testing/test_4_juspyce.predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# JuSpyce API test: predictions methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/llotter/projects/juspyce\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/llotter/opt/anaconda3/envs/juspyce/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.2, the latest is 0.5.3.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n",
      "/Users/llotter/opt/anaconda3/envs/juspyce/lib/python3.8/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.1, the latest is 0.2.2.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "from glob import glob\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# current path\n",
    "wd = pathlib.Path().resolve().parent\n",
    "print(wd)\n",
    "\n",
    "# import juspyce\n",
    "sys.path.append(os.path.dirname(os.path.join(wd, \"juspyce\")))\n",
    "from juspyce.api import JuSpyce\n",
    "from juspyce.stats import *\n",
    "from juspyce.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load JuSpyce data from test_juspyce.fit.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:juspyce.api:Loaded complete object from /Users/llotter/projects/juspyce/testing/test_juspyce_vol.pkl.gz.\n"
     ]
    }
   ],
   "source": [
    "juspyce_vol = JuSpyce.from_pickle(os.path.join(wd, \"testing\", \"test_juspyce_vol.pkl.gz\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions\n",
    "\n",
    "The JuSpyce core consists of several methods  to \"predict\" Y from X data.  \n",
    "The JuSpyce.predict function has a variable named \"store\". If \"store\" is False, predictions will be calculated and the prediction dataframe(s) will be returned in a dict. If store is True, predictions will be stored as dataframes in a dict as JuSpyce.predictions[\"prediction_name\"]. This way, results from different methods will not overwrite each other\n",
    "\n",
    "The results are in part compared to those from other functions to check for errors. This is relevant as JuSpyce uses mostly custom implementations for, e.g., correlations and linear models that are significantly faster than the \"standard\" python implementations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pearson correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d39be6510c854882a46148bdcb743ada",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (pearson, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>-0.426663</td>\n",
       "      <td>-0.079370</td>\n",
       "      <td>-0.233269</td>\n",
       "      <td>-0.085829</td>\n",
       "      <td>-0.399700</td>\n",
       "      <td>0.033192</td>\n",
       "      <td>-0.015343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>-0.191406</td>\n",
       "      <td>-0.146331</td>\n",
       "      <td>0.121694</td>\n",
       "      <td>-0.094603</td>\n",
       "      <td>-0.081260</td>\n",
       "      <td>-0.113993</td>\n",
       "      <td>-0.140186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>-0.095277</td>\n",
       "      <td>0.357360</td>\n",
       "      <td>0.289272</td>\n",
       "      <td>0.291627</td>\n",
       "      <td>0.019122</td>\n",
       "      <td>0.073981</td>\n",
       "      <td>0.252232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>-0.612772</td>\n",
       "      <td>0.459167</td>\n",
       "      <td>-0.440161</td>\n",
       "      <td>0.281464</td>\n",
       "      <td>-0.545530</td>\n",
       "      <td>0.045298</td>\n",
       "      <td>0.718468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.152093</td>\n",
       "      <td>-0.346891</td>\n",
       "      <td>-0.065294</td>\n",
       "      <td>-0.513260</td>\n",
       "      <td>0.127016</td>\n",
       "      <td>0.055487</td>\n",
       "      <td>-0.289138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.292311</td>\n",
       "      <td>-0.079031</td>\n",
       "      <td>0.183084</td>\n",
       "      <td>0.037154</td>\n",
       "      <td>0.102660</td>\n",
       "      <td>0.141787</td>\n",
       "      <td>-0.051926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>-0.129552</td>\n",
       "      <td>0.005238</td>\n",
       "      <td>-0.227872</td>\n",
       "      <td>-0.401938</td>\n",
       "      <td>-0.020586</td>\n",
       "      <td>-0.062006</td>\n",
       "      <td>-0.070281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>-0.378508</td>\n",
       "      <td>-0.115512</td>\n",
       "      <td>-0.089019</td>\n",
       "      <td>-0.061578</td>\n",
       "      <td>-0.320610</td>\n",
       "      <td>0.097178</td>\n",
       "      <td>-0.001743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>-0.161098</td>\n",
       "      <td>-0.163284</td>\n",
       "      <td>0.160802</td>\n",
       "      <td>-0.094786</td>\n",
       "      <td>-0.051313</td>\n",
       "      <td>-0.098216</td>\n",
       "      <td>-0.151470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>-0.344174</td>\n",
       "      <td>0.232815</td>\n",
       "      <td>-0.153768</td>\n",
       "      <td>0.456580</td>\n",
       "      <td>-0.457828</td>\n",
       "      <td>0.412249</td>\n",
       "      <td>0.674056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.155851</td>\n",
       "      <td>0.101755</td>\n",
       "      <td>0.294627</td>\n",
       "      <td>-0.017066</td>\n",
       "      <td>0.158509</td>\n",
       "      <td>0.024202</td>\n",
       "      <td>-0.073483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>-0.436906</td>\n",
       "      <td>0.658280</td>\n",
       "      <td>-0.081946</td>\n",
       "      <td>0.564267</td>\n",
       "      <td>-0.394731</td>\n",
       "      <td>-0.048291</td>\n",
       "      <td>0.435309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>-0.536215</td>\n",
       "      <td>0.570776</td>\n",
       "      <td>-0.456912</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>-0.504236</td>\n",
       "      <td>-0.333634</td>\n",
       "      <td>0.206677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>-0.078710</td>\n",
       "      <td>-0.011604</td>\n",
       "      <td>-0.188707</td>\n",
       "      <td>-0.131451</td>\n",
       "      <td>-0.019445</td>\n",
       "      <td>-0.241521</td>\n",
       "      <td>-0.081082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>-0.108046</td>\n",
       "      <td>0.207072</td>\n",
       "      <td>0.320499</td>\n",
       "      <td>0.226727</td>\n",
       "      <td>-0.013785</td>\n",
       "      <td>-0.000465</td>\n",
       "      <td>0.078779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.182947</td>\n",
       "      <td>-0.290807</td>\n",
       "      <td>-0.323089</td>\n",
       "      <td>-0.640463</td>\n",
       "      <td>0.205062</td>\n",
       "      <td>-0.042164</td>\n",
       "      <td>-0.245244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>-0.162986</td>\n",
       "      <td>0.414607</td>\n",
       "      <td>0.263682</td>\n",
       "      <td>0.371580</td>\n",
       "      <td>-0.035982</td>\n",
       "      <td>0.053405</td>\n",
       "      <td>0.269862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.159329</td>\n",
       "      <td>-0.229724</td>\n",
       "      <td>-0.357602</td>\n",
       "      <td>-0.594562</td>\n",
       "      <td>0.190547</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>-0.183069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>-0.313444</td>\n",
       "      <td>0.225558</td>\n",
       "      <td>-0.238350</td>\n",
       "      <td>0.297867</td>\n",
       "      <td>-0.196917</td>\n",
       "      <td>-0.164257</td>\n",
       "      <td>0.125711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.200819</td>\n",
       "      <td>-0.183087</td>\n",
       "      <td>-0.088408</td>\n",
       "      <td>-0.492880</td>\n",
       "      <td>0.188322</td>\n",
       "      <td>-0.132824</td>\n",
       "      <td>-0.281707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.208299</td>\n",
       "      <td>-0.111638</td>\n",
       "      <td>0.032104</td>\n",
       "      <td>-0.227814</td>\n",
       "      <td>0.132971</td>\n",
       "      <td>-0.063295</td>\n",
       "      <td>-0.276997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>-0.498272</td>\n",
       "      <td>0.449823</td>\n",
       "      <td>-0.132637</td>\n",
       "      <td>0.576072</td>\n",
       "      <td>-0.463948</td>\n",
       "      <td>0.338581</td>\n",
       "      <td>0.809666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>-0.189114</td>\n",
       "      <td>-0.354762</td>\n",
       "      <td>-0.080444</td>\n",
       "      <td>-0.354075</td>\n",
       "      <td>-0.128793</td>\n",
       "      <td>-0.036854</td>\n",
       "      <td>-0.219735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>0.059201</td>\n",
       "      <td>-0.261971</td>\n",
       "      <td>-0.346649</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.104491</td>\n",
       "      <td>-0.050071</td>\n",
       "      <td>-0.162754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>-0.319979</td>\n",
       "      <td>-0.212951</td>\n",
       "      <td>-0.175016</td>\n",
       "      <td>-0.251795</td>\n",
       "      <td>-0.235275</td>\n",
       "      <td>-0.003427</td>\n",
       "      <td>-0.110738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.258957</td>\n",
       "      <td>0.123731</td>\n",
       "      <td>0.215582</td>\n",
       "      <td>-0.111046</td>\n",
       "      <td>0.297072</td>\n",
       "      <td>-0.061046</td>\n",
       "      <td>-0.138175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>-0.563259</td>\n",
       "      <td>0.497114</td>\n",
       "      <td>-0.184624</td>\n",
       "      <td>0.620211</td>\n",
       "      <td>-0.580265</td>\n",
       "      <td>0.337349</td>\n",
       "      <td>0.835748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>-0.349846</td>\n",
       "      <td>0.279355</td>\n",
       "      <td>0.083837</td>\n",
       "      <td>0.284518</td>\n",
       "      <td>-0.286897</td>\n",
       "      <td>-0.178272</td>\n",
       "      <td>0.101480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                            -0.426663                  -0.079370   \n",
       "touch                              -0.191406                  -0.146331   \n",
       "interoception                      -0.095277                   0.357360   \n",
       "learning                           -0.612772                   0.459167   \n",
       "attention                           0.152093                  -0.346891   \n",
       "language                            0.292311                  -0.079031   \n",
       "interaction                        -0.129552                   0.005238   \n",
       "inhibition                         -0.378508                  -0.115512   \n",
       "somatosensory                      -0.161098                  -0.163284   \n",
       "decision                           -0.344174                   0.232815   \n",
       "auditory                            0.155851                   0.101755   \n",
       "taste                              -0.436906                   0.658280   \n",
       "metabolism                         -0.536215                   0.570776   \n",
       "memory                             -0.078710                  -0.011604   \n",
       "empathy                            -0.108046                   0.207072   \n",
       "vision                              0.182947                  -0.290807   \n",
       "autonomic                          -0.162986                   0.414607   \n",
       "visual                              0.159329                  -0.229724   \n",
       "fear                               -0.313444                   0.225558   \n",
       "perception                          0.200819                  -0.183087   \n",
       "cognition                           0.208299                  -0.111638   \n",
       "reward                             -0.498272                   0.449823   \n",
       "mirror                             -0.189114                  -0.354762   \n",
       "orientation                         0.059201                  -0.261971   \n",
       "movement                           -0.319979                  -0.212951   \n",
       "default_mode                        0.258957                   0.123731   \n",
       "dopamine                           -0.563259                   0.497114   \n",
       "pain                               -0.349846                   0.279355   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                         -0.233269                        -0.085829   \n",
       "touch                            0.121694                        -0.094603   \n",
       "interoception                    0.289272                         0.291627   \n",
       "learning                        -0.440161                         0.281464   \n",
       "attention                       -0.065294                        -0.513260   \n",
       "language                         0.183084                         0.037154   \n",
       "interaction                     -0.227872                        -0.401938   \n",
       "inhibition                      -0.089019                        -0.061578   \n",
       "somatosensory                    0.160802                        -0.094786   \n",
       "decision                        -0.153768                         0.456580   \n",
       "auditory                         0.294627                        -0.017066   \n",
       "taste                           -0.081946                         0.564267   \n",
       "metabolism                      -0.456912                         0.214205   \n",
       "memory                          -0.188707                        -0.131451   \n",
       "empathy                          0.320499                         0.226727   \n",
       "vision                          -0.323089                        -0.640463   \n",
       "autonomic                        0.263682                         0.371580   \n",
       "visual                          -0.357602                        -0.594562   \n",
       "fear                            -0.238350                         0.297867   \n",
       "perception                      -0.088408                        -0.492880   \n",
       "cognition                        0.032104                        -0.227814   \n",
       "reward                          -0.132637                         0.576072   \n",
       "mirror                          -0.080444                        -0.354075   \n",
       "orientation                     -0.346649                        -0.592266   \n",
       "movement                        -0.175016                        -0.251795   \n",
       "default_mode                     0.215582                        -0.111046   \n",
       "dopamine                        -0.184624                         0.620211   \n",
       "pain                             0.083837                         0.284518   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                            -0.399700                    0.033192   \n",
       "touch                              -0.081260                   -0.113993   \n",
       "interoception                       0.019122                    0.073981   \n",
       "learning                           -0.545530                    0.045298   \n",
       "attention                           0.127016                    0.055487   \n",
       "language                            0.102660                    0.141787   \n",
       "interaction                        -0.020586                   -0.062006   \n",
       "inhibition                         -0.320610                    0.097178   \n",
       "somatosensory                      -0.051313                   -0.098216   \n",
       "decision                           -0.457828                    0.412249   \n",
       "auditory                            0.158509                    0.024202   \n",
       "taste                              -0.394731                   -0.048291   \n",
       "metabolism                         -0.504236                   -0.333634   \n",
       "memory                             -0.019445                   -0.241521   \n",
       "empathy                            -0.013785                   -0.000465   \n",
       "vision                              0.205062                   -0.042164   \n",
       "autonomic                          -0.035982                    0.053405   \n",
       "visual                              0.190547                    0.006826   \n",
       "fear                               -0.196917                   -0.164257   \n",
       "perception                          0.188322                   -0.132824   \n",
       "cognition                           0.132971                   -0.063295   \n",
       "reward                             -0.463948                    0.338581   \n",
       "mirror                             -0.128793                   -0.036854   \n",
       "orientation                         0.104491                   -0.050071   \n",
       "movement                           -0.235275                   -0.003427   \n",
       "default_mode                        0.297072                   -0.061046   \n",
       "dopamine                           -0.580265                    0.337349   \n",
       "pain                               -0.286897                   -0.178272   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                          -0.015343  \n",
       "touch                            -0.140186  \n",
       "interoception                     0.252232  \n",
       "learning                          0.718468  \n",
       "attention                        -0.289138  \n",
       "language                         -0.051926  \n",
       "interaction                      -0.070281  \n",
       "inhibition                       -0.001743  \n",
       "somatosensory                    -0.151470  \n",
       "decision                          0.674056  \n",
       "auditory                         -0.073483  \n",
       "taste                             0.435309  \n",
       "metabolism                        0.206677  \n",
       "memory                           -0.081082  \n",
       "empathy                           0.078779  \n",
       "vision                           -0.245244  \n",
       "autonomic                         0.269862  \n",
       "visual                           -0.183069  \n",
       "fear                              0.125711  \n",
       "perception                       -0.281707  \n",
       "cognition                        -0.276997  \n",
       "reward                            0.809666  \n",
       "mirror                           -0.219735  \n",
       "orientation                      -0.162754  \n",
       "movement                         -0.110738  \n",
       "default_mode                     -0.138175  \n",
       "dopamine                          0.835748  \n",
       "pain                              0.101480  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.426663</td>\n",
       "      <td>-0.079370</td>\n",
       "      <td>-0.233269</td>\n",
       "      <td>-0.085829</td>\n",
       "      <td>-0.399700</td>\n",
       "      <td>0.033192</td>\n",
       "      <td>-0.015343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.191406</td>\n",
       "      <td>-0.146331</td>\n",
       "      <td>0.121694</td>\n",
       "      <td>-0.094603</td>\n",
       "      <td>-0.081260</td>\n",
       "      <td>-0.113993</td>\n",
       "      <td>-0.140186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.095277</td>\n",
       "      <td>0.357360</td>\n",
       "      <td>0.289272</td>\n",
       "      <td>0.291627</td>\n",
       "      <td>0.019122</td>\n",
       "      <td>0.073981</td>\n",
       "      <td>0.252232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.612772</td>\n",
       "      <td>0.459167</td>\n",
       "      <td>-0.440161</td>\n",
       "      <td>0.281464</td>\n",
       "      <td>-0.545530</td>\n",
       "      <td>0.045298</td>\n",
       "      <td>0.718469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152093</td>\n",
       "      <td>-0.346891</td>\n",
       "      <td>-0.065294</td>\n",
       "      <td>-0.513260</td>\n",
       "      <td>0.127016</td>\n",
       "      <td>0.055487</td>\n",
       "      <td>-0.289138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.292311</td>\n",
       "      <td>-0.079031</td>\n",
       "      <td>0.183084</td>\n",
       "      <td>0.037154</td>\n",
       "      <td>0.102660</td>\n",
       "      <td>0.141787</td>\n",
       "      <td>-0.051926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.129552</td>\n",
       "      <td>0.005238</td>\n",
       "      <td>-0.227872</td>\n",
       "      <td>-0.401938</td>\n",
       "      <td>-0.020586</td>\n",
       "      <td>-0.062006</td>\n",
       "      <td>-0.070281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.378508</td>\n",
       "      <td>-0.115512</td>\n",
       "      <td>-0.089019</td>\n",
       "      <td>-0.061578</td>\n",
       "      <td>-0.320610</td>\n",
       "      <td>0.097178</td>\n",
       "      <td>-0.001743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.161098</td>\n",
       "      <td>-0.163284</td>\n",
       "      <td>0.160802</td>\n",
       "      <td>-0.094786</td>\n",
       "      <td>-0.051313</td>\n",
       "      <td>-0.098216</td>\n",
       "      <td>-0.151470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.344174</td>\n",
       "      <td>0.232815</td>\n",
       "      <td>-0.153768</td>\n",
       "      <td>0.456580</td>\n",
       "      <td>-0.457828</td>\n",
       "      <td>0.412249</td>\n",
       "      <td>0.674056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.155851</td>\n",
       "      <td>0.101755</td>\n",
       "      <td>0.294627</td>\n",
       "      <td>-0.017066</td>\n",
       "      <td>0.158509</td>\n",
       "      <td>0.024202</td>\n",
       "      <td>-0.073483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.436906</td>\n",
       "      <td>0.658280</td>\n",
       "      <td>-0.081946</td>\n",
       "      <td>0.564267</td>\n",
       "      <td>-0.394731</td>\n",
       "      <td>-0.048291</td>\n",
       "      <td>0.435309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.536215</td>\n",
       "      <td>0.570776</td>\n",
       "      <td>-0.456912</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>-0.504236</td>\n",
       "      <td>-0.333634</td>\n",
       "      <td>0.206677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.078710</td>\n",
       "      <td>-0.011604</td>\n",
       "      <td>-0.188707</td>\n",
       "      <td>-0.131451</td>\n",
       "      <td>-0.019445</td>\n",
       "      <td>-0.241521</td>\n",
       "      <td>-0.081082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.108046</td>\n",
       "      <td>0.207072</td>\n",
       "      <td>0.320499</td>\n",
       "      <td>0.226727</td>\n",
       "      <td>-0.013785</td>\n",
       "      <td>-0.000465</td>\n",
       "      <td>0.078779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.182947</td>\n",
       "      <td>-0.290807</td>\n",
       "      <td>-0.323089</td>\n",
       "      <td>-0.640463</td>\n",
       "      <td>0.205062</td>\n",
       "      <td>-0.042164</td>\n",
       "      <td>-0.245244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.162986</td>\n",
       "      <td>0.414607</td>\n",
       "      <td>0.263682</td>\n",
       "      <td>0.371580</td>\n",
       "      <td>-0.035982</td>\n",
       "      <td>0.053405</td>\n",
       "      <td>0.269862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.159329</td>\n",
       "      <td>-0.229724</td>\n",
       "      <td>-0.357602</td>\n",
       "      <td>-0.594562</td>\n",
       "      <td>0.190547</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>-0.183069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.313444</td>\n",
       "      <td>0.225558</td>\n",
       "      <td>-0.238350</td>\n",
       "      <td>0.297867</td>\n",
       "      <td>-0.196917</td>\n",
       "      <td>-0.164257</td>\n",
       "      <td>0.125711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.200819</td>\n",
       "      <td>-0.183087</td>\n",
       "      <td>-0.088408</td>\n",
       "      <td>-0.492880</td>\n",
       "      <td>0.188322</td>\n",
       "      <td>-0.132824</td>\n",
       "      <td>-0.281707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.208299</td>\n",
       "      <td>-0.111638</td>\n",
       "      <td>0.032104</td>\n",
       "      <td>-0.227814</td>\n",
       "      <td>0.132971</td>\n",
       "      <td>-0.063295</td>\n",
       "      <td>-0.276997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.498272</td>\n",
       "      <td>0.449823</td>\n",
       "      <td>-0.132637</td>\n",
       "      <td>0.576072</td>\n",
       "      <td>-0.463948</td>\n",
       "      <td>0.338581</td>\n",
       "      <td>0.809666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.189114</td>\n",
       "      <td>-0.354762</td>\n",
       "      <td>-0.080444</td>\n",
       "      <td>-0.354075</td>\n",
       "      <td>-0.128793</td>\n",
       "      <td>-0.036854</td>\n",
       "      <td>-0.219735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.059201</td>\n",
       "      <td>-0.261971</td>\n",
       "      <td>-0.346649</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.104491</td>\n",
       "      <td>-0.050071</td>\n",
       "      <td>-0.162754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.319979</td>\n",
       "      <td>-0.212951</td>\n",
       "      <td>-0.175016</td>\n",
       "      <td>-0.251795</td>\n",
       "      <td>-0.235275</td>\n",
       "      <td>-0.003427</td>\n",
       "      <td>-0.110738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.258957</td>\n",
       "      <td>0.123731</td>\n",
       "      <td>0.215582</td>\n",
       "      <td>-0.111046</td>\n",
       "      <td>0.297072</td>\n",
       "      <td>-0.061046</td>\n",
       "      <td>-0.138175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.563259</td>\n",
       "      <td>0.497114</td>\n",
       "      <td>-0.184624</td>\n",
       "      <td>0.620211</td>\n",
       "      <td>-0.580265</td>\n",
       "      <td>0.337349</td>\n",
       "      <td>0.835748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.349846</td>\n",
       "      <td>0.279355</td>\n",
       "      <td>0.083837</td>\n",
       "      <td>0.284518</td>\n",
       "      <td>-0.286897</td>\n",
       "      <td>-0.178272</td>\n",
       "      <td>0.101480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6\n",
       "0  -0.426663 -0.079370 -0.233269 -0.085829 -0.399700  0.033192 -0.015343\n",
       "1  -0.191406 -0.146331  0.121694 -0.094603 -0.081260 -0.113993 -0.140186\n",
       "2  -0.095277  0.357360  0.289272  0.291627  0.019122  0.073981  0.252232\n",
       "3  -0.612772  0.459167 -0.440161  0.281464 -0.545530  0.045298  0.718469\n",
       "4   0.152093 -0.346891 -0.065294 -0.513260  0.127016  0.055487 -0.289138\n",
       "5   0.292311 -0.079031  0.183084  0.037154  0.102660  0.141787 -0.051926\n",
       "6  -0.129552  0.005238 -0.227872 -0.401938 -0.020586 -0.062006 -0.070281\n",
       "7  -0.378508 -0.115512 -0.089019 -0.061578 -0.320610  0.097178 -0.001743\n",
       "8  -0.161098 -0.163284  0.160802 -0.094786 -0.051313 -0.098216 -0.151470\n",
       "9  -0.344174  0.232815 -0.153768  0.456580 -0.457828  0.412249  0.674056\n",
       "10  0.155851  0.101755  0.294627 -0.017066  0.158509  0.024202 -0.073483\n",
       "11 -0.436906  0.658280 -0.081946  0.564267 -0.394731 -0.048291  0.435309\n",
       "12 -0.536215  0.570776 -0.456912  0.214205 -0.504236 -0.333634  0.206677\n",
       "13 -0.078710 -0.011604 -0.188707 -0.131451 -0.019445 -0.241521 -0.081082\n",
       "14 -0.108046  0.207072  0.320499  0.226727 -0.013785 -0.000465  0.078779\n",
       "15  0.182947 -0.290807 -0.323089 -0.640463  0.205062 -0.042164 -0.245244\n",
       "16 -0.162986  0.414607  0.263682  0.371580 -0.035982  0.053405  0.269862\n",
       "17  0.159329 -0.229724 -0.357602 -0.594562  0.190547  0.006826 -0.183069\n",
       "18 -0.313444  0.225558 -0.238350  0.297867 -0.196917 -0.164257  0.125711\n",
       "19  0.200819 -0.183087 -0.088408 -0.492880  0.188322 -0.132824 -0.281707\n",
       "20  0.208299 -0.111638  0.032104 -0.227814  0.132971 -0.063295 -0.276997\n",
       "21 -0.498272  0.449823 -0.132637  0.576072 -0.463948  0.338581  0.809666\n",
       "22 -0.189114 -0.354762 -0.080444 -0.354075 -0.128793 -0.036854 -0.219735\n",
       "23  0.059201 -0.261971 -0.346649 -0.592266  0.104491 -0.050071 -0.162754\n",
       "24 -0.319979 -0.212951 -0.175016 -0.251795 -0.235275 -0.003427 -0.110738\n",
       "25  0.258957  0.123731  0.215582 -0.111046  0.297072 -0.061046 -0.138175\n",
       "26 -0.563259  0.497114 -0.184624  0.620211 -0.580265  0.337349  0.835748\n",
       "27 -0.349846  0.279355  0.083837  0.284518 -0.286897 -0.178272  0.101480"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"pearson\", \n",
    "    r_to_z=False, # apply r to z transformation\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "display(juspyce_vol.predictions[\"pearson\"])\n",
    "\n",
    "print(\"check\")\n",
    "check_res = np.zeros((juspyce_vol.Y.shape[0], juspyce_vol.X.shape[0]))\n",
    "for y in range(juspyce_vol.Y.shape[0]):\n",
    "    for x in range(juspyce_vol.X.shape[0]):\n",
    "        check_res[y,x] = check_pearson(x=juspyce_vol.X.iloc[x,:].values,\n",
    "                                       y=juspyce_vol.Y.iloc[y,:].values)\n",
    "display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spearman correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6747cc1ed21d41738ef0072475a65f35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (spearman, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>-0.477046</td>\n",
       "      <td>-0.192757</td>\n",
       "      <td>-0.167770</td>\n",
       "      <td>-0.093991</td>\n",
       "      <td>-0.421097</td>\n",
       "      <td>0.033699</td>\n",
       "      <td>-0.133764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>-0.201768</td>\n",
       "      <td>-0.207688</td>\n",
       "      <td>0.060185</td>\n",
       "      <td>-0.101657</td>\n",
       "      <td>-0.158459</td>\n",
       "      <td>-0.183677</td>\n",
       "      <td>-0.140968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>-0.203906</td>\n",
       "      <td>0.413593</td>\n",
       "      <td>0.219152</td>\n",
       "      <td>0.551124</td>\n",
       "      <td>-0.070080</td>\n",
       "      <td>0.112728</td>\n",
       "      <td>0.376650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>-0.442394</td>\n",
       "      <td>0.260362</td>\n",
       "      <td>-0.567470</td>\n",
       "      <td>-0.078076</td>\n",
       "      <td>-0.343198</td>\n",
       "      <td>-0.231407</td>\n",
       "      <td>0.312182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.079514</td>\n",
       "      <td>-0.323892</td>\n",
       "      <td>-0.044409</td>\n",
       "      <td>-0.511921</td>\n",
       "      <td>0.084673</td>\n",
       "      <td>0.091093</td>\n",
       "      <td>-0.240818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.301565</td>\n",
       "      <td>-0.106593</td>\n",
       "      <td>0.185515</td>\n",
       "      <td>0.091154</td>\n",
       "      <td>0.041218</td>\n",
       "      <td>0.220544</td>\n",
       "      <td>-0.133587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>-0.166325</td>\n",
       "      <td>-0.020144</td>\n",
       "      <td>-0.285004</td>\n",
       "      <td>-0.476031</td>\n",
       "      <td>-0.042894</td>\n",
       "      <td>-0.145127</td>\n",
       "      <td>0.003744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>-0.384400</td>\n",
       "      <td>-0.182163</td>\n",
       "      <td>0.025049</td>\n",
       "      <td>0.019121</td>\n",
       "      <td>-0.373067</td>\n",
       "      <td>0.130319</td>\n",
       "      <td>-0.129874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>-0.154261</td>\n",
       "      <td>-0.206866</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>-0.056080</td>\n",
       "      <td>-0.122408</td>\n",
       "      <td>-0.147626</td>\n",
       "      <td>-0.164564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>-0.110583</td>\n",
       "      <td>-0.010241</td>\n",
       "      <td>-0.077077</td>\n",
       "      <td>0.207558</td>\n",
       "      <td>-0.303656</td>\n",
       "      <td>0.344166</td>\n",
       "      <td>0.036743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.007719</td>\n",
       "      <td>0.085526</td>\n",
       "      <td>0.242533</td>\n",
       "      <td>0.100488</td>\n",
       "      <td>0.066098</td>\n",
       "      <td>0.053350</td>\n",
       "      <td>0.031169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>-0.320732</td>\n",
       "      <td>0.491262</td>\n",
       "      <td>0.077561</td>\n",
       "      <td>0.561988</td>\n",
       "      <td>-0.134448</td>\n",
       "      <td>-0.019375</td>\n",
       "      <td>0.441564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>-0.500319</td>\n",
       "      <td>0.342583</td>\n",
       "      <td>-0.294768</td>\n",
       "      <td>-0.187991</td>\n",
       "      <td>-0.171476</td>\n",
       "      <td>-0.448460</td>\n",
       "      <td>0.325237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>-0.036636</td>\n",
       "      <td>-0.069934</td>\n",
       "      <td>-0.231592</td>\n",
       "      <td>-0.070503</td>\n",
       "      <td>-0.065659</td>\n",
       "      <td>-0.154730</td>\n",
       "      <td>-0.080890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>-0.179287</td>\n",
       "      <td>0.141868</td>\n",
       "      <td>0.255526</td>\n",
       "      <td>0.340168</td>\n",
       "      <td>-0.086910</td>\n",
       "      <td>-0.040557</td>\n",
       "      <td>0.177957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.033230</td>\n",
       "      <td>-0.334379</td>\n",
       "      <td>-0.344489</td>\n",
       "      <td>-0.727206</td>\n",
       "      <td>0.092031</td>\n",
       "      <td>-0.223650</td>\n",
       "      <td>-0.272118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>-0.266336</td>\n",
       "      <td>0.450605</td>\n",
       "      <td>0.227971</td>\n",
       "      <td>0.582393</td>\n",
       "      <td>-0.072041</td>\n",
       "      <td>0.070857</td>\n",
       "      <td>0.421566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>-0.039926</td>\n",
       "      <td>-0.245193</td>\n",
       "      <td>-0.411786</td>\n",
       "      <td>-0.636174</td>\n",
       "      <td>0.049883</td>\n",
       "      <td>-0.190313</td>\n",
       "      <td>-0.198701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>-0.220198</td>\n",
       "      <td>0.454019</td>\n",
       "      <td>-0.199516</td>\n",
       "      <td>0.373283</td>\n",
       "      <td>-0.079499</td>\n",
       "      <td>-0.271995</td>\n",
       "      <td>0.434490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.075216</td>\n",
       "      <td>-0.189436</td>\n",
       "      <td>-0.124023</td>\n",
       "      <td>-0.527513</td>\n",
       "      <td>0.115857</td>\n",
       "      <td>-0.219321</td>\n",
       "      <td>-0.088387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.149579</td>\n",
       "      <td>-0.129758</td>\n",
       "      <td>-0.050675</td>\n",
       "      <td>-0.082651</td>\n",
       "      <td>0.100765</td>\n",
       "      <td>-0.059424</td>\n",
       "      <td>-0.243294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>-0.367739</td>\n",
       "      <td>0.372890</td>\n",
       "      <td>-0.148895</td>\n",
       "      <td>0.322162</td>\n",
       "      <td>-0.156091</td>\n",
       "      <td>0.084450</td>\n",
       "      <td>0.409641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>-0.212617</td>\n",
       "      <td>-0.393918</td>\n",
       "      <td>-0.086103</td>\n",
       "      <td>-0.448268</td>\n",
       "      <td>-0.229562</td>\n",
       "      <td>-0.036812</td>\n",
       "      <td>-0.287787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>-0.126283</td>\n",
       "      <td>-0.260393</td>\n",
       "      <td>-0.393596</td>\n",
       "      <td>-0.689671</td>\n",
       "      <td>-0.049083</td>\n",
       "      <td>-0.229362</td>\n",
       "      <td>-0.161873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>-0.347372</td>\n",
       "      <td>-0.260108</td>\n",
       "      <td>-0.198309</td>\n",
       "      <td>-0.329881</td>\n",
       "      <td>-0.337093</td>\n",
       "      <td>-0.077953</td>\n",
       "      <td>-0.164641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.282928</td>\n",
       "      <td>0.125514</td>\n",
       "      <td>0.103771</td>\n",
       "      <td>-0.066521</td>\n",
       "      <td>0.323215</td>\n",
       "      <td>-0.092385</td>\n",
       "      <td>-0.008965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>-0.333887</td>\n",
       "      <td>0.301672</td>\n",
       "      <td>-0.049475</td>\n",
       "      <td>0.509637</td>\n",
       "      <td>-0.253858</td>\n",
       "      <td>0.251036</td>\n",
       "      <td>0.247645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>-0.410902</td>\n",
       "      <td>0.176350</td>\n",
       "      <td>0.089363</td>\n",
       "      <td>0.328497</td>\n",
       "      <td>-0.239034</td>\n",
       "      <td>-0.217853</td>\n",
       "      <td>0.266521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                            -0.477046                  -0.192757   \n",
       "touch                              -0.201768                  -0.207688   \n",
       "interoception                      -0.203906                   0.413593   \n",
       "learning                           -0.442394                   0.260362   \n",
       "attention                           0.079514                  -0.323892   \n",
       "language                            0.301565                  -0.106593   \n",
       "interaction                        -0.166325                  -0.020144   \n",
       "inhibition                         -0.384400                  -0.182163   \n",
       "somatosensory                      -0.154261                  -0.206866   \n",
       "decision                           -0.110583                  -0.010241   \n",
       "auditory                            0.007719                   0.085526   \n",
       "taste                              -0.320732                   0.491262   \n",
       "metabolism                         -0.500319                   0.342583   \n",
       "memory                             -0.036636                  -0.069934   \n",
       "empathy                            -0.179287                   0.141868   \n",
       "vision                              0.033230                  -0.334379   \n",
       "autonomic                          -0.266336                   0.450605   \n",
       "visual                             -0.039926                  -0.245193   \n",
       "fear                               -0.220198                   0.454019   \n",
       "perception                          0.075216                  -0.189436   \n",
       "cognition                           0.149579                  -0.129758   \n",
       "reward                             -0.367739                   0.372890   \n",
       "mirror                             -0.212617                  -0.393918   \n",
       "orientation                        -0.126283                  -0.260393   \n",
       "movement                           -0.347372                  -0.260108   \n",
       "default_mode                        0.282928                   0.125514   \n",
       "dopamine                           -0.333887                   0.301672   \n",
       "pain                               -0.410902                   0.176350   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                         -0.167770                        -0.093991   \n",
       "touch                            0.060185                        -0.101657   \n",
       "interoception                    0.219152                         0.551124   \n",
       "learning                        -0.567470                        -0.078076   \n",
       "attention                       -0.044409                        -0.511921   \n",
       "language                         0.185515                         0.091154   \n",
       "interaction                     -0.285004                        -0.476031   \n",
       "inhibition                       0.025049                         0.019121   \n",
       "somatosensory                    0.140276                        -0.056080   \n",
       "decision                        -0.077077                         0.207558   \n",
       "auditory                         0.242533                         0.100488   \n",
       "taste                            0.077561                         0.561988   \n",
       "metabolism                      -0.294768                        -0.187991   \n",
       "memory                          -0.231592                        -0.070503   \n",
       "empathy                          0.255526                         0.340168   \n",
       "vision                          -0.344489                        -0.727206   \n",
       "autonomic                        0.227971                         0.582393   \n",
       "visual                          -0.411786                        -0.636174   \n",
       "fear                            -0.199516                         0.373283   \n",
       "perception                      -0.124023                        -0.527513   \n",
       "cognition                       -0.050675                        -0.082651   \n",
       "reward                          -0.148895                         0.322162   \n",
       "mirror                          -0.086103                        -0.448268   \n",
       "orientation                     -0.393596                        -0.689671   \n",
       "movement                        -0.198309                        -0.329881   \n",
       "default_mode                     0.103771                        -0.066521   \n",
       "dopamine                        -0.049475                         0.509637   \n",
       "pain                             0.089363                         0.328497   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                            -0.421097                    0.033699   \n",
       "touch                              -0.158459                   -0.183677   \n",
       "interoception                      -0.070080                    0.112728   \n",
       "learning                           -0.343198                   -0.231407   \n",
       "attention                           0.084673                    0.091093   \n",
       "language                            0.041218                    0.220544   \n",
       "interaction                        -0.042894                   -0.145127   \n",
       "inhibition                         -0.373067                    0.130319   \n",
       "somatosensory                      -0.122408                   -0.147626   \n",
       "decision                           -0.303656                    0.344166   \n",
       "auditory                            0.066098                    0.053350   \n",
       "taste                              -0.134448                   -0.019375   \n",
       "metabolism                         -0.171476                   -0.448460   \n",
       "memory                             -0.065659                   -0.154730   \n",
       "empathy                            -0.086910                   -0.040557   \n",
       "vision                              0.092031                   -0.223650   \n",
       "autonomic                          -0.072041                    0.070857   \n",
       "visual                              0.049883                   -0.190313   \n",
       "fear                               -0.079499                   -0.271995   \n",
       "perception                          0.115857                   -0.219321   \n",
       "cognition                           0.100765                   -0.059424   \n",
       "reward                             -0.156091                    0.084450   \n",
       "mirror                             -0.229562                   -0.036812   \n",
       "orientation                        -0.049083                   -0.229362   \n",
       "movement                           -0.337093                   -0.077953   \n",
       "default_mode                        0.323215                   -0.092385   \n",
       "dopamine                           -0.253858                    0.251036   \n",
       "pain                               -0.239034                   -0.217853   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                          -0.133764  \n",
       "touch                            -0.140968  \n",
       "interoception                     0.376650  \n",
       "learning                          0.312182  \n",
       "attention                        -0.240818  \n",
       "language                         -0.133587  \n",
       "interaction                       0.003744  \n",
       "inhibition                       -0.129874  \n",
       "somatosensory                    -0.164564  \n",
       "decision                          0.036743  \n",
       "auditory                          0.031169  \n",
       "taste                             0.441564  \n",
       "metabolism                        0.325237  \n",
       "memory                           -0.080890  \n",
       "empathy                           0.177957  \n",
       "vision                           -0.272118  \n",
       "autonomic                         0.421566  \n",
       "visual                           -0.198701  \n",
       "fear                              0.434490  \n",
       "perception                       -0.088387  \n",
       "cognition                        -0.243294  \n",
       "reward                            0.409641  \n",
       "mirror                           -0.287787  \n",
       "orientation                      -0.161873  \n",
       "movement                         -0.164641  \n",
       "default_mode                     -0.008965  \n",
       "dopamine                          0.247645  \n",
       "pain                              0.266521  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check\n"
     ]
    },
    {
     "data": {
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       "      <td>0.041218</td>\n",
       "      <td>0.220544</td>\n",
       "      <td>-0.133587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.166325</td>\n",
       "      <td>-0.020144</td>\n",
       "      <td>-0.285004</td>\n",
       "      <td>-0.476031</td>\n",
       "      <td>-0.042894</td>\n",
       "      <td>-0.145127</td>\n",
       "      <td>0.003744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.384400</td>\n",
       "      <td>-0.182163</td>\n",
       "      <td>0.025049</td>\n",
       "      <td>0.019121</td>\n",
       "      <td>-0.373067</td>\n",
       "      <td>0.130319</td>\n",
       "      <td>-0.129874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.154261</td>\n",
       "      <td>-0.206866</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>-0.056080</td>\n",
       "      <td>-0.122408</td>\n",
       "      <td>-0.147626</td>\n",
       "      <td>-0.164564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.110583</td>\n",
       "      <td>-0.010241</td>\n",
       "      <td>-0.077077</td>\n",
       "      <td>0.207558</td>\n",
       "      <td>-0.303656</td>\n",
       "      <td>0.344166</td>\n",
       "      <td>0.036743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.007719</td>\n",
       "      <td>0.085526</td>\n",
       "      <td>0.242533</td>\n",
       "      <td>0.100488</td>\n",
       "      <td>0.066098</td>\n",
       "      <td>0.053350</td>\n",
       "      <td>0.031169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.320732</td>\n",
       "      <td>0.491262</td>\n",
       "      <td>0.077561</td>\n",
       "      <td>0.561988</td>\n",
       "      <td>-0.134448</td>\n",
       "      <td>-0.019375</td>\n",
       "      <td>0.441564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.500319</td>\n",
       "      <td>0.342583</td>\n",
       "      <td>-0.294768</td>\n",
       "      <td>-0.187991</td>\n",
       "      <td>-0.171476</td>\n",
       "      <td>-0.448460</td>\n",
       "      <td>0.325237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.036636</td>\n",
       "      <td>-0.069934</td>\n",
       "      <td>-0.231592</td>\n",
       "      <td>-0.070503</td>\n",
       "      <td>-0.065659</td>\n",
       "      <td>-0.154730</td>\n",
       "      <td>-0.080890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.179287</td>\n",
       "      <td>0.141868</td>\n",
       "      <td>0.255526</td>\n",
       "      <td>0.340168</td>\n",
       "      <td>-0.086910</td>\n",
       "      <td>-0.040557</td>\n",
       "      <td>0.177957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.033230</td>\n",
       "      <td>-0.334379</td>\n",
       "      <td>-0.344489</td>\n",
       "      <td>-0.727206</td>\n",
       "      <td>0.092031</td>\n",
       "      <td>-0.223650</td>\n",
       "      <td>-0.272118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.266336</td>\n",
       "      <td>0.450605</td>\n",
       "      <td>0.227971</td>\n",
       "      <td>0.582393</td>\n",
       "      <td>-0.072041</td>\n",
       "      <td>0.070857</td>\n",
       "      <td>0.421566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.039926</td>\n",
       "      <td>-0.245193</td>\n",
       "      <td>-0.411786</td>\n",
       "      <td>-0.636174</td>\n",
       "      <td>0.049883</td>\n",
       "      <td>-0.190313</td>\n",
       "      <td>-0.198701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.220198</td>\n",
       "      <td>0.454019</td>\n",
       "      <td>-0.199516</td>\n",
       "      <td>0.373283</td>\n",
       "      <td>-0.079499</td>\n",
       "      <td>-0.271995</td>\n",
       "      <td>0.434490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.075216</td>\n",
       "      <td>-0.189436</td>\n",
       "      <td>-0.124023</td>\n",
       "      <td>-0.527513</td>\n",
       "      <td>0.115857</td>\n",
       "      <td>-0.219321</td>\n",
       "      <td>-0.088387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.149579</td>\n",
       "      <td>-0.129758</td>\n",
       "      <td>-0.050675</td>\n",
       "      <td>-0.082651</td>\n",
       "      <td>0.100765</td>\n",
       "      <td>-0.059424</td>\n",
       "      <td>-0.243294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.367739</td>\n",
       "      <td>0.372890</td>\n",
       "      <td>-0.148895</td>\n",
       "      <td>0.322162</td>\n",
       "      <td>-0.156091</td>\n",
       "      <td>0.084450</td>\n",
       "      <td>0.409641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.212617</td>\n",
       "      <td>-0.393918</td>\n",
       "      <td>-0.086103</td>\n",
       "      <td>-0.448268</td>\n",
       "      <td>-0.229562</td>\n",
       "      <td>-0.036812</td>\n",
       "      <td>-0.287787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.126283</td>\n",
       "      <td>-0.260393</td>\n",
       "      <td>-0.393596</td>\n",
       "      <td>-0.689671</td>\n",
       "      <td>-0.049083</td>\n",
       "      <td>-0.229362</td>\n",
       "      <td>-0.161873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.347372</td>\n",
       "      <td>-0.260108</td>\n",
       "      <td>-0.198309</td>\n",
       "      <td>-0.329881</td>\n",
       "      <td>-0.337093</td>\n",
       "      <td>-0.077953</td>\n",
       "      <td>-0.164641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.282928</td>\n",
       "      <td>0.125514</td>\n",
       "      <td>0.103771</td>\n",
       "      <td>-0.066521</td>\n",
       "      <td>0.323215</td>\n",
       "      <td>-0.092385</td>\n",
       "      <td>-0.008965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.333887</td>\n",
       "      <td>0.301672</td>\n",
       "      <td>-0.049475</td>\n",
       "      <td>0.509637</td>\n",
       "      <td>-0.253858</td>\n",
       "      <td>0.251036</td>\n",
       "      <td>0.247645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.410902</td>\n",
       "      <td>0.176350</td>\n",
       "      <td>0.089363</td>\n",
       "      <td>0.328497</td>\n",
       "      <td>-0.239034</td>\n",
       "      <td>-0.217853</td>\n",
       "      <td>0.266521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6\n",
       "0  -0.477046 -0.192757 -0.167770 -0.093991 -0.421097  0.033699 -0.133764\n",
       "1  -0.201768 -0.207688  0.060185 -0.101657 -0.158459 -0.183677 -0.140968\n",
       "2  -0.203906  0.413593  0.219152  0.551124 -0.070080  0.112728  0.376650\n",
       "3  -0.442394  0.260362 -0.567470 -0.078076 -0.343198 -0.231407  0.312182\n",
       "4   0.079514 -0.323892 -0.044409 -0.511921  0.084673  0.091093 -0.240818\n",
       "5   0.301565 -0.106593  0.185515  0.091154  0.041218  0.220544 -0.133587\n",
       "6  -0.166325 -0.020144 -0.285004 -0.476031 -0.042894 -0.145127  0.003744\n",
       "7  -0.384400 -0.182163  0.025049  0.019121 -0.373067  0.130319 -0.129874\n",
       "8  -0.154261 -0.206866  0.140276 -0.056080 -0.122408 -0.147626 -0.164564\n",
       "9  -0.110583 -0.010241 -0.077077  0.207558 -0.303656  0.344166  0.036743\n",
       "10  0.007719  0.085526  0.242533  0.100488  0.066098  0.053350  0.031169\n",
       "11 -0.320732  0.491262  0.077561  0.561988 -0.134448 -0.019375  0.441564\n",
       "12 -0.500319  0.342583 -0.294768 -0.187991 -0.171476 -0.448460  0.325237\n",
       "13 -0.036636 -0.069934 -0.231592 -0.070503 -0.065659 -0.154730 -0.080890\n",
       "14 -0.179287  0.141868  0.255526  0.340168 -0.086910 -0.040557  0.177957\n",
       "15  0.033230 -0.334379 -0.344489 -0.727206  0.092031 -0.223650 -0.272118\n",
       "16 -0.266336  0.450605  0.227971  0.582393 -0.072041  0.070857  0.421566\n",
       "17 -0.039926 -0.245193 -0.411786 -0.636174  0.049883 -0.190313 -0.198701\n",
       "18 -0.220198  0.454019 -0.199516  0.373283 -0.079499 -0.271995  0.434490\n",
       "19  0.075216 -0.189436 -0.124023 -0.527513  0.115857 -0.219321 -0.088387\n",
       "20  0.149579 -0.129758 -0.050675 -0.082651  0.100765 -0.059424 -0.243294\n",
       "21 -0.367739  0.372890 -0.148895  0.322162 -0.156091  0.084450  0.409641\n",
       "22 -0.212617 -0.393918 -0.086103 -0.448268 -0.229562 -0.036812 -0.287787\n",
       "23 -0.126283 -0.260393 -0.393596 -0.689671 -0.049083 -0.229362 -0.161873\n",
       "24 -0.347372 -0.260108 -0.198309 -0.329881 -0.337093 -0.077953 -0.164641\n",
       "25  0.282928  0.125514  0.103771 -0.066521  0.323215 -0.092385 -0.008965\n",
       "26 -0.333887  0.301672 -0.049475  0.509637 -0.253858  0.251036  0.247645\n",
       "27 -0.410902  0.176350  0.089363  0.328497 -0.239034 -0.217853  0.266521"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"spearman\", \n",
    "    r_to_z=False, # apply r to z transformation\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "display(juspyce_vol.predictions[\"spearman\"])\n",
    "\n",
    "print(\"check\")\n",
    "check_res = np.zeros((juspyce_vol.Y.shape[0], juspyce_vol.X.shape[0]))\n",
    "for y in range(juspyce_vol.Y.shape[0]):\n",
    "    for x in range(juspyce_vol.X.shape[0]):\n",
    "        check_res[y,x] = check_spearman(\n",
    "            x=juspyce_vol.X.iloc[x,:].values,\n",
    "            y=juspyce_vol.Y.iloc[y,:].values)\n",
    "display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partial Pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2970ca900f141dca290c92cecffe3f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (partialpearson, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>-0.439563</td>\n",
       "      <td>0.212916</td>\n",
       "      <td>-0.012629</td>\n",
       "      <td>0.287942</td>\n",
       "      <td>-0.315345</td>\n",
       "      <td>-0.262424</td>\n",
       "      <td>0.085017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>-0.163370</td>\n",
       "      <td>-0.047337</td>\n",
       "      <td>0.246797</td>\n",
       "      <td>0.038643</td>\n",
       "      <td>-0.012413</td>\n",
       "      <td>-0.240124</td>\n",
       "      <td>-0.108650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>-0.133766</td>\n",
       "      <td>0.291374</td>\n",
       "      <td>0.218537</td>\n",
       "      <td>0.202578</td>\n",
       "      <td>-0.049240</td>\n",
       "      <td>0.187051</td>\n",
       "      <td>0.227438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>-0.629198</td>\n",
       "      <td>0.469656</td>\n",
       "      <td>-0.503226</td>\n",
       "      <td>0.282278</td>\n",
       "      <td>-0.585490</td>\n",
       "      <td>0.077689</td>\n",
       "      <td>0.717281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.253804</td>\n",
       "      <td>-0.191335</td>\n",
       "      <td>0.148254</td>\n",
       "      <td>-0.362209</td>\n",
       "      <td>0.306896</td>\n",
       "      <td>-0.163743</td>\n",
       "      <td>-0.257716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.288817</td>\n",
       "      <td>-0.106219</td>\n",
       "      <td>0.178105</td>\n",
       "      <td>0.016272</td>\n",
       "      <td>0.093469</td>\n",
       "      <td>0.172564</td>\n",
       "      <td>-0.059090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>-0.099141</td>\n",
       "      <td>0.121093</td>\n",
       "      <td>-0.145599</td>\n",
       "      <td>-0.326164</td>\n",
       "      <td>0.051351</td>\n",
       "      <td>-0.179585</td>\n",
       "      <td>-0.036060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>-0.369359</td>\n",
       "      <td>0.127019</td>\n",
       "      <td>0.146589</td>\n",
       "      <td>0.270324</td>\n",
       "      <td>-0.220519</td>\n",
       "      <td>-0.139754</td>\n",
       "      <td>0.087999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>-0.132953</td>\n",
       "      <td>-0.071924</td>\n",
       "      <td>0.284019</td>\n",
       "      <td>0.030963</td>\n",
       "      <td>0.016054</td>\n",
       "      <td>-0.216157</td>\n",
       "      <td>-0.121893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>-0.332831</td>\n",
       "      <td>0.309674</td>\n",
       "      <td>-0.115176</td>\n",
       "      <td>0.593241</td>\n",
       "      <td>-0.443135</td>\n",
       "      <td>0.395865</td>\n",
       "      <td>0.703941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.154976</td>\n",
       "      <td>0.103257</td>\n",
       "      <td>0.310357</td>\n",
       "      <td>-0.028606</td>\n",
       "      <td>0.159577</td>\n",
       "      <td>0.033005</td>\n",
       "      <td>-0.076528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>-0.484386</td>\n",
       "      <td>0.634425</td>\n",
       "      <td>-0.184731</td>\n",
       "      <td>0.531273</td>\n",
       "      <td>-0.483670</td>\n",
       "      <td>0.041996</td>\n",
       "      <td>0.419043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>-0.535700</td>\n",
       "      <td>0.638349</td>\n",
       "      <td>-0.475041</td>\n",
       "      <td>0.266916</td>\n",
       "      <td>-0.511040</td>\n",
       "      <td>-0.377619</td>\n",
       "      <td>0.214742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>-0.094733</td>\n",
       "      <td>-0.059716</td>\n",
       "      <td>-0.248722</td>\n",
       "      <td>-0.210409</td>\n",
       "      <td>-0.050255</td>\n",
       "      <td>-0.217073</td>\n",
       "      <td>-0.097653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>-0.120506</td>\n",
       "      <td>0.190268</td>\n",
       "      <td>0.312890</td>\n",
       "      <td>0.213426</td>\n",
       "      <td>-0.036818</td>\n",
       "      <td>0.033263</td>\n",
       "      <td>0.068320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.231521</td>\n",
       "      <td>-0.204899</td>\n",
       "      <td>-0.245426</td>\n",
       "      <td>-0.599895</td>\n",
       "      <td>0.300483</td>\n",
       "      <td>-0.166197</td>\n",
       "      <td>-0.217650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>-0.208302</td>\n",
       "      <td>0.348680</td>\n",
       "      <td>0.182766</td>\n",
       "      <td>0.286722</td>\n",
       "      <td>-0.114487</td>\n",
       "      <td>0.173593</td>\n",
       "      <td>0.244324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.199054</td>\n",
       "      <td>-0.151312</td>\n",
       "      <td>-0.297069</td>\n",
       "      <td>-0.561292</td>\n",
       "      <td>0.270844</td>\n",
       "      <td>-0.093622</td>\n",
       "      <td>-0.156099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>-0.411447</td>\n",
       "      <td>0.072752</td>\n",
       "      <td>-0.469548</td>\n",
       "      <td>0.121177</td>\n",
       "      <td>-0.351122</td>\n",
       "      <td>-0.006499</td>\n",
       "      <td>0.075833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.235332</td>\n",
       "      <td>-0.113629</td>\n",
       "      <td>-0.012856</td>\n",
       "      <td>-0.458070</td>\n",
       "      <td>0.256437</td>\n",
       "      <td>-0.232125</td>\n",
       "      <td>-0.261554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.251034</td>\n",
       "      <td>-0.016944</td>\n",
       "      <td>0.137648</td>\n",
       "      <td>-0.128992</td>\n",
       "      <td>0.211102</td>\n",
       "      <td>-0.173982</td>\n",
       "      <td>-0.253446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>-0.517937</td>\n",
       "      <td>0.449610</td>\n",
       "      <td>-0.182026</td>\n",
       "      <td>0.605309</td>\n",
       "      <td>-0.508808</td>\n",
       "      <td>0.406062</td>\n",
       "      <td>0.807781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>-0.138971</td>\n",
       "      <td>-0.168128</td>\n",
       "      <td>0.184249</td>\n",
       "      <td>-0.103513</td>\n",
       "      <td>0.031814</td>\n",
       "      <td>-0.345233</td>\n",
       "      <td>-0.174354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>0.110061</td>\n",
       "      <td>-0.153125</td>\n",
       "      <td>-0.255642</td>\n",
       "      <td>-0.523773</td>\n",
       "      <td>0.209217</td>\n",
       "      <td>-0.199141</td>\n",
       "      <td>-0.125891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>-0.299446</td>\n",
       "      <td>0.005112</td>\n",
       "      <td>0.040469</td>\n",
       "      <td>0.017766</td>\n",
       "      <td>-0.112722</td>\n",
       "      <td>-0.275876</td>\n",
       "      <td>-0.042412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.238234</td>\n",
       "      <td>0.044825</td>\n",
       "      <td>0.150168</td>\n",
       "      <td>-0.247628</td>\n",
       "      <td>0.255882</td>\n",
       "      <td>0.021296</td>\n",
       "      <td>-0.172941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>-0.567796</td>\n",
       "      <td>0.540966</td>\n",
       "      <td>-0.197411</td>\n",
       "      <td>0.706474</td>\n",
       "      <td>-0.600035</td>\n",
       "      <td>0.362522</td>\n",
       "      <td>0.844043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>-0.353448</td>\n",
       "      <td>0.301598</td>\n",
       "      <td>0.088702</td>\n",
       "      <td>0.321206</td>\n",
       "      <td>-0.298144</td>\n",
       "      <td>-0.190718</td>\n",
       "      <td>0.101904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                            -0.439563                   0.212916   \n",
       "touch                              -0.163370                  -0.047337   \n",
       "interoception                      -0.133766                   0.291374   \n",
       "learning                           -0.629198                   0.469656   \n",
       "attention                           0.253804                  -0.191335   \n",
       "language                            0.288817                  -0.106219   \n",
       "interaction                        -0.099141                   0.121093   \n",
       "inhibition                         -0.369359                   0.127019   \n",
       "somatosensory                      -0.132953                  -0.071924   \n",
       "decision                           -0.332831                   0.309674   \n",
       "auditory                            0.154976                   0.103257   \n",
       "taste                              -0.484386                   0.634425   \n",
       "metabolism                         -0.535700                   0.638349   \n",
       "memory                             -0.094733                  -0.059716   \n",
       "empathy                            -0.120506                   0.190268   \n",
       "vision                              0.231521                  -0.204899   \n",
       "autonomic                          -0.208302                   0.348680   \n",
       "visual                              0.199054                  -0.151312   \n",
       "fear                               -0.411447                   0.072752   \n",
       "perception                          0.235332                  -0.113629   \n",
       "cognition                           0.251034                  -0.016944   \n",
       "reward                             -0.517937                   0.449610   \n",
       "mirror                             -0.138971                  -0.168128   \n",
       "orientation                         0.110061                  -0.153125   \n",
       "movement                           -0.299446                   0.005112   \n",
       "default_mode                        0.238234                   0.044825   \n",
       "dopamine                           -0.567796                   0.540966   \n",
       "pain                               -0.353448                   0.301598   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                         -0.012629                         0.287942   \n",
       "touch                            0.246797                         0.038643   \n",
       "interoception                    0.218537                         0.202578   \n",
       "learning                        -0.503226                         0.282278   \n",
       "attention                        0.148254                        -0.362209   \n",
       "language                         0.178105                         0.016272   \n",
       "interaction                     -0.145599                        -0.326164   \n",
       "inhibition                       0.146589                         0.270324   \n",
       "somatosensory                    0.284019                         0.030963   \n",
       "decision                        -0.115176                         0.593241   \n",
       "auditory                         0.310357                        -0.028606   \n",
       "taste                           -0.184731                         0.531273   \n",
       "metabolism                      -0.475041                         0.266916   \n",
       "memory                          -0.248722                        -0.210409   \n",
       "empathy                          0.312890                         0.213426   \n",
       "vision                          -0.245426                        -0.599895   \n",
       "autonomic                        0.182766                         0.286722   \n",
       "visual                          -0.297069                        -0.561292   \n",
       "fear                            -0.469548                         0.121177   \n",
       "perception                      -0.012856                        -0.458070   \n",
       "cognition                        0.137648                        -0.128992   \n",
       "reward                          -0.182026                         0.605309   \n",
       "mirror                           0.184249                        -0.103513   \n",
       "orientation                     -0.255642                        -0.523773   \n",
       "movement                         0.040469                         0.017766   \n",
       "default_mode                     0.150168                        -0.247628   \n",
       "dopamine                        -0.197411                         0.706474   \n",
       "pain                             0.088702                         0.321206   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                            -0.315345                   -0.262424   \n",
       "touch                              -0.012413                   -0.240124   \n",
       "interoception                      -0.049240                    0.187051   \n",
       "learning                           -0.585490                    0.077689   \n",
       "attention                           0.306896                   -0.163743   \n",
       "language                            0.093469                    0.172564   \n",
       "interaction                         0.051351                   -0.179585   \n",
       "inhibition                         -0.220519                   -0.139754   \n",
       "somatosensory                       0.016054                   -0.216157   \n",
       "decision                           -0.443135                    0.395865   \n",
       "auditory                            0.159577                    0.033005   \n",
       "taste                              -0.483670                    0.041996   \n",
       "metabolism                         -0.511040                   -0.377619   \n",
       "memory                             -0.050255                   -0.217073   \n",
       "empathy                            -0.036818                    0.033263   \n",
       "vision                              0.300483                   -0.166197   \n",
       "autonomic                          -0.114487                    0.173593   \n",
       "visual                              0.270844                   -0.093622   \n",
       "fear                               -0.351122                   -0.006499   \n",
       "perception                          0.256437                   -0.232125   \n",
       "cognition                           0.211102                   -0.173982   \n",
       "reward                             -0.508808                    0.406062   \n",
       "mirror                              0.031814                   -0.345233   \n",
       "orientation                         0.209217                   -0.199141   \n",
       "movement                           -0.112722                   -0.275876   \n",
       "default_mode                        0.255882                    0.021296   \n",
       "dopamine                           -0.600035                    0.362522   \n",
       "pain                               -0.298144                   -0.190718   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.085017  \n",
       "touch                            -0.108650  \n",
       "interoception                     0.227438  \n",
       "learning                          0.717281  \n",
       "attention                        -0.257716  \n",
       "language                         -0.059090  \n",
       "interaction                      -0.036060  \n",
       "inhibition                        0.087999  \n",
       "somatosensory                    -0.121893  \n",
       "decision                          0.703941  \n",
       "auditory                         -0.076528  \n",
       "taste                             0.419043  \n",
       "metabolism                        0.214742  \n",
       "memory                           -0.097653  \n",
       "empathy                           0.068320  \n",
       "vision                           -0.217650  \n",
       "autonomic                         0.244324  \n",
       "visual                           -0.156099  \n",
       "fear                              0.075833  \n",
       "perception                       -0.261554  \n",
       "cognition                        -0.253446  \n",
       "reward                            0.807781  \n",
       "mirror                           -0.174354  \n",
       "orientation                      -0.125891  \n",
       "movement                         -0.042412  \n",
       "default_mode                     -0.172941  \n",
       "dopamine                          0.844043  \n",
       "pain                              0.101904  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.439563</td>\n",
       "      <td>0.212916</td>\n",
       "      <td>-0.012629</td>\n",
       "      <td>0.287942</td>\n",
       "      <td>-0.315345</td>\n",
       "      <td>-0.262424</td>\n",
       "      <td>0.085017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.163370</td>\n",
       "      <td>-0.047337</td>\n",
       "      <td>0.246797</td>\n",
       "      <td>0.038643</td>\n",
       "      <td>-0.012413</td>\n",
       "      <td>-0.240124</td>\n",
       "      <td>-0.108650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.133766</td>\n",
       "      <td>0.291374</td>\n",
       "      <td>0.218537</td>\n",
       "      <td>0.202578</td>\n",
       "      <td>-0.049240</td>\n",
       "      <td>0.187051</td>\n",
       "      <td>0.227438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.629198</td>\n",
       "      <td>0.469656</td>\n",
       "      <td>-0.503226</td>\n",
       "      <td>0.282278</td>\n",
       "      <td>-0.585490</td>\n",
       "      <td>0.077689</td>\n",
       "      <td>0.717281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.253804</td>\n",
       "      <td>-0.191335</td>\n",
       "      <td>0.148254</td>\n",
       "      <td>-0.362209</td>\n",
       "      <td>0.306896</td>\n",
       "      <td>-0.163743</td>\n",
       "      <td>-0.257716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.288817</td>\n",
       "      <td>-0.106219</td>\n",
       "      <td>0.178105</td>\n",
       "      <td>0.016272</td>\n",
       "      <td>0.093469</td>\n",
       "      <td>0.172564</td>\n",
       "      <td>-0.059090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.099141</td>\n",
       "      <td>0.121093</td>\n",
       "      <td>-0.145599</td>\n",
       "      <td>-0.326164</td>\n",
       "      <td>0.051351</td>\n",
       "      <td>-0.179585</td>\n",
       "      <td>-0.036060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.369359</td>\n",
       "      <td>0.127019</td>\n",
       "      <td>0.146589</td>\n",
       "      <td>0.270324</td>\n",
       "      <td>-0.220519</td>\n",
       "      <td>-0.139754</td>\n",
       "      <td>0.087999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.132953</td>\n",
       "      <td>-0.071924</td>\n",
       "      <td>0.284019</td>\n",
       "      <td>0.030963</td>\n",
       "      <td>0.016054</td>\n",
       "      <td>-0.216157</td>\n",
       "      <td>-0.121893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.332831</td>\n",
       "      <td>0.309674</td>\n",
       "      <td>-0.115176</td>\n",
       "      <td>0.593241</td>\n",
       "      <td>-0.443135</td>\n",
       "      <td>0.395865</td>\n",
       "      <td>0.703941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.154976</td>\n",
       "      <td>0.103257</td>\n",
       "      <td>0.310357</td>\n",
       "      <td>-0.028606</td>\n",
       "      <td>0.159577</td>\n",
       "      <td>0.033005</td>\n",
       "      <td>-0.076528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.484386</td>\n",
       "      <td>0.634425</td>\n",
       "      <td>-0.184731</td>\n",
       "      <td>0.531273</td>\n",
       "      <td>-0.483670</td>\n",
       "      <td>0.041996</td>\n",
       "      <td>0.419043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.535700</td>\n",
       "      <td>0.638349</td>\n",
       "      <td>-0.475041</td>\n",
       "      <td>0.266916</td>\n",
       "      <td>-0.511040</td>\n",
       "      <td>-0.377619</td>\n",
       "      <td>0.214742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.094733</td>\n",
       "      <td>-0.059716</td>\n",
       "      <td>-0.248722</td>\n",
       "      <td>-0.210409</td>\n",
       "      <td>-0.050255</td>\n",
       "      <td>-0.217073</td>\n",
       "      <td>-0.097653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.120506</td>\n",
       "      <td>0.190268</td>\n",
       "      <td>0.312890</td>\n",
       "      <td>0.213426</td>\n",
       "      <td>-0.036818</td>\n",
       "      <td>0.033263</td>\n",
       "      <td>0.068320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.231521</td>\n",
       "      <td>-0.204899</td>\n",
       "      <td>-0.245426</td>\n",
       "      <td>-0.599895</td>\n",
       "      <td>0.300483</td>\n",
       "      <td>-0.166197</td>\n",
       "      <td>-0.217650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.208302</td>\n",
       "      <td>0.348680</td>\n",
       "      <td>0.182766</td>\n",
       "      <td>0.286722</td>\n",
       "      <td>-0.114487</td>\n",
       "      <td>0.173593</td>\n",
       "      <td>0.244324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.199054</td>\n",
       "      <td>-0.151312</td>\n",
       "      <td>-0.297069</td>\n",
       "      <td>-0.561292</td>\n",
       "      <td>0.270844</td>\n",
       "      <td>-0.093622</td>\n",
       "      <td>-0.156099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.411447</td>\n",
       "      <td>0.072752</td>\n",
       "      <td>-0.469548</td>\n",
       "      <td>0.121177</td>\n",
       "      <td>-0.351122</td>\n",
       "      <td>-0.006499</td>\n",
       "      <td>0.075833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.235332</td>\n",
       "      <td>-0.113629</td>\n",
       "      <td>-0.012856</td>\n",
       "      <td>-0.458070</td>\n",
       "      <td>0.256437</td>\n",
       "      <td>-0.232125</td>\n",
       "      <td>-0.261554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.251034</td>\n",
       "      <td>-0.016944</td>\n",
       "      <td>0.137648</td>\n",
       "      <td>-0.128992</td>\n",
       "      <td>0.211102</td>\n",
       "      <td>-0.173982</td>\n",
       "      <td>-0.253446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.517937</td>\n",
       "      <td>0.449610</td>\n",
       "      <td>-0.182026</td>\n",
       "      <td>0.605309</td>\n",
       "      <td>-0.508808</td>\n",
       "      <td>0.406062</td>\n",
       "      <td>0.807781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.138971</td>\n",
       "      <td>-0.168128</td>\n",
       "      <td>0.184249</td>\n",
       "      <td>-0.103513</td>\n",
       "      <td>0.031814</td>\n",
       "      <td>-0.345233</td>\n",
       "      <td>-0.174354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.110061</td>\n",
       "      <td>-0.153125</td>\n",
       "      <td>-0.255642</td>\n",
       "      <td>-0.523773</td>\n",
       "      <td>0.209217</td>\n",
       "      <td>-0.199141</td>\n",
       "      <td>-0.125891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.299446</td>\n",
       "      <td>0.005112</td>\n",
       "      <td>0.040469</td>\n",
       "      <td>0.017766</td>\n",
       "      <td>-0.112722</td>\n",
       "      <td>-0.275876</td>\n",
       "      <td>-0.042412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.238234</td>\n",
       "      <td>0.044825</td>\n",
       "      <td>0.150168</td>\n",
       "      <td>-0.247628</td>\n",
       "      <td>0.255882</td>\n",
       "      <td>0.021296</td>\n",
       "      <td>-0.172941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.567796</td>\n",
       "      <td>0.540966</td>\n",
       "      <td>-0.197411</td>\n",
       "      <td>0.706474</td>\n",
       "      <td>-0.600035</td>\n",
       "      <td>0.362522</td>\n",
       "      <td>0.844043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.353448</td>\n",
       "      <td>0.301598</td>\n",
       "      <td>0.088702</td>\n",
       "      <td>0.321206</td>\n",
       "      <td>-0.298144</td>\n",
       "      <td>-0.190718</td>\n",
       "      <td>0.101904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6\n",
       "0  -0.439563  0.212916 -0.012629  0.287942 -0.315345 -0.262424  0.085017\n",
       "1  -0.163370 -0.047337  0.246797  0.038643 -0.012413 -0.240124 -0.108650\n",
       "2  -0.133766  0.291374  0.218537  0.202578 -0.049240  0.187051  0.227438\n",
       "3  -0.629198  0.469656 -0.503226  0.282278 -0.585490  0.077689  0.717281\n",
       "4   0.253804 -0.191335  0.148254 -0.362209  0.306896 -0.163743 -0.257716\n",
       "5   0.288817 -0.106219  0.178105  0.016272  0.093469  0.172564 -0.059090\n",
       "6  -0.099141  0.121093 -0.145599 -0.326164  0.051351 -0.179585 -0.036060\n",
       "7  -0.369359  0.127019  0.146589  0.270324 -0.220519 -0.139754  0.087999\n",
       "8  -0.132953 -0.071924  0.284019  0.030963  0.016054 -0.216157 -0.121893\n",
       "9  -0.332831  0.309674 -0.115176  0.593241 -0.443135  0.395865  0.703941\n",
       "10  0.154976  0.103257  0.310357 -0.028606  0.159577  0.033005 -0.076528\n",
       "11 -0.484386  0.634425 -0.184731  0.531273 -0.483670  0.041996  0.419043\n",
       "12 -0.535700  0.638349 -0.475041  0.266916 -0.511040 -0.377619  0.214742\n",
       "13 -0.094733 -0.059716 -0.248722 -0.210409 -0.050255 -0.217073 -0.097653\n",
       "14 -0.120506  0.190268  0.312890  0.213426 -0.036818  0.033263  0.068320\n",
       "15  0.231521 -0.204899 -0.245426 -0.599895  0.300483 -0.166197 -0.217650\n",
       "16 -0.208302  0.348680  0.182766  0.286722 -0.114487  0.173593  0.244324\n",
       "17  0.199054 -0.151312 -0.297069 -0.561292  0.270844 -0.093622 -0.156099\n",
       "18 -0.411447  0.072752 -0.469548  0.121177 -0.351122 -0.006499  0.075833\n",
       "19  0.235332 -0.113629 -0.012856 -0.458070  0.256437 -0.232125 -0.261554\n",
       "20  0.251034 -0.016944  0.137648 -0.128992  0.211102 -0.173982 -0.253446\n",
       "21 -0.517937  0.449610 -0.182026  0.605309 -0.508808  0.406062  0.807781\n",
       "22 -0.138971 -0.168128  0.184249 -0.103513  0.031814 -0.345233 -0.174354\n",
       "23  0.110061 -0.153125 -0.255642 -0.523773  0.209217 -0.199141 -0.125891\n",
       "24 -0.299446  0.005112  0.040469  0.017766 -0.112722 -0.275876 -0.042412\n",
       "25  0.238234  0.044825  0.150168 -0.247628  0.255882  0.021296 -0.172941\n",
       "26 -0.567796  0.540966 -0.197411  0.706474 -0.600035  0.362522  0.844043\n",
       "27 -0.353448  0.301598  0.088702  0.321206 -0.298144 -0.190718  0.101904"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"partialpearson\", \n",
    "    r_to_z=False, # apply r to z transformation\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "display(juspyce_vol.predictions[\"partialpearson\"])\n",
    "\n",
    "print(\"check\")\n",
    "check_res = np.zeros((juspyce_vol.Y.shape[0], juspyce_vol.X.shape[0]))\n",
    "for y in range(juspyce_vol.Y.shape[0]):\n",
    "    for x in range(juspyce_vol.X.shape[0]):\n",
    "        check_res[y,x] = check_partialpearson(\n",
    "            x=juspyce_vol.X.iloc[x,:].values,\n",
    "            y=juspyce_vol.Y.iloc[y,:].values,\n",
    "            z=juspyce_vol.Z.values[0,:])\n",
    "display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partial Spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "477ff10277b844d2bb75ea9b3928f5bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (partialspearman, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>-0.468742</td>\n",
       "      <td>0.229559</td>\n",
       "      <td>-0.007421</td>\n",
       "      <td>0.285056</td>\n",
       "      <td>-0.249232</td>\n",
       "      <td>-0.150657</td>\n",
       "      <td>0.231564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>-0.146357</td>\n",
       "      <td>-0.026371</td>\n",
       "      <td>0.164177</td>\n",
       "      <td>0.067679</td>\n",
       "      <td>-0.029191</td>\n",
       "      <td>-0.289144</td>\n",
       "      <td>0.022426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>-0.279984</td>\n",
       "      <td>0.313140</td>\n",
       "      <td>0.154563</td>\n",
       "      <td>0.487672</td>\n",
       "      <td>-0.210166</td>\n",
       "      <td>0.196064</td>\n",
       "      <td>0.281528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>-0.462153</td>\n",
       "      <td>0.274384</td>\n",
       "      <td>-0.601871</td>\n",
       "      <td>-0.115365</td>\n",
       "      <td>-0.394325</td>\n",
       "      <td>-0.225309</td>\n",
       "      <td>0.323729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.228117</td>\n",
       "      <td>-0.038223</td>\n",
       "      <td>0.119903</td>\n",
       "      <td>-0.351110</td>\n",
       "      <td>0.385361</td>\n",
       "      <td>-0.044721</td>\n",
       "      <td>0.017599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.297722</td>\n",
       "      <td>-0.158726</td>\n",
       "      <td>0.178679</td>\n",
       "      <td>0.076494</td>\n",
       "      <td>0.023610</td>\n",
       "      <td>0.238912</td>\n",
       "      <td>-0.176691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>-0.128013</td>\n",
       "      <td>0.124505</td>\n",
       "      <td>-0.240980</td>\n",
       "      <td>-0.429157</td>\n",
       "      <td>0.049370</td>\n",
       "      <td>-0.208579</td>\n",
       "      <td>0.125271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>-0.341566</td>\n",
       "      <td>0.194105</td>\n",
       "      <td>0.222828</td>\n",
       "      <td>0.396114</td>\n",
       "      <td>-0.198793</td>\n",
       "      <td>-0.005853</td>\n",
       "      <td>0.193408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>-0.099540</td>\n",
       "      <td>-0.043936</td>\n",
       "      <td>0.242049</td>\n",
       "      <td>0.105816</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-0.239968</td>\n",
       "      <td>-0.021148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>-0.067162</td>\n",
       "      <td>0.145859</td>\n",
       "      <td>-0.016548</td>\n",
       "      <td>0.367091</td>\n",
       "      <td>-0.235510</td>\n",
       "      <td>0.305577</td>\n",
       "      <td>0.171131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.018838</td>\n",
       "      <td>0.136881</td>\n",
       "      <td>0.265835</td>\n",
       "      <td>0.141359</td>\n",
       "      <td>0.094705</td>\n",
       "      <td>0.041727</td>\n",
       "      <td>0.063871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>-0.430261</td>\n",
       "      <td>0.373203</td>\n",
       "      <td>-0.018548</td>\n",
       "      <td>0.475821</td>\n",
       "      <td>-0.321566</td>\n",
       "      <td>0.072738</td>\n",
       "      <td>0.329236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>-0.510940</td>\n",
       "      <td>0.402309</td>\n",
       "      <td>-0.306210</td>\n",
       "      <td>-0.213713</td>\n",
       "      <td>-0.187787</td>\n",
       "      <td>-0.459505</td>\n",
       "      <td>0.363059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>-0.034657</td>\n",
       "      <td>-0.074169</td>\n",
       "      <td>-0.235891</td>\n",
       "      <td>-0.072169</td>\n",
       "      <td>-0.065409</td>\n",
       "      <td>-0.162217</td>\n",
       "      <td>-0.083965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>-0.190897</td>\n",
       "      <td>0.142190</td>\n",
       "      <td>0.253669</td>\n",
       "      <td>0.361197</td>\n",
       "      <td>-0.111035</td>\n",
       "      <td>-0.032047</td>\n",
       "      <td>0.179262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.119937</td>\n",
       "      <td>-0.161269</td>\n",
       "      <td>-0.275486</td>\n",
       "      <td>-0.670558</td>\n",
       "      <td>0.283223</td>\n",
       "      <td>-0.349523</td>\n",
       "      <td>-0.114233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>-0.349513</td>\n",
       "      <td>0.354381</td>\n",
       "      <td>0.162210</td>\n",
       "      <td>0.521366</td>\n",
       "      <td>-0.216780</td>\n",
       "      <td>0.153537</td>\n",
       "      <td>0.330800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.032036</td>\n",
       "      <td>-0.066690</td>\n",
       "      <td>-0.355009</td>\n",
       "      <td>-0.568870</td>\n",
       "      <td>0.217088</td>\n",
       "      <td>-0.300477</td>\n",
       "      <td>-0.041064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>-0.367277</td>\n",
       "      <td>0.260737</td>\n",
       "      <td>-0.383811</td>\n",
       "      <td>0.194834</td>\n",
       "      <td>-0.330259</td>\n",
       "      <td>-0.188105</td>\n",
       "      <td>0.271578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.126478</td>\n",
       "      <td>-0.076975</td>\n",
       "      <td>-0.068212</td>\n",
       "      <td>-0.485641</td>\n",
       "      <td>0.229217</td>\n",
       "      <td>-0.289100</td>\n",
       "      <td>0.022600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.194981</td>\n",
       "      <td>-0.029093</td>\n",
       "      <td>-0.000567</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.194360</td>\n",
       "      <td>-0.109753</td>\n",
       "      <td>-0.175533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>-0.404685</td>\n",
       "      <td>0.359914</td>\n",
       "      <td>-0.190862</td>\n",
       "      <td>0.296012</td>\n",
       "      <td>-0.226268</td>\n",
       "      <td>0.119439</td>\n",
       "      <td>0.395386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>-0.111668</td>\n",
       "      <td>-0.035946</td>\n",
       "      <td>0.129933</td>\n",
       "      <td>-0.207749</td>\n",
       "      <td>0.053642</td>\n",
       "      <td>-0.280290</td>\n",
       "      <td>0.047189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>-0.050867</td>\n",
       "      <td>-0.045823</td>\n",
       "      <td>-0.326733</td>\n",
       "      <td>-0.617039</td>\n",
       "      <td>0.132988</td>\n",
       "      <td>-0.369091</td>\n",
       "      <td>0.038006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>-0.296637</td>\n",
       "      <td>0.118974</td>\n",
       "      <td>-0.050543</td>\n",
       "      <td>-0.063130</td>\n",
       "      <td>-0.134048</td>\n",
       "      <td>-0.295120</td>\n",
       "      <td>0.180138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.244730</td>\n",
       "      <td>-0.021918</td>\n",
       "      <td>0.038376</td>\n",
       "      <td>-0.218819</td>\n",
       "      <td>0.248212</td>\n",
       "      <td>-0.032994</td>\n",
       "      <td>-0.153475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>-0.341902</td>\n",
       "      <td>0.352033</td>\n",
       "      <td>-0.053340</td>\n",
       "      <td>0.567673</td>\n",
       "      <td>-0.278125</td>\n",
       "      <td>0.259899</td>\n",
       "      <td>0.274298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>-0.420063</td>\n",
       "      <td>0.204637</td>\n",
       "      <td>0.090648</td>\n",
       "      <td>0.365232</td>\n",
       "      <td>-0.261405</td>\n",
       "      <td>-0.222268</td>\n",
       "      <td>0.296370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                            -0.468742                   0.229559   \n",
       "touch                              -0.146357                  -0.026371   \n",
       "interoception                      -0.279984                   0.313140   \n",
       "learning                           -0.462153                   0.274384   \n",
       "attention                           0.228117                  -0.038223   \n",
       "language                            0.297722                  -0.158726   \n",
       "interaction                        -0.128013                   0.124505   \n",
       "inhibition                         -0.341566                   0.194105   \n",
       "somatosensory                      -0.099540                  -0.043936   \n",
       "decision                           -0.067162                   0.145859   \n",
       "auditory                            0.018838                   0.136881   \n",
       "taste                              -0.430261                   0.373203   \n",
       "metabolism                         -0.510940                   0.402309   \n",
       "memory                             -0.034657                  -0.074169   \n",
       "empathy                            -0.190897                   0.142190   \n",
       "vision                              0.119937                  -0.161269   \n",
       "autonomic                          -0.349513                   0.354381   \n",
       "visual                              0.032036                  -0.066690   \n",
       "fear                               -0.367277                   0.260737   \n",
       "perception                          0.126478                  -0.076975   \n",
       "cognition                           0.194981                  -0.029093   \n",
       "reward                             -0.404685                   0.359914   \n",
       "mirror                             -0.111668                  -0.035946   \n",
       "orientation                        -0.050867                  -0.045823   \n",
       "movement                           -0.296637                   0.118974   \n",
       "default_mode                        0.244730                  -0.021918   \n",
       "dopamine                           -0.341902                   0.352033   \n",
       "pain                               -0.420063                   0.204637   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                         -0.007421                         0.285056   \n",
       "touch                            0.164177                         0.067679   \n",
       "interoception                    0.154563                         0.487672   \n",
       "learning                        -0.601871                        -0.115365   \n",
       "attention                        0.119903                        -0.351110   \n",
       "language                         0.178679                         0.076494   \n",
       "interaction                     -0.240980                        -0.429157   \n",
       "inhibition                       0.222828                         0.396114   \n",
       "somatosensory                    0.242049                         0.105816   \n",
       "decision                        -0.016548                         0.367091   \n",
       "auditory                         0.265835                         0.141359   \n",
       "taste                           -0.018548                         0.475821   \n",
       "metabolism                      -0.306210                        -0.213713   \n",
       "memory                          -0.235891                        -0.072169   \n",
       "empathy                          0.253669                         0.361197   \n",
       "vision                          -0.275486                        -0.670558   \n",
       "autonomic                        0.162210                         0.521366   \n",
       "visual                          -0.355009                        -0.568870   \n",
       "fear                            -0.383811                         0.194834   \n",
       "perception                      -0.068212                        -0.485641   \n",
       "cognition                       -0.000567                         0.007815   \n",
       "reward                          -0.190862                         0.296012   \n",
       "mirror                           0.129933                        -0.207749   \n",
       "orientation                     -0.326733                        -0.617039   \n",
       "movement                        -0.050543                        -0.063130   \n",
       "default_mode                     0.038376                        -0.218819   \n",
       "dopamine                        -0.053340                         0.567673   \n",
       "pain                             0.090648                         0.365232   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                            -0.249232                   -0.150657   \n",
       "touch                              -0.029191                   -0.289144   \n",
       "interoception                      -0.210166                    0.196064   \n",
       "learning                           -0.394325                   -0.225309   \n",
       "attention                           0.385361                   -0.044721   \n",
       "language                            0.023610                    0.238912   \n",
       "interaction                         0.049370                   -0.208579   \n",
       "inhibition                         -0.198793                   -0.005853   \n",
       "somatosensory                       0.000007                   -0.239968   \n",
       "decision                           -0.235510                    0.305577   \n",
       "auditory                            0.094705                    0.041727   \n",
       "taste                              -0.321566                    0.072738   \n",
       "metabolism                         -0.187787                   -0.459505   \n",
       "memory                             -0.065409                   -0.162217   \n",
       "empathy                            -0.111035                   -0.032047   \n",
       "vision                              0.283223                   -0.349523   \n",
       "autonomic                          -0.216780                    0.153537   \n",
       "visual                              0.217088                   -0.300477   \n",
       "fear                               -0.330259                   -0.188105   \n",
       "perception                          0.229217                   -0.289100   \n",
       "cognition                           0.194360                   -0.109753   \n",
       "reward                             -0.226268                    0.119439   \n",
       "mirror                              0.053642                   -0.280290   \n",
       "orientation                         0.132988                   -0.369091   \n",
       "movement                           -0.134048                   -0.295120   \n",
       "default_mode                        0.248212                   -0.032994   \n",
       "dopamine                           -0.278125                    0.259899   \n",
       "pain                               -0.261405                   -0.222268   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.231564  \n",
       "touch                             0.022426  \n",
       "interoception                     0.281528  \n",
       "learning                          0.323729  \n",
       "attention                         0.017599  \n",
       "language                         -0.176691  \n",
       "interaction                       0.125271  \n",
       "inhibition                        0.193408  \n",
       "somatosensory                    -0.021148  \n",
       "decision                          0.171131  \n",
       "auditory                          0.063871  \n",
       "taste                             0.329236  \n",
       "metabolism                        0.363059  \n",
       "memory                           -0.083965  \n",
       "empathy                           0.179262  \n",
       "vision                           -0.114233  \n",
       "autonomic                         0.330800  \n",
       "visual                           -0.041064  \n",
       "fear                              0.271578  \n",
       "perception                        0.022600  \n",
       "cognition                        -0.175533  \n",
       "reward                            0.395386  \n",
       "mirror                            0.047189  \n",
       "orientation                       0.038006  \n",
       "movement                          0.180138  \n",
       "default_mode                     -0.153475  \n",
       "dopamine                          0.274298  \n",
       "pain                              0.296370  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.468742</td>\n",
       "      <td>0.229559</td>\n",
       "      <td>-0.007421</td>\n",
       "      <td>0.285056</td>\n",
       "      <td>-0.249232</td>\n",
       "      <td>-0.150657</td>\n",
       "      <td>0.231564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.146357</td>\n",
       "      <td>-0.026371</td>\n",
       "      <td>0.164177</td>\n",
       "      <td>0.067679</td>\n",
       "      <td>-0.029191</td>\n",
       "      <td>-0.289144</td>\n",
       "      <td>0.022426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.279984</td>\n",
       "      <td>0.313140</td>\n",
       "      <td>0.154563</td>\n",
       "      <td>0.487672</td>\n",
       "      <td>-0.210166</td>\n",
       "      <td>0.196064</td>\n",
       "      <td>0.281528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.462153</td>\n",
       "      <td>0.274384</td>\n",
       "      <td>-0.601871</td>\n",
       "      <td>-0.115365</td>\n",
       "      <td>-0.394325</td>\n",
       "      <td>-0.225309</td>\n",
       "      <td>0.323729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.228117</td>\n",
       "      <td>-0.038223</td>\n",
       "      <td>0.119903</td>\n",
       "      <td>-0.351110</td>\n",
       "      <td>0.385361</td>\n",
       "      <td>-0.044721</td>\n",
       "      <td>0.017599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.297722</td>\n",
       "      <td>-0.158726</td>\n",
       "      <td>0.178679</td>\n",
       "      <td>0.076494</td>\n",
       "      <td>0.023610</td>\n",
       "      <td>0.238912</td>\n",
       "      <td>-0.176691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.128013</td>\n",
       "      <td>0.124505</td>\n",
       "      <td>-0.240980</td>\n",
       "      <td>-0.429157</td>\n",
       "      <td>0.049370</td>\n",
       "      <td>-0.208579</td>\n",
       "      <td>0.125271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.341566</td>\n",
       "      <td>0.194105</td>\n",
       "      <td>0.222828</td>\n",
       "      <td>0.396114</td>\n",
       "      <td>-0.198793</td>\n",
       "      <td>-0.005853</td>\n",
       "      <td>0.193408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.099540</td>\n",
       "      <td>-0.043936</td>\n",
       "      <td>0.242049</td>\n",
       "      <td>0.105816</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>-0.239968</td>\n",
       "      <td>-0.021148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.067162</td>\n",
       "      <td>0.145859</td>\n",
       "      <td>-0.016548</td>\n",
       "      <td>0.367091</td>\n",
       "      <td>-0.235510</td>\n",
       "      <td>0.305577</td>\n",
       "      <td>0.171131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.018838</td>\n",
       "      <td>0.136881</td>\n",
       "      <td>0.265835</td>\n",
       "      <td>0.141359</td>\n",
       "      <td>0.094705</td>\n",
       "      <td>0.041727</td>\n",
       "      <td>0.063871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.430261</td>\n",
       "      <td>0.373203</td>\n",
       "      <td>-0.018548</td>\n",
       "      <td>0.475821</td>\n",
       "      <td>-0.321566</td>\n",
       "      <td>0.072738</td>\n",
       "      <td>0.329236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.510940</td>\n",
       "      <td>0.402309</td>\n",
       "      <td>-0.306210</td>\n",
       "      <td>-0.213713</td>\n",
       "      <td>-0.187787</td>\n",
       "      <td>-0.459505</td>\n",
       "      <td>0.363059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.034657</td>\n",
       "      <td>-0.074169</td>\n",
       "      <td>-0.235891</td>\n",
       "      <td>-0.072169</td>\n",
       "      <td>-0.065409</td>\n",
       "      <td>-0.162217</td>\n",
       "      <td>-0.083965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.190897</td>\n",
       "      <td>0.142190</td>\n",
       "      <td>0.253669</td>\n",
       "      <td>0.361197</td>\n",
       "      <td>-0.111035</td>\n",
       "      <td>-0.032047</td>\n",
       "      <td>0.179262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.119937</td>\n",
       "      <td>-0.161269</td>\n",
       "      <td>-0.275486</td>\n",
       "      <td>-0.670558</td>\n",
       "      <td>0.283223</td>\n",
       "      <td>-0.349523</td>\n",
       "      <td>-0.114233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.349513</td>\n",
       "      <td>0.354381</td>\n",
       "      <td>0.162210</td>\n",
       "      <td>0.521366</td>\n",
       "      <td>-0.216780</td>\n",
       "      <td>0.153537</td>\n",
       "      <td>0.330800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.032036</td>\n",
       "      <td>-0.066690</td>\n",
       "      <td>-0.355009</td>\n",
       "      <td>-0.568870</td>\n",
       "      <td>0.217088</td>\n",
       "      <td>-0.300477</td>\n",
       "      <td>-0.041064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.367277</td>\n",
       "      <td>0.260737</td>\n",
       "      <td>-0.383811</td>\n",
       "      <td>0.194834</td>\n",
       "      <td>-0.330259</td>\n",
       "      <td>-0.188105</td>\n",
       "      <td>0.271578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.126478</td>\n",
       "      <td>-0.076975</td>\n",
       "      <td>-0.068212</td>\n",
       "      <td>-0.485641</td>\n",
       "      <td>0.229217</td>\n",
       "      <td>-0.289100</td>\n",
       "      <td>0.022600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.194981</td>\n",
       "      <td>-0.029093</td>\n",
       "      <td>-0.000567</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.194360</td>\n",
       "      <td>-0.109753</td>\n",
       "      <td>-0.175533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.404685</td>\n",
       "      <td>0.359914</td>\n",
       "      <td>-0.190862</td>\n",
       "      <td>0.296012</td>\n",
       "      <td>-0.226268</td>\n",
       "      <td>0.119439</td>\n",
       "      <td>0.395386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.111668</td>\n",
       "      <td>-0.035946</td>\n",
       "      <td>0.129933</td>\n",
       "      <td>-0.207749</td>\n",
       "      <td>0.053642</td>\n",
       "      <td>-0.280290</td>\n",
       "      <td>0.047189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.050867</td>\n",
       "      <td>-0.045823</td>\n",
       "      <td>-0.326733</td>\n",
       "      <td>-0.617039</td>\n",
       "      <td>0.132988</td>\n",
       "      <td>-0.369091</td>\n",
       "      <td>0.038006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.296637</td>\n",
       "      <td>0.118974</td>\n",
       "      <td>-0.050543</td>\n",
       "      <td>-0.063130</td>\n",
       "      <td>-0.134048</td>\n",
       "      <td>-0.295120</td>\n",
       "      <td>0.180138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.244730</td>\n",
       "      <td>-0.021918</td>\n",
       "      <td>0.038376</td>\n",
       "      <td>-0.218819</td>\n",
       "      <td>0.248212</td>\n",
       "      <td>-0.032994</td>\n",
       "      <td>-0.153475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.341902</td>\n",
       "      <td>0.352033</td>\n",
       "      <td>-0.053340</td>\n",
       "      <td>0.567673</td>\n",
       "      <td>-0.278125</td>\n",
       "      <td>0.259899</td>\n",
       "      <td>0.274298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.420063</td>\n",
       "      <td>0.204637</td>\n",
       "      <td>0.090648</td>\n",
       "      <td>0.365232</td>\n",
       "      <td>-0.261405</td>\n",
       "      <td>-0.222268</td>\n",
       "      <td>0.296370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6\n",
       "0  -0.468742  0.229559 -0.007421  0.285056 -0.249232 -0.150657  0.231564\n",
       "1  -0.146357 -0.026371  0.164177  0.067679 -0.029191 -0.289144  0.022426\n",
       "2  -0.279984  0.313140  0.154563  0.487672 -0.210166  0.196064  0.281528\n",
       "3  -0.462153  0.274384 -0.601871 -0.115365 -0.394325 -0.225309  0.323729\n",
       "4   0.228117 -0.038223  0.119903 -0.351110  0.385361 -0.044721  0.017599\n",
       "5   0.297722 -0.158726  0.178679  0.076494  0.023610  0.238912 -0.176691\n",
       "6  -0.128013  0.124505 -0.240980 -0.429157  0.049370 -0.208579  0.125271\n",
       "7  -0.341566  0.194105  0.222828  0.396114 -0.198793 -0.005853  0.193408\n",
       "8  -0.099540 -0.043936  0.242049  0.105816  0.000007 -0.239968 -0.021148\n",
       "9  -0.067162  0.145859 -0.016548  0.367091 -0.235510  0.305577  0.171131\n",
       "10  0.018838  0.136881  0.265835  0.141359  0.094705  0.041727  0.063871\n",
       "11 -0.430261  0.373203 -0.018548  0.475821 -0.321566  0.072738  0.329236\n",
       "12 -0.510940  0.402309 -0.306210 -0.213713 -0.187787 -0.459505  0.363059\n",
       "13 -0.034657 -0.074169 -0.235891 -0.072169 -0.065409 -0.162217 -0.083965\n",
       "14 -0.190897  0.142190  0.253669  0.361197 -0.111035 -0.032047  0.179262\n",
       "15  0.119937 -0.161269 -0.275486 -0.670558  0.283223 -0.349523 -0.114233\n",
       "16 -0.349513  0.354381  0.162210  0.521366 -0.216780  0.153537  0.330800\n",
       "17  0.032036 -0.066690 -0.355009 -0.568870  0.217088 -0.300477 -0.041064\n",
       "18 -0.367277  0.260737 -0.383811  0.194834 -0.330259 -0.188105  0.271578\n",
       "19  0.126478 -0.076975 -0.068212 -0.485641  0.229217 -0.289100  0.022600\n",
       "20  0.194981 -0.029093 -0.000567  0.007815  0.194360 -0.109753 -0.175533\n",
       "21 -0.404685  0.359914 -0.190862  0.296012 -0.226268  0.119439  0.395386\n",
       "22 -0.111668 -0.035946  0.129933 -0.207749  0.053642 -0.280290  0.047189\n",
       "23 -0.050867 -0.045823 -0.326733 -0.617039  0.132988 -0.369091  0.038006\n",
       "24 -0.296637  0.118974 -0.050543 -0.063130 -0.134048 -0.295120  0.180138\n",
       "25  0.244730 -0.021918  0.038376 -0.218819  0.248212 -0.032994 -0.153475\n",
       "26 -0.341902  0.352033 -0.053340  0.567673 -0.278125  0.259899  0.274298\n",
       "27 -0.420063  0.204637  0.090648  0.365232 -0.261405 -0.222268  0.296370"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"partialspearman\", \n",
    "    r_to_z=False, # apply r to z transformation\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "display(juspyce_vol.predictions[\"partialspearman\"])\n",
    "\n",
    "print(\"check\")\n",
    "check_res = np.zeros((juspyce_vol.Y.shape[0], juspyce_vol.X.shape[0]))\n",
    "for y in range(juspyce_vol.Y.shape[0]):\n",
    "    for x in range(juspyce_vol.X.shape[0]):\n",
    "        check_res[y,x] = check_partialspearman(\n",
    "            x=juspyce_vol.X.iloc[x,:].values,\n",
    "            y=juspyce_vol.Y.iloc[y,:].values,\n",
    "            z=juspyce_vol.Z.values[0,:])\n",
    "display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple linear model\n",
    "\n",
    "Will calculate $R^2$ for single linear models predicting each Y from each X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c75a3d7a5ff5466ead0ecfc7839f3c67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (slr, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.182042</td>\n",
       "      <td>0.006300</td>\n",
       "      <td>0.054414</td>\n",
       "      <td>0.007367</td>\n",
       "      <td>0.159760</td>\n",
       "      <td>1.101792e-03</td>\n",
       "      <td>0.000235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>0.036636</td>\n",
       "      <td>0.021413</td>\n",
       "      <td>0.014809</td>\n",
       "      <td>0.008950</td>\n",
       "      <td>0.006603</td>\n",
       "      <td>1.299454e-02</td>\n",
       "      <td>0.019652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>0.009078</td>\n",
       "      <td>0.127706</td>\n",
       "      <td>0.083679</td>\n",
       "      <td>0.085046</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>5.473213e-03</td>\n",
       "      <td>0.063621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>0.375489</td>\n",
       "      <td>0.210834</td>\n",
       "      <td>0.193742</td>\n",
       "      <td>0.079222</td>\n",
       "      <td>0.297603</td>\n",
       "      <td>2.052000e-03</td>\n",
       "      <td>0.516197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.023132</td>\n",
       "      <td>0.120333</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.263436</td>\n",
       "      <td>0.016133</td>\n",
       "      <td>3.078766e-03</td>\n",
       "      <td>0.083601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.085446</td>\n",
       "      <td>0.006246</td>\n",
       "      <td>0.033520</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>0.010539</td>\n",
       "      <td>2.010372e-02</td>\n",
       "      <td>0.002696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>0.016784</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.051926</td>\n",
       "      <td>0.161554</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>3.844739e-03</td>\n",
       "      <td>0.004939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>0.143268</td>\n",
       "      <td>0.013343</td>\n",
       "      <td>0.007924</td>\n",
       "      <td>0.003792</td>\n",
       "      <td>0.102791</td>\n",
       "      <td>9.443612e-03</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>0.025952</td>\n",
       "      <td>0.026662</td>\n",
       "      <td>0.025857</td>\n",
       "      <td>0.008984</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>9.646291e-03</td>\n",
       "      <td>0.022943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>0.118456</td>\n",
       "      <td>0.054203</td>\n",
       "      <td>0.023645</td>\n",
       "      <td>0.208465</td>\n",
       "      <td>0.209607</td>\n",
       "      <td>1.699490e-01</td>\n",
       "      <td>0.454351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.024290</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>0.086805</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.025125</td>\n",
       "      <td>5.858030e-04</td>\n",
       "      <td>0.005400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>0.190887</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.006715</td>\n",
       "      <td>0.318397</td>\n",
       "      <td>0.155813</td>\n",
       "      <td>2.332084e-03</td>\n",
       "      <td>0.189494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>0.287526</td>\n",
       "      <td>0.325785</td>\n",
       "      <td>0.208768</td>\n",
       "      <td>0.045884</td>\n",
       "      <td>0.254254</td>\n",
       "      <td>1.113114e-01</td>\n",
       "      <td>0.042716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>0.006195</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.035610</td>\n",
       "      <td>0.017279</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>5.833246e-02</td>\n",
       "      <td>0.006574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>0.011674</td>\n",
       "      <td>0.042879</td>\n",
       "      <td>0.102720</td>\n",
       "      <td>0.051405</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.623426e-07</td>\n",
       "      <td>0.006206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.033470</td>\n",
       "      <td>0.084569</td>\n",
       "      <td>0.104387</td>\n",
       "      <td>0.410192</td>\n",
       "      <td>0.042051</td>\n",
       "      <td>1.777842e-03</td>\n",
       "      <td>0.060145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>0.026564</td>\n",
       "      <td>0.171899</td>\n",
       "      <td>0.069528</td>\n",
       "      <td>0.138072</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>2.852129e-03</td>\n",
       "      <td>0.072826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.025386</td>\n",
       "      <td>0.052773</td>\n",
       "      <td>0.127879</td>\n",
       "      <td>0.353504</td>\n",
       "      <td>0.036308</td>\n",
       "      <td>4.654822e-05</td>\n",
       "      <td>0.033514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>0.098247</td>\n",
       "      <td>0.050876</td>\n",
       "      <td>0.056811</td>\n",
       "      <td>0.088725</td>\n",
       "      <td>0.038776</td>\n",
       "      <td>2.698036e-02</td>\n",
       "      <td>0.015803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.040328</td>\n",
       "      <td>0.033521</td>\n",
       "      <td>0.007816</td>\n",
       "      <td>0.242930</td>\n",
       "      <td>0.035465</td>\n",
       "      <td>1.764233e-02</td>\n",
       "      <td>0.079359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.043389</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.051899</td>\n",
       "      <td>0.017681</td>\n",
       "      <td>4.006361e-03</td>\n",
       "      <td>0.076727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>0.248275</td>\n",
       "      <td>0.202341</td>\n",
       "      <td>0.017593</td>\n",
       "      <td>0.331859</td>\n",
       "      <td>0.215248</td>\n",
       "      <td>1.146372e-01</td>\n",
       "      <td>0.655558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>0.035764</td>\n",
       "      <td>0.125856</td>\n",
       "      <td>0.006471</td>\n",
       "      <td>0.125369</td>\n",
       "      <td>0.016588</td>\n",
       "      <td>1.358118e-03</td>\n",
       "      <td>0.048283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.068629</td>\n",
       "      <td>0.120166</td>\n",
       "      <td>0.350779</td>\n",
       "      <td>0.010918</td>\n",
       "      <td>2.507067e-03</td>\n",
       "      <td>0.026489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>0.102387</td>\n",
       "      <td>0.045348</td>\n",
       "      <td>0.030631</td>\n",
       "      <td>0.063401</td>\n",
       "      <td>0.055354</td>\n",
       "      <td>1.173123e-05</td>\n",
       "      <td>0.012263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.067059</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.012331</td>\n",
       "      <td>0.088252</td>\n",
       "      <td>3.726554e-03</td>\n",
       "      <td>0.019092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>0.317260</td>\n",
       "      <td>0.247122</td>\n",
       "      <td>0.034086</td>\n",
       "      <td>0.384662</td>\n",
       "      <td>0.336707</td>\n",
       "      <td>1.138044e-01</td>\n",
       "      <td>0.698475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>0.122392</td>\n",
       "      <td>0.078039</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>0.080951</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>3.178104e-02</td>\n",
       "      <td>0.010298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                             0.182042                   0.006300   \n",
       "touch                               0.036636                   0.021413   \n",
       "interoception                       0.009078                   0.127706   \n",
       "learning                            0.375489                   0.210834   \n",
       "attention                           0.023132                   0.120333   \n",
       "language                            0.085446                   0.006246   \n",
       "interaction                         0.016784                   0.000027   \n",
       "inhibition                          0.143268                   0.013343   \n",
       "somatosensory                       0.025952                   0.026662   \n",
       "decision                            0.118456                   0.054203   \n",
       "auditory                            0.024290                   0.010354   \n",
       "taste                               0.190887                   0.433333   \n",
       "metabolism                          0.287526                   0.325785   \n",
       "memory                              0.006195                   0.000135   \n",
       "empathy                             0.011674                   0.042879   \n",
       "vision                              0.033470                   0.084569   \n",
       "autonomic                           0.026564                   0.171899   \n",
       "visual                              0.025386                   0.052773   \n",
       "fear                                0.098247                   0.050876   \n",
       "perception                          0.040328                   0.033521   \n",
       "cognition                           0.043389                   0.012463   \n",
       "reward                              0.248275                   0.202341   \n",
       "mirror                              0.035764                   0.125856   \n",
       "orientation                         0.003505                   0.068629   \n",
       "movement                            0.102387                   0.045348   \n",
       "default_mode                        0.067059                   0.015309   \n",
       "dopamine                            0.317260                   0.247122   \n",
       "pain                                0.122392                   0.078039   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                          0.054414                         0.007367   \n",
       "touch                            0.014809                         0.008950   \n",
       "interoception                    0.083679                         0.085046   \n",
       "learning                         0.193742                         0.079222   \n",
       "attention                        0.004263                         0.263436   \n",
       "language                         0.033520                         0.001380   \n",
       "interaction                      0.051926                         0.161554   \n",
       "inhibition                       0.007924                         0.003792   \n",
       "somatosensory                    0.025857                         0.008984   \n",
       "decision                         0.023645                         0.208465   \n",
       "auditory                         0.086805                         0.000291   \n",
       "taste                            0.006715                         0.318397   \n",
       "metabolism                       0.208768                         0.045884   \n",
       "memory                           0.035610                         0.017279   \n",
       "empathy                          0.102720                         0.051405   \n",
       "vision                           0.104387                         0.410192   \n",
       "autonomic                        0.069528                         0.138072   \n",
       "visual                           0.127879                         0.353504   \n",
       "fear                             0.056811                         0.088725   \n",
       "perception                       0.007816                         0.242930   \n",
       "cognition                        0.001031                         0.051899   \n",
       "reward                           0.017593                         0.331859   \n",
       "mirror                           0.006471                         0.125369   \n",
       "orientation                      0.120166                         0.350779   \n",
       "movement                         0.030631                         0.063401   \n",
       "default_mode                     0.046476                         0.012331   \n",
       "dopamine                         0.034086                         0.384662   \n",
       "pain                             0.007029                         0.080951   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                             0.159760                1.101792e-03   \n",
       "touch                               0.006603                1.299454e-02   \n",
       "interoception                       0.000366                5.473213e-03   \n",
       "learning                            0.297603                2.052000e-03   \n",
       "attention                           0.016133                3.078766e-03   \n",
       "language                            0.010539                2.010372e-02   \n",
       "interaction                         0.000424                3.844739e-03   \n",
       "inhibition                          0.102791                9.443612e-03   \n",
       "somatosensory                       0.002633                9.646291e-03   \n",
       "decision                            0.209607                1.699490e-01   \n",
       "auditory                            0.025125                5.858030e-04   \n",
       "taste                               0.155813                2.332084e-03   \n",
       "metabolism                          0.254254                1.113114e-01   \n",
       "memory                              0.000378                5.833246e-02   \n",
       "empathy                             0.000190                1.623426e-07   \n",
       "vision                              0.042051                1.777842e-03   \n",
       "autonomic                           0.001295                2.852129e-03   \n",
       "visual                              0.036308                4.654822e-05   \n",
       "fear                                0.038776                2.698036e-02   \n",
       "perception                          0.035465                1.764233e-02   \n",
       "cognition                           0.017681                4.006361e-03   \n",
       "reward                              0.215248                1.146372e-01   \n",
       "mirror                              0.016588                1.358118e-03   \n",
       "orientation                         0.010918                2.507067e-03   \n",
       "movement                            0.055354                1.173123e-05   \n",
       "default_mode                        0.088252                3.726554e-03   \n",
       "dopamine                            0.336707                1.138044e-01   \n",
       "pain                                0.082310                3.178104e-02   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.000235  \n",
       "touch                             0.019652  \n",
       "interoception                     0.063621  \n",
       "learning                          0.516197  \n",
       "attention                         0.083601  \n",
       "language                          0.002696  \n",
       "interaction                       0.004939  \n",
       "inhibition                        0.000003  \n",
       "somatosensory                     0.022943  \n",
       "decision                          0.454351  \n",
       "auditory                          0.005400  \n",
       "taste                             0.189494  \n",
       "metabolism                        0.042716  \n",
       "memory                            0.006574  \n",
       "empathy                           0.006206  \n",
       "vision                            0.060145  \n",
       "autonomic                         0.072826  \n",
       "visual                            0.033514  \n",
       "fear                              0.015803  \n",
       "perception                        0.079359  \n",
       "cognition                         0.076727  \n",
       "reward                            0.655558  \n",
       "mirror                            0.048283  \n",
       "orientation                       0.026489  \n",
       "movement                          0.012263  \n",
       "default_mode                      0.019092  \n",
       "dopamine                          0.698475  \n",
       "pain                              0.010298  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.182042</td>\n",
       "      <td>0.006300</td>\n",
       "      <td>0.054414</td>\n",
       "      <td>0.007367</td>\n",
       "      <td>0.159760</td>\n",
       "      <td>1.101730e-03</td>\n",
       "      <td>0.000235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036636</td>\n",
       "      <td>0.021413</td>\n",
       "      <td>0.014809</td>\n",
       "      <td>0.008950</td>\n",
       "      <td>0.006603</td>\n",
       "      <td>1.299450e-02</td>\n",
       "      <td>0.019652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.009078</td>\n",
       "      <td>0.127706</td>\n",
       "      <td>0.083679</td>\n",
       "      <td>0.085046</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>5.473164e-03</td>\n",
       "      <td>0.063621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.375489</td>\n",
       "      <td>0.210834</td>\n",
       "      <td>0.193742</td>\n",
       "      <td>0.079222</td>\n",
       "      <td>0.297603</td>\n",
       "      <td>2.051914e-03</td>\n",
       "      <td>0.516197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.023132</td>\n",
       "      <td>0.120333</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.263436</td>\n",
       "      <td>0.016133</td>\n",
       "      <td>3.078823e-03</td>\n",
       "      <td>0.083601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.085446</td>\n",
       "      <td>0.006246</td>\n",
       "      <td>0.033520</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>0.010539</td>\n",
       "      <td>2.010363e-02</td>\n",
       "      <td>0.002696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.016784</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.051926</td>\n",
       "      <td>0.161554</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>3.844762e-03</td>\n",
       "      <td>0.004939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.143268</td>\n",
       "      <td>0.013343</td>\n",
       "      <td>0.007924</td>\n",
       "      <td>0.003792</td>\n",
       "      <td>0.102791</td>\n",
       "      <td>9.443520e-03</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.025952</td>\n",
       "      <td>0.026662</td>\n",
       "      <td>0.025857</td>\n",
       "      <td>0.008984</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>9.646353e-03</td>\n",
       "      <td>0.022943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.118456</td>\n",
       "      <td>0.054203</td>\n",
       "      <td>0.023645</td>\n",
       "      <td>0.208465</td>\n",
       "      <td>0.209607</td>\n",
       "      <td>1.699490e-01</td>\n",
       "      <td>0.454351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.024290</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>0.086805</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.025125</td>\n",
       "      <td>5.857630e-04</td>\n",
       "      <td>0.005400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.190887</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.006715</td>\n",
       "      <td>0.318397</td>\n",
       "      <td>0.155813</td>\n",
       "      <td>2.332022e-03</td>\n",
       "      <td>0.189494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.287526</td>\n",
       "      <td>0.325785</td>\n",
       "      <td>0.208768</td>\n",
       "      <td>0.045884</td>\n",
       "      <td>0.254254</td>\n",
       "      <td>1.113115e-01</td>\n",
       "      <td>0.042716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.006195</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.035610</td>\n",
       "      <td>0.017279</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>5.833240e-02</td>\n",
       "      <td>0.006574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.011674</td>\n",
       "      <td>0.042879</td>\n",
       "      <td>0.102720</td>\n",
       "      <td>0.051405</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>2.080154e-07</td>\n",
       "      <td>0.006206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.033470</td>\n",
       "      <td>0.084569</td>\n",
       "      <td>0.104387</td>\n",
       "      <td>0.410192</td>\n",
       "      <td>0.042051</td>\n",
       "      <td>1.777777e-03</td>\n",
       "      <td>0.060145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.026564</td>\n",
       "      <td>0.171899</td>\n",
       "      <td>0.069528</td>\n",
       "      <td>0.138072</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>2.852078e-03</td>\n",
       "      <td>0.072826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.025386</td>\n",
       "      <td>0.052773</td>\n",
       "      <td>0.127879</td>\n",
       "      <td>0.353504</td>\n",
       "      <td>0.036308</td>\n",
       "      <td>4.659280e-05</td>\n",
       "      <td>0.033514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.098247</td>\n",
       "      <td>0.050876</td>\n",
       "      <td>0.056811</td>\n",
       "      <td>0.088725</td>\n",
       "      <td>0.038776</td>\n",
       "      <td>2.698035e-02</td>\n",
       "      <td>0.015803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.040328</td>\n",
       "      <td>0.033521</td>\n",
       "      <td>0.007816</td>\n",
       "      <td>0.242930</td>\n",
       "      <td>0.035465</td>\n",
       "      <td>1.764233e-02</td>\n",
       "      <td>0.079359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.043389</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>0.051899</td>\n",
       "      <td>0.017681</td>\n",
       "      <td>4.006307e-03</td>\n",
       "      <td>0.076727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.248275</td>\n",
       "      <td>0.202341</td>\n",
       "      <td>0.017593</td>\n",
       "      <td>0.331859</td>\n",
       "      <td>0.215248</td>\n",
       "      <td>1.146372e-01</td>\n",
       "      <td>0.655558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.035764</td>\n",
       "      <td>0.125856</td>\n",
       "      <td>0.006471</td>\n",
       "      <td>0.125369</td>\n",
       "      <td>0.016588</td>\n",
       "      <td>1.358178e-03</td>\n",
       "      <td>0.048283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.068629</td>\n",
       "      <td>0.120166</td>\n",
       "      <td>0.350779</td>\n",
       "      <td>0.010918</td>\n",
       "      <td>2.507081e-03</td>\n",
       "      <td>0.026489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.102387</td>\n",
       "      <td>0.045348</td>\n",
       "      <td>0.030631</td>\n",
       "      <td>0.063401</td>\n",
       "      <td>0.055354</td>\n",
       "      <td>1.173316e-05</td>\n",
       "      <td>0.012263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.067059</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.012331</td>\n",
       "      <td>0.088252</td>\n",
       "      <td>3.726576e-03</td>\n",
       "      <td>0.019092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.317260</td>\n",
       "      <td>0.247122</td>\n",
       "      <td>0.034086</td>\n",
       "      <td>0.384662</td>\n",
       "      <td>0.336707</td>\n",
       "      <td>1.138044e-01</td>\n",
       "      <td>0.698475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.122392</td>\n",
       "      <td>0.078039</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>0.080951</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>3.178107e-02</td>\n",
       "      <td>0.010298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4             5         6\n",
       "0   0.182042  0.006300  0.054414  0.007367  0.159760  1.101730e-03  0.000235\n",
       "1   0.036636  0.021413  0.014809  0.008950  0.006603  1.299450e-02  0.019652\n",
       "2   0.009078  0.127706  0.083679  0.085046  0.000366  5.473164e-03  0.063621\n",
       "3   0.375489  0.210834  0.193742  0.079222  0.297603  2.051914e-03  0.516197\n",
       "4   0.023132  0.120333  0.004263  0.263436  0.016133  3.078823e-03  0.083601\n",
       "5   0.085446  0.006246  0.033520  0.001380  0.010539  2.010363e-02  0.002696\n",
       "6   0.016784  0.000027  0.051926  0.161554  0.000424  3.844762e-03  0.004939\n",
       "7   0.143268  0.013343  0.007924  0.003792  0.102791  9.443520e-03  0.000003\n",
       "8   0.025952  0.026662  0.025857  0.008984  0.002633  9.646353e-03  0.022943\n",
       "9   0.118456  0.054203  0.023645  0.208465  0.209607  1.699490e-01  0.454351\n",
       "10  0.024290  0.010354  0.086805  0.000291  0.025125  5.857630e-04  0.005400\n",
       "11  0.190887  0.433333  0.006715  0.318397  0.155813  2.332022e-03  0.189494\n",
       "12  0.287526  0.325785  0.208768  0.045884  0.254254  1.113115e-01  0.042716\n",
       "13  0.006195  0.000135  0.035610  0.017279  0.000378  5.833240e-02  0.006574\n",
       "14  0.011674  0.042879  0.102720  0.051405  0.000190  2.080154e-07  0.006206\n",
       "15  0.033470  0.084569  0.104387  0.410192  0.042051  1.777777e-03  0.060145\n",
       "16  0.026564  0.171899  0.069528  0.138072  0.001295  2.852078e-03  0.072826\n",
       "17  0.025386  0.052773  0.127879  0.353504  0.036308  4.659280e-05  0.033514\n",
       "18  0.098247  0.050876  0.056811  0.088725  0.038776  2.698035e-02  0.015803\n",
       "19  0.040328  0.033521  0.007816  0.242930  0.035465  1.764233e-02  0.079359\n",
       "20  0.043389  0.012463  0.001031  0.051899  0.017681  4.006307e-03  0.076727\n",
       "21  0.248275  0.202341  0.017593  0.331859  0.215248  1.146372e-01  0.655558\n",
       "22  0.035764  0.125856  0.006471  0.125369  0.016588  1.358178e-03  0.048283\n",
       "23  0.003505  0.068629  0.120166  0.350779  0.010918  2.507081e-03  0.026489\n",
       "24  0.102387  0.045348  0.030631  0.063401  0.055354  1.173316e-05  0.012263\n",
       "25  0.067059  0.015309  0.046476  0.012331  0.088252  3.726576e-03  0.019092\n",
       "26  0.317260  0.247122  0.034086  0.384662  0.336707  1.138044e-01  0.698475\n",
       "27  0.122392  0.078039  0.007029  0.080951  0.082310  3.178107e-02  0.010298"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"slr\", \n",
    "    adjust_r2=False, # adjusted R2?\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "display(juspyce_vol.predictions[\"slr\"])\n",
    "\n",
    "print(\"check\")\n",
    "check_res = np.zeros((juspyce_vol.Y.shape[0], juspyce_vol.X.shape[0]))\n",
    "for y in range(juspyce_vol.Y.shape[0]):\n",
    "    for x in range(juspyce_vol.X.shape[0]):\n",
    "        check_res[y,x] = check_slr(\n",
    "            x=juspyce_vol.X.iloc[x,:].values.T,\n",
    "            y=juspyce_vol.Y.iloc[y,:].values.T,\n",
    "            adj_r2=False)\n",
    "display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate linear model\n",
    "\n",
    "Will calculate multiple linear regression models predicting each Y from all X's. Will store the following dataframes:  \n",
    "- `JuSpyce.predictions[\"mlr_beta\"]`: len(Y) x len(X) dataframe with beta coefficients  \n",
    "- `JuSpyce.predictions[\"mlr_full_r2\"]`: len(Y) x 1 dataframe with full model $R^2$'s  \n",
    "- if `mlr_individual==True`:\n",
    "  - JuSpyce.predictions[\"mlr_individual\"]: len(Y) x len(X) dataframe with predictor-wise $R^2$'s in a \"leave-one-out\" approach: for each predictor, the difference between an MLR with all predictors and an MLR with all predictors expept for the current one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d7c29602d2c4b9d87b487a2518d3cbf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (mlr, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlr_beta\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>-0.463210</td>\n",
       "      <td>0.076000</td>\n",
       "      <td>0.333371</td>\n",
       "      <td>-0.285619</td>\n",
       "      <td>-0.665450</td>\n",
       "      <td>0.233285</td>\n",
       "      <td>-0.553785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>-0.469161</td>\n",
       "      <td>-0.071388</td>\n",
       "      <td>0.548121</td>\n",
       "      <td>-0.221619</td>\n",
       "      <td>-0.368110</td>\n",
       "      <td>-0.013959</td>\n",
       "      <td>-0.428344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>-0.028536</td>\n",
       "      <td>0.328041</td>\n",
       "      <td>0.335662</td>\n",
       "      <td>0.027684</td>\n",
       "      <td>-0.101708</td>\n",
       "      <td>0.086049</td>\n",
       "      <td>-0.020376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>-0.229210</td>\n",
       "      <td>-0.002593</td>\n",
       "      <td>-0.538202</td>\n",
       "      <td>0.059453</td>\n",
       "      <td>0.349082</td>\n",
       "      <td>-0.011153</td>\n",
       "      <td>0.731421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.067232</td>\n",
       "      <td>-0.016630</td>\n",
       "      <td>0.206701</td>\n",
       "      <td>-0.596517</td>\n",
       "      <td>-0.343371</td>\n",
       "      <td>0.060219</td>\n",
       "      <td>-0.110556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.569370</td>\n",
       "      <td>0.139994</td>\n",
       "      <td>0.244959</td>\n",
       "      <td>-0.073424</td>\n",
       "      <td>-0.507938</td>\n",
       "      <td>0.109270</td>\n",
       "      <td>-0.049817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>-0.130126</td>\n",
       "      <td>0.240889</td>\n",
       "      <td>0.009086</td>\n",
       "      <td>-0.603852</td>\n",
       "      <td>-0.128782</td>\n",
       "      <td>-0.015525</td>\n",
       "      <td>-0.021447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>-0.520893</td>\n",
       "      <td>-0.015881</td>\n",
       "      <td>0.448087</td>\n",
       "      <td>-0.252478</td>\n",
       "      <td>-0.586494</td>\n",
       "      <td>0.262675</td>\n",
       "      <td>-0.498871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>-0.458738</td>\n",
       "      <td>-0.086753</td>\n",
       "      <td>0.579207</td>\n",
       "      <td>-0.216110</td>\n",
       "      <td>-0.364322</td>\n",
       "      <td>-0.006177</td>\n",
       "      <td>-0.426464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>0.042789</td>\n",
       "      <td>-0.193624</td>\n",
       "      <td>-0.274825</td>\n",
       "      <td>0.328225</td>\n",
       "      <td>0.030235</td>\n",
       "      <td>0.331625</td>\n",
       "      <td>0.584626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.285039</td>\n",
       "      <td>0.434584</td>\n",
       "      <td>0.546822</td>\n",
       "      <td>-0.322500</td>\n",
       "      <td>-0.572890</td>\n",
       "      <td>0.065049</td>\n",
       "      <td>-0.282755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>-0.020245</td>\n",
       "      <td>0.614269</td>\n",
       "      <td>0.045627</td>\n",
       "      <td>0.241977</td>\n",
       "      <td>-0.346316</td>\n",
       "      <td>0.106922</td>\n",
       "      <td>-0.249177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>-0.038831</td>\n",
       "      <td>0.769566</td>\n",
       "      <td>-0.109230</td>\n",
       "      <td>-0.140051</td>\n",
       "      <td>-0.561459</td>\n",
       "      <td>-0.087907</td>\n",
       "      <td>-0.458173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>-0.157765</td>\n",
       "      <td>-0.070712</td>\n",
       "      <td>-0.245446</td>\n",
       "      <td>-0.031770</td>\n",
       "      <td>0.280619</td>\n",
       "      <td>-0.240516</td>\n",
       "      <td>0.068342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>-0.131411</td>\n",
       "      <td>0.296001</td>\n",
       "      <td>0.591985</td>\n",
       "      <td>-0.044198</td>\n",
       "      <td>-0.417338</td>\n",
       "      <td>0.082570</td>\n",
       "      <td>-0.357444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.077576</td>\n",
       "      <td>-0.146613</td>\n",
       "      <td>-0.453045</td>\n",
       "      <td>-0.505319</td>\n",
       "      <td>0.398909</td>\n",
       "      <td>-0.136013</td>\n",
       "      <td>0.374071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>-0.105943</td>\n",
       "      <td>0.359611</td>\n",
       "      <td>0.311603</td>\n",
       "      <td>0.127752</td>\n",
       "      <td>-0.093989</td>\n",
       "      <td>0.101483</td>\n",
       "      <td>-0.116952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.069486</td>\n",
       "      <td>-0.131361</td>\n",
       "      <td>-0.567318</td>\n",
       "      <td>-0.435330</td>\n",
       "      <td>0.513443</td>\n",
       "      <td>-0.091656</td>\n",
       "      <td>0.436615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>-0.497420</td>\n",
       "      <td>-0.145438</td>\n",
       "      <td>-0.531248</td>\n",
       "      <td>0.598636</td>\n",
       "      <td>0.682025</td>\n",
       "      <td>-0.098592</td>\n",
       "      <td>-0.029972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.251693</td>\n",
       "      <td>0.176160</td>\n",
       "      <td>0.085151</td>\n",
       "      <td>-0.613190</td>\n",
       "      <td>-0.243663</td>\n",
       "      <td>-0.147974</td>\n",
       "      <td>-0.012402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.310911</td>\n",
       "      <td>0.278097</td>\n",
       "      <td>0.243597</td>\n",
       "      <td>-0.333141</td>\n",
       "      <td>-0.497586</td>\n",
       "      <td>-0.004040</td>\n",
       "      <td>-0.335724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>-0.223340</td>\n",
       "      <td>-0.140317</td>\n",
       "      <td>-0.364663</td>\n",
       "      <td>0.443432</td>\n",
       "      <td>0.390232</td>\n",
       "      <td>0.257701</td>\n",
       "      <td>0.680076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>-0.470680</td>\n",
       "      <td>-0.209029</td>\n",
       "      <td>0.412648</td>\n",
       "      <td>-0.442862</td>\n",
       "      <td>-0.418574</td>\n",
       "      <td>0.054643</td>\n",
       "      <td>-0.355945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>-0.049164</td>\n",
       "      <td>-0.188437</td>\n",
       "      <td>-0.412194</td>\n",
       "      <td>-0.503133</td>\n",
       "      <td>0.374171</td>\n",
       "      <td>-0.134956</td>\n",
       "      <td>0.394274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>-0.549777</td>\n",
       "      <td>-0.111696</td>\n",
       "      <td>0.290969</td>\n",
       "      <td>-0.355162</td>\n",
       "      <td>-0.366794</td>\n",
       "      <td>0.126699</td>\n",
       "      <td>-0.380487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.331544</td>\n",
       "      <td>0.416546</td>\n",
       "      <td>0.224724</td>\n",
       "      <td>-0.269052</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>-0.067028</td>\n",
       "      <td>-0.103261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>-0.135726</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>-0.270136</td>\n",
       "      <td>0.379826</td>\n",
       "      <td>0.070405</td>\n",
       "      <td>0.309037</td>\n",
       "      <td>0.533036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>-0.229564</td>\n",
       "      <td>0.390564</td>\n",
       "      <td>0.544207</td>\n",
       "      <td>-0.044146</td>\n",
       "      <td>-0.676566</td>\n",
       "      <td>0.003901</td>\n",
       "      <td>-0.570636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                            -0.463210                   0.076000   \n",
       "touch                              -0.469161                  -0.071388   \n",
       "interoception                      -0.028536                   0.328041   \n",
       "learning                           -0.229210                  -0.002593   \n",
       "attention                           0.067232                  -0.016630   \n",
       "language                            0.569370                   0.139994   \n",
       "interaction                        -0.130126                   0.240889   \n",
       "inhibition                         -0.520893                  -0.015881   \n",
       "somatosensory                      -0.458738                  -0.086753   \n",
       "decision                            0.042789                  -0.193624   \n",
       "auditory                            0.285039                   0.434584   \n",
       "taste                              -0.020245                   0.614269   \n",
       "metabolism                         -0.038831                   0.769566   \n",
       "memory                             -0.157765                  -0.070712   \n",
       "empathy                            -0.131411                   0.296001   \n",
       "vision                              0.077576                  -0.146613   \n",
       "autonomic                          -0.105943                   0.359611   \n",
       "visual                              0.069486                  -0.131361   \n",
       "fear                               -0.497420                  -0.145438   \n",
       "perception                          0.251693                   0.176160   \n",
       "cognition                           0.310911                   0.278097   \n",
       "reward                             -0.223340                  -0.140317   \n",
       "mirror                             -0.470680                  -0.209029   \n",
       "orientation                        -0.049164                  -0.188437   \n",
       "movement                           -0.549777                  -0.111696   \n",
       "default_mode                        0.331544                   0.416546   \n",
       "dopamine                           -0.135726                   0.003212   \n",
       "pain                               -0.229564                   0.390564   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                          0.333371                        -0.285619   \n",
       "touch                            0.548121                        -0.221619   \n",
       "interoception                    0.335662                         0.027684   \n",
       "learning                        -0.538202                         0.059453   \n",
       "attention                        0.206701                        -0.596517   \n",
       "language                         0.244959                        -0.073424   \n",
       "interaction                      0.009086                        -0.603852   \n",
       "inhibition                       0.448087                        -0.252478   \n",
       "somatosensory                    0.579207                        -0.216110   \n",
       "decision                        -0.274825                         0.328225   \n",
       "auditory                         0.546822                        -0.322500   \n",
       "taste                            0.045627                         0.241977   \n",
       "metabolism                      -0.109230                        -0.140051   \n",
       "memory                          -0.245446                        -0.031770   \n",
       "empathy                          0.591985                        -0.044198   \n",
       "vision                          -0.453045                        -0.505319   \n",
       "autonomic                        0.311603                         0.127752   \n",
       "visual                          -0.567318                        -0.435330   \n",
       "fear                            -0.531248                         0.598636   \n",
       "perception                       0.085151                        -0.613190   \n",
       "cognition                        0.243597                        -0.333141   \n",
       "reward                          -0.364663                         0.443432   \n",
       "mirror                           0.412648                        -0.442862   \n",
       "orientation                     -0.412194                        -0.503133   \n",
       "movement                         0.290969                        -0.355162   \n",
       "default_mode                     0.224724                        -0.269052   \n",
       "dopamine                        -0.270136                         0.379826   \n",
       "pain                             0.544207                        -0.044146   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                            -0.665450                    0.233285   \n",
       "touch                              -0.368110                   -0.013959   \n",
       "interoception                      -0.101708                    0.086049   \n",
       "learning                            0.349082                   -0.011153   \n",
       "attention                          -0.343371                    0.060219   \n",
       "language                           -0.507938                    0.109270   \n",
       "interaction                        -0.128782                   -0.015525   \n",
       "inhibition                         -0.586494                    0.262675   \n",
       "somatosensory                      -0.364322                   -0.006177   \n",
       "decision                            0.030235                    0.331625   \n",
       "auditory                           -0.572890                    0.065049   \n",
       "taste                              -0.346316                    0.106922   \n",
       "metabolism                         -0.561459                   -0.087907   \n",
       "memory                              0.280619                   -0.240516   \n",
       "empathy                            -0.417338                    0.082570   \n",
       "vision                              0.398909                   -0.136013   \n",
       "autonomic                          -0.093989                    0.101483   \n",
       "visual                              0.513443                   -0.091656   \n",
       "fear                                0.682025                   -0.098592   \n",
       "perception                         -0.243663                   -0.147974   \n",
       "cognition                          -0.497586                   -0.004040   \n",
       "reward                              0.390232                    0.257701   \n",
       "mirror                             -0.418574                    0.054643   \n",
       "orientation                         0.374171                   -0.134956   \n",
       "movement                           -0.366794                    0.126699   \n",
       "default_mode                       -0.155408                   -0.067028   \n",
       "dopamine                            0.070405                    0.309037   \n",
       "pain                               -0.676566                    0.003901   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                          -0.553785  \n",
       "touch                            -0.428344  \n",
       "interoception                    -0.020376  \n",
       "learning                          0.731421  \n",
       "attention                        -0.110556  \n",
       "language                         -0.049817  \n",
       "interaction                      -0.021447  \n",
       "inhibition                       -0.498871  \n",
       "somatosensory                    -0.426464  \n",
       "decision                          0.584626  \n",
       "auditory                         -0.282755  \n",
       "taste                            -0.249177  \n",
       "metabolism                       -0.458173  \n",
       "memory                            0.068342  \n",
       "empathy                          -0.357444  \n",
       "vision                            0.374071  \n",
       "autonomic                        -0.116952  \n",
       "visual                            0.436615  \n",
       "fear                             -0.029972  \n",
       "perception                       -0.012402  \n",
       "cognition                        -0.335724  \n",
       "reward                            0.680076  \n",
       "mirror                           -0.355945  \n",
       "orientation                       0.394274  \n",
       "movement                         -0.380487  \n",
       "default_mode                     -0.103261  \n",
       "dopamine                          0.533036  \n",
       "pain                             -0.570636  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlr_intercept\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mlr_intercept</th>\n",
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       "  <tbody>\n",
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       "      <th>control</th>\n",
       "      <td>-7.437190e-07</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>-1.522009e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>-9.135920e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>-1.270915e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>-1.209767e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>5.113604e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>-8.684092e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>8.367875e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>-7.932928e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>8.811165e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>4.636519e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>4.735417e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>-3.439393e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>-4.479788e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>-1.216584e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>8.698919e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>-1.981400e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>-4.855015e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>-4.761657e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>-1.089683e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mlr_intercept\n",
       "control        -7.437190e-07\n",
       "touch          -2.263309e-07\n",
       "interoception  -6.983125e-07\n",
       "learning        2.107224e-07\n",
       "attention      -7.896185e-10\n",
       "language       -4.860270e-07\n",
       "interaction    -1.889216e-07\n",
       "inhibition     -6.434822e-07\n",
       "somatosensory  -2.434419e-07\n",
       "decision       -1.522009e-07\n",
       "auditory       -9.135920e-07\n",
       "taste          -1.270915e-06\n",
       "metabolism     -1.209767e-06\n",
       "memory          5.113604e-07\n",
       "empathy        -8.684092e-07\n",
       "vision          8.367875e-07\n",
       "autonomic      -7.932928e-07\n",
       "visual          8.811165e-07\n",
       "fear            4.636519e-07\n",
       "perception      4.735417e-08\n",
       "cognition      -3.439393e-07\n",
       "reward         -4.479788e-10\n",
       "mirror         -1.216584e-08\n",
       "orientation     8.698919e-07\n",
       "movement       -1.981400e-07\n",
       "default_mode   -4.855015e-07\n",
       "dopamine       -4.761657e-07\n",
       "pain           -1.089683e-06"
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      "mlr_full_r2\n"
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       "               mlr_full_r2\n",
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       "      <td>0.002994</td>\n",
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       "      <td>0.000163</td>\n",
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       "      <td>0.062099</td>\n",
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       "      <td>0.023962</td>\n",
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       "      <td>0.059251</td>\n",
       "      <td>0.003453</td>\n",
       "      <td>0.028297</td>\n",
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       "    <tr>\n",
       "      <th>taste</th>\n",
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       "      <td>0.162501</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>0.021652</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>0.021975</td>\n",
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       "    <tr>\n",
       "      <th>metabolism</th>\n",
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       "      <td>0.255054</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.056910</td>\n",
       "      <td>0.006305</td>\n",
       "      <td>0.074298</td>\n",
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       "      <td>0.000428</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>0.047202</td>\n",
       "      <td>0.001653</td>\n",
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       "      <td>0.031443</td>\n",
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       "      <td>0.028728</td>\n",
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       "      <td>0.006926</td>\n",
       "      <td>0.001595</td>\n",
       "      <td>0.008403</td>\n",
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       "      <th>visual</th>\n",
       "      <td>0.001548</td>\n",
       "      <td>0.007431</td>\n",
       "      <td>0.131027</td>\n",
       "      <td>0.080419</td>\n",
       "      <td>0.047593</td>\n",
       "      <td>0.006855</td>\n",
       "      <td>0.067470</td>\n",
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       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>0.079342</td>\n",
       "      <td>0.009109</td>\n",
       "      <td>0.114895</td>\n",
       "      <td>0.152070</td>\n",
       "      <td>0.083976</td>\n",
       "      <td>0.007931</td>\n",
       "      <td>0.000318</td>\n",
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       "    <tr>\n",
       "      <th>perception</th>\n",
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       "      <td>0.010718</td>\n",
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       "      <td>0.000054</td>\n",
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       "      <th>cognition</th>\n",
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       "      <td>0.044698</td>\n",
       "      <td>0.000013</td>\n",
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       "    <tr>\n",
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       "      <td>0.054137</td>\n",
       "      <td>0.083440</td>\n",
       "      <td>0.027492</td>\n",
       "      <td>0.054188</td>\n",
       "      <td>0.163694</td>\n",
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       "      <td>0.018817</td>\n",
       "      <td>0.069321</td>\n",
       "      <td>0.083225</td>\n",
       "      <td>0.031630</td>\n",
       "      <td>0.002436</td>\n",
       "      <td>0.044842</td>\n",
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       "    <tr>\n",
       "      <th>orientation</th>\n",
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       "      <td>0.107420</td>\n",
       "      <td>0.025275</td>\n",
       "      <td>0.014861</td>\n",
       "      <td>0.055019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>0.096924</td>\n",
       "      <td>0.005373</td>\n",
       "      <td>0.034467</td>\n",
       "      <td>0.053527</td>\n",
       "      <td>0.024288</td>\n",
       "      <td>0.013098</td>\n",
       "      <td>0.051238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.035248</td>\n",
       "      <td>0.074725</td>\n",
       "      <td>0.020559</td>\n",
       "      <td>0.030718</td>\n",
       "      <td>0.004360</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>0.003774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>0.005907</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.029708</td>\n",
       "      <td>0.061219</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.077927</td>\n",
       "      <td>0.100561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>0.016899</td>\n",
       "      <td>0.065694</td>\n",
       "      <td>0.120569</td>\n",
       "      <td>0.000827</td>\n",
       "      <td>0.082637</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.115248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                             0.068804                   0.002488   \n",
       "touch                               0.070583                   0.002195   \n",
       "interoception                       0.000261                   0.046344   \n",
       "learning                            0.016847                   0.000003   \n",
       "attention                           0.001449                   0.000119   \n",
       "language                            0.103955                   0.008440   \n",
       "interaction                         0.005430                   0.024990   \n",
       "inhibition                          0.087007                   0.000109   \n",
       "somatosensory                       0.067482                   0.003241   \n",
       "decision                            0.000587                   0.016146   \n",
       "auditory                            0.026054                   0.081337   \n",
       "taste                               0.000131                   0.162501   \n",
       "metabolism                          0.000484                   0.255054   \n",
       "memory                              0.007981                   0.002153   \n",
       "empathy                             0.005538                   0.037734   \n",
       "vision                              0.001930                   0.009257   \n",
       "autonomic                           0.003599                   0.055694   \n",
       "visual                              0.001548                   0.007431   \n",
       "fear                                0.079342                   0.009109   \n",
       "perception                          0.020314                   0.013365   \n",
       "cognition                           0.030998                   0.033307   \n",
       "reward                              0.015995                   0.008479   \n",
       "mirror                              0.071041                   0.018817   \n",
       "orientation                         0.000775                   0.015292   \n",
       "movement                            0.096924                   0.005373   \n",
       "default_mode                        0.035248                   0.074725   \n",
       "dopamine                            0.005907                   0.000004   \n",
       "pain                                0.016899                   0.065694   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                          0.045244                         0.034617   \n",
       "touch                            0.122310                         0.020842   \n",
       "interoception                    0.045868                         0.000325   \n",
       "learning                         0.117923                         0.001500   \n",
       "attention                        0.017394                         0.150995   \n",
       "language                         0.024428                         0.002288   \n",
       "interaction                      0.000034                         0.154732   \n",
       "inhibition                       0.081740                         0.027050   \n",
       "somatosensory                    0.136576                         0.019818   \n",
       "decision                         0.030748                         0.045715   \n",
       "auditory                         0.121730                         0.044135   \n",
       "taste                            0.000848                         0.024847   \n",
       "metabolism                       0.004857                         0.008323   \n",
       "memory                           0.024526                         0.000428   \n",
       "empathy                          0.142669                         0.000829   \n",
       "vision                           0.083558                         0.108355   \n",
       "autonomic                        0.039529                         0.006926   \n",
       "visual                           0.131027                         0.080419   \n",
       "fear                             0.114895                         0.152070   \n",
       "perception                       0.002952                         0.159554   \n",
       "cognition                        0.024157                         0.047095   \n",
       "reward                           0.054137                         0.083440   \n",
       "mirror                           0.069321                         0.083225   \n",
       "orientation                      0.069169                         0.107420   \n",
       "movement                         0.034467                         0.053527   \n",
       "default_mode                     0.020559                         0.030718   \n",
       "dopamine                         0.029708                         0.061219   \n",
       "pain                             0.120569                         0.000827   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                             0.079944                    0.044406   \n",
       "touch                               0.024463                    0.000159   \n",
       "interoception                       0.001868                    0.006042   \n",
       "learning                            0.021999                    0.000102   \n",
       "attention                           0.021285                    0.002959   \n",
       "language                            0.046577                    0.009743   \n",
       "interaction                         0.002994                    0.000197   \n",
       "inhibition                          0.062099                    0.056299   \n",
       "somatosensory                       0.023962                    0.000031   \n",
       "decision                            0.000165                    0.089735   \n",
       "auditory                            0.059251                    0.003453   \n",
       "taste                               0.021652                    0.009328   \n",
       "metabolism                          0.056910                    0.006305   \n",
       "memory                              0.014216                    0.047202   \n",
       "empathy                             0.031443                    0.005563   \n",
       "vision                              0.028728                    0.015095   \n",
       "autonomic                           0.001595                    0.008403   \n",
       "visual                              0.047593                    0.006855   \n",
       "fear                                0.083976                    0.007931   \n",
       "perception                          0.010718                    0.017867   \n",
       "cognition                           0.044698                    0.000013   \n",
       "reward                              0.027492                    0.054188   \n",
       "mirror                              0.031630                    0.002436   \n",
       "orientation                         0.025275                    0.014861   \n",
       "movement                            0.024288                    0.013098   \n",
       "default_mode                        0.004360                    0.003666   \n",
       "dopamine                            0.000895                    0.077927   \n",
       "pain                                0.082637                    0.000012   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.108542  \n",
       "touch                             0.064939  \n",
       "interoception                     0.000147  \n",
       "learning                          0.189344  \n",
       "attention                         0.004326  \n",
       "language                          0.000878  \n",
       "interaction                       0.000163  \n",
       "inhibition                        0.088083  \n",
       "somatosensory                     0.064370  \n",
       "decision                          0.120969  \n",
       "auditory                          0.028297  \n",
       "taste                             0.021975  \n",
       "metabolism                        0.074298  \n",
       "memory                            0.001653  \n",
       "empathy                           0.045220  \n",
       "vision                            0.049525  \n",
       "autonomic                         0.004841  \n",
       "visual                            0.067470  \n",
       "fear                              0.000318  \n",
       "perception                        0.000054  \n",
       "cognition                         0.039892  \n",
       "reward                            0.163694  \n",
       "mirror                            0.044842  \n",
       "orientation                       0.055019  \n",
       "movement                          0.051238  \n",
       "default_mode                      0.003774  \n",
       "dopamine                          0.100561  \n",
       "pain                              0.115248  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check beta\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.437190e-07</td>\n",
       "      <td>-0.463210</td>\n",
       "      <td>0.076000</td>\n",
       "      <td>0.333371</td>\n",
       "      <td>-0.285619</td>\n",
       "      <td>-0.665450</td>\n",
       "      <td>0.233285</td>\n",
       "      <td>-0.553785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.263309e-07</td>\n",
       "      <td>-0.469161</td>\n",
       "      <td>-0.071388</td>\n",
       "      <td>0.548121</td>\n",
       "      <td>-0.221619</td>\n",
       "      <td>-0.368110</td>\n",
       "      <td>-0.013959</td>\n",
       "      <td>-0.428344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.983125e-07</td>\n",
       "      <td>-0.028536</td>\n",
       "      <td>0.328041</td>\n",
       "      <td>0.335662</td>\n",
       "      <td>0.027684</td>\n",
       "      <td>-0.101708</td>\n",
       "      <td>0.086049</td>\n",
       "      <td>-0.020376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.107224e-07</td>\n",
       "      <td>-0.229210</td>\n",
       "      <td>-0.002593</td>\n",
       "      <td>-0.538202</td>\n",
       "      <td>0.059453</td>\n",
       "      <td>0.349082</td>\n",
       "      <td>-0.011153</td>\n",
       "      <td>0.731421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-7.896180e-10</td>\n",
       "      <td>0.067232</td>\n",
       "      <td>-0.016630</td>\n",
       "      <td>0.206701</td>\n",
       "      <td>-0.596517</td>\n",
       "      <td>-0.343371</td>\n",
       "      <td>0.060219</td>\n",
       "      <td>-0.110556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-4.860270e-07</td>\n",
       "      <td>0.569370</td>\n",
       "      <td>0.139994</td>\n",
       "      <td>0.244959</td>\n",
       "      <td>-0.073424</td>\n",
       "      <td>-0.507938</td>\n",
       "      <td>0.109270</td>\n",
       "      <td>-0.049817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.889216e-07</td>\n",
       "      <td>-0.130126</td>\n",
       "      <td>0.240889</td>\n",
       "      <td>0.009086</td>\n",
       "      <td>-0.603852</td>\n",
       "      <td>-0.128782</td>\n",
       "      <td>-0.015525</td>\n",
       "      <td>-0.021447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-6.434822e-07</td>\n",
       "      <td>-0.520893</td>\n",
       "      <td>-0.015881</td>\n",
       "      <td>0.448087</td>\n",
       "      <td>-0.252478</td>\n",
       "      <td>-0.586494</td>\n",
       "      <td>0.262675</td>\n",
       "      <td>-0.498871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.434419e-07</td>\n",
       "      <td>-0.458738</td>\n",
       "      <td>-0.086753</td>\n",
       "      <td>0.579207</td>\n",
       "      <td>-0.216110</td>\n",
       "      <td>-0.364322</td>\n",
       "      <td>-0.006177</td>\n",
       "      <td>-0.426464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.522009e-07</td>\n",
       "      <td>0.042789</td>\n",
       "      <td>-0.193624</td>\n",
       "      <td>-0.274825</td>\n",
       "      <td>0.328225</td>\n",
       "      <td>0.030235</td>\n",
       "      <td>0.331625</td>\n",
       "      <td>0.584626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-9.135920e-07</td>\n",
       "      <td>0.285039</td>\n",
       "      <td>0.434584</td>\n",
       "      <td>0.546822</td>\n",
       "      <td>-0.322500</td>\n",
       "      <td>-0.572890</td>\n",
       "      <td>0.065049</td>\n",
       "      <td>-0.282755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.270915e-06</td>\n",
       "      <td>-0.020245</td>\n",
       "      <td>0.614269</td>\n",
       "      <td>0.045627</td>\n",
       "      <td>0.241977</td>\n",
       "      <td>-0.346316</td>\n",
       "      <td>0.106922</td>\n",
       "      <td>-0.249177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.209767e-06</td>\n",
       "      <td>-0.038831</td>\n",
       "      <td>0.769566</td>\n",
       "      <td>-0.109230</td>\n",
       "      <td>-0.140051</td>\n",
       "      <td>-0.561459</td>\n",
       "      <td>-0.087907</td>\n",
       "      <td>-0.458173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.113604e-07</td>\n",
       "      <td>-0.157765</td>\n",
       "      <td>-0.070712</td>\n",
       "      <td>-0.245446</td>\n",
       "      <td>-0.031770</td>\n",
       "      <td>0.280619</td>\n",
       "      <td>-0.240516</td>\n",
       "      <td>0.068342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-8.684092e-07</td>\n",
       "      <td>-0.131411</td>\n",
       "      <td>0.296001</td>\n",
       "      <td>0.591985</td>\n",
       "      <td>-0.044198</td>\n",
       "      <td>-0.417338</td>\n",
       "      <td>0.082570</td>\n",
       "      <td>-0.357444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.367875e-07</td>\n",
       "      <td>0.077576</td>\n",
       "      <td>-0.146613</td>\n",
       "      <td>-0.453045</td>\n",
       "      <td>-0.505319</td>\n",
       "      <td>0.398909</td>\n",
       "      <td>-0.136013</td>\n",
       "      <td>0.374071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-7.932928e-07</td>\n",
       "      <td>-0.105943</td>\n",
       "      <td>0.359611</td>\n",
       "      <td>0.311603</td>\n",
       "      <td>0.127752</td>\n",
       "      <td>-0.093989</td>\n",
       "      <td>0.101483</td>\n",
       "      <td>-0.116952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8.811166e-07</td>\n",
       "      <td>0.069486</td>\n",
       "      <td>-0.131361</td>\n",
       "      <td>-0.567318</td>\n",
       "      <td>-0.435330</td>\n",
       "      <td>0.513443</td>\n",
       "      <td>-0.091656</td>\n",
       "      <td>0.436615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.636519e-07</td>\n",
       "      <td>-0.497420</td>\n",
       "      <td>-0.145438</td>\n",
       "      <td>-0.531248</td>\n",
       "      <td>0.598636</td>\n",
       "      <td>0.682025</td>\n",
       "      <td>-0.098592</td>\n",
       "      <td>-0.029972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.735417e-08</td>\n",
       "      <td>0.251693</td>\n",
       "      <td>0.176160</td>\n",
       "      <td>0.085151</td>\n",
       "      <td>-0.613190</td>\n",
       "      <td>-0.243663</td>\n",
       "      <td>-0.147974</td>\n",
       "      <td>-0.012402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-3.439393e-07</td>\n",
       "      <td>0.310911</td>\n",
       "      <td>0.278097</td>\n",
       "      <td>0.243597</td>\n",
       "      <td>-0.333141</td>\n",
       "      <td>-0.497586</td>\n",
       "      <td>-0.004040</td>\n",
       "      <td>-0.335724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-4.479788e-10</td>\n",
       "      <td>-0.223340</td>\n",
       "      <td>-0.140317</td>\n",
       "      <td>-0.364663</td>\n",
       "      <td>0.443432</td>\n",
       "      <td>0.390232</td>\n",
       "      <td>0.257701</td>\n",
       "      <td>0.680076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1.216584e-08</td>\n",
       "      <td>-0.470680</td>\n",
       "      <td>-0.209029</td>\n",
       "      <td>0.412648</td>\n",
       "      <td>-0.442862</td>\n",
       "      <td>-0.418574</td>\n",
       "      <td>0.054643</td>\n",
       "      <td>-0.355945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.698919e-07</td>\n",
       "      <td>-0.049164</td>\n",
       "      <td>-0.188437</td>\n",
       "      <td>-0.412194</td>\n",
       "      <td>-0.503133</td>\n",
       "      <td>0.374171</td>\n",
       "      <td>-0.134956</td>\n",
       "      <td>0.394274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.981400e-07</td>\n",
       "      <td>-0.549777</td>\n",
       "      <td>-0.111696</td>\n",
       "      <td>0.290969</td>\n",
       "      <td>-0.355162</td>\n",
       "      <td>-0.366794</td>\n",
       "      <td>0.126699</td>\n",
       "      <td>-0.380487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-4.855015e-07</td>\n",
       "      <td>0.331544</td>\n",
       "      <td>0.416546</td>\n",
       "      <td>0.224724</td>\n",
       "      <td>-0.269052</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>-0.067028</td>\n",
       "      <td>-0.103261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-4.761657e-07</td>\n",
       "      <td>-0.135726</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>-0.270136</td>\n",
       "      <td>0.379826</td>\n",
       "      <td>0.070405</td>\n",
       "      <td>0.309037</td>\n",
       "      <td>0.533036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.089683e-06</td>\n",
       "      <td>-0.229564</td>\n",
       "      <td>0.390564</td>\n",
       "      <td>0.544207</td>\n",
       "      <td>-0.044146</td>\n",
       "      <td>-0.676566</td>\n",
       "      <td>0.003901</td>\n",
       "      <td>-0.570636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1         2         3         4         5         6  \\\n",
       "0  -7.437190e-07 -0.463210  0.076000  0.333371 -0.285619 -0.665450  0.233285   \n",
       "1  -2.263309e-07 -0.469161 -0.071388  0.548121 -0.221619 -0.368110 -0.013959   \n",
       "2  -6.983125e-07 -0.028536  0.328041  0.335662  0.027684 -0.101708  0.086049   \n",
       "3   2.107224e-07 -0.229210 -0.002593 -0.538202  0.059453  0.349082 -0.011153   \n",
       "4  -7.896180e-10  0.067232 -0.016630  0.206701 -0.596517 -0.343371  0.060219   \n",
       "5  -4.860270e-07  0.569370  0.139994  0.244959 -0.073424 -0.507938  0.109270   \n",
       "6  -1.889216e-07 -0.130126  0.240889  0.009086 -0.603852 -0.128782 -0.015525   \n",
       "7  -6.434822e-07 -0.520893 -0.015881  0.448087 -0.252478 -0.586494  0.262675   \n",
       "8  -2.434419e-07 -0.458738 -0.086753  0.579207 -0.216110 -0.364322 -0.006177   \n",
       "9  -1.522009e-07  0.042789 -0.193624 -0.274825  0.328225  0.030235  0.331625   \n",
       "10 -9.135920e-07  0.285039  0.434584  0.546822 -0.322500 -0.572890  0.065049   \n",
       "11 -1.270915e-06 -0.020245  0.614269  0.045627  0.241977 -0.346316  0.106922   \n",
       "12 -1.209767e-06 -0.038831  0.769566 -0.109230 -0.140051 -0.561459 -0.087907   \n",
       "13  5.113604e-07 -0.157765 -0.070712 -0.245446 -0.031770  0.280619 -0.240516   \n",
       "14 -8.684092e-07 -0.131411  0.296001  0.591985 -0.044198 -0.417338  0.082570   \n",
       "15  8.367875e-07  0.077576 -0.146613 -0.453045 -0.505319  0.398909 -0.136013   \n",
       "16 -7.932928e-07 -0.105943  0.359611  0.311603  0.127752 -0.093989  0.101483   \n",
       "17  8.811166e-07  0.069486 -0.131361 -0.567318 -0.435330  0.513443 -0.091656   \n",
       "18  4.636519e-07 -0.497420 -0.145438 -0.531248  0.598636  0.682025 -0.098592   \n",
       "19  4.735417e-08  0.251693  0.176160  0.085151 -0.613190 -0.243663 -0.147974   \n",
       "20 -3.439393e-07  0.310911  0.278097  0.243597 -0.333141 -0.497586 -0.004040   \n",
       "21 -4.479788e-10 -0.223340 -0.140317 -0.364663  0.443432  0.390232  0.257701   \n",
       "22 -1.216584e-08 -0.470680 -0.209029  0.412648 -0.442862 -0.418574  0.054643   \n",
       "23  8.698919e-07 -0.049164 -0.188437 -0.412194 -0.503133  0.374171 -0.134956   \n",
       "24 -1.981400e-07 -0.549777 -0.111696  0.290969 -0.355162 -0.366794  0.126699   \n",
       "25 -4.855015e-07  0.331544  0.416546  0.224724 -0.269052 -0.155408 -0.067028   \n",
       "26 -4.761657e-07 -0.135726  0.003212 -0.270136  0.379826  0.070405  0.309037   \n",
       "27 -1.089683e-06 -0.229564  0.390564  0.544207 -0.044146 -0.676566  0.003901   \n",
       "\n",
       "           7  \n",
       "0  -0.553785  \n",
       "1  -0.428344  \n",
       "2  -0.020376  \n",
       "3   0.731421  \n",
       "4  -0.110556  \n",
       "5  -0.049817  \n",
       "6  -0.021447  \n",
       "7  -0.498871  \n",
       "8  -0.426464  \n",
       "9   0.584626  \n",
       "10 -0.282755  \n",
       "11 -0.249177  \n",
       "12 -0.458173  \n",
       "13  0.068342  \n",
       "14 -0.357444  \n",
       "15  0.374071  \n",
       "16 -0.116952  \n",
       "17  0.436615  \n",
       "18 -0.029972  \n",
       "19 -0.012402  \n",
       "20 -0.335724  \n",
       "21  0.680076  \n",
       "22 -0.355945  \n",
       "23  0.394274  \n",
       "24 -0.380487  \n",
       "25 -0.103261  \n",
       "26  0.533036  \n",
       "27 -0.570636  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"mlr\", \n",
    "    adjust_r2=False, # adjusted R2?\n",
    "    mlr_individual=True, # calculate individual R2?\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "for mlr in [\"mlr_beta\", \"mlr_intercept\", \"mlr_full_r2\", \"mlr_individual\"]:\n",
    "    print(mlr)\n",
    "    display(juspyce_vol.predictions[mlr])\n",
    "\n",
    "\n",
    "print(\"check beta\")\n",
    "check_res = np.zeros((juspyce_vol.Y.shape[0], juspyce_vol.X.shape[0]+1))\n",
    "for y in range(juspyce_vol.Y.shape[0]):\n",
    "    check_res[y,:] = check_beta(x=juspyce_vol.X.values.T,\n",
    "                                y=juspyce_vol.Y.iloc[y,:].values.T)\n",
    "display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dominance analysis\n",
    "\n",
    "Will calculate dominance analyses predicting each Y from all X's. Will store the following dataframes:  \n",
    "- `JuSpyce.predictions[\"dominance_total\"]`: len(Y) x len(X) dataframe with each's predictors individually explained $R^2$ in multivariate regression model for each Y\n",
    "- `JuSpyce.predictions[\"dominance_relative\"]`: len(Y) x len(X) dataframe with each's predictors relative contribution to the full $R^2$ corresponding to `dominance_total`. Each row in the dataframe should add up to 1.\n",
    "- `JuSpyce.predictions[\"dominance_full_r2\"]`: len(Y) x 1 dataframe with full model $R^2$'s. This is the sum across rows of the `dominance_total` dataframe nd should equal `mlr_full_r2`.\n",
    "- `JuSpyce.predictions[\"dominance_individual\"]`: len(Y) x len(X) dataframe with each's predictors individually explained $R^2$ corresponding to a simple linear redression (`slr`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1655dda23500498e8ea8f7ea3b822db8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (dominance, 1 proc):   0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dominance_total\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.147913</td>\n",
       "      <td>0.014475</td>\n",
       "      <td>0.032636</td>\n",
       "      <td>0.026925</td>\n",
       "      <td>0.121830</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>0.057992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>0.080976</td>\n",
       "      <td>0.022301</td>\n",
       "      <td>0.073290</td>\n",
       "      <td>0.017817</td>\n",
       "      <td>0.029836</td>\n",
       "      <td>0.008097</td>\n",
       "      <td>0.047151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>0.007750</td>\n",
       "      <td>0.076401</td>\n",
       "      <td>0.067108</td>\n",
       "      <td>0.033618</td>\n",
       "      <td>0.010086</td>\n",
       "      <td>0.005171</td>\n",
       "      <td>0.024266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>0.118375</td>\n",
       "      <td>0.072957</td>\n",
       "      <td>0.130110</td>\n",
       "      <td>0.025580</td>\n",
       "      <td>0.088513</td>\n",
       "      <td>0.007599</td>\n",
       "      <td>0.284321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.006852</td>\n",
       "      <td>0.047395</td>\n",
       "      <td>0.010261</td>\n",
       "      <td>0.190173</td>\n",
       "      <td>0.014129</td>\n",
       "      <td>0.003143</td>\n",
       "      <td>0.028408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.091593</td>\n",
       "      <td>0.005207</td>\n",
       "      <td>0.021725</td>\n",
       "      <td>0.004623</td>\n",
       "      <td>0.023399</td>\n",
       "      <td>0.013086</td>\n",
       "      <td>0.003791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>0.021849</td>\n",
       "      <td>0.017200</td>\n",
       "      <td>0.027849</td>\n",
       "      <td>0.177235</td>\n",
       "      <td>0.010744</td>\n",
       "      <td>0.001937</td>\n",
       "      <td>0.007066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>0.142532</td>\n",
       "      <td>0.022376</td>\n",
       "      <td>0.037338</td>\n",
       "      <td>0.020763</td>\n",
       "      <td>0.091369</td>\n",
       "      <td>0.029603</td>\n",
       "      <td>0.045106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>0.073340</td>\n",
       "      <td>0.025789</td>\n",
       "      <td>0.087558</td>\n",
       "      <td>0.017290</td>\n",
       "      <td>0.027851</td>\n",
       "      <td>0.006430</td>\n",
       "      <td>0.047328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>0.034437</td>\n",
       "      <td>0.024866</td>\n",
       "      <td>0.025416</td>\n",
       "      <td>0.093338</td>\n",
       "      <td>0.075440</td>\n",
       "      <td>0.149707</td>\n",
       "      <td>0.246052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.017374</td>\n",
       "      <td>0.035445</td>\n",
       "      <td>0.085246</td>\n",
       "      <td>0.013106</td>\n",
       "      <td>0.022728</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.011677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>0.054872</td>\n",
       "      <td>0.253703</td>\n",
       "      <td>0.008795</td>\n",
       "      <td>0.137506</td>\n",
       "      <td>0.056479</td>\n",
       "      <td>0.003929</td>\n",
       "      <td>0.053792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>0.098644</td>\n",
       "      <td>0.266274</td>\n",
       "      <td>0.098375</td>\n",
       "      <td>0.021244</td>\n",
       "      <td>0.110732</td>\n",
       "      <td>0.053995</td>\n",
       "      <td>0.048458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>0.008053</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>0.027792</td>\n",
       "      <td>0.011223</td>\n",
       "      <td>0.007347</td>\n",
       "      <td>0.050472</td>\n",
       "      <td>0.004554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>0.016200</td>\n",
       "      <td>0.030244</td>\n",
       "      <td>0.124896</td>\n",
       "      <td>0.024343</td>\n",
       "      <td>0.018974</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.016515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.015487</td>\n",
       "      <td>0.033709</td>\n",
       "      <td>0.118275</td>\n",
       "      <td>0.269454</td>\n",
       "      <td>0.046866</td>\n",
       "      <td>0.006169</td>\n",
       "      <td>0.032679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>0.015662</td>\n",
       "      <td>0.098884</td>\n",
       "      <td>0.059734</td>\n",
       "      <td>0.060661</td>\n",
       "      <td>0.009833</td>\n",
       "      <td>0.004794</td>\n",
       "      <td>0.023673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.015018</td>\n",
       "      <td>0.022288</td>\n",
       "      <td>0.154842</td>\n",
       "      <td>0.235079</td>\n",
       "      <td>0.056200</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.034573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>0.063832</td>\n",
       "      <td>0.020191</td>\n",
       "      <td>0.067986</td>\n",
       "      <td>0.095754</td>\n",
       "      <td>0.035109</td>\n",
       "      <td>0.014663</td>\n",
       "      <td>0.008635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.018367</td>\n",
       "      <td>0.014617</td>\n",
       "      <td>0.011403</td>\n",
       "      <td>0.185987</td>\n",
       "      <td>0.016985</td>\n",
       "      <td>0.019435</td>\n",
       "      <td>0.023461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.025558</td>\n",
       "      <td>0.011876</td>\n",
       "      <td>0.006911</td>\n",
       "      <td>0.034834</td>\n",
       "      <td>0.016410</td>\n",
       "      <td>0.003419</td>\n",
       "      <td>0.045508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>0.075211</td>\n",
       "      <td>0.062310</td>\n",
       "      <td>0.027848</td>\n",
       "      <td>0.142944</td>\n",
       "      <td>0.066818</td>\n",
       "      <td>0.103760</td>\n",
       "      <td>0.329932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>0.089246</td>\n",
       "      <td>0.088640</td>\n",
       "      <td>0.031229</td>\n",
       "      <td>0.108036</td>\n",
       "      <td>0.043984</td>\n",
       "      <td>0.002475</td>\n",
       "      <td>0.053277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>0.006297</td>\n",
       "      <td>0.034316</td>\n",
       "      <td>0.109095</td>\n",
       "      <td>0.252661</td>\n",
       "      <td>0.031275</td>\n",
       "      <td>0.006273</td>\n",
       "      <td>0.029099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>0.132062</td>\n",
       "      <td>0.040333</td>\n",
       "      <td>0.022467</td>\n",
       "      <td>0.068648</td>\n",
       "      <td>0.054764</td>\n",
       "      <td>0.005852</td>\n",
       "      <td>0.042079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.041205</td>\n",
       "      <td>0.051922</td>\n",
       "      <td>0.024803</td>\n",
       "      <td>0.016932</td>\n",
       "      <td>0.036156</td>\n",
       "      <td>0.006385</td>\n",
       "      <td>0.010509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>0.089542</td>\n",
       "      <td>0.075487</td>\n",
       "      <td>0.029937</td>\n",
       "      <td>0.154254</td>\n",
       "      <td>0.103883</td>\n",
       "      <td>0.114778</td>\n",
       "      <td>0.304493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>0.071629</td>\n",
       "      <td>0.051957</td>\n",
       "      <td>0.063230</td>\n",
       "      <td>0.033353</td>\n",
       "      <td>0.071934</td>\n",
       "      <td>0.015856</td>\n",
       "      <td>0.050025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                             0.147913                   0.014475   \n",
       "touch                               0.080976                   0.022301   \n",
       "interoception                       0.007750                   0.076401   \n",
       "learning                            0.118375                   0.072957   \n",
       "attention                           0.006852                   0.047395   \n",
       "language                            0.091593                   0.005207   \n",
       "interaction                         0.021849                   0.017200   \n",
       "inhibition                          0.142532                   0.022376   \n",
       "somatosensory                       0.073340                   0.025789   \n",
       "decision                            0.034437                   0.024866   \n",
       "auditory                            0.017374                   0.035445   \n",
       "taste                               0.054872                   0.253703   \n",
       "metabolism                          0.098644                   0.266274   \n",
       "memory                              0.008053                   0.001382   \n",
       "empathy                             0.016200                   0.030244   \n",
       "vision                              0.015487                   0.033709   \n",
       "autonomic                           0.015662                   0.098884   \n",
       "visual                              0.015018                   0.022288   \n",
       "fear                                0.063832                   0.020191   \n",
       "perception                          0.018367                   0.014617   \n",
       "cognition                           0.025558                   0.011876   \n",
       "reward                              0.075211                   0.062310   \n",
       "mirror                              0.089246                   0.088640   \n",
       "orientation                         0.006297                   0.034316   \n",
       "movement                            0.132062                   0.040333   \n",
       "default_mode                        0.041205                   0.051922   \n",
       "dopamine                            0.089542                   0.075487   \n",
       "pain                                0.071629                   0.051957   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                          0.032636                         0.026925   \n",
       "touch                            0.073290                         0.017817   \n",
       "interoception                    0.067108                         0.033618   \n",
       "learning                         0.130110                         0.025580   \n",
       "attention                        0.010261                         0.190173   \n",
       "language                         0.021725                         0.004623   \n",
       "interaction                      0.027849                         0.177235   \n",
       "inhibition                       0.037338                         0.020763   \n",
       "somatosensory                    0.087558                         0.017290   \n",
       "decision                         0.025416                         0.093338   \n",
       "auditory                         0.085246                         0.013106   \n",
       "taste                            0.008795                         0.137506   \n",
       "metabolism                       0.098375                         0.021244   \n",
       "memory                           0.027792                         0.011223   \n",
       "empathy                          0.124896                         0.024343   \n",
       "vision                           0.118275                         0.269454   \n",
       "autonomic                        0.059734                         0.060661   \n",
       "visual                           0.154842                         0.235079   \n",
       "fear                             0.067986                         0.095754   \n",
       "perception                       0.011403                         0.185987   \n",
       "cognition                        0.006911                         0.034834   \n",
       "reward                           0.027848                         0.142944   \n",
       "mirror                           0.031229                         0.108036   \n",
       "orientation                      0.109095                         0.252661   \n",
       "movement                         0.022467                         0.068648   \n",
       "default_mode                     0.024803                         0.016932   \n",
       "dopamine                         0.029937                         0.154254   \n",
       "pain                             0.063230                         0.033353   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                             0.121830                    0.018801   \n",
       "touch                               0.029836                    0.008097   \n",
       "interoception                       0.010086                    0.005171   \n",
       "learning                            0.088513                    0.007599   \n",
       "attention                           0.014129                    0.003143   \n",
       "language                            0.023399                    0.013086   \n",
       "interaction                         0.010744                    0.001937   \n",
       "inhibition                          0.091369                    0.029603   \n",
       "somatosensory                       0.027851                    0.006430   \n",
       "decision                            0.075440                    0.149707   \n",
       "auditory                            0.022728                    0.001225   \n",
       "taste                               0.056479                    0.003929   \n",
       "metabolism                          0.110732                    0.053995   \n",
       "memory                              0.007347                    0.050472   \n",
       "empathy                             0.018974                    0.001585   \n",
       "vision                              0.046866                    0.006169   \n",
       "autonomic                           0.009833                    0.004794   \n",
       "visual                              0.056200                    0.002232   \n",
       "fear                                0.035109                    0.014663   \n",
       "perception                          0.016985                    0.019435   \n",
       "cognition                           0.016410                    0.003419   \n",
       "reward                              0.066818                    0.103760   \n",
       "mirror                              0.043984                    0.002475   \n",
       "orientation                         0.031275                    0.006273   \n",
       "movement                            0.054764                    0.005852   \n",
       "default_mode                        0.036156                    0.006385   \n",
       "dopamine                            0.103883                    0.114778   \n",
       "pain                                0.071934                    0.015856   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.057992  \n",
       "touch                             0.047151  \n",
       "interoception                     0.024266  \n",
       "learning                          0.284321  \n",
       "attention                         0.028408  \n",
       "language                          0.003791  \n",
       "interaction                       0.007066  \n",
       "inhibition                        0.045106  \n",
       "somatosensory                     0.047328  \n",
       "decision                          0.246052  \n",
       "auditory                          0.011677  \n",
       "taste                             0.053792  \n",
       "metabolism                        0.048458  \n",
       "memory                            0.004554  \n",
       "empathy                           0.016515  \n",
       "vision                            0.032679  \n",
       "autonomic                         0.023673  \n",
       "visual                            0.034573  \n",
       "fear                              0.008635  \n",
       "perception                        0.023461  \n",
       "cognition                         0.045508  \n",
       "reward                            0.329932  \n",
       "mirror                            0.053277  \n",
       "orientation                       0.029099  \n",
       "movement                          0.042079  \n",
       "default_mode                      0.010509  \n",
       "dopamine                          0.304493  \n",
       "pain                              0.050025  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dominance_relative\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>control</th>\n",
       "      <td>0.351694</td>\n",
       "      <td>0.034418</td>\n",
       "      <td>0.077600</td>\n",
       "      <td>0.064020</td>\n",
       "      <td>0.289676</td>\n",
       "      <td>0.044704</td>\n",
       "      <td>0.137889</td>\n",
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       "    <tr>\n",
       "      <th>touch</th>\n",
       "      <td>0.289750</td>\n",
       "      <td>0.079796</td>\n",
       "      <td>0.262250</td>\n",
       "      <td>0.063753</td>\n",
       "      <td>0.106760</td>\n",
       "      <td>0.028972</td>\n",
       "      <td>0.168719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interoception</th>\n",
       "      <td>0.034537</td>\n",
       "      <td>0.340466</td>\n",
       "      <td>0.299055</td>\n",
       "      <td>0.149814</td>\n",
       "      <td>0.044947</td>\n",
       "      <td>0.023046</td>\n",
       "      <td>0.108136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning</th>\n",
       "      <td>0.162725</td>\n",
       "      <td>0.100291</td>\n",
       "      <td>0.178856</td>\n",
       "      <td>0.035164</td>\n",
       "      <td>0.121675</td>\n",
       "      <td>0.010446</td>\n",
       "      <td>0.390843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>0.022811</td>\n",
       "      <td>0.157793</td>\n",
       "      <td>0.034161</td>\n",
       "      <td>0.633150</td>\n",
       "      <td>0.047041</td>\n",
       "      <td>0.010464</td>\n",
       "      <td>0.094581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>0.560462</td>\n",
       "      <td>0.031862</td>\n",
       "      <td>0.132933</td>\n",
       "      <td>0.028290</td>\n",
       "      <td>0.143181</td>\n",
       "      <td>0.080077</td>\n",
       "      <td>0.023195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interaction</th>\n",
       "      <td>0.082800</td>\n",
       "      <td>0.065180</td>\n",
       "      <td>0.105538</td>\n",
       "      <td>0.671649</td>\n",
       "      <td>0.040715</td>\n",
       "      <td>0.007342</td>\n",
       "      <td>0.026777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inhibition</th>\n",
       "      <td>0.366324</td>\n",
       "      <td>0.057510</td>\n",
       "      <td>0.095964</td>\n",
       "      <td>0.053363</td>\n",
       "      <td>0.234828</td>\n",
       "      <td>0.076082</td>\n",
       "      <td>0.115928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>somatosensory</th>\n",
       "      <td>0.256804</td>\n",
       "      <td>0.090301</td>\n",
       "      <td>0.306590</td>\n",
       "      <td>0.060542</td>\n",
       "      <td>0.097522</td>\n",
       "      <td>0.022517</td>\n",
       "      <td>0.165724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decision</th>\n",
       "      <td>0.053041</td>\n",
       "      <td>0.038299</td>\n",
       "      <td>0.039147</td>\n",
       "      <td>0.143761</td>\n",
       "      <td>0.116194</td>\n",
       "      <td>0.230582</td>\n",
       "      <td>0.378976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auditory</th>\n",
       "      <td>0.093006</td>\n",
       "      <td>0.189749</td>\n",
       "      <td>0.456344</td>\n",
       "      <td>0.070159</td>\n",
       "      <td>0.121669</td>\n",
       "      <td>0.006559</td>\n",
       "      <td>0.062513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taste</th>\n",
       "      <td>0.096423</td>\n",
       "      <td>0.445815</td>\n",
       "      <td>0.015456</td>\n",
       "      <td>0.241629</td>\n",
       "      <td>0.099247</td>\n",
       "      <td>0.006904</td>\n",
       "      <td>0.094526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metabolism</th>\n",
       "      <td>0.141379</td>\n",
       "      <td>0.381633</td>\n",
       "      <td>0.140994</td>\n",
       "      <td>0.030448</td>\n",
       "      <td>0.158705</td>\n",
       "      <td>0.077388</td>\n",
       "      <td>0.069452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>memory</th>\n",
       "      <td>0.072667</td>\n",
       "      <td>0.012470</td>\n",
       "      <td>0.250778</td>\n",
       "      <td>0.101270</td>\n",
       "      <td>0.066293</td>\n",
       "      <td>0.455427</td>\n",
       "      <td>0.041096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empathy</th>\n",
       "      <td>0.069601</td>\n",
       "      <td>0.129938</td>\n",
       "      <td>0.536594</td>\n",
       "      <td>0.104583</td>\n",
       "      <td>0.081519</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.070955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>0.029632</td>\n",
       "      <td>0.064498</td>\n",
       "      <td>0.226304</td>\n",
       "      <td>0.515566</td>\n",
       "      <td>0.089671</td>\n",
       "      <td>0.011803</td>\n",
       "      <td>0.062526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>autonomic</th>\n",
       "      <td>0.057321</td>\n",
       "      <td>0.361894</td>\n",
       "      <td>0.218613</td>\n",
       "      <td>0.222005</td>\n",
       "      <td>0.035986</td>\n",
       "      <td>0.017545</td>\n",
       "      <td>0.086637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visual</th>\n",
       "      <td>0.028867</td>\n",
       "      <td>0.042842</td>\n",
       "      <td>0.297641</td>\n",
       "      <td>0.451873</td>\n",
       "      <td>0.108029</td>\n",
       "      <td>0.004291</td>\n",
       "      <td>0.066456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fear</th>\n",
       "      <td>0.208486</td>\n",
       "      <td>0.065948</td>\n",
       "      <td>0.222054</td>\n",
       "      <td>0.312748</td>\n",
       "      <td>0.114671</td>\n",
       "      <td>0.047890</td>\n",
       "      <td>0.028203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perception</th>\n",
       "      <td>0.063280</td>\n",
       "      <td>0.050360</td>\n",
       "      <td>0.039287</td>\n",
       "      <td>0.640771</td>\n",
       "      <td>0.058517</td>\n",
       "      <td>0.066957</td>\n",
       "      <td>0.080828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cognition</th>\n",
       "      <td>0.176855</td>\n",
       "      <td>0.082180</td>\n",
       "      <td>0.047822</td>\n",
       "      <td>0.241039</td>\n",
       "      <td>0.113550</td>\n",
       "      <td>0.023656</td>\n",
       "      <td>0.314897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reward</th>\n",
       "      <td>0.092988</td>\n",
       "      <td>0.077038</td>\n",
       "      <td>0.034430</td>\n",
       "      <td>0.176731</td>\n",
       "      <td>0.082612</td>\n",
       "      <td>0.128286</td>\n",
       "      <td>0.407917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mirror</th>\n",
       "      <td>0.214077</td>\n",
       "      <td>0.212624</td>\n",
       "      <td>0.074911</td>\n",
       "      <td>0.259149</td>\n",
       "      <td>0.105506</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>0.127797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>0.013425</td>\n",
       "      <td>0.073166</td>\n",
       "      <td>0.232604</td>\n",
       "      <td>0.538706</td>\n",
       "      <td>0.066683</td>\n",
       "      <td>0.013375</td>\n",
       "      <td>0.062042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>0.360624</td>\n",
       "      <td>0.110138</td>\n",
       "      <td>0.061351</td>\n",
       "      <td>0.187458</td>\n",
       "      <td>0.149544</td>\n",
       "      <td>0.015981</td>\n",
       "      <td>0.114905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.219277</td>\n",
       "      <td>0.276311</td>\n",
       "      <td>0.131995</td>\n",
       "      <td>0.090104</td>\n",
       "      <td>0.192408</td>\n",
       "      <td>0.033977</td>\n",
       "      <td>0.055927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>0.102642</td>\n",
       "      <td>0.086530</td>\n",
       "      <td>0.034317</td>\n",
       "      <td>0.176821</td>\n",
       "      <td>0.119080</td>\n",
       "      <td>0.131570</td>\n",
       "      <td>0.349039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>0.200089</td>\n",
       "      <td>0.145137</td>\n",
       "      <td>0.176629</td>\n",
       "      <td>0.093170</td>\n",
       "      <td>0.200942</td>\n",
       "      <td>0.044291</td>\n",
       "      <td>0.139742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                             0.351694                   0.034418   \n",
       "touch                               0.289750                   0.079796   \n",
       "interoception                       0.034537                   0.340466   \n",
       "learning                            0.162725                   0.100291   \n",
       "attention                           0.022811                   0.157793   \n",
       "language                            0.560462                   0.031862   \n",
       "interaction                         0.082800                   0.065180   \n",
       "inhibition                          0.366324                   0.057510   \n",
       "somatosensory                       0.256804                   0.090301   \n",
       "decision                            0.053041                   0.038299   \n",
       "auditory                            0.093006                   0.189749   \n",
       "taste                               0.096423                   0.445815   \n",
       "metabolism                          0.141379                   0.381633   \n",
       "memory                              0.072667                   0.012470   \n",
       "empathy                             0.069601                   0.129938   \n",
       "vision                              0.029632                   0.064498   \n",
       "autonomic                           0.057321                   0.361894   \n",
       "visual                              0.028867                   0.042842   \n",
       "fear                                0.208486                   0.065948   \n",
       "perception                          0.063280                   0.050360   \n",
       "cognition                           0.176855                   0.082180   \n",
       "reward                              0.092988                   0.077038   \n",
       "mirror                              0.214077                   0.212624   \n",
       "orientation                         0.013425                   0.073166   \n",
       "movement                            0.360624                   0.110138   \n",
       "default_mode                        0.219277                   0.276311   \n",
       "dopamine                            0.102642                   0.086530   \n",
       "pain                                0.200089                   0.145137   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                          0.077600                         0.064020   \n",
       "touch                            0.262250                         0.063753   \n",
       "interoception                    0.299055                         0.149814   \n",
       "learning                         0.178856                         0.035164   \n",
       "attention                        0.034161                         0.633150   \n",
       "language                         0.132933                         0.028290   \n",
       "interaction                      0.105538                         0.671649   \n",
       "inhibition                       0.095964                         0.053363   \n",
       "somatosensory                    0.306590                         0.060542   \n",
       "decision                         0.039147                         0.143761   \n",
       "auditory                         0.456344                         0.070159   \n",
       "taste                            0.015456                         0.241629   \n",
       "metabolism                       0.140994                         0.030448   \n",
       "memory                           0.250778                         0.101270   \n",
       "empathy                          0.536594                         0.104583   \n",
       "vision                           0.226304                         0.515566   \n",
       "autonomic                        0.218613                         0.222005   \n",
       "visual                           0.297641                         0.451873   \n",
       "fear                             0.222054                         0.312748   \n",
       "perception                       0.039287                         0.640771   \n",
       "cognition                        0.047822                         0.241039   \n",
       "reward                           0.034430                         0.176731   \n",
       "mirror                           0.074911                         0.259149   \n",
       "orientation                      0.232604                         0.538706   \n",
       "movement                         0.061351                         0.187458   \n",
       "default_mode                     0.131995                         0.090104   \n",
       "dopamine                         0.034317                         0.176821   \n",
       "pain                             0.176629                         0.093170   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                             0.289676                    0.044704   \n",
       "touch                               0.106760                    0.028972   \n",
       "interoception                       0.044947                    0.023046   \n",
       "learning                            0.121675                    0.010446   \n",
       "attention                           0.047041                    0.010464   \n",
       "language                            0.143181                    0.080077   \n",
       "interaction                         0.040715                    0.007342   \n",
       "inhibition                          0.234828                    0.076082   \n",
       "somatosensory                       0.097522                    0.022517   \n",
       "decision                            0.116194                    0.230582   \n",
       "auditory                            0.121669                    0.006559   \n",
       "taste                               0.099247                    0.006904   \n",
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       "memory                              0.066293                    0.455427   \n",
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       "fear                                0.114671                    0.047890   \n",
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       "mirror                              0.105506                    0.005936   \n",
       "orientation                         0.066683                    0.013375   \n",
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       "pain                                0.200942                    0.044291   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.137889  \n",
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       "cognition                         0.314897  \n",
       "reward                            0.407917  \n",
       "mirror                            0.127797  \n",
       "orientation                       0.062042  \n",
       "movement                          0.114905  \n",
       "default_mode                      0.055927  \n",
       "dopamine                          0.349039  \n",
       "pain                              0.139742  "
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      "dominance_full_r2\n"
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       "               dominance_full_r2\n",
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       "      <td>0.155813</td>\n",
       "      <td>2.332084e-03</td>\n",
       "      <td>0.189494</td>\n",
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       "      <td>2.852129e-03</td>\n",
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       "      <td>0.036308</td>\n",
       "      <td>4.654822e-05</td>\n",
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       "      <td>0.038776</td>\n",
       "      <td>2.698036e-02</td>\n",
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       "      <td>4.006361e-03</td>\n",
       "      <td>0.076727</td>\n",
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       "      <td>0.331859</td>\n",
       "      <td>0.215248</td>\n",
       "      <td>1.146372e-01</td>\n",
       "      <td>0.655558</td>\n",
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       "      <td>0.016588</td>\n",
       "      <td>1.358118e-03</td>\n",
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       "    <tr>\n",
       "      <th>orientation</th>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.068629</td>\n",
       "      <td>0.120166</td>\n",
       "      <td>0.350779</td>\n",
       "      <td>0.010918</td>\n",
       "      <td>2.507067e-03</td>\n",
       "      <td>0.026489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movement</th>\n",
       "      <td>0.102387</td>\n",
       "      <td>0.045348</td>\n",
       "      <td>0.030631</td>\n",
       "      <td>0.063401</td>\n",
       "      <td>0.055354</td>\n",
       "      <td>1.173123e-05</td>\n",
       "      <td>0.012263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_mode</th>\n",
       "      <td>0.067059</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>0.046476</td>\n",
       "      <td>0.012331</td>\n",
       "      <td>0.088252</td>\n",
       "      <td>3.726554e-03</td>\n",
       "      <td>0.019092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dopamine</th>\n",
       "      <td>0.317260</td>\n",
       "      <td>0.247122</td>\n",
       "      <td>0.034086</td>\n",
       "      <td>0.384662</td>\n",
       "      <td>0.336707</td>\n",
       "      <td>1.138044e-01</td>\n",
       "      <td>0.698475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>0.122392</td>\n",
       "      <td>0.078039</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>0.080951</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>3.178104e-02</td>\n",
       "      <td>0.010298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               5HT2a-cimbi36-29-beliveau2017  NMDA-ge179-29-galovic2021  \\\n",
       "control                             0.182042                   0.006300   \n",
       "touch                               0.036636                   0.021413   \n",
       "interoception                       0.009078                   0.127706   \n",
       "learning                            0.375489                   0.210834   \n",
       "attention                           0.023132                   0.120333   \n",
       "language                            0.085446                   0.006246   \n",
       "interaction                         0.016784                   0.000027   \n",
       "inhibition                          0.143268                   0.013343   \n",
       "somatosensory                       0.025952                   0.026662   \n",
       "decision                            0.118456                   0.054203   \n",
       "auditory                            0.024290                   0.010354   \n",
       "taste                               0.190887                   0.433333   \n",
       "metabolism                          0.287526                   0.325785   \n",
       "memory                              0.006195                   0.000135   \n",
       "empathy                             0.011674                   0.042879   \n",
       "vision                              0.033470                   0.084569   \n",
       "autonomic                           0.026564                   0.171899   \n",
       "visual                              0.025386                   0.052773   \n",
       "fear                                0.098247                   0.050876   \n",
       "perception                          0.040328                   0.033521   \n",
       "cognition                           0.043389                   0.012463   \n",
       "reward                              0.248275                   0.202341   \n",
       "mirror                              0.035764                   0.125856   \n",
       "orientation                         0.003505                   0.068629   \n",
       "movement                            0.102387                   0.045348   \n",
       "default_mode                        0.067059                   0.015309   \n",
       "dopamine                            0.317260                   0.247122   \n",
       "pain                                0.122392                   0.078039   \n",
       "\n",
       "               mGluR5-abp688-73-smart2019  MU-carfentanil-204-kantonen2020  \\\n",
       "control                          0.054414                         0.007367   \n",
       "touch                            0.014809                         0.008950   \n",
       "interoception                    0.083679                         0.085046   \n",
       "learning                         0.193742                         0.079222   \n",
       "attention                        0.004263                         0.263436   \n",
       "language                         0.033520                         0.001380   \n",
       "interaction                      0.051926                         0.161554   \n",
       "inhibition                       0.007924                         0.003792   \n",
       "somatosensory                    0.025857                         0.008984   \n",
       "decision                         0.023645                         0.208465   \n",
       "auditory                         0.086805                         0.000291   \n",
       "taste                            0.006715                         0.318397   \n",
       "metabolism                       0.208768                         0.045884   \n",
       "memory                           0.035610                         0.017279   \n",
       "empathy                          0.102720                         0.051405   \n",
       "vision                           0.104387                         0.410192   \n",
       "autonomic                        0.069528                         0.138072   \n",
       "visual                           0.127879                         0.353504   \n",
       "fear                             0.056811                         0.088725   \n",
       "perception                       0.007816                         0.242930   \n",
       "cognition                        0.001031                         0.051899   \n",
       "reward                           0.017593                         0.331859   \n",
       "mirror                           0.006471                         0.125369   \n",
       "orientation                      0.120166                         0.350779   \n",
       "movement                         0.030631                         0.063401   \n",
       "default_mode                     0.046476                         0.012331   \n",
       "dopamine                         0.034086                         0.384662   \n",
       "pain                             0.007029                         0.080951   \n",
       "\n",
       "               GABAa-flumazenil-6-dukart2018  5HT1b-p943-65-gallezot2010  \\\n",
       "control                             0.159760                1.101792e-03   \n",
       "touch                               0.006603                1.299454e-02   \n",
       "interoception                       0.000366                5.473213e-03   \n",
       "learning                            0.297603                2.052000e-03   \n",
       "attention                           0.016133                3.078766e-03   \n",
       "language                            0.010539                2.010372e-02   \n",
       "interaction                         0.000424                3.844739e-03   \n",
       "inhibition                          0.102791                9.443612e-03   \n",
       "somatosensory                       0.002633                9.646291e-03   \n",
       "decision                            0.209607                1.699490e-01   \n",
       "auditory                            0.025125                5.858030e-04   \n",
       "taste                               0.155813                2.332084e-03   \n",
       "metabolism                          0.254254                1.113114e-01   \n",
       "memory                              0.000378                5.833246e-02   \n",
       "empathy                             0.000190                1.623426e-07   \n",
       "vision                              0.042051                1.777842e-03   \n",
       "autonomic                           0.001295                2.852129e-03   \n",
       "visual                              0.036308                4.654822e-05   \n",
       "fear                                0.038776                2.698036e-02   \n",
       "perception                          0.035465                1.764233e-02   \n",
       "cognition                           0.017681                4.006361e-03   \n",
       "reward                              0.215248                1.146372e-01   \n",
       "mirror                              0.016588                1.358118e-03   \n",
       "orientation                         0.010918                2.507067e-03   \n",
       "movement                            0.055354                1.173123e-05   \n",
       "default_mode                        0.088252                3.726554e-03   \n",
       "dopamine                            0.336707                1.138044e-01   \n",
       "pain                                0.082310                3.178104e-02   \n",
       "\n",
       "               D2-raclopride-156-malen2022  \n",
       "control                           0.000235  \n",
       "touch                             0.019652  \n",
       "interoception                     0.063621  \n",
       "learning                          0.516197  \n",
       "attention                         0.083601  \n",
       "language                          0.002696  \n",
       "interaction                       0.004939  \n",
       "inhibition                        0.000003  \n",
       "somatosensory                     0.022943  \n",
       "decision                          0.454351  \n",
       "auditory                          0.005400  \n",
       "taste                             0.189494  \n",
       "metabolism                        0.042716  \n",
       "memory                            0.006574  \n",
       "empathy                           0.006206  \n",
       "vision                            0.060145  \n",
       "autonomic                         0.072826  \n",
       "visual                            0.033514  \n",
       "fear                              0.015803  \n",
       "perception                        0.079359  \n",
       "cognition                         0.076727  \n",
       "reward                            0.655558  \n",
       "mirror                            0.048283  \n",
       "orientation                       0.026489  \n",
       "movement                          0.012263  \n",
       "default_mode                      0.019092  \n",
       "dopamine                          0.698475  \n",
       "pain                              0.010298  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "juspyce_vol.predict(\n",
    "    method=\"dominance\", \n",
    "    adjust_r2=False, # adjusted R2?\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "for dom in [\"dominance_total\", \"dominance_relative\", \"dominance_full_r2\", \"dominance_individual\"]:\n",
    "    print(dom)\n",
    "    display(juspyce_vol.predictions[dom])\n",
    "\n",
    "# check dominance analysis, requires dominance-analysis toolbox\n",
    "if False:\n",
    "    print(\"check\")\n",
    "    from dominance_analysis import Dominance\n",
    "    check_res = np.zeros((correlater.Y.shape[0], correlater.X.shape[0]))\n",
    "    for y in range(correlater.Y.shape[0]):\n",
    "        check_res[y,:] = check_dominance(x=correlater.X.values.T,\n",
    "                                        y=correlater.Y.iloc[y,:].values.T)\n",
    "    display(pd.DataFrame(check_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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69fjoo48YNmwYubm51K9fn/Xr13Prrbdy5513snLlSt59913effdd7r//fl5//XXq1avHvHnzauBIhRBCCHElWLrrJCt+Pc3iB7rV9FCEEJcJ41dfk7JiBc0WfFrTQxFCCFGNJOZ1dVNa65oeQ6WIjo7Wjpk7HY4cOUK7du1qaESipsn7L4QQQtSsaV/s44s9Cfz20mA8XK+cmn5KqT1a6+iaHkdlKOwaWoiadOb56Ri/+IK2Bw+gXCWfSghRcySmIET5Ffb5Ke4aWkp8CCGEEEKIKpGcaXtEMzEtp4ZHIoS4XFhTU/P9XwghhBBXPglQCyGEEEKIKmHMNAFwTgLUQohSsqak2P5vTKnhkQghhBCiukiAWgghhBBCVIlkR4A6VQLUQojScQaoU4w1OxAhhBBCVBsJUAshhBBCiCphdJb4yK7hkQghLhe5zgxqY80ORAghhBDVRgLUQgghhBCi0mmtMWbZAtRS4kMIUVoXM6ilxIcQQghxtZAAtRBCCCGEqHSp2RasuRqAs6mSQS2EKJk2m8nNyAAkg1oIIYS4mkiAWgghhBBCVDrHBIkgGdRCiNKxpqVd/FkyqIUQAoDmzZsTHh5OVFQU0dHRAIwdO5bly5fna+fr68uBAweIiooiKiqK4OBgWrRoQVRUFDfeeCOxsbF0796d0NBQIiIiWLp0aaWM74MPPmDBggWlbh8XF0dYWFih68aPH8/hw4cBGDx4MJGRkYSGhjJp0iSsVquz3bvvvkvbtm0JDQ3lySefLLSvadOm0bZtWyIiIhg6dChG+41Pk8nE/fffT3h4OJGRkWzZsqXQ7efPn8+DDz5Y6uMC23t1/vx5AHr06FGmbSuiqGMFeOWVV2jVqhVt2rRh7dq1zuXjxo2jfv36Bd6LESNGOP8NNW/enKioqGo5BglQV7MZM2Ywa9asCvdTnf/Qy+Lo0aNERUXRoUMH/vjjjyLb5f1l2rdvX3bv3l3qfYwePZo2bdoQFhbGuHHjMJvNFR63EEIIISpXsr3+tKuLkkkShRClYjVeDErnSoBaCCGcNm/eTGxsbImxk/DwcGJjY4mNjSUmJobXX3+d2NhYNmzYgLe3NwsWLODQoUOsWbOGRx99NF8gs7wmTZrEmDFjKtwPwJw5c2jfvj0Ay5YtY9++fRw8eJDExES++OILwHYuVq5cyb59+zh06BBPPPFEoX0NGDCAgwcPsn//flq3bs0rr7wCwMcffwzAgQMHWL9+Pf/3f/9Hbm5upYw/r59++qnS+yxKUcd6+PBhlixZ4nzPp0yZ4gz0jx07ljVr1hToa+nSpc5/Q3fccQfDhg2rlmOQAPVlqjr/oZfFihUruPPOO/n111+59tprK71/q9XK6NGjOXr0KAcOHCArK4s5c+ZU+n6EEEIIUTGODOqW9Xwkg1oIUSrWFOPFn6XEhxBCVKrWrVtz3XXXAdCoUSPq169PYmIiADNnzqRz586EhYXxwAMPoLUutI8FCxYQERFBZGQk9957L5A/EbNv37489thjREdH065dO3bt2sWwYcO47rrreO6555z9WCwWRo8eTbt27bjzzjvJzMx0bu8Iwvv7+zvbmkwmlFIA/O9//+Ppp5/Gw8MDgPr16xc61oEDB+Lq6gpAt27dSEhIAGxB2xtuuMG5bWBgYJGB//j4ePr27ct1113Hiy++6Fz+2Wef0aVLF6Kiopg4cWK+7G4HX19fAEaOHMn333/vXO5I2LRarUybNo3OnTsTERHBhx9+CEB6ejr9+/enY8eOhIeHs3LlSqBg5vmsWbOYMWNGsce6cuVKRo4ciYeHBy1atKBVq1bs3LkTgD59+hAcHFzocYNtPplly5YxatSoIttUJtdq2Ust8OK3hzh8OrVS+2zfyJ8Xbg0tsd3LL7/Mp59+Sv369WnSpAmdOnUiNjaWSZMmkZmZybXXXssnn3xCUFAQffv2pUOHDmzbto2MjAwWLFjAK6+8woEDBxgxYgQvvfQSYPuHnp6ezpYtW5gxYwZ169bl4MGDdOrUic8++wylFDNnzuTbb78lKyuLHj168OGHH6KUom/fvnTt2pXNmzdjNBqZO3cuvXv3xmq18tRTT7FmzRpcXFyYMGECDz30EHv27OHxxx8nPT2dunXrMn/+fBo2bFjgOFetWsVbb72FwWBg48aNzJs3j1tuuYWDBw8Ctg9Penq68wNUWr6+vkycOJENGzYwe/ZshgwZ4lzXpUsX5wdPCCGEELWH0Z5B3bqBH98fOIPFmourQXIjhBBFy021f19zdc2XTS2EEDWtJmNKSikGDhyIUoqJEyfywAMPALayDo4YUVnt3LkTk8nkTCx88MEHmT59OgD33nsv3333Hbfeemu+bQ4dOsRLL73ETz/9RN26dUlKSiq0b3d3d3bv3s3bb7/Nbbfdxp49ewgODubaa6/lscceA+C3335j7ty59OzZk3HjxvH+++8Xmgk9aNAgdu7cyU033cSdd94JwLFjx9i2bRvPPvssnp6ezJo1i86dOxd7vJ988gkjRowAIDIykm+++YZRo0YRHx/Pnj17iI+Pp0uXLoWep4MHD+Lt7U3nzp25+eab8fHxYenSpfz444+4ubkxZcoUFi1aVGQW+YgRI1i2bBk333wzJpOJjRs38r///Y+5c+cSEBDArl27yMnJoWfPngwcOJAmTZrw9ddf4+/vz/nz5+nWrRsxMTHFHl9Rx3rq1Cm6devmXBcSEsKpU6dK1c+2bdto0KCB86ZGVZNvCVVsz549LFmyhNjYWFatWsWuXbsAGDNmDK+99hr79+8nPDw8350Yx4d50qRJ3HbbbcyePZuDBw8yf/58Lly4UGAfv/76K2+99RaHDx/mzz//5McffwRsv2B27drFwYMHycrK4rvvvnNuY7FY2LlzJ2+99ZZz3x999BFxcXHExsayf/9+Ro8ejdls5qGHHmL58uXs2bOHcePG8eyzzxZ6rEOGDGHSpEk89thjbN68udLOYUZGBl27dmXfvn306tXLudxsNrNw4UIGDx5cafsSQgghROVItmdQt73GD63hfLqphC2EEFc7R91p98aNpQa1EELYbd++nb1797J69Wpmz57N1q1bAZzlOxz/ldaZM2e49957mTdvHi4utrDg5s2b6dq1K+Hh4WzatIlDhw4V2G7Tpk0MHz6cunXrAhSZfesIpoaHhxMaGkrDhg3x8PCgZcuWxMfHA9CkSRN69uwJwD333MP27dsL7Wvt2rWcOXOGnJwcNm3aBNjiWUlJSezYsYPXX3+du+66q8iMb7Aljbq6ujJ69GjAVns5JCSE6OhoHn30UXr06IHBYCh02wEDBlCnTh28vLwYNmwY27dvZ+PGjezZs4fOnTsTFRXFxo0b+fPPP4vc/0033cTmzZvJyclh9erV9OnTBy8vL9atW8eCBQuIioqia9euXLhwgePHj6O15plnniEiIoIbb7yRU6dOcfbs2SL7L+5YK2Lx4sXVlj0NV1EGdWnuSlWFbdu2MXToULy9vQHbBzUjIwOj0cj1118PwH333cfw4cOd2xT2YQacH+Y6derk20eXLl0ICQkBICoqiri4OHr16sXmzZv5z3/+Q2ZmJklJSYSGhjrvgDlqyHTq1Im4uDgANmzYwKRJk5yPBQQHB3Pw4EEOHjzIgAEDAFuJjcKyp6uSwWDgjjvuKLB8ypQp9OnTh969e1freIQQQghRsuRMM0pBq/q2xxvPpWVzTYBnDY9KCFGbObKm3Zo1xfRH0V/2hRCiutVUTAmgcePGgK0cxdChQ50lGsojNTWVm2++mZdfftmZWZudnc2UKVPYvXs3TZo0YcaMGWRnZxMfH++MIU2aNKnU+3CU3nBxcXH+7HhtsVgAnOU6HC59nZenpye33XYbK1euZMCAAYSEhDBs2DCUUnTp0gUXFxfOnz/Pk08+ya+//kqjRo1YtWoVYJvo8LvvvmPjxo3Ofbi6uvLmm286++/RowetW7fm66+/diZwOkrJFjZOrTX33Xefs85zSTw9Penbty9r165l6dKljBw5ErCV0Hj33XcZNGhQvvbz588nMTGRPXv24ObmRvPmzcnOzsbV1TVfrezs7OwC2116rI0bN3beFABISEhw/nsqjsVi4auvvmLPnj2lOsbKIBnUtVBpPsyFtQdbMNdisTh/wSxfvpwDBw4wYcKEfP94Hds42hdFa01oaKjzjtyBAwdYt25dqY6jpA9PaXl6eha4m/Xiiy+SmJjIG2+8Ua4+hRBCCFG1jJkm/D3duCbAC0AmShRClMhqL/Hh3qSpZFALIQS2J8rT0tKcP69bty5fHeKyMJlMDB06lDFjxjjLZcDFWE3dunVJT09n+fLlgC3L2RELmjRpEjfccANffPGF88n+okp8lMbJkyf5+eefAfj888/zPS0PtjrMZ86cAWzB0u+//562bdsCcPvttzuf2j927Bgmk4m6desyb948Z/UCgDVr1vCf//yHb775xpk0CpCZmUlGRgYA69evx9XVlfbt2zN06FDn8UZHRzvXJyUlkZWVxYoVK+jZsyf9+/dn+fLlnDt3znke/vrrr2KPd8SIEcybN49t27Y5qwAMGjSI//3vf5jNZuexZGRkkJKSQv369XFzc2Pz5s3Ovhs0aMC5c+e4cOECOTk5+aokFHWsMTExLFmyhJycHE6cOMHx48cLLWVyqQ0bNtC2bVtnMmx1kAB1FevTpw8rVqwgKyuLtLQ0vv32W3x8fAgKCmLbtm0ALFy40JlNXVmK+gVTnAEDBvDhhx86A9ZJSUm0adOGxMRE5y8Os9lc6KMehSnuw1MRc+bMYe3atSxevNj5OIoQQgghapfkTDNB3m7U97PdFJeJEoUQJbGmpODi54chOIjc9HS0/Uu7EELUFK01ueVMtqsMZ8+epVevXkRGRtKlSxduvvnmcpc5XbZsGVu3bmX+/PlERUURFRVFbGwsgYGBTJgwgbCwMAYNGlRkPefQ0FCeffZZrr/+eiIjI3n88cfLfVxt2rRh9uzZtGvXjuTkZCZPnpxvfUZGBjExMURERBAVFUX9+vWdWdzjxo3jzz//JCwsjJEjR/Lpp58WmoH94IMPkpaWxoABA4iKinJuf+7cOTp27Ei7du147bXXWLhwYZHj7NKlC3fccQcRERHccccdREdH0759e1566SUGDhxIREQEAwYMcAbTizJw4EB++OEHbrzxRtzd3QEYP3487du3p2PHjoSFhTFx4kTn5JG7d+8mPDycBQsWOAPzbm5uTJ8+nS5dujBgwADn8uKONTQ0lLvuuov27dszePBgZs+e7UwAHTVqFN27d+e3334jJCSEuXPnOvtbsmRJtZb3gKuoxEdN6dixIyNGjCAyMpL69es7P+iffvqpc5LEli1bMm/evErdb95fMNdcc02JBePB9uE4duwYERERuLm5MWHCBB588EGWL1/Oww8/TEpKChaLhUcffZTQ0JIfb8n74WncuHG+D09FTJo0iWbNmtG9e3fAVq7EUcxfCCGEELWDMdNEoLc79ZwB6pr7cieEuDxYU4wYAgIwBATaXqem4npJeUMhhKhOVqMR86nTeLZpjXJzq/b9t2zZkn379hVYPn/+/ALL0tPTi21zzz33cM899xS6n5deeqlUEy7ed9993HffffmWzZgxw/nzli1bnD/37duXvn37Frru6NGjhfaft41jDrdLubu789lnn5U41t9//73Q5c2bN+e3334rcfuxY8cyduzYQteNGDHCORFhXo4SupD//XBzcyuQce7i4sK///1v/v3vfxfox5EkeqmHH36Yhx9+uMDyoo4V4Nlnny10LrnFixcXuU1h/76qmiqukPjlJDo6Wu/evTvfsiNHjtCuXbsaGpGoafL+CyGEEDXnlne3Uc/Xg3n3d6HTv9YzMPQaXhkWXtPDqhRKqT1a6+iaHkdlKOwaWoiaEj9xEpbERILHjeP0E0/QctX3eLRsWdPDEkJcpY4cOUKrwCAsF87j3qIlBh/vkjcSQgCFx+SKu4aW+ghCCCGEEKLSJWeYCfK2PcJYz8+DRMmgFkKUwJqSgiEwAENAgO210VizAxJCXPW02WT7wSIlh4SoSlLiQ5TL1KlT+fHHH/Mte+SRR7j//vvL3WfXrl3Jyclfn3LhwoWEh18Z2VZCCCHE1cRR4gOgvr+n1KAWogaYEk5hTTHiVYryfLWBNTUV14bXYAh0BKhlokQhRM3SJluAWmriC1G1JEAtymX27NmV3ucvv/xS6X0KIYQQovqZLLlkmKwEedtqNdb38+DY32k1PCohrj6Jb71F9sGDXLtmdU0PpVSsKSkY/AMwBAY6XwshRE3KlQC1ENXiii/xcaXU2BZlI++7EEIIUXOMmbYvc4H2AHUDfw8S03Ow5srfZyGqk+X8eSznz9f0MEpFa20LUAdIiQ8hRO2grVbIzbX9LAFqIarUFR2g9vT05MKFCxKsvMporblw4QKenp41PRQhhBDiqpScafsS5yzx4eeJNVeTlGGqyWEJcdWxGo3kpqdfFoEVnZkJFguGgABc/PzAYMCaYqzpYQkhrmLaarX9oNRl8XtUiMvZFV3iIyQkhISEBBITE2t6KKKaeXp6EhISUtPDEEIIIa5KyfYM6iBngNoDgHNp2dSz/yyEqHqODGRrSgqudevW7GBK4CjnYQjwRymFwd9fMqiFEDXLYgGDARdPT7TZUiNDUErx+OOP89///heAWbNmkZ6ezowZM5gxYwYvvvgix48fp1WrVgC89dZbPPbYY+zatYvo6GiaN2+On58fAFarlWHDhvHcc8/lS+h79NFH+eKLL4iPj8fFpXLyWLdu3cqjjz7K/v37WbJkCXfeeScAmzdv5rHHHnO2O3r0KEuWLOH2229n06ZNPPHEE5hMJjp16sTcuXNxdS0Ythw9ejS7d+/Gzc2NLl268OGHH+Lm5kZycjLjxo3jjz/+wNPTk08++YSwsLBKOZ7mzZuze/du6pbxb+np06d5+OGHWb58eZFtijoerTWPPPIIq1atwtvbm/nz59OxY0diY2OZPHkyqampGAwGnn32WUaMGAHAe++9x1tvvcUff/xBYmJimcdbk67oALWbmxstWrSo6WEIIYQQQlxVLi3xUd/fEaDO4fKYqk2IK8PlGKB2sZf3MAQESA1qIUSNcmRQu/j4YDl/Hq01SqlqHYOHhwdfffUV//znPwsNNoaHh7NkyRKee+45AL744gtCL5kYd/PmzdStW5f09HQeeOABJk6cyKeffgpAbm4uX3/9NU2aNOGHH36gX79+lTLupk2bMn/+fGbNmpVveb9+/YiNjQUgKSmJVq1aMXDgQHJzc7nvvvvYuHEjrVu3Zvr06Xz66af84x//KND36NGj+eyzzwC4++67mTNnDpMnT+bf//43UVFRfP311xw9epSpU6eycePGSjme8mrUqFGxwWko+nhWr17N8ePHOX78OL/88guTJ0/ml19+wdvbmwULFnDddddx+vRpOnXqxKBBgwgMDKRnz57ccsst9O3btxqOrnJd0SU+hBBCCCFE9XOU+AjyuVjiAyAxNafGxiTE1SY3OxudnQ1cHrWcrSmpAM7604bAQHIlQC2EqEkWC8rNDeVuu56piTIfrq6uPPDAA7z55puFrr/99ttZuXIlAH/88QcBAQFFZs36+vrywQcfsGLFCpKSkgDYsmULoaGhTJ48mcWLFxc5jn/961+0adOGXr16MWrUKGfg+Y8//mDw4MF06tSJ3r17c/ToUcCWcRwREVFsRvby5cu56aab8Pb25sKFC7i7u9O6dWsABgwYwJdfflnodkOGDEEphVKKLl26kJCQAMDhw4e54YYbAGjbti1xcXGcPXu2wPaJiYkMGDCA0NBQxo8fT7NmzThvn6/h9ttvp1OnToSGhvLRRx8Vuv833niDsLAwwsLCeOuttwB4+umnmT17trPNjBkzmDVrFnFxcc4sbqvVyhNPPEFYWBgRERG8++67xR7PypUrGTNmDEopunXrhtFo5MyZM7Ru3ZrrrrsOsAXA69ev76wc0aFDB5o3b17kOa/NrugMaiGEEEIIUf2MjgC1PYPaUdbjbGp2jY1JiKtN3qD05RGgdpT4uJhBbU48V5NDEkJc5bTVinJzR7m54Rb7FuqXBHAxVN4OrgmHm14tsdnUqVOJiIjgySefLLDO39+fJk2acPDgQVauXMmIESOYN29ekX35+/vTokULjh8/TteuXVm8eDGjRo3itttu45lnnsFsNuPm5pZvm127dvHll1+yb98+zGYzHTt2pFOnTgA88MADfPDBB1x33XX88ssvTJkyhU2bNpXq8JcsWcLjjz8OQN26dbFYLOzevZvo6GiWL19OfHx8sdubzWYWLlzI22+/DUBkZCRfffUVvXv3ZufOnfz1118kJCTQoEGDfNu9+OKL3HDDDfzzn/9kzZo1zJ0717nuk08+ITg4mKysLDp37swdd9xBnTp1nOv37NnDvHnz+OWXX9Ba07VrV66//npGjBjBo48+ytSpUwFYtmwZa9euxeqoYw589NFHxMXFERsbi6urq/MmQVHHc+rUKZo0aeJcHxISwqlTp2jYsKFz2c6dOzGZTFx77bUln/BaTjKohRBCCCFEpTJmmnB3dcHLzfYlztPNQICXG+fSJINaiOpy+QWojUDeDOoAco2SQS2EqDnaYkG52wLUtgW6Rsbh7+/PmDFjeOeddwpdP3LkSJYsWcKKFSsYOnRoif1p+3GYTCZWrVrF7bffjr+/P127dmXt2rUF2v/444/cdttteHp64ufnx6233gpAeno6P/30E8OHDycqKoqJEydy5syZUh3TmTNnOHDgAIMGDQJstbaXLFnCY489RpcuXfDz88NgKP5mwJQpU+jTpw+9e/cGbFnMRqORqKgo3n33XTp06FBoH9u3b2fkyJEADB48mKCgIOe6d955h8jISLp160Z8fDzHjx8vsO3QoUPx8fHB19eXYcOGsW3bNjp06MC5c+c4ffo0+/btIygoKF9wGWDDhg1MnDjRWVc7ODi42OMpyZkzZ7j33nuZN29epdUOr0mSQS2EEEKIWik5w8TbG4/z9E1t8XSrxGwVUeWSM00Eebvlq9NY38+Dc2mSQS1EdckfoK79gd7c1IIlPqQGtRCipuRmZUFuLsrdDeXqijnqUbjmmhqr5//oo4/SsWNH7r///gLrbrnlFqZNm0Z0dDT+/v7F9pOWlkZcXBytW7dm7dq1GI1GwsPDAcjMzMTLy4vIyEhnEHrSpElF9pWbm0tgYKCzpnRZLFu2jKFDh+bL1u7evTvbtm0DYN26dRw7dgyAQYMGcfbsWaKjo5kzZw5gy4JOTEzkww8/dG7v7+/vzB7XWtOiRQtatmzJ7Nmz+fjjjwFYtWpVkWPasmULGzZs4Oeff8bb25u+ffuSnV36a9fhw4ezfPly/v77b+ekhaVV2PE0btw4XxZ5QkICjRs3BiA1NZWbb76Zl19+mW7dupVpX7VVjYTYlVKDlVK/KaV+V0o9Xcj6pkqpzUqpX5VS+5VSQ2pinEIIIYSoOdt+P8/8n+LY+1dyTQ9FlFFyppkgb/d8y+r7e0gGtRDV6PLLoE6x1Xr1tNWsdwkIIDcjA20y1fDIhBBXI7O9DrBydweDAVxcaqQGtUNwcDB33XVXvnIUDt7e3rz22ms8++yzxfaRnp7OlClTuP322wkKCmLx4sXMmTOHuLg44uLiOHHiBOvXr6dOnTrExsYSGxvLpEmT6NmzJ99++y3Z2dmkp6fz3XffARfLhXzxxReALSi8b9++Uh2Po7RIXufO2co65eTk8NprrzmD42vXriU2NtYZnJ4zZw5r165l8eLF+TKHjUYjJvvfjDlz5tCnTx/8/f2ZOnWq83gaNWpEz549WbZsGWALhCcn275rpKSkEBQUhLe3N0ePHmXHjh0Fxt27d29WrFhBZmYmGRkZfP31186M5xEjRrBkyRKWL1/O8OHDC2w7YMAAPvzwQywWC4CzxEdRxxMTE8OCBQvQWrNjxw4CAgJo2LAhJpOJoUOHMmbMGO68885Sne/LQbUHqJVSBmA2cBPQHhillGp/SbPngGVa6w7ASOD96h2lEEIIIWpacobtAjM+ObOGRyLKyphpItA7f/3C+n6enJNJEoWoNs6gtMFweQSojSm4BAY4n7xwZFJb7ZnVQghRnUz2zFUXd3fbBHaubjUaoAb4v//7P+dkfpcaOXIkHTt2LHRdv379CAsLo0uXLjRt2pQPP/yQzMxM1qxZw8033+xs5+PjQ69evfj222/zbd+5c2diYmKIiIjgpptuIjw8nAD77+hFixYxd+5cIiMjCQ0NdU7YuGvXLkJCQvjiiy+YOHEioaGhzv7i4uKIj4/n+uuvz7ef119/nXbt2hEREcGtt97qnPDwUpMmTeLs2bN0796dqKgoZs6cCcCRI0cICwujTZs2rF692lnL+VIvvPAC69atIywsjC+++IJrrrkGPz8/Bg8ejMVioV27djz99NOFZiZ37NiRsWPH0qVLF7p27cr48ePp0KEDAKGhoaSlpdG4ceN8daIdxo8fT9OmTYmIiCAyMpLPP/+82OMZMmQILVu2pFWrVkyYMIH337eFRpctW8bWrVuZP38+UVFRREVFObPY33nnHUJCQkhISCAiIoLx48cXeg5qI6WruYaOUqo7MENrPcj++p8AWutX8rT5EPhTa/2avf1/tdY9ius3Ojpa7969uwpHLoQQQojq9NaGY7y14TgP9mvFE4Pa1PRwRBnc+MYPXFffl//d08m57JXVR5i3PY7fXhqcr/TH5UgptUdrHV3T46gMcg195Tr/wQckvvU2bs2a4tm6DSHvFl67tLZIeORRcn7/nWu/t2XmpXz/Paf/7wlafvctHq1a1fDohBBXmwvz53OqSRPCr78e5epKTlwcWK14XAGT0ZVHeno6vr6+ZGZm0qdPHz766KMiA+K1XU5ODgaDAVdXV37++WcmT55crjIlonhHjhyhXbt2+ZYVdw1dEzWoGwN5p+JMALpe0mYGsE4p9RDgA9xYPUMTQgghRG0hGdSXL1sG9SUlPvw8MVlzMWaaCfJxL2JLIURlsSYbUd7euNard3lkUKekOLOmAQwBgc7lQghR3czxCdC0qa28B6BcXcnNvnqfBHvggQc4fPgw2dnZ3HfffZdtcBrg5MmT3HXXXeTm5uLu7u6sTy1qVm2dJHEUMF9r/V97BvVCpVSY1jo3byOl1APAAwBNmzatgWEKIYQQoqokZ9oeo4xPkgD15URrbQtCFyjx4QHAubQcCVALUQ2sRiOGwAAMgYGYT8aXvEENs6ak4HbNNc7XhsBA53IhhKhupviTKNc+zqe+lJsb2mJGa33ZPwlWHo5yFFeC6667jl9//bWmhyEuUROTJJ4CmuR5HWJfltc/gGUAWuufAU+gwFSpWuuPtNbRWuvoevXqVdFwhRBCCFETkjMdGdRZNTwSURZpORYsubrgJInOAHXpZ0MX+ZU00bi9zV1KqcNKqUNKqSvn26QoM1uAOhBDYOBlkUGdm5KCwd/f+doQaK9BnWysoREJIa5m5vgEZ/Y02ALUQI3XoRbiSlUTAepdwHVKqRZKKXdskyB+c0mbk0B/AKVUO2wB6sRqHaUQQgghapQjQJ2YlkO22VrDoxGlZcywfXErMEmivyeATJRYTqWZaFwpdR3wT6Cn1joUeLS6xylqD6vRiGtgIK72AHV1zz1UVtaUFGdQGvJMkigZ1EKIaqatVswJCSjXi0UHnAFqi6WmhiXEFa3aA9RaawvwILAWOAIs01ofUkrNVErF2Jv9HzBBKbUPWAyM1bX9ikoIIYQQlSo5w4yHq+1SJUHqUF82HDcWisqgPisZ1OXVBfhda/2n1toELAFuu6TNBGC21joZQGt9rprHKGoRRwa1S0AA2mRCZ9Xep1G02UxuRgYueWpQu/j6gsEgAWohRLWznD2LNptRhWRQIxnUQlSJGqlBrbVeBay6ZNn0PD8fBnpW97iEEEIIUXskZZho38ifX08aiU/KolV9v5oekigFZ4DaJ38GtY+HK74erpJBXX6lmWi8NYBS6kfAAMzQWq+5tCOZx+XqkLfEh+O1i7d3zQ6qCNa0NAAM/hcD1EopDAEBl0V5EiHElcUUn2D7IW8Gtf1nKfEhRNWoiRIfQgghhBDFyjZbyTJbiQwJBCBeMqgvG0b75JYBXgUnQqzv50FimgSoq5ArcB3QF9uk4x8rpQIvbSTzuFz5tNWKNTU1f4C6FmciW422sRnyZFA7XtfmcQshrkzm+JMA+TKoMRjAxaXWBqjXrFlDly5daNu2LVFRUYwYMYKTJ23HMXbsWJYvX17s9vPnz6devXpERUXRtm1b3nzzzULXRUVFMWfOnDKNbcaMGcyaNatM25hMJh544AFat25N27Zt+fLLLwE4efIk/fr1o0OHDkRERLBqlS331Ww2c9999xEeHk67du145ZVXCu339ddfdx5HWFgYBoOBpKQksrOz6dKlC5GRkYSGhvLCCy+UabyVyWg08v777ztfx8bG0r17d0JDQ4mIiGDp0qXOdSdOnKBr1660atWKESNGYDLZEkW2bt1Kx44dcXV1LfDeP/XUU4SFhREWFpavr5omAWohhBBC1DqOLNzrGvji4epCfJIEqC8XF0t8uBVYV8/PQyZJLL/STDSeAHyjtTZrrU8Ax7AFrMVVxpqaClpjsNegBmp1JrI1xQiQrwa17XWgc50QQlQX08l4cHXNX+JDKZSrW60MUB88eJCHHnqITz/9lKNHjxIbG8vo0aOJi4srUz8jRowgNjaWH3/8kZdffpn4+PgC62JjYxk/fnwlH0FBL7/8MvXr1+fYsWMcPnyY66+/HoCXXnqJu+66i19//ZUlS5YwZcoUAL744gtycnI4cOAAe/bs4cMPPyz0+KdNm+Y8jldeeYXrr7+e4OBgPDw82LRpE/v27SM2NpY1a9awY8eOKj/OS1kslgIBam9vbxYsWMChQ4dYs2YNjz76KEb73/SnnnqKxx57jN9//52goCDmzp0L2J6Qmz9/PnfffXe+/r///nv27t1LbGwsv/zyC7NmzSI1NbXajq84EqAWQgghRK2TbJ9oL9jbnSbB3sQn1d7aqSK/ZGcGdcEAdX1/T85JBnV5lWai8RXYsqdRStXFVvLjz2oco6glHMHoS0t81Fa59i/HBn//fMttJT4kg1oIUb3MCfG4NWoESuVbrtxcqz1AHRcXR9u2bRk7diytW7dm9OjRbNiwgZ49e3Ldddexc+dOXnvtNZ555hnatWvn3C4mJoY+ffoU6K958+acP38egN27d9O3b98CberUqUOrVq04c+ZMmcb67bff0rVrVzp06MCNN97I2bNnnev27dtH9+7due666/j4448B2LJlC3369OHmm2+mTZs2TJo0idzcXAA++eQT/vnPfwLg4uJC3bp1AduNAkdANSUlhUaNGjmXZ2RkYLFYyMrKwt3dHf9L/qZcavHixYwaNcq5va+vL2DLxjabzahL3n+AM2fO0KdPH2cG9rZt2wDw9fVl2rRphIaGcuONN7Jz50769u1Ly5Yt+eYb2+VaXFwcvXv3pmPHjnTs2JGffvrJeR569+5NTEwM7du35+mnn+aPP/4gKiqKadOm0bp1a667zpZv0KhRI+rXr09iYiJaazZt2sSdd94JwH333ceKFSsA2/scERGBi0v+sO/hw4fp06cPrq6u+Pj4EBERwZo1BarB1YgaqUEthBBCCFGci3WM3WkS5MVJyaC+bKRkmvD3dMXVUDAPooGfB2dTs9FaF3rRL4qmtbYopRwTjRuATxwTjQO7tdbf2NcNVEodBqzANK31hZobtagpeQPUjokHa3OA2lHGo0CJj8BAso/9VhNDEkJcxUwn43Fv0oS8oejXdr7GkTP70FYrLscrp55/2+C2PNXlqRLb/f7773zxxRd88skndO7cmc8//5zt27fzzTff8O9//5uTJ0/yxBNPVMqYwFZGIzs7m4iICOeyL7/8kq1bt9K6dWvefPNNmjRpUmC7Xr16sWPHDpRSzJkzh//85z/897//BWD//v3s2LGDjIwMOnTowM033wzAzp07OXz4MM2aNWPw4MF89dVX3HjjjQA8//zzbNmyhWuvvZb33nuPBg0aMGPGDAYOHMi7775LRkYGGzZsAODOO+9k5cqVNGzYkMzMTN58802Cg4OLPMbMzEzWrFnDe++951xmtVrp1KkTv//+O1OnTqVr10un+oDPP/+cQYMG8eyzz2K1WsnMtH1HycjI4IYbbuD1119n6NChPPfcc6xfv57Dhw9z3333ERMTQ/369Vm/fj2enp4cP36cUaNGsXv3bgD27t3LwYMHadGiBXFxcRw8eJDY2NgC+9+5cycmk4lrr72WCxcuEBgYiKu9PnpISAinTl36cF1+kZGRvPjii/zf//0fmZmZbN68mfbt2xe7TXWRDGohhBBC1DoXy0TYM6ilBvVlIznTTJBPwfrTAPX9Pcg255KWY6nmUV0ZtNartNattdbXaq1fti+bbg9Oo20e11q311qHa62X1OyIRU3JG6B2vYxqULsUVoNaMqiFENXMFB+PW9OCAViUAq2rfTwtWrQgPDwcFxcXQkND6d+/P0opwsPDC5SxuHDhAlFRUbRu3brMdZ+XLl1KREQErVq1YsqUKXh6egJw6623EhcXx/79+xkwYAD33XdfodsnJCQwaNAgwsPDef311zl06JBz3W233YaXlxd169alX79+7Ny5E4AuXbrQsmVLDAYDo0aNYvv27VgsFhISEujRowd79+6le/fuzgD84sWLGTt2LAkJCaxatYp7772X3Nxcdu7cicFg4PTp05w4cYL//ve//Pln0Q+Rffvtt/Ts2TNfENtgMBAbG0tCQgI7d+7k4MGDBbbr3Lkz8+bNY8aMGRw4cAA/P9sk7u7u7gwePBiA8PBwrr/+etzc3PK9R2azmQkTJhAeHs7w4cM5fPiws98uXbrQokWLYt+fM2fOcO+99zJv3rwCmdGlNXDgQIYMGUKPHj0YNWoU3bt3x5C31noNkgxqIYQQQtQ6yRmODGo3mgR5k5ZtISXTTEAhdY1F7ZKcaSLQu4gAtZ/ti8651Bz8PeW9FKKqOCcdDAxEubvj4u2NNdlYs4MqhrWoEh+BAejMTHJNJlzcC/+9IoQQlcmakkJuSgruIfkD1E91eQpLUhLm06fxaN26Wn8neXh4OH92cXFxvnZxccFisdChQwf27t1LZGQkderUITY2llmzZpGenl6gL1dXV2cZjezs/POCjBgxgvfee4/du3czcOBAYmJiuOaaa6hTp46zzfjx43nyyScBePbZZ/n+++8B20R+Dz30EI8//jgxMTFs2bKFGTNmOLe79Mk5x+vCltepUwdvb2+GDRsGwPDhw521lefOnessSdG9e3eys7M5f/48n3/+OYMHD8bNzY369evTs2dPdu/ezerVq50lRVatWuUsCbJkyRJneY9LBQYG0q9fP9asWUNGRgYTJ04EYObMmcTExLB161a+//57xo4dy+OPP86YMWNwc3NzHkth7xHAm2++SYMGDdi3bx+5ubnOGwAAPj4+hY7FITU1lZtvvpmXX36Zbt26AbZSLEajEYvFgqurKwkJCTRu3LjYfsD2vj377LMA3H333bRu3brEbaqDZFALIYQQotZx1DEO9HKnSbAXgGRRXyaMmeZCJ0gEqO9nu1iXiRKFqFp5M6gd/6/tJT5c/PzyTUgGF8efW4uzv4UQVxZTfAJAoRnUys12faMttetJsCeffJKXX36ZI0eOOJc5Sk9cqnnz5uzZswewle0oTHR0NPfeey9vv/02QL5a1N98842z1vXLL7/snHAQbDWhHQHSTz/9NF+fK1euJDs7mwsXLrBlyxY6d+4M2EpWnDhxgtzcXJYuXUqvXr1QSnHrrbeyZcsWADZu3OgsQ9G0aVM2btwIwJEjR8jOzqZevXo0bdqUTZs2AbZyGzt27KBt27ZMnTrVOUZHcDolJYUffviB2267zTm+xMRE58SDWVlZrF+/nrZt29K1a1fn9jExMfz11180aNCACRMmMH78ePbu3VvoOSxMSkoKDRs2xMXFhYULF2K1Wgtt5+fnR1pamvO1yWRi6NChjBkzxllvGmzB/H79+rF8+XLnOc97TIWxWq1cuGCr/rZ//37279/PwIEDS30MVUkC1EIIIYSodZIyTPh5uOLu6kJIkK3OX7zUob4sJGeaCCoqg9rfFqBOlIkShahSVqMRDAZc7I8euwQG1PIAtbFA/Wm4WJO6No9dCHFlMSfEA+DetGmBdY4ANdU8UWJJwsPDefvttxkzZgxt2rShZ8+eHDlyhLvvvrtA2xdeeIFHHnmE6OjoYks7PPXUU8ybN4+0tDTeeecdQkNDiYyM5J133mH+/PmFbjNjxgyGDx9Op06dnJMaOkRERNCvXz+6devG888/7wwWd+7cmQcffJB27drRokULhg4dCsBrr73GjBkziIiIYOHChc5a1v/973/5+OOPiYyMZNSoUcyfPx+lFFOnTiU9PZ3Q0FA6d+7M/fffn6+Gdl5ff/01AwcOzJe1fObMGfr160dERASdO3dmwIAB3HLLLQW23bJlC5GRkXTo0IGlS5fyyCOPFHkOLzVlyhQ+/fRTIiMjOXr0aJFZ03Xq1KFnz56EhYUxbdo0li1bxtatW5k/fz5RUVFERUU5bwq89tprvPHGG7Rq1YoLFy7wj3/8A4Bdu3YREhLCF198wcSJEwkNDQVsZUZ69+5N+/bteeCBB/jss8+cNaxrmtI1UD+nKkRHR2tHcXEhhBBCXN4eXfIre04ms+3JG0jJMhP54jqeGdKWB/pcW9NDEyUIe2Etw6NDeOHW0ALrUrPNRMy4/N9LpdQerXV0TY+jMsg19JXpzAszSFu/ntY//QjAyXHjyM3IpPnS2lmWPH7iJCyJibT4Kn82X/qPPxL/j/E0+2wh3tFXxEdOCFHLnf/oYxLfeIPWu3dzLP6kM1sYbJnT2UeP4nbNNbheEoAVZbdlyxZmzZrFd999V9NDEVXgyJEj+T4/UPw1tGRQCyGEEKLWSco0E2zPwg3wcsPf05X4pKwaHpUoicmSS3qOpcgMaj8PVzzdXDiXKhnUQlQlq9HoLI8B9hIftbhMhjUlBUNgIRnUl8EEj0KIK4s5/iSGOnUw+BaS3WowgIsLupZlUAtxJagdedxCCCGEEHkYLykT0STYW2pQXwaMWfbJLYuoQa2Uor6fJ+ekxIcQVarQAHUtLpNhTU3FteE1BZYbAgJt62vx2IUQVxZTfALuISGFrlNKoVzdJEBdSfr27Uvfvn1rehiilpAMaiGEEELUOkkZJoJ98gSog7ylBvVlwOiY3LKIDGqwTZQokyQKUbUKDVCnpqJzc2tuUMWwpqRg8C8mg9ooGdRCiOphPnkSt0LqTzsoN1cJUAtRBSRALYQQQohax5hpJjBPFm6TYC8SkrO4UubOuFIlZzgyqIsJUPt7SAa1EFWsQIA6IAByc8lNTa25QRVBa20LUBcySaKLjze4ukoGtRCiWmiTCfPff+PepEmRbZSbG9psqcZRCXF1kAC1EEIIIWoVRx3j4EtKfORYckmUwGatluzMoC68xAdgK/EhNaiFqFKFZVBD7azlrDMzwWIpNECtlMIQEFArxy2EuPKYT5+G3FzcSgpQW8ySNCFEJZMAtRBCCCFqFWOmLQs38JISH4DUoa7lHO9dkE/xGdTpORYyTZJ9JERVyM3KQufkFB6groWZyI7gsyHAv9D1tX2CRyHElcMUHw+Ae9PiA9SAlPkQopJJgFoIIYQQtUqSPciZP4PaC4D4pKwaGZMoHUcGdVGTJIItgxqQLGohqogjCG0IvJiRfDkEqF0KyaAGW3mS2jhuIcSVxxGgLimDGkBbqudGu1KKe+65x/naYrFQr149brnlFgBmzJjBrFmz8m3TvHlzzp8/X+ljOXr0KFFRUXTo0IE//vijzNuvWLGCw4cPl3v/p0+f5s477wRgy5YtznNwqdGjR9OmTRvCwsIYN24cZvvNBK01Dz/8MK1atSIiIoK9e/fm2y41NZWQkBAefPDBQvudP39+kevKIi4ujs8//7zC/ZTFokWLiIiIIDw8nB49erBv3z7nujVr1tCmTRtatWrFq6++6lxe3vNYXhKgFkIIIUStkpxRMMgZYs+gPikTJdZqxkwT7gYXvNwMRbap7+cBIHWohagiFwPUgc5ltTtAbauLXViJD8dyyaAWQlQH88l4lKcnrvXqFdnGEaCmmjKofXx8OHjwIFlZtiSN9evX07hx42rZd15Wq5UVK1Zw55138uuvv3LttdeWuY+KBqgbNWrE8uXLS2w3evRojh49yoEDB8jKymLOnDkArF69muPHj3P8+HE++ugjJk+enG+7559/nj59+pR7fKVVEwHqFi1a8MMPP3DgwAGef/55HnjgAcD2vk6dOpXVq1dz+PBhFi9e7HyPynsey0sC1EIIIYSoVZILKRPh6Wagvp8H8RKgrtWSM00EeruhlCqyTX1/R4A6u7qGJcRVpdAAtT34WzsD1I4SH0UEqKXEhxCimpji43FvElLsdYxydQWqt8THkCFD+P777wFYvHgxo0aNKlc/6enp3H///YSHhxMREcGXX34JwOTJk4mOjiY0NJQXXnjB2b558+Y89dRTdOzYkaVLl/LWW2/xv//9j379+gHw2Wef0aVLF6Kiopg4cSJWqxUAX19fnn32WSIjI+nWrRtnz57lp59+4ptvvmHatGlERUXxxx9/8PHHH9O5c2ciIyO54447yMy0XeePHTuWhx9+mB49etCyZUtnUDouLo6wsLBSnS+lFEopunTpQkJCAgArV65kzJgxKKXo1q0bRqORM2fOALBnzx7Onj3LwIEDS3Uuv//+e7p378758+fLfBxPP/0027ZtIyoqijfffJPs7Gzn+9KhQwc2b94M2DK2hw0bxuDBg7nuuut48sknnftft24d3bt3p2PHjgwfPpz09HTne/bCCy/QsWNHwsPDOXr0KAA9evQgKCgIgG7dujnPyc6dO2nVqhUtW7bE3d2dkSNHsnLlynKfx4pwrXAPQgghhBCVyBmg9s5fx7hJsLfUoK7lkjPNBd63SzWwl/g4KyU+hKgShQWoXfz9wcWlUgO93+8/Q+/WdfH3LLqkT2lYU4xACRnUtTCwLoS48pjj43ELKby8x9///jc5R46igdzMTJSrKy7uxV/zlMSjXVuueeaZEtuNHDmSmTNncsstt7B//37GjRvHtm3byry/f/3rXwQEBHDgwAEAkpOTAXj55ZcJDg7GarXSv39/9u/fT0REBAB16tRxlnA4duwYvr6+PPHEExw5coSlS5fy448/4ubmxpQpU1i0aBFjxowhIyODbt268fLLL/Pkk0/y8ccf89xzzxETE8Mtt9ziLNMRGBjIhAkTAHjuueeYO3cuDz30EABnzpxh+/btHD16lJiYGOc2ZWE2m1m4cCFvv/02AKdOnaJJnvItISEhnDp1igYNGvB///d/fPbZZ2zYsKHEfr/++mveeOMNVq1aRVBQEMOGDSvTcbz66qvMmjWL7777DoD//ve/KKU4cOAAR48eZeDAgRw7dgyA2NhYfv31Vzw8PGjTpg0PPfQQXl5evPTSS2zYsAEfHx9ee+013njjDaZPnw5A3bp12bt3L++//z6zZs1yZj47zJ07l5tuuqnIc/LLL7+U6zw2bNiwxHNXHAlQCyGEEKJWSc6wT5J4SR3jJkFe7IpLrokhiVJKyTQXeN8uFejthrvBRTKohagihQWolYsLBn//Sgv0nknJYurne3kxJpT7ejSvUF+5qSWU+AgMRGdlkZuTg4uHR4X2JYQQRdFaY0pIwKd7t2LbKWx1odG6egYGREREEBcXx+LFixkyZEj+8RSR7V3Y8g0bNrBkyRLna0dG7bJly/joo4+wWCycOXOGw4cPOwPUI0aMKLT/jRs3smfPHjp37gxAVlYW9evXB8Dd3d1ZH7pTp06sX7++0D4OHjzIc889h9FoJD09nUGDBjnX3X777bi4uNC+fXvOnj1b6PYlmTJlCn369KF3797Ftnv//fcZMmQIISEhJfa5adMmdu/ezbp16/D396+U49i+fbszoN22bVuaNWvmDFD379+fAPvfx/bt2/PXX39hNBo5fPgwPXv2BMBkMtG9e3dnf8OGDQNs5/6rr77Kt6/Nmzczd+5ctm/fXuKxOpT2PFaUBKiFEEIIUaskZ5rxdjfgeUkd4ybB3nyz7zRmay5uBqlSVhslZ5q4tp5vsW2UUtTz8yBRMqiFqBKOILRrngA12EtlVFKAOtFeQ/5sasVvNFlTUlBubihPz0LXOyZ7tBpTcGlQv8L7E0KIwljPn0dnZuLWpGmh6/NmOuecOAG5uXiUow5zecXExPDEE0+wZcsWLly44Fxep06dAuUV0tLSCAwMZPbs2Xz88ccArFq1qtB+T5w4waxZs9i1axdBQUGMHTuW7OyLv9t9fHwK3U5rzX333ccrr7xSYJ2b28VybwaDAUsRE0qOHTuWFStWEBkZyfz589myZYtznUeeG5K6hJsBgwYN4uzZs0RHRzuzhV988UUSExP58MMPne0aN25MvH0iTICEhAQaN27Mzz//zLZt23j//fdJT0/HZDLh6+tL165defHFFwGc/V577bX8+eefHDt2jOjo6Eo9jsLk3d5xLrXWDBgwgMWLFxe7zaXnfv/+/YwfP57Vq1dTp06dYs+JQ1nOY0XJtzshhBBC1CrJGaZCy0Q0CfImV8MZo2Te1lbJmWaCfEp+3L+en4dMkihEFbEajbh4e6MuefS8MktlXLA/6XI+veKfY6sxBZfAgCKzAJ31s+2lQIQQoiqY4m31dd2blJxFq9zc0ObCg65VZdy4cbzwwguEh4fnW96nTx+++eYb0tLSAPjqq6+IjIzEYDAwdepUYmNjiY2NpVGjRgwYMIDZs2c7t01OTiY1NRUfHx8CAgI4e/Ysq1evLtV4+vfvz/Llyzl37hwASUlJ/PXXX8Vu4+fn5xwn2ALpDRs2xGw2s2jRolLttzBr164lNjbWGUSeM2cOa9euZfHixbi4XAx7xsTEsGDBArTW7Nixg4CAABo2bMiiRYs4efIkcXFxzJo1izFjxvDqq68ydOhQ5/lzBKObNWvGl19+yZgxYzh06FC5juPS89C7d2/ndseOHePkyZO0adOmyO27devGjz/+yO+//w5ARkaGM+O6KCdPnmTYsGEsXLiQ1q1bO5d37tyZ48ePc+LECUwmE0uWLCEmJqZc57GiJEAthBBCiFolOdNUaJAzJNgLQOpQ11Jaa4yZJgJLqEENUN/PQ0p8CFFFrEZjvvIeDobAQCyVFaBOtwWoEyvhRpM1NbXI8h5wsVRJrkyUKISoQub4kwBFZlDnpdzc0BZzuTJiyyskJISHH364wPKIiAgefPBBevXqRVRUFB988EGBmsMOzz33HMnJyYSFhREZGcnmzZuJjIykQ4cOtG3blrvvvttZNqIk7du356WXXmLgwIFEREQwYMCAEifKGzlyJK+//jodOnTgjz/+4F//+hddu3alZ8+etG3btlT7LY1JkyZx9uxZunfvTlRUFDNnzgRsk/61bNmSVq1aMWHCBN5///1y9d+2bVsWLVrE8OHDy3UcERERGAwGIiMjefPNN5kyZQq5ubmEh4czYsQI5s+fny9z+lL16tVj/vz5jBo1ioiICLp37+6cDLEoM2fO5MKFC0yZMoWoqChnwN3V1ZX33nuPQYMG0a5dO+666y5CQ0OBqj+Pl1LV+YGqStHR0Xr37t01PQwhhBBCVNBts3/E39OVhf/omm95fFImvf+zmVeHhTOyS8lfHkT1Sss2Ez5jHc8MacsDfYp/5PX5FQf5Zt9p9r1QupnSaxul1B6tdXRNj6MyyDX0lefkxIlYE8/T4qsv8y0//dTTZO7aRatNGyu8j4+2/sG/Vx0lrLE/3z1UsZqUf429H20y0fzzwrPOsg8f5sSwO2j87jv4DxhQoX0JIURREt99j/Pvv0+bfbHOyQ+PHDlCu3btCrS1JCVhPn0ajzZtcHGr2ESxQlypCvv8FHcNLRnUQgghhKhVjJkmgn0KZuE2DPDE4KIkg7qWMmaaAUqdQZ2SZSbbbK3qYQlx1Skug7qyS3xUSgZ1SkrxGdT2dZJBLYSoSuaEeFyvucYZnC6Osgeltdlc1cMS4qohAWohhBBC1CpJRdSgdjW40CjQk/ikrBoYlShJcqYtYFXYe3ep+v62xxYrI7glhMivyAB1UCC5mZlok6nC+0hKd9SgNpGbW7EncnNTUjD4+xe53nEsVglQCyGqkOlkPO5NmpSqrSNAjQSohag0EqAWlUZrzff7z2Cx5tb0UIQQQlymzNZc0rItRQY5mwR5SwZ1LZVsz6AO8i75Udf6fp4AMlGiEFXAakwpPEBtz0SujDrUSfYMamuuxphVsQCNNSUFQ2DRGdTK2xvc3Cot+1sIIQpjSojHrRQTJAIoV1dAMqiFqEwSoBaV5td4I1M/38vGo+dqeihCCCEuU44yEYVNkgj2ALVkUNdKRnsGdWlKfNTzc2RQy0SJQlQmbbWSm5paZIkPqJxSGY4SH1CxJyG02UxuRgYuxZT4UEphCAzAapQMaiFE1cjNzMSaeB73UkyQCIDBAC4uEqAWohJJgFpUmtNGW8AgPkky24QQQpSPsYQyEU2CvTifnkOWSWoX1zbJGY73ruQM6gb+tgzqs6mSQS1EZbKmpoLWxQaoKyMTOSnDRMMA2+e4IgFqa1oaAAb/ogPUYMv+lgxqIURVMcUnAODetJQlPpRCubqizZaqHJYQVxUJUItKc87+JfO0UbKhhBBClE9SRkkBam8AEqTMR63jKPER4FVygLqOjzsGF8U5yaAWolJZk42Ard70pRwB6soq8dHmGj8AEtPL/zl2ZEUXN0mibX2g1KAWQlQZc0I8AG6lrEENtjrU2lzxmv7CRmuNNTUVrSs2r4G4fEmAWlSas/YvmY5MaiGEEKKskksq8WEPUJ+Up3VqHWOmCX9PV1wNJV9eurgo6vq6O29uCyEqhyPLuLga1BXNRM42W0nPsTgD1OfTyh+gsabYxlJcDWrbeglQi8vXr+d+xZIrmba1memkLUBd2kkSwR6gtlTP+3r27FnuvvtuWrZsSadOnejevTtff/21c/2jjz5K48aNyc29OB/Y/PnzqVevHlFRUYSGhnLnnXeSmZn/+jkqKoqRI0eWa0xffPEF7dq1o1+/fmzZsoVbbrmlfAdnl5uVhenkSXJTUyvUT0mmT5/Ohg0bAOjbty+7d+8utN3+/fvp3r07oaGhhIeHk51d8s3Y4vpzGDt2LMuXLy/7wO3mz5/P6dOnna9Hjx5NmzZtCAsLY9y4cZjtZWe01jz88MO0atWKiIgI9u7d69xm8ODBBAYGFnjPNm7cSMeOHYmKiqJXr178/vvv5R5neUiAWlQax5fMUxKgFkIIUU7JJZX4CLIFqKWcVO2TnGkmyKfk+tMO9f08ZZJEISpZsQHqSirx4XjSpVmwDx6uLiSml/9z7AhEGPz9i20nJT7E5erPlD8Zs3oMa+LW1PRQRDHM8Sdx8fcv9HdnUWwZ1OYqz/jVWnP77bfTp08f/vzzT/bs2cOSJUtISLCVJcnNzeXrr7+mSZMm/PDDD/m2HTFiBLGxsRw6dAh3d3eWLl3qXHfkyBGsVivbtm0jIyOjzOOaO3cuH3/8MZs3b67YAdppewBYm6o2K33mzJnceOONxbaxWCzcc889fPDBBxw6dIgtW7bg5lbyE4JVzWq1FhqgPnr0KAcOHCArK4s5c+YAsHr1ao4fP87x48f56KOPmDx5snObadOmsXDhwgL9T548mUWLFhEbG8vdd9/NSy+9VPUHlYcEqEWlOZsqGdRCCCEqpqQAdV1fd7zcDMQny9+a2iY501SqCRId6vt5SIBaiEpWXIBaeXmh3N0rPEmiI0Ad7ONOPT+PitWgTiltiY8AyaAWl6Xfk20ZiH8a/6zhkYjimOITcA8JKdM2yh6wrOos6k2bNuHu7s6kSZOcy5o1a8ZDDz0EwJYtWwgNDWXy5MksXry40D4sFgsZGRkEBQU5ly1evJh7772XgQMHsnLlSufyjz/+mM6dOxMZGckdd9xRIOsabEHe7du3849//INp06blWzdjxgxmzZrlfB0WFkZcXBxxcXG0bduWsWPH0rp1a0aPHs2GDRvo2bMn1113HTt37ADgl5076d69Ox06dKBHjx789ttvAIwfP56oqCiioqKoV68eL774IgCvv/46nTt3JiIighdeeAGAuLg42rVrx4QJEwgNDWXgwIFkZdm+O5Qmg3ndunVEREQQGRkJQJ06dTAYDAXaZWVlMXLkSNq1a8fQoUOd+wDw9fV1/rx8+XLGjh1bYPvnn3+esWPHYrVamTx5MtHR0YSGhjqPA6B58+Y89dRTdOzYkcWLF7N7925Gjx5NVFQUWVlZDBkyxFYTXSm6dOnivHGxcuVKxowZg1KKbt26YTQaOXPmDAD9+/fHz8+vwHiUUqTabxynpKTQqFGjYs9TZXOt1r2JK5rjS+aFDBNZJite7gU/wEIIIURxkjNMeLq5FPk3RClFSJCXZFDXQsZMM8FlyaD29yQ23lh1AxLiKlRsgFopDIGBFa5BfcEeoK7jWwkBansNapeSAtSBgejsbHKzs3Hx9Cz3/oSobidSTgDwV+pfNTwSURzzyZN4tGtXbJtty45xPj7d+VpbrejsbJRXLMqlfLGPuk186X1X62LbHDp0iI4dOxa5fvHixYwaNYrbbruNZ555BrPZ7Mz2Xbp0Kdu3b+fMmTO0bt2aW2+91bnd0qVLWb9+PUePHuXdd9/l7rvvBmDYsGFMmDABgOeee465c+c6g+EO06dPZ9OmTcyaNYvo6Gi2bNlSquP9/fff+eKLL/jkk0/o3Lkzn3/+Odu3b+ebb77h1bfeYukbb9C6aVO2bduGq6srGzZs4JlnnuHLL790Zgb/9ddfDB48mLFjx7Ju3TqOHz/Ozp070VoTExPD1q1badq0KcePH2fx4sV8/PHH3HXXXXz55Zfcc889pRrnsWPHUEoxaNAgEhMTGTlyJE8++WSBdv/73//w9vbmyJEj7N+/v9j36VLTpk0jLS2NefPmoZTi5ZdfJjg4GKvVSv/+/dm/fz8RERGALUDuKNExZ84c53nPy2w2s3DhQt5++20ATp06RZM8JWtCQkI4deoUDRs2LHJMc+bMYciQIXh5eeHv788O+02D6iIZ1KLSnE3Ndk6MdDpFMtuEEEKUXXKmmeASsnCbBHtLBnUtZMwyEeRd+scf6/t5cCHDhNmaW3JjIUSpWI1GMBhwyZO5lZchMLASSnzYAtJ1fNyp51vBAHUZSnwAkkUtLjsnUm0B6vi0+BoeiSiKtloxnT6Ne5MyZlArZe+geif1mzp1KpGRkXTu3BmTycSqVau4/fbb8ff3p2vXrqxdu9bZ1lHi4++//yY8PJzXX38dgN27d1O3bl2aNm1K//79+fXXX0lKSgLg4MGD9O7dm/DwcBYtWsShQ4cqbewtWrQgPDwcFxcXQkND6d+/P0opwsPD+Sve9hkxJiczfPhwwsLCeOyxx/LtPzs7m+HDh/Puu+/SrFkz1q1bx7p16+jQoQMdO3bk6NGjHD9+3LmvqKgoADp16kRcXFypx2mxWNi+fTuLFi1i+/btfP3112zcuLFAu61btzqD3hEREc6Ackn+9a9/kZKSwgcffOD8d7Rs2TI6duxIhw4dOHToEIcPH3a2HzFiRIl9TpkyhT59+tC7d+9SjaEwb775JqtWrSIhIYH777+fxx9/vNx9lYdkUItKkWmykJZtoW+bemz5LZHTxiyurVf4hbEQQghRlOSMkstENAnyYteJJLTWF78ciBpnzDCXrcSHvwcA59NzaBjgVVXDEuKqYjUaMQQEFPm7sTJqOV9It2dQ+3hQ18+DPX8ll7sva0oKLn5+qEIenc7rYv3sFNwaNCj3/oSobnEpcYAtg1quW2ony99/g9mMWwkTJF6a6awtFrKPHsXtmmtwrVu3ysYXGhrKl19+6Xw9e/Zszp8/T3R0NGvXrsVoNBIeHg5AZmYmXl5eBSa/U0px66238u677/L000+zePFijh49SvPmzQFITU3lyy+/ZMKECYwdO5YVK1YQGRnJ/Pnz2bJlC1arlU6dOgEQExPDzJkzixyvq6trvska804u6OHh4fzZxcXF+VphCwqDYuYbb9C3Xz++/vpr4uLi6Nu3r3ObSZMmMWzYMGcNaa01//znP5k4cWK+McTFxeXbl8FgyFd+41Jff/21s2TInDlzCAkJoU+fPtS1v69Dhgxh7969pKam5mtXnLyf9UsnWOzcuTN79uwhKSmJ4OBgTpw4waxZs9i1axdBQUGMHTs23zY+Pj7F7uvFF18kMTGRDz/80LmscePGxMdfvDGWkJBA48aNi+wjMTGRffv20bVrV8AWFB88eHCx+61sNZJBrZQarJT6TSn1u1Lq6ULWv6mUirX/d0wpZayBYYoycEyQ2KGJrabRKclsE0IIUQ7JmaYSy0Q0CfYmLcdCSpa5mkYlSmK25pKWYymydnhh6vvZHtN3XEMIISrOajQWO8mXITCwUmpQu7oo/L1cqefrQVJm+Z+EsKYYS6w/DWAItGdQy0SJ4jKiteZEygk8DZ5kWjK5kH2hpockCmGyB/HcmzYt24YGA7i4oM1Vez16ww03kJ2dzf/+9z/nMkdd6MWLFzNnzhxnjecTJ06wfv36QutGb9++nWuvvZbc3FyWLVvGgQMHnNutXLnSWb86LS2Nhg0bYjabWbRokf1QDcTGxhIbG1tscBpsNZMd5Sj27t3LiRMnSjxGx8SILt5epKSl0fiaawCYP3++s83s2bNJS0vj6acvhhAHDRrEJ598Qnq6rfTKqVOnOHfuXIn7u9TQoUOdxxcdHc2gQYM4cOAAmZmZWCwWfvjhB9q3b1+gXZ8+ffj8888BW+b5/v37nX02aNCAI0eOOCexzGvw4ME8/fTT3HzzzaSlpZGamoqPjw8BAQGcPXuW1atXFzlWPz8/0tLSnK/nzJnD2rVrWbx4MS4uF0O8MTExLFiwAK01O3bsICAgoNjyHkFBQaSkpHDs2DEA1q9fT7sSyt5UtmrPoFZKGYDZwAAgAdillPpGa+3MX9daP5an/UNAh+oepygbR/3piJAAXJRMlCiEEKJ8kjPNNAosPps2JMgbgPikrDJl7IqqY8y0fTkL8ilbiQ9AJkoUohKVJkBd0RrUSRkmgnzcUUpRz88DrW3LGviXvTZ0bkpqieU9IG+JD2OZ9yFETTmXeY5MSyZ9Q/qyJWELJ1NPUter6jJtRfk4AtRuIcVnUF9KKYVydUWbq3aSRKUUK1as4LHHHuM///kP9erVw8fHhxdffJHHHnuMDz74wNnWx8eHXr168e233wIXa1Dn5uYSEhLC/Pnz2bZtG40bN843AV6fPn04fPgwZ86c4V//+hddu3alXr16dO3aNV8wtDTuuOMOFixYQGhoKF27dqV16+JrbAPkOgLUfn48fv/9PPDcc7z86qvcfPPNzjazZs3Czc3NWbZj0qRJTJo0iSNHjtC9e3fANjHhZ599VuiEhmURFBTE448/TufOnVFKMWTIkHxjcZg8eTL3338/7dq1o127ds4sc4BXX32VW265hXr16hEdHe0MojsMHz6ctLQ0YmJiWLVqFR06dKBt27Y0adKEnj17Fjm2sWPHMmnSJLy8vPj555+ZNGkSzZo1c56DYcOGMX36dIYMGcKqVato1aoV3t7ezJs3z9lH7969OXr0KOnp6YSEhDB37lwGDRrExx9/zB133IGLiwtBQUF88sknFTqPZaV0NdfLUUp1B2ZorQfZX/8TQGv9ShHtfwJe0FqvL67f6OhovXv37soeriilb/ed5qHFv7LusT7c98lOelxbl//eFVnTwxJCCHGZiZq5jpjIRsy8LazINodOp3DzO9t5f3RHhoQXnQkgqs/xs2kMeHMr74zqQExk6Wb8/jslm26vbOSl28O4p1uzKh5h5VJK7dFaR5fcsvaTa+gry58xt+EWEkKT92cXuv7cf9/gwvz5tN2/r9ylBsZ/upuE5EzWPNqHtYf+ZuLCPXz3UC/CGpecCX2puJGjcPH2omkJX4LNZ87we78buOZfMwkaPrxc4xaiuu04s4MJ6yYwvft0Zv48k5k9ZjL0uqE1PSxxCefvxdhfC5QbOnLkSLFZpDknTkBuLh7XXlvVw7yimc+exZKYiMe115Lzxx+4hYTgWszNVnF5KOzzU9w1dE2U+GgM5J0hIMG+rAClVDOgBbCpGsYlKuBsqq0+Tn0/DxoHenHKWPCREiHERX+nZJfcSIirjDVXk5JVch3jJsGODGr5W1NbJDsyqMswSWJdX3eUkgxqISpTyRnUAWA2k5tR/t+fSRk5zlJMdX1tT0Ikppfvc2xNScGlNCU+AqTEh7j8OOpP92rUC1flysm0kzU7IFEoU3w87o0alVgLvzDKzQ1tqdoM6quBzjGh3N1R7ra/LVVdNkXUTjVSg7oMRgLLtdbWwlYqpR5QSu1WSu1OTEys5qGJvM6l5eDu6kKAlxuNAr04bZTgmxBF+eFYIt1e2cjBUzITvRB5pWSZ0RqCSwhy+nu6EeDlRnyyBKhri+RM26OZZalB7WpwoY6PO4lpcs0gRGXQWpeqxAdULNCblHFxrgBHqZ7Ect5osqamYvAvOUCtvLxQbm4Vrp8tRHU6kXICb1dvrvG5hka+jTiZKgHq2sgcH1/iBIlFUW5uaLOZ6q5McKXROTm4eHigDAaUwdVZk1pcXWoiQH0KyPvpD7EvK8xIYHFRHWmtP9JaR2uto+vVq1eJQxRldTY1mwb+HiilaBToxZmULHJz5Ze0EIVZ+PNfAJyU7E8h8knKsAc5S5gkEaBJsBfxSTLfQW1htAeoA8uQQQ1Qz89TJkkUopLorCy0yVS6AHUFajlfyDA5M6edGdTlCFBrrbGmpJRqkkSlFIbAQKwSoBaXkRMpJ2gR0AKlFE39m1Z6BnXm3r0k2SexE+Vnio/HvWn5A9SAZFFXgNaaXFMOysP290S5u0kG9VWqJgLUu4DrlFItlFLu2ILQ31zaSCnVFggCfq7m8YlyOJeaQwM/28QojYO8MFt1uR/1E+JKdjY1m82/2WYWviCfESHyMZYhC7dJkLdkUNciF0t8lG3Syvp+HlLiQ4hK4siKNgQWHfCtaAa1yZJLWrbFmUHt5W7Az8O1fAHqzEywWEoVoAbbcUmJD3E5OZFqC1ADNPNvxsnUk5WaaZv8+WLOvfYftLXQB85FKViNRnJTU8s8QaKDI0CNBFTLTZtMoPXFALWbu2RQX6WqPUCttbYADwJrgSPAMq31IaXUTKVUTJ6mI4ElWp6VuCycTct2ztzdOND2/1NGyWwT4lJf7I7Han+64Hy6/OEVIi9nBnUpgpxNg71JSJKndWqL5EwT7gYXvN3LVr+xgb+Hcx4LIUTFXAxQBxbZpqIBakc5n+A8T7rU8/MoV2KKIxvaEOBfqvaGgECsRsmgFpeHTHMmf2f8TXP/5gA08WtCpiWTC9kXKm0flr//RptMmM+cqbQ+rzam+ASAimdQS4C63HSO7e/HpRnUEgq8+tRIDWqt9SqtdWut9bVa65fty6Zrrb/J02aG1vrpmhifKLtzqTnUs9egaxxom7zqtASohcgnN1ezdHc8Pa6tQ5C3GxcyJGtQiLyMjixcn5LLRIQEe2Oy5kr2bS1hzDAT6O2GUqpM29X38+R8eo7zxp0QovxKFaCu4GSDF+w31+vkCVDX9fXgfDl+FzsC1KWZJBHARTKoxWXkr1RbSb+8GdR5l1cG89mzAJhOnKi0Pq825nhb2RW3Jk3Ltb1ydQUkQF0RjgC1i32CROXmBlpL2ZSrUG2fJFFcBjJyLKTnWJwZ1I0cGdTJEqAWIq+f/rhAfFIWI7s0pY6vB+fTJINaiLySylTiwwtAynzUEsmZpjKX9wCo7+9BrkZu2AlRCcoUoC5nLWfHky6Vk0Gdmm9MJTEEBEgNanHZOJFiCxo7A9R+tgB1ZU2UqLXG4gxQx1VKn1cjZwZ1SOPydWAwgHKp8gB18+bNCQ8PJyoqiujoaADGjh3L8uXL87Xz9fXlwIEDREVFERUVRXBwMC1atCAqKoobb7wRgMGDBxMYGMgtt9xSYB/nz58v9xj37dtH9+7dCQ8P59ZbbyU1NTXf+pMnT+Lr68usWbMAyM7OpkuXLnS8/no63X47M/71LwCUPVCdt8zHyZMnGThwIO3ataN9+/bExcU5z4Hj+KKiooiNjS33+PPasmWL8/zMnz+fBx98sFL6BYiNjWXVqlXO14sWLSIiIoLw8HB69OjBvn37nOvWrFlDmzZtaNWqFa+++qpz+XvvvUerVq1QSuV7z7TWPPzww7Rq1YqIiAj27t1baeOuDhKgFhXmyF5r4G/LoPbzdMPP01UyqIW4xOJdJwn0dmNQaAPq+LhLQEaISyRnmnB3LV2ZiCbBtqd14mWy0VrBmGku8wSJYKtBDchEiaWklBqslPpNKfW7UqrAk4ZKqbFKqUSlVKz9v/E1MU5RMyylCFArNzdcfH3Ln0Ftv3ap43tJgLoCGdSlr0EtkySKy8eJ1BO4KBea+tsycxv6NsRVuVbaRIlWo9EZwDPFSQZ1eZniT2KoWxcXH59yba+UQrm5os1Vn+27efNmYmNj2b17d7HtwsPDiY2NJTY2lpiYGF5//XViY2PZsGEDANOmTWPhwoWVPr7x48fz6quvcuDAAYYOHcrrr7+eb/3jjz/OTTfd5Hzt4eHBpk2b2PXtd+xctYo1a9awY8eOiwHqPEH/MWPGMG3aNI4cOcLOnTupX7++c53j+GJjY4mKiqr046pslwaoW7RowQ8//MCBAwd4/vnneeCBBwCwWq1MnTqV1atXc/jwYRYvXszhw4cB6NmzJxs2bKBZs2b5+l69ejXHjx/n+PHjfPTRR0yePLn6DqwSSIBaVJijdqQjgxqgcaAXp4xSU1IIhwvpOaw79DfDOoTg4Wqgrp+H8zFZIYRNcoaJoFKWiWgcaM+gTpKbobVBeTOo69knWC5PcOtqo5QyALOBm4D2wCilVPtCmi7VWkfZ/5tTrYMUNcoRdHYtJkAN9kBvBUt8BPt4OJfV8/MgLdtCtrlsE7VZU2xjKH0GdSA6J4fcLPm9L2q/uJQ4Gvk0wsNg+6y4urjS2K9xpWVQW/7+2/lzjpT4KDfzyXjcm5Sv/rSDcnO7rEp89O/fHz8/v0LX/ec//yE8PJwuXbrw+++/F9rG19eXxx57jNDQUPr3709iYiIAx44do0+fPgAMGDCAL7/80rnNihUraNGiBaGhoc5lSil8fHzQphwsBgNms9ke8LfX9bbfgDl8+DAWi4UBAwY49+/t7V2mY161ahVt27alU6dOPPzww87s6J07d9K9e3c6dOhAjx49+O2334rtJzExkTvuuIPOnTvTuXNnfvzxRwCGDBnizOIOCAjg008/JTs7m/vvv5/w8HA6dOjA5s2bMZlMTJ8+naVLlxIVFcXSpUvp0aMHQUFBAHTr1o2EhATn2Fq1akXLli1xd3dn5MiRrFy5EoAOHTrQvHnzAuNbuXIlY8aMQSlFt27dMBqNnLmMatS71vQAxOXPEaB2ZEGBI0AtF49COHy19xRmq2ZUF9sFUF0fd86X43FYIa5kSRnmUgc5Pd0MNPD3kBIftURyprlUtcMv5Xj6SiZKLJUuwO9a6z8BlFJLgNuAwzU6KlFrWI1GXLy9ndlnRTEElL+Wc1KGCRcFgV4XP+917dnU59NzCAkqfdAgN7XsJT7Alnnt4uVV6v0IURNOpJxwlvdwaOLXpNIyqB31p92bNZMSHxVgSojH214yoySb53/Eub/+LLBc5+Sgrbm4eJf991L9Zi3pN/aBEtsppRg4cCBKKSZOnOjMsp02bRovvfRSmfdblICAAA4cOMCCBQt49NFH+e677wq0ycjIIDo6mjfffJOZM2fy4osv8t577xEaGsrKlSu5/fbb+eKLL4iPjwcgPT2d1157jfXr1zvLezhYc3LoMnQof8YnMPXBqXTt2tV2vK6uzqD/sWPHCAwMZNiwYZw4cYIbb7yRV199FYPB9sTls88+y8yZM+nfvz+vvvoqHh4e+faRnZ3NxIkT2bp1Ky1atGDUqFHOdW3btmXbtm24urqyYcMGnnnmmXyB9Us98sgjPPbYY/Tq1YuTJ08yaNAgjhw54syI3rNnD/fffz+33347s2fPRinFgQMHOHr0KAMHDuTYsWPMnDmT3bt389577xXof+7cuc4s81OnTtEkz82TkJAQfvnllyLHVtQ2p06domHDhsVuV1tIBrWoMMdjufXzZFA3CvSSEh9C2GmtWbLrJJ2aBXFdA9vd6jq+HqRmWzBZcmt4dELUHsYyZuE2CfKWEh+1gNYaY6aJwHJlUNtLfEgGdWk0BuLzvE6wL7vUHUqp/Uqp5UqpiqWFicuK1WgstryHgy2DunylMi5kmAj2ccfF5eKTLo7PcVmfhLCmpKDc3FCeniU35mLpEinzIWq7XJ1LXGpcgQB1M/9m/JX6F1pXfGJgy9+2ALV3t25Y/v6b3Ey5HiqrXJMJy5m/cS/nBIlOygW0hiqc73n79u3s3buX1atXM3v2bLZu3QrkL29RGfWXHcHbUaNG8fPPPxfaxsXFhREjRgBwzz33sH37dgA++eQT3n//fTp16kRaWhru9pulM2bM4LHHHsPX17dAX8pi5Zfly/nrt6Ps3LmTgwcP2pa7uTkzqC0WC9u2bWPWrFns2rWLP//8k/nz5wPwyiuvcPToUXbt2kVSUhKvvfZagX0cPXqUli1b0qJFi3zHCJCSksLw4cMJCwvjscce49ChQ8Wenw0bNvDggw8SFRVFTEwMqamppKenA3D+/HnuvfdePv/8cwICAti+fTv33HMPYAuEN2vWjGPHjhXZ9+bNm5k7d26hx3C1kAxqUWHn0rLxdHPB3/PiP6fGQV6kZJlJz7Hg6yH/zMTVbfdfyfyRmMHrd17rXOao3ZiUYeKagNJ9MRPiSpeUaaLdNf6lbt8k2JudJ5KqcESiNDJMViy5Ol9GZWl5uBoI9HbjXJpkUFeSb4HFWuscpdRE4FPghksbKaUeAB4AaNq0gl/MRa1RlgC1KSG+xHaFScrIyTdBIkA93/KV6rEaU3AJDChVWSfIk0GdbCzTfoSobmcyzpBjzSkQoG7q15QsSxYXsi9Q16tuhfZhPvs3uLjg3aUzxqVLMf31F57t2lWoz6uNOeEUaI1bk5BStS8q09mSlIT59Gk82rTBxa3s10Kl0bix7X50/fr1GTp0KDt37qyS/eT9fayUwmq10qlTJwBiYmKYOXNmkdu0bduWdevWAbas5++//x6AX375heXLl/Pkk09iNBpxcXHB09OTBx98EJ1ju/4Lql+ffv36sWbNGjIyMnhg3DjIzeVfr71GSEgIUVFRtGzZEoDbb7+dHTt28I9//MOZGezh4cH999/vzNAeNGgQZ8+eJTo6utgJDp9//nn69evH119/TVxcHH379i32/OTm5rJjxw48L7mxarVaGTlyJNOnTycsLKzYPgqzf/9+xo8fz+rVq6lTpw5ge88dWegACQkJzn8HRSnPNrWJZFCLCjubmkMDf898v8wa2WuDSha1ELB450n8PFy5OeLiozV17LUbpcyHEBeVdaK9JkFenEnJwmyVJxFqUnKGLcOlPDWowVYiTCZJLJVTQN6M6BD7Miet9QWtteNkzgE6FdaR1vojrXW01jq6Xr16VTJYUf2sxpQqz6BOsmdQ5+XMoC7jNY01NbXU5T0ADEGBtu0kg1rUcnEpcQA092+eb7ljwsS/Uv+q8D4sZ8/hWrcuHq1aAWCSOtRlZrbfqHOv4I1aR81kqqgOdUZGBmlpac6f161bV64gaGksXbrU+f/u3btjMBic2dmO4HRubi7Lly8H4PPPP6dXr14AnDt3zrn+pZdeYtKkSQBs27aNuLg44uLiePTRR3nmmWd48MEHSUxMJDkxEVxcyLZYWL9+PW3btqVr167s3riRHcuXc+utt9K5c2eMRqOz1vWmTZto3942BYejvrLWmhUrVjjPy9q1a4mNjWXOnDm0adOGP//8k7i4uHzHCLYMakcA15GVXZyBAwfy7rvvOl87staffvppIiIiGDlypHNd7969WbRoEWAL2J88eZI2bdrg5+fnfD8BTp48ybBhw1i4cCGtW7d2Lu/cuTPHjx/nxIkTmEwmlixZQkxMTLHji4mJYcGCBWit2bFjBwEBAZdNeQ+QALWoBGdTs2ngl/8OUuNA22upQy2udilZZlYdOENMVCO83S8+TeCo13ghQyZKFAIgN9dWJuLSwEdxQoK9ydVyM7SmGTNtX8jKcnMhr/p+nlLio3R2AdcppVoopdyBkcA3eRsopfJ+C4kBjlTj+EQNK0sGdW5qKtpatkkNwXbdUscnf31Px1Nh5SnxYfAvQ4DakUFdzvrZQlSXEym2YHGBEh9+zQAqZaJEy99/43rNNc7gao49+CZKz3TSHqCu6CSJrrbveFU1UeLZs2fp1asXkZGRdOnShZtvvpnBgweXu7/evXszfPhwNm7cSEhICGvXrnWuS05OJiIigrfffps333yz0O19fHzYuXMnYWFhbNq0ienTpwOwePFiWrduTdu2bWnUqBH3339/seM4c+YMA+64gy7DhtGlSxcGDBjgnLxQubuD1mizGYPBwKxZs+jfvz/h4eForZkwYQIAo0ePJjw8nPDwcM6fP89zzz1XYD9eXl68//77DB48mE6dOuHn50eA/e/Jk08+yT//+U86dOiAxWIp8dy988477N69m4iICNq3b88HH3wAwKxZs1i3bp1zosRvvvmGKVOmkJubS3h4OCNGjGD+/Pl4eHjQr18/Dh8+7JwkcebMmVy4cIEpU6YQFRVFtL0muqurK++99x6DBg2iXbt23HXXXc5JJt955x1CQkJISEggIiKC8ePHA7bJGlu2bEmrVq2YMGEC77//fonHVJtI7QVRYefScmjfKP8j2Y0DbROknEqWoIG4uq2MPUW2OZdRXfLfma/ja/tyd0EyqIUAIDXbTK6mTHWMm9gn44pPyqJZHZ+qGpooQXKmPYO6DDcX8qrv78Gff6RX5pCuSFpri1LqQWAtYAA+0VofUkrNBHZrrb8BHlZKxQAWIAkYW2MDFtWu1AHqgADQGmtqKq5BQWXaR2EZ1G4GF4K83cr8VJg1JQW3a64pdfu8kyQKUZudSDmBv7s/wZ7B+ZY39G2Iq3KtlIkSzWfP4tGyJS5eXrg2aigTJZaDOT4e5eWFoW7Fyq04MqirKkDdsmVL9u3bV2B5YRm/jnrIxbXZtm1boftxZBiXpgbyG2+8UWDZI488wiOPPFLsdjNmzHD+HBERwY6vvsLFxwf3kPxlVpSb7e+MNpvB3Z0BAwawf//+Av1t2rSpxLEC9OvXj6NHj6K1ZurUqc4gcPfu3fPVhXZMONm3b19nuY+xY8cyduxYAOrWrZsvA9uhqLry8+bNK7AsODiYXbt2OV+PGDGCOXPmFLr9kCFDGDJkSIHlDz/8MA8//HCB5UopZs+eXWhflwPJoBYVdq6QDOp6fh64uijJahNXNa01i3fGE9bYn7DG+TOEHNlGF9Ilg1oIsAU9AIJ9ylDiI9hWTio+WSYGqknOAHUFMqgT03MqZdKoK53WepXWurXW+lqt9cv2ZdPtwWm01v/UWodqrSO11v201kdrdsSiumiLhdzU1NIFqB2lMspYy9lszcWYaS70SZd6fh5lzqDOTUnB4F/6eQdcvLxQHh5YU4xl2o8Q1e1E6glaBLQoUF/d1cWVxn6NK6nEx1lcGzQAwKN5CynxUQ6m+HjcQ0JKXQe/SAYDKJcqC1BfqbTVijabUe4eBdYpd3vQ31Q535U//vhjoqKiCA0NJSUlhYkTJ1ZKv6JySYBaVEh6joUMk5UG/vl/qRhcFNcEeEqAWlzVDpxK4ciZVEZ0LljXzM/DFXeDC+czJINaCIBkZ5mI0mfhNgzwwtVFEZ8kAeqaZCzHe5dXfT8PzFbt/DcghCg7a2oqQKlLfABlDvQ6bkY5brLnVZ4AtTUlBUNg6Ut8gC2LWkp8iNouLiWuQP1ph6Z+TYlPK98kpQ7W9Axy09Nxu8YWoHZv0QJTXJzc6C0jc/xJ3CphomClFMrNFW0uuUTEleDSLO3y0jm2vykuHgX/plR2Vvpjjz1GbGwshw8fZtGiRXh7e1dKv6JySYBaVMjZVNusqw38PQusaxzoJTWoxVVt8c54vNwM3BbVqMA6pRR1fN05nyYZ1ELAxYn2gssQ5DS4KBoFehEv5aRqlCNoFehVzgxq+03uc2nZlTYmIa42jqCtIzu6OM4AdRkDvRefdCkkQO3rUaZJErXZTG5GBi5lmCQR7AFqKfEharE0UxqJWYkF6k87NPVvyl+pf1UomGw5dxYA1wa2EjnuzZuTm56O9fz5cvd5tdFaY4pPKFBaoryUm5tkUJdRrsn2N0N5FJJB7eKCcnVFm+ScXk0kQC0qxBGgru9X8JdK40AvThvly6a4OmXkWPgm9hQ3RzTE37PwoE0dX3cuSAa1EEDeMhFly8JtGuzNScmgrlHGTDN+nq64Gsp3WVnfXibsXKr8PhSivJwB6tLWoAasxrIFepPsZckunSQRbBnU59NMpQ66WdPSbGMpwySJYJ/gsYzjFqI6xaXEAQUnSHRo6teULEsWF7IvlHsflr//BsC1QX3AlkENkCNlPkrNkpiIzs7GrWnFJkh0UG5uaIsEU8tC5+SAUrYJEQuh3N3RZknmuppIgFpUiOPLZP1CMqgbBXrxd2o2FmtudQ9LiBr3/f4zZJisjOpS9EVPHR8PqUEthN3FifbKloXbJNiLBAlQ16jkTFOZbyzk5SgT5rjpLYQouzIFqMuZQX0ho+gSH3V9PcgyW8kwWUvVlyM4bihrBnVggNSgFrVaXGocUEyA2t9WUqIidajNf9syqB2TjHq0aA4gEyWWgTneVmbFvUklBqjNFimzUgY6Jwfl5o5yKTwsqdzcK60Gtbg8SIBaVIjjcdxLa1ADNA7ywpqrOVvGenRCXAkW7zpJq/q+dGwaVGSbur4eXCjjjPdCXKmSM824GRS+Hq5l2i4kyJsLGSYycq6Oun+1UXKmudwTJEKeDGq5XhCi3BwTHpYmQO3i5wcGQ5kDvcWW+LA/TVnaOtSOfZe5BnVgYJkzv4WoTidSTuCqXAnxK7x0RDO/ZgCcTD1Z7n04S3zUt2VQuzZsiPLwwBQXV+4+rzYme4DarRID1KDRFrkeLa3cnBxcPAvGkRyUuwT9rzYSoBYVcjY1B293Q6EBhUaBXgAyUaK46vz2dxq/njQysnOTYmeFruvrzvmM0j8OK8SVLDnDRKC3e5lnUm8SbJvkJEHqUNcYY6ap3BMkAni5G/DzcC3zBGtCiIvKkkGtlCrXZIMXMkwoVXgpprIGqHMdkzr6+5dpDI4a1HLtJGqrEyknCPELwc2l8Bu3DX0b4qpcOZlW/gC1+e+/MQQG4uJpu8GrXFxwb9YMk5T4KDXzyXhQCvfGjSulP8ekflRBHWqDwUBUVBShoaFERkby3//+l9xc21Pq69evp1OnToSHh9OpUyc2bdpU6fsHmDFjBrNmzaqUvnbv3s1DDz2ENplQ7vkD1M2bN+e8vZa6M+hfzDm9cOEC/fr1w9fXlwcffDDfur59+9KmTRuioqKIiori3LlzznXLli2jffv2hIaGcvfdd1fKcV2qb9++7N69u1zbLlq0iIiICMLDw+nRowf79u1zrluzZg1t2rShVatWvPrqq87lo0ePpk2bNoSFhTFu3DjM9vNWXF+1TdnSlIS4xNnUbOr7eRQaUGgcaPuDKQFqAbBsdzxtGvgR2SSwpodS5RbvPIm7wYVhHYufdKOOrzsmSy7pORb8iqhTLcTVIjnTVKYJEh2aBNluhsYnZdLmGr/KHpYoheRMEy3r+lSoj3r+HjJJohAVYDUawdUVF1/fUrUvTyZyUkYOgV5uGFwKXveXPYO6fCU+XAIC0CYTOisL5e1dpm2FqA4nUk4UWd4DwNXFlcZ+jStU4sPy91lc7eU9HNxbtCDn6NFy93m1McXH49rwmiLrH5eVcrWF1qpiokQvLy9iY2MBOHfuHHfffTepqam8+OKL1K1bl2+//ZZGjRpx8OBBBg0axKlTp4rtz2Kx4OpaM6FAi8VCdHQ0HcPDyTl+HOVR9Pl3vDfaZIYi3idPT0/+9a9/cfDgQQ4ePFhg/aJFi4iOjs637Pjx47zyyiv8+OOPBAUF5Qtc1xYtWrTghx9+ICgoiNWrV/PAAw/wyy+/YLVamTp1KuvXryckJITOnTsTExND+/btGT16NJ999hkAd999N3PmzGHy5MlF9lUbSQa1qJBzqTmF1p+GixnUktUmcnM1z684yLwfr/y7+tlmK1//eopBYdcU+ghsXo5JhqQOtRCQnGEmsBxlIhwZ1PHJUoe6phgzzBXKoAbbZMsySaIQ5Wc1GjEEBJT6KZRyZVCnm4q8tqnra7umOV/K0mWO4LhLmWtQB9q2T5EyH6L2seRaOJl2stgANdgmSqxIiQ/zubPOCRId3Js3x5SQUCUB0iuROT4e9yZNK60/RwZ1VZ//+vXr89FHH/Hee++htaZDhw40atQIgNDQULKyssjJKfh7eP78+cTExHDDDTfQv39/0tPT6d+/Px07diQ8PJyVK1c62y5YsICIiAgiIyO59957C/QVGxtLt27diIiIYOjQoSQnJwO2jOFHHnmEqKgowsLC2LlzJ2DLvr733nvp2bMn9957L1u2bOHW224DIDk9nYEDBxIaGsr48ePzPR2z6Isv6D1qFB27d2PixIlYrQXnOPDx8aFXr154ehYekyrMxx9/zNSpUwkKCnKe08KMHTuWyZMn061bN1q2bMmWLVsYN24c7dq1Y+zYsc52kydPJjo6mtDQUF544YVC+1q3bh3du3enY8eODB8+nPT0dMCWMf7CCy8434ej9ptMPXr0cI6vW7duJCQkALBz505atWpFy5YtcXd3Z+TIkc73bsiQISilUErRpUsX5zZF9VUbSQa1qJBzadmEhwQWus7b3ZUgbzfJoBacS8shx5LL31fBBFhrD/1NSpaZkZ1LrmfmmGToQkYOzSuYfSjE5S4500Sr+qXL/Murjo87Xm4G4pPkb01NMFtzScuxVGiSRIAG/p7sPZlcSaMS4upjNRpLVd7DwRAYiPnMmTLt40KGyXlz/VJB3u4YXFTpM6grUOIDbMfr1rBhmbYVoqqdTj+NOddMc//mxbZr5t+M3Wd3o7Uuc2kzsGVQe4WF51vm3qI5WK2Y4hPwaFl8gFzYMqj9buhXpm2M3/6B6XRGEWs1uZmZKNcslPvpUvfp3siHwFuvLdM4WrZsidVq5dy5czRo0MC5/Msvv6Rjx454eBT+e3rv3r3s37+f4OBgLBYLX3/9Nf7+/pw/f55u3boRExPD4cOHeemll/jpp5+oW7cuSUlJBfoZM2YM7777Ltdffz3Tp0/nxRdf5K233gIgMzOT2NhYtm7dyrhx45xZzYcPH2b79u14eXmxZcsWsJcomfmf/9CrVy+mT5/O999/z9y5cwE4cuQIX3z5JZsWLMCrUSMeefFFFi1axJgxY8p0ru6//34MBgN33HEHzz33HEopjh07BkDPnj2xWq3MmDGDwYMHF7p9cnIyP//8M9988w0xMTH8+OOPzJkzh86dOxMbG0tUVBQvv/wywcHBWK1W+vfvz/79+4mIiHD2cf78eV566SU2bNiAj48Pr732Gm+88QbTp08HoG7duuzdu5f333+fWbNmMWfOnHxjmDt3LjfddBMAp06dokmeuukhISEFsqHNZjMLFy7k7bffLnA8efuqjSRALcpNa83Z1Bxu9Cu6sH2jQC8JUAviLtj+kP+dcuUHqBfvPEnTYG+6t6xTYtuL2UaSQS1EcjnrGCulaBLsJRnUNcSYacsUCvKpWJkiRwZ1eb+sC3G1K0+AOruM5QCSMky0qlf4jUSDi6KOj3uZSny4+PmhDIYyjcEQEOjcXoja5kSK7WnRkjKom/g1IcuSxfms89TzrlemfeSaTFiTknC9pkG+5R4tbPs0xZ2QAHUJcjMysF64gFtI5UyQaKNAKaih+viHDh3iqaeeYt26dUW2GTBgAMHBwYAtlvPMM8+wdetWXFxcOHXqFGfPnmXTpk0MHz6cunXrAjjbO6SkpGA0Grn++usBuO+++xg+fLhz/ahRowDo06cPqampGO1P6sTExODl5eVsp3NzUa5ubNu2ja+++gqAm2++2Znpu3HjRvbs3Uvvu+8GFxeyLZYiM52LsmjRIho3bkxaWhp33HEHCxcuZMyYMVgsFo4fP86WLVtISEigT58+HDhwgMBC/obeeuutKKUIDw+nQYMGhIfbbgyFhoYSFxdHVFQUy5Yt46OPPsJisXDmzBkOHz6cL0C9Y8cODh8+TM+ePQEwmUx0797duX7YsGEAdOrUyXkuHDZv3szcuXPZvn17qY97ypQp9OnTh969e1e4r+omAWpRbmk5FrLMVhoUUeIDoHGglzM4Ka5ef9n/DZxJyb6igw8nzmew488kpg1qg0sh9Rkv5cyglgC1uMpprUnONBNcziBnkyBv4pMkQF0TUrJsv78qXuLDkxzL/7P35mGOZYV59+9I92otrbV37d2zbzAww2DALMMMizHEkPgzi22wExy+hJjYyZfYiY1tghPbxDh2jFfi2CbAeImNMRgzAwz7MgsMM0z3LN1V1VXV3SVVqaSSSirp3iud74+rq67u2rRcLaW6v+epp7pV0tWp0nbue97zvhWyRYOI38nkd3BolHImgzpd/3Z1M4M609B9bOQ1Buf2f60Ph7ys1RvxsZlpOH8adkR8NJif7eDQCeoVqGfCMwAs5ZYaFqiNal6uOnqlQO2ZnQVAW1hs6HjHEa0aceCZbkygPszpXFpYgIrEe+pk02Orh/n5edxud02wXVlZ4Y1vfCN//ud/zqlT5hj/9m//ll/5lV8BqDlyg8HLO3Y/+tGPsra2xqOPPoqqqszOzlIstm4mu/o83/r/zvsGoFJB+PY3Okopefvb384vv/OdAHhPmn/Tq3+vq/OldzJRLcAMhUK89a1v5aGHHuLHf/zHmZyc5K677kJVVebm5rjuuut49tln+cQnPsGnP/1pgFrmt+VGd7lcVzjTXS4XhmGwsLDAf//v/52HH36YWCzGO97xjl1/Rykl9957Lx//+Mf3HKd1XLfbjWEYtcsff/xx/sW/+Bd85jOfYXBwsPY7LS8v166zsrJS+z0BfuVXfoW1tTX+8A//8Ir72OtYvYiTQe3QNMlqXMNI+GAH9YX0ttO0fcw5nzKFo5JRqbnt+pH7Hl7C7RL88PMPLke0sHIcU3WezDk49CvZokG5IpuOiZiKB1hxPmu6Qrr6nh5tUVS25hJrTlGig0NTmA7q+gVfdySC3N6mskdW6Z7Hr0jSBY3BA/o1hkPeuh3Ulc1sw/EeQO13bFRcd3DoBIvZReK+OBHvwa/F6ZC5mNRMDrWxugqAMnplSaI7EsEdj6Mt9n/nT6toS+bf3V4HtZlDLY32nuuura3xrne9i3e/+90IIchkMrzuda/j137t12oOXYA3vvGNPPbYYzz22GN7iribm5uMjIygqioPPvgg58+bpZ133303f/VXf0UqlQLYFfERiUSIxWJ85StfAeAjH/lIzU0N8Bd/8RcAfPWrXyUSiRDZYyFSSgmVCi6Ph5e+9KV87GMfA+Azn/lMLc/6la98JX/913/NWjaL1HU2NjY4f/78ob+XhWEYrK+vA2bkxac+9SluueUWAH7oh37IjBnBjN945plnOHnyJL/6q79aO3a9ZLNZgsEgkUiERCLBZz7zmV3XeeELX8jXvvY1zp49C0A+n6/FjOzH0tISb3rTm/jIRz7CddddV7v8zjvv5Nlnn2VhYQFN07jvvvt4wxveAJiC/Wc/+1k+/vGP43K5Dj1WL+I4qB2axiozGgnt76CejPnJa2XHEXXMsQRqMF3UsUPKA48iernC/310hbtvGNm3OPRqvIqbkE8hlXcc1A7Hm0zBfA00K1BPxvxslQwyBb0v3196mXS+tcfOYrgaF5bMlrhmJNTyuBwcjhNSyqYiPsAUel1XOTH3IlPQkJIDC6CHBrw8vZqr6/7Lm5sNCeoWtQxqJ+LDoQdZ2Fw4NH8aYHxgHEUonM+eb/g+9NUEAOrY7tetZ27OdPE6HIi+3JyD+jCEqiJ1w/Ydw9vb2zz3uc9F13UUReHHfuzH+Nmf/VkAfvd3f5ezZ8/yvve9j/e9732AWch3WBzG2972Nl7/+tdz6623cscdd3DDDTcAZnTFf/7P/5mXvexluN1ubr/9dv70T//0itv+2Z/9Ge9617soFAqcPHmS//2//3ftZz6fj9tvvx1d1/mTP/mTPe9blsvm38jr5Zd+6Zd4y1vews0338yLXvQipqs7gW666Sbe//7387of/VEqmoYnFOJDH/oQMzMzu443OztLNptF0zQ+8YlPcP/99zMzM8OrX/1qdF2nXC5zzz338M6qG/vVr341999/PzfddBNut5sPfOADTbuKn/Oc53D77bdzww03MDU1dcUigcXw8DB/+qd/ylve8pZageX73//+A8Xi973vfaRSKf7Vv/pXACiKwiOPPIKiKPzu7/4ur371qymXy/zkT/4kN998MwDvete7mJmZqcWHvOlNb+K9733vvsfqRRyB2qFpElWX0+ghDmqAC+ltR6A+xiym8sQCKumCzqXNbW460bhjptf5/JkE61sab3lBYxOdoYH6t8M6OPQrG5bI2WzERzwAwHK64AjUHcbaFRMNtPYZb8WFJRwHtYNDw8hCAanrTQrUm7uiAvbCep+OD+w/7x8OeVnfKlGpyEOjzsqbmyjjYwdeZy9cPh/C53MEaoeeZGFzgbun7z70eopLYTI0yVKuCQd1whSolT1et565Wba++KWGj3nc0JaXcEUiTcUMHYRQVUAiDaP6b3sol8v7/uwXfuEX+IVf+IVDj/GOd7yDd7zjHbX/Dw0N8Y1vfGPP67797W/n7W9/+xWX/fIv/3Lt38997nP55je/uedtf/RHf7RWmLjXbQFe9sIX8n0f+hDC62VwYGDf3Owf+ZEf4Z++6lXoFy7gve46XJ695/iLi4t7Xv7oo4/uebkQgg9+8IN88IMf3PPnFjuF+dnZ2Vrh49U/u1rAt7Bc2mA60x9++OFd19k59jvuuKN2mw9/+MO7yhItfuAHfoAf+IEf2HX5zniQnRx0rF7DifhwaJqE5aA+wC1qCdROUeLxRUrJUqrAXXPmquSlPi1K/PhDy4xHfLzsusbKGwaDHifiw+HYUyvaazbiI1YVqDecz5pOk7bc7y0uDIzscFA7ODg0hhV30ayDuh6s3V4HRnwMeNHLks3tw7e4l7NZ3OHmxCF3JOJEfDj0HJlihnQpfWj+tMVUaKqpiA89sYorEMA1sLuw1Ds7S3l9nXKuvp0MxxV9aRnPZH2RjI0glKr/U+/fSEs7kFUXsfDuv+BpYQn9UnN2HB8HHIHaoWkS2SJBj5sB7/5G/AnLQe0I1MeWjbxGrmRwx2wMt0uw2ocC9Uq6wJefXeOH75jCXUc54k6GBrxOSaLDsWejxZiIqbj5WbPkFCV2nHRBR3ULgh53S8cZ8Cr4VTfJOvNrHRwcLmM0I1DHzOvWLVBX5yoHRXxYUT2H7QyTUpoRH026F93RqOOgdug5FrOLwOEFiRYz4RmWcksN92cYiSTK2NieERKeOfO+NSfm40C0lWVUm+M9YIeYekwF6i9+8YsH5kJbyFIJ4XJfFvQPQFRd045AfTxwBGqHpklmS7UtufsxGPTgUVyOg/oYs1jNnz45HGQk5O1LB/VfPWLmmP0/dzS+Ej844HEyqB2OPa26cEM+lVhAZTntCNSdJlPQiAY8LWctCiEYCXsdgdrBoQkskVlpRKCONFY2uJE3X5sHOaiHqvEf64e8jmWhAIbRvEDtOKhtIafl+PMn/5yKrHR7KD2BrFSoVPaPUjiMhU1TFJ4L1++g3ja2Wd9eb+h+jNVVlNG9d2w6AvXhSMNAv3ARz9S07cc+7gJ1vVRKJYS3vrmj+TcVzt/0mOAI1A5Nk8wVGTkgfxrA5RKciPgcB/UxZmkjD8B0PMhYxMdqtr+eC+WK5K8eWeb7rx1mshoz0AiDA17SBQ2j7JwcOBxf0gUNt0sQ9jVfjTEVD7DsOKg7TrqgEWsxf9piJOQlme2/RUwHh3bTyYiPgxYS63VQW+5nd6S5ThJ3JEJ5M9PUbR0u87nzn+MDj3yApzee7vZQeoLPffj3+LsPvL/p2y9sLqC6VE4MnKjr+jNhs+yt0aJEPZFAHd07v90zOQluN6V9MnkdqiWThoE6Vb+xqG6Xu9sNwuWIqYcgS6W64j3ANDAIVUFqzt/0qNHo7hBwBGqHFkjU4aAGmIj5HYH6GLO4XkAIcwv+eMTXdw7qLz+zxsXNIm+5s7ltYkMDHqQ0t8k7OBxX0gWdWEBtyYU7FQuwknY+azpNuqATbTKa5WpGQj7HQe3g0ATNCNSNlg1u5DUifhXVvf/pY02gPuR1bN2ny4n46CqrhVUA1rbXujyS3mB1/lmSi/NN335hc4GZ8AxuV32RV9Nh08G7nFuu+z5kuYyxtrZnQSKYcQjq5ATawmLdx+w0le1tSvPdc3jry2bud70Oap/PRyqVqktsq4mp+5TVOZjPYWkYuOoUqMF8Xkvd2XF8lJBSkkql8PkO1wt30rxVyeFYI6UkkS3WSo0O4kTEz5efdSY+x5XzqTwnIn68ipuxsJ8vPr2GlLLl7eC9wscfWmJowMMrb9x7ongYg0HzNZTKl2ondg4Ox410XmtZ5JyM+3ngdIJKReJqMAveoXkyBY25oaAtxxoJe/ni0/21iOng0AlqAnWDgq87Gm3IQX1QvAdA2KfgUVx1CNRZ8/6bFqgjlDObfTWf7AbJQhKAtYJzngaQW1+jmN+iUinjqlNk3slidpFrY9fWff3x4DiKUBpyUBvrKSiXUcf2P+/wzs71dMRH6n/9Cak//EOu+fKXUGKxjt+/tmQuCHjqdFBPTk6ysrLC2lp9rxNjfR2kRNnaanqM/YzUNIz1ddy6jmu9vnibcjpNpVRCLTkmhqOEz+djssEyUkegdmiK7LZByajU5aA+EfWTzJXQjAoexTHtHzfObxSYGTSjL8YjPgpamWzRIOK3Z0t4N0nminz+qST/4iVzTT+3BwfMkz2nKNHhOJMuaMRbFKinYgG0coVErsh4xG/TyBwOI13QeZ6NDuq8ViZfMggeUMDs4OBwJeXMJq5gsFYmVS+NZDlvbGkHFiSC6R4cHvDW7aBupSQRXUcWCoigPQtkx5FEPgFAcjvZ5ZF0H71UZDtnLpxsZ7MEo40Jp3pZZzm3zL0z99Z9G8WlMBmaZCm3VPdtjITpelf2ifgA8MzOkv/Wt5CVCsLVe+fe29/9LlLX2Xrwi0Tf9MaO37++sgyqijK2/99wJ6qqMjdXX644wMX/+HPkH3qIax/8QrND7Gsyf/O3XPpP/4lT//gZPLOzdd1m7UMfYv13P8T1330MV4Ofcw5Hi957x3I4EiRzpsNppM6IDylhtc+iHRzq43yqwMygefIwFjGfL/3yXPjrR1coVyQ/0mS8B5gRHwDrh+Q1Ojj0M+m8TrTFHOOpuLkQtrzhxHx0CillrSTRDqxdWU7Mh4NDY5QzmYbiPSwacVBv5A8XqAGGQt46MqjN+2ylJNE8jhPz0QptdVAXNqCYtf+4bSKXuuzkzGfSDd9+ObdMWZaZi9QvZIJZlLiUrV+g1hPmosJ+JYlgFiXKYhFjdbWhsXQCKSXF06cByD3wQFfGoC0t45mYQLgbd8nXgzI2hpFMIsvNF272M9r8OYSqojbgrFUnJkBKjIsX2zgyh17AEagdmiKRNSeeo3VEEkxETSebk0N9/MgWdTbyGrM7HNQAlzaP/nOhUpH8xcPL3DUX5+TwQNPHqUV8OA5qh2NMulCf8HEQUzHzs8YpSuwcea2MXpb2lSRWi5edokQHh8bohECdymu1XV8HUY+DupJtLeLDyq6ud+wOe9NWgfrjb4Y/eQ0YR2PBMbt++W+wlU41fPuFTTNS42TkZEO3mwnPsJRbqrtMzFg1BWr1APevp+r2LfVgzIeRXKOcSuEKh8l/7WuUt/IdH4O2vIQ61by56DDU8TEol82oD4ddlM7N45mdQSj175TzTEwAoF240K5hOfQIjkDt0BSJ6sljvREfABcdgfrYsZQyhSIr4qOfHNTfnE9xPlXgLS+or2BjPyJ+FbdLkMofjQm8g4PdSClJ2+DCnYj5EQKW045A3SnSeXNhLWZjxAdAwnFQOzg0REsCdR0u5EpF1r2QOBzyHrorrLy5iVBVRIPlSRZK9Xd1HNTNo5U10iXTKWx7SWLZgIuPQfJJ+ML77T12m8jtEKjz6cYd1AtZUwyeCc80dLvp8DTbxjbr2/WJmUZiFaGquA/IbrZiE7TFxYbG0gmKZ0z39OBP/iRS08h/9SsdvX8pJfrSMp42CtRWgWUvOth7AW1+Hs/JUw3dxnJb6yuOQN3vOAK1Q1MkahEfhzuoLdes46A+fiymzFVxK+JjJORDCLjUBwL1xx9eJuxTeM0t9eWX7YfLJYgHPY6D2uHYYrlw48HWXLhexc1oyOdEfHSQTEEHaDmexWLUcVA7ODRFqwL1Ye7NzW2dckUSDx4+7x8OeUnlNYxyZd/rlDObuKKRpgsOHQd161juadWl2u+gTi9AuQSRafj6/4TFr9l7/DaQXV+D6vMxn95o+PYLmwuM+EcY8DS2q3I6ZBpd6i1K1BNJlJGRA7OllZFhXIEA2sJiQ2PpBMXTp0EIYm99C+54nNz9nY35KGcyVLa2UKfb6aAeB0C/5AjUV1PRNLTlZbynGttpoIyMgKKgOw7qvqcrArUQ4jVCiKeFEGeFED+3z3X+HyHEaSHEk0KIj3V6jA4Hk8yWCHkVAp7Dt2b4VDdDA17HQX0MOX+Vg9qjuBga8B55B3U6r/HZ763ypudN4lNbzy8bDHqcDGqHY4vlwrUjx3gq7ncc1B0kXag6qFuMZ7GI+FU8iuvQeAAHB4craVqgjkTAMKhsbR14vVT1fXqorogPD1LCRmH/hfdyNtt0vAeAOxI1j+M4qJsmUTCjIq6PXc96cZ1yxca83KTpkuWNvw+xWfjEu3o+jzq3vsZAfBBfKMxWEwL14uZiw/nTYDqowcywrgdjdfXQcj8hBJ65ObQejPgonTmDZ2YGdzhM6JV3s/WlL1HROmfS0ZfNv3NHHNQJR6C+Gm1xESoVPHONCdTC7UYdH3cE6mNAxwVqIYQb+BDwWuAm4C1CiJuuus61wM8DL5ZS3gz8206P0+FgkrliXe5pi4moz3FQH0POp/IMh7xXLGSMR3xcOuLuuL/5zgW0coU3v8CeyY25HdZxUDscTyyRM26HQB0LsOJkUHeMzLbpoLYrg1oIwfCA1ylJdHBoAGkYVHK5ph3UcLgTeaMqUNcb8QEcuNBU3tzEHW5BoI5aDmpHoG4Wy0F9y9AtVGSFjWLjouy+JE4DAk48D970R7C5Ap/9efuO3wZyqSThwWEGojHymcb+FlJKFrILzEZmG77f8eA4ikup30GdTKBWBdCD6FWBunj6DL6bbgQgdO+9VPJ5Ct/4RsfuX6sK1O3MoHZHowiv13FQ74E2bz4nG3VQA6iTE+grK3YPyaHH6IaD+gXAWSnlvJRSA+4D/slV13kn8CEpZRpASpns8BgdDiGRLdWVP20xEfM7AvUxZDFVqBUkWoyFfawe4ZJEKSX3PbTEc6ei3DAWtuWYg0GPk0HtcGyxhI9YixEfAJPxAJeyRTRj/63lDvaRKdjnfrcYCXtJ5o72IqaDQyexXMStCdQHC70b1TmKrQJ1Cw5ql9eL8PudiI8W2ClQAyS3bTzdTp6G+Bx4AjD1AnjJz8B3/g889Wn77sNmsutrhIaGCcbiDWdQp4opclquKQe14lKYHJhkKbd06HWllBiriZpD9yA8s7Poly5RKfbO52k5k0G/cAHvjaZAHXjhC3EFg2Qf6FzMR81BXc00bgdCCNSxMXTHQb2L0vw54HKRZyOoExNoFx0Hdb/TDYF6Ati5h2WletlOrgOuE0J8TQjxTSHEazo2Ooe6SGSLjITqd1CfiPi5mNmuu6HYoT9YShVq+dMW4xHfkc6g/vZSmmeTW7z5TvtW3gcHvE4GtcOxxcoxtqNobyrmR0qn86BTpPPmYxfx2+OgBhgJeUlknQU7B4d6sUTadjqorYiPwXoyqAdMA8tBAnWlRYEazHgSJ+KjeRKFBH7Fz8mI6WRcL9RX0lcXyTMwsmOD9Mt+DsZug0/+NGzZnHdtA7JSIZdaJzw0TDAaazjiY2HTdIXOhRsX3cCM+VjKHi5QlzMZZKmEOlaPg3oWpEQ7f/hxO0XxqacA8N1kPjdcHg8DL385W5//ArJsY8TMAWjLy7iHh3AFAodfuQWUsTEMx0G9C+3cPOqJE7j8/oZv65mYoLy23lOLLg7206sliQpwLfBy4C3AHwsholdfSQjxU0KIR4QQj6yt9d6HXb8ipSTZoIP6RNRPUa+QrgoRDv3PtlZmNVtkJn6VgzriJ1c02CoZXRpZa9z30DJBj5vXP+eEbcccHPBQ0MoUtKP5N3FwaIWag9qWDGrz/WbZifnoCOmCRsiroLrtm06Ohn1OSaKDQwO0JlDXVza4sVX/TpehkPlevnZAt4bpoG5tF5pV8OjQHIl8gpHACMOBYcBGB7W+DRvnrhSoFY8Z9VHKwd+/B3rMsFTIblLWdUJDwwzE4uQz6YZMVTWBugkHNZhFiUu5pUPv00iaj1E9Dmpv1aHaSzEfxSfNbHJLoAYz5qOcTlN49NGOjEFfWsYzNd32+zEd1Im2389Ro7Qwj+fUqaZuq1Zd7/rFi3YOyaHH6IZAfQHYaT2crF62kxXgk1JKXUq5ADyDKVhfgZTyj6SUd0gp7xgeHm7bgB2uZHNbRytXGGkw4gPgQtpxtR0XlqoC0czQbgc1cCSLEnNFnU89fok3PPcEQe/hBaH1MlR1JDkuaofjSKag4RIQtsGFO20J1E5RYkfIFDSiNkSz7GQk5CVbNCjqnXFTOTgcdTrloA55FbzK4cXQAY9C0ONmPbf3nEbqOpV8HpcdDmon4qNpkoUko4FRBv2DCARrBZvMXuvPgKzAyI1XXj5yI7zyvfD0p+Gxj9pzXzaRXTeF33A14qNSNtjO1V/quJhdxK/4GQ0eLhzvxXR4mm1jm/Xtg13sxqrpyK0r4mNmBqiW0vUIxTNnUMbHUWKx2mUD3/8ShMdD7oHPdWQM2vIynqn2xXtYKGNjGMlkx5zhRwFZqaDNL+A92dxCjjphhi44RYn9jS0CtRCikT0SDwPXCiHmhBAe4M3AJ6+6zicw3dMIIYYwIz/mWx+pgx1YW29HGypJrArUzrbrY8P5VB5gdwb1ERao/+6xi2zrZd58p70r74MDptvI2kLr4HCc2ChoRPwqbpdo+VijYR+qW7C84XzWdIJ0QbfF+b6TkdDh8QD9RINzaAeHXdQE6li04du6w+ErjrEfqbxGfKD+1/pwyLuvg7qcy1Xvu0WB2nFQt0SykGQkMILqUon74rVM6tYPfMb8Pnrz7p+98F/B7PfDZ34O0vWVAnaC3LopzocGhwlG4wDkG4j5WNhcYCY8g0s0J63MhEwx+bCiRH3VdOSqY2OHHtMVDKKMjvaWg/rMGXw3Xrlw4QoGCb7kJeQ+97m2R4FWSiWMRAK1Iw7qUSiXMdZtjM454hiXLiGLRTwnm3RQWwK1U5TY17QkUAshXiSEOA08Vf3/c4QQv3fQbaSUBvBu4LPAGeAvpZRPCiHeJ4R4Q/VqnwVS1WM/CPx/UspUK2N1sI9EdeutdRJZDyeqAvVFR6A+NpxPVR3U8b0d1JeOYFHifQ8vccNYiNsmWzupuprBActBfTwEGQeHnaQLOrE6irfqwe0STET9joO6Q2QKmq0FiQDD1cXvfi9KbGYO7eCwF604qIWi4AqHDxV6N/KlugoSLYZDXtb2eQ1bhYy2ZFA7DuqmqMgKyW1ToAYYCYwc6t6tm8ST4PZA/OTun7lc8EO/B0LAJ/5fqPSGuzRbFajDQyMMxJoTqJvNnwaYCpubyw8rSjQSCRACZWioruN65uYoLfaGQF0pFNDm56+I97AI3XsvxqVLFL/3ZFvHoF+4AFLimbavR2g/lOoiguV6d4DSvOk39Z7a472hDpThYYSqOg7qPqdVB/VvAa8GUgBSyu8CLz3sRlLKf5BSXielPCWl/NXqZe+VUn6y+m8ppfxZKeVNUspbpZT3tThOBxuxBOpGHNSxgIpfdTsO6mPEYipPNKASCVy5/dvKLj9qDurvXdjkexeyvOUF0wjRutNzJ4PVkz4n4sPhOJLOa7a6cKfiAVacDOqOYDqo7Y/4AI5DUWJTc2gHh6spZzKgKLiCwUOvuxfuaPRwB/WWVldBooUpUO/joN7MVO/XnpJEp4C9cTaKGxgVoyZQD/mH7HVQD10H7n0+G6LT8Npfh/Nfg298yJ77bJHc+hqqz483GCRYFai3Mum6bls0ilzcuth0/jTAeHAcxaUcWpSoJ1ZRhoYQan2fu57ZGbSFxZ54jRSffhqkxHfTjbt+FnrFy8HtJvfAA20dg7Zk/n3VyfYL1JbLXXeKEmuUzp0DwHOyOYFauFyoJ06gOQJ1X9NyxIeUcvmqi3pjKdShbSSrE85GHNRCCE5EfY6D+hixtFHYVZAI4FPdxIMeLh2xEqz7Hl7Cq7j4oedO2H5sK+JjPd/3goyDwy7sjomYjAVYdvoOOkK6YO/iAlxexDwORYnOHNrBDsqZDO5otOnF83qcyBt5rbaYXg9DA17W91l0r2TNbF8rXqRZ3NEoGAaVvLMg2SiWGD0aMLOMRwIjrG3blEGdPHNlQeJePOctcMMPwhf+i+m47jLZ9TXCQ8MIIQhW85HrdVAv5ZaQyJYEasWlMDkwebiDejVRc+bWg3dujko2Szldn9jeToqnqwWJN+4WqN3RKIEX3Enuc+3NodaXzWiIjjqoE45AbaHNL+CORlHi8aaPoU5MoK84AnU/06pAvSyEeBEghRCqEOLfY8Z2OPQxyWyRsE/B7zm8KGUnJ6J+x0F9jFhM5ZkZ3NvNMx7xHSkHdUEz+LvvXOR1t47vcoTbQcCjEPC4HQe1w7HEdFDb97qaivvZyGvkS4Ztx3TYjVGukCsaRG1+T4wHPCguUVsM72OcObSDLZgCdfNu5MMc1FJK0oUGM6gHvGxu65SM3WsuVpxIyxEf1d/ZiflonKsF6uHAMKntFEalxc/N7QxkV3YXJF6NEPD63wZfFP7mX4LR3ff7XGqN0NAwAKrHizcYZKtOgXph04zQmI3MtjSG6fD0oRnURjKBMjpS9zE9c6Zo3gs51MUzZ3DHYvsK7KF770Wbn6+5bNuBtryECARwDw627T4s3NEowut1HNQ7KM2fw3OqufxpC3Vy0on46HNaFajfBfxrYAK4ADy3+n+HPiaRLdUcTo0wGfM7DupjgmZUuJDe3lWQaDEe8XHpCAnUn378ErmSwY/c2b4V98EBj5NB7XDsqAkfNmVQA0zFzPcdJ4e6vWS2dQDbHdQul2BowHscBGpnDu1gC+V0pqn8aYvDygazRQO9LBtyUA9Xo3r2clFbGdQuGzKo4XJkiEP9WAK1FfEx7B9GItko1p+7vCdrT5nfD3NQAwSH4A3/ExJPwBf/W2v32yKWg9oiGI2TzzQmUM+EZ1oaw3RomuXc8oFxHPpqAnW0fge1Z3YWAG1xsaWx2UHptFmQuN9Oj9ArXwnQ1pgPfXkFz+Sk7VGNeyGEQB0bQ3cc1DW0c/N4Tza/0wBMB3V5Y4NKwZnj9ystCdRSynUp5duklKNSyhEp5Y86ZYb9TyJXZKSB/GmLExE/61saRd3ZwdrvXMhsU5EwvY+DeiziY/UIlSTe9/AyJ4eDvGCu+S1JhzEY3H87rINDv7KtlykZFVuL9qaq0ULLG0fnPeYokimY71d2O6gBRsL9L1A7c2gHuyhv2iBQH+BC3sibr/VGSxKBPXOoy3ZGfACVQwoeHXaTKCRwCReDftNJOuw3xdm1QosxH0kzxoHROgRqgOtfA8/7cfjab8PSN1u77ybRtRLb2U3CQ5edyQOxWEMO6hPBE/gVf0vjmA5Ps21s7xu1UsnnqeRyKGOjdR9TnZhAqGrXHdRS0yg++yy+m/d/Xqijo/if8xxyD7Qv5kNbXkLtQLyHhTI2hrGa6Nj99TJGOk05ncZzskUH9YQZtem4qPuXlgRqIcSfCSGiO/4fE0L8ScujcuhpktkSow3kT1tMxMwPbsdF3f8spvIABzio/aQL+pFYrHg2kePR82nefOdUW1fczbzG/hZkHByuJl0wXbjxoI0RH9XPmmWnKLGtWI+d3Q5qMIsS+z2D2plDO9iFkWlRoI5EqORySGPveIeNaj+GbQL15iauUAjhbiwq8GpqDmon4qNhEvkEQ74hFJcCXHZSt1yUmDgNngGINCACvvq/mtf/238JpVxr998EufV1gFrEB0AwNki+ztzmxexiS/nTFjMh04G9X1GinjAfG3W0foFauN2oM9OUFhZbHl8rlM6dA13fM396J6FX3UvxySfbIj7KSqXqoO6cQK2OjaKvOg5qAG1+HgDvqeYKEi08k6ZA7RQl9i+tRnzcJqXMWP+RUqaB21s8pkMPI6UkmSsy0kTEx4moJVD390mnAyylTGFovwzqserz5yjkUN/38DKqW/Cm50229X6GBjyk8o6D2uF4ka4+5+0UOeNBDwGP24n4aDPteOwsRsK+vndQ48yhHWxASkk5s4nSooMa2Dfmw9rdNRisf/fk0IAV8bGXQJ1pOX8aDh+3w/4kC0lGg5eFzuFA1UHdalFi8oyZP92IocMbgjf+IaTPw2f/c2v33wS5dfN3Dg/ujPiIkc9sHBi3Aebrb2FzoeX8aYCpsCmc7leUaJXtKQ1EfIBZlNhtB7VVkOg9TKC+5x6AtpQlGmvryFKpww7qcYxkElnufUNWu7GyxT0nWxOoHQd1/9OqQO0SQsSs/wgh4oDS4jEdeph0QUcvS0abiPiYiDoO6uPCYipPwONmaJ9CnfGIKVD3eg51ySjzN99e4d6bRmsnW+1icMDDRl6jUjl4Muzg0E9YW8djNmZQCyGYigWciI82k6k6qNsS8RHyspHX0IyK7cfuIZw5tEPLVPIF0PWWIz5gfydyLeKjgZLEwep193JQVzazLcd7wOUMa8dB3TjJQrLmmgaI++K4hKs1gVpKSD5ZX/701cx8H7z4PfDtP4On/7H5MTRBNmU6k3c6qAdiccq6TimfP/C2iUKCbWObuXDrDurx4DiKS9m3KFFPmFERagMRH2DmUGvLy/vukOgExdNncAUCeGYOzun2zMzgve66tsR86Mum8O+Z6qyDmnIZo+rSP85o8wsIrxf1xImWjuMeGjLLJ1ccgbpfaVWg/k3gG0KI/yKEeD/wdeA3Wh+WQ6+SqG65HWki4mMs4kMIWHEE6r7nfKrAzGBw30iMsapAvZrt7efCZ59MkC7ovPnO6bbf12DQS7ki2awWjzk4HAfShfa4cKfiflYcB3VbyWzbv7hgYc0x+jz2yJlDO7SMJc7aIlDv40S2BOpGShK9iptoQN034sMdbd1B7fJ4EIFArXTRoX6uFqgVl0LcF28tg3orAdvp5gRqgFf8Jxi9BT75byDfOUEvt74GQjAQH6xdFoyZnTNb6YNrAayCRDsiPhSXwuTAJMu55T1/bmUZKw1EfAB4ZudA17vqOC2ePo33xhsRrsOlp9C991J49FHbRV1tyfy7dlKgVsZMt7vRpZgPbTnH6gceprzZ/blUaf4cnrm5up4DByGEQJ2YcBzUfUyrJYl/DvxTIAGsAm+SUn7EjoE59CaWQN2Mg1p1uxgN+RwH9THgfCq/b/40XBaoe91B/dePrjAZ8/OSa4bafl+W2yiV7/4kwsGhU1yOibDXhTsZC7C0UTh0e65D86QLOqpbEPS0liO7FyPV/Np+jvlodg4thHiNEOJpIcRZIcTPHXC9fyqEkEKIO+wbtUOvYatAvY8TObWlEfS48amNvdaHB7z7Z1DbEPEB4I5GnIiPBinoBXJ67gqBGsyixJYyqBstSLwaxQtv+iMoZuBT/9Z0ZHeA7PoaA7E4buXyBpaBqClQH5ZDvZhdBLAl4gPMosT9HdSruCMRXL7GTGKeOVM8L3Up5kOWyxSffhrfTfU9L0L33gNSkvvCF2wdh76yDC5Xyw7eRlCrArXepaLEzQfOY6SKlJY6n+1+Ndr8At4W4z0sHIG6v2nVQQ3wFPA3wCeBLSFE+62GDl3DOlkcbSKDGuBE1BGo+51yRbK8sc30AQJ1wKMQ8as9n0F9+mKWF50axOVqXzmixeW8RieH2uH4kC7oCAERv70C9VQ8QEEr15x/DvaTKWhEA562lMeOVBfBE31elEiDc2ghhBv4EPBa4CbgLUKIXWf9QogQ8B7gW7aP2KGnsEegrkZlpDN7/nwjX2oo3sNiv/LncjaLO2yTQB2JOhEfDZIomGLZaOBKJ+5IYKS1iI9EVaBu1kENMHoz3P0LcObv4bv3NX+cBsitr10R7wEQjJnpS/nMxoG3XdhcIKgGGfYPH3i9epkOTbOcW95zcd1IJBt2TwN45mYB0BYXWxxdc2jnl5CFwqEFiRbe669HnZqyPYdaW1pGHRtDeOzf9bUflx3Ulzp2nxbahS1Kz5gLLEayuzsKK8Ui+oULeFosSLRQJ06gr6zYciyH3qMlgVoI8W8wnR8PAJ8CPl397tCnJKsni1Y7d6NMxAJccATqvubS5jZaucLsPgWJFuMRX087qAuawfpWad+iR7upOagdgdrhGJEuaIR9KorbjvXyy0zHzQWy5bTzedMu0nnddue7hbUI3s8O6ibn0C8Azkop56WUGnAf8E/2uN5/AX4d6N0PWQdb6IiDOq8Rb6Ag0WI45GXtKoFaSmlGfNjloI44DupGsVzSVwvUw4FWHdRnIDgMwRZ3HX7fu2H6RfCZ/wCZveMu7CSXWruiIBF2RnwcLlDPhedsW6idCc+wbWzvuVBgrK6iNJg/DaDEYrgjEbSFRRtG2DhWQaLvpvoEaiEEoXvuIf+Nb1LO2ef81ZaXUKc766N0R6NmXnIXHNS5Ly4jfG5cAyrGWncFam1hAaS0zUHtmZykvLlJeWvLluM59BatnhG+B7heSnmzlPI2KeWtUsrb7BiYQ2+SyJaI+NWGt/lZnIj6uJQpOkVwfcxSyvwQnDnAQQ1mzEcvO6hXqsLWVPzg38MuBqsnf07Eh8NxIl3Qibchw3gqbpbyLm84OdTtIl3QiPrb40QaDHoQAtb620HdzBx6Atip2KxUL6shhHgeMCWl/LS9w3XoRewQqF3BICjKgSWJjeRPWwyHdkd8VPIFMAz7BOpo1BGoG8QSofeK+EgX0+iVJrtQkqdbc09buNzwxt8HWYFP/L9QaV9ZrpSS7B4Oao/Pj8fvJ1+PQG1D/rTFdMgUUPeK+dATCdTRsaaO65mbM0XCLlA8cxqhqnhPnar7NqF77wVdZ+tLX7ZtHPrySkfzp8EU25WxUfQOO6j1ZIHt760z8H0nUE8MoK9116xRmp8HwHOy/ufAQagT5rRHv3DRluM59BatCtTLgDMrOEYkssWm8qctJqJ+tHKFdUeE61sWawJ1PQ7q3nU3WkL7VMzfkfuLBVSEcCI+HI4X6bxGtA0u3KmY5aB2BOp2kSnobXnsABS3i8Ggt68d1LRhDi2EcAEfBP5dHdf9KSHEI0KIR9bWWtjW79BVagJ1C4KvEOJAoXcjrzW1kDgc8lLQyuRLRu2ySta8D3ck3Nxgr8IdiTgRHw1iRXzsEqgDw0gkqe2DiwH3pFKBtafsEagBYrPwml+Dxa/At37fnmPuwXZ2k7KuEx7aHdERjMbZyuyfQV3QCyQKCdvyp8HMoAZ2FSVKTaOcSjUV8QHgmZ3tWsRH8fRpvNddh1Drny/4n/sc3MND5B54wJYxlLfylDc2UDssUAOoY+O1gstOkfvSCkJxMfDiE6jDfoxkAdlFc6B2bh5cLjyzM7Yc77JA7cR89COtCtTzwBeFED8vhPhZ68uOgTn0Jslcqen8aTAFaoALzrbrvuV8Ko9HcTF+yPNkLOxnfUujZJQ7NLLGWKo6L6c75KBW3C5iAQ+pPfIaHRz6lXRBIx6w34Ub9CrEgx6WN5zPmnaRLmjE2vDYWYyE+l6gbmYOfQHYeYY9Wb3MIgTcUj3uIvBC4JN7FSVKKf9ISnmHlPKO4WF78lMdOk85k8E1MNCQ+LMX+wm9UkpSW006qKvdGjtd1JYIbl9JoimsO4W49ZPIJwipIQLqlfPbEb8pWK8VmliwyiyCXoCR+mIc6uL2H4XrfwA+9ytmfEgbyK6bv2toaGTXzwZicfLp/cV6qyDRTgf1WHAMxaXsclDrSXOcahMRH2A6qI1kkvJWvuUxNoKUktLpM3UXJFoIl4vQK1/J1pe/TKXY+k4qfcUU/D3T3RCoR9FXVzt2f0a6SOE7SYIvGMM94EEZCSD1CuVs9+ZTpfl51MlJXN7mTY47UScnAdBXnKLEfqRVgXoJMzvPgzkptr4c+pRktshIqHmB+kRVoL6Y6ettu8ea86kCUzH/ocWC45FqxmgXPzAPYmmjQNDjbkv8wH4MBj17Fgo5OPQrpoO6Pa+xqZifFcdB3RaklKaDOtgeBzWYRYl9XpLYzBz6YeBaIcScEMIDvBmzYBEAKeWmlHJISjkrpZwFvgm8QUr5SDt+AYfuU85kWor3sHBH9y4b3CoZaOVKU3OhoZBV/rxToM6a92djBjXlMhUni7RukoUko8HdQudwwFyoSm43kUNtCcijN7cytCsRAl7/O+ANwd/8FBj27zDMVQXqPR3UsTj59P4O6oVNMzJjLmyfQK24FCYHJnc5qI2EKXAqTUd8zAKdL0o0Ll2ivLlZd/70TkL33ovc3ib/9a+3PA5taQkAdbLzArUyNo6RTCLLnTFk5b68AgIGXmqKuOqwuRBlJLtn2NDm523LnwZwx2IIvx/9giNQ9yNKKzeWUv6KXQNx6H0qFVl1UDe/+nVZoHZcbf3KYip/aEEimBnUAJc2ix3LeW6E5Y0CU/GAbcUn9TA04HVKEh2OFWYGdXtEzsl4gCcvOClk7aCgldHKlbY6qEdDPp68mG3b8btNM3NoKaUhhHg38FnADfyJlPJJIcT7gEeklJ88+AgOvYyeyKOMNDbvsFOg1pd3F9Jt5M05yeBAEyWJBziobRWoq8d1hxyPVD0kC8ld8R5gZlADrBfWGz9o4nT1INe3MrTdDAzDG34H7nsrfPk34O5fsPXwlx3Ue0V8xNjKbCCl3PM1ubC5gEu4arEcdjETntnloDYSZkSEMrr7casH75wpomsLC/hvsXER4RAuFyQ2Hv0SfMELcIXD5O5/gNDdd7c0Duu9rVsOaspljPUUapOPX72Ucxr5h1cJPm8UJWK+/yojpvaiJwv4rou19f73QpbLaIuLBL//JbYdUwiBOnECzYn46EtaclALIYaFEB8QQvyDEOIL1pddg3PoLTYKGkZFMhJqXqCO+FVCXoULjkDdl0gpWdooHJo/DZcd1L2aQ720UehYvIfF4ICHVN4RqB2OB0W9zLZebqODOsCFzDZlp5TXdtIF830q1qYMajAd1KmtUt8+fs3OoaWU/yClvE5KeUpK+avVy967lzgtpXy5454+GmgXt0j81rcpPrO/Y3Mv7BOoI3tmUFtzkmZLEgHWrnBQZ8z7s0ugjkXN42acxch62U+gjvviuISrSQf1aYhOm25nu7nhdfDcH4Wv/CYsP2zroXOpJKrXhy84sOtnA7E4RqmEtr33TqzF7CITAxN43PbOYaZCUyznlq+IrdGrGcbqWHMOanV6GoTouIO6ePoMuFx4r7uu4dsKVSX0ipeTe/BBpN5kcWcVbXkZdySCO2xP9n0jWK53owNFiVtfvQBlycDLJmuXuYIqroCCsdadHYX6ygpS02x1UAN4JiadksQ+pdWIj48CTwFzwK8Ai5jbDx36ECuKoZUMajBd1I5A3Z+sbZUoaGVmBg8Xdi0H9epm723htoT2TgvUQwNeJ+LD4dhgiZztitGZivvRy7LfYyK6QqZgniy2a3EBzAzqiqSfc/mdObRDDf1S/orv9dLuiI+Nrebfp+NBDy5xpYO6km1DxAc4RYl1YlQM1ovrewrUbpebId9QcxnUyTMw0kZn7mv+G0Qm4W9/CjT7cpSza2uEhob3dEgHY3EAttIbe952YXPB1vxpi5nwDNvGNmvblx8HI7GKCARwNblLwOX1ok5MoC0s2DXMuiiePo3n5Bwuf3OF86F776WyuUnhkdbWWfWl5a4UJAKo46ZArbe5KLFS0Nn65iX8tw2jDl3+ewshUIYD6F2K+CjNzwPgsVmgVicmnIiPPqVVgXpQSvm/AF1K+SUp5U8Cre3BcOhZEjnzJH+kZYHa50R89CnnU+bqbD0CdcinMuBVuNSDAvVarkTJqDBdx+9hJ4NBD7mi0bPFkQ4OdmJtHW+XC3cqZr5+lzecHGq7ueygbp9APVztu+jjokRnDu1Qw3K3GWuNzY9tE6gjUWSpRGX7yvu33qebEajdLkE86L0qg3oToaoIX2vnErX7qEV8ZGw5Xr+zvr1ORVYYDexdtjccGG7cQW1okHrW3oLEq/GF4Yf+ADYW4P5ftO2w2fW1PfOnAYJRU6DeK4e6Iiucz563NX/aYjpkRobsjPnQE0nUkZGWYgc9c3OUFjssUJ9pvCBxJ8EXvxjh95N74IGWxqEtL3cl3gNAGeuMg3rrG5eQpTKhl+/+PZVhf9cc1FpVoLbbQa1OTFDJZiln+zcK7rjSqkBt7be4JIR4nRDidiDe4jEdepRk1YXWSgY1wETMcVD3K5ZAXU8GNZgu6l50UC9VBa1OZ2NbGY8bTsyHwzHAcuG2S+S0Xr/Laefzxm7StceuvREfQD874J05tEMNy91mrNf/fiV1ncrWlm0RH7DbiVyL+Bho7n16OOS9MoM6s4krGrGt38P63feKJ3HYTbJgis8HCdQNZ1CnnoWKYW9B4l7Mvhhe9G545H/Bs5+z5ZC51Nqe+dMAwZiZ15tPp3b97FL+EqVyqS0OaivTemdRorG6WhM6m8UzO4u2eP6K6JB2YqRSGIkEvhubF6hdfj8DL3kJuQc+h6xUmjqGNAz0ixdRp+zNCq8XdzSK8Hrb6qCuaGW2vnYB3w1xPOO7z8HVkQCVLZ1KobWolGYozc/jHhqybdeMhTppxpg4Lur+o1WB+v1CiAjw74B/D3wY+JmWR+XQkySqER/DLWRQgxnxkSno5EuGHcNy6CHOp/K4XYKJWH1bucYjPi71oPhgCdTdyKAGnKJEh2NBzUHdpoiPE1EfQjgO6naQqTqo2xnxYcWJ9bGD2plDO9SoOajX63+/qhUO2hTxAbsF6o18CZ/qIuBRmjruLoE6m7VVqLAyZZ2Ij/qwBOq9Ij7ALErcGS1R30HPmN/b6aC2eMUvwMhN8Hf/GrYzLR1K10oUNjOEB/cWqAdigwBsZXY7qBc2TSfybGS2pTHsxXhwHMWlXOmgTiZaLtjzzM0iCwWMZBMZ401QPG0+L1pxUAOEXnUvxtoaxccfb+r2+qVLUC7jmZo8/MptQAiBMjaKkVht233kH1qlUjAIvWJvl7gyYp7P6g3u0LED7dy87e5pMB3U4AjU/UhLArWU8lNSyk0p5feklK+QUj7faRDvX5K5IrGAildxt3SciagpXjoxH/3HYqrARNSP6q7vrWUs7GO1B0sSlzYKCHH5udophqoCtZND7XAcyLQ5JsKruBkL+xyBug2k81YGdfsc1MPVHSVW/0W/4cyhHSxkuYKRKiI8bip5o26XmyXK2ipQX+VETm1pDAabN6YMD1wlUG9u4g7bJ1ALjwdXMEilzx3U5axGpdR6/FuiYLo49xWoA8NsFDfQyw04LRNPgkuBwWtbHt+hqD54za/B1iqc/3pLh9pKmU7x/RzUHr8fxeslv0cGtSVQt8NB7Xa5mRyYZCm7BIAslzGSa7WyvWbxzplj7VQOdfFMVaC+8YaWjjPwspeBqpJtMuZDXzad6N1yUAOoY+Pol9ojUEujQu7LK3hPRvDO7F0CqQ6b57NGsrPzYSklpfl5PCftf52oEycA0FZWbD+2Q3dpSqAWQvyH6vf/KYT4nau/7B2iQ6+QyJZaLkiEy6KfE/PRfyyl8nXlT1uMR3wkcyX0cnPbttrF8sY2Y2EfPrW1xZhGsU4CHQe1w3FgowMi51QswHLaEajtJl3QCHmVuhcjm8GjuIgFVJK53ttl0wrOHNrhaoxUESoS3/VmpIBeZ8yHrQJ1JHrFMS1Sea3peA8wHdTrW1otVqC8uWn7Vm93JEI5078CtZSS5O89Rvaziy0fK1FIoLpUYr7Ynj8f8ZvC9fp2AzEfyTOmOK20b0fNFVhO7c3WhKnsuukUDw/vLdYLIRiIxvcsSVzYXCDijRDz7v13bJWZ8AxLOVOgNlIpMAyUsb1jWerFMzsLgLa42OLo6qN4+jTq5GRtl0OzuMNhgnfdZcZ8NBFPoi2ZAnW3HNQA6tgoepsc1PlvJ6hktX3d0wDumA8Ugd7hHOpyKkUlm8V78pTtx3ZHo7iCQfQLF20/tkN3afbMorqXh0eAR/f4cuhDktliywWJYEZ8AFzM9NdJp4PpoG5EoB6L+JHyyob3XmB5o1ArWOsktYiPfG/9PRwc2kG6oBHytVfknIz7Wd5wFkPtZnNbJxps38KCxUjI148RH84c2uEKrHgP302D1f93QaDeN+JDa6og0WJowINWrpDdNmP9Km0QqF3RSF9HfJQzJcqZEtqlfMvHShaSjARGcIm9P3eHA6abuKGYj+STnYn3sAgOg+KDzaWWDpOzBOp9HNQAwVicfGa3QL2YXWQuPGdblvrVTIWmWM4tI6XESJiRHOpoawK1MjqK8Ps76KA+3XK8h0Xo3nvRl5YoPfNsw7fVlpcQqorS4t+vFZTRMYxEEllufRfETmRZkvvSCurkAN5rovteT7gE6lAAI9nZ+XDpnFmQ6Dllf8SHEAJ1YsKJ+OhDmjorlFL+vRDCDdwqpfyzq79sHqNDj5DIlhhpMX8aYCTkxe0SXMg4rrZ+IlPQ2NzW6y5IBNNBDXCpx4oSlzYKHS9IBBjwKngUl+OgdjgWpAta2+I9LKbjARK5IiXD3pOC404nHjswixKTPdhT0ArOHNrhaqxcUN91MXDVX5Roq0Adi15xTItWBWqrt2Zty3wdmw7q1hyVV6NEo31dkqgt5wAop1oXlyyBej+G/VWBulCnQF3KQWbJzIXuFEJAZBIyy4df9wCy62sgBAPxwX2vE4zFyaf3zqBuR/60xUx4hm1jm2QhWcsubjXiQ7hceGZnKXVAoC7ncujnl/DdZM/CReiVd4MQ5JqI+dCXV1AnJxHuzu6K3Yk6PgblMsb67sLNVth+Yo1yqkj4FVOHLpYoI/6OO6i1+XMAbcmgBjOHWnciPvqOpm1LUsoy8GIbx+LQw5QrkrWtEqPh1gVqxe1iLOxzHNR9xvmU+aE304BAPVYVqFd7SKAu6mVWs8WOFySCuRo8FPSw7gjUDseAdEFvW0GixVQsgJRwIe24qO0kXdDbWpBo0acOamcO7XAFRrKAK+zBHVRR4v6uCNQujwcRCOyKykjlSwzaIFAncyWkrlPJ53HZ7aCO9LeDWlvZAqo51Fpri62HCtRVB3Vyu84ivbWnze+jHRSoASJTsNmaQJ1LrTEQjeFW9t8NNBCN7Yr4yGpZ1rfX25I/bTEdNvOSl3JL6KtmbrjaYsQHgGd2Bm3x/OFXbJHSU08BrRckWihDQ/if9zxyn/tcw7fVlpdRuxjvAZcXF4zVS7YdU1Yk2QeXUUYC+G7cf5GlNobhAOWNIlLvXKxmaX4BVyCAMtba4sp+qJOT6BcuNBX94tC7tLqv9jEhxCeFED8mhHiT9WXLyBx6io28RrkibcmgBjOH2smg7i8WU+bWw0YzqAEu9VBR4kpVyJoe7GxBosVQyOuUJDocC9J5jVgb86eB2k6IZUegtpVMof2PHZgO6rVciUqlL08+nDm0A2BGelglVsqQv/6Ij81NUFVcQXsW1N1XRWUUNIOiXiHeQkmitfNyLVeinDOdwG3JoO5jB7W+kqv9u7zRvKFDSkkinzhQoI774riFu34HdeJJ83snIz7AdFDbkEG9X0GiRTAWRy9uoxUvvyYXNxcBmAu3UaAOVQXq7BJGIgGqijseb/m43rk59JUVKlp7jTCXCxLte16E7r2H0lNPoS3VH+0ipURfWsLTxYJEqDqoobbYYAfFpzYwEgVCr5hCuA6PmlFHAiDBsGEnRr1o587hOXmybVE46sQJKvl835fkHjdaFah9QAq4G3h99esHWx2UQ++RqG6xHQnZJFDH/I6jrc+wHNSNOI8jfhWf6uopB/XyRuO/h50MBj1OBrXDsSBd0Ii32YU7FTdFH+t17WAP6bxG1N+JDGovRkWSLvTlrhJnDu1gCijJAsqwOedQhvwYqW1kHYsy5UwGdzRi28m/OxK9QqC24sZaclAPmOcNa7lSzZ3tDtssUFcjPmSltwq37UBWJNrKFp6pENCauJTVshTLRUYD+ztxXcLFkH+IZKFOB3XyDKgBiM42Pa6miE7DVgL05s8fcutrhIb2F+sBBmKmKJzf4aJezC4CtNVBPR4cR3WppoM6sYo6PIxwtd7X4Zmbg0oFvQGRtxmKT55GGR5GGT54AaARQvfcC0Dugfpd1OVMhko+j2d6/wLBTmA5iA2bihKllOQeXMYd8xK4rb6/sVJdBNWTnZsPl+bn8Zxs3+tEnZgAQFtxcqj7CaWVG0spf8KugTj0NsmcOQGwI+ID4ETUx2q2SLkicdex6ufQ+5xPFRiP+PCp9Wd8CSEYj/i51EMZo0tVIasbGdQAgwNenlrNHX7FPqKgGeRL5dpWYIfjQTqvtT0mYjTkw+N2sZx2BGq7MMoVskWjYxEfYMYDDA701/uDM4d2AKjkdGSpfNlBPexH6hXKWQ0levBzvpzJoNgQ72FxtYN6I28K1K1kUIf9Ch63i/UtjXJ1t5w7areDOgqVCpWtLdzhg/OtpZT8zF88xg/dPsHLrz9YnOwFjLUCUivjf84w2nIOI9X8fNkSnQ8SqMHMoV7fXq/zoKdh+AawQTxtiEhVcMxegMFTDd9cSklufY1Td9x14PWCUVOg3kpvEBs3xbCFzQUUoTARmmj4fuvF7XIzGZo0HdSrG7ZFJHhmTbGwtLCA95prbDnmXhTPnMFrU/60hWdyAu9NN5L73OcY/Oc/WddtLCFenequQO2ORhFeL/olewTq0vwm2nKO6A9dg3DXp6Oow34QZqRUJyhv5TFWV/GebPz1WS+eSTO6Rb9wAf8tN7ftfhw6S0ufJkIInxDiXwshfk8I8SfWl12Dc+gdElnT0TliU8THiaifckXWhG+Ho8/5VL4p1/FY2NdTDuqljQI+1cVwl8SQwQEPqS3tWOVp/dpnnuKNv/e1I/U7lyuSbFHv9jCOLCWjTF4rEw+214XrcgkmYn5WNpwdO3axuW0+7zsV8QGXd3H1E84c2gGolVYpI5cd1GAKk4dRTmdMcdYm3NGrHNTV3VzxgeYFaiEEQwMeM6onmzXv5xARuVGsDO56Yj7Wtkp84rGLfPCBZ2wdQ7uw8qd910ZxBZSWHNSWQH1QxAeYOdR1Z1AnT3e2INEiWhUcM805gbdzWQxdIzR4sPt0IL7bQb2wucBUeArV1d7PwOnQNOdz5zESCZRRexZTPHOzAGiLi7Ycby8qxSKlc+dsjfewCN97L9vf+Q56sr7np7ZsxsB4uixQCyFQxkZtc1DnHlzGFVIJPr/+XHKhunHHfLVS3najVcs4O+Gg1i84Dup+otXlzo8AY8CrgS8Bk8Dxsv4dE5JVgdou0W4iak7AnZiP/uH8RoHZBgoSLcYjvSdQT8cDbcvLOoyhoBetXCFXMrpy/93gseUMK+ltVo+QCPXhr8zzst94kJLRWmHRcSVTMEXOTrhwJ2N+x0FtI+nqY9fugkswHfBAXxYl4syhHbgsRFsCteWkrqcosbyZwR2L2jYWKyrDwor4GGohgxrMosS1rVLt2O3IoAZ2FTzuxbmk2Zfy+Momj69kbB1HO9BWcgiPG2U4gDLob8lBnSiY+beHCdQjgZH6Mqi31iC/1vmCRLjsoG4yhzq3bv5+4cMyqKsO6nwmXbtsYXOhrfnTFtPhaZY3l9ATCdRRexzU7oEB3MNDaAuL9d3gkT9p+G9cevZZKJdtK0jcSeieewDY+vzn67q+vlx1UE92tyQRQB0ds8VBrS3nKJ3NEPr+SYTamJSnDvs75qDWFuYB8J5qn4PaHQ7jCofRV1rLo3foLVoVqK+RUv4ikJdS/hnwOuDgvTIOR5JErshg0INHsWcLV02gdooS+4J8yWAtV2K6gYJEi7GIj0Q17qUXWK4K1N1isOpUsk4M+51yRfJMwtRkHl85OiUXXz+XIl3QeeIIjbmXsDKFW9k6Xi9T8YCTQW0jmepj15GIj/DlgrU+xJlDO2AktxEeF+6w+XpyhTwIj7sugdrIZGruYTuwygatLOdaxEcLDmqoCtQ7MqhddgvUUUugzhx63XNrpiNZcQk+9q325vDagbacwzM5gHAJlEFfXc+L/ahXoB72D5MpZdDKh8xD18wivI4XJAKET4BwweZyUzfPrpsO3MNKEr3BIG5VZavqoDYqBku5pbbmT1tMh6Zx54vIYhFlrH6n7GF4Z+dq7tYDyV6CT/0MPPpnDR2/eLpakNgGgdpzzTV4ZmfrzqHWlpZRhodx+btTfL8TdXwM3QYHdfbBZYRfIXjXeMO3VYYD6Gv1dRy0SuncPCgKnun2FlSqExN1O6j/5KsL/MxfPNbW8Ti0Tqtqo7W/OSOEuAWIAL0f6OXQMMls0bZ4DzAjPgAuZo6OY9Jhf6yCxGYd1EZFktrqvgAhpWR5o9C1/GmglrPaC3+PTrC8UaComyfDR0XslVLyxAVzrA8vpg+5tsNeWMJHtAMxEVOxAOmCztYx2pXQTmoO6g48dj7VTcinkDxCuysawJlDO6CvmQWJ1q4tIQTKsP/QbdhSSsqZTXsF6mjUzHLOmYvGG3kNj+Ii6Km/W2Qvhga8rG+VKPdAxMe5tS0CHjdvet4Ef/fYxZ6O6pJGBf1SHnXSLEh0D/opb5aQRnNlkMlCkrgvjsd98ILDcMAUbQ/NoU6cNr93I+LDrUJoHDLNCdT1OqiFEAzE4rWIjwtbFzAqBrOR2abutxGmw9PEq3tq1FH7BGrP7Gx9ER+ps+b3dB1i9g6Kp0/jCodr8Qt2IoQgdO+95B96qK4FKX15GbXNAmm9KKNjGIkkstz8zkt9NU/xdIrQi0/g8jb+vqyOBMCoUM60/xxTmz+HZ2oKobZ3rqhOnEC/WJ9A/bkzCf72Oxe4tOkYJHuZVgXqPxJCxIBfBD4JnAZ+veVROfQciWyJERsLzIJehWhA5ULGcbX1A0sb5pbJmSYc1OMRc7HiUg/EfGzkNfJaubsO6qqjdP2YOKitQkiv4uLxC0dDoF5Jb9cE1ocXNw65tsNeWBEfnXFQm+8xjovaHiz3e6wDDmqAkZC31oPRZzhzaAeM5HYt1sNCGfIf6pSt5Aug6/YL1Fx2IqfyGoNBT8uRZ8MhL6mtEkYmgysUQrhbE7yv5nLER+bQ655NbnFqeIAffeEM23qZv/tO72aX6qt5KEs8kwMAKIM+kGCkm5svJwvJQ93TYDqoresffMDT4I/DgH3iaUNEplpwUK+heL34BkKHXjcYjZPPmHO9hU1TrO2Eg3omPMNgznS6KjZFfAB45uYop9OHv15Sz5rfN+YbOn7xzBl8N97YtqjE0L33gGGQ++IXD72utrxcK9LrNur4GJTLGOuppo+R++IywuNi4EUnmrq9MmJ+1uh1dBy0Sml+Ac+pk22/H8/EJNrKhbp6jKy4vwdOJ9o9LIcWaEqgFkKcFkL8AvCglDItpfySlPKklHJESvmHNo/RoQdI5oqMhu0tjTsR8TsO6j5hseqgbkagHouYzvxeEKiXqgJWNwXqIctBne9LQWYXT6/mEAJedfMYT6xkjkRRouWevmk8zCOLG1R6JJ7mKGEJ/J0QOadi5uvZEajt4XLER/sd1AAjIV9fFSo7c2gHi0qpTHmzhDJ85ZxDGfJTThcPdMpa4lJbBOqqE3kjr9myiDgc8lKRsJ1K254/DZcd2eXNzKHXnV/Lc2o4yG2TUW6diPDRby317LxDWzEX8D1TpoiqDFbzyZvMoU7kE3UJ1NZ11rYPyaFOnjHd013qbCE61XRJYm59jfDgcF0iajAWYytt7pazBOrZ8GxT99sIY4ExhrfMxRzVppJEuFyUWDos5iN1zvzegEAtDYPS00+3pSDRwnfLLShjY+Q+d3DMR6VYxEgkUKe7W5BoYS0yNFuUaKS2KXx3jeALx3E1Of+yPmvanUMtdR3t/Hm8J9uXP22hTkwgt7cppw/e0WqUKzXd6f4nHYG6l2nWQf0WIAjcL4R4SAjxM0KIxoNwHI4E5YpkLVdi1MaIDzBjPi46GdR9wflUnsGgh5Cv8Q/M8apAvdoD220sgbqbER/WyeBxyaB+JpFjOh7grrk46YJ+JHLpv7uSQXULfvz7ZsgWDZ5JOr1mjdJJkdN6PS85ArUtpAs6iksw4FU6cn+jYW+/lSQ6c+gqX3w6yaPnj29MkuWStgoSLdRhv+mUTe3/edgOgVrZw0Fti0BdXXgvpjO2x3sACFXFNTBwaMRHQTO4kNnm1LDpSH7bXdM8tZrj20u9+RzUlrdwBVXcUfPvpwya8+Vmc6jrdlAH6nBQS1kVqLuQP20RmYTsRag0HpmQTa0dmj9tMRAbrEV8LGYXifviRLz2L7RcjdvlZrYUQgpQhusbaz14ZmcB0BbPH3xFK+JjO21+1UFpfh5ZKuG7uX2xL8LlInTPPeS/8lUqhf3ndVYucbszkOtFreaIN1uUmPvyCrgEoZc07wh3B1VcQRXjkAipVtGWl8Ew8Jxs/04DddKMkjksh3q12nc1EvLyzfkUm4XejXc67jQlUEspvyul/Hkp5Sngp4Fp4FtCiAeFEO887PZCiNcIIZ4WQpwVQvzcHj9/hxBiTQjxWPXrXzQzTgd7SG2VqEhszaAGmIz5uZDufTHK4XDOpwpNuafBFGQ9bheXeiBj1HJYWo7LbuBRXIR9yrHJoH5qNcv1oyFumzQn+0chh/qJlU1uGAvz4muGAHh4wYn5aJSNvE7Q48ar2LvVey9iAZWgx82K83ljC5mCRjTQ+rb/ehkJ+0jmSj3rcmyUVufQ/YJmVPilTz7JT3/8Oz2dA9xOjOo2a3Vkd8QHHCxEtkOgdl0VlbGRL9Vix1phuBoRaJY6tkfYc0cih0YWzK+ZcXSnRkyB+vXPOcGAV+GjPVqWqK1UCxKr77WuoIrwug9cuNj3WGWNdCnNaODwOI6oN4riUg7OoN5cBi0Ho13In7aITEFFh63G3ZC59bVD86ctgtEYpUIeXSuxsLnQkXgPi4ltH1sDboTHvt1mnslJUJTDixJTZ0Gtng9t1JdDXTxt5pK300ENELrnHmSpxNZXvrrvdbQl83Wt9kjEhzJurkM346AuZ0vkH0kQvGO0Vqjb9DiG/ehtdlBr86br3nuqEw5q8/HVV1YOvN7yhvm++fYXzWJUJF942nFR9yqtZlAjpfymlPJngB8HosDvHnR9IYQb+BDwWuAm4C1CiL0+3f5CSvnc6teHWx2nQ/NY2Y+jNmZQA5yI+siVjGN7YtJPmAJ14wWJYBZejEV8rPZIxMdwyIu/xUKgVhka8LKe738HdVEvs5gqcMNYiOvHQqhu0fM51JWK5ImVTW6bjDAZ8zMa9jpFiU2QKWjEOpA/DeZ7zFQ84ER82EQ6r3ekINFiJORFMypkt/uv5LLROXQ/4VFc/NaPPJfVbJFf/MT3uj2crqAnCyAuRzdYWAL1QUWJbY34sBzUWxrxYOtzfyu6rLKZrYngduOORg91UJ9b2wKoOaiDXoU33j7Bpx6/VNvV0ytUSmWMZKFWkAjVAs0hf1MRH5Ybuh6B2iVcDPmHDnZQJ8+Y37tRkGgRrTpjGyxKNDSNfCZdt4M6GIsDkE+nOy5QD20J1gekrQu0QlXxTE0dLFCXdUgvwtzLzP/XWZRYOnMG4fPhmWvv3yhwx/NxR6MHxnzoy+bzolcc1O5oFOH1NuWgzn3lAkhJ6KWti+3qSKDtER+lc6ZA7Zlrfwa1OmHmcR/moLbyp3/g1nFGw94jE/Ox9Y2LrP7Wo31j0qiHlgRqIcSdQogPCiHOA78M/CFwWGr7C4CzUsp5KaUG3Af8k1bG4dBeElVnq90O6hNRcwLuxHwcbUpGmYub2007qMHMoe6VDOpu5k9bDA14We+vLe17cja5RbkiuW4shFdxc/1YqOcd1IupPLmSwW2TEYQQ3Dkb5+HFjWM1cbCDjYLWsZI9MGM+rMmpQ2tktjv72Fnuy0Qf5VBD03PovuJ50zHe88pr+bvHLvK33znY/dSPGGvbKHEfQrnydMzlU3CF1I47qN3hMAhBOZOhqJcpaGUGB+xzUIutHO5w+xzUlcxhAnUel7iyL+Wtd02jGRX++tHeev7pF7ZAXs6ftlAGfZSbcFBbYnM9ER8AI/4R1goHZFAnnjS/D9/Q8FhsI1LNFm6wKDG3YTrDw0P1/S0GqgL1pcQimVKGuXDnBOpQRmd9QB5eWNkgntlZtMUDROf0eagYcO095v/rzKEunj6D7/rrbS9CvRqhKAy88m62HnwQqe29uKQtryACAdzxeFvHUi9CCJSx0YYd1OW8Tv5blwg8Z2TXYmYzKMMBKgWDcr59BkFt/hzK6CjugebMa43gHhjAHYmgHSJQr6S3EQImon7uvWmULz2zRlFvPB6o05TOZjASBcpNdg8cRZotSfyvQohzwO8BF4AXSylfLqX8AynlYdWkE8DOT5KV6mVX80+FEI8LIf5aCNEb6fbHFCv70e6SxImqQO3EfBxtlje2kRJmm3RQg5lD3QsO6uWN7Z4QqAcHPKSOgYP6mYSZ3XzDmHkCdutElMd7vCjRKki8bTIKwJ2zcS5tFo9EdnYvkS7oHXNQgxnbY75X9e5z66iQKegdK0gEsyQRIJntj0W7FufQfce/fsU1vGA2zi9+4kmWUsdrEUlPFnYVJFooQ/76BGobHcnC7cYdDlPObNbmIHZEfAS9CgHVhZLPtaUkEcAdPTzi49zaFlPxAD71snh243iY501H+dhDvVWWWCtInBy44nJl0I+RLiHL+xdo7kWiYLoF6xWohwPDB5ckJs9AeBL80YbGYSuRqpu0UYF63fy9QoONOaiXLj0LwGxktqH7awXvxhapECzl7I2h8czNoZ1fQpb3Eeis/Omx58DAWF0RH7JSoXjmTFvzp3cSuuceKltb5L/1rT1/ri8t4Zma6lgcWT2oo2Poq405d7e+fhGpVQi93J6oEitSqp0u6tL8At5T7XdPW6iTk+grhwjUGwXGwz48iotX3zxGQSvz1WcPiDHqEfTqPMD6TDgONOugLgKvkVLeKaX8TSml3cvOfw/MSilvAx4A/myvKwkhfkoI8YgQ4pG1tUOahh2aJpEtIsTlLXp2MeE4qPuC8ykz02+6RQf16maxqycHmlHh4uZ2VwsSLQYHPMcig/rp1Rwet6u2uHHbZIRs0ejpMrvvLm/iU11cW82wvHPWPHF5eNHJoW6EdF7raEzEVNzPtl4+Fgs/7SbdYfe7tTie7B8Hdbvn0EcKt0vwW29+LkLAT9/3HfQGhbejiqxIjPVtlJG9HXHqUODAIqtyJoMrFEIo9paVuqpC70a1qNmOkkSASR+4KuU2CtR1RHwkt2rxHjt5210zzK/l+eZ873yOays53FEv7qsc7MqgDyqScqaxOWIt4iN4eMQHwLB/+JCIj9PdLUgE8A6AP9ZwxEe2KlA3kkENcDGxCNCxiI9KoYBrq8BGSLCUtVugnkVqGvqlS3tfwRKoB09B/GRdArW+skJlawtvm/OnLYIvehGuQIDcA3vHfGgrK3ime8vjqIyNoq/u8zffg0rJYOtrF/HdPIg6ao8b2VoU1dfac64lpUSbn+9IvIeFOjFRV8THZPUc/665QUI+hftPN1dY2SlkRdY6B7RlR6A+ECnl+6SUzzZ5nxeAne8Wk9XLdh4/JaW0Pnk/DDx/n3H8kZTyDinlHcM2tts6XEkyV2Qw6EV1txxZfgVDA148bhcrjkB9pFmsOp5aclCHfWjlChtdFI8uZEwneE84qINe0gUdo89P1J9azXFqZACl+t5y64R54vp4D8d8PHEhw80nIrUxXz8WIuRTnBzqBum0yGkVnzo51K0hpSRd0IkGO+igrsaLJfsk9qjFOXRfMhH181/feCuPLWf4nc/3+J/me38DT/x1y4cpp4tQlqj7OaiH/VTyOpXC3tuwy5mMrfEeFu5olHImQypvvt7siPgAmFTMDHl3JGzL8a7GFYlQzmaRlb3nTeWKZGE9z6nh3XPV1902TsSv8tFvnW/L2JpBW9naFe8Bl/PKG82hThQS+BU/IXX3MfdiODBMVstSKu/xvlvWYf2Z7hYkWkSmmnZQDwwO1XV9fyiMy62wsXYJj8vDiWBnkpj0hOm03Yy4OZ+z97nprWZE75tDnToL/jgE4lWB+vCIj8sFiZ15Xri8XgZe/jJyn//8Lie4rFTQl5dRJ3tLoFbHxjGSa/s7168i/81VZNEg/HL7fg931ItQXRjJ9ugvRjJJJZ/H00kHdVWgPsjotryxXTsX8CguXnnDCJ87k+zpc+1ypgSG+Ts5AnV7eRi4VggxJ4TwAG8GPrnzCkKI8R3/fQNwpoPjc7iKRLbEiM0FiQAul2A86uNipm9cUceSpVSekE9pyQ05FjEn3N3MobZcu70gUA9VTwg3eqy0x26eSeRq8R4A142G8CiuWoxGr2GUK3zvQrYmpIPp/nv+TIyHF3rHedXr6OUKuaJhmzOvHqydEctOpFRLbOtlNKPS0cWFAa9CwOPum4gPh715/XNO8M+eP8mHHjzLQ738fvqlX4cv/reWD2MVICoj+0d8wP5CZLsFasswYEdJIsAJlym0t60kMRKBSoVKbu+T+IuZbUpGZU8HtU9188+eP8lnn1xlrQcWwsp5nfJGEc/kXgK1uWBnNJhDnSwkGQmM1B13MOw3jV975lBvzENZ625BokV0uikHdTAaQ1HrO28RQhCMxdhKbzATmcHt6kyRulEVqF2jIyxnG/sdD8MzOwuAtrC49xVSZ2HwGvPf8VnYWgUtf+Axi6fPgKLgve5a28Z5GKF77qGcSrH92GNXXG4kk0hN60kHNYaBsX54mpfUK+S+soL32uiei1XNIlzVstU2Oai1c+cA8J481Zbj74U6OYEslSiv7x3ZUTLKJHJFJmOXdyy96uYxNvIaj57vXYOR9RipUyG0i1tIo3fFdDvpuEAtpTSAdwOfxRSe/1JK+aQQ4n1CiDdUr/bTQognhRDfBX4aeEenx+lwmWSuaHv+tMWJiN+J+DjiLKYKzAwGWsr4Go+YE+5u5lAv95BAPViN00lt9a9AvVnQubRZ5PodArVHcXHjeJjHVzLdG9gBnFvLs62Xec7UlSfYd87GeTa5RdqJj6iLTNUR2MmID2tS6jioWyPdhccOYCTk7buSRIfd/PIbbmYqHuDf3vcdNvdxDncVQzOFm9S5Q8WaQw9Vzf+0hOirsS7fbxt2uwRqZZdAbc9i1KjLPF47Iz6AfWM+zq5tAXDNyG6BGuAtL5hGL0v+6lF7hcBmsLJG1cndY3WFPKb7sVEHdT5Rd/40XM6q3jOH2ipI7HbEB1x2UDcQEZhLrdVdkGgxEI2jZbeYDc82OMDmsQTqgfFp2x3U7sFBXKHQ/kWJqXM7BOqqEza9eOAxi2fO4L3mGlyezi1gB1/6MoSqkrv/gSsu15fN17E6Nd2xsdSDOmZ6MOspSsw/ukplSydko3vaQhkJoLcpg7p0znTbe052rkxUnTDr7PaL+biYKSIlV8R4vuy6YTyKi/tPN5YJ3kmshezg7SNgSPTV1uYdR4VmSxKfd9DXYbeXUv6DlPI6KeUpKeWvVi97r5Tyk9V//7yU8mYp5XOklK+QUj7VzDgd7CGRLTFa3WJrNyeifqck8YiztFFgpoV4D7gsUF/Kdleg9iiutuwWaBSrlKifBeqnqwWJOwVqgNsmInzvQpZKpXfKiiy+WxXOb52IXnG5lUP9SA+vwvcS6erOgE6WJAa9CoNBDytpR6BuBWsRJuLv3GMHZlHiWp84qFudQ/czA16F337z7SRzJf7TJ57oqdI6wBSnKwYgzZK4FtCTBVxBFfc+cTlK3Acu9i1KbKuDetMsSVTdgrDPnozr4WpyYzm4t0DcKpbwvV9R4rmkKVDv5aAGU7h+4ck4H39oqevzD305B2J3QSKYbl5l0NeUg3o0UF/+NJgRH9btdh/sDAgXDF3f0BjaQmQStC0oZuq+SXZ9jVCd+dMW/mgEV17vWP40UCvTi02dYjm7TEXa554UQuCZm6O0V8RHaQtyF838aYBY9Xc+IOZDSknx9Gl8N3XWVe8eCBJ80YvIPfDAFZ8X2pIpUHum7CkWtAt1zHwN6pcOFqhluULuSyt4pkN4T9q/qKeOBChnSlS0+qJGGkFbmMcVCqF0MH7XUxWotX0EasucMrXDQR30Knz/NUN89snV3ptrVDHWtxE+Bd8N5nnmcYn5aNZB/ZvVrw8B3wL+CPjj6r8/ZM/QHHoBo1xhfatUy4C0m4mYn0SueGxKcfoNo1xheaPAbAsFiWA6hhWXYHWze4sVSxsFpmJ+XK7utz3XHNT5/hBk9uLp1SwA149eKVDfOhlhq2SwkOq9VeInVjYZ8CqcHLpyQea2yQget8spSqwTS+TsZEwEwGQ8wPKGsyDaCt1wvwOMhL39VJLozKEP4LlTUX7m3uv49OOX+OtHe6w/Mnn68r9XH2/pUMbaNsrw3u5pAKG4UGK+jgvUrkiESj5POpMnFvC0tDtuJ7Gy+Xtk1fbsUjvMQX1uLU886DlwYfRtd82wvLHNV87uvU28U2grWyjDflzevRcH3IP+hgTqiqyQ3E425KC2Ij7Wt/f4WyRPQ/wUqO05N2yIaNVdWmfMh5SSXBMCtQx68JVcHRWojUQCVyTC5PA1FMvFveNWWsAzO4O2uIcze8OMaGCoGtURtwTq/YsSjeQa5VQKX4cKEncSetW96BcvUjpzedFQW14Clwv1RGfywutFGa/PQV347hrldInQK6Zsew++YhzDfpD7L4C2QuncPN6TJ9sy7v2oOahX9haoV6pmyMmrdkm/6uZRVtLbnLnUm8KvsW7OE9wxL66g6gjUB1F1Nb8CuAQ8r1pU+Hzgdq4qPHQ42qxvaUhJ21ylE1EfUnY32sGheS5mihgVyUy8NQe12yUYDfu41MU88qWNwhVbf7qJlUG93ucO6pBPqbnnLW6bNJ0CT/RgUeLjKxlumQjvWsTwqW5um4w4AnWd1BzUHRaop2J+lh0HdUt0w/0OpoO6j0oSnTn0IbzrZae4ay7OL33ySRbXe2ixcu0pEG7whGD1ey0dylgroO6TP21h5oTuFhCkrlPZ2sIdtd9ZZwm9hdRGbbHcDsKGOb/bcLVH1HRHogCUM/sJ1Ft7FiTu5NU3jzEY9PDRb3avLFFKibaS2zN/2kIZ9GOkisg6nd7pYhqjYjQkUEe9URSXso+D+nRvxHuAGfEBdRclbueyGFqJcIMC9bavgk93MxPoXKaxnkigjowwHTZjKpZyS7Ye3zs3h3HpEpXCVfOi1FnzuxXx4Y+ZhYkHOKiLp83YF99NnX9eDNx9N7hcZB+4HPOhL6+gjo8j6swZ7xTuaBTh8dTc8XshK5LcF5dRx4I156zdWJ89RhtiPkrz5/Cc7FxBIoArEMAdj+8b8bGcLqC6BWNXGS5feeMoLgH3nz48cqUbGGsF1CG/ueNhKlSLf+p3Ws2gvl5K+YT1Hynl94Ae+cRysAPLsdTOiA+AC04O9ZHk/IZ54jjTooMaYCzi61pJopSSpVShJ/KnAcI+FcUlSG31hyCzF0+vmgWJV6+wXzM8gE918XiPCdSaUeHMpRy3TUb3/Pmdc3GeWNlkuw3b5fqNWo7xPlvb28VUPMCF9DblHoyPOSpkqgJ1tAsO6oJWZqtkdPR+24wzh94Ht0vwWz/yXFS3i/fc953e2WWXPGNuex+7FRLNC9TlvE6lYKAMHyJQDwcw1rd3CZGWS7hdGdQApY10LW7MDga0ArrLzVqb1t0tsX6/iI/5ta194z0sPIqLH75jis8/leyacaa8WaKypR9YiqYM+qAsKdcZe5QomGJYIxEfQghG/CO7XbtawXTSjt5c97HaSrSaMVyngzq3bv4+jTqoNxVTyBuqhBu6XSsYq6soY2OXBeqsvQK1Z850Rmvnr1qQSVUd1PEdImN8DtL7O6iLZ86AEHivv8HWMdaDEosRuOMOcjsEam15GbXHChKhGtEzNoaxemnf6xRPpzCS24ReMdk2F7Iy5AdxOePYLsrZLOW1dbynOitQg+mi3leg3ihwIurHfZXBaGjAyx0zcT77ZO/lUFe0MuVNrbbTyjMVwljbplLsq3nwnrQqUD8uhPiwEOLl1a8/Blrb8+bQUySqk592lSROVAVqpyjxaLKYMidsrWZQgylQr3Ypg3pzWydXMnpGoHa5BPGgp28zqKWUPLWa47rRPRrq3S5uPhHhiQuZzg/sAJ5ezaGVKzWH99XcORvDqEgeW850dmBHkI0uRXxMxQIYFcmlLkYJHXWsxYVoxzOozTlIoos9BW3AmUMfwImon1970618d2WT33rgmW4Px8Ryjo7dYjqoK80J50a1+FAZ2T/iA0wRQeoVyrkr5wKWCNuuDGoAI7NpW0EigL+4RU4NsNameY07bAqHe0V8ZAoa61vaoQI1wFtfME25IvmLh7tTlqgtm1nZhzmoAYz1+t4PLRd0Iw5qMHOok9tXOajXnwZk7zioA4Og+Ot2UGdTpkAdHmxMoE66zOeV3OrcZ5CeTKCMjjAWGEN1qbYXJXpmZwHQFhev/EHqrOlMV3e8P8VPHuKgPo1nZgb3QOvng80QuvdetLPnKM2bIrq+vIynxwoSLdSxsX0d1FJKsg8uowz68N/avgxnobhQ4j7bHdTavFWQeMrW49aDOjmBvrJ3LNhyepup2N7n+K+6eZQzl7I9V6Juxa/sFKiRHAsXdasC9U8ATwLvqX6drl7m0CdYJ4PtdlA7AvXR5Px6Hp9qT7HgeNjHpc3trhQVLFnlCT0iUIO5qtuvGdSr2SK5osENY3ufgN1aLUrsJafr41XB/LarChItnj8TRwicmI86yBQ0/Kobn+ru6P1Oxc3PGyeHunnSBY0Br4JHaXX62BgjIXMOkuyTosQqzhz6EF576zhvvnOK3//SOb5+rru5wOjbpnN0+EbTQa3nD3QUHnioqiigHuagHqoKkWtXnji3U6B2VcsGZSZjq0Dt2c6T8wRYa1NUj1AUXKHQng7qc2vVgsSRw8Wz6cEAL71umPseXsLognNfX8mBW6CO7z9WZch8P6w3h9oSqBtxUIOZQ71euOp1l6jmsI90tgxvX4QwixLrFKibdVAvS/NvmE93pgxbahrl9RTq6Bhul5vJ0KT9DuqZGYDdRYnrz14uSLSIzcHmChh7LzCVTp/peEHiTkL3vBKA3Oc+R3lri3I6jdpjBYkWytgo+j4O6tKzGfQLW4RePoVocyeSMhzY9dnSKqVzpkDtPdm5rHYLz8QE+sWLyD0Wji+kC0zG9l4QftVNYwB89sneivmw4r2UIXOeYJXmWouY/UxLZxhSyiLwB8DPSSnfKKX8replDn1CMlvEJbB1m99OfKqboQGPE/FxRDm/UWAmHrSlWHAs4qOoV9jc1m0YWWNYAnWvOKgBBgc8bXMadZunVs3V3+vH9t4qedtkhG29XDup7AUeX94kGlBrIufVRPwq14+GHIG6Djbyuq3CR71Y7gknh7p5MgW94/EecHkXVx8VJTpz6Dp57+tvYm4wyM/+xXdrETNdYW2Hc3T0FvOyJmM+jOQ2KC7c0YMX99Vhyyl75Ry5nQK1FfGh5HO2zv1lNsu2N8B6G6PL3JHIng7qc0kzjq4eBzWYLupLm0UefNreUrp60JZzqONBxAGLgO6wFxSBkarv7SJRSOASLgb9gw2NZU8HdfI0uL1Xxj90m+hU3REf2fU1FI8Xf6j+qA4pJfO66czcSqeaGmKjGGtrICXKmLmoMBOasT2D2uX3o5wYR1tYvHyhlGbEx+C1V145fhJkBTK7x1DOZNAvXuxK/rSFOj6O79ZbyT3wAPqSOcbedVCPYyTXkOXdkYDZB5dxRzwEbm9st0MzKCMB9D0ipFpBW5hHqCrqZOcXB9SJCaSuY6xduahW0AzWt7R9TWjTgwFuGAtx/+neivkw1gogQK0uSLoCKsqQ/1gUJbYkUAsh3gA8Bvxj9f/PFUJ80oZxOfQIiWyJwQEvirt9bqkTUT8XuliO59A851N5pm3InwYYj5gnYt3Ioe5ZB3WfZlA/bQnUe0R8wOWixF7KoX78wia3TkQOzIO7czbOt8+nu+K6OkpkClpXRM4TUT8uASs9to3vKJEuaB2PZoHLDup2uS+7gTOHro+AR+G333w7qXyJn/u/T3RllxVgFiSCKVCP3AjCBatPHHybfagVHx2yuO8KexCqa1dRoiVQK22M+AjrBeID9r3Wy5ub6IEB1topUEejlDczuy4/t7aFx+1icp8t3lfzyhtHGA17+di3OluWKCsS7cLWgfEeAMIlUOI+yg04qId8QygupaHxjARGyGk5to0d95M8A8PXg6uzO6AOJDLVkIM6NDTcULZvqphi3ZUFIchnOuOg1hPmwoA6agrU0+FplrPLVKS980vv7CzaTgd1fh1Km5cLEi2sBYk9Yj6KZ86Yx7qxu7EvoXvvpfjEExQefhgATw9mUIPpoMYwMFJXLnaUFjfRFjYZ+P7JAxeo7EId9oMhKaftO+8unZvHMzuDUBp7r7EDSxTXL1wZ87GSNt+/9nNQA7zq5jEeWdzoqfNufX0bd8SL2LHb1DM54ER81MEvAS8AMgBSyseAznv6HdpGMldsW/60xYmI34n4OIJUKpLzqQKzNgnUYxFTgOhGMc3yRoHBoIcBb+c/UPdjsI8zqJ9ezTEW9hHZR6ScGxog6HHzxEqmswPbh22tzDOJHM/ZpyDR4s65OHmtXHOIO+zNRkHrioPao7gYj/hZTrf386ZSkTy8uEGlhyJq7CLdJQd12G/GiiT7SKDGmUPXza2TEf79q67nH59c7Vo2sOkc9ZhCjeqHoevMHOom0Ne2D82fhmqh1pC/ow5qEQggVZWwlrfVQV3Z3KQ8EG7rIpM7EqGc2cNBvbbF3FBwV0HWfqhuFz9y5zRffGato7mkxvo2slSubeU+CCXur99BnU80nD8NZsQHcGXMR/J07xQkWkSmIL9mxvAcQm59jXCD8R4LmwsgwBMeYCvdmV1yRsKMG1BGzfiB6dA0xXKxFtdiF57ZObTFxcsLf6mz5vddAnX1o2mPWKPiaVOg7mbEB0Do3nsA2PjzjwCgTvWmQK2OmY+psXplpETuiyu4ggrBF4x1ZBzKiHn+rtuYQ63Nz3clfxpMBzWwqyhxpbpr8qAFylffPEpFwufP2Pv6agVjfbuWP22hToWoZDWMzb6aC++iVYFal1JePRPovzOyY0wiW2I01J78aYuJmJ8L6e5kDzs0TzJXomRUbClIBBivCtTdclD3knsaYHDAy7ZepqD1X1vv06s5rt8nfxrA7RLcPBHh8Qu94aA+fcnMw751n4JEiztnYwA8tODEfByEGRPReYEaTAdFOwWHSkXyn/72CX74D77Bn359sW330y0yXXJQCyEYCXn7rSTRmUM3wDu//yQvvmaQX/n7092Jf0o+ZW57d1cXaEZvaSriQ+oVyukiyiH50xbKsB99D4FaqCoiYP+8RQhBJRRmQNsmHrTPoFLe3ESE2ixQ7+ugzteVP72TN985hQDue9jeWIWDsJxxnqmDHdQAyqAPI1XfuVOykGQ02Fj+NJgRHwBr29Wok8IG5C71TkGiRbQqRG5eOPh6mCWJoQYLEhc2TVF2IDZIvkMCtVWip45ddlADLOfsXaDzzM1R2dqivF5dhKgJ1FeJjMFh8Azs7aA+fRplfBwlFrN1bI3inZvDc80p9AsXcEejuEOHv466gSVQ6zsEau3iFsWnNhh48QQuT2d2J9QipNbsMW1UNA1teRnvqe7E/6gnTgC7BWqrd2a/iEaAm8bDTET93H+6N3KopZQYa9u1HgoL67NB7/OYj1YF6ieFEG8F3EKIa4UQ/xP4ug3jcugRkrkiI20qSLQ4EfWzrZfJFDqfPezQPIspM9NvxiYH9XDIi0vA6mbn3fTLG9s9lT8NZgY10HcuaqNc4eza1r4FiRa3TUQ4fTGL3gNxGY9XndyHOajHI34mY34nh/oQNvIa8S64cMGM8WlXBrWUkl/++ye57+FlogGVP/jSOYr67ozBo0ymoBPr0mM3EvL2W0miM4duAJdL8Js//Fy8qov33PcdNKPDnw3JM1cKc2O3mLEC241t+dfXt0GCWoeDGsyixPJGEbnj9zUyGdzRaEMxBY1gBEKmg9qmiA+p61TyedyRSNsd1JWrHNQlo8zSRqHu/GmLE1E/d98wwl88vNKxeYi2nEN4XHUtXihDfqReoZI7/NwpWUi25KCu5VDXYm56pCDRImIJ1AcvJhi6Tj690ZSD2q/4iQ6OdEygNhIJhM+HK2xmZVsC9fmsvbEznjnTGV0rSkw9a+4UiV6V3yyEWZS4T8RHt93TFqF77wV61z0NoOzhoM59aQXhdTPwfSc6Ng5XQMU1oNrmoNYWF6FSwTPXHYHa5fPhHh5CW7ky4mN5o4BPdTE8sP+CqxCCV908ypefXSdf6r4xrJLTkaXyriJlz/gAuEXf51C3KlD/G+BmoAR8HMgC/7bFYzr0CHq5wvqWxkiovREfE1FTAHeKEo8W56sC9axNDmrV7WI45O24g9ooV7iQ6T2Beqh6YtjOQqFusJjKoxkVrtsnf9ri1skIJaPCs4nuFyU+sbLJcMhbV9zRnbNxHl5MOztC9sEom0Wo3XJQT8UCJLIl24VjKSW/+ukz/Pk3zvMvX3qSD731eSRzJf7ykS7FEbSBckWSLXbvsRsN+/qqJBFnDt0wYxEfv/5Pb+N7F7L85v1Pd+6OSzlT/NopUI/ean5vMObDWDPFgPod1AGQYGxcfu6XqwJ1uygGBghrBdsiPsq5qjM4HiWvtW9nmDsaoZzNXlE+tpQqUK7IhgVqgLfdNcP6VokHOlSepa9soU4MHJpNDqAMVt2Ph+RQF/QCOT3XlEBt3WatUHVQJ56s/qA3xMgaloP6kKLErQ0z8zfUqECdXWA2PMtALM5WxzKoV1FHR2uLUGOBMVSXantRomd2FqiKi2AWJMZP7p0xHp+DjSsjPiqFAtrCAr4u509bhKsCtaeHBWp3LIbweGoueVmRFJ/aIPCcYVz+zkZNqiMBDLsE6nnzudEtBzWA58TEbgd1usBkLHDogu6rbx5DMyp8+ZnOl+NejV6bJ1y5kC1UF+p40BGoD0JKWZBS/mcp5Z1Syjuq/+6rs4fjjCWMjbbZQT0RNSfpjkB9tDifKqC6RS2aww7GIn5WO7yF+9JmkXJF9pxAPVjdWttvDuqnV03B+aCID4Dbqm7lJy5k2jyiw/nuSobnTB5ckGhx52yc9a0SiymniG8vNrdNt1c3Mqjh8hY/Oz9vpJR84LNP8+GvLvCOF83yc6+9gRedGuSOmRi//8VzlIz+cFFvbutISXcd1H2UQe3MoZvj1TeP8da7pvnDL8/z1WfXD7+BHaxVxfArHNRVgbrBmA8jWQDBrq27+6EO7d6G3W6BuuALEtYLhH32vNatXGh/PArAeq498xp3NApSUsldPnm34mCaEahfet0wE1E/H+1AWaI0KmiXDi9ItFAGzbn3YQJ1omCKYKOBxiM+wp4wHpfnskCdPAPeCIQ75/Ksi9AJs7R0c+XAq+XWTSd4ow7qxc1FZiOzBKNxtrOblI327/g1VhM1py2A2+VmKjTFUtZegVo9MY7wetEWFs0LUmd3509bxE9CehEql+c0xaeeBil7xkHtvfFGBu55JQN3393toeyLEAJlbAxj9RIA5XTRdMvWkT1vN8qwH33NnpjV0vw54LIrvxuoExPoK1dnUG8fWJBoccdMjFhA5f4OLUgehNU7cbVADWbMh7ayhezDnhuLlgRqIcR1Qog/EkLcL4T4gvVl1+AcuksiawnUbS5JrDqonaLEo8X5lLkiqbjtaxoeD/s67qBequbRTh6QTdUNahEf+f4RZACeXs3idgmuGTl4IjYTDxDyKTy+0t0c6lxRZ349z60T0bqub+VQOzEfe5MumMJEN4r2gFrWvJ051L/z+bP83hfP8da7pvml19+EEAIhBO+551oubRb560cPPmk+KliPXaxLiwsjYR+5osG21h+CvzOHbp5ffN1NnBoO8rN/+Rgb+Q4s4iZPm993CtShUTOXtUEHtb62jTvqrTtn1BKydxYltlug3vKYArWrzlLBw7ByoQeG4wCsbbVnnueOmD0RVokkmPnTACeHG9/t53YJ3vKCKb52NsXCet6WMe6HniiAIevKnwZwR33gEocWJVqles0I1EIIhgPDlyM+kqfN10CbomWaxq2YIvXmwQ7q7LoptDfioC4aRS5uXWQuMsdA3Hz+5nc8v9qFkUigjF7pep8OTdse8SFcLjwzM2gLC6bwvDG/O3/aIj4HFR2ylwXA4hnzvdF3U284qIUQTP3u7xL5wdd1eygHoo6N1RzU2kXzvcUz3gWBeiSA3DaobLW+6KKdm0c9cQKXv3vn0+rkJPqlS1fsolneKDB1QEGiheJ28cobR/n8mUTX4yWNtW2E6sK9hwbnmQwhtXJtN1Y/0qqy9FfAd4BfAP6/HV8OfYBVRtRuB3U86MGnuhyB+ohxfiNvW/60xVjEx2qXBOpedVCv95mD+qnVHLODAXzqwSfnLpfg1okIT3S5KPF7F7JICbdNHVyQaHHNyACxgMrDTlHinqQLXXZQVyepy2l7Pm9+/4vn+K3PPcMPP3+S9/+TW65w2b/kmiFun47yew+e63xebhvI1BYXuvPYDVfjxvoo5sOZQzeJ3+Pmd95yO5mCzn/8v4+3P1Ip+RQofojOXnn56C2w+nhDhzLWCnXHewC4/IqZE7rjZLSc2WyrQJ1R/IRKBdv+rpVsFoDISFWgbtNOCJclUG9enjecS25xIuIj6G1u6/z/c8cUikvw8YcOd65uljb5wlJza0zWlu16HdTCLVBi3kMd1JZA3UzEB5g51OuFdZDSFKhHe8Mpu4vo1KERH9mqgzoUH6r7sOez55FI5sJzBKOWQN3e+Z2sVNCTSdTRsSsunw5Ps5JboSLtnU94ZmfNiI/NZShrBzuo4Yoc6uLp07hjMZTRxhdAjjPK2Ggtg1q/tAUuUMc6fx5qZRzbIXaWFubxnNpncaNDqBMTYBgYSfO1vrmtky0aBxYk7uTVN4+RLRp8a76753DG+jbKoH/PuCdrEbOfYz5aFagNKeXvSykfklI+an3ZMjKHrpOsCtTtzqAWQnAi6nciPo4QUkrOrxdsy5+2GI/42CoZ5IqdK8xc2iiguATjkd5yUPs9boIed/9FfCRyh8Z7WNw6GeHMpWxXIxKsiJHbJuoTqIUQPH8mziPnO5NTeNSw3I6xLomcIyEvHsXFig0O6g9/ZZ5f/8en+CfPPcGv/dPbdrkNhRD89Cuv5UJmm7/9ztF3Uafz5vtyNyM+gH6K+WhqDi2EeI0Q4mkhxFkhxM/t8fN3CSGeEEI8JoT4qhCiRxWl1rj5RIT/8JrreeB0go9+y95t77tInobh68F11WnT2K1mcVy5vjmLrEiMtW3UPbbtHoQy5K85qKWUbXdQb7j9KBUDWbDHoWUJxvFRUxhca9O8Rqn+Ta4QqNe2OHXIjq2DGAn7eNXNo/zVI8uHdhf8nzP/h/c8+J6aKNwI2koOV0DBHav/nMs96D/UQW1FfDQtUFsO6twlKG72Xv60RWTq0JLE3PoagUgUxVP//GMha+bqzkXmGIiZAvVWm4sSyxsbYBi7RN/p0DTFcrGp59dBeObm0FZWkKvVKKP9BOpYNbphp0BdLUhsV2Frv6KOjqEnk8hyGf1iHmU4gDjEuNMOlBFTIBNDMQABAABJREFUoNaTrWkwslJBm1/Ae7J7+dNQFagBvVqUaO2WrMdBDfD91w7hV93cf3r18Cu3EXMhe+95gjLkR/jcjkB9AH8vhPhXQohxIUTc+rJlZA5dJ5kr4RIweEDrqV1MRP1cyPSNK6rv2chr5EqG7a7jsWqedSdd1EsbBSZjftw2bWW1k6GQt68iPgqawdJGgetHw3Vd/7aJKHpZ8sxq94oSH1/ZZCLqb+h98AVzMRbW8/3k9LSNTJdjIlwuwWTUz3K6NeHlI99Y5P2fPsMP3DrGb/7wc/Z9/3j5dcPcNhnhdx882/Utg61Si/joYkkiQDLbN++JDc+hhRBu4EPAa4GbgLfsIUB/TEp5q5TyucBvAB9sx+B7gZ988Rzff+0Q7//0ac4m23iytvbUlfEeFmO3mo7D9WfrOkx5s4TUKzVRoF52CtSVfB4Mo60C9ZrLfK2VbYoysDKo4+PDCNEBB3V13FJKzq3lm8qf3snb7pohXdD5x+8dLFo8sfYEAGczZxu+D30lhzoZakjoUwZ9GOsH58cm8glCaoiA2tx8fSQwYmZQJ/aIueklolOQvXhFPvLVZNfXGs6fXthcQCCYDk8TrArU+XR7DQhW9IM6dpVAHZ4GYDlnb/myZ24WDAPtqW+bFwxeu/cVwxPg9taKEqWmUXr2bM/EexwllPEx0+mbSqFf3MJzovPxHgDuiAfhcbXsoDYuXUIWi3i6LFB7Jk2BWqsWJa5Ud0tO1ilQ+1Q3L71uiPufTFDpUsazNCoY6eK+ArVwCTyTIUegPoC3Y25H/DrwaPXrkVYH5dAbJLJFhkPejgh3JyJ+Lti05dqh/ZyvrkjODtkrUFsu5k7mUC9vFGq5tL3GYNDTVw7qZxNbSHl4QaLFbZPmyebjXSxKfHxlszaOerlz1jyJeWTRcVFfzUaXXbgAk/EAyxvNf978xcNL/OLfPck9N47y22++/cAcfiEEP333tSxvbPN3j11s+j57gUw1niXSdQd13yz8NDOHfgFwVko5L6XUgPuAf7LzClLK7I7/BoG+bdJxuQS/+cPPIeBR+Dcff6w9u20KG6Z7dC9hbvQW83udRYlW0WGjDmp12E9lS6dSNGriazsF6kvSXIQy7BKoqxEfnmiEwaCnbQK19TexBPFkrsRWyeBUE/nTO/m+k4PMDgYOLEuUUvLEuilQz2fm973eXlS0MnqiUHf+tIUy6EeWylQKxr7XSRaSTbunwYz42NK3KKx+17ygZx3Uk1AxILf/IkJufa2h/GkwBeoTAyfwK34CkQhCuNoe8WEkzN9BuSriYyY8A2B7DrV3dhYA7ZnTZglmcJ8IFJcLYrM1B3Xp7FnQ9Z4pSDxKqNUCTG3xIuWshjpu747kehFCoAwH0JOtCdSlefM54T3VXYFaOWEWuOo1gbrqoG6gZ+rVN4+xmi12LWLS2ChC5eAiZc9UCH01jzxkV89RpSWBWko5t8dXd5+ZDraRyJbanj9tMRHzs75VOnT7nENvcD5lFjpMx+2P+IDOO6h7LX/aYnDAy/pW37gFeXrVXO29oU6BejLmJxpQeaJLRYmZgsbSRoHbJqMN3e7mExF8qsspStyDTEHDq7jwd2Ero8VUzF/Lnm+Uv/n2Cj/3N0/wsuuG+dDbbketoyT2lTeOcNN4mA89eBbjCLuo0wUNxSUINZnl2iqxgAfFJfom4qPJOfQEsNM+t1K97AqEEP9aCHEO00H90/aNuvcYCfv4wD+7jTOXsvzGPz5t/x2sPVW9o5uQUvK3z/4tnz//efOyoWvB7ak7h9oSARrJoAZQhqyc0G3K6QwA7li0oWPUi16ukMCci1U27fnsLW9u4gqFEG43QwPe9gnU4XDt/sDMnwZadlC7XIK33jXNw4tpnkns7Vpbzi2T1UwhvlEHtX5hCyR4JhsbpzJoPk4H5VAnC0lGg83nAw8HTEF3PfkEDIxBoEc3SkdMd/F+RYlSSrKpxh3Ui5uLzIZnAXC53AQiEbY22ju30xOmg/rqksTRwCiqS2Upa2+kkWfOjO7Qzp83CxIPcvHH5yC9CJjxHgC+Gx0HdaNYAnVpft38f5cc1GAugFqLp81SOncOoOsOapfHgzIygr5iCtTLGwVCXoWIv35jxd03jOB2ia7FfFxeyN5/nuCZDEHlcsFmv9GUQC2EuLv6/U17fdk7RIdukcgWGQl1RqA+ETVXiTpdkOfQHIvrBYRobEWyHkaqbbWdclBvbutkCnrPCtRDA56+Kkl8OpHDp7rqdqwLYRYlPt4lgdq630Yd1B7Fxe1TMUeg3oONvEYs4OlqXuFUPFAtTmks6/5Tj1/k3//Vd3nRqUH+8Meej1epT2S3sqgX1vN86vFLzQy5J0gXdKIBtWuPncslGA55awXOR5VOzKGllB+SUp4C/iNmCeNe4/gpIcQjQohH1tbW7LjbrvHKG0f58e+b4X99dYEvPWPz75I0RRgtfor3fv29vPfr7+WXvvFL6GUd3KrprF6t10FdQFRLDxvB2uprrG+33UGdzmvkPOZntG0RH5sZ3NX4jeGQl7U2LbwLtxtXOFwb99m1qkDdQga1xT97/hQet4uP7ZN3brmnY94Y85uNOai1lcYKEi2Uwerz4oAcajsc1ADJ1NO9W5AIZsQH7FuUWNzKYZRKhAbr/1tUZIXF7CJzkbnaZcFYvP0O6tUEKArK4OAVl7tdbqZCUyzl7BWo3ZEI7ngc7eL6/vnTFvGTpoNaSopPnsYVDKJOTzd8n/8w/w/888/+cwq6PTn3Rw2lKlDrVYHRc6I7Dmowc6jLmRKVUvMmQW1+AXc0ihLv/gKWOjm5w0G9zUTM39C8NRrw8MKTcT77ZKJdQzwQY91ayD7YQQ2gLfVnzEezDuqXVb+/fo+vH7RhXA49wFquVBMM282JqCmEO0WJR4OljQInIv66BZp68SpuhgY8rGY78zywyhN6VaAeDHrZyJe6loNlN0+v5rhuNNRQbNBtkxGeSeS6srvC2t51S50FiTu5czbG6YtZtkr7b709jqQLetfypy2sspTlBlzUn31ylffc9xh3zMT54x+/A1+DDvBX3TTKDWMh/ucXnqV8RF/PmYJGtEv50xYjofa5LztIK3PoC8DUjv9PVi/bj/uAH9rrB1LKP5JS3iGlvGN4uDFXYS/yn37gRq4bHeDf/eV37d15lDxDyhfmnQ/9Mp84+wleMfUKNkubfHnly+bPR2+F1SfggBxgC6sgsdFFHiXuAwH6WqHtAnVqh0BtV8RHZTNbczcPD3hZb+Nr2B2NXuGgHvAqtpS9x4MeXnvrGP/32ysUtN2f699b/x4+t4+7p+/mbObsgbnQV6OtbOGOenGHGnt/tZ4XVj751RgVg/XieksCtXXbtdyF3o33ADPiA/YtSsyumwtX4Qbe65KFJNvG9hUC9UAs3vYMaiOxijIyjHDvnmdMh6dtj/gA8MxMU0oV6xOo9QJsJSieOYP3xhsQV5fHHsJmaZP/9tB/46HVh/jwEx9uYdRHF3cshvB4MDIV3FEvri7G3qkj1g6d5hcLSvPn8Jw6ZdeQWkKdmKgJ1Mvp5mI8X3XTGGeTW5xb63wHkr62jWtAxeXbf7eiO+zBHfHWFjf7jaYEainlL1W//8QeXz9p7xAduoFmVEjlNUY75KCejJpvHo5AfTRYTOWZGWyPqDse8XfMQX05m6pHBeoBDxUJme3GnJ69ylOrOa4fbcwhdOtEFKMiOXMpe/iVbea7yxnmhoINbQ2zuHMuTkXCt887OdQ7SRe0ruZPw+UFqXpzqL/wVIJ3f+zb3DYZ4U9+4k4CnsYjLlwuwb+5+1rOreX5hyeOpou6Fx67kbDvyJcktjiHfhi4VggxJ4TwAG8GPrnzCkKInQ1XrwPqa/A74vhUN7/95tvJFnX+418/3pBAeBBPrz3OW8finE6d4QMv+wAffPkHGfIP8clz1T/72C1QWIetw91W+lqh4XgPAKG4cMd9HXFQb7TFQb2JO3qlg9qux+dq3JFIbdxmQWLQtl0fb7trhlzR4FPf3f0e/sT6E9w0eBPXxq4lp+VY316v+7jacq7heA+oPi8iXsr7RHysb69TkRVGA61HfCRFpXcLEgE8QfDHYXNlzx/nLIF6qH6x3nLCX+Ggjsba7qDWE0nUkb0fs+nQNCu5FSrS3rgwz3gcLafA0CECdcz8W8i1cxSfegrfjY0vWvzx43/MZmmT5408jz998k9Zztpb+ngUEEKgjI0hS96u5U9b1HbotBDzoZ2bx3ty7vArdgB14gT66ioVXWd5Y7tmSmmEe28yX3/3d8FFbaxvH5g/beGZGujbosRWSxIRQrxOCPEfhBDvtb7sGJhDd7G23412yEE9GvEiBFx0BOojwflUgZnB9nygjkV8HYt6sXJop9sktrfK4ID5+kv1QQ51aqvE+lap7oJECyteoxtlFU9caLwg0eL26RguAY84MR9XkC5o3XdQV6OJrAWqg/jyM2u86/98mxvGwvzpT7yAgRbyl197yxjXjgzwP7/w7JHcFZEp6D3hoO6jksSG59BSSgN4N/BZ4Azwl1LKJ4UQ7xNCvKF6tXcLIZ4UQjwG/CxmGWPPYqdQeeN4mJ9/7Q18/qkkH/lm6y7DL5z/Aj8mL2K4FP70tX/Ka2Zfg+JSeN3c6/jyhS+TLqZh7FbzyofEfFS2DSo5HXWkuWg0dch/pUBddSTbTSqvYbgUCARtFahdOyI+NKNCttie3UXuSOSyg3ptq+X86Z3cORvj2pEBPvrQlS5dvaJzJnWGW4Zu4ZqoKfCd2zxX1zHLeZ3yRhG1wXgPC2XIv2/ER7KQBGhJoA6pIbxCYd3t7m0HNZgxH/tEfFgO6kZKEhc3FwFqGdQAwdgg+c0MlXL7dvUZq6u1CIirmQnPUCwXa4+tXXgHvZSLbsreEwdfMV7Nqz79CHJ7u+GCxOXsMh976mP80DU/xAde9gFUl8pvPPIbzQ77SKOOTSAZ6Gr+NFSjglw0XZRopNOU02k8J3vDQe2ZnIRymbWFZbb1clNxpCeifm6bjHQlh9rcaXW4LuGZClHeKFLO94eJbSctCdRCiD8AfgT4N4AAfhiYsWFcDl3GynjsVEmiV3EzPODlQtoRqHudbFFnI68x2zYHta9jDuqljQLRgErY111X4H4MVYW8fsihfrpaLNSoQD0e8TE04Ol4DnUyV+TSZpFbm4j3ABjwKtx8IsJDjkB9Bel89124Eb9KyKscGvHxjXMpfuojj3BqeICP/PMXNOWk34nLJXj33dfwTGKLf3yyO+UrrdATDuqQj3RBp2Qc/ULlZufQUsp/kFJeJ6U8JaX81epl75VSfrL67/dIKW+WUj5XSvkKKeWTbfw1WuITH/w2X/lLew3e73jRLC+/fpj3f/oMlzabm1NKKfnwEx/m337x33JK1/j4ybdw8+DNtZ+/4Zo3YFQMPrPwGRitXp544sBj6mvNFSRaKFWB2khncIXDCKU9ZaUb1QVxVzRiX0liNos7fFmgBtpXlFiN+NgqGVzaLNqSP20hhOBtd03z3eUM39uxaP5s+lm0isatQ7dyKmqKNOcy9QnU+gVzC3mj+dMWyqBv35JES8RsJeJDCMGw20dSccPw9U0fpyNEpvYtScyl1lBUD/5Q/Qs7C5sLDKgDDPmHapcNxGIgJYXNTKuj3RMpJXoigTq696LCVMhMeLK9KDFsLhhpm4dIQ9FpEG6KT5rFsL6bGnPV/9a3fwvFpfDu29/NSGCEf/mcf8kXl7/IVy98tZlhH2ncY9cghOhq/jSYOzGUuL/piA9t3txp4D3V3YJEC3XC7I1efWYRgMkmHNRgRvN9ZylDsoO9J5WCTiWvH5g/bVHLoe5DF3WrDuoXSSl/HEhLKX8F+D7gutaH5dBtrBfjsA25bfUyEfNzscmTCYfOsZQyP8DaFfExFvGxua3vmfFnN0tNbv3pFDUHdf7oO6ifXm1OoLaKEp/osEBt3d9zpqJNH+OO2RiPLWfQDHu3Yh5VyhXJ5rZOvMsuXCEEk/EAywcsiD6yuME//7OHmYoF+D///AW2OYd/8LYTnBwK8jufP1ouaimlmR/ebQd1uL3iVoc59nNot+LiwtP2xiAJIfh3916PZlT49vlMw7cvlUv8/Fd/nt/+9m/zmuHn8b8vJRkZf/4V17kudh03xG8wYz78MVMYWz1YoDaS5vuNMtKkQD3sR2oVypvFtsV7gBnxIQSo0agtGdRSSjPio+qgHqrOa2zNCd+BFfGxsGaWj50atlf8eePzJvGpLj66oyzxe+ume/6WoVsY9A0S8UbqFqi15RwImor4ANP9WCkYVAq7XXSJgrk9vRWBGmCkIlnzBs0YjV4mOm06qPfYlZFdXyM0NNxQ3MtCdoG5yNwVtwlGzRK4fKY98W2VXA65vY2yj0A9EzbXMO0uSvR4zTmvdvGQSAO3CtEpis8uIDwevCfrFyW/nfg2D5x/gJ+45Sdqz8kfvfFHmQnP8OsP/bpZPHuMcIfNxQZltLldNXaijATQk81pMKVz5nudp4HnQjuxBOr02UWAphzUAK+62dzFcP/pzsV86NU+gXoiPtSJEAhHoN4L65lcEEKcAHRgvMVjOvQAyZwV8dEZBzWY2ykuZvpn626/spgyJ/3tivgYj5jPuU7EfCxvFHq2IBHMDGqAVD84qFdzxIMehgcaX/S6dTLKs8lcRxYtLL67solLwM0nmt9G/YLZOEW9wvcudj6epBfJbutUJF2PiQCYivn3dVA/tpzhHf/7YcbCPj76zrtqC0V24K66qJ9azfHAme40hDfDtl5GMypdf+yssrNkfwjUx34OfeK6KBsX8xS37BUmrh0dwCUu79ypl7XCGj/5jz/Jp+c/zU/f/tP8euwufFLuGW3whlNv4MnUk6YQOXbroREfxloB3AIl1ty82jphreTblz8NsJ7XiAc8KNGoLREflXwBDKMmUHfCQV3JZjmXMD937Yz4AHMHzutvO8EnH7tQK0F+Yv0JYt4YEwMTCCE4FTlVv0C9kkMZ8h9YiHUQyqD5fDI2ds+ZE4UEiksh5os1dWyL4dI2a57uf24fSmQK9Dxs7xaPc+vJhuI9wIz42Jk/DRCMmX/LrXSq+XEegL5q7q5Sx/YWqMeCY3hcHvsd1PICCCgtLBx+5fhJikspvNddh1Dr21VVkRU+8PAHGAmM8PabLqdOedwe/sOd/4HF7CIfPfPRZod/JBGeIaSWR5Y7X8R3NeqwHyO1jSw3bpzQ5hcQPh/qiUPiYTqEOjYGLheFZXM3RbMO6mtHBpgbCnZUoLZywOtxULu8bnNhoQ+LElsVqD8lhIgCHwC+DSwCH2/xmA49QCJbxO0SDHYwK3Qi6udCZvtIucqOI+fb7aAOm2/K7RaoyxXJSpPtvp0iFvAgRH9kUD+dyHHd6EBTZUW3TUSoSDh9sXNFiU+sZLh2JNRUIZ7FHbOm0+bhBSfmA8yICIB4lzOowSxGXUlv78q//d6FTX78f32LeNDDx975QkbaUBT8huecYHYwwO98/tm2FYXZTabqzut2xIe1aH7UixKrHPs59IlrogBcPJux9bg+1c3sYJCnV+v/zDidOs1bPv0Wns08y/94+f/gnbe9E7H+lOmQHtjtQH3t3GtxC7fpoh69BVLPgr6/A01f20YZ9CPczRX2KUPmXEVqaq1wsB1sbGnEgx4zKsMOgTprCsXuiLnYay1St02grgrhS0sJ3C7Rlo6Rt71whrxW5hPfuQCYDupbhm6pzW9ORU9xNnP20Pd3KSXaSq7peA+o5sfCnjEfyUKS0cAoLtHC6b5eZLiQZY0jsBMsMml+3yPmI7e+RrgBgTqv50kUElfkTwMMxAbNn6fb46A2EmYsy34Z1C7hYjI0abuDWmTOoQ4G0BYWD72ujM5SXC3hu7H+eI/PLHyG76W+x3ue9x4C6pWvyZdOvpSXTr6U3//u77NWWGt06EcWaQQoby5TTtqbJ94MykgAyhKjzvLwnZTmz+GZm0O4Wq62swXh8aCMjlK5eJFYQG26O0YIwatuGuUb59bJFjvj7jfWt8ElUOL1nXt4pkJoy7kjcy5RLy09k6SU/0VKmZFS/l/M3LwbpJS/aM/QHLpJIltiJOTF5bKn+boeJqJ+NKNCKn/03aL9zPlUnuGQtyXh7iAsB3W7c6hXs0X0suxpB7XbJYgHPKwf8ddEpSJ5ZjXHDWPNuZFvrRYVdiqHWkrJ4yubtfttluGQl7mhIA8vtudE5qhhCdTRLoucYDqot/XyFfnuT61m+bH/9S1CPpWPvfMuxiLt2UGkuF3861dcw5MXs3zhqe6fmNTD5ceuNxzUa31QlOjMoWFkJoxbdXHx2Yztx75+LMQzifqcafcv3s/bP/N2hBB85LUf4ZUzrzR/kDxjuqf3WFgd8g/xkomX8Kn5T1EevQlkxbz+PhjJAmodrqj9cIc9CNWFrPjbHvFhCdSVTOufuVZhoVWSGPGrqG5RK2O3G0u8X11OMB0P4FXctt/HcyYj3DQe5qPfWmJL2+Jc5hy3Dt1a+/mp6CmyWpZU8WCXbTmrUcnpTcd7ALirQsZeRYnJQrLleA9SzzJc1slLg7yeb+1Y7SZqRiZcXZRYNnS2MmlCgw0UJGYXAXY5qAORKAjBVro9xgMjYTqolZH9iy2nw9Ocz7ZeAlujsAHbG3hODKMtLh56dUMOU9EEvmvrqx0rGkX+x7f/BzfGb+QHT/7gntf5j3f+R/SKzv/49v9oYOBHF1mWlPMuKptL6JcudXs4Nceu0UTMhza/gHdu7vArdhB14gTq2mrLJrRX3TyKXpY82KG5urFWQIn7EO76JFrPVIhKwaC8xw6ao0xTArUQ4k1XfwGvA15Z/bfDESeRLdZOBDvFiaj55ngx4+RQ9zLnU4W2FSQCNVFotc2lBFaWdi8L1GDmNR51B/WFzDZ5rdxw/rTFaNjHaNjLExc6I1Bf3CySymvc1qJADXDnbIxHzm84O0OAdLVpulcc1ADLafN94Gxyix/98LfwKm4+9s67mt4SWC8/dPsEU3H/kXFR94qDenDAi0sc7YgPZw59GbfqYmwu3BaB+rrREIupPNva/oWaUkp+/7u/z7/70r/jhvgNfPx1H+f6+PXWDyH5FAzfsO/tX3/q9SQLSb7lrrpL98mhlkYFY2O76fxpAOESZsyHO9JWgTqVLzE44MEdjVDOZpHl1gpJLYHacja7XIKhAW9bIz4A1i+u254/bSGE4G0vnObMpSx/d+YhJJJbhm6p/bzeokS9mh2qTjXvoHZ53LjCnvYJ1InTDFdLaXve3RqZNr9f5aDe2kiBlA05qBc2zaiLqwVqt6LgD4XJt0mg1lfNOAF1ZP+xTof+f/beOzyyuzD3/5wzXdMljbq0Klu8RWvvendtY4MrYAK2ccBAIJcQCAQSEriUS3LzS3JveqGEcCGFNJJgEqoxptjG2Liu19t7k1arPk2aoukz5/z+ODOqM9L0Ga31Ps8+2p05c85Zzcwp7/f9ft4exoJjSHKZUu1e5XOq6+0lPjKCLK2+3uiMMuijb89vYOU/zv4H06FpPr3/0znT/D2WHt674708OvQox13H89/3daqkJwwpSPnHSU7XHvemSZ+bEgUWJUrRKImJCbR1UpCYkbazC9Osu+SeqT3ddppNuqphPpKeSF54j4yu1aLEYhPU963yJ/vQ2IbWldzBGC1V5E8DdNiU7U1sGNR1ravecMX406BMzbU3aJiqcGFmhj9b7wZ1k0m7JOW5HnU+XZC4tbX4m7DBTisnx31l2qPVdXJM2c7uLlvJ69rX24gvnGDIXXvGXK01k07h1rpoDxYZ1DNhrnhCvPurBwGBr3/wpooe3zLSqER+847NnBj38/OLdX7Tz0KC2l7jwQWVKNBk0uGsYqt6BbRxDb1I7VtseMaCxCPl7Ri4rs2MLCuDT9kUSUb49LOf5ivHv8L9A/fzz2/8Z5oNzQsLBCYh5oeW3NPY7+i+A7PWzA9cr4DWBM7sHOrkTBSk/LiSq0ndqEM0NFUtQY0skwqUhtZK+ZXXZwxqUAbeK1mSCBB0eRloKS9/erEeuKETo1bFd8+8BLDUoLYqBvVl3+VV1xEfD4IooM3T6MsldZN+BeJDlmWcIWfpBrXrLC2yMoPAHanzc1VDI2gaViSoAx5lvwthUI/4R1AJKrrN3SueM9kbmfNVKkHtRNXUhLAK83uTZROxVAxXuEypTq/yOdVu3YkcjZJMc7BzKToVAkFGZ1/bIPdEPPzTqX/iru672N+2f9VlP7T7Q7QYWvjzQ39ePvO9TpWYVGYjyKGpee54LSXq1YhmLUlXYQZ1/MoVkOWCyjKrIXVHB9aQj25LabO9RVHg9Ttaeea8i2hijcHaNQZ21pIsySQ80bwKEjPStBoRNOKGQQ0gy/KvrvLn/eXeyXqVJMn8+n8c5gtPXqz1rpRdzkCUVkt1E9RdNsUw2EhQ16+iiRTTgSibKmzqtlkNFWdQj86EUYkC7bbqDsQUqqZrIEF9MV1UVWyCGmCw08awJ0SwChywkxN+1KLAdSXsb0YH0hzqQyMbHGpfnZicAF125QLw4LCX93z1IElJ5uEP3lT2Qq3V9It7u+i0GfjiOkhRz6YT1PWAZ2kx69Z1gnrjGnqpOrbYkGWYGi7vDJmt6eP3+SwcamfIyft+8j6eGHmCT974Sf7k1j9Bq1p2XMrgOrIUJGakU+m4t/denhr7GaHW7TmLEjM3/ZoSEtQAollAaGhGtNhKWk8upSQZXyRBo1E3b4KXyqFO+ZXXLzaoHeYKJqjT2zFEQxU9npt0at66p5PzM2foMHYtKSJsNjRj1poZ9g2vuo74+ByadsVgKEXqJsMKgzoQDxBNRWltyI2KyEuuszgsSjK5bIZopSQICod6WYI6mDaoC01Qd5o6Vx4XAKO9sWIM6oRzGk3r6u9ZT/r9KFtRovcSCCq0O/YCaxclRkem0VmSiKGVrO/l+vLxLxNPxfnEvk+suWyDpoFP7PsEZ71neeTyI3nt+npVfHIO1AKiSVhzQKBa0rQY5kv68lVsWDnGafsHKrFLRSvS1IoKmX65dCzRG3a2EoqneGloFWRTyAt/2QvnflD0dlK+GCSlggayBZWAptO0YVAvliAITYIg/K0gCEcFQTgiCMIXBUFoKtfO1btEUSCakPj2kfFravp2LJliNpygtQLlUKvJYlBj1Ko2EtR1rNF06nhTc2UThu1WfcUZ1GOzYTpsejR5cp5qpSajFu81kKDushuKLqoA2N1lRZbhTBWKEk+O+7iu3YxeUzq7clNTA80mHYc3ONTMhBJoVAJGbfmZoIWqQaum2aTlG4fGCMVT/OcHbiop4V+MtGqRj9wxwLFRHy9cXp1VWmv50hx8m6H2gwutFv01UZL4ar+Gzqitz4ooCmXHfPQ2GdGqxfkB0oxOuU/xSz/8Ja4GrvKlu77E+3a9L3t5rztjUK9eBHb/wP1EkhGetDYrCeosg02J9E1/qQlqUZtEEFWI+saS1pNLs+E4sqxcd5TLoJYCKxPUjiogPsyJcMUHHN99Uw/oxzALSzEQgiCw2bZ51QS1LGUKEkvfR3WTASmYQIotJPwyZnLpBvU5HM0K5sYT8ZS2rmrI2r3CoM4kqE1NzdlekVVXAldW4D0yMtrshGYrc85OTjtzFiRm1GNWDOqrwTJxqL2Xwd6LdmALwJoc6ui5C+gcGphZ3ci+OHuR7176Lu+67l1ssuTHq/6Fvl9gT8sevnj0iwTi1StGr7YSUyE0bUY0rY66SFCDUpSYcIULCkzEh4ZBFNH25vf+Vktes3Ip1RX1lbyu1ww0YdKpeeLsKu/T8NPKjKsLPyl6O0mPcp2gaS5sIFvbZSY+OYecunZmHZTqzPwX4AbeBrw9/ff/LnWn1pMe3NPJhC/C4avXjvmQufFrrTLiQxAEOu0GJmY3DOp61YhHGYmsJIMaFA51NRLU9Y73AGg2aQnGkmtPLapjXZgOlJxG3tWp3NyeqnBR4nxBYqetLOsTBIEDfXYOXdlIUPvCcewN2uxmUA3U12zErFPzHx84wI6O4go8S9VD+7pos+j54lMX6zpFPRtOYNKp0aprP6C33hPUi/Sqv4YG0OhUODaZmSqzQa0SBba0mOYRUwA/HP4h7/vJ+9CqtPznm/6T27tvz70C1zkwtSrYgFV0veN6Nlk28ajsh1gAfCtNo6Q7jMqiRSxhkBZAUKWvj8XKDKbNpAeiGhcb1P7Szrkpvx9Bo0HQL9xTOMw6vKE4qQqEe0SzGVkQsMRDFWNQZ9RqTyBqfEw4m1ccv/ut/Qz5h3Ie15PeCHI0hbar9PdS3ZQpSly4f8oY1CUhPqIB8I9hatmFQW2o/wQ1KEWJvpUJ6garDY02v5nBKSnFVf/VnAa1yd5EyO9Dksp/XZ50OlG3rv6etRnb0IpaxgJrJ5jzkncImregbnEgNjQQvzKSe/88HpIuF/qeRphZfYbA5w5/DpPGxIev/3DeuyIIAr974HeZjc7yd8f/Lu/XrSfJskxicg5tuwl1W1v9JKgdDcixFFIw/5mqseFhNF1diLrqzrpfSxN6GwDNodLvvXRqFXdsc/DkWWfuc9bwM8rP0ZeK3k4yzf8udCBb222GpExiujA8Sz2r1DuN9nQL+ZX0nz8BShyqXV96w85WGrQqHjk+UetdKZsyN36OKiM+QClKnKwwe3hDxetqulhwU2OFE9QWPd5QvKKm7Ng6MaibTMr3MHPjuN4UT0oMu0Mlp1MdZh0dVj0nK1yUOOINE4wmub4MBYkZ7dvUyIQv8qrHF2XYpvWiv3779fzgt24rC2u8WOnUKj5yxwCvjMxycLh+BzF84ThWQ+3xHqAY1N5QjOT6T4u86q+hM+rYYsM5EiC5SqFhMdrWZuaiM4gkS/zt0b/ld577HQYdg3zjzd9gs33z6i92nVu1IDEjQRC4r/8+XgmPM6lWZcV8JFzhkgoSM5JTgfTPygRIMlzoJpN2PvFcMuLD50e0WZcMTDabtKQkeZ5tX04JKhUxvRGHHMNW4b6D0x7lvXa5W1cMQm+2bcYf8+ONZk/axscVNrq2hILEjNRNiqGxuCixLAZ1GnMjtO7CYXDUf0kiKAnqsAfiC2ZNwOvG3JQ/3mMqNEVciudOUNvtyJJEpEQ++3JJkQgpvx9N6+oJalEQ6TJ3cTVQhgS1JCkGddNmBEFA29encIVzKHpO+UzoN29a1aB+fuJ5Xpx8kQ9f/2GsusKup7c3beftW9/ON85/g8uzq3Pc16NS/jhSOImmw4imrY2Ey7VmMWU1pG5RjiOJAjjU8eHhuuNPA4wIJlKCiGmmPINqb9zZhmcuzrHRLIFUWVYMakGEmSGYK26bCU8EQadCNBV2rX0tFiWWalA/IQjCuwRBENN/3gE8Xo4dWy9q0Kp54842fnhyilhy/SYcF8uVLh+qNuID0ga1b12XH13TujoTwtagwVphDmmbVfnsVWoadyiWxDMXny9Kq2c1pQ299Yr5GPbMkZTkkvjTGQ12WTlV4aLETBHjYBkN6gN9SgLvlVc5h9oXTtQFwzij3mYjvRXGFeWjd+7vpsWs42+fulTrXcmp2XAcu7E+3juHRY8ss+7LY9m4hp5XxxYbUkrGeaW8hs+2VjPOYICP/vRjfPXUV3nblrfx1dd/dQkvOKskCdznV+VPL9ZbBpRuyx+YTDB9aslzsiyTdEdKxnsASCEfUiyAHK0MJikzEN5UTgZ1ILAE7wHgSN9fVKooMaRtoFWo/PHhlOcUKkFFA918/eWlPOB+m2La5OJQJ8aCCBoRtaP069BsCerpsJLKLM2gPqv8bNmOo8GBK7IeEtQK/oLAQnAs4HYVzJ8G6LX0Zn3eZFOu6eZmy3tNl3Q6AVC3rT1O2WPpYTRYBgZ1cBKSEWhSGMLa3t5VER/Rs2mDeucuCLkgtrKENikl+ewrn6XH3MO7tr2rqN36rT2/hVFj5C8O/UVdzy4rRolJ5Xem6TAp73UySdJTe3yOJn0syiR515KcShEfGUE7UH8G9ag/xkyDDXl6qizru2ObA41K4PEzWdLuM8MKVmj3O9MbP1jUNjLXCYXOMlXZdYhGzYZBvUgfBB4GYuk//wX8uiAIQUEQrl1w0DK9dU8n/kiCZy6sg5HlPOTMGNQ1SFB32gzMhOKE4+Vtc99QeXTVG654QSJAu1W5katUmn5sVjn5rqcEtSe0Pqe0X0hPr76urXSEwu4uGyPeMP5w5YoST4370anFsvKIr2szY9SqXvUG9Uy4vhLU9SK9RsWv3z7AS8PeukXBzIYT2CucRsxXLWblmOgKrvvB7I1r6LTaN9tAgMnLvrKu12EP09D7dzw/+Syf2f8Z/vCWP0SjymOgxT8KiTC0rJ2gBug0dbK/bT8/sFiRlxnUUjCOHEuVXJAIilkszblIBStj2CxGfIhmM4hiGUoS/agsyw1q5TtcKQ71rNpAs1T548Npz2k22zbztj0D/Pj01JJC6802JaGfi0MdHw+i6TQhqEpHXol6NaJJQ2pZgrpR35i15C9vuc6B1gTWbhwGxzphUHcpP32KeSvLMkGPG3MRBnXuBLViUId85T1fJ5zKAMBaJYkAm8ybGAuOIcklJm896YHxJuXzqu3rIzE5iRTN/v2JnjuHprsbVVeazT+7Mm393UvfZcg/xCdu/ER+x9sssuvtfHTPR3l5+mV+OvrTotZRr0pMzoGAwqBubwcWBidqKdGiRdCp8k5QJ8bHkePxukxQj82GCdodJCbKQzgw6zW8ZqCZJ846Vw6YDD+t/Lz1Y6DWl2RQa4oYsBQEAW23ecOgzkiWZbMsy6Isy5r0HzH9mFmW5doAHWugWweaaDbpeOTYtYH5cAZjaFRCTW5GO21pY3IjRV2XGvGG2NRU+cRhJkFdKQ71aBpV0m2vf4O62bS+E9Tnp4OoRYG+MiRVB9Mc6tOTlcN8nBz3s6PDUtbyTLVKZO8m+6u+KNEXjld8yvV61bsP9NBs0vKln9Vnirqe3rtMP8Z6L0rcuIZekM6gprnLxMRFX9nWedx1nC+c/SiixsfbOv+QX97xy/knk1yZgsT8EtQA9/Xfx1UVnPAsNagTrvIUJIJiUMthF8nZynz2M9cZ9gYNgiiislrLwqBemaCunEE9E4ozqzZgjleWxynLMqc8p9jVvIv33NRDIiXz7SPj8887DA7MGjPD/pUJajklEZ8MlYU/nZG6ybCCQV1SehqUBHXLdhBFJUEddtV/mtXarfxMFyVGQ3MkYtHCEtSBK9h0tpwzLUwZg3q2vNd0SaeSzlSvgfgAJUEdS8VK54J70wMo8wZ1L8gy8avZ09nRs2fR79gB9rR5v6wocS4+x5ePf5kbW2/krp67Stq1h7Y+xBb7Fv76lb8mkrx2EHnxyRDqJgOiToU6PRiRmCpP0rcUCYKA2mEg6c7vdx0bVo5t2no0qGcixJvbSIyPr71wnnrjzjauesNcdC6bNTD8DFh7FCRY576iONRSPEXKH0PdXNx1grbLRNIdRopeGwHPku7ABUH4wLJ/qwRB+MM8XnevIAgXBEG4LAjC76yy3NsEQZAFQdhXyn5WWmqVyH3Xt/PUORf+SOWSfdWSMxDFYdIhitUvsupIG9QTr3JWaz0qnpSYmI1UvCARFgzqqUoZ1DPrL0HtrdBU2Err4nSQAYepLOVqGYP6ZIWKElOSzOlJP9dXgEl8oLeRC85gRdPf9SxZlpkNJ2isE5Oz3mTQqvjQ6/p57pKHI3VYuqwkqOsD8bGQoF6fx8SMir2GvlbVscWGc9hPKlk6i/Pi7EXe//j7sWhNCFO/TTy4pbAVZNAGeTCoM3pD7xvQCyp+QBCiC+eozHTpYpJRy5Xy+UAKIgUTFbkRnQnFsTVoUKcHaFVWa8kJaqnKBvWQe46gpgFDLFT2dS/WaHCUYDzIYPMgW1rNHOhr5OFDo0jpEi1BEBiwDTDkG1rx2sR0GJIS2m5T2fZH3aRfwaAuyaCWZXCeUQxqoMXQQiQZIZSo7O+1ZJnbQVDNFyUGPcrs5kIS1CP+kZzpaYAGm2Jcz81m54sXq8S0kqLVrFGSCIpBDTAaKBHz4R0CjVH5vaEgPoCsmI9UMEhidBT99u3QmDGolw7A/NOpf2ImOsOn93+65EJstajmdw/8LlOhKf7t9L+VtK56UmJqDk2HEtqZT1BP1z5BDaBpaSCZZ4I6njao6y1BnUxJTAeiCO0dJF0upHh5Al737GhBEFiK+ZBScOVZ6L8dBAF6boKpExAv7DiZ9JQ2kK3tsYAM8YmVyJ31qFIdg7sFQfiRIAjtgiDsAg4Cqw4HC4KgAr4MvAnYAfySIAgrIgqCIJiBjwEvl7iPVdGDezqJpyR+fKr2I2Clyh2M0WKpPn8aoNOeSVBvGNT1pglfBEmGniokqE06NWa9mulKIT5mwph16rri4eaSUatCpxbxrtOSxPPTwbLwpwHsRi3djQZOTfjKsr7lGnLPEY6n5o3wcmpfbyOyDEdG6xPhUGkFoklSkrwuvnO10i/fvIlGo7buWNQpSSYQTdRNgrrZdM0gPgq+hr6W1bHFRjIh4R4tfZrq06NPk5SS/Nu9/8b2ps3zqKm85ToPli7Q5x9kN2qM3N10PT82GolNHp9/POEKI2hViJbSvz8pnx9E5booc0NbTi0vslXZbOVBfFiX/h6NWhV6jVgRBvWQa46gtgH1XGUpOafSSfldzbsAeM9NPVz1hnlhaAGDkcugjo8rn8eyJqgb9aT8MeR0ubgz5CzNoA65ITIzP4vA0aAYvHXPoVapwdIJfiU5GUgb1JYCShKv+K/k5E8DqDUa9GZLBRLUTkSzGdG49n1Wj1kxqK8GSyxK9F5W+NNpM1mXMaizFCXOFyTu2A56KzQ0LTGoJ+Ym+I+z/8F9/fexs2lnafuV1v62/dzbey//fPqfmZhb/zPVpXCC1GwMTYcyOKWy2xE0GhLTWdjGNZDa0UAqEM9rADQ2PIyquXnFAGStNeWPkpJkDD2dACQnJ8uy3haznj3dNp44u+i9mjyuDEj336H8u+cWkFMwcaSgdc8b1CUkqOHaKUosFfHxbuBrwCngh8DHZVn+1BovOwBclmV5WJblOApz74Esy/0x8JfAurgDGey00u8w8r1rAPPhDERrwp8GaDXrEIUNg7oeNeJVRgOrkaAGaLfqK5qg7m5sKHl0vxoSBIFmk65iZUKVVDCaYMIXKZtBDbC701axBHVmvdd3l/9i64ZuGxqVwKEr9ZeOrYZmF7FNN5RdDVo1H3xtPz+/6Ob4mK/WuzMvfySBLFM3CWqtWqTRqMW5/hEfxVxDX7Pq2GwDYPKSr+R1HXUdZbN9M44GB1vbTFxwBgtDE7jOzSdHC9H9295OUCXy86Efzj+WdEdQtxRefJRNKZ8PUacYB5UwqL2h2HwxM2QM6uLPt3IigRQKIS4zMARBwGHWVSxBHdYbYW4OOVm56c6nPacxqA0M2JRyuXt3tdFo1PL1gwuJ1gHbALOxWWaiSwemE+NziA1qVI3lCwNljI3kTJR4Ks5sbLY0g9p5RvmZMagNisHrCa8TDrU/k6BWDPV8E9T+mB9v1LtqghrAZLNXgEE9jSaPgkSANmMbWlHLWGCstI16L8/jPQBEoxF1a2tWgzo2b1Cnc4WN/UsY1F888kVEQeS39/52afu0TJ/c90kEBD53+HNlXW8tFJ9S7qW1aYNaEATUbW0k68Sg1rSkjyNrYD6kWIzQiy+h37atGrtVkMbSs6Ttfb0AxMfL5829cWcbpycCCzP9h3+m/MwY1F37AaFgDnXm912sQS02aFA36TcMagBBELagpJy/A1wF/ocgCGu5V53A4qPpePqxxevdC3TLsvxDVpEgCB8SBOGwIAiH3e7aFhQKgsCDN3Ty8pWZdY+ncAZi84zHakutEmmz6JmYXd+/w2tRGW5zNRjUAG1WA9OByhnU6wHvkVGzSbsuGdQZTte2MhYODnZZGZ+NzBue5dTJcR9GrYq+5vJNu83IoFWxq9PK4VdpUeJsOMM23TCoV9P/uGUTtgYNX6qjFHU9vnctZh3udZ6gLvIa+pqVwazF3tZQskGdlJIcdx3nxpYbAdjWZiEYTeY/4J1Kgudi3gWJi3VT35toSUk86j60sD/ucFnwHqAY1CqTCAIk8uSEFqJyJ6hTQeVmOVvCzmHS4a5EgtodQme3KdsPVC5Ffcpziu2N21GLagB0ahVvv7GLJ88553GPA1bFvF6eoo6PBdF0mcsaklA3pY0lb3SeS9zWsDbLOKeWcdjXTYIawNY9j/gIeNyoNBoarLa8XjoSGAFyFyRmZLQ3Mjdb3uu5pNOVF38aQBREus3dXA2UkKBOxsF3dYlBDQrmIzaSJUF99hxqhwN1c7PyQGP/PIP6hPsEPx75Mb+y81doM5bwucuiNmMbvzb4azx59UkOThVXQFcvSkwqBrWmfeFeWtPWRqIOShIB1Oky37WKEmcf/gbJqSmafu0Dqy5XC42nPaSWrcp3uFxFiQBv2Kl8tp/IYD6Gfw5tg2BMfycMNmjdWTCHOumJoLLpELWqovdN020msWFQA/AD4PdlWf514HbgEvBKKSsUBEEEPg98cq1lZVn+R1mW98myvM/hyH/qTqX0wA2Kz/7o8fJMJaiFookU/khinvFYC3XaDeve5L8WNeIN0aBVzZf2VVrtlsokqCVJZmw2Qk+VkuDlUNM6TVBnplWXN0Gt3Oiemih/ivrkuJ9dnVZUFeLvH+ht5OS4n2h6Cu6rSfMm50aCelWZdGp+7bY+njrv4nQFPuPFyJfmptcTnsVh1q17BjUVuIZe7+rYYmPqsm+e41uMLsxcIJwMc2Nr2qBOD5BecOZ54zZ7BVKxggoSM1Kp1LxFZeP5uBdvxIsUS5Lyx1G3lF6QCGmD2mZBZddXJkE9F5/vvYC0QV1CSWImfa2yZDGoK5igNrU0Ldl+uZVIJTjvPc9g8+CSx1+3xUFKkjmRngGTSVcvNqileIqEKzQ/JbtcUjcpwaKkNzJvUJeUoHadBaMDTI4l63KHaxsIy0vWbghMQCpJ0OPG0uzIezBgxD8CsCriA5SixLIjPqanUefBn86o29LNaLAEBvXsFZCllQZ1Xy/xKyMrZp3MFyRmZO8D/zhyIspfv/LXNBuaef+u9xe/P6vofbveR6epk794+S9ISOu3yyUxOYdo1qIyL1wLq9vaSNZBSSIoqCBEYdUEdSoYxPv3f4/x1lsx3nJLFfcuP43NhhEF6NjcDRpNWQ3qvmYjW1pMPHHGqXCmx16G/juXLtRzM4wdUga781TCHS46PZ2RtttMKhAn5V/318YlG9QHZFl+CkBW9DngwTVeMwF0L/p3V/qxjMzALuAZQRBGgJuBR+u9KBGgp6mBfZvsfO/YeP23HOdQ5mKxVgxqUIoSJyvEHt5Q8Rr1htnUZKwaFqPNqsczFyNehsKkxXKn19m9jhLUTcb1maC+MB3AqFXRZS/PzTnAzgoZ1PGkxNmpALu7KsdS29fbSDwlVQxRUs+aDSk3FPWCiahnvfc1vVj06rphUfvqMEHdatHjWueID4q7hr6m1bHFRjyawjtefNHPYedhAPa27gUWGdT5cqiLKEhcrPubbyQlwI+GHpu/yS9HglqWZYXnbLOhbjaU3aCWJJnZcHwZ4sOKHA4XXTKV8vvm17NclTCoo4kUYzNh7G2OJdsvty76LhKX4uxy7Fry+O5uK4LAPKKppaEFk8bEZd/l+WUSk3MglZc/DcoUb8GgJumN4gw757dftFxnl2BujBojDeqGefO7rmXrVjiwwSkCXjfmAvnTalFNp7lz1eWMNjsh3yyyVJ57FDmRIOnxoMkzQQ2wybyJseAYklzkPnjTn8vmpQa1rq8PKRAgtciAl6JRYsPD6HYsQh819gMyj5/7BifcJ/itPb9Fg6Yy91Y6lY7/tf9/MeQf4r/P/3dFtlENJabm0HYsnYmsaWsj4XKV7bNUigSViLpZv2qC2vtP/0zK76flk5+o4p7lr7GZMO1WA1qtBk17O4nx8bKu/4072zg0MkPw4rOQii/gPTLquQXic+A6k9f6ZFlWUGBFFiRmpO1WzinXAuajKINaEIT/BSDLckAQhIeWPf2+NV7+CrBFEIQ+QRC0wLuARzNPyrLsl2W5WZblXlmWe1FKY+6XZflwMftabb11TycXnXOcm1qfHw5nGqlQK8QHKAb1lE8B3G+ofjTiDVWNPw0Kg1qWy1+ENZpmU60nxEeTSYc3FFt3A1/np4NsbSvvNFarQUNfs5GT476yrRPgojNIPCmxu8tW1vUu1r5NSvP7K69CzMdGgjp/WfQa3n9bH0+cdXJ2srJFX/loNpwZXKif967FrOABSkna1kolXkNf02ovA4f6qPMo3ebueXPO2qChzaLnYt4G9XlAAEdxbM2BzlvYGYvxg0vfWeBKlnjjCSDNzUEyicpmQ9NsIOmOlPWawBdJIMmsQHwApGZ9Ra1TSiM2siE+mk06ZsMJEqnymTJXvWEkGVq7lPe+1ILHXDrtPg2wIkFt0WvY0mLi2Khi7AmCwIBtgGH/QpFcPD34Um6DGpQUdVkS1JKkfA+WzSJoaWjBE1knDGoA/zhBjztv/jTAuZlz9Jh70IirD6Yb7Y1IqSSRufLc7yc9HpBl1K35MagBeiw9xFKx4gcNMgZ148CSh7VZihJjFy9CKoV++2KDuo+YAH9z9t/Yat/KAwPZKsXKpzu77+Q1Ha/hK8e/gjfirei2KiE5IZFwhecLEjNSt7VCMql8BupAakcDSXd2gzrhdDHzta9hectblqbp60hjsxG6G5Vzrqazo6wJaoA37GwlJclMHf0JqLSKIb1YPTcrP/PkUEtzCeRYCk2pCep2E4jCfAnvelaxCep3Lfr77y577t7VXijLchL4KPA4cA74pizLZwRB+CNBEO4vcn/qRm8ebEejEnjk+PosS8yUDtWqJBGg02YgKckVmfp3LUiSZH7nOyd5aah6J+eUJDM2U10sRptVGSSZLjPmI8PSXk8GdbNJSyIlE8ijVbleJMsyF51Brisj3iOjwU4rp8qcQs6kmiuZoLYbtWxpMXHoyqvToFaLAmaduta7si70q6/pw6xT8/+ern2KOpOgthnrJ/3eYtaRkmS8FWDRV0FFX0Nf6zI36rE065m87Cvq9ZIscdR1dB7vkdG2NjPnC0lQ23tBW2TfRtsu7psLcS5wBefoOIgLfOBSlDFbVTYbaocBOZ5CCpbv8z8TUq65sxrURRq9GTyIymJZ8ZwjjRIs5+ywIbdi/nb1tC7Zfrl1ynOKRn0jHcaOFc/d0G3j2JhvfvBgwDawBPERHwuismpRWco/4KduMswnqA1qAxbtyt97XvKPQiK0wqBuNjSvjwS1tQeA1MxV5mZnsORpUE+Hpjk4dZC7e+5ec1mTvRGAUJk41Il0SV6+JYmgGNQAo4EiMR/eywrGxWBb8rC2T2H3xkdG5h+Lns0UJO5cWLCxn4ctZiZiM3xq36dQicXzc/ORIAh85sBniCQjfOnYlyq6rUoo4QyBBJrlCer2dgCSdcKh1rQ0kPRGkbMMHnq+/GXkVArHx8pbhFlOjc+G6bIr9/iazk7ik+X15AY7rbRb9RjGn1PMaO0yP8HaBZauvDnUCwPZpfkSgkZE02589SaoASHH37P9e4VkWf6RLMtbZVkekGX5T9OP/YEsy49mWfaO9ZKeBsWAuH1rC98/PrEuE8DzCWpz7RLUnTblQn6DQ51dz1328F+vjPF/Hj1TtfTYdCBKPCXRW6WCRIB2q/I5KDeHenQmjCAsfM7Wg5rS3G/vOuJQu4MxZsOJshYkZrS7y8qkP1rWQaxTEz6sBk3FBy729zVy9Orsujw/lKKZUAJbg7ZqiKD1LmuDhvfd2suPTk3njyaokOpxcCGDISv3DJsqqaRr6GtdHVtsTF7yFZUOvuK/gi/mY2/L3iWPb2szc9k9RzKftK77/BK0QcFq3sYvhOOoEZgeHUPdaEBQl0pUXGZQp5NW5SxKzBjFTcalDOrF2y5UGQa0mKMkESjreXzIpRjUm/oUw0eqkEF92nOaXc27sp7P9vTY8YUTjKTDEAPWAWaiM8xEFSMzMa4UJFZC6iY9qdkonjk3LQ0txZ9vnWnMzTKD2tHgwB1ZDwxqJUE9N3EJZDnvBPV3Ln0HWZZ5+9a3r7ms0VZegzpjTqrbCkN8AFwNFlmU6B1awZ8GxdQTNJolCerouXOIViuazoVBmRlR4B9tNm7XOrilozos4n5rP+/Z/h6+e+m7nPHkh1CoF2UKErXtyxLU6dR8ZpCi1lI7DCDJJL1Lr69iw8P4vvMd7O98J9ru7hyvrq2iiRTOQIzutEGt7eoi5fYgRct3rSgIAg9u0dAdHyKx6fbsC/XcrCSo87iOSXiUc0WpDGpQMB/x8TnkdX6PWewVk5zj79n+/arTg3s6cQZiHBxef9NPXMEYWpVY0zKkjg2DelU9/PJVREEp/HnibHVGW696lJPqpmsgQT02E6bDakBbhhvGailzw7ie0oLn5wsSi0zwrKLBNIe6nCVyJ8b87O6yVtxAPdDbSDCW5Px07dEN1ZQvHN/gTxeo99/ah1Gr4v89fXnthSuo2XACW4OmrgYXMkXO67QoceMaehV1bLERnUswO5WbgZlLR5xHANjXurS2ZlurmXhSmjcNcyoZV1KFpRjUai32pm28lgYEbwKVozyBj+UJaqCsHOqZ9PXFkgR12lguluWcyiA+VklQu+fKd4035J6j02bA2GgDUSRZAcRHKBFi2D/MruZdWZ/f02MD4PiYgvlYXJQohRMkvdF5Vmi5pW4ygAyJmUjp/GmAlqUc9hZDC+6wu/5xc9oGaGgmOD0GgKVp7d9FUkry3Yvf5bbO2+gwrUzGL5cxnaCeK7dB3ZL/+9ZqbEUraotPUHsuQdPAiocFlQrNph5iV0bmH4uePYt++/Yl1wFfOfF3RASBTySrF14C+PD1H6ZR38ifHfqz4vnbNVB8ag5Bp0LVuPScoEkPSiSn6sOg1rQo9/rJZRxq9xf+BlGno/kjH67FbuWljHe0gPhQWPKJycmybucBq3Jdfli1O/sCPTdDcAp8a383k+4IqEVUttLpBdpuM3IslRPRsl5UrENzvSAIAUEQgsDu9N8z/x5c68XXuu7e3oJZp+Z7x9Yf5sMViOIw62p6I9phUw7ckxsG9Qo5A1F+es7F+2/to7epgS/97FJVLhQzN3WbqpigtujVNGhVFUlQl7O0rxpajwnqi86MQV3+G7GdnUoRUbnKBqOJFBedwYriPTLa16twqA+PlLf9vd41E4pv8KcLlN2o5b2v6eWxk5NcdhVfGleqfOE4tjriT8NCT4Z7fRYlblxDr6KOLTaAojAfR5xHcBgcdJm7ljyeOQ9lzks55b0MUhIcJRjUAG27eMAXoDXWiMtQnmP9YoNaZdEhaMT5qcHlUGYAPHO9kdnW4m0XqpTfj2g2I6hWTv1vTieoPWXElAy5Qwy0mBBEEZXFUpEE9VnvWWTkFfzpjLa0mDFqVRwb9QELBvWwb5j4RIY/bcr62lKlblKOi6JPKt2gtvWAbun1m6PBQTQVJZjIb1ZP5NRpYpdqhKmydRPwKDiSfBLUPx//Oa6Ii4e2Lq8FyC6jXbmWC/nK8/1OTDsRdLr571w+EgWRbnN3cQZ11A8hFzRtyfq0trd3HvEhJxLELlxYwp8e9g3z7Yvf5iG1g/7Z6vodJq2Jj9/4cU66T/LY8GNV3XYpSkyG0LQbEcSlHouqsRFBoyHhrA+DOjMAmlhkckaOHyf45JM0fuD9qJuaarVra2p8NmNQLyA+gLJzqLeEjuDHyHcmm7MvkOFS58GhTrojaJr1Kz4XxWihKLF29wzlUFEGtSzLKlmWLbIsm2VZVqf/nvn3qz4ipdeoeNNgGz85PU00kar17hQkZzBaU/40gFmvwaJXbxjUWfTfr4yRkmR++eZN/OadmzkzGeBn5yvPg7s6E0KrFmmvYnmmIAi0WfVMB8r7ORidCa8r/jQsupErI6ux0jo/HcRh1i1JY5VLJp2aAYeJUxO+sqzv7FSApCQz2Gkry/pWU5e9gQ6rnkOvsqJEXzixkaAuQr92Wx96tYov1zBFPRuqv/fOMZ+gXn+Ij41r6NVlaTZgtGoLLkqUZZkjziPc2HrjipDF5hYTosDaHOr55GiJBnXrLm7xSmhkDYcSx0tbV1qZokKV3YYgCgpvuAIJ6sVlqAsGdXFGb8rvy1qQCIsT1OUZZJIkmSH3HAMOJUihslorUpJ4ynMKgJ1NO7M+rxIFdnfZ5g3q1oZWjBojl32X59mg2s4KJqiBhqCG1ob8WcYr5Dq3Au8B4DAoRq8nvHaZmyzLTHz840z9n/9b/H6UImsXwRkfAObmHCbSIn3rwrdobWjltV2vzWv1Gq0OndFYxgT1NOq21oIDYj2WHkaDRRjU3jQXPQviA0DX10d8dBQ5mSQ2fAU5Hke/Y+G4+Lkjn6NB3cBHHDeB7yqkqtuRc//A/exu3s0XjnyBuXj9m3GyJJOYmkPbsXJwShAENJ2dRA4fQZZqnwgXdWpUVi1Jl3J+kWUZ12c/h6qpiab3va+2O7eGxmYUUz0TRNN0KoPVifHx8m1ElhGHn2HEvI+fXvBkR4e1bAedNS8OddITKZk/nZG62YCgU637osT1M8d9nemtezqZiyX56bn6AN7nK2cgNp9MqqU6bAYmZjcM6sVKpiS+cWiU125pprfZyFv3dNJlN/C3P7tc8RT1VU+YbrsBsQyje4Wo3aova4I6Ek/hCsbWnUGdMXnLWSZUaV2YrkxBYka7O61lS1BnChev7658ghpgX28jh0dm6n+abBk1E45XZLDiWleTScf/uGUT3z8+wZU0aqnamq3DBLVeo8KiV88XO2/o2pEgCEVxqCdDkzjDTva27l3xnF6jorfJyMU1DepzIKigOXuqMG+1DSLIyo3xT0PPEIyXfrOY8vlAEOZxGWpHeQ1q71wMi169BH8mGgwIOl3RRq/kD2TFe4Dynpj16rIxqKcDUcLxFAMOxQASbdaijfXVdNpzmi5TF3a9Pecye3psnJsKEE2kEASBAesAw/5h4uNzqJsNiIbK8PxFkwa0Iq2xpuIT1Mk4eC5mHaRxNCgGtSuydjAmMT5OYmKC6JkzyMkaFHxbewgEIxgsVjTa1YNXY8ExXpx8kbdteRtqMf/3xmhrLF9JotOFpqXwQYUecw9jwbHCURdrGNTa3j5IJJT38JwycKffoQxavDT5Es+OP8uHdn+IRsd2ZdZJoIwGYB4SBZHfvel38UQ8/MPJf6jqtotR0htBjkto2rPPRG764K8ROXGCma/9e5X3LLvUjob5BHXo2WcJHz5M82/+BqKxujiXQjU2G0arEue71NSOZiWdXs4EtXcIAuOoN9+JL5zIHjgSVdB9YM0EtZySSM5Ey8KfBhBEQeFQr/OixA2DukK6ua+JdqueR9YZ5sMZiM6zHWupLrthg0G9TM9ccDPlj/LuA0prs0Yl8ht3bObEmI/nLq2dZihFI95QVQsSM2qzGMrKoB6fVU62PVVkaZdDmjQX3htaH2ZMSpK56AxWpCAxo8EuK65gbL7YtRSdGPfRbNLRVqXBuf19jTgDMcZmXh3HOFmW6xITsV70wdf2o1WLNUtR+8IJbIb6C/a2WPTrMkG9obXVscVGyBcj4Mn//c3wp29svTHr89vazFxYC/HhPq8wWdUlXge3DZKUlRKpYfU4T4w8Udr6UAxq0WKZx2Womw0kZyLI+RQ/5iFvKE6TaeX/W2WzFc+g9vtR2XIP/DrMurIZ1ENuJUWZMaiV/S6/QX3Kcyon3iOjPT12kpI835MxYBtQEtTjwYrxp0EZ3EnZBDrijuIT1BnMTcvKhHjG9HaH1y5KDB1UjBk5GiV2uQbnLls3wZgKiz33QEJG37n4nXTx2YMFbcJktzPnK1OCenq6oILEjHosPcRSMVzhAmfTei8DAjT2ZX1a29cLQHxkhOjZswgGA9reXlJSis8e/iydpk7evf3d0NivvGBmuOB9L1W7mnfx4OYH+c+z/8mwv/rbL0SJKSVgoMmSoAaw/uIvYrrrLtxf+ELtsDiLpGlpIOmKICWTuD73eTSberA/lB/+ppYan4nQuShQJ4gimo4O4uU0qIefBqD/5regU4s8cSZHGLXnJnCfg3DuY0RyJgqSXDaDGkDbZSYxFUJeZxSHxdowqCskURS4/4YOnrngnp82V++KxFMEo0la6iRBvYH4WKqHD43iMOu4Z8fCRefbbuyk3arnb5+qHItalmVGZ8JV5U9n1G7V4wrGsk+fKUKj6ak/3essQQ3QZNSumwT16EyYWFJiayUT1GledDlS1KfG/VxfhYLEjPanOdSvvEowH3OxJImUTOOGQV2UHGYd77lpE987NsFVb/VT1LPh+uSHt5h167UkcUNrqD3DoS4A83HUeRSL1sJmW/ZE4NZWMyPeEJH4KjdtrrOl4z0AGhpJqLciqiM4Glt5dOjRkleZ8vmWmL3qZgNI6RvcMmgmlH2Wi4LKKBbx4UfMgfgAcJjKaFCnOf0DLasjPqSUxH/98SHOvVh4aZY77GY6NJ2zIDGjG7ptAEs41EIwhRSIo6kQfzqjiDlJR8JRfIJ6FcxNBvGRjxkafukggl65n4ycPFncvpQiazeBpA6zZfXr/UQqwfcuf4/bu26nzViYQWy0NxGaLZ1BLUsSCZcLTVsRCWqLElq6Grha2Au9lxTOeI7BOG2fYlzHrlwhdvYc+m3bEFQqvj/0fS7OXuR/3vg/0aq0YE8b3DNXCt73cui39/42erWevzr0V3U9KzExOQeigKY1++dREATa/+j/IppMTHzmM8jx2t7vqVsMyPEUvu/8iNjFi7R87GMImvoLKizX+OzKnilNVxeJ8XIa1M+ArQdDy2Zeu8XBE2ems3/2MhzqsUM5V5Xpkchwv8shbbcJJJn4ZG1mXZZDGwZ1BfXWGzpJSjI/PFne5tBKKZNEqhfERyCaJBhN1HpX6kLjs2GevuDiXfu70agWvrY6tYqP3DHA4auzvDTsrci23XMxwvEUm2qQOm6z6klJctnYyxmDer0hPkCZ6u9ZJyWJF6YDABVFfOxotyIKcGrcV9J65mJJLrvnGKxCQWJGW1vMWPTqV41B7Qsrx3FbnXGM15N+/XX9qESBrzw9VNXtRuIpYkmpLt+7Vose1wbi45pUY5sRvVFTUFHiEecR9rbsRRSy39pc12ZGlsldOJqIKCZLqQWJaSXFftSqSe4fuJ+jrqOMBcdKWp9iUNvm/525oS1XUWJOg9pmK74kMRBAZcl9bm02l++6ZsgdwqxX40inwHMlqL2TIbwTc5x+tvB7s9Oe0wAMOlZPUDvMOrrsBo6NKeblgG2ArdFeQEm3VVK+hhCt8SYc+rWLAbPKdQ5ENTRvXfFUg6YBo8aIJ7L6rE1Zlgm9/DLme+5BtFqJnjpV3L6UINnSSSChw9KwutXx1NhTzERn8i5HXCyjzU7IVzquLTU7C4kE6iIQH5vMmwAK51B7L6+KMlLb7aisVuLDV4ieP49+x3ZCiRBfOvYlbnDcwBs2vUFZ0NwOan1NEtQAzYZmfuOG3+CFyRd4ZuyZmuxDPopPhtC0NiCoc38e1c3NtP/R/yV29hzuv/u7Ku5dln1JM5Fnv/Eo+p07Md97b9W27Y14ed9P3seIf6Tg147NRuiyL73H13R2lg/xkUrCleeg/04QBN6ws5VJf5Qzk4GVy3bsBVEDY7kxHxlMl6acCepuBau1njEfGwZ1BbW93cJ1bWa+t04wHxmWY61LEgE6bcoXddK3MX0XlHJEgHfu717x3Dv2ddNi1vGlpyozhe6qVzF1a2FQd9iUwZIpf3luwMZmIjRoVTTVYRpwLTWbtOvGoD4/HUQQlDb7SsmgVbG11czJidIS1Gcm/MjyQiK7GhJFgX29ja+aosTMLKINBnXxarHoefeBHr5zdHy+BKYamg2vLE6rF7Wk8QD1nJraUHESRIH2zda8E9SeiIeRwEhW/nRGmRk956ez3EgCuC8AclkS1LIsk4g70KQu8ZbuexAQeGzosZLWudygztzQlotD7Q3Fs14bFWtQy7KsID6qlaB2zzHgMM3PhFJZrUhzc8iJpUGX6SHlmsE1EiBYYPr8lOcUKkHFdY3Xrbnsnh77QoLaOsDWyCYkQUbbUdxsxKf+5e859/wzay7n0s+gQY09VuT1l+uswiVWZz/mOwyONRPU8cuXSXm9GG+5GcPgIJGT1TeoY7pmEpIas251/vW3L3ybTlMnr+l4TcHbMNkbSSUSREOllfQlpqcBUBeRoG41tqIVtYwGCjCoZVnh6ObgT2ek7e1l7vnnkObm0O/Ywb+c/hc8EQ+f3v/phRmHogj23polqAHedd276Lf281ev/BWxVH3eJyUm53LypxfLfM89WB98EO8//COR48crv2M5pGlR7vnluJaWT30SQayeZfizsZ9xxHmEn4z8pKDXhWJJZkJxuhuXJag7O0nNzCCFy3DtPHkMYn7ovwOAe7a3Igrw+JnplctqG6DjhlU51El3BNGoQSxjCERl0aKyajcM6g3l1lv3dHJ01FeTabmFKsNyrZcENcCEr3o34vWqREriv14Z485tLStGBUEpmvnQ6/p5adhbkURmxqCuFYMaKBuHenQmTE9jQ9VQDuVUk1GHd53ggi46g2xqbMCgVVV0O4OdVk6N+0syqE6lDe7BTluZ9io/7e9tZNgdwrtOBh1KUcbk3GBQl6Zfv70fURD4u59XL0W9YFDXX4LaYdYRT0nzCf0NXVvq2GIj4I4wN7v2MfKo8yiQmz8NyjWMVi1yMReH2n1e+VkGg1oKJZCTGtSM0hb2caD9AI8OPVrSuSrl86FeZFCLDRpEo7osBrUsy8yWOUEthcKQTK5uUJt1BGPJ1bEreSpjUGekstoAJcW9WFNDftQ65dpk+PjaLOXFOu05zRb7FgzqtdNue7ptTPmjTPujtBnbuC7Wx6wlhKAp/LooFg5z/IkfcvrpJ9dcdlyd5qHOFnlcXANz09LQgjuy+u8tdPBlAIw334x+cBexS5fKYw4VoMCc8v+3qHJvd8Q/wsvTL/O2LW9DJRb+vhjtjQAlFyUmnYrhr2kt3KAWBZFuc3dhBvWcE+JzaxvUfX0kJ6eUl/S28O9n/p039b2J3Y7dSxds7IfZ2hnUGlHDZw58hvG5cf79TH2UDC5WKhhHmkvk5E8vV+v//l3Uba1MfOYzVf/eZCTLUeREBN2WPRhvuaWq235h4gUADk3nRmNk0/isci7sXp6g7uoEKE+KevgZQIC+2wEleLO/t3EVDvXNMHEEEtl9jIQ7XFa8R0baLjPx8Q2DekM5dP/1HQgCPHKs/jEfGZZjvZQkAkxsJKh56pwTdzA2X46YTe+5aRNNRi1/+1T5ixWuekOoRIFOe/kPoGup3ZpJUJfnczA2E16X/GmAJpMWXzhBokw87krq/HSQbRXEe2S0u8uKNxRnsoTPx4lxPx1WPY4qH/cWONSl8wvrXRmTcyNBXZrarQYeuKGDR49PEk9W5ziwgGepv/cu05exwaG+NtW5VTlGTuWB+TjqOopBbWB7U25jTSUKbGkxcX46x02b6yyotAulXyUo6UpP2xXGwXmaBwYeYHxunGOuY0Wvc3mCGkDd3ECiDIiPQCRJUpJzM6j9hQ8ESwF/+vWWnMtkzrulzg4LRhM4A7F5/jQw/7tajvmYHvKzaWcjjR1Gho/lb1BLssRp7+k1+dMZ7elRtn88jfnYGu3lirE4g8Q5fBlkGdeVoTXfh2FRMSqT3iI+F/EQzI5kLUjMyNGwdoI6dPAgmu5uNJ2dGAZ3gyQRPXeu8P0pQUGv8t6a5dzm8bcvfhu1oC64HDEjky1jUJd2HZd0phPUrYWXJILCoT7hPsHXznyNFydexBV2rf458aZn3DYNrLreDIcatZq/D/wQSZb4+N6Pr1ywsV9JUNdwNtNrOl7D3T1389VTX2U6lCXNWkPFJ5WEfb6zJ1RmMx1//hckro7i+uxnK7lrOTXzz/+CFJxE25ff8a5cSkgJDk4dRCWoOO46TiSZ/3EsM7twOYNa26kY1GUpShx+Btp3g7Fp/qE37mzjgjPIiCdLGLXnFkjFYep41tUlPZGyFiRmpO0xk/JGSYXWZ4Bjw6CusDpsBm7ua+L7xyfqfhqqKxBFqxaxGmqflHKYdGhUwkZRIvD1l0fpsOq587rchScGrYoPvq6f5y55OD7mK+v2r3rDdNoMS9jX1ZKtQYNOLTIdKN2gzpQ9rkf+NCgMaoDZOk9RRxMpRjwhtrXlviktlwa7bEBpHOpT4z52p9dTTQ12WdGqRQ6/CjAfs+kLpHpM4a433bOjlblYkiNXqzOwUe+ID1joz9jQtaWmLhMavYqJPDAfR5xH2O3YjUZc/Rizrc2cO0HtOqdwd1WlH6cSbuVGWa31wvRp7u65G4PaUHRZohyPI4XDWQxqQ1kS1N6QYhA3m1YO1KpsNkgmkUKFzQTNGMNrlSSC0nVSiobdyr4tTVAr212c/p6bjRGcidI+YKN/j4PJyz7CgfyuqUYDowTjQQabV+dPZ7Sjw4JWJXJs1EfSG6UhqeO4ujiTduryBQCioTn8rhxJvbSGkiMkxCRJbxHHRdfaswgcBgeeiCfnPa2cShE+dAjjzTcBYBhUDK5qYz4CHsWgtiSym5WxVIxHhh7hzp47aTY0F7UNo10ZRJubLa0DKDHtBJUKdXPT2gtn0Rt6FR70Zw9/ll//6a9z97fu5rX//Vre95P38acH/5RvXvgmR51HCcTTswk86TBTHogPALmvi++P/Yj37nwvHaaOlQvaeyEZgWBtjeFP7/80kizx+cOfr+l+LFciXVaXb4IawHjTARp/5VeYffgbzD33fKV2LasSThczX/saKpuGVKi6M45PuE4QSoR4cMuDJKQEx13H837t2Kxy3l0eRNN0lilBHZuDsZfn8R4ZvX6HMvPhibNZPv/dynGQ0ZdWPCVFkkqy3lF+X0KT7jpYrynqDYO6CnpwTyfDnhAnx0tjpVZazkCUVouuLvAHoijQZtUzMfvqNqivekM8d8nDO/f3oBJXf19++eZN2Bo0fKnMKeqr3lBN+NOQbjW26suSoPbMxYkkUuvWoG5OJ5vKVRhZKV12zSHJlS1IzOi6NjNqUSj62OoPJxjxhqtakJiRTq3ihm7bq6IocTYcRxTAot8wqEvVawaaUIsCz1xcPcFWLmUS1PU4uJDBkW0UJV6bEkWB9gHbmhzqYDzIhZkLq+I9MtrWasYZiOELZzmPus6DY222cD5KuiMIGhFVaws4T9OgaeD1m17P4yOPE00Wfj2TTJusKwxqhwEpGEeKrc7ZXUveVXoC5pPIBWI+Mgb1WogPoGQO9ZBbSSguMahtGYN64fpgelj5e1u/lYE9LSDnj/k45VEM1nwT1Dq1ih0dFo6N+kikTYKjqjP4Y4Vfr0xfvohKrQbAdWX1vhlnxMWcMVakQX1W+bmGQR1LxRbMzmWKnj2HFAzScNPNAKgdDtQd7URPnSx8f0pQ0ONGJUJDJDv64smrT+KP+XnHtncUvY15xIev1AS1E7XDgaAqDov3lv638Mw7n+Hn7/w5//LGf+F3DvwOr9/0elJSiseGH+OPD/4xv/KTX+HWb9zKPd+6hw9f+k8+39TEo+5jnPOey3lM0vb1AnDKHqJR38gHdn0g+w5kZp3UqCgxo05TJ7+661f58ciPOTx9uKb7sliJyTlUjXpEvbqg1zk+8T/Rbh5g6n//76KLaouR58tfRk6lMN56PVIwgRQp7fxSiF6YfAG1oOYj138EtaAuCPMxNhPBoFnZM6VqbkbQ6UiMl2hQj74EUmKFQd3d2MCOdkt2zIexGZq2ZOVQZwaXK5Kg7jKBAIl1yqHeMKiroHsH29CqxbovS3QGYrSaa8+fzqjTZnjVJ6gfPjSKShSyliMul0mn5gO39vHUeRenSyyOW6wRb7hmBjVAm1XPdBlKEkdnMiOr1UeVlEOZBHUm6VSvupCePr21tfIGtV6jYlubeZ4jXagyr7u+BglqUDAfpycDhOPVu/irhWZCcWwNWsQ1Btk2tLbMeg37eu38/EJh7NRi5atjfvhCgrq+j4kbKl4dW6zMToWIBHMPzB5zHUNG5saWPAzq9MDpheWYj1gQ/KNl4U8DJN1h1M0GhPadMH0SZJn7B+5nLjHH02NPF7y+VA6Der4osUTMh3duFYParmwzNesraJ0pv2JgVsugVovCkmvVbIiP6SE/ao1Ic4+Jpk4jVochb4P6tOc0BrWBAevqWITF2tNj4+SEj+hoAEkNV3VTDPkK7xCYvnyBgRtvQlSpFdxHDoUTYYKJIHGrVBziw3UO1Aaw9+VcpKVBmc3pDmf/vYVfVoyYTIIawDC4uyYJarPZgBDxKuiSZfrWhW/RY+7hQNuBoreh1RvQGgwlM6gTzumi+NPL1ahvZH/bft6z/T384S1/yH/8wn/w4i+9yBNve4Iv3/1lPr734+xv248nHuA/zUZ+78X/j3c89g5uevgm7vvefXzimU/wleNf4cmrT3LFfwWxp4tkVwtPdnr5zRt+E5M2RwK4Mf15qbFBDfD+Xe/HrrPz7UvfrvWuzCsxOYc2j4LE5RJ1Ojr+8i9Jzs4y/Ud/XIE9W6nY8BV83/kO9ne+E/2WdmBhRlA19MLEC1zfcj0tDS0MOgZ5eerlvF87Phumy25YEbQUBAFNZ2fpCerhZ0ClU7Ady/TGnW0cGZ3Nfi7ruVkxqKWlaL75mVYVYFCLOjXqloZ1W5S4YVBXQRa9htdvb+UHJybrmh/rCkZpsdSeP51Rx6vcoI4lU3z78Dh3X9dCmzW/gYNfubUXs17Nl35WnhS1LxzHH0nUpCAxo3aroSwJ6gybar0mqJtMyo2jt84T1BecQbRqkd4qDWrs7rJyssiixBNpNMhgZ/UT1KAUJaYkmWOjvppsv1ryhRN1mcBdr7pjWwvnp4NlK49dTbPhBEatCq26/i4XjTo1776ph21t+U+b3dD6UseWDIc69yDkEecR1KKaQcfa6IV5g3o55sOtIBTKZVAn3BHULQ3QNghRP/jH2d+2nzZjW1GYj1wGdebGtlTMx0w6QZ25zlisXCzntZTy+5TXr2JQNxq1CEIZDGqXMtNvMYouG+JjashHS68FlUpEEAQG9jqYOD9LNA9O52nPaXY07SioTG9Pj51oQiJwxY/YpkcSJC77Vk9AL1fQ62FudobO7Ttp7t6E80pug3ueDW3XkPRGkaUCr4tcZ6DlOhBzH+8dDQ5l0Uj2WTyhlw6i27IZdfMCNsMwuIvE+DjJmerNGAt63JjTKXr840ueuzx7maOuozy09SFEobRzm9HWyFypJYnTTtRtxfGn15IgCLSb2nld1+v4wOAH+PPX/jnfDsgc0l/P99/6fT57+2f50O4Psdm2mYuzF/n7E3/PJ575BPc/cj+3fOu1fOBXI3hv3sovbvnF3Bux9oCormlRYkYGtYH9bfs5PH24LtCqUkzB7RSC91gsw86dOH7zNwj86Ef4f/jDMu/dSrn/5m8QdTqaP/Jh5RzGQqdCpeWJeDg3c47bOm8D4Kb2mzjjPUMwnp/JOjYbydkzVRaDeuhpxWzWrDSU37CzFVmGn5zJgvnouQWiPvBcXPJw0h0BEdSNlQmHaruVosR6+B4Uqvq747hG9cANHXhDcZ6/7Kn1ruSUKxCjpc4S1NOBaF2b+pXU42eceENx3nPzprxfY9Fr+NVb+3j8jJPz09mn3xWiq17F1N1UQ4O6zarHGYgiFXqhvUyj8+UJ69OgbjaWp0yo0jo/HWRLiwl1lZjlg502/JEEYzOFX0CdGvfT29SAtUbm6d5NdgSBax7zMROK1yXDeL3q9q2KQfDzKmA+ZsPxukxPZ/RnDw5y13WlJ882VJ9q2WRGpRFXxXwcdR5lZ9NODOq1U0htFj0WvXplgjoPtEG+khMpUrNRNA4DtKZNc+dpREHkvv77eHHyxZzp01zKaVA3GkAoh0GtXFfkKklcvA/5SgqsnaDWqEQaG7QlM6iH3HNL8B4AotkMKtW8UZ6Ip/CMzdE2sLA//Te0IEkyIydXvzdLpBKcnzmfN386oz3dNlSA4Ipg3NRIg7qBYX9hKdPpy4qp0b55G639AziHL+c0HJxhZYq5ttkISYnUKjMPssp1Dlp2rLpIi0FJUHsiK39ncjxO+MiRebxHRvrB3QBET58ubH9KUMDrxtKc7u7xjy157tuXvo1G1PDA5gdK3o7J3kjIV/w1nCzLJJxO1K25e4bKqlQSZq+gbt5Cv7WfN/a+kd+84Tf5wp1f4LEHH+PQew7x32/5b/7k1j/h3dvfzU3tN/GHt/whanEVPIVKDbaeukhQA9zYeiPOsJPJ0GStd4XEVIY/Xfx9dNMHP4jh+uuZ/r9/RMK5OoO+FEWOHyf4xBM0fuD9qJuaUNv1oBKqlqB+cfJFAG7tuBWAA20HkGSJI84ja75WlmXGZ8J027NfB2i6OkmMj2d9Li8FncoA3jK8R0bXtZnZ2mriO0eybKMnfTxcxqFOeiKoGw0IFQqAaLvNSKEkqZn119OyYVBXSXdsa8HWoOGROsV8hGJJgrHkPNOxHtRpMyDJChv71aiHX75Kd6OB124urLzj/bf2YtSq+H8/KyylkU0jXuXEWkvER7tVTyIlzzMSi9XoTJhWiw69pjjGW61lMajRqISSfw+V1sXpINuqgPfIaHeaH31ywlfwa0+O++aLFmshi17D9jbLNW9Qz4bj2LMYHxsqTte1mWmz6Pn5xcpjPnzhBHbjRvp9Q7WRSi3S1m9h8rIv6/PRZJTT3tN58adBSRNuazNnMajTaANbb2k7jJKeRga1owFa02bftII3uG/gPiRZ4kdXflTQOnMZ1IJGRGXTKdssQd5QHJNOjU698vqoFAa1oNEg6Fe/r2g26fCUkKBOpiRGvCEGWpYa1IIgoLJY5pPfrpEAkiTTvsigbuk1Y7LrGDq2+rH0ou8icSmeN386oy67gT0GHaqUjK7bTL+1v+AE9dTlC4gqNY5NfbT0bSY6FyToyb6/mQS1pVVhIxc0cBHywpxzTYO6uaF5ybYWK3LyJHI0ugTvAaDfuRMEoWqYj1QySWhmBnN7Go/oWzCoI8kIj15+lNdvej12vb3kbRntjYRmi2dQS3NzyOEwmtbKJKhXyHcVpGTOgkS9Ws+Oph08sPkBPrnvk3zpri9xQ8sNa6/X3gcztU9QA/Png3yMzUqrmILE5RLUajr+8i+QEwmm/vfvVSQRK8syrs9+DlVTE03ve5+yXZWgFPG6shvUZycDnJksH1L0+YnnadI3sa1xGwDXO65Hr9LnhfkIRBQfK1eCWtvZScrvJzU3V9zOXXlW+TlwZ9anBUHgHfu6OT7m49LyGVqN/WBsWcGhTrojFeFPZ6Rdx0WJGwZ1laRVi7x5sJ3Hz0wzV2KZSSWUYTi21hniA2DS9+ozqC+75jg4PMMvHegpmNtqa9Dy3tf08sNTU1x2FXkgTmvUW3ssRlt60KTU6eyjM+F1i/cA5eTXZNThreMEtT+cYDoQnZ9GXQ1tbTWjVYmcKrAo0R2MMemPcn0NChIXa3+vnaNXfdf0TJHZcHwD8VFGCYLA7VsdPHfJQ7LCnxvlvdsYXNhQ7dSx2YZnLEgsS1HTKc8pklIyb4MaFMzHBeeyaa+uc+DYtiraIF9leNBqhwF0ZsW4SRvUfdY+djfv5vtD3y/IZMgU/S03qJXtNJQF8ZEtPQ3FJ6hTPj+izbpm8brDrCspQT02GyGRklckqEHZ98x+Ly5IzEgQBPr3OBg7O0M8mvve7LRbSf4WmqAWBIG7rMp+abvMDNgGGPYVnqBu6e1DrdXS2q/wr3NxqDMJ6sYOZVZJqpCixDxnERjUBswac9ZZAKGDL4Mo0nBgKddZZTKi2zxApEpFiaHZGWRZwtLRp6AnFiWoHx95nGAiyENbHyrLtow2O3OzM0WbhslpBQmgbqvSTCBv+rPTvKW8623sVwzqOsAJbLFvwaK11IVBHZ+cQzSqUVlKu47S9vbS8ulPEXrhBWa/8Y0y7d2CQs8+S/jwYZp/4yOIxoW0t6alIWvHgSTJPPJvf8UT//nZsmw/JaV4afIlbu28dR67o1Vp2dOyh5en1zaox2Yzs6RzJKg7OwFITBSZqh9+Ggx2aNudc5G37ulELQp8a3mKWhDSHOqFBLUsySS9lTWoNW0NoBaJj5XmBdVCGwZ1FfXgnk6iCYknsvFpaqxMSrmeEB8Zg3rCVz04f73oG4dGUYsCD924djliNv3abX3o1Sq+/HRpKeoRb5h2q76mqeN2q/I5mCqxKHF8JpxzZHW9qMmkrWsGdQYrU02DWqsWua7dzMkCDepT6cR1rfjTGe3vaySSSHF2snQkTz1KlmVmw4mNBHWZdfs2B8FokqMV5pf7wom6Rnxs6NpXxxYbsqwU3C3XYedhBIT8En5pbWs1E4wml3Zb5IE2yFdJdxgEFMQHKBxq5wLa4P6B+7k0e4kLsxfyXmfK50PQ6RANK29mNc0Gkp5ISam61QxqQa1GNJsLN6gDgVXxHhk5zLqSGNSZIMaAY+UUepXNhpROUE8N+bG3NaBfNiNkYE8LqaTE1dPenNs45TlFo76RdmN7wfu3W60mgEzQIDJgG8AdceOP5Xe9Ikkppocv07ZZSRQ6evoQRBHnlezX9q6wC7PGjKnZBiqhsKJE1znlZ+vONRd1NDhwR7IZ1C+h37EDlcWy4jn94G6iJ09VhYca8CjpbrOjFSwdSxLU37rwLfqt/QUNaq0mk72RZDxGPFLcvWrCqexrOUoS81LGoM6RoC5ajX0Q80O49jMCRUFkb+veujCoE1MhNB2mNQfq8pH9l34J42234fqrvyZ2pXxpdTmVwvW5z6Pp6cH+0NKBG7XDQNIbQU4uDUMcueLiI7F/5X2hf2EuXDqj+qz3LL6Ybx7vkdGB9gNcmr2EN5L7+AwLPVO5MJ6ari4AEhNFYD5kWSlI7HsdrNJB0GzScdd1LXz36MTK0FHPLcrshYBikKf8MeSEVJGCxIwElYi207QuixI3DOoq6sZNdrrsBh45Xnsm0nLVY4K681WaoI4mUnz7yDhv3NU233BeqJpMOn755h6+f3yCEc/K9up8ddUbqnnqOFMQOV0C6iWWTDEViNb8/1Kqmkw6PHWM+MgUT13XtvLmpJIa7LRyesJfEKf85LgfQYBdtTaoe5WpuNcq5iMcTxFPShsp3DLr1s3NqESBZy5UlkO9kX7fUK3V2m9FVAlZOdRHnUfZat+KRZv/OWdb+vw0X5QYnoG5aaUcrgxKuCOo7HqEzMB+26DCZo0p27u37140oobvX/5+3utM+XxZ09MA6mYDciyFFFy76C+XvHNxmrMUJGakslqLKEn0o7Lkb1AXa1wOuRWDuj9Hgjrp8yFLMtPD/iX86YzaBqwYzBqGjubGfJz2nGZX866iTKaOiMR5UpwY9zNgUxLQ+XKoZ8bHSEQjtG/eCoBaq6W5qydnUaIz5KSloQVBFFA36gs0qM8qCUHT2kapo8GxAvEhhcNETpxcgffIyDC4i9TsbPEJxgKUQaBYmh1KgV+6JPH8zHlOek7y0NaHymIYgoL4AIouSkw6MwnqKiE+vJeV97mhsbzrbexXftZBUeLDL4/Sqd/J1cDVgnn/5ZSckkhMh9C0l6fIWRAE2v/0TxB0OiZ/53eQk+WZkR947DFiFy/S8vGPIWiXngc0LQ0gs+JYcuL5H2IX5rALc4wceaLkfXh+8nkEBG7puGXJ4ze1KceTV6ZfWfX1mQT1aiWJAInxIlC73ssQmID+7HiPxXpoXzeeuRg/v7Dsc9eTPi6mMR+ZWU+aChrUkC5KnJhDXmezdDcM6ipKEAQe3NPJ85fcuIL1Zbq6MgnqOmJQG7QqGo1aJnzVaY+tF/3w5BT+SIL33NRT0no++Lp+NCqRrzxTfIr66kyY3hoWJAI0GbVoVMLStFOBmpiNIMu1RZWUQ81GbUmsxkrrwnQQi15d9YGu3V1WgrHkPDM9H50c97PZYcKoW6X4pQpqtejpaWy4Zg3q2bAyoNK4YVCXVVaDhht77BXlUKckGX9kI0G9odpKo1XRssmywqBOSAlOuE8UnITMdCTMc6jd55Wf5UpQu8JLbzpb09xip4JQsOqs3NF9Bz+68iMSUn6m8qoGdXpbSU/xsw29oVjOBDUoSeRiGNT5JKibTVpiSalo/OGQaw6HWYfVsHIgTWWzIvn8zDrDxELJJfzpjERRoP8GB1fPeEnGUyuen4vPMewfLpg/DUphpnY2xjlSHBv1zRvU+XKop9IFiZkENUBL/+acRYmusIuWBqVsT91kIFko4qNlhzIdfQ05DI4VJYnho8cgkVhRkJjRfFFiFTAfgbRBbW5qBmvXPOLjWxe+hU6l476B+8q2LaNNMXpDRRrUmdI7dUuVShK9l8ufnoYFg7rGRYn+cILfe+QUxy4q70stU9QJVwRSMtoSChKXS9PaStsf/D7REyfxfvWrJa9PisVwffGL6HfuxHzvvSueVzuU++bEIg51NJHCcuWHRAU9EVmLdO4HJe/HCxMvsKt51wou/Pam7Zg0pjUxH+OzEcx6ddbzAIDKbkcwGEhMFGFQDz2t/MxRkLhYd2xz0GzS8s3DS4tZadsNmoYFgzqDAmuurC+h7TZBUiIxvb5oBBsGdZX1wA2dSDL84MRUrXdliZyBKHqNiEVfW7NmuTpseiZmX10G9cOHRulvNnJLf1NJ62kx6/mlAz189+jE/NSXQhSKJXEHY/TUsCARlJuHVou+JAb16EztWdrlUJNJizdUfNKo0rowHeS6NkvZkin5arDTBsCpifwSXrIsc3Lcz+4aFiQu1r5eO4dHZuv2fS1FsyHFgLFtpHDLrtu3OTgzGajYgHcgkkCW2UhQ15kEQbhXEIQLgiBcFgThd7I8/wlBEM4KgnBSEISnBEHYVIv9LKc6tthwXQ2QWGQgnveeJ5KMsLd1b0HrsjZoaLPouZgxqDNoA0fpCWpZkkl6IvM39YCSoAZwLhTE3dd/HzPRGV6ceDGv9a6VoAZIFMmhlmU5jfjIPbBcjEEt5WlQZ2YKFov5GHLPZcV7QHq//f6s/OnFGtjTQjKWYvTsSpPxrPcsMnLB/GmA+GQIJAjatBwf89FubMegNuTNoZ66fAGd0Yi9bQEt0tq/mUjAT9DrWbG8K+yi1agkoNVNepLeaH7XFbJcEOYmk6BevO7wywdBo6HhxuzfR/3WLQhabVWKEoNeNwazBY1OD7ZuCEwSivp5bPgx3tj7Rqy68s2cMzWWZlAnp52oGhsRtVUaCPZUyKC2bQKEmhclHhqZQZbh+OUGDGoDh52Ha7YviUlldkcpBYnZZH3zm7H8wi/g/vJXiJw5U9K6Zr/xDZKTU7R86pMIWToY5gdAXQvnl5+eneRO+RDB7rs4rNlLl/NpkIpP6Ppjfk55TnFr560rnlOLava17ePQ1KFV1zE2E6Y7B94DlJCotquTeDGIj+FnwN6rYGzWkEYl8ot7u/jZeReexd0KKg107YMxxaBOuMMIOhWiubLX19puZcbYeitK3DCoq6zNLSZ2d1l55FgRIzgVlDMQo9Wir7qxtJY6bQYmX0UJ6vPTAY5cneXdN/WU5b349dv7EQWBv/t59umAq+lquiCx1glqgHarviQG9dg1Y1DriCYkwllSPrWWLMtccAaryp/OaEurCZ1azJtDPeWP4pmLsbvGBYkZHehtxBuKM1wCjqdeNZ+g3mBQl123b3UArJxKWCZl3ruNwYX6kSAIKuDLwJuAHcAvCYKw3FU6BuyTZXk38G3gr6q7l+VXxxYbUkrGObxwjM8k44phyW5tM3N+sUGtNStJyxKV8mXhSlq7QG+F6QUO9W1dt2HX2Xl06NE815vboFZZdaAWsxZZ5aNgLEkiJdNUkQT12ugVh0mZuVmMQS3LMkPuUNaCRADRakUKhZi6OIPeqMHWmv0asGObDV2DmuFjK4+lpzyKobqrqfAEdYb9adxk4fiYD2SBfms/Q778rsmnL1+kbWDrEuOotU9JYbuWYT6SUhJP1LOQoG7UI8dTSHN5pPT94xALrFmQmFGLoYWElFjC0g69dBDD9bsRG7L/jgWtFv327VUpSgx43JiblfMj1m6QU/zo/H8RTobLVo6YUSZBPeebLer1Ced09QoSY3MQnISmgfKvW6MHS2fNE9QvDyus4lhSoLthR20T1JNzCBqxIkV4bX/w+6gbG5n8zGeQYsUN7qWCQbx/9/cYb70V4y23ZF1G1KpQ2XQk3AtBt9MvPY5DCNB44B1MtN5NY8qDPFH87/mlqZeQZGkFfzqjm9puYjQ4ytRc7nDn2GyE7sbVf8+ajs7CEUOpJIw8l1d6OqOHbuwiKckrvb6eW5TC5FgwPZBtqLjvprLrEI1q4qPZDep6DUZtGNQ10AM3dHJqws9lV/2MZriCUVqK5B1XUh1pg7pev0Dl1sMvj6JVi7xtb+k3SqAUDD60r4tvHx4v2OAdnVHMsk01TlADtFkNJSE+RmfC6NRi0UzvelHmBrIeixKn/FGC0SRba2BQa1QiOzosnMrToM4Y2fViUO/vS3Oor1x7mI8Fk3PDoC63dnZYcJh1FcN8zIYz6feN966OdAC4LMvysCzLceC/gAcWLyDL8tOyLGfuJg8C5bmgqKHaBqwIAkswH0dcR9hk2USzobng9V3XZuaye45kSkonR6/LC22wlpLpm3hNy6LrJkGA1kHlxjQtjajhF/p/gafHns6rMG81g1oQBTTN+nmmZaGamVt7EDGTRM5XciKBFAohFpKgnivcZPGG4vgjiZwGdeZ3NjXkS3+Gsr/HKpVI3+5mrpz0kFpWBnbac5puczc2va3g/UuMBxEtWrZubsQfSXDFG2LANpCXQZ2IRvGMXp3nT2fk2NSHIKwsSvRGvEiyRGuDYnaq0qZYXhzqzCyCAhLUAK6IwqFO+f1Ez57FmAPvkZF+926iZ86WjZ2bS0GPW+FPA9i6kYFvDX2frfatXO+4vqzb0hoMqHU6QrOrl7jlUtLpQtNSJYM6Yx43banM+hv7am5QH7zi5cZNdsx6NXKkn8u+y/iivprsS3wyhKbNiCCW34RU2Wy0/+mfEr88hPvzXyhqHd5/+mdSfj8tn/zEqsupWxrmB0DdwRgdE4+TEHWotrwe9Y43kZBV+I99r6h9AAXvYdFacmKUDrQfAMiJ+ZBlmfHZ1RPUoHCoE+MFJqgnjyqDdwUY1FtazVzfbeNbh8eX+lc9N4MswfgrJN0RNBUYuFguJTluzpqgllIS//R/XuKlJ69WfD8K1YZBXQPdd307ogCPHKufskRXIFZX/OmMOm0GQvEU/kjx5S/rReF4ku8dneDNg+3Yy5g2/MgdA0iyzD/8vLCLhpF0groeDGolQZ3nVMUsGp0J093YUHczBApVs0m5kfOE6o9DneF5XlcDgxpgd6eV05N+UnkUJZ4c96EWBba3V7fMMZf6m400GbW8MlJcAqeeNRvaSFBXSoIgcPtWB89d8ihGW5nlSw8ubBRc1pU6gcVww/H0Y7n0AeDH2Z4QBOFDgiAcFgThsNtduyKpfKQzqGnuNjN52QeAJEscdR4tKj0NsLXVTDwpKSXSrrN5J0fXUiLDlVxefNS2S9mOtDD76f6B+0lICR4feXzVdcqyrKSRcxjUoGA+ik1QezPH6DVKEqVAIG9jMRUMzr9uLZWC+BhyKVPoB1pyGNRWK3GNEb8nnpU/vVj9e1uIR5JMXFh6Hj7lOVUUfxogPj6HtsvMnh6Fq3o8zaF2RVwE4oFVX+u8chlZlpbwpwE0Oj1NXd04h5ca1M6wwjJezKAG8uNQuxQ+er7fA4dBMX89YQUzEj58GCQpZ0FiRobBXciRCLGhypqYyxPUZ7Razs2NlbUcMSNBEDDZG5mbLe76LTldxQS1N/2ZqQTiAxSDuoYliYFogrOTAW7b3Myd21q4Mq78Xo+6jlZ9X2RZJjE1h6aM/OnlMr32Nuzv/iVmvvY1QgdXZzQvV8LpYuZrX8Py5jej37H6wJTGYSDpCiNLMj84Ps4bxUNEN90JOhODA728JO1AdeExBRVUoGRZ5oWJF7il4xbUYnbM7BbbFhr1jbw8lf3/6JmLE01IdNnXSFB3dSEFg6QCqx97l2j4GUCAvtvzfw3wjn1dXHAGl6Inu/aDICJfOUTKH6tIsj6btN1mkq4w0rKehxefGiXujPL81eIG1yqpDYO6Bmox67lti4NHjk8g5WGmVEPOQJRWc30a1MCroijxBycmCcaSvLvEcsTl6rI38It7O/nGodH5Msx8dNUbpsmoxayv/fTuNoueeFKaT/QVqtGZyLrHe4DCoIb6TFBnpktvba2NQT3YZSMcTzHsnltz2VMTfra1mdFrVFXYs7UlCAL7eu3XZFHiTDiBIJCzuGRDpen2rQ78kQQnxn1lX3fmeLvBoF6fEgThl4F9wF9ne16W5X+UZXmfLMv7HA5HdXeuCHVstjE9HCCVlBjyDRGIB9jbUhh/OqPMQOrI6AhEZspXkOgOIzaoEY3LvjNtg5AIL+Gzbm/czmbb5jUxH1IwCKnUGgZ1A8mZKHIRA1UzaYN6LcQHkPeNfcqn3JSrLGsb1DaDBpUoLOV15qkhtzLTLyeD2mrDb1HK23LxpzPq3m5Ho1MxtAjz4Qq7cIadRfGnpUiSpCeCttvEZocJs07NsbFZBqwKXmEtDnWmIHF5ghoUDvXyokRXWEkzZxLUapsOxHwT1GcVPIPBls9/bUWCOnTwZQS9HsP1q6eT9YPK77GSRYmxcIh4JIylKWNQd/EtiwmDoOYt/W+pyDaNtkZCvsKv36RolJTPh6atrQJ7lUXedHI/U2hYbjX2Q8gNsdrMED88MoMkw039jdyzo5XZ2TbUgrYmmI/UbAw5mio7f3q5Wj71KbSbNjH5v393fmAwH3m+8hXkVArHxz+25rLqlgbkhETKH+PcKz+lVfBh3vN2ALa0mPi5eBPm0NWFwuECdHH2Iu6Im9s6b8u5jCAIHGg7wKGpQ1mDamOzSqCue437fE2nMpZfUFHi0NPQfj00NOb/GuC+6zvQqcWlZYk6M7QNkhi6CDJLuyoqKG23GWRlwDSjRDzF0R+OMKWW+OW3l2eAvpzaMKhrpAf3dDA+G+HIaO0Tc3OxJKF4ilZL/eEPOtIG9aSvMiVQ9aSvvzzK1lYT+zbZ1164QP3GHZtJpCT+8dn8UwtXvaG6SE+DkqAGiuJQy7LM2Ez4GjGole+ot4gbuUrrwnSADqu+ZkZkBtexFod6oSCxPvAeGe3vbWR0JoyzgEGk9SBfOI41bUBsqPx67ZZmRAGeqQCH2reBZ6lHTQDdi/7dlX5siQRBuAf4PeB+WZbr74RRhDq22EglJFxXgyXxp0HpgxEFmB1JG2VlKEgESLiUgsQVKc3WdAJ3esGYEwSB+wfu54T7BFcDuafYZtjPqxrUDgNIMsnZwt/qmfSMrLUQH4v3ZS2l/L7069Y+z4qiQLNJW1yC2j2HXiPSYc2eRFPZbPit/YgitGxaffBcrVHRO9jElRPu+fDQaY/CDS+qIDE9pVrbZUYUBXZ3WzmWTlADa2I+pi9dwOJopcFqW/FcS98AYb9vSTHf8gS1oBZR2fT5J6gLmEWQSVC7w8p5J3zwJRpuvBFhjaI/7aZNiBZLRYsSAx5ln8zNyu8hKCf5scnIL2gcmLSVMQuN9saiShKTLsXgV1cL8eG9BJYu0FbofsieLpGrUVHiy8MzaFUie3vs3LHNgVrQ0KjeXJOixExBorbCBrXY0EDHX/4FyWknzj/9s7xeExu+gu/b38b+znei7e5ec3lN2kgdueBl28zTpAQNbH2jsn1RwNlxNxICnPtBwfv/wuQLADn50xkdaD+AK+JiJDCy4rlMz1S+BnU8X8xHbA7GDxWE98jIotfwpl1tPHp8kmhiUW9U980kpxTvb8VMqwpJ06Wc+zKdCACHHh9BFZOQB22026qzH4Vow6Cukd6wow2DRlUXZYmZVG1LHRvUE7PhNZZc3zo17ufkuJ93HyhPOeJy9TYbeesNnXz95dG8zc2r3jCb6qAgEaAtbVBPF8Ghng0nmIsl1zxxrQfNM6hD9ZegvuCcqwl/OqMBhwmDRrV0OlUWjc6E8UcS7O6yVWfH8tT+3jSH+hpLUc+E4jRuGJwVk61By54ee0U41L5wApUoYNFnn3a5oZroFWCLIAh9giBogXcBSyK4giDsAf4BxZx21WAfK6L2zYrZOXlplqPOo7Q0tNBpWo1uklt6jYreJiPSdAZtUL4EddabTsd1IKjAeXrJw2/ufzOiIK6aol4wqHObvZmpwkl34dfK3vkEde57gEINaimdtM4H8QEK5qNYg7q/2YSYYwBUZbPit/RjNydRa9eeMdW/p4VIMMFUmnV+2nMalaDiusbCBzAyaTVtp2JQ7em2c346iF3bikFt4LLv8movZ2roYtb0NEBrn4JoWMyhdoVdqEU1dv1CyEXdpF87QZ1KgvtiQd8BvVqPRWvBFXaR9HiIXbpMwxp4DwBBFDHs2kXkVOUM6mDaoM4wqB8bfoyIIPBQonIz5opFfCSmpwHQVBPxUYmCxIwyyewacagPDnu5vtuKXqPCotdwc38Tc/4ezs+cZy6+9uzKcio+OQcCqHMUs5ZThhtuoOnXP4T/kUcIPPnkmsu7/+ZvEHU6mj/y4bzWr25Rzi+nTk7zC6pDJPvvAv0CIrG/b4Bj0mZSZ4swqCdeYJt92/ysjFy6uU3h22fDfIzPKse4zjWMVm1XgQnqqy+ClISBO9dcVJZkho+5iUcWMBoP7esmEE3y+JnphQV7biaZVHozqoX4UBk1qJr0JNIGdXQuwfEnRrmsTvE/Hsh+jqm1NgzqGsmoU/PGna08dnKKeLL87MhC5AwoF4X1iPhoNmnRqkUmSyjIWw96+NBV9BqRB8tUjphNv3HnZqLJFP/0/Noj27Fkikl/pI4S1MpBvJiixNH0yOq1kKDWa1SYdeqipsJWUomUxJBrjm01NKhVosCuTgsn10AdnEgnrAc76ytBvaPDgkGjuuaKEn3hBLYNRERFdcdWByfH/WU/LsyG49gMmnXP7r+WJMtyEvgo8DhwDvimLMtnBEH4I0EQ7k8v9teACfiWIAjHBUFYnSGxTmQwa7G3G5m85OOI8wg3tt5Y0mdza6uZBv8lMNjB1FLy/knhBNJcYj5ttkQaPTi2wfRSg7qloYVb2m/hB0M/QJKz3wvklaDOGNRFFCV65+I0aFUYVjFwM+Z4Bt2xljKFiipLfj0PDpOuqJLEIfccm3PwpwEwmglaNtFsyM+479nZiEojMnRcMTlPeU6x1b4Vvbrw+6P4eBB1swExff7b02MjJcmcmQzSZ+1j2J/bxAv5Zgl63LTlMKhbevtBEJZwqJ1hJy2GFkRh4dZe3WQg6VmjaH5mGFKxggdpHAYHnoiH0MuKYWS8efWCxIz0g4PELl5Eilbmvm4hQe1AlmW+eeGb7BAM7Ax4KrI9AKPNTiIaIR4pbIAo6VRS7+pqID5kWTGomytUkAgKgxpqYlDPxZKcngxwc3/T/GP3bG/B4+5EkiWOu49XdX8SUyHUjgbEPAbGyiHHRz6Cbsd2pv/gD0l6cn/WI8ePE3ziCRo/8H7UTU05l1ss0ahBMKiRR8fpELzoBh9c8vyeHjs/Se1H5TwJsyN573MoEeKo6yi3dq6engboMnfRbmzn0PShFc+NzypIUqNu9TCFaLUiGo0kJvLsgBt+GtR66F792CZJMj/7z/P8+B9O8czDF+Yfv6W/iU6bgW8fWZTY7rmZpNSFSh+v2mcDFMxHZlbPwR8OIyUkIteZuK6tPrqYlmvDoK6h3rqnE38kwTMXahtwcQUzCer6M6gFQaDTZmBi9tplUAejCb5/fJL7dndUFI+wucXEmwfb+fcXR+anbufS2EwEWYbeOklQO8w6VKJQVIJ67BoyqEHhUNcbg3rEEyKekmpWkJjRYKeNM5OBVQvjTo370KrFmprp2aRRiezdZLvmihJnQvGNgsQK6/ZtSvLk2TKnqDcGF+pTsiz/SJblrbIsD8iy/Kfpx/5AluVH03+/R5blVlmWb0j/uX/1Na4fdWyxMXnZhzvs5saW4vAeGW1rM9Mev4Lk2A5lGISZL0hsyZGKat21IkENcN/AfUyFpnJyUvMxqFVGDWKDuiiDOp9jdMGIj7SRLeaZoG426fAEC7uuiSZSjM9GGHDkNqi9MzKSqKFRzG/gV6tX07OjkeFjblKpFGc8Z4ouSEyMB9F0LezbDd02AI6NKhzq1RLUC/zpbVmf1+j1NHZ04byygAlxhV20GpcmcdVNBuRoCim8SrllBjtTYFGoo8GBK+IifPBlRLN5zaK1jAy7ByGVInr2XEHby1dBjwtRpcZotXHCfYLLvsu8w7wVfGNFlbjlI5NdmQFXaIp63qCuBuIj7IWov3IFiaAwdo2OmhQlHh6ZISXJ3NS3yKDe0UoqsgkRVdU51InJyhYkLpeg1dL5V3+FFAox9ft/kHVQSpZlXJ/7PKqmJpre97781y0IRMwaBpJRJEEN2+5d8vyeHhuPS/uVf5z/Yd7rPTR1iKSUXJU/vXgfDrQd4ND0oRWDuWMzEbryuMcXBAFNZyeJfBEfw89Az83KAHMOSSmJn/7rWc6/OIWjx8ylV5yMp4t2RVHg7Td28fxlz0KXmqWDhKoftbq63p+2y0zKH8c34ufMzyc4rU3xP+6t4GBVidowqGuo2zY302zS8sjx2mI+MszTemRQA3TY9Nd0SeIjxycJx1O85+ZNFd/WR+/aTCie4l/WSFFf9SrFMz11kqBWiQItZl1JCeruxvpjLBWjJpMOb6i+EtQXnLUtSMxod5eVWFLikiv3VL6T4352tFvQqOrv9LdvUyPnpgMEosWVgdajfOH4BsO4wtrVYaXZpC075mM2HMe+8d5tqI7UucVGMibRFOosmj+d0bZWE1uFMWaN5ZnynsFrZE1QA7TtgsAEhJeapXf13IVRY+T7l7+f9WX5GNSgpKiT7iIS1KH4qgWJi7edt0GdQXzkm6A26/DMxQoqjr/iCSHLMNCS2wRyXlH2wxafzrnMcg3sbSHki3HyzCWCiWBR/OlUIE7KH0fbtXBN1GTS0dPYMM+hdoVdBOPZS82mL19AEEVa+nN/Nlv7N+MaXor4yPCnM1I3KcZKamaVa+fLT4HetsBJz1MtDS24w25CBw/ScOAAgiq/NGClixIDHjfm5mYEUeRbF7+FUWPkTa0HIBlRTNoKyGhTDOpCixIT005EkwmVqQpGpjf9WamkQQ0K5qMGDOqXr8ygFgX2brLNP9Zlb2B7WzNaqaeqBnUqlFC+/xXmTy+XbvNmHJ/4n8w9/TT+7353xfOh554j/MorNP/GRxCNhX3mLiUTNGFE7rtdmXW0SLYGLermfsa0/QVxqF+YfIEGdQM3OG7Ia/mb2m/CH/NzYebCksfHZsN02/O7x9d0dRG/cmX1WSUAQafC5u/PjfdIJSUe/6czXHrFyS0PDvCLn9qLpVnPs9+4QCpNR3j7jV3IMnwnnaKWZZmk1IE6cbFiA2bZpO1WzkXnHh0mJYNnk57XDOSXoK+F6u8O/VUktUrkLbs7+Ok5F/5I7QwJVyCGQaPCtMbUiFqp02Zg8ho1qGVZ5uGXR9nRbuH6KpS2Xddm4d6dbfzriyOrmmBXvcqNVr0kqEHhUE8HCv8cjM2EaTbpaNDW5+e7UDUZtQUnjSqtC9NBVKKw+lTbKmgw/R06laMoMSXJnJ7wV+W7VowO9DUiy3D06rWTop4JbySoKy1RFHjdFgfPXnSTKsDgWUuz4cTG4MKG6krtm20A9Id30W/rL2ldO8xzWIQIV9XlCQck3BFQCajsOdJWbWmjc3opf9egNvCGTW/gyatPEk6sRASkfD4QhDXNXnWzgURRCerYmsdo0WQCtbqAkkQ/otmct2npMOtISjK+Au6FhtzKQPRqCeqpIT+GpA/tXP6Dd72DTYgqgVOvjAAUlaCeL0jsXjpov6fHxrGx2TWLEqcuXcDR04dGmzs41Nq3mbnZGUK+WSUZmc2gzqBfcnGoJQkuPwmb7wFVYdfIDoMDecpJYmwM401r86cz0rS0oG5rq1hRYsDjxtLkwB/z85MrP+Et/W+hoTFt9PtGK7JNU2PaoC6wKDHpnEZdLf6055Lys5IMalCKEmtgUB8c9rK7y7riXu/121sIzPZwyn2KaLI6uNBMQaKmvfr30I3vfS8NBw7g/NM/W1IGKKdSuD77OTQ9PdgfeqigdYZiSYZ8Y4AVYcsvZl1mT7edHyVuRB49CHNrp4NlWeb5iee5qf0mNKr8ZuodaDsAsATzkZJkJn0Ruuz5BepMr72N+MgIkSNrDFgMP6P8zFGQmEyk+PE/nGL4mJvbHtrC3jduQq1V8dp3bmV2OsyJp8YApbjxlv4mvn1kHEmSkUIJ5JQWdepSVVE42g4jiBAZ9nNYm+C9dw/UNb5vw6CusR7c00k8KfGT01M12wdnMEarRVe3H9QOmwFXMEYsmVp74XWmY2M+zk0FeM/NlSlHzKaP3rWZYDTJ114YybnMVW8Is16NvY6md7db9UUnqHuukfQ01GeC+vx0kL5mIzp19Xha2dTXZMSkU3Nywpf1+SueOULxFIN1VpCY0Z4eGypRuGaKEiPxFNGEtIGJqIJu3+ZgNpxYk8FeiHzheF2dAza0IZNdR8gwy5bo9UtYu8WoOz4CwOlERxn2DJKuMOpmA4Iqx7Vca9qgzoL5uH/gfsLJME+NPrXiuZTPh8piWdPsVTsakAJxpFhh18ozc3EaVylIBGVqtMpqLcCg9uVdkAiKQQ0UVJQ45AohCNDXnN0EkmWZ6SE/jZKblN+X93p1DRq6rrPjPy9hUBnotxY+EBIfD4K40qDa023DGYhhUSl9M9k41LIkMT10ifYtq5dXtfYpRqNz+DKBeIBIMkJrwzLEh10Pwips8sljEHLD1jfm+1+bl6PBwXUjCjokn4LExTIMDlasKDHocWNudvDo0KPEpTgPbX0IrN3Kk/48p/YXqEyCeq5AgzrhdKGpBt4DlAS1qAFrT2W309gPgXFIVC9YFo4nOTXuX8KfzuieHa0kw30k5SSnPJUr51ysxKQyC1lT5QQ1KEWkHX/+ZyAITP7O7yCnlPNB4LHHiF28SMvHP4agLSx48JPT07RyAoCE/fasy+zdZON70RsRkOHCj9Zc59XAVSbmJvLCe2TUamyl19K7pCjRGYiSSMl5z5K2vvWtqOx2vP/8L6svOPwMGBqhbfeKpxLxFD/6ykmunvJy+7u3cf3d3fPP9Q4203d9M6/88ArB9MyVd+zvYnQmzMtXZuZnOWmECRg9mNc+l0OCRkVIJWJVC4y2aPiFwfaqbbsYbRjUNdbuLiv9zUa+d6x2mA9nIFqX/OmMMq2sxfCH610PvzyKUavigRuKa6IvRrs6rdx9XQv//MIV5mLZuXQj3jCbmhrqatCizWJg2h9de1rOMikGdX2gSsqhZpOWmVC8rEnJUnVhOlgXTGcxXZSYK0F9Ykx5vF4T1A1aNbs6LLxy5dpIUM+mWfeNGynciuu1WxwIAmXFfMyG49g30u8bqiO5w27GTBcwe1uRSzwHqrzKNOGDwfIYREl3BI1jlZtkkwNMrSsS1AB7W/fSaerkB0Mrp0enfL418R5QXFGiLMsK4sO09vdcZbXOlx+uJckfyBvvAUpJIhRmUF92z9FlN6DXZDfuA54o4UCcRm0g73LHjAb2tCAG9exT34ZKLHzgPT4WRNNqXFGCtadHmRo/6TGgV+mzcqhnJieIR8K0DaxuULf0pYsSr1zGFVYSi8sT1IJGRGXRkfTmuH+69DgIopKgLlAOg4NdIzLYrei2FMYy1Q8OkhgdzXvAI19JqRRzM17MTQ6+eeGb7HbsZlvjNrCmC+j9Y2XdXkY6oxGVRkPIVyCDenq6OgWJoBjUjf0FJ+ULVqYocfZqZbezSEeuzpKUZG7KYlDv6rDSpNoKCByePlyV/YlPzaGyalEZazPAr+nspPX3fo/I4SPM/NvXkGIxXF/8IvqdOzHfe+/aK1im7x4d42bNswAkg9n/T3u67ZyXuwk1dOeF+Xhh8gUAXtPxmoL25ab2mzjiPEJCUmbbZHqmuvNMUIsGA/Z3v5u5p58mNpwjwSzLikHdfzuIS63SeDTJY186wfj5We7+le3set1K/+a2d2wBGV74ljJr4d6d7Zh1ar51ZGzeoFbrgzD6Ul77XA6NnZvBNZfAohL45dduqkvM5WLV9969CiQIAm/d08nB4ZmaYSxcgSit68CgvtY41P5wgh+cmOSBPZ1Vx6v81t1b8IUT/MdL2S8gRmfCbKojvAcoCepwPEUgukrZyzIlUhKTvsg1ZVA3GbVIMmsWXVZL4XiS0Zkw22rMn85od5eNc1NB4smVRYmnJvw0aFX0rzIluNba39vI8XHfNTFjJGNQb2AiKq9Go5bru2w8c6E8BnU0sZF+31D96YjrCFOWIYiqmJkOlbYy1zkCqkaOlOErIyclkjMR1C1rXGu07oLplQlqURC5b+A+Dk4dZDq0lJecr0GdMceTnpWYkFwKx1PEklJeGCaVzVYQ4kNly38guDmdoPbMFZKgnlsV7zE9rJjSDnO8oAQ1QNcuKxIS23z7CnodKKZ/YmJuCX86o+3tFrRqkRNjfvqsfQz7Vhok00PpgsQt2QsSM9IaGrC3d+IcHpo3qJcnqEHhUOdEfFx8HLr2Q0PjWv+tFWoxONh1VSZ2w9aCwyyG3cpsgsipld+FUjQ360WWJWa0IUYCI7xj6zvSG7SD1qQUJVZAgiBgsjcWhPiQk0mSHg+aaiE+vEOV50+DYoJDVYsSXx6eQSUK3LjJvuI5URS457pe5Fg7h6pkUCsFibW9z7A++FZM99yN+2/+Buef/hnJySlaPvkJBLEw62/KH8F75ThdnAZRIuHKfn7Z1mbGqFVzzHQbDP9cKeRcRc9NPEevpZcuc1dB+3NT+02Ek2HOeM4AMD6rHNu6C7jPt7/n3Qg6HTP/+q/ZF/BchODkCrxHLJzg0S8eZ2rIz+s/sJPrbsmeQrY0GbjxF3oZOuZm9IwXg1bFW67v4MenpglNz4FaQNW7uWoJalmSeel7Q7gFMAgCD/U7qrLdUrRhUNeB3ppOzz56YrLq25ZlGWcgRqu5PgsSQUF8AEzMXlsG9XeOjhNLSrz7QIWnW2XRDd02XrfVwT89N0w4vtTwTaYkxmbC9NZJQWJGbVZlEKWQJP2kL4Ikk1e773pRUzpp5A3Vh0F90amw1uohQQ0w2GklnpK46FxZPnRi3MeuDisqsX5mBizXvt5G4kkpZwp8PWk2pCQcNhjU1dHtWx2cGPcxU4ZjQ2ZwYaMkcUP1pKPOo8zYlRmHU5d8pa3MdZaAZTPOQKzkAd+kNwLSKgWJGbUNgvs8JFdu7y39b0FGXoH5SOaboG5K4xwKKErMHCvWKkmEwg1qsYKID0mSGfasblBPDfnR6lXY7aqCE9RjyRGmLEMYxgo3D1MzUaRwEk33yn3TqkV2dVjmixKzJainLl1AazDQ2LG2cdPaN7BqghrS5ZnZEtTBaZg6DlvesPZ/Kosa3VEa52BmZ2EGE4B+504QBCJlLkoMeJTRpkOhk5i1Zt7Ym0aXCIKC+ahQghoUzEchiI+kxwOShLoaiA8ppfBuK82fhgWDuop83YPDXnZ1WnOGvV6/o5VEqJcT7hMkUpXt/JLiKWU2TYEG9VPnnBwf85VtPwRBoP2P/gjRYsH3zW9ifM1rML6msLQywCPHJrlXPASCjGaVIl6VKHB9t43vR/eClICLT+RcZzQZ5fD04YLwHhntb92PgDCP+RibDSMI0GHLP2ipbmzE+uBb8T/yfZLuLCPU8/zphYLE6FyC7//NcdyjQe794C627Fv9e7vnnh5srQ08+18XSSZSPLSvi0gixdQVH+omA8Kmm8B7CUKeNfc3NnwF/2M/zPv/t1yXjjhxjwb5Icr5Ve1ceA+/fu7rnJ85X/S6K6WaGNSCINwrCMIFQRAuC4LwO1me/7AgCKcEQTguCMLzgiDsqMV+Vks9TQ3cuMnOIzXAfMzFkkQSKVos9WtQt6cPOpO+awfxIcsyDx8a5fpuG7s6a4Mb+O27NuMNxXn45aWlIZO+KElJZlNj/SWoQRnNzVej6ak/11SCOj0Vt5CkUSV1YToAwHV1YlDvTuM7Ti4zeBMpibOTgfnn61X7e5UEyF8/foHHTk7mxPCsBy2YnBsp3Grojm0OZBmeu1R6JDQzuLDx3m2onnTEeYTN3T0YbTomSzGoJQncF5Ad2wEFU1WK5qftrob4AMWglhLgubDiqU2WTQxYB3h69Oklj+eboBY0KlRWXUFFiZnriLwQH4UY1IEAKkv+51qzTo1OLeLO87pm0h8hmpBWT1AP+Wntt6K2W5HDYaR4/oMQpzynGG48QcIrMDNVWFJ/viAxS4IaFMzHqQk/fZZ+nGEnc/G5Jc9PXb5A28CWvJKOrX0DzHk9TLuV6/isBnWTHimUQFo++/DSk8rPIvjTAIbjirk+tqXwayqV2Yy2v59omYsSg2mD+rngKzww8AB69SLTylZhg9puLyhBnZxWZkpUpSTRPw6pWHUS1AY76KxVK0qMxFOcGPdxc3/uWQC39Dehig+QkGKc8Z6p6P4kpkMgg7aAgsRANMFHHz7Gb379aFlnT6obG2n/0z9B3dZGy6c/VfDrZVnmu0fH+UXdEYRNt6JuM+dMUIPSo/OIpwPZ2Arnc2M+jjiPEEvFuLXz1oL3yaa3cV3jdbw8nTaoZyK0mvUFdyA1ve99yMkkM1//+sonh55Wyj7tSoFyOBDnkS8cZWYyxJs+PEj/nrUTyCqNyOveuRW/O8KxJ0bZ021jwGFUBi+aDdBzi7Lg2MurrkeWZaZ+93eZ/NSniuL2pxISL39/mIRZzXO6FGhF4mPKOcoT8fCXh/6SZ8aeKXi9lVbVDWpBEFTAl4E3ATuAX8piQD8sy/KgLMs3AH8FfL66e1l9vXVPJ+eng5ybClR1u86AcjFYz4gPnVqFw6yrGQKlEjp0ZYbLrjnec1P109MZ7ett5Jb+Jv7h2WGiiYUT4tUZ5WJ80zWQoL4WDermTIJ6rj4S1Oengxg0qrz5X5VWT2MDFr2aU8uKEi86g8SSEoN1blA3mXR88vVbueya46MPH2PvHz3Jr/7rIb5xaLQgPmc9aN6g3khQV0W7u2zYGzT8vAyYD98GnmVDdSZ/zM+l2Uvc2HYjHVtsTF7yFdxJMS/fVUiEMfcoqIFsM24KUcKtXGuo10pQt+5SfmbBfADc0X0Hh52H8ccWBlhTPn9eBrWyfUNBDOpMgnqtkkTIn0Ety7KC+CggQS0IAg6zLu9z3JBbuU4dcGQ3gWKRJN7JOdr6rfO/OylPfjYoBrWvQzF9h48VdjyNj82BWkTTmv2zsKfHRiwpoZWUcs7FRYmJeAzP6Ahtm1fHe2TU2q8Yjt6rV7Hr7GhVK4/X6qY0+mV5ivrS42DpXPhMFqjYocN4rSJjluKuRQ2Dg0ROny7+O5xFmQR1QBdTyhEXy9pVMcQHgMneVBCDOuFUUu+a1ioY1F6FhVsVg1oQFA51lRLUx0ZnSaRkbu5byZ/OSK9RcUv7fkAxRyupxFThBYk/ODFJJJFiwhfh6wdH135BATLfcQebn/4Z+u3bC37t6YkAkvsCPamrsOMB1I4GUrNR5MRKhCLA3h47CUnA1Xm3MgCWoyjz+Ynn0al07GstHKEEcKDtAMddx4kmo4zNhvMuSFwsbW8v5nvuYfYb/4UUWjQImUrAyPPzeI+52RiPfP4ofneEN390N72DzXlvo3tHIwN7Wzjyk6sEvVHeubeLpqRM0KiGjj2g0q3JoQ49+yyREydAEHB/4QsF/z9PPztBwBPlx2KEB/Z2ous2zxvUz4w9g4zMXT13FbzeSqsWCeoDwGVZlodlWY4D/wU8sHgBWZYXu7RGoH7awCqkNw+2oxaFqqeoXQHlgqXFXL8GNSiYj2uJQf3woVHMejX37S5Pe3yx+q27N+MOxvjvVxYu2ka8yo1WvTGoWy16BAGmCjSotSqxrgdgClVmKq63ThLUF51BtraaEOsEmyEIAru7bCsS1BlkxvVdthrsVWH6rbu3cOj37uFbH76F996yicvuOX73u6c48Gc/5e1/9yL/+OwQV70l8leroEwK12bYSOFWQypR4HVbHfz8ohupxAK52XD6vdtIUG+oTnTcdRwZmb2te+nYYiPkjxMowIxdIrcypdXasxuzXs35UhPUrggqqxZRt0aKq2kzqPXgzG5Q39lzJyk5xfMTzwMgxePI4TAquy2v/VCnp2Dna/p5C0R8yNEoUnT1azApFIZksiCDGpTB97wNapeSOh5oyW4COa/4QYb2Aev8fhRSyHfac5rNnb209lkYOubK+3WgJKi1HUaEHCVUN3TbAPAHlMTnkG9o/jnXlWGkVIq2zasXJGbU0qcgGyLjLlqN2Y1O1bxBvei7kozD0DOw5fWKoVigZEki/PLLXNlixh1Ze4p6NukHd5HyeEhOTRX1+mwKeFzEtTLXd+6l39a/9ElrN0RmIF6ZayejzU4sHCIRy+8eJenMJKirUJLoTX/GmgsrsyxaVTSoD16ZQRRgX+9K/vRivWnnFlKxFp65unpitVQlJucQ9GpU9vxnpv/3K2Nc12bmNQNN/L+nL5d95mShjPiMvnN0nLeoX1H+sf0taFoMIJNzlk6mBPYVw62QCCtJ5Cx6YfIF9rXuWzrDoQAdaD9AQkpw3H2cidkIXUUGpBrf/6tIfj++73x34cGJoxAPwsCdBLwRvvf5o8z5Ytz3WzfQfV3hrP7bHtqMIAo8998XeaCvGTUCh/xhUOugc++qHGpZlnH/7ZfQdHbi+MT/JPTiS4Reyr9YMRZJcvhHI8gtOi6Q5IOv60fbbSYxFUJOSPxs9Gd0mbrYYqvScaEA1cKg7gQWD2GOpx9bIkEQflMQhCGUBPVvV2nfaqZGo5Y7tjn4/vFJUiXeWBYiZ1A5kbbWMeIDlFTkRWew5JvuetBMKM6PT03ztr1dGLSFt4OXU7f0N7G/187f/3xoflrRqDeEXiPSUmdcco1KxGHSFZSgHpsJ02U31DVzuFDZG7SIQv0wqC9MB+uGP53RYJeVC9PBJTMDToz7sejVdTczIJdUosD+3kb+v7fs4NlP38mPP/ZaPn73VsLxFH/2o/Pc/tfP8MYvPMvnn7jA6Ql/WVNI5dJsOI5Fr0Zd523R15Ju3+rAG4pzerI0hvkGg3pD9aYjziNoRA2DzYN0bLYB5MR8SLEUkbNe5FzXjK6zAAgt27muzVyWBPWa6WkAlRpatsN09qm6g82DNOmb5qfcpmZ9ysvyTFBrmg3IsRTSXH6c1YUEdX4GNaxt9EoB5dijslry2oeMHGZd3uiyIfccVoMmp7E+NeRHEKC1z7Kw33kmqIPxIFf8V9jVvIuBPS14xubyHgiRU+mCxO7c10SdNgMOs44rUzp0Kt0Sg3r6crogMc8Eta7BiK2tHdkZyIr3gDSbnGUG9eiLigmzpTi8R+z8eVJ+P54dbbgjxc3YMezeDUCkjJiP0YlLBPXxlelpAFt6xmqFUtRGu2JehWbzS1Enpp0IWm3e3+2S5L0MOgsYq1SO1tiv4FQqzHsGhT+9s8OKWb/6YPpd17Ughfs4M3OClFS5EvLEZEgZoMrTFD4z6efkuJ937e/mf917HTOhOP/8XPUKJnMpkZL4wYlJ3m44At03gaVj/hyXdGfHfDQatfQ2NfDDwADorXBuJeZjYm6CK/4rReE9Mrqx9UbUgpoXJw4y5Y/QbS88QQ3QsGcPhr17mfna15CT6UGB4acBAb/pAN/73FFioQT3f+wGOrbYitqGya7nwJv7GDnlJXJWOTY8OuYlmZKU3+vkcYhn/33OPfUU0TNnaP6N36Dxve9F3d6O6/NfyPt+79gTV4mGEvyAMHde52Brq1k5N0kygTEPB6cOclfPXUUPYFRSdXvXKMvyl2VZHgA+A/x/2ZYRBOFDgiAcFgThsDsb5Hyd6a17OpkORHl52Fu1bbrSiI+WOk+YvmFHK65gjJeq+LuplL59ZIx4SuLdNcR7ZCQIAr911xam/FG+c0RJ7494w2xqNNZNInax2q16pgKFGNSRgpp914NEUaDRqMNTB4gPz1wMz1ycbW2F3YhWWrs7rSQleUkq7tSEj91dtro8Ea8lQRDY3m7hY/ds4Ucfey3P/a87+f237MDWoOH/PX2Zt3zpeW77y6f5P4+e4cUhj3LhUweaDcc38B5V1uu2KjegpWI+FhAfGwnqDdWHjriOsKt5F3q1Hnt7A3qTJqtBHbsawPm3R/H++1mCP8sxXdp1HixdoLewtdXM+elg0YN8siyTdEXQtOR5rdG6SzGos2xPFETu6L6D5yeeJ5FKzJvB+SM+MgZCfobqTCiOTi3SkEdYIl+DOmMEF1KSCBSI+JhjwJHbBJoe8tPUZUKrV8/vR74G9VnvWWRkBpsHGdirHE+H8sR8JN1h5ISUkz8Nyvl8T7eNE+MB+q39XPYvFCVOXb6AucmByZ5/Uq+1bzMGbzKnQS1qVYhm7VLEx8UnlOnl/bfnvZ3FCh1UUqjhwYH5gsZCpdu2DUGjKWtRont6lLhR5PWbXr/ySWu38rNCHOrMezbny49DnXQ6Ube2Vuea1HtZKUis1vVvYz9IyYoyvwGiiRTHx1bnT8/vklHLJuMuEnKYC7MrOwDKIVmSSUyH0BTAn/7mK2No1SJv3dPJDd023rizla8+N1yWsutS9OxFN6bwKN3xIdihgA40DoNSxLsKh3pvj51XxkLIW98IF38MqaVp8BcmXgAoyaA2aozsat7FixMHkWToKuE+v+kD7ycxMUHwiXSp4/AzzDbezfe+PEQyJvHAx/fQ1lcaGnL33V00dhi58pzis5wIR3n2klvhUEsJmDy64jWyJOH+2y+h3bQJ6wP3I+p0OD76UaKnThF88sk1txnyxTjx0zE0fSYuxON88HXKjJLM4OmlM6dISIm6xHtAbQzqCaB70b+70o/l0n8Bb832hCzL/yjL8j5Zlvc5HFUaFayg7tneikmn5ntVxHw4AzGMWlXO5tt60et3tGLWq/nOkfFa70pJkiSZh18eZX+vna2t9ZE6fe2WZq7vtvGVZy6TSElc9YboqdOUaZtVz3SBJYnXEn86o2aTti4QHxfTBvC2OvksZ5ThTJ8a9wHKRez5qWDd86fzVXdjAx+4rY///vVbeOX37uGv3r6b7e1mHj40yru/+jL7/vSnfPKbJ3jizDSReOWSImtpJhTfSOBWWc0mHbu7rDxzsTSDejacoEGrKrh4ZkMbqoTCiTBnPWe5sfVGQDH5MhzqjOSUhP+JEdx/fwIkGd1WO4GnRokNZzEmXeeUJDNKwW8wmmS6gMHvxZICceR4au2CxIzaBhXUQDA72uCO7juYS8zxivOVwg3q5jTOIc/Er3cuTpNRm5dJVqhBXSjiw2HSMROOk8hjgHXIHcpZkCilJJxXArT1W9P7YVP2K0/ExymPkujd1bwLS7OB5m4Tw3liPqIXFHNS07U6f3ZPj50rnhBdpl6GfQsohOnLF2jbXNh06+bePhrCIg5sOZdRN+mXJqgvPQ69t4G2OJRf6OWDaPv6MHX24I14keTCB8VFrRbd9u1lK0p0h93IgSjt7b1ZWdzYKmtQF5ygdk5Xhz8NaYO6CvzpjOx9ys8KFyUeH/MRT0rctAp/erHeMHAzAE9dyR+TUIiSnghyQsqbPx1NpPjesQnetKttvu/jU2/YRjie5CtPX17j1ZXVd49O8Iv6NK97+/1AuojXriexygDonh4bnrkY3u43QmQWrr6w5PkXJl6gw9hBn6WvpP070H6Ai75zIEZL6kAy3Xkn2t5evP/8L8jRAN4rTr439EEkGd76iT04ekq/v1WpRF73rq1ooilSahGNUcu3Do9D9wFlgSwc6uDjjxO7eJHmj/4mglrx6KwP3I+2vx/333xxIfGdQ4ceu4IkyfxACjHYaeWWfuU7orLoUFm0+K+4sevs3OC4oeT/XyVUC4P6FWCLIAh9giBogXcBjy5eQBCExWfnNwOXqrh/NZNeo+JNu9r48enpJVPTKylnMLou+Lx6jYq37G7nx6enCZWZzVRNvTTsZcQbrov0dEaCIPDbd21mfDbC945OMDoTprdODep2qyFvBrU/nMAfSVyTBnWTSVsXiI9MQrneEB+dNgONRu08h/r8dJCkJHP9NWJQL1aTScc79nXzT7+yn2O//3r+7j17uXNbC0+eneZD/3GEPX/8BL/+H4f5zpHx+VRsteQLJ7BvJHCrrtu3Ojg2OlvS+z0b3hhc2FD96JTnFEk5yd6WvfOPdWy2EfBECc5ESbjCuL5yguDPxmjY20rrx/bS9J7rUDfqmfnv86RCi6aap5LguQAt1wHMhwWK5VDnXZCYUZtSzLi8KFGWZQ4Oe9nfehN6lZ6nR58u2KBW2XSgFkh4cifcFmsmFKPRlN/3XGXLsJxXTyKn/EqNUMEGtVmHLLNmctAfSeAOxnLyp72TIRKxFO0DaYN63ljPL0F92nOaHnMPVp3y+oE9DqaHA8zNrh4KiF6Ywf/4CLottvmBglzKcKh1UgdToSlCiRDhgB+/y5k33iMjTYdijFpnc9/Sq5sMCwnq/5+98wyI6zyw9nOnDwPMUIbeESAJod5ldfciF7nGNU4vtmMnm823cTZ1d7PZZLNx4lTHjkvcq2zZliyr2GqooQqii84MzDDMML3c78cFBGKoAgnLnD+yZ+7ce6dwy3nP+xxLtWRY5o8N7yH6/bgPHkK3bClGrZGAGKDDM/JywL7SFhXhOXUKMXj+97xvnnwVVUBGUc7i8AtEJoJMMXGID4PE33V2jGymb6DVdGH403639J4vpEEd283/nmAOdXGNFUGARdkjm3Fwc9EsQr5YdpyZGA61v1li44/UoP7gZAt2T4A7Fp3NbeYlRnHL/DSe219H80Xq3up0+fmozCThPVLmnx3cQUpRD5Wg7uFQ7xfmgkLbD/PhD/opbi1mReqK8545sDR5KSIh5BE1pI0R8QEgyGTEfvGLeE6douHFV3mr/SfIlCpufmwecakjL7ocTqn5MRgNKjq9QTbmJbKtzIRVjATjDKjv/3sUg0Ha/vAkqmm5RF977dl9VSgwfucRfDU1dL7zzqDb6mh1UranmehZMZzodPHVVTn9Pm9Fmo7odjWr01cjl03OEMoFN6hFUQwA3wa2AGXAq6IonhIE4WeCIGzoXuzbgiCcEgThKPAYcP+F3s+LpZvnpdLlDfBx2dimTI1WZruHhEnOn+7RxvlpuP1BPjjZerF3Zcz6Z3Edhggl18xKvti70k/rpidQmBLNr7aU4/GHJl1BYo+S9BocnsCICiQaOqQT6KWG+ACI042c1TiRKm91EKdTYZxkvHJBEChK1XOiSbohPd6dpC76DBQkno90agXXFCXz2zvmcvhHV/DCl5Zw24J0jjbY+O5rx1jwi218/fnDvbz5iZbVOYX4uBhaU2AkJMKnlWMrrwLpBmUK7zGlyaLDpsPIBBlzE+b2PtbDhGz78AymJ0oI2jzE3TOD2NvykWkUyNQKYu+aTrDLT8frFWcRHh21EPRBwkzg7ABrxRgN6h6chjJhhDfJiYXSv6390Qa7q9q586/7+fCEhWUpy9jZuJOgTTL+RmpQCzJBMiNHgfiI043s/D3yBLX0/FhKEoFhMR81bZIJNG2QBHVrtXTe70lQy3QRoFCMKkE9K35W7//nzJPQGbXHBp+V4mt0YPlnGcpEHXF3zxjWfJmdpkcmgMsppdpqbDW9/OmRFiT2KNCd3Fe2D/65KeI0hOw+Qr4gVHZPZc+7clTb6ZH7xElCLhcRS5b2YkXGyqHWFM0i5HLhqzk/IzMYCrLlhJR1y0wb5POTySE6dcIS1NqoaGRyBV224c16URS7ER/hsSzjKmstIF5YgzoqSTInJzhBvb/GwoykaPQjLOLOMUYSEcqjpuvEhPS2+JqdIBdGfC54+UADmXERLD0nAf6dy/NAhN9tuzj5zM0nWkgImkh1ne7Fe/RIYYzA3+YetN9helIUWqWcQ81emLYeTm+GkDTD4mjbUZx+53nhPXo02zgbOUqUuhqS9ecXtNTfuAFH2mw+2J+CSubh5u/OJyZp/H2QSJmAU4Sceh/+gMjbJU2QsRQaDkAfLrp982Z81dUYv/1tBHl/AznqiivQFBXR9ocnCXnDH/P3vVWNQi1nc9BJWoyWa2b1HwgzGTpJ9sVzuXFy4j3gIjGoRVF8XxTFfFEUc0VR/I/ux/5dFMVN3f/9iCiKhaIozhVFca0oiqcuxn5eDC3JiSMpWnPBMB9mh5eEqMmfoAZYkBlDZlzEZxbzYXZ42HrKxK3z09AoJ9eIVQ+Lusf0nKxFcj0noZEUJdZbJYP6kk1QTwIG9WnT5CtI7NHsND0VJgduX5DjjZ3ER6pIOc+LmM+SlHIZl+XF8/ObZrHvB+t5+1sruGtxOh+eaj1vPvFIZZtK4V4UzUkzoNcq2XUemI+pBPWUJpOOmI5QEFNAlOrs+cYQpWRFlALV8TY00wwkfmcB2lnx/V6nSotCf002njIrzn3dSI3ugsQexIchQkVitJrysSaozS4EtcT5HZE0eqmwzdQ/Qf12STMAH5xsZW36WlqdrbQ2SwbFaIrUlPHaESM+2rsRHyPRhCM+uge624YZfK9ucwIMmqBuqe5Ep1cR1V0OKAgCcr1+RAxqs8uM2WWmKL6o97HYZB0xSRFUD4L5CFjctP/jFLIIJfFfnIVMMzwyUadWUJAUTUub9BlV2apoqapAEGQk5ozOSOzAgT3CT6BlcGNUEScZZkGrByq2QHw+xI5tir2reD8AEYsXYYyQ8Jpj5VCPtShRFEUsTV29JuOe5j24rBJeJSpuCOSnIWPCEtSCIKCLicHZMTyDOtjRgej3o0y8AAlqSzcq4kIa1IIg/b46Js6g9gaCHKnvYGnOyPAePZpjnE9Q6OK4uWLc98nf3IUySYcwgmLwmrYuimut3L4wfUDnU1pMBHcvzeC1ww1UmbvGfT+H01sljdwdfUz6n5kb+j2nTIiAQIigLfxxWiGXMTtNT0l9B8y4ARzNvYzlPU17UAgKliQtOe99VMvVRAt5qKNqzruIvbXBTUn+l1F67FyX8Sb6ZMN579+5CnkCiE4/cTNisdbYuTwqilcPNSBmLAVvp4QdA8RAgLYnn0RdUEDUlQMHEQVBIOG7jxFoaaHjpZcGPN9SZaP2WDtJixPY22jjS5dlD/h8ioUSAOYFZ477+xwvTdqSxM+r5DKBG+emsLPcPOGAfFEUMdk9JH5GEtSCIHDLvDT21Vho7BjZ9MXJpNcONRIIidw1ifAefXXlzMRelnDWZE1QR4/eoE6PHfvUn8mq+Eg1Xd7ABUMBhVMoJFJpckwalvq5KkrVExKhtMXO8UYbRan6z2RB4nhIJhOYm27gxzcUEqtTselY84Rv0xsI4vQFiZ1KUF9wKboHJ3ZVtBEaJOUynGxTCeopTRL5g36OtR3r5U8DuI6aMf+uhBiFQIVCTtz9M5EPYhBHrkhBMz0W2+YafM1dUkEiAsSfRSkUJEVTbhp7glqREDG680vS7H6ID48/yNZTrchlAp9WtrEocQUCAg2NpQgaDTLNyAdXFUYtAasHMdjnb3/vH2DLDwcsa3X6RnyMlqnVCFrtsAZ1yG5HUCoRRrHPAAlRI0tQV7d1oZQLpA8yrbu1upOk3P7ne7nBMCKD+mS79J30TVAD5M5PoLnChtvR/74s2OWj/emTEBKJf3AW8uiRn+/mZRgoa1Cikqmo6ayhtaqc+PQMVJrRXbOaXCYs0T66GgY/ryu6zfpAazcTdozpaZAKEtUzZqCIicGolczgdvfYZuuosrKQRUaOuijx8Ad1vPzzA1QUSzNqX6t4jcSAZPZHxw9hUOvToXPiQk6Rhli6RmBQB1ql/VYkXQAGda9BnTvx2+qr2JwJRXwcb+zEGwixZAQFiX11y4yVALx6cue47o8oivhbukZckPjqoUbkMoFbF6SFff5ba6ehUcr5348mptBxMNVZnBw808HN6kPSeaoH19ItRXc63D9UUWJmDKea7Xiyr5CwOt2Yjz3Ne5ibMJdI1figM2TefELKZizukWF1wqmhzMq7TxwjMkbNghP/i7diYmYn98xqSl2cSHx6JPOtItUtDirU3eeabg515zvv4K+rx/jwQwiy8DatbulSdMuXYfnzXwh2nR3AEEWRvW9WE6FX8aHfiV6r5PaF6f1eK4oibznfR0SEprH1blwITRnUk1A3zk0lEBLZfCJ8gcp4ye4J4PGHPhMM6h7dMj8VQJoW8RlSMCTy0oF6lufGDVrscrElkwn8+IaZXF2YRIphcpq6yXppv1pGUJRYb3URq1MRpbn0TJaexNPF5FA3drhx+YJMn7QJagMgTQGsMnf1/v/nWUq5jGuLkthWZppwlr/NJTFfp0zOi6M1+UbaHF5KW+xjev1UgnpKk0Wl1lI8QQ/zE+cTcvmxvHQa68vlKBO02BYnU9buwe3wD/p6QRCIuTUPmU6J9aXThFoqICYLVGdnVxUkRlJp7iIwgoK+cxUwu1COtCCxR4mzJOPIJ6WBd5a34fAG+PrqHLyBECfqgsxNmEu76cyo0tPQXZQYFAl2dN98lr0HW38I+/7QLz3q9gVx+4MjZlADI0oiB22dyAyjHxAeKeKj2txFVpwubGquq8OLw+ohOdcwcL9HgPg42X4ShaBgeuz0fo/nzDUiilB77KwRG/IFaX+2lECnj7j7C6Vk4Sg0N92AwxMiRZdJVUclrVUVo8Z7gGRQ2w0hHG1teLrCpy17EtSBylIJbzNG/nTI48FdUoJuqVQ2F6+VZiyMNUEtyGRoimbhOXFy+IW71VzZwYF3a0CAwx/W0eJo4ZPGT5ijmY5MruhlQYeVPk1KdAYHP16cj3QxsSNKUPtNJoALU5JoqYbIJFBf4Gv1mCzoONOLdxhvFddIpuTirNEZ1FfmF0Igmv3Nh8Z1f4J2HyFnANUI+NP+YIjXDzeytiBhUA8mPlLNl1fm8P6J1l5M4YXQWyVNJAsWkuzHB+A94GzXQqBtCA51uoFASOSkVYCslVD2Lm1OM6etp8cF79Ejhy0LgIOmg2N6/ZkT7Wx+8jj6hAhuvr6FxLRWOour8JvGH7Pr757VpErUsfquAkRXkMt8Sl48LUJUMtTvR/T5aH/yj2hmzSJy3dD4DeOjjxK02bA+/UzvY7XH2mmt6SR3TSofnDZxz9IMdOr+M3pKraXUeRvwxITwNV74dP5INWVQT0LNSI6iIDFqwk1Yc3djecJnyKBOj41gSXYsbxxpmhB+1ETpk8o2Gjvck6ocMZyWT4vnz/cuQC6bnEnTHl76SBLUDVbXoAmbz7rium/kLBeRQ326VTK+JiviIzFajTFKzauHGgiJEvJjSrBhTioef4htZaYJ3U7PDKDYKZPzomh1gZQiGwvmIxQS6XRPFVxOaXLosOkwAHMc+Zj+7wjuE+1EX5mJ8WtzSJwj/c5bqm1DrkMeqSL2jgIC7W5slYW9eI8eFSRF4wuEqLOObnZeyBsgaPeNvCCxR0mzALF3Wu+7x5uJ06l4eH0eMRFKPjzVypr0NQRtNsTo0c1o69kXf7tbMqfe/sbZtPjJN3qXszil64eRIj6gO4k8HOLDbh813gNAq5ITqVaMKEE9WNCjtaY/f7pHI01Qn2g/QV5MHhpF//ui+PRIouM1VJdIx1MxKGJ98TT+Rgdxd01HnRk97LrP1fwMAwCRslRaG2vxOLtIGmVBIkjmsNh9HWaqrQq7jEyrQKZTEGhsBnU0ZCwb9XYA3CUliD4fuqXSFH2VXEWMOoY219hxUtqi2XjKywflqfbbvsPH1qdOEW3Usvbu6XS0unhz6zZEUSRDTCAqLm7Q1CEglb2JIbBPzCwyXUwszhEwqAPdBvUFKUm0VF5YvEePYnMg4AHHxITt9tdYmZ4UNeqeE4VcRpJ6JmZfKb5x7GM5W5A4/PF6+2kz7V1e7lyUPuRyX1mZTUyEkv/ZcmFS1KIo8uaRJr6R0I3BCmNQy3VK6VhiHjwoNj9TGiQ60oP5sFazt1w691yWetm47KvHH8RiMaIUIjjQcmDUr68paeODP58gNkXHTY/NI6J1B7FzFBAS6Xjh+XHZx74KtLlAAEWshqQcPTNWJLPAo2DXoRaCaUugfj+2N9/E39wspaeHGeDVFhURddVVWP/xDwIWC6FgiP1vV2NIjGCr24FSJuP+ZVkDXre9fjtyQU5UZjy+Bsek9dKmDOpJKEEQuGleKofrOjh0ZviR2LHKZJcuBhInWcHZcNo4P43adiclDbaLvSsj1ovF9cRHqrhy5gW4GLmEpVHKidOpaLGPDPFxKRYkgsSgBi4qh7qiezp03iRFfAiCwOxUPXUWyXAomjKoAViYGUOyXsOmoxOL+ehwSb9Nw5RBfVGUEKWhMCV6TLxxu8dPSJz67qY0OXS8+Rj/Yn0Q3wv1CGo5Cd+cQ/S6DAS5gDEjCoVSRnOlbdj1aHINRK1OweVahCu4pt9zPXiz0XKoewsSx5KgBmg9gdMb4OMyE9cWJaNWyLl8RiLbT5u5LGUNkW4Rm3p0Jooivjst29oJr94nlcPd8zqkLYITr/cu1zuIOMKSRBihQd3ZiTx6bOdbY9TQBdD+YIg6i4vchPAmUGt1JwqljPiM/gb2SJLfITHEqfZT/fjTPRIEgZx5CTSetuJx+rC9XYXntBXDTdPQFo6OgdujnPhIojQK/J4EQi3SviWPIUFtdpnRpEhJZlNNeIMaQBGrJWD1Qe5akI9t8NG5vxjkcrQLFvY+ZowwYnaPPXGoKZoFgQDesrIhlxNDItv+UYrHGeCqr8xi+vJk9AlabPvkLE9ZTqDTSdRQeA+QEB8wYUWJkYYYPF0OAr6hr839JhPIZCjixvbbGZUsVRB/MQzqbsb5BGA+/MEQh+s6WJI9uvR0j5anLgaFnQ9Ojzy5P+w+NTtBYESIj1cONpAQpWZNwdC/1yiNkm+tncanle3srRp76fVIdbiug3qri2vkB6QS4fi8sMtJRYmDD+bGR6rJiI2gpN4G068DBPbUvE+8Np6CmNEPwoVTY4cbkJMTWURxS/GoXltxsJUP/3aShMwobnx0HpoIBVTvQDVnNVFXXknHy68Q7HKOy372KNDuRh6rQVBI1uuym3JRqOQs6RAoVRYS6mik/ckn0c6di27lyhGt0/jII4S8Xtr/8hfK9rbQ0epi1tUZvHakiZvnpYYNoG6v3878xPlEZsURcvoJdly8oNtQmjKoJ6nuWpxOVlwEX3r20JiLW4aT2fHZS1ADXFOUhEYp+8yUJbZ0uvm4zMRtC9NRKab+5M5XSXrNsAnqQDBEU4f7kixIBIjvvqEc6kZuonW61UF6rJZI9fCFQBdLPaZ0sl7zmSmDnWjJZALXz07mk8o2bK6JG+DocEpTaKcY1BdPq/ONHK7voNM9uunMHd14lhjdVIJ6ShdXnkY7d+5ZxTrTQiKXp5D48DxUaWcHReUKGYk5+hEZ1ADRc9yohFN0nJ7er0gwLzESQRiZQW156iksf38aOMvhVIwS74AhU0qymk7yUakJjz/EhrkpAFxVmITDE6ClPYpYn5IWxeim4coiFAhaBYEjO8B0Cm55SiqHK7oNTCd6U9uWXoN6nBPUnZ1jSlADGCPVQyao6ywuAiFx0AR1S7WNhKxo5OfgP0aC+Kiz1+HwOwbwp3uUO89IKCjS/FolzoOtRK1LJ3JJ8tBvaAj1dEO0Ww3E21TIVSri0kc/y9LsMhMXm0S0MQFTbfWgyyl0HgL+WMgbG94DwLV/P9qiIuSRZ004o9Z4fgnqERYllnxUT/0pK5fdNg1jehQymUDEIjeGriSuld+Ow9JG9FAFiSD9HcCEcah1sZJhOlyKOtBqQmE0Iigm+PrZZQWX5eIlqGFCihKPN3bi9gdHXZDYo9tmrQLg7dO7x22f/M1dKOK0yIa5J2rpdLOz3MxtC9NGVO53z9JMkvUa/ntL+YSnXd8saSJd6SDeeiRserpHyoQIAkMwqEFi7B+p70CMTCSYtpC9XXWsSFkxbl1ADd1dZPMTFlHvqKela2RJ/cpDJj56upTkXD03PDwXtVYBbeXQ1Qq5a4l78IuEHA5sr782LvvZo0CbG2X82YFsbZSKFbfkkhGUs+n0NGzVOgJt7RgfeXjEn5E6JxvDLTdjeeVNDrxTRVKOnh1dDryBEF9ZNbAEt95eT5WtinXp61ClS9dRvoaJ8RjPV1Nu2SSVIULF819agkYp476ni2kY5bTDkagnQZ3wGUtQR2mUXF2YxLvHmi9qSdxI9fKBBkTgrkWTG+/xWVGyXkPLMAZ1S6eHQEi8ZA3q3gT1RWRQl7c6KEgc/bTWC6kerMcU3qO/NsxJxR8U+eBk64RtoydBPYWJuHhaU5BAMCSyZ5TJm6n0+5QutsSgiP3jetr+eAxNUEnVtV0YNuQiKOUDlk3NN9De2IXXPTxXX2g/Tazq1yCXY3npNGJA4qNqlHKy4nTDGtS+ujrMv/0/2n7/e4IOh5SglknTdkclmQwSC6H1BO8eayZFr2FBhjQt+rK8eCJUcracasXgkVMvdODwjfwmUhAElNouAu0uWP19yLtceqLwZhBkcEK68bZ2z8CKHw2D2jC80Rs6H4M6Sk3bEAPv1W2SWR/OoPb7grQ3dJGUO3DbcoMB0e0eEiMxWEFijxKzosnTK1GdthKxIJHoKzKHfC8j0bx0Aw2tURhtapSpschkA3/fQykkhjC5TCREJJCYPQ3zIIgPAEWghiBGxOz1Y9rXYFcX7pMniVi2tN/jxggjbe6xG9TKxEQUCQm4TwxuULdU2dj/Tg258xMoXJXa+/hW9Wu4NJ14D+pwWNqJik8YemPR3a+1TVSCusegHnr2c8DUemEKEnvSyxfDoI5OA5lyQhLUxbXd/OkxJqgL4/NRiJEcbz8ybqavr8U5ovT064caCYkMKK8bTBqlnO9cnsexBhtbSycOzefxB3nvWDMPp55GQBzSoFYYIwi5AgSdg4cf5mfEYLJ7ae70cCp7KZ2CyGWG8UlPAzR2+2JrM5cDcKB1eMyHzxPg01cqSMyK5vqH5qDSdA8m1OyQ/s1Zg3b2bCIWLsT67HOI/vFh1YshkUC7ewAKrHBlKqEYJaqGaFrKjUTkxhKxdOkgawmv+G99i/qU1bi6gizYkM1z++tZPz2BaQkDZzfvaJDe59qMtSiTIkAhmzKopzR6pcdG8NyDS/D4Q9z79+JxT0ua7B6i1IoBAPXPgm6Zn4bdE2D76fEH2Y+nAsEQrxxsYGWekYy4S9MsvdCSEtRDlyT2jKxeqga1Tq1Aq5RfNAa1NxCkpt05aQsSe1SUakAuE5ifMURpzudQs1KjyY7XTSjmo8M5ZXJebM3PMBClUYwa82HrHVyY+u6mdOEVaHfT9pdj2D+qoy3TyTdyfkH+/DmDLp8yzQCiZGINK3MZCpmV2I3T8Dd10fnhmd6nChKjetFVg6ntySdBEBA9HuybNxMwu1DEaXun7Y5KibMQW0/yaaWJ6+ekIOvu/tAo5awtSOCjky0onV46NSH2NO0Z+XqbS1DY9xGQ5cDqfz37eGQC5KyRDGpR7IP4GE1JosRyFocoPpMS1GMbvDZGDZ2g7jGoc4wDjSDzGTuhkEhyWINa37tvg+lE+wm0Ci05+pywz3vKrcwQwBwQibwue1ySgPMyYhC9emIdKjzxoz/edng6CIQCJEYkkpgzDVtrC15X+KnpcsdBQEbAN7bvxnXwIASD6JacY1BrjVjcFoKhsQeGNLOL8AxiUHu6/Gz9+ymiYtWsvXd67+fe6Ghkb8seIha6MVW3IoZCRA+H+FBqQJcAnfVj3tehpIuRDNOuYYoS/SYzyoQLUZDYPWBxMQxquUJKrFvHP0G9v8ZKXkJkbx/PaCUIAjlRRXjkVVSYzr8oLuQOELR6UA5TkBgKibxyqIHluXFkxo28W2Dj/DRyjDp+vaWcYGhiUtTbT5uxewJcIe6H+HwwTh90WUVCN0ZqiBT1vG7Gfkl9B3siIpCJIktt4+fZNHS4USlkLEqZSawmdkSYj6Mf1eN2+Fl5Rz5KVZ/BwJqdUuK/e4ZF7JceJNDSgv3DLeOyr0G7D9EfQnEOCkwmE1h1Zz7akEBV8gaM8/yjPq8EImOpz7qa+PbjHDp1AqvTx1dXhT+Hba/fzvTY6aRGpiLIZahSI/GVVYJtYo6H56Mpg3qSqyApiqcfWEir3cP9Tx/A4Rm/5mGzw9NbOvdZ04pp8SRFayY95mP7aTOtdg93T/JyxM+SkvVaOlz+IdPzPTMOLlUGNUgp6ovFoK5pcxIMieRPcoPaGKVm07dXcP/yrIu9K5NKgiBww5wU9tdaMI2A5z4Wdbj8RKoVU1ijiyiFXMZl0+LZVdE2qpRQD55lKv0+pQspURTpKm7B9Lsj+M1uYu8s4MXp24iM1pMSmTLo6xKzo5HJhZFhPtpOQ1wu2tlJ6JYl07W7CfdpyUzKT4rijMU56LWFt6oK+7vvEXv/fajz87G9/gb+toGpqBErqQjB7yQpZGbDnP7v78rCRNwdnQghkWBURG/yaVi5rPDqfSjUdoKBKELn3jIU3S7djDYepN3pRSWXjQrTJTcYIBQi1CWZOqIoUnu8HZ9HSq+Lfj8hpxPZGBPU8ZEqHJ7AoN9BtdlJYrSaKM3AY9NgBYlAb6J7qPT3yfaTFMYVIg+TYvbW27G+eBohTsOBrgD1ZcMX4Y1Ec9MNGH0dyEMCJv3QM2Xb//JXuj75pN9jZpdk+CRGJJKQnQuAqSYM5sPZjqJjLwABy9ABj8Hk2l+MoFKhnTe33+MJEQkExSAd3rF/Jtqi2fjOnBkwgCCKIh8/W4rL4eOqr8ySpuN3643KN6TOpuvXoNJI72lYBjVIRYkTlaDuNqidwxjUgdbWC1OQ2F4JglxCCl0MxeaMe4I6EAxx+IyVJTljS0/36PLspchUVt4+ceq898nfMrKCxL3VFho73NwxTDniuVLIZXzvygIqzV28VdI05v0cSm8eaaQgyoPeXAwzNsAQRqmyp4h3CIN6RnI0GqWMI3U2dnecYlZIgaFi27jtb2OHizSDFoVczqKkRRS3Fg95neuy+yjZ1kDufCOJWX0G6YJ+OLMbctb2PhS5ejWqnBwsTz89Lgn7QDevWxE/sKtiVm4kieb9NKauxin6wG0b1boPvX+GIHKmtX6E/69/Yk6aPuzMAovbwtG2o6xLX9f7mCrCjN8iQyz+++je0AXQ1J3jZ0ALMmP50z0LKG918OVnD40b1sJs935muaxymVQkubOibdjG74upFw/UkxitZv30YaadTWnESupmpg/Foa63ulDIBJL1n83f90gUF6mm/SIhPnqmQU/2BDVAYYoeTZhp4Z93bZiTgijCe8cnpmG9w+WbYhhPAq0pMNJq93B6FF0WU4iPKV1oBR0+LM+WYnurClVmNImPzkc7x8hh02EWJC4Y8rUKlZzErOiRGdTmUkiYAYDh2hyUSTo6XisnaPcyPSmKkAhV5vCJurbf/wFZRARxX/4yhls34jlVSqDdNfqCxB4lSSiJtfpWClP6p1rXTk8gNiiZbmmpM/i06VP8A9zmcxQKwVtfA3sLipW3AWHMyOnXgUIDJ17D2uUjVqcaVWJLbjAAZ43epgob7//xOAffk1KSQYd0nDkfxAcM3q9R3dY1BH+6k5ikCDRhzjs9+x0aJEHtC/o4bT0dtiDR3+bC8uwpZFEqEr9ahDJSSU3J+CQBY3QqZipsAJRrBkduOQ8coO23v6XpX75PwHrW/OwxqBMiEkjMkVKyYTEfVdtQCNKMqYBlbIPSzuJitPPnI1P3DzYZI4z99mUs0hZJfwvuk/1L645ua+DMCQsrNk4jIfPs30iHy81Lpa9zWcoqUg0ppM+QvnOfZwR/i/r0CStJ1EZFI5PLh2RQB7u6CDmdKC8E4sNSBTGZoLhI5/LYbClBPY7s5JPNdpy+sfOne7Q6U5oJsLVm33nvk69ZmrWgGiZB/fLBevRaJVcVjn5w4ppZSRSl6vntRxV4A+OLN7V0edlZ3sYjqZUIYmhIvAeA3KBGUMp6S4LDSSmXMTvVwMH6Bk62n+Sy2JlQvw+6xufY2WB1k9YdQluctBizy0ydvW7Q5Q+9f4agP8TSG3P7P9F4CHxd0uyibgkyGXEPfhFvWRmufef/++jpuwh3rdDxwgsUVL5BUPDzif2riA0HR7zezjY3J3c1MWN5Mr7L1zC37hgPJXnDntM/afyEkBhibUa3EW+tRXXmb4ho8M98ZGxvbAI1ZVB/RrS2IIHf3D6H4lorD79UQiA4+PS6kcrk8JD4GU1QA2ycn0owJLLp2MRNUz8fNVhd7Kpo445FGSMqQpjSyNRjOg/Foa63ukmN0V7Sn3u8TnXREB+nWx0o5QLZ8SOfojalyaVpCZHMTI6esONnh8s3hYiYBFqdLw2O7qoYOebD5vIjlwlEaz57+K8pffbkPtmO6beH8VTZMNyQQ/yDs1Do1TQ4Gmh3tzM/Yf6w60jOM9BW58DvHeLG3eeSzBKjZFALShmxX5iO6AthfaWc/G7jM9xgjqe0FMeWLcTefz+KmBiib7gBWXQShBhzgtqsySEoClwT3z7ghjJao2S5UTLdCrIW4vA5OGI6MvQKd/8GKrfC1f+FokAy/AYYCJpoyL8aTr5JZ5dr1CW2vaiMboO6ZKtkCJTuacHnCRC0SQawPPr8DOpwwRNRFAc1qMWQSGt1Z1j+NJw1zAODJKgrOirwh/wD+NNBh4/2Z6SEZfyDs1DqNeTMiefMCQuBcQoLZYsWXEoFNcEmXP6BiURRFGn7zf8ij4sj5HRi/s1vep8zuSQmbUJEAhHReqLijOGLEiu2INNpEDTyMSWoA1Yr3tOn0S1dMuA5o1YyqNvdo+s66CvNLOlz74v5aK3pZP9b1eTMM1K0Jq33cac3wF0vPIMzaCPQuRgAQ4L0XZQfCI836SdDulSSOAGFc4JMRoTeQJd18AR1wCR9Z4oLgviohri8id/OYIrNAZ9DKmocJxXXnB9/ukcFMQUoBS2N7lOYz3Mmob+5C1mUEnnU4MdTq9PH1lMmbp6XOqbQjCAI/MtVBTTZ3LxYPL5IhnePNRMIiawO7oWYbEgaOFDXb19kAop47ZAJapAwH5X2I4TEECum3w6IUP7+uOxzQ4eL9BjJ8F2SLB2XBsN8dLa5OfVpEzNWJGNIPOd8XbNT6mbIXtnv4egNG5DHx2N5+pnz3tdAmxtBJUd2zu8j6HBgeeYZopcvYm9UiFb/dMo+GfmMg+JNNchkAouuz+F3hvnYNVFMe+fZsKnv7fXbSdGlUBBTAH4PvHY/KoV0rvA1T76g56Xr3lyCunFuKj+5YSZbS03821snzmvagSiKmOxeEqM/uwnTvMQoZqfpJy3m44XiOgTgzlFO5ZnS0ErqNqhb7YNfZNdbXZcsf7pHcZGqcefSj1QVJge5xkiUl/AAwOdBG+amcKzBRp1lBDd1o1SHc8qgngxK0muYnhTFzvKRp1Y6XD70WuW4ta1PaUrhJAZCWF+rwPJCGfIYDYkPzSVyRSpCN4v5sOkwAAsTFw67rpQ8A6GQSGvt4Ixh2isAsTdBDaBMiMCwIRdvdSfxJ62oFLKwHOq2J36PTK8n9oH7AVDExKC77CoA5DFjG8jZfNpGjZjCLEX4NOdlCZJBHakvQi1XD435qN4B2/8Dim6DRV9GESddJ/Ukt/qp6DZwtZNqO9BbuDxSyfUGQDKo2xsd1J+ykjU7Hp87QPn+VoKdNmk5wxgN6khpv8MZ1G1dXhyeALlh+NMdJhdeVyAsf1raH2m/B0tQn2iXjNG+CeqQN0D7P04RcviIf2AWyu7p2TnzE/B7gzSOE+Yj2tFMq1YPAtR2DuT1dn38Me5jx0h49DvEPXA/nW+8iaukBJAMapkgI14bD0BiTi6mmnMS1MEAVH+MkH8lijjtmBLUrgNSAZkuTIFXQoQ0CHo+CWp5dDSq7GzcJ6QEtcfpZ+tTp9DFqFnXhzvt8gX44j8OUufbjVyMYushPYfrOnDaLChUETSUOrA0DcMU1qdDwAPOsRvqQ0lniB2yJNHfKiXlJzxBHQqBtfri8Kd7FNvNwh1HzMf+Ggs5Rt15zwCXy+TMipuDPKKWj8+zz8rf7ESZPHR6+q2SJnzB0KjxHn21Mi+eZTlx/GF7FV3e4UuBR6o3S5pYkiSga9ojpadHcO2nSIjoRVcMpnkZMRBRjk4RTWHe9RCTBWXvnvf+Ojx+bC5/L8YzIyqDJF0Sxa3hDeoeI3fxddkDn6zZCSnzQNu/q0imUhF7zz04d+/GU15+Xvvrb3ejMGoHXFNb//Esoc5OEh95mMzFaUQra9l3NBXPEOWTPWqrd1B50MTs9emU253sb/XQccvduA8exLm7f2eFy+9ib/Ne1mWsk/Zh6w+h5RjyW34ulSUGJ4Zrfj6achc+Y3pgRTYPr8/j1UON/PLD02NeT6fbjy8QIuEzbFCDVBxQ2mKnrMV+sXelnyxdXp7fV8d1s1NIMYxx+ueUwqrHoG62DX6R3WB1XdL8aZAQH5Yu37g1UI9G5a0OCj4DeI8pDa0burmn705AirrD5Z9iGE8SrS4wcuhMx4g7LGwuP4ap725KEy25gOgPErU2nYRvzEGZ2N94PGw6TIw6hmx9mJvKc5Sco0cQGBrzYS6T/u1jUANELExEO8dI1/Z6rtJHDkhQu48epWvnTuIefBB59FnMgGb2cgC8p4YvZwqnTceaaVLnEtlRFvb5OXrpZvZAu8DS5KXsbNgZ/nzf2QRvfEkqtbrhdyAIyFRy5AZ1eIM67wrQ6FnStX0MCWoDIJUNlmytR6GWs/7+GSRkRnF8R2MvQ/h8ER9tYQbfq83SQGpuwkAjqLV6cP503/0ZrCTxZPtJ4jRxJOmkqfdiMITln6fxt3QRe/cMVOlnr3fSCmJQaRVUjwPmw93lIGhrw6RKBqC6s3/6WQwGMf/2/1Dl5KC/6Sbiv/ENFImJtP7s54iBAGaXmXhNPAqZNEiSkJ1LR0sTXlcf46ihGDydkH8lijjNmBLUzv37kel0vUnnvorTSqiFNtfoynjPlXZ2Ee7jxwiFQnz8bBnOTi9XfXkW6u5zkdsX5Ev/OMSh+mY00ae5Jf86kqN1/OCN43S2mdEnJqBUyzn84eDT/AHJoIYJK0qMjI0dkkEdaO1OUE80g9rRAn4XxOUOv+xEKab72D1ORYnBkMihMx0syT4/vEePVmcsRa4280Fp5ZjXIQZC+M2uIfEeoijyysF65qQbmJE8tpJS6E5RX12Axenj6d3j85lWmhwcb+zkWykVEArAzA0jep3SqCVo8xLyDT6TZF66HnlkBanqOcjlCphxA9Tsko5H56HGjm78VXeCWhAEFict5mDrQUJif8JAW0O3kbsuHZ3hHGqAxw6NB/vhPfoq5q47ESIisD799Hntb6DNNYA/HbTZsD77LFFXXI62sJDbFqXj1h3GG1Cz/83hf49736xCo1My/6pM/rKrBkOEkpWPfQVlaiptv/1tvyLjvc178YV8rMtYBydeh4NPwfKHEGZcS+J3FhC5fPCOj4ulKYP6M6hHL8/j3qXSD/Ivu8JM5RqBzN3phISozy7iAySDRSkXePPI5EpR//XTGtz+II+sv4gj15eoIlQK9FrloAxqh8eP1em79BPUOhWBkIjdPX6j6COR3eOnyeaeMqgvAaUatCzMjOHdY+PPoe5w+ogZpfkxpYnRmvwEAiGRvdUjm2Y7hWeZ0oWQIAjE3jUd/VVZCGHKVA+bDjM/cf6IkvwqrYL49ChahjKo28pArjqb6uuzHzE3T0Nu0PANu0BTS3+Duu2J3yOPjSX2nrv7PS6LMCL6HHS+/caw+3euGqwuSuptaNLmSDxc98A0rs4jJUE/bHCzJn0NTV1NVHRU9F8o4IPX7oeAF+54HlRnTX5FvBZ/uISbQg0zb2RlYD8J2tHhAuUxBgDsLQ4qD5kpvCyFTqwUrknBZnLRWC2ZyH2N/NGoJ9Hd7hjYr1HdJn0e08IY1C01nWh0yoHTt7slRESAUjloSeKJ9hMUxRchCAKiKNLxRiXeig5ibs5DO70/SkCukJE1O47aY+0EzxO3aKqSvk+LOhsZCqps/dPPnW+/g6+6GuN3HkFQKJDpdCT+vx/gLSuj4+VXMLvMvQlmoJdD3XamT2K1cgvIlJCzFkWclmCHB3GU++3aX0zEwoUIioGzBZQyJbGaWMzu8zPsNUWzCba1c/SdMs4cb2fZzbkkZku/I48/yJefO0hxrYV71tkJiH5uzLue/7i5iEpzF2fqGjEkJjJrVSpVh0zYhkIPGLoN6gkqStQZYugayqA29yA+JribyNJtcl3MBHVMJiCMW4K6tNmOwxtg6XkWJPaop9/gQMthXL6x3Uv5TS4IiUMWJJY02KgwdY3LjOr5GTFcOTORv31Sg3UceojeLGlCLhNY4tkjDd6kDI/UAilBjTjILJ1u2YL1yBQOcE+XHph+A4T8ULH1vPa5wSr9fafHnD3eL0legs1rG3CO3P92NeoIBfOvyhi4oro9IAYHNajlej2GWzfSufn93pkPo5XoDxK0eQfwpy1PP0PI6ST+2w8BsCAzhiZDOrMjNnNqTyumM4MHL+tLLTSe7mDhtVk0dXn4qMzEvUsz0UVGYHz4oV4kWY92NOxAr9YzTxYF7z4C6Utg/Y/H9H4ulKYM6s+gBEHgJxsKuX52Mv/1wWlePTT6k6ypm7f0WUZ8AMTqVKwtSOCtkuZx4XKPh9q7vDy3t44Nc1KYljBl4k2EkvWaQRnUDVbpZNn3xHUpKj6yu0zIeWExHxWfoYLEKQ2vDXNTKDc5eosvx0P+YAiHNzBlck4SLciMIVKtYGf5yBJutqn0+5QukAYzn01OE41djcMWJPZVSp6B1lo7Qf8g14LmMojPB/nA37ZMoyDurulEBkS+6BCwdd/4Ow8cwLl3L3Ff/QoyXX8DItDmRhYl4Ny7F19j04j3E+Dd49Ksldyibqav6dSAZQI2G6IgcLQjyDSdtNzOhp39F/roR1IC7MY/QHx/1qwiXkug3R02de2buRGd4GGhZ3Tpb3l0NAgCZWeUCEDMYpGr37iaH5seRRkpUFYpfZ+yMSaolXIZMRFK2roGXt9Vt3URoZL3FmX3VQ9/erDfkyAIyA36XkZ2Xzl8Dmo7a3v50/atdbiOmIm+PAPdovAp19x5CXhdAZorbKN4dwPVUlUBgkBCdj6KUCI1trNGXsjrpe0Pf0AzezZRV1zR+3jUVVehW76ctt/9DmdrU3+DOlsyI019ixIrtkLmMtBEo4jTQgiCHSO/bvS3tuI7c4aIMHiPHhm1Rtpd54fM0BbNwh6VQfHWVrJmxzNnvWTmefxBvvLcIfZWW/j1bXNoDe4jLTKN2fGzWTs9gZvmpuC1WQlq9cy5PB2ZXMaRLUOkqHsT1BMTbNIZYnE77AQD4Wcs+VtNyGNiBpRNjrss3b+Bi2lQK9SgTxs3g7q4VhpkP9+CxB4VxhWilKkRNdV8Wjm236+/WRo4Uw6RoH7lQAMRKnnvrMXz1feuKqDLF+BPO8MUoo5CoZDI2yVNXJWrRV23c8R4D5DwWACBIQaDdjftBqCxuZshn7YIIhPh9PlhPhq6E9R9Z0ovTpJ49H051E3lHdSfsrLg6qzemRj9VLMTFFrJsB1EsffdD6KI9bnnh9wnl9/FL/b/gmpb/+BowOIBERR9DOqA1Yr1hReIvuYaNAX5gHSOypq/jsWRL6PR+Nj1YjmhkHTutjY3UbZnFyD1Lex7q5qoOA2zVqXy1O5alHIZ9y3LAiD6+utR5+XR9n+/Q/T7CYQC7GzYyerkFShef1AapL/1mbDXQZNJUwb1Z1RymcD/3j6XlXnx/OCN42w5NbqRHZNdujj5LJck9mjjgjTau7xjPrmMt/76SQ3eQJCH11/EYopLXEl6zaAM6vrukdVLPkHdnTSydJ3/CPpoVN7N58xPnDKoLwVdW5SMXCaw6djoDJah1OGSfpNTCerJIZVCxvLcOHaVm0eEBLK5fBimBhemdBF1xCwVAs5PHFmaCySDOugPYa4bJHlkPi1hMAaRKj0KywIja1DSuKNeKqd74gkUCQnE3Hlnv2VFUcTf5kaTnwKCQOebb454PwE2HW1mfoYBY94i6YHWkwOWCdpsCFHRhAQZB6v9zI6f3Z9DfeJ1KP4zLP0mFN484PUKoxbREyQUhmdpiVtIqxhDoWXLgOeGkiCXE4hJpKbDQN7iRF5rehG5TI7Vb2Gv4X1abBqc2oQxJ6hBwnyEY1BXtznJNUYOMKHdXT5sJteg/OkeyfX6sIiPUkspIPGnu/Y149jRgG5xElHrwyTuupUxMxaFWk51yflhLVqrK4hLTWd2TiJuZ3y/BHXHiy8RaGkh4bHH+r1nQRBIfPxxQh4Pa95t6GdQ6wwxRMbEnuVQ2+qlmQN5Ei9dEd/NJreOnEPt3L9fWveyIQzqCON5J6jJmMbJwi+jkftZf/8MBEHA4w/ytecPs7uqnV9tnM2qGRqKW4u5Nufa3s/kB5dnow552d7oRxOlYuaKZMr3t+IY7D1q9KCKkmYuTIAiY6V0r/PctL7fDe89RqChZuLxHiAVJCojIPoiT9+PzYaO8cFR7K+xkBUXMW7hOqVcydyEOah0Z9hWahrTOnzNXQgqOYrY8PvU5Q3w7vFmbpidQqR6fIqn8xOjuGVeGs/uq6Olc/TInh7tr7HQ0unhy4kVEPRJBvUIpYjTggD+c4t4+2hP8x6MqmxMHWppP2UymH4dVH4k/T2MUY0dLiJU8n5BiiRdElnRWRxolXj5oiiy961qImPUFK1NDb+i6h2QuVwaSBlEqrRUoq+6CtsrrxB0DB7k+fPxP/NK+Sv89vBv+z3e8/ko4s96Epa/PYXo8RD/7W/3W/bapXNoJpq82G201Tso3S0NZG976knef+J/MNVWU3HQRHtDF0s25NDh9fPG4UY2zk/txWMJcjnGRx/FV1eH7c23OGI6gt1nZ535DJhL4Za/gX6Qz2MSacqg/gxLpZDx53sWMDvNwEMvlbBvhNN34WyC+nxLBiaD1hYkEBOh5I1JgPloc3h5bt8ZbpybGrZpfErjo2S9ZlDER8PnxaDWSScjywUuSixvdRClVpA6xVa/JBQfqWZ5bhzvHmsZN555R7chEjtlck4arSlIoLnTQ6V5mAIppvjhU7r4Omw6jE6pkxrnR6iUaQYAmqtsA5/02CXm7Dn86XOVsD6DYgJE7W3B/sEe3IcOE/+NryPT9L9WDjn9iO4Aqsx4dMuXY3vrLcTg4CzOvqo0OTjd6mDDnBQpTRYRD60nBiwXtNlQxUjM0i2nWlmbsZZTllOYnCZoK4dND0P6UrjiZ2G301PqFwhjIFhcQd4NLiOlbQ+4BscRhFNz2mqCopzsldG8W/0uG3I3sOmmTcxclYwo+jmTuYbNdR+M+XwyqEFt7gpbkDgcf7pHcr0hLOKjpyAxvy0N26ZqNNNjMdw4bUi0jEIlJ7Mwjpqjbb0pt9FKFEVaKstJys1nXkYMAU8CzV3NuPwugg4Hlr/8Bd2KFeiWDkz3qXOyib7/HlYc95FX338AIiFnGqba7hRfRfcARH63QR3X/ZsYBYfatb8YucGAOj9/0GUSIhLOi0EtiiI7X63BqzYwz/kxGp0SbyDIN/95hF0VbfzyliJuW5jOh7UfEhJDXJd9Xe9rFW7p+6/oUvCPvWeYd1UmiFDy0SCMaUGQMB8ThvjoNqjPxXyUvgOH/o6/sgRlgnFCtt1PliqJP32xy45jc8YlQR0MiRyotY4bf7pHixIXgrqFj8vrCI7hb9nf4kSZrOst9z1X7x1rxuULcsfi88d79NV3Ls9DFEV+t21oXrGlqYFXfvqDsNiZN440EaVWMMe+C6JSIHX4QuIeCUoZ8lgNAVP4knWn30mJqYQlyVJXQ0m9TXpixg0SG716iNLfYdRgdZMeEzHgGL04aTGHWg/hD/mpOdqG+YydRddno1DKB67E3gzt5ZC7dtjtxT74ICGnE9urr4Z9vtxaznOnniNWE8uuxl39UtSBdsmT6GFQ+81mOl58Ef0NN6DO6d+vkRitoSFqDkXBl0nJN7D/7WrqTpTRcOo4AHtf+SfFm2qIT48kf1Eiz+2rwxsI8eWV/bFlkWvXoJ07l/Ynn2Rn9VbUgoJlpVtg1fcg7/Jh3+9k0JRB/RmXTq3gmQcWkREbwVeeO8TJppGB5812D9EaBVpVmD/az5hUChkb5qSwtdREp3tkJVATpb/sqsYXCPHQuin29EQqKVpLe5cPb2DgDWFDh4tojQL9JW6wxPewGseBQTYanW51kJ8UNSIu6JQ+G9owJ4V6q4ujDbZxWV9vgvoS/xv8LGl1gXRDvGsYzIfHH8TtD04lqKd0UXXYdJi5xrm95W8jkSZSSWyKLjx2oa1c+ncYgzrFoOW3ah8euYB9mwVlWiaGjRsHLNczrVmZEIHh1o0EWlpw7t03ov1891gzMgGunZ0smUdJs8AU3qCWGwxcXZjE4foOZscuA2DXmS3wyr2gioDbBp+q23NDHI4RanH6eDu4Apnoh7JNI9pvgIAvSL1hEQmhJrY4NuEL+bh35r3olDq+v+oxkuVNtBmX8uMdP+MrW79Cbefok5PxkWraz5kZ5vYFabK5wwY/Wms6kckFEjKHntUlNxjCJqhPtp9knbAcz+sNqNKiiP3CdAT58Nc3ufOMuO2+XoN8tLK3mXA77CTn5TM33UDIm4iISK29FsvTTxO02TA+9uigr/fds4G2aMh/ehdi4Cw/NzF7GtbmRnweN1RulYrqujEPskglgko2JDe2r0RRxFlcTMSSJQiywS0Do9aIxWMhGBrZIM25OrGziZqSNgr19WiP78DrC/Ctfx5h+2kz/3lzEXcsktLsm2s2MyN2BjmGs4aMo106p+XnpvPrLeXYCJG/NInS3c247INcH+vTJ64kMUYyqLts5xiCJS+AKoqA3YtCHFtad1SyVF1cvEePYrLBZTnvYrzTrXbsngBLc8eHP90jCSMl0kklRxsGdgEMJTEk4m92DsmffvlgA/mJkcxLN5zfjp6j9NgI7l6SyWuHG6lpGzx4cHDTGzSWnuTUzm39Hnf5AnxwsoWbC/XIaz6WjOMh/sbDSZ2tx11mxdcy0KQubikmIAbYkLcOlUJGSX33Z5u1UprFcPq9UW2rrxo7XKTHDgxJLUlegivg4qTpJPvfriEmKYLpSweZrVAjITMG40/3lXZWIRFLlmB97nlEX/9jSjAU5Kf7foperefZq59FLVfzXOlzvc8H2tzIo1XI1JLfZvnr3xADAeK/9c2w29IXrCJGcBA7y43fE2Tb0/9EpY1g0YaN1JQcoNN0huU3T8MTCPH8vjNcPiNxwHlREAQSvvsYAbOZ0CubWOZyEpF5Gaz5f8O+18miKYP6ElCMTsXzX1qMXqvkgWcOUNsefjSrr8wOLwmfcf50X21ckIYvEGLz8fEv+xqpzA4PLxTXcdO8VHKm0tMTqmS99Ns12wembOqtLjLiLu30NEj8dbiwCWpRFKkwOabwHpeYrpqVhEohY9Ox5nFZX4dzCvEx2ZRq0JKXEMnOiqGnYdtc0iDvFD98ShdLNo+NKlvVqPjTPUrJM9BS3Uno3E6StjLp32EMakEQSEqKYrunGpQGIq9+DEE18G+hd9pugpbI9euRGwzY3hi+LFEURTYda2ZZbtzZGYxJRRJ+JNg/YBG0dSI3GLhqViKiCBUNOtKj0tlR8lepAG3j34ecvi+P0YBcCDsF2+r0ckrMwmfIlVAhI9TpfS34ZFqyOot5+fTLrElbQ7b+bAosP1hBSK7mIe3jlFpK2bhpI08efRJvcOTXKcZIKUHdN4HdU5CYG64gsboTY0YUimECN3K9PmyCur2hhYcqb0dhUBP3QCGyEQZ3MovikCtk1IwR89FSKQ2aJE0rIFmvIUYpcVrrao9h/cezRF97DdrCwkFf3yba+cflMtRnWun45z97H0/MyQVRxFxZBrWfSOnp7kCBIAgoYrUSF3UE8tfVEWhpCZvi7iuj1khIDGH1jC6ND9BW72DPG5Vkzopj9tI4Qk4nP/7DZraVmfn5TbP4whLJnK6z13HScpLrcq7r93p7t0H96E2LkcsE/t+bJ5h/ZQahQIhjHw9iQhvSJ45BHdOToO5jdnacgTOfElr8LYJeOYqOw9BybEK2D0jlqR11k8Og7imltZ4f5qO4RvptjXeCerZxNgqZAmVELVtHifkIWj2IviCqQfjTp1vtHG2wcceijAkJ9Xx73TTUChm/+agi7PNuh53T3ezi0k939DumbjnVissX5D5jBQQ8o8J79Eh/TTYyrYKOV8sRA/3PuXua9qBT6liYNI+iVD1HehLUciXkXw3l70Nw9MWUoijS2OEmLUzP1KIkCZlVvKscm8nF0ptykckHsTprdkizlxIGP8b2VdyXHiRgMtH5/vv9Hn+l/BVOtJ/g+4u+T5Y+i5um3cS71e/S7paws4E2dy9/2t/Sgu2VVzDccjOqjPAIqelLpL6BxurtFCyLwNZ8jNwFa5h39UYEmQaF/CDpM2N5/XADHS4/X1udE3Y9EYsWwZI5rP/UweVdMtj4FMg+O6HUKYP6ElGyXstzX1pMSIR7/17ci/AYTCa755LgT/eoKFXPtIRI3ryImI8/76zBHxR5eN0Ue3qildRtUIcrSqy3ui55vAeAortM6EIyqM0OLzaXf6og8RJTtEbJ2gIj7x1vGdMUx3PVMWVyTkqtKTBysLYDp3fwm4Kp9PuULrZKzCXA6PjTPUrJM+D3BmlvPCdRZi6TypAMWcOuoyBBR/anzxE07SPQrsV1bKABGTC7pCnO0WpkKhXRG27A8fHHBDqGTuCdbLJzxuKS8B49SiyCoJegqYKKg634vVIKtSdBXZAYRWZcBFtLzaxRxFIcsOFc86+Qs3rIbQkyAUWcNnyCussHCIQKb4Uzu6Fz+A6CUEik5KN6YmQ2lC2H6fB2cF/hff2WieqsIzZkRnYqnrdvfIcrs67kz8f+zC3v3MLepr3DbgMkxIfbH8TpO5vG7TWozwl/BAMhzGccJA3Dn4bwCerW1iYerbgLmVxO/BcLketGftxTaRSkz4yl+ujI2P7nqqWqAoVSRXx6JoIgMC9pGohyZM++gej3Y3zkkSFfb3KZOJgvIFu2gLYnfo/fLA0+9hQlmo9sk4ynvCv7vU4Rp8Hf7sZmdtFQZqV0TzPHtjfgDnMt6dwvFY4NVZAIEoMaGDWH2ucO8OHfTqKNVLH+gRmoCmcC0H6ohJ9uKOTepZm9y75f8z4CAldnXd1vHQ5LGzK5nJzMZP71munsrmpnW6OV3AUJnNjVhCcMgx19Grg7wDs88mq0itDrEQQZzo4+2M1jLwMCgeT1AChjIuCtb0BgggImHWdADE4Sg7p7AOs8MR/7ayykx2pJGWe8oEahoSi+CH1Mw6g51L6egsTk8AnqVw42oJLLuHnexDB/4yPVfOmybDYfbwk7g/7E9q0E/X7mXX0D1qYGzLVn0RNvHmkiPVZLbtvHoEuAjKH/xsNJrlMSc/M0/C1O7DvOInNEUWR3026WJC1BKVcyP8PAiaZOfD0m9owbpL+/uj2j3qbN5afLGyAtZuDvIEYTw4zoQtz7I0nKiSZ7Tnz4lYiiVJCYs2bEqXHdypWo8/KwPv1M7/He5DTxRMkTLE9ZzrXZ1wJw38z7CIQCvFj2Ym9XhcIoeRLtf/4LIhD/9a8Puh1VQgFOhQFtywE8rsOAgNWcx4mdZuTqhbhslTScLuOp3bXMTTewMDNm0Pd4cI6ZSA/Ms10OUReAez+OmjKoLyHlGiP5xxcX0eH0cd/fD9DpGhx3YbJ7SbwE+NM9EgSBjfPTOFTXwZkRJMjHW2a7h38W13HzvFSy4gef6jOl8VFyr0Hd/8YrFBJptLr7NfteyoqLVGNxXrgE9elWqSCiYMqgvuS0YU4qbQ4vxTUj7zIYTD0mp2HK5JxUWlOQgC8YGrKv4ux3NzW4MKWLo8Omw6hkKmbFzxr1a3s51JW2/k+Yy8BYMKKb0aWNx8i0NSNcU4AqI4qONysHMHv93amoHu6oYeOt4Pdj3zQ0LmPTsSaUcoGrC5PPPpgkvc/jH9Xw0d9L2fe2ZCL0GNSCIHBVYRLumr2sLduOXxDYmzEy815h1PYyMPvK6vShkAmo590OiHBy+PR3TUkb9nYP+TEmxM5OZsbNZGFif2ZpsLOTXE09DouHrkr45cpf8rcr/4YgCHxt29f4/q7vD8sq7il76suhrm5zIhMg85zZcW31DoKBEMnD8KdBSlCLHg8hjxRsCHkCdD1fRVRIh+vW6F4+82iUO89Il9WLuW7w8qzB1FpVQULONOQKCWOzIDOeeJOBtI/LMNy6EVVm5pCvN7lMIAgkPf4jRJ8P8//8GoDI2Dgi9DE0nDhJfWAppxqnse/tarb+/RRv/OowZSct+Npc/PPf97Ppd0fZ8fxpdr9ayfM/3Me+t6vxdJ29d3QW70eRmIgqK2vIfekpahwNh1oURXa8cBqHxcOVXy5EqVXwrwfsuBRq7tZ3cf/yrH7Lvl/7PouSFpGoS+y3Hnt7G5Gx8chkcu5enMHirFh+/l4p2SuT8XuCnNgZJrik7+YBT0BRokwmJ0Kvp6tnsCoUgqP/hJzVBFzS8UKx/ltgPgU7fznu2wckvAdA3CQITMV0G9TnUZQYCokcODP+/OkeLUhcgEdWR3V7x5C4jHPlb3GCTECZOPC+3+MP8lZJE1cWJvbOeJ0IfWVVDoYIJb/aUt7v8VAwyNGtm0kvnM3y2+5GrlBQ+qnEfW7t9LCnqp3biuIQKrd24z3Glq7VFsYTMS8Bx44GfE3SZ1drr6XZ2cyK1BUAzMuIwRcIUdrSXWCcu14aMC57d9Tba+iQzmeD3ecvs1yLyhPB/A3pg6fWzWXQZRoR3qNHgiAQ+8Uv4q2owLlbMtZ/eeCXBENBHl/6eO+2MqIzWJ+xnlfKX8HZaUf0BFDEa/E1NmJ74w1ibrsVZeoQAxaCQDBtCYXBSso+3UZ64RI62+SUfFRPwbKr0Ebr2fyPZ6izuPjaqpzB3+Phf7ApsolTs7S4Nu8l0HZ+hb4XWlMG9SWm2WkG/nbfQmrbnTz47EFcvoFJKVEUabvEEB8AN89LRRC4KCnqP+6sJhASp9jTF0g9CepzixJNDg++YOhzkaAGiNOpBrAaJ1LlrdLFRcEU4uOS0/oZCehU8nHBfHQ4fUSo5GjCFZNM6aJpYVYMESr5kJiPXsTHKJKEU5rSeOqI+Qiz4mehlo9+lp/OoEZv1IY3qBNmDvt6MRAg+91/UhudRO3sZcTeOR0EsLxcjtgHGxJoc/WmogA0Bfloioqwvf7GoGnaUEjkveMtrM439u/IiM/HhZGDB9UolDJO7mykrcaK6HYjjzEAcF2ugt/Jf0e+EIteFc2Oxp0j+jyU8RLOQQz23yer00eMToUQPw1S5sOJ14b+XESRI1vq0CdoCUXUoPGK3J9/z4Cb46DdTqrBRWSsmuPbJfNvafJS3tjwBt+c+00+rv+YDW9v4KXTLw3KKw5vUHeRHhsx4JzSWtNdkDiSBLVeWibY2YkYCGF5oQyVVeCXaU+TN2Nk07zPVdbseGQygZqS0SWHg4EA5tpqkqedNRDnphu4Y7ePoAzivxmeT9ojnydAe5ODAscC6ht0NG74IXurjbzy+E6e/v5uPC4D1U0C77b/Kztfrubo1npMtZ3IlQKalEjkgsDlt03jpkfnce8vlnHnjxaTVRTHkS11PPf4Xva/U43b4cVVfADd0iXD4gmM2u4EtWvkn8OpT5upOmxmyYZsErKjefTVY7x3yoQvt4Cs9v5ojlJLKWfsZ3pTin3laG8jOl7avkwm8F8bi/AEQvzfoTNkzY7n2PYGfJ5z7oUN3dPrJ6ooMSYWZw+Dum432Oph7j34TdLno1xwLcy7B/b8HzQcHP8d6DWow0//v6BSR0plsOeRoK4wO7C5/CzNmTiDOkQQubaOj8tG/hv2N3ehTNAiKAfaaVtLTdhcfu5cFB7lMF6K1ij55ppcPqlo6xc+qD5yAEd7G/Ouvh5NZCQ58xdzes8uQsEg7xxtIiTCHbEVUmHhzA3ntQ+GG3KQ6ZRYu1Efe5okA7fHoJ6fIaV8j9R1D9qoImDaeji9WRrAGYUarNJgcXoYxIfH6UdzMo06wynM+jODr6Rmp/TvKAxqAP3116FISMDy9N/ZUb+DbfXb+Pqcr5Me1b8A8/7C+7H77Ow4+hEgDRS3//FPCDIZcV/72rDbic5fia1TDn4fa+67i4zCWGQKgeUbp7P4xltx1pYyT2XlysJBUtEtx2nc+v8oV6sIfO1eRL+f9j/9eVTv9WJryqC+BLV8WjxP3DWXkvoOvvnPI/jPYfHZXH58wRAJUZcO4gMk0/KyafG8WdI05lbtsai108OLB+rZOD+VzLip9PSFUJRGSaRaMQDxUW+RRlY/LwZ1fKT6gjKoy1u7SIhST7GFL0FplHKuLEzig5OtZ6fhjVFWl28K7zEJpVbIWZ4bx87ytkFNtLOIj6nvb0oXXi6/i1JL6Zj40z1KyTPQXGVD7LkOdFmhqxUSpg/72s733kPeWM8L06+i3OxEEashZmMe/gYHnVvrAAj5ggRtXpTG/olbw8aNeCsr8ZwYWHgIcKiug5ZODzf0xXsAyJXs932NYFDgxsfmoY5Q8skrFYhIWApCQWbv/x5xgoMn4x5nVdpqPmn6hEBoeH6nIl4LQZGgrf+1ksXpI67nPD77dmg9frZIMoyaKmy01TuYd0UG+10nAVhrWNRvGVEUCXZ2ojToKVqdRlOFrRe1opar+cacb/DmjW8yK34W/1n8n9z9/t2UWkoHbCs+Uro3ae9zbVNt7gpbkNhS3Ul0vAadfvj7GbnBAEhs7443KvFW2Xhn+m660oNjGgwB0OiUpBYYqD4y+DE1nNrrzxDw+0iaVtD7WEFXC6srO9i8SCAQ2z8EYK6zs+P5Ml79z4P8/Xuf8rfvfELU23NYe/I+Pnm5gkpbAl2GLMTGWrIKY0nLTUMMWbjh6ibu+8/lfO0Pa7j3F8u56dH5zLpeSrRmZkaRWhBDdLyWuNRIrvzyLO780WIyZsZx+IM6nv/hXiqjl6GYP/zU/zhtHAICbe6RpfTaGhzsfrWSjMJY5lyewXdfO8a7x5r5wTXTyV21GM/p04T6lJFtrt2MUqbk8szLB6zL3t5GVLdBDdJs4kfW5/H+iVYCBZF4nQFOfXrOwHtvgnriihJ7GdQl/wR1NMy4noCpFQBFUhJc9V8QnQpvfx18A2c5nJcsVRJfVzvI9P8LrZhssJ4Z88vP8qfHtyCxR3ONc5EJMhITm/mobOSYD1+zE+Ug/OlXDtaTFqNlee7EmOp9dd+yLJKiNfxqy+ne49DRD98lKs5I7gKJHz9j1VpcnTbOHDvCG0camZ9hILFxC2hjIfOy89q+LEJJzMY8AiYX9m117GnaQ7Y+m9RIKSmcpNeQotdwpL4PAmvGDeBohuYjo9pWY3eCOi1MSeKRLXWEvHAo832KW4oHXYdYvVPC3xjSB10mnASVitj77sW1bz/PvvVj8mLyBmCuAOYmzGVewjyOne4efPJa6XznHWLuuhNlYuKA5c9VIHkhJdZUnNoo2lXxXP21Iu760RKi47UE8pfilEew1lmCXBZm4NDTCa/dzw699LeyYsmtGG7dSMerr+JrmJgBuYnQlEF9ierqWcn8x81F7Cxv43uvHetn2Joc0oVq4iWWoAbYOD+Nxg43B86MvqhjrPrTzipCIZGHptjTF1RJes2ABHW99fNlUMdFXuAEtck+hfe4hLVhTgqdbj+fVJzfVDCbyz+VwJ2kWl2QQGOHm+q28CisngT1FJ5lShdDx9qOERSD521Qe50BrC3dv/G209K/wySoRb+f9j88iWbmTKoLFlBukrANEUVGdEuS6NrViKeiQ2I6i6BI6H+dEX3dtQgaDbbXw+MyNh1rQqOUcfmM/jeo5jo7Zdb5zI7eRlK2nmU359Ja58KUsFAyVXf+F0LtTjanfZfnzxhYkbKKTm9nL6t7KPWWM53DobY6fcRFdhvUhTeDIBuyLLFkax3aKCXBvA5OBSRTT2bvPxU+5HRBIIBcr2fmZSkolDKO7+h/Q5wZnclfr/grv1r1K1qdrdy1+S5+eeCXdPnOruvcBHUwJFLb7iTX2D8AIooirdWdI0pPA8gN0nKeKiuuEjOR69J4UfkuRfFFI3r9YMqZl0Bnmxtr88jxgi1VUqlZch+D2v6HJ3Cq1byzVKC2s5agP0R5cSuv//chXvuvQ1QcMqONVJI7z8jSm3I4PW87las/4oH/XsHXf7+GW++JZ87+XzEnWMy8XClZrsjMIipWg6yPkdGDMglXlBiXEsnVX53FHY8vJjHSyZmsa3lnbwwHN9fidQ8+IKKQKYjVxI4I8eHzBNjyt5NodArW3jeD779xnHeONvMvVxXw9dW5aGYVgd+P97T0dxsMBfmw9kNWpq5Er+7/XYdCQbqs7b0J6h59dVUOM5Kj+Y/9NSTlGTj6UT0Bf5/EfmQiyJQTV5RoiKGrwwIeO5S+A7NuAaUWf6sJmU6HPDISNNFw4x8kM3n7z8d3ByxVk4M/3aPYnPNKUO+vsZBq0A7AOgSDITpandQeb6fko3p2vHCat35zhOf+bS9HugcUR6JIVSTTY6eji67j0BkrVufw91VBh4+Qw4cyeaBBXW9xsafKwh0L0/v97Q0lS1MXnWEKbUcijVLOI5fnUVJv46NSE5bGeupPHmfOldcik0uzTnLmLUQTGcW+rVupMHVx6xwjlH8IM64HuWJM2+0r7fRYIhYm4tjVSGetmRUpK/o9Py8jhpKeokSQyltlilFjPho6XOi1SqI1/a9Ruzo8HN/RSP7iRFIy4yhuHWhQiyERy/MnMZfeQChj4GDXSGS4/XYCGgUrdrXz42U/RikLf618f+H96BwqQjIRy/N/RVCpiPvKV0a0jbJqG86giqRYP68dbkCpkmNIlH77T+1rotS4EH9jBfUnj5/zBkXY9BB01LE9bQZ5MXmkR6cT/41vIigUtD3x+zG954uhKYP6EtZdizP4l6sKeOdoMz97r/Qs1N0uXfRdSiWJPbqqMAmdSs4bhy8M5qOl081LBxq4dUHa54Z7PFmUrNfQck4ZaIPVhUxg3Es0JqvidGo63f7zTryORMGQSKWpa6og8RLWZXnxxEQozxvzYXVOJagnq9bkSzfyuwYZhOjBs6gVU3iWKV14HTYdRibImJswd8zrSMkzAH041ONBXnEAAQAASURBVObulK5x6AS17c238Dc2YvzOIxQk6ylvPcsVNlyfgyIxAuur5Xi7sRLKcwxqeVQU0VdfjX3zZkKu/onIQDDE+ydauXxGIjr1WTNAFEV2v1qJVhNkofo5cJiYsTyZ+HgZVbk3E+qohk/+B+bdQ/yqL+P2Bwm5ClDKlOxs2DnsZ6GI7zYjzzE+LF1eYnXd9wBRSZC9Ck68Kt3gnqP2Rgf1p6zMXpfOCxXP4Y+Uwi1Bm63fciG79LnI9dFodErylyZRccA0oHxPEASuyb6GTTdv4vb823mx7EVufPtGtp7ZiiiKxESokMuEXoO62ebGGwgNSFDb2z247L4R8ael/ZKWc5/wIItSYp0fosvfNSbWeV/lzDWCANVHRo4GaK0qRxutJ9oosZudBw7g/ORTjq+9EkGI4dCmep79tz1se6YUryvAyjvyeOCXK7jh4bmsuXs6C67OojRmH5HpcnR6NYJMIGrdWiLXrKH9D38gtlVK7plMtoGfQ7QKFMIArnpfxadFMr/jfZY1PUPq9FgOvFvL8z/cy6H3a/ENYlQnRCQMm6AWRZGd/yzH3ubm8gcL+cnW07xZ0sR3r8jnW2slQ1U7WxowcB+XZiIcNB2kzd3GdTnXDVif09ZBKBgkKq6/Qa2Uy/jVxtm0d3k5Hh3CZfdxem/L2QVkMtCnTiDiIw6XvZPQyTch4Ia59wAQMJlQ9E1Q5qyBRV+B/X+SykrHS5POoM6W0rL+0RuwoVCIk1VWVhuiOPlJE7tfr+S9J4/xwr/v4y8P7eLFnxTz/h+Ps/eNKmqOthEKikToVex7s5qaoyMPWyxIXIAlUEWIADtOD/+37O8eAFWmDJw5/eqhBmQC3LowbUTbbqt38PovD/HSz4o5vqNxTKWrty1IIydex6+3lnPkg3eRK5UUrTtbkCpXKClYtpKW44fQCQE2RJWDzwEzbxz1tgaT4focAjp4uPEuViaea1AbaLK5Mffct2tjIGulZFCP4v02WN2kh0lPH3yvFjEksuSGHBYnLeZU+6l+A58Ane/X4j7VgT+URUfrlWP6nE/7Gvhwdojlp2Gmb5ASRmBN2hryQllYFRYc771H7N1fQBE/+PI9EkMhDr2/CWOUyJXx9bxd0tR7j19l7mJbmYlFV19HZEwse197of97OPBXKH0H29p/5UhnFevS1wGgTEwg9t57sb/3Hp7ywWdKTSZNGdSXuL65JpcvX5bNP/ae4ffbJSaVyX7pJqi1KjnXFiXz/okW3L7wfLvx1B93VBMSxd4LqyldOCVFa2g9pySx3uoiWa9FKf98HNp6ElA90/InUmUtdryBEAVJ0RO+rSldHCnlMq4pSuajUlPY/oKRyjaF+Ji0So+NIMeoY2d5+BuwDpd/6rub0kXTEfMRKcmmHDsuLSpOQ2SMmuYqm/SA+TSookA/uFkQ8npp/9Of0M6bh27lSgoSI6k0dxHsnn0oKOXEfWE6ojdI5we1IBC2VM9w60ZCTif2LVv7Pb6n2oLV6RuA96g6ZKalupOla9SoZS4wnUCQCSwq9OFTGzi+7wwkFcG1v2ZpThxRGgW7TttZnLyYHQ07hr3BlumUCBqFlPruo36ID4Ci26DjDDQdHrCOkq31KNRyjAsUbD2zlaUFUvIs2NnZb7me/5d1G8Gz16YR9IcG4hW6Fa2K5odLf8iL171InDaO7+76Lt/4+Bs0OxuJ06l6Deqq7tKy3IT+BvVZ/rRhyM+gR3KDAXlcPkGbnKjV6ZzsPCW99fNMUEdEq0jO1VNdMnIzrKWqguRp+QiCgCiKmH/zv3RmLyGYcBN3H/kx9gNKknL0bHhkLl/4yRJmr01HrT07sBEIBWj3tA8oDEz84b8hBgO4tlWh1Sgw1VQP2LYgE1DEasMmqHskBgK4Dh4keV4O135jNrf/2yKSpxko3lTLc4/v5fCHZwZwnY0RxmET1KW7m6k8aGLR9dn8/kQDrx9u5JH1eTy0/uwMVEVSEnJjfC8qZ3PNZnRKHavSVg1Yn6Nd2t65CWqAojQ9X1mZw7NVJiKSIziypZ5gX+SlPn1CShIBImNiQBRxFb8E8fmQJpWJ+k2tKJPOmeJ/xU8hJgve/iZ4R1+2OUAeu1QAFz+J7ktju1nYHWcGXcTrDmA6Y6e8uJXid2vY8tRJXvmPA/z1O59wZ6uc1KMOdr1YzqldTXRZvdIgylUZrH9gBhu/v4Av/WYlX/r1SjZ+fwE3PTaPhMwotv2jlI7Wkc1sWJi4EH/IR3yciW0jwHz4mqXjkiq5/7kqEAzx2uEG1hQkkKwfPizldvh4/8/H0UQqSc0z8OkrFbz3+2M4baPDNyrkMh67Mp8zLRZO7NrO9OWriYjuP3hXsGINQtDPtVFtRFZvBo0BslePajtDSaZRsGvuKdJ9SeQfT+j33PzMbg513xT1jBvAWn12ltMI1NjhGsCf7mh1Ura3hVmrUomO17IkeQlBMchh09nzmfNAK127m9Cl1hGteB53rQrngdZRvb9AKMBP9/2UfSvjkclkWJ97btBl5TI5+aEcgpYGRI2a2C99aUTbqD12GGtTAwsXTiPDX4XX5eDj7t/j33fXoFbIuH/lNJbcfAdNp0upO3G0+4M5DFt+CPnXsCu5gJAYYm3G2t71xn35S8iiomj77f+N6j1fLH0+XJzPsQRB4N+uncEt81P5348qeH5/Xe9Fn/ESY1D3aOOCNJy+IFtOje7AM1o129y8crCB2xamT6WnL4KS9RrMDm8/xnq91fW5wXsAxHcb1O0XgEP90oF61AoZl89IGH7hKX1mtWFOCm5/kG2jKIo5V1anb0Jby6d0flqTn0BxrTXsIK7N5ZvCe0zposgX9HG87fh54T1Auu5NyTPQXGmTDFxzmcSfHqLozfbKqwRaWzE+8giCIFCQFI0vEOKM5ay5oUzUob8+B4Ii8hhN2GIs7YIFqLKysL3eH5ex6WgzURoFawrOGml+X5C9b1ZhzIhi+hXdJmmrZMrFhMwkt+zhpO8KrGv+DkotKoWM9dMT+LjMxOrUNTQ4GqjpHHravCAIKI3afga1LxDC4Qn0P0bPuAHk6gFliXaLm8pDZgovS+G1upcBuHbuncDABHWPQd2TVI5LiSRtegwndzX1NwbP0az4Wbx43Yv8YPEPOGo+ys3v3IwqbgfmLumzrzZ3G9TnJKhbqjtRaeTEhkkwhpNcr0c1/XqQB4hcksSJthNEKCLI1meP6PVDKXdeAtZmJzbT8Cxhr8uJtbmRpGn5+DwBDv11OztVN3A48z7E9gAlifvZt+Idrv3GbNJnxIYtKLS4LYTEEIkR/c1OVXo6cTdehqNeQ3xMHObaqrD7oIjTEBwiQe05dYpQVxe6pRK/1pgRxXXfnM2tP1hIUrae/W/X8Pzj+ziypQ6/VzqPGLXGIUsS2xu7+PTVStKmx/Cmx8Erhxp4aN00vnN5fzyiIAhoi2bjPnECb9DLtrptrM9Yj0YxMFRlb5O2FxXGoAb4zuX5ZMVHsEV04bB6qDzQx3g0ZExcgtog8V+76k/C3C/0HnsCrSYUiecUm6l0cNOfpCLFrT86/41buwclJluCGvphPmxmFzteOM2bvz7M09/fzVOPfsLrvzzEtmdKOfz+Gcxn7EREq5DnRvGR1seSL07nvv9czld/t5o7f7SYq79axNIbc5m+NJmkHD2aPlg5hVLO1V8rQq6Q8cGfTwwsyQyj+QnzAchOM7Grog2Pf+iQm7+5C3mMGtk510u7Ktow2b3csWh4vnEwGGLL307idvi55utFXP/QHFbdmU9zpY2Xfl5M1eHRXYdfOyuZdbI6RL+XWVcMLBStJA6bIpppnWVw+n0ouBbk43u997p/M4dTK/DsNeGtPTuIWZgSjUouo6Qvh3r6dYAwYsyHKIo0drhJi+lv/O9/pwaFSs6Ca7IAiQGtkql6MR+eahsdb1ehjrNgaH+IqCIP6vwYbO9W9w40jET/LPsnZdYyvnHl40Rfcy0dr70+YKC2d1+DIhEOBYaWVg6tTkIRMzIe/KFNbxIZG0fBmmuRiUHWRtbx2uFG2hxe3jjSxMYFacRHqpm17kqi4ozsffUFRKcFXnsAopLhpj+yvWEHiRGJzIw9izWT6/XEffnLdO3cievwwIHoyaYpg/pzIJlM4L83zmb99AT+/Z2TvF3ShF6rHNCGfalocVYsaTFa3jgysZiPJ3dUISLy7XWT6CLgc6RkgxZR7N/0Xm91f64M6rjuMiHLBHOoHR4/b5U0sWFOCoapdOUlrcVZsSRGq9l0dGyYj0AwhN0TmDI5J7HWFBjxBULsr7EMeK5jKv0+pYukUksp3qCXBQnnZ1CDhPlwdfpoq7NLiI+EGYMuG3K5aP/rX4lYurTXkCtIlFBWFa39E426xUnoliUTMTe8ISYIAvqNt+A+fBhvTS0AHn+QradauaowqR86p2RLHV0dXi67PQ+ZLhai06BVKiAMHnyF3JpNKFVyPv3Q1ZuUvnpWEh0uP9HiHAB2NOwY9rNQxGsJtJ01TntmXPUzqDV6iQl68k0InjVzjn3cgADkrYrljco3uDLzSpJT8qV9HGBQ24GzBjXAnHXpOG1eao4MnaxVyBTcPeNu3rnxHVanraZTu4ljoX9nV8Muys0WYiKUAwY9W6s7SczRj5jx6mv1oTBOR65pQlDKOdl+ksL4QuSy878Xypkn/R6qS4Y3lFqrK0EUaW+M5B8/2MOBEgGFQmDdPfk88F/LOZh4mqrQwPLIvjK5JKM1IWJgYCCu0IsySkR7ppX2xnr8voEBBkWclKAeLIHv3C8ZOxFLlvR7PDErmuu/PYeN/7qAhIwo9r1VzfOP76Xko3qMygSsHmvY8s4e7rRaq+BwsoyXDjbwzTW5PHZFflgDXju7CF9NDZ+Wb6HL3xUW7wFSQSIwAPHRux6VnP+6ZTb73W6C0QoOf1h3tpNJnw6OFgj6w772fBQZIxnUzqAaZksDOmIgQKC9HUVimJBH5jJY/m04/AxUbTu/jVsmoUEd02NQS8fEULcxW9GdYM2aFceym3O55utF3PXvS/jaE1Kp5w0PzeWoUaDVqGTB4mSiYjUII/x7j4rVcNVXZmEzudj+bNmws00MGgPTDNNAU4PLF2RfmOujvvIPUpD48sEG4iPVrJs+fJhnz+tVNFXYWHvPdBIyoxEEgaI1adz+w0Xo47Vs+dtJCfMzBP+9rwREZnWeoEWdyMdtA6/j3ixpoj5mOl115Ti63OeN93BYPXR1nD2+NDoaOWM/g2uVBnmMBuvrFYS6gxBqhZzC1Oj+RYlRSZC+eMQGdZvDizcQ6hcKbK3tpKakjXlXZhARLb1ntVzNvIR5FLcU4293Y3mhFIXSQlzXNxAW3Iuw8Slib89HFqHE+s8yQiMYwGjuaubJo0+yOm01l2dcTtyXHkR0ueh45dWwywc7PBCCgK+dP05voMY2PIO9tbqShtITzL/2RuRZSwGBu5Ka2Vlu5n+2nMYfDPGVldJsBIVSydKNd9BSWc6Zv35NOpbd9g/cKi17m/eyLmPdgGNr7L33oDAaMf/mf8eEN7mQmjKoPydSymU8efd8FmXGUmnuuiT50z2SyQRumZfK7qr2ASV646XGDhevHmrg9oXppH5OeMeTTUl6KU3R0v0du31B2ru8ZMR9jgzq7hs2i3NiE9RvlTTh8gW5d1nmhG5nShdfMpnA9bNT2FVhptM1+hs3m1t6zVSCevJqcXYsGqUsLObD5vKjnxpcmNQSBOFqQRDKBUGoEgThB2GeXyUIwhFBEAKCINx6MfZxLDpkOgTAvMR5572unHlGtFFKPv7HSQKuriELEjtefJFgezvGhx/ufSwvMRJBgNPnGNSCIBBz4zT0V2YNuj7DTTeBXE7nm1JZ4s7yNhzeABv64D0cVg9HttaTtyiRlGkG6cGkIjCdhKMvEqw9iho3S28poPF0BzXd+IhV+UbUChkHq0IUxhWO2KAOdvp6jYKeAe24c4/RRbeB0wxnPgHA0+WndHczeYsT+bD9Pbr8XdxfeD8yXQQolWEMaun/+xrUmbPi0Bu1HNs+sqRqoi6R36z5DfPV/0JA9PPt7d/mQ8eXkKf/Lz/Z+xPerHyTyo5KXE4vluYukkbInwZwbG9A9DkQvZX4gj5Od5w+b/50j6JiNSRkRfd+T+EUCoaoOdrG9md3AtBQriAtxs3Cw7/ixjvjmXFZGiq1gmRtJq6QGU9g8PuXnqTyAIM6FER25mOSbplFZIsJMRSive7MgNcr4jSI/hAhR/hwg6t4P+r8fBRxcWGfT8rWc8PDc9n4/QXEp0Wy940qhJfyKWpejdne/zMQRZFPXqrAZnbRND2C50oa+dqqHP7lqoKw5jQgFSUCJbteJ04Tx+KkxWGXc1jaUOt0qCMGv+5flhvHXUvSeT/owmZynf2O9GmACPamQV87EomiSNDhwNfQgPvECbo+/ZTQQYkB3tqWTev//Y2mxx6j/sEvQTCIMikp/IrWPg7xBfDOQ+C2jX2H2isB4awpPBkUESvhJLoT1Cd2NtHe0MX6+2dyy/cWsO6+Gcy/KpOcuUZiU3TIu2eniKJIcY2VJTnhZxIMp7SCGJbdMo3qkjZKttYPu/yCxAXUOU8RoYJtpYNjPkLeIAGLewDew2z3sP20mVsXpA2Lmizb28yJHY3MuTydgiX9fxMxSTpu+f4CFl6XRcVBEy//vJimio5B1nRWZ46X4LWaceUu5ffbK3F6zxqvnW4/W0tNZC9dBSKUOdMhd+0QaxtcXR0edv7zNC88vo+Xf15Mc6W0b3ub9wKwLGsFsbfmEbR4JCRWt+alx3C8sbPfzGemXw+tx6Fj+FLLhg5poLUH8SGKIvvfqkYbpWTO+v6J9cXJi2lub8D89yMIXjvx/ADZNT+GG34HChXySBVxd00n0OGh483KIQ1bURT5j+L/AOCHS36IIAhopk9Ht3w51uefI+QbeBx1HpKwJdo1CwhEaniudHAcSI8OvfsmKm0Es9dfLQ0aJ85ivlBOSIRXDzVy5cxEsuPP/uYKV1+OPlrLnqNtiFf+AtIWsK95H56gh3UZ6wasX6bVEv+tb+I+coSuXbuG3Z+LqSmD+nMkjVLO3+5fSFGqnhnJlzZH9pb5aYiiZKxNhJ7cUY2AMMWevohK7jaoewYhek9cn6MEdXzUxCeoRVHkuX11zEk3MDvNMGHbmdLk0YY5KfiDIh+eahl+4XNk607nTSXtJ680SjnLcuLYGaYoUUpQTxnUk1WCIMiBJ4FrgJnAXYIgnOu+1gMPAC9e2L07Px0xHSFHn0OsJva816WNVLH+gZlYW73sddw7aEFisKsLy9+eQrd6FRHzzxrjGqWcrDgdFabRM2EVRiORq1dje/sdRL+fd481E6dTsTz3rNm3980qBGDZzblnX5g0C9or4L3HCCqTkcfFU7gyhbi0SHa/VonfFyRCpWBVvpEtp1pZk76GE20naHe3D7M/3UWJ3ZgPqzNMghog70pQR8NxCfNx8pNGAr4QRetT+WfZP1mQuIDC+EIEQUCu1w+L+ACJd1y0Ng1TrR1TrX3En+FMw2LctY/yh3VPIrdfQbQyjq11W/nx3h9zy6ZbuPuZL4EI+0Mfs71++7CfgfdMJ94qG0HrQUKdVsqt5QRCgfPmT/dV7jwj5joHDmt/Y9nt8HH4wzM8/6N9fPDnE3RZ6tBGJ3Dfzy4jb+evSMiKJurKK3qXnx6XB4JIaftAfnSPehLU5yI+aDoMbiuR199J8nyJe9x8dOB07h5+eqB9oAke8npxHT5CxNIlA547VxInex43f28+EQkyltfdzOZflHNsewOBbkTC6X0tlBe34pqm46mKFr58WTY/uGb6kIajdlYhAM5jJVyTfQ0KmSLscvb2NqIHSU/31Q+umYEtVkGXCg69XyuZUYZuQ+sczIcYCOCtrsZ16BCObduwvf46lqeewvzrX9P8+OM0fPvbnLnnHqqvv56KFZdxumg2FYsWU33FlZy57XYavvJVOn76CyklX+fBvvl9PKdKEb1eIi9fj+6yleF3UqmBm/8k8aM//H/DvqdBZamS3ptykvVMxWaDtYauDi/Fm2rIKIwld/7Q311Nu5P2Li9Lc8IPlIxEcy9PZ9rCBPa/XU1DmXXIZRcmLsQdcDM/z8W2MtOgpqW/1QkiAxLUrx9pJBgSh8V7tNZ2svPFctKmx7C87zmgj+RyGUtuyOGW781HLpfx9m9L2PtGFUH/4Likkg/fRWeI4Yv33ER7l49n9pw1hz840YIvEOLmlYUk61yUuTIQ5aO7TnfZfXz6agUv/Gg/ZXtbmHFZChHRKt753VEqDrayu2k3qZGpZEZnos4xELkiBee+FjzdfRDzMw14AyHKWvqcC2ZcL/17+r1ht9/YIZ3DekoS60utNFXYWHhtNipN/2PEkoTF/FvjVwh2+IjT/hrFPb+HpV/vh/pSZ+uJvjIL9/F2nMWD3+9srdvKJ42f8O253yY5Mrn38dgHHyTY1o793YH73vn+TgCS77uDm6bdxKbqTUOepzrNJir272H25VefHXDLWEKE6QiLMyTP7qurcvq9Rt58iKWRJzB5oqiWzwWkWVVRqqhBUWmGjRtRZmTQ9tv/QwwN/lu62JoyqD9n0muVvP2tFfz29rkXe1cmVFnxOhZmxvDGkbG14Q6lBquL1w41cMeidFKm0tMXTcnR0mff0l2UWG+RDOrPE+IjSq1AJZfRPoEG9f4aK1XmLu5dOpWe/rxodpqezLgINh0bPebD6uxOUE8Z1JNaawoSqLO4qG0/y9gNhUQ63VMliZNci4EqURRrRFH0AS8D/ebpiqJ4RhTF48Dkvfs4R8FQkBJzyXnzp/sqszCOOTM7OOG6njOW8IaB9dlnCXZ2Ynzo4QHPFSRGUd46ttIyw60bCba30/7xDj4+beLaomQU3Ym65kobVYfMzL86k6jYPiZS4iwQQ6A1EIyZhdwQg0wuY9Wd+XR1eDn8wRkAripMoqXTQ6pqISIiuxqGTkIp4vsb1D0zrnoQYb1SamDGBih7l4Czi+M7GsmcFceRwF5anC3cP/P+3kXlBj1BW3/2ZshuR1AqETT9jbEZy5JRauQc3zFy3q8xUo0/oCBTuwBb8zruyvg5u+/czaabNvEfl/0HK+RXIBLiBctTPLLjEda+uparXr+K7+36Hs+eepYSc0m/BLJ9Wz2ySCVCsJagrZOTFgmlMp4GdQ/moyehazpjZ9s/SvnH/9vD/rdr0BsjuPqrs1Ao28ieU4j3vTcItLSQ8N3H+pm1y9Kl8aZdNccH3ZbZZUYhUxCjOYdrWrEFBDlMW0/2vz2OMhCk7r2B0+cVcdJ3FAjDoXYfPYbo9aJbunTE7z1lmoFFX0nknZlPoIgNsfvVSl54fB8HN9fyyUsV+ONU/NHczhdXZPHD62YMm4aVGwz4UuLIag5ybfZAlm6PHO1tg/Kn+0qvVfKzm2bxidyHpclJ3UmLhPiAfkWJoijS8LWvU3Pd9dTdcy+N336Ilsd/hPnXv8H67HM4d32Cv64eQZChzs4hav164h58kITvf5/k//xP0v74RzJffJFpj8xFqwiivfM28ov3k7vlQ7JeeZn0P/wBVVrq4DuaugBWPgbHXoTTm4d9X2FlqYK4vOGXu9CKzYGOWna/VkEoJLLqzvB4l74qrpEM5SXZYx+0FASBdffOICZZx9anTmFvH5y93nP+SUxoxmT3crIp/KCav5tb3NegFkWRVw42sCQ7tl/K9Vw5O718+OcTRBrUXPXlWciGSVon5ei5/YeLmHlZCiUf1fPaLw9haRrITe5obab26GFmX341i3KNXD4jkb/sqqGje0DyzZImcow6ZgdOMjOqmfbOAG11tQPWE06eLj/73qri+cf3cmJnE/lLErn7Z0tZ84UCbvmXBSRl6/no76V0HVBzWcplvd9r9FVZKOK1dLxeQcgbYH6GdLwq6VuUGJsjnfvKhjeoG6zSfX6qIQIxJLLvrWqi4zUUruxfPiyKImnvtTDPNZ3dxpdQf/1JyLs87DqjVqWhKYjB9m4NvjCfq8Pn4JcHfsmM2Bl8YcYX+j2nW7Ec9fTpWJ55up/Z6yopIWBygSyAMsHAvTPvJRAK8GLZ4JmBI++/gyATmH/NhrMPZiwDXxc/WSLy6OX5LMjs83fgbIfXvsjMTA2GhET2vvZP/AEfOxt2sjptNUpZ+KCJoFRifPhhvOXl2De/P+j+XGxNGdSfQ8llwoiZbZ9l3TI/jSpzFyeawgPsx6ond1QhEwS+uTb8qOeULoyitQq0Snlvgrre+vkzqAVBIC5ShWUCSxJf2F+HIULJ9bOTh194SpeEBEFgw5wU9lVbMDtGh0nq6E1QT6VwJ7N6ytp29cF8ODwBQuJU+n2SKxXo6/Q1dj82agmC8FVBEA4JgnCorW1oRvBEq9JWSZe/i/mJ88d1vUvTPyVO1cDHr7bg7Ox/ngzabFif+QdRV1zRm9jsq/ykKM5YnMOWZYVT5KpVyI3xnHnuZTz+EBvmSjfQoZDIp69WEBmrZu4VGf1flHUZpC2G258j6HAjNxgAyfzLX5JIyUf12MwuLp+RgFwmUHpGR4ouZVjMR69B3dY/QT0A8QEw+zbwOTj97i7cDj9zr0jn2VPPkhWdxer01b2LyQ2GgQlqWycyg36A6aTSKpixLJmqw+YB38Fg6pkdVlwrcWBzE3TIBBnZ+mw25G4gzzsbY3o0n9y7k+eueY7vLfweRcYijrcd59eHfs19H9zHsheXccd7d/DXzU/grbLhXxyBLDqSYGcnJ9tPEq+NH5hAPg8ZEiKIS43k5CdNvPbLQ7z+y0PUlLRRuCKFu/59CTc9Og9jBrg6bSSkZ2L581/QrVgxwAi+PK8QUZRRYiofdFtml5kEbQIy4Zzb+MotkL4EtDGo09MwGpOwdLTT9enufovJDRqQCQQsA8/vruL9IJMRsWjRqN6/UWukRV+NbmM7Nz46j2ijlgPv1uKTwd/8ndy3PJN/v37miFENVckyClplQ2JYJIN6ZMXdVxYmkTXfiF0msmdTDWJ092Gz82xfUeebb+Hcs4e4r36V9KeeIuuN15n28TYKDh+i4Pgx8j79hJx3N5H5/HOk/f4Jkn/2UxIee5S4B7+I4ZabiVq3logZWajbPyYyWofTPvJZA71a9X1ILIJ3HwHn0BzkARJFiUE9mfjTPYrNoa41nuojbSy8Jgu9cfh7tf01FoxR6iEN35FIqZZzzdeKCIVEPvzrSQJhCqIBjBFGMqMzcQqVyAT4qCw85sPf7EQWoUCuP3sM3V9jpc7i4s7Fg6eng/4QH/7lBF53gGu+PhtN5Miuk1UaBWvvns5135yNy+7l1f86yNFt9YihswG8Y1s3I5PJJDwE8C9XFdDlC/DnXdU0WF0cqLWycX4aQtkmCmKdyORySj/ZPuR2ve4AB96t4bnH93Jkaz05c4184cdLWHfvDKK7Z2FodEo2PDyX2FkKFpy5hpzjywl1IzxkKjkxt+UT7PTSubmWZL2GxGh1fw41SJiP+n3QNTTDv8HqJj5SjVYlp/KQCUtjF0s25CBX9DkOhkI4n3kad1Uk+2K382JWAxgLBl2nIBOIub0AeaQSy4sDedS/O/I7rB4rP17+4wEzOQRBIO7BL+Krqqbrk096H2974gnkMakoU6TZRJnRmazLWMcr5a/g8g8s0vV0dXFi+1amL19FVFz82ScypHPDTH8pj/Qtkw2F4M2vgMuC7I5nWXbb3bTV1bLt45exeW2sTR8a3RJ97TWop0+n7YknEMPgSSaDpgzqKV2yum52MiqFjDcOj19ZYoPVxeuHG/nCkgyS9VPp6YspQRBI1mtosZ81qCPVis/d9PS4SBUW58ScYEx2D1tOtXLHwvRLtlR1SuG1YU4KIRE2Hx8d5qNjsOnjU5pUyozTkRUX0Q/z0TO48Hk7hn5eJYriX0VRXCiK4kKjcfgU4kTqsEnCECxMXDiu61VYS7myYBt+b5CPny3rd0NvefoZQk4n8Q99O+xrpydFERKhyjwwVTWcBIUCw003EVFSzHSllwXdybHTe1tob+hi+S3TUKrOOafq4uHLH0H6YoI2W69BDbD8lmnIFTJ2v1aJIULFkuxYtpaaWJuxlv0t+8Pe9PZIppIj16vPJqi7fMhlAnptmL/zrJWEdEmU7PeTkBVNS3Q1pyynuHfmvf3M0LAGtd3eD+/RV0Vr0wiFRE7uGhl2z9id7t7fnaDMNZ5NKoaCIUy1dpJy9GgUGuYlzOP+wvv59epfs/XWrey4fQe/W/s7Hpj1AFHKKFJLdNjkDjY2fZEPLJ9iMZ1hd9NuZsXPGhPXdihNW5iAzeTC7wmw6s58HvjlClbdVUBsimSwtVZVABBxsoygzYbxsUfDvHcdimACZ+yDl2qZXKaB/Gl7M7SegPwrex9KXbESh0ZF8y9+0Y+TKsgFFLGasAlq5/5iNLNmIY+KGvb9+oMh2hxeKkwOKltEBGRsr6zknWYLpwo0lM/U8ozKxc3LM/jJhsIRf95ml5niGAsGe5CAObxp5XO78Di7iB5BgrpHP7lpFscjQ9gaumisdkFkItgkNnHAasX8q1+hXbAA43ceIfKyFWgLC1GmpiLT6Ub+Wzn5BgS96BIzcHYMjZQIK4UKbv6zxKF+/7uje22XGXyOSWlQB6Ky+cT+JQzxCuadOzgXRqIoUlxrYUn22PjT58qQGMEVX5xJW72DXS+WDzq7ekHiAk5YSliQaeCjQTjUvuYulCmR/fbrlYP1RGkUXDMrfJhHFEU+eaWC1ho76++fSXzawILF4ZQ1O567/n0JmYVx7Hm9ind+V4LD6sHv8XByxzbylqwgMlbCoRQkRXHz3FT+sfcMf9ol4YJumpMEZe+inbme7HmLOL1nF6HgQLPe7w1KWKLH93Jw8xkyZsRy548Wc8WDhRgSBw4syJUyWpce5mjqx3SUwPt/PoGv2+hVZ0YTuTIN54FWvJU25mfEDDSoZ9wAiFA+dKK30eYiPVZLMBCieFMN8emR5C3sM8jo7cLz1L9hq8hFE9uE++oM6roaaXW2DrleuU5J7F3TCXZ46HjjLI/6qPkor5a/yhemf4HCuIGD2ADR11yDIikJ69PPAOA8cADXvv3I4zNRJp79jh8ofAC7z85bVW8NWMexj97H7/Ww4Pqb+z+hT5NmetTv6//4p7+B6u1wzX9D8hymX7aamJQ0Tm16D5WgYkXqiiHfryCTkfDYo/gbGuh4/fUhl71YmjKop3TJSq9VcuXMRDYda8YXGJ+Zrr/fXolcJvCNNVPp6cmgJL3mLIPa6iI9NmLcbzgmu+J06glLUL90oJ6gKPKFJcNfTE7p0lJeYhTTk6JGjfno6C5WnMJETH6tKUhgX7WlNyF61qCe+u4msZqAvhGttO7HPtM6bDpMii6FJN0gBWJjkSiCuYzYrAQuuy2PhlJrb1lfwGLB+vzzRF93HZr8/LAvz0+UDLqxYj5k196ATAzxZVcZMpmA1x1g/zvVJE/TM23B0KlPyaA+a/bq9GoWX59N3QkLZ463c/WsJKrbnORHLsEb9LKvZd8Qa5M41H6zZGJbnBJnPuxMSpmcmtivY/dEM39tHM+VPYdBbeCG3Bv6LRbWoO7sRB4d3qA2JESQNSuOU5829bKJh5KxO0G9r9qCSi4jLeasKWJpduL3BknODb+teG086zLW8cj8R3hy+v8ypysf3coU/u2yHxKflIPa6cfmtbEiZeib+LFo3hUZ3P7DRdz14yUUrUlDpe2fuGupqkCuUCC+8TbR116DtjC86RGnSsfmHxxRaHaZSdSdk/6u3Cr9m3dV70NJefmIgoDF3Ir16af7La6I629Q+4MhzKYO3MeOYSuYzYcnW3ixuJ4nd1Txi/dKeezVo3zxmQPc+OQeVv/PDop+soW8H37Aov/YxpW//YS7/3aIoF/Hjuoq/mdLOa8faCDb4uVnOYn85OrhsR599UHtB1QlS8t7TpwIu4zDIvFcR4L46JExSs0tGwvoEkQ2v1IumT/diA/zf/83QZeL5J/+BEF2HvbI0RchoZDIpKyxGdQg8ejX/ABOvSUZ3iOVpUr6N27y3aMeLkvHHkxi9VpfbwniUKqzuDDZvSw5D/70ucqaHc+i67I4vb910MGyBYkLsPvszJ/mpazFTmNH/8E/MRjCb3KiTDmb6u50+Xn/ZCs3z0sdNMxz6tNmSnc3s+DqzGGP/0NJG6Ximq8Xsfbe6ZjPOHj55wfY8fw7eF1O5l11fb9lH70in5Ao8mJxPcty4ki1HwVnG8y8kZmr1uK0dVB/8ljv8gF/kGMfN/D843vZ/3aNhBf5t0Vc/bUi4lKGNtT3NO8htLiV1V8ooP6khbf/t6R3xoz+ikwUCRLqY1FyNA1WN22OPvetiYUQkwVlA3FEfdVgdZMWE8GpT5uwt3tYelMuQs95zNaA/8/3YalZgzLaR+zDt7IkbTkAxS3Fw36u6iw9+quycJ9ox7mvBX/Iz0/3/ZREXSIPzXto0NcJSiWx992H68AB3CdO0vbEEyiS0yGg6O1/AJibMJe5xrk8X/o8gdDZlHbA76fkw3fJnD2PhKycgRvIWAr1+6XrGYDaT2Dnf0LR7bDgAQBkMjnLbr0Lod3Fek8ROuXwMw50K1cSsXAh7X/8EyHX4APcF0tTBvWULmltnJ9Gh8vPjvKhp42MRHUWJ28caeILSzJIjJ5k5ROfU/U1qOutLjJiP3+p9rhI1YQwqP3BEC8dqGd1vpHMuPObXjelz6Y2zE2hpN7Wy30biTpcPjRKGdpz04FTmnRaXWDEGwhRXCvdRNu6Bxem8CyTWgeBPEEQsgVBUAF3Apsu8j6dl0RR5LDp8LjypwEpUeq1g3E6hStTyJ4Tz763qmmrd2D5698QfT7iv/XNQV+eFReBSiGjfAxFiQAfOTSciMtm5tFdiKLIwc21uLv8rLx9aPaqGApJaeQ+CWqQUsgxSRF8+moF6/Mkg6OxNZkoZRQ7G3YOuS+aPAP+pi7c5VasTu+gM1xEUeRI43z08mZkgXfY1bCLOwruQKvof23VU5LY10ANdnYOmqAGmL0uHbfDT+XB4a/HewzqVruH7Hgd8j5memu1hO1Lyhl8Wz2yb6tDplOSua6IW/JuYdXMa1EGRA5u3M2d0+8c9vWjlVwhw5geNej321JZjkGpQfB6MT48kHveo2kx0wgp2qlptw14ThRFCfFxboK6YqtkuCbM6H0oMUdK0noXzKX9z3/B13jWlFPEaQlYPLx+qIEFP/+IvB9+wDd/8HcIBvlZUwRff+EI//bWCf5nSzkvHqinuMZKW5eXaI2CuekGNs5P49HL8/n5jYX84QvzePHLS5gWm8KKAhUnH17N1oREbvPKmVvnwvy/h3EeNvWbwTCU3q99H+3MGaBQ4D5xMuwy9nZp9k/0CBEfPbp1cTqWVDXBFjd1wdlga8C5bx+d72wi7stfQj3tPNLH5tNSUeW8u9HFxOLstBEKjR4RBMCK70hM6s3fBUf4JO8AWSqlfydZgrqj1cmR4gAFmh2kRQ4+M6CvevA+y3LOvzS3rxZdl01WURy7X62kpXogArTnPBRpkJL1H5f1P14F2twQEFElnzVs3z7ahC8QGrQcsbnSxqcvV5A5K47FG8KYkKOUIAjMXJHCHY8vJiYpgpPb30cTlUJsWv/vPT02gi8slgJGt8xPhdJ3QKGFaVeQM38xap2O0k93EAyEOPlJEy/8aD+7X6skLjWSjd9fwPXfmoMxY/iZFGaXmfKOclakrmDWqlSu/eZsOlqdvPHfh7G2OBGUMmJvKyDY5eOyeumevaRviloQpBR1zS7whMeyBkMizTY36ZFqDr1/htR8Axkzu38bDQcI/uVG2lvvRtBGEPfNVcg0CvJi8ohRx3Cg9cCIPtfIlWlopsdi21zDW5+8TJWtih8u+SERyqFxNIbbb0MWGUnzv/4r7kOHMXzhKwAo4/ufNx8ofICmriY+rv+497Gy3Ttw2jpYeMMt4VeesRS6WsFWJx0HXv+S9Pd9/W/7FT4KBQl0RPpIO+4f0TFHEASMjz1GsL0d6/MvDLv8hdaUQT2lS1or8+IxRqnHBfPxxMdVKGQC31g9+UamP69K1msw2T0EgqFug/rzw5/uUXykmvYu77iXgX5UasJk906VI36OdcNsiZs6mhR1h9M3lcD9jGhpdhwqhYyd3QO4UwnqyS9RFAPAt4EtQBnwqiiKpwRB+JkgCBsABEFYJAhCI3Ab8BdBEE5dvD0eXnX2Oqwe67jzpzGXSf8mSNzbtfdORxOpZOtfj9H+yhvob7oRdXb2oC9XyGVMM0aOOUH97rFmjhSuRGhqoHXbAU5sb2TmipRhb/hDdjuEQgMMarlcxso787G3e2guNjEn3cC20nYuS7uMTxo/ITjETWnkilQURi22t6twOAY3qJsqbLS1BJmXsIcXql5FKVOGNXLlBgOi34/oPpvADQ1jUKdNjyE2RcfxHQ3DXq9Ea6QCaJD4033VUt2JTq8iKm7ooIi3zo630kbUqjRk3QOmPal0weEc6qVjl6kUiv8KYb6LUDCIqbqSqMYWDLfeiiora9DVLEyZjiCIbKseaM46/A7cAXd/fnbACzU7Ie/KfqZFtDERjS4Sz6wZIAiY/uu/ep+Tx2oQvUF+8foJcow6Hr08n2/pbYgKJd977Lb/z95Zh0d15W/8c8cnmWQmLsQVEiK4u1OklFJoKbTUbbvbdtvtdn+17bbb3drW3Z1S3J3iEiSBQIS4u8vY/f1xQyDEQ6jO53l4gHvPPfeM3Zn7nve8XzY+OIZDf5/Eueemk/jP6ex/fCLr/zSGL28fxuuLBvDMnEj+PDmUJSMCmBXtzcgQV3wdPemX40nlu6ewlDfgsiQCt3tjkOvVlP+QTNHbJ2lM67guUHplOomliUwLm40mLIyGhLaLRVY3C9TdiycSBIF7b4+hQRDZmTIAS2kO+U8/g9LfD9e77+5WX604+RXIFBC9EHsnJ0Srlfqe5FADyBVw7Xtgqod1D150UF6GKIpUldTTUGuSHNRy9cUCkL8CRFFkzzdJKFUKRjovh/KuFeY7lFaGq07VIt6nNxBkApOXRaBz0bD5g4RWufgXVvJk1CQQ7GbP9styqI3NBRKl65Ioinx7JIuoPnoivVtf/6rLGtj8QQKOblqm3BbRqzXA9G5ahlyjQrSWYrX25/vnjpCd2NK1//CUcB6ZEsbsaE9IXAshk0CtQ6FUEjZsNMkH9/PVUz+x55skHJw1zH1oAHP/MqBLE4AXOJB3AKB5VUpAlCvzHhmI2Wxl5Utx5KWUo/J1wGGcL3ZJFYyRKTh+aaFEgL6zwWqSJtraIL+yHrNVxDXPSH21ieHzgqWJwFPfI346l9KaB7HI3HFZFovCIH03yAQZQzyHcDj/cJfukQWZgNOCMER7GUE7HJjlPZPxvuM7PU6u02FYeAPGtDQUXl5oY6Xn4VIHNcB43/H4Ofjx2enPEEUR0Wrl2LpVuPkH4h8V23bnvk01CjL2w4+3Q2M1LPgc1C0/F7tz93AqtBJTcSVJB/Z2OmYAu4EDcP3TA9iP6v3VRFeKTaC28btGIZdxbaw3u5KKmgvD9IT0klpWncjh5uH+uNvc078aPPVazFaRcwXVNJqt+P4BBWoXexWNZiu17RT96ClfHszEx0nL+PCeL0Wz8dvG19mOgX4G1nVHoK6zCdS/FbQqOcODXNiTJN3o2+JZfhuIorhRFMUwURSDRVF8vmnbU6Iorm3691FRFH1EUbQXRdFFFMW2cwR+JVzIn+51B3VRovR3k6NUq1MxeVkEFSWNJAfMxfXe9t3TF+jr6dAjgbqoqoGDaaV4zbkGmU7H/jUZKFQyhnXBPXchOuNygRrAt68zwQPdiducyVR/F+JzKol1GU1ZQxnxJW0LeQCCQobTvBAs5Y2MKTbjYq9us92JrZloHZR4DPdgjbWSWb4TcdW6tmp3YWyXxnxIDmrH9scgCERP8KEku4b81I5FSkEQml3UlwtUBecr8QxuXYzxcqp2ZCGzV2A/4mImrFxvaB5rryKKcOwT+HACbHoUtj/dqklJdiZmkxFDownX+zp+7431lwoDHslJbLWvsFYSzFo4qDP2gakWwqa1aCsIAu6BwZQU5uN6373U7NhB9e7dWKwi35+XJiYXhbjz9R3D+fPkUAKyzmI/IJZR/X2I8HbEU6/pcv0Ra72ZGxLHc2PqZFR+Dnj8eSDaSBfU/o643xuD86JwrDUmij+Ip/TLxDbzr0FyTwsITA+cjiY6ivqE04jW1jGNVSXFCDIZ9k5OXRrfpQR6OaCLcqKu1oe0xCBMWVl4PfMMMs0V3N9ZTHDqewibDvau6AySu7OmpzEfAG5hMOkpSN4MJ7/BbLRQmFFF4r48fvoumZUvx/HRQz/x5f8d5It/HKA0q0yK97iSiJJeJvlIIbnJFQyfF4ydmyuUde6gFkWRw2mlDO2l/OnLUdspmXlPFMZ6M1s+OI3lkhhQQRAY5DGIuMI4JvVz51BaKVUNpub9prxaUMhQuEr3mwm5lZwrqG7TPW02Wtj0XgJmk5UZ90Shvgqr005tWY/GwZHr/7EYlVbB2jdOsvf75OZCkHo7JX+aFIqm4LjkxI24FtEqknKskNxUDyxmI1ZjKrMeiOG6RwfiE979z9P+3P24ad0Ic7oYl+Xu78j1jw3CzlHFmtdPkny0AMdJfig97XkcO5LSL/tc+AyRMuHPtR3zkVNej50VGk9XEDTADU9/B9j+LOLKuyhXPInRFIzzDeGo/Vp+Bw3zGkZhXSGZVZldeiwyOwWfhq7H1WTggdxFXTZ/OS9dikyvx/2hv2CpMIEgrVK5FLlMzi2Rt3C69DRxhXGkn4yjLDebwbOva/997t4P1HrY+g/I2AuzXgWPiFbNdmbtxCk6DDe/AA6u+KbNbPG2cLv//jaLRP/S/CJXMEEQpguCkCQIQqogCI+3sf9hQRASBUGIFwRhhyAINgufjR5z3UAfTBaxWyLL5by5IwWVQsY9Nvf0rwqvpsmCC0vU/5ACdVMxod7MoU4tquZgWimLh/m3WFpr44/HnBhvzhVUk9zFZe7ldSac7G0REb8Vxoe5kVZSS1ZpHRV1RmQCOGgUnR9ow0YvcbzoOM4aZwIcA3q34+Jz0g2v3cUl4h66OvxydpLnOZKc4s4nYsI8HSioaqCyztRp20vZkJCPKMI1QwOpm3wzhWY3Bk32xs6x83N2JFADjLo+BEEAjzRJ4KsuDUIhU7Ara1eH/aqDDNgN8mBGvUCIrLXoWJJTQ9aZMqIn+rLCUUmjTMZS2hacLxeoRZMJa20tsg4c1ABhwzxR2ymIb8oC7wjXNgTqmvJGqssa8Ao2dHhsY1YVjcnl6MZcdE8DzQ5vS3lFp+fvMg2V8MOtsP4h8B8JA5bAgTfh2KctmmXv+wkA/+kzUXp0PPEfZAgEUUZy+flW+4rqJGG5hUCdshUUGggY06q9R1AIJVkZGBbfhCooiIJ/Pc9DXx7m87NS4bAHBviiUsiwVFTQcPYsdsOHdefRA9CYUUnh68cJyHfnE7fV6Jf1Ra6/OAkiyATsYt3xeGQQjlP8aUgpp+DVOCo2pGGtv5jHKooiG9M2MtRrKO527mijorFWV2PMaC0uVZcU4eDiiqyN93JXuHlJf0TBzEnLPLQTRmA/YkSP+mkmdQfUFkHsTQDYO0nXndqK7gvUoihSU95IRkIJceUz2WL8N998LPLBn/ew4sVj7PrqHGcP5iNaRUKHejJmYRhKlZyNpyZQ79BavOoppqI6ytekUnM4H7EL2fGX01BrYv+KFDwCHYkc7S3lDJd17qDOKa8nr7KBYYG9lz99OS59dExc0o/885XsX5HaYt9gj8GUNpQSE2jCZBGbJ/EBTHk1KL3sEeTSvdF3R7PRKGXMifVu0Ycoiuz+OonirGqmLIvA2av3oxKrSopIPXqIqIlT8Qp24YYnhhA9wYf4XTksf+EoxVmX/G5PXIMoU5HWOJTvnz/C1o/OoNb5Yad3xcGQgX9/lx5NBlisFg7kHWBUn1Gtjnd01XLdo4PwDNSz7eNETuzIxrAgFAcRJmQ3YrZcMvEkk0HfayBlm7Rq4DKyy+oY3qDEarYyfIYnLF8C+16l2vPf1FVF4TDJD7uY1tfVoZ5DAboc87EhfQM/1K0ja3A14rkaag50TTtSengQdugg+jlzMBfXIXfSIChay6yzg2fjpHbi8zOfc2zdSnQuroSPaH3dbkYmB9+hUF8OA25uvr5cSn5NPmfLzjLBbyIjblhMeX4eZ/ft7tK4f6387AK1IAhy4G1gBhAB3CgIwuVX0xPAYFEUo4EVwH9/3lHa+D3Rz8uRCC9Hfjzes5iP88U1rD6Zy5Lh/s2ODhu/Djz1kkB9pCmr7I8Y8eGik254ezOH+suDmajkMm4Y7NNrfdr4bXJNtDcygS5P8NkiPn5bjAuXlkfvSS6ivM6IwU7Vq0tQbdjojAv5073ulCtKBLe+LTaVvPMuwTmbcfXWsPOrs9SUN3TYRbhnU6HEbuZQrz2VRz8vRwKd7TlDLHZ1hfhVxXXpWHOT6KtoR6B2cNYweGYAhYnljLS3Z/e5GoZ4DGFXdscCNYBumj81iEzIqG+VB3xiayYKtZyw0W58m7WV0RYlwck72uynWehtGqulurrF9vZQquREjPYm7WQxVe04aC/gpmstUBc0xUN4tlMg8QJV27OQ2SnQjWgpGsmdDNJ4e8tBnRsH742RintNfgYW/wiz/gchU6Ts4PM7m5tmbtmI0mIl4IH2C25dQClX4qjwotSYRaO5pTB4QaBujvgQRUjeAoFjQdX6N7BHUAgWs5nSgnwc/vY45pwcdKu+5dZpYSCApUz6DNQeOQKiiP3w4V1++KJFpGp7JsXvx4Nc4MysMn5w3UpZY9uirEwlx3GSH55/HYLdAHdq9uVS8NJRag7mIVpETpecJqs6i2sCrwFAEyU5yduK+agqLcbBpXvxHpdiZ68goOYkRW6D2B3Yv8f9NHPyK7B3k2JWAF2TQF1T1rFAbTFbKc6u5tzBfPb9kMLq107wyV/38fnf97Ph7XgOrU2n0NofvSKfQX0OM/3OSBb/czh3vTaW+Y8NZvxN4URP8GHGXRHUmhzYknwNFktrx3l3sFQZKV+ZQuH/4qg9nE/FqlTyXzxK1fZMLLVdn6w7tPo8DbVmxi8Ol4rZOQdJER+dZOQeSpPu6Yb3YoHEtggd4kHsZF8Sdudw7lB+8/YLq3nqZCm42KuaYz5EUcSYV4uqSWyuM5pZezKPa6K8cdS0NGbE78wh6XABQ2cHEhjT8/dpR5zatgmA2CkzAVCo5IxZGMacB2Mx1ptZ8eIx4jZnYLVYyTqaxIqqN9n08XnMJitTbo9g0ZPDiJ40iazT8dSUlfZoDKdLT1NlrGJUn7ZjIjT2SuY8GEvoYHcOrjrPwT155EcYmCgqOL/vMl2m32ww1cH51t9l2TnVxBrlhA9ywGnDPEjaSH30O1RlRaGNdsVxsl+b5/d39MfDzqNLhRIrGyt56ehLRLtGM3LeDDT9nKncmI4xu2vf/xd+w5iL61G6tV0TS6vQsqjvIhJOHyA7MYFBM+YgV3RiChmwGMJmwIyX2ty9M1v6npnoN5GQwcNxDwjm4I/fYjGb22z/W+CXcFAPBVJFUUwTRdEIfAfMvbSBKIq7RFG8UJXpEFKVchs2esz8QT7E51SS0oNiN2/uSEGtkHO3zT39q8OrSaA+mlGOIEAfwx+vSKKrfe86qGsbzfx4PJdZ0V7N7mwbf1zcHNSMDHZl7am8Li11s0V8/LYIcrXH11nL7qRiyutMtgKJNn5WCmoLyK3J7f14D6sVipPA/aL/pTE9ncrVq3FZdAPT7o7FYhbZ/lki1g4Kt4V7dF+gzi6r40RWBXNivEnYnUNlhYV+9YeoXvljl47vzEENEDvJD72blhGVMo6mlTLUYwwZVRmkV3bsTqwU4B0aca00UXfsYrZqVWk9KceKiBztzfaCLZQ1lHGL31TIOwElqa36aXZQNwm9lgrpb7lj57mlUeN9QBA4vTu3w3YXDCFBbhedhwXnK1EoZbj6tp9La8yultzTY32QqVu6a5uF9cqKTsfZIVar5JL+eCqIVrhtM4x+SHIByhWw4FNpcmT5LVB0lrqjRymuLMfd3RNFJyL+BfwdAhGUhSTmtcwwLqy7LOKjNFUS/ZqE0ctxD5TuXVLPnuOWk/CTTyyLz+9iSYgKuUHdHLVRd+gwglaLNiqqS+MzlzdQ/GE8VduzJHf0gwOw85PiAS6I6O0hd1ThfH0Y7g8MQOlpT8Wa8xS+HsfJgwdQyVRM9p8MgDo4GMHOrs1CidUlxd3On76Uih9/xPfkCuSYKcvVs/NcFwsRtkVtKSRthuiFIJe+Q+0M0nNxqYO6vsZIdmIZJ7Zmse3TM3z33GE+eHAPy58/yo7Pz3L6p1xMDWYCY10ZszCUeY8M4I5Xx7D0xbFcs9iZYeYXCbaux+BuJwm+l+DpVM4E/TvkFhvY+11yj+rSWBvMVG7NoOClo9QeK0Q33BuvJ4bhemcUKl8HqrZnUfDiEcpXp2Iu6XiCqSCtkjP78oie4IOrT1PuvnMgWIxSAdsOOJRWhpOdklD33s2fbosR84LpE2ZodjsDBDgG4Kxx5kTRcSb2dWfXuSJMFiuWikbEBjNKb2lcG+LzqWk0s2hoy3iP7HNl7P8xlaBYNwbPCOjaQDL2w6nvpLie8gwwd2w6MhuNJOzYQvDgoTi6tXQO+0Y4s+ipYQQNcOPQ6jQ+e2wP63Lvpx4XJi7ty01PDyNsiCcymUC/MRMRRStn9+/p2jgvY3/ufmSCjBFe7a9AkCtlTLktkoHT/DnzUy45xUaSRAvKHdlYLjVXBYwBjR7OrW/Vh/FEGYIgMrz4XqjIxDh1BWUnAlD6OOC8oP3Cw4IgMMxrGEcKjmAVO564eTXuVaoaq3hqxFMo5AqcF4Qhd1BR+s1ZrF1cRSVaRcwl9Shc29cjFvVdRHSGAVElJ2rS9M47jZwHN33X5gQkwK6sXQTrg/F39EcQBEbesJjKwgISf9rZZvvfAr+EQN0HuHRtV07Ttva4HdjU1g5BEO4SBOGYIAjHiouL22piwwYgLVOXywR+PN7xD+LLSS2qYe2pPJaO8MfVJtb96nC2V6GSyyirNeLp2PWsvN8TFxzUpVeQsX4pq0/mUtNo5uYRtmQlGxJzYrzJLK0jPqdj15nFKlJRb8KpnQJcNn59CILA+DB3DpwvpaiqwTa5YONn5UL+9ED3Xi6QWJEpObGa8qcBSt56G0GjweXOOzB42DHmhlBykyo4sbX9bEovvQYHjYKkgq4XOlsXL4kvU4JcObohA79IF8JmD6Lh9GkakpI6Pb4rArVcKWPMwjBkNWYGNigQayUhfnf27g77Lq1tZCMmatw1VGxKbxYHTu3IRgCiJ/rwxZkvCHcKZ9iQBwEBEn5off5LIj4KzqdgLCtp2t65+OrgrCEo1pXE/XmYGtt3Us6O8eKeccHYqy+6y/LPV+Ae4Ihc3v7ta9X2zCb3tFerfRed31fgoK4thW8Xwdb/k/KG79krLcG+FLUD3PQ9KLWIXy0g96UXqdGo8B0zrsunifEIR1CVcjSz5f1tUV0RTmonVPKma3XyFunvy/KnL2Dw8EKhteObjQfILK0l4rknUSiVFD7/AgoXLebSJgf14cPYDR6MoOr8O6AuvpjC149jyq/FeWE4zgvDkakVuNlJgnFxfdfuyVV9dLjeGYXLkghEi8iEAxH8r+DvaMqk11eQy9FGRlJ/mYPaarVQXVqKQw8FanNJCUUvvYwhti/9HPagbvTnhR9OU93QvSifZhJ+kAq8XbL8XqFUonFwpLKohNN7clj1ynE+eXQfa984yYGVqeSeK8feoCF2ih9Tb4/kxqeHcdf/xrLg70OYuKQf0RN88Q51uphbPPg2CJoAW59sO8e59Dzh2j0MHKHgzN48Tu/p+v2uaLZSsz+XgpeOUb0zG00/ZzwfGYRhTjBynQpNsAHXWyPxeGgg2hg3ao8WUPDKMUq/TKQxq/W10WqxsvubJOz1aobOvqQQrXNTBn8nhRIPp0v50z/Hai6ZXMbUO/qj1SnZ9H4CDTWm5hzqY4XHmBzhQVWDmaMZZRibHuuFAonfHc0myM2ewf4Xc5urSurZ8uFpnDztmHRrv1YTCa0w1knxQJ/NhFV3w2fXwOsx8C93eDkcPpwEy5fCln/AoXelQoe5x0natYH66ipip81qs1uNvZKpd0QyeVkEDqpKxuk/ZPHTA+k30hvZJddPZ+8+eIaEcXZv5ytw2mJ/7n6iXKPQqzu+9gsygRHzghl3UziFKZWcqbciM1mpWJ16cTJFrpScwkkbwXLR/VuSU4OusJEBduvQ2ZmxLNpC6W4dMjsFrksiEDq59x/qOZSKxgpSylPabXOs4BgrU1ayNHIp4c7hAMjslDjf1BdLpZGyFSldmvSxVBsRTdZWBRIvRV5lwi9fy1mfSqqFunbbdYXKxkqOFR5jot/E5m1BA4fgGRLGoZXfYTH38Jr2C/PrSdFvA0EQbgYGA2162kVR/EAUxcGiKA52c7s6yyds/D5wc1AzPsyNVSdysHTglrmcN3akoFHKuWts54VtbPz8CILQHPPxR8yfhksE6l5wUIuiyJcHM4n0dmSAr+GK+7Px+2Baf09UchlrO4n5qKo3IYrgZHPh/qYYH+5GvcnC8awK22tn42fleOFxdEpdi+JKV4woSjfyAJ6SG7QhKZmqjRtxXrIEhYu0bLzfSC+CB7pzZG06hRltC9CCIBDu4UByQU2XT7/2ZB4D/Qxk783H3Ghh9IIQHGfPRlAqqVjRuYvaUlEBMhkyx/YLDgL493chINqVkY1Kjp8209e5b6cxH2VNgnT12D6IRguVG9JpqDGRuC+P0KEenKo/xvnK89wSeQuC3hsCx0ji22U35hfiR9JTk/j6iYfYu2EV0HnExwWiJ/rSWGcm6XBBu21GBrvy+IyLES0mo4WS7JoO4z2M2dU0JDVlT6tbL5uWabUIanXPHdQZ++G90ZC2S1puvfAr0LZTVMzgCzd+R01SJYXn00AQ8Orb9XzgGI++CILIwayzLbYX1hVelj+9Bdz6gaHtJe4nsyvIkTmjry3k2zuHM2ZEBK4PPEDN7t2IDaVYSusxFRZhPH8e+07yp61GC2Urkin75hxKNzvJNT3g4lgujKu4ruumMUEQ0Ea6kL7IyPseP+BX7U7h68cpX5WCpcaIJjqKxsSziMaLJoy6igqsFnOPHdSFL/4Hsb4ez2efYaDPSQRBxL/Ewn83dz6B1CYnvwKvWPCQio011pk4eyAPq0XL2f2p7Pk2mfpqI0OuCWTOX2K57eXR3Pqf0cz+Uwwj5gUTOsQDZy/7FsJhKwQB5r4lZdKuvl9y8V9KqbTSYdh1EQREu7J3eQrZ5zqOFxFFkbpTxRS8FkfFujSUHna43x+Ly039WhV4A1B62ON8fRhefxuKwzhfGs5XUvzOKYreO0X9mdLm2KD4XTmU5tQwZmEoqktrWlwQqDsolJhbUU9Oef1VzZ++HDtHFdPvjqK2spGtH5/GahUZ5DGI/Np8QryNqBUyDh7Po2JtGgoXDSpvHSmF1cRllrNoiG+ze9fUaGHjuwkgwox7olo+9rbIj4cPxksFVkf+Ce4/CktWw5y3YPzjEDoZ1DooTISjH8Pmx2H5EsQPJnDim//hrK7Hb+cy+Hw2rL4Pdj4PcZ9LeejFSQjGWsKHerDA82n6RxmRO7T9nEaMmUBxZjrFWRndet4qGipIKEloN96jLfqP7cPMe6PBDKcbLNSfLqX+1CXXi36zpLzlzP3S/61WDn20CY1Qi4vrOcRbt1Oy0Yi1zozLLZHIu1DXYZiXdF1rL+bDaDHy7MFn6aPrwz0x97TYp/ZzRD8jkIbEUmr2dx51aC6WBOcLRTTbIm7jamSCjDP+lXx77ttO++yIn3J+wiJaWgjUgiAwasFiqoqLOL1r+xX1/0vxSwjUucClayF8mra1QBCEycA/gDmiKPZe9S8bf1jmD/KhsKqR/aklXWqfUljNuvg8lo4IsEUd/Iq5IFD/EfOnAdQKOQ4aRa9kUB/LLOdcQTVLR/hflcrZNn6b6LVKxoW7sT4+r8MJvrI66T3obHNQ/6YYEeyCSi7DYhUx2BzUNn5G4grjiHWPRd7DQmetEEXY9hQceR+G3QveAwAoeetNZDodLrcta24qCALjF4djp1ex7eMzGBvazmsM93TgXEFVl9xTKYXVnCuo5hpfFxL35RE1wQcnT3sUTk7oJk+iau1arMaOv6stFRXIHR0RZJ3foo1eEIpCAOWZKkZ5jeNk0UnKGtoXpS6stNL7OuAwzoe6E0Ukr0nFbLQyYIofnyd+jrvWnekBTcuOoxZA2Xkp6uMSBJUKi86ew0nxyORyzibGU2GnRt6JqH4Br2A9bn4OxO/M7nIUQVFGFVariFcHAnXVjqbs6ZGt3dMXkOv1lBUVYGroOH+8BVYL7P4PfD4LlFq4YzsMu0sSDTtA9IyhKC2YGifpuuoZGNrlUwYZJDEvsaSl66+orggP+6b86YYqyDwAYW3He+xKKuKmDw9TrfPAzVRGfy8pmsB5yc2oQoKpPbAda52Z2v1SATG7Ye3nTxtzayh64wR1cYU4TPDF7Z7oViKmk9oJuSDvNOKjLTZkbmSHxzHcHx2IboS35NJ96Rgy/UBEi0hDUnJz2wtCWk8c1DX79lO1fj0ud92FOigIB3dH+jqdZIBJwY8HMjma0c2ihvnxUJCAsf8Sko8UsOGdeD55dB87vziHiB12jmYWPTmUG58extBZgfj2dUar6+H3rN4Hpr8IWQfg8Lst95WmgsaATOfClNsicPK0Y8sHp6kobNuh2XC+gqK3T1L27TkEhQyXWyObozw6Q+6oQj89AK+/D0U/KwhLRSOlXyZS+GocpbuyOLYuHf/+LgTFXvb6OPYBmbLDQomHf6b86cvxCHBk3I3hZJ8t5/DaNAZ7DAYgsewUkwJdGHmyAtFixeWWSASFjO+PZqOQCVw3UEqhFUWRHZ+fpSyvhqm3R2Jw7+Ce1GqFg2/DR5OkIqtLVsPUf4FbGARPgIFLJIF67tuwdA386Rj8Ix8eTYO79pA/+lUKGxwYMDAUwTsWTA1SbvPel2Hdg/DVdfD2UPh3H/iPvxQZEjG33eGEjxyLTC7vdiTEwfyDiIiM9h7dreMCol1RTvbkbIOFcqtI2cpULFVN34vBk0ChlXL9G2vIe/+vZBa4ILM7xdYBr1K2oRhTTjXOi8JReXctAsbT3hN/R38OF7QtUH+c8DEZVRk8OfxJtIrWEzO60d5oIlyo3Jje5oqBSzEXS9E37WVQ19dUc3rnNvqNGsewsLF8n/Q9daaeu6h3Ze/CXetOhEvLyU//mIF4h/Xj0KrvMXfym+PXyC8hUB8FQgVBCBQEQQUsAtZe2kAQhAHA+0jidPe/5WzYaIOJfd1x1ChY2cViia/vSMHO5p7+1eP1BxeoAVx16l6J+PjyYCYOGgVzYjpKXbLxR2ROjDeFVY0cSW//5q2iSaC2iZy/LexUCoYGSgWdbA5qGz8n98Xex5KIJb3TmSjCzn/BgTdg8O0w/d8gCNSfPkP1tu04L7u1lcNXY69kym2RVJXUs/f75Da7Dfd0oKrBTGFV516ZdafykAG6szVo7JUMuSageZ9h/vVYKiup2d6xo8lSUdlhvMel6N20eA/3ILxRjjY3ChGRPdntZ4mW1V6cRHSc4IvcWYM6voSASGeKtTkczj/MTf1uQtmUo0u/2SBXQcKKVn0lebtQZzJy3d+fxU6t5XQfN9B1TTAQBIHoiT6UF9SRc7a8S8fkn28qkBjUtkBtzK6m4VwZujF92nRPX6DKSc+mrCR+/PdTmE1dWP5clQ9fzIXdL0iC/d17wCumS2OuXLMWY3YhtaH+GJT12MW90aXjQMrBFZBRbs6muPrie6+oruiigzptF1jNENo63mNFXA53fH6MIDd7ls0Zg9VsojQnCwBBqcTzyacw50mu29pjZ5A5OqLp17dVP6JVpHpvDkXvnEQ0WnC9Iwr9tACENty+cpkcF61LlyM+LtBgbmBH1g4m+09G66DDMCcYj78MQh2opzFNhf2kZ6nZJ0UBWMwm9nz5MQ4ubvj0615xQ2t9PQXPPosqIACXu+6UNup9GahdjkyECYKWv/0YT4Op4yJ+FzAbLZzfuI3NlY/x6XfBbPskkZLsaqIm+HD944MJHRyIQB0ufXS9Z/qIvUmKQdj+LBRfcs0qTQWXEBAEVBoFM++NRhAENr4bT2P9xck3U0EtJZ+epuTDBKzVRpyuD8PjzwPR9nXu9hhlajkOo/vg+egQnG/si6CWU78lkwkagWH+Oqx1l036yeTgFNChg/pwWhl6rZK+np0L5b1NxChvIsZ4c3xzJvIMAw4qB47nH+eeKgEvC9TNDEDpbkej2cLKE7lMifBojv88viWT88eLGD4vGL/IDsT16kL4+nrY8gSETIZ7D0iidGcIAti7gHcsJ86WodLaEXHPa1Le/R3b4JGz8H9F8JcEWLYJrvsIJj0N/a+HmJsg4tp2u7Zz1BMQO4hz+3Zj7aSA5QVEUWRz+mYMakMrcbQrDBrgyZe6RhJkApZGM9mfnpYmK1V2EDIJzq5F/Hg6BxJD0WpNvKAYwLAiK/XxJThOC0Ab6dqt8w31HMqxgmOYrC2v+WmVaXyY8CEzAme06wQXBAHn60OR61WUfXOuwzxqc3E9gkqGrB1nd/y2TZgaGxg8+zqW9V9GZWMlq1NXd+uxXKDB3MC+3H1M8JuATGh5Pb6QRV1TWkLCzi096v+X5GcXqEVRNAMPAFuAs8ByURTPCILwT0EQ5jQ1ewnQAT8IgnBSEIS17XRnw0aX0SjlzI7xZvOZgk5zxpILq9mQkM8tIwNsbsBfOX90BzWAi73qiiM+iqsb2XQ6nwWDfNGq/nhZ3jY6ZnI/D+xU8g5jPsqaqrs72wTq3xzjwyWnk21ywcbPydSAqYz0Htk7ne35r+QgG7gUZr7c7G4tfuN15AYDzkuXtnmYd6iBQTMCOHewgJRLCgde4EKhxHOd5FCLosjaU3nMctZTklbF8LlBF/NjAfuRI1B4e3Ua82GprOiyQA1wzQ3hVMtFavaIeGo8O8yhLq01IgjgZKdCUMqpDDZgL0CMm4YvznyBVqHl+rDrLx6gdZKK753+UXIRN5GXfJYMjZJQlQ7/qFgGB4ZTZafm9JH9XR536CAPtA5KTu3K7rwxUtE1J087NPZtT6JV7chC0CrQjfButw+L2cwJrYACgdxziWz/6O2OHdwp26VIj9w4mPsOzHtfypfuAtbGRorfehNN//6UoMXT2xX2vgInvu7S8Sq5Cg+tDzJVISezKwBpKXpZQ9lFgTp5q1RUzPdiNIcoiry7+zx//eEUw4Oc+e6u4YRGSFnshekXC17aDxuKdogkLDWcycB+2FAEecvffpZqIyWfnaFyQzqacGfc/zwQTbChw3G7ad26LVDvydlDramWmUEzm7cp3e1wvTUSl9v7A2YaM10ofj+eU9+upzQni0m334NK073C6CXvvocpOxvPZ59Fpm5aGWvwxWBJJiTWmf71MnILa7n/6+O8vj2Fzw9ksOZkLruSijiRVU56SS3FlQ2cP1XMtk/P8Mlj+9h8dAB55lj6jfRm3l8HsvT5kYy+PhSPAEd0Ts7UVpQhXh7HcSUIAsx+XRLyVt9zMav3gkDdhN5Ny/S7+1NZVM/Wj05jKmug7IdkCl8/TmNmFfoZAXj+dTD2gz06z0nubEhyAbsYN+rH+bCvxgwuWhr350kFFdekNhfiBKRCiR04qA+llzIk4OfJn26LsTeE4RHoyM7PzzFCM57AY444FzbwKg1srZWcrtsTiyirNbJwiBQIkJFQwqE1aYQO8WDAlLajdgApL/7dkVJ8xTWvwqJvJNG5G9RWlJN8aD+R4ye1fv/LlVLUj/9IiF4AYx6GWa/CvHdB0/HqlogxE6gpLyP7dEKnYzBbzTx14Cl2Zu9kYfjCHq2Aiuqjp04JuaNcyNWpUeTXcvbzROl63G821BSSnu9KoSkc59EBjBOU+CRWYDfQHYdxPt0+31CvodSZ60gsTWzeJooizx18Do1Cw2NDHuvweJmdEpeb+mGpNlL2Q/tFSE1NBRLbmuwxm0yc2LwO/+gBuPkHEuseS4xbDF8kfoGlixMDl3I4/zD15nom+k5sc79f/xh8+vXn8OofMBl/W2EUv0gGtSiKG0VRDBNFMVgUxeebtj0liuLapn9PFkXRQxTF2KY/czru0YaNrjF/kA8NJiubEtrPvQN4fXsK9ioFd46xuad/7Xg5/rEzqEHKoS69woiP749mYbKI3Dy8gx9XNv6waFVypkR4sOl0PkZz2zdb5c0OapsL97fG+HBJ8PBsup7asPGbYu8rksM1djHMeh2a4jHqjh+n9qe9uNx5B/IO3L2DrwnAI9CR3V8nUXWpmILkoAbJuNARp3OryCmpo1+hFRcfHf1GtRRKBZkMw7zrqD14EFNu+wXMuuOgBlBrFNRHOqKpszKlZhEH8w/SYG47vqKsthEnOxVymYDVKnL0RDElSjkklBB/Lo7rQq9rXewqagHUFEDGXml8ZhNb338TO5mMfkbpJt1HqcWtrpH9y7+mprxrEQlypYzIsX3ITChtN4bgAqJVpOB8Zbv508YcyT3tMKYPsg5yX4+u/ZFK0cLARpER19/Imd3biduwunVDi0mKivl6Pujc4a7dMGBxp5Eel1L+7beY8/Kxu/sOaspK8Rq7EILGw7o/Q/reLvXRzzUUuVoSR4Hm6AwPOw8pJiBlq7QkXi49ZqtV5J/rE/nP5nPMjvHmk1uH4KBR4uTphUqrpTDtfIv+3f9yl/QP0a5VvEd9UpkkZqZVYpgXgsuSfsjbmRy4FDc7t25lUANsTNuIm9aNIR5DWu3ThjohmHZjyt2MsagW9zOuTOm3DP+Q2G6doyEpmdJPPkE/bx72wy4paqmXxK6BwwWsRit3e7tzOL2M17Yn8/TaM/z5u5Pc9slR/vz6If79z/18/vh+Nr+bQPyRAirkecxxepqS0DS+F2t5KyGbl7Ym8cFP5/n+aBZZDQqsFgunzueRd76M4m/PUbHuPLXHCjHm1SBaeihcO3jANa9IEyf7/wfGWqjKbSFQA/QJc2Ls9cHozleQ/9JR6k4WoRslOZ4dxvl2WlyuO5iMFvYuT8HqqiXg4UEXCyoeKaDg5WOUftVUUNE5SCqS2IbAV1DZQGZpHcODnHttXN1FrpQx/a7+KFQyJh2exMSCwciHO5Ppa8/WRGkC87ujWXjrNYwJdaOisI5tnyTi6qNjwpK+bbvQTQ2w8TH45gZw8IS79sCQ27t1PblA/PbNWC1mYqe2XRyxpwQNGopKa0fi3o5jPhrMDTy06yFWp67mvpj7uD/2/h6dT6uS08/LkeP5VQx9bDB1GgXas6Xs+/ws1rDZWEc+xCHxYZw87ZBpBJ5ACz46nK4L7dFqhKGe0mf+0hzq1amrOVZ4jEcGPYKrtnNHtsrXAf3MQBrOllGzt+3vcHNJPQq3tvWIs/t2UVtRzpDZ85u33Rp5K7k1uWzP6n5W9M7sneiUOoZ4tr5uwkUXdW15GfHbNne7/1+SX3WRRBs2epsBvgYCXe35sYOYj3MFVWxIyOfWkQE42dzTv3rGhrkxuZ8HEV5dyz78PeKiU1NyBQ5qs8XKN4ezGBPqSpBb15bo2vjjMSfGm4o6E/tS2775LL9k+biN3xYh7jrW/2k0s2Laz261YeNXyYE3Ycc/JSF1zpvN4jRA8etvIHd1xemmmzrsQi6XMeW2SERRZPsniVgvEY4Mdio8HNWcK+hYoF57KpdhRgXWWjNjbght0wFouG4eABUrV7Xbj6Wiew5qgDHjfUlXWLA/GQB1inaLQZXVGpuvz2kniqkqacBhRgBGmYl78hdwc9/FrQ8KmwYqB6lYInBk9QpKc7IY7OyJrEKK3bBWVRLdKDTHL3SV/mP7IJMLxO/uOHqvvLCOxjpzu/nTze7pke27p0tzszn047f46Qx4lFYxYv6NhA0bxU9ffUraiaOXnCwTPp0B+1+HQcvgzp3gFt7lxwRgqamh9L33sR85kiq9NMHhGdYPFnwuCXTf3wyXZUu3RahTMDJVKcezpe/cFgJ1/kmoLZJeH6DRbOFP353g0/0Z3DYqkNcXxqJWSAKkIJPhHhBM0SUOagBVH08EhRmZzgPdaGl5u2i2UrHuPKWfnkGuU+Lxp1h0w7y6LAq5a927JVBXNlayN3cv0wOnt+vE1ET3p/7YKg5bNpJUcwxnkweFLx+jek82oqXzDHPRaqXg6aeROzjg/tijLXfqJVOGq7aAgGhXHLPrOf73SSQ/N53Ni4fyVrg/fzc7sLBWTayoRBegwzzKheppHlzj9Tk6u1yOyGM4l1/F1jMFfPBTGi9sPMfffkzgkxNlCAisfz+O+g9PU3aqiIqD+ZSvSKbojRPkPnWAwjeOU7YimZoDeTRmVGJtbDsLvxX950PkPNj9IiQ2LTR3vShQiyYr1T/lYNibS4hGTm6jlZoJvhhmBXVpoqG7xG3MoKqkgXE3hSNXyFoXVExtKqh4egL19RGINa0TXA+n/zL505ejc9IwdWYA/UUlaVRwMiqbqREenMquIC6znH2pJSwY7Iul0cLGd+ORyQVm3BOFsq3Vp4WJ8OEEqS7C8Pvgjh3g3jpKpytYzGZObd9EQOwgnL17N4pRqVITNnw0KYcPtJvPX9lYyd3b7mZPzh7+Mewf3Bt77xVF1wzwM3AqpwJBKSPw/hjkchl2CcVs/DiFBMXtlJeYGTbZl36HiihFxKMp/7snOGucCXMK40i+lLdf1lDGK3GvMNB9IPNC53W5H91Ib7SRLlRuzqAxs+WqKtFkxVLegMK19coO0Wrl2LpVuPkH4hd1MSJqgu8E/Bz8+Oz0Z12uxwBgsVrYnb2bMT5jLkZytYFvRBR+/WM4suaH7tVd+IWxCdQ2/lAIgsD8gX04nF5Gdlnbjo3Xt6fgoFZwx5jAn3l0NnpCkJuOj24Z/IeOpXC1V1FWZ+ywgF1H7DxXRF5lAzcP9+/lkdn4PTEm1A29Vsnak23HfJTXmVDJZdj9gT+Lv2X699E3Cxo2bPwmOPQebP0/Sai59j0p47SJ2kOHqDt8GNe770am7TwKQO+mZdyN4eSfryRuc2aLfWEeDh06qK1WkZ3H8xneqCR4oDt9wpzabKfs0wf7ESOoWLUS0dL2kt6eCNSjQ9044GhBNAuMypnHruxdbbYrrZEEalEUOb4lE727Fo9Bej53X0tsXThOqW1MLiq10pLrxHWUZqRyeNX3hI8ci49HHywVFdLjr6xC7+DI0LnXc27/HrJOn+rSuO31akIGu3PuQH6LnNzLKeggf9qYW0PD2TIcRrfvnhatVra+/yZKtYahIRFYKitBEJh+30O4+Qey4fWXpHzmxLXw/hgoToIFn8Hs/0mPv5uUffIJlooK3B5+mILUZGRyOe4BwaA1wOLlIFPA1wugtrTDfkIMISBYSShMxWIVmwVqdzt3yT2NACGTqW4wsezTo2yIz+fxGX15cla/VhMkHkHBFGdmYL3sfaf0dcFu+ERUAQGYiuooevskNfvz0I30xv3+ASg97Lv12N3s3ChvLMdk6UK+N7A9czsmq4lrAq9pt402Kppcgz0Z507iMiccz0cGow51onJTBkVvn8CYW9PhOSqWL6f+5Enc//YYCqfLPpsG36ZGWQya4U9jrZlN7yXw7VOH2PF2AjnHS/ALd2b6Xf2565Wx3PO3Yfx5SQzPTHIjsvYITsOXsOL+sex4ZDzH/m8KKc/P4Myz09j/+ESev2YoE70Wc6vCmWovOz7qa8dEayXfxzhiWBiObnQfZPZKGs6WUrH2PMXvxZP39EEKXjpK6ddnqdqVTX1SGZbqdlZIznxFek+t/4v0f5cQRKtI7fFCCl45RuXGdFQ+Otzvj6HIz5Fdq9LIT63o0uvSHcryazmxLYu+wz1bXfsuFlQcIhVUbNRSanqKwreTqY0rbCHKHUorw0GjoN+VGo7qyiTXcg8xFdUh7MnG6qDibIU9Z3YUMCHYiYjqs3z20suoLY1cP7AP2z5NpKKonul39sfxsoKhiCIc/gA+GA+1xbD4R6kugrLnq9RSjhygtryMAdN61z19gYixEzA1NpB67FCrfUV1Rdy6+VbiS+L577j/sqjvois+30A/J+qMFpIKqlG62eE8OwgPpQySy9m3PAWvAAd0cYXILCKv6a0oHa7M/DLUcygnik7QaGnkpaMvUWuq5ekRT7fKb+4IQRBwuj4MuUFN2TfnsNRevM6ZS+tBbLtAYvrJOMpysxk8+7oWor5cJmdpxFJOl57meNHxLo8jviSesoYyJvq1He9xKSMXLKausoKT2zZ2uf9fGptAbeMPx7ymirsrj7denpGYV8Wm0wUsGxVgy+K08ZvBRadGFC9GLHSXLw9l4q3XMKmvey+PzMbvCZVCxswoT7YmFlJvbC2ulNcacbJX9l4xIBs2bPyuaUxJof7MGRrOnqUhKYmGpGQaU1JoTE2lMS2NxrR0jBkZGLOyMGZnY8zJxZSbiyk/H9OW1zCt+jvmPtMwj30Rc2UV5vJyLBUVWKqqKP7f6yi8vDAsvKHL4wkf5knYUA+Ork9vLsoH0NfTgZTCmnYngY9lltO3yIJMEBg5P7jDcxgWXI85L5/ag61FAGtjI2J9fbcFao1SzoD+7iToREIKBxF/OhWr2Do+oLTWiIu9itzkCoqzqhkwxY81aatZ5bADk5ecyg3pbReAiroesaGSbW+/iFKtYcItdyI3GLBUVSFarVgqK5HrHRky93oMHl5s//jdrhUgBGIm+mJqtHDuQP7FjaIoTT58OQ82Pkr+kaNotGDQt+6zansmgkaBblT77umT2zaSl5TIuKV3YO/qhmg0ItbXo9RomPvo/6FQqVj9zJ+p/2YZOAfD3T9Jkx49wFxSQulnn+MwYzra/pHkpybj5h+IQtV0T+EUADd+C1V58P1iMLe/+i1IL8UMNgr5pBRVU1gnRQy427lLebY+gymy6lj4/iGOpJfxyoIY7hkX3OZ3sEdgCGZjI6W5LTO/FS4aLFVmao7kU/TmCSxVjbjcGolhTjCCsvsygZtWqmdQUl/SpfYb0jcQ4BjQYaE1McCPs96uuOmdiZk8HYWzBtelEbjcLGXCFr11goqNaVjb+F1iKiqi6JVXsRs+HP3cua07t3eXCoFW5uAZqMcv0oW8lApcfR2YclsEt700mul39Sd4oDuKSyff478H0QIDbm7RnSAI2CnlOCaU4bPXjIPSmdooIzEPDuK/twziznFBvHkqh0fPZKOZ7Ifb7VF4/d9wvP4+FJdbInCc4o/S0x5jbg1VWzIo/fQM+c8fJu9fhyj+5DSVm9Opiy/GVFKPqHWG2W9AU6RPQ5krRW+eoHx5MjI7Ba6398ft9ijUvo5Mu7M/Di4aNr2f0CrG6EoQRZE93yShVMsZOT+k3XYytUIqqHiXN87K/yIIRsp/SKZiVWpz1MnhNCl/Wt7T/OmqPFj3F3g5VPqz+j5I3XExp7sLWGpNlHx2BkEho899MZR4JKLYn8mOp+5jUsluAopPsbhsI1m70siIL2H0ghD6hF826VFbAt8ugk2PQuBYqRBi6OSePaZLOLF5PXoPTwJjB11xX23h0zcSB1c3Eve2nODMqMxgycYl5NXk8c6kd5geML1XzjfQT3reTmRLEUb2w7xQB+uJcVTh6a5lmF6FqaCWj5wFZG2Ivt1luNdwjFYj7516j/Vp67m9/+0EGbof5SrTKnC5qS+WGuk9LDb9LjCXSJ+rthzUx9atROfiSviIMa32zQmZg0Ft4LMzn3V5DDuzdqKQKRjtPbrTtn36RhAQM5Cja1ZgbOi9z/7VpP2gLhs2fqf0MWgZEeTCyhM5PDgppMUPudd3JOOgUXD7aFv2tI3fDi466cantMbYXFG6q6QV17A3pYS/Tg1D0UZVdhs2LmV2jDffHslmx7lCZkW3FATK64w42Sb2bNiw0UVy/vQgxoyMK+jBE0iAVya0vfefzyJTde+aNPbGcArSKtn2yRkW/t9Q1FoFYR4ONJqtZJbWthmDtWl3BhEmBTHTfFs76S5DN2kScoOBihUrmmMVLnDBkdxdgRpgWqQnj5zKJ8pORXTSFOKL4on1iG3R5kLEx4mtmWgdlIQMdeeR9V8S4x5Dn2kxFL15nMpNGTjND23ZeeA44utDyc0qYNo9f8be4ESjwQCiiLWqCktlJQovT5QqNROX3c3KF58hbv0qhs3rfHLA3d8RzyA98buyiZrgg6yhHNbcD0kbJbE4+wgFudF4Ks4i/GceOPqAez/wiMCoiKHhrDuOE9t3T1eVFLH3m8/xjx5A5LhJVBSvAMBSWYnMzg5HKpkblM3yk/asq5nC/KWfINf0vKZJybvvITY24vbgg1itFgrTUug35jKXm+9QqXDZittg7Z+k4ottiMqB+kBkyJCpCzmRVUGhUIhGrsHR2AB5xykb9ijz3z1ASbWRj24Z3FxLoM3nOVASDwvTUnHzC2jernDVYq0xUbEyFXWoAecF4cgde/497mYnCdRF9UV46TqOjCqoLeBYwTHujek4KmD/uh8xy2UMVNghXBLho+3vijrYQOWmdGp+yqX+dClO80LQhF4UDItefBGxsRHPp59q+xwymZRDXSkJ9zPvicJisaLqIMscUYSTX4PPUHBt+Vkxl9RTtiIZY0YVqjADa7a/yeCR85vP/fcZ/XB30PDc+kRKa4/w4dLB6LVK5Ho1Wr0abb+L8RbWBjOmvFqMeTWY8mow5ddS/VMFNAligkqO0qsPSsP/MFdA4xepyJ3UOC8MRxvj1qL4ocZeyTX3RbPiP3FsfDeB6/46sOPH2EWSDheQl1LB+MXhaLvgcBWc/bFT7EM7eDhV5sVU78rGXN6AdXYgaSW1zYUHu0VtCex7DY58CKJVKpRrMULiOul1sneDyOukGCifwe1mP4tmK6VfJmKpaoSpOjZ/9j80SQcwiyBoQmgYM46tyXnMKd7K4ZWvEDH+PqLGX1awL3UHrL4X6itgxn9h6F09ypq+nML089Ik25LbW3wGehNBJiNizASOrF5BbUU59gYnzpSe4b7t9wHwybRPiHSN7LXz+TprcbFXcTyzgsXD/BFkAk7zwyj833FGKMCaWYVhdhDbdiUy2cnjis83yGMQckHORwkf4e/oz53Rd/a4L5WPA4ZrgqhYe56avbk4jPPBVNwkUF8mphecTyE7MYFxN9+GXNH6M6dVaFnUdxHvnXqPtMq05onJ9hBFkR1ZOxjmNQydqmuxnCMXLOab/3uEE5vWdel7+ZfGJlDb+EMyf5APf/3hFHGZ5QwOkIoxnMmrZMuZQv48KRS9rciXjd8QLvaSKF1a0wh0rcL8Bb4+nIVSLnBDT34U2vjDMSzQBXcHNWtP5tkEahs2bFwRns88jbW2FkQR0WoFEUlgsFqlpd9WERBb/j/rIMR9iejeDwbfDoK85fFWKyAiaLUY5nXfBavWKphyWyQrXz7Onm+SmHJbBH09pSXnSQXVrQRqo8mCeLwck0rGsJmdR8PJVCoc58ym/NvvMJeXt4gcuBKBekK4G4JCRmWgHW4nfflpWwKxN8de7NsqUl5nxNUskHWmjGFzg9iTv4vcmlz+OvivqLzs0Y32oeanHOwGuaMOuBinUVNZyU953vjZlxM5fEiLMV5wrMsdpfaBAwYTOmwkh378jr6jxqJ39+x07NETfdj60Rkydx8i8NRdUJ0P01+EYfdQX91IxWMH6DfCA/yfhqJEKDoLabupangMATt0B8ZAkpckXLtHNAnYkYiGALZ/9A6iaGXKnQ8gCAJyvb553MqiPbD+YbwVaqZe9xc2/biNXV99xuQ77uvWcy+azZhycmg4e5by5csxXH896sBASrIzMdbX4xUS1vqg/vOhLA12/ksS4sf/rVUTlVyFr6MvmbUlnMyqwOJWhLudO0KqVFDr/qNu1AoWvr1rOLG+hg7H6OTtjVKtoSj9PIy/6OZUBzgiqGQ4TvZHN7pPC1GzJ7jbSSJ5V3KoN6dvRkRkZtDMdttknT7FmT076Ofoivpscqv9Mq0Cp+tCsYt1o3xlKiUfn8ZuoDv6a4KojztI1cZNuD74J9SBHXw29T5QIQnUcqUMeWfO8dzjUHwOZv2veZNoFak9lE/lpnSQCzgtCMNuoDviPqgpbxnlcvvoQNwc1Dyy/CQL3z/IZ8uG4qlvHf0g0yhQB+lRXxJtI5qtmArrMOXVSMJ1fi11ZeEIcgH9NX7oRni1m9Pr5GnP1Dsi2fDWKXZ8dpbpd/W/ote7odbE/hWpeAY5EtHBCoYWKFSg90GoSEc/PwCFs4byVanUf3wGD4Tu5U/XV8DBt+DQu2Cqg5gbYdzfwKkprnDmK5C6TcrPj/tMyoE2+EtCddSCFlnQoihSuiIJY0YVp8VDnHl7Dxp7Hb4TR/JB3VbmZ8zGr8SOqUMC0e63p6FiFfnnPqW2IgSdk7O0EmLHP6XxuPWFm1eCZ/+uP5ZOOLllPQq1mv7jp/Ran23Rb/QEDq9azrn9ezAN9OTPO/+MQW3g/SnvE6AP6NVzCYLAAD+n5iKwAApnDfpZgVSsTMV+mCeywR6UrDuJr3PPJw0voFPpiHSJJL4knieHP4la3j1D1+XYj/CiMb2Syi3pqPwdMBfXIXNUIVO3lFePrVuJSmtH1KT2neeLwhfx6elP+eLMFzwz8pkOz3u+4jzZ1dncGnlrl8fqFRpO0MAhHFu3kthps1DbXfnzeTWxCdQ2/pDM6O/Jk6tP8+Px3GaB+n/bU3DQKLhttC172sZvC9cmB3VJbfciPuqNFn44ls30/l64O/Q8F83GHwe5TGBWtDdfHcqkst6EXntxMq+8zkS4R/cmSGzYsPHHxX748O4dEP8DxH8AU8bBjd/1KB+4K3gG6Rk6K4DDa9Pxj3QmZKA7ggBJhdXMiGrpCt24LhU3k4DHNG+U6q5luBvmX0/5F19StXYtzrfc0rzdUl4B9EygdtAoGRniwuaiGq5xLUB92JmGa01odNI1uqLOiCiCNq0WQS2n/9g+3L7nH/g6+DLBV3KgO072oz6+mPKVqXg8OKBZ6Nr56ftYRRmTPZMRzm2AAYtbCtSVlc3CL8D4pXeScfI4Oz/7gHmPPdXp2INiXbG3MxG/Op7AIOC2LeAjLWMvSJeyvz2HDYXQqc3HGLMraHg7AcfoWmSe90nCdeEZOLsOaaYCzlV7k54TzPhYR/TJ30JFBHKFFBNi2fAs1GwC/1Fw3YdE6PtQYtZzdM0KXH39iZ3WOhPZajRiTM/AmHaextTzNKadx3g+DWN6OmJTpIncyQnX+ySBOz81SRp7WwI1wJi/Qmka7H5BKp4YvaBVkxBDCIWViZzILsfTvggPew+Kjq9FFJ3IVoWw4vZhXSpuLZPJcQ8MojCtZaFEdYAe72dH9lo014WIjwt52R2xMX0j/V364+/Ydv0Ts9HI9o/exuDhxeDYEZTuewlzcTEKN7dWbdVBBjz+PJCqnVlU78mh4VwZDfE/ogoOxuWOOzoeiN4Pzu/o/MFd4OTXoNBC/+ukcZY1UL4imca0StRhTjjND0Whl8Qve4MzteXlrbqYE+ONs52Ku788xvx3D/D5bUMJce/8dRQUMlR9dKj66LiQDn4hYqArYrN/pAujrg9l3w8pHFmfzrA5PV8xfHD1eRrrzIy7qW/3hG7nIGlyBrAf4oncoCb3s9N8iI5QsQvuYGMtHH5fKmTaUCHF8Yx/Atwu+5wpNVJ+fr/Z0FAF59ZLYvW+V2Hvy+ARBVHzqe4zkfS1GTjnO3O6fB+FDjlMuesB+o0ej1Uh8J9vVlDvl4p1WwhORfVoHIOZvOwpNr/9Asuf/TsL7r8dhx0PQ0ECDLkTpj7Xq99N9dVVnNu3h4ixE9Horm4hexcfXzyCQji0fQ0fl53C39Gf96e83zzx1NsM8DOw/WxhU0SgdC9rP8QTlY8DSk97UoulfHkfp955Pu+LvY+MqgyGeQ274r4EQcBpfijGvBrKvjmHoFGgvCzeo7KogORD+xk069oORWEXrQtzguewJnUNDwx4AFeta7ttd2bvBGj+3u4qIxcs5qu//4Xjm9YwYv6N3Tr258a2ntvGHxJ7tYIZ/T1ZH59Hg8nC6dxKtiUWcsfooBaCiw0bvwUuxHpIDuqus/ZULlUNZpaOsBVHtNF1Zsd4YbRY2XKmoMX28lojBtvqExs2bFwNzqyCVXdDwGhY9O1VE6cvMHB6AF4hevZ8m4yxspEAF3uSCloWSjTWm8ncnUe+0sqsWR1nT1+KJjwMTVQUFSt+bFEkrNlB7WTo0ZinRXqSXV6PcpQJhUnF9h8uFissqzXiaBWwZNQSOdqbc7VniC+O5+Z+NyNvKi4pU8kxzA3GXFRH9V6pTkvKkQOkHDnAiAWLcfLwlAQeaBakTXl5YDa3EKgdXd0YseAm0uKOkHrscMeDri9H/sPNRMm+I8cYTemci+I0QEFaJTK5gLt/y8nPqt15CBo5unmTYeI/YNHX8OBxeCIP7tpN3dTX2FkchpdBYIDyBGx/Gr5ZgHztUgAsZ3dLbsula0HfB4DRi5YQNGgoOz97n5S1q6lYvZqiV14l+777OT9tOkmxA0ifO5fchx6m5J13aDh9BqW3N05Ll+D1/PMEfP8dIdu3ofSQxJyC1GTUdvY4e/Vp+7ELAsx+HfxHw5r7IKt1LnmQPohGoZCUogoKagtprLdDm7WH4+ohrLxvVJfE6Qu4BwZTlJmG1doyq7k360Y4aZxQCIpOM6jTKtI4W3aWa4LaL454eNX3lOfnMfnO+9HFDgCgPuF0u+0FpQz9tADc/zQA0ViBKuR6dFP+jrWudR57Cwy+UF0A5i6YPEwNcHoF9JuNqHak5nA+hf87jjG3Bqf5obgui2wWpwF0Ts7UVJS12dXoUFe+v3sEjWYr1793gONZrYXsriDIhG4JxNETfeg30otjGzNIOVbYbrvTuZXc8fkxvjiYgfWy/P3885Uk7s0jZqIPrj7dFE2dAqEsvfm/mlAnntVbERQCZR8mUJ/YTvFQc6MkTL8eCzueBd9hUl78gs9ai9OXo3GE2JtgySp4JAlx+n/IrdWy7rPv2PjPz3DOd6ZMSCfijtEsfektoidNR6nWoJariXKL4qhqF8PnBiFXyphxV3/Chg1k/hPPUltayPJ/PkFVSZE0aXrNy73+3ZSwcytmk5HY6VenOOLlNIQZaMgrZrC8H59N/+yqidNwMYf6ZHZF8zZBEFB56xBkAtnldQD4OPWO43dUn1Es7re4V/oCaZWDy039sNSaMBfVtYr3iNu4BkEmMHDGnE77WhqxFJPVxLfnvu2w3a6sXUS7RTfHKXUVj6AQggcPJ279ahpqOy4s+0tjE6ht/GGZP8iH6gYz2xIL+d/2ZBw1CpaNDvilh2XDRrfRa5XIZQKlNV13UIuiyBcHM+nr6cBgf6fOD7Bho4lYXwN+znasO5XXvM1qFamoN+Fsb4v4sGHDRi9zdj38eIeU3Xvjd6C6+stTZTKBKbdFIpMLbPskkTA3e5IKWwrUhzakIzdascY6oVF2b1GqYf58GlNSaEhIaN52JREfAJP7eSAIUEsQCV4/kXm4kqLMKkAqkDioQQECxEzy5fMzn+OocuTakGtb9KHt54I20oWqHVnU5Zax45P3cPMPZNCsedKy+PQ9UF3YPEZjVpY0Zr1ji34GzpiDq68/Oz99D1NDQ9sDzjkG742FlG1EzhqOXCkj/kBViyb55ytx83NoUaDOmFdDw5lSdKP6INNe9ryr7MB7ALuOlWI0C0z9x5vI/noW/pYByzYjn/F/AFgGPoA59l7qTp6ifPlyCv/9b3Luuoe+m3djX1vPps/fJ/Wppyj99FOMWZmow8NxveduvF9+mcBVKwk/cZyQbVvxfe9dPB59FMP869DGxCCzt7849tRkPIJDO86MVahg4Zeg94Xvbmp2ll4gxBCCiBVBVUJBbRF5aSU4CPWMm3Uz7o7dW/nmERiCubGR8rzWReJ7C5kgw0Xr0qmDekP6BmSCjOmBbS97L8nO5MiaFUSMmYB/VCyafn1BLqc+Ib7TMVgrs6la9RiC7CzmMih8NY7qfbnNTuNW6H0BEaq68LycWw8NlZhDbqLkk9NUrEpF5eeAx18GYj/Es5XYb+/UtoP6Av376Fl570gMWiU3fXiInefaF4x7C0EQGHdjOF4henZ8frb5GnGB2kYz/1qfyJy39rE3pZin1pxhwfsHSWm6/lktVvZ8k4TOSc2QWT1YdewcBPVlUC89LyU1jfxUVsORMe4oPOwo/TKR6v2XvBYWMxz/At4cBJseA7dwuG0rLF4OXjHdOrXZaORMXAJfrT7Ld0fVVDCIER5zkCkyiVI9jN+maxG+uQHil0OjJOIN8hjE2bKz9J3kxh2vjMErxAB1ZfQ5+QLXex+j3qrh+/yRVDoN6P5z0QlWq4VT2zbiGxHVIjv+aiCKIu+cfIf3TasQBbjROgG9Wt/5gVdAjK8emUC7kzPZZVKus6/z1Z2QvhJUfXQYZksrERTuF3+b1NdUc3rnNvqOGoeDS/uO6AsE6AOY4DuB75O+p85U12abgtoCTpee7rZ7+gIjF9xEY10tcRvW9Oj4nwubQG3jD8uIIBe89Rpe257M9rNF3DkmCEeNzf1n47eHTCbgbK+itLbrDuoT2RWcyavi5uH+veqesfH7RxAEZsd4sT+1hOJq6T1X3WDGYhUx2DKobdiw0ZskbYYfbgXvAbD4B1Bf3SXOl+LgrGHcTeEUplcRUQ4ZJbU0mCT3aUVhHQk7s0lQWZg+xq/bfTteMxNBo6FixY/N265UoHZzUDPE35kDSSLl/VMwqRv46btkRKtIcWkd0UY5nlEuVCiK2ZG1gxvCb8BO2Vrs188JRpAJZH18kLqKCqbe/aBU3ClqgZQRfmblRYE6IxMAmb6lkCFXKJh0x31UlxRzaOV3LU8ginDwHfikSZy8bQua8XcTPtSDpMMFNNQ0xXCYrRRlVOMZ3LLv6h1ZCBo5DqPbdianHT/Kuf17GDZvAa4XRB2tE/iPQD7uXgAK31tOyoiRZC6+mYKnnqb8++WYy0pxHDSYqRNmILO359TYwQTs20vw+vX4vPE6bg8+iH7WNWj69UOm6VgcNjU2UJKVgVdIeIftALBzlt7bohW+vqFZuAMINkjOfLldGiJmxmhrEOUq7MIndd7vZXgEXSyUeDVxt3OnuL79DGpRFNmYtpFhnsPaXMouWq1s++AtVHb2jFsqxXPItFrUYWE0xCe0at/iWIuF/KefRq53xPOxG/B4aBDqQD2V69MoevcUpoLa1gfpmwrdNRVK7LD/E99Qq7qBwpVqjJlVGK4NwfX2/iic2n4/2BucqC0vbbFS4nL8XOxYce9IQt0duPOLOJYf7XwcV4pcKWP6XVHYOajY+G4CtZXSb7ntiYVMeXUPH+1LZ9FQP448MZlXb4jhfHEN17yxj/9tT+b49ixKc2sYszCsZ4UWnZtE7SYX9ZF0yWE+oJ87bndFo+nnQuW6NCrWpCKeWgFvD5WKiercYclquGUd+HUvoqGmrJT933/JB/cvY/M7r2ExmZh68wNMClyK0mCPx2MLEO7dDSMegMJEWHknvBQCK25jkNGKRbRwsugkcoUM0vbAuyMheQtecx9nwbOvYqxv4LtnHqe8IK/TsXSHtLijVBUXXXX3tMVq4fnDz/PuqXeZFjGbwJhBJB/YJ9V2uIrYqRT09XTkRFZFm/tzyutQK2S46a4sL/pqYz/MC5dbIrAfdLGYY/y2TZgaGxg8q+u1MG7tfyuVjZWsOd+2gLw7ezcAE/0mtrm/M9wDgggbNorjG1dTX1Pd+QG/EDaB2sYfFplM4NoBfUgrrsVgp+TWUQG/9JBs2OgxLvYqSrrhoP7qYCY6tYJ5A9pZemrDRgfMiemDVYSNCfmAVCARwNneNslnw4aNXiJ1OyxfIhWbWrwC1D9/xn3oYA/6jfRCca4ab5OM1CLJVbf/x1QsApxxFhgZ3I3CXk3IHRxwnDaNqg0bsNZJbilLRQWCVotM3fOb8amRHpwrqCbGcwj7fFdSmF7FuUP5FB4vRYVA9CQfvjz7JXKZnBv7tp1DqdCrEWPV6OocGTvqJjyDQ6UdbuHgGQ0JPyBzdARBuMRB3dpp59M3kshxkzm2fhWlOVI76svh+5thy9+lTOl7fmqO9Iie6IvFZOXMPsk9WZxVjcVsxeuSInHG/Frq23NPA411dWz/6B1cfPwYeu0NrfbLNBqcly3Dcc5s3B97DN/33yN4+zbCj8cRtHIlfV76L4EPPcy1jz9DVVkpG995FavF0qqfzihMP49oteIV2kn0wAVcgmHh11CeAd8vaY6bCNAHIBNk+PfJAWCMkIfgP6pHEzXO3j4oVOqrLlC7ad06dFDHl8STU5PTbnHE+B1byEs+y7ibb8PO8eJrr42Koj4hoUOxt/y772g4FY/H3x9HbjCgcNLgcmskzovCsZTVU/jGCSq3ZCCaLhHeDE1Fwis6FoYtOZmUnhtNedVSVH10ePxlELrhXh2aPHROLljM5k6X1Lvq1Hx313BGBrvw2I/xvLUzpcPH2RvYOaqYeV80jfVm1rx1ins+O8odXxzDQaPkx3tH8MK8KPR2Sq4b6MP2h8cxI8qTj7emsn91GoYQRwJjOneFtolzU+51uSRQH04rxU4lJ6qPHplKjsvivugijdQczKf0+3SsMp0U63THDgieIEXjdAFRFMlLPsv61//Lhw/cxqFVy/EO68v1//cvlrzwBu4ZHoiNVlxvjUTuqJa+Z6Y8C39JgGWbpUiQ87uI3fw0clEkbt+/YdPj8MVcUOngzh0w8gE8QsJY8NQLWIxGvn/mcUpze2+C4cTmdehcXAkZ3M16Dd3AaDHy6E+P8n3S9yzrv4znRj1H5NiJ1JSWkJ3YfqRObzHQ38DJ7AosbaxwyC6rx8dJ+6s3UgmCgLafC7KmCRuzycTxTWsJiBmIm3/XVxnEusUS7RbNF2e+wGJt/b2zM2snAY4BBOl7nh0/4vobMTY0cGzdyh73cbWxCdQ2/tDMH+SDXCZw19ggHGzuaRu/YVx16i5nUJfVGlkfn8/8gX2wV9tq5droPuGeDoR7OLC2KeajrEmgtjmobdiw0Suk7YbvFkui6M0rQWv4xYYy+oZQ7FzUXFOr5GxGBVmJpWTEl3BQY2ZCrBcKec9upwzXz8daW0vVlq2AJFD31D19gWmRngCIdZEkuR5F5W3h4KrzNCRWcF5hwclHxurU1cwMnNlutqjZZGLb3o+otJTgXRmAtcF8cWfUAsiNQyhPR+7oiDFTclC3JVADjL15GSqNlh0fv4uYfQzeHwvJm2HaC1JutPZixJhLHx19wp04vScXq8VKQVolQAsHdfWOTAS1HIdR3m2eb++3n1NdVsLUu/+EQtn273qPvz2G9/PP43LbMnTjxqHy8WkVw+ET0Z9Jt99Hxqnj/PT1J2320xEFKU0FEoO7KFADBIyCuW9Bxl7Y8BCIImq5Gl8HX2pkUn8e5dkQNq3b4wGQyeW4BQRSmH6+R8d3FTc7tw4d1BvSNqCSqZjsN7nVvpryMvZ+8xm+kdFEjmvpEtdGR2GtqsLU9J67HFNhEcWvvob9yJE4zrroOBUEAbtYdzweHoxdrBvVu7IpfP04jU3vLxx9AKFdB7UoitQeL6TgvTQarVEYJulxvSMKhXPnESv2TtL7u7a87RzqFm3VCj6+ZQjXxnrz8tZknl57pk3Rrjdx8rZHNtyF8uwalCcqeGxaOOsfHM0gf+cW7Vx1al5fNIBH3dwBkf8WFfHsukRqGs1td9zhSQOkv5vibA6llTHI3wmlXAZpuxE+mYLh/HUY9D/QIA6j2Po/LN6TuyxMm00mEvfu4pt/PMy3Tz5Kxsk4Bkyfze2vf8i1jz6JX2QM5cuTMeXV4HxTX5Se9i07kMnAfwTMehX+mozdjcuJlDsQV5kKh9+FgUvh7j0t4kXcA4K44akXEK1Wlj/7d0qyMrr/vFxGaU42WadPETtlJjJ51wrwdpdaUy33bb+PbZnb+Ovgv/LwoIcRBIHgwcNQabUk7t15Vc57KQP9nKhpNDdP/l5Kdnkdvs5XP9Krtzm7dxd1lRUMnnVdt44TBIFbI28lpyaHHVktC7dWGas4WnC0x+7pC7j6BRA+YgwnNq2jrqryivq6WtgEaht/aILddOx6ZDz3jO16cRsbNn6NuOhUlNZ2zUH9/dFsjBYrNw+3FUe00XPmxHoTl1lOTnkdFRcc1DaB2oYNG1dKxj74ZhE4B8OSNVIEwi+ISqNg+u2R2IsCudtz2bc8BbmjkiMKE3Ni2xZKu4J28GBU/v5U/LgC6B2B2tfZjggvR06k6nCzc+V81H4aakwIjVYSHUVWn/+RenM9SyOWttvHkdXLKcvLxm6GD9ZaE5VbMi7u7D8fEOD0j8gNBiylUkGz9gRqO0c9o29cSnZiAudeWyzFe9y2BUbc36bgFDPRh5ryRtJOlpB/vhJHVw32TUXnTAW11J8uRTfKG1kbBXlzzp3h1NYNDJw+G++wfl1/0tohetI0Bs6YQ9yGNSTs3NqtY/PPp+Do5o69oZs1PmIWwdjH4MRXsP9/AATrg6k1SdEU7maL5DzvIR6BIRRlpF3VpftuWjcqGysxWlr/JjVbzWzJ2MI433HoVK1d4Ls+/xCzycjkO+5v5ZrUREUD7RdKLHz+eUSzGc9nnm7TcSm3V+J8Qziut/VHtFgp/iCe8pUpWE0ycPBsU6C2VBsp/SKR8uXJKGWZeAR+hm5KdJeLEuoM0rWrpgsCNYBKIePVG2K5a2wQXxzM5E/fHm+OFeptEnIqufbt/Tx/KpNsHxV9G+UMa1RIQnEbZMSXUH2+iqGzgpg32p/PD2Yw9dU97DrXcd54K1T2oPOEsgzKao0kFVYz1yUHPpsluZOrC2D2G+geewWXW/pjLqmn6O2TbcezXEZFYQGfPXIvm956BWN9PZNuv4+73v2M8UvvwOAhTd5Vbc2g4Uwp+muC0Pbt5LtFroSwqQzqt4AErR0N9x2EOW9Ij+EyXP0CuOHpfyPIZCz/5xMUZaS10aE04ZFdnc22zG28cfwN7t1+L5OWT2L+2vm8cfwN4ovjsYpWTmxZj1yhIGpSzyakOqO0vpRlm5dxrPAYL4x+gVsib2nep1RrCB06ipTD+zE1tlNDoJcY0FQosa0c6pzyenx7qUDiz4VotXJs/SrcAoLwi+peRjrARN+J+Dr48tmZz1qsotiXsw+zaL5igRokF7XZaOTo2h87b/wLYBOobfzh8XOxQ9aN6sc2bPwacbFXU1LduYPaYhX5+nAmI4JcCPX4+ZdL2/j9MDtaEmbWncqnrFbKDHWyCdQ2bNjoIkkH95GfktRyKXvWISmH1+AHS9eAfffjM64GXkEGkt3lKPMbKC+oI9lLgbtBwyC/nhcZFgQB/fz51B+LozE9vUmgvvKiVNMiPTmRVckwzzHsrNtIzDQfqlyVNDrJ+ObsN4zwGkG4c9vZyCXZmRxe9QP9Ro8ncOIQdCO8qT2UjzG7Ka9S3wf8R0H88hZiensCNfXlRJd8haemit0lYTQs2QQ+g9sdu3+UK46uGuJ3ZlNwvrKFe7pqR5bknm4je9psNLL1/TdxdHNn1KIlnT9JXWTcktvxjx7A9o/eIacby90LUpO6556+lAlPSBMB25+BM6ubc6hlgKshQIoD6SEeQSGYGuopy796hRIvOPPbclEfyj9EWUMZ1wRd02pf2vGjJB/cy/B5C3H2bv0aq4ODELTaNgslVu/cRfXWrbjeey8qv44z4TVhTng8NAjdmD7UHi2g4NU46uWTW0R8iKJI3ckiCl+LoyGlAv1IGW7CQyiGth1L0h72zpIA2hUH9QVkMoEnZvbj/67px8aEAm799AhVDaZunbcjahrN/HNdInPf3kdBVQNv3TSAF54YSehgdw6tSSP9VOvXzWS08NN3yTh52TN0mj/PzIlkxT0jsVcrWPbZUR789kSXV3ECUsxHWRpnTxzgI+VLXH/yNig+B9P/A3+Kg0G3gFyJtq8zbnfHIIoiRe+eoiG5/YKT5fm5fP/s4zTW1nLd489w66vvEjt1JirNxQJ7tXGFVO/OwX6YJ7p2VmG0xSCPQZisJhKsHYvkLn18Wfj0v5GrVPzwzyfITT1HSnkK686v479H/8ttW25j1LejmLlyJg/vfphPTn9CYV0hw7yG4ahy5JPTn7B442Kmfj2Rk7s2oY8JA23vr/DOqc5h6aalpFem88bEN5gdPLtVm4ixEzDW13P+2OFeP/+lBLjY4WSn5Hhmy9e2qsFEZb0JH6dfb4HEtkg/GUdZbjZDZs3rUTSJXCZnacRSEkoSOF50vHn7zuyduGpdiXKNuuIxuvTxpe/ocZzcsoHaivY/U78UNoHahg0bNn4HuOhU1Bot1Bs7dlrsSS4ip7yeJSNs7mkbV4afix2xvgbWnsprdlA72TKobdiw0QVEq5XdX37EN//3CJ/8+S72L/+KsrhN8NX14OgFt6wFndsvPcwWKCP0ZNqJ+A9wZV1xObNjvK/Y4KC/di7I5VSuXNkrDmqA6f09EUXQmqKoN9djHVxIvJ8Shf4UxfXFLZxyl3KxOJ0d42+5EwDHqf7IHFSUr0xBtDRNJEQvgNIU5FrpNlJQKhHaKhqYGwfvj0VI2cLkedOoNwrsX7u+w7HLZAJR433IP19JXZWxOX/aVFBLfUJJu+7pQyu/pzwvhyl3PtBCkLpSZHI5s/7yN/TuHqx99QUqiwo6Paa2opyq4iK8QnooUAsCzH0HfIbCqrsJskrvMRezBUXo9J712YRHoCRuF13FHGo3O+lzW1zXWujcmLYRB5UDY/qMabHd2FDP9o+l7PAhc+e32a+gUKCJjGhVKNFaW0vBv55DHRqCy23LujRGmUqO4Zog3O+PRa5TUlqwkJL0qViqGrHUGCn7+ixl3yWhcNHi8eAAHMRvEVRaiJjbpf4vcMFB3RMR6I4xQby+KJa4zHJueO8ghVVX7mTdeqaAKa/u4dMD6dw0zI/tD49jVrQ3MpmMiUv74e7nwLZPEinNbRm3cGxDBtVlDYy/KUwqFAgM8ndiw4NjeGhyGJtO5zP51T2sPJ7Ttexs50DIOcKo7XMZIkvGPOFJePAkDL8HlC2vJao+Otzvj0XhpKHks9PUHMlv1V1ZXg7Ln/07FqORBU8+T+CAwa3Ewcb0SspXpqAOMWCYE9wt8TDWPRYBgWOFx9pt02hp5HTJabZV7adoTh8qqeWLpx/mri8W8cS+J1ietJxGcyMzAmfw1Iin+Paabzm8+DAr56zkhTEv8On0T9mzcA8vjnmRUeXBCCYrn6t3MOa7Mdy3/T6WJy2nsLawy2Nuj6SyJJZuWkpFYwUfTv2QsT5j22znGxGFzsWVxL27rvicHSEIAgP8nDiRXdFie3aZVJvhtxbxcXTdjzi4uBE2YkznjdthbshcDGoDn535DJBywvfm7GW873hkQu/ItyPmL8JiNnH0V5hFbROobdiwYeN3gKtOcq6W1nbsYPjyYCbuDmqmRHh02M6Gja4wJ8abs/lVHEkvQyET0NkyzW3YsNEFBJmMW19+m2n3/BlHN3cOrfyeT//7Nl+lRhDn/SA1pl/fZFe4lyPLVQ3k9rXHZBWbV5FcCUp3d3Rjx1KxajXmsrJeEajDPHQEuNiRlOGJVqFld/ZuSmsbqVJvJ8QQwkjvkW0ed2rbJvKSzzJ+ye3NxelkGgWG2cGY8mupOdDkuu03B2RK5BYp3kNm0LcUe0QRDr0HH0+T/r1sMx7X/oPYaddwcuvGTov09RvljVItZa56BhuAjt3TRRlpHF27goixEwmIGdiNZ6praOx1XPvYU1itFlb/9zmM9XUdti84nyyNPbRtl3qXUGrgxm9B50HI3jcB8LCYe5w/fQEXHz8UStVVzaF200oC9eWFEuvN9ezI2sFU/6mo5C1XWx1Y/jXVJcVMvvN+5Ir2P/vaqGgazp5FNF10FBe/+RbmvHw8n30WQdW9VVwqHwfcH4jFMTiFhoa+FLwSR+FrcdSfLUM/IwC3e2NQGkQ4swoir+12cUqlRoNKq6WmvLRbx11gbmwfPrl1CNlldVz3zgHOF3dcbLE98irqueuLY9z1ZRx6rZIV94zkX9dGob/EnatQyZlxTzRKjZwN78RTXy0ZD0rzaji5LYu+I73wDm25YkSlkPHnyaFsfHAMQW46Hl5+iqWfHGkWF9vFbwQotHyrWcgj3l+gGPfXDp9bhV6N273RqEOcqFiZSuWmdMSmfO7SnCyWP/t3rFYrNzz1Au4BrQvImUvrKf0yEYWzBpeb+iJ0s26AXq0nzCmMuMI4AKqN1RwtOMqXiV/yxN4nmLdmHsO+HsaNG27kuUPPsalsF5nXOKO0t2NOnB8fRbzCoZsO8fU1X/PkiCdZELaA/q79UcvVrc4zM2AG/ucVeISE8Z8b3uGG8BtIr0znuUPPMXnFZG5YdwPvnHyHxNLEbhfSjCuMY9nmZQiCwOfTPyfWPbbdtoJMRr/R48k4dfyqu2wH+hlILaqhsu7i5zq7rB7gNxXxUZCaTE7iaQbOnINc0fP7Ia1Cy6K+i9idvZv0ynQO5x+mzlzHRN8rj/e4gJNXH2Y+8AhDZncvJ/vnwCZQ27Bhw8bvABd76UdOaU37OdRZpXXsTi7mxqF+7WbM2bDRHWZFeyETYPvZQpzsVb/6Sts2bNj49aC2s6f/hCksuGMhd0cmMt6/DFxC2L38Bz64bxk/PPcPTu/aRmNd59mjPwfhTbFY7+05T6CrPf37OPZKv4YF12MpKcFaVdUrArUgCEyL9OTw+SqGeoxgd/ZuSswJ1JHD0oilbV6nq0tL2PvtZ/hHDyBibMubYG1/FzR9nanalom5okHKBA+dgry+jQKJ9RWwfAls/huETIa7fwLfIQCMWngzdo56tn/0NlZr+6u91FoFkWP7YK9X4extj6mwlvrTJehGtnZPWy0Wtr7/BhqdA+OX3tGzJ6wLOHv3YdZfHqc0N5sNb77c4fjzU5IRZLJmt3KPsXeFxT8Q0NCATBRxtwqSsHcFyORy3PwDKUz/GRzUl0V87MneQ525jpmBLWMyCtNSOb5xLdGTpuPTN7LDvrXRUYiNjTSmpADQkJhI2RdfYFi4ELuBPZucEOQyHGMseKoeQNVHjcLVTnJNj/OVsqYT14KxBmIX96h/e4MzteU9F/jGhLrx3V0jaDBZuP7dA5xoI6u3PcwWKx/vS2fKq3v4KaWYv8/oy7o/jWaQf9vRRDonNTPviaauysjmD05jMVnZ800SSq2ckde1/34O9XDgh7tH8NzcSI5nljP1tZ/4aG9a+0UeBy6h8i/pPFE5l6jgrq3olKkVuN4Sif0wT6r35FD27TmK09JY/s8nALjhqX/j6hfQ6jhrvZmSz84A4HpLZJsrMLrCII9BHC88zowfZzDy25HctuU2/nv0vxzKP4SXvRe39b+N18a/xqbrNrH/xv28P/9z7n7xPfTO7hx+8wPyz53t0nky409Qnp/LoOmzGeY1jL8N/Rsbr9vI6rmr+cvAv6CWq3nv1HssXL+QySsm88+D/+SnnJ9oMHfssN+VtYu7t92Ni9aFr2Z8RYhTSKdjiRgzAdFqJeng3i6NvacMbIrKOplT0bwtp/yCg/q3E/FxdP0qVFo7oiZeeW74ovBFqGQqvkj8gp3ZO7FT2DHMa1gvjPIifUeN636dhJ8Bm0Jhw4YNG78DXLrgoP76cCYyQeCmYR3n89mw0VXcHTUMD3LBKoJTD3/027Bh4w9M4Rn4Yi46ew2D/v4tN7/0Dre++i7DrruBquIitrz3Ou/edTPrXv03KUcOYDb1XhZrdwn3lATq8joTs2O8e21CTjd2LHI3VwAUvSBQA0yN9MRsFXGRDaSovohGww9oZYY2s39FUWTHJ+9htVjbLE4nCAKGOcEgQsXapsJfUdcjF6RcanmT25rc4/D+WEjaBFOflxzAlxS4VNvZM37pHRScTyFhx5YOxz9iXjA3PTscmUyQ3NNKObo23NNxG9dQmJbKxGV3o3XonQmD9giIHsCEW+4kLe4I+7/7st12BeeTcfULQKluI/aku7iFo1n4BVPqGhjpGAyKK6/z4B4YTFH6+atWKNGgNqCQKVpFfGxI24C7nTuDPAY1b7NaLGz78C20jo6MuenWTvtuLpQYn4BosZD/1NPInZ1xf/ihKxy0HwpZPm4zRdzvjUHpcUkRvJNfg1MA+Le98qAzdE7O1FZ0PYO6LaJ89Px470gcNEpu+vBwlwoTxudUcO07+3lufSJDA53Z9tA47h4X3KlBxSPQkYlL+pKXUsGK/x4jP7WSkdeFoNV1/N6TyQSWjAhg28PjGBnswr82nOW6d/ZzNr+qzfZHMsoQRRgW1PUiuIJcwHBtCPqZgdQnlJD/dhwauR03PP1vXHx8W7UXLVZKvzmLuawBl5v7oXDtudg5I3AGgfpA+rn048EBD/LOpHfYdcMudt6wk3cmv8ODAx9ksv9kfBx8mq+hDs6uLHzmRRzd3Fn572fIjD/Z6XlObFmPnd5A2IjRFx+3IBBsCOb2qNv5cuaX7F64m3+N+hfRrtFsSNvA/TvuZ+z3Y3lw54OsTFlJSX1Jiz5Xpaziod0PEeYUxhczvsBL59Wlx+zq649bQBCJP13dmI9oXwMygRY51Dnl9ejUihYu/18zlUUFpBzaT/Tk6ajtrtz17aJ1YU7IHNamrmVH5g7G+IxptfLk94ptLa4NGzZs/A5w1UkO6pJ2HNQNJgvfH8tmWqQHHo69cNNkw0YTc2K8OXC+1FYg0YYNG92j6Bx8PgcUarh1HThJTjqXPr6MuuFmRi5YTEFqMmf37Sbp4F6SD+9HbW9P2LBR9Bs9Hp9+/RFkP5/XxkuvwUGjoLrBzJyYrt3gdwVBocBw7bWUfvhRrzioAQb4GnB3UJOb648MGajKGGC4uc0b3JQjBzh/7BBjb74Ng4dnm/0pnDU4TvajclMG9WdK0YbNQN40KSnX6+Hw+7DlH6DzgGWbwHdom/30HTWO07u2svfbzwkdOhI7fduPVyYTUGkUkns6oQSH8b7IL6txUF6Qx4HlXxM8eBhhw0e32U9vEzttFiXZmRxZswIXX38ixkxosV+0WilITSb8CvJHWxE0npfnrwFd269Nd/EICuHUto1UFObj5NVa9L9SZIIMN61bCwd1RUMF+3L3sbjfYuQyefP2E5vXU5iWyqy//A2NrvP4DGUfb+ROTtQnxCMajTScPo33Ky+3X6Szq+h9mgaa1bKIZ3kGZOyFCf+QssF7gL2TM/mpSVc2PiDA1Z4f7x3Jss+OcMcXx3jxuigWDG4tylY3mHhlazJfHMzAVafmncUDmdHfs1sTamFDPSnLqyVucyZewXr6jej69c7boOWjWwazPj6fZ9aeYfab+7hnXDAPTAxBo7z42h9OK0WlkBHra+hy3yCJtXW+jRyp3Mwgx8lM9VuGg6K1yC2KIhVrz9OYUoHT9aGog7p3nsuJdY/lxzk/dvs4e4MTNzz1Aj/86/9Y9d9nmfvX/yMwdlCbbSsK8kk7cYzh1y3sMOrGWePM3JC5zA2Zi9Fi5GjBUXZn72Z3zm52Ze9CQCDKNYpxvuMwWU28d+o9RnqP5LXxr2Gn7J54GjFmAnu+/JjS3Gxc+rR+v/UGOrWCMA8Hjl+yOiC7rA4fJ+1vZmVm3MY1CDKBgTPn9FqfSyOW8mPyj5Q3lvdqvMevHZuD2oYNGzZ+BzQ7qNsRqNfH51NRZ+Lm4bbiiDZ6lxn9vVDKBZtAbcOGja5jboSvF4BMDresA+fWuaGCIOAVGs7EZXdz97ufc93fnyV44FDOHdjL8n8+wQcP3Maerz6hKCOt21mcPUEQBKL66Onfx5EQd4de7duwcCEqf380ERG90p9MJjA10oP9yQ2EGaIRrUrGe7W+cW6oqWHnJ+/hHhjMoJkdF4DTje6D0tOOirXnsYpq5IFSpIK8PB42PQYhk+Ceve2K0yA9hxNvuxdTQyM/ff1pp4+jamd2m+5pURTZ9sFbyORyJt1+788mYgiCwMRl9+AbEcXW998gL/lci/3lBXk01tXiGdrDAont4REJ9i6901WQtLS/syzwK8HNzq1FBvXWzK2YRXMLB39VSRH7v/+SwAGDuzzBIAgCmugoag8cpPh//8N+zBgcZ87s/MDO0DcJb5XZLbef/BYQIObGHndtb3Citry8V65Rbg5qvrtrBCOCXHh0RTzv7E5t7lcURTafLmDKqz/x+cEMbh7uz/ZHxjEzyqtHn49hc4IYd1M4U26PlKJOuoEgCMyO8Wb7w+O4dkAf3tqVyszX93I47WIW96H0Ugb4GlqI1l2h4HwKPzz3BCWyPHQ3BiCYBYrePUVjWkWLdjUH8qg9XIDDOB/sB/fO5E5PsdMbuOGpF3Dp48eal57jfNzhNtud3LoBmUxGzOQZXe5bJVcxqs8o/jH8H2ydv5UVs1dwf+z9WEUrb554k/dOvceMwBm8NfGtbovTIE0qCoKMs1e5WOIAPydOZldgbYqFySmv/00USKwsKmTfd1+QsH0LfUeNw8HZtdf6DtQHMt53PAqZgtE+P88k7K8Bm0Btw4YNG78D7FQK7FRySmvajvj48lAmIe46RgT1zg2ODRsX0NspeW5uf5aOtE1+2LBho4so1DD7NVi6FlxDO20uk8sJjB3EjAce4d4PvuSaBx/F3T+Q4xvX8OXfHuTzv97P4VXLqSwqvKrDfn3RAD65dUiv96vy8SF4y2bUoZ0/F11lWqQn9SYLQcJS6rNvwUff+sb5p28+pa6qkql3/QmZvGOhSJDLMMwLxVLVSNW2TOSRkqNLXp8NU/8FN37XItKjPVz6+DJkznWc2bODnMTT7bYzFdZSH1+MbqR3K/d0ws6tZJ+JZ+ziZb0qCHQFuULB7If/js7ZhTUv/4uqkotO4fwUySnrFXIFBRKvMi4+vsgViqteKPHSmIGN6RsJ1AfS17kv0BQr8/G7iIhMuq17EwzaqGjM+fmIViueTz/VO5MTGkfQ6KHiEoHaaoVT30DQODD03Dmqc3LGbGzstLhml/tTK/jk1iHMifHmv5uTeHZdItllddz5RRz3fBWHwU7JyntH8s+5/XHU9DweQZAJ9B/bBwfnnq+6dLJX8fKCGL68fSgmq5WFHxziiVUJ5FbUk5hXxbBu3pPkpySx4l//h8Zex8KnX8QlNhD3+2OR65QUf3ya2uPS9b8+qYzK9WloIlxwnBbQ4/H3JloHRxY8+Txu/oGsfeUFkg/vb7Hf1NDA6d3bCBk6Ep1zz+7VBEEg3Dmcu2Pu5ttZ37JzwU4+m/4ZL455EaW8Z+8FnZMz/tGxJO7dddVigUAqlFjdYOZ8cQ2iKJJdLjmof41YrRbOxx1m5YvP8NGDd3Bk9Qr8omMZfePSXj/XUyOe4pNpn+CouroRVr8mbBEfNmzYsPE7wUWnorS2tYM6PqeCU9kVPDsn8jezVMrGb4tFQ2255jZs2OgmIZN7dJhSraHvqHH0HTWO+uoqkg/t4+y+3ez77gv2ffcF3mH9CB85Bje/APQenuicXZDJuufSaw83B3Wv9PNzMDzIBUeNgm2nwFIXgrN9y1Uu2YkJJOzYwuDZ1zW7ajtD7e+I/VBPag7kYpg+Cngb2fCbYeSfujW2YfNu4Oy+3Wz/+B2W/OcN5IrWt6SSe1qGbkxL93RNWSk/ffUJPhH9iZ505cWoeoLWwZFrH32Sb5/8K2te+heLnv0PSo2G/NRklBotzn18fpFxdQW5QomrX+DVdVBr3ThacBSAgtoC4grjeCD2gebfoCmH95N2/Cjjbr4NvbtHt/rWxsZK53jgflQ+vfg86/2gMufi/zP3SZEfE5+6om7tnaRJm5qyMtR29p207hoqhYz/LYzFzUHNx/vS+eJgBmqFnCdm9mXZqMBfXSH0MaFubPnLWF7blszH+9JZdTwXqwjDA7ueP52bdJaV/34KO0cDC556AUdXqRinwlmD+70xlH51lvLlyRizq6k7XoTSyx7nReHddn9fTTQ6Hdf/379Y+e9nWP+//zDzT3+l78ixAJzdt5vG2loGTJ/Va+dzs3NrLlp6JUSMmcDGt14h59wZfCOiemFkrRnYVLjzeFY5zvYq6owWfJ1+XQ7q6rISTu/cRsLOrVSXFmPv5Mzw6xYRNXFq8/uxt3HVuuKq/XknYX9pbAK1DRs2bPxOcLFXU9KGg/rLg5nYqeTMG9j7WYM2bNiwYcPGL4XWwZGYKTOJmTKTyqJCzh34iXP7drPrsw+a28gVChzdPNB7eKJ398TgLv3b4OGF3t0DlfbXdRPcWyjlMib182DViVxA+o1wAbPRyLYP3kLv7sHIBTd1q1/9tADqz5RSE2cGBBSB3RcslGoNE5fdzer/PsfxjWsYMmc+oigiNlqw1powFddTH1+Mw1ifFu5pqaDju1hMJqbe9aefNYP8clx9/bnmwcdY9d9/svmd15j1l79RkJqMZ3Bor02IXC08goJJOrAXURSvinHB3c6dKmMVDeYGNqZvBGBmoBTF0VhXy87PPsA9IJiBncTKtIX9yBH4ffIxdsOG9eqY0fu0jPg48TWoHaFv68Ki3cHeIImwtRVlbRby6ykymcCTsyLwddJyMruCv04Lx+dXJuhdip1KwT+uiWB2jDd/+zGB/Mp6Bvg5denYnLOnWfnis+icnFjw1AutVk3I7JS43taf8pUp1B7MR+agwuWWSGSqX9/nUG1nz/wnnmXVf/7Jxjdexmo202/MBE5sWY+bfyB9wnsn5qk3CRkyAqVaQ+JPu66aQB3oYo9eq+REVgXhnpJb+NcQ8SFarWQmnOTUtk2cjzuMaLXiHz2ACbfeSdDAoW1Ortq4MmzPqA0bNmz8TnDVqciraGixraLOyNpTeVw/yOeKlvrZsGHDhg0bv2b07h4Mu3YBw65dQGVRIRUF+VQWFVBRVEBlYQEVhfnkJ5+jsa62xXFaRz0Gd8+LArbHxX87OLv8oiLolTIt8qJA7XSJ0Ht41feU5+cy/x/PoVR3bwm/zE6JYVYQZd8l4fb42zjOajtzWrSKWOtMWGulP5baC/82Y601YajVMyPsTqx7LOTGH0Sst4DlYk6voJG3ck+nHN5P6tFDjLnp1qtS4K+7BA0cwtjFy/jpq0/Y990XFGemM2jWtb/0sDrFIyiE+O2bqSwswODZewU/L3DBtVlcX8zGtI1Eu0bj6yiJs3u/+Zy6igquffTJTmNl2kKQybAfObJXxwtIMR6ZB6R/N1RB4hqIWQiqKxPJdM5NAnV52ZWOsE1uHRV4Vfq9WkT7GFj/p9HUGs1ouyAgZ5+JZ+V/nsXRxY0FT72Azqlt17WgkOG0IAxNqBPKPjoU+l/vaheV1o7rHn+G1S/9k03vvEZe8jlKsjKYeveDv8qVrkqNhtChI0g+tI9Jt92DQtX7NWdkMoEBfgaOZ5UzOlSagPB1/uUiPuqqKjm9axvxO6TrpNZRz+BZ84ieNP2qXDNtXMQmUNuwYcPG7wQXezUJuZUttq2Iy6HRbLUVR7Rhw4YNG38Y9O4e7UYHNNTUSMJ1YUHT3/lUFhaQn3KOpIN7W+RsXuq+NjSJ1u4BQXiHR6BQ/vonfceGuaFRylDKZagVkhhUnJXBkTUriBg7kYDoAT3qVxvjhvpYIY3p1dTsK8Zal3eJAN30p94M7dSFEzRy5PZKnFy9yU47g9lsIWD0YOT2SmRNf1Re9sh1F4WQ+ppqdnzyHu4BwQyeNa9H474aDJ41j9LsTI6sWQGAV0gvF0i8CngENhVKTE+9OgK1VhKoD+UfIqk8iceHPg5IMQ2ntm9i4Iw5eAb3Xt56r6D3hcZKaKiUxGlzPcTefMXdXnBQ11wlgfq3iFwmdMk0k5lwktX/fQ69uwcLnnwee0PHjmtBELAb4N5bw7yqKDUarv3b06x9+XlObduIRudA39HjfulhtUu/sRNJ3LuL83FHCB9xdQr2DfB1Yk9yMWfzqwB+9hUBoiiSc/Y0p7ZtIuXwAawWMz4R/Rm9cAkhQ0f+Jr7zfw/YBGobNmzY+J3golNRWmPEahWRyQSsVpEvD2UyJMCJfl5/nOIKNmzYsGHDRntodDo0upA2c5ctZjPVpSXNonXlBfd1UUEL97VSrcEvKoaAmEEExg7qdo7uz4WdSsGkfh6kF0vjtlotbHv/TdR29oxbcnuP+xUEAadrQyh88wTVP2Ujs5NEZbm9EqWnfbPIfFFwViCzV0n/t1MgKC660rNWZ7Dj28+ZNzeAoAHtF6Dc8+XH1FdXcd3fn+2R8/ZqIQgCk+98gLL8XPKTz/2qCyRewMXXH5lcKpQYPmJMr/d/wUH9xZkvkAtypgVMw2I2se2DN3FwdmXUwisXfnudC4UQK3OkeA+XUPAZfMXdqrRaFGo1tRU2gbo7ZJyMY83Lz2Pw8mbBk89j56j/pYfU6yhVaub+9f/Y/cWHuAeGoFT9el3ffv2j0Tk5k7h351UTqAf6GxBFWB+fj5OdEp3655EqG2pqSPxpB6e2baIsLwe1vT2xU2cSPXlGr8by2OgaNoHahg0bNn4nuOjUmK0iVQ0mDHYq9qaWkFlaxyNTf/03SzZs2LBhw8YvjVyhwNDklm6L+ppq8pISST8RR/rJOM4fOwyAs7cPgQMGERAzCJ9+/a/KEuie8p/50TSYLACc3LKR/NQkZj7wyBULPgpXLd5PDQdBuKJCZINnXUvinh3s/PR9fCOj2xRpMuNPcmb3dobOvR6PwOArGfZVQaFUct3fnqEwLRWds8svPZxOUSiVuPr5X7VCie5aycWaUZXBKO9RuGpdObxqOaU5WVz72JOoNL/c0v120TcJUWm7IfsQTH4GeiFuQRAEdE7O1JSXX3FffxTSThxl7Ssv4NzHl+v/8dzvUpy+gEKlYvId9//Sw+gUmUxO39HjOb5xDXVVlVflNYnxNSAIkFlaR7TP1X3NRVEkPyWJ+O2bSDqwF7PJiFdoONPve4iw4aO6HX1lo/ewCdQ2bNiw8TvBtWkpbEmNEYOdii8PZuKqUzE9su0bbRs2bNiwYcNG19HqHAgeNIzgQcMQRZHy/FzST8SRcSqOk1s3ErdhDQq1Gr/IaAJiBhIYO/gXz6vUqRXo1AqqSorZ990XBMQMpO/o8b3StyC/8nxuuULJpNvv44fnnuDI6h8YdUNLd62poYGtH7yJk5c3w6+/8YrPd7XQ6HT4R8f+0sPoMh6BwaQcPnBVCiXq1XqUMiUmq4mZQTMpL8jj4I/fEjpsJMGDerm4YW9xQaDe/zoIMohe1Gtd2xucr1oGdW9iamigsriQyiLpT3VpMS4+fgQNGIyd3vCzjOF83GHWvfpvXP0CmP+P59DqHH6W89ronIgxEzi2biVJB35iwPTZvd6/o0ZJqLuO5MIafJyuziSWsb6Os/t2c2rbJooz01FqtESOn0z05Om4BwRdlXPa6B42gdqGDRs2fie42Euuo9KaRjRKGTvPFXLf+BBUit9ugScbNmzYsGHj14ggCDh7++Ds7cOga+ZiamwgOzGBjJPHST95jLTjR4H3cfLybo4C8YmM+kWWcYuiyI6P30EUrUy+4/5fXSEuv/7R9Bs9nqNrVtBv9AScvS8WQNy//EuqigtZ+PSLv+ol8L81PIJCSNi5lariol6PqBEEATetG6UNpUz0ncim//wbuULJxFvv7tXz9Cr2biBXQ00hhE4Fx96bWLJ3cqY443yv9ddTLkQYVRYWUFlc0CxEVxUVUllcSF1lRYv2gkwmZfILAl6h4QQPHErQwCG4+gVclWtIypEDrP/ff3APDGb+E/9EY6/r9XPY6Dlu/oG4+QWQuHfXVRGoAQb6OZFcWINvJ/nToihibmyksa6Wxro6GutqMdbV0lhf17zNeMm+C/8vSEvF1FCPW0AQU+58gL6jxqLS/rxZ1zY6xiZQ27Bhw8bvBJcmB3VprZE9ycUA3DjM75cckg0bNmzYsPGHQKnWEDRgSFOO8t2UF+Q1u6sTdm7lxOZ1KJQqfCL6Exg7iIDYwTh5ef8sYnHyoX2kHT/KuCW3/2rzssctuZ2040fZ8cm7XP+P5xAEgfzUJI5vXEfMlBn4RPT/pYf4/+zdd3hUVfrA8e+ZSYEkECAhCAm9ExIChC5IkSIqCoq0paiIIKwuq6hrBZS18dO1rlIURaSIBVfpHWwQMHQElAgBBBIEUsmU8/tjZi7pBZLMJHk/z8NDZu69Z86dIcw5733ve8qVzAsllsS/ic51OuPn5ceJn3ZwYv8e+tw3ybPLn5hMEBgGF36DqJHF2nRA9Roc/yWmWNvMjbbbSbn4lyPwfP6so4Z+pgB0UkICWl9dBFaZTFQNrklgyA00bt+RwJAbqBpSi8CajkVm/aoGci7ud37btYPfd+9k+5JP2L7kE6oE16RRu440bt+Ruq0iiqWk0a8/bue7t17lhibNuOtfM/D187/uNkXxa9mjN1s//ZALp+OpUSes2NrVdjuXE87T0hJP1KU9VDt0nA1/bsk36Jx5QePcKGXCx68yvn7++Fb2w8fPnxZduxPRpz83NG7mcRdqhYMEqIUQopwIDnBkFp2+mMbSnSe5uWUtQqt5YJ0/IYQQopyrfkMdqt9Sh3a33I4l4wqnDh3geKyjdvWmj+fCx3MJrHWDkV1dLzwS70rFX/cyLTmJjR99QK1GTWl3y6Bib7+4+FerTrfho9n44fsc+Wk7TTp0Zu37b+FfvTrdR97r7u6VO8H1GmAymzn7+zGadepW7O3P6DqD1MuXWPDPSdRu1oI2fW8p9tcodtXqQdoFaD6wWJv1r1YdS3oaGWmp15Wtacm4QvKFRJISEkhKPE9SovPvhPOOQPT5c1gtGTleOzDkBkKbtyKwey1nAPoGAkNqUSUouMAFR2s1ciwo23XoSJIvJPL7L467Qw5sWc+etd/h7VuJ+pFRNHJmV/tXq17k8zr8/RZWvvN/1GnWgiFPTpeMVg/WolsPti76iEPbN+cox1QYWmuSEs6TEP8HifEnSTx5gkTnz5Yr6QB0B1J3Kg75+WUJLlepEYRvWD18/PzxdW3L9HPW5/3wrlRZgtBlkASohRCinKju541S8NmOEySmZDC6S313d0kIIYSo8Lx9fGnQph0N2rSj19gHuHTuT447S4Ec3LKBPWu/QykTJq98gkVaX8smI2PyrqdmFhiMcrc2fW9h/6Z1bPp4Ln/+dpSEk39w5+PP4usnAavi5uXjQ1BYvRJbKBFg66cfciU1hX4PTEGZykC5ub4z4cpl8CreUjIB1WsAkPzXX9TII/hqs1qyBp8vJF4NQjufS0u6nOO4SlWqUiUomKCwejRs14HAEEf2c2DNG6gaElKsZXECagQR2ac/kX36Y8m4QvyBfUZ29bGdPwFwQ+OmNGrfkUbtOhLSoFGBAcKD2zax+t03CG3ZisFPPO+ZC2gKQ5UawdRr3YZD2zbRdeioPD9frTVJiQkkxp8g8eQfJMSfcPwcfxJLepqxn3+16gSF1aV1r74EhdUjqG49gkLrUimgigSXKygJUAshRDnhZTZR3c+H38+n0DDYn26Ng93dJSGEEEJkExhyA1H9BhLVbyBWi4VThw8Qf+gANqsl3+Pyna7nM5kPbdGqTCwAZTKZuXn8Q3z2zGPE/O9Lmnfp7rmL6pUDtRo14beYn9Fag9ZordHajrY7/9Y628/2q/vZ7dn2z3pswok4DmzZQMc7hxJcr4G7T7VwakeWSLP+zgD1H3t3cy7ut0yZzwkkJSaQfCGBlEsXc1xp8vX3p0pQTarUCOKGxk2pEhRMleCaBNQIcjwfFIS3b/HfdVEY3j6+NGwbTcO20WitOf/HcX7fvZPfd+3gh88/44dliwioEUSjdh1o3L4TdVtH5giW79+8njXvv0m98AjunPZcidxBIopfq+69WP3eG5z69SChzVuRfCExWxDaEYjOSEs1jvELrEZQWD3Cb+pDcN16jmB0WD0qV6nqxjMRnkgC1EIIUY4E+ftwISWDv3Wuj8kkV56FEKI8UUoNAN4EzMA8rfXL2bb7Ap8A7YFEYJjWOq60+ykKz8vbm/oRUdSPiHJ3VzxC7SbNaT9wEL/+uJ3e93rwonrlQK2GTdi/aR2vDy+ZBc+q1apN57uGl0jbZUnV4JoAbPzoA+M570qVHQHnoGBq1m/oDD4HU6VGsCP4HBxcZrKJlVKENGhESINGdB4yjJSLf3E8dhe/79rBoe1b2Lt+NV4+vtSLaEPjdh1p2C6a47/sYt3cd6gfEcUdjz3ttkC7KLqmnbqyfv57fPufV4yFCl0qVw0kOKweLbv3ItiVER1WD7+qgW7ssShLJEAthBDlSFCADyf/MnF3u+JbuEIIIYT7KaXMwLtAXyAe2KmU+kZrfTDTbvcDf2mtmyilhgOvAMNKv7dCXLueYx6g+8hxmL283d2Vcq3FjTdxJTUFm9WKMimUMqGUQpmcf2f6GWXKtk/u+5Pp57CWrYu1xERZVb12KIOffB6FMrKgy/MigP7VqtO658207nkzVouF+IP7+H33Tkc5kF07jP0aRLXnjkefLpZFFkXp8alUmc6DhxG3dzdBoY4gtCMYXV8C0eK6KZ1f0bIyJDo6WsfElPzquEII4ck2HDrLpTQLQyRALYQQJUYptUtrHV3Kr9kFmK617u98/C8ArfVLmfZZ49znR6WUF/AnUFPnM+CXMbQQQoiSprUmMf4Ev+/eiTXjCh3vvAcvb7kIJURFk98Y2i0Z1IW4PbEH8B8gEhiutV5e6p0UQogyqE/LWu7ughBCiJIRCpzM9DgeyF6g19hHa21VSl0CgoCEUumhEEIIkQulFMF16xNcVxZxF0LkrtSX0810e+ItQCtghFKqVbbdTgDjgM9Kt3dCCCGEEEKUb0qpCUqpGKVUzPnz593dHSGEEEIIUcGVeoAa6Agc01r/rrXOAJYAd2TeQWsdp7XeC9jd0D8hhBBCCCE8zSmgbqbHYc7nct3HWeIjEMdiiVloredoraO11tE1a9Ysoe4KIYQQQghROO4IUOd2e2KoG/ohhBBCCCFEWbETaKqUaqiU8gGGA99k2+cbYKzz57uBjfnVnxZCCCGEEMITuKUGdXFRSk0AJgDUq1fPzb0RQgghhBCiZDhrSk8B1uBYx+VDrfUBpdRMIEZr/Q0wH1iolDoGXMARxBZCCCGEEMKjuSNAXZjbEwtFaz0HmAOOFcivv2tCCCGEEEJ4Jq31SmBltueey/RzOjC0tPslhBBCCCHE9XBHiY/C3J4ohBBCCCGEEEIIIYQQopwr9QC11toKuG5PPAQsc92eqJQaBKCU6qCUiseRAfKBUupAafdTCCGEEEIIIYQQQgghRMlySw3qQtyeuBNH6Q8hhBBCCCGEEEIIIYQQ5ZQ7SnwIIYQQQgghhBBCCCGEEBKgFkIIIYQQQgghhBBCCOEeEqAWQgghhBBCCCGEEEII4RYSoBZCCCGEEEIIIYQQQgjhFhKgFkIIIYQQQgghhBBCCOEWEqAWQgghhBBCCCGEEEII4RYSoBZCCCGEEEIIIYQQQgjhFkpr7e4+FAul1HngDze9fDCQUIbaLattS59Lp23pc+m0XRb7XJJtS59Lp23pc9lvW/pcem3np77WuqYbXrfYyRi6QrQtfS6dtqXPZb9t6XPptC19Lp22y2KfS7Jt6XPptZ2fPMfQ5SZA7U5KqRitdXRZabesti19Lp22pc+l03ZZ7HNJti19Lp22pc9lv23pc+m1LUqe/Jsrnbalz6XTtvS57LctfS6dtqXPpdN2WexzSbYtfS69tq+VlPgQQgghhBBCCCGEEEII4RYSoBZCCCGEEEIIIYQQQgjhFhKgLh5zyli7ZbVt6XPptC19Lp22y2KfS7Jt6XPptC19LvttS59Lr21R8uTfXOm0LX0unbalz2W/belz6bQtfS6dtstin0uybelz6bV9TaQGtRBCCCGEEEIIIYQQQgi3kAxqIYQQQgghhBBCCCGEEG4hAWohhBBCCCGEEEIIIYQQbiEBaiGEEEIIIYQQQgghhBBuIQFqIYQQQgghhBBCCCGEEG4hAWohhBBCCCGEEEIIIYQQbiEBaiGEEEIIIYQQQgghhBBuIQFqIYQQQgghhBBCCCGEEG4hAWohhChGSqkFSqkXi7nN7kqpX4uzzfJCKaWVUk3c3Q8hhBBCiIpMxsBXXW+/M49vlVLvK6WeLcQx9ZRSyUopcx7bpyulPr3WPuXVPyGEKC4SoBZCCA+ntd6mtW7u7n4UlVKqp1IqvqT2F0IIIYQQ5VdZHQMXZ7+11hO11i8UYr8TWusArbWtOF5XCCFKmwSohRBCCCGEEEIIIYQQQriFBKiFEOI6KKXaKqV2K6WSlFJLgUqZtj2glDqmlLqglPpGKVUn0zatlHpIKXXUeewLSqnGSqkflFKXlVLLlFI+zn2zZBYrpeKUUo8ppfYqpS4ppZYqpSo5t1VXSn2rlDqvlPrL+XNYpmM3O1/re+frrlVKBWfafqOzDxeVUieVUuOcz/sqpWYrpU4opc46bzesnM/74g+sAuo4bzdMVkrVcbbzH6XUaeef/zify2v/jkqpH539OaOUesf1vgghhBBCCPeQMXC+702h++3cPs05zj2tlLovW1tG6RSl1CGl1G2Ztnk5z7edUqqB8731cm5rqJTa4jzXdUDmc81x16Kzjzc7f5bxtxCi1EmAWgghrpFzoPY1sBCoAXwO3OXc1ht4CbgHqA38ASzJ1kR/oD3QGXgcmAP8DagLtAZG5PPy9wADgIZAJDDO+bwJ+AioD9QD0oB3sh07ErgXCAF8gMecfa6PI0j8NlATiAJince8DDRzPtcECAWey6tzWusU4BbgtPN2wwCt9Wngaef5RgFtgI7AM/nsbwOm4hhUdwH6AA/l874IIYQQQogSJGPgvMfARe23UmqAsx99gabAzfm0sZis701/IEFrvTuXfT8DduEYQ78AjC1CX2X8LYQodRKgFkKIa9cZ8Ab+o7W2aK2XAzud20YBH2qtd2utrwD/AroopRpkOv5VrfVlrfUBYD+wVmv9u9b6Eo5Bctt8XvstrfVprfUF4H84Bs1orRO11l9orVO11knALOCmbMd+pLU+orVOA5a5jsUxaF+vtV7sPJ9ErXWsUkoBE4CpWusLznb/DQwv4vvlel9maq3Paa3PAzOA0XntrLXepbX+SWtt1VrHAR/kcj5CCCGEEKL0yBi46HLtN47A9Uda6/3OhI3p+bTxGTBIKeWXqd+Ls++klKoHdACe1Vpf0Vpvdb5mocj4WwjhDl7u7oAQQpRhdYBTWmud6bk/Mm0zshm01slKqUQcWRdxzqfPZjouLZfHN+Tz2n9m+jnV+Xo4B6xv4MjQqO7cXkUpZc60aEr2YwOcP9cFfsvltWoCfsAuxzgdAAXkukp4Aepw9T3C+XOdPPZFKdUMeB2IdvbBC0c2iBBCCCGEcA8ZAxddrv12/p15bJt5nJyF1vqYUuoQcLtS6n/AIHIP5tcB/nIGvDO3W7cwHZXxtxDCHSSDWgghrt0ZIFRlGrHiuKUQ4DSOWwwBoyZzEHCqhPv0KNAc6KS1rgr0cHWhEMeeBBrn8nwCjslCuNa6mvNPoNY6IJd9M9O5PJflfcHxfp3OZ///AoeBps7zeYrCnYsQQgghhCgZMgYuPmfIGjiul9eOTq4yH3cAB7XWx/Jos7rzvc+t3RQcgWcAlFJmHMF4Fxl/CyFKnQSohRDi2v0IWIGHlVLeSqkhOGoqg2PweK9SKkop5YvjdsCfnbfJlaQqOAbSF5VSNYDni3DsIuBmpdQ9zkVXgpRSUVprOzAXeEMpFQKglApVSvUvoL2zQJBSKjDTc4uBZ5RSNZ0L0zwHfJrP/lWAy0CyUqoFMKkI5yOEEEIIIYqfjIGLzzJgnFKqlTMLvKB+LwH64RgTf5bbDlrrP4AYYIZSykcpdSNwe6ZdjgCVlFK3KqW8gWcA30zbZfwthCh1EqAWQohrpLXOAIbgWOTkAjAM+NK5bT3wLPAFjiyGxlxbvbqi+g9QGUfGx0/A6sIeqLU+AQzEkYFyAcfiMG2cm58AjgE/KaUuA+txZKnk195hHJOU352rgNcBXsQxYN4L7MNxC+iL+ez/GI76ekk4JghLC3s+QgghhBCi+MkYOP8xcFForVfh6PtG5+tsLGD/MzguEHQl/3HxSKATjvN5HvgkUxuXcCx6OA9HZnsKEJ/pWBl/CyFKncpaNkoIIYQQQgghhBBCCCGEKB2SQS2EEEIIIYQQQgghhBDCLSRALYQQ4poppZ5SSiXn8meVu/smhBBCCCFESZAxsBBCFC8p8SGEEEIIIYQQQgghhBDCLSSDWgghhBBCCCGEEEIIIYRbeLm7A8UlODhYN2jQwN3dEEIIIYQQ5dyuXbsStNY13d2P4iBjaCGEEEIIURryG0OXmwB1gwYNiImJcXc3hBBCCCFEOaeU+sPdfSguMoYWQgghhBClIb8xtJT4EEIIIYQQQgghhBBCCOEWEqAWQgghhBBCCCGEEEII4RYSoBZCCCGEEEIIIYQQQgjhFuWmBnVuLBYL8fHxpKenu7srQpQplSpVIiwsDG9vb3d3RQghhBBCiApH5rJCXBuZywpRNpXrAHV8fDxVqlShQYMGKKXc3R0hygStNYmJicTHx9OwYUN3d0cIIYQQQogKR+ayQhSdzGWFKLvKdYmP9PR0goKC5AtdiCJQShEUFCTZGkIIIYQQQriJzGWFKDqZywpRdpXrADUgX+hCXAP5vRFCCCGEEMK9ZEwuRNHJ740QZVO5D1CL4mE2m4mKiqJ169YMHTqU1NTUa25r3LhxLF++HIDx48dz8ODBPPfdvHkzP/zwwzW/lhBCCCGEEEKIikvmskII4fkkQC0KpXLlysTGxrJ//358fHx4//33s2y3Wq3X1O68efNo1apVntuv5Uv9WvsihBBCCCGEEKJ8kbmsEEJ4PglQiyLr3r07x44dY/PmzXTv3p1BgwbRqlUrbDYb06ZNo0OHDkRGRvLBBx8AjoUKpkyZQvPmzbn55ps5d+6c0VbPnj2JiYkBYPXq1bRr1442bdrQp08f4uLieP/993njjTeIiopi27ZtxMXF0bt3byIjI+nTpw8nTpwAHFeyJ06cSKdOnXj88cdL/00RQgghhBBCCOHRZC4rhBCeycvdHRBli9VqZdWqVQwYMACA3bt3s3//fho2bMicOXMIDAxk586dXLlyhW7dutGvXz9++eUXfv31Vw4ePMjZs2dp1aoV9913X5Z2z58/zwMPPMDWrVtp2LAhFy5coEaNGkycOJGAgAAee+wxAG6//XbGjh3L2LFj+fDDD3n44Yf5+uuvAcdK1z/88ANms7lU3xMhhBBCCCGEEJ5N5rJCCOG5JINaFEpaWhpRUVFER0dTr1497r//fgA6duxIw4YNAVi7di2ffPIJUVFRdOrUicTERI4ePcrWrVsZMWIEZrOZOnXq0Lt37xzt//TTT/To0cNoq0aNGrn248cff2TkyJEAjB49mu3btxvbhg4dKl/oQgghhCi3lFIDlFK/KqWOKaWezGX7G0qpWOefI0qpi27ophBCeBSZywohhOeTDGpRKK66Xdn5+/sbP2utefvtt+nfv3+WfVauXFnS3cvRFyGEEEKI8kQpZQbeBfoC8cBOpdQ3WmtjhS6t9dRM+/8daFvqHRVCCA8jc1khhPB8kkEtik3//v3573//i8ViAeDIkSOkpKTQo0cPli5dis1m48yZM2zatCnHsZ07d2br1q0cP34cgAsXLgBQpUoVkpKSjP26du3KkiVLAFi0aBHdu3cv6dMSQgghhPAEHYFjWuvftdYZwBLgjnz2HwEsLpWeCSFEGSdzWSGEcC/JoBbFZvz48cTFxdGuXTu01tSsWZOvv/6awYMHs3HjRlq1akW9evXo0qVLjmNr1qzJnDlzGDJkCHa7nZCQENatW8ftt9/O3XffzYoVK3j77bd5++23uffee3nttdeoWbMmH330kRvOVAghRGmwXb7CxW9/p/pdzTD5ym2vosILBU5mehwPdMptR6VUfaAhsDGP7ROACQD16tUr3l4KIUQZJHNZIYQnST92kfRfL1Dt1kbu7kqpUVprd/ehWERHR2vXCrouhw4domXLlm7qkRBlm/z+CCHcLTX2HBeW/ErNByPxbRjo7u4IYVBK7dJaR5fya94NDNBaj3c+Hg100lpPyWXfJ4AwrfXfC2o3tzG0EML9ZCwuxLWT3x9R1v319TFSfj5D6KwbUSbl7u4Um/zG0FLiQwghhBAeyZbiuM3W7vxbiAruFFA30+Mw53O5GY6U9xBCCCGEKJPsqRbQoK/Y3N2VUiMBaiGEEEJ4JFdg2pYqAWohgJ1AU6VUQ6WUD44g9DfZd1JKtQCqAz+Wcv+EEEIIIUQxsKdZHX9XoHmQBKiFEEII4ZHsqc6BWYrVzT0Rwv201lZgCrAGOAQs01ofUErNVEoNyrTrcGCJLi91/IQQQgghKhgjQJ1WceZBskiiEEIIITySK2NASnwI4aC1XgmszPbcc9keTy/NPgkhhBBCiOJVEQPUkkEthBBCCI9klxrUQgghhBBCiApGS4BaCCGEEMIzGCU+KlDtNSGEEEIIIUTFpe1aMqhFyWjQoAERERFERUURHR0NwLhx41i+fHmW/QICAti3bx9RUVFERUVRo0YNGjZsSFRUFDfffDOxsbF06dKF8PBwIiMjWbp0abH07/333+eTTz4p9P5xcXG0bt06123jx4/n4MGDAAwYMIA2bdoQHh7OxIkTsdmurj769ttv06JFC8LDw3n88cdzbWvatGm0aNGCyMhIBg8ezMWLFwHIyMjg3nvvJSIigjZt2rB58+Zcj1+wYAFTpkwp9HmB47NKSEgAoGvXrkU69nrkda4AL730Ek2aNKF58+asWbPGeP6+++4jJCQkx2cxbNgw499QgwYNiIqKKqWzEEKI4mUskigZ1EIIIYRbyFxW5rIFkbmsEMVLX7GBcyWRihSglhrUpWTTpk0EBwcXuF9ERASxsbGA44v/tttu4+677wbgyJEjfPLJJzRt2pTTp0/Tvn17+vfvT7Vq1a6rbxMnTryu4zObN2+e8fOyZcuoWrUqWmvuvvtuPv/8c4YPH86mTZtYsWIFe/bswdfXl3PnzuXaVt++fXnppZfw8vLiiSee4KWXXuKVV15h7ty5AOzbt49z585xyy23sHPnTkym4r3e8sMPPxRre/nJ61wPHjzIkiVLOHDgAKdPn+bmm2/myJEjmM1mxo0bx5QpUxgzZkyWtjIP9h599FECAwNL7TyEEKK4aK2xuWpQp1acgZkQQgjhaWQuK3PZ/MhcVojilfnu0YoUoJYM6jKkWbNmNG3aFIA6deoQEhLC+fPnAZg5cyYdOnSgdevWTJgwgbwWbv/kk0+IjIykTZs2jB49GoDp06cze/ZsAHr27MnUqVOJjo6mZcuW7Ny5kyFDhtC0aVOeeeYZox2r1cqoUaNo2bIld999N6mpqcbxMTExAFStWtXYNyMjA6UUAP/973958skn8fX1BSAkJCTXvvbr1w8vL8c1lM6dOxMfHw/AwYMH6d27t3FstWrVjNfM7uTJk/Ts2ZOmTZsyY8YM4/lPP/2Ujh07EhUVxYMPPpjlirhLQEAAAMOHD+e7774znndlDNhsNqZNm0aHDh2IjIzkgw8+ACA5OZk+ffrQrl07IiIiWLFiBZDzav3s2bOZPn16vue6YsUKhg8fjq+vLw0bNqRJkybs2LEDgB49elCjRo1czxscwZ1ly5YxYsSIPPcRQghPpS12sGpQYE+WDGohhBCiLJO5rMxlZS4rROFkDkprCVCL4qSUol+/frRv3545c+YYz0+bNs24faWot67s2LGDjIwMGjduDMCUKVPYuXMn+/fvJy0tjW+//TbHMQcOHODFF19k48aN7NmzhzfffDPXtn18fIiJiWHixInccccdvPvuu+zfv58FCxaQmJgIwK+//spDDz3EoUOHqFq1Ku+9916ubfXv35+QkBCqVKmS5er5tm3b6NSpEzfddBM7d+4s8Hw//PBDbrnlFgDatGnDN998g9Vq5fjx4+zatYuTJ0/m+T598cUX7N27l88//5yYmBgOHTrE0qVL+f7774mNjcVsNrNo0aI8X3vYsGEsW7YMcNyStWHDBm699Vbmz59PYGAgO3fuZOfOncydO5fjx49TqVIlvvrqK3bv3s2mTZt49NFH8xxkFXSup06dom7dusa2sLAwTp06Vah2tm3bRq1atYyBoBBClCWu8h7m6pXQGTZHwFoIIYQQpUrmsjKXlbmsEKUrc4C6Iq3FU2FKfMz43wEOnr5crG22qlOV528PL3C/7du3Exoayrlz5+jbty8tWrQA4LXXXjO+6ODqVc6CnDlzhtGjR/Pxxx8btwJt2rSJV199ldTUVC5cuEB4eDi33357luM2btzI0KFDjduz8rpiOWjQIMBxi1Z4eDi1a9cGoFGjRpw8eZJq1apRt25dunXrBsDf/vY33nrrLR577LEcba1Zs4b09HRGjRrFxo0b6du3L1arlQsXLvDTTz+xc+dO7rnnHn7//XfjqnR2s2bNwsvLi1GjRgGOelWHDh0iOjqa+vXr07VrV8xmc67H9u3bl6CgIACGDBnC9u3b8fLyYteuXXTo0AGAtLS0PK98A9xyyy088sgjXLlyhdWrV9OjRw8qV67M2rVr2bt3r1F/7dKlSxw9epSwsDCeeuoptm7dislk4tSpU5w9ezbP9vM71+uxePFiueIshCizXAFq75qVsV1Ix55qwRzo6+ZeCSGEEKVP5rIyl5W5rBAVhytArXzNFarER4UJULtTaGgo4LiFZ/DgwcZtLdfi8uXL3HrrrcyaNYvOnTsDkJ6ezkMPPURMTAx169Zl+vTppKenc/LkSeOLvSi1uVy3K5lMJuNn12Or1fmLku0LOK8vZIBKlSpxxx13sGLFCvr27UtYWBhDhgxBKUXHjh0xmUwkJCTw+OOP88svv1CnTh1WrlwJOBaH+Pbbb9mwYYPxGl5eXrzxxhtG+127dqVZs2Z89dVXxq1PrvphufVTa83YsWN56aWXCvV+VKpUiZ49e7JmzRqWLl3K8OHDAcdtR2+//Tb9+/fPsv+CBQs4f/48u3btwtvbmwYNGpCeno6Xlxd2+9UMwPT09BzHZT/X0NDQLFfU4+PjjX9P+bFarXz55Zfs2rWrUOcohBCexlV32qumH/z6F7YUCVALIYQQpU3msjKXlbmsEKXLmAcFVZIAdXlUmKvDJSElJQW73U6VKlVISUlh7dq1PPfcc+zfv7/IbWVkZDB48GDGjBmT5Wq168shODiY5ORkli9fzt13303dunWNRSrAcVvU4MGD+ec//0lQUBAXLlzIt+5Tfk6cOMGPP/5Ily5d+Oyzz7jxxhuzbE9OTiYpKYnatWtjtVr57rvv6N69OwB33nknmzZtolevXhw5coSMjAyCg4P56KOPsrSxevVqXn31VbZs2YKfn5/xfGpqKlpr/P39WbduHV5eXrRq1YpWrVoxePBgY7/9+/ezbt06Lly4QOXKlfn666/58MMP8fPz44477mDq1KmEhIRw4cIFkpKSqF+/fp7nO2zYMObNm0dMTAwLFiwAHLd8/fe//6V37954e3tz5MgRQkNDuXTpEiEhIXh7e7Np0yb++OMPAGrVqsW5c+dITEwkICCAb7/9lgEDBuR7roMGDWLkyJH885//5PTp0xw9epSOHTsW+PmsX7+eFi1aEBYWVuC+QgjhiVy3s3mFVHY8Tqk4t7cJIYQQmclcVuayMpcVouJwBaW9giqTcTLJzb0pPRUmQO0uZ8+eNb5orFYrI0eOZMCAASxZsqTIbS1btoytW7eSmJhofLEsWLCAqKgoHnjgAVq3bs0NN9xg3O6TXXh4OE8//TQ33XQTZrOZtm3bGu0UVfPmzXn33Xe57777aNWqFZMmTcqyPSUlhUGDBnHlyhXsdju9evUyrnzfd9993HfffbRu3RofHx8+/vjjXK9aT5kyhStXrtC3b1/AseDC+++/z7lz5+jfvz8mk4nQ0FAWLlyYZz87duzIXXfdRXx8PH/729+Ijo4G4MUXX6Rfv37Y7Xa8vb1599138/1S79evH6NHj+aOO+7Ax8cHgPHjxxMXF0e7du3QWlOzZk2+/vprRo0axe23305ERATR0dHGbXDe3t4899xzdOzYkdDQUOP5/M41PDyce+65h1atWuHl5cW7775r3AI2YsQINm/eTEJCAmFhYcyYMYP7778fgCVLlsgtUUKIMs3mKvER7JjoVKT6a0IIIYQnkLmszGVlLitE6bOnWcDLhLmKT4XKoFZFKXjvyaKjo3X21W8PHTpEy5Yt3dQjIco2+f0RQrjTpXV/kLTxBLWf6sSZWT9TbVBjArrWcXe3hABAKbVLax3t7n4Uh9zG0EII95OxuBDXTn5/RFl2YfkR0n/9C/+ON5C04QSh/74RZcq7FFFZkt8Y2lTanRFCCCGEKIg9xYKpshcmP29QVzOqhRBCCCGEEKK8sqdZMfl5YfLzMh5XBBKgFkIIIYTHsadaMPl5o8wKU2UvKfEhhBBCCCGEKPd0mtWRqFNZAtRCCCGEEG5lT7Vi8vcGwOTnLYskCiGEEEIIIco9e2rWALWWALUQQgghhHvYUyzGbW0mfwlQCyGEEEIIIco/u2RQCyGEEEJ4BkeA2pVB7YU9pWIMzIQQQgghhLgeloQ0zv5nF7bkDHd3RVwDe5pjHuSaC9nTKkaijgSohRBCCOFRtNbYMpf48PfGJjWohRBCCCGEKJDlZBKWP1Ox/Jnq7q6IItJWOzrDnjWDOrViJOpIgLqEKaV49NFHjcezZ89m+vTpAEyfPh2lFMeOHTO2/+c//0EpRUxMDAANGjQgIiKCiIgIWrVqxTPPPEN6enqW1/jHP/5BaGgodru92Pq9detW2rVrh5eXF8uXLzee37RpE1FRUcafSpUq8fXXXwOwceNG2rVrR+vWrRk7dixWa+6/RKNGjaJ58+a0bt2a++67D4vFEXT466+/GDx4MJGRkXTs2JH9+/cX2/k0aNCAhISEIh93+vRp7r777nz3yet8tNY8/PDDNGnShMjISHbv3g1AbGwsXbp0ITw8nMjISJYuXWq09c4779CkSROUUtfUXyGEKA+0xQ5WO2Z/x6DM7CzxobV2c8+EEEKIikPmsjnJXFbmsmWBzVkaTxYZL3tc5TxMflLiQxQzX19fvvzyyzz/g46IiGDJkiXG488//5zw8PAs+2zatIl9+/axY8cOfv/9dx588EFjm91u56uvvqJu3bps2bKl2Ppdr149FixYwMiRI7M836tXL2JjY4mNjWXjxo34+fnRr18/7HY7Y8eOZcmSJezfv5/69evz8ccf59r2qFGjOHz4MPv27SMtLY158+YB8O9//5uoqCj27t3LJ598wiOPPFJs53Ot6tSpk2VQk5u8zmfVqlUcPXqUo0ePMmfOHCZNmgSAn58fn3zyCQcOHGD16tX84x//4OLFiwB069aN9evXU79+/RI9LyGE8GSuetNGiQ9/b7Bp9BWbO7slhBBCVCgyl81J5rIyly0LXGNpWcOl7DEC1JW9UF4mlLdJAtSieHh5eTFhwgTeeOONXLffeeedrFixAoDffvuNwMBAgoODc903ICCA999/n6+//poLFy4AsHnzZsLDw5k0aRKLFy/Osx8vvPACzZs358Ybb2TEiBHMnj3beM0BAwbQvn17unfvzuHDhwHHVdrIyEhMprz/iSxfvpxbbrkFPz8/EhMT8fHxoVmzZgD07duXL774ItfjBg4ciFIKpRQdO3YkPj4egIMHD9K7d28AWrRoQVxcHGfPns1x/Pnz5+nbty/h4eGMHz+e+vXrG4OmO++8k/bt2xMeHs6cOXNyff3XX3+d1q1b07p1a/7zn/8A8OSTT/Luu+8a+0yfPp3Zs2cTFxdH69atAbDZbDz22GO0bt2ayMhI3n777XzPZ8WKFYwZMwalFJ07d+bixYucOXOGZs2a0bRpU8AxaAgJCeH8+fMAtG3blgYNGuT5ngshREXguo3tag1qZ/01GWQLIYQQpUbmsjnJXFbmsmWBEaCuIKUhypPMAWrX3xXlc5QAdSmYPHkyixYt4tKlSzm2Va1albp167J//36WLFnCsGHD8m2ratWqNGzYkKNHjwKwePFiRowYweDBg/nuu++MW3Iy27lzJ1988QV79uxh1apVxi1XABMmTODtt99m165dzJ49m4ceeqjQ57VkyRJGjBgBQHBwMFar1Wh7+fLlnDx5Mt/jLRYLCxcuZMCAAQC0adOGL7/8EoAdO3bwxx9/GF+Qmc2YMYPevXtz4MAB7r77bk6cOGFs+/DDD9m1axcxMTG89dZbJCYmZjl2165dfPTRR/z888/89NNPzJ07l19++YVhw4axbNkyY79ly5bl+CzmzJlDXFwcsbGx7N27l1GjRuV7PqdOnaJu3brG9rCwME6dOpXlmB07dpCRkUHjxo3zfa+EEKIiMTKonSU+TAHOAHUFGZyVJ9aENNIOyG2+QghRVslcNncyl5W5rCeTDOqyyxWgVs4AtarsVWEyqL3c3YFSs+pJ+HNf8bZ5QwTc8nKBu1WtWpUxY8bw1ltvUbly5Rzbhw8fzpIlS1izZg0bNmzgo48+yrc9Vw3OjIwMVq5cyeuvv06VKlXo1KkTa9as4bbbbsuy//fff88dd9xBpUqVqFSpErfffjsAycnJ/PDDDwwdOtTY98qVKwWeD8CZM2fYt28f/fv3Bxz1yZYsWcLUqVO5cuUK/fr1w2w259vGQw89RI8ePejevTvguPL7yCOPEBUVRUREBG3bts21je3bt/PVV18BMGDAAKpXr25se+utt4xtJ0+e5OjRowQFBWU5dvDgwfj7+wMwZMgQtm3bxsMPP8y5c+c4ffo058+fp3r16tStW5e4uDjj2PXr1zNx4kS8vBy/NjVq1Mj3fArzHo4ePZqPP/4436v7QghR0bjq5V3NoHb8v2uTQXaZk/T9KVJ2niX0hSCUUu7ujhBClE0yl5W5LDKXFYUnNajLrhzzIAlQFw+l1ADgTcAMzNNav5xt+0RgMmADkoEJWuuDzm3/Au53bntYa72mJPta0v7xj3/Qrl077r333hzbbrvtNqZNm0Z0dDRVq1bNt52kpCTi4uJo1qwZa9as4eLFi0RERACQmppK5cqVadOmjfHFPXHixDzbstvtVKtWjdjY2CKfz7Jlyxg8eDDe3t7Gc126dGHbtm0ArF27liNHjgDQv39/zp49S3R0tFHTasaMGZw/f54PPvjAOL5q1arGgEZrTcOGDWnUqBHvvvsuc+fOBWDlypV59mnz5s2sX7+eH3/8ET8/P3r27JljEY78DB06lOXLl/Pnn38WePU/u9zOJzQ0NMuV9/j4eEJDQwG4fPkyt956K7NmzaJz585Fei0hhCjvrmZQO75jzP5S4qOssqdYwGpHX7GhKlWcvAghhChPZC4rc1mZy5YtriCnTe4+LHNylPjw88Z2ofD/F5RlJTZTUEqZgXeBvkA8sFMp9Y0rAO30mdb6fef+g4DXgQFKqVbAcCAcqAOsV0o101pf++pIhbg6XJJq1KjBPffcw/z587nvvvuybPPz8+OVV14xal7lJTk5mYceeog777yT6tWrs3jxYubNm2fcmpSSkkLDhg2ZO3duli/qnTt38uCDD/Kvf/0Lq9XKt99+y4QJE4xbrD7//HOGDh2K1pq9e/fSpk2bAs9n8eLFvPTSS1meO3fuHCEhIVy5coVXXnmFp59+GoA1a7JeW5g3b55xhT3z1daLFy/i5+eHj48P8+bNo0ePHlStWpXJkyczefJkY79u3bqxbNkynnjiCdauXctff/0FwKVLl6hevTp+fn4cPnyYn376KUe/u3fvzrhx43jyySfRWvPVV1+xcOFCAIYNG8YDDzxAQkJCrot09O3blw8++IBevXrh5eXFhQsXqFGjRp7nM2jQIN555x2GDx/Ozz//TGBgILVr1yYjI4PBgwczZsyYAldVFkKIisiWagWVaWAmAeoyy1WWxZ5swSQBaiGEuDYyl5W5LDKXFYUnJT7KLmMtnkpXa1Bb0irG51iS92J0BI5prX/XWmcAS4A7Mu+gtb6c6aE/oJ0/3wEs0Vpf0VofB4452yvTHn300TxXQB4+fDjt2rXLdVuvXr1o3bo1HTt2pF69enzwwQekpqayevVqbr31VmM/f39/brzxRv73v/9lOb5Dhw4MGjSIyMhIbrnlFiIiIggMDARg0aJFzJ8/nzZt2hAeHm4scrFz507CwsL4/PPPefDBB7OsxhwXF8fJkye56aabsrzOa6+9RsuWLYmMjOT22283FonIbuLEiZw9e5YuXboQFRXFzJkzATh06BCtW7emefPmrFq1ijfffDPX459//nnWrl1L69at+fzzz7nhhhuoUqUKAwYMwGq10rJlS5588slcr+a2a9eOcePG0bFjRzp16sT48eNp27YtAOHh4SQlJREaGkrt2rVzHDt+/Hjq1atHZGQkbdq04bPPPsv3fAYOHEijRo1o0qQJDzzwAO+99x7guGK/detWFixYQFRUFFFRUcYg7K233iIsLIz4+HgiIyMZP358ru+BEEKUZ/YUi2PlapOjJITyNYNZyW2KZZBrYiTlWYQQomyTuayDzGVlLuvptF1fTRCQsXOZo9OsKF8zyuyYB1WkEh/KVQOq2BtW6m5ggNZ6vPPxaKCT1npKtv0mA/8EfIDeWuujSql3gJ+01p8695kPrNJaL8/r9aKjo3XmBRPA8SXRsmXL4jytMis5OZmAgABSU1Pp0aMHc+bMyXMQ4emuXLmC2WzGy8uLH3/8kUmTJl3TrV0if/L7I4Rwl8TPDmE5ncINj0Ubz52e9TOVW9Sg+l1N3dgzUVRnXtqB7dIVgka3pHJ4sLu7U2yUUru01tEF7+n5chtDCyHcT8biV8lcVhRVRf79saVYOPPCT2BWKLOJ0Jld3d0lUQQXlv7KlbhL1H7CkaN7ecMJLq/7g9BZ3VDmsl/vPb8xtNvvtdRavwu8q5QaCTwDjC3ssUqpCcAEgHr16pVMB8uJCRMmcPDgQdLT0xk7dmyZ/UIHOHHiBPfccw92ux0fHx+jppcQQojywZ5qNcp6uJj9vSQLtwwyaiAmy2cnhBDi2shcVojCc9295hVcGevZVLTVjvIq+4HNisKeZjXKHMLVxeLtaVbMAT7u6lapKMkA9SmgbqbHYc7n8rIE+G9RjtVazwHmgCP743o6W965buEpD5o2bcovv/zi7m4IIYQoIfYUC+ZqvlmeM/l5Sx29MkZb7GiLHXDUoBZCCCGuhcxlhSg813jZu6YjQG1PtWCu6lvAUcJT2FMtmPyuJuq4gtX21PIfoC7Jyyg7gaZKqYZKKR8cix5+k3kHpVTm+3RvBY46f/4GGK6U8lVKNQSaAjtKsK9CCCGE8BDZB2bgWChR6uiVLZk/L7m4IIQQQghR8owM6pp+jsepFaN+cXmRPYNaVb6aQV3elVgGtdbaqpSaAqwBzMCHWusDSqmZQIzW+htgilLqZsAC/IWzvIdzv2XAQcAKTNZa20qqr0IIIYTwDFprbCk5S3yY/CWDuqyxZZoQ2ZIz3NgTIYQQQoiKwVUSzyvEz3jsnd8BwqPkKPEhAeriobVeCazM9txzmX5+JJ9jZwGzSq53QgghhPA02mIHq92ot+Zi8vfGnmZF2zXKpNzUO1EURga1khIfQgghhBClwSjxEVw5y2Ph+bTWFTpALZXShRBCCOExXEFNc/ZFEv28QCNlPsoQ47OsUUkWuBRCCCGEKAX2FAvKx4w50FF3Wkp8lB06ww42nSVRxxWg1hVgDiQBag+xevVqOnbsSIsWLYiKimLYsGGcOHECgHHjxrF8+fJ8j1+wYAE1a9YkKiqKFi1a8MYbb+S6LSoqinnz5hWpb9OnT2f27NlFOiYjI4MJEybQrFkzWrRowRdffAE4Vi3u1asXbdu2JTIykpUrHQn2FouFsWPHEhERQcuWLXnppZdybfe1114zzqN169aYzWYuXLhAeno6HTt2pE2bNoSHh/P8888Xqb/F6eLFi7z33nvG49jYWLp06UJ4eDiRkZEsXbrU2Hb8+HE6depEkyZNGDZsGBkZjlugt27dSrt27fDy8srx2T/xxBO0bt2a1q1bZ2lLCCHKA3uKYxCdWw1qkEF2WeL6rLxr+kkGtRBClGMyl5W5rMxlPYc9xYIpwNsIckoGddnhypJWkkEt3GX//v38/e9/5+OPP+bw4cPExsYyatQo4uLiitTOsGHDiI2N5fvvv2fWrFmcPHkyx7bY2FjGjx9fzGeQ06xZswgJCeHIkSMcPHiQm266CYAXX3yRe+65h19++YUlS5bw0EMPAfD5559z5coV9u3bx65du/jggw9yPf9p06YZ5/HSSy9x0003UaNGDXx9fdm4cSN79uwhNjaW1atX89NPP5X4eWZntVpzfKn7+fnxySefcODAAVavXs0//vEPLl68CDi+oKdOncqxY8eoXr068+fPB6BevXosWLCAkSNHZmn/u+++Y/fu3cTGxvLzzz8ze/ZsLl++XGrnJ4QQJc01iDb55yzxkXm78HyuDGqvED/sqRa0Xbu5R0IIIYqbzGVlLitzWc9iS7Vi8vNCeZlQvma5+7AMcX1WpspXE3WU2fk5SoBaXK+4uDhatGjBuHHjaNasGaNGjWL9+vV069aNpk2bsmPHDl555RWeeuopWrZsaRw3aNAgevTokaO9Bg0akJCQAEBMTAw9e/bMsU9QUBBNmjThzJkzRerr//73Pzp16kTbtm25+eabOXv2rLFtz549dOnShaZNmzJ37lwANm/eTI8ePbj11ltp3rw5EydOxG63A/Dhhx/yr3/9CwCTyURwcDAASinjS+jSpUvUqVPHeD4lJQWr1UpaWho+Pj5UrVo13/4uXryYESNGGMcHBAQAjivYFosFpXLWKD1z5gw9evQwrlpv27YNgICAAKZNm0Z4eDg333wzO3bsoGfPnjRq1IhvvvkGcHyW3bt3p127drRr144ffvjBeB+6d+/OoEGDaNWqFU8++SS//fYbUVFRTJs2jWbNmtG0aVMA6tSpQ0hICOfPn0drzcaNG7n77rsBGDt2LF9//TXg+JwjIyMxmbL+ih48eJAePXrg5eWFv78/kZGRrF69Ot/3SQghyhJjYJZXBrUEqMsMe4oV5W3Cq7qvlGcRQogySOayMpeVuWzZY0+xGKXyTH5ecvdhGeIKQmeuQe16LAFqUSyOHTvGo48+yuHDhzl8+DCfffYZ27dvZ/bs2fz73//mwIEDtGvXrthe78SJE6SnpxMZGWk898UXXxAZGcndd9+d5Wp0ZjfeeCM//fQTv/zyC8OHD+fVV181tu3du5eNGzfy448/MnPmTE6fPg3Ajh07ePvttzl48CC//fYbX375pXFF9dlnn6Vdu3YMHTrUGCBMnz6dTz/9lLCwMAYOHMjbb78NwN13342/vz+1a9emXr16PPbYY9SoUSPPc0xNTWX16tXcddddxnM2m42oqChCQkLo27cvnTp1ynHcZ599Rv/+/YmNjWXPnj1ERUUBkJKSQu/evTlw4ABVqlThmWeeYd26dXz11Vc895xjXc+QkBDWrVvH7t27Wbp0KQ8//LDR7u7du3nzzTc5cuQIL7/8Mo0bNyY2NpbXXnsty+vv2LGDjIwMGjduTGJiItWqVcPLy/GfT1hYGKdOncrznAHatGnD6tWrSU1NJSEhgU2bNuX5eQohRFl0NYM69wC11DIuO+ypFkx+XlcvLkiZDyGEKHNkLitzWReZy5YN9hSLMfYy+XtLgkAZol0B6uyLxVeQALVXwbuUD6/seIXDFw4Xa5starTgiY5PFLhfw4YNiYiIACA8PJw+ffqglCIiIoK4uLgsVxYTExPp06cPqampTJgwgccee6zQ/Vm6dClbt27l8OHDvPPOO1SqVAmA22+/nREjRuDr68sHH3zA2LFj2bhxY47j4+PjGTZsGGfOnCEjI4OGDRsa2+644w4qV65M5cqV6dWrFzt27KBatWp07NiRRo0aATBixAi2b99Oz549iY+Pp2vXrrz++uu8/vrrPPbYYyxcuJDFixczbtw4Hn30UX788UdGjx7N/v372bFjB2azmdOnT/PXX3/RvXt3br75ZqPt7P73v//RrVu3LF/8ZrOZ2NhYLl68yODBg9m/fz+tW7fOclyHDh247777sFgs3HnnncaXuo+PDwMGDAAgIiICX19fvL29jc8IHFezp0yZQmxsLGazmSNHjhjtduzYMcv7lZszZ84wevRoPv744xxXkwurX79+7Ny5k65du1KzZk26dOmC2Wy+praEEMIT2VKtoHJmDphddfRkkF1m2FOtmPy8MQc4Ly4kW/Au4BghhBA5yVxW5rIgc1lROFkC1H7ektxRhkgGtShxvr6+xs8mk8l4bDKZsFqthIeHs3v3bsBxS1NsbCwTJkwgOTk5R1teXl7GrUfp6elZtg0bNoy9e/fyww8/8OSTT/Lnn38abbpec/z48ezatQuAp59+2likAeDvf/87U6ZMYd++fXzwwQdZ2s9+i5HrcW7PBwUF4efnx5AhQwAYOnSocX7z58/nnnvuAaBLly6kp6eTkJDAZ599xoABA/D29iYkJIRu3boRExPDu+++a/TRdaUbYMmSJcYtUdlVq1aNXr16sXr1an7++Wfj+G+++YYePXqwdetWQkNDGTduHJ988gkA3t7exrnk9hkBvPHGG9SqVYs9e/YQExNjLAIB4O/vn2tfXC5fvsytt97KrFmz6Ny5s/G5XLx40Wg/Pj6e0NDQfNsBx+cWGxvLunXr0FrTrFmzAo8RQoiywp5qwVTZC2XK9v3ibUb5mIxFFIXnMzKoA3wcj1MyCjhCCCGEp5G5rMxlZS5bdtgzbGiL3QhQm6XER5ni+qwkg7qcK8zVYXd5/PHHGTx4MJ07dzZqd6Wmpua6b4MGDdi1axe33HKLsZpwdtHR0YwePZo333yTl156iTNnzlC7dm0AvvnmG+M1Zs2axaxZs4zjLl26ZHypfPzxx1naXLFiBf/6179ISUlh8+bNvPzyyxw5coQdO3Zw/Phx6tevz9KlS5kwYQJKKW6//XY2b95M79692bBhA61atQIcCyZs2LCBcePGcejQIdLT06lZsyb16tVj48aNjB49mpSUFH766Sf+8Y9/cM899zB58uQsfbl06RJbtmzh008/NZ47f/483t7eVKtWjbS0NNatW8cTTzxBp06diI2NNfb7448/CAsL44EHHuDKlSvs3r2bMWPGFPgZuV43LCwMk8nExx9/jM1my3W/KlWqkJSUZDzOyMhg8ODBjBkzxqjRBY4BUK9evVi+fDnDhw/n448/5o477si3DzabjYsXLxIUFMTevXvZu3cv/fr1K1T/hRCiLLCnWHLUn3Yx+XtLDeoyxJ5qxbu2/9XyLFLiQwghronMZWUuCzKXFQVzjZPNmUt8yNi5zLCnWcEEyifrnQWqcsW40CAZ1B4gIiKCN998kzFjxtC8eXO6devGoUOHcqx6C/D888/zyCOPEB0dne/tME888QQfffQRSUlJvPXWW4SHh9OmTRveeustFixYkOsx06dPZ+jQobRv395YCMIlMjKSXr160blzZ5599lljQYgOHTowZcoUWrZsScOGDRk8eDAAr7zyCtOnTycyMpKFCxfyf//3fwD83//9H3PnzqVNmzaMGDGCBQsWoJRi8uTJJCcnEx4eTocOHbj33nuz1B3L7KuvvqJfv35ZrvSeOXOGXr16ERkZSYcOHejbty+33XZbjmM3b95MmzZtaNu2LUuXLuWRRx7J8z3M7qGHHuLjjz+mTZs2HD58OM8rzUFBQXTr1o3WrVszbdo0li1bxtatW1mwYIFxBdw10HjllVd4/fXXadKkCYmJidx///0A7Ny5k7CwMD7//HMefPBBwsPDAcetWd27d6dVq1ZMmDCBTz/91Kj7JYQQ5YHdufJ4bkz+cptiWWJkUFf2ApPUoC4OSqkBSqlflVLHlFJP5rHPPUqpg0qpA0qpz0q7j0KIikXmsjKXlbms58i+lovJzxt9xYa22t3ZLVFI9jQLpsreOe7uMPlVjAxqpbV2dx+KRXR0tI6Jicny3KFDh7KsJiyK1+bNm5k9ezbffvutu7siSoD8/ggh3OHsm7sxV/MleGx4jm3nP9yPPdVCrSlt3dAzURTarjn19Haq9KxLYP8GnJ71E5VbBlF9SFN3d61YKKV2aa2jS/k1zcARoC8QD+wERmitD2bapymwDOittf5LKRWitT6XX7u5jaGFEO4nY/GSJXPZ8q2i/v6k/3qBhI8OUHNSG3zrVyX5p9Nc/Po3aj/dCXMVH3d3TxQg8bNDWE6ncMNjWYeYlzed5PKaOEJf6IryLtt12/MbQ0sGtSg22q6x/pWOtsnVOSGEENfGkXWbe4kPs9ymWGbodCtojM/S7O8jJT6uX0fgmNb6d611BrAEyH4/9QPAu1rrvwAKCk4LIYQQovyw5ZJBDcj4uYzI605S16KJ5T2LWgLU4pr17NkzyxVnnWHDnmLBfiX3elZCCCFEQWwpVkz+eZT48POSRRLLCFu2RV5MAd7Yk2WRxOsUCpzM9Dje+VxmzYBmSqnvlVI/KaUGlFrvhBCiDMk+lxWiPHCNk82u8ZczQC0l8soGe5rVCEZnJgFqIYpI253lYuzlo2yMEEKI0mXPsIHVnvciiQHeaOfq5MKz2VOzZfAESP3wUuIFNAV6AiOAuUqpatl3UkpNUErFKKVizp8/X7o9FEIIIUSJsKdYHIvsOQOarnFYRVhgrzyQALUQxcUZmNYSoBZCCHENXEFN18rj2RlZIKkS6PR09mwZ1GZ/b1kk8fqdAupmehzmfC6zeOAbrbVFa30cR83qHIW/tdZztNbRWuvomjVrlliHhRBCCFF6XKXyXIvsuTKp7TJ2LhPsqVbj4kJmJuNzlAC1EIViBKZtEqAWQghRdK7bEnOrvQZXA9dSR8/zuT4js58rg9rHsYq8RcqAXYedQFOlVEOllA8wHPgm2z5f48ieRikVjKPkx++l2EchhBBCuIkt2WJkTUOmGtQSoPZ42q7R6dZc7ySVDGohisomJT6EEEJcu+xlIbKThV7KjhwZ1AFSA/F6aa2twBRgDXAIWKa1PqCUmqmUGuTcbQ2QqJQ6CGwCpmmtE93TYyGEEEKUJnuqJcudiMrbhPIxyRouZYBOd46dpcSHKClKKf72t78Zj61WKzVr1uS2224DYPr06cyePTvLMQ0aNCAhIaHY+3L48GGioqJo27Ytv/32W5GP//rrrzl48GDeO7hKfOSRQX369GnuvvtuADZv3my8B9mNGjWK5s2b07p1a+677z4sFsdkVmvNww8/TJMmTYiMjGT37t1Zjrt8+TJhYWFMmTIl13YXLFiQ57aiiIuL47PPPrvudopi0aJFREZGEhERQdeuXdmzZ4+xbfXq1TRv3pwmTZrw8ssvG89f6/sohBDu4go851eDGiQLpCywp1pAgaqUrQailPm4LlrrlVrrZlrrxlrrWc7nntNaf+P8WWut/6m1bqW1jtBaL3Fvj4UQZVmFmssWQOay107msqXHnmLJkehh8vOWsXMZYCR35BKgdo2nJUAtrou/vz/79+8nLS0NgHXr1hEamn3B9ZJns9n4+uuvufvuu/nll19o3Lhxkdso6Eu9oEUS69Spw/Llywt8nVGjRnH48GH27dtHWloa8+bNA2DVqlUcPXqUo0ePMmfOHCZNmpTluGeffZYePXoU8myunTu+1Bs2bMiWLVvYt28fzz77LBMmTAAcn+vkyZNZtWoVBw8eZPHixcZndK3voxBCuMvVAHXuJT6M+msS5PR4jhqIXiiTowai6+KCTT47IYQoMyrSXLYgMpe9djKXLT25Bqj9veXuwzLAFXzONUBtUqhKXmgJUIvrNXDgQL777jsAFi9ezIgRI66pneTkZO69914iIiKIjIzkiy++AGDSpElER0cTHh7O888/b+zfoEEDnnjiCdq1a8fSpUv5z3/+w3//+1969eoFwKeffkrHjh2JioriwQcfxGZz1IUMCAjg6aefpk2bNnTu3JmzZ8/yww8/8M033zBt2jSioqL47bffmDt3Lh06dKBNmzbcddddpCalAHD/wxN4+OGH6dq1K40aNTK+yOPi4mjdunWh3i+lFEopOnbsSHx8PAArVqxgzJgxKKXo3LkzFy9e5MyZMwDs2rWLs2fP0q9fv0K9l9999x1dunQhISEh53mkpgIwbty4XM/jySefZNu2bURFRfHGG2+Qnp5ufC5t27Zl06ZNgOMq95AhQxgwYABNmzbl8ccfN15/7dq1dOnShXbt2jF06FCSk5ONz+z555+nXbt2REREcPjwYQC6du1K9erVAejcubPxnuzYsYMmTZrQqFEjfHx8GD58OCtWrLjm91EIIdzJlmoFBabKeWRQV/YG5dxPeDR7atYaeuYAH8fzEqAWQogypcLMZQuYA8pcVuaynk7bNfY0ay4Z1F4ydi4DjAB1Pok65T0TXgLUpWD48OEsWbKE9PR09u7dS6dOna6pnRdeeIHAwED27dvH3r176d27NwCzZs0iJiaGvXv3smXLFvbu3WscExQUxO7duxk5ciQTJ05k6tSpbNq0iUOHDrF06VK+//57YmNjMZvNLFq0CICUlBQ6d+7Mnj176NGjB3PnzqVr164MGjSI1157jdjYWBo3bsyQIUPYuXMne/bsoWXLlnz42ceOF9Vw5swZtm/fzrfffsuTTz55TedrsVhYuHAhAwYMAODUqVPUrXt18fqwsDBOnTqF3W7n0UcfzXF7WV6++uorXn75ZVauXElwcHCO85g/f76xb27n8fLLL9O9e3diY2OZOnUq7777Lkop9u3bx+LFixk7dizp6ekAxMbGsnTpUvbt28fSpUs5efIkCQkJvPjii6xfv57du3cTHR3N66+/brxmcHAwu3fvZtKkSbme0/z587nlllvyfU+u5X0UQgh3s6daMFX2QplVrtuVWWGq7CVZIGWAaxV5F6M8S0qGu7okhBDiGlSUuWxBc8CikrmszGVLmz3VAposNajBmUFdzgOb5YE9zXknaS4Z1K7ny3uJj9zPvBz689//5sqhw8Xapm/LFtzw1FMF7hcZGUlcXByLFy9m4MCBWbYplcckPJfn169fz5IlV0sJuq5CLlu2jDlz5mC1Wjlz5gwHDx4kMjISgGHDhuXa/oYNG9i1axcdOnQAIC0tjZCQEAB8fHyMmlrt27dn3bp1ubaxf/9+nnnmGS5evEhycjI339gbnEGFOwYNwmQy0apVK86ePZv7G1OAhx56iB49etC9e/d893vvvfcYOHAgYWFhBba5ceNGYmJiWLt2LVWrVs31PPr372/sf+eddxZ4Htu3b+fvf/87AC1atKB+/focOXIEgD59+hAYGAhAq1at+OOPP7h48SIHDx6kW7duAGRkZNClSxejvSFDhgCO9/7LL7/M8lqbNm1i/vz5bN++vcBzdSns+yiEEO5mT7HkWX/aRerolQ32VCvmar7GY5OPGeVtkhIfQghxDWQum1VJzGWLOgcsiMxlZS5b2oxSef5Zw3xmPynxURZcXWA8rztJJUAtismgQYN47LHH2Lx5M4mJVxdTDwoKynFLSlJSEtWqVePdd99l7ty5AKxcuTLXdo8fP87s2bPZuXMn1atXZ9y4ccYVT3DUDcuN1pqxY8fy0ksv5djm7e1tDCrMZjNWa+6/BOPGjePrr7+mTZs2fPThh2xatQHl5UjK9/G+OinVOvea1C79+/fn7NmzREdHG7WlZsyYwfnz5/nggw+M/UJDQzl58qTxOD4+ntDQUH788Ue2bdvGe++9R3JyMhkZGQQEBNCpUydmzJgBYLTbuHFjfv/9d44cOUJ0dHSO81iwYAGbN282XsPXt/DnkZvMx7veS601ffv2ZfHixfkek/2937t3L+PHj2fVqlUEBQXl+564FOV9FEIId3OUhch/aCJ19MoGe6oF7zoBWZ4zBXhLiQ8hhCiDyvtc9nrmgDKXzf0YmcuWvqsB6pwlPnS6DW2zo8xSRMFT5VeD2vW85dKV0uxSqaswAerCXB0uSffddx/VqlUjIiIiy5dGjx49GDVqFE8++SRVqlThyy+/pE2bNpjNZiZPnszkyZONffv27cu7777Lf/7zHwD++usvLl++jL+/P4GBgZw9e5ZVq1bRs2fPAvvTp08f7rjjDqZOnUpISAgXLlwgKSmJ+vXr53lMlSpVSEpKMh4nJSVRu3ZtLBYLiz77jDrVa6G8nf/h5bFQYm7WrFmT5fG8efNYs2YNGzZswGS6+h/ooEGDeOeddxg+fDg///wzgYGB1K5d27idCxy1smJiYowVgAcPHmxs279/P/Xr1+e1115jyJAhfP7554SHh2c9j0WLCvyCy/4+dO/enUWLFtG7d2+OHDnCiRMnaN68eZ4rCnfu3JnJkydz7NgxmjRpQkpKCqdOnaJZs2Z5vuaJEycYMmQICxcuzLJfhw4dOHr0KMePHyc0NJQlS5YYi14U9X0UQgh3s6dYsmTd5sbk743tQlop9Uhcq9wuNpgCfLDJxQUhhCgymctmVexz2ULMAfMic1mZy3oKW0ruGbiugLU91Yq5ik+p90sUjj3VivI2GUmf2VWEDGq5fFJKwsLCePjhh3M8HxkZyZQpU7jxxhuJiori/fffN66QZvfMM8/w119/0bp1a9q0acOmTZto06YNbdu2pUWLFowcOdK41aYgrVq14sUXX6Rfv35ERkbSt2/fAhcXGD58OK+99hpt27blt99+44UXXqBTp05069aNFk2bA6C8zY6dr+EKrcvEiRM5e/YsXbp0ISoqipkzZwKOhRIaNWpEkyZNeOCBB3jvvfeuqf0WLVqwaNEihg4dmvM8WrQo8PjIyEjMZjNt2rThjTfe4KGHHsJutxMREcGwYcNYsGBBlqvN2dWsWZMFCxYwYsQIIiMj6dKli7GARF5mzpxJYmIiDz30EFFRUcYVcy8vL9555x369+9Py5YtueeeewgPDwdK/n0UQojilr1ucW5Mfl7GAFx4Jm2xoS32HJ+l2d8be7LUoBZCiLKm3M9lCzEHLCyZy+Ykc9nS4cqgNgdkz6B2BaglScCT2dOseWZPw9UA9bXcDVFWqPJyctHR0TomJibLc4cOHaJly5Zu6lHFYku1YLuQjldNP6znUzEH+srVuTJOfn+EEKXt1LPf49+lNtUGNspzn0urjpO0/RShL3bLs/alcC/rpSv8+dIOqg1uQkCnq1n/73RjAAEAAElEQVRNF5Yf4cqRv6j91LUtsOVJlFK7tNbR7u5HcchtDC2EcD8Ziwtx7Sri78/lDSe4vO4Pxxg5UxZu+tG/SJi/n5oTIvFtFOjGHor8JHxyENuFNGr9o32u25O2nOTSqjjqzOyKycdcyr0rPvmNoSWDWhQPZ0kP5aVAgS5CiQ8hRMVmPX+e9F+PuLsbws3sGbln3WZn8vcGm0ZfsZVSz0RRGTUQs2dQB3hjS7GU68wPIYQQQgh3sKdYUL7mHCUirpb4kAxqT2ZPs6DyyaB2bXMtplgeSYBaFAttc042TcrxxyaTTyFE4Zx/+x1OTpjg7m4IN3MNms3+hQhQgyyU6MFcn2WOGtT+Po6LC+lycUEIIYQQojjZUi05FkiEq2NnmwSoPZo91Yqpct7zIFf5j/Jch1oC1KJ42DWYFEoplElJBrUQotAsf57BevYsOkNq01ZkdmNhl/zXbzYG2RKg9liuzI7sFxtcNRFtUodaCCGEEKJY2VMsuSZ6mJ1ja7us4eLRdCFqUDv2K79zIAlQi2Kh7RpldtYCNZuMkh9CiDzY7e7ugcewJSQCjlIfouIysm4LyqD2K/+3t5V1eWZQB0j2uxBCCCFESbCn5J5BrbzNKG+TlPjwcPY0a76JOq7s6vI8B5IAtSgeNkcGNeDIoJYSH0Lk7uJJWH4fvFgTEo66uzcewZroCFBbzp5zc0+EO+VVtzg7s5T48HhXs+GzrSLv+uyS5bMTQgghhChOeQWowTEmk7Gz59IWu2MtnkJkUJfnEh/530crRGHZNcrbeb3DpCSDWojsMlLh+zcdf2xXQNvh/GEIburunrmV1hrrhQsAWM+ddXNvhDu5sgEKW+JDBtmey55qQfmYcizSYw7wAcAmAWohhBBCiGKjtcaWYs07QO3vVa4zb8s6V9A53wxqv/IfoJYM6lJw9uxZRo4cSaNGjWjfvj1dunThq6++Mrb/4x//IDQ0FHumW/4XLFhAzZo1iYqKIjw8nLvvvpvU1NQs7UZFRTF8+PBr6tPnn39Oy5Yt6dWrF5s3b+a22267tpNz0nYNzhIfyqxA6xKpQ/3cc8+xfv16AHr27ElMTEyu++3du5cuXboQHh5OREQE6enpBbadX3su48aNY/ny5UXvuNOCBQs4ffq08XjUqFE0b96c1q1bc99992GxOCbtWmsefvhhmjRpQmRkJLt37zaOGTBgANWqVcvxmW3YsIF27doRFRXFjTfeyLFjx665n6IYaQ37lsM70bDlZWh+C9y72rEtJcG9ffMA9kuXwPnv3npWAtQVmS3FAop8FwcBUL5mMCsJUHswe6o110x4k79zYC01qIUQosyoCHPZ0iJzWZnLlhSdYQerHbN/7gFOk7+3lPjwYHZnXen8MqiVrxlMEqAW10FrzZ133kmPHj34/fff2bVrF0uWLCE+Ph4Au93OV199Rd26ddmyZUuWY4cNG0ZsbCwHDhzAx8eHpUuXGtsOHTqEzWZj27ZtpKSkFLlf8+fPZ+7cuWzatOn6ThDHOWLXKGeJD1epj5LIop45cyY333xzvvtYrVb+9re/8f7773PgwAE2b96Mt3f+AY/SYLPZcv1SP3z4MPv27SMtLY158+YBsGrVKo4ePcrRo0eZM2cOkyZNMo6ZNm0aCxcuzNH+pEmTWLRoEbGxsYwcOZIXX3yx5E9K5O/ULviwP3xxP/gHw72rYOhHUCfKsT1VAtSu7GkAyzkp8VGR2VMtmCp7XV3PIA9KKUx+3rJIogezp1pyzQBRZhMmPy/57IQQooyoCHPZ0iRzWZnLlhSjVJ6U+CiTjAzqfBJ1lFKYKnlJgFpcu40bN+Lj48PEiRON5+rXr8/f//53ADZv3kx4eDiTJk1i8eLFubZhtVpJSUmhevXqxnOLFy9m9OjR9OvXjxUrVhjPz507lw4dOtCmTRvuuuuuHFeqwfHFuH37du6//36mTZuWZdv06dOZPXu28bh169bExcURFxdHixYtGDduHM2aNWPUqFGsX7+ebt260axZM3b+EgMmxY4dO7ixd3c6DriRbt278euvvwIwfvx4oqKiiIqKombNmsyYMQOA1157jQ4dOhAZGcnzzz8PQFxcHC1btuSBBx4gPDycfv36kZaWBhTuqu/atWuJjIykTZs2AAQFBWE2m3Psl5aWxvDhw2nZsiWDBw82XgMgICDA+Hn58uWMGzcux/HPPvss48aNw2azMWnSJKKjowkPDzfOA6BBgwY88cQTtGvXjsWLFxMTE8OoUaOIiooiLS2NgQMHopRCKUXHjh2Nwd6KFSsYM2YMSik6d+7MxYsXOXPmDAB9+vShSpUqOfqjlOLy5csAXLp0iTp16uT7PokSlPQnfDUJ5vaGC7/DoHfggU1Qv6tju5cv+FSRDGrAmnD1PbBKDeoKLa+s29yY5TZFj5bfZ2ny95Ya1EIIUUZUhLls06ZN2bFjBwA7duygS5cutG3blq5du8pcVuayZUZBa7mY/LywydjZYxmlDvPJoHZtlwC1uGYHDhygXbt2eW5fvHgxI0aMYPDgwXz33XfGbTEAS5cuJSoqitDQUC5cuMDtt9+eZdvw4cMZMWJElsHAkCFD2LlzJ3v27KFly5bMnz8/x2s+99xzREdHs2jRIl577bVCn8uxY8d49NFHOXz4MIcPH+azzz5j+/btvPrSq7zyzv+BSdGiRQu2bNrCjtXbmf7MdJ566ikA5s2bR2xsLCtWrCA4OJhx48axdu1ajh49yo4dO4iNjWXXrl1s3boVgKNHjzJ58mQOHDhAtWrV+OKLLwrdzyNHjqCUon///rRr145XX3011/3++9//4ufnx6FDh5gxYwa7du0q9GtMmzaN8+fP89FHH2E2m5k1axYxMTHs3buXLVu2sHfvXmPfoKAgdu/ezd/+9jfjfY+NjaVy5crGPhaLhYULFzJgwAAATp06Rd26dY3tYWFhnDp1Kt8+zZs3j4EDBxIWFsbChQt58sknC30+ophY0mHb/8Hb7WH/cuj2CPx9N7QbDaZsA0v/IAlQAzbnAommwEAp8VHB2VNyz7rNjclfskA8WV4Z1ACmAG+pQS2EEGVERZjLzp49m3//+98AtGjRgm3btvHLL78wc+ZMmcsic9mywnV3mikg7wxqnWZF22StME9UmBrUAKqcB6grzCKJ25YdIeFkcrG2GVw3gO73NCvSMZMnT2b79u34+Pjw/fffs3LlSl5//XWqVKlCp06dWLNmjVGPadiwYbzzzjtorZk8eTKvvfYaTz75JDExMQQHB1OvXj1CQ0O57777uHDhAjVq1GD//v0888wzXLx4keTkZPr3719s59uwYUMiIiIACA8Pp0+fPiiliAgP54/4Eyiz4lLiJcb+/e8cOXQEk5cJi+3qL096ejpDhw7l7bffpn79+rz99tusXbuWtm3bApCcnMzRo0epV68eDRs2JCoqCoD27dsTFxdX6H5arVa2b9/Ozp078fPzo0+fPrRv354+ffpk2W/r1q08/PDDAERGRhIZGVmo9l944QU6derEnDlzjOeWLVvGnDlzsFqtnDlzhoMHDxrtDRs2rMA2H3roIXr06EH37t0Le5o5vPHGG6xcuZJOnTrx2muv8c9//tO4zUqUMK3h0Dew9lm4+Ae0uA36vQA1GuV9jH9NKfEBWBMcAepKLVtiOXO6gL1FeWZPsWCu5luofU3+3lhOF/2WYFE6bKlWfPPI4DEH+GA5K5+dEEIUhcxlr1+ec9mICGOueenSJcaOHcvRo0dRSmUJuMtcNm8yl3U/V+KGOa/xl2uR8TSLsWi18BxXS3wUkEHt512uA9SSQV3CwsPDsywK8O6777JhwwbOnz/PmjVruHjxIhERETRo0IDt27fnemuUUorbb7/duCK7ePFiDh8+TIMGDWjcuDGXL182rsqOGzeOd955h3379vH888+Tnp6OzWYzbkl67rnn8u2vl5dXlgUuMi/I4Ot7NXBgMpmMxwqF1WoFk+LZZ5+lZ69e/LLhZ75e+mWW4ydOnMiQIUOMultaa/71r38RGxtLbGwsx44d4/7778/xWmaz2dF+Hr766ivj/GJiYggLC6NHjx4EBwfj5+fHwIED2b17d4798qPU1Rqo2Rel6NChA7t27eKCs3bu8ePHmT17Nhs2bGDv3r3ceuutWY7x9/fP97VmzJjB+fPnef31143nQkNDOXnypPE4Pj6e0NDQPNs4f/48e/bsoVOnToBjIPHDDz/k+7qimPy5Dz6+HZaNAR9/GLMChi/KPzgN4BcMKYml00cPZk1MALMZ32ZNsZ4956hpLyokR9Zt4Up8mPxkoRdPpe0anW7NO4NaSnwIIUSZURHmsiaTyZhrPvvss/Tq1Yv9+/fzv//9T+ayyFy2rLAXmEHtXKhaynx4JHuqY7F4VangEh+6HM+BKkwGdVGvDheX3r1789RTT/Hf//7XWBzAVUtr8eLFzJs3jxEjRgCQkpJCw4YNc621tX37dho3bozdbmfZsmXs27fPqMu0adMmXnjhBR544AGSkpKoXbs2FouFRYsWERoaitlsJjY2tlD9bdCgAd9++y0Au3fv5vjx4wUf5LxNRJkUly5dIiwsDJTi408/MXZ59913SUpKynKrTv/+/Xn22WcZNWoUAQEBnDp16poWgBg8eDCDBw82Hjdu3JhXX32V1NRUfHx82LJlC1OnTuXWW2/Nsl+PHj347LPP6N27N/v3789yK1OtWrU4dOgQzZs356uvvspSJ2vAgAH079+fW2+9lbVr13L58mX8/f0JDAzk7NmzrFq1ip49e+ba1ypVqpCUlGQ8njdvHmvWrGHDhg2YTFevFw0aNIh33nmH4cOH8/PPPxMYGEjt2rXzfA+qV6/OpUuXOHLkCM2aNWPdunW0bNmySO+jKKKUBNj4Auz+BCpVg1v/D9qNA3Mh/1v1D4IzsSXYwbLBlpiIuUZ1vG+ojU5Px56UhLlqVXd3S7iBPdWKKY+Vx7Mz+TuyB7RNF7iooihd9jQr6LxrIJoDvLGnymcnhBBFIXPZ2EL195rmsplcunTJCKQuWLDAeF7msg4yl/Vc9lQLmBXKN2e9cri6eKKUyPNM9jQrytcLZcp/bFzea1BXmAC1uyil+Prrr5k6dSqvvvoqNWvWxN/fnxkzZjB16lTef/99Y19/f39uvPFG/ve//wGO2lzbt2/HbrcTFhbGggUL2LZtG6GhoVkWDejRowcHDx7kzJkzxi07NWvWpFOnTlm+QArjrrvu4pNPPiE8PJxOnTrRrFnBgyFtd2Y8mhSPP/44Y8eO5QXfmQzsN8DYZ/bs2Xh7exu3Ok2cOJGJEydy6NAhunTpAjgWc/j0009zXQSiKKpXr84///lPOnTogFKKgQMHcuutt+bYb9KkSdx77720bNmSli1b0r59e2Pbyy+/zG233UbNmjWJjo4mOTnrLXVDhw4lKSmJQYMGsXLlStq2bUuLFi2oW7cu3bp1y7Nv48aNY+LEiVSuXJkff/yRiRMnUr9+feM9GDJkCM899xwDBw5k5cqVNGnSBD8/Pz766COjje7du3P48GGSk5MJCwtj/vz59O/fn7lz53LXXXdhMpmoXr06H3744XW9jyIP1gzYMQe2vAqWFOj4IPR8AipXL/jYzPyCHUFurUFV3CCNNfECXkHBeNUKcTw+e1YC1BWQPcOGttiLsEiiN2i5TdETuTLb81xF3pnZY0+1YK4in50QQniyijCXzcw1l33xxRezzB9lLusgc1nPZUt23Imo8phXusbYcgeiZ7Kn5X33YWauALW26wKD2WWRKi+3U0dHR+vst7ocOnRIrryVAutf6djTrfjUvrpasOVcKijwrunnxp6J6yG/P9loDUfWwNqnIfEYNOkL/f8NNa8xo+WHt2HtM/DkCagUWLx9LUOODxuG2T+A4Icm8cffRlN33jwCbsx7YCzKJ+vFdP58eSfVhzTFv+MNBe6fuuccFxb/Sq1/tsc7RL5nPMmVPy5z/r97CLo3nMrNa+TYnrovgQuLDhHySDt8aud/27AnU0rt0lpHu7sfxSG3MbQQwv1kLC7Etatovz8JnxzEdiGNWv9on+v2oo61RelK+Gg/tmQLtf7eNt/9krbGc2nlcepM74KpgHIgniq/MXTZPCPhUbQtl6s3JgU2e+4HCFHWnDsMa56C3zZAUFMY+Tk063d9bfoFO/5OSajQAWpbQiI+9evjVasW4MigFhWPPcW5MEhhS3y4skCSLRBSYt0S16DARXpcGdTJGUDZDVALIYQQQngKe4olz7vX4OrY2SYZ1B7Jnlr4DGpj/zIaoM5P+TsjUfrs2hGQzkSZFHZL+cjOFxVY6gXY/DLsnAc+AdD/Jej4AJiLXl8uB/9MAeqgxtffXhmktcaamOgo8VGzJgDWcxKgrogKKguRnVFHTwbZHse1+E6eiyQGSA1EIYQQQojiZE+x4F0n7wv/Jh8zeJlk7Oyh7GlWvKv7FrifEaAup3WoJUAtrp9do7xNWZ8zK7BptNZ51kESwuN9cT/8vhnaj4NeT18NKhcHvyDH36kJxddmGWNPSUWnp+MVHISpUiXMgYFYzp1zd7eEGxgrjxeyBrUrQG2TIKfHMS425JVB7frskuWzE0IIIYQoDrYUC74FJHqY/b2MuxaFZ7GnWYzgc35cCSDlNUBtKniXa6eUGqCU+lUpdUwp9WQu2/+plDqolNqrlNqglKqfaZtNKRXr/PNNSfZTXB9t046AdCZGyQ+7ZFGLMuzsAWgzEm57o3iD05A1g7qCsiU6zt0c5AjWe9WqhfWsBKgrooKybrMze/BCLxnxp/i1Q0fSDx1yd1fcwp5qBROoSrkvEqUqe4FJSQa1EEIIIUQx0DaNTrMaSQB5Mfl5e+TYuaLTWjsWSaxccKKOcu5jTyufn2OJBaiVUmbgXeAWoBUwQinVKttuvwDRWutIYDnwaqZtaVrrKOefQSXVT3F9tNagc6lB7QxYawlQi7LKZoXkc1C1TsH7XgtXDeoKnEFtTbwAgFeQ471wBKilxEdFZEuxgKJQAzMA5W1C+ZgdNag9TPq+vdiTkkjbt8/dXXELe2r+q8grpTAFeHvkZyeEEEIIUdYUtlSeyd9bEgQ8kL5iA3vhEnXKe4mPksyg7ggc01r/rrXOAJYAd2TeQWu9SWud6nz4ExBWgv0RJcHmDEDnUoMakAxqUXalnAc0VKlVMu37+IG3P6Qklkz7ZYDVmUHtFVTD8XetECxSg7pCsqdaUJW8UObCl4Qy+XsZmdee5Mrx4wBYzpxxc0/cwxGgzn+Abfb3xpacUUo9EkIIIYQovwodoPbzzLFzRecKNheqxIdzHy0B6iILBU5mehzvfC4v9wOrMj2upJSKUUr9pJS6M7cDlFITnPvEnD9//ro7XFIaNGhAREQEUVFRREdHAzBu3DiWL1+eZb+AgAD27dtHVFQUUVFR1KhRg4YNGxIVFcXNN98MwIABA6hWrRq33XZbjtdISLj2TMw9e/bQpUsXIiIiuP3227l8+XKW7SdOnCAgIIDZs2cDkJ6eTseOHYlq15aoPh2Z8e+ZWRt0Bqi1TXPixAn69etHy5YtadWqFXFxccZ74Dq/qKgoYmNjr7n/mW3evNl4fxYsWMCUKVOKpV2A2NhYVq5caTxetGgRkZGRRERE0LVrV/bs2WNsW716Nc2bN6dJkya8/PLLxvPvvPMOTZo0QSmV5TPTWvPwww/TpEkTIiMj2b17d7H1W1yDJGdwqUrtknsN/6AKnUFtS3QE583ODGrvkBBsCYloa/n8whV5s6cWfFtidiZ/b4+sQZ1xPA4A6+nT7u2ImzhWIS9gghQgGTxCCFFWlOe5bJs2bQgPD+f555/Ps22Zy8pc1tO51vUocPwlJT48klHqsBABauVjArOSDOqSpJT6GxANvJbp6fpa62hgJPAfpVTj7MdpredoraO11tE1a9Yspd5em02bNhEbG0tMTEy++0VERBAbG0tsbCyDBg3itddeIzY2lvXr1wMwbdo0Fi5cWOz9Gz9+PC+//DL79u1j8ODBvPbaa1m2//Of/+SWW24xHvv6+rJx40Z+2bGLnau/Z836tfz000/GdiMLzq4ZM2YM06ZN49ChQ+zYsYOQkBBjP9f5xcbGEhUVVeznVdyyf6k3bNiQLVu2sG/fPp599lkmTJgAgM1mY/LkyaxatYqDBw+yePFiDh48CEC3bt1Yv3499evXz9L2qlWrOHr0KEePHmXOnDlMmjSp9E5M5JT0p+PvKjeU3Gv4BVfoGtTWBEeA2qtGdcffIbVAa6zXMUERZZM9peCs2+zM/p45yM5wTlwtpytyBnUBi/QE+MgiiUIIUYaU17nsnj17iI2NZfXq1VnmspnJXFbmsp7OddHfHFCIEh9pVinD6mFcwWZVmAC1Upgql99M+JIMUJ8C6mZ6HOZ8Lgul1M3A08AgrfUV1/Na61POv38HNgNtS7CvZUafPn2oUqVKrtteffVVIiIi6NixI8eOHct1n4CAAKZOnUp4eDh9+vTBlXl+5MgRevToAUDfvn354osvjGO+/vprGjZsSHh4uPGcUoqAgAC0XWOxWrBYrFnrTTozqA8ePIjVaqVv377G6/v5+RXpnFeuXEmLFi1o3749Dz/8sHFFeceOHXTp0oW2bdvStWtXfv3113zbOX/+PHfddRcdOnSgQ4cOfP/99wAMHDjQuPIdGBjIxx9/THp6Ovfeey8RERG0bduWTZs2kZGRwXPPPcfSpUuJiopi6dKldO3alerVHcG1zp07Ex8fb/StSZMmNGrUCB8fH4YPH86KFSsAaNu2LQ0aNMjRvxUrVjBmzBiUUnTu3JmLFy9ypoLeIu4Rkp0B6oASDFD7B1foDGprYgLmatVQ3o7BlFctx4Bf6lBXPI4AdREzqP08LwtXa02Gq8RHBc2gtqVaC7zYYPKXGtRCCFERedpcFsBisWCxWHJdO0HmsjKXLQuMEh8FZlB7gS6/9YvLKteCh4WdC5kqe5Xbz7AkA9Q7gaZKqYZKKR9gOPBN5h2UUm2BD3AEp89ler66UsrX+XMw0A04WIJ9LVFKKfr160f79u2ZM2eO8fy0adOML5PiuOIaGBjIvn37mDJlCv/4xz9y3SclJYXo6GgOHDjATTfdxIwZMwAIDw83vnQ+//xzTp50VGdJTk7mlVdeyfW2J5vNRvvO0YRFNebmvjfTqVOnLOeMSXHk6BGqVavGkCFDaNu2LdOmTcNmsxn7Pf3000RGRjJ16lSuXLmS4zXS09N58MEHWbVqFbt27SJzKZcWLVqwbds2fvnlF2bOnMlTTz2V7/vzyCOPMHXqVHbu3MkXX3zB+PHjAcegITY2lvnz51O/fn3uvPNO3n33XZRS7Nu3j8WLFzN27FjsdjszZ85k2LBhxMbGMmzYsCztz58/37gyf+rUKerWvXp9JiwsjFOnclyfyeJajilLbMkZ2DNsBe/oKZL+BBQEhBS46zWr4BnUtoREzMFBxmPvWo563xYJUFc49lRrgXXzsvPEhV5sCQnYk5MxVa2K5exZtK0M/Z9XDLTWhcqgNgV4ozNsZes7QQghKqjyPJeNiooiJCSEvn37ZpnLuhw5InNZkLmsp3Nd9Df5F7wGCOBx4+eKrig1qF37ldcAddHupy0CrbVVKTUFWAOYgQ+11geUUjOBGK31NzhKegQAnzuvWJ7QWg8CWgIfKKXsOILoL2utrytAvWnBHM798fv1NJFDSP1G9Bo3ocD9tm/fTmhoKOfOnaNv3760aNECcNwSdPfddxv7ua7iXqsRI0YYf0+dOjXXfUwmk/Fl9Le//Y0hQ4YA8OGHH/Lwww/zwgsvMGjQIHx8fACYPn06U6dOzbVvZrOZmG07uHDqHMMmj2H//v20bt3a2K5MCmuG1fjirVevHsOGDWPBggXcf//9vPTSS9xwww1kZGQwYcIEXnnlFZ577rksr3H48GEaNWpEw4YNjXNzDYwuXbrE2LFjOXr0KEopLJb8/6Ndv369cWsSwOXLl0lOTiYgIICEhARGjx7NsmXLCAwMZPv27fz9738HHIOH+vXrc+TIkTzb3rRpE/Pnz2f79u359qEiO/ffPVQOD6LawEbu7krhJJ0B/5pgLlrQrEj8gxwBaq0hl6yN8s564QJezvrTAF7OALX17Lm8DhHllD3VUuCgOjuTvxc6w4622FDe5hLqWdG4Fkj079SJpHXrsCYkGBdeKgJtsYNVF2qRRHBmzvt4xmcnhBCeTOayDsU9l42NjeXixYsMHjw4x1wWwGqVuazwfPZUK6qSGWXOP//UlUDgiSXyKjKjBnUhyx2aKnuV21J5JRagBtBarwRWZnvuuUw/35zHcT8AESXZt9IUGupYGzIkJITBgwezY8eOEnmdzLclKaUcGc7t2wMwaNAgZs6cmecxLVq0YO3atYDjSvF3330HwM8//8zy5ct5/PHHuXjxIiaTiUqVKl1drMGmqVa9Or169WL16tWkpKTw4IMPAvDco08TekMdoqKiaNTIEZS88847+emnn7j//vupXdux+Jyvry/33nuvsWhF//79OXv2LNHR0fkuCvHss8/Sq1cvvvrqK+Li4ujZs2e+74/dbuenn36iUqVKWZ632WwMHz6c5557LsegpDD27t3L+PHjWbVqFUFBjmzQ0NBQ48o9QHx8vPHvIC/XckxZoS12bInpWBPS3d2Vwks6C1VKOLDkFwy2K5CRDL653+5YntkSEqgU3sp4bK5eHby9sZ6TAHVFYs+woS32ayrxAY6SEl6BnhHkdNWf9u/WlaR167CcOl2hAtSuCY+5EBnU4Mz4qV4p332FqCgs51Lxqlk51zIDQrhTuZ7LAtWqVct1Ljtz5kzCwsJkLovMZT2dLcVSqMXGXQFQe0r5zL4tq3SaFcwK5V24Ahemyl5YzqeVcK/co0QD1J6kMFeHS0JKSgp2u50qVaqQkpLC2rVree6559i/f3+xv9bSpUt58sknWbp0KV26dDGuCmdmt9tZvnw5w4cP57PPPuPGG28E4Ny5c4SEhGC323nxxReZOHEiANu2bTOOnT59OgEBAUyZMoXz58/j7e2Nv92X9CvprFu3jieeeIJOnToZr2lNTMOabuHixYucP3+emjVrsnHjRmP15zNnzlC7dm201nz99dfGF+qaNWuM10xLS+P3338nLi6OBg0asHTpUmPbpUuXjC+9BQsWFPj+9OvXj7fffptp06YBGItZPPnkk0RGRjJ8+HBj3+7du7No0SJ69+7NkSNHOHHiBM2bN+fo0aMkJSUZ+504cYIhQ4awcOFCmjVrZjzfoUMHjh49yvHjxwkNDWXJkiV89tln+fZv0KBBvPPOOwwfPpyff/6ZwMBAY+BT1tkuO255sydluLknRZB0BqqU8Pvv71zcNSWhQgaorYmJmDNlUCuTCa+awVjPSYmPisSVNVBQUDO7LLcpBvoWe7+uRcbxOJSvL5XbtQPAcuY0FWkJDdeEp8AM6gBHZptNbjEVAgDL2RTOvrGb4PtbU6lpdXd3R3ggmcs6FPdctlq1aqSlpeU6lwVH8FfmsjKX9XT2FEuhSuW59pEMas9iT7NiquxV6AvUShZJFNfq7Nmz3HjjjbRp04aOHTty6623MmDAgGtur3v37gwdOpQNGzYQFhaW5Qvwr7/+IjIykjfffJM33ngj1+P9/f3ZsWMHrVu3ZuPGjcZtSIsXL6ZZs2a0aNGCOnXqcO+99+bbjzNnztCrVy/a9ehAl1t60LdvX2PBB4NJYVImZs+eTZ8+fYiIiEBrzQMPPADAqFGjiIiIICIigoSEBJ555pkcr1O5cmXee+89BgwYQPv27alSpQqBgYEAPP744/zrX/+ibdu2WK0F/4K+9dZbxMTEEBkZSatWrXj//fcBmD17NmvXrjXqp33zzTc89NBD2O12IiIijFu5fH196dWrFwcPHjQWlpg5cyaJiYk89NBDREVFGQMWLy8v3nnnHfr370/Lli255557jIU53nrrLcLCwoiPjycyMtKoHzZw4EAaNWpEkyZNeOCBB3jvvfcKPKeywnbJEZi2lakA9Z8QUMKZj/7O4GxqYsm+jgeyX7mCPTkZr6CgLM97h9TCIiU+KhRXHbyil/jwvDp6GceP41O/Pt51HBPOirZQYqEX6XF9dsll6DtBiBLkusPMmlA+M6JE2VXe57KRkZF06NAh97ksjjIgMpeVuaynK3SAWkp8eCRXgLqwTH7e6CtWtF2XYK/cQ2ldPk4qOjpax8TEZHnu0KFDtGzZ0k098kwBAQEkJycXW3sZf6Zg8jHhVaNyjm22y1ewXc7AOzTgum5XdNXW0lozefJkmjZtmmddMlF8ivP3JzX2HBeW/ApeJkJf6Or5t6/arPBCMPSYBr2fLrnXid8F83rDiKXQ/NoH+2WR5dQpjvW5mdovvkC1TPUL4x/5B1eOHKHxqpX5HC3Kk/Sjf5Ewfz81H4zEt2FgoY+znEvl7Ou7qDG8OX5RJbiYaRH8NuAWfJs1I+ytN/m1U2cCbx3IDdnqUZZnqXvOc2HxYWpNbYd3Lf8897Nn2Dj93A9UHdCAqj3r5rmfJ1NK7dJaR7u7H8UhtzG0KF3JP53m4te/UaVnGIEDGrq7O8JDyFw2p+Key5YGmcu6R0X6/Tnz75/xbVadGnc3y3c/rTWnnv2egG6hVLtFvms8xfm5e9FWTcikNoXaP2n7KS59+zt1nutc5BKJniC/MbRkUIvrY9NgyiPY6Hr+Oq/szJ07l6ioKMLDw7l06ZJRF0yUHbZLzlWtrXb0FVv+O3uClHOAhio3lOzr+Duzh1MTSvZ1PJA10ZE1bs6WQe1VKwTrWSnxUZEYWbeFyPzI7GodPc/IAtEWCxnx8fg4F0LyrlMHy+kzbu5V6Sp0BrWPGeVjMladF6Kis1123ml2Se4qEKK8kbmsKElaa2yFzKBWSmHy8/aYsbNwKHIGtXNfe1r5K/NRYWpQC4fivOKs7Rq0RpnyuM7hDFBru0Zdx/pVU6dOlavMZVzmCZctKQNTJQ//ryfpT8ffJR2g9nOW+EipuAHqnCU+QrCnpmJLTsEckHcGpig/Clu3ODuTnzcoxyKJniDjZDxYrfg0bACAd+3aWOLj3dupUmasQl6IQbYpwEcmSEI4XQ1QX3FzT4TwbGUtexpkLitKls6wgU0XapFEcKz5IuMvz2JPteZ752F25TlALRnU4tq5MqPNuWdQK9fztvJRRkZcO+vFqxOuMrFQYmkFqH38watShcygtuURoPaq5aj7LQslVhyuhfJMlYuWQa1MClNlL48ZZGfExQHg26AB4MygPlPxMqiVrxnlVfDw0uzvjU1qUBeZUmqAUupXpdQxpdSTuWwfp5Q6r5SKdf4Z745+iqIxAtSX5XdCCCFE4bnuRitsqQeTf/ldYK+sKnoNaglQC5GDqyi7KqDER3ks3i6Kxnb5CuZAH8fPSZ4RTMpXkjOoVKWEV55WypFFXREzqBOcJT6Cg7M87xXiDFBLmY8Kw55qQVX2unpRswhM/p6TBZJx/DjA1RIftWtjT0rClpTkzm6VKnuqtdCZ8KYAbynxUURKKTPwLnAL0AoYoZRqlcuuS7XWUc4/80q1k+Ka2C87LuTbLl2hvKwPJIQQouQZd68FFDJA7ectiyR6EG3T6Cu2It1JamRQl8MLDRKgFtfOFXjOI0BtBK4lg7rCs126gndYFcfPZSGDOvksoMC/FBZe8w+qmAHqxERMAQGYfH2zPO9dy/GeWyRAXWHYU62Fvi0xO0+qo5cRdxxzjRqYAx0LPXrXcVzgqkh1qG0pliJk8Hgb2fOi0DoCx7TWv2utM4AlwB1u7pMoBrbLGaBAW+zo9DKwVocQQgiPYNyJWNgEAX8JUHsSe7ojyKykBjUgAWpxHbQz8Jxn1ptkUAtAW+3Yky143+APZoW9LNzSnXQG/GuCuRRqZfvXrKAlPhJylPcA8ApxBKitZ8+VdpeEm9hTLEWuP+3iSYPsjONx+DjLe4CjxAeA5cxpN/Wo9NlTC/9ZmgN8sCdbJFu0aEKBk5kexzufy+4updRepdRypVTd3BpSSk1QSsUopWLOnz9fEn0VhaStduypVrxC/ACpQy2EEKLwXIkahU32MPk5SnxIjMYzFHaB8cwkQC2umdlsNlbtbdOmDf/3f/+H3W4HYN26dbRv356IiAjat2/Pxo0bS6QP06dPZ/bs2cXSVkxMDA8//LDjQbYM6gYNGpCQcDXQppRy1KfO4z+/xMREevXqRUBAAFOmTMmyrWfPnjRv3pyoqCiioqI4d+5qsGrZsmW0atWK8PBwRo4cWSznlV3Pnj2JiYm5pmMXLVpEZGQkERERdO3alT179hjbVq9eTfPmzWnSpAkvv/yy8fyoUaNo3rw5rVu35r777sNisRTYVllhS8oADV7VfDEHeJeREh9/lnz9aRe/YEhJLJ3X8iDWhMQc5T0ATH5+mKpUwXrOswPUSRs2cOKBCRJcKwaOoOa1ZVCb/b2xpXjG4OxKXJyxQCKAV21XBnVFClBbC59BHeANdo0uh4NrN/sf0EBrHQmsAz7ObSet9RytdbTWOrpmzZql2kGRlavutE/dKlkeC+EJyvVcNpvsc9mCyFy2YsxlPZ0rQG0qdIDaG3T5DG6WRa7PoSg1qJW3GbxM2NPKQFyliEohPbBiq1y5MrGxsQCcO3eOkSNHcvnyZWbMmEFwcDD/+9//qFOnDvv376d///6cOnUq3/asViteXu752KxWK9HR0URHRwPOzGiF408elEkZmdbZVapUiRdeeIH9+/ezf//+HNsXLVpkvJbL0aNHeemll/j++++pXr16li97T9GwYUO2bNlC9erVWbVqFRMmTODnn3/GZrMxefJk1q1bR1hYGB06dGDQoEG0atWKUaNG8emnnwIwcuRI5s2bx6RJk/JsqyxxZQKZA30xVfEpIxnUpRig9g+ukBnU1guJ+DZslOs2r1ohHr9IYvKWraRs24b13Dm8nQs7imtjT7HiXTvgmo51LPTiyMJVqug1rIuLLSkJW0ICvs760wBewcEob2+sFWihxKJlUDsmUkUpCyI4BWTOiA5zPmfQWme+4jkPeLUU+iWug81Zf9onrAqpMWclg1p4lPI8l71eMpetGHNZT2dLsYBZoXzNhdrfFci2p1quucSeKD7XEqB27S81qMV1CQkJYc6cObzzzjtorWnbti11nLcAh4eHk5aWxpUrOQelCxYsYNCgQfTu3Zs+ffqQnJxMnz59aNeuHREREaxYscLY95NPPiEyMpI2bdowevToHG3FxsbSuXNnIiMjGTx4MH/99RfguMr6yCOPEBUVRevWrdmxYwfguGI9evRounXrxujRo9m8eTO33XYbAInnExg48k5at27N+PHjs2QSfvrpp3Ts2JHoPl146J9TsNly1tPz9/fnxhtvpFKlSoV+D+fOncvkyZOpXr268Z7mZty4cUyaNInOnTvTqFEjNm/ezH333UfLli0ZN26csd+kSZOIjo4mPDyc559/Pte21q5dS5cuXWjXrh1Dhw4lOTkZcFxlf/75543P4fDhwwB07drV6F/nzp2Jj48HYMeOHTRp0oRGjRrh4+PD8OHDjc9u4MCBKKVQStGxY0fjmLzaKkuuBqh9MAf4lI0a1KWaQR0EllTISC2d1/MQtoREzME5S3wAeIfUwuLhJT4szglYxu+/u7knZV9RgprZmfy8wbm4iDtlxMUBVxdIBFAmE161a2M5VTEyqLVNo9NtRapBDchCiUWzE2iqlGqolPIBhgPfZN5BKZV5dd9BwKFS7J+4BkYGdZjjQp0EqIWnKndz2cRE+vXrR3h4eJ5z2aioKB588EGZy1Jx57Kezp5iweTvXehEDbNf+V1gryzS1xGgLo93IUqAupQ1atQIm82W42rpF198Qbt27fDNtmCYy+7du1m+fDlbtmyhUqVKfPXVV+zevZtNmzbx6KOPorXmwIEDvPjii2zcuJE9e/bw5ptv5mhnzJgxvPLKK+zdu5eIiAhmzJhhbEtNTSU2Npb33nuP++67z3j+4MGDrF+/nsWLF2dp64VXZtGtU1cOHDjA4MGDOXHiBACHDh1i6dKlfP/99+za+jMmk4lFixYV+b269957iYqK4oUXXjAGDEeOHOHIkSN069aNzp07s3r16jyP/+uvv/jxxx954403GDRoEFOnTuXAgQPs27fPyASYNWsWMTEx7N27ly1btrB3794sbSQkJPDiiy+yfv16du/eTXR0NK+//rqxPTg4mN27dzNp0qRcbz2bP38+t9xyCwCnTp2ibt2riU9hYWE5sgwsFgsLFy5kwIAB+bZVltguOSZe5kBfTGWhxIfNCinnIaAUM6ihQmVRa4sF28WLeAXlLPEB4FWrFlYPXyTRFaC+IgHq62LPsKEt9kLflpidEeR082J7GcePA2SpQQ3gXbs2lgqSQe26zdBc2EV6AnwAsEmAutC01lZgCrAGR+B5mdb6gFJqplJqkHO3h5VSB5RSe4CHgXHu6a0oLFeA2qtGJcfioVLiQ3iw8jSXnTFjBjfeeGO+c9nY2FjMZrPMZam4c1lPZ08pWia0p4ydhYPrQkFRk3VMfl7lskxLhSnxcfF/v5FxOqVY2/Sp40+12xtfdzsHDhzgiSeeYO3atXnu07dvX2rUqAGA1pqnnnqKrVu3YjKZOHXqFGfPnmXjxo0MHTqUYGddV9f+LpcuXeLixYvcdNNNAIwdO5ahQ4ca20eMGAFAjx49uHz5MhcvXgRg0KBBVK5cOUeftv24nWULlgBw6623GldHN2zYwK5du+jQoQPapklLTaV2g9zW8MnbokWLCA0NJSkpibvuuouFCxcyZswYrFYrR48eZfPmzcTHx9OjRw/27dtHtWrVcrRx++23o5QiIiKCWrVqERERATiu8MfFxREVFcWyZcuYM2cOVquVM2fOcPDgQSIjI402fvrpJw4ePEi3bt0AyMjIoEuXLsb2IUOGANC+fXu+/PLLLK+/adMm5s+fz/bt2wt93g899BA9evSge/fu192Wp7BduoLyNWOq5IW5ig/2lAy0XaNM7rsdP18p5wBdujWoAVISoFq90nlNN7NecGS7eOWRQe0VEoI1IQFts6HMhbtdrTRpu92oK5zx+3E396Zscw3KzNdY4sE1yLalWPAKyvk9VVquHD8OZjM+dbOuR+ddpw4pP/7opl6VLmOAXchJkqvEhz1FgnFFobVeCazM9txzmX7+F/Cv0u6XuHa2yxngpVCVvTAH+kgGtciVzGWLfy67detWY/6W11wWIC0tLc9M57zIXLb8zGU9nSuDurBcd7p5yiLjFd31lPiwXSx/44UKE6D2FL///jtms9n4kouPj2fw4MF88sknNG7sGCB89dVXxtXgefPmAY5biFwWLVrE+fPn2bVrF97e3jRo0ID09PTr7lv220JcjzO/dhaaXIOMWmvGjh3LSy+9hO1yBrbLV/CuE5DjvPKr/xUa6ghoV6lShZEjR7Jjxw7GjBlDWFgYnTp1wtvbm4YNG9KsWTOOHj3K119/zXfffQdgXFF2XcE3mUxZruabTCasVivHjx9n9uzZ7Ny5k+rVqzNu3Lgc76PWmr59++a44u7iatdsNmO1Xr2CtXfvXsaPH8+qVasICgoyzunkyZPGPvHx8cZ5guMq/vnz5/nggw+yvEZubZUltktXMAc6MuXMVXzA7qx55cye8zhJzmzHKrXz36+4+GcKUFcQtkTHuZrz+PfsVSsEbDasiYl4F3FCUBqsCQnoDEdQ7crvv7m5N2WbsbDLNZb4MHtIFkjG8Ti8w0JRPln/X/OuXRvruXNoiwXlXb7r/BV1FXJjgiQZ1KKCs1++grmqL0opzFV9JUAtPFq5msvmIfNcNjOZy1bMuayns6dY8K5e+DIzJn8p8eFJ7KkWlI8ZZS5acQtTZS8sZ4r3oqUnqDAB6uK4Ony9zp8/z8SJE5kyZQpKKS5evMitt97Kyy+/bFzVBBg8eDCDBw82HmdfdOHSpUuEhITg7e3Npk2b+OOPPwDo3bs3gwcP5p///CdBQUFcuHAhy5XnwMBAqlevzrZt2+jevTsLFy40rkADLF26lF69erF9+3YCAwMJDAzM93xu7NSNJV8u5bl201m1apVRA6xPnz7ccccdTJ06lSD/6lz46wLpVxJynFderFYrFy9eJDg4GIvFwrfffsvNN98MwJ133snixYu59957SUhI4MiRIzRq1IhZs2Yxa9asAtvO7PLly/j7+xMYGMjZs2dZtWoVPXv2zLJP586dmTx5MseOHaNJkyakpKRw6tQpmjVrlme7J06cYMiQISxcuDDLfh06dODo0aMcP36c0NBQlixZwmeffQY4Bjlr1qxhw4YNmEymAtsqS2yXMjAHOgdYVZzZjkmeHKB2lpaoUkoL3/k5B2oVqMSHNfECQJ4lPlyLDlrPnvPIALWrvIc5KEgyqK+TEdS81hIfrjp6Ke4dZGfExeUo7wHgHVoH7HYsZ8/hE1a0O4nKGtdnUNiLDcqsMPl5SYkPUeHZLv8/e38eHtl6lvfCv3dNJZVKUktqSS31oKGHPfQeem/vvc0QMCbEAwQTIMnB4RCGk4CTkBBykpzwgTE45uQkEPhOggGbIfjwhTiQ4RwneOJgTHCI7d327t2799zdUnerNM9DlWpN7/fHqlWaalhVtWpev+vSpe5SDW93laqe937v535M7wAfb16HeX+7wSuKaEaivWz4e9lv/MZv5Hd/93f5qZ/6qYJ72ZGREdbX19nZ2Yn2sh26l212nD2brjLqaGGooIqGmzsiPNy0XbZ7GrJDEqOIj4hySafTXLt2Dcuy0DSN7/u+7+Mf/IN/AMAv//Ivc/v2bT74wQ/ywQ9+EPCGGJRqIfre7/1evv3bv53HH3+cZ555hocffhjw2n1+8id/kre97W2oqspTTz3Fb//2bx+57cc+9jHe9773kUqlmJ6e5t/8m3+T+1lXVxdPPfUUlmXxW7/1W0XXIF3JT/34/8Zf/7G/wdWrV/m6r/s6Llzw4gkeffRRPvShD/GOd7wD13HQhMYvf/iXmbw4deJ+Jicn2d7exjRN/u//+//ms5/9LBMTE7zzne/Esiwcx+FbvuVb+Jt/828C8M53vpPPfvazPProo6iqys///M9XfBL75JNP8tRTT/Hwww9z/vz5I4WVz/DwML/927/Ne9/73tzQjw996ENFP2A/+MEPsra2xt/+238bAE3TuH79Opqm8cu//Mu8853vxHEcfuiHfoirV68C8L73vY+JiYlcy9V3fdd38dM//dMF76uVcLYyxEa8ljl/A+bumDBWnpuhbtTdQT3sfe8gB7WddVBrQ4N5f66NZAXq5SXgsXotKzD+0LvEn/t6tv6fT+Ds7qEmmvT13ORULVD3NL5NUbou5uwsPW9964mf6WPe+4g1n2x/gbpMBzWAktCjDVJEx+Nsm+jj3meI2hfDTdlIy0Xo0aigiMbTrntZgA984AO8973vLb6XdV10XefDH/4wExMTJ+4j2su2/162mZGOi9y3y+pEFEKgxHWcqP5qCtx0ec+fj9KtITMO0nHLdl83M+LwtNpW5plnnpHH3+xeffVVHnnkkQatqLX4pm/6Jn7hF36haKvSYaTtYi3uoQ50FQ3ldzMO9koK7XQ3Sld0HtJKhPH7Ix1J8qe+QO/bz9P/jkns1TSLv3Cdgb9yhZ631MmhXC6f+zn4bz8P718FtQ6vWSnhQyPwNX8L/sIHa/94TcDab/4myz//C1y5fj2vsGstL3P7G9/G6E+/n8G/9tcasMLirH7ko6z80i8x/vP/gvl/9I+Z/P3fp/vx5hPSW4HdP5tn8xN3GPupt1bUVSGlJPlT/53eP3eW/nefPAStB9b8PLe/+c9z5md+hoHv+Z+O/CwzM8Pdd38r4//8/6D/O76jIeurFzt/MsfWp2YY/5mvDfx5v/yRm4Bk5EeerO3iaoAQ4itSymBFU5OTr4aOqB/Jn/4zep4d5dS3X2Tv+hIb/+ENzvyjZxqaqx/RHER72eCUu5eNaH864ffH2TFZ+LkvceovXSTxNeOBb7f4S19BG+rm9F9/tIariwjC8q+9iFAEwz/8ROkrH2L3vyfZ/C93K95DNZJiNXT7SO0RdUW63sFGqUF3QvV+Lp32OAiJKA9nxwTJoYiPrIO6mVu6dxc9V3M9xGkAIbxBiXtr9Xm8JsBeXUN0daH0xPP+XBsaAlXFXlrO+/NGYyWTqIODdGVdI+bM3QavqHXJuW67K3NQCyFQehrrAjFnZwHyR3zkHNTzdVxRY3BTFigCEQs+2FRN6M39eRARUWPcjI00HdS+bA5sdmaHsxUND42IiIiIKM7BLJfy6mi1R4+GJDYJbqqyiA/hz3Jps5iPyNIaAcDnP//58m7gC84lBOrcz91IoO5E/EE/OYE6piJ0xROum5WdReg9U9/H7BnqsAzqVbShoRPDbHyEqqKdPo293KQC9dwc+tmzGOfPg6aRuRsJ1JXi7FmIbi13mFkJaryxMRGZGS+H3Jg66eBWurpQh4aw5hfqvay646a8FsVCv9f5iCI+IjodZ9urh9Q+P4M6lr08GpQYEVEOZe9lIyLaAN+gUW5UntKjYy2134C9VsRN24gKM6j927cTkYM6oiJyDupSooIAhMhdP6Kz8AVq7dShydO9Bs5uMwvUC/XLn/aJn+6oDGpndQ31dPG8PW10FHtpqU4rKg8rmUQ/exah6xjnz2PeiQTqSnFTNmoFuWuHUXq0hk4iN2dmUeJxtJHhvD/Xx8exFtpfoHZSVtkbJM/BYyMdt0ariohobnyntOIL1H2RgzoiIiIiIhj+IX+xyNV8KHGt4QPGIzzctFW2Ax4igbplaZeM7abDDeagFkJ414kiPlqKsH5v/A2Wv+ECb1Ci29QO6iXorXM+ds/pznJQr6+jDZ0ueh19dARrufkEaum6WPPz6Ge9nDdjeppMFPFRMW4FouZxlJ7GunDN2VmMycmCzmF9bKxjIj7KHfKiZDPzok1SRKfid5T5dZLSpSFiau6APyIi2stGRJRPp/zeuJU6qOM6btqKTIQNRloO2LKiiA//NjISqFuHrq4u1tbWOuYNqp5IR3rZuQE6eYUaOahbCSkla2trdHV1VX1fzlYGoStH2lbUhI6z06Qt3Y4FeyuRg7rGeBEfg0Wvo42MNmUGtb2yirQsjHPnAIhNT2Heu4+026s4qBfuXmWugcMoDc7RM2dm8sZ7+OhjY1gLC21fi3gRH2U6qBPe9Zu6qyYiooa42SgPP4Pa+7MRRXxEANFeNiKiEsLcyzY7BxnUZRoE4jq4IPej/Usj8TtAy33+oH0d1G2dQX3u3Dnm5uZYWVlp9FLaDnfPQtou6nas5HWdXQtcibrSWtNFO5muri7OZQW4anC2M6j9sSPOQqXXwJ3Zqvq+a8LuMiAhUW8H9RCYu2Dtg97exZR0XZz1DdShEhEfIyO4Ozu4qRRKPP8wxUZgJZMA6GfPAmBMXwTLwpqbyzskL6I47p6NfqanqvtQ4n5MhKwqy7oS3P19rPl5+r/zOwteRz87jkyncTY30QYG6ri6+uKmLNR4X1m3UbICdZRDHdGpONsmIqaiHBouqvbHooiPCCDay0ZEVEpYe9lm52CWS3m+U6XHkwGdCswFEeHhi8vVOKgbGXNYC9paoNZ1nakirqaIyln5rVu4KYfRH32k5HXXf/8NMrc3GPuJJ+uwsohmwtnM5CbS+6i9hicm2S5Ca7Imjt1F73u9HdQ92eza1Cr0t3cx5WxuguOUjPjQRkcAsJeXm0r4PS5Qx6a9z5jM3ZmmWmerEEbEh5+756Yt1ER9D0LN+/dBSoypyYLX0ca89xNrfr5tBWopZW5IYjn4z727GwnUEZ2Js20eiUEDz0GdubPZmAVFNBXRXjYiIqIY7p5Vdv40kBOlvQ7E7pBXFRGUnIO6AoFaaArCUNrOQd1k6lBEq1DOm6GS0HH2rKg9rQNxtszcRHofpTfb0t2MjrkdX6A+U9/HjWfF2g6I+bBXvX+jVmJIoj7quditJov5yAnU49kM6uzG0YxyqMvGNR2k5YaQQZ11EDTgPcWcmQUoejjhv1bsNh6UKE0HHFlBxEd2IFwkUEd0KHkF6v4Yzo4ZxeNFRERERBTF3avM6JEzdzTjfryDqMZB7d8uEqgjIvDcTkHfDNUeHWyJzDg1XlVEMyFdibOTOSFQ+4JEUw5KbJRA3ZMVqDtgUKKztgZQOuIjK1DbTTYo0UomUYeGULo9t4Ha14c6fJrMnUigLpdqctcOk3PhNmDQnjkzA0AsgEDdzoMS/f/7cp9L0aWCKnD3mvDzICKiDjjbmSP504DXeeaCG2WzR0REREQUwd2zKxKo/Xqt3eIhWg037WeIV2bWiQTqiAi8Vl5nz0IJ2Eqdy5iMHFIdhbtrgstJgbo365hrWoFaQM9IfR8356Beq+/jNgB7bR0ArWQGdVagXmo+gdqP9/CJTU1j3o0E6nLxBxuq1Q5JPNKmWF/MmRm00VGUnsI52uqpU4jubqz59nVQ+//35RbYQgjUHj1yUEd0JFLKAhEfXt0U5VBHRERERBTD2bMqG7AXOaibgmod1KJbz4nc7UIkUEeUjTQdsN3AER+5Ft7oDbCj8DdWxzOo/YgPd6cJXw87C5AYAbXO8fw9WbG2IxzU2YiPEgK1muhBicebMuJDPzt+5DJjeorMzEwUY1QmucnjIWVQN+IzxpydLZk9LoRAHxtrbwe174bvqWCTlNCjA+yIjsRN2V40Tp6IDwBnK9OIZUVEREREtADe/A8LNVF+HS1iKiiiIeaOiAPclA0i+3xUgNKttZ0LPhKoI8rG30gqAd8MD4YgRU6QTsLfWBWK+GhKB/XuEiRG6/+4XadA0Tokg3oNdB2lv7/kdbXRUezl5hGopetizc9jHHdQT1/E3drCWV9v0MpakwPXbZURH/HGuECklGRmZ4sOSPTRx8aw2jiDulIHNYCSMKID7IiOxNnOHuSfEKizdVIkUEdkcXZMXDOKSoyIiDhAZiqb/wGeeUKJt5+42Wq4aRulW0MooqLbK90aMor4iOh0nDJdb/6pXtTC21nYm/kFaqEpiG4NpxkPLHYWoHes/o8rBMSHOsJBba+toQ0OIkTpD2JtdLSpIj7slVWkZZ2I+DCmpwGimI8yOXDdVuegFrqCMNS6C9TOxgbu1hax7KDMYujj420uUFeeJ6726NEBdkRH4m5n66RjGdRKXAdV5ATsiIiVj9xk+zOzjV5GREREE1FtJ6LSo0cGgQbjC9SVosSjDOqIiJyDOmg7yYGDOnoD7CSc7QxoIq9gofbqzTsksd4DEn3ipzvDQb22WjLew0cfHcFqoiGJVjIJcDKDetoTKDN3Z+q+plYmV1h3VydQQzYmos4uEHN2FqBkxAeAPj6Gs7qKm2lPR2TOQV3BcxlFfER0KoUc1EIRqH1G5KCOAEA6LvZaGns13eilRERENBHlmgaPo8T1KOKjwbhpG1GNQN2tIS0XabshrqqxRAJ1RNmUe1onNAXRVX93W0RjcbZM1P5YXqesmjBwmi2D2rE8gbhRAnXPUEcI1M7qGurpYAK1NjKKvbyCdJvjQ9dKzgEnBWrtzBlEdzfm3TuNWFbL4uxZiC4NoVbW1nYYJa7V3QVizngHEkZABzWA3aYuajdlI7rUip5LNaEjLTdqX4/oOHICde/JoeNqXyxyUEcA2Ug8GQ3NjIiIOIqvrQSdC3YcNa7h7rWX+7bVcFNWRREtPr77up1c1JFAHVE2lZzWqVHGZMfhbGXQjsV7+Ci9RvO1dO8uA7KBAvVwZ0R8rK+jDQYVqEfAtpsm2znnoB4/OiRRKArG1GTkoC4TN2WjVjBULx9qj173Q1BzZgZ0/cSBRT60MS86qF0HJTpVFNhKjyfORS7qiE7D2c6g9GgI7eR2TO2PHNQRHr4w7exEr4eIiIgDwoj4iBzUjUVWG/ERCdQREd4mUhgKihF82qgSZUx2HM5W5kT+tI/a24QO6t1F73uikREfa4157DohpcRZXUUL6qAeHQFomkGJVjKJevo0Snf3iZ/FpqajDOoycVNW1fnTPkq8/gJ1ZnYW48IFhFr6s9A/1GjXHGo3ZVc87FLJzamIaoSIzsLZNk/kT/v4DmopZZ1XFdFs+AcV7p7dVm3cERER1eG7n6uN+Ig+ZxpH9RnUeu5+2oXAArUQIl7LhUS0Du6ehZI42Y5YDCWhR0MSOwjpSm/j1Z//daIkdKTp4GaaqKV7JytQN8xBfRoyW2C3r0jj7uwgLQt16HSg6+ujowBYTTIo0Uom0c+O5/2ZcXEaa34eNx1lRAbF3auure0wjXCBmDOzGFOTga6rj46CEFjz7SpQV/5c+vMsOtVBHdXXnYsnUOevk9T+mJcr2UabzojKOBztEcW+RERE+Dh7FmgCYVTmOVV6NHBB7jfRfryDkK6sXqD2HdRt5IQv+WoWQnydEOIV4LXs358UQvxKzVcW0bQ4u2bZJ3Vqov7utojG4e5Z4MiiDmqguVz1O1nhqHesMY8fz7qKU+3rorZXvX9bcAe1J1DbS83hoDaTSYwCcQ6x6WmQEvPevTqvqnWpxnV7HKVHR5ou0qpPkS1tG/P+fWIB8qcBhK6jjYy0bcSHm7JRq3RQd1qNENXXEc62iZInfxrIHfBHgmSEs53J++eIiIjOxt2zUHv0vPOegpBz37aRuNlKyIwDkqr2QqJDIz5+CXgnsAYgpXwR+MZaLiqiufHfDMtByeaDSjdqIekE/HbEgq2r2Q2Zs9NEG6+dJUB4WdCNoCfrKm7jHGpnzfu3aUMBBerTp0EI7OXGO6il62LNLxTMGzampgGimI8ycPdCjPjIZlk7dRr2Ys3Pg2VhTAYTqMGL+WjbiI8q3PB+PdGBXVZRfd3BSEfi7hZxUGcvt6Mc6o7H2cpAVn+KDiwiIiJ8qu1E9GvwaE5YY/BF5SiD+iiB+gGklA+OXRT1AXQw7m75ooLao4OMTug6hZxAXSTiA2iuHOqdBUiMgBqOo7Ns4lmBeq99BWp7zXNQB434EJqGenqoKSI+7JUVsKzCAvXkBAhB5k4kUAdBWg7SckOL+FDj9XXhmjPeQMygER8A+thYWzqopeMiM07FDhChq4iY2lwdNXUiqq87F3fPBEnhTrPs5e5W5/1eRBzF2TbRR+O5P0dERESAH7tahUAd9+Mh2kfcbCV8XUzpruI57PKew3aKAwsiUD8QQnwdIIUQuhDiHwKv1nhdEU2KlBJnz8plRgbFz6zutBbeTsXPy1NPtVLEx2Lj8qfhwEHdAQK1NjQY+Db6yCj28kqtlhQYK5kEKChQK7EY+rlzmDORQB0EJ+UPdgkv4gPqdwiayQnU5Tiox7AXFpBuew25clPVDemB7JyKzqsPovq6g8nVSYUiPnojB3WEh7OVQTvTA5poPYE6Gr4WEVEznCqHjdfb3BFxlDAc1EIVnsmjjQ4ZggjU7wP+DnAWSALXsn+P6EBkxgFHlv1mmHPMdl4Lb0fibGVAFQXdkUqPDqLJIj52FyHRQIE63gkRH2ugKKgDA4Fvo42OYjeBg7qUQA1gTE+RuTtTryW1NH4xrIY4JPHw/dYac2YWtb8frZzX8vg40rK834M2IucAqSJDT+3RO3FIYlRfdzC+0Fgo4kNoCkpCx201QTIiVPyh41p/DLUv1loZ1L//A/D/RG9pERG1wt21qqqj623uiDhKTqCuch6P0q21VcRHyf8NKeUq8L11WEtEC+BvIMttJ1E7dAhSp+JsZVD7Ywgl/9AGoQhv49VMgsTOIow/3bjH7x4AobS3g3p1DXVgAKGqgW+jjY6Q/spXariqYOQE6vHxgteJTU2T+uKXkK6LUCqbqN0p+J8FYUV81DtHz5ydxZicLOs2+pg3gNWan0cbblDWfQ1w9/wCuxoHtYGzvh/WklqCqL7ubJyd4rM6wIv5iBzUnY2byg4d7zNQe43WOrB48GUvPu9t/xsMTDR6NRERbYW0s/FqVTioRZcKShTx0SjCcFCDJ3C3k0BdcgcthPiYEOLUob8PCCF+K8idCyHeJYR4XQhxWwjxT/L8/B8IIV4RQtwUQvyREGLi0M++XwjxZvbr+wP+eyJqjL/5r2RIItBcgmREzbC3Cg/+8VETRvM4qB0L9lagd6xxa1AUiA+1tYPaXlsLPCDRRx8dxdnawt1vrHhlJZOop0+jdHUVvI5xcRqZyWDNt+cgvDBxw4746NZA1DeDupx4DwB93HPft9ugxAMHdRVtpgkdZ69JPg/qRDX1dUTr42yZoBQ3fKh9Bm4rOWYjQicXBdMfQ+03Wifiw7E944d04flfb/RqIiLajlztVY1ALbxu58hA2Bhye6HIQX2EIBavJ6SUm/5fpJQbwFOlbiSEUIEPA+8GHgXeK4R49NjVXgCekVI+AfwH4F9kbzsIfAB4K/Ac8AEhRPA+2oiaceCgLi4+HkeJZyMdmilzOKJm+A7qYii9TSRQ7y5733tHG7uO+Om2dlA7q6top8sTqLXhEQDs5eVaLCkwVjKJfrawexogNj0NEOVQByCMwvowQhFegVYHF4izu4e9vFyBQJ11UCfba1BiGAW20uNtkKTbUXmlFdXXEe2Bs22iJoyCnWbgO6ibpE6KaAgHQ8djqL0GznYG2Qq5znvLIB3QuuGr/xeYe41eUUREW+HsVT//A7Lu2yjioyG4aRs0gdCDdxbnwxOo2+c5DCJQK4fF4ax4HGQX8hxwW0p5V0ppAh8HvuPwFaSUfyylTGX/+kXgXPbP7wT+UEq5ni3Y/xB4V4DHjKgxvsOp3DdDoYjcBjSivZFSBhKo1YSOu9Mkr4edRe97Ix3U4A1KTLVXPu1h7LU11KHTZd1GG/UODRotUJvJJEaR/GkAwxeo70YCdSlyER9VTK4+Tr0+Y8x7swBlR3yovb0oiUTbOaidEBzUSkIHl7ZygASg0vo6og1wdkyUUp1m/QYybSMtp06rimg2/Mxptd9A7Y8hTa+tv+nZzh7Efu3fhv0tePHjjV1PRESb4WY1GbXKTkQlrufE7oj64qasUPZBSrfeVvVzEIH6XwL/QwjxT4UQHwL+jKzTuQRngQeH/j6XvawQ/wvwqQpvG1En3AojPsATD6Ihie2Pu5fNy+svsfHqNXB2zeZwguxmBepEox3UQ23toLbX19EGB8u6jT7qOaitBg5KlI6DNb9QdEAigDYwgHrqFJk7kUBdCjdlI7o0hFrYPVgudROoZ2YBMKYmy76tPj6ONd+GDmpVIIzKc9c7dE5FpfV1RBvgbmeK5k/DQT61E7moO5aDKBgjF53XEjEf297cDq5+J4xdgy99BJqh3o+IaBPcsBzUPXrkoG4QMm1XnT8NILIZ1E2hqYRAyd2ElPL/Ar4bWAIWge+SUv5OmIsQQvzPwDPAz5d5ux8WQlwXQlxfWVkJc0kRBXB3LURMRejlb0TVniYbihdRE/yNlBYg4gNHIpvhxG8n62hsCgd1ewrUbiqFTKVQy4348B3US41zUNsrK2BZJQVq8FzUkYO6NM6eVbXr4zieC6QeAvUMCIExUf7QJ31srO0c1G7KQonrCFH5YYPS4wkvbgfFgNWjvo5oXpztALM6sgf90aDEzsXZyqD2elEwBwJ1C7wetrICdd9Z+Jq/Bauvw53PNXZNERFtRK4TsUqBWo1HAnWjcNN21fnTkJ3DY0uk5YawqsYTVGV8DfhPwCeAXSHEhQC3SQLnD/39XPayIwghvgX4SeA9UspMObeVUn5USvmMlPKZ4eHhQP+QiOpwdq2iA12KoSSiiI9O4HBeXjHUXu911BQ51DuLIBToafD7SPw0pDe8oY1thr3mRZdoZUZ8KL29iO5u7AY6qK2k9/Gjnz1X4ppgTE+RmZmp9ZJaHjdlhZY/7aPWyQVizs6ij4+jxIq/x+VDPzuO3YYO6moLbN9B3YFdVpXU1xEtjrRc3JRdWqDOOqjdVnDMRtQEZ9vM1dNKKznqt5Ne/nT3gOei7hnxXNQRERGh4OxZIKqPylN6NNy99nHfthJuSA5q/z6awvQXAiUFaiHE38Vzd/wh8F+BP8h+L8XzwGUhxJQQwgC+B68AP3zfTwEfwROnD9vjPgO8IzvRfAB4R/ayiAbj7lkVxXsAqAmjEzefHcdBXl4JB3V20KbTDDnUO4ueOK02OP6zJyveptYbu44aYK96zvByhyQKIdBGhrGWm0GgLu2gjk1fxFlbw9ncrPGqWhtP1AxXoK5XkW3OzJQ9INFHGxvD2drC3WufgVG+g7oalA6M+Kiivo5ocfyD+chBHVEKZyuTe53kHNTNYOwoxfY89I2DEKDF4Jkfgjc/A2t3Gr2yiIi2wN2zULqrj8pT4jq4sjWy7dsMNxWuQN0uOdRBHNQ/BjwkpbwqpXxCSvm4lPKJUjeSUtrAj+IJy68CvyelfFkI8UEhxHuyV/t5IAH8vhDihhDiE9nbrgP/FE/kfh74YPayiAbj7lbuelN6dOS+jbTbo/0gIj9eXp4o6bRXe5uopXtnEXrPNHoVhwTq9ov5cLIOanWoPIEaQB8ZxV5uXIxTTqAeLx0BY0x7wmXkoi6Ou2eF0tZ2GKWn9kW2lBJzdrZigVofGwdoq5gPN1V9XIsS10F0nIO6ovo6ovXJHeSXyKBWYhoipkYO6g7miIPaUBFdaq5TsanxBWqfZ34IFB2+/NHGrSkioo1w98LpRPQNBp1kEGgWwnZQd5JA/QDYquTOpZSflFJekVJelFL+XPayn5ZS+kL0t0gpR6WU17Jf7zl029+SUl7Kfv2bSh4/InycKt4MO9Eh1Yk4m57bQyjFT3R9gbppHNSNzp8GL+ID2nJQor2ajfg4XV7EB3g51I2M+DCTSdTh0yhdXSWvG5ue9m5zNxKoixFWYX2YXJFdQ5HTXl7BTaUqGpAI3pBEoK0GJYbhhheKQInrzXFgWT8qrq8jWht/yF0pBzV43WiRg7ozcfdtZMY5cpCh9sVa48Bie97Ln/bpHYXHvgte+Lewv924dUVEtAmhCdRZg4Gbag9xs1WQjos0nVC6SXMCdZs8h0Ek+7vA54UQfwDkKiQp5S/WbFURTYmU0ov4SJQuqPNxOGOyVPxDROvibGUCPb+iSwVV4DSDILG7CGefbvQq2tpBba9nBeqBgbJvq42OYC8vI6WsahBbpVjJJMZ46XgP8GJAhK6TuRu1sRZCWg7ScmsQ8ZH9jElZaHSHet8+ZtYZH5ucrOj2vgvfmm8PB7WUMrS4FiXRcYOUo/q6QylPoDZy14/oLA5muhy8TtS+Fng9uC7sHHNQA7z1R+Dmv4cbvwtf877GrK0N2f7DPyT9wg1G//E/avRSIuqIs2ehDVVf6/r1mxMNSqwrvts5clCfJIiD+j5ePp4B9B76iugwZNoGV1YV8QGRg7rd8doRS2+6hBCovQZuo7P0HAv2VprMQb3W2HXUAGd1DaW/H2GUf8Clj44iTbNhuc5Wcj5Q/jSAUFWMycnIQV0EJ3vCr1QZC3EctQ6fMeas97xWnEE9PAya1jYRHzLjeHVBCHEtao/uDf3pHKL6ukNxtk3QBCLAxlTti7VGpENE6OQOMvoPO6hbQKDeWwHXPilQn30LnHsOvvwRT8SOCIXtT/wX1j/2MVyzyV8XEaHipqyc+a8acvpMm7hvWwX//zuM+jnXQdomAnXJ/xEp5c/WYyERzY+/caz0zTA3FK8ZHLMRNUFKibOVoeuRwUDXV3qNxg972c1GRzRDBnV8EBDt6aBeW0OrIH8aQBsZ8e5jaakiB3Y1SMfBWlig753vDHwbY3qazGuv1XBVrY0vIKs1clC7e7Ur0MyZWUR3N9roaEW3F6qKPjraNhEf/nMZloPamm+f4ZGlqLS+FkK8C/g/ARX4DSnl/1Hget8N/AfgWSnl9YoXGhE67nYGtS8WqCNI7fcO8qUjqx6GFdFa5BzUfYcd1DGcHRPpypJReg1j25vbcSTiw+dr3gf/4Yfg9h/CleB1VURhzOQcOA7mzAxdDz3U6OVE1AGvqz2c7jU1K5BGBsL64ovJQQ6qSyFiKghw0+3xHJZ0UAshhoUQPy+E+KQQ4nP+Vz0WF9Fc5DaiFQrUapRB3fbItI203MARLmpCx210BvVOEwnUigrdA+2ZQb22WoVA7YmB9vJymEsKhL2yApYV2EENELs4jTk3F7lZCuCmwhM1D5PL0avhZ0xmdgZjYgKhBGlAy48+Noa10CYCdYgOEDVhdNSQxErqayGECnwYeDfwKPBeIcSjea7XizeE8Uu1WHtEdTjbZqB4D8i6Z2Vk7uhEnC0/CuaQg7rfAFc2915qO/v51p+nbnrkPdA7Dl/81fquqY2x5rwDgcwbbzZ4JRH1Qu47VXW1H0Z0aZ642czvKW1ImBEfQhGILq1tHNRBdlj/FngNmAJ+FpgFnq/hmiKaFD8bstI3QxHzMocryZhMv/giO3/0RxU9bkT9sP1iOkDEB3iDEhu+6drJtto3g0ANXg51GzqondU11NOVCdT6qOegthowKNFKeoV/OQK1MTUNjoN1/36tltXS+A7nsCM+hJHNta9hjp45M1vxgEQf/ex4+zioU9XVBYdRenTkvo20O6b1u5L6+jngtpTyrpTSBD4OfEee6/1T4J8D+6GtNiI0yhKos9eLYj46D2c7g9KjIfSD7XpuwHgzx3wUc1CrOjz7v8DdP4blqNOsWpztbdxtb+hk5vbtBq8mol74Xe1h1NHekGotV89F1IcwBWr/fjpJoB6SUv4mYEkp/0RK+UPAN9d4XRFNSLURH0II1IRekUNq5cMfZvFDP1fR40bUj4OBLsEc1EqvgbtnIR1Zy2UVZ3fR+55oEoE6frpNHdRraIMVOqiHh737WKq/g7oigXrayyfO3L1bkzW1OmGKmocRQqD06DVzgbimiTU3R6zC/GkfbWwMe2kZabd+IRlqhl7ndVlVUl+fBR4c+vtc9rIcQoingfNSyj8IdbURoeFsmzmhsRR+PeW7aSM6B2fLPOKehkOvh+0mPrDYToJqQLxAzfeWHwA15mVRR1SFNTeX+3PmzchB3SnkovJCqqOVuB5lUNeZsLtJlbjmzYtrA4II1P5OYUEI8W1CiKeAYAGzEW1FzkFdxS+SkjAq2nxaD+awl5baYkPfzpQrUKu9Okgae2q7swhCgZ7hxq3hMD3tJ1C7pom7s4NWoYNaGAbq0BB2Ix3U48GHaMYmJwEwI4E6L7m4qO5wBWrwcq1rJXBaDx6A62Jkn99K0cfHwXG8+JgWxwmxwPYPvzso5iP0+loIoQC/CPyvAa77w0KI60KI6ytt8FpsFdx9G2k6wesk30HdzIJkRE1wtjInXidKXys4qOe9AYmFMtZ7TsMTfwVe/DikN+q7tjbDzArUxsREJFB3ELk6OiyBuobmjoj8+GKy0hWig7pNDhmCCNQfEkL04xW7/xD4DeDHa7qqiKbE3bMQXRpCqzx7U+nRy450kK7rnRC7bkMEqojgOFsZEF6WaBD86zV0UOLOAvSMgBpu3EDFtGHEh7O2BoBaYQY1eIMSG5FBbc7NoQ6fRunqCnwbpacHbWwsclAXwE3Z3mdJDQZ+KYnauUDMmRkAjCod1PrYOEBbxHy4KRtEOC2K/iDlDtokVVJfJ4Hzh/5+LnuZTy/wGPB5IcQs8DXAJ4QQzxy/IynlR6WUz0gpnxkebpID2g7AFxaDRnwoPboXXRQ5qDsOZ9s8EZmnJnQQrSBQl+g6e+v7wErBV3+nPmtqU/z86cTb34714AFuKtXgFUXUg9AF6rgeRXzUGTdlI2JqaHuhjor4kFL+VynllpTylpTy7VLKt0gpP1GPxUU0F86uWXG8h4/ao5edQW0vLyMt7zbWwkJVjx9RW7x2RCPwm62SbXF1GypQL0HvaOMe/zjx05BaB9dp9EpCw171BGrt9OmK70MfGcFqgEBtJecxzp4r+3axqSnMuzM1WFHlSMfBvHev0cvA2bNQQ86f9lHiWs0EzowvUFftoPbc+NZ863+euanswbVSfYHtt6o2fC5Bnaiwvn4euCyEmBJCGMD3ALnbZO/vtJRyUko5CXwReI+U8nrN/iERZeELi0rAiA8hBGp/LHJQdxjScnH3rBMRH0JVUBJ6c2eSbyc9B3UxzjwOE38Ovvzr4LSHqNIIrLkHKH19dL/laQAydyJjRCfghC5Qazh70e9hPXHTdmj509AhArUQ4h9nv/9rIcS/Ov5VvyVGNAvunlX1G6GS8FpIpAyeOWw9OIhbjATq5iZfO2Ixci3dOw2O+OgNHt9Qc3pOA7Kt2h7tNc8RrlXjoB4dbVjERzn50z7G9DTm3btlvdfVms3f/33ufNtfxM462huFm7JCy1w7jtKj5wr3sDFnZ1FPn0bt7a3qfvQxX6BuDwe1GkL+NBzKoG7ziI9q6msppQ38KPAZ4FXg96SULwshPiiEeE/tVx9RLX7HWFm1Up/R3IJkROj4BxL5ho6rfbHGGjuKIeVBxEcp3vojsHUf3vhU7dfVpphzc+jnzhK7dAmIcqg7BTdlIXQFxVBDuT+lx3NQN9Oepd1x03Yo81t8lG4dN223xXNY7H/l1ez3yHURAXi5kNpQd1X3oSZ0pOUiTRcRC/amaj44GADRDo6zdsbZyqCf6Ql8fd9B1FDH3M4CnHtL4x7/OP5Qmb3VrFjd+hxEfFT+79FGR3DW13FNE8UI5jyrFuk4WAsL9L373WXfNnZxGjeVwl5eRh9tDof+3n//M7BtMnfuVHVYUC1uyg48IKxclLiOTNtIR4YeIWLOzObyxatBicdRT53CWmgHgTq8wwYRU0ETNTtgaCKqqq+llJ8EPnnssp8ucN1vquQxImpHblZHGe+Ban8Mc26nVkuKaEJyUTB5DjLUPgNno0kPLFJr4JilIz4AHvpW6L8AX/w1eOTba7+2NsSaSxK7eBHjwgWEYUQCdYfg7oZr9FDjOjgSaTqIWJNEXrY5tXBQ47bHc1jQQS2l/C9CCBV4XEr5seNfdVxjRJPg7llVR3woPeVnTFpzD0BRUPr62mJD365IKT0HdcBcRQDFUBExtXFOEMfy8p4TZxrz+PnwRek2yqG219YB0IYqn//li7z2cv2GednLy2DblTmop6aB5hmUKKUkdd3Tw8zZ2Yauxd2zQnUNHMb/jHLT4Yuc5sxM1fnTPvr4eFt0BLmp8BwgQgjUHqPtHdRRfd3ZuNsmIqaiBDRpgOeidbYybeGMighG7iAjT02t9hk4O00qUG9nI/GDOKhVDZ77G3DvC7D4Um3X1YZIKb0uv3PnEKqKcekimdu3G72siDrgpuxc11kY+HWcG8V81I2wu0l9sbsdYj6KZlBLKR3g6+u0logmRrrS+0UKIeIDynPMmg/m0MfGMM6da4sNfbsi9x2k6ZbVtgqei6hhQxJ3s5ERvU0kUMezAvVe+wjUztoqSjyO0l15B4Y2MgJQ10GJVtLbaFUW8eEJmc2SB2jevYuz4cXGNDqHuqYRH9n7DTuH2tncxNnYCE2g1sbHsNsh4mMv5AI70RmT5KP6unNxdsyyDvLBi3TAljUbABvRfBR3UMdw92yk7dZ7WaXZzn6uBRGoAZ7+66DH4Uu/Vrs1tSn2ygoyk0E/59WosUuXIgd1h+CEELt6GP++okGJ9SOog9peWyN940bJ6+UOGdqgTig5JBG4IYT4hBDi+4QQ3+V/1XxlEU2Fm7bBperTOrWCjEnrwQP08+fbZkPfruTcHmUK1Eqi/MGZobGz6H1vJoHad1Dv1c8pXGvs1TXUKgYkgpdBDWAv1y+H+kCgDrjROoQ2PIySSDSNgzr1vOeeVhKJhgrU0vIOssIsrA+j9PgukHDfU3zXebUDEn30sXGs5HzLOyLDdFCDVyN0ypBEovq6I3G2zfIP8rM5xFEOdefgbGUQhorSdfL91T/g8EXspmIrG8vYF3C4dPcAPPk9cPP328qYUQ+sOa9GNc6fByB2+TL24iLO9nYjlxVRB9w9K7T5H3BYoG59cbMVkFIGFqjXPvrr3PuBH0TaxZ8b0SkO6ixdwBrwzcC3Z7/+Yi0XFdF8+Jt9NSQHdTnigTk3h3H+XNts6NuVnEB9qoUc1M0oUPsZ1KnGDrILE3ttrerM45yDuo6DEk1foB4vX6AWQmBMT5OZaRKB+vp1tOFh4s8+i9VAgdrJFr++kBw2vps37GnkmZlZAIypyVDuTx8fx02lcFt4IyltF2k64Tqoexp4YFl/ovq6A3G2MmVn8Kt9Xl3VlIJkRE3wDjLyv04OBOomPLDYngdFg57h4Ld57kfAycBXfrtmy2pHrKR3GKCf8w4DYpcvA5C5fadha4qoD27YDup4bcwdEfmRlguOzInKxTDv30fu72PNzRW9ni92yzYQqEv+r0gpf7AeC4lobvwNY9UO6h4/4iPYG6CbSuGsrqKfO4/QdW9Dv7OD2tdX1ToiwsfZ8tsRy9x49Rrsv7lZgxUFYCcbGdM71pjHz4eqQ9eptnKSOGurVTtP1VOnEIaBtVTfiA9teBglVt6hi09saoq9L34x5FWVj5SS1PPPE3/2GbSRUfb+7M+QrotQgpxRh0vusLNGER+5Lp2Q2xTN2VnQNIxzAV1hJdDHvPcca2EBtb8/lPusN24NDhuUhIG7502SFyLcIZfNRlRfdx5SysoiPrKO68hB3Tk4W5mCTnulmQ8stuehdxzKqS9GHobpt8Pzvwlf/2NeHRxREvPBA+Aghi52KStQv/km8aefati6ImqLtF1kxglZoPbNHZFAXQ9y9XMAF7zfzZuZmSm6l+6YDGoAIUSXEOLvCCF+RQjxW/5XPRYX0Tw4e14R5A85rBShqwhDxQ3YwmtmT4uM8+fQxw829BHNh72VAVHeZHrwDj3kvu2dJtab3SUQSnlOj3rQc7q9hiSurqFW6aAWQqCNjtbVQW0l5yvKn/YxLl7EXlrC2d0LcVXlYyWT2EtLdD/zDMbkBDKTwV5cbMhafOG41TKozZkZjHPnEHo46/ZjY6z51v08q8VzqSZ0pOUizSbMVg2ZqL7uPNyUDY5EKVeg7tVBNKkgGVETnK3CBxlNHfGxnQyeP32Yt74Pdubh1U+Ev6Y2xZo7aqLQx8dQ4vEoh7rN8evbUAXqbg1ElEFdL3wRuVTEhz8IFcDMdnIWIueC7wSBGvgd4AzwTuBPgHPATi0XFdF85FxvIUyMVRJ64BM6v51BP3/+wHHWIjnU7v4+9mr7iIylcLYyKAkDoZbnyvQF7Ybkju4sQM8IKGr9H7sY8dNt46CWto2zuYk2WJ1ADV7MR72HJFYjUMeygxLNmZmwllQRfv50/JlnMCYmgMYNSvQnhNcq4kNoCiKm1kagDmlAItByn2f5OBCoa5CD2Bk51FF93WHkBt/1nXTGruxk+Au/+CfcXj75EhCqgpIwIgd1hyBdibNTxEEd10ATTSpQz1cmUF9+BwxMwRejYYlBsebmcvEeAEJRMC5fInP7dgNXFVFrnJBiVw8jFIHSrUUZ1HUiVz93F38O3a0t3D3P5FRqLykMFZT2yBEPoiRdklK+H9iTUn4M+DbgrbVdVkSzkYv4CGEjqib0wOKB5bcvnTuXy4FtFQf16kc+wsx3flfHZGZ77YjlO+wVX6BuRA71zmJz5U/79JxumwxqZ2MDpEQ9Xb1ArY+OYNVpSKJ0HKyFheoc1NPTAJh3G5sHmLr+PGp/P7FLlxovUNfYQQ3ZHOMQBWrpOJj37oUqUKtDQ15kzUIrC9R+i2K4DmromDbTqL7uMPzM4HzO2JeSm7y5vMuXZtbz3lbtN5pTkIwIHXfXBLdwZJ4QArUvhttsGdRSVi5QKwq89Udg7suQ/Er4a2tDjgvUALFLlyIHdZtTCwc1eLVc5KCuDzKgg9qfhQQBBGqRPWRIt/5zGESg9v+Vm0KIx4B+YKR2S4poRpw9CyWule2OzUc5Q5DMB3MoiQTqqVPehl7XsVtEoM68/gb2ygrOev7NRrvhbJU/mR4OZcbuNOANdWepOQXq+FDbOKjtNU9o14ZOV31f2sgo9tJyXQ597OVlsO3qBOrz50HTyNxtsIP6+nW6n3kGoShoZ84gYjHM2UY5qMN33R5HiWu5YYxhYC0sIk0TY3IitPsUQqCPjbW0g9qpwWGDkvAEmQ4ZlBjV1x2Gm3NQnxQe5zbSADxYT+e9rdoXixzUHUJupksep72P2tuEBxbpDbDT0Fdh3XTte8FIwJc+Eu662hBpWViLi+jnjv5fxy5fxllby9XeEe1HzQTqkM0dEYXJRXyU2Av58R6xRx8hMztb8n6Vbr1jIj4+KoQYAN4PfAJ4BfjnNV1VRNPh7oY3LVZNGIGHJFoPHqCfP48QwhNXxsawkq2xoc9lBt273+CV1AdnK4NWiUDd6IiPZhSofQe12/o5rPZqVqAOwUGtjY4i9/dxt7ervq9S5OKFzlUuUAtdxzh/HvPu3bCWVTbW0jLWvfvEn3nGW5OiYFw430AHtY3oCuewsxBqyEW271qIheigBtDGx7BbOoM6+JCXoPiDmDtEoI7q6w4jF/GRZ1bHgUCdyntbtd/ICZcR7Y1/EFHM9NGUjvrt7P6sEgc1QFefJ1Lf+k+egSSiINbiIrguxrnzRy6PXfYHJUYxH+1K7RzUUcRHvQiaQe1rXomv/3M4q6s4Jfa/noO69Z/DgjtEIcQrQoifAv5YSrkhpfwTKeW0lHJEShkdbXYY7l54ArWSjfiQbmkXpDk3h3GofUkfG2uJiI8jofb3GyME1RN330ZmnIoc1DlBot4RH7bpDSLsHavv4wYhfhqkA/ubjV5J1ThrnhNcq3JIIngRHwBWHQYl+m1VRhUOavBiPjIzjROo0185yJ/20ScmGiZQOymrZvnTPmG7QHyBOsyIDwB9fLylHdRuygJNQTHCy/D3MxX9wcztSFRfdy7OdgalR0doJ7dfvjB9v5BA3RdD7tu4plPTNUY0ntxBRpHYPM9BnWmuGMGcQF1F3fTWHwHXguvRvNhiHJgojkd8ZAXqKIe6bXH2LBClxc1yUeKRg7peuCkbFBCx4vWzlUyiJBJ0X3sSCBDz0e4CNfBeoAf4rBDiy0KIHxdCNKGSE1EPnF0rtDB+pUcHVyL3i/8CSdf18rXOH5wOt4pA7W5t4e7uAmDdb38HdZBiuhBCVVB6tMCu+tDYyw7bS4zW93GD0JONw2iDmA/fQa2eDiPiwxOo7eWVqu+rFP4BkzZeoRMoS2x6GvPefaTdmIIhdf06SjxO1yMP5y4zJiawHjxAOvUXOtw9C7WG+dMQfpFtzs56UVMhHLIcRh8bx15ZwTVbU4x192zUkKNackMu29tBHdXXHYqzbeaN94ADB3VBgTpbX0UxH+2Ps5UBVRSNT1L7Y0jTRWaa6MBi2xNN6a9CoB666A1MvP6bYEev9UKYWYHaONblp40Mo/T3RznUbYybsr3YVUWEer9Kj46Tsprr0KtNcdMWSreGEMWfQyuZRD97NmeQyZQQqNveQS2lfFFK+RNSyovA3wMuAF8SQvyxEOJv1m2FEU2Bu2flnK7VkhuCVGIDaq+sIjMZjPOHHNTjY9jLy0iruTevR0Lt7z9o4ErqQ5B2xGIoCaP+QxJ3Fr3vzeig9gXqVBsI1OtrCMNA6emp+r60Ue8wwa6Dg9pKzqONjKAY5R+6HMaYngbLyrld6k3q+et0P/00QjsQEo2JCS+/sAGHfW7KDr0t8ThKj4603NCchubsDMbUVMlCslz8wb/1eD3XAjdl1WTYpZrQ639gWUei+rpzKSZQP9hIoSqCrbTFVp4hR34ecRTz0f74r5NiApT/OmqqmI/teRBq9caPt74P9lbg5f8czrraEOvBHGga2pmjMYVCCGKXo0GJ7Yy7V5vaS4lrYEuk1frxks2Om7ZRuks/hzmB+tw5UFXMmdmi11fiWm4AYysTKARSSvlFKeWPA38dOAX8ci0XFdFcSFd6G9EwHdSUzpi05jxhVz+Ur6WPj4PregPMmhhrzhOo1YEBzE5wUG9WJ1CrvUb9Iz5yAnUTOqjj7eOgdlbXUE8PhSLuHTio6yFQJ6sakOgTm86eejcgh9re2CDz5ptH4j0AjIlJgIYMSnSzA3drid/tE1aWXmZmFmNqMpT7Oow+7h2OtcpcheP4Lp6wURJGx7SZRvV1Z+EJjyfrpJ19i82UxeNn+4H8OdQ5B/V25Cptd5ytTNEBiXBYoG6i18P2vDfXRaky9uniN8Pph+CLvwqRmzMv1twc+tgYQj35fx27fJnM7duRE7ZNCTN29TC52rlD6q9G4gnUxetnPy5WP3sWYRgY586VjPjwHdRBYnSbmZICtRDiWSHELwoh7gE/A3wEqK7nOaKlcFMWSG+4YRgo2fsplTFpPvAE6sMOam0su6Fv8pgPPx6g52u/BqtBWa/1JOegLuAMKkVDHHM72ddQ5KCuKfbaGtpQ9fEeAEoshnrqVF0yqMMSqP22rEYMSkx/9asAxJ89JlBPTnhrujdb7yXVzHV7GD/jOowi202nsRcWMCYnq76v4+gt8nlWiDAPrg+j9Oi4jRiaW2ei+rqzkI7E3TVR8tRJfrzH1130YoTyC9SRg7pTcLbNkpF5SjM66reTlQ9IPIwQ8NYfhoUb8ODL1d9fG2Im5woO8Y5duoS7vd30Zq6IynD2wotdPYxfm0eDEmtPEIOHu7WFu7eX67Y0pqYCCdRImiv6qQKKDUn834UQd4BfAZLA10spv0lK+WtSyrW6rTCi4YQ9LdaP+CglHlgP5kCI3C8meJmdQNMPlrLm5lB6e+l67HGcrS2czc1GL6mmONsmSiL/4J8gKFkHdV1P+3cWQSjQM1y/xwxKPJt1u9f6b7WeQB1edq82Ooq9VNuiW9o21uJiKAK12teHOnyazN3iRUUtSD1/HWEYdD3++JHLtZERRHd33QclSstFmm5dIj4gHIHa/z+KhTwgEQ4duM4nS1yzOfEOG8J3ULd7xEdUX3cm7q7pmT2KCtTeYW6+HGrFUBFdWnM5ZiNCR0rpOahLdCTmHNT17j4sxvZ8OAI1wJPvhVg/fOlXw7m/NsOaS3pt/3mIXc4OSnwjivloR2rloA7T3BFRHDdtI0o4qP24WP1sVqCensa8d6/o/CDfld3qOdTF1KR94F1SymellP9SStmYAM2IhuNvFMPKoM6d0AWI+NDGziAOZcDqY17WljXf3I6zXGbQBS+exHeDtytBiuliqL2GJ17V88RvdxF6RqpvRawFWgxifW3hoHZWV1FPhyhQj4zU3BViLy+DbeeKgmqJTU03xEGdun6d7iefPJGjLYTAuHCh7gK1m8p+ltQ44uPABRKCQJ11Kxg1EKgVw0AdPt2SDmov+suuTQ5iwhty2eotikWI6usOJDdMuvekQO07ph8Z6+VUXOfBRuFBiU3lmI0IHZm2kZZbMuLDO7BQm2doppSwlYS+6g/2ATB64Onvg1c+4d1vRA43lcJZW0M/W0Kgvn27nssqCzeT6YgIzLAJO3b1MGHWzhHFcVOlIz6snEDtvacaU5NI0yxq0vRzrdtWoJZSflBKGR29ReRO0sJqJxGqQIlrJR1S5oM5jEP50wBKdzfqwEDTb+it+ST6ubPoFy4AtP2HcLUCtdLbACfIzqKXldesxIdaPoNaui72+jraYJgO6hGsGmdQHy8KqsW4OE1mZqauHQLO7h77r7xyIt4jt6aJCaw6Z1A7IX+WFMIv3J0wHNSzs4D3/1UL9PFx7CY/cM2H3LdBUpshiT06yNYvsAsR1dedSU6gzlMrzW2kiRsqgz0GFwbj3F9P570PtT8WOajbnIPXSenIPLUvhtssQxIz22DtheegBnjuhwEJz/9GePfZBuRq1PP5BWptYAD19OmmHpS49hu/wd3v+Eu4+/uNXkpLIfdtcMPraj+Mbx6JHNS1RboSmQkgUGeFaCO7F/U7OYvFfOQc1C1+yFBZP35ER5GL+AjJQe3fV+mIjwd5P3z1sTGsheaN+JBSYs4lMc6exTjvCexWmwvU9lbpvLxi5GJf6tnWvbPYnPnTPj2nW95B7WxtgeOgheig1kdGcVbXkFbtXit+W5URkkAdm5rG3drCWV8P5f6CkH7hBXDdEwMSfYyJCcxkEmnXTwQ8cFDXWKDu1kCEU2RnZmbQxsZQurtDWNlJ9LHxpo+syoefUVirIYlAR+RQR3QOvrCcP+IjxbmBboQQnB+M582g9m8bOajbG3sr+NBxtc/ICdoNZzv7ORamQD0wAQ99K3zlt8HKf2jTiZhzXtNNoYgPgNjlS00tUKeuX0em02Ru32n0UlqKWho9lG4dBDgNzqC2lpa5863f1tSv32oIavCwkvMoPT0o/d7wZCOIQB1v/4iPiAggG/EhwhUVlB4dp8jm002nsVdWcgLvYbTxMewmdlA7GxvIdBr97DmU7m600VHMe+0rULumg0zbVUd8QCMc1KP1e7xyiZ9ueQe1s+bFqaohZ1AjJfZq7f5vfHeKNh7ORsuYngbqOygxdf06aBrd167lX9PkJNh27t9aD3KiZk9tIz6E4nXphJJBPTNLbGqy+kUVwDtwXahv/n4IOP5hQy02SdkDy3bOoY7oPJxtE5T8vzMPNtKcH4gDcGEwztxGCidPxI3aH8PdNZGOW/P1RjQGd6scB3UzCdTZWiKsiA+ft74P0uvw0u+He78tjPXAE6j1ogL1ZTJ37iDd5nuvkI7D/s2XAMi88UaDV9NahD0X7DBCFYgureHu29SXvoh59y57/+OLDV1HrfDF4yARH/rZswghAFAHB1H6+sgEcVC3q0AthHi62Fc9FxnRWNxdEyWuIRQR2n2qCaOoWzbXvnTupECtj49jJeebdkNvzfmFQzYz6Pz5to74cMpwexTCj/hw6yVQ26bnTm52B3WLC9T2qidQa0OnQ7tPbXTEu++l2sV8WMl5tJGRE9nNlRKb9k69M3fqK1B3XX0UJR7P+3Nj0ousqGcOda6wrrGD2n+MaieRSykxZ2YwJsPPn/bRx8eRmUxd3fVhUEsHddBByq1KVF93Js62iZow8tbSvoMa4PxAHMuRLG2fbH1X+wyQ4Oy05+9GRNZBLfJnlR9H7Yvh7JjNkdfv50SH6aAGmPxzMPoYfPHXvJzrCKzkHKK7G3VwsOB1YpcuIVOppuzQMu/exd3bAyKBulzcPd/oUZs6Wu0p3eFea9Iv3QIgc7c93fW5+jmgQO0jhMCYmsScmS14G9HuAjXwL7NfHwa+BHwU+PXsnz9c+6VFNAvetNhwhBofpcQboD9U0Mgb8TGOm0rh7uyEuqawOJ5fq09cwHzQAQJ1nrbVoCjdGih1dMztZsXNZs6g7jkNqbWWLsjtNU9gDzfiwxOorRoOSjxeFFSLduYMorsbc6Y+ArW7v8/+zZsF4z3gIFPZrGMO9YFAXVsHNZT+jAmCs7aGu7vruc1rhD7uHZI1++Df49QyrsXfeNU18qm+RPV1B+LsmCh5DvK3UhY7+zbnBw8c1AD388R8+EaAphmMFxE67raJktARaukmZ7XfAFc2XFACshEfInzjhxDw1h+B5Zdh9gvh3neLYs4lMc4dOCvzkRuU+EbzxSSkb94EQO3vjwTqMjlwUNemjlbiWtXmjmrZv+UJ1Gabxr/kHNRF9kJSyrx70djUdNGID6EroIr2FaillG+XUr4dWACellI+I6V8C/AUEI3T7SCc3fCnxaoJz90mnfziW659KU/Ehz7mb+ib71QY8kxdvTCBs7KaOy1uN/w8RK0KB7VQBErCqF/Ehy9QJ5pYoI6fBteC/a1Gr6RiahbxAdhLNRSo5+aKtk6Wi1AUjKlJMncLFxVhkr55E2lZRQVqdWgIpaenvg7qlI3oUgNtvKtF6dGrHpLoF4F+7lst0LMxMs08VyEf/gZGrUUGdTybg9imGdRRfd2ZOFuZvK7YBxueEO07qIsK1FkjQDQosX2xtzKofcHq6Vw8XjPEfGwnITEKag2cnY//FegehC/9Wvj33YJYc3PoZ4vXqDmBuglzfNMv3kTp6yPxTW9j/81IoC4Hp8azXJR4Yx3U0rbZf/VVADJ1jEWsJ246+xwWcVC729u4u7snBGpjagp7eRlnN7+mJIRA6daQ7SpQH+IhKeVL/l+klLeAR2q3pIhmw92zci23YeFnTBbKOTLnHqDE46gDAyd+1uyOM3NuDrW/HzWRAMCYuOBdnnWFtxthRHyAV2jXLeJjJ/vaaXYHNXgu6hbFXl0DVUXNDngIA3VgAHQde7k2ER/StrEWF9HPhtumGpuarlsGder6dRCC+NOF0wKEEN6gxDoK1E4q/MPOQoTRppiph0Dd5AeuhXBT3mwK0RW+QC0UEYoDvgWI6usOwt0xCw5IBDiXzaAeO9WFqoi8gxIPHNRNIEhG1AR3OxO4ns69HprhwGJ7Pvx4Dx+9G97yA/D6J2FjtjaP0SJIKQOZKNREAm1sjMzt23VaWXDSN2/S/fjjxK48hLOyir2x0egltQzuroXQFRRDrcn9Kz16QzOoM7dvI/f36bp6FWd9vS1fGwcZ1IX3Qwdmx6PvqUZ2Jk6pQYlt66A+xE0hxG8IIb4p+/XrwM1aLyyiefAiPkIWqLORIYUiHawHc+jnz+dtX8pt6JvUcWYl548UDr4LvF0HJTpbGZQezWsrqQI1odcv4mNn0fvezAJ1PCtQt3AOtb2+hjY4iFDCc8wKRUEfHsaqUQa1vbQEjhNqxAeAcXEaa34eN137SfTp69eJPfRQyYMBY7K+ArW7Z6HWIX8a/Axqq6pZBebsPYRhoI/V7n1C6e9HxONNPfg3H27KRukOdzbFYbxBym0vUEf1dYcgLRc3ZRcQqL3PBH9Ioq4qjPV35RWolbgGmmgOQTKiJthbZqABiQBKXzM5qGsoUAM8+zcAAV/+9do9RgvgbG7i7u3ljcA8TuzypaZzULt7e2TeeIPuJ58gduUK0JwxJM2KW2OjR6MjPtIveWf2/d/xHQCYd9ov5iNIBrV5qBtfuhLH8oadxrKGGXO22KDExh4yhEEQ1eAHgZeBH8t+vZK9LKIDkI70iuqQHdS5IUgFWnituQfoBT581aEhhK437Ybea706ELeMC56D2mrTHGpnywzcjlgMpbeOER87iyAU6Bmuz+NVQk82FiPVugK1s7qGejq8AYk+2uhozSI+/KLACFmgjk1Pg5Q1F4SlZZF64UbReA8ffWICK5lEmvX5vXNTdl3ypyGbz+eC3Hcqvg9zZgZjYgKh1sapAp6TXR8fa0kHdS2HXaoJvZ0zqH2i+rpD8AXlfLXSg/UUvTGNvkOb1QuD8bwRH0II1P5Y5KBuU1zTQabt4BEfiWwcUtMI1OHWTUfoPwuPvge++juQ2a3d4zQ51lxWuAoQQxe7fBnzzh2k3TxuyvTLL4Pr0vXEYYE6ivkISi1Mg4dRenTvQNWsvHauhv1bL6P09pL45m8G6jtcvl64aRuhK0WNfdahvehXP3uPf/uBLyKlRJ+YAEUp7qDu7gAHtZRyH/g14J9IKb9TSvlL2csiOoDcIKTQHdRZgTpPC6+UEvPBHMa5k/nT4DkotfGxpoz4kFJizc8fEajV3l7UwcG2dlBXG+8B2YiPXas+08h3Fr2sPKV2wlPVtIODem0NLcT8aR9tZAS7RkMSraQnFIbuoJ6aBiBTYzfA/iuvINPpQAK1MTEBros5V5/Y21oX1ofxxdNqYiLMmZmaxnv46GPjTfl5VoxaHzYoCaPtIz6i+rpz8A/fCzmozw3Gj3QMegJ1/m4btc+IhiS2Kb7QHNRBLVQFJaE3/vWQ2YHMVm0d1ABv/Vve49z8eG0fp4mx5ry4yEAC9aXLSMvCvN88EZP72QGJ3U8+iTYyHA1KLBOn1gJ1tq5rlIt6/6WX6HrsKvr4GKK7m8yd5ouoqRa/A7EYVnIepacHpb+f5Bub7Kzvs7O2j2IY6GfP5iII89ERArUQ4j3ADeDT2b9fE0J8osbrimgSctNia+SgztfC66yuIvf3CzqoIbuhb0IHtb2ygsxk0M8dC7W/cAHzfjsL1MGK6WKoCd2bRl6PN9XdrEDdzPgZ1HsrjV1HFdhrq7URqEdHvCiOGmAlkyAE2li4k+iNyQkQArPGgxJT168DEH/mLaXXNDEBgHlvtpZLylFr1+1h/M8sp8I2N2lZmHNzGJOTIa4qP/p4c36eFcPdq7GDukdv2yGJPlF93TnkhMc8AvWDjVRuQKLP+cE4q7sZ0nlcbGp/rDkcsxGhk5vpUkZXotoXq9/8lkJsZz+/aumgBjj/HIxdgy99BKqI72plzLk5gJJDEqE5ByWmX7yJfuEC2sAAQghiV65EAnUZuHsWahkCtbW0TPIf/K8429uBrq+GYO6oFDeTYf+NN+h+7DGEohCbnsZsVwd1SYE6mTNKrT7Y8b7PeZ0jxtQk5sxswdt2hEANfAB4DtgEkFLeAGpvKYpoCvwNYjlvhkEQXRooIm8Lr/nA+/A1zud3UIOXQ92MLdHWocygw+gXzrelQC0tx4uACcFBrWSnkdel0N5ZhN5wBcjQ0bvBSLTskEQpZTbiI3yBWh8dxU2lcHbDb/O0kkm0kREUo/pDl8MosRj6uXOYM7UttlLPX8eYmkILEK3ii6/1yKGWlos0XS96ow7kiuwKYyLMuTmw7To5qMdw1tfrkk8eFrV3UOvIfQdpuzV7jCYgqq87hEICtZSSuY10Ln/a5/yg9/cHG3kGJfbFcLYyVeXrRzQn5TqowXfUN1qgznZh1dpBLQR8zd+C1Tfgzudq+1hNijWXRD11CjXRU/K6sYvTIERzCdQ3b9L9xBO5v8euXCHz5ptIt60/60Oj3E7E3T/5PNuf/CR7X/hCoOvnOtwbkGGcee01sG26Hnsc8Gb3ZOo0XL6euGmrZP1sJZPo4+Oktk3SO95zsZb09ryxqWnM2dmCvzOiW/Pq53p0pNeIIAK1JaXcOnZZ6/6LI8riwEEdrlgjFOFNis1zQhekfUkfG8NeXkZazdUC7McDGMfWblyYwF5cxM20V1umXxSHEvGRfY3VJYd6Z6G5ByT6xIdaNuLD3d1FmibaYC0iPjz3ey1c1IdPrYvhSpd9u7xufGN6ikwNHdTScUh95SuB4j0A1FOnUPr66iJQ5+Ki6uWgrrLI9t0JsezE7Fqij/uDfxdr/lhhUWs3fM4B394xH1F93SE42xnQlBOuqY2URcp0TjioL2QF6vtreQTqfgMc2fYROJ1IzkFdRk2t9hk4Ow3eW9RLoAa4+p3QMwJf+rXaP1YTYs3NBYr3AFC6u9EvnCdzuzliEqzFReylpRMCtZtKNaXprNmQlpM1egSvvfwhg6kXbgS6/kHER/0/X9K3bgHQ/fhjAMSmL2IvLODs7tV9LbVEpm2U7sLPoZQytxddfZA1YonDDuop5P4+9mL+PYMfH9LKLuogAvXLQoi/BqhCiMtCiH8N/FmN1xXRJPgRHLXIO1IT+Vt4zQdZgbqISKSPj4GUWDUalFYplt96NX60SDMmLnjrzf68XbArKKYLofQWjn0JFdv0XMmtIFD3nG7ZIYnOmuf81mrgoNZGR4DGCtS/88rv8K7/+C5MJ/iBSmz6IubMTM2cIpk338Td2SH+bDCBWgiBMTGBVQeB2hcaw+7GKYTv1Hb3KivQzNlZgLpFfAAts0GTlou0grvhzfv32fmjPyrrMdSebEdNew9KjOrrDsHZNlH7jCM50+ANSIQDx7RPTqDOMyjRj3+IYj7aD2crg+jSUIzg81HUvhjunt3YbpPt7GdXPQRqLQbP/BC8+VlYbz93ZSnKEajBy6FuFgd1+kU/f/qQQO3HkEQxHyVxsvVsOZ2ImTe9w4n0jRuBrn8wI6z+4ub+S7dQh4ZyEYuxSxcBat55Wm9KZVC729u4u7ueQD3nxXuce2jgiEANFDQ8+YcMss0F6r8LXAUywL8DtoG/X8M1RTQR7p4FgpJh7pWgJAo4qB/MoY2OosQKi57+m5e90FwbeiuZRB0aQokf3WwYFy4AtN2gxEraEQuh1iviYzcraraCQB0/3bIOajsrUKtDpaMmykUf8QRqK+RBidK2sRYX0c+W3mR9IfkF1vbXeGn1pcD3b0xPITOZmg3ESz3v508HE6jBy6E2Z+vpoK5PxIcwVNBExRnU5swM6sAA6qlT4S4sD/qY76Burs+zQpTrhl/96EeZ+7G/j2sGf2/3HdRue+dQR/V1h+BmBerjzG14sT7HHdQDcZ1ETMsf8ZGttxo+GC8idJwts+x62n9dNfTAYjsJPcOeeFwPHn2P9z351fo8XpMgXRdrfh7jXGkThU/s8iXM2dmyPn9rRfrmiwhdJ/bII7nLYlcigToobgVGD38w+/6rr+Lul+769J29jXFQewMS/YNcY9oTqGs9XL7euOniArVvVvEE6l16h7o4e+UU2ytpzH0bI9vZaRYYlNgRDmopZUpK+ZNSymellM9k/xyor1kI8S4hxOtCiNtCiH+S5+ffKIT4qhDCFkL85WM/c4QQN7Jf0dCYBuHuellHQhGlr1wm3hCkPBnUcw+KDkgEb0gi0HSDpazkXF73pe4L1PdrLwTVk0raEQshYipCV2of8bGTbYlp9gxqyDqoWzOD2l6tpYPaj/gIV6C2l5bAcUo6qG3X5uaK5wT58uKXA99/bHoaAPNubYqt1PXr6OPjJzo4imFMTGAtLNQ8fsifCF7L6eOHEUKgxvXKM6hnZuqSPw3Z17OitIyD2vGfy4CHDebtO2DbuVbTIBQbpNwuVFNfR7QWTgGB2hegjwvUQgjODXTnHNaH8eutyEHdfjjbmbLr6ZxA3chBidvz9XFP+wxkP5s7zEHtR1vq5wrPaDpO7PJlcJyCYlY92X/xJrFHHzky40VNJNDHxyOBOgC52NWAdbSzve1Fqjz9NNg2+9kIjWIIVSC6tLrHqzm7e5h37tKdzZ8GMC6cB10vq3ZsdqSd7UAsUj8fnme2+mCX0+cSDJ1NALA+v4c2PIzS09PZArUQ4ooQ4qNCiM8KIT7nfwW4nQp8GHg38CjwXiHEo8eudh/4AeB389xFWkp5Lfv1npL/koia4JQZxl8OSk9+8cB6MIdR4sM3l9lZIydipZjJZF73pXrqFEpvL9b9Bw1YVe1wNjOI7vLaEQshhEDpNWrf0r2bFagTo7V9nDDwM6hbcBiSveY5v7Wh8AVqpbsbpa8v9IgPM1sUGCUE6jc33iRle8LB9cXrge/fyArUtRj6IaUkdf164HiP3JomJ7z4oQe1fW/KFdZ1yqCG7GdMhS6QzOxszqVQa4SmoY2OYjfZ51khynFQSynJZIvo/ddfD/wYOQd1G+fsVlpfR7QWUkpPeOw7KTzObaQ4Fdfp7Tr5u3RhMJ4/4iNhgIgc1O2Is5X/IKMYih/50sjXw/Y89J2sm5ytrZoMs8aIe4/XYQK1X6eVG/EBB1EPjULaNumXX6b7iSdP/Cx25Qr7kUBdklztFVCX8Z3Hp777uwFIvfBCoNupPVrOVFIvMq++AlLSlc2fBq82jk1OkLnTPr/nvmhc1EGd3YsyPMrmcorT53sZOucJ1Ktzu14849QU5mwpgbp16+cg9pffB34N+A3AKeO+nwNuSynvAgghPg58B/CKfwUp5Wz2Z9Ho1ibF3bVqlhmqJAyk6eCaTk7gdDMZ7KWlkg5qpasLdXCwqRzU0nGw5hfoe8c7TvxMCIFx4QLm/TaL+NjKoIUQ7+GjJvTIQX2YntPgZMDchVhvo1dTFs7qGgiBOjBQk/vXR0ewlsMVqP0hp6WK/xeWvSLvWy58C3+a/FMyToaYWtr1pGUjI8waDEo0Z2Zx1tboLiPeAzwHNYB57x6xS5dCX5fPgUBdn4gPoOAg3lI4Ozs4q6t1yZ/20cfGmurzrBjlCNTO2hruljcHMPN68A2oF9GitLWDmsrr64gWQma8wVZ5HdTr6RPuaZ8Lg3H+9M1VpJRHsquF6h3m+0OqI9oD6bi4u2blDupGR3xc+JoTF8/9nR9FHRzk3L/6P8N/zMFpWGsfZ2UQzLmsiaKciI+pSdC0hudQZ27fRqbTRwYk+sSuXGH3C1/ANc0j7uqIo+TmggU0evjDMePPPYsxMUE68KDEys0dlZJ+KTsg8bHHjlxuTF8k89prdV1LLQkSd2gmkyjxOJu7GkjYjsHv3JzD6NaO5FCnruc3SPmvj7Z2UAO2lPJXpZRfllJ+xf8KcLuzwGFL1lz2sqB0CSGuCyG+KIT4S2XcLiJE3D0r52QKGzWPQ8o/NTLOl25f0sfGmqol2l5ZAcsqGA9gTLShQL0dvJj+3G9/hNf/xxeKXkfpNeojUAvFE3+bnXh2jXsrjV1HBdhra6inTiG02giS2sho6BEf1twcCIF+png++Y3lG4zER3jPxfeQcTK5uI8gGNPTmDVwUKeuPw+Ulz8NhwTqGudQuykb0aUi1CBlRzgo8cpcIP6AxFidIj7AG5TYTJ9nxfD/T9UAhw0554uikHk9+CZDCIGa0Ns9g7rS+jqihcjN6sibQZ3i/ED8xOUAF4bipC2H1Ty/A2p/DGc7clC3E86OBbL8mS5KXPPmLTRKoDZTkN44EfEhpWT/lVdIvxR8TkdZDE53noM6W6NqZcS4CcPAmJxouECdvvEicHRAok/syhUvBqwJYkiaGTdV3lww8/YdRFcX+tmzdD/1FOkbN5ABOnIrNXdUw/6tl9DGxtBOH92bxy5exHzwoOYxhPXiwEFdWFuzkvPoZ8+yltwD4NNza/z8Z96g/0yctezQxNj0FPbCAm7qZJdVzkFdZxd8mATZKf4XIcTfFkKMCSEG/a+arwwmpJTPAH8N+P8KIS4ev4IQ4oezIvb1lZXGCDiuK9nNtO4LoBTObm0jPoAjkQ4H7UsBBOrxsaYaKnWQGZTffamfv4CVTCKt9nGEOVvB8vIc2+LGZ/6AV7/w+aLXU3uN2gsSO4tevIdSfSxJzekZ9r7vtV4OtbO+VpP8aR9tZAQ75CGJVjKJNjqKKOHgeGHlBa4NX+MtZ96CQJQV8xG7OJ2LPAiT1PXrqKdPl+36Vfv7PVf3vdoK1E6qdp8lhVB69Ipy9PxNUr0yqCF74Lq0hHSa30hbjoPan77e8zVvZb8MBzUUHqTcRjSqvo6oI4UEaiklcxuFHdS+cJ035qMvclC3G7mZLnmiYIohhEDti+E26sBiJ9v5cyziw1ldxU2lsBcWcHZ2wn/cwWlIrcL+Vvj33aRYc3Noo6Nlu4xjly7n3LSNIn3zJurAAHoeA1o0KDEY7p6FEg8+Fyxz+zax6WmEotB97RrO+jpWAKNcpeaOaki/dOuEexrAuDgNrluXYe71IDePp0TEhz8g0ejWeGnDE6ozCZW15B7Slbn9Sb69m9AUhK60vYP6+4F/BPwZ8JXsV5DdeBI4/C50LntZIKSUyez3u8DngafyXOej2cEyzwwPDwe969BwXcm3/NKf8M8++WrdH7seSNtF7tte3l0N8J3ZhwUE88EcAEaJiA8AbWwMe34h0GlgPbDmvLUXdFBfuACO0zJt3KWQtutFwAQQqDcXF5Guy+Zi8QMFNaHj7tlIp4apPzsL0FvcIds09GQF3tRqY9dRAfbqGupQ7Vzq2ugI9upqqIKeXxQUY3FvkcW9RZ4aeYo+o4+HBx8ua1CiMTWNs7aGs7lZ5WqPkrp+nfgzzxxpBQ+8pomJmgvUfmFdT9QeHZku//3EnJ0FRcm7kaoV+tlxsCzs1eb/XXf3bITuFcClyNy5i4jHSbztbThra2X9+woNUm4jKq2vI1oI3+msHBMeV3YzZGyX84P5HdT+5fkGJWr9sSiDus3wXyeVDB1Xe43GOai3s1v7Yw7qwzVFTcTRoaxvrYNc1GZyDr2MeA+f2OVLWA8e5HVb1ov0zRfpfuKJvDVqbGoKdD0SqEvgljkXLHPnDsYl7/ek+ylPRkvfuFHydkq8vuYAZ3MT68EDuh5//MTPYhe99Zt3GnvAEhZBM6i9AYk7DJ7t4faqJ1DP42BlHLbX0jmButBMI6Vba2+BWko5ledrOsB9Pw9cFkJMCSEM4HuATwRZlBBiQAgRy/75NPD1HMqubhYURfDIWB+feXkRx20OkTRMyg3jLxdf+D7uoBZdXainSwtb+tg4biqFu71dk/WVi5lzUOdvvTImLnjXu9ceMR85V1CAdsT1BU+831ycx3ULC4pK78nXROjsLkGiRQTqXMRH84tWx7HX1moyINFHHx0Fx8FeDc9dbhUYcnqYG8s3AHhqxCv2nj3zLDdXbpJxgokFxrRfVITnoraSSez5hbLjPXJrmqyDQJ2yA0VChEmuS6dMJ0hmZgb93Lm6ZiHqY/7g3+bpCiqEmwp+2GDeuUNsaorYQw8D5Q5KrMPQ3AZSRX0d0UIUclDPbaQBCjqo/cvzOaiVPgOZcXDbuIOz0/Ad8eUOSQSvDm+cQJ39zDrmoPajsoDaxEsMZt8qOyiH2ppLYhTo0i1G7PJlkLJhw+ac3V3MO3fpyhPvASB0ndjUVDQosQROGQK1s7ODvbiYG5IZu3QRJZEINChR6dGQlou06tPRl771MgDdj+dxUE9OehFxbTIoMSdQF9gPOdvbuDs7qONnWUvuog7GcFyJpghu7nm1wOrcrhfPKATmzGze+xHtKlALIb45+/278n2VumMppQ38KPAZ4FXg96SULwshPiiEeE/2vp8VQswBfwX4iBDi5ezNHwGuCyFeBP4Y+D+klE0nUAO8+7EzrO6aXJ9db/RSQsd3Lqk1yqBWchnUB0WVOTeHcf5cIBegns3gahZHsjWXRBseRonldz/oF7IC9f32aFNxNoO7PTbmPfHesW12VgvH8fiHFjXNoW4pB3VWoG5BB7WzulrbiI/RUQDskAYlStvGWloq6aB+YfkFurVurgxeAeC5M89huiYvLr8Y6HFi096myo8+CAN/UEb82coEan1iAntxETedDm1NxynX+REGuUEhZQ57MWdmMaYma7CiwmhZgdpuks+zYrgpO/Cwy8zduxgXp4k95P2+ZF4rR6DWcfbMpumSCotq6+uI1sLdNhFdam4YuI/vjC6UQd2lq5zp6yrooAaimI82wtnOgKZUNEjYc1BnGvNeueUZUI4PHjfv3QNdR3R3k3mzBu7HgWwE13pn5Ba7pom9tFRRZ5cvUjYq5mP/pZdASrqfeLLgdWJXrpB5o7E52c2Ou2ej9gTMn77jHdzEsg5qoap0P/FELgu8GH7t7NQp5mP/lpdT33X16sm1dHWhnztH5m57HET5orHoyv88+nGx+/1nsU2X7Zinh73rsTM8v76DELA2t+v9v4yNFcxtV7rrH9MSJsUc1G/Lfv/2PF9/McidSyk/KaW8IqW8KKX8uexlPy2l/ET2z89LKc9JKXuklENSyqvZy/9MSvm4lPLJ7PffrPDfV3Pe/tAIMU3hU7cWG72U0PHbO2qWQW2oCF050sJrPXgQKH8avAxqAGu+OTb0peIBtOFhRHd3oPynVqCcdsT1+bncnzeK5IYrvdkPxVq55mwTUmsnCummxegBrbvlHNRuOo2bSqEO1jKDOitQL4UjUFuLS+A4GAEE6sdPP46ueK/Vp0efRhEKzy89H+hx9LNnEbpesC2rElLXr6P09XkumQrIDUq8/6DENSunHNdtWOQc1GW0KkrXxbx3j1iZWd7VkjtwbRUHdYC6wNnd8xw80xfRBgbQRkbIvBFcoFZ7dLAlMtP8udxlUlV9LYR4lxDidSHEbSHEP8nz8/cJIV4SQtwQQnxBCPFomIuPKA9nO1NgQKJ3IHi2gIMa4Pxgd0EHtX/fEe2Bs2Wi9hsVxXSp/TGk6TbmvXJ7HroHwTh60GLO3sM4f57YpUtkbtdAeDTi0DsO6+0hXJXCSiZByooiPowL5xGG0bBBiekXvUHi3U+cjHDwiV2+7OWVN0lXdDNSjtHDP4yIXbqUu6z7qafIvPEGzu5u0duqFdTO1ZB+6RbGxARqX1/en8empzFvt8fvuZuyEF1awRxxX6DeVr39833XIm6o/OW3nMMEjFMxVue858+YmioqUMt2dFBLKT+Q/f6Deb5+qH5LbG56YhpvuzLMp24t4LZZzEdOoK6Rgxqyk2KzYqSUEnNuDj1A/jQ0X0u0lUyinyu8diEExvnzNRWB6kluoEuAiI+N+SQDY15RtbFQOIpe9SM+auWg3s2Kmb2jtbn/WtBz2hPVWwh7zVtvbYckenMHrJAGJR4MOS1c/KesFG9svMG1kWu5y3qNXh4ZfITnF4MJ1EJVMSYnMUOM+Eg9f534W96CUIKMlTiJMTEJgHlvNrQ1HUZaLtJ0UQI6P8LCL+TLGZRoLy0h0+m6DkgEUBMJlL6+pjlwLUZQB3Vu2GQ21ib28ENlDUrMdVm1WcxHNfW1EEIFPgy8G3gUeG8eAfp3s+aOa8C/AH4x/H9FRFCcbTPv4Lu5jRSnEwZxo/Dv0vnBeF4HtRo5qNsOZytT9oBEHzV3YNGA18P2/Il4D/AiPozJSWKXL9fGQQ1eDnWHZFBbc16NahTZZxZCaBrGxYsNFKhfxJiaKihAwqFBiQ1aY7MjXRnYHACQuX0HEYsd2dN0X7sGrsv+zZtFb+vXd+V2H1bK/q1befOnfWKXLmLOziLt1hVcfWS6eP3s70U397tQFMFLuykePtPLM5ODKAJScYW15CGBenY2b+eMEtfbM+LjMEKIbxNC/GMhxE/7X7VeWCvxrY+PsbSd4YUHm41eSqjkIj5q2JbttfB6j+OsryNTKYyADmp1cBBhGFhFHLn1Qto21sJCyXgAY+ICZggO6v9+e5WNvcZuTJytbNtqrLRQsb6Q5Pyjj6N3dRd1UOciPnZr9G/byXY6tIqDGiA+1HIOaicrUKs1zKDWhoZAVbGX6idQ31y9iSMdrg1fO3K5n0O9b+8HeixjehozJAe1vbKCOTtbcf40eBnUkH8adBjk5hlU4KA2Z2e5931/Pff8lIPfCunuBS/ScqLqZH0FavBc1M1y4FqMoG54M9uS6Q+56XroITJ37iCtYJue3OdBHYf11JsK6uvngNtSyrtSShP4OPAdh68gpTxsQesB2ss90WI422bu8P0wcxtpzhaI9/C5MBhnYXufjH3UGatGDuq2w9k20QIYPvLR0NfDdvLEgETpupj372NMTBC7dAlndRV7YyP8xx6c6pgMaivpdaIWM0IVw3Oy1z/iQ0pJ+uZNup8sHO8B0HUlGwMW5VDnxU3bIIN3tWfu3MG4OI1QD6Klup98AoQgVWJQ4kH3Ye0FTmt5GXtpKW/+tI8xfRFpWVhzcwWv0yq4abvogEQzmUTE46yvWAyMxXllaYdHxvpIxDSujvdz37XYXt3HTNsY01O4qRR2HqOWNySxdWvnkgK1EOLXgP8J+LuAwMuLnqjxulqKb35kBF0VfOql5nc+lYO7Z4EiCubkhIGaMHCzYqT1wHMWB3VQC0VBGzvTFJmd9pIXD1Cq9Uq/cAHr/n2kU3kb3va+xff95pf4xT9s7Ie4vZUJFO+R3tlmf2ebgfGzDJwZZ2OxsAAjdAXRpeHu1OhNdSf7WmmVDGrIOqhbS6DOOaiHSg87rRShqmjDw+FFfCSTIAT6mcKvjRvLNxAInhw5Wmg/e+ZZLNfixZWAOdQXpzHn5nDN6g9iUl/5ClB5/jR47l11aKhmArUvMFYiUO98/vOknn+ehQ9+sOx8zVwGdRkCZyY72KneDmrwuoKaZaZCIaQrvQI7gIM6c+cuaBpGdv5C7MpDYFmBB4TmNkm1OrBsMBXW12eBw21Yc9nLjt/33xFC3MFzUP+9Ao//w0KI60KI6ysrhWdDRFSOlBJnx8zbafZgPXViQKJlHq0NLwzGkRLmN48efiqGiujWIgd1myClxNnKoASoqfOh9DXQUb89f0KgthcXkZlMzkENtRqUeNGrj/e3wr/vJsOam0PoOtrISEW3z0Vo7OyEvLLiWMl5nLU1TxwtgjY+jpJIRIMSC+DXsUFNg5nbt4ldvHTkMrWvj9ili6RfuFH0tpXOb6mE/Vu3AOh6rLBA7edoZ+60/mFUKYHaSs5jnB1nNblL90g3O/s2j457nQfPTg7y0p4XDbaW3CWW3afkMzwp3ZoX++S4NfhX1J4gDuqvk1L+dWBDSvmzwNcCV2q7rNair0vnGy4P86lbi201zMfdtVB6CufkhIHSo+fedM0H3smYUcYACH1svClaok2/9aqUg/rCBNKyqhLVXlvYwZXw2VcWGxor4wQUqNezAxIHx88xMDZeNOIDQO3Vazck0Y/4SLSSQD3ccg5qe9Vbby0jPgC00ZHQhiRaySTa6CjCKOxgurF8g4unLtJnHG1TfHrEy6H+8uKXAz2WMTUNjoMVgiCcev46Ih6n65FHqrofY2ICa7ZWDmrPhRF0uMth9rPTvff+5L+x85nPlnVboSmImFpWkW3OzKLE47kImXqij483v0C9n3XxBHFQz9zFuHABoXvXzQ1KDJhD7Q9ortlMgsZTs/paSvlhKeVF4H8DfqrAdT4qpXxGSvnM8HD9X++dgLtngSNPOKhdV5LcTB8ZkLi5nOI3fvy/MX97M3fZ+UHv5/lyqLV+Ixe1FtHa+K8TLU9WeRByDupaDhjPh7XvCcTHIj78w25jYoLYZU8kq41A7Q2d7oSYD3MuiT4+XnGU28HzUF8X9f5Nz7jR9URxgVoIkY2DiSI+8pHrRAw0/2MXe2HhSP60T/e1p0i/+CLSLSxc5iI+6tC9ln7pJVCUonsYIztcPnOn9X/PS0XkWckkzvg0qS2T/R7P/f7ImLfnfG5qgHm8Q+zVud2ckSaTJ4faF8FbNeYjyLtcOvs9JYQYByyghfrj68O7HztDcjPNS8n2OcV19izUnsqKpaCoCR1n10JKiTWXdVCXEHkP0yyOsyDxAOANqoDqhpG9tuh17y5tZ3hxbrPi+6kWZ8vMO/jnOOvz3r91YPwsA+Nn2V5exrELf+gpCaN2RfbOAgjVcyW3ClHER0H0kVGsECM+iv3+Oq7Diysv8tTIUyd+ljASPDr4KNcXrwd6LD+TN6iTtBip69eJX7uWEwErxZiYqH3ERwVxUfu3bpF4+9uJPfoISz/3c2W7fw4fggbBnJnBmJysaFBVtejjY7jb2yUH2DQSf6p7kOcyc+du7rUOEJua8gaEvh5MoD5wULetQF1JfZ0EDp/in8teVoiPA3+p0gVGVIefCawcyxZe2tnHcuQRB/XKvR1cR7J452AfcaGIQK30xRqTORwROr7zOYjpIx+KoSK61PofWPhdiccc1DmBenICbXQUpbe3NvESQ56zshNiPqy5uYrjPYDaOtmLkH7xJiIWy0V4FCN25QqZN95sK7NfWPh1UCBzQNZp7DuPD9P91FO429tFYwaF6ps7ai9u7t96mdilSyjxwnFXaiKBNjqKeaf+ETVhU9pBnSQ15B0sLCoOQsDDZ3oBeGZykB0hwVBYTe56pqp4HHNm9sT95A4Z2lig/q9CiFPAzwNfBWaBf1fDNbUkf+HRUTRF8MmXFhu9lNBwd82aDkiE7BAkRyIzDuaDObThYZTuwhPNj6OPj2MvLwfOtKwV1tycFw8wVnxv6bc6m/crF4JeXdghEdPQFMFnXg7HPVou0nZxd81AxfTGfBJV0+gfHmXgzDhSumwuFf49UXv12gkSO0uQGAFFLX3dZqHnNNhpMPcavZLA2KtrKL29KEXcyGGgjYzkzd6qBCuZxCgS0XN78za71m5egRrg2bFnubl6k7Sdzvvzw8QmJwHPYVoNzuYmmTfeqCrew8eYmMBeWcHdC/915lYY8eHs7GDOztL95JOM/ewHsdfWWPmlXyrrPpQevawMY3N2tiHxHtB8g3/zcfBcFnfDS8vCvH+f2PTBBknoOsalS+y/FkygFlo28ql9M6grqa+fBy4LIaaEEAbwPcAnDl9BCHH50F+/DYgsaQ3CP2w/fpg/t+F9TvgOaYCNRe+9d2Ph4D14OBEjpil5ByVq/bHIQd0m+NnRlQrUAGpfDLfeBxbb2c+q4wL1zCyiuxttZKS2ztiB7Gf1enhDp5uVagVqfXwcEY/XPYc6/eKLdF29GshEEbtyGXd7O7TovnbC8Y0eAXSZzG1foM7noL4GQDpADrVT44gPKSX7L71EV5H8aZ/YxYst76CWUuKmLZTu/M+hs72Nu7PDTtzbC7y+n2FqqCc3SPl0Isb0SA87McHa3C5CCIzJidzsnMMI30Fdh0OGWlBSoJZS/lMp5aaU8j/iZeM9LKV8f+2X1lqciht87cUhPn1roW1O/ty94NNiK8W/f2fXwnrwAL2MeA/wHGdIGZqLslKCxAMAaGfOIHQdq4pBia8ubPPY2T6+9uIQn325MbEyzo4J0tsglWJ9PsmpM+MoqsrAmCcAFh2U2FtjB3UZ+dP2xkbd89pOEM+6vVvIRW2vrXlDDGuMNjqKu7ODmzq5eS8HadtYS0tFHdQ3lm8AcG3kWt6fPzv6LLZr565XDKWnB21sjEyVgxJTX30BpKxqQKJPblBiCENcj+MXSEFyiw+z/8qrAHRdvUr3448x8D9/Lxv/7uMlC+vDqHEtcIHmZjLeQUX2AKHe6OPeJr+pBershkUtcdhg3r8Ptk3s4vSRy7uuXAnsoAa/y6o9XaKV1NdSShv4UeAzwKvA70kpXxZCfFAI8Z7s1X5UCPGyEOIG8A+A76/dvyKiGL5geDyDem7D+8w67KDeWPIuWz8kUCuK4NxAd16BWukzcHctpN2aGZMRB+Qc1BVGfPi3rbujPidQn4z4MC5cyMVRxC5dIvPm7fD3K0Ycesdhvb0d1M7uLs7mJkbAGU35EIqSfR7qd14pTZP9V14pOSDRJxqUWJhcBnUAo0fm9m2EYeQ90DCmJlH7+0m98ELR+1B69JqLm1YyibO5SXeR/Gkf4+JFMnfvtrTGJk0HXAo6qP3af4tTJAZivLS6wyPjRyMln5scZNYxWUvuIl1JbHIqr0DdthEfQojvOv6F58T489k/Rxzj3Y+NMbuW4tWFBgtaIeHsWrkMyFqhJrxizN01Mefmyv7wPXCcFc81rjVWMllyQCJ4g9308+cx71UmArmu5PXFHR4+08c7rp7h7uoet5fr3w7uu3byDf45zsb8XE6YPjXmCTDFcqiVXgOZcXDNygdJFmRnEXqDJxQ9eN/7WHj/T4e/jnLw40haaFCis7aGWuP8aQB91BsWY1XptrAWF70hp8UE6pUbDHUNcS6R/z3q6dGnUYXK84vPB3rM2NQUZpURH6nr1xG6XjLbLwjGRFagrkHMh7tnIbpUhFpeduLB8JSrAAz/vR9DGxlh4QM/E7hrppyID/PePZCyYQ5qLfv+2AyDfwsR9LDBP3zxswN9Yg8/jL2ygr2+HujxlEQNO2oaRLX1tZTyk1LKK1LKi1LKn8te9tNSyk9k//xjUsqrUsprUsq3Sylfru2/KKIQuVopcbRWerDuOajPnjokUC9mBerFFPLQfJELg/ECGdTZwXj1zh2OCB1nOwOKV/9WSmMEam92EH1H62rz3r1cTQFevIS7tYUd4jDWe2vZg5zB6bbPoLbmvP/nahzU4OVQ11Og3n/9DaRplhyQ6BOLBOqCuHs2wlAReuk6OnPnNsb0NEI92S0shKD72rWSgxLVeO271/ZfegmArsceL3nd2MVpZCrV1PVxKUrVz35c7OaezqnxHh6sp3l07KhA/ezkIHPSwTZdtlbSGFNTWPPzuPvHBilnBWrZbgI18O1Fvv5i7ZfWerzj6iiKgE/fat1fHh9pu8iMUzcHtb2Zxl5cRD9XnoNaywrUjX7DMpPJkgMSfYzz5zEfVJZBfX89RdpyeGSsl3c8OgrAZ16uf6xMzu1xqriD2nUcNpcWGRz3/m+6E7109fYVFagPDi1q8MG4uwiJ0UBXlZbF/iuvki5xylxzcg7qtcauoww8B3Xtc761Ue+5tKvsoLDmSmfIv7D8Ak+NPFUwm7hH7+Hq0NXAArVx8SJmlW6A1PXrdD35BEqs8rbg3Hr8+KEaDEp0UlbZ8R4A+y/fQj97Fm1gAAA10cOZ9/8UmddfZ/1jHwt0H2UJ1NkcN2Nqsuy1hoE2fBp0vSkG/xYilydeykGdbcU0po45qB8qbwOqlhnR0iJE9XWH4OyYKD06Qju63ZrbSDHSG6NL9wQE6Uo2l1LE4hp2xmFn42CzeWEwzv211InPCiVrEIhiPlofZ8tETRhVDaVX+2I4O+aRw42asz0PsX6I9eYukraN+eDBkU6ksPOPP31rkbf9/Of5yr11GJpu+wzqnEB9tkqB+tJlnLW1wAfE1ZLODkjsDmiiUPv70UZH2Y8E6hN4Xe3BuhAzt2/njffw6X7qKcy7d3E2NwteR4mXN7+lEtK3bnkmm4cC5JNf9OLiWjnmw3czF3RQJ5M4is7WpoPb79XYxwXq56YGWVG9rqnVuV1vzouUJ4yPbeugllL+YJGvH6rnIluF04kYb50a4pO3Wj+H2t8Q1jqD2ndoW8kVkBK9Ugd1AwVqaZrYJeIBDqNPXMC8f78iYerVBW9A4iNjfYz2dfHUhVMNyaE+cFAXF8a2lhdxHZuB8YPndWBsnM2iER/Z2JewXUF2BlJrgR3UmZkZsCzspaW6FXN56ck6kVvIQV23iI+RrEC9XKWDusSQ05XUCsndZMF4D59nzjzDrdVbpKzSkSOx6SncVKrirD13b4/9l18OJd4DsrEjw8O1cVCn7IoOO9O3XqbrWOtf77d8C4lv+fOs/PKHMbObtmIoPTrScgN1ZJizswAYE5NlrzUMhKKgnznT5BEfNigguorn+Gfu3kE7cwY10XPk8thDDwGw/9prgR6vHR3UUX3dOTjb+YdJP1hPH8mf3lnfx7FcJp/wDnY3Fg4+Q84PxtnJ2Gylj/4e5BzU0aDElsfZzlSVPw3ZjkZX1jezf3se+o/WTdb8PNj2MQe1J5aZIeQfSyn515/zhO6bc1uegzq1CvtbJW7Zupg5B3WwfWYhDg4K6pNDvX/zJurw6ZyZLAixy5fJvBGNTTiOEzB21dndw55fyDsg0af7KW+WTvrFFwteRykjHq9S9l+6Rezhh0vGo4Jn6gEw77buYZT//ymKCNSpoSmkhPXsU/3osYiPcwPdaKdiSGAtuUss2/F5fKZRTqCucY54rSjZJyCEGBJC/CshxFeFEF8RQvyfQojaKw8tyrsfP8Pt5V3eXGrtmA9/Q6jWy0G9tAl47uKybt/VhTo42FDHmbW4CK4b+GTbuDCBTKVwVssXHF9d3EERcHnEcyu849EzvJTcYn6z9GC2MHG2Ml6rUay4SLGejV7xHdQAg2NnS0Z8ALhhC9S7WSEwYAb14QIpE1BMqQktlkEtTRN3a6suER/aiBfxUe2gRCuZBEVBH83vrn9h2XPRFxqQ6PPcmeewZbAcat9ZWmySdjFSN26A4xB/5tmKbp93TRMTNYv4UMvMn3a2trDu36fr6tUTPzvzkz+JUBQWf/aDJQ/6/Ly+IEWaOTODNjJyQlStJ/rYWEMPXEvhprwBL4U6CXzMO3eJHYv3ANCGhlBPnybzejCHlJIwcFNWfV2BdSKqr9ufQgL13GYqb/709LVhANbnD3KofSHbjwXx8e/X72iLaF2crUygyLxiqNnaua4HFtvJkwMS/YPeyQOBWhsaQh0cZD8EB/XnX1/h5XnPrPPm8i4MZoW4No75sOaSKD09qKdOVXU/YTvZS5G+8SLdTzxZsl44TOzKFcw7dwLHuHUK7p4VSJPxBdyiDurHHwNV9fYRBVB6dKTp1GzGgXRd9l9+ORfhVwptcBB1YCA3ALIVKeWgNpNJ0uOPAnDHMhnsMRjpPXpwKYTg6YuDbGnSc1BnO1WO51ALVUEYavs5qA/xcWAF+G7gL2f//O9ruahW5p1XzyAEfKrFXdRuzkFdXcFUCqEpiC4Ne80T9MuN+ABvsFQjN/Sl3JfHMSayrfQVDCN7bWGbydM9dBueMPzOq56g9tk6x3z4xXSpomNj3jv1P+ygPnVmnN2Ndcz9/KK6H/ER+mCsnXIF6jcgO+Bl/9UGCtSxXlAN2Asvu6+W+G5zbbD2Ooua6EHp6al6SGqpIacvLL9ATI3xyOAjRe/nqZGn0ITG80ulYz6Mae/UO1NhDnXq+nVQ1dxE7jDQJ2snUJcb8bH/yisAdOcpXvWxMYb//o+x96d/ys6nPlX0fvyWSHevdJFmzsw0LH/aRx8fb3oHdan8aSklmZmZE/nTPl0PPRR4UKKa0EG2rgukBFF93eY42xnUvqMbTNtxmd/cPyJQb2bzp8cu9dPdZ7C+eCBQX8gK1MdzqEW3htCVKOKjDXC2zBOvk3JRc476Or4etufzCNReDXF82HAYA/qklPzyH9/m7Klurp0/xe2lXc9BDW0uUM+hnztXltCbD21kGKWvj8zt2gvUzuYm5r17geM9fGJXLiMtqya1aCvjBnRQ+wKu7zjOhxKP0/XQQ0VzqJUyzB2VYM7M4O7t0R0gf9rHuDhd9XD5RuKmi0fkWcl5dk9NYXSp3Nra5dGxvry/889NDrAgHJbub6PE42hnznhd38dQ4lpbC9Rj2UnjM9mvDwHBQlw7kNG+Lt5yYYBPvtS8Dqgg5CI+auygBm8D6mxnELGYl8FZJp7jrHEb+pxAHXB4he8SN++Xn0P96uI2jxzKI5oeTnB5JFH3mA9nywzUjri+kKS7t4/uxEE+nT8wcXMx/++I0qODAGcn5A/FnezjlSFQxy5Oo42NBW5HrwlCQM+wF0/SAthr3jq1OjiowcuhrjQmw8dKJtHPjhf8+Y3lGzx2+jF0tfj7YVyPc/V0sBxqbXgYJZGo2EGdfv46XY8+Gqrb15iYwFlbw9kNd/BqJREfaX9AYh4HNcDA934vXVevsvi//zOc7e2C9+M/bpC2Z3N29sSmut7o42PYy8sF3UNfvLvG/3OjcUOBgxw22IuLyFSK2MX8AnXsoYfI3L6NtEsXzrnnr81iPrJE9XUbIx2Ju2uhHHNQL27v47iS8wMHER8bi3t09eh0JwwGx+JsLJx0UJ8QqIXIDsaLBOpWxt23kRmn6ogP/3VWNwe1bcLuMvQdNeeY9+6hJBKog4NHLo9dvoz55u2q5m588e46X7m3wY+8bZpHxvp4Y3kHOTDp/XCtdYWrUljJuaoHJIL3nhG7fLkuER/p7AC87iefLOt2XdGgxLwEF6hvIwyjZEd697Vr7N+8WbAO880dTgBzRyXkhqA//liJax4Qm76Iebu695BGIgNkUO8YIwyeTfDa0i6PjPXmvd6zU4Msq5L0pkkmZWFMTeZm6BxG6W5vgfqzQojvEUIo2a+/Cnym1gtrZd79+BivLe4ws7pX+spNipt1r6o1zqCGbBvJvuOdDitBXpJH0cfHsOYXGvaGZc7Ngaqinwm2r9THx0FVMe+Xdzq8s2/xYD3NI2eOvmG98+oZvjy7zsZe/dr6PAd16WJ6Yz55xD0NXgY1UDDmQ6jCG2xWs4iPgBnUb7xB7PIVuh5+mP1XXwl3LeUSH2qZiA8nK1CrdcigBtBGR6oWqM35wkNO03aa19Zf49rwtUD39eyZZ3l59eWSOdRCCIzpaTIz5W+q3EyG9M2boeVP+/iZkWEOSpSWizSdwMNdfPZvvYx+4QJqf3/enwtV5cwHfxZnfZ3lX/zFgveTEzhLuEDsjQ2cra2GDUj00cfHwXULxtb868+9yU/+51s4DYq8COKg9ofYGNP5HTxdD11BmmauFbwYfg3itKdAHdXXbYy7a4LkRMSHH9Vx7ohAnWLgjPf3wTM9rC/s5WraRExjqMfgwcbJzxS1PxZFfLQ4vqBcdcRHwvDMHfUSqHcXAZk34sOYnDzh/ItdvuzN3aiiQ+jDf3yb04kYf/WZ81weSbCZslg1da+ub1MHtZQScy6JEYJADV4eeObNN2u+Z06/eBOEODFHpBTGxYugqqHEwbQLrukgLTeYQH3nNsbUFEIrXqd1P/UUbipVsKsh56CuUaZ9+qVbiO7u3PDDIMQuXcTZ2sJp5FyoKnBTNqgCYZzUupztbZztHbacBPpQDNN2T+RP+1wZ6SXV7d3HWnKP2NQU5szMyUHKbS5Q/03gd4FM9uvjwI8IIXaEEIVtSx3Mux7zHJqfutW6Lmp31/J+iUpkDB9m397nV278Crtmee47JaHj2mrFH77a2BgylcLdasyADCs5j37mTMkPAx9hGF4b973yIj5eX/RiUB45NtH1nVfP4LiS//fV+riopSNxdsxAxfT6/NyR/GmAgTNZgXq+sAtQTRjhD0ncWQChHmQ6F8HZ3cWanyd25QpdjzyMeXcGd38/3PWUQ8/plhmSaK/6DuryuyEqQR8Zxaoig1paFvZi4SGnt1ZvYUu7ZP60z7NnnsWWdi63uhix6WnMCiI+9l96CWmaxJ8NW6CeBMC8NxvaffrCcNkRH7du0XX10aLX6b56lcHv+z42P/7vSb2Q///bf9xSAqef3xZrcMSHP0yoUMzH64s77Gbs3MDceuOmSjuocxmIRRzUAPsBYj78mDG3jgewdSSqr9uYnPB4TKCeywrN5wePZlCfygrUA2M9WPsOuxsHzuhzg3EerOcRqCMHdcuTGzpeZcSHUAVKQq9f5Mt29jPquEB9796RAYk+/qDESoXHGw82+cLtVf7mN0zRpatcHk0A8ObyjpdDvd662bTFcNbWkOl0KA5qgNily7jb29jLtY0NTN98kdilS2V3+SmxGMbERDQo8RB+HR0og/rN20Xzp326n7oGULB2VgOaOypl/6WX6Hr0UYQaXGfyTQ+ZO635u+6mbZRuLW9shzU/T7r7NLarsNvt/fy43uOjKIJzU97PVud2MaamcXd3T8w2E+0sUEspe6WUipRSz34p2ct6pZT5/+c6nLOnunny/Ck+9VLr5lA72TD+cvKuPjXzKX71xV/lj+7/UVmPpfToCBFDL3NAoo+edeQ2KofampsLnD/tY1y4UHYG9atZgfrhY29Yj53t4+yp7rrFfDi+K6iEgzqT2iO1tZmL9PDRu7pIDA6xsVjYRaH06uE75nYWITGay5Uuhl8Yxa5cIfbww+C6dRsqkpf46ZZxUNtr3jq1ejmoR0awV1aQbmWDPKylpeyQ0/y/w/7Aw2sj1wLd37Xha2hC48uLXy55XWN6GntpqexIjdT16wDEn366rNuVXM+FbPxQiNl/TnZqdTkCtb2xgZVM0h3AeTP89/4u2tgYiz/9gbyxGEq3BqJ0ke23xzU8g7rI59nqbobVbHfTl2Ya4yBxUnZJN3zm7l2Uvr6CXRSx6WnQtECDEn3HUDs6qKP6ur3xhePjwuPcRhohYKzfE6j39yzS2yYDo56QMzjufT8c83FhMH4i4gOyDuptsy2HiHYKvgO+Wgc1eK+10LsPC7GdNZkcivhwTRNrfj6/QJ0VzczblcVL/PLnbtPfrfO9X+Pd95VRr5v09vIuDE61rYPamvNm+ejnyttnFqIegxKllOy/eJOuJ8vLn/aJXbkSRXwcwo84K1VHu3t7nrnqUmlXsn72LOrwadIFBiXWMoNaWhb7r70WqMY/jG96MFtcoM6HlUyym/AOoR5IB0NVuDicKHhfT14ZIiUkczNbuX3L8RzqtnZQCyH+l2N/V4UQH6jdktqDb33sDC8lt/I6HloBdzdY1tFhPj37aQBe3wg2/MhHaA7oPRWfDuvjWcdZowTqZLJsgVq/cL5sgfq1hW36ujTG+7uOXC6E4C88OsqfvrlCyqz9G1HO7VFCoF7PDkgcHD/5vA6MnS0Y8QHeNPLQi+ydRegNFsPiF0aeg9objLf/6qvhrqccek63TAa1s7qGiMdR4vHSVw4BbXQUbLvili9rrviQ0xeWX2C6f5r+WP6oiePE9TiPnX6M64vXS143lh2UeHz6cilSz18nduVK1RPdj6N0d6OdOYMVokDttweqZUR87L/sRep0XS1dvCo9PZx5//vJvPkma//mt0/8XCjCGxRSok3RnJ0BXfciNhpI7vMsj4P6jewhJcDzDRCoXdMB2y3toL5zl9j0dMEDbmEYxKan2X+9dLa/0q2B0p4Z1FF93d4UclA/2Egx1teFoXlbsM0lb5+Qi/gY8wTq9SMCdTfJjfSJaB+1zwBHtusQ0Y6g0EFGJXiO+joJ1Fu+QH3wmWk9eACumzcqS+3vRxsdrUgYfW1xm//31SV+8OsnScS8WmKkN0Zvl8YbSzswdNEbJL7ffo0nZrZGDTPiA2orUFv37uFsbZU9INEnduUy1oMHuHutG5UaJq5v9CgRu+oPEDQCOKiFEMSvPVVwUKIf5RZkwHi5ZG7fRmYydD0efEAigHbmDEo8nouRazVKCdQ7ibMIBV7eS3F5NIGuFpZpn5saYkV1mZ/dIpZ9vz3ekavEtdxrp9UIEvHx54UQnxRCjAkhHgO+CORP7Y7I8e7HvE3mp2+1pova3bNKvhEeZi29xpcWvgTAGxvlnXrKzA5CKGijFTqos4KCNV9/gdrNZLCXl8s+2TYuTOBub+Nsbga+zasL2zxcYKLrO66OkrFd/tsbtW3ZAnA2gwnUfoTHwPjJ/5uBsXE2igy2VHoNnF0z3Iy0ncWy8qeVnh70s+Po586hJBJkGjkoMT4E5i5YDYwZCYi9toZ2bDhOLdFGR4CsE7oCckNO8wjUrnS5sXIjcLyHz7NnnuXltZfZs4oX18Z01g1QxqBEadukXngh9Pzp3JomJkLNoM5FfJRx4JkbnlIi4sOn95vfTu9f+AusfvjDmA9ODp9VevSSRVpmZgbjwoXAUU21QunqQh0czPt59vqSJ1B/w+XTfHl2ve5zF3KbpFIZ1HfvYhSI9/CJPfRQIAe1ULIzCWqUg9hgovq6jXG2TVBOvvfNbaRP5E8DuYiP7l6DroR+wkFtu5KFrfSR+/LrsCiHunVxtjIocQ2hlz+D5zh1jXzZngcjAbGDZg+/+yqfgxo8F3UlA/o+/Md36DFUfuDrJnOXCSG4PJLgzaVdGMx+3rShizrnoC7TCFUIbXAQdWiopgJ1+uZNALqfvFbR7XODEit027cbzp7voC5Re93249VKC9TgDUq0HjzAXj3ZoSs0BRFTa3L4mRug+Vj+IeiFEEJgXLyYi5FrNYpF5FnJJLt9Ewyc6eGVpR0eLRDv4fP42X7Wdcn+6j7K6BlEV9cJs5PSrYHtIq3KOowbSZCIj78GfAx4CfgD4O9LKf9hrRfW6lwYinN1vK9lc6j9iI+gfPbeZ3GkwxPDT/DG+htlbZzdPc8Jpg6eKXud3u0GEYaBVUTwrBW+y63siI+JCwCBXdSuK3l9cefEgESf5yYHGYjrdYn58DdCWol2xPX5JEJRODV68nkdODPO/u4O6Z38bgc1YYAtkftO9Qv22VmA3mCvMW9A4mWEEAghsoMSGyhQ92TznFsgh9pZX6tbvAeAPuq54u2lynKorWQSFAX9zMnXxt3Nu+yYOzw5XN4U8mfPPIsjHb669NWi1zPOn/eiDsrIod5/9VVkKhV6/nRuTRMToUZ8VJJBvf/yLYyJCdTe4Frd6E/9JELTWPyZnz05KCReOjLInJ1t+IBEH318PK+D+vXFHQZ7DL7t8THW90zurJQXDVMtuRzEIs+ls7mJs7ZGrMCARJ+uhx/CXlwMdEir9hjtGvER1ddtjLNtovYaCOWoqWBuPcW5Q/nTm0t7KJqgb+igO25wrIf1hYMOzPNZQft4zMeBQB3lULcqzrYZaOh4ENS+GO6ejbTrIEhsJ714j0OmmVxUViGB+vJlMnfuIJ3gtf3M6h5/cHOe//lrJzgVP7rvuDzSm434yH7etGEOtZWcQx0aCrUrMXb5ck3F3/SLN1Hi8UBRE/mIZQXq/SjmAzjciViqe+02QtdzcX2l6H7KM98UjPkIYO6ohP2XbqH09aEXeJ8oRmx6OifEtxrFHNRmMslu73kSZ+Ks7poF86d9DE0hPtKNcGF7dd/LbZ89LlDrucdtNYJEfFwGfgz4j8A94PuEEPXp3W5x3v3YGb56f/OE46EVcHet3HCiIHxq5lNcOnWJb536VjYyG6ykgzt5nQ1PWFITlYlaQgj0sTHsBkR8WElPRCi39cq4kBWoAw5KnNtIs2c6J/KnfTRV4c8/MsofvbqE5dS2MHW2MghdQRR4k/XZmJ/j1OgZVO3kB6rvqi7kolZ7s7mjYcV82BlIr0OitEAtpWT/zTdzBRJA7JFH2H/99YpzjqvGH+y4V3uHfDEebD9g3y7u4rZX11DrNCARshEfgL1cuYNaOzOK0E++Tm+s3AAo20F9beQamqLx/NLzRa8ndB3j/PmyHNSp573okO63vKWsNQXFmJjwRMaQhs767YGlnB+HSb/8ctmT3/XRUYZ//MfZ++//ne0/+OSRn3lFdmGBUzoO1r37xCYny3rMWqGPjeWNrHp9aYcrowmem/I6FL48s1HXdR0cNhR+Lv3DFmO6eJZ37Io/KDFADnVCx91tP4doVF+3N852BuVYbINpuyxu759wUJ8aiaMcauf1BOq93GHb+UHv+sdjA/3c4lKu2fTLL7Pz+c9X/G+JqB3OViZEgdp/PdTh/XJ7Pu+ARHVgALU/fyRa7PJlZCbjRYEE5Fc/fxtdVfgbf+5kV87l0QRreyZrRnYdbeigNufmQsuf9vEF6lrtadI3b9L12GNlDcA7jH7uHKK7OxqUmMXds0AB0VXCQf3mbYypqcCdgF1XHwVdL5JDreXc22GSvnWL7seuljXnzMe4dBF7eRlnZ6f0lZsMN1VYoN5dWCej9ZJJeL8zj46XHkMydfEUQC6H2j8g9PEfy023nsEjSD/RfwHeL6X8EeBtwJtA8V13BADvfrw1Yz6k5SBNJ3BL9sLuAi8sv8C3Tn0rVwY8Ua+cmA971Wtfknbl7W3a+FhOLK4nlbZe+Xnb5v1gTsVXFjyncbETtXdePcP2vs0X79Y2q9jZ9orpUh8s6wvJEwMSffzLC+VQ+4cjoQnUu1nxMoCD2l5ext3aOiJQdz38MDKVwiozNzw0eoa973uNy6Ge2Zrh2/7zt/ENH/8G/u7n/i7/6c3/xGr6pKPbXquvg1obGgJFwV6uzEFtJucw8sTQgJc/Pdg1yERfeaf83Vo3T5x+gucXSn9UGtPTZGbKEKivX8eYmEAfGSlrTUExJr1/a1guanfPQnSpiCJZaoex19aw5xfKFqgBBt77PXQ9/jhL/+yfHRHY1RIREVYyibSshg9I9NHHPYH6sBPcdSVvLO7w8Jk+pk73cDoR48sz9X0/cAMMvPRbL2MXizunYg9lW3hfLz2zQknoNdkkNQFRfd3G+A7qwyxspXElnBs4cFBvLKYYGD16LjEw1oOZtkllO9bG+rvQFHHCQa0kDFBKR3ws//N/wcJP/H/qHgsUURpnyzyRU14pOYG6HoMSt+ePDEgEr24o5J6GQ/nHAd27yc00/+mrSb7n2fMM954U8S9nByW+uSm9CL+19hOorbkkxtlw8qd9YpcueXuaPJ1a1eJmMt4AvAoHJAIIRfHiYCIHNZCNXY3rJ7pxjpO5cyc3jDQISixG96OPkiqYQ13c3FEJ7v4+mTffpOux8vKnffzastUGJUpHIjNOQXPfxpb33C4r3qHRI2dKC9RPPz6Ci+TVV9cwpiax5uZwzYP3/gOBug0d1MBzUso/ApAe/xL4ztouqz24OJzgodFePtViArW/EVQDZlB/avZTALxr6l05gfr19eCDEu15TwipxiGlj403ZEiilUyCrqOVKRYpXV3eMLL7wVwEry1uIwRcGS080fUbLp8mbqh85uXavt6cLbPktHHpumwuzDOQZ0AiQP/IKEIobC4Wd1CH5prbyf6fBMigPhiQeDl3WdcjDwMNHJTYBBEf15euI5G8Y/IdvL7+Oh/4sw/w9t97O9/7B9/Lr9/8dd7YeAPXtnE2NtBO10+gFpqGNjRURQb1fMEDphvLN3hy+MmKTvmfOfMMr6y/wq5ZPIYhNj2Nee8+0i5dQEjXJfWVr9Bdo3gPOGjNDU2gLpK5lo/9l18Gys+mAxCqytgHfxZnc5Plf/mLuct9B3UhccbPbWsegXocmUodib9IbnpdNFdGexFC8NzUAM/PNspBXfj5zNy5izCMkoe22vAw6sAA+2+UrhXUHr0thyQS1ddtjbtzslaa2/A6Kv3IDsdx2V5J5/KnfQbHvL/7gxI1VeHsQDcP1o92ZApFoPYaRSM+3EyG9I0bOBsb2CuN7cKKOIq0Xdw9KzQHte/Yr3nki2PD7uJJB/XsLEaRTiRfXAqaf/zRP/FEqB9+W/4Dz8sj3p7ozeVsDnWbOailbWPNz6Ofr2xGUyFil739TS1yqPdfeQUsi64KByT6xK5cJvNGeZGh7YqzZ5U0DbqpFNbcHEaZsSrd166x/9JLSPPkflutwZC9zGuvgW3TVUGND4feQ1psUKK7X7ib1NnZYYcBAN7IZDh7qpv+APumZ6aHWFcli/d3iE1Pg+seMdLlBOoWHJRYUKAWQvxjACnlthDirxz78Q/UclGthGO5pIucVL/rsTM8P7vO8k7zDzfz8TeCQR3Un575NE+cfoLzvefpj/Uz1jPG6xvBBWrr3m2QsiqHlD42hr2ykvcNtpZYyTn0sbGK2piMCxcCZ1C/trDD5FAPcaNw206XrvK2K8N89uUlXLd2H+hB2hG3V1ewLZPBAs5UVdPpHxllvWDEh+8CCUmUyAnUoyWv6gvUXYcc1MalS6BpjcuhjmcF373GCdQvLr/IYNcgH/r6D/GZ7/4M/+Hb/wM/eu1HkUj+1Qv/iu/+xHfzV//tu8F1ua9tYzn1E5S00dGKMqilaWIvLeUV01bTq9zfuV92vIfPc2eew5UuX10ukUM9PQ2WlXe433Eyb97G3dqq2YBEwNsICRHaoEQnZZc1IDF96xYIQeyRYAMSj9P1yCMMfv/3s/l7v0fqK18BsoKqS8FMe3N2FqDoxrqeaGPeQdrh2KrXF712xoeycwiemxwkuZlmbiN18g5qRJC4FvPuXa/FtMRnohCC2MPBBiUqCQOZcZBWiDMJGkhUX7c/0nJwU/YJZ6wf0eE7qLdX0riuZOBMz5HrDYx5f18/NCjx/ED8hIMavNzhYpEO6Rsv5mrjqGW+ufCft9Ad1LWO+NhdAukeEajdVAp7aSnXhZUPb/j42UDC6MpOho8//4DvevosZ091573OWH8XiZjG7aWdrEDdWq7KUliLS+A4NYj4yDrZKxhYWYp9f0DiE+XNbjlO15UrOBsbOHkG+HUaboC5YH68WtABiT7dTz2FNE32Xzu5v63FgOr0S94Q9O7HK3NQ6+fOIQyDTIsNSvRdzPkiPqz5eXYS54h3SW6t7ZbMn/bpiWmYCQ1rLYMx6RlsMocGJfq1ers5qL/n0J9/4tjP3lWDtbQcUkp+5/3/gz/7T4Xf4L/18TGkhM/WYXhdWOSmxQZwUM9szfDq+qu8e+rducuuDFzhzY1gRbA0TazFBVDtqhxS+vgYSIlVYZt/pZjJJPrZ8dJXzIMxEVygfnVxm0fGSg8Me+fVMyzvZLgxt1nRmkohXRlooMvGvBd9MjhWuC1tYGy8YMSH6NZAFbhhtSmW6aDWRkZQT53KXaYYBrGLF9l/rUEO6q5+UPSGOqhfXHmRJ4afyA2OfGjwIX7kyR/hd7/td/ncX/kcH/jaD/CY8IroX7v/7/jGf/+N/MM/+Yf817v/la1MOHnGhfAE6vLfY62lJXDdvAL1i8svAuXnT/s8OfwkuqLz/GLxjv1YNqv3+PTlfKSue/cVf+bZitYUBCUWQx8bCzXiQy0jf3r/5VcwpqZQEz2lr1yA4R/9O2jjYyx84ANI00Tp8R6/0CFoZmYGpb8fdWCg4scMEz17sHe4/fb1JU+g9rtons3mUD8/u163dbkpC2GoCK1w6Zi5e7dk/rRP15WHyLz5ZsmBWX43VxvFfET1dZuTEx6PxRLMbaRRFcFYvzcQcWPRE5wHjjmo430GsR7tqEA9GD+RQQ1eDnUxx2zqy1/ODbKLWuabCz87PDQHdVwDTdReoN7OfjYdivjw9zPFIj4gm38cQBj9jS/cxXJc/tY35RfcXMdFCMGlkcSBg3pvBfbzD19vRfwYyXLnHJVC7e1FGxuriYM6/eJNtLEx9NHqYuj8mMVarLHVcAM4qDO3vf8n//AhKLlBiS+8cOJnSlz3zAEhDl3dv/US6unTaHkG0wdBqCrG5CRmiw1KLNaBaCWT7CbOMjBicHdlN1D+tM+psThdliQz5Okb5t1DAnWbRnyIAn/O9/eORAjByEQvC7cLiy9XRhNMn+7hU7fqHz9RKb5QXOq0DrzhiALBOybfkbvsysAVZrZmyDilW8yshQVwXZSYwKlKoPZE4lrkaRXDSs5XXDjo5y/grK3h7O4Vvd5exubeWoqHA+QRvf2hETRF1OxAxN01wZUlIz7Ws8LzQAEHNcCpsXE2F+bztm8JIVATRlWviSPsLoJQD4YNFmH/jaMDEn26Hn6YTKMc1EJ4LuoGOag39jeY3Z7l2vC1vD8fjg/zl6/8Zf7RpR8G4Ae+4e/zzsl3cn3xOj/xpz/B2/792/jBT/8gH3v5Y9zbDkf4PIw+OlJRBrWV9F6n+QTqGys30BWdR4YeqWhNXVoXTww/UVKg9mMlggxKTF2/7hX+FR6KBcWYnGhcxMetWxW3/vko8Thn3v9+zNt3WPutf5Mr7As5QcyZWWKTkxVFudQCfdwrNK35ow7qs6e66e3y/i0Pn+mjt0vjyzP1FKjtou5pd38fa26O2HSwFtPYQw8h9/dLDgvOPX/tE/MR1ddtjp8BfDLiI+XlSWcz+TcWvfrv1LEMaiEEg2M9bBwSqC8MxlnbM9nLHN1slnJQp770JbquXkUbHg6U+R5RP/yDhVI1dVCEEKh9MdwSQzOrZjtrLjnkoPa7rkp1IsUuXyYzO4u0Cr+fb6ZM/n//4x7f9sQ4U6dPHlYv3N7koz/231id2+XySII3lnZhKPu500YxH1YyO+coZIEavBzqoFng5ZC+eZPuKuM94ECg3o8O1bw6uoQmY965A9nB6+Wgj46gjY+RyjMo0Td3hBkRkb71Mt2PPVZVvR27dJFMGcPlmwFZxEG9fz9JqucMynAcV8KjAQyJPhcve2aVr97eQxsePmJ28odqtptALQv8Od/fO5axS6fYWkmzV8C9IITg3Y+f4Yt311nfa40p9G5AB7WUkk/NfIpnzzzLSPzgpPShwYdwpMOdzdKnW+aDuexjGVW1keh5WqJrjZtO46yulj0g0ce4cAEA60Hxzflr2dbuIC0f/XGdr704xGdfXqxJbpc/iEftK+72WE/OEYv3EO8/VfA6A2NnsTL77G3kF1iUXj28QS87i5AYBaV47L60bcw7d/IL1I8+gr2ygt2odrOe05BqzJDEmytey961kWtFr2eveet75pFv4We+7mf43F/9HL/7rb/LDz32Q2yb2/zC9V/gL/7nv8i3/+dv5xev/yJfXfoqjlt92742MoKztYW7X16UUk6gztM++cLyC1wdukpMrdzZ9OyZZ3l1/VV2zMLTptW+PtTh07n2vEJIKUldv078mWdqLqTqE55AHcZ7iLsXPOLDWl7GXlqiu4IBicfpffvb6X3nO1n9lV/B3V7NrqWAQF0iN7PeqAMDiK6uI3MV3ljaycV7AKiK4NnJQb5UV4G6+CbJnJ0FKYldnA50f10PPwRApkQOtV+LhHZg2Xii+rrNKRTd8GAjncufBthcTNFzKobRdXLTOjDWw/r8Xu59+MJgPHsfR13Uan8MmXFyGZeHcff3Sb/4IvHnniN25Qr7b0ZiTzORq6lDclCDF5FXewd1HoE6e6jt720KEbt8yYs1K3II/tt/Nsue6fB33p7/sPP5T87i2C4Ltze5PJpgdTfDdndWmGsjgdqcmwNFQa/QbVqM2OXLmHfuBJp/EhR7fR1rbi4UgVobGkIdGur4WCLpSs8cUMpB/eZtz2ihBzeE+MSvPUU6z6BE31wS1qBEZ3cP8+7dioagH8aYvugNBCxzz9dIikV8rM1uIIXKRp/XWfXoWH/g+33uKS+69NXX1jCmpo4K1IpAdKk5cbyVKKbWPCmE2BZC7ABPZP/s/72y4Jg2ZOyS9yIq5qJ+92NjOK7kD19pjWGJzq4FmkAYxTMkX11/ldnt2Vy8h+NK/v3z95ns89pLggxK9NuX1IF4VQK13ypSTwe1/1h6hdOVjQmviCvlHntt0WtXe/hMsBO1d1w9w93VPW4vFx/OVgkHbo8SER8LcwyOnysqpA2Mnc1eN3/Mh9prhBjxsQC9pQs88949pGkeGZDoE3vYc9Luv9YgB1IDHdQ3Vm6gCY2rQ8WdrfaqJ1D7QxIVofD48OP8vaf/Hv/xPf+RT3/3p/mJ536CsZ4xfufV3+H7P/39fNPvfRM/+YWf5Mby/5+9/w5vJE3Me9FfVSEDTGAEc2527p7p7om7k3Yn7JFmvZIsaXVk2Ud+LAfpyrZ87jm+ko5sy9Kxfe17JB9Lx0r2Kq0kS5a1QTuzM7OzE3p6UndPs7vJDswJzCSIHKvq/lEokCAyCbC7Z/k+zz69AxRAkACqvu/93u/3Du/59RmatAt0qSnquNutDf6b09nkUTnKrY1be8Z76EpxqFfyc6jNPb0FE9Tx2VnktfWK8qd1mbq6UHy+tJK+vUhNKKgxOZXCKCS9INFyfH8Jal3NP//zCCYTG7/9G0D2QbYSDGrczPukIBG0hW2jy5W6xsRlhcm1QJpBDXC+28nUWpD1QIXTckkVSlDrn2FTX3EJalNfH0gSkQKpTsmhmXyfogT14fj6U66U8ViVmaDW+dMAnpVQBt5Dl7PFTjSUIJzs4uhwao+b29htUOfmDoeHh1HjceyPXMB85AixifIaUofan2RvVMMmmUvvscklqeYgDOpFMFjBuo3Fis3MYGhsRLTnx3MVKugLRBN85dIMnzvanHXn6Nqcn/lb2sLs+kKAgWbtujieSAalPkUc6viCG2NLy55Mx0IyDwygxmLE5gr3nxSr8HUNjWc9vX+DGraLEr+XpYQToFIQlRednMxZkLhRYIxoPXOGxPJyWigCtg3qcuHVIqOjoKpYT+7PoDb39YKqFoVGvF+U16Be087XM3Ich9mQNkYopDaXg4gEa/N+TD092u6UHeEi0Wb8dCWoVVWVVFWtVlW1SlVVQ/L/6/9d/jPlA6rGzioMJpHFia2cxxxvrabDaeWVmw+GQa3B+E0FU3rfnv42BtHA57s+D8D7k+v87395k9EZIxbJwpin8EUlvjCPYDRiqK9CDux9QCVaLEj19Wlboist3Vzfa4La2JE0qAtwqO8s+akq4YT1/DHNbHtttPyft0SR2xE3F9158R4AdS1a8sKznKMo0WHa12ciTf6VogzqbAWJulJpv3vFobY33DMG9fW16ww5h7AYLHmPkzfWEYxGxKrsiyltjjZ+7OiP8TvP/w4Xf+Qi/+Gp/8CTbU/y9vzb/NQbP0UovrfCN0OSdVcqhzrudmNoac4Y/I+ujxJX4gUT44V0qvEUJtHEx8sf5z3O1NdLdHo6b2I5dOUKALbzB2NQw3Z54F6Vj7mWTZHRWyCKWIaG9vVzdRmbm2j8uX9K8IN3tdeTZZAd1QsS7yODGrRdQfpkYXo9SFxWOdKc/r26oHOoDyhFXQjXEp2cAkEoyCDVJZrNmHq6iRZY9NMT1MoDsgutkA7H159+yf4oGEStTyOpSFxmxRelI5mEVlUVz3KIuuYcBrVelLiohQ30BPXuokR9R1s2DnXo449BkrA+/DDmwaQhVSZ806H2L63TpfB8qxRpCepoRXZRpuRza+npHa87Njtb1E4kU28viGJOg/qrH87iDcf5mWezs3SvfnsWk0WiqasqhfgAuLupgKMFNh8c06qQ4gsLFcF7gIb4gG12cTkUvn4dJKlsIQPL4CDRiYmCPRWfZhWzq10JhzW8Wn/md+b9yXXO/+p3UmG3bEpxqHdhPlJ4tTIlqCMjWkGiZY8Fibr0EER08sHZLaFjUrIZ1B6/AUmNc3MryFFXFaK463qwdAM+/t2szysIAtQYUT0xjN3dKF4vsseTul+0Gsr2/h2k8u93P1RBSZJIc08NS3kMakEQ+MIJF+9PruN9AD4kSiBWEO+hqAqvzrzKE61PUGPWUuQ3FrQU+a3FAAN1A0UZ1LF57eIrOUyokf2B+HdO6A9CsTz82mIkOexI9fXE5vJPFm4v+RhyVRU9gG2utnC2s5bXKsChlr0xMAj5t3lHwgQ2N3C25h9UVTU0IBmNeJayG9RilRElEEdVyjDILjJBHRkbA0nKmgCUamowtrYSuVccanvjPUlQx5U4I+sjnG4q3MidWN9Aqq8v6rPqMDl4ofsF/s1n/g2/8dxvEE6EeWP2jT29Rj0BHV8pLUEdc7sxZVlIubaqlYXs16A2S+aiONTmnl5tULGRG+ESunwFyek8ECPV1NUNsG8jQw4mB2TFGtQjI5j7egsmsEpR3Y/8CJYTR1CVOInNzF0lsZRB3V22n1kOGdtaUwnqu0nM0+4E9cm2GixGkY8PqChRLpCgjk5NYmxvR7TkX8jaKcvgkYJcXNEkIRjFTxPi41CfcmUzHhe3wgCpsEHIFyMWTlDbkv1852xNGtRLmiFdYzVSZTFkFCWmEtTezAWc4EcfYzl+HMnhSC28f68nEu8nyd5oWfEekES+xBTUaAVNPd8i1KSPnTSDuvDipGg2Y+rszFqUGInL/O7FaZ7sb+BMR23G/VsrISavrXLi6XZcfbVsugO0VFmwmSTGVvwah3rj05Ogji3MY+yokEHd1wuCUNYSwsiNG5iPDCJai0+A5pN5cBA1EiE+X76U94MmfedY3nDA1FQSr5ZpUL83vo6iwoeTuecXlqEjCBYLoV1FiXpqu1wM6vDITYytrRiczn09j6m7W1vkmiw/Q71SUsIJBJOYtWTcq1RRYwpxZyWQHef6wW/AK/+rFrbLovo2B3UJgeVqzefYmSwXrYZPV4L6UMWrtb+G9YUA0TwfgBdPtBCXVb5zuzLldeWUXERb7PDqMMvBZV7s2S6cH13UDOqRRS+DdYPc9dwtuIIfn9cuvinG5D451AdpUMcX3AgmE4bGwsV7uWTq7CSeZ3uVqqrcWfYXVZC4Uy8cb+Gm24s7OSEql/TBdD4D0rNYuCARQBQlaptdeREfqLm5sUUrEYXwJlS5Ch4aHRvH1NWFaM4+YTAfPUrk9j1KUNsaIOrTfp8D1JhnjHAinLMgcacSGxsY6utL/hlnGs/QWdXJ1ye/vodXCIbmvSI+FrMXJK4O013djdOyv0EUaJiPO5t38MVypxdMvRqzN1/px0HxpwFM7W0givs2qPXvrlQE4kNVVcKjI1iO758/vVOCJOH65V9GjQYIfjyccX9sekZL/RbgZh60DC4X8vo6SjTK3WU/kijQ25huZJkMImc76g6kKFFVVNRIIu8kKTY5hbm3OP60LvPQEPHFRWR/bk47aOmhTxHi41CfcsneWAbeY96jG9RaEnprWTOacyE+bDUmTFZDqihREAQ6nbbU8+jKlaBWwmHCN25gv3Ae2IHUOTSo7xvJ3lgGp3y/0p+vopgP3yJUb4+dZL8feWOj6C4H88BAVmP0z6/Msx6I8tPPZE9Pf/L6LJJB5PSzHdS3O0jEFfzrYfqbHBrW0NnzqWFQK5EI8to6pgolqEWbDWNHR9aFgr1IVRTCN26WhT+tS8fB3C/nrPjqKhv/5b+gKnsP05Wq1E7EfMGwZNmleSDze3N1VkvTXpvfyvl4wWjEeuIE4eHrabenGNTlQnzcHNk3fxpANJkwdXYSe6AS1HFEa+Z7mPD68FtasDsUAtEEx7IZ1IvD2r9Tb2d97sEjTowIDIe1EMuhQX0oAFwDtaDC8lRuDvWZjlpaayy8OnJwBupepQTiSAUM6lemX8EiWXi249nUbTfd2u8/6vYxUDuIN+plNZTfMIotLGBq70DSt/DuYwJqbG0lvrRU2W1tOxR3uzG2tiIUKN7LJ1NnZ17Ex4InTCCaYKiERlfQDGqA18uM+ZC90cIFiUnD2ekqnCyvc7XlTlAnuaP7LkoMJBeFHM35j0NLFmUrSNRlGRoiNj2NEtobimJfsieN3wMuSry+qg1YikkTyxsbSA2lG9SCIPBy38tcXr7Mgn+h5MeLDgeC1VoS4kONxUisrGRsn1RVleG1YU43Fk6MF6NzLedQUbm6fDXnMeZeLRUdy1GUGF9cJO52Hwh/GkAwmTC2tx8o4iOxuoq8tl62raE7ZTlyBMlhIrG4Qehyepo9Nj2NsbW1pNTvQcjo0hBIieVl7q746W2wYzZkckrP9zi5teTDF6mseatzEHMlqFVZ1soms+w+mb52he/+/m9nfZzlSDLVWShF7TCVjYN4qENVWoo/03hcSJYb6ixpz0p+g1oQBJwuG5tJgxqgo86WgfgQjCKizYDsSzeow9euQTyO7ZFHgGRytbub6N37w+z5XpeqqMj+WPkT1CmDukJhBkXWdiXuLEicSRYkFol3Mg/0E5ubQ4luv8a4rPDb70zxcFcdj/ZmhgMCnih3P1zm6OMubNUmGto1tIeG+ahifNUPzj4IrkIkdyDgQdF2iXdlDGrQMB/lSlDHpqdRAgGsp8ozdoYkhkQQ7puiRM8f/iGr//4/pFAVByE5FfTIk6CemASjMSNoEZcVri9sAXBtbivvz7GePUvk1q204kHBKCKYxLIkqBMeD/GFBSz75E/rMvX1EZ16cHZLKOFEVrzH5p15ZIMVpUob32ckqKMBWE9esyffzPrcA0e08+WtNRHBZCI6dWhQHwpo6alBEAWWxrdyHiMIAi+ecPHu+Dr+Ck8k9yslGM+L+EgoCd6YfYOnOp7CZtQG1t5QnPnNMN31NvzRBLUGbZBy15N70il7vSg+H8aOjpQZqeyDOWxsdaGGQije3AsF5VTc7d73wMHY1UlieTlnE+3tJW2QlXXLRx71NNgZbHaUnUMte6MYCvCnPYsLIAjUtrTmPQ6gztXK1vISipK5FVGq2v+iBQD+5N+gQIJaCQaJz89nLUjUZTk6BKpa1i1xRcuWTOofMOZjeG2YJlsTLfbCiBQtQb23HQUv972MgMA3p75Z8mMFQcDY1ER8tXiDOr6yAoqSkaCe8c2wFd3ad0GirlONpzBLZi6v5MZ8GFpaEKxWYtPZ0wChq5q5fRD8aV2mrq79J6iLSH7oSrHpTpTfoAYwdrYgVjlZ+hf/EiW2fZ2Jzczcd/xp0BZcQVucuLvsZzBHSe4jPU5UdTslUynp76WUY7Eh7nZrBbO9mX/LG2++xrVXv0nIu5Vxn/mIxvYvWJRoN5YtxXOoQ1VSqqoi+zIX8+c3wxglgaYqbTHMsxzEYJaw1+Y2KOtc9jSDurPexvxmCGUX+kyqMWcgPoIfJfnTZx9K3XZYOnb/SAnEQVHLnqAWU4n6CiWog2ugJHYZ1DNAKQb1AChKWjn0X11z494K8zPP9GfdKTb85hyqCmc/r5lwdS4bgiiw4Q4w0OxgxRcl6Ej+fM+Dz6He7jmqoEE9MEBsdjZtTLRXha/fAMpXkAg7Ut73yTnL/9bbgIbcOyilGNR5DeoJzN1dGX06t5d8ROIKp9trmNsM5S3Utp49A4lEqqxcl2grz9grMqI9r3Wf/Gld5t5eYjOzqPEHY1yohLMj8lbvavPWNZsRUchE+bF8E1A1zOfkW5Alve9ssaMKsLkYwtjZmTVBfVDhzXLp0KAug4xmicYOR96iRICXTrYQSyh8905p29APUkpMRo0rqURzNn209BGbkU1e6nkpddtIEu/xI+e1gUM4oJWW5eNQx+a1i6+poz21MrgfxqTBpRmQB4X5iC8s7Jk/rcuULErMxde6o7NHm0tLUAM8f6yFj6c38ZSpWEpV1CRXsUCCetFNTWMTBlPhQXedqw1FTuBbW8u4T98eu+8EtT/5eSjAoI4mt0hlK0jUZTl6FODecKjtSeP3gIsSb6zdKArvoaoqic3NPSE+AFwOFxdaLvCNiW/s6UJqaG4mUQKDOp6DIT+8OgxQNoPaLJk53Xg6L4daEEWtLC5Hgjp0+QpiVVXedH+5ZerqIj4zu69BjZJiUBdGfERGR7VynTIVJO6WVG3B0NRObGqKjd/7PUD7zMamp4velnyQMrZq17Pg/AJzmyGGclwDznbWYhCFimM+UgUvOd7L6KSWZDH1ZiaoV6a0c+vSRKYJbWhuRqypKZjq1BAfn46SxEN9uqVGZdSYkjVB3VZrRUoWIG0lCxLzYZucLjuRQJxwchzU4bQRTSis7TIapGpTRmI29PHHWE+cQHJso4EsR44QX1hADgQ51L2V/n5VLEG937FzLvmSWL4diI/Y7CwIAsYiUVk6ukEPe8iKyn9+e5LjrdU8faQx4/hIIM7oxUUGzjdR3aDtQDAYJepabGzsKEqcJblT8lPAoY7pBnX7/uaZ+WQeGIBEQkOd7VPh69cRq6rKvuB/vyyqxebmiCXHOXpp+UFICcYRzFJWdrGu6OQkpiz8aT248JNPau/JcJ4UtfXMGSC582aHRLuxLCV7kZGbAGXbJWnu79M+uw8In1wJJ9JKk3Wtz/lAVbhpsNDb6MBi3LVTcjH5fjz209rukNXRjOeQjCJSrQlbWEFu60gzqAWrAWQVNX5wWJpy6NCgLpNcA7WszviR83wAHu6so6nKzLdHyptqLadSMP48K3WvTr+Kw+jgybYnU7eNJPEeP/hQG0ZJYGJFptXeythm7otKfEE7qWgJ6v1zjvQt0XqxVCUlB4LIW1v7N6i7tMFcrhPsnWUfXfU27ObCBs9uvXC8BUWlbNxzJRgHWS04mPYsuqkrUJCoqy75nm1l4VCXI1UPbJcKFDCodcaZPnDOJkNrK2J1NZE794BDnUpQHxziYzW0ijvgLgrvoXi9EI9j2APiQ9cX+7/IQmCBT1Y/KfmxmkFdQoI6lU5J/w5fW71GjbmG7prukl9DLp1rOcfdzbt4o7l3d5h7+9LSRDsVunIF20MPIUiZiIdKydTVhRIKIa/vfUFECSUH1lLhoUZ4ZARzf3/ZynV2S7QZUBUj1V94iY3f+m2i09MkVtdQQqH7riARksWfgsDKuJZiz5WgtpkMnGir4XKlDeoCKR79s2vuS2dQh7xb+De0BcjFscyFPUEQsBwpXJQoOYzIgfgDlwI51PeedPbvboN63hNO8acBPMuhnHgPXU6XXpSoGcqdTu34zKLE9AS1EgwSvnkT24ULacfpi5zR8Xtv+HyvS2eGlz1BbZIQLFIGk7xs8iXnWDsT1LOzGF2unP0tu2Xq6gKjMcU/fuXmEtPrQX46R3r6xtsLJKIyD72QntCub3OkEB8At8LJcfKngEMdX3AjmM0YGjMN+3JJZxaXY1do+MYNrCdP7gt7mU2WwUENB5Njp/FBKfDWWwDYHn2U0NWrB8ahVgr0ginJEklzf3aDuq3WyvPHWpBEgeE8HGqD04mxq5PQ8HDa7aLNgFwGxEf45gim7m6kqtIDd9mkhyH0cMT9LiWUA/GxIWMLr3LVT3b+9NKwtgP81I9q/z2RHfPR1FFFkyyyXN1MbGEhlSzXQyUPGubj0KAuk1r7a5ETCiuzublXoijwwvEW3rq7Sih2f35QUpNQR/YBU1SO8ubcmzzX+RxmaXsgctPtpa3WSlO1hcHmKkYXvQw6B/MiPnRT1tjWjmCWQBL2V5KYTJzFFyufoNbTl6Z9rmzrvKjYbHYO9e0lP0dLLEjUdaKtmrZaK6+NlsegTg2m8xjUqqriWXIXxZ8GLUENsJmFQy2aJQSThOzfL+JjCQRp2+DNoejYOILVirGjI+cxgiBgGRoius8EdSyc4KNvTBEvpWX9HiSor69p/OlieMyJDc04l5x7N6if63wOm8HG1ydKL0s0NjeRWF0t2sCKud0gSRhb0tnk11avcabxDKJQvsvjhZYLqKhcWcmdujD19hBfXEQJpxdgJTY2iE1NHSjeA8DUrU0E94P5KDSw1qWqKpGR0Yrwp3VJdiNqJEHj//t/RzCbWf5Xv5xCqpjvQ8SHVsDbiG9WW0jJt4vmkR4n1xe2iMRLOJ+UqO0Edfb3Mzo1hdTQgFRTk3a7np42GE0sjWcfD5iPHCEyPp53wifaTVoKpJRz5qEOdQ+USsbuMh7dnlCKPx2Pyfg3IwUN6rqkQa0XJXbUaY/fzaGWqk0owThqQvsOha4NQyKR4k/rMg9qSJ37hen6vazUQkaZE9SgFWcqlSpJ9GZJUM/MpMYMxUgwGjF3dxMdH0dVVX7zrQn6Gu28eDwzSBKPytx4a57uUw3UtzrS7mtodxDwRGkwGbAYRW5vKuBo+ZQY1Nou3UoWY5t6ekCSiE7s73yghMNEx8awlBHvocs8OAiKcs+NSP9bb2Pq76P2S38Dxec7sFS3HMzfCxabmgJV1RLFu/TJrIeHuuqwmiSOuqq4Np8fBWc7c5bwteG0eZRYJrxa5OZNLGXCe8CO7p4HxaDOgfjYCpupVj24vZHsONfFYXCdgWoXNB2Dye9mff6evlqqVIHbUkMyWa7NHXRTvBwc8YPUoUFdJrn6tUnZUhGYj0hc4e27mUiD+0GFYPzvLbxHIB7gCz1fSLt9xO3lZJv2NzjZVsOI28uRuiPM+GaIJLKvesbnF5CcTiSHHUEQkBzGffGGJacTwWQ6EMRHLjxAqZJqaxFraojNZZpAoViCmY1gyQWJugRB4PPHmrk4vlaWBRE9nSPlYVAHNjeIRyNFJ6htNbWYrFY8WRLUAGKVsTwliY5mKLCqHx0bw9zfX3D133J0iMjYGKq8d6Pk5jsLXHllhpmbJZjNllrNaD9ABvX11euYRBNHnUcLHptY1wzq/SSobUYbz3c/z2szrxGKl1ZEaWhqQo3Hkbe2ijo+7nZjbG5GMGwPGDwRDzO+maIS46XoZMNJzJKZK8u5DWpzby+oakYxYehKkj99QAWJunSW5H4MajmUKMqgTiwtIW9uVow/DdvJX8lRR9M/+zlCH37I+m/8JsB9ifgAMLpcxBcXsRjFVHIym853O4nLat50zH61XXiZfTdPbHIqq9G/PDUOgsCRxz/D8sQYSpbzpuXIIGoolBN1BaSwY/vBgB3qUAch3XgUdzCowzGZ9UAslaDeShYk1jbbM59ghxx1ZowWic0l7fi2OiuCkMWgTpqc+s8OffQRGAzYHkpHVRnbWhHt9oI7Fg5VecneKEhCUdfIUqUhXyqI+JBMYNPGeqqqEpudLfk6ah4YIDo+zpu3V7mz7OcfPd2PKGaasbfeWyQaTPDwi5kGuF6U6FkM0t/kYHw1AM7eT4VBHVtYqCjeA0A0mbTi1GSSfa+KjI6CLGM9VSGDmnu7qCb7/YSuXKHqmWdSY/GD4lArBcbRunG/O0G9uBVm0Rvh4c5aAM521HF93ous5A7xWM+eRd7YSBuLSbb9Iz7iK6sk1tawlqkgEUC02zG0uohO3v/fdTUuQ0JBtKa/j5FgnDA2LAbten6sdXdBol8rSGxNXsf7noW5DyCWOT9u6NDOhaOxOgBiMxrmQzeo1fCDNXY+NKjLJKvDRF2LjaWJ/AV9F7qd1NtNvHqfYj50nEKuk+Er06/gtDi54NreNuiLxJnZCHGyXTOoj7fV4AnFaTB3o6gKk97sq1vxhXmMHdtGpugw7QvnIAiCNqHPksYtt8rZrmzq6CCeJUF9d9mPqpZekLhTLxxvIZpQeKcMCyLF8PI2F7UVO2drcYMqQRCoc7XlNKglhwmlHAzqAngPVVU1gzpPQaIu89BR1HB4z8adqqjcek/7jK7OlNA0LorahCB4cItbw2vDHG84jlEqPIGSN5MJ6j0yqHW93PcyoUSIN+eyb2PKJUOTloQuFvMRdy9mLDDpifFimNulyCSZONN0Ji+H2tSjoRGiuzAfoStXEKxWLMeOlfU1FZKxtRUMBmIz+0hQh+JIRfCnw8lSFuuJ8g1ed0u/pinBOLU//MNYz5zR/rYWC4aWwgWgpWg5uMwPfuMHmdzaX7LD2NaKcX2VweaqrBN3Xee7nQgCFeVQK6EEiIK222mXVFUlOjWFaRfeA2BlahKnq42uk2eIRyOsz2d+nsxHNO54vqLEFAbskEN9qPtc2RAfCx5tQtmeTEBvLWv/XShBLQgCTpedzaUAAGaDhKvakseg1sZpoY8/xnryJKLNlvF85oH7g+n6vS7ZG0OqMiHkObfvVZU1qBc1vEcy2StvbaH4fEUXJOoyD/QTd7v5nddG6HBaeflMZrG6nFAY/s4crQO1tPTWZNxfnzSodczH+Iof6ns/FQzq+MICpvbcOzrLJXN//74RH9sFiYV3W5YqU2cngsl0T89ZwYsXIZHA8cwzGNvaMLS6DoxDrQTy70SMjk+AwZDaka1L508/3OUEtL6SQDTBxGog53NZz54BILwD8yHaDKgRGVXeO9IkxZ8+Ub4ENWhoxOjk/hZXDkI6XmM34mN9QXsvFLMW3Di6O5CoFyS2ntH+u+9ZkGMweynjZzS0a49di2v/6hxq3RQ/RHx8D8vVX8vSpDejXXunDJLI88eb+e7tlYpux92rthEfmSfDYDzIOwvv8HzX8xjE7S/ZqFsz2Y4nV35OJP9NhLVJfy4OdWw+/eIr2o37QnyANqFPHATiY2EBwWJBcjr3/Vymzs6sDGq9IHGviA+A89111NmMvDa6/wUReatw2sOzqBnNdUUa1AC1La1sLWdfVJCqjMj7ZlAva/ymPJLX15E9nrwFibosR5Nmyu29cajn72ziW48gGgRWSjGoQcN8hA6GQR2TY9zauFW0WbudoM6PUimkh5sfps3RxtcnS8N8GJq1YtbiDWp3Vv60QTRwoqH8Run55vPc9dxlK7KV9X5TdxcIArFdRYmhK1ewnjmNUETpaDklGAyY2tv3j/jIgYTYqcjIKBgMmI8c2fPPKiT9dcjBOIIo0vKv/pU2qO/uLjsz8c25NxnzjPHG7Bv7eh6Dy4XDt8GRxvwpyxqbkSPNVRU2qOOINkPW7cby+jqKz4c5a0HiOM19A7gGtV0YS+OZeCRzfx+IItE7eQxqfYHhMEF9qPtcii+GYJEQTduLOQseDd2kJ6g9y0EEAWqaCjP361z2VIIatKLEhc10FFSqGM8bRQ4ECY+MYHsknT+tK4XUOeS531PJ3mhF8B6gIT5kfww1z3x0z/ItpuM9kgV7e0lQA2zdGeMfPNWHMUtXxd2Plgl4olnT0wC2ahPWKiMb7gD9TQ6WvBGi1d1amVjUX9LruZ8ke70ofn9ZQlCFZB4YID4/n4GXK0XhGzcwtrdjKMOceLcEgwFTf989Naj9b72NVFeXMuBt584RunLlQM6hSiiOaM8d9IhOTmLq6sqYI1yd9WA1SinT82ynlqy9Npcb82Hu70e02wntKEpMjb32gYgI37yplaAfLW8Jurmvl9jU9IHxwPeqXCXjq5PavHndCA0OM01VlvQHLg5r/7rOaP92PQ6SOSvmw1Ztwmg3UCOYkWvqUmGnFOLj0KD+3lVrfw2xcILNxdyrUwAvnXARjMlcHD+4rfrFSg7EEYxi2sBa11vzbxGVo7zU81La7aOLWmr8RBLxcdRVjSQKrKzbsRqsWTnUaiJBfHExLUG9X8QHaBP6A0F8LLrLxgYzdnUSd7tRY+lG7J0lH3aTlErc7EUGSeRzR5t5884qscT+TuCyN4pUnT/tsbm0gNFixVFXfIq2ztWGd22VRDzzvRerTGVgUC9DVXPeQ1IFiUUY1ObeXq3c5c7eONSjFxexOIwcfbyVtTk/Simr0rb6A0N83Nq4RVyJc7qpuEREYmMdJAmptnZfP1cURL7Y90U+XvqYpUDx32Vjs/Yex4swqNVYjMTKSoZBPbw6zDHnMSwGS45H7l36rpOrK1ez3i+azRjb21NcZADZ5yN6586B4z10mbq69mlQF4f4iIyMYB4YKLpgaS/aHmRr5xPLkUFaf/VXaPgHf7/sP+s993sAeRPzxShW34RJSXDCXngx+0KPk0/mPMT3kXLJp3yLDdHkooqpNz1BHdzyENjcoLmnn5qmZmw1tVmLEkWrFVNXF5Gx3Aa1lOzF2O8i9qEOVWnJvmiWgkTNYNYZ1J6VEFUNVgzGwsW3zhY7YV+MSHJ83OG05U5Qe2OEr30Csoz9Qg6DenAAxestqVT4UOWX7IvlRebtR1KNCRS1LOzYDPnc6QZ1coxQcoI6iSQ4FVvnhx7ONGIVReXa63M0dDjoOJbd+BQEIVWUOJjsaVgUk0nsBxjzEdNLvCuM+IDkQoGq7guVEL5+vSLpaV2WgcF7ZlCriQSBd9/F8dRTqaJy27lzyBsbqZRqpaTEZNS4kpdBHZ0Yz1qQ+MmchzMdtRiSCz/d9TZqbUauzW3lfC5BkrCePkV4+HrqNn3ctx/MR+RmZUrQTX19qJHIgXSP7Ue5EtRr4+uYoluMC+bM9DQkCxJbtz0Mo1UzqXNwqJs6qmhWRDacLamFw1RJ4iGD+ntXrv5aABbH82M+Huurp8Zq5NWb998XKl+p1avTr9Jib8ngs950e3HVWGhwaANki1Giv9HByKKfgdoBxjyZF5X48jLIMqYdhXSiw4gciO9rRdLoaiWxtpZh9pZbsQV32QYOps4uUBTii+kp4ttLfoZc1Xm3dhej54+34I8k+Gh6f8nbhDdWMO3hWXRT52otybh3ulpBVfGuZH4fJIcJNZxIFf+UrEQUwpsFE9Q626wYg1owmTD39xPZQ1Fi0Btl+vo6Q4+5aO2vIRFT0pJRBWVvOLCSxFIKEgHkjQ0kZ11Z0qjf3/f9qKh8c+qbRT9GbzpPrBZGoMSXl0FV0wzqmBxjZH2k7PxpXSfqT2CRLHy8/HHOY8y9vSmzDyD0ySegqtjOna/IayokU3cXsbm5PZ2T1YSCGpNzMotTx6kqkZERrBXkT8N2r4IS3B6k1Xzxi1S/+GJZf05UjnJl+QqSIHF97TpRObrn51q21AIwKAQLHnuhx0koJjO6WOKujCIlh7IXvADEppIMxF2ID70gsbmvH0EQcA0cyVuUGL2bewKqJ4gOE9SHut8l+2JI1eljpQVPGLNBpDE5TvYshwriPXTVubTjNpe180Cn08ayL5K2C1OwSAhGEdkb1fjTRiPWs2ezPp8lxXQ9xHzcK6mqmgx9VChBXZVc0Cs35kNRNGxe9TaOIzYzoxVOl9jJc1NxEBUNvOgIYzZkLtRMD6+xtRLioRe68s4pGtodbC4G6avXdhqNJ7TddA+yQR1f0Hajmg4kQa2Zm3vFfMRXVkksL2OtQEGiLvPgAIm1NRKe/CV/lVDok09QvF4czzyTus12XhuTV5pDndrVniMcoEQixOcXMPel714LxRKMLvp4uKsudZsgCJztqC1YlGg9c5bo3bvIAe16kxp77XGxSx/jW8rIn9al/96x+xzzkUpQ7zKoNxZDVAXcXI9bM/nTAIvXtvEeuvqfg7U74F3IOLyxo4oGWWTS0pBaPBFMEgiHCervaVXVW3DUmQsWJRolkc8fa+aN2yv7TrWWW3IgnhXvsRXZ4n33+7zU/RKikP6xGXF7U+lpXcfbqhlZ9DHoHOTu5t0Mg0MH8Bt3ID4kuwkSCmps738To8sFqlpUinI/irvdmPZZkKjL1Kn9DWJz2xxqVVW5vexjqGVvBYk79ZmBBmwmad+YD9lXeDvi5qIbZ5EFibpqXdpA15OFHZ4aZO/VlPAnf+cCDOro2BhSfT2GIvnJlqNHiewhQX370hKqonL8yVaaurWLUUkcalvDgSWor69dp93RToO1OGRHYn0Dg3N//Gld7VXtnGs+xzcmv1G0OSoYjUj19UWlwrKVnN7auEVMiXG2Kfukfr8ySkaNQ72Sh0Pd20tsejpVwBm+ckUzGio48M8nY1cXajhMYnW15MemSvUKJKjj7kVkrxfL8crxp2FHiqDCCdyrK1eJyBF+YOAHiMpRbqzd2PNzTYna+b89VvgccaFbS5hdrhDmQ0N85EhQT04h2mwZLO/lyXEEQaSpWzOuXQNDeJbchP2Zv49l6Ajx+fnUpGi3BElEtBn2j3w61KEqLM2g3pWg3gwlCw4FVEVlayVEXXNxBrWzVTPeNhe3DWrYxoaAZj5INWZkX4zgRx9jPXUqZ1rNfGhQ33Op4YSWjKxYgjqdSV42hTY0BuquBLWpvR3BWFrZ42++O81ibQt9wcwxm6qqXP32LDVNVvoeasr7PA3tDuSEQlUCTAaR4WDSlHuAOdTxVIK68ga1qbMTwWjcs0EdvqGFWSpRkKjrXhYlBt56G8FoxP7EE6nbTN3dSA0NFedQpwzqHOPo2PQ0KEpqkUHXjQWtDHGnQQ0a5mN8NYAvknscbD17BhQlxY3eTlDvzeCMLywge71Yy8yfhu1de/d7UWK2BLUcV9jyqTgCCyxYajm2u28s6of18W28h66+Z7V/J9/K+DkN7Q5EFcZNbcgeD/LWFoIoIFoNhwb197IEQdA41BNbBU2Vl05oqdZLk/cX5kMJxrNuJXlj7g0SaiID7xGIJphaD3KiNd2gPtlWw5o/Squ1F1/Mx0oofQCiM5dNaSWJ+y9BMrZqSdlKbveQfT4Unw9jW3kGDnqxQWxum0O96I3gjyQY2kdBoi6LUeKpwUZeH13Jy0fPp1TaI49BHY9F8a2vUucqzbiva9GOz1aUKFYlPxN7LUoMJD93jsIGdTEFibosQ0PI6+sk1oovLFQUldH33LQdqaO22UZNoxWzzcDKdP4dF2myN0BkC+TKmmyqqnJt9VpJaeLExkbRBn8xernvZWZ9s6kkdzEyNDcRX92bQZ0qSKxQghrgQssFxj3jbEaym4im3h7UaDSFKQpdvqIVXVnKjxwpRvqW3b0UJcpBnbmWf9IaGRkBwJKjIFFOJJi4/OG+WX+CQUQwSxU3qC+5L2ESTfz9U38fAWFfmI9RWTOYrJ7C55mmagvd9TY+qphBnT9BbertzUi5rUyN42xrx78pc+eDJVoHNMb40kRmito8qN2XzzQT7caKv3+HOtR+pCoqsj/ToF7whOlI8qf9mxHkuEJdS362vK6qOgsGs4RnSTOodUyIjg3RJVWbSHjCREZHsefgTwNINTUYWlqI5NmxcKjKKlWkWSEGtVhdoQS1LzlO35mgnp3F2F0a3mN00ct376xi6R8gMZlpJC/c9rA25+eh57sK7iKtT5aDeRYD9DU6uLWhgKMZNiuLX6ik4u4FxOpqpOr9zwELSTAYMPX1EZ3Ym/kbuXEDjEbMR4+W+ZVt614uqgXeegvbhQtIju3ztSAIGof68uWKcqgLGdTRCe27Y9qVoNYLEs921qbdfrazFlWFG/O55506qiWc5FDrP3uveLXITb0gsfwhFENdHVJ9PdGp+3sxSglnJuE3l4OoqoA9soLX5Mg0qHcXJOpqOqZ5GlkwHw3J0li/XZvbRlNFiYcG9fe8WvtrCHpj+NYjeY97cqABh9lw32E+lEAc0ZG5ov/q9Kt0V3cz5EwH3N9a9KGqcLI9/YuVSlTHNcN4N+YjPr8ARiOG5m028H5PgpBMUAPxLGnccimbubUfSQ0NCDYbsbltE+h2cpv2sWxMoj3oheMtrPqjDC9s7enxSigBPunenwABAABJREFUCTVv2mNreQlUFWcJBYkAFocDa3VNVoM6laDeq0HtT36/8iSoVVkmOjFRVEGirlRRYgkp6rnRDQKbUU58Vvv7CIJAc3c1KzMlFLnYkgZwqHJlaACLwUXWw+tF4z0gifhoKJ9B/Xz381gNVr428bWiH2NsaiaxUjjtG3O7tS2pLdvnn2ur10pKjO9F51u0bYG5ONTmZBogNjWFEgoRHh29Z/xpAFNXt/Z6ZmdKfmwqQV0A8REZHdEmNzkWiMY+uMjX/8Ov4L4zWvJr2C3RbkTeB0evGF1yX+Lh5odptjcz5Bzal0E9siUTNVlILBW3++V8t5PLM5t7XojMJVVVk0U9uRnUpt6ejNtXpidp7u3nw69N8eYf3CYcdCIIIktZONSWI/oENE9RYhl6Kg51qEpKCcVBVlNjF13znlCqT8SzohnLtUUiPgRRwNliYzNlUGuPm8/CoU6sB0BRsOXgT+syDw4cJqjvoWSvlmzevZBRLkkOEwiVMKiTc6ukQa2qqpagLpE//f+8NUmV2cDgY6dJrKwge9MNs6uvzWCvMXHkkfzhEoC6FhuiJLCxEGCw2cH4SgCcfbB5f5tW+RSbXzgQ/rQu88AA0fG9YRLC129gGRqqaIeIoakJsabmwM9Z0elpYjMzaXgPXbZz50gsLxN3V85v0P2QXAzq6MQESBLmXQWln8x66G9yUGvTzi+xpDl5uqMWQchflChVV2Me6E8VJUr7ZBiHb44gGI1YSgiBlSJzby+xifv7u66EEyCAYN5GGa3Pa311ohTCZJToadi1YL2YLKrcnaAWBC1FPfUWKOn9NLXN2rkQizaP1TnUwqFBnS5BEF4UBOGuIAgTgiD88yz3f1YQhE8EQUgIgvBDu+7724IgjCf/97cr+TrLKZ1DXQjzYTZIfO5oE6/fWqlYqVGpUlUVOQuDeiW4wpXlK7zU81JGQmrEnV6QqOuoqxpBAO+WxoW9u5k+6YwtzGNqbU0VDoBWkgj7Y0wakgZ1ooJFiSmDukxbrwRBwNTZSXx2G/FxZ1kzqPXSj/3qmaEmDKKwZ8yHvKUNpg150h6eRW1LWl2JiA+AupZWPMuZF3l9sWTP27pTiI/cDOrY3BxqNFoUf1qXeShpUJfAoR69uIi1ykjP6W0DtKmnms3FAPFo4RI0QEtQQ8U51NdXS0sTq6qaTFCXz9y1G+18vuvzvDbzGpFE/gU/XYbm5qIRH8aWFgSDNvDSE+OVwnvoOt5wHKvBmtO0TG1Xm5oifP06JBLYzt87g9roakEwGvdUlKgUGFjrCo+MYBkcRDRln6i7k2ZmtnK9UlXpBO5SYIlJ7yRPtGlbQc+3nOf62vWiP787paoqY6tBws7GjH6CXLrQ48QbjjO+mr+oueTXElM00y3LYoMcCJJYXsbcm57gCWxuEPRs0tDRy/wtbUHtvb+Ypr69i8UsHGpDaytiVRWRu/mLEveMezrUoQ5A2ZKx/kicrVA8ZSxvLWvGcrEMaoA6lz2VoG50mLEYReY2dhvUJtSIimA0YT1zJu/zWY4cITo1hZqlnPpQlVelE9SCJGgLehVLUGvmaWJ1DTUUwrTLIMunidUAr4ws8ROPd1F7TBtLRye2zdHlaS/uu1uc+XwnkrGwTSEZROpa7KwvBBlocuDeChOv7X7AGdQLmHYgMCstc38/iaUlZH8JgRm0gE94ZKSiBYmgzZMtAwN7xpDsVYG33gag6pmnM+7b5lDvrww7nwomqCcnMHV1IewYP6uqytU5Dw93aniPxXEPv/fPLrKxGKDaYqS/0cG1+a28P9d65gzh6zdQFQXBqPUb7HXsHBkZwXz0aNprLKdM/X3atayCSfb9SgklEK0GhB27QdYX/EhqHK9BYKilKlVmmdLicHpB4k71PQthj1aiuEOSQaTOZafRWk1ClIhNa+fAwwT1DgmCIAG/CbwEHAO+LAjCsV2HzQF/B/iTXY91Av8CeAS4APwLQRDqeADkdNkx2wwsFjCoAV484WIrFOejqcqmIYuVGpMhoaSMYl2vz76OipqB9wDNoG6qMtNUlb4F3WE20NNgZ2wpTpujjbue9ElnfH4BY0f6xVcsg0Etms1IDQ0VRXyk2pXbWgscWbxMHR1pDOrby346nFaqLKUx3XKpxmrksb56Xh9d2dNJPJX2yDOY3lzUBq7OEhEfAHWutuwMav0z4d8Hg1o0bCePs6iUgsTU66qqwtjeTuT2raKOD3gizN5c5+jjrUiG7dNuc3c1qgprc0UOCu3agg/B4tEie9Hw2jBWg5X+2sxm6GxSgiHUSARDGRPUoGE+AvEA353L3li8W4bmJmSPB6VASWrcvZi2A2LeP89mZLOieA8Ao2jkbNPZnAa1oa4OqbaW2NS0Vr4iijmLrg5CgiRh7Ozcm0FdBINaVVUio7fybv1bGr+T/De3cVmsJLuxok3WlxYvAfBk25OAhnSJK/E9cajdW2EC0QRCc0vRO4Ie6dG+fx/vsxB3t7bT8JnvpT4ANu0uSJzWDAdFbUROKHz2RweJhhPIShPLE3dRdiU/BEHAfGSwQFGiESV4yKA+1P0r3XgUdyRj3VsaKzqVoF4OYrEbsWbZrUiO8ZnTZSfojRENxREEgU6nLRPxUWMGRKxnHymIhTIPDkI8ntoCfKiDleyNgkBG0r6ckqrN5WdQ+9zamDo5FtV3V5WSoP7Pb09iMUj85BM9mPv1gr5tg/qTb89ithk49mTxc6yGdgcbC376m7RQz7qpXUP8RUszXO8HqYqihSgOgD+tyzygpVt3LhQUo+jEBGoodCA9KebBQaLj4wdqRAbeegvz4GDWHdPmgX7EmhpCVyppUCdAFBAsmSWiALHxidR3SNfUepCtUDzFn54d2URVVBZub2M/rs158v4drWfOoni9qaI90W5MjQNLkSrLREZHsVYA76HL3NuH4vMhr99fyNydUsIJhF0FievzARyhJWYNVRxtyYLyWRrOxHvo6n1a+zcH5sOZEFmy1xNOsrlFmxH10KBO6QIwoarqlKqqMeDPgC/uPEBV1RlVVW8AuyPELwBvqKq6qaqqB3gDeLGCr7VsEkQBV18NSxOFubJPH2nEZpJ4ZeT+wHzoxvBuQ+HV6Vc56jxKT03mFt6RRS8nd6WndZ1orWHE7WWwbjAjQR2fn8fYkX7xlVKIj/1NQI0uV4rhWgnF3YuINhtSbW3ZntPU1Ul8YSFVjnZ7yZf9hLUPvXC8hen14J7SdfogNx/iw7O4gKO+AeMeeLl1rlaCnk1i4fQJl2BIFmPtGfGxrLHoxNynuuj4OAhCxkW+kCxHh4gWmaC+dWkJVSVjwN3Upb3HK8UWJdqSCeUKFyUOrw5zquEUBjE/nkGXvKG9HqmMDGrQEqguu4tvTH6jqOONTVqZTmI1v4Efd7vTBpzXVrWtVJVOUIP2O01sTbARzm4imnp7iU1NEbpyBcvRo0gOR8VfUz6ZurqI7ylBnb21eqfi8/MoPh+WE8ez3x+NsD43A8ByFm5xqRJthooiIi65L9Fib6G3RjNrH2p+CFEQ+Xj545Kf6+6yNrG2dbSTKHILaYfTSnO1mY9nytt0n2ogz5Kgjk1pA+DdLfLLkxMIgsjmkhVrlZHjn23jke/vJeCpIxYOs7Ewn/FclsEjRO/eRVWy7yoTkwsMqnz/JmUO9b0tPbG6E90wv6kZ1DqD2rMcyp6eXhmF//QQzFzKuMvpShYlLoVSzzW3GU47RjBo40fL6UcKvs5t5vvBl44dSlvIEO1GBEPx0/DVWR8BT/G7caRqU2UQH1WtqTF1bGYG2MaBFdL8ZoivDbv58oVO6h1mbeeMzZZKxm4uBpm+vs7JZ9oxWYobfwLUtzsIemN0O7QQzYyaRIM8gBzqxNo6aix2sIiPJH6h1IRy+HrlCxJ1mQcHUYLBiiI1dkr2egl98klWvAeAIIrYHn64okWJSjCOaDdk7F4HUKJRYvPzmPuz86cfShrUS5Nbaf+e7azDE4ozu2sHzk5Zz54BIDw8DCTHznsId8Smp1FCoYrwp3WZ+/SixPsX86GEE2lzIVVVWZ/349iaZc5Uw7HWHAWJrTnmpI5GaDmVuygxqjBX3UFgYkeCusJ4w3KrkgZ1G7BzBrKQvK1sjxUE4acEQbgiCMKVtRLKyiotV38tWyshQgUGBhajxDNHmnh9dBm5zMzIvUhnHYk7EtTzvnlurt/Mmp4OxRJMrAY4nsugbqtm0Ruh09HPnH+OcEIbTMs+H7LXm7F9STBKWonVPg0Eo8tV9JbovSi+sICxvT3rBWOvMnZ2osbjJJaXCcdkZtaDZSlI3KnPH9O2ibw2UjrmQ/bGQBSy8sl1bS4u7Ck9DZpBDeBZzlxYEB2mfZQkJg3qPIqOjWHs7MjZeJ9L5qEhYrOzKMFg3uMUWeH2pUU6jjmpadz+Gb5IHFu1iap6C6vFGtQpxEd5E5I7FYqHGPOMcbqp+C17iQ3t9ZSzJBFAFES+v+/7+WDpA1ayNL3vls60T+wsSpy+mJaiUWIxEisrGQZ1lbGKvtr0gV4lpHOor6xkH9Sa+3qJjo8Tvn79nvKndZm6uojNzec0DXNJCcURzFLeCbhekGg9nt2gXpmeRJFluk6dJeDZxL+xv4WZvaZAilFcifPh0oc80fpE6tpQZariqPPonjjUd1e0z2x9byey11vwPANaCvlCTz0fT2+UNWWUL0EdnZwCgwHTrh1RWkFiB3O3/fSeaUQUBc58vpPmXm2nyszwzYznMh85ok1Ac1y/JYcRVB64gfahvneUWszfMVZaSCaddzKoM/jTqgqv/byGJXjj/8hIUtclDWrPDg71/GYo7XseW9DSj6a+3ZtVM2Xu6QaDgWgepM6hKqdCpeO7pSgqX/u3H/D2f3yn6MdoBnW5E9SLGQWJgsmE0VWYFQ3w2+9OIgkCP/VZzVQSBAHTQH/KGP3k9VkMJpHTz+THW8x4Z3juL55jckszpfRyMEtQwSSJ3Iomx8oPIIc67tZ26ZoOMEFtbG1FsFpL5lCHb9xAqq3F2NlZoVe2rYMuSgy8exFkOSveQ5ft3Dnis3PEi+i+2YvkYDwnJi82PQ2KkhGu+mTWQ63NSG+DnURcTgWglia8qKqaKk68Np87yGDq7kaqqUlxqPeKxwvfTI7xT1bOoDb1JXdhPEAGtX8jQiwi4wgssGJzZhrUSzcANZM/vVP9z8H8RxBJ9w/q27Rz4VpdH7gXUBPaz1Yiifsag7JbD3RJoqqqv6Oq6jlVVc81Njbe65eTUutALbC9WpVPL51sYT0Q4/LMvcd86MbwzpPhqzOvAvBid2aA/faSD0Uld4I6ebtJbkNRldRAIq4jMjoyL76iw7hvxqSeoK7UF3F3+rIcMnVq2+Nic3OMrfhR1PIVJOpqrrZwtrOW128VNvp2S/ZGkapNafyknVJVlc1F957406AhPoAcRYn7+Ez4l/Pyp0Eb7JRSkKjLcvQoqCqRAoOl2dFNAp4oxz+zPaj/q5sjPPqVH+CtibvJosQiDWprHSBUNEE9ujGKrMolFSQmklurym1QA3yx74soqsJfT/11wWMNTUmDWudQr0/AH3wfXPq/t1/rklbmufM7fH3tOqeaTiEKlb8kHqs/lp9D3dOL7PWiRqP3lD+dej1dXajRKInl0ha2lCx9BrsVHhlFMJly7l7QsR5nX/x+7b/3maIW7UbUuIISK5L5XoJurt0kEA+k+NO6LrRc4Mb6jdQCbbG6u+ynrdaKI3mdjBf597/QXceKL5pKbZZD+Qovo1OTmDo7EYzb77WqqqxMTWCv6yARlek7q+1sEEWB53/qCRCsDL9xOeMabRlKpjpzmGYpDFgFOeIPgorod/k5QRBuCYJwQxCENwVBKK3B7FB7luyPITrSk7Hzm2FsJgmn3UQkGCfsi1HXvKsQafwNmHobOh4F91UYey3t7qp6CwajmCpK7HTaCEQTeHYs1kRva2aCob7w+FQwmTD39BwWJd4j6WPqYrU+tkJcNbK0VLzJIFWbUYIJ1EQZe458bqjZ/nzFZmYxdnak9Qnl0qovwp9fWeAHH26npWZ7p6V5YIDoxAS+jTDjH69w/Mk2LI78Y4d3F95lNbTKd2a/A2wb1FtLQXob7Vz11WoHPoAc6tQc+QANakEUMff3E50oLUEduX4Dy6mTZQ1s5VIq5X1QBvVbbyHV12PJkw5PcagrhPnIN46OJosBdYNW19VZDw911iGKAmuzfpSESudxJyFfDN96mIGmKuwmiWtzWzl/riCKWM6c3pGg3lu4IzIygmCzpfp1KiFDUyOiw0HsPjao1VA8LeCxvqDtZHcEF1ix1THUssvv0dnSuRAfoHGolQTMvJd2s34uxNmFKCeIu92aOa6AWmzf1X2gSs7G3cDOJdD25G2Vfuw9V2NnFZJRZGm8MObjmSNNmA0i395DqrXcUrIkqF+dfpWzTWdxOTJNvhG3ZqqdaMue9D3eqhnUwYA2OdUxH7H55OpwR+YKuVSGEitjqws1HEbe2trX82STqqoVYYOZOrW/RWx2LlWQOFRmxAdomI+bbm+KiVisCqU9Qt4tYuEQzj0a1LUt2ucrm0EtVpn2UZK4BFW5kx1KJEJsbg7zwB4NaiB6Jz/mY/SiG1u1ie5TWqJDVVV+/f3/gWSb4Y9HvkZTdzX+jUjBHRcAiBLYnBUtSby+pm3ZK8Wglje1BTapjCWJujqrOznbdJavT3694MTM2Kyda+K6QX3rr7R/x19PHZMqOU0y5L1RLxNbE5xtPBjWs1E08lDzQ1xZzp6gNvVuo5SsDz98IK8pn0zdycWzEjEfciiR1dDcqcjoKOahoZzlKUvjd6hpaqb79Fkko3HfHGp98bUSCdz33O8hCRKPuNK3159rOUdCSTC8OlzS891d9jPY7Eh9TosvStQWiT4qI4d6G/GRhUE9OZXaYqkrsLlByLuFLNdjthloPVKbuq+u2U5jZz/+9RlG300/35v7+0EQiOQ4p0r2fZbmfgpUZL/LNeCcqqqngP8O/H8P9lV+70r2xjK4wgueEO11VgRBYGslS0GinIDXfxGcvfC3/grquuGtX4Edu1ZEUaC2xZZmUIOGTNAVunIJVVVQgsUZkuYjR4iMHxrU90KyL1ZSgnrh0m0AYoKVjbnCc0vYxsyUDfOhqlkS1DNFFyT+7sUpZEXlHz6VvlPNMjCAvLnJtb8eBwFOf65wOeDw2jAAHy59CIC1yoSt2sT6QoD+Jgc312Vt9+TGg2dQb/ccHRziA5ILBSUkqOVAkOjEBNZTlS1I1CU5HBhbWw/EoFbjcQIXL+J4+imEPIhIy9EhRJutYpgPJZTHoJ6cAEnC1NOdus0b0kqydf603od29nltHL804UUSBU611+Y1qAFsZ88Sm5hE9no11GawdMRHeOQm1mPHilrA2qsEQcDc16ft5rtPtTtBrRnUKo7AIoa2tsy+Mb0g0dGU+0k7HgGjLYNDba0yYasxYa/VfJXw1HTqZz9IRYmVNKgvAwOCIPQIgmACfhQoDiQKrwHPC4JQlyxHfD552wMhySDS3F1dVFGi3WzgqcFGXh1ZQrnHmA95V4J63DPOxNZEVrwHwE23lwaHiZbq7MzhGqtRK3JZsWIz2BjzaBeVeJI9mc3kFe3GfSM+DK3a4ClRAQ61vLWFEgyWtSARwNDSgmAyEZuf4/aSH5tJSk1AyqkXjmtm7eujpS2IyN5YXv705qI2oHK27m1AZTRbqKpvZCtrUeIeER+JqNZym8egjk5Maluk9pCgNrS0INXUEMnDofZthJkd2eDoEy6kZEPvW3dXWYlp5YojW+/T3K0tRBSN+bA1VDRBPbw6TG9NLzXm7DsjsimxnkR8OCvTZfvFvi8y7Z3m5nomFmCnxJoaBLN5m0E9+jXt36VhCGhb8GJJg9qUHPzrhvxB8Kd1nW8+z6R3kvVw5vtoTiYNzAP9GOrufTewXn5UqkGthHJvTQStCEgrT8mO9wBYmhjDNTCEZDDS1N27b4NaN1grwaG+tHiJ042nqTalLyw+3PwwkiCVhPmIywqTawGOtFRjdGmDzGKLfweaHNRYjXw8Xb5dWanF610LDmo8Tmx+HlPvLv70lJbC2lqroudUQ+rcp2vw0dOoyibv/cUIW6vbBptot2Ps7MhZlFiOIuVPgYrpd3lLVVX9D/shWsjjUAcg2R/LSMbOe8K07+BPA+mIj0/+ANbvwud/GUw2eOqfw/JNuPPNtOdxttrTEB8Ac0mDWvZ6id6+jWBIFI11MA8OklhcQvYVOfY4VFmkxmWUUCLvmHq3Fu9uIMraOHj2veIMupRBvVdE3m6FPZCIQLU2dlJlmfjcfFEFiZ5gjK9+NMfLp1vprE+f25j6+4kZHdz+eJ3BR1qocubvsVFVNdUbMrw2TCiufQca2h1suAMMNlex4Akj1/Y8mAnq+QUMjY2I5uIXMMohc38/8vo6CU9xHRaRkZugqlhPH4xBDbqJXnmDOnT1ExS/n6oc/GldgsGA9aGHCFfKoM6ToI5NTGDq7ETcEfD4JInteKhT5097qWs00Pbu38RsEVnSDevOWm4v+Qjn2U1oPaPNicLXr2sle5HS+j/UWIzo7TsV5U/rMvX1EZ26PxPUqqJmGtTzfqqMEWRBpb0ni5+0eC03f1qXwQzdn4HJNzPuamh3YBG1XVoLN25vG9QVLIkvtypmUKuqmgB+Bs1Yvg38uaqqo4Ig/LIgCC8DCIJwXhCEBeBvAr8tCMJo8rGbwL9GM7kvA7+cvO2BUetALevzfmKRwh+GL5x0seKL5uUBHYSUYBzBJCEYtZWuV6dfRRIknu96PuvxI24vJ9pq8m7tOdFWzeiSn4G6Ae569AT1PFJtLVJVJsJCcpjKUJKYTJxVwKDWyxnKvbItiCLGjg7ic3PcXvJxpKUKMQdOYz/qabAz2OzgtRIMalVVk9sRcw+WPIua6bfXBDVAncuFJ5tBXWVCjSkopW5N8Sd/x3wGdXIlfi8GtSAImI8ezZn2A7h9SfsM6uWIqqrya98Zw+SYQUAiJEwhO8MIQglFifaGijGoVVXl+tr1ktLTAImNdaTa2rRt/uXU893PY5EsBcsSBUHA0NSkIT7WxmBlBM78z9qdE9pW0LjbDZKU4lUPrw4jCRInGio/iNJ1oeUCkJ1DbWxrQ7TbsT3y6IG9nnwyNDcjmM3EZko0qIPxrIlbXbHZWZRAAEsO/rR/Y53AxjquAQ374Oo/wsr0BIq89y1qor0yg7SN8Aa3Nm5l4D0A7EY7x+uPl2RQz6wHicsqR1ocGJqaQJKKTlCLosD5bmdZsWFKKKHxxHcZzbG5OUgkMO9I/QOsTmkFiYl4Hb1nM/FrroEhAFR5hTd//3ba4rxelJhNUtKg3i8G7AFXqf0ufxd4taKv6FApyb7MsdKCJ0RHkj+9tRJENAhU1ydNuIgP3vo/ofNxGPo+7bZTPwwNg/DWvwFl+3zndNkJeKLEwgk6nNrz6QZ16MoVUFUMNRZkb7EG9d6K0Q61P8levUizeANyfUuiPjyNJbKB+1ZxnUti8vmL/TwUlC+546Van2Mto8ZiRRnUX7k0TSgm84+ezuz5MA8MMN/+NLKs8tDzhVnG7oCb9fA6z3U+R0JJpMZR9e0ONpeC9NfbtbC3rfPBZFAne44OWuaB0s4H4es3ALCeOlmx17Rb5sFBotMzqLHK7qIKvPVdBJMJ++OPFzzWdu4c0fGJoo39YqXKSWMzxzg6OjGZlT8tiQKnO2pQFZXlSS+ummUE92VarFMsTWq7L8521pFQVEYWc+/GsJ48AaJIeHh4e/dhuPixV2R8HDUWw1JB/rQuc18v8to6sre43SUHKTUmg0pGgroqscGytY5jrbXpD4j6YWMiP95DV9+z2iLcrjLY+jYHCb/MltnB+ugYwmGCOl2qqr6iquqgqqp9qqr+avK2X1JV9RvJ/39ZVdV2VVXtqqrWq6p6fMdj/6uqqv3J/32lkq+zEnL116CqsDJV2HR69mgTRkng1Zv3FvOhBGKphJKqqrw6/SqPuB6h3prJlY3EZcZXA5xozZ+yPN5aw+xGiO6qfsY2xzRExvwCxix4D9ASUkowjrqPNLmxtbTEWSnS2WCVKK8wdXYmER/+iuA9dL1wvIWPpzfZLHIhQA0nUONK3u2Im4sLGExmqvaBeKhztWVHfOipuVJTICmDOjeDOjo2hmA2Y+raW8GHZWiI6N27qInMk74sK9y6tEjX8Xqq67XJ5Nt31xhdHUcVgzzeqIXe/nLiDZytjhIS1PUVS1DP+mbZim5xpulMSY+T1zeQKsCf1lVlquLZzmd5ZfoVonL+yZahOWlQ3/oaIMAzv6Bt9Rx/A9AWmYwtLQgG7YJ9bfUaQ84hbMby71jIpaP1R7EZbFxeyjQtBUmi+8/+lMZ//LMH9nrySRDF5Lmp1AR1fsRHZGQUIGe6QudN6wZ1y8AREtEo6/OlvY6d0pMo5WYYv7/4PkBWgxo0zMfI+kgq6VVId5a1gsQjzdUIkoSxuZl4lsW7XHqkx8nMRohVX6Tox+RTrm2meilNZoJ6AktVMyaLhY5jzozHufoHEQSRtsEIy1Neht+YS91nHjpCbG4OJZT5txIsBhCF73kGdbESBOHHgXPAv89x/31ZNP6gSpVVlEAccUeC2huK448k0hLUtU02RH2x571f05BdL/wq6GEPUYKn/zms3YaR/5F6rroWLRG1uRzEZjLQ4DCnEB/Bjz5CMJsxuGqLRjpYjmjn1shhUeKBKpE0jItNUAc3gwTFGprbbTgj86xsSEXNkcqO+PAlr0HJBHVsdgagIOLDH4nz++/P8OLxFgaaM4NJiqMOd9vTtJrXU5/xfNLT0z954icxiaYU5qOh3YGSUHElcQKLogsCKxANFPPb3TeKuReydjRVWqUuWIVv3EiV6R2UzIODkEgQnZ4ufPAepaoq/rfexvboI4i2wvMC2wWNQx2+erWsr0MJx0HdXphPuy8WIzY3h6k/fex1ddbDMVc1NpOBzaUg0VACl3ANBBFX/CKe5RDhQIwzHbUAXJvLbaqLdjvmoSOErl3bDneUMPbSx/jWk5VfwDD1aX+H+xHzsY3I0/6G0VAc/0YE89YcK7Y6ju7uGyumIFFX37Pav1Nvpd3c0O5AlVUWm44Rn5nZ3j16aFAfqqW3BkGgKMxHtcXIZwYaeXVk+Z42bO5si725fpOFwEJOvMedZT+yoqaKEHNJv98udOCP+1kOLhNbmMeU4+Ir2o2g7O9LJNXVIZjNFUpQ6/za8rPBTJ2dROfm8IZimSesMuqF4y0oKnzndnFliQk97ZFnMO1ZclPX4srL6tr8wz8k+PHHOe+vc7URCQYI+9ONWp3nWDJ3NJA0qB3NOQ+Jjo1h7uvbMx/LcnQINRolNjOTcd/sjQ1C3liqHFFVVX79O2M0NGqLHD915sdRYk6+M/tdmnu0osSivv/2hooxqHWuX+kJ6o2KFCTu1Bf7v4g/5uft+bfzHmdsaia+ugqjfwWdj2mFPv2f0zhdycII/fsbV+KMrI8cKN4DwCAaeKj5IS6vZE/VmgcGsu4wuVcydXeVZFCrCQU1KuctSYyMjiKYzZj7MhNVoBUkSgYDjV0a8qQ1aVQvjednvueTfn2TK2BQOy1OjjqPZr3/QssFEmrxHOqxFT+SKNDXpE3WDa0uEiUsuJ7v0Uzhj8uUos7FE49NaZOBnQlqvSBRlhvoOlmPwZh5bjVZbTR0dBILu+k728hH35xiw62ZCJYjR0BVs06SBVEoCwbsAVdRHS2CIHwO+AXgZVVVs67q3a9F4w+q5EBMMxR2GNTzHs1A1hPPnuUQdc1J02NrHj78f+DkD0PbQ+lPduxL0HQc3v43GqMaLUENpDAfnU5r6vlDH1/G+tBZDLVagrqYsYShpQWxquqwKPGApfj0MXVxCeqFd0cAaD3lorlBJYYpxSLPJ9FmAINQRoM6PUGtj3tNXd15H/ZHH87iiyT46WeylyGPvrdIwmClZ+P9ol7G8OowDqOD4/XHeaj5IT5Y/ADQEtQA5oCMURIYl5Nj/wcI86HG4ySWVyoSgiokQ1OTdj4owqBWVZXwjetYT+cuEKyE9N2ulTxnxaamiM/NFcR76LKcOIFgNhO6XN6ixG20Wpbuj+kZkGXMOwoSE7LC8PxWij+tp6VdwVfh7N/C1awNA5bvrNBYZabDaWV4fivva7CdOUPk+g0EizaOK2X3YWTkJmJNTc5AYjmlzyNi9yHmQ/ez9AS1XpBoX7nLqs3JsdZdgcRFbQGuqAR1wwDUdMBEOuZDPxdGWo5gX3Wn3j/10KA+lMlioKGjKsX7KaSXTrTg3gpz033vticoge2U1KvTr2IUjTzb+WzWY/XXmasgUdeJ5BcvHtIwC3fXb2sJxvbsJyx9pXA/CSlBEDC6XEVviS5FcfcCYnU1UnX5E87Gzg6IRKiL+jnqqlyC+nhrNW211qI51HIq7ZE/QV2XB++R8HhY+Tf/lvX//J9zHlPbog16d6eoRd2grkCCOjI+tie8hy7zkGZIZeNQj150Y68103VCM27fvrvG9QUvPe0rNFmbOOvqx5o4xVz4OrUdZqKhBN61Isor7Y0Q2kzb+lsuDa8OU2Wqoqemp/DBO5TYWMfQUFmD+pGWR2iyNfH1ia/nPc7Q3ExieRl15RYc/5J2Y//nILIF7qtpBvXdzbtE5Ainmw6OoafrQssFpr3TrIXu/+SiqauL+Pw8apF4Db2EMK9BPTKC5ejRVJJ9t5bG79LU04chiY2pbmzGWl3D0j74g4LFAEJ5SxIVVeH9xfd5vPVxRCH7kOps01kMgoGPl3Mv0O3UnWU/PQ12zAZtUGlsbS3pena8tRqbSSobh1oJZce1RKemMLhciPbt1Jt/Y42wz4uiNtF7Jrfp6RoYYnn8Lp/90QHMVgNvfOUWckLBXCDVKTmM39MliRTR7yIIwlngt9HM6dV78Bq/J5UyHncY1Ase7ZreXmdDlhV8a+Ft/vSbv6z9+9wvAeCP+fnlD35Z6yYQRXjm5zVEwY0/A6C60YpkENlc3OZQz22GSHg8RO/cwX7hAlKNGTWmLRAWkiAImI8MEh07RHwcpFIJ6iIRH4vX3QiKTPvTZ2g/qZVmzV2dL/Ao7f2Vqs0oRTLJC8rrBkFKhT5is7MINhuGptzn+XBM5r9cnOapwUZOtmcGmhJxmevfmafJ5MF694OiFlaurV3jVOMpJFHiUdejTGxNsBZao67ZhmgQ8CyG6Gmwcz2Y3L3zABnU8aUlUBSMbQdvUAuCUHRRYmJpCXltHcupAzaoe7rBYKjoOSvwlpZGdTz9dFHHiyYT1tOnCV0uL4c6ZVBn2702of3+5oFtg/rOsp9QTOYh3aCe2MLmEKiWp6H3KZp+8GcRibP0jmZmnu2oK1iUaD17FiUUQl5fTHtNxSh8cwTr8eN5MbDlkrG1FcFs1nql7jOl5kNW7X1cn9cM6pr1MbZq6mmrtaY/YGlY26WSryBRlyBA3zMw/W5qIRugNnkuFBq7qY34WVjXkvKlIFrutQ4N6gqqtb+W5WkfcqJwo/bnjzVjEAVeuYeYDyUYR3QYkRWZ12Ze4zNtn8kofNI1suClzmbM/GLtUr3DTGuNhZUN7YQ5M3EVEomc25e2S5D2yaFudRW1JToUS/Bjv/th3m0uOxXbYW6VW6ZOjePWGljnSEvu9KSqqlz99kwqcVaqBEHg88eaeXd8nWC08GpayqCuzT6YlhNxvKsreQsSg+9dAlUlfPUTlHB2E7bOpT1+N4daqtIRHyWeWP1LIBo0JEYWJTwe5LX1/RnUvT0IRiORO7fTbveth5m7vcmxJ1yIkphKT7fVWViL3+bhlocRBIHTdU+gkmDeopluRWE+bA2AqpnUZZbOn85ltOWSvLGJtA+8SzGSRImX+17m/cX385q6hqYm1FgMJS7CsZe1G/ueAUFEuf1tEqurqe+wvlX0bOPBJqgBzrdo2wKzcajvNxm7ulDj8aJ3peze0rZbqiwTuXUrJ39aTiRYmZpIsYpBO2+5+gdT6I+9SBAFRJuxrIiI25u32Yxs8nhrblahzWjjeMPxnIn53Rpb8XNkx1Zoo6uV+MpK0QsERknkoc66MhrUORLUk1OYe9IXs1YmtcmtwdySWpzLJtfgENFQkLB/lWd+fIiNhQCXvzWdYrDnK0r8XkZ8FNPvgob0cAB/IQjCsCAIxRaUH2of0ssJdxqPC3qCus6Gby2MoqgaxsB9FW7+OTz6j6BWC2z85dhf8hdjf8ErU69oDx76n7Rtvu/8O0jEEEWB2hYbm0vac3Y6bSxuRfB/rJ1XbI88ktrpVix32DI4SHRs7J7u3vxek+KLIVgkRHNxO/dWluJUxVextjbR+MhJLOF15oeLW7CUqkzlRXxUtWgIGjSD2tTVldeA+tOP59gIxviZZ7Onp+98sEzIF+P4EQXF79fwbPleQszHhGeCM41nAHis9TEAPlz6EFEScbrsbLgDDDRV8eFW0hB/gDjUOkbyXjCoIVlCODFR8HwQvq6Vi1tPnzmAV7UtwWTC3NNT0QS1/623MR89miqoLka2c+eI3LmD7PeX7XXIweQ4OotBHZucBFFMw+t8kvQx9AT14sQWrvotjRzV9QSG3kdpqvWyNBOG1duc7axlyRthyZs7GGU9cwaA6IQ2xy02Qa2Ew0THx7EcAN4DNDSiqbf3vixKTCWok2PodXcAi03EHPNhamvPPH8uDheH99DV9yxEfdqYIikpeS4ULNq8fOTqiIbHO0xQHwo0DrUcV1ibK3zCqrWZeKyvnldHlu7JQFFV1RTi4+rKVdbCa7zUmx3vATCyWLggUdfxthruLMZod7SzPqkxiUw5tnxIjuTgep8TUIOruC3RH05t8P7kBl/9aK7gsaAhPkztFTKokxzk46qPakvu5KF3LcyHX5vi8rf2zuB64XgLsYTCu2OF05uyNwrC9nuzW1vLy6iKkjdBHbj4LqC1+oauZOd01TQ1I4hihkEt2owg7gHx4V/Rkh45sCO6AbIfg1owGrUB3a4E9eh7iwhslyPq6ekff9LOWniNc83nAHi+71HUhI3Xtr6DwSQWV5RoT5o+ZcZ8+GN+JrcmS8Z7KNEoSiBQccQHwMt9LyOrMt+a+lbOY4zN2qpzvPbh7YJMax20XyAx/DqoKsb2bYO61d5Ksz03BqZSGnIO4TA6SirPu1fSS5CKLUqU82xN1J5nBiUUysmfXp+bIRGLpvjTulz9R9h0zxMJ7p0pKdoNZTU4L7kvAeQ1qEFLzI+ujxKM59+aHYolmNsMpS1SGltbQZZJrBYfhr3Q4+Tuip+t0P7NCSUUR9r1XqqKQnR6OsX+07U8NQ6IdJ0YxGTJzSDX39vF8Tv0nG5k6LEWPvn2LCszfsyDg0TuZke5SHbj93pJYjH9Lp9TVbVZVdUzyf+9nP8ZD1UOyTkS1FVmA9VWA55lzViua7bCa7+o7YZ68p9qj1Vk/uyulpS+upIcIwkCPPuLsDUHw38MaJgPHfHR4bQhKyrrF99HsFqxnjiRMsf1Ir5CMg8OogQCJCqw4/BQ2ZXwRovGe8gJGY9cQ0O1ds4zHztGnW+ClWW5OA51TTkNancK7wHadbxQQeLEO3P8RGMt57szuwgUWeHa67M0dVfTeU6b/xRK795Yu4GKmupJGXIOUWuuTeNQry8E6G9ycMejotqbHqgEdSzVc1SZeWYhmfv7UbxeEqv554bh6zcQzGYsR/Y+f9qrzIODRPaxky6fEh4P4WvXqHrm6ZIeZzt/DhSF8LVrZXst+jhVypagHp/A1NmJaN4+j1yd9dBSbaG1xoJ/M0JgM4rLMALOvtRcyHX2KKvxPhLf/OecTXKoh/OkqI3t7UgNDURuab+XXOTuw8idOyDLWtHiAcnc20vsfkxQ70Z8zPupq9KCq7W9u/qvIr5kQWIJoamep0AQYTId89HQ7iAc0cYi7pt3EG2GQ4P6UJpc/bUALI5vFXX8SydczG6EuL1UvhW4YqVGZZBVRIeRV6ZfwWaw8VT7U1mPjSZkxlb8BfnTuk601jC1HqSvdpDQrGaq5kJ8pEqs9jkBNba2klhbQynQ9Pv+xAYAb95eISHnT7qrqqrhSfIkhfcjY2srsiByVM1vUs7f0lJxMzc3iEX2drI5311Hnc3Ia0VgPmRvDKnKhCBlX4zYXNIGVE5X9r+LqigEL75H1ec/j2AyEbx0KetxksFATVNzBuJD446a9pag1g3KLNJX4PVikL3KfOwokTt3UgtLckLh9qVFuk424KizaOnpN8dpr7PS0KD9brpB/VhvE4nAEB+tXqShs8iiRFsyqVzmosSbazfTBv7FSl7XXkelER8APTU9nGo8xdcnv55zIc9g0orhEs5H0u8Y+Bzxae09N7W1oaoqw6vDJf++5VKKQ/1AGNTdwHYpUiHlG1iDhvcAsJ7InqBeGk8WJPanG9QtSVNzeXLv2ztFmzGVTLkys8md5SLLSXPokvsSx+qPZS0T3qlzLeeQVTmV2s+l8ZUAqgqDOxPUevFvCb0KF3qcqCpcmdlfs7wqq6gROSNBnVheRg2FMPf1pt0+P3oHQaqn/9y2kfHO2Bo//1c38e7YXuh0tWGxO1ga04zoJ394EHudmTf/4DbS4BDRu9lTnaLD9L3OoD7UfSrZFwMxPfE2vxmi3WlDEAQ8y5qxXOt9F+beh6f/P2DRdim+534Pd8BNk62Jq6tXUdTkeLT/c9B+Ad759xCP4HTZ8G9GiEUSdDo1VEjkymVsZ88imEwp47PYBLV5MInUOeRQH5hkbzRtESOflj8eR5FMuAY0g1c0mWhyhIkpBjYWCy/Uagnq4pjkBeVbTBnUajxOfMGNqTu3QT2/EeTLfvjxoJD1509+soZvPcLDL3Zh0dnCBfjHw6vDiILIqUYNLSEKIo+4HuGDRQ0P0tBeRdgXo7fKgqpCqKoLNh4cgzo+vwAGA4aW3HOXSso8UFxRYvjGDSzHjiEYc4epKiXz4CCJxaWyppV1Bd99FxQFR5H8aV3WM2fAYCgrh3qbQZ250B+dnMxakPhwVx2CILA0uQVAa+g16N4u73YddaFgYHVylRMb38ZkELmWh0MtCAK2s2cIX/sEDGLReLzITW2Mf1AJagBzfx/xxcWsBdv3UropLFgNyAmFzaUgZlXD5LYPpY+fWU4WJBbDn9Zlc0LrQ1rP0g41tFcRDilETDWEJqYQrYaSGOL3WocGdQVlqzZR22xLgeoL6fnjzYgCvDpS/nK/QtITSapV5I3ZN3im8xmshuz4jrHlAHFZ5eQugzoWSfDOn94ltGu1/kRbNaoKtVIX0vI6GCSMLdkTi3ribr8JKaNLG0QV2i72/uQGVqOEJxTncoGJvLy5iRoOVwzxEVUFVmx1dIY38h43f3sTySAixxWmr+/NpDRIIp872sybd1aJFUDQyAXSHp5FzXTNlaCOjIwgezxUPf881ocfymlQg4b52G1Qg4b5KJlBHVgBRx6DenwMqbYWwz7LoSxDR5E3N1OJg+nr64T98VQ54ttja1yf3+JnnulneO0TnBZnivHc4bRil08TVYKojSHW5gLIBRZKsCcN6jInqIfXtIH/yYbSBhSJDe3zKjkrb1ADfLHvi0xsTXBr81bW+w0eDZmRsO7aUtr/eWJBbaBnbGvDHXCzFl478ILEnTrffJ4Z3wyrofsbE2toakSw2YouStxGfGSfvIRHRxGsVky9vVnvXxq/g62mlurGdAabq38QBIHl8b1jPiS7kXggxs/92TU++L1/ysXf/6U9P5cv5uP62nWeaH2i4LFnGs9gEAtzqO8ua5OuoZ0J6uRW03gJRYlnOmoxSgKX91mUqDPrdr+X0SltsXnne6iqKmuzk4iGFrpPaucpRVH5l98Y5U8+muNLv3mJyTXNVBFEkZaBI6nFCLPVwHM/cZStlRB3DA9p272zGPKiw4gak1Fi5WfwH+pQ+5HsSy7mi9uL+QueMO112lh6azmEvdaE6Z1fgsYheOhvp4770zt/SpO1iZ8+89N4o14mtpJJUkGAZ38B/Itw9fdxurTyI89yiA6njZpoAMPMFLYLF4Dt9HaxqVl9gT4XUudQ5ZfsixVfkPiRZhS2P7GNu2o7opnVC6OFx4ClMMnzSlWTBrU2zo+73SDLeQsSr3yySB0itpBMfFepo6qqXH1tlroWGz2nGjDU1SE1NBCdyJ+gHl4d5kjdEezG7d6Dx1yPsRZeY8o7lSoHa5I1e2PD1PZAJajj7gWMLteei9v3q9T5YCK3Qa3G40RGR7EeMH9aV+o1FlHmWKr8b72N1NiQEz+XS2JyB0s5OdRKMI5gkRAM6VadEosRm53F3L89x1nxRVjwhHfwp70YTQL1yih0PZk6ztWr+TZLlucwfOeXuNAiFkScWs+cIT43h2gVUYLFGZzhkZtIjQ0YmorgKJdJpl7NsI9O7313eSWkhBJgEBCMIp7lEEpCBa+bqGhgcGjXAt/isPZvKYgP0DAf7qsQ3n4v69uSpcptJ6hacyObxMME9aG25eqvYWliq6itWA0OMxd6nLw6cvAcan2l7k50HF/Mx0vdufEeqYLE1nSDenp4jZF33Nz9MP3160lrJdpCs0dFbW7IWYwlSIK2DWGfW7BTibM8E3pPMMatJR9/54luzAaR12/l/7tXmg02sRpg0d5AvTf31ipFVnDf9XDkkWYcdWbGr+Q34PPpheMt+CMJPpzaNsRvb9zm333877bTO+gGde60x+biAvbaOsw2W9b7A++8C4KA/ckncDzxBNHxceI5tqrXudrwLC9mpC2kKtMeEB/5E9SRMa0gcb8FDpaj2sQhmuRQj15043Ca6Txen2RPa+npH3ionasrV3m4+eHUzxQEgUdaHgfVyLT5NnJCYWOhQCqmQgnq4dVhBmoH0gb+xSixrn1+DiJBDfBiz4uYRFP2skRVxbD4BgDx3Vy1llPEY9UgChiamhheGwa4twa1S+NQ3+8pakEQMHV2Fm9Q50l+AERGRrWCxByTsKWJMVwDQxnfTbPNjrO1fc8calVVmYvE8KyFaBr5Hf5fhq/xE+E/Zn11b9fcj5Y+QlZlnmx7suCxNqONkw0nubyU/72+u+LHYhTpcG6fT7cN6uK34VuMEqfba/lonxzqXDxxvS3dvMOg9q6ukIiFqG/vwZJMkX7n9grT60F+6rO9eMNx/sZvXuLtu9r5v3VgiPWFOaIhzbxoH3Jy6pl2xtw2NmuPZC1K1FP538sc6k+TErJSFgzN/SDZF03jT6uqyrwnlDKoPSsh6qwezTD7/L8GSftOzXhnuLR4ib955G/yiEvb+XNleYfR0fMUdH8GLv7/qGvQpm2epSAt1RZOe7TJuO0RzaAWDCKi3Vh0glpyODC2tlaU6XqobamyiuKPFZ2gXpn2YYr7cJ7eNqPqzx3DGl5jfjgzzLFbpS5Y5FTEC/FgKkEdnZkByIv42BjdnluER9ODN7MjG2wsBHjoha7Ugo55oD+v6ZhQEtxYv5Gx603nUH+w+AENSYPa4E8giQKzqgsCyxDdOxbsIBVbcGPK0dF0EDI4nUj19Xnfh8jYGGo0ivX0vTGoU2n7Mp+z1FiM4MWLVD39NEIONGQ+2c6fIzwykrNnqVTJwXh2/vTMDMgy5r7tc8Ins+n86aUJLy0NAURBga5t/JzFYaSuxcaS7XkIrfOz4p9zY8FLPE8wynpWnyPFih53RW6OYD1x8kAKEnXpu/lik/cX5kMNJxCtBgRBYH1BC6CIqxOs2ero37FTEthRkFhicK7/OVAVmHondVNDu/bc0dYh2gJreFX10KA+1LZa+2uJhhJs7lo9zqUvnHQxsRpgfOVgMR96KeE7m+9RbarOy9O86fZSbTHQ4UxPWM8l0ROzI+nGWXO1hcYqMx5PI01bKsFGR97XIjqM+y9JLGJCrxuznzvaxGcGGnh9dCXvNri4WxsMVipBfWvJx6K9HvNqpkGra2XGTywi03Gsnv5zzcyPbhLZ40T9yYEGbCYpDfPxa1d/jT++/ccpBqKqqkUlqOvyYE8CFy9iPXUKQ10d9ie0pGHw/fezHlvX0koiGiXgSR/Mig4TSikJ6nhEW0msyl5yoSoK0fGJffGndZmPJLfI3r7D1mqIhTsejj/ZiigKaenpjcgK7oCbh5sfTnv8Y70uEoE+3o1+ByiiKNGWZPmF8iftS5GsyNxcv7kn3IW8mTSoD4BBDVBtqubZzmd5ZfoVYvKuz8TqbUTPGJLDksnrFUXitGC0yQiiwPDqMHajnf7a7OU9B6GhuiGqjFX3vUEN2kQ0XiSDWgnFEcyZyQ8ANZEgcvs2lhx4j7Dfh2fJncGf1uXqP8LSROmlXu6tMD/5+5d5dXKdauB/k/6MQPN5zEKcpXe/UtJz6brkvkSVsSq13biQzrec59bmLQKx3JPlu8t+BpqqkHakMEW7Hammpqji37Sf1+NkxO0lFNv7oDTVQL47QT05hVhTg7Tjez9xRdvW2ffQ9nv7uxenaKu18r+9cISv/8wTtNVa+cnfv8zvvjulvceqyvKOtNajX+qjptHC7aEfx38rc5K8XaR8aFA/6FJVlef+r3f41W/dLnzwAyDZF0PcYTx6QnFCMZmOOhuqquJZClIX+BB6n4aBz6eO+293/xsG0cAPDf4QrfZWWuwt2xxq0FLUz/wCBFepmfljRElgcymIJAo85pshZjRj3cHzl6pL4w6bjxwhWiGm66HSJQdioFJ0gno9aKXe6EPcYZhZz56hdmuM5flIwfDTtkFd3IJFTvmS156kQR1PLlaberqzHh6XFapWw/hMIqbuaiK7DOpPXpvF4TQzcGF7N22qoE/Jbpbd9dwlnAhnhApaHa10VnXywdIHWOxGHHVmthaDdNfbuBVNGj2e+ytVmUvxhQWMbffOoAaNQ52PBb5dkFhaX025ZGht1YqUy2xQh65cQQkGcTzz7J4ebzt3DhKJ1N9nv1JC8ayYvFhyl4F5YHvucnXWg9kgcsxVTTQUZ2MxgMt8B2o7UyW8ulz9tSy5QX3473J+9X/QL09yJw9a1nL8OBiNqBF/UYgPORAgNj2N5QD50wCmzk4wGIhO3l87JpRQHNGqvY/r8wEMRhHz0hj+2kYsxl0hncVrpfGndbU9DObqNMyHxWHEXmsmXttJW2Cd1Vj80KA+1LZc/cntFBNbRR3/wvEWBAFeuXmwKWq91Oq7G2/z+a7PY5Ryc6VGsxQkqorK/O1NBEFbuYvuOomdaK1matlIi1dgtTb/x060m/aN+ND5Xfkm9O9PbmAzSZxqr+X5Yy24t8LcWsptDsYqbFDfWfKzVt0IgQDy1lbWY+Zvb4IA7UN1DJxrQlFUpq4VLjrMJotR4qnBRt64tYKiqEx4Jvhg6QMAXpnWWuTVqIwaU/IOpjeX3Dhd2QdUiY0NIjdvYv/sZwBtIiTV1xO8lMOgTnKsPbsWFqQqrRgr22BcVmRG1kfSbwwkk+VV2VEycbdb46fukz8NyQRSZyeR27e5dXERQRQ4+nhrRnr6yoqWiNL507oe6XGSCBxjRr2D0V5EUaJkBEttWRPUk95JAvFAyQWJsJ2glhoayvZ6CunlvpfxRr28u/Bu+h23vgaCiMHVRmIlM6Ufj1gw2uLgvsq11WucajiFJN6brZQAkijxcPPDqc/G/SxTVxcxtxs1UXiAo4QSWZMfANGpKdRwOM1Q2anlCW3SsZs/rcs1MEjY58W7WtzuEUVR+YP3Z3j+/3qHj6Y3eapPRESA5gtYf/Lr3GCA5vE/1bYwlyBVVbm0eIlHWx/FIOYuA9yp8y3nUVSFT1Y/yXnM3RV/WkGiLkNba0kJatA41AlF5VqeEpxC0rd0ZiSoJycx9/amjQMmr44AIiefOQNorfKXZzz83Sd7MEgi7XU2/vIfPs4Lx1v41Vdu859GYiAILI5vG5RGk8TnfvI4UXMtV25lpgzLVaR8qHsvQRA421HLG0V0gDwIkn3pydj5TY2F2V5nJeSLEYvI1DIFz/+KZjoDoXiIr018jee7nqfB2oAgCJxrPseVlSvpi3Bdj0Hfs4jv/zq1TZZUUeLx1QmmXf1pLFipxlx0gho0pmt0arpgZ8uh9i/9fSnGoPZNLxE21tLckR4GMjY306CuEUuIrBfYcSeWWJqZ+8XoBrU2Ro/OzCBWVyPV1mY9/NqshxOKiNxhx3q8nvhykMSGlixdnNhiacLL2c93Iknb80Fzfz9qOJzzOje8OgxouKzdeqz1MS4vXyauxKlvc7DhDjDQVMVVv5YoZeP+SlVmkxIMIm9uVmyXbrEyDwwQy7NQELl+A6mhAUNra9b7Ky1BEDAPDJSdm+9/620Esxn7Y4/u6fHWhx4CUST0cXkCJ0oge4I6OjEJooippyd129U5D6fbazEZRA0rq4Ir8l3oysTPufpriIUTbB79Zyg2J79i/ArX5nIHnkSzGcuxo8jetaIYxpGRUQCsB8ifBhBMJm2n59T99V1XkglqgPWFAM42Bw7PKmrLrgCdXpBYKt4DNF+g57Mw+VbaXKah3YGfWkxKgtUt3yGD+lDbqm6wYqsxsThRHIe6udrCw511B86h1tNIK+o6X+j5Qs7jYgmFO0v+DP702ryfsD/Osc+0oShqKk2t60RbDW73JlUhlVlHJO9rkRzGfW/fFc1mpIaGrAxLXe9PrnOhx4lREnnuaBOiAK+N5jY94gtupNpaJEdpCIRidXvJh6FDW+mM59hKP39rk6bOKix2I42dVdQ0WveN+Vj1R7k2v8VX73wVs2TmibYneH3mdeJyvOBgOuz3EfH7ciaog++9B6qK47Na4aYgitgfe4zg++9nHfzUJQc8W8vpA1TRYQI5+/aUP7v7Z3z5W1/m7uaO7eD+5AJPjgS1vvJuKUOCGsAyNETozhi3P1ii51QD9lpzWnraZBC5unKVKlNVRmK3v9GBLXEKBIF4g4+VmSJ2T9gbIbi3hYlsyjfwL6TExgaiw5HWJl1pPdb6GI3WRr4+uQPzoaow+lfQ9QQGV2tW/nx8M4TRLuO/+y3GPeP3FO+h61zLOWZ9s6wE9/49PgiZurogkUjtJMknORjPjfcY1djhlhwG9eL4XQRBpKUve7LdNaAhdYrBfIyv+Pmh33qff/GNUR7udvLGTx3jzPpXtdf4hd9GMtu51vg3aIrOos7mZuNn05R3iuXgct7dRrt1pvEMRtGYMzG/GYyx5o9yZPe2P7RehUQJDGogWZjDvjAfqQT1rolSdHoa066CxNWZSUy2ZqrqtWvk712cotpi4IfPbyd47GYDv/ljD/FPPjfAX97cIGitZ+ZWOk++paeGAXGMebmTqeH089x2kfKhmfZp0IsnWtgKxfl4nyiaey01LqOGE2kG9YJHM+Q6nDa2konEuqEhaNmeuP/11F8TiAf48tCXU7edaz7HZmSTad+u1OczvwjhTZxGN5tLQRIbGzSuu/nEmf49lGpMJSVmLUcGQZaJTd1fybNPo3SjuBjEx9w7WvCi7aHujPtcvdo51j2Wnx+bSlCX2uGyW77kdX9HgtrU1ZVzC/+Na0vUIOI62YT1uBZe0DEfn7w2i8Vh5OgT6QZnqqBvLDte4trqNZptzbgcmeP6x1yPEU6EubF2g/p2B56lEIMNdj7Y0kpIHwQOdSoE1V6ZEFSxMg8MoIRCORGZ4Rs3sJ46daD4ht0yDw4SHRsvT/knWuAg8NZb2B97DNGavX+rkKSqKm0ueKU8gRMlFM/a4xKdmMDU0ZGac0XiMiNu7zZ/etKLKEKzciW7Qd1Xqx23ICM+/yucFScw3/hq3tdiO3OGxNpiUcGAyMhNIPcYv5Iy9/VqBv59JCWcQLQZUFWV9QU/jgYjVdEg1t0on+Ub2r+lFCTuVN8z4J3TTO6k6tsceEMGFEHC791EjSSKQg7fDzo0qCssQRBo7a8tOkEN8NJJF3eW/UyvF4cFKYeUYJyoIU6tvS4DQbBT46t+YrLC8V0GtW5In/9CN2a7gdmR9NW44601NAa0225bNvNeVESHcd8JagBja2vOC+yKL8LkWpDH+7TtyfUOM+e6nLw+mju5Hne7K5aeVlWVO8s+avq0FdHY/HzGMdFwgpUZHx3HNMSDIAgMnG/GfddDsIS0zE49M9SEQRT45sg4fz3513xf7/fxY0M/hi/m49Lipe3BdA4G9WayINGZoyAx8O5FpPp6LMePpW6zP/EE8sZG1u1ZVc4GDEYTm7uKEqUq7ednMyV0FnFamtaffN9zMKj1n23q33+CGjQO9WLYSSSglSOqqsp//M44bbVaehrQ+NNND2ckdkVR4EJnF4ZYN9PmW3iWg8QKbcOxN5QV8XF97TpOi5P2qtKTG/LG+oHhPXQZRAPf1/d9XFy4yHo4mSRfvQXrY3D8SxibmzM450osRmJtHWNrCzem30BF3RPSpNy60KKxQy+v3N+YD1O3xposhkOda2siaKWpos2Gqbs76/3LE3dp6OjEZM3OtG/o6MJgNuctSowlFH79O2N84f++yPR6kF/7kdP8wU+cpu31v48U1boEFEE7jxpP/yA+1Ubo/d8r+Hvt1Hvu9wCKKkjUZTFYONV4KmdRol6QmC1BbXS5iOdZcM2maouRY65qLu/LoM4svJS3tpA3NjD3bBtjnuUgsdAijV1aUc3cRohvjyzzPz/ahcOcvlghigL/5HOD/NaPP8SCoYnp27cY3lXUc2pIwRGY5+0/vkN4h7miIz7KMUY41L3XZwcbsRjFNNTYgygdqbGTQb3g2U5Qey5+DYC6z/8vqftVVeVP7/wpR51H03Yv6WPwNA41QPvDMPgSTt/b+DYieN/XziMfVXfji2x/H6RqM0owgRovLpWuo86iWZjvhyqvSklQL91aRVAStH02M4lYf3YIa2iF+Zv5F7ZFk4RgkUpK1GeVzw0IqTF1bGY25zUcwJc0zmuHnBicFowuO+HRDdYXAsze3OD0s+0YTeljYb30LRf/eHh1OGeo4LzrPKIgpjjUiqLSaTLhV60krI2weX+ZVtkUX9DmPaZ7nqDW34fMOZrs9RKbnr5nBYm6zIODKD5f1iDKXhQdHye+sIDjmWf29Ty28+cIX7++790oqqpqQQ9HFoN6chLTjoLEEbeXuKzu4E9v0VgfwSjEoDtzfFrdYEkFJ4XTP8qY5RQvLv82BHPPKa1nz6KGvajhBKqc3+AM3xzB2NaGoa6u2F+3bDL19RGbm0O9j3YD6QnqgCdKNJggLmu7Xhr6u9MP3GtBoq6+57R/d2A+GtodqCoEbS0Yw1sAqJEHI0V9aFAfgFz9tQQ8UXwbxYHzXzyhDQAOMkUd8YXYELd4ofuFvFveR5IFibsT1PO3NmnocGCvNdN5rJ650Y20VZoTbdW4kie/2aoIi8HcW5UluzF5Etzfls98E/oPJrXX8njfNpbg+ePN3Fn2M7cRyvqY+MJCxbZerfqjeEJxWo/2gSAQm53LOMZ914OqqHQcdaZuGzjXjKrC5Cd7S9PWWI081lfPqzNfIyJH+LGjP8ZjrY9Ra67llalXkLfyD6Y9i5rZky1Brcoywffew/Hkk2mFE/bHtcRh8FJmYlEQRWpbXHgyDOqkKeFPNyUmPBPc3tS2h190X9y+Q0d8OLIb1JGxMYzt7WVLw5uHhnC7nsRRJdJx1Mk7Y2sMz2/xM89q6en18Dozvpmciz+P9DgJeoYYla6CCqtzBVLUtvqyIj6ur13ndOPpPSUiEusbaRzag9IX+76IrMq8MqXhaBj9KxBEOPoyhqZm5I0N1Pj25yWR3DZqHDjNteACImLR7OBKarBukCrT/c+h1suQYkVwqJVQImvyAzSD2nLsWNYSGlVRWJq4m0pJZ5MoSTT39OdMUF+d9fB9/+kiv/6dcb5w0sV3fu4pvnSmDeFb/wzm3kd8+h9qrzGZBHnkSAf/Q34Sy/hf5x2g79Yl9yX6avqyprny6XzLee5s3sEXy0T5jK3kMahbW1ECAWRfAQTQLl3ocfLJnIdYYm/XUyUUB0lAMG2/X9Fk0nJngnr04m1Qo/Qm+dP/9dI0kijwdx7vzvncL55w8YMvPYFZjvJTv/EqX99R+mUbGuTY7T8kGorz9p/cTS1qiyYJwSQeMqg/JbKZDDw12Mhroxpq7EHVtkG9A/HhCVFrM1K1ehXP/DoGg4y9Y3s3wZWVK0xsTfDloS+nXXu7qrtosDakc6h1PfPz1DEBKqx8NIpisTJe257CicB2oKDYFLWpqwvBaCz7lvlDZUr2xcAg5NxhtFNrm1CjbmC0WzLus549S93WOEtTvoLfG6najLLfkkSfGxzNIBlRolHiS0s5CxI3AlFc3jgBq4ShVnvt1uP1xOZ8XP/WNEazxImnMudSUlUVBpeL6EQm/3gpsMRKaCVnqKDaVM2J+hN8uPRhqijRmfyVvbZO2Lz/GdTxBS2YdM8RH/pCgf4+JKIpbED4ppbqv1cFibp0PGO+MsdSFHjrbQAcTz+9r+exnjuHGo0SGRkpfHAeqTEFEirSrnG0GosRm51NvUcAV5IFiQ911iLHFVZn/LisU9oO4roedksQBFx9tSxPekEQuHbyF7GpISLf/j9y/15nzqAmu1OUcP6xV2RkBMsB4z10mfv6tN1ARRa6H4SUkGZQ6zimoFfzpTqP9aUfuHgNqttLL0jU5ezR3u8dBnV98lwYbOjHHtKCKg8Kh/rQoD4AtQ7oHOriMB9ttVZOd9Ty6gFyqDc2VtmS/LzU81Le40bcPqrMBrqc2+m2WDjB8qSXzmOaSdV9qp6wP87K7PZEuq3WSk98C4CVWhjbzD0QTpUgBff3JdIN6mxp7fcn16mxGjnqqk7d9vwxzcx8/Vbm311VFOKLixUtSAQY6mzA4GohNpd5cp2/vYnBLNHSu7044Gy1U99mZ2IfmI/PHWsgYH6Xk/XnGKwbxCgaeaH7Bd6af4uwJwDCdoJ5tzaX3IiSgZrGTNZz+MYNZK8Xx1OfTbvd2NyEeWAgq0ENGod6axc7XMyRoP7m1DeRBIkfGvwhrq9dxxtNfsf8SyAaNCM3i6Jj42UpSNQVaexjq26Q3votEODXk+npH0ymp3XGcC6D+kKPk3jgGGt2bWGiYFGivQFC5TGoPREPs77ZPaeJExsbB56gBuir7eNE/Qm+MfmNbbxH92fA0YihqQlUlcT69t9I3z5pOv00wxYzg9ZG7MbK4HpKkc6hvt8Naqm+HtFuLy5BnQPxoSYSRO7cybn1b3PJTTQYpGUg/3fTNXCE1elJEjsWIALRBP/yG6P80G+9TyCS4Ct/5zz/8UfPUu8ww4f/Ga79EXzmf0U89WLqNQL0Nth5w/oSkhqH639S8HcDjRt7ZeUKT7QVn57WdaHlgsahXsnkUN9Z9lNrM9JUlbkgaGxNFv+WmKK+0O0kmlC46S5u/LFb+mLDTgMtmmxJN/dtD7AnLmsTsq4TR9kKxfhvl+d5+XQbzdWZ5spOPXz+DADnbH7+8Z8N829fvYOsqFiODOIILnKqL8TUtTXGPt6+xokO074xYIe6f/TC8RaWfRGuL2zd65eyZ2UzqBc8YdprLfDaL7BFL3Wu6rTv0Z/e+VNqzDUZ4+6cHGoA1ymcRzRzZuP2IsKpMyiixPzmdgBGDxQUyx0WjEZM/f050QqHKp/00vFCYYC4P4hXbKCpfvu4eCSCnOyAsAwNUReYJh4XWJ/PH2gotTQzq3yL23iPuTlQ1ZwJ6vfG1ziNAUP39vzKcrwBVIiMrnPis21YcuywMg/0ZzUdr61eA8iLZXu09VFG1kcQa2UMRhHRF0cUYEl0PRAM6tjCAoLViuR0Fj64gpKqqzG0tGjvg28R/sMAXNZ2mIVvXAdBuGcGpC4dz1iuosTAW29hOX4cY3PTvp7Hdk7rGApd3h/mQx/f7EarxWZnIZHA3L899ro666GnwU69w8zqnB85oeCKvavhPXKcZ1x9Nfg3I/g3I3QfPcd/lV/EcvOrMJ99d5+xpSUVOsnHMU5sbhJ3u7EecEGiLlOvFpq4X4oSVVlBjcmaQT3vBwECSzNAlgT10vDe8R66+p6F6YuQ0M73tU1WJINI2HWExqgWwDk0qA+VkrPVgclqYLEEzMcXTrRw0+1NS0VUUhFfkKg5zsmG/Bedm24vx1qrEcXtk97CXY+2neq4dlHtPFaPIMDsze00miAIHFX9hMw2whaRu57cWwlFezL9sU/GpLHVhRoOZy0cfH9yg0d7nUg7fo/OehtDLVW8fivT7E2sraPGYhVjg+kNukMt1Zg6OolnSVDP396kbbAWyZD+te0/18zSpBf/Zn62dy5ZakYRjV5a2W6V/0LPF4jIEdyLs4gOI4Ih+6nCs7hAbYsLUcpM3QfefRdEMZWY3in7448TunIVJZL5mutcrWytLKPIcuo23SDfydGTFZlvTX2LJ9qe4It9X0RRlVTJI/4VLT2dJaWpxGLEZmbKUpCo6+7tCIIq0+6/mZGeBri6fBWrwcrR+qNZH3/MVY2NFozmWiJ2f+GiRFsDhDYhR4lJKbq+pjVO76UgEUDe2EBqOHiDGuDl/pe567nLnfG/1rhbx78EgCE5yNy5/U9nJwsnnuCG2cyZ+P2T1rvQcoF5/zzLwft3m7sgCFpRYgGDWk0oqFE5e7nL5CRqNJrToF5KYjta8ySoQTOo5USCtVltEPrW3VVe+LV3+YMPZvjbj3Xz+s89xTNDyYnG+Bvw+i/A0PfBM7+wzTBOspUFQaBl4GGGOYJ69feLKku8snKFuBLfk0F9qvEUJtGUFfMxtuJnsLkqq3lhTPL54+7SihLP92jX5b0yfuVQ5mJDbGoawWxOvSbfRpit5RkE0UBDZxdf/WiOcFzm7302M72zW/VtHZhtdr7oivNjj3TyW+9M8vf+8AqRJheC1Upv5Aauvhre/bMxAh7teiHZjfseHxzq/tFzQ80YRIFvP8CYj2yIj/nNEF8yfgTuK3jEI9S5HKn7loPLfHfuu/zAwA9gMWQu4pxrPsdqaJUF/0LGfTUv/kNEEnhDRmoefzT1s3SluMOlcKgHBw4RHwcg2Rstij/tfucGimjEdVwLf6iqyh/985/l3a9+BdAKwVpc2rjbPbaV97nKbVDrY4BcCepbwytUI9BycjsJaGyxETeJuEwipz/XkfVxAOb+AWJTUxllzNdWr2E1WBmsy714/ajrUWRV5urqFZytdrYWQ3TX25mUmyGwDLGDw2buRfEFN6b2tnvKdtZl7u8nOj4B7/w7iHjh498BVSV8/Trm/j4kh6Pwk1RQUm0thqamshjUiY0Nwtev7xvvAWCoq8M80E/o8v4CJ9sGdfrYKxUOSCaoVVXlk1kPD3Vu4z0AXMoH0JW7H8XVrwXdlie9nGyv4TeUH8JvaoK//jmQsxuYpi4tJKGPnbNJT45bjt8bg9rc2wuCQPQ+KUrUzWDRZmR9IUBNo5XY4hxxgxGpYXsH/74KEneq/zmIB2H+I+3nSiLOVjsBRzt1Pm18FdlvH8EB6dCgPgCJokBLbw1L41tFP+b541qa983blS/O2ghvYIiI1NbV570wJmSF20u+DLzH3K1NjDuSvRa7kZa+GmZupic82yMe3FYn7VUdjHlyX1SkVIJ6fwkpgyuZONvVCD2/GWLBE07De+h6/ngLV2Y22QikD+x1c8tUoQT1nWUfrTUWamxGrYV2F4Patx7GuxqmYyhzZX3gnGbETFxZzbivGH1z5s8xKo3cnt7+3c40ncFld7G1vpGXlbe56MaZqyDx3YtYz5zJ2vJtf/IJ1FiM0JXMLay1rlYUOYFvbfv3EcwSGMQ0xMfllcushFb4/r7v52TDSapN1by3oHFh8S9BVWaqGyA2OQmyXLaCxERc5s6Hy7SoC3D3RkZ6GjRD62zTWQxi9m2dBknUCi5Cx1mwjrM8tZX/h9obQJUhUuC4IjS8OoxBMHC8/njJj1XjceStLQz1md+lg9BL3S9hFI18/eZXQJDg6PcDWss9QHxl+zMUdy+CwcC02UdYFDi7PguKnPV5D1rnW84D3PcpalN3YYM6G7NY1/bg9VjGfaDxp01WW06mvS5X/xEAJkdv8U/+7Br/y1cuYzVJ/Pd/8Bj/8uXj28zjtbvw338Smo7Dl34bRFFDVRhE5B07dJ4cqOcPY88gbEzAzMVsPzJN7y++j0Wy5O1ryCWzZOZ00+kMvqyqqowt+xnKgvcAbUcQQHypNIO6wWGmt9HO5Zm9GdTZinqiU5OYursRkguT08PrqPIK9e1dJBD5/fdn+OxgI0Mt1dmeMk2CKNLSP8jK5F3+zy+d5F//jRO8M7bGD/zWh9DTR+zuXZ7920dRZIXv/tEdVFVFdBgPER+fItXYNNTYayPLZSu9OmjJ/iiCUUSwaN8JVVVZ9Xj5Qc/vEW98CH/AQF3L9s7DP7/75yiqwo8c+ZGsz5fiUK9kJvEk1zGqJC9BuwvnmSPUWI3MpSE+SktQg8Z0TayuZg10HKp8kn2xovjT7k+062zn05rRszY7jWdpkfGP3099R+pOH8EWWsF9Jz+aSqo2I/tj+yvH8i1CjXZdjs3MANu9FDulKCrxaS1gYU2WsQGEfDHmAgkajSJWc26MpHlgQMMYzKXPgYbXhjnVcCrnGBq0EmKrwcoHix9Q3+5gYyFAf6Od66HkvOk+L0rUMJK5zfuDlHlggNjkBOqVP9KwAetjqHMfErl+A8s95k/rMg8OEinDro/AO++CquJ45ul9PxdomI/wJ59kLLKUIjlHgjo6PgGiiKlHW/yf3QixEYyl8adra+JYRR90P5nz+RvaHRjMEksTW9hMBjpbGvmvjp+ClZuptPxuWQa7AYi5cyNFwzdvagn7E6XPJ8sh0WrF2NpK7D4pSkwZ1MkEtbPNgWl9haizKd1vW9KCYrTm3iFSlLo/o82Fd3GofdRg3NJ2X864S8ME3isdGtQHpNaBGjzLIcJFpn56Guz0Nth5887eTMdS9PrM69QkHLS3ZF8N1zWxFiCaUDixw6BWVZW50Q3ajtSlJXu7TtSzPh8guLVt9NZ511iyOXFZeri7mSdBrRvU+5yAGl3aan9i15bo9yc141wvSNyp5481o6jw5u30v3s81a5cGTbY7SVfCjdi6upE3txE9m9v25u/rZkLekHiTtU02mjqqmJ8D5iPm2s3ub52nUcbvsioO5Aq9REFkZd6XsIQUFEc2RctFFlma3mJuixmUmJtjcjoKI7PfibrY23nziEYjVkxH3UuzfDeyaEWBAHJYUTZsfL3zclv4jA6eLr9aSRR4vHWx7m0eAlFVcC/rPG3skhfcS8X4mPykzWiwQT9rVHCd8e4ObvBTz+znZ7eimwxsTXBueZzeZ/nkR4nK8v9rDhmCHnjBDx50k+2pCFcBg719bXrHK0/mjXFVUiJTY19ZrhHCepaSy1Ptz/Ft3zjxLuf1Ix7wJA0qHcnqI0uF9c2tKbks75NcGdiFu6FBusGqTZV3/cGtbGri7jbnbeARE9X7E5+AIRHRhAdjpzJq8Xxu7gGjmTlU++Uw1mP5Kjhz771Ht+6ucQ/fm6Ab/3skzzcteP8GNqEP/kRMJjhy38KZi3xIwgCkt2QtgD6eF8D31IeJWKohitfyfuzQeNPn285j1kqbDRkk86hTiGJgEVvBH80wWBzdoNaqq9HMBozrmfF6JEeJ5dnNpH3YFAooQTS7gT15BTmHfzpiU9WUJVV2o4M8vXhRdb8Uf7eZwqnp3W5BoZYn5slFg7xtx7t4o/+7gXWA1G+E60icPsONY1WnvjBfuZvbTL6rhvRbiyqTf5QD45ePNHCzEaIsZXAvX4pe5LsjSFWm1KTzrVAlB9TX6U2tszWQ78EQG2zhpSKyTH+cvwvearjKdoc2Rf4+2r7qDPXZTWoARxClKC9Bcv6t+h02tIMasEsIZikkhLU5kFt0e+QQ105qaqK7IumpexzaXUhgiXho7pDSyFPX9M+B/71tdTY2HrmNLVbYyyOb6Hk6eyRakygqHsP/UR8EPWlJail+nqkqsxr1e1lH4MxlbDDkGbEX39znqWYgqBC5E7uxVLzQCZbOBgPMuYZK4ihM0pGzjWfS3GoI8E4gzU2LvuTZW33sUGtqmpFe45KlbZQECcWscHf+iswVRH/zu8gb21hPbW33ZbllnlwkNjk5L6MYNDwHobmZizHsgcnSpXt3DmUUIjI7Tt7fg79u7q7bDw6OYmxox3Ros3Xrib50w931aEqKkuTXlodc9ocsSH3HFeURFp6qlmc1MagZztr+d2Nk6h9z8F3f0WbQ++S5ZS2Azg6NpPzeSMjo5h6eu5pwt7U35fqSbnX0gM7siTgW4+g1hhpCm4iJP2plJaGtX/3i/iwVEPHhQwOdTRhIIa2MDi/eGhQH2qHXP21QPEcaoDnjjbx0dQmgWhleTFvjb+JAYmG+uyJU103F7TXvtOg9q6G8W9E6Dqebpx2n9SMotkRbWVflWVM6yss2+ux0M68f55QPDu+RD8hy/s1qHVm5+Jug3qDBoeZ/qbME+jx1mraaq0ZHOq4eyH5nK0Zj9mvogmZybUgQy5tsGfs7AQgNreN+Zi/vYm91pyWwNmpgfPNrM352VopDQnzx7f/GLvRzk+f+zIAr49uG3pf6PkC9fFa5oXshoh3dRlFTuB0ZU6wAhe1JLPjs5/NuA+0VU7ruYezGtT683mW05OCUpUpta07FA/xxuwbvND9QspYfbLtSdbD69zZvKNt53Nk/zxHxsY05mIOk6xUjV50U9NopeNsK0I8xkOCjx96eHuQeXVVS4kXSlte6KlHCXcQqNEG76uzeS4i9qQhvE8OdVyJM7I+sg+8h/bz70VJoq4v1p/GI8LFzu1Uh1RXB0YjidVdBnVbG8OrwzRbG3EpKky8cS9ecoZEQeRc87ms2If7SaauLlAUYgvunMekkh9ZE9SjWI4fz2pAxyMR1mdncA0cyfsaFjwh/s5XLjOmOGmJrfKtn/0M//Tzg5gNO1JZchz+/Ce0Yqcf/ROoTU8liTZj2mS9udpCZ5OTd6zPwe1vQiB3QmTeP8+Mb2ZPeA9d55vPo6KmmU93l5M9BDkS1IIoYmh1ZewIKkYXepz4IwnuLJc+MFVC8bQUjxKJEHe7MfVqDMSgN8ri+CyqEqWpp5/ffXeKoZYqnuwvfldF6+AQqqqwPKmZEo/3NfCNn34ST0sXot/HV791hWOfaaXjmJNLfzlBXBRQAvEHNm17qEx9/lgzggDfHnkwMR+yL5bW1bG8uMBPG77GeuvTbBk040Mfv7028xqbkU2+PPTlnM8nCAIPNz+cvSgRsKzPELY0IH/yVU5VBZn3hNIeK9WYkL2lGNRJpuvd+8egVlUVtQwYs/tFSiihFZ/V5Ed8qLLMRqKaBvs2Am/q2hUcyZ1qM9c1HrP1zBnqtsaJx9RUAVc2pRB5e8V8+JNzgGptbB6bnsk5fn7n7iqnMWDvr0vdFg3FGXnXjfN0A6LDSHg0d+Lb3Jfcoj+xbVDfWLuBoip5+dO6HnU9yoxvBiHZkNguGpiSk3OB+5hDLW9toYRCmCqEkSxV5lptd2G06X/SytdO/ADhD94E7n1Boi7z4ECqNHCvUmIxgpcu4Xj66bKhVWzntB2RoSt751DnYlBHJ8Yx920XJF6d81BlNjDQ5GBzOUg0lMAlJ/EeBX4fV38tG+4A0XCCMx11BKIyMxf+BchReP0XM463ntTG5rHZ7GNQVVUJj9y8Z/xpXebePmLT06jyvd8hqyeofUk/y2OC5pCHqu5dOyUWh7WCRHsZdiP3PaclspMBtoY2zevym7Vz8vLq/Y060nVoUB+QmruqkQxiig9UjJ4daiYmK7w3nnuyvCdFfOCZBUVhKbDE3LLWbqyjNXJpdNGHzSTR07BdLDZ3SxtodBxLN6icrXYcTnMK85FYXYV4HE9NI5FgMyoq41vZt+YIVgOIAkpwf5wcqa4OwWJJK5VSVZX3Jzd4vC87zkQQBJ4/3sy74+sEdywMxN1urSTMat3Xa8qmidUAsqJuJ6iTBnU8aVArisrCHQ8dx5w5L6D9D2uYj1JS1KuhVV6feZ0v9X+J464mBpsdacZ8v60Xh2LjRuRW1sdvLmomVbYEdeDiuxgaGzEfzc5cBo1DHR0bI76anla3VtdgstrSEtSgFSXqCeo3594knAjzfb3fl7pfN4zem3sbwp48CepxTH19CMb8n/ditLEYYGnC+/9n77/D47jva3/8NTPbd1EWfdE7QBLsRSIpierFTbHcJFt2EsdO4sRxyTfXSe7N79pOnDjViZ1iO3GXZMmSLcuybHWxk2IFCBJE771jsb3N/P6Y3QUWWAC7AEjQ9+F5Hj18tDvls9idmffnfM77HDbfnk+TWbXl+XhuIKqeBrgwegG9pKcua/mH9rbCNHQaDVJqHrIQYrhreumNzWFvP9fa7g1t0214Q16256yOoA5Oqtf/RoQkRnBgtJvMUIgXg3OqHEEQ0GZnx/y2VII6n4bxBnbm7obCvao/8Q2CvXl7GXQOMuRMnoC8XohMSv29PUtuE1FQL1R+KH4/vtbWJVv/RrraURQ5at+xECFZ4fsnu7n/X49xrmeKrTu3YfRMU2hcQGAoCrz8BdWq493fUNUECyCatYt89A5WZvH1mdtADkDDU0t+vlODp9Tt81dPUG/L3oZe0sfYfLSOqARD1RIKalC7ghYuuCaCvaXqAvK5JH2oFUUJhyTOKaj9PT2gKOjLVYV096UJlKD63OmXMmkfc/L7d5QnNdmLfOcRD3JQMyH+6BNqoOULPzvKXz5/hdseq0aURDquTIKsoPyGhL3cxNKYnfQwNewiJ8XA7mLrb6wPteyItW6wvP0vmPDhvOOLTI+4QIC0HLV+fKblGUpTS7nVduuyx9yTt4dB5yDDzthrPjA6hmGwGQSR6UAe73M/w8CUB3leh4SUpk+KkNTkZCOlp69b6Nh6YPI736HzgQdvCKJhPRBZMNCsYPExWd+CT5dOXrk6J/A4HQy3tVB3532k59nobVQ7v7Q5OWTr1UXHgdal68Wo5UsSivoYzIZr8XkK6qUI6s4r46QgkD7PjvDykUEC3hC7HirFuDkTb+s0SiD+woNoNKItKlKtDMJoGGtAQGBb9srE6P78/QC0iWqnXKoXXBjx6rNuaAV1YCAsgrpRFNTdTwDg04TrsV2/jWdMQdBro/7HG431CEp0nzmL7HaTcvfa/acj0ObmoC0pXpMPtewKgCSo9pZhKIEA/p7emHDqi73T7CyxIopCVABpC55c1t4jAltlGigw0mVnZ3E6AGdnrXDb5+Hyc9B1NGZ70axHUYIERuLfa4Kjo4TGJzbMfzoCfUU5is8X7XzfSEQI6ukp9d7b554lze8io6w4dsOh+rWrpyOouBtQoOsIoCqoARymPEJKiNlp76q6Ka83bhLU1wmSViSnNIWhJBTUe0qtpBo0i+wm1own3gtf3wZ/Z+Plp99DekidEIvjb8PIZQh44u52edDOlvzUmGDBvqYp0rKNpGXHEreCIFBal0V/yzShgBz1VDYWFzEyrhYuS9l8CIKgtvCuUUEtCAJamy2GoO4cdzLu8MW194jg/s15+IMyx9rmyD//wMA1C0hsnheQCKArUlfWIh5s430OfO4gRZus8Q8AWKwGbJVptJ8bTVhV9kzLM4SUEB/e9GEAHtiSx9nuKabCCwPyrPr3v+S7Epc0mx5SC6qFHtRKMIjr5CnMt98eQ1J4nH6CgbmJhuWgSvC4T5+O2V8QBKy2AqaHFyqo534TL3W9RIGlgF25u6LvZxmz2Jy5mRMDR9QXUvLifm5fW9u6BSRePT6EqBGovTWPf23xEhA1bPfFXq/nR86rwWjS8qoZg1ZiR2E6Tkc1E6ZBOtqXebiuk8VHw1gDoHr3rQbBiQ0mqBUF7dVf8E4pk6Mjp5n2zhVOmtxcgmEPatnvJzg2hi87jRHXiNoqWnmfWhSsg03KeuA3wYdaV1oKsKxiRXbF96D2dXSg+P0Yt8QnqCPkZF7l4rbE1hEH7/vmKb78y6vsK8vg9T89xLvuVcmdkc4Fk5Nz34Hz34ODn4Ud8RWKolm7qN35QEUmVwM2ZnP2woUfLBlAemLoBAWWAkpSV9+BoZN07MjeEaOYb43kEBiXXjjT5uevSkFdaDVRkG7kbJI+1Io/BCEl5ruMhPTowpOkzotjaPVTSFotP27zkZdq4F3bFnQaBTwwE+spOh8Gi4WM/EKG2ppjXk/foi5wfiTLz0/O9/P7P21g58NljI+rNcpNm4/fbCiKwvP/dJEzL6rE0YN1eTQPz9I3eX3CwdcLUeuGiIJ6vI3S7p/wdOhucsq3MT3qJjXTgEYrcWXiCo0TjTxa+yiisPwUbCkfavfZs5jdal07XfgY28dfJEceZdQxp7iVUpNTUAuCEPZ0vXGCEmdf+hWB/n48ly5t9FDWBZEFA3GFkMSBk+p3ULhffRb2XLqIosiU79xDybZd9DddJhRU733pW6sw+yYYWiYoUUxdo4J6NvzMSc1HdrkIjo9Ha4H5cPqCGIZUdZ6+PB2AgD9E4+F+irdkklWYgnFLJoo/hLdjaUJdX1UVY/FRP1ZPlbWKFN3Si7cRVKZXkmXM4szUaVIyDAgzfgQBJnUFNwnqRNF1FLH/CNrsVHw94ed2wS489jSM2SBolvYBv57QlZeDKK7Jlsh5+DCC0Yjp1uUXC5OFac8e3BcurLoDJORSO9fmz6H9vb0QDKKvUhcIZr0BWkcd7J4XkGgyhUiVRpYNSIwgtzQVQRQY6bRTlmkmzailvm9GJajTS+DXfwbBuXuGIAgIkozs8CH7Fj9bPJcvA2DYYAV1pDaN1KobCSUshJkcd2OwaBnuVYWHuvnXudcOU53rR1Dn7wCjNWrzYTBrsVj1uLOrQPahDyk0D9/4Nh83CerriPzKdCb6HAR8iakBtJLIoZocDreOxSgj1oTBizB4HnZ8BPZ+gld0Ctv8YYL6+P+Fb90Gf2uDf9sGT74fXv0/cOEHhLpPMjQ0EGPvEQrIDLZNU7wlPjlVsjWToC/EYPs0gX714ZtTU07HkBaL1rJiUOJaQxKBMEE9N6E/1akSavECEiPYW2rFatLy2tX59gBD1y4gcXgWvUakNFNt/xTNZqTsLPx9KgnUf1UlFQprFvtPz0fVnlymR9xMDa3cvuENevlp20+5s+hOilJUQvyBLXnICrwRDuaMTG4mtDP8uvvXi44xNTyIMSUVY0psEJbn0iXk2dkYe4+AL8TTf32Wo0/Pfef62lqkjAyccX2o8xcR1KJFh+wKMOoc5e3ht3ln+TsXTfBuK7iNS1Mt2EUhLkEdstsJjo6uS0BiwB+i9cwIFTuyOTcyy4UBB/6ScgJtc75jDr+D1unWFf2nI9hXlkFXv42p1EFmBwJLh9qYIhYfy4fjrIRLY5fINeWSZ45P5q+E0JR6fmmDQhIZvgTT3byn8mGCcjDmd6oS1OpvORgm9fot6m96R84OqLoXUKDjzes96rioslaRrk+/oQlqKT0dMTV1BYI6YvERO4nxRAIS6+IXr8PtraTn2TClxobwXuid5l3/fpy+KTdff3QH3/+dvRSkG8ktr0QQRIbb5z1HOt+Cl/8cqh+Ce7649OeI42F8a0UmogAn0t4N093QfXTRfoFQgLPDZ7mt4LY1t4PuzdtL23QbM+Gg09ZRJ9VL2HtEoLXZCI6PL+sBvuT5Sq2c7Z5OyhZjbrFhnoK6s0sN6SktxesKMNg2g0YzgcVWzMmuGX7nYGlMBwk+B/zgnfDvu6F76QBKW1Utw+2tMeOTUlPR5NvYJ0/x74/t5MqQnc+casMQVoRMd9/4RfZNLA1BECjdmknf1SmCgRAPhMPBX/0NU1ErvhCKX0aKEI+v/1/8op4f6h7DpNMwPeLGmqd2Hj7d8jQmjYmHKx5e8bhV6Sopt9Dmw332DBaNF0GEKeu9CILIn0g/p39qTlwipSUfjKevrsbX3nFD2GoEhofxtapErfPI4nvxbyISVVCPdEwjygFyd4W7VOrPY0xJJbeiktLtuwj4vAy1qot5xh07SJ+4ylDb9JI+1JJFB8IaCGp7WCyRYos+++MpqE93TrJdkQik6aLXQvPJYTyOALsfVLfXV6Qj6KXlbT4qK/H39iL7/YTkEI0TjQnZe4B6T7nVditnRs6QWWhmesilerRju6EJan94jqwt2GCCWlHgjS9BaiH6LTuiCwVyIIBvEoyWKVXIdgNANBjQlZTgW2VQoqIoOI4cxnzgAKJ+dXkiS8G0Zy+y3R7TCZAMZFcAaZHII1Yc0NA3g6IwLyDRji11GMGYrgaDrwCdQUNWoYXhjhlEUWBHUToN/TOgNcI7/hkm2uD0f8TsI5o0oDHibWpadDzvlSbQaDAs0zl9PRBRmPtvAII6oqAeHXKpYYVhglpbME/EMax2e2BbY0BiBKIE5Xeq86FwPZ1VaMFpKUAIuElFWHVo+vXETYL6OsJWmY4sK4x0J+FDXZvDhNPPpYGZ9RnEhe+D1gQPfpXuWz9BMz72FT8CgPSxH8L7vwd3/gUU7FZN8s99B375WaQfvoPT0if5i8Z3wHcfgBf/hKEXf0DQL1NcHAB5MeleUGNF0or0Xp7EP9APkkTZ5gp8QYVCc8WyBLVo0a45JBFY5Nl5qmOSgnQjRRlLW3VoJJF7NuXyZvMogZCMEgoRGB6+ZoVD88gsNXkpaKS5y1FXXEIgfCPrb54iq8iCaQXVRcWuHARRoP3cyjYfv+7+NdO+aR7f9Hj0taj/dnhyGCmms3NscQnq6aHB+PYeR4+BJGE+sH/uM54awjPrp+3sSDQoVBBFzPv34zp1ehFpYrXlMzsxRnAeESOl6ECBN6++hqzIvLv83YvOfXvB7cgonDYa4xLU0YDEqrUrqDvOj+FzB9l8ez7/9kYbBelG8nZvw9fcEv089WP1yIq8ov90BPvKMgiFNGiztYgBDVNLBUZpdKBPW7P699L4pRWDZ5ZDcGISwWBANMf3Rr/maPo5iBpqdn6cTRmb+EXHL6JvaXNzCIyNoShK1DO5WTeBUWOkxloDedtVq5T21zZm7AsQ8aFeKhTrRoAgCOhKSggsR1C7A2pIlya2vPBeaUJMTUVbtDilXlEUhttbsFXVLnrvm0c6SDFoef3zd/DwjoIoMawzGMkqKmakI6z4m+iA534Hsmvgff+jFmlLQDRpULwhlHkT+lSDlm2F6fxwZhsYM9Rn5QI0jDfgDrrXZO8RwT6baj1yfvQ8gZBM55iTmpUI6nwbKAqB0ZXv8YvOV5bJhNNH90Ti/nPRwMv5CuruLrSFhYh6Pd2XJpBDMh77IANSJmadxGP75rUuBn3wzEdUf72UXHj6sSWDSfOra/E4ZpkZjbUzMNTU4mtr5d3b8/npHx4AAb5lV4vrS7/uJhTceDLtJlaP0m1ZqpChbYaiDBNb8lN/42w+IsSflKqD7mPQ9jK/SHkUc6YNRVaYGXWTnmdiyjvFK92v8O6Kd2PRrRwiJYkSu3N2L3omuM6exbJ7J2nZJqanJJxbP8b7pGNM9c11IEipOpBBTjCYHUBfU43idt8QrdHOY+pilibfhvPIkY0dzDohZPeBoIotlsP4rA6rNItGIyHLIXoaLlC6YzeiKFG0eSuiJNHTGPah3rmT9Jl2An6Z8b749aIgCeqcatUK6kG1VtLo5wjq0sUE9fGw/3RqWEyjKAqNh/vJK08jvypdHYtGxFCbgbd5EiUUf/FEX1UFwSD+7h7aZ9pxBVxJ1an78/cz5Z2CTB8zo26qsyy0+LNUL23/jem/GhgYQEpPR7KYV974WqL5lzB0Ee76S/TVtfh7elV7tuZmlGAIQzZw8YmNHeM86KurV23x4WtrIzg0TMpdd67rmABMe1VRkvv86gQnsjuIaFnchYggoC9XA6ov9E4jCrC9KA3HlBfHlBebcl5VT68QNB6BrTKN0e5ZQkGZncXptI461Nyz6vuh9l1w7J9iut80GRZEnQVPff2iY3kvX0ZfVRUNcNwoSKmpSNlZ+Do3fkFK9gQR9BJTw24MOQZSZtQ5u3a+4HG9AhLno+Ju9X43ptYEmQUWnEIasmeWLEm6SVDfRCzyKtJASCwo8dzIOe545g5+OfpldBkneP5y/doDgbx2uPwzqHsEDGm83P0yAgJbjKrHlFi6FerepxLUH/g+fOoE/O9h+GwjJ2/5Fn8TeBxP1TtBEKHl1/SdbEAkQP4rd8Pf5cM3D8Lzvx9dbdfqJAprrfRcmcTfN4DWZqOuRC1cUsRi2qbbkJUlVvzjKNxWA63NRmh8AtnvR5YVTnct7T89H/dvzmXWG+Rs91TUP1t7DRTUiqLQPOxYFIylKyrC39eH3xtkpMtO0abl1dMAplQdhbVW2s8vb/OhKApPNj9JtbU6aisAsf7b4w4fIbtazB6ovZ326fZFCwpTQwOL7D0AnMePY9q5EylVVVbLIZmGN/pJzTYiBxWaT84REOaDBwlNTCwqMKy2AlCUGLJCSlEf1m93nGBb1jZK00oXnXtr1lZSRT3HjYa4HtSRVjD9Oiiom44Pkp5ropMg9X0z/NFdFZg2byI0MxNV7l4YvYBG1CTknQewq8SKJAooqarHdP3lZVKgzZlrCkkcdY0y5BpadUAiQHByAk3mytfTNYGiwNUXoOwQmDJ4uPJhmqeao79TTU4uituN7HRGJ9znhF62ZW1DI2rUAq7yXuh8M+4C20Yg4jk66Nx4gmAp6EpK8PcsR1AHFwW7AHibmjDWbYn7W3FMjuOamca2wN6je8LFmy1jPH5LMZmWxQoXW1Utwx2tKK5JePpDIGrgsadBvzzRGxlfJGE7gtsqszg/6MFf9yFo+RU4YongE4Mn0IiaKLm8FtRl1mHUGDk3co7eSRf+kEzNMv7TMBfSuxof6n1lqsrmbBI+1JG/z0IFtb5MVfZ1NYxjSnER8Hk45zTz6L7iOYuSUBB+9nuqEv3h/4SPvwomKzz5PhhfbCNgq1YXJ+b7UINKmvm6upF9PuoK0vjFp28jPV8l9/xTPs6+1J3w57mJGw+FtVY0OpGeS+qz7IEteVzonWZs1rvCnjcOIt6+UopW7TpMK+K7wYcotBpxTHkJBWSsuSaeb38ev+xfNhxxIfbk7aF3tpdxt2o5FxgZIdDbh2nfXjJsZqaGXRju+jMCaCi58o3oflHfYXvipGTU07V1420+nMeOoc3PJ+Mjj6tZJauwNrrRELKrQZqCtHS95OkbxKHPJSdfJbFHOzvwOGYp26kSXnqTCVtVLb1hgtpQW0OGRxWzDLYt40Odql+DB/VQjP80zGXlzEd/8wRmBIwV6QCM9Tiwj3nYdDC2FjduyUR2BfH3xp8LRywMfB3t1I+pnzMZG7qIt/uQvhtFgWqjnnpXeA41dWM+LwIDA3EX768rQkF4628gqwa2PRpdKPD19OC5pKo8jfsPQeMzS1qBXm/oq6sI9Pcju5O3hXK+pVogWA4dWu9hoS0oQGOz4T63OsGJ7AosDkjs7FDFAeEsrIt909TkpZJi0DLSqV5L+cHjCdl7RGCrSCcYkBnvd7Cz2IqiQGP/jPrmg19V/33lL6LbS2kmBHM6noaGmOMoioInXOPfCNBXVOLrugEU1O4g6CRCQRmnQSTXPYWi1aHJmtd1PFQPaUXrE5AYQcXd6r9hm4/MQgsKAsFggDyJpLspNwI3CerrCL1xrp1iOciKzD+d+ycEQWDcO4I+9yVemPgsDz3/EF95+ysc7juMO7AKj77GZyHggj0fR1EUXu5+mT15ezD69QgGzSLFG6CSONYS3gxu5ynxXVje9x/w8ZfhC530pz2KrUSP7rf+CfZ9EtIKofkl+PEH1bZeoLQuk9lxDzNDs2iLCinLsmDUSoS8ubgCriUDwUSLLinlx1LQ2tSiKjgywtXhWeyeAAcrV74J3F6VjUEr8lrTSJTcuhYE9bjTx5TLH/WfjkBXUkxwbIzBK2PIISUhghqgak8OsxNexnodS25zbuQc7dPtPL7p8UVk0UdvLUGWFf71jTZCdh+iRct95fcjCRIvd78c3c7nduG2z6hE8jwERkfxNTdjnmfv0XlxHMekl4Pvq6SgOp0rxwajljXmg+qD1HUi1uYjctzpkbnfhxj2d3RMz/DuisXqaVAVRwf02Zw0GZEN6Yve97W1I6akoMlbnaVFBBMDTka7Z9l8m42vv9lOfpqBD+wuwlCrtjZ5m9VVywujF6JEVCKw6DVsyU+l31mEX/LS3NKz9MamrDUpqC+Nq76Oq/WfBghNTCJtlP/0cANM98CW9wLwUNlDaAQNL3a8CIAmRw0ODY6NqdewRuJ8qCtWiVN5rxqouYSq83rjN8KHuqSEwPBwXA86CHvnLbD3kP1+vG1tGJb0n1YXFRYqqH9wshutKPL4/vh+z3lV1fhcLqZ/+HE1+PdDT4K1dMXPECWoF/pQV2YSkhXOZz4MchAanox5/+TgSXbl7MKsXbvKSStpoz7ULSPq/XpFBbVNnegHhpMnayqyLWSYdUn5UC9UUCuhEP6eHnQVFfi9QfqvTpGRp9Yi4/psfvdgqbqjosBLn1XVWA/+veoFnpoPH31BXUR44r2LPKkzC4vQGY0MtcUuyhlqaiAUirZrZqfo+dYnbwFgRiNz4ZVeultvfDXITcSHRitRtCmDnssTKIrCg3Vhm4+ryXcJbBQiJLA09DqMNCLf/X/pmQlRZDUxPapeH6m5Bp5tfZZb8m6hIr1iucPFINJ9FbH5cJ9VfevNt9xCRr4Z+7gHyZzLzzTvpGb8VRhTr5+IxUIyPtSR8LO1eLquB2S/H9fp05gP3YHlTpU8ch47tqFjWg+EZn2IK9h7DBxpRBE1FOxQuxO76s8jCCKl2+fyVkq37WS0uxP3rB1BqyW1tgRLaGoFglq3Ng/qVLUm93f3oMnNRTTFds31TLiwzaoLmvpy1aar7ewIkkakYldOzLaGmgzQCEvafOjKykCS8LW30zDWQLYxmwJL4vOvHFMOFWkVNMrqtWKTJTpDueqbUxtPWsWDf/Da5RwljEtPq7YO9/z/QNJEs3p87e14GhvR5OaivfOTqtit+aWNHWsYEbGRryN5Kw3H4SMYtm1Dk5293sNCEATVh/r8+VURgfHqaH9HR/QeHZIV6vtm2F2SDqj+01qtQqamB0oS7/CzVaaF97ezo1A9Vn2EoE4vhjv+F7S8BG1qp6lo1iLqU3HXN8R8rkB/P7LdjqFua9Kf9VpAX16Ov6Nzw0lY2RMkGM5tGyRIrnsabX4+wnyF+1AD2FYvFIuLtEJ1oalTtbDMCtvi+RBJlRUmnD56bvCsj5sE9XWGrTKdkS47oSW8wgBe7XmV5qlm/mzPn/Hib73Ixwq+jXf4tyiyVPBi54t85vBnOPjMQT7x6if4wZUf0D7dvvJFqChw/vuQtw3yd9E81UzPbA8PlT1EyOlHsixWvM3HlUE7m22pURsK14yPyWEvxbvKYNfH4P6vwId/Ah96Qm0p+OnHIRSkZKtKBg+509AVFiGJApvzU5mcUl9vnY6v1BAtWhS/jOxfm7JxvuLsVKdK5u1fJiAxAqNO4o6qbF67OjrnDXYNiodIQOImWyxBrQ2rE3ovDCBpxehDZCWU78hGlATazy89uXui+QmseivvKH/H4v2zLTx+awnPnO3DPuZCStOTaczk1vxb+XXXr6O/s6loQGKsxYfruNqWaTmkEtSKolD/eh/puSbKtmVRd6gQx6SXvnBhqs3NRV9VievkQoJa/d6mh+aUpFK4LTIzZOXB0geX/Hy3K0YmJYmWmcWTLDUgsXrNit+m44NIGpHpHB0X+2b447sr0WlEtVgSBLzNzbgDbpommhK294hgX2kGjQMBvNYZHAPBpTc0Z63Jg/rS+CX0kp7ajMW2CokiODm5cQGJYXsPat8JQIYhgzsK7+ClrpcIykE0uWGCenSUwOAgoZwMQoIS62VYcbfaEdLx+kZ8gkWoTK/Eqrfe2AR1aYlqM9EfP/ROdgcWByS2tkEgsGS693B7CxqtjuyS0uhrdk+A5y4M8O7t+eSkxG8ZtFWq3T/DbVfhXV9LWDkSIagXdunsKrZi0Iq8Pp4KpbfDhR9GwxLH3GO0TrdyID9xdcpK2Ju3l46ZDhoHB5BEgYrs5dv+NRGCehVqQkEQ2FeakZyCeoGfeGBgAMXvR19RTu/lSdVeQxonKEjcsmszhVaTWmu89ldQ/yTc8QW49VNzB8ysgI/+HPxOeOK3wDkXRCyKEnkV1QwvIKj1Nep37G2du58b9BpEk4ayijRmRZmfvnJjkg43kRjKtmfhnPYx0e+kKsdCeZY5ajX2m4CQIxx+d+ZvIX8XYyXvwh+SKbQamRlRJ4FXQw0Mu4aTUk8D1GbUYtaaozYfrjNnENPS0NfUYLWZVAuRMTfHsh7DJxjgyN8B8xTUSZCSotmMtrh41Z6u6wX3uXMobjeWO+5AV16OtqgI5+EjGzqm9UDI7kezglXf0BW1O6bwNlXs0F1/Hlt1LUbL3OJlyfadoCj0Xm4AwLRjB2ljTQy1zyw5t1QJ6tUqqAdjFNTxAhKPtY+zCw1KhgEpRYcckmk/P0rp1kz0xliiTdRLGCqteJom485dRZ0OXWkpvvYOGsYa2JGzI+mafX/+fk67jqHRiZg9Cr1KhKDe+Lb/hVBCIQJDw7HBadcbAS8c+apq71n7LiB2ocDT2Ihx+3YovUMN0Kv/0caNdR6iXR9JLqoFx8fxNjZeE3uPCEx79hCamMDf05PUfkpIRvEEkeYpqJVAAF9PL/pKdXGzLWzFEfGfHuq0k5c2gag3qzxPgjCn6UnNNjLcMUOaSUtFtpn6vnkLXfs/DVnV8PL/goBHrQUFLaGJyWiwJ8wFJBo3OCAxAl1lhRroOja2oeOQPUH8soKkEbnq8FDkn0E/n0ta74DE+ai4G3pPQcBDWo4JSSvgE3Row7TauSTmAhuBmwT1dUZ+ZTpBv8zEEl5hgVCAb1z8BjXWGt5ZrhIv7922lcDMrdyR9gVOPnqS797/XT666aNM+ab4lwv/wiMvPsJ9P72PL536Eq/3vo7DH0c9O3AOxppgz8dBEHil+xU0gob7iu+L20oyH7Ks0DRkjwlI7LuqEmPFWxYoeyvvgXf+s+rr+upfkpJhICPPyLihLNq+VJefStdQCgICbVPxHyqRG/Nafai1+RHF2TCnOiepyDaTm5qYP9L9W/IYtnsZaOkMHyt/hT2SR0s4SXWTbYHFR7GqGBzocJBflY5Gu7Sf6nzoTVqKt2TScX4sbjhO/2w/R/uP8oGaD6CX4is5PntPFRa9hokhZ1SF886ydzLkGoqqbiPEsXWBxYfz2HE0ubnRVe2B1mnG+xzsuLcIQRQo25GFKVXHlaNzxLP5wAHc588je+daevUmM6a09JigRMWkFqi7TNtIj6OOjuCAV52QnRg8EfO6oij42tujqoDVIuAL0XZmhIpd2fzHya6oehpAspjRFRfja26hcaKRoBJkT15iAYkR7CvLwB+U0eXqsTgy6Z7sib+hKXNNCuqG8Qa2ZG5BKy2/OLUcgpOTaLI2gKBWFJWgLr8LTHP3oIcrH2bSO8mpoVNoc9UJSWBUVVDPZugREGLtVkwZULAH2m8MgloURPbk7eHcyLkNX/lfCpFwpKWCEmV3bGEN4G1aOSAxp7wSSTO330/O9eH2h/j4baVLjiVj+A10YpDhtAPqQmmCkJZQUBu0EntLMzjZMQG7fwdmeqFLbZE7NXQKUINY1wsRxfyFsQuUZpowrHCfF/V6pKwsgsPJW3wA7C3LYGDaw9BMYu25oYjFR9i2w9elTu515eV01o9jTNXR09vBuC6LTx5SlT2c+JoarLP3k3DX/1580Lw6+PCzqhXYk4+oBXoYtqpaxvu6Ccx7FuhKShD0+kW2A6JFS026iUOf2can/nCdwmVuYkNQUpcFAnQ3TiAIAg/U5XG6c5IZ99q76K4H5Fk/giaI6OyBB/6O/hn191toNTI94kJv1vBc39PkmfM4VJRcO7lG1LAjZwfnR1SC2n32HKa9exBEkQyb2skxNewiPTuPHwvvhKu/gOFGtaaXhKQU1KC2zG+0xYfr2DEEnQ7zrbciCAKWQ4dwnTkTUyP+JiJk90UXDpbC+FgIk+LAbDXhmplmtKud8p2xNWRueSUGSwq9l8I+1Dt2YJ1qJeiXGV+ie1JK1SO7gijJevb7XeCdmVNQ9/TEDUg83jLGdiQs1SphNtAyjccRoHpf/G5F45ZMQjM+AkuEuusrK3G3tTDkGko4IHE+9ufvxyd70eeAPO3DJZhwazNg8sZbzJyzkdxAgvrcd9SFiHu/BOHFAFGnQ1dSgvvsOQJ9fRi3b1O7qnd9VPXavwHIfm1REYLRmHTXh/OoGrxqueuuazEsYL4PdXI2H1FrtXl1tL+vDwKBqIL6Qq9KIu8pycDnDjA56MQmNkDxLSBpFh1zOeRXpDHSZUdRFHYWW6nvm5mbf2h0amDidA+c+New+EQArSnG5sN7+QqCTrcu+U7rAX35jRGUKHsCeP0hMvLNNI85yHHPLPCfVjkV8q9BDVtxNwS90HcaURTIzLfg1hgRFIksU3LdlBuBmwT1dUZECTu0hM3Hc23PMeAc4HO7P4coqF9PRbaF4gwTbzWPopW07LPt40/3/CnPv+d5Xn//63z5wJfZlr2N13pe40+P/Cm3P3M7v/3yb/Ody9+hebJZvdGc/z7oLLD1/ciKzMs9L7M/fz/phnRCzsAiM/756J504fKHFhDUU5hSdWQWxFF87fm4uup29r/h7W9RWKjBnlYJeerDt64gDbdPwmYqXDIocakW7GQRsXLwDQ5ytnuKAxWJe/zcU5uDJAoMNXehyclZ95RfgJYRB7Y0A+mmWFWFrrgIrz4du0NI2N4jgqq9ObhmfAx3LvZ3+3HLj5EEiQ/VfGjJ/a1mHZ+5pwqTX2Yk7BF+d/Hd6CU9v+r6FQBTQ4MIokh67lzxqQQCuE6dwnLH7VG1Q8NrfRhTddTcqm4nSSKbb8+nt2kS+7hKkpgPHkTx+3FfiE2qt9rymZln8XFm8hwu0cNW4+ZlP3+Wc4LNgnERQR0cGkJ2OqMr7qtF+/lR/N4QwTIzF/tm+KO7VPV0BPpNm/C2tHB+5DyiICZtobG3VP2+5ZQcJEXD4YbT8Tc0Z6se1KsgMn0hH82TzWzPWX1bkSLLhKamNsbiY+gizPRF7T0iuL3gdqx6Ky90vIAmTFBHFNSDlgCV1kpSdAtsFKruUz3A1hg4uV7Ym7eXYdcwA86BlTfeAEQJ6iV8qOU4rYmeK1eQ0tNjk6vDCAUDjHZ3xPhPB0MyPzzVy63lGWzJX6J7pPsY4itfIC9Dy7AvPanPEFF4Ryws5uNgZRZto07GCu9TF4HOq2GJJwdPkmXMotq6dv/6CLZkbcGoMdLrblzR3iMCrc1GYHB1fqy3lKn3lkQDUmR3QLX/Cnum+sMEtVRUSm/TJCVbM/CO9iFmF7GtMB3Ofw/e/GvY+gF46B+jE91FKL5VtWMZa4YfPxr1s8yvrkWRZUa65hScgiShr6rC2xqrrBbNOkJOPwdrc7Dok5uQ3cSNBVOqjryyVHoa53yog7LCm80bq35KFKGpWSR5GDa9B0r2MzCtqqaLMkxMj7gxZkmcGTnDh2o+pOYfJIk9uXvotHcy3n2VQH8/5n2qB356rglBUAnq4gwTX3c/gGJIgyNfRRAFpJTkbR0M1dX4e3s3lAx2Hj2G6ZZbol6rlkOHULxe3GfOrNs5Jpw+gst0sq43ZF8QxRdCSltaQR10OJgWs8lOVyVu3Q1qTVy2gKAWRYnirTvobbyIoigYd+wgfUa9Zy5l8xG1fEnW5mM2vBiaWkDIbic0M7OIoPYHZSY7ZzAizLP3GEVv0lBSF78+NGzKAAE8TfHrLn1VFaGBIXQBZVUE9e7c3WgEDbOWcaaHXBSmGxnW5N8QpOpCRJSo2o1SUHvtcPyfVUKr7I6Yt/RVVXguqhZ4xm1hcceOj6idh/VPLjzSdYcgiugrK5Pu+nAcPoLGZot2aF0L6MrKkDIz8SRLUEc61+YR1L4OlWjVVagE9cXeabJT9BRajYx0zYICtsDxpOw9IrBVpuNxBLCPedhZnM6ky0//1DwRQ/khqHs/nPg3JEWtHaW07JigRM+Vy+g31SJoVy94Wk/oK9QgycjfbaMgu4M4PSHS8k2MjE5jcs/GEtRDDeq/tmtAUJceBEkHHXM2Hy6tOs84WJh+wwcl3iSorzPmt1MshCvg4tuN32Zv3l4O5s/dZARB4J5NOZzsnMTtj235zzPn8UjVI3ztzq9x9NGj/PDBH/Lxuo/jCXr4+sWv88GXPsjdz97J/xl+nZdr7sCOTMNYAyOuER4qewhQb4YLFW/zcWVQJTq3hglqWVbob56ieHPG0m1X9/212ib06l9i855DESXGQqrPU4TotmpLlrX4gMUt2MlC1OmQsrMY6+jF7Q9xIAF7jwisZh37SjPwDgxcE/9pgObh2UUBiQBSWhoz4RW1ZAnq0q1ZaLTiIpsPp9/Jzzt+zgNlD5BjyllibxUf3V1EKgJvDEwRDMmYtWbuLLqT13pfIyAHmB4aID03L0bx6K6vR3Y6o/7TEwMO+q5Ose2uwhgF+JbbChAEgabjqoratGcPglaL6+SpmDGk5+UzPTyntP5l5y+xa53ksYJfmGOY2ww2Lo1fwu6bI+nXKyCx6dggVpuJ/7k6oKqn98QWlYbaWgL9/VzuOcOmjE1YdMu37S+E1ayjOtdCi1f927Ys5UNtzlJ9cr0zSX+G5slmAnJgbf7TMzMgy2gy1zHYIVE0/RxELdTG2tRoJS3vLH8nR/qP4BB8iGlp+Pv7CI6P02aYYWd2nCKg8l5AiT7ENxp7c1VVbUQxd6NBSktDSk+Pq6BWgjKKL7TI4sPbdBXDlvgBieM93YQCgRj/6VeaRhic8fB7t5XHH8RkJzz7MciowLb/PUz09RDwJ64UjBDosmuxhc7B8CLmqR6nOhFrfZmQfYBTQ6c4mH9wXQNBtaKW7Vk7cUtt1OSmrrwDoCsqxNfejhJK3v5qky0Vi16TsM2HGng5R6j5OruQsrIYHAgS9IUYFibRygH27t0GV34GL/0pVD0Av/XNlVPkq+6FR74Nfafh2d+GUIC88CJF3KDEltaYrgLJol1zh9VN3Dgo3ZbFeJ8D57SPbQVp2NIMvPIbYvMRGuhBYhLu+zJAdHJfkG5ketTNqK4frajlkapHVnX8PbkqQdn25s8BMN2ierBrtBKp2Uamh1wUZZiYxczktt+H1l/DwAWkNP0qFNQ1IMv4Nkh55u/txd/Tg2Vejolp314EkwnHkSPrcg67J8Bd/3SEf33j+nltR33Kl1FQj5+8hF+Xiq1WfQZ115/HbM0gu6Rs0bal23binJ5icqAPTVYW5tx0UoRZBttm4h47SlA7kiWowzV4av5cQGJZacwm53un2BxUn4v68jQC/hBdDeNU7MxG0sZ/DkgWHbrStCV9qPVVVQiKQvm0jpqM5ElEs9bMtuxtdGmu4nMH2ZxupiuYc0MS1BEbSd1GeVCf+nc1i+WeLy56K6LYRZLmMkRS86Hqfqh/Sg1W3GDoq6uSsviQfT5cp06Rcted1zTgPeJD7TqXnGVfKB5B3dkBghAlXi/0TbO72IogCAx3zCCKCrnatlUR1HkVc8LJnUVqB0R9/4KFrgf+FiQd4uXvAaDfvA13WEGthEJ4rzZjvEH8pwGkrCzEtLQNDUpUFAXZE8TrDxFI0ZDrVv+msQrqhnBA4jUQeunMqhik8zAAmYUpeFF/U7fYUumddN/QYdQ3CeoNQH5lGsMd9kUt3D9q+hFT3ik+v+vzi26a99Tm4g/KnOxY2nNWK2rZlbuLz+z6DM+++1kOf/Awf3vb37JXm8lRg44vOK9wx0/u4PNHPo9e0nN38d0osqJ6hi6joL4yaEenEanMUYm2sd5ZfK4gRQvtPeZDlOCR/4a8baSd/mc0AReD4+pEtzLHgk4jIgYK6Hf04wosbvGK+A2vV1CivUf1S721PLmbwP1bckmZGceXvbZQvXjwB2U6xpzU2uITE9N529ErHjILkgvk0hk0lG7LovPiGPI8hcgvOn+BK+Di8U2Pr3gMMfyAbHb6+Ml59W/3jrJ3MOWd4szwGaaGBxcFJLqOHQONBvP+/QDUv96HRi9Rd0fsdharnvLtWTSfHCYYCCGaTBh3747jQ12Aa2Yan9uNK+Dirb63VG9y5zLETMAL3hluT6tGVmROD82pjyMr7GtpQRrvczDW68BYm8bFfjt/dFclek1sW75hk0q0Oa5eTtp/OoJ9ZRmcGbajmAL4RySmvHEIJVOYGHYl70PdMNYAwPbs1SuogxOq8uW6W3woCjT9AiruAqN10dvvqXgPATnAy90vo83JxnNJbaEatARiAxIjsO1Q1eg3iA91RXoFOcYc3uh7Y6OHsiR0JSVxCep4rYmy14uvvX1pe48OlYy0Vc1NQr93opuSTBN31+agKAoTA46556XXDk+HfVwfe5q82m3IoRBjSRSigkZEMEhxO3Q256eSbtLO2XwoIZrOfINZ/+y62ntEUGrehqQfIz8zMbI15YEHCY6P41wFWSOJArtLrEkQ1LF+4v7OTvTl5XTVj6M3aTjcolq3HCoIwPN/oBbDH/gBJGobVPc+1Tu8/VV44VOYLClYbflxgxJD09OEJubUdqJFu+YOq5u4cVC6TX2e9VyeQBQFHtiSx7G28UWijBsOo02EXDJSdhZkqOTBwLSbnBQ9+GU8s36uBM/zUNlDZBiSExtEsCVzCwbJgPPMaaT09JgaJsNmjiqoAZqKHgNjBhz+W6S05BXU0dCx1utH3s6H81hsjgmo1kbm/ftxHj26LtZXv748jMMX5Jmz/fiTtbxYJSL+z9IyHtSD59RnWOHBWkLBIL2N9ZTt2BOXRCvZtgOA3sY5m4/0yRaGO+L7UIupYU/yJBcsmA1366TmR710Fyqoj7aNsxsNYo4RyaKj59IEAV9oSXuPCIxbMgmOuglMLLac0lepxOgtnny04upUmfvz99OkqOrfSq2Oy95scAyrtiU3EAIDAyAIaK6BjeSKcI7B6f+ELY/E9cGN3Gv0VVWxwZg7PwrOkRuibjZUVxOamorOSVaC++23UTweLHfdfY1HpgqwgkPDalB7gojUNfOFg/6ODrQFBYhGI+MOH72T7qj/9HCnnaxUO1qduCqrCGueCYNZy3CnnepcCyadRH3fTOxGKXlw9/9BHFHDavWVm/C1tCK7XPi7ulDcbgw3iP80qIsDkaDEjYISkCGk4FdgTFLmEdTzrvNrEZA4HxV3q/a+jhGyCs0Ewo/P7Snqc+hGtvm4SVBvAGyV6XhdAaZH5hI0Jz2T/KDpB9xXch9bsxevQu0ry8Ci1/BWS+LJ5lnGLN5T/m7+cWiAo6FcnnrHU/zhtj+kKKWIxzc9jllrRvYEQWZZD+rLg3Y22VLRhgMS+69OgZCAsldnhseeIeTVkzlzlf42O4qsoJVENuWlMGtXJyTt04tbc6IK6nVQSGltNpTRETbbUrGalw8oWYh7qzPJ9tjpkRJTtyWDznEnQVlZFJAIoMgKk/oSMhwdq1rhrdqTi8cRYLB1BoCQHOKp5qfYnr2duqyVHyIRtUdGrpmvvdaGwxvgtoLbSNGl8OvOXzEzPIR1QUCi89hxTLt3I1kszE56aD83xpaD+Rji/LbqDhXgdQXouKC28JoPHMDX2kpwfC40KyNMgM+MDPF67+t4Q16smVnLL1o4VcXV1qw60vRpHB88Hn3L19aGxmZDSl39d3nl+CCSVuSZsam46mlQLT4ACkYCUeVTsthXlonTF8SUZyDbUcTR/qOLN4qsuLqTt6a4NH6JopQiMo2rJ5dDkyoxLmVcZ4J68CLYF9t7RFCbUUu1tZoXO19Ek5MbLVDG0oX4raKiCBX3qApqeW2hrOsBQRB4d8W7OTF4gjH3jdniritdiqCOKD/mqW5bWyEYxFC3Je6xhttbsVgzSAkr8ev7prnYN8PvHihFEgWaTw7zk6+c4/TznSihIPz099RQkQ/+CDIrotYgEaI7UYhmbdwOHUkU2F+eycmOCZSMcig7xMnOXyMKIrfabk3qHInAJKvj90iJEUIp99yNJi+P6aeeWtX59pVl0D7mZMq1MnElu4NIYbW5oij4urvRlFfQ3TiBuSwF/2gfgkZD1tHPQU4tPPYM6EzLH3Qh9nxcVW5dfg5e+XNslTUMt7fEEFH6GnXRb35QomTWIruDKNexTf8mrh0ybGZSswwxNh++oMzR1vEV9txYKK/+FSElA6lyR/S1/imPGpA4qtb4Y7qBpMMR50Mradmes52UK32Y9u5FmNedYLWZsY95yA9nq3TPinDb56DzTSQmCdl9SZG6upJi1fM9SU/X9YLz6FF0ZWXowkHhEVjuPERwaBhf+9oDHJ+/OIBRKzHp8vP61cTnVGtBIgrqkV4XkuwnuzKb4bYWfG4X5bvi15CpWTlk5BfScylsv7BjB2lDDQT9MmM9i32oV2/xEbYaS81Xbb1EMZonFMHJ1gm2CxqMFekAtJ0dwWLVk1+VvuyhjVvU2tEbx+YjaMsiIMGW2cSsr+LhVtutTJpVYjBbFukKhTtHp7pXfcxrgcDgAJrcXERdcvPTdcGxf4KgD+7+q7hvRzJ7jNsXkGjVD4A5By4+ca1HuCIiJHqi9yzH4cOIJhOmW/Zdy2EBq/OhnqujYy0+Imr2i+EQw10lVkIBmdGeWWzaJijaq3pGJwlBEMirSGO4YwaNJLKtMC02KDGCvZ9EzFLJVW1eIcgynstX8FxWhQrGJUQoGwVdRXk0N2UjIHvUxfWAotDh91IaVDu6owrqaEDiNcxQqQgvwnS+RWaBBX+4FChwz2DSSTd0UOJNgnoDkF+ZDhBj8/Hfjf+NL+TjT3b+Sdx9dBqRO6qzeLN5DDlO+N2S6D0FE61Ie36Pbdnb+NSOT/HkO57kc7s/B8xbqVtCQS3LCk2Ds9Tlz5F6fU1T5BSnYLQkcCNMteFP2Umu5zIeZ4ixDrUY3FKQRu9I2Kssjg+1qJMQtOK6tPAKuXmkzE5yoDx59Uquz4GkyFzwJxasmAyaIwGJcSw+Jgac+NGRPtSA4k9eRV5cl4HWINEWtvk4Pnicfkc/j29eWT0NcyqLj9xXyaTLzzePdKKTdNxfcj+nW48QDPjJmBeQGBgextfWhuWO2wFofFMtarffW7T44EBBjZX0XFM0LNF88AAArtNzimerTX0QTg8P8svOX1KcUkxGVvbyLYoOlaCWUgs4YDvAycGTyGEfbV9b25oCEv3eIO1nR0mtTuXM4Exc9TSAJjsbf5qJ0lGFXbm7VnWufWEfan9KCuneXI52nli8UVRBnRxBrSgKDeMNa1JPAwQnVIL6uiuom55X7T1q3hH3bUEQeLjiYS5PXMZjNUZfl3MzKbAs0UJZdR94plQv6hsA7616L7Ii82Lnixs9lLjQlZYSHBlB9sQqn6KtifNUt54ryxevw+2t2Kpqowtx3z3RTYpBwwf2FKEoCpfe6kfSiNS/3se5f/+Bqth5xz9FvRLN6VZSs3MX2UKsBMmkjetBDXCgMoshu5eeSTfs+V1OiH7qzIXLhrOuFg57Hoqso8fVmND2gkaD9dEP4Tp1elVt+PuS8KGer6AOTUwgz84yY63F7wnydsBDRXAQm96OmJoHjz8PxvSkxwPAbZ+HA38CZ/8bG7247TPMjs8RR5H7tm+eD7UY6bKKY9NyE795EASB0m1ZDLRME/CF2FtqJcOsu7FtPtrfQO48D2iQMue88gdm3GH/aVWpmZtvTUgYsBwOClVYpwMIu2IFLBk2M7KsoHGHMOkk+qY8akCpOQdp5E2UgIziSfwaESQp7Ol6/YMSZbcb99mzMfYeEVjuUMMlnUfiLNYngd5JF+d6pvmjOysoSDfyzLm+NR0vUaykoFYCAaZ8FjKMHkRRoKvhPKKkobhux5LHLN2+i4GrVwj6/aqC2t4BxPehFk0a0AirIKiHVEW+1oi/txdtfn4MkTo264URFzoFDBXpeJx++pqmqNqTiyAuL67RWA1oCyxxbT6aZloYzATb2Orv73VZdRgMOkIWD0ZXiG4lrOieurGCEv0Dg+g2wn96qlvN2Nj1McisiLuJrqSElIceJO3h98S+IWlhx4eh7ZXovGujEOn6SCQoUVEUnEeOYj548LosCOirqxFTU5MjqJ2ROjosDggG8Xd3o69Uv6OLvdPoJJG6glTG+hyEAjL5gRNQsvoOP1tlGvYxD+5ZPzuLrTQNzeINLBDsSBrEd6oLGRqneg15GurxXrmMaDKhK1tsRbSR0FdUqsr66fi+/NcakY5SjUVH05iTWlwIOh2arPDcPRqQuOPaDSJ3q9oh3PkWepMWrUmlfUND4+wqtnK2Z2P+NongJkG9AUjLMWJM1UWDEvsd/Tzb9iyPVD1CWdrSF/g9tbmMOXw0Dc0mfrIL3wd9mtq+EwfRG+ESCuq+KTcOXzDqP+11BRjttlO8JXFSKjBmpyBPQiBEz0+fADlEXX4aDmcKZo2F1qmlfajXo4V3xJCOPhTgYG7y4TSR8IqzXgNjjvX16mkZcaDTiJRlLbbw6G9WyYOMqav4k2gNikCjlSjfkU1X/TihgMyTV58k15TLPcX3JLR/hKDeUpPFIzsL+M6Jbgam3byj7B3o7OpNN8M2V1BF2zLvuAOvK0DTySGq9uaQkhGf2BcEgbpDBYx2zzLWO4th0yYkqzXG5iMtzwZAX28r50bO8a6KdyGl6FG8IZSFD84IIoVSSi63Fd7GpHeSlqkWFL8fX3f3mgIS286OEvCFeN3nxraEejry2YZsOmom9aTplwh4WwF5aQZKMk20BdTJRFf7MJ7ggjZIc/ghl6SCetA5yIRnYk3+0wDBybDFx/UMSVQUaHoBKu9Zlgx7R/k7kASJNo068QlKUFaxa+luhIq71dCX9o1vVwQwkMvOnN38vP3n69LSvN6IBiX29ce8HiF857cmepuuImVkRANr58M9a2dmdDjqPTw04+HlKyM8urcIs17DQMs0U0MuDn24htpqF+dayqnP+Kqqup0HW2X1qhTUSz1fbqtUr60THRPMlB7kil7HQffiNuT1QPuYG1OoinMjifsUpn/gAwhaLdNP/Tjp820rTEOnEROy+ZDdwegkydepKlEGAzlIOpFLoy1YPRPkWgLw0RfAsnyuwbIQBLjvb2DnR8kf+ClAjM2HxmpFk5uLt3XuO5aiXVZrtwG7iRsDZduyCAVl+pun0Egi927K4a3msetmw5AUQkF47a8IpaoLvRHiMRiSGZ7xUmg10tTRQUgI8vCu+IupyWDHgHoddlXEdihk2NT6cXrYTXGGib4pt9rFcPufIk2r95TV2Hx4kwwdWw+4zpxB8ftj7D0i0ObmoN+8CefRtRHUP68fRBDgkd2FfHBPEcfbJ+ibdK+84xoRsvsRTRoE7WJRA4CjsRmnyUZeifp9dl88R+GmzehNS3eklGzfSTDgZ7DlKoaaavQamTSti8HWxaSDIAhIqXrk2VVYfKSqC/v+np5F9h7H2ifYhfqZdGVpdF5QRVTVt+QmdHjj5kz8fY4ogR9B/Vg9/VkCpv7Vh1drRA178/YyZurHP+GjVwmP6QbzoQ4MDGxMQOKRr6p2nIf+fMlNBEmi8F//FdOuOGKbXR8DJQQNydch6wlNVhZSRkZCQYm+5maCIyNY7rrrOoxMDXE07d6N+2zi9Z3sDobDqVWKzt/XjxIIoAsrqC/0TrO1MA29RooKHfO0zWoo3ioREU6OdNrZWZROUFZoGrIv2k4o3wdCCGWoBV1pEe76ejxXmtSMGSn+vW2jEPHr9m9QnoLiUecXpmwjLSMOivwzaPPz5zqgrmVAYgSiCOV3qT7UskxqoWrVGxiZYm9pBi0js9g9N6ZV3k2CegMgCILqQ92uXvz/Uf8faAQNf7j9D5fd786abAQB3kzU5sM1CVd/AdsfXbLtNhRu85WWUENfCd+gIsGGAy3TKAoUb05MjazIMoGBAcyb6sjL8dM7lAav/VWY8BbIMZTGVVCDqpBaj8nn1ZCqotymTZ5gjvhGjZqs654o3zw8S3WuBY20+DLsb54i3Sqi988S6FudwqNqTy5+T5DTZy9zZuQMj9U+lrCXW8juixbTf/ZADaIA//hKK7tzd1PgV32vrPMU1M7jx9Dk29BVVnLl2CBBX4id9xUvdXgAam/NQ6MTuXJsEEEUVY/BU6eihJxWpyclK5uWzosoKLyr/F1zpIRjiRtqlKC2cSBfVWWfGDyBr7sHgsFVByQqikLT8UEMWQYOj8f3no4gIAdosrrJG/WjBFZ/499bmsGJKXUxyjpri/HTBlatoL40rq7axvVjTgKhySnQahHTVkfCrwoD59WW0yXsPSLIMmZxe8HtnA+pE5GJFNieu0wRYMqAgt03hJ+e3R3g3n85ijK7lz5HH+dHb7ywRG2EoO7tiXk9omadr6D2XrmCoS5+QOJIh3rvzw8HJP7wdA+KovDbB0oBaHyrH2OKlurcLu5yfJzKzDZOXa3l8pGBmOPYqmpwTIzjmklcDSCaNEuqb0szTeSnGTjVMcHbYxeRBYGDg01gT36xcCW0jjgoNG6lZ7aHcXdidgaazExS3/EQ9hdeIOR0JnU+vUZiR9HKCd4LAy99XZ0oCPQPCXjSQvyH8C8EFZG8+z8F1pJlj5UQBAHe/XWytt+NVggxfCq2e0BfWxPjixuxAfv+2e9wZvjM2s9/ExsOW1U6OqOG7rDNx4N1eTh8QU51rp6kumaofwLGmwlt/SMAxDBBPTLrJSgrFFlNdPT04zRO8WD5A2s+XVbzCLMmOKMfink9Pc8EAkwNq0GJA9NhsnX37yKFtQ+hmSSDEmuqCU1MEJxMPt9iLXAeO4ZgMmHcE9/WIuXOO/HU16sBzauAoig8f3GQ/eWZFKQb+eDeQkQBfnL+2quoQ3YfUurS9h6DJ6+iCBIFe8uZnRhjor+Xsh3LW8QVbdqKKGnoabyIoNVirKvDOtvFSKedUJxFHSkleU9yZgchNR9FUfD39qIrLY15+1jbOPskHZo8E5JZS9vZUTLyzWQWJBYObqxTBQ6eq7G/tfrxetzFWYSGR5J+xs3Hrfm3MqjrZHbCQ0ZqJg7JqoYs3yCQ/X6CY2PXn6AeuQKNz8ItfwipttUdI7NCVe1e/JEqHtlA6KurE7L/cRw+DIIQdxHsWsG0Zw/+3l4CY4lxCCFXIKar3dcRzk+qqMQXDNE4aI/xn063uDDpPOocZpXILkpB0ooMdc6wozgdYLEPNSp/JZr1yGIGxtRpPPUN+JqbMWy9cQISI9BXqIrziLjiesMfFvopVh2+oEyWcwrtfJ/5oXpIK742AYnzUXG3KmQbaSS9JF0d26SDvWVWFEVV5N+IuElQbxBsFek4przUd17h192/5qObP0qOaXkFUqZFz86i9MSJ0oanIOSHPb+75CYrKagvD9rRSSLVuaoNRf/VSXRGDbllifn4BsfHUfx+dEVFlBzYwniwEtfJp6kd+AkaUUAXKqRtui1qwzAfklm7LhYfZ1zqZ9NNJu9lGBgcBFHEkJ/Pq+vcato87KA2b/HfMeAPMdxhpyi8CODvXV3xXLjJisGs5czxqxgkA++vfn/C+4bs/qhXXn66kd+/vZwXLw1xaWCWzUIpfo1MwKASTorfj/vUaSy330EoKNN4eIDizRlkFS7vHac3aanel0f72VG8rgDmgwcJjU/ErIJbbQVMDg+wK2cXRSlFiGFj/yUXLhzDqv2DMYMsYxabMzdzfOB41JtstQT1WK+DiX4nl/RBbOkGPriEehqgebKZjuwgUlBek//VvrIMxrwBTFl68l3lHO4/HLuB1gA6C7iTm0Q2jDVg0pioTK9c9dgAgpOTaDIyrmkK9iI0/RwkHdQ8tOKm76l8D7161YtxSf/p+ai8T/W3TpLwX288dbYXhy/I6cYCTBozP2//+YaOJx50JaUAi3yoZVdsa6Ls8eDr6FjG3qMFQRTJLa/E5Qvy9Jk+HqqzUWg1MTPqpufyJFtuSUX62UcRM4q59y8/SOm2LI4900bzqeHocfIqa8LHS1xFLVqWtvgQBIGDlVmc7prk+OAJUrUW6rxelZRaR0y7/Iw5fOzMUYmIZFTU1o98BNntxv7CL5I+7y1lGVwZtOP0Ld0+HfHPi3yX/q5uZnO34HWH2O3/PqHwLTh3xzpO9EQJ8QPfJc8qMdR0EVp+FX3LUF2Dr6srankVqVma+hrpsfes3xhuYsMgSSIlWzLovTyBLCscqMjCotese+21ZvgccPhvofgAcrr6XImQjwPTaqeF0ThLaFokNUePTlpbK7miKHjPXWCoKoPz4xdi3tPqJFIzDUwPuyiyqgpqRVFAa0C69QMAhDoakjpfpNPsevpQK4qC8+hRzPv3L9l6bzl0CGQZ5/E4lmcJ4ELvNH1Tbh7ZpdZvtjQjd9bk8Nz5AQLX2Ms+NOtHSlv6dzDcqtYdBbtK6K5Xv+OynXuXPabWYKCgdnNMUGJK9xmCYV/ahVhNaKaqoM4nNDmJ7HTGKKhDssLptnG2yCKG8nRmJzxq0Nq+3ITrQk2OCU2mIcbmQ1ZkGscaMVerC9f+jo7kxjwP+237mTANgQJbU4z0k3dDeVAHBgdBUdAWLmE/d63w5l+DIVX1q18Ldn0UpruhZ3XX5HpBX12Fr6MDRV7+Ona+dRjj9u3XtfMz4kPtuXBhhS1VyK5ADCcTUQDry8toGprFH5TZVWxFkRWGO2ew6dugYA9ojUsdckVIWpGckhSGO+zkpBgotBrjEtSgChrlrL2YDP3Is7MogQDGJTJmNhIamw3BaMSfRID6emJ2WF0snjaqVKthamzOfxpguAHyr2FAYgQV4W6BzrfILEohqCj4HAo7i6xoJeGGDUq8SVBvECLhEc8c/QVp+jR+t25pEnk+7tmUy+VBO6OzK6iBZRku/ACK90POpqU3i+MZOh9XBu3U5KWg04goikLf1SkKa62IcVS/8RDoV1vAtYVFlNSpis/e9I+hffXP+XBGK25XLu6gm0HHYmXaci3YicLpC3LSoU6wA0NDK2y9GJHwinu2FnCqYxKHd31aIcYdPiacPmrj+E8Pt88QCsqU7MxHNJnwr1JBLUkihdtTkfqsvLv44aTsJkJ2X0yYyx8cqiA7Rc9XXrpKlseE3Rzgzb43AXBfvIjsdmM5dAetb4/gmfWz4/7l1dMR1B0qIBiQaX17ZM6Hep7Nh5JuQG8P8a7yd6mfKUxQy0v5UDtHwZKrtrUAtxfcTuNEI7MtV0CjQb9Kj6ym44OIGoFXnI5l1dMAF0Yv0JOrFufe5uZVnQ9UEgkgkKbF5i7naN9RQgtD/MxZq1JQb83eiiSurR0rODlxfe09ZBmuvgCV94Jh5d/yocJD+DPU62sqXaI2s3b5HaruBRTofGvtY10l/EGZH57qoa4gFRQdueKtvN77Og7/4tCjjYRkMSNlZS0mqN0BBL2EoFGvP29zC8gyhiUI6qH2VrKLy9AaDDx/cYBZb5CP31YKQOORAURJoE77ghom8ujTSJYMHvjkFoo2WTn8RDPtYY/9nLJyREliuL0l7nniQTRpUQIysj++XdDByixm3H6O9Z/gQMFtSOV3qUqh0Pp5HreOqt/rweLtWLQWzo6cTXhf47ZtGLZuZfqpp5K2gdlbmoGsqITNUogG9YRrA39XJxMlBxGFALt0h+nPexit3oB1viJkPaDRYzvwMOM+M4GffBy6w6nxNTUQCODrVsmFCVEde51xMx+o+cD6juEmNgyl27PwOAKM9cxi0ErcVZvDa02jhJLJXrnWOPl1cI3DA1+JEn5Sinqd9E+pk9L66ZdJ9WZRWxHf2zUZBAYGCA4Pw646mqeacfpjFaUZNjNTwy6KM4y4/SEmI52Rt34IgNCVY0kpHPUbQFD7OzoIDg0vq2w0bN2KlJGxapuPn10cxKiVeLBuzm7qsX3FjDl8vNVybQOJF9bU86EoCuPTEhbRicGspbvhPKnZuWQUrKyqLdm2k/Heblwz0xh37iA9bJk4FMeHWlVQJxGaGfCqAoi0guizXlc6R1BfGbRj88hoFdBXpNF2Tn0eV+1NzN4D1MVgw5YsfJ326DOnY6YDR8BBwTY1lNi7hmDMktQSpCz1eiiVtLQEslFuIA/qiI3kdfWg7j0F7a+q2Q9G69qOtek9qo3oKhbvz42c4x/P/SNBee01laG6GsXjiXIO8RAYHcPb1HTd7D0iMGzahGAy4T6XWDek7ApEhQEAvvYOtAUFiGZzVO26qySd6RE3PlcQW/Dkmuw9IrBVpjPR5yDgC7Gz2Bo/KBG1JpS1Noyb5wRfN6KCWhBF9GVl+Do25np3hUOSO0UZi+JHmJmeI6g9M6rVkG3HtR9ISh7k1kHnW2QVWggo4A/pMGhF6grSbtigxJsE9QYhs9CCqANHb4hPbv0kKbrEkorv2aSqrFcspnqOqUEQu5cnvkNO9UYoSItXuxVF4crgrEqWoHrcOad9Cdt7APj7ww/fokIyC8xYrHp6je+F3C38H/c/oBlV1Sat04uVb5JFS8gVWJMH67nuKaY1JhS9nsDQ8Mo7LBz/4CDagnweqMvDH5I52rY+ifItI6q6YbNtsYK6v3kKUSOQX21FW1KCv3/17Yc92Y1oZR13856VN56H0KwvRu1h1mv4s/urudg3g31okpBVz6+7fw2A8+gxBK0W0959NLzRT3ZxCoU1iRU92UUp5JWncvnoAJqcXHSVFTEEdY80ij4ocShrPzA3CQwtpax3DKs34zBuK7gNWZEZu3wOfVkpwipCMXyeIO3nRhlIFclcQT0NcH70PLrSUgSDAV9z4oTZQhRnmMhN1TMghJC8egIOaBhviN3IlKVOlBOEO+Cmbbptzf7TAKGJSaTrSVAPnlfbTTf/VkKb6yQdu7bcC4CUn7eyvY1tp/r33EAf6pcahxid9fFn99fwyK4C2jo24w15ebn75Q0b01LQlZQQ6FlIUAdjlB/epiYADFsWqysUWWakow1bVTWyrPC9kz1sL0pnV7EVnydIy6lhKndmYL76Xdj8MGSrxbBGK/HQH24jryKNN753le7GCbQ6Pdkl5Qx3JE6oRHyyl1oEPVCZiagfYcY/ycGCg2on0uzgutrAtI6oBPXmvHR25+5O2s7F+pEP4+/uxn369Mobz8OuEiuSKCxbmM7ZtagTJW9nJ6P6Eop1F3ky/38x6/CTU1aBuMaFrniwbdqGrAiMaqvg6cdg8CKGmjBp1tqKoij8zcW/JSAEeSjnPkThZin7/wqKN2ciigLdl9SF1we25DLp8nP+RlH5+Bxw5tvqc6hgN6FZP6JFG/ULHZj2IIgBzrSfQlQkioqy13xK9xnVwib/9vuQFXlRHZCRb2Zm1E1Ruqqg6wuT5ILBgKgPqZZo87oRVoImKwspMzOh0LH1gvOYuhAVLyAxAkEUsdxxB87jx1GCyZFa3kCIlxqHeLAuD4t+jvy5qyab3FQ9z5y9djYfSlBGdgaWDEj09/VhNxaQkyMRDATovdxA2c49CamQS7er3sC9jfUYt29HG3STbvIx2DazaFspTY/iV62bEoIjLOpJLcDfEyGoS6NvH2sbn/OfLk2l7ewotso0UjOTU3Iat2SCrOAJe2c3jDUAsKXubgSjMSHrhqUgCALbyjcRkLxYA9AVykVwDIPftepjriciBPV1s/hQFHjjy2DJg31/sPbj6Uyw7QOqnagncauAHnsPn3nrMzxx9Ql+dPVHax5GIkGJziNHALDcdeeaz5cMBK0W086dCQclhhYoqH2dnejCAYkXeqcpzjCRk2JguHMGAJu2CUoOrHmctoo0ZFlhtGeWnUXpDNm9jNgXiyFFs9p9qHv83xB1MpJJuzEe6glAV1mxpk7mtcA75UVWFBrtTvYY1EWyKEF9PQIS56PiLuh7m9Q0mYCioGhTCI6Nsa80g8YB++JAzBsAN6v6jYKgMJHWT5GzmkdrH014t5rcFArSjSvbfJz/vroyuvnhZTdb2EoyHwPTHuyeQNR/ui/sEZZUQGJ/P4giWpsNQRAo2ZpFf+ssoQ88TUhr4V+930dEjOtDLVq0EFJQvKu/cE51TqDTSOhs+QSGkyeoAwOD6AoK2VVsJdOs47WmBP2/V0DLsEpM1MYlqKexVaSj1UnoiosJrNLiIyAHeHb2hwQMHhxXE7dhUAIysiu4SO3x/t1FbM7WE3RMU1CkhnqNukZxHj+Gae8eejs9zIy62XlfcVK2D3WHCrGPeRhomcZ84ADu8+eRfT4CcoBzfpXgCkyofy/RrAVhGQW1YzSGoN6atZU0fRpyZw/6qtXZe7SdGSHol3nT71pRPR2SQ9SP1rM7f68aNNSyeoJaEAT2lmZw3qFONvNcZRzuW2DzYc5KKiTxysQVQkqI7dlrbysKTk5eXwV1089B0idk7xHBQ7sf4z/fKRJ6190rbyyKavhi55uqWvs6Q1EU/ud4N1U5Fg5VZ/OpOyvxu/NJk4p4vv356z6elaArKVmkoA4tUH54r1xBys5Ck7PYvmpqaAC/x42tqpbDrWN0T7j4vdvKEASBllPDBHwhtudfAp8dbvlUzL5avcS7/ng7WUUWXvnvy/RfncJWVc1IZzvywi6DJRBRBi9FUOekGMjL6wHgYP5BqHmH2p1x/vsJHT8RtI46SDNqyU3VszdvL72zvYy6En/GpD70EFJGBlNJhiVa9Brq8lOXDUqcr6AOzdqZcpvwSmmc0xrY/cBHGO/pJrd8bTZBSyG/KmzZUvUJ1R/+yfehswQQtFq8ra281PUSx4eOEzKCObD6ttabuPFgMGuxVaXRc1l9rt1Zk4NOI/LqOtVea0bDj8E3Cwf+BAhbN6TMEY/9024yc66imVV/l+l5i0Owk4Xr7FmkzEzq9jyERtBwfiSW6LDazMghhczwlC6i4gaQMlMJaYrh8N8l9VzTV1fFeL5faziPHkNfU4M2TpjufFjuPIRst+O5dCmp47/VMobDG+SRXbFWChpJ5AO7izjaNs7gzLUJwo2q7JdQUI8ev0RAayF/az4DzVcI+nyU71refzqCnJIyjKlp9DTWo8nMRFtcTIa3n+FOO6FA7PcdIcgTtvmYjRDU+fh7ekCjifFQPdo2zu16A1qbmakpH9PDLqr3Lf/9xYOuKAUxRYf3inrNN4w1kGHIoDitBH1FxZosPgD2F6g2H/KMgx4lPL4bxObDPzCAoNXGrZGuCdpehf634c4/XzKbKmns+hgEvXD5pwlt7gq4+Nzhz6ERNdxqu5X/avivNdt06SsrQRCW7fpwHj6MtqAAfVXVms61Gpj27sHX1kZwenkSX1EUZFcgKqBQgkH8XV3oKytRFIXzvdNR/+mhjhmMej9p2gkoumXNY8wrTwMBhjtm2Bn2oW7oXzxe0aRRgxwLd5G6r5IU2wzCBgdlLgV9eQXB4WFCzuu/IOW3+wiJAldHnWzXqs+WOYK6Qf33WgYkzkfFPSAHEPtOokgCaM34u7vZW5qBPyRzqX/m+owjCdwkqNcIf78D2Zt8e8prva/RYbhEqisHxZv41yAIAvdsyuFkx8TSKx6OUWh5CXZ8RPWpXQYhZyAaNrQQVwbDAYn5EYJ6CmueiZSM5Y85H/6BfrR5eVHlamldJgFfiKFRMz0PfJ88xUlhCFonry7aV7Ss4DecAE51TrKrJB1dQfIEteL3ExwdRVtQgCQK3Lspl8Mt65Mo3zwyS26qngxzrKLCZfcxOeikaJP6ANIVF+MfHExaLQLwes/rjHlHydtmovfKJD5PYscIhY39FxbTkijw2T3quHy6zSgovHX2GfwdnZhvv4P61/pIyTRQsSs5xVDlrhwMFi2Xjw5gOXgQxefDc+ECpwZPMahVH47Tw6oFjCCJKmGyJEEdq6CWRIlD6XuxTLrRVSdflCiKwpXjgziMAkKGfkX1dNt0G46Ag925uzFs2oS3pWVNHQC3lGXQ4vUiSgLblVs43H849nimLDUMNUFEAhK3ZW9b9ZhA/buEJifRZF0nglqWoemFsL1HYv73AJszN/PQH/8jH9z/+4ntUHmf2tI6VL+6ca4BpzonaR6e5RO3qyRtWZaZd24rYHp0J02TTbROJe6vfD2gKykhOD6O7Jor/GR3IMYuytN0BeOWurgLVkNhOw5bVQ3fO9mNLc3AQ3V5yLJC4+F+8spTyen8uhr8UrTYi1Nn1PDuz+zAmmvm199sxGApIuD1MDWwdIvnfESee7J76fuiIbUDxWcjRZsBkhZ2Pq4qqGcSO8dKaB1xUJOboi5G5amf8dxo4j7Uol5P+gc+gPPwYfwDyQU47i3NoGFgZsk6IvJ3EU0S/ic+w3j2dlBkrhbvpVTjIuj3kXeNCGpTWjppuXkM9w3Bx34Bkhbhx+9DV1aC4+pl/v7s37M9ezuW9LR1yam4iRsLZduymRpyYR/3YNFruKMqi1ebRtb0LF0XyDKc+RYU7oVClUAMOfwxtVL/tBsh7SQVymYArLlrI4EURcF95iymfXsx68xsydqyqNMiw6aS4Aa3WpvGENRpekLGKhhrUi2yEoShukb1dA1de2VVyOHAffHisurpCMwHD4JGg/NIcjYfz18cIDdVz4GKrEXvfWhvEQrw7Ln1ua8vRGg2fk0dwdAlVUVbeGsl3RfPodHqKNqcWMu8IIqUbN1Bb2M9iixj3LGdlJ6zhOL4UM8R1AmGZs7OU1D39qIrLETQqAvQs94ATX0zVARAX5FO29lRRFGgclfyRKsgChg3Z+Btm0YJhKgfq2dnzk4EQUBfVbUmiw+AW2y3MGkaxD8RpEcO249MbYyqciECA4NoCwoQxOtAx8ghePPLkFEOOz+6fse1bVf/u/DDFa2EFEXhr078Fd2z3fzzoX/m7277O3SSji+e+mLcLKpEIZpMaIuKYjKM5kP2eHCdPo3lrruub25OGKZw8Kvn4sVlt1N8IQgpUeGgv78fJRBAX1HJwLSHcYePXZGAxA47+aZuhPwdoFv7QqjBrCUz38xwp53N+anoJDGuD7VoUhXUiqxg+/YvsH1wO7z0OTXE/gaDrqIcAH/39b3e5ZBMyB1A1kqMO3xUyeq9OEpQDzVcn4DECIr3g8YAnW8hmjSIWgO+rm72lKq/pZVC0zcCNwnqNUAJyUw+cZXhvz/H7Bu90VChlRCQA/z7xX9HtKlFwnCnPanz3l2bgycQ4nTnEsRUw5MgB2H376x4rPkrdQtxedCORhSoyUsh6A8x1D5D8ebkLqZA/wDaoqLo/xfUWpG0Ij1XJiirO8Bng5+m1uOkdeC0+vCch5VasFfCtMvP1eFZDlRkoc23ERhOzoM6MDwcDq9QScn7t+Sq4WVda082XyogcaBFJWSLNqk2KtriIggECIwkHxL0ZPOTlKaWcsehHYSCMt2XErOCCEYJ6sXtiMUa1fvwpU4TNdbNDLz+SwCcFfsY6bKz497ihP3JI5C0IpsP5tPTOIFcuQ20WpwnT/Ji54to01MQJYmZkbnvTkrRqi2rCxHwgHcmhqAGOBRQfafHbMkr7Ua7Z5kadHEaH390V8Wy6mlQ/acB9uTuwbCpFtluV70jV4l9ZZnIAkiZemyucvocfXTZ5z1ozZmqgjrBiXvDeAPlaeVJ+ZHHg+xwoAQCSJmLJ3vXBANn1XbTLe9NajdBEHhn+TvJNCZ436q8BxDW1cYhUXzneBdZFh0P75hTeP3xXRW4p7YjouGFjheu+5iWQyQsab5HvuwOzrtvu/B3di3pPz3c3orBbGFEsXCyY5KP7S9FK4n0Xp5gdsLLts0zMNmxSD09Hwazlvd8dgeWDAOXj6n3hKEEgxIjSu/QEs8XV8DFVKgVv6OaixEvvl2/rV5rF9fekqooCm0jDmrCOQQ11hpSdClJBSUCWB9VfWZnnnk6qf32lWXgD8o0DsSvP0IRBfWZf8R79lXGs3cyLPj4+N0VjHapirbcimunRMqvqmWovQXFWgaPPw8BFwahG3vTJbxBL3998K+RLLolv7+b+M1F6Tb1ft3TGLH5yGNwxsOVwcXBb9cV7a+pxNatc/ekkN0XY93Q52jGK/ZRp9mFOU2HzqiJd6SEEejrIzg6ivkWVSG3O3c3TRNNuANzJHR6mAR3jnvISdFHLT4gTFD7jJBdC0f/MeFaQV9djeL1rjoDJRm4Tp6CYBDLnYdW3FZKScG0e3e0XT8RTDh9HGkd57d2qmKThSjKMHFbZRbPnu+/Jl7nIXtEQR3f4mN02I9W8ZNhs9DdcJ6iLVvR6hcLgVy+YNwwx9Ltu3DbZxjv68G4YwepfSoJNrjAh1oMB3lGxrMi7CpxTooNf09PjL3HqY4JahQRjQy6slTaz41SXJeJYQnB00owbslCCciMXeljwDkQDbXWV1YSGp9YUXm6HDIMGWizZYSAhGgKf4YbxIc6MDBw/ewRLv8Uxq7C3X+lLrivJ3Z+FEYvzylDl8B3r3yXN/re4E93/ym32G4h25TNF/Z+gYtjF3mm5Zk1DUFfXbWkgtp1+m0Un++623tEYNi6FUGnW9GHOpoLFq6jfeHuAX1VZbQO3V1sxTntxTHpxRY6vS7+0xHYKtIZ6bKjFUU256fGJ6jNWlBA8QZB0sAHfggpNnjmIzC7+vnufPTP9nP/T++PzqlXC32FKqLwdV7f631mzINWgYBO5UMKfDNqp0R2eM58vQISI9AaoOQgdL6FzmpEK4rMdAySbtLxV+/cxO1Va7ciW2/cJKjXAEESyfztLejL05h9o4/hfziL/fXeaGvsUni+7Xn6HH187M4PIEoCwx0zSZ331vJMTDqJN1vitDxGwhFLb4eslSePsssfVSovxOVBO1W5KRi0EkPtM4QCMkVbEvefhrCCumju4avVSRRUW+m9PIlRJ9GXdQifZieD+HG+9r9j9o0q3FapkDrTPYmiwIGKTDQ2G6HxCWR/4mrswKCqSIuseB2szMKkk3htjYnygZBMx5iDWtti3/H+q1MYzFqyi9T3dMUqCRRIcpJwafwSlycu8+FNH8ZWnk5KhoH2c4mFwCzXjjg9NACCwKiQgtazm7zGIYT8PK5cCaI3a9h0wJbUOCPYcns+CtB8YRrTrl04ThznSP8RHqx4iLScPKaH5tSBYooOOZ6q3hm+HiyxBPWmGXVl+Zwp+Rbhy0cHCIowmanlg3uLVtz+/Oh5CiwF5JnzMNSqoXxrsfmoyrGQbtIyqQdl3ICgCLzVNy/Ez5QFIb/qi7kCFEXh0vglduTsWPV4IghOqIs0101BHbX3ePDanseUoSrjEvCh9nuDjPWuD2HSMebgcOs4H9tfikE7twhSm5fKPdXlyK4tvNj5S/yh1XeTrDciYUnzbT7mh7t4W1pAUTBs2Rx3/+H2VvIqq/n+qV6MWonH9qnX16W3BrBY9ZRPfku9llewqTKl6nj4czsxpeUgCAZ6LjUlNP6VFkDPDp8lpARR3DWc6ggvSlpL1EWM+ifWHJY4ZPfi8AWpDhPUkiixO3c3Z4cTD0oE0NpspNxzDzPP/RTZu0J48jzsLVWf5UspJ2R3EEQZ4fTXGDM+gMeYzVCmnvs25zHa1Y7OaMSat84BifNgq6rBNT2FY3Ic8urgw8/RY/VgmvXzmaIPUZ5WjmTWxn8W3MRvNNKyTVhtZrrDBPW9m3KRRIFXmtZn8rtqvP1fkFqgBoOhilRkVwAxbPHhD8rM6o6gFUykurPWx94j7D9t2rcPUBe/g0qQxonG6DY6g4aUDANTw26KM0yxBHWqDtkTRNn3GRhvVgPSEsBcUOLa1KuJwHnsGGJqKsbtiU3YLYcO4WtvTzj4/JeXhgjKCo/sXJoIfGxfMcN2L0fb1j8sMaqgTl1cUwenp5kmk8zUADOjQ0wPD1G2c7G9hzcQ4r6vHeUvn7+86L2SrTsA6Ll0EdOOHWiDHqyp8iKCOqqgXqoDcSFmh8CQhqJVw9oji9Kg2nvcKulAgKkQuGZ8VO9LPBxxIfTlaQgGifEGtZ6I1Kn6cOfjWm0+ysrVuUmxKYUZIe0GUlAPoC0sWHnDtSLog8NfgbxtsDk5oUdC2PoBVaF5cemwxBODJ/jGxW/wUOlDfGzzx6KvP1zxMAfzD/JvF/+NQWdynWDzoa+qwt/bG7cOch4+jGg2Y967uBvvekDU6zFu376iD3VoAUHtDxOr+vJyLvROY9ZJ1OSlRIWNNu1lKLlt3cZpq0wj4A0xOehkZ3E6jYMzixbFouKOSPehKQMee1qdh/7kcTVcdY14uvVphl3DfO3819bUOaUrLgKtNvp3vF6YGHCgFcAdns6lz06izc9XOyUiAYn518neI4LKe2CiDb1FRivAZL8qOPzE7eVsL0q/vmNJADcJ6jVCV2Ah62ObyfnMTvQV6Tje7GP4H85hf60nLlHtDrj55qVvsitnF3eWHSKnJDVpgtqglbitMou3mscWX7idb8FMnxrotAIUWVkUahV9T1FoGpplazggsa9pCkkjkl+VnvA4ZY+H0PgEusJYYq90ayb2cdWvuC4/jbed6s21vf57Mf6eEQJhyUC8FXCqcxKTTmJbYTpamzqRDiahRPaHCWpduHgwaCXurMnm9aujyGtQWXSNuwiElEUBiYqi0N8yReEmK0JY5aErKVbHkiRB/dTVp0jRpvBwxcMIgkDV3hwGmqfwJDCZj1p8xCmmp4YGSc3K4dH95TRcyqeuV6FzSxU9jRNsvbMQrX51gVmpWUZK6zJpOjGE4dYDBFrbMTp8vKfiPVht+VGLD0BVzcUrsB3h7zYlliTXdg/h04u86W9cvM8yGOmy03ZmlAZtkD+4Z2X1tKIoXBi9wJ5cdXKhr64GQcB7tTmp886HKArsKcmgxe8j5JfZq7uNw/3zfKjN4dXYBHyoe2Z7sPvs6xOQOKme77p4UEfsParuA31iYbJrQuV9MHhhReuUY0+38dxXz3PpzbW3BX/3RDd6jchHbile9N6n767EPbmbWb+dt/rfirP3xkBXHL43hcOTlKAavhSx+PBeuQLED0j0e9xM9veRWlzJLxqGeN/uAtJNOiYHnQy2TlO3R4/U/Trs/QRoVg42tVj1/Nbnd6Ex5NN18TLTIyv7zQkGjepnvwRBfXLoJEaNkbrMbZzomHd97f5d1Uqo7ZUVz7Ec2sIBibV5c7/pfXn7GHAOMOxMjoizfuQjhOx2Zn/168T3MeuozrVwZgkfarnvKpI8hVD3CG3BXaDI3HFPGZIoMNrZQU5ZxTVtS86v3gTAUJu6wDedU80PKtV2xPe+9SvwuxEt2psWH/+PomxbFsPtM/jcAaxmHbeUZWysD/VoE3QfhX2fjKoPQ84AKHPK2OaxQaTUy+xMvw/7qBdr3to9Xt1nziJlZ6ErUzvBdubsRBTERT7UGflmpoZcFGeY6J+a81KOCA1ChQ+BIQ3Ofzeh8+orK0AUl/V0XQ8osozz+DEstx2M2keshIjS2nk0MZuP5y8OUleQGu1WiYd7N+WSZdHx9Nn1t/kI2f0IOhHBsLiGtJ+px2W2YavKoLtBVQuW7VxMoj11po8hu5fnLw7QNe6Mec+SkUlWcSm9jfXoq6sRTCay5BFGOmcJzrNwEnUSgkGK1vgrYnYIUgsIjo2heL3oykoBtdY91jbBbXo92nwLbZfG0eolSretvqNO0IgYazMwdssYBAObMtT7f8QveK02H3s2bUVBJlOx0yXnokxuvII65HAQstvRXQ8F9YUfqNzAvV9U81bWG8Z0NTj28nPgdy96u3+2ny8c+wJV1iq+dOBLMTYbgiDwxf1fREDgy6e+vGpC0lBdDbK8SC2ryDLOI0cw33571G50I2Dauwfv1auEnM4lt4lYq0X4D197B9r8fESzmfM90+wsVgOuhzvsaDQhsrR9ULy8/7SiKIx2J/Z7z6tQu2uHO+zsLLbiDcjRMO8IxHjijtwt8N5vqWH2v/rThDt14sEb9PKLjl+QYcigcaKRYwPHVn0sQaNBV1KMr/P6LkhN9DvRiQITgkx+mgFGRxYHJNp2XNcxUaFmMZmFATSCwPT0BlumrYCbBPU6QZdvIeujKlFtqErH8Va/SlS/2hPTgvrE1SeY9E7y+d2fRxAE8qvSGOt1EPQn5/N276ZchuxemocXKCcvfF9VVda+e8VjyO5wcR2nJWvI7mXK5WfrvIDE/Go1uC9RRNOJi2IfviVbw+2blyfYUpDG5LTaWtBasA1+9f9Bx5vAEjfBJHCqc5K9pRnoNGpII5Cw4kId/yBIEprcOVXA/ZvzGHP4uDQws6oxATQPq6rLhRYfU0Mu3HZ/1N4DQJOTg6DX408iKHHENcJrva/xSNUjmLTqBKlyTy6yrNB5cWWbj5Ddh2DQIMYhm6eHBskoKOSz91Sxc2oKQwAu62uQtCLb7lxbkVV3qBDPrJ+JvN0A3D2axebMzSpBPTqMEg74EcMWH4uKGEeY1Flg8eFra8NTnEPj5GXsvsTsdEIBmbeeaMarhY5sKSH1dOdMJzO+GXbnquMXTSZ0paV4W+IT1MFAnM8QB7eUZdDoViece8XbuTxxmTF3WOVjCk8IEvChjiSjr1dAIoB0PQjq/rfBOZK0vceqUXUvoKiLfUtgdsJD27lRDBYtJ55r58IrPas+3YTTx88uDvK+3YVkWhYvCu0oSmd//q0QTOdnrTdOWKJoMqHJyYkqqKOexeH7tudKE5rcXLRxwn9GOjtQFJkrgTT8IZnfPaiSL41v9aPRimwRngNJl5BNVQSpWUbqDu0iFJjghX85g318+cArQRSiXnoLoSgKJwZPcIvtFm6rzKNxYIZZb3i76gfVRbALawtLbAkX/dU5c6TJSj7UiqJw+oVOXv72ZbzznoumW/ahr6pk6qknk5rc7S3N4ELPFMGFbeNXf4HcdxXRoMB7v81YMBfRO8IHbyshFAwy3ttNbvm1DRrKKi5Fo9MzHLZs+Ydz/0BLpvobC7R1wHO/jWSS1FDfJOunm7jxUbotC1lW6GtSF1AerMujY8xJx9jK3ULXBG9/EzRG1eYnDDnSbRZezH+29acIQoh35D6C3xNcM0GtKArus2cx790XJXQsOgubMjYt8qG22szMjLopTDcybPdEs1KiqlmPCNs/DFdfBOfKdaBoNKIrLsbXdm2zD7zNzYTGJzAn4D8dga6sDG1xMY4EbD7aRh1cHrQvq54G0GlE3re7kLdaxhidXbsCcD5UGxh9/CyGs+0giBTcUkF3/Xms+YWk58bWsB5/iG8e6WR7YRo6jch/Hl5MNpVs28lgSxPBUBDj1q2kDtYTCsqMdi/0odZHf7crYnZwLiCROVuvznEn4zMeir0y2tJUOi+OU74zO6n5YTwYtmSh92t5l+YedJL6u9Xk5iJaLGtWUO8t2s2sYRKdy0u3nEtoYuMV1NE58rUmqH0O1d6n9HY1LO1aYdfH1ADZq7+IedkdcPPZI59FQODf7vq36Lx0PmwWG5/f/XlOD59etZ3dUl0f3qarBMfHSbnrzlUdd71g2rMHZBlP/dIZN5EF96jFR2cnusoKnL4gLSOzUf/poY4Z8swDiLYt6sLjMrh67C2e/IvP0tOwsl1GSoYBi1XPcOcMO8Oq2voFAXpSJGB8Ye28+T1w6M+h4Sk48+0Vz7UUXu15lVn/LF+9/asUpRTx7/X/viZ/cn1F5QYoqFWCetgXZJMtlcDg4OKAxCQU1FePvcXzX/0ioVXkkUWRXQspNrQulZNwC9akui6vN24S1OsMXb6FzMc3k/u5XRiqrTiO9DPyD+ewv9LN5OQY32/6PncX3R1tX7JVpiOHlEVFxEq4s1Yldd+ab/MxOwStL6tBTgmozhZ6Hc1HJCBxS0Eajikv0yNuijcnae/Rrz58dUWx5F5qppGMfDM9lyepy09FCaZhlCy0le9XL6DnfgdGryJoRASDZlUhiWOzXjrGnByoUAk0bX6EoE5cmRYYHFQDHuepOu6qyUEjCmtS8jSPzKKTRMqzY9s/+5vVidh8gloQRbRFhUkpqH/S+hMUFB7b9Fj0taxCC+m5JjrOrzzukN2PJn3x70dRFKaHB8mwFZBp0fNxaQinIR2Tayu5O/UYU9a2Ml28OYPULAOX23zMGuGekUwEQcBqKyDo8+GcVv8+kkUHYbVmDBzhzzaPoFYUBW97Oym1W5AVmdNDpxMay/lXepgedpNpgG8ZUvC+2Y+3fRp54TnnIeo/nTfXnmnYVIuvebHFx0BLE9/46Pv4r098mKf/7xd47dvf4MKvXqC74QKz42NRMh5Ur9hpUUHUi9jcFQAc6T+ivhkJWEhAQX1p/BKpulRK00pX3HYlRC0+rgdB3fSC2jpY/cC1PxeoqcqmLNVvdAk0vN6HIMAH/nIPVXtzefuFLs78smtVyo8nTvfiD8p8PEzSxsOn76rGN72bt0feZsiZnJf+tYSupGQeQR15noQtPq5cWcZ/Wr0mnusVubs2h4psCx6nn9azo1TvtmJo/pHaMmpJzhetbEcdoOBzD/GLf6vHMbV88SWatXEXQPscfQw6B7kt/zYOVmYhK/B2JPNB0qh+ix1vwnTvon0TRduoA1uagbR5oZLV1mrS9GlL+lCf/WU3F1/ppat+nJ/94wVmxlSlkiAIWD/yEXxXm/E0NCQ8hn1lGbj8odiF7s634GefQNblI9rKaGyewmvIIc3ixKTTMDnQRzDgJ/caBSRGIGk05JZXMtzWwpH+I/yq61c8uv/3kbKz8KUcgPbXEFvV5PibKur/95BblooxRRu1+bh/s/pc3xAVtWsCGp+F7Y+q7cxhRLvNUnQE5ACHh35B0FlNuaTWm9bctVl8+Lt7CI6PY7olViG3O3c3l8cv4wvNKWEzbCZCQZl8rRZZgaEZdYEuqqC2+9XOSjmg5tQkAH11Nd5rrKB2HTsGgoDl9tsT3kcQBCyHDuF++wyyZ/mFyOcvDiKJAu/ZsbId0aN7iwnJCs+dX18VdWjWv6T/9HCXAxSFrBIL/VcvUx7H3uOpM71MOH38n3du5iO3lPBCwyC9k7FdQqXbdhIKBhlobsK4Ywemq8cRBBhsXWzzEUqYoB4KE9Tqcy5CUB9tm2ALEqIMdlHA7wlSvXf19h4RCBVGfIKfO9xzf4NIUOIi0tHp5OiT38MxuXLtC2DQGJAzPWjtWrrlPDSu4bhK3+sJf4SgLrjGBPXb31TnCPd+Ca5lQGDJAcioiMnoUBSFL576Ih3THfzjHf9IUcrSYp8P1nyQ3bm7+adz/zQnwkkCupISBJ0O3wK1vfPwYRDFpBbBrgWMO3aARrOsD/X8OloJhfB3daGvrOJS/wyyArtLrPg8QSYHndiUc6qv8DJQFIWLL78IQP1rv1pxjIIgYKtIY7h9hoJ0A1kWPfV9sfeQiMWH7IpDlh76C6h5J7z6v6EruSDbCJ5te5aytDL22/bzqe2fonW6ldd6l56PrQR9RTn+/n5kX4KdI+uAiQEHGmDA46cuS09ocnJeQGI9pBfH1BLLQZZDnHz2KbobLnDp9cS7JBdBEKDibsTJBgD8KflJiR+vN64pQS0IwoOCILQKgtAhCMJfxHlfLwjCT8LvnxEEoTT8eqkgCB5BEBrC/33rWo7zWkCbZybzI5tUorrWiuPoALNfu8qjg/fz2dpPR7fLK08DQV0NSwY5KQa2F6bxRvO8m/jFJ0AJwe7fXnrHeQg5lyeoJVFgsy2VviZ1Yp50QOKAWuRpixY/kErqMhlun6EywwQIpEsltM52w4d/Aloj/PhD4BxDssQnEFZCJMgwktitycsDQUgqKDFeeEWaScut5Zm8dnX1PtTNww4qcyxoF4QJ9jdPkZ5rIiUjNhxFV1xCoC8xIsQT9PBc23PcVXQXBZY5XzPV5iOXwfYZXDPL36RDdl9c/2nn1CQBnxdrvvo3Ke1q5Er1byEqIu3FyfmmxoMgCtTdUchkt5dLlQXkXhlBURTSwz6nEZsPKWUJHz3HMIhaMM7d9INjY8h2O3nbbiFNn8bxweMrjmNy0MnFV3rxmOFeSUtGCBxH+5n47hWGvnyasf9sYObX3XhappC9cw/o86PnyTHlUGiZ+83oazcRGBwkNBu7AHX+l8+jt1iovvUgoijScf4MR370HZ7/6hf5n09/nG/8zgd44i8+y6//419wnX2FTf4e3LoZPIMhilKK5qwezGECz7WyIurS+CW2Z29HFNZ+2w9OToAoIlmtaz7WspBDqhrjetl7gNr+WHkPdL6p2ossgHvWz9VTw9TcmkdqppF7f3czmw7YOP+rHk4/35kUSe0NhHjy7V7uqc2hMsey5Ha3lGWwyXI3oPB82wur+FDXBrrSOYI66p1n0hJyOvH39CztP93RiiYjlyGvGCXmr54YIhSQ2Zb1NgTccMsfJj2evEpVQVO9F3yuAC9+vQH3MpNxTYYBb6edwFjsRPXE4AkADhQcYGdxOgatyKn5ocS7PqYWexd/mPQYI2gZcVCdG/ubFgWRPbl74hLUDW/0cf7XPWw6aOO9/99OPE4/P/uHCwy1zwCQ9u53I1osTD/5VMJj2Fem3ivPdIc/28B5eOZxyKxCtlQhWgy8+bxqjbQn7C862q0q2fKuMUENqg/1aE8nf3vib6hMr+STWz+JoaYW72QI7v0S4uAbQBKeqjfxGwNRFCjZmkXvlUlCIZm8NAM7i9N55craMkBWhfPfh5Bv0T0p8ruTUnUc7juMIzhJcOYAklOtC9LXqKB2n1XrKtO+WMuHPbl78Mt+Lo/P+RFbbSoZbg2qBFTEhzpCjIZmfZBdo/qVnv9+3GfbQuhrqgn09SO7rx2R5zxyFMPWrUkvdlvuPITi80U9uuMhJCu8UD/IndXZZMXpTlqIsiwz+8sz+cn5/jXZ+C0aR1hBvRCyz8ekx0ia3sNIZxOhQGCR/7THH+JbR7s4WJnJvrIM/uCOciRR4D8PxyqKCzZtQdJq6W28iHHHdrQ+BxmZIoNtMzHbJUxQB/3gGoPUAvy9vQh6vTqPAo61jXOPyQgCtPU5MaZoKaxdey3YNNvMRXMzpSM5MXWUvqoKX3t7zGsXX36R8798nue/+kV87pUtvUAV6pg96XQQniNMd695zGtBYCCcc3QtPahdk3DyG1D7LjVf5VpCEGDXR6HvFEyoJPGPrv6IV3pe4TO7PsPBguXJVFEQ+fKBL+OX/Xzl7a8kLfgQNBp0FRWLbIkcRw5j3LkTzbWer6wA0WTCuGXLsj7UIVcANAKCTiLQ34/i96OvqOBC7zSCoHZUjnTZQQGb1LgiQT3c3spYdyfpuTa6L55ndmJl4t9WmY7L7sc55WNncToNC4ISo93t8fLWRBEe+baagfbcb8N0z4rnm4+WqRYaxxv5YPUHEQSBd5S9g4q0Cv6z/j8JyqtTD+sqKkCWowtt1xouu4+gI4AA2FGok9Tn5xxB3ZCUvUd3/Xlmx0cxp1s5/dyP8TjX0EVWcTdiQF3kDxoycbdv7D1wOVwzgloQBAn4T+AhYDPwmCAIC2ervwdMK4pSCfwr8A/z3utUFGVH+L/kZ6o3CLS5ZjI/vAk+WcApSz3vm7wX/TfHmPl1FyGnH4NZS2a+OWp4nwzu2ZTLpYEZxh0+NbDp4g9Vj5mM8oT2jxC/8Sw+rgzaqcy2YNBK9F+dwmLVY7UlV2z7+wcQzWak9PRF75VuVds3Z7oclGeZwW+jfbodOa0AHntGJdyefhTRJK1KHXWqY5JUg4bN+aqNhqjTocnKIjCcpIK6YHHh8MCWXLrGXXSMLe0jtRxahmcXBSQGAyGG2mYoiqNS1xUX4+8fiFHVLoVfdf0Ku8/O45seX/Re1Z4cUKDjwvIPqKUI6qkhdbU/I78Af18fnr4hvJm76Ejt5uejzxOS195mXbs/j5AYxFv0DpTJKXxt7Vht6ncQIajFlPDD0bHgd+EYUdXT8/zVIoWKsaaGA7YDnBg8sWyrkCwrvPWjZkKSQK1GIGTUkPv53eR/cT9ZH68j5VAhiALOk4NM/qCJoS+fZvQbF5l+sRNaXdxm3R/TxmnYtDgocWZkmM4LZ9lx30Pc98lP86Ev/T1/9D9P8an/eYoPfenvue+Tn2bbPQ9iTEll4OoVTj/7JPcOvQx932Hwyle5+3UL+l+2c/jH36P5UgtjXjMB+/Kqsln/LB0zHeti7wEQmpxCsloRpLW1dK6Ivuts7xFB5X3gnlRXuheg8a1+QkGZnfepHsyiKHDX47XU3VFA/et9HH+2HSXBye3P6weZdPn5xO3L37MFQeBzd91K0FXJMy0/W1O723pCV1JCaHKSkNMZLVYlsxbv1augKBjjKKgVRWG4vZU+KYvavBQOVmYSCslcPjJIYU06me3/oRbdtm1Jj8eYkorVlo9jood3fXo7zmkvv/i3erxLPEPS312OIAlMfPcKwXm+nCcHT1KSWkJRShF6jcS+skxOzvehTi9SfyP1T0Io+edTMCTTOeaM8Z+OYG/eXgadgzFhQc2nhjj50w4qdmZz50dqya+y8v4/34PBouUXX6+n9cwIotlM2iPvZfbVVwmMJaY+sqUZKcowUt89oqqsnnhEVa1/9Hlkr4xfKxAY9JE6203RbpX8H+3sQGc0kZ67ukDcZJBfXYscDCKPzfKVg19BK2nR11Tjb+9AueXTSDseAkA+++w1H8tNXH+Ubc3C7wky3KHWxw9syePyoJ2B6euofAz64dx31Nb4nNqYt0KzfhAFRLOWp1uexkA2OZrt2Mc8aPQSlvSVSdHl4D57Bk1ODrrS0pjXd+XuQkCIsfnICBPUepdah/WH/0aiXoOgl1QFNagq6ple6Fo5z0BfXQ2Kgm+N9gpLITg9jaexEcsqlI2mvXsRTCacy9h8nO6cZGTWyyO7EleoPrqviP4pDyc7E1PmrgRFVsIK6sW/Bc/ly8ymlJBbaKS7/jxag5GC2tjMhoh6+nP3qvffnFQDH95XzPMXB+mfF4ap1ekp3FRHz6V6VakJZElTjHbH+lBLqXpCDv/KNUrEMi+1AH9PD7riYgRRxBsI8XbXJLdqdGhsZrqaJqnak4sorZ1OaBhv4FTKJbROgcDA3PxKX1lJyG4nNKF+J6FggMY3XiajoIipoQFe/Je/IxRc+Tm8qUpdDHeZwmKADfahDgwsPUdeN5z4GgRccPf/79qdYz62fxgECeqf4PTQab524WvcV3Ifv1f3ewntXpJawqd3fJrD/Yd5tefVpE9vqK6KIagDw8P4rjZvuL1HBKa9e/Bcvrxk54fsCiCZtAiCEPXS1ldVcqF3muqcFNKMWoY7ZhAEhVxtu6paXwYNr/0KndHIw//rr1BQaHxj5b+prTLsQ91pZ2dxOl0TLqZdc4tagl4CUYhPUIMqJnr0x6DI8PSHwZc4V/Js67MYJAPvrlBtaiVR4o93/jE9sz38qmtlBXjc4VSoncf+rutzvU8MONGGaYBZFMqDqkBNW1CgBiROd0P+joSPV//KS1gys3jvX3wJn9vN2z99evWDK78LUVC/D50oMt5243TkLsS1VFDvAzoURelSFMUPPAM8vGCbh4GIBOmnwD1CPJOu/wfwn4P/w78WPYX2U2UYt2TiPD7IyD+cY+alLgpLUhjptCMv9IFcAXfX5qAocLh1DDpeV/3Cdq8cjhjBQq+jCBRF4fLgLHUFacghmf6WaYo2Z8T1T1sOgf5+tEVFcffLK09Fb9LQc2WSLQVpTM9k4Ql6GHAMQMEueN93YPAikv3yqiw+TnVNcGt5JpI4d25Nvo1gghYfss9HcHw87sr2vZtVJdlqVNSTTh9jDh+bFvhPj3TaCQbkGHuPCHQlxSheL8Hx5VWyiqLwVPNT1GbURn2Q58OaZyaryEL7MjYfSlBGdgaivoXzMT2kEibW/AKcx44zZDuIrEgM2QScwSmODyytZEkUbd5mOjIvoPFvIygZcJ06RUpGJhqdnumw+j2qoF74u3COgCW2zTBSqBiqq7m98HamvFM0Ty0dWtj4Vj9jvQ569AF2CBoy7y1G1EmIeg2GaitpD5SS86ntFHxpP1mf3ErqPcWIBg3Os8N8tusxPvnGA4z86wWmX+jAfWkcbZGqMvTNI6jrX/kloiiy/b53xJzblJpG4aY6tt37IHf99id5///5G37/v77Pn/zgWTS/9XkuWe9BMuwm3VpI2qyGiy/+nF//1zd4onsX3/jWUb7zJ7/Hz//hyxx98nuMdsVOKCNKq4i10FoRnJy8TvYeP1d9P6uuk71HBBV3A4J6X50HvyfI5aODVOzMxpo3174tiAJ3PFbN9nuLuHx4gCM/bl1xAijLCt890c2W/FRuLV+51evO6mzyhNuxB8Y4PbT2a209oA23/Pp7eqPtfqJJi/dKExA/IHF2fAy3fYZWOYOPHyxDEAS66sdxzfjYVj0C9r5VqacjyKusYbijjbyKNN7xR9uwj3l48RsN+DyL1ReaTCNZv1uH7A0y8b0ryO4AvpCPcyPnOJg/p0w5WJFJ+5gz1pt0z++CcxRak2+565l04Q/JixTUMM+HOqyi7qof5/ATLRRtsnLfx7cghp9p6Tkm3veF3djK03jj+1c588su0h99FIJBZp57LrGByCH+OP0sf9X1UXjlLyB/O3zsRRRLLrI7SNOoE7NoJnu8AX04IGu0q53c8sprGpAYwWCKWki/U38bW7LU35KhpgYlEMDf04N49x+rHyNj1zUfy42EBLoT7xAE4aIgCEFBEN6/EWNcDxRusiJpRHouqcTUA1tUBedr19Pm4+oLam1x6x8teis060dK0dFub+f86Hks/jsospqZGXFjzTVFw65XA0VRcJ09h+mWWxbV0Gn6NKqsVTEEtc6gwWLV45/yodOIUQU1qCrqaDDepnerFlbnvrfiGAw1NQDXLCjRdeIEKAqWQ8kT1KJOh/nAfpxHjy2ptHz+4gApBg33bFqcg7AUHtiSh9Wk5emz69P6LLsCICtxLT5GTl0hqDFh21lMV/15SrZuR6Odm4+5/UG+dbST2yqz2Fs6VyP84aEKREHgv47EEi6l23YyOdCHRw6hKykhffwqoaDMSNdcB5+UpgNZWbkzdTZMXKTm4+/tRVeqPuvPdk9BUCbXGcRl1CIHFar35S1zoMRRP1bPSL4dRPA0zXUs6avVvIOIdUPbmVO4Zqa586O/x/1/8Bn6rlzitW//+4qK2x21avBixN+aqY31oQ4MDCw5R16IkBzCHXAz5Z1i2DlMt72b5slmGsYaeHv4bY72H6VtesF1OtMPZ/9HJY0XLK5dM6TkQs1DDDY+zReOfYHytHK+cvArSfEHH938UbZmbeXvzvwdU974Ic5LQV9dTXBsjNDMDEB0Acty111JHedawbhnDwQCeC41xn1fdgXm/Kfb1Xmctqyci33TUf/p4Q472eYxdHnly9pEuO0ztJ0+zuY77iGrqITynXu4/NarKy7mZORb0BkklaAuUs/ZMC93SxAERLMmvsVHBJkV8P7vw3gzvPCphEITnX4nL3W9xINlD5Kmn/PVvrf4XjZlbOKbl75JYBWCEF1pKQgCvo7rRFD3O9CFf+8BjUC6Q72XaQsKkg5InBoaoLexnu33PkRuWQVb776fhtd+FRUMJg1zJkKO6mqgFWCyf4MyPRLAtZxhFADzjbwGwq/F3UZRlCBgByKsR5kgCPWCIBwVBCFxc7IbEK1Trfyq61d8ZNNHyCspIuPRWnL/dDfGuiycJwcpaZmiRlAYX+AVthK25KeSl2rgreYxtWXPkgc1DyW8f8gVAEElFOZjdNbHhNNHXUEqo92z+D3BpO09APwD/eiK4isXREmkeHMGvZcn2GJLYXJateJonQ4Hsmx6F9z/N4jOZuTp5NTl/VNu+qc8Uf/pCLS2/IQV1IFBtTjTxVFQ29KMbC9MW9UkKRKMtckWS1D3N08jigIF1emL9tEWqUrNSCv9Unh7+G06Zjp4fNPjSxYDVXty+f+zd56BcVRn275me1PvvUtukiVb7gUbV2x6LwESEkhICAkhhQTSSCF5Q94khEDooZfQweACxr1bvVi997Yq28t8P0ZdK2kliyTfG+4/4N3ZmVnt7sw5z7mf626r6aOv0/Pq7VDrn0cHdUsjSo0WQ0AQfYcP0xi/hcgUf+69/GJEl5pHTpy/i+2Dqg+oiDyF6JTRuWgHpqNHEWQy/MMjRhzUhskQH60eAxIVoaHI/f1ZHSmtNB9pPOLx2L0dZk6+V02vv4LtCiX4qtCv8OwSFJRyNEn++G6OI+SODM58qZ174x7GeYEvcj815px2ul89R8ffK9Fv+y3mEgWmnDbMzT0UHdhH2qp1GAK9+02ptDpWrsjkrG8qSu06lmy8mwPbLLR/fR63/OFRLk7pYNVCH8KTUunr7CD34/d567c/w2EfcYTmdeQhE2SkB6d7dczp5OrsRBH8OReoh/AeqVtBPTn+4nORPgiilkLF2AJ10aEm7BYnS7bFTXiJIAisuSqZ7B3xlBxp5tPnS6dcdDxY3kFl+wC3r0v0avAuCAL3rL4K0aXlsTOvzPw9fQ4aYlLa62pH2Hk6BdaiIhSRER4XMYb401b/6GEuaMH+BnxDtMS3/Q38YmHezlmfU0RyKqaebvq7OomZF8j2ry+iq2mAXY/m4/DAkFdFGQi6eQHOTgudL5SQ03QGq8s6ph11TbJ0fzo22lWXvAV8o6R77wxV1ioVXtM8OKiT/ZPxV/tzuvU0Dee62fNMEaHxvmz/ejpy5dghm0av5JK7M5k3iJg59JkFzdr1GF97HdExxUBeFKH0A3h8Ndc3P0S725fGS16FWz+AgDiJ7+8WqWqU7lcRQjMyvR6X00FHfe3nzp8GMDlM/Lb4Yaw6SDQHDz+uHiyaWcvKpTwCwCWbGav8/2d52Z1YD3wZ+M+4UMxSKo2C6HkB1BR2IooiCcF65oX7sLv4X4T5EEU48RgEpw4uWo6Vq8+G3FfFa+deQy1XY+rMIiZAR0+rGf+w88N72KurcXV2TsB7DCk7LJv89vwxE/bACD09rSaiA7Rj3LVyXzXOIayDQi214Zd/DL1N43c7RsroaASd7nPjUA8cPIQ8KMjjQqY38tmwAWdLywQ+MYDJ5uTjolYuzohEo5zY6VV6rHmY4T9aGqWcK5dEs6+kjc6B82eWDnPKPSA+WkqkThd9iJv+zo4JeI+XTtTROWDnu5vHBtKG+2m4blkMb55toMk4MpaPWywt1NUVSC5qbeFnEoe6fGRuOWzwmA7z0Sd9N0RdOPaGhmEX/6HyDrJkSgQ31HVZ8AvREhp//vg1t+gmrz2P1Kj5qOP9sBSP3GvVyYNGj0Enf+7uDwiIiCR+8RIWXrCJ1dfeRMmh/Rz759R4K98gLS6lA71FRqfoi/gvdlA73U5eKX2FP575I78+8WsaK3Ip1xi5e//dfH3f17n141u57sPruPzdy9n+1nY2vrGR1a+sJuvFLDJfzGTFKyu44PUL2PrWVi5991Ku/fBabv74Zm7fezt37b+Lq9+/msfzHh/pZj3wO0CEDRPWMD9XWRffwD0+MlxO66ShiFNJLpPz4OoH6Xf087tTv5vRa4eCEoeuWf2ffYYyNhZVoned5Z+3dEuWgCBMivlwmxzIDCMBiYrICKrNIv1WJ0vjAqTg09o+IsiZFu9RuH8vLqeTzG3SeHrx1h2Ye41Unj4x5etkMoHwJD9aKo1kRPshEyB3POZDp8Q1mYN6SMmbYMuDUPo+HHp46m2BD6s/xOK0cF3adWMeFwSBb2d9m6aBJt6umHlQvEyjQRkTg23QQd3Rb6O87fMrzHY2DuA3uCAZGKTD1dyEoFSiCAke6cr1MiAxb+8u5AoFGZskk9bqa29CoVJx8KXpF5gnkyxJGlOoBTfdMys7/kv1nxqS2ALEiqKYBXwPeEUQBN/xGwmCcIcgCGcEQTjTMY279N+pP+f8GYPKwG2Lbht+TBmiI/C6NMLuzUa1IIgEtQz7S6UY36+SWHFeSBAELpwfSmVFCWLFXmnQKZ+I65hM7gE7Mp1ygstjKCAxPcqP+pJuBIEZ88VEUcTR0IgyevJAhLj0YCz9DpLkKty2MARklHWPSgxfdReyiATcDgXimRcm3c94HR9kha5ODh7zuDIiAkdLi1dcK0fTIBvMQ4EaYOvCcPIajLT2ziwBtbRFcjKMR3w0lHYTluiLSqOY8BpVnFSgdkwTlPhy6csEagK5KGHyRYrkpZKTZDLMx/Bg2kOBuqe5iYCISESbjZpKB1aFL1lbY1mREE6EchnlA0ep7Zo5qmZIdped3bW7SV+QQkisD40hqzCdPo3bZiMgInLYQS3TKkAuTIL4GFtQtpZXDA9YgrRBLAxaOMyXHS1RFPnspTLcAlS7LcwT5QRsjkNQeHeJPNN5ltZAI3HbMwi5bRGRP19F6Lcy8duRgCC3ILpC6HmjnO5HitgadCuLtRdgym33GgWxOMYfh1KGWyuns26A9dHrOdx6FP/oKNJitaxOtHPxd3/ErX94lKt+8iCW/j5KD382/Pq89jxSA1JnPFCcTM6uLuReFthnrbpjEgNxweWf73EmU8oWaDorMfyQMDx5nzYQMz+A0LgJtyNAuiavuDSRFZcmUnaylb3PlOCapEj91OFqwn017MzwHpVwcXosOvsyCnqO0GMxzvgtzbVUsSOLZ26TA0EtR1DIsBQXoZ2k6HCusAiHoOCiC5agUcppq+2jtbqPjCUyhPojsPx2kM0eHRORIjmFWiqke0l8ejBbbltIa3UvHz1eMKbdeUiaZH8Cr0vDXteH8E4nGkFDdthIsWBBhC8BOiVHKkZxqOUKiUVd/dmMnVhlrX3IBDxyx2WCjGXhy6gsa+SjxwvxD9Vx8V2LPd4bpNOQceHN81h5eSIVp9s4HXYdZqOF/k8+8XzwmkPw9CZ4/UvgdtG2/Skus/+Kg86Rz2vIXae2utHbOwgYdNF0NtTjcjj+JQXqP539E62mVuLmpdNWOVKAUickgFKJrawMQSnHZ2MMqph/EZ/+P0PTdieKolgrimIB8J/BAjoPxWcE09dhoadFKiZuWxjO6dpuCW33eavhpDShXPGNMeiwIbn67LgNguT6it9Be6+CaB8N/d1WAuaIP60fF5A4pOzwbKwuK8VdxcOPBUTopUDzAO04B7Ua9yiEEUtulYrvOVOPqwWZDHVyMrayuS9Qiy4XpsOHMaxbN+tujKHQM0+Yj91FrVgcLq5aMnEM317Xx/4XznH4Nc/v64blMThcIm+dnaVLbZSG0CrjHdSi201HF6gEOx31RQAkZI7cc8x2J08crGZdSjDZ8RNdkt/YILWtP35gpFsuOCYOvX8AtQW5aLMykXU0ExyuHhOUOByaOd08c7BA7TDLwOEYFZDYwQ4/PQhQUTdA6vKwGXfXelJtby199j4yQzLRLgzC2WEZzoaQBwUhDwjAVlFBW3UlLeXnyNx28fD3ZuWV17No41ZOvPUahfsnD1MTBAFNKPiZgjiuCMHW8fmgaybT04VP89Cph3j13KvsrvkYXUc/tXoLTQNNDNgHkMvkBGoCSfBLICs0iwuiL+DS5Eu5ZcEtfDPzm3xv6ff48fIf88vVv+R3637Hnzf+mb9v/jvPbXuOV3a8wiVJl/BY/mN8fd/X6Ww4DvmvwLLbJSTZv0iiKPLLjiOcUyn5nRhMnO9EM4c3Sg5I5o6MO/i45mM+q/9s+hcMami+ZyuvwG02Yz5xEp+NG+bkOzoXkvv6op4/b+oC9aBp0FZZiTpJwnuAFJDYUd+Py+EmQlEwJd7D7XKRt+8jYhctJihK+vzjFy/BNySM/L3Td/1FJPnR3WxC7hRJC/f1EJToZT7Yqrsg4zr47NdwbnJEhyiKvFH+BvMD57MwaOLcYW3UWrJCs3iy4EmszpnVXQDUiYnYBx3Ud750lu1/PsSj+yvmNGtgSJ0NAwQEStfZ8DA99qYmFJER0vWqJc/rgES7xUzxgU9JXbUOnZ8/AHr/AFZccR3VZ09RV5g3q/OTpa0FwEdmpdehmzHr/V+lz7NA3QSMvipGDz7mcRtBEBSAH9AliqJNFMUuAFEUzwJVQOr4A4ii+KQoitmiKGaHhPxnOmhOt57mSNMRbk+/fUzLwpCUwVrCvjSfkwo5PTolAyeaafmf0/S8VznSkjeFNs0L5VL3J4PhBN6FIw5pdCvJaBU29SIIsCDSl/qSbsISfNF42G4qOTs6EG02lJM4qAFiFwaCAOoOG4hK/BSRY1uUBGGQMSnDvesXUH3Aq2Mfq+ok2KAiZdzkXxkRgWi14uqZfsnI0TSYrhzt+fy3DmI+9pXOzEV9rrWfEB/1mMAWS7+djoZ+j3iPofNGocBeP3myeF1fHQcbD3Jt2rUj7Wse5BusJSzB1yPmQ3S6cbRJYSNyD+zE7uYmAiOjMZ06RV3EBvz9pLBLgG+vuBZBbuX+PV62lnvQ4cbD9Np6uTT5UhZdEEWfU0ePJgbL2bMERETR296K2+VCkAnI9cqxDmqHBaxGqb1s+P04sVdVDQ9YQLrJFXQW0GsbW0gvPdZCU1kPOX4iX1FqkQVq0C31vi30TOsZloYtHR4ECXIBVYwPPuujUUe1MrD7+wR/YwElthNYVCaEFhc9r5dhPuvd90ejlJMZ40+7UqStto+NsRvpt/dztu2s1K5rGnGbRC9IJzQhibMfvovoduNyuyjsLJwz/jT8ixAfJe9KeI/UfzHeY0jJWwARqiRW57njrVj67CzZHj/tS7N3xLPm6mSqctrZ/UQRLsfYOlFxcy/Hqrr48pr4CWGpU0kmE7g1/VoQnPzpxGszeTefi2QaDYqICBx1dbjNTmR6Ja6+Phx19WgWTuRPA5QVFNOpDuHm1RIPsmB/A0qNnPmuV0CpkxZaz0MhcfHIlUpaKkcWO5OXhrLp1vk0lvWw+8kiXM6JdTtdRgj+FycS3RLIA8Y70Sq0I+9TJrA6KZhjVZ1jB3NZN4Mgg7MzC0ssa+snPljv0dkHkKVYyfKcq1HpZVz6ncxp77+CILB0ezzbbl9Ed5+Ms8t/TN0r4yYDzbnw4hXw/CXSYt6lj8I3TxC64hpCfDRS6/agnIMTD71LRmh7DqokyX3UVi0VisMTxzr65lqnW0/zetnr3DT/Jhakr2Sgq5P+LukaJ6hUqBMTsZZJTny/bfFokv0/1/P5D5M33Yn/ZxSfLt1nagokE8r2ReGIInwyw7HXrHTiMdD4w+LrPT7t6rNT427A4rSwIVxaIwgfXFwbjYCajUwnT6EID/cYMg4MY9zGcKgj9bgcbhI0ahq6R5y1cl+VxB12DV67AhMkd1vO81J+zRTSpKViKyub80msJb8AV2/vrPAeQ1KGhqJZsICBgwcnPPd2biOxgTqWxk001+Tuk8we9SXddDZOdNIlh/qwLD6A1043nPf7HioEjzd92KurMWqjCQkSqM09Q0hcAj5BI6aaF4/X0WWa6J4eUpS/lquXxvDG6UZaeqXPWhAE4jKyqCvMQ5MhZTiEaPtpq+3DYZcWZmW+3jqom0Hlg71FWpRVxcXRbLRQ0T5AFgocPiqcInOK9wAJQ6dZKP0dhjAfgiBIQYnlFeTu/gClRsvCCzYPv1YQBDZ/7ZvEL17CvqcepSbv7KTHiUkIIcgcySGNz7+UQV3cVcwT+U9wUcJFnPnSGQ5sfheVQ+TaDXfx1qVv8fLOl3l227M8vvlx/rTxTzy07iF+sfoX3Lf8Pu5Zeg93Lr6Tryz6CjfOv5ErU65kZ+JONsVuYk3UGrLDs0kPSefXa37Ng6sfJL8jn6v3f5OTej9Yd++/7D0CvHLuFT6s2cU3/RaxvvrEtF0aU+lri75GSkAKvzrxK/rsfdO/AFCEhiLz86M9v5iOA4cR7fb/GLzHkHTZ2Vjy8hDtE3+DLpMDuV6J6HJhr64eDkgM1KuID9LRXGkEIEJZOqWDuursSQa6OsncfvHwYzKZnIzN22koKaSrcfJ6AkBEkj8ArdUShzqvwTimmCvXKXCbvQgtFAS45C+SY/jtO6DdM2IzryOPip4Krku7zuNiwpCLut3Szutlr09/3HFSJSVir63lVGU7Z+p6SAox8PDecm57/vQYvvb5ymFzYWw3o9ZJhpK4KF8cTc0j3fgzCEgsOXwAu8VM1raLxzy+5KJL8Q0J4+ALT+OeRfaXELsCATMGuZUBbfi0+Nh/lz7PAvVpIEUQhARBEFTA9cD747Z5Hxiqql4N7BdFURQEIWSwjRFBEBKBFODfC4uahURR5E9n/0SYLowb5t0w5bYBKQGc6bUTdm82usxQTCdbafnDGaxlU/OX1iT4cb38IBU+K2e8SuoacHgMSCxu7iUpxIBgd9Ne10fswpkXohyNUoFXNcngGkBrUBGe4EdbmZHoAC1KV9QEhpbMVwOA2zcd3rod7FOnNYuiyLGqLlYlBU+4yCkjJaeiwwsOtaOpCZRKFJMsfCSHGkgM1rN3hq2mpS19E4KxGs/1gIjHgEQYTCaOkoIJJ9Mrpa+gkCkmtMYAiC4RV68Ne0M/luIuFoVpCe4w0/pCCR3PFtH257M0P3icpgeOYny3CmTCBLeHw26jr7OdgIgoqvYWYjJEkbUzefhvvCN5HRqZH2e79pPfYJzR32RI71e9T5AmiJURK0lZFoZKK6cp+gJMx44REBGF2+Wit0OalMp8VLhHM6j7Bz+HUQ5qe12dlICcOjLAXxu1Frfo5ljzseHHTEYbR9+sRBaqwWW3E+cS8N8ah+Bl4bB5oJlmU7NH7jcMBiU67FTn7qew+SC6y6KIeGAFymgDfZ81IHrJnl+WEEC5w0Zfp5Usn2Vo5BrJWaAPlgL9BiUIAtk7L6e7uZGa/LNUGisxOUxzVqB2m0yIFgvyOUR8VOd1cHrXqDThYbzHNlCd30R/1orMAl0QVO7D7XKTu7eOsARfjxgeT8rcHMv661OpLejko8cLhieIAM8crkGnknPD8tgZn9bXV65D7ohmV817uL0ITv28pYqLkxjUZoeE9ygpAUDjISCxu9eM0N2IT2wyoT4aTL02Ks+2M3+ZP6rSV2HxDaA9v6R1uUJJaEISraMK1ABpKyO44IY06gq72PdssUf8ykCmnNeCdrO0OYW+T8Zeb1cnB9HSa6W6c9Q9yC8KUrdD3stSmJqXKm8bIM0Dfxqgr9OC7b0wXDInhiu60HvoZplMyUtDueJ7SxB1PhxTb6dqTx50VsAbt8KTG6TB8dbfwLdzBjuuFAiCwPL4QE7VdA8XY3LLpGKwQ4SQ5tOoE4cK1JWo9Xr8wuamKOFJFqeFnx/7OdGGaL6d9W0iUocc8SMcf3Va6ufi6vxv0/8PXYiGAA0hsT7UFkj3uHnhPsQG6tjzeWM+jPUSBmfprR7vQaLDhWhxctqUS1ZoFiq3dC33dUhjovNxUIuiiPnUKfQrlk/q/AvUBJLol+gxKDEcBb0WB71DwbV+ahAZO2bKvk0Kwiv/eMpzUaek4jIa53wSO3DoIMjl6FdPHfI1nQwbNmDJy8M5ynjSbLRwrKqLK5dETfj79XVaqDrbzoK1kSjVcnL2eB5XX78slppOEyeqZ8bAHS9X70iQ5mj1nMjFrAsnNNWXprKSMXgPs93JE4ck9/TSuMmddt/ckIRbFHni4Mj0OD4jC2t/H0alDJlOR4CxArdTpLVaMmXIDSoQvER8+EZir60FJGuRFpcAAQAASURBVI7r4YoONIB/r502q4vQOJ/zRtkMKbc9lwB1APG+8Sj81SijDRMwH33VVZw7doiFF1yIWjf2uHKFgkvuuY/g2Hg++NPvaKvxXHyOiw9H6VZTI/dHY2kD++cfuGp1Wvnx4R8TqA3k/hX3A2BvHDJBzd3aoiAIXJFyBa9k34+v3cLtIT48XvHGnATYe6PTraf5w+k/sCFmA3es/40Ukpc3e8qUUq7kV2t+Rbe1mz+e+aNXrxEEAXdcAiVHc9j91D+R+figW+p5bvbvki47G9FqxVJUPOZx0eVGtLqQ6ZU4GhsRbTbUKcnk1PWwJDYAQRBoqezFT2tEFxo6xpA1Xnl7PsQnKISkJcvHPJ6+cQsyuYL8T6Z2UYcm+CKTScfLivGn3+qkunMk7FCmV04ekjheSi1c97JkQHntRrBMNAm+UfYGBqVhyg7wZeHLWBmxkmcKn8HkmLoWNF7qpGREh4PX3jtBkF7F+3et5deXL+JYZRc7Hzk87FI/X3U1D4AIDqSxdFKMH46mpsGAxJ7BgMTp8R6iKJK350PCElMITx7rz1WoVKy/6St01NdS9Nm+SfYwhRQqZEonOmEAp0JHd9F/Znn1cytQDzKl7wL2AKXAG6IoFguC8KAgCJcObvYMECQIQiUSymMIlLQeKBAEIQ8pPPEboiie3yjh36BP6j+hsLOQb2V+C41CM+W2EUl+WPodmJwigVenEv79bJShWjpfLMVaZZz0dZrqvYQKPTxj3TDjlf6pHNTpUX40lk5dOJ1KjgZpdW4yB/KQ4tKDaK/rJyvEB1N/CE0DTfTbRxwNQ+fnWvEjqd3/1JNT7q+qw0R7v20CfxpAGSnxTh0t06eW2hsbUUZGIMg9O9wEQWDLwjCOV3XRa/HuIu10ualoG/DAn+5GrVNMig0AUMbFYq8fy6AW3SKufjvGujbqc8r4nux2VEdN9LxdQec/imn7ay7NvzlB0wNHaHnoFO1/y6PrxRIM57pJUcuwVRpxmx3IA7VoM0Lw3RJHwFUphH5zMTL12HZyY2sLiCKBkVGUNujQiGbSVo98tgqZgkuSLkJpKOWXu87O+LtotBo51HSInYk7UcgUKFVy5q+OpCM4k87jeQSES5/dEIda7qPCNTDq7z4w6KYyjBRORgckDik9OB0/td8w5kMURQ6+WobL4eZVVz/fVulQhOnQZnjfkXG2TXJrjEYCjJZ6MJglb/9efIJDSF62CkEQ8N0Ui6vbijnHM25lvJYnBNEkl4pqfY12VkauZH/DfkRt4BgHNUDqIOP67IfvkN8hhTLMWUBit3QpVgQFT7Old2qr6WPP00Wc+qCG2sLB91F3FEwdsPCKOTnGrCSTQdImqPyUyjNt9HVaWbItbkatgukbotl48zzqS7vZ9Wg+dquT1l4r7+c3c212DH7amXWmACjkMjZFX4Jd3sir+cdn/Pq5llSgrsU12JpoLZLalTULx2Nx4bU9x5GLbtatkSYMRQebcLtF0v0Ogst2XuGIoxWRnEZbdRUu51iHx6L1UYPO9g72v3BuAmLnaNNRng95H1eGjv5P6xk4MXKvWDvEoa4c+1tj6Vek7+q5D706N4vdRW2XySN/2tRr470/54JL4HDmy+RYZx6GGZbgy1Xfy0BjM7Ln7U6K/3C/xFJf/0P4Th6svguUY8cjyxMCaem10tgjOfEOFkiLuEqdiN7cMsxvbKuuJCwh+XNtl30091Ea+hv45epfolPqCI1PRK5U0lwxsuCgSZuHs61tTFHqv0jedCd6pf8fuhBBwny01vRi7rMjCALbF4VztLKTPuvMA5O81qknAUFqkfegoQJfjauOG+bdQEOPVOhSWVwggF+o1uPrvJG9shJXdze65cun3C47LJu89jycbuk6N1QU9x38swyd01DotXN0V2bKtkGG/tQsy9Et83OpgUOH0GZlIveb2Fk6Exk2XAButxS4OKh385oQRbgya+L8I39/A4IgsGxnAgvWRVJ5tt1jJsuO9Ah8NApeO31+YYmuXolTPh6l2JwjjecFQy9ul2tMgfqF43V0m+x8d/OExuExignUcdWSaF45VT8c4BuXIRU/6osK0CzOQFd2BEEm0FxulI4nF5AZlLi9cVAPBiTKdDrkwcEcLO/gAp0WwQ313bY5c0+D5KBcHLp4+N6iXRiEo3Fg+DurTk2hTqvA5XCQOc5ROCSVVseVP/o5Gr2Bd37/S/o6J46tg6KlzlqX6IcDpILR56w/5/yZmt4afrXmV8Od1I5G6ZKtmmaOPGOJIiknnuY1o4Odcdsl5McnX6fT0jn9a89DraZWvn/w+8T4xPDbtb9FFpQECRdA7gtwHkaKhUELuXXhrbxd8fYYY9FkcrtFTogBxPW1klxdQHncIlB4xqP9u6TLln7r4zEfw0HjeiW2KmmBxRoVR3WniaVxAYhukdYqIxGyqfEeXY0N1BcVsHjLRcjG1TB0fv6krlxDycH9OKyTozKUKjkhcT60VBnJipVMIzmjONQynVSg9nqu7xcF170kBXe+eduYzp0eaw97avdwSdIl02Io7866mx5bDy+VvOTdcQelHuwCbC4o5Str4tGq5HxpZRxv3bkauVzguieO88yRmvPumOlskIr4ZocTKyKpQWpcXV1jAxIjM6fdT0NxIV2N9WRtv9jjeDt15Rqi5i3g6OsvYTPPfJFNplOhEaTPv730X5TpMUN9rgxqURQ/EkUxVRTFJFEUfzP42M9EUXx/8P+toiheI4pisiiKy0VRrB58/C1RFBeKopgpiuISURQ/+DzP8/OQ0+3kkZxHSPJL4pKkS6bdPjLFH2C4fUMRqCH4q+koAjV0PV+MrX6S9pazz2HShPPPvgVUtA943mYSjYbxD6m930pbn42Fkb7Ul3Sh1k9dOJ1M9oZGEIRJGc5DGmrfnCcq6RwMSqzoGRkIDzm83fpkqeX+yJ/BOjnn+PhgiJWnArUiQnLXOr0IShzTkjGJti4Ix+kWOVDmXYGxptOE3eVm/ij+tCiKNJR2E5UWgGyKxHdVTCyOunpEUcRtd9H+93yaHjhKy29OMvB4OffXfZWNhen0fVKPpaRLCu/xUaGdH4TPhbH4X5FM0K0LCP12FhH3ryAnVM9RmYzQb2USfMsCAi5PxndTLPpl4aiiJxZOegYTY52dTrq1ccxLcCIfx2e+LOVikDkp6Dk6Y3fT7trdON3OMb+VReujEAUZtQMh+KilgopxiENtGIf46B/8TEeFJFrLy0EuR5WUNPyYXCZndeRqjjQdwS26qcrpoCa/k64ELRlugRAH+G2NmzCZmEpn287io/IhJcBzK6YqLpY+fx9a2lvI2nbx8IBBMy8QZZT3LuqlcQF0KNyIQHttHxfGXEirqZVzShk4LWO6C+QKBVnbL6G+qICikhMEaYKINszNQNjZKf3G5iIk0dJvZ/eThej91PiH6TjyRoWEwyh+R1ptT9l63sc4L6VsQTR1krOrjIAIPQkZMy/KL1gTyZavLKC5spcPHsnn+UPVuEWR29YkzPq0frz+BhCVPJH76qz3MVdSxcWBKhJHiwlFgBpLUTHKqCgUAWOd0E6XmxMncgBYvjwLp8NF8eEm4hcG4H/ub9JiQMjUE3JvFZGShtNuo7O+dsJzmZtjWXFpAmUnWzn4WvmYAenR5qOE6cOIuTYLzbxAjO9VYS6UnIOxgTqi/LUcGV+gTt4EfjFw1ruwxMr2AUSRCQ5qq8nBB4/kYe53cPFdi0lNiuNU66mZD5jN3fhX/JWNPb8hsKeEA73f4GjibtwbfgIaz8WgZYOM09O13eQ3GDG2SQPeUJ9+BECdlITT4aCjrpawpM8P75Hfkc+LJS9ybeq1LI+QinNyhZKwhGRaykc7qKWgxP9SF7U33Yn/p5SQEQwi1BVJLuptC8NxuEQ+O+fd2GvGsg1IfOYFl07amThUoHbqYXPsZhp7LCjlAo4eO75BGhST4Hu8kemkxJ/WTcKfHlJ2eDYmh2k4u0WtU6L3U6EySY7JoaDEIbzEmKKkXCFhAav2T8nQV6cNFajn7rfmaGvHVlKK4YILzntfmkWLkAcFMXBAwnyIosjbOU0siw8gNmhsscNqclBytIWU5WEYAtRkbopBECDvk4nt7lqVnCuzovi4sPW8WsBdfTaPmS5tTRYE3BibS1Hr9UQOZieYbE6ePFTN+tQQj3iS8frWxmRc7hEXtc7Pn9D4JGoLctBmZuI6V0xItH4sh9pX7QWDuhl8o7DX1aGKj8flFjlS0cl2Xz2iAD0ukeRs7zF4U6nL0kVdXx1ZoSPOQu0g5sM6iPlQJCZSH+RHdHTcMFPXkwyBQVz541/gtNl4+6FfYDWNnRcHRepBEPG3hFOoVs84P2KmOt58nJdLX+bGeTcOB7XDKIzkNPPMGatqP9QeRrf+h/z2gv/hwdUPkteexzUfXMOpllNze6xB2Vw27vnsHmwuG3+58C/4qAbHNktukTpRaiYieGaiOxffSbxvPL889kvMjqmLcS+drOO0LACd00agrZ+31Ak8e7T2vI4/11IEBqJKSsJ85vSYx4eDxvUKbBUSH71YIY3NlsYF0NNmxmpyEinPh/i1k+4/b++HyBUK0jd5xiMu3nIRNrOJc8cOTXmeEUl+tNf2E+enxVejGBOUKNMrwA2idQbu/NgVsPNh6Tv66S+GH3638l0cbgfXpl477S7SQ9LZELOB54ufn4DqnEpDdYBkSyc3r4of2V+0Hx/etY6N80L51Ycl3PlSznktfHc2DqDWKTCbHZhloOiUzHPKqCipgxG8Qnzk7fkQjY8vaavWeXxeEAQ23Pw1zL1GTr03c7SqzM8XpUKqc3Q1eIfP+VfrPzUk8f97vVP5DrV9tdy95G4UsulX7/zDdGh9lLRUGIcfk+uVhHwtHZmPis5ni7E3jytAd1dD1X7cWbfgRsanpd4P1kWXiNvsRD7OQV3cJH1RFw3yp2PmB05ZOJ1MjoYGFOHhyFST85ABgqIMGALU+PQ4cVulAnJZz4hTSmYY5KUNOODC+yXO8PHHJt3fsaouovy1xAZOXIWT+/sjaLVeIz6UUVMX9LJi/Ak2qNlb7B0LsWQoIDF8pOBvbDMz0GOblD89JFVcLG6TCVdPD/0HG7HX9mFYHYnvJQn8LflNHs96j/D7lhP1mzVEPrCSsLuXEPzlhQRcmYLfljgMKyLQzg9CFWVA7qMieVk4Pa1mOhu9W9TobpZW+6tO9CF3Wlh8deaEbTKCM4gyROEfUsRDH5/D5vT+xvVB9QekBKSQFpA2/Jh/mI6oGBXNkWtx5hej1uvpHixQywcRH8MOSA+ID1t5Baq4OGTqsZODdVHr6LZ2U9BYzKHXyjCE63iho5NvqfQoow1oFsys8Hqm7QxLQ5ciEzxfTgW5nIa4SORA+oUjA4YxLurc6X+7BrWCtGg/zBqBttp+1kevR0DgM/tgwcw0tgU3Y/N2lGoN5uNlLA5ZPGeuR1eXNGE435BEt8vNnqeLsQw4uOjr6ay7LoXeDgt5+2qh5H0JnaCam/bRWStpE3W2bLra3SzZFjujhYvRSl0ezravLaS9rg/znmZ2pIZOmDzPRMG6ANJ8VtPNSY5XT389+zwl84lHu/JbyH0EfLfGYy0u9oj3+LioFa2xCaVvAIbAICpOt2Ppd5CRUAcDrbDyzjk7p4hk6TrSUum5qLL0oniWbIuj+FAT+18oxWpy4HA7ON58nLVRa5EpZATeOA9VjA/dr5VhrTIiCAJrkoM4XtWFa7TzWiaXCj01h7xiWp5rle4Dox3UDpuLXX/Lp6fNzI5vpBOe6Mfy8OW0m9tp6J+aFTgs2wAc+gP8ZTEce5TQHdmkFz5BSmgfeQc72f1EIQ6b52tyWrgPvhoFp2q6eepwNcmCHFEUCbFUIPfzQx4YSFdDHW6Xk7CEzycg0eay8bOjPyNMH8Y9S+8Z81xE6jzaaipxOaWJg2a4aFY2YT//1+VNd6IgCMsEQWgErgGeEAShePI9/ucrOEYaJ9YWSPe6rBh/Qn3U7C76nJw/+a9KRoiV35x0k7Y2aSyyLGUVSrmShm4zUf5ajG3m8+ZPm0+dQhEZMW3xajIOtaNHKj7WDxeoPTioQcL8CHI4+49Jj6EICEAREoKtbO5+a6bDUmHEsP78C9SCTIZh/XoGDh9GdDopbOqlsn2AK5dMHL8XH27CaXORuVnCsRgCNKQuD6P0aDOWgYlF6OuXx2J3uXk7d/YcXVevfdjBPiRHezs9BOOndVBXmEN8xpJh48KIe9q7hcDYIB1XZEXx8sk62vsHXdSLs2guO4diwQJwuQj1s0kc6sHrv9xXNTXiw+WQxtSDiA9VfBz5jUb6rE4WOAT6RIiYHzgj/NRUyuvIAxhToFaG6lCEaIcxH439RqwqBWnB04dKB8fEcem999PT0sz7D/8Gp2Ok4KRQyfEN1RJkiuKEVoP4OXKo++x9/PToT4n3jee7S7875jl7YyPy4GBkujkc47rd8OkvpRC2pV8eQX7sfAUflQ+377udx/Mfn1PkhyiK/PrErynqKuI3a39Dol/iyJPzLpaQbdOEsU4njULDg2sepMXUwl9y/jLpdi29Fv5ndxl+C6SuVeRyfC9Yz28/Kp3Y+fZvlm5ZNpazOYiukc9iqCtYrldiq6pEER7OmU47CplARrQfLUP8aVXppA5qm9lM8cH9pK1ah87XsyEhat5CgqJjyd83NeYjItkfl9NNZ+MAmbEBY4ISh4IcvQpKHK2lX5a6ko79FfJfxy26+Wf5P1kSuoTkAO/Glndl3sWAY4B/FP/D68M22WR0avxYpxqY0Lnqp1Py5M1LuX/HfPaVtnHJX49Q3Ox98Xu0Ohv6CYoy4DA7cKpkEi6WwQK1lwGJfZ3tVJ4+QcaFW1FMUUMLT05l/rqNnN31Lr3tM8vkkPn6gNwPnb2T7p7/vpDE/1pZnBYez3uczJBMNsZ4B+cXBIGIJP9hB/WQ5L4qqUitltP5TOFwqjEgBTMJcnxWfYWFkb58OoPQmOGVunEO6sIm6UcZKSgw99qJnQXeA6SbrzetS4IgELcoCEuDCcHhi0bmM+wGAZBpFSCAe8AhcXvmXwLH/wbmicQXt1vkeHUXq5KCJoXsKyMicEzjoHabzSMtGVNIJhPYsiCMA2XtWB3T3/DPtfajlAskhYyENzaUSu9jugK1MlYaUFtKaxg41Ig2Ixj/ixM5HVPGh8r9bFi5DYW/2mtuctKSEGQygUoPYYme1NPciN4/iMZuH2JNBRiS4ydsIwgCOxJ2YFeWUW9s48XjdRN35EG1vbUUdBRwSeIlEz63jItSsKkDqD5UQUB45BjEB+6R7zH9rSBTjrnw28rLxwQkDmnIxXDszSpsJif7fZ1cq9LiY3PjtzV+RoXcDnMHdX11ZId7xnsAmHuNNAguonvNqPVjJ66a+aNd1NPfJJbFB1IrOmmr7SVQE0hWaBb7BwYdIKauMdtq9AaS168nrB4yNGke9jY7OTsHHS3n6aA++X41TWU9XHBDGiGxPsQuCCJhcTBnPq5loF+EhZfPwdmep/RB5DhvxqDqJWXZ5Lw3b5S0JBTVBWEEOgWW1jgwT9diO43uXnYjgtzKQwdnH0x6vrJWGjHlKXEPtKNJ6QaHCUdDA5pFE1O4nz1aQ5Sjg/j5CxBFkYLPGgiM1BPd9AgEJUsO6jmSb0goOj//CRzqIQmCwMrLE1l6URxlJ1p5+Wcn2LfrDCa7iTVRUvCMTCUn+MsLUQRp6HqhBHvzAGuSg+mzOilqGjd4zfrStIWeIZW39aNWyIgLkq4FLoebj/9eQFtNH1u/unD4XrAsYhkAp1qncT057XDySXgkC/b/GuLXwZ3H0HzzRfRLs0g8/hhrr0mmtqCTd/6Yg8k40TknlwlkxwfySWk7HxW2kCxX4hQEdA35qBITEQSB1qrBgMSkz6dA/UT+E1T3VvPzVT/HoBobcByZkobL4aC9VrrWyYODkQcGYp3Dotn/T/KiO/G0KIrRoijqRVEMEkVx4g/y/yMJgkB8ejD1JV04HS5kMoGtC8M4UNbh1dhrRnK74eTfIWopRC+bdLO8KqkovC19BwCNPRaiBwvU/ufDn3a7Jf708hXTjkVCdaHE+sRypnWkQB0Qoae3zUyAVjlcoJbplCAXJhYlfSMh7SLIfQmckztq1ampWCvmzkE9cPAQivDwMfkg5yPDBRfg7uvDkpfH2zlNqBQydqSPLWS6HG4KPmskZkEgwdEj15esLXE4HW4KP2ucsN/5Eb4sjvHntVP1s2r9FkXRo4PadCaXPt94/MIsmIw9w3gPyT1dxQWpISyJ9T6L4Vsbk3G43Dx9WMJVxGdk4XY56RpE9QWa63C7RnGofVVTO6gH2gARURcmmXXi4jhY3oke0HXbaLO6SF1+fuOh0cpvz0cpU7IgaCwWTLswCFtNLy6Tg/xDn6JzugnumRhq6UmxizLYfud3aCgpZO/f/zLm8wuN8SXUEsdhrR5L69yia0brtyd/S6elk4fWPTQmdBkkxMd0XbozVsk7EkZg4/2gGPnOpQak8trO19iRsIPH8uYW+fFG2Ru8W/kud2TcwabYcWM4pQYyrpPwZx7m7DNRVmgWN8y7gVfPvUpOW86E50VR5KfvFuN0u/nGl7cAoFuyhN/euobEYD3feiVnuKPkP0G67GW4TSaspSOdYSMOaiW2ykrUycmcrethYZQfGqWclspetEoLfkEq8PNcXyk5vB+H1TIpBgek++nirTtoq66kdRITB0gOaoCWSiNZMf6Ut/UzYBvBkAC4vOVQj9b2hyBuLbz/bU4UvkhDf4PH/KzJlBaYxvb47bxc+rLX3+MnD1fR6BtGgsmzGUwQBG5fn8jrd6zE5nBzxWPHeHWG132Xw01X0wC+ETqUDjcyrXJsgbo51yv+dMEnuwFYvGXHtNuuu+FWBEHG4Vf+4fV5Asi0StxCAH6Oenod/6acp2n0RYH6c9DLpS/TYengnqX3zKjYFZHsR1+ndcIEUhGgIfj2dJAJdDxViLPLIk1Kc1+SBpe+kWyaH0ZOfQ/dXraiDa16jWdQFzX1khisp7NSGsjEzJ9dEcrR0DBp+vh4xaUH47S5yFBr0YjRYxAfwmC4yPAq3cb7wT4AR/88YT+lrX0YzQ6PeI8hKSMicDRPzaAeet6b1qttC8Mw2V0cr+qadtvSlj6SQgyoRqExGkq68Q3R4hcyNa9QFRsHgOlYD6IIfjskPMDLpS8ToY/weiFkSFqDiuj5gVScbvfqAtzd0oRMEYAgulmwYHJu7s7EnYi4mZ9cw18+rfDq+/hh9YfIBBk7E3dOeC5+cQhawUJFuw/+EVEYWwcRHz6Dq7cDowrUPuFSYjBSkJ+jocHjBChIG8Qa1zbEMl/0mQGcaO/lK4IGVbwv6kHUjrca4k9PFpAIkL/vY9yiSFxLJ46msd+9YRd1l3cu6uUJgTTKXNhMTvo6rWyM2UiZuYUmhRzME2/UsqUxIIJvsXeDem/k7BpEfATObvEKoCq3nZw99SxcH8X81SMTybXXpCC6XBwzfVVC+vyb1VxppGUgliz1P5HbjOe1L5db5PnaNgoSVNh77Lz7v56Lhd5qbcxKfORhVFg+nVgw/RfIWmWk6/liFEEaLMf/jLOpFkuxZNTUjnNQ59T3cK6mGb29l8iUNFoqjXQ2DJCx2InQfFZiT8vmbjgiCALhyam0VExewBQEgZWXJXHt/csJitJTs8vC1QU/JH5gZJIs0ykJvi0dmUZO53NFrAqSXM9Hq8b91nwjpHtx3stTFnpAWqhMCTMglwm43SL7niumobSHDV+aR1LWSMt0gm8CwdpgTreOtIG6rU6MH9dg/Kga0eWE/Nfh0Wz4+AcQnApf/QRueAXCpPcQeNNNOBoaSJRVseObGRjbzPzzd2foaJh4PVieEEjngA0NAlqbG9Ry7NXVqAbZfW01lWgMPviGzF1hYkglXSU8W/QslyVdxtqoia2rw0GJg5gPQRDQzEv7b0V8/FcqfnEwTrubpjIjANsXRmBxuDhUPsfhjpWfQFel5J6eZPxudphpaq3HIXMSGiBhxRp7zMRp1bgcbgLOIzjOVlGJy2iclj89pOzwbM62n8UtSpiwwAg9TrubVB/tcIFakAlSUXK8gxqksERzl9S1NInUaWnYK6sQxzH9hzSTRQLRbsd07BiG9evnrKtLv2Y1KBT0fnaA9/Ob2bIgbIJLrvx0G+ZeO1mbxwYTB0bqic8IpuBAo8cOkxuXx1DRPjCrEC3R6kK0uyeEjreeKsUlV4OmEwSBhExp/Pj88Vp6zA6v3dNDSgjWc1lmFC8er6NzwEZk2gIUajX1lWWoEhIwVJ1EJhOGMR9yXzVukxPRKX1nznWf477D92G0GqUd9knjVLtZA2436vh4DpV3cFmIH4II3SIkZs4dsz63PZcFQQtQy8cW8rULg8EN7UfO0XSumGSdL44q7x3P89dtZO31t1B65ABHX39x+PHgaAN6qy8VcgPd7Z/PIuee2j3sqt7F1zO+zqLgiR1ljsbGaTOaZiSXQ1qgDl0A6ddMeFqn1PHbtb/ll6t/OWfIj9z2XH53+nesi1rHNxdP0m2SdTO47FDw+nkdC+A7S75DpCGSnx/7OVbnWH7yx0WtfFLaxve2pBIXF0bALTcT+NXbMKgVPHlLNk63yNdfPIvFPscLmrOULlv6zY/GfAzVOQSNDHtVNYrERPIbjCwdXKxqqTQSoSxBSPCM95CC9XZ5DNYbrwXrNqJQq8mbwkWt9VHhH6ajpaqXrFh/3CIUNBoBkOmkxS+32fM9YUrJlXDt82AI5Y0TfyBQ7c/muM0z2sWdmXdic9l4pvCZabft6LfxxplG1ElJuOtqp6x5ZMcHsuvutaxICOTHbxdy7xv5mO3Tv8fuZhNv/s8ZnHY3zhA1PghofFRSPUmpRGFQQE/ttHgPp91OwSe7Scpejm/I9Agln6Bgsi+5krLjh2kqK512+yEJWgVul4YAsRaz0h9b/8Qchn+3vihQz7GMViPPFD7DhugNLAlbMqPXjudQj5YyWEvIV9PB5ZaK1Gd3SQWppV8BYNO8UNwiXvOQXYPtbHL92IFTUVMvi6L8qC/uJihKjyFg5i1cbqsVZ3s7qhjvbr7RaQHIFTIWy1XYzWFUGCvGtCDJ9MqRQLzQ+dLN9+STI1iHQQ0ViVdNVaCOnN5BPZN05VVJQRjUCvaWTN9qeq6lf0xAosvlpqncOK17euhcZIFJOLvU+KyPQuGv4Vz3OU63nuaGeTd4hZEZr5RlofR3W2mrmZo/JIoi3U2NWAb0hLedJvjCVZNum+SfRFpAGtqAfEw2J498OrU7wS26+bD6Q1aEryBUN/FiLJPLSE0U6TYkoXIp6evswGGX+NrACId6oHUMf9pWKfG7NB4c1Hark/SSzRi1bTzeWc83fX1R2Vz4bZuZexqk1lqdQse8wHken3c6HOTt3UVsUioGmwNracmEbTTzA1FG6un7rH5aF/Wy+EBaB4MS22v72BgrLUwc0GknBCUClLpraQi30nYsB7t1bm5Arq5uZH5+CNPgeyZTT6uJT58vJSzBl3XXjJ2I+QYoyfLdRYV5FU21sy/ezpVydteh0QnM1+2TuGnnoX0lbdR3m7lqRzKX3L2YgR4b7/wxh/7uyYNKppJMkHH9/KtQ6Kt5eP/04TFzKVu1ka5/FCMP0BByRwaKIB/sdXVYB1PJNQvGOqGeOVJDglv6fkakzCN/fyNqvYJU6z9A7QeLb5jzc4xMmUd3cyPWgakxRsHRBi67J4uSpfswiL7s/WsZu58soq9L+r0o/NUE37YI0Snifr2c7FAfjlV6WJDM/opU6CmdOjKjvK2f1DAfKaD15XNU5XSw5upkFqyJHLOdIAgsC1vG6dbTuF1uTGfbaH34DAMHGxk41IT5j/fAO3dIXOkvvQVf/hBixjo+fTZvRhESQs/LrxCfHsyVP1iCIMDbD+dQUzD2erE8QboPXR0RhBJQGBS4urtRJ0rsvraqSsIS5z4g0eFy8NOjPyVAE8APlv3A4zY+gcEYgoLHBCWqU9OwVVRMWjT7Qv+3FJXqj0ItH/7erkgMxE+rZPcM8y6m1YnHJFTYgssm3STv3jvYUKRGZuun5f4HaHnkURaXHCOlRRo7+odOHYo+lcwnpWBUrwvUYdn02/uHjR0BEZIbKl6lGg49BYlD7er1YBhI3AgBCVOGJapTUxDtdux1E7vi8huMLPnVPp485F3h0JyTi9tkwnDBeq+290ZyHx902dl07P2UbpOdq5aMHbuLokjeJ/UERRuInj/Rmbxkayw2k5PSYxPNKxdnRKJXyXn1lJeopVEacinLfcfOo1oH53g9raWEJ6Wg8/NnwObkqUPVbEgLGQ4lm4m+tTEZq9PF04drUCiVxCxIp64gF21mJo78s4TE+9A0GJQ4hBxx9dnpt/dzz2f3sKt6F3/L+5u0sz7J9Wc3SvMwa1gU+Y1GLtRqcYvguzAIlWZugudsLhvFXcVj8B5DUkYZkPuq6DlRi0KtJi11EbaqqjFYhOm0/PJryNi0nZPvvDHsTAyKkhz0AZZICq0TnfPnqw5zB7868SsWBS3iaxlfm/C86HTiaGmZ2wL1iccl7Oemn0vYMQ8SBIErU67klZ2vYFAazgv50W5u53sHvkekPpLfrf8d8kmOSfgiqRsl5wU4zwA6nVLHz1b9jNq+Wh7Pf3z48V6zg5+9V8yiKN/hbJfwn/wEnw0bAGkB55Hrsyht7eO+twvOOwhvLqQMD0cZEzMmKHGoQO3qaUe02egKjsLmdLM0LoCBHht9XVYi5PmT4j0aigvpbmogc9vOacdpap2e+Ws3UHbs8JRj5IgkP1qqjCyOktzUQxxq+WwRH0PSB9N2+aMcUMHlFgeqGX4mCX4JXJp0KW+UvUGraeoxwHNHa3C63KSvWYxoNk+bQxZkUPOPryznns2pvJPXxGWPHqWy3bPJSxRFCg808sZDpzEZbez4ZgbNOvBBwMdfI3WgREYgtBVKL5gmILH8xBEs/X1TOuDHa/mlV2EICOTAC08hehlIKtMpwC0QomoEQUZ73ufL4p+NvihQz7GeKnwKs9PM3UvunvFrg6MNKNRSG4cnKcP1BN+2CLfFSeeHMlw+iyDpQgDSo/wI8VHzqZehMcMO6lGIj64BG829VhaGGWipMhIzQxbvkIZbGqK9c1Ar1XKi0vwJ6RfpNgZjcVrGcDflBuXYi+CG+6QV2cP/O2Y/x6q6SAzWE+E3uRtZGRmJq7MTt23y4teYloxppFbI2ZAWwr6StrFc0nHqMdlp7bMybxR3tK1aYsLFeBgwj5egUKJd8iUQLfhskP6uL5e+jFah5cqUK6d9vSclLA5BrpBRMQ3mw9xrxG4xgxBAbMcRdMsmb3sF2JG4g/LeYi7NVvPSiTqqOia/+eW259I00DRlkGj6ZRkIbic9dU4QRXpbW0bY5EMF6v5WMIw4+6yDgT6eEB8n36sGk4oDia9iEku5wi5HnRqAOmHmafJn286SFZo16QJB2bFDmHuNZF9xLchk2Ea1cw1JclHHSS7qvKl/v4F6FQGROlwyaKvtI843jkTfeD7T6Tw6qPM78rFmBmMzmyg++OmM358nObu6UATN7tpgtzr5+O+FKJQytt+xCLly3C2o9jBL1C9j8HFz+PVy3F6ER35e6mwcoK6oi8Wb4lDqDFCx77z29/ThamICtWxbGE5kSgCXficTy4CDdx7Oobdjdq2H1867AgEZx9s/nnQANdey1fTS+Vwx8gA1IbenIzeoUMXFSQXq4mKUsbHI/UZ+S01GC7uLWrkgwIJMLkfrG0lNXgcLl/miLHtb4qCqDVMccXYacpC0Vk3vsu22dnNI9SH6m9tYfkkCdYWdvPKLk5z8oBqH3YUyTE/wlxfiNNq436ygsKZ7omsw8ULwj5sS82E022nrs5EWZuD421WUHG1h6UVxw0zU8coOz8a3R03j307S889yFEIbocF/RCUUYuy5FOf2f8AdByF5s0e3p6BU4n/9dZiOHMFWU0NwtA9X35dNYLiOjx4vIP/ThuHJ2uJof+7ZnEqWXI1GIUOlku656qREnHY7nQ21hCXOPd7jmaJnKO8p56crf4qfevJrcGTKPFoqxgYlijYb9vr6OT+nL/SfJ4VSTuyCQGoLOhFFEaVcxub5YXxS0oZjru4T7aVQ/Rks+5rk8vKgzk/3ELD3LII+CJnLgunoUYyPP8b3c14jcs/H0jbX7aRy8xbqbv0yzT+5n46//Q3ju+9iPn0aR3PzlAU28+lTKKOiUHlhkACpQA0jHOrAwQJ1mCinscc8PC5VBGhwtplwW8ct6Mhk0uJa/THp/XuQZiiUdFxQYq/FwbdeycFsd/GHPWXDfP2pNHDwIIJSiX7lSq/en7cyXHAByoZa0hhgXcpYd299cTfdzSaytsR6LNxEJPsTkeRH3r4GXOO+S3q1gsuyothV2EyvZWbFmKEFgdEOarfJRKdJi0roo722koRM6fN7/tiQe3p2QcHJoQYuyYjkheO1dJvsxGdk0dPShDMlGVdPDxFhMtpr+7BbncMFamefjQePS2zflREreaP8Dcp7yqF3sEDdKQVvn3HqEUWIMzrpcblJWTk9B9pblXSV4HA7yAzNnPCcIBNQpPigNxtYuHoTvgvmI1qtOBq9LyoLgsCmr95JQlY2nzz9GNW5p4cRLyEDUeQqrGCfO/SDKIr89NhPsTlt/Hbdb1HKJl5HHK2t4HJ5ZYLy4oBw+I+w76dSbkuq52C80UoNSOX1i1/nooSLZoX8sLvsfO/A9zA5TPx545/xVflO/YIlt0B7CTSd9foYk2l15GquTLmS54ufp7hTMkQ89HEpPWY7v7syA8UkmMuN80L5/tY03strHkbh/LulW7YMy5mzw0VFl8mBTKfAXi0VC0vVUlDokjh/WqqMwBB/eo3H/Q0H6632HKw3Xou37MBpt1FyaPK5YUSyHzaTE7HPSWKwfrhAPdR9754N4mNQb/eW4hZkXN1UDh99f8YLGN9Y/A3cuHmi4IlJt+mzOnjxeB0XpUcQmSGxyW1V0xdj5TKB72xO4cXbVtBtsnPpo0d5L29sFoG5z86uvxVw6LVyolIDuP6nK0jICKa0pQ8/BPS+KuxNgyif5lzpRdM4qHP3fEhgZDSxixZPe45DUmo0rL3hVloryzl31LtAUplWqlmEREvjhbaSf2+WkSd9UaCeQzUPNPPquVe5JPESUgJmzlaTyWWEJ/h6dFAPSRXtQ/CVfrgcOjqsP8NlkQa6MpnAhWmhHCrr8GqwPuRIHo34KGqWBpexTjlupzh7/nSDVFz21kENELcoGGHAic+ANFEfH5ToHh1gEpQEWTfB2efAKB3L4XJzcpA/PZUUEdLAytk6+Yqbo6kZQa1GEeJdC9vWheF0DtjHBAiMV+ngwH20g7qhtBtBkBzk08mc047MEIWz4zAylZwuSxcfVX/EpUmXTjmpn0pqrYK4RUFUnm3HPUVxvaNe+hv72y2ELU6YNvjyoviLAIiPK0ejlPPQRxOLskP6oOoDtArtRHbZKPmlxhJmqaSzT/o8elqbkQ8hPvqHEB8tEwISBZ1ugkOhpaqXggONxK0IpUXfxu0aPTKrC7+tcVO+J0/qsfZQaaycFO8hiiI5H71PYFQM8dkrUCUkYD3n+W+hWRCIMkJP/34vXNSJQbTL3bTVSt+pC2M3cUajprd/rPvH4XZQ3FlM0sIlRKSkkbPrPdxzEI7i7OqcVYFaFEX2v3AOY5uZrV9bhCHAg8us+B2UahVrr51HV5OJokNT43g+T+XsqUOplrNoQwwkb5Jav71cnR6v3PoeztT18JXVCcgHgxbDE/24/J4sHDYX7zycQ0+racb7DdeHszx8FUr/HP62//NjKQ7JVttL53NFyP3VhNyeMdzJoIqXCtSWokK04/jTLxyrBSDS3k5IXAKlx6W25kX63eB2wfLbP5dzDU9KBUGgZRIO9WgdbzkOwNq41SzbmcCNv1xJ4uJgzuyq5ZWfn6DiTBuqWB+CbpxHsMnFz1wacmrGMRVlMlh6K9Qehk7Pn0VZq7SIEFhvI3dfPYsuiGLFpYkTN3S7cVfnsnKfk7/U3IerpYsAxZ8IEe9EFaEkcEcAqPR05yciMrVTJuDaa0GppOfVVwHQ+6m5/N4lJGaGcOSfFRx6TVoIkssEvrU+kfYyI1qVDNEmfR9ViYl01tfidrnmvEBd0VPBEwVPcFH8RVwYe+GU20akpNHX0c5Aj/R318wbLJr9l3Ko/xsVnx6MyWijs0Fa9N6+KJw+q5OT1efHNx3Wyb+DQjPcmThebquV2l88QFMQaKJS0K1YTMrBA7S+sZevbv4RfRuuQKVwE37L9WgzMyWcxZEjdP71UVru+zF1N99C5YWbOLc4k8otW6n78ldovv9+Oh57jN733sN85gzmU6fRrVjh9SlHGCKI1EcOc6g1eiU6XxU+dhGHS6S1T+rQMayOxG120v+ZBydw5k0gV8GZ5zweQ5WUBHL5GOa7KIr86M0CWnutPHNrNn5aJfe+kT/t/GPg0CF0y5YhG5fHcb4SV0quwluEJpTjilS5++rR+6tJzp68ZTprayz93VYqz0w0CdywLBarwz2hQDGdhpAqox3UlsJCen3j0embQRRJzMqW3NOHq9mYFkJmjP+MjjFad12YjMXh4tkjNcRlSB28HWrJ2RroaMbtljjUssHzOV5+mN21u/lW5rd4+IKH8VH58PtTv0fsbQKlDntTG3I/Pw602AnXKNH02ekRhFnPDT0pt10q3GSGZHp8vslSiVxQsChtA+pk6f4z1CHprWRyORd/90eExCfw4Z9+z0B3A2q9gmhTMse1Gqn1fo70RtkbHG06yveyv0eCX4LHbYYK7N7kNE0ptws+/iF8+qDUWXzti5NiicZLp9Tx0NqHZoX8+N2p35Hfkc+v1vzKu1rHoqtAqYec573a/3S6N/tegjRB/PTYTzlS2cprpxv42toEFkVNPRf+5oYkLloUzkMfl3KkYvYMbmePFVevDbfNdV5ubF12Ni6jcfj77DY5kOmUw/8+7vIlyl9LhJ+WlqpeFHIHIQFWCIifsK++zg4qz5wgfeMWlCrvOt/DEpKISE4jf9/Hk76PiCR/AFqqjGTG+pPX0IMoiggaOcjAbZpdB5vT7eTNijdZHbWamJXfkRz2p5+e0T6iDFFcnXI171a8S0Of5w6Xl0/U029zcucFScPXD3u195igtSnB7Lp7HQsjffnOa3nc/04hVoeL2oJOXvvVSRrLelh3XSoX35WBbnDhr7y5Dw3CIIO6eZA/nScZWKYISGypLKO1spzM7RfPuFNxwbqNhCUmc+jV53HYpu/KHSpQ+y7KQmUzYmmZWcjiv0JfFKjnUH/L+xsCAt/K/Nas9xGZ4k9X0wC2KVal1K2vEKT+LU6zns5ni4YdEZvmh9Jvc3J6/MTZg9wmB8hGvqTAMMdU2W5DoZIRkTy7wqejYRCR4SWDGiA+XSp4JZjCEJBJq/iDko9GfAxp/Q+l/x78PQAFjb2Y7C7WJAdPeRxlhNRGPRXmw9HYiDIy0usLxMa0EJRygb0lk//Az7VIhYl5ESMO6obSbkLjfVHrJmc6A7htTnr31IDQi71EWun8Z/k/sbvt3Dj/Rq/OcTIlZ4di7rXTUmGcdJvSI9IqdWzdSQzrp1+ZjTBEsCR0CZ817uGbG5L4pLTNY4Ky1Wllb+1etsRtQaecmtmYFu/ErRgsULc0I6jkCEqZhKpxWMDaCz4jDmpbeTnq5GSEUVxbl8PNZy+WYghQs0u0YBhIZ1tXGpqFgaiifSYcczrltEtBHZMFJDadK6a9toolF106yEydh/WcZ4eSIAj4bo7F2WXFnD+1i3pZfCBNMjftdX24XG42xl6ISxA41Df2plveXY7VZSUzNJOlO6/A2NZC1dnzY84BuDq7kM+iQJ33SQNVOe2svCLJ86KMyyHhEdIuIjE7iuh5AZz6oBpLv4e25M9ZvR1mKs+0sWh9FBq9UuJhmzulFOZZ6OkjNfhoFFy7bOw1MSTWh8u/l4XbLfLO/+bS1TQ1jsKTrpt3FYKilw+rDlDf9fmFwNjq+uh8thi539jiNIAqLg53fz/O5hY0C0d4iyabk1dO1bN9QSg9dVWEJaZSerSZxMWB+JQ8AWk7PA6250JqnY6gqJgpQ2CGdLTpKIGaQOYHSg4Ln0ANW7+2iCvuXYLGoGTv08W888ccBgwqdJcksgIFrl01iOMX9jK/BDLFpC7qsrZ+FtvktB9tI2VZGOuvSx25z/S3Qt4riG/ezsCvv0brk03IGiP5JOAATy96Cv1t30G4rwZu+ieKdTfgf0kS9po+Bo5MXTRRhITgu20bvW+/g9skFZ2VKjnbb19E1tZYig42setvBdgsTupLunHa3ShEcJt6ENRqlJGRtFZLE6bwxLkJNQNpgvKzoz/DV+XLfSvum3b7yCEO9aCLerhodu6LAvV/i+LTg0CAmnyJO70uJRidSs7u4jlw/5i7If81KdRL7/n+lv/wT9F3DFB/+0UozcLwNbBxwEmzIQS3KoDA2ADCvvddoh7+A/GvvkLKoYOk5eeR+PFHxDz9NOG//CVBt92GNiMD0WrFdOgwnY/8leYf3Ufdl27G1duLfqX3BWoY5FC3nR0uMgRE6FH0S/OCoXuCKsYHXVYo/UeapByb0dIHS0iT/NfAPnGhVKZSoYqPx1Y+svD2wvE6dhe38sPtaWyaH8ZvrkinuLmPR/dPXjy0NzZir6qaU7zHkHb3qmnSB5PVVDTm8Y76fprKelh8YQzyKULE49ODCYjQk7t3YjBWerQfCyN9efVUw8xCs4YL1CP3yu6TeVi1IbjlrWh9/QhLTOb5Y7UYzQ6+M0v39JBSw3zYsSiCfxyrRRYQiiEomKbWRmR6PYb6nEEOtXH4fA6V7GdlxEq+mv5V/NR+3JV5F6daT/GpsRh8I7HX1aOMi+NQRQfXhvojANoUCck4V8ptzyXON44g7cTfnNvt4syp93BgR9UuRzUY0GurmPlivEqj5cr7foHW15d3fv9L/IIdhFjjqFUpaWk+M/0OvFBdXx1/PPtHVkeu5vq06yfdzjGMkTyPArXDCm9+BU49CavugiueBMXMsHtDyI+Xd7w8jPz4e/7fp0R+vFX+Fv8s/ye3LbqNbfHTu7UBUPvAwiug6G2wzXyMO16+Kl8eWPkAFT0V3Lv3T8QG6rzqPBAEgYevWUxyqIG7Xp1daGLvvjpaf3+alodO0fzzYzT95AhNvzhOy+9O0fbns7Q/nk/nc0V0vXqOnncqMH5cQ9/+egaONWM624aluAtrlRF70wCqlMUIKgOmU9L3z21yINMrsVdVoggL40SbjSVxg/zpCiPhqkpkCas8LkIUfLIbURS9CtYbrcVbd9Dd3EhDcaHH5/1CtWh9lIMc6gA6B+w09lgQBAGZTjlrB/XBhoO0m9u5NvVauPABSNkGu++D2iMz2s8dGXcgl8l5LP+xCc9ZHS6eOVLDupRgFkX5oQgMRO7vj63S+wI1QLifhlduX8nXL0jk9RP1/Oxnh9n1WAE6PzXX/DibjI3Rw+N4h8tNS5t0DxWU4OrslArULXnT4j3y9uxCpdWycP3UZg1PEmQyNtzyNQa6Ojnz4TvTbj9U+5PFruGSru+Qlfif14n4RYF6jlTeU84HVR9w4/wbiTDMvv0pItkfRMnp6VEOK+S9jGZhLEE3L8DRYqLzuWLcg8VZlULmFebDPSCt1AmykQtdUVMvcUE6WsuNRKUGoFBOwpSaRo7GBgSdDvkMQtR8g7UEROhIc6vRCRGUd48UFWQGJaLNhegY5czwj5HCXfJega4qjg+GVq1MnLpwpoyUPhtH8xQF6qYmr/AeQ/LRKFmdFMye4tZJB6/nWvsINqgI9ZFco1aTg/baPmK8cCL0f9aIu9+BOroXl7GH1888y4slL7Imag2Jfh7cdzNQfHowCrV8UsyH2y1Sk18JyAntqkK/zruJxc7EnVT3VrN2oZ0ofy2/3lU6AYFyoPEA/Y5+Lk6cnrUUu3ERBlMnMrmO7uYm6eboo8Ldbx9hkQ86qEVRlArU4wISz3xcS0+rmdCNkXxc1s4PZBejcavoyJ7dCvCZ1jOo5WoWBi30+HzOR++j0RtYsF7iRGvmz8PZ3ILLaPS4vWZB0KCLumFKF/XyhEBa5G7cTpHuJhOLghcRIgp8Zh3rNs7ryANgcchiUpavwjckjLNe3Lim02wQH01lPRx/p4qkrBCytnjGGVBzCCzdsPAKBEFg3bWpOKwuTrw7s8HEXCh3XwOCXGDx5sGCcvImQJBc1DNUQ7eZjwtbuHFFLAb1RBRMUJSBK+5dgkyAd/83l476maE6NkRvwF8VgNLvDH/3kgM6U0nF6SLkvioJ6+E7diKkjBvpQNAsHPk9vJXTSL/VybXJKhxWC6IYhs3sZHFMqfRZr/zG53K+QwpPTqW5omzKooJbdHOs+RirIlchE8YOiSJT/Lnmx8vYcFMaPa1m3njoNAU1/XzsK5DQZqN397hWUZ8wmLdTCkt0THQx1OV2sMWiJG5REJtuSkCo+Qz2PgCPrYY/pmF7+0+0567DaP4KihANoV9LpHyVmaOKXsSE9aAYccboloaiWRBE755aHNO47wNuuhH3wAC9H4zwsQWZwOork9l48zwaz/Xw9h/OUnyoCY1egeB04+xpQ5WQgCCX01ZdidbHF5/guQvGerHkRYq6ivjxih8TqJn+PhiakIxcoRgOvpSpVKgTE75wUP8XSeujIiLRj9pCiQGvUUqItT3FbVN2gXmls8+B0wor7/T4dE9ZEYpXPyQn08D1Vz+I6BgJv2vosaBWyBjotBIQPnGxXaZWo05IwLB2DQHXXUvo9+4h6o8PE//aq6QcPiQVsD/6iJinniLyD3/Ad/v2GZ16dlg2PbYeqnul1uXACD22bhuI0NAzUoTx2x6PIBMwfuShxT37NrD1QtFbHo+hSUsdRnwUNfXym12lXDgvlK+tlcag2xaGc0VWFI9+Vklho+f5y8ChQwDo1899gfrtnEYqEzKQ5efgtowU4HP31aPUyFmwLnKKV0vXw6wtsXQ1DVBfMtHkc8PyWEpb+iiY5L15kqvPjsygRBhV0G0paEEU3RjbyknIXMqA3cVTh6u5cF7oebmnh/TtTckM2Jw8d6yO+Iws6osLUGdk4CjIJTTel6byHuwqJw7BSZg7mIfWPTR837s69WqS/ZN52FaPzTdcwnaFR9HWZ2OFRcAlikSvnwMsxaBEUSS/PZ/FIZ7b2avOnqK3ow0hVo2ltBuZVosyKmrMQslMpPcP4Mr7fonL6aC96mU0Jg2CKHCg4fwzPJxuJz85/BOUMiUPrn5wSoOTvbERZDKU4eGTbjOlLEZ46SooeQ+2/hq2/ea8QqbTAtN47eLX2B6/nb/l/Y1vfPINj8iPgo4CfnPyN6yOXM3dWTPEmC65BewDUHz+cxCAjbEbSdCsxaTbw7e26tGqvKtX6NUKnrw5G7db5I4Xz3oVgDcka6WR/v31aBcG4X9FMn4XxeOzIQZdVgjqRD/kgVoEuYBrwIGjsR9LURcDR5vo21uH8f0qev5ZTteLJXQ+VUj7X3PpfrkJw47/xVIUQ+MDR7BV9yLTK6UCanwCLb1Wlsb6Y7M46WoaIEKW7xHv4XQ4KNy/h8Qly/ALnVmIdeqqtWj0BvInCUsUBIGIZH9aKo1kDV6fchuMgBQiPlsG9RvlbxCmC2N99HqJmX7VU1IWwhu3gNH7YmmILoQb5t3ArupdVBnHzn/eymmkc8DGnRuShh9TJSdhq545b1kpl/G1hdH8SOZHXI+bPJ0L/0ujCYociyes7jChGZy/u61Sl7My2G/agERzXy9lxw6xYP0mVNrZhSxHz19EyorVnHrvTQa6PeTkjNJQgdptSCJ8tQxD7Nxmy8yFvihQz5EeyXkEg9LA19InBiLMRGEJvshkwuQF6pL3wNID2behnRdI4PVp2Ov76HqhBJ1MxqrEID4tbZt2ld81uFI3WoVNvWQFGOhtt3hVOJ1M9oZGVNHRM25RiF8UTIRdQGaOGYf4UA6f8xit/Z7UlnjgIY5VdTE/wpdA/dQryIqwMBAEKVl1EknpyjMbhG1dGEZdl5mKds+rw6Ut/cwLH8F7NJX1IIpMG5Do7LbSf6QRcZGej2XSIOrlT/6XeN94vr/0+zM6R09SquUkZARTldMxgb0HUJ3bgc3Ujk6mQpOU5DUXcUvcFhSCgk8advOji+ZR0tLHWzljuXEfVn1IqC6U5eHThwEZVqwguu0oohBAe61085L7qCRn/XCBWhrsuTo7cfX0oEkZKVB3Ng6Qs7uOlOVh/KmonnR/HStagznoe4aD9uNevafxOtt2lsUhi1HJJ37netvbqDx9gozN21GqpUUJ9XzJnTmZ409iUcfi7LRgLuiY9LgRflqEIOmYbbV9yAQZGwQfjroHsLlG2Op57XmE68MJ14cjk8tZctGlNJ0r8Qp5MJncNhvu/n4Uwd4XqAd6rOx5ugj/UC0X3jp/8utC8Tug8oEkCfcSGKkn/cJoSo610F43PdtyrmTqtXHuWAvzVkWg9xssCOqDIWrJrDjU/zhWi0wQ+PLq+Em3CQjXc8X3l6BUy3n3T7m0Vns/CVbKlVyafAkKnxLezD1Ha+/sQhcnk61+sDhtUA4Wpye2D6rGFKilgES3W+S5o7VkxvgTYJKut621WkJifQiveQTCFkG8d6y82SoyZR7W/j562yZHOpV2l9Jt7WZNpGeun0wmsHBdFF96cCWLN8Zw7mgLthYHh1wOBg410X9oHA9z6Veke3Tp+2MerivsJKCgD7mmh22G3yB/OAFevAJOPoFLHUN3yD/osP8vLn0agdenEfK9TaiSY1kevpwuaxc1vWOLSoIgEHBlMjKtgu7XyxCdk7fWazMz0SxYQM/LL08YGyxYE8kldy/GZLRRX9JN0uA9ydXRjDpRKj61VVfMaUBiTW8Nj+Y+yqbYTWyL886FpVAqCY1Porl8FIc6NQ1r+RcF6v8mxWcE01Hfz0CPdJ3btjCcjn4buQ2TI9amlcsBp56SAgND5094WhRF8n58FzYFpP/ijyjN0tRpaKGuscdMvK8GS58dfw8F6ukkU6tRJyZgWLcWv0sunnEA8TCHunWIQ63DZXfjhzDGJSj3U+OzIQbroItvjGJXQcj8ScMS1ampOBob6e0y8q1XcggyqPjjNYuRjTK5/OKShQQbVNz7zzxszokuTNPBQyjjYlEneEYfzFY1nSZy6o0EbL4Q0WbDdOIEgITsONvOwrWRqLUTF4fHK3V5GHp/Nbl7JoZBXpYZiVYp59VT3hdPXL025H4j90vR6aS9w43oasZuGSBxybIR9/SmuelOmRfuy/aF4Tx3tIbQ+YuxmUyYkxOwlZcTmaCnva6fPxz/XzoVRjYErCNYO9J1qpApuG/5fTQJLl4U3DhbWqgdXDwM6rHTJwhEps48wHEy1fXV0WPr8RiQCJC3+wN8gkMIWZeKaHViq+5FnZw8Y8THaAVFx3D593+K3dyNo+99YvuCOG6cHEPorZ4pfIaCzgIeWPkAYfqpi4SOxiaUEREIyqm7Zz2qrxme2wENJ+HKp2H1t2d5xmOlV+r53brf8YtVvyC3PZdrPriG062nh5/vtHRyz4F7CNWF8vt1v588FHEyxSyH4DQJ5TAHOtfaR0nRRlSCjncb/4TT7X2hOT5YzyM3ZHGutY8fvuldaKJrwE7362UogrUEXJuGYUUEPhfE4LctnoDLkgm8No3gWxYQckcGYd/OIvwHy4j86Uqif72WqF+vIeKBFYR/P5vQb2cRfHs6QTcvIOCaVHAV4Kjbi2F1JPrl4RhWR2CrrqYzSJprL40LpK26F1GcnD9dceII5l4jWVt3ev8HHJRSpWbhhs1Unj4+jE8br4gkP/o6rcRoVGiUsmGcqUynwGWeucGrvq+eY83HuDr16pH8Jo0f3PCqdC9+7UaPnTyT6bZFt6FT6kZCXgGny80TB6vJjPFn1SjjojoxCXtl5Yw6YUS3SM6eOt78/RmUosCq2+bTlqjlG6/k8ptdJWOwVqUtffgOYvfcA9LfSakarAtFer7OARR+ugeX00nmtpl/hqO1/qbbEF0ujrz24pTbDReorSLcUyxlu/2H6YsC9RzobNtZDjYe5Lb022bNAx6SUiUnJM6Hlsk41Gefg8AkSJAcCLqMEAKuTsVWaaTr5VK2pIVQ22WmunPqH7d7wIF8VECi0Sy1baSK0mNxC2cXggbgaGiYEd5jSHHpQchE8G9No8XUQp9dKkrJh2D8A+Na/X3CYMXXEQvfpLeugNXT8KdBcl0pgoNxtHguULsGBnD19s7IQQ2wZb40INlTNLEQ4nS5KW/rHxOQ2FDajVIjJyxh8nAJURSpefssdreDW6zf4U2T1Prym/i7eHnnyyQHzA0PNCU7FKvJQeO5sRM8URTJ3VuHgBFfYxeGGbheAjQBrI5azcc1H7MzPYysWH8e3lOGySbdzLosXRxpOsLOxJ1eDXRkej0J4Xbkgh89zVJLu9ygxGU0SdxIGHZQjw9IdLsktIdar6ApXk152wC/DgsCt8jJlHKONM2spQig395PWU/ZpPzp3D0fgsCYJF7NPKlFfTLMBwy6qMP19H9aPxEfMEoLUwKxyMRhDvVGbSRmQeRky8nhbfI78sdw/dIv3IJKq+Psh+968xY9ytUtDWC8RXy4HG52P1mE0+7mom+kT5787nLAuQ8h7SJQjrCpl+9MQOuj4tBr5VP+PeZS+Z824Ha5WbJ1nNM7eQs0nZFawQc13SCnz+rg9dMNXJwRMWV4K4BfiI4rvr8ErUHJ+3/Jo/Gc91zVK5KvQMSF4HOWpw7PXRqzvaGfzmeKkBmUBN+RMWayPVqq6GiQy1HFxyP3ka5z+8+1U9Np4ra1CbRUlKPSGujrUrM43YTQUQwrvu41L3G2GgpKnGpR5liTtPC3OtJzMvqQ1Dola69N4bqfLscvykBPP7QAvR/VYMod1bWUcIHkBDnznPRdKXqL5ud+xu7HzhCiqOY232+jHKiFpV9BvP6f9F9witaG72BuDsFwQTTh9y5Flxk6XAxeFi6F0o6eLA5JblARcGUKjhYTfZ9MLKoMSRAEAm66CVtFJeaTEzE/0fMCueqHS4lLD2L+UonT6mxvRJWYiMNuo7OhjrA5wnu4RTc/P/ZzNAoN96+4f0ZF74jUebRVV+JySvcR9bw0qSul71+3gPWF/r2Kz5AKarUFksPvwnmhqOQydnsYe3mtkvekHIuV3/T49KmX/kR4SRt1168hY976EXSDj3Q9bOi2kKSR7lsB4XPLVvZG0T7RhGpDR4ISI6VzSNVqqB/Xxu6zPgq5v5reD6rH3lMFQXJRN+dCU86EYwyNpx55eg+NPRb+ekMWAeMMIX46Jb+7KoPytgH+tG+sy9VttWI6eRLD+gvO+/2O1zs5jcgEuOCarch0OgYOSEFR+Z82IAAZF3o3H5ErZCzeFENTuZG2mrHXFB+NkoszIng/v5kBm3dFGVevfUy3ka28HKMuBrWsBkEmIyh1EU8drmHTvFAWz4F7ekjf3pRMv9XJoQF/EAQ6NEpwuwmWdyG6RU7k5KHwVRPkmDhfXRGWzWaThY/apXF2jtuH5QEG9A4XsijDmK7b89UQf9pTgbqzvpb6ogIyt+5EmxaEoJRhKe5CnZqCraYG0TH7cLboBYtYc903EJ1NrCwKIMfZjVucfdBqcVcxf8//OxfFX8RFCRdNu71kgpoF3qOjDJ7ZCsY6uOmfkHHNLM52cgmCwFWpV/HyRS+R4Irhndde4MxfP6L51yfY98zrDFj7+fPGP+Ov8Z/NziUXdeOpScNYvZXLLfKjtwrxUwXwkxU/pqiriJdKXprRPjakhfKDbWl8WNDCk4emHjOLokjPmxW4zQ4Cb5iHTD2z4rygkCE3qFAEa1FFGdAk+aNdGIR+aRj6pcFYc99Et1BOwBUpyDQmRIuFCm0IWqWceRE+tFT1Ighuwvy7IXjiOCx37y4CIiKJy5i8ADqVFm+5CLfLRdH+vR6fH+JQd9T2kxHtPyYocTaIjzfL30QuyLky5cqxTwSnwFXPQGsRvHeX16GJAZoAbl5wM/vq9lHSVQLAR0Wt1HebuXND0pgxpjopEVdv7/Bcdjr1d1t578+5HH+nioSMYK7/6XKWLI/gn99Yxc0r43jqcA3XP3mCll6pa6ekpY+AwW4GV480L1AyODaJ8Nwp4na5yN/3MbHpmQRFzbx2Nlr+YeFkXXQpxYc+pa168oW84QK1xfm5hNTPhb4oUJ+nRFHkT2f/RKg2lJvm3zQn+4xI9qettg+nY5wDob0U6o/D0i+Pmdjrl4bhf3kS1nPdbKgwIQP2l06N+XCPc1AXNUmDMR+jE58gDX6hUxdTJpMoitgbG2cV/hCe5IdMLSOuT3LjDWE+ZAZpgDfBQQ2w5ju4lHq+LbzhVYEaQBEZgXMSBrWjSRqUzfT8Q301ZMX6e+RQ13aZsTndEwISo1IDPDLxbC4b71S8w49e+g7aSjfvhn7GFUuu4ckvvwdAhHFGpzatYhcEodIqqDw99tyby4201RpxOY0YLNYZcwN3JOyg1dRKXkceD+xcQHu/jZ++V4TN6WJ37W5cootLEi/xen9+a5bja+rFaR+gtywPWcdx3J1dUPYRrPwWhErOzaH2v6EJVf6njbTX9bPk8kT+dKiKnXFBBFX2oV8azvyUDAo7C+m1ee9YBWlg7Rbdw86l0bJbzBTt30vqijX4BI1ypwQFoQgNxVY6uVtDkA2yqDstWPInd1EvTwiiWeamcXAha4VPIjq3yGcNnwHQZmqjxdQypnVSpdWRsXk75SeP0tcxPQbIk5ydUtuQInhq1vuQjrxZQVtNH5tunT/1xL1qv+Q6XXjFmIdVWgWrr0iiraaPcyfOowDhpWxmB0WHmkheGopfyDgnXMoWEN2IlfuxVhppfyKf1odOTeR5jtJrp+oZsDn52jrvUDw+gRquuHcJhkANHzyST/Fh70KZkgOSyQjOICAsj1dO1tFtOn9ut72xn45nCpHplYTcnoFikuI0gKBUoklLQ7d8pBvi2aM1RPhpuGhROC0V51BqItH5qUnuexq0gVKoz+es4Jg4FGr1lAXqI01HWBC0wCMD05MCI/Rce+8SPvR1cMLupsPhpvv1MrpOD95TZDLpHl1/DP4nkY5XH2TXqeXoNBZyfWr4cMPbcNcpbGn30b47iN6PGlHF+BD2nSX4X5SAbBwGJsYnhjBdGKdaPfPjtQuC0C8Lp/9gI7baya9jvjt3IPf3p+fllz0+HxCu5+JvLcZvsKAi2gZQJyXSWVeL6HYTlpjk8XUz1avnXiW3PZcfLf8RIbqZIUMiUtJw2m101tcCoEn7Iijxv00B4Tr8QrTUFEj3Ih+NktXJQewpnr5zcFKdeAyCkiF584SnerqaER95hqYoNRff+1dAQjfAWAd15KATLCBsdu255yNBEFgavnSYQx0QId1r4xTKCQVqQSnHb0cCjlYTptPj7qmLrwOlTjLCjJM6VfqtNecWce/WVLLjPXf/bUwL5brsGJ48VEXOqOBw86lTiFbrjIwO3sjtFnk7t4k1ycGEB/mgX7OagYMHsZrslBxpJnlZKD6BHgKZJ9HCdZGodQpy905c8LthRSxmu4sP8r0Lb3b1jXVQ95/Jpd8nFpfYSGTqfF7N66TX4uA7m+eO7Q+wMNKPzfPDePZMOyHxSTR3SeM9se4YLsHJYudqoiPjhr/HYzTQzr3d3YR2S7+lw2Ydl8nUCIJAyIpZIikmUV5HHr4qX49hgrl7PkShVJF+4VZkKjnq1AAsxV2oklPA4cBef37M1CU7tqDQrUPbaye5wo+KntlhQ6xOKz85/BMCtYHcv/J+r15jn0WXLg2n4Nlt4LTBVz6CpI2zONvJ5TY7MBd20PN2Bb5P9fFg/u18s/VaxHYbebJS1jVm8Gz/70jVn8d3dfH1IFNCztTuzun0wvFa8huM/OySBVyVdjEbYzbyaN6j1PVNvkjvSXdekMTO9Ah+v/sch8onn28NHG3Geq4b/x0JqCLntpinWybNIc2nJQPCEF/9NH4sjvFDKZfRUmkkWNWIKmHpBFNHW3UlLeXnWLxl55jMpZkoICKK2PRMCj7dg9sDfzw41oBCKZ1HVqw/Jc192Jwu5LNAfNhcNt6pfIcLYy8kVOchtDZ1K2z+ORS/DUf+5PV+b1lwC74qX/6a+1dEUeTxA1UkheiHzYNDGubYV02PQ6w408brvz5FW10/F94yj213LJIyiQC1Qs6vLl8kOfFb+tj5yBEOlXdQ2tJHyqAZydXVAkolCnPFlAGJVWdP0t/VQda26XGn3mjlldeh9fHlwAtPTzomEjQKEMBtmf1C3+etLwrU56n9DfvJ78jnzsw70SpmV9Qdr8hkP9xOkfbacSzSM89JSIvMiYVww8pI/HYkwLkeHtL68GmJ52KO9dw5qi+7HEdrN7ZzhVjPScWywqZeZCKYG03ELgicdSuvq6sL0WKZlYNaLpcRnhpAvMUfRGEY8zHioPbwQ9IFcir8BrbLT7NS493NSRkZOSmDeqhAPVMHNUitpoVNvTQbxxasSluk4v9QQGJvh1lqlxmH92g3t/NIziNs+ecWfnH051xZcwE2g4s7v34fdy+5m/DgeBQhIdjr5hZmL1fKSMwKoTqvY8yiSM7eetTaARBFDKIM3ZIlM9rvxpiNaBVaPqr+iKVxAdy9KYW3c5q47okTvFX+LvMD53uXAD0o/Zo1hHdKK915f3sYuTEHN76Id+XB9t8O37ht5eXIg4JQBAVhbDdz6oNqEhYH80ZbFya7ix8MOjx9NsWwNmrtMIN2JjrTdgaFTEF6SPqE54oPforNbGLJjssmPKeePw9r6dTuAclFraNv/+Qu6uUJgbQq3Ay0W7BbnagMoayxWDhQ/xlu0U1+Rz4AmaGZY16XtV1aEMj5+P3xu/RKzi7JteYNg/rciRaKDjaRtSWWpCWh0mp4XzNUfQYnn4Rd98I/LoaH0+CVaxFVvpA0MRwibUU4YQm+HH+nEptldrxwb1V4sAmH1UXWtrgJz4kRmVgVa+j4QKDz6UKcXVbcdjedz5cMB9WOlsPl5h9Ha1mZGDhtuvho6f3VXPXDpUTPD+TAy2Ucer0ctwf8znhdkXIFJrEJu6KWZ494YIzOQPbGfjqeLkKmUxJyRzoK/+lTwWNfeJ6w+38CQElzH8equrh1dTxum4WupgZslmAWLdMjr/gQsr8Cyrm5X04lmVxOeGLKMLd4vPrt/eR35E+K95hMaqWc0HkBvB/hhgtj6XOL9L9ZwdkXS7FbnbD0Vki/BmP2L/nA+mdUfgGE3biM18Q1xIUm0vXqOTqeLMRtdRH0pfkE37YIZajnwpYgCCwLX8aZtjOTDjb9Lk5AHqCh+41y3JO4+2QaDf7XXE3/p59OibhyD7ZsinYTqsQkWqulCdNcOKgb+hv4S85fWBu1dkaLk0MaCkpsLpeuoerBArW1bPogzC/0f0OCIBCfEUxjWbf0WwO2LwynvttMacvM+P0ANJyGprOw4hseOa6f/fwOfPrdRPziF6hVgxPPweBema+KAZuTHrODAJeATC7gG+x9MXQulR2WTYelg/r+erQGFVofJaGijIbuiQuo2vRgVPG+9O2tlRxUQ9L4waKroPBNKXh6lCoFA2aFmjVCD99YP/Vi1QMXzyfCT8v338jHYpfGlAMHDyFoNOiWLzv/NztKp2u7aeyxcNUSyVRi2LABZ2srBe8U4LC5yNw8SebFJFJpFCxaH0VVXgfGtrHF/awYf9LCfLzCfIgOF26zc5hTDtB2thKXYMfc10JkehZPH6lh8/xQMqL9Z3SO3ug7m1LotTjoDkigpaYSEhM5d+hNunwaWWRbgdJXg6vPNvGe0tdMtNPFDqdUNG4INBHf68QNhGTNjG87nXLbc8kMzZyQ/WAdGKDk8GfMW3sBWh/J2KNdGIS7347cXxqbzSYocbQUSjkh8Rcg1yeRXu3HZ+89P6v9/CXnL1T3VvOrNb/yqoPabbHg6uycmQmq7GN4/lJpYf+reyd1Ys5EosuNrbaX3r21tP8tj+ZfnaD75XOY8ztQRhrwvzyJsO8vpe5mFz+L/Rtns2rxa1TS8fd8nEbb9AfwJH0wzNsB+a9KhfZZqMlo4Q97yrggNYRLF0ciCAIPrHwAlUzFz4/9fEZOeEEQ+J+rM0gN8+Hbr+ZS1zWx89zeNEDvxzVo5geiXz01x342UiUmIg8MxHxa6n6xDxZOD9p8WBoXgMvpprW6l0h5HsStnfD6vL27UKjVLNyw6bzOI3PLDvq7OqjOmRgYKpfLCEv0lYISYwKwu9wUN/ch0ytwm50zWhjeV7cPo83INalTGFTWfFe6D336IHz0A+ibPgTZR+XDbYtu40jTEZ47+xmlLX1844KkMQgqkBzUMPJ39iS7xckn/yhh79PF+IfpuO7+ZcxfHemxLnbp4kje//ZaQgxqbn3uFCeru0nUS/MlR2ujhPJpzZsS75G350N8gkNIXDo390a1Ts+aa2+isbSIylOeEaaCTEDQKMbe///D9EWB+jzkdDv5S85fiPeN5/Lky+dsv0PtFM2jMR92s5SwveCySRPGfdZH47s5ljUWgTW1FoyjXHSiKNL9wovUXnMtrp5eBIUWS+EZai6/gurLr0Dx1quslrlw2lzEngfew97QAIAqZnbpxPOXhmIQ5YSbU4ZXtIcY1JOt1D1m3Uaf4IP+6O+9OoYyIhJHS4vHi6qjcbBAPQsH+NYF0uBt3zgX9bnWPhQygeRQaeW1YTCAJWa+xHIr6Cjgh4d+yLY3t/F04dNkhmbyctQTRJtDibh0IdpRwHxlXCz2hrlPW03NDsNudVFfJJ1bV9MA9cVdRKdJF+TgefNnzEXUKXVsiNnAnro9OFwOvrcllcdvWkJFdyUVxnMs8ptBUm3TWTQFvyXUIi28lNmXIWz6DgAu2Vg371BAoiiKHHjpHDK5QPjGCF493cBdmdEoSroxrIhA4a9hUdAi/NX+HG48PKP3drbtLOnB6RMWpUS3m9zdHxCRnDZcTBktzbz52KqrcdsmH5wJMgGfTbE4OyxYJmFRxwXpsBikNrOO+n7QBXOhyUyntYuiziLyOvLQyDWkBaaNeZ1vcAhpq9ZRuH8PNvPME6xdXZJrbUrEh8tBR34RB14sISqsn5X8EZ7cCA/FwP/Ohxcvh49/AAVvgMOCM+YS2rWv0mx5hZ4PG7C3jB0kCjKB9denYhlwcPrD8yu8TiWH3UXB/gZiFwYREjOC4xFFEWtlDx1PFdM58GNcAwr8L0kk4gfLCPrSfJydFrpeOTch2PKjwhaae63c7qV7erTUWgU7v5VB5uYYCj9r5MNH87FO41TYHr8drUJLQkIRzx+vpc86u5Vxe9MAHc8UIdPKCbk9HYW/dwUXucGATC0NzJ49WoNWKeeGZbG0VlaAKCJXR7BI9b4UiLLs/LIaZqKIlDQ6aqtxemgJPtlyEpfoYm3UxIH/dFqbHExFl5mItRHE3p2FWyXHv7CDd35+nLJ8K/0bH+X9Y8sQBQWXfjeTmgErN6Ii/PVKLMWd+GyKJex7S9EuCp52QXh5+HK6rd0TQmCGJFMrCLw2FVePld4pfiP+110PQM/rb0y6zVDLpug0o4qPo62qEp2f/5hukNlIFEV+cewXyAQZP1/181ktgvsEhaAPCBxecFCEhqJfswa5r880r/xC/5eUkBGM2ynSWCo5dDcvCEMmwO7iWXTZnHgM1H6w+IYJTx3+7AXSPq2iefMiFq67fPhxV68NQaNAppLTOBhCqLW68QvVIfPQFfev0HgOdUC4Hr1VpHPANiEITBAE/C9Jwm120vfpuPFk9m3gMEP+68MPDdic3PVqLk0BkawUjBMm/ePlo1Hyh6szqO408Yc9UkjtwKFD6FeuHL5HzJXezmlCr5KzdaE0/jasX49bkFN0spvoeQFj7uXeKuPCGORyGbn7xv5tBEHg+uUxFDT2Utw8ddfdiMteer+iKNJaZ8LlqAUg1x0huac3pc74/LxRerQfF84L5aNuX0S3m6IwOWG1faQtiqG3yQY6JaLdjWgb55jsk+ZAmfYIjHoB39CT+FndOP3VCMq5+24brUZqems84j2KDuzDabMNGyoAtPMCQSbg6tOBTDbroMTRCo72QaXfRlewiYE9BVSdPTn9i0bpRMsJXip9iRvm3TAtImxIwyYob+eYZ5+XmLyh86XidODs+O2iKOLotDBwvJnO54tpfvAEHX8voP+zBhDA58JYQr6RQeTPVhJ8ywIMKyNRBuu4OvVqjt5wlMuuu5ngLy/C2W2l/W+52BtmsRgIEubD0i11vs7iPfz03SJEEX59+aLhMUSoLpQfLPsBZ9vO8kbZ5GMbT9KrFTxxs4Rq/Pq40ES3zUn3q+eQ65UEXJ06ZxkcoyUIArrsbMxnpOu2raISd1AwfQoN2XGBdNT343KKEn86fqyRwtLfx7kjB1mwdiMa/fk5u5OyV2AICJw0LDEiyZ/Ohn4WhUrX09x6IzKdEtzixGvIFHqj7A3ifONYEbFi8o0EAS59VPqunHkW/rIYPv7RtIXqG+bdQJAmiCcK/kaEn5rLMicaDRXh4ch0OmxVnrEuLVW9vP6bU5SfbCV7ZzxXfH8J/pOYR4aUFGLg3W+t4cqsaOwuN3GGwQJ1Uy3K8FAJyROZ6fG1XY31wygj2Uy57lMo/cJtBEXHcvDlZz3OfUDCfHxRoP4/Kpkg4+sZX+e+5feNgN7nQBqDksBI/VgOdfHbUsJ29m1TvtZnUyymzGCuQEXlP88hiiLO7m4av3Enbb/9Lfo1a4h98TUAQu7+OmE/fQBBpWLtvpe5Lv8ogujGp/YUbuvsgrYcjVJg1Kz4WkjsaxGI61xGWbc0CRXUclBIybjjNWBzcqLJQU7MrVD5CdRNH3injIhAtNlw9UwM1XE0NSJotcgDZh4EkhhiIDnUwN5x7vXSln6SQgyoFdLFp6G0B0OgmuPmQ9z00U3c9NFNHG48zA3zb2DXlbv48+r/JfCEgCreF2362KKAKjYOxxw7qAGi0vzR+iipOCMV13P31aNQydALktMufN3suIE7E3bSa+vleIv0uVyUHsHVG9pAlPGPvf78/WDV1KuvdcfgxSvhqQsRGo4RnBoNIlgsA3SbBxcu+kctxLhc2Cor0aSmUnKkmaZyI6uvSuZ3ByoJ0Km4waFAkMvw2Sg5/OUyOasiV3G0+ajXK+9mh5mSzhKP/OmavLP0tDSzZMelHl+rmT8PnM5pQ160C4NRhOnom4RFLQgCsSn+ABIrUR/MOosFuSBjf/1+8tvzWRC0AKVsYgjL0p2XY7dYKNy/x4t3O1bDiI+gILD1S6zK/Nekle7XboJHl2P9VTK7nyxAK3ax1f1tZLUHQOMLmTfAjofhlvfh3jK4rx7LqtdpK70Kh9kfzfxgTGfbaf9LDu2P52POax8OfguN82XBmkgKP2uku9n78IyZ6NyxFiz9DpZulxw6oihireih44kCOp8uwtVtxX+pkXDVVzHEtyEoZWiS/Qm4PBlbeQ+9u0YGPKIo8syRGhJD9GxM89DG5oVkMoE1V6dw4S3zaCo38tb/nJ3g6Botg8rA1rit9MpO028z8eLxmbU7AtibB+h4uhCZWi5hPQJm7gbs6Lfxfl4zVy+Nxk+npLFUYsKlLJmPruQZaZHVd+6dKJMpIjkNl9NJR630+fTb+ynsKOT9qvd5seRFDEqDx06I6bQ6WVqkOVrZiU+0DzHfWYJap2CpTODw86W8/PMTWM0OLvn2YrR9dlYdaOObaNAk+xN+z1L8tsQh8zJ5fohDPRnmA0Ad74fP+mhMp1uxlHhO8FZFR2HYuBHjG29Mukg25KBWhPgjU6tpq6kkLCHpvCdnb1a8yanWU9ybfS/h+tm1iguCQGTKPJorzg3/O/aZp/G71PP19gv931R4sh9qnYKawQXcYIOaZfGBHjNAplRvo8SfXnrLBA5jj7mbvt88jFknZ92Dfx/z3Gi28LBDuc9BwCwCEudKCX4JBGoCx3CohX4niHh0UauiDOiWhjFwrBlHx6j7StQSye115lkQRURR5IF3CqntMhGbnYGryruAqdXJwdyyKo5nj9Zw+lAujoYGDBvmlj9tdbjYVdjCRekR6FTSHEwREkJ31mVYHAqytszMPT0kna+KeavCOXeiBVPv2OvkFVlRqBUyXjvVMOU+hjnlgw5qZ3MzPbJgBFc1+oAgnim2sHl+GOnR55dbNJW+fWEylQQhKhQUY8TXAqtioxDdIr2DC94TMB990pjf3Wqk3TeUzN54/OTgu2j2xiVPGu7yG5WTAuB2u8jbu4uoeQsIjR9Z3JfplKgT/bCW9aKKiTmvoMQhBUcbEB0afBM66Pa18+Ff/ofWSu+6cfrsfTxw5AHifeO5Z+k9Xh/TPjRHjppmjiyKcOD38MHdUnD4rR9IDuQZSMJ2dNLzdgWt/3OatofPYHyvCkerCV1mCEFfmk/kz1YR+s1M/LbEoY73Q/CwwKaWSwU3TWoAod9cjKCQ0fFkAebCybEYkypxI/jFzCos8cOCFvafa+feranEBI691l6efDmrIlbxp7N/onnAOwTPkOKC9Pz1hizK2/r5wajQRON7VTi7LARenzbcyf15SJedjaOpCUdzM7aqKowh0ncjK9aflkppISzcp0UKsR2logOf4HTYzztYD6Ruw/RN26jNz8HoIVQ8ItkPUQSxy0aUv5bc+h6pQM3k5sHxKu8plwI4U6+Z0DUxQSodXPoIfPssZFwrhRhPU6jWKXVsj74Rs7ycrUv7USkmHkMQBFRJSdiqxl4/3C43Jz+o5p2HzwJwxfeXsuKSRI8YVk/SquT88drFfHT3OhYE6BE0chxNTSj9B419EZkeX5e7ZxdypZJFG7d4dRxvJZPL2XDL1+htayV39weet9EqEL8oUP/flEyQsTNxJ2uiZtYe7I0ikv1prerFPVScOvOclIAbu2rK1wmCQNI1aXykcBJ+rpeufxyl+rLLMB0/TtgDDxD9+GMISolPpwzxI/Cmmwh8/iVu3/RDWuNW42dppPO+71Oxdh0tP/0p5jOTtxV70pCDejaIDACtjwpXgJLI7mQqjJW43C4EQUCuV00MSURq73O6RVSrvgGGMNj/q2nB+spIKUzPE+bD3tSEMspzK4c32rogjBPV3RjNI+d6rqVvGO/RZeqmuqSVIs1JfnT4hxitRu5bfh+fXPMJP1z2Q2J8YujbX4/b7MD/kolFAVVsLM6ODtyzcL9OJZlcRtKSUGoLOulpNVFxqo0FayLpKStC7XASuGl27UOrI1fjp/ZjV/UuQArIOtq6l9WRq7loQSq/+/gc33jpLP2j3Z6iKDGJn9sBz10ErQWw+Rfw3UJ8r7gNjcOBTGakatA9NXrhwl5fLy0+xKZx7K1KotL8qfKBU7Xd/GxlAs6iLgyrI5H7jLjB10Wto9vaTWmXd8Ed+R35OEWnR/702Y/ewxAYRMoKz9eEoaBE27mpU8MFmYDvNC7qpanBGGVuast7QBeMn1sk2zdJCoroLpmA9xhSeFIK0fMXkfPx+7hd3q9847DizNuFTCUge3wpPBQNT22Ed74OR/8CneWIQSns448MiGFs+3IcugeK4d5SuOU92PEHWH47JF6AqAml5/0qul4qRRGsIezuLIJunE/kT5bjtzMB94Cd7tfKaHnoFL27a3B2W1l5WSJKjZzDb5TPnjU6iVwuN7l76wlP9CM8yVcqTP+9gM5nBgvTlyUR/sNlGHasQhCc0mLYoPTLwzGsjWLgWDMDx6WB8amabgoae/nq2oRp3WbTaf7qSC67JwurycGbvz8z3IHhSVemXInVZWFRai3PHKmZ4JybSvbmATqfLkSmkhNyRwaKGXA7R+ulE3XYXW6+siYegMozBQiyQJbElkmLrCvunNV+ZyKX20VjfyOHGw9zxF0AwMMf/IyNb2xk9aurufGjG7n/yP0UdBRwZcqVHhdyptP8cF8C9SqOVknYG2WwltCvpaNVCmyJ0RMWpWfnLfOQH2qk89kiHE43/4hUEHzrQhRBM8ObRBmiiNBHDBefJpPvljiU4Xp63q7A5eF+CRB40424enro+/hjj8+7zQ5EtwN1QiwOm5WuhnrCks4P71FlrOKPZ/7IivAVXJ1y9XntKyIljd62Vsy9xvPazxf6/1dyuYzYhUHUFnYNj5G3LwqnrK2fmmkCwsfo1FOACMvvmPDUW/97J4mNDny+dxeawJHinOl0K9bSLlSDztzGHjMyEaw99n8Lf3pIgiCwNEziUIPEyhftbgwiEzjUQ/LbFo+glNH70biui+zboKMU6k/wzzONvJvXzHc3pxKbnY67txdn28SsFU+676J5xAXp+PiZtwAwrFs3+zfoQXtL2hiwOblyych8QxRF6kLWoB9oJiJi9vfezC2xuF0iBfsbxzzur1OxIz2Cd3Obpry/jndQm3NyMPrE43LUYw5Loc/m4rtzzJ4er6zYAFakaWgIMKN2D6LtOsuRKQQ6u6yD5zluobKvEeRqrPVN1ChjWWtcgyAIKFLnNvwztz0XhaBgYfDCMY/X5J6lt611jHt6SNqFQTg7LajTlp434gOkAjVA+kAw+7LbkOu1vPM/D3os0I3XQycfotPSyUPrHpoR3nOkS3eKObLbBR/eAwd+C4tvhBte9SrIbBjbsa+O9seGsB2lErYjQsJ2hH8/m/AfLiPgihS0i4KHw9K81f9j763D2zjT7+/PiG3JJMvMFDOFwWFO2jRQTJrSppvilnG77Za23G6ZU26atE3DzMx2HHBsx8zMsoXz/jGOE8d24kC3/X3fnuvyJVkajUYjzcz9nOfc5yi9tHjem4TSV0ftDydp3FJ0cfW4TA7JN0tWf3W9F1HUG808v+I4Cf4u3D6sq4pcEASeG/ocIiIv7HnhoscII/p48PikKFall/HJtlxaUisxHq7EaUwg6lDXi1rXxeJsH2pTTg55Ok/CPXW4Oqooy6nHRVmFNjSukwWV3W7jyPpV+EXF4hF0aar6cxE/diKCIJC+sWtt6B3igiBA2al6kgKloESZtj1oz9i7ccbizMWoZCquCetqf9kj3ILhmg86E9XvJcGaJ6Gp63GadSoerK5kmhf3+BtwSEjAuP8AzdukMN2GKiNL3jzMwVX59BnkzQ3/HIhP2KVNHMb4OkOrFZlGIVn5OLSfX7ux5TEZWzixbRNRQ0fi6HzlJyqDE/sSktyfvb/+hLGxa8ePzPEvBfVfuAT4RbhibrNJQR1l6VByUCoae0GcKuQysqOdyGnIoy1TRBU6juCfF6O/eQ6CIHTMdp22zjhe2kCt1hOz6ETkjSMJ/PornMaNo2HVagpunkvO+AlUvfd+r0IpLEXFKDw9kWku3YfPPcIFrzY9slYlhU2FHdva3SzdnpwaVHIZyWG+MPxRKNgFuVvOu36lz2mCumsAmaW4BNWFZrbPg4mx3tjsIptPSqEkDUYLpQ1teOhreW73c9z8zTwwy8G/hQ/HfsiKGSuYEz0HbfukgaW6leZdpTj280Ll17UgUQUGYJbL+OnfT16yj3BPiOjvhdViZ80nRxGBxLEB1JWV4iRTdOyzi4VSrmRC0AS2FG3BaDFyoPwAFcYKpkdM44ObknlmajQbMyq55oNdZJU3Sl5rX4yF72ZAbS5MehUeSIeUh0DjjG7oUHQmCwqxivz2DoOzFdSmrGxE4FCRJ3abyODrI3h1zUlifZ0ZVmZCUMlxGtn5+x3mNwwBgR0lvbP5OFhxEJkg60IAVxfmU3g0jaQJU5Erui/6lIGByBwdaTtPUOJpOMS1q6h78KIeGKKnTG6nIr+xQ1kxWhdCYVMhVru1U0Diueg3dTpN1VVk778I7+21T2LLSUPuIIPg4TDmX3DD93DvAfhnOdx3gAO6Fyks1zP8xii8Bw4Cddf2WkuVkcqP0mjZU4YuxQ/PuxI7CDuZoxKn4f54PdIfw9/iUAU507StmPI3DtDyazbDBntTfLKO3NRLUG6cB6cOVNBU20b/RHeqPz0qEdP1bVJB//gAdEN8ERQyaT/7JkP2hk6vd5kSgiZKT/2KHNqy6vh8Rx5ujkpmJl/6ueRs+Ia7ct2T/dG5qVnxwRHStxR3W3wleyYT7ByMyvUgtS1mFl5A5XUa5rIWqr84iqCUSZ7Tl0hOt1lsfL+3gDFRnoR66LDZ7NQU56B1C8Ij5z3w6wcBV86DtMXSwvHq46zMXcn7qe/z8NaHmbl8JgN/GMjkJZO5Z9M9vJ31Ia0aO5pKC8P9hvNQv4d4b/R7rJi+gv037+exAY9d0nvLZAJDwtzZdaq647tQ+TvhPjcGeYuFISoZ4pJTtGXVoZsQxC00Iwt3vaT3Ou1DfaD8wHk7PQSFDP2NkdhbrdQt6V7p6DhkCKrQUOp++LHbddibzYimZlRhoVQV5CGK9ovyn7aLdrLqslh0chGPb3+c8b+MZ/qy6dhFO88NvTRrj7Phc9qHugdf8b/w/w+EJBpoa7ZIHUTAhFhJlb+utzYf5hY49DVEXw2unZW2G9OWkPhLOg3RfkTOnt/xeNPOEup+zUYd4YbrNZIPc1FtK14yOaJd/EMV1CDZfJS1lFHSXNIRlOhuk1HUA0Etd1LhNDqAtoxa2rLP6iiMmwVqZxp3fsqzy48xLNyde0eHnwklzeqdytRRpeDN6xKJyDtKvVfAJQtXesKSw8X4umgYHHJmAqEoo5YGk4bAoo0Yd+685HW7ejoSluzBse0lmM8ZxN84IIAmk5VV6T23nJ+roK45cIxWhRG73cymZnfGx3hdVDbFpcBmt2F3/5ESgxFVm0CrmyuW9FS8Q1woLWlu386uCmqbxgextoYq13D87TrMWPm6buEV3bbUylSi3aO7kLupa1eg07sTPqCrGMshRvqe5V4JmAsKzmuV1xu4txPUupYQLCoR68wY7FYrS179N61NjT2+bn3+elbmruTvCX8nzhB3Ue9pKS5GUKtRePQQEGxphcW3SEGlKQ/D9I9AfuEJ9KadJWdsOzZL4+bubDsUBofLvgbLdSo85sXjkORB47p86n7O6uh27BVO52ildR/Y3B1eXpVBndHCqzMTkPcg+vDT+fFg3wfZVbqLZTnLer897Zg/IpSrEnz4cW0WNb9moQp2xnnMpXVhXAzUffogc3KiYdlyxNZWDsvc6BfohiiKlGXX4aNI72LvkZ92mIbKCpInXZlgPQAnvYGwfoM4tmVDF1sIlYMCd39duw+1KyX1rTS015c244UV1EaLkZW5K5kUMglXjevFb5w+5AxRHX8t7P9MUlSvfaqDqM6qaGJzRi0phps4XnOMrUVbu12Vx0MPoYmMpOiBBzmycC+LXjpAfYWRCfNiGXdbDKqLnLQ5F/ZWK4JCOh6UilqJZO8mIPH4ts1YTG1X9Ds8FyNv/hsWUxu7f+5a78scFL2eXPgj8BdB/SdFaF8Pwvt7sndpLju/24cod5AStnsBc0EBc358Cc+tr2GylaLwGYO1zrnj+dNhg7L2lpXjJY0EW6VW48BYd7SDB+P76iv02bkD39dfQxUYSPXHH5MzYSL5s+dQt2gxtsbuL96WoqJLCkg8G/EDpIFGYH30maBEnRJbQ9dAj9051SQHuuKgkkvBVC4BsOn8KmpFO9lqLetaXFpKSi6riI73c8HbWcP64xXY7DYWHl+DQ+DnLCp7kNW5qxkjuxoEePH6JxnhP6JLm0vDqlwEhQyXicHdrl/uH0BakBdlBbls+fozjmzoXgV3KfAJc0Hrqqau3Eh4P0+0GhuNFhNu3pdGTp/GlJAptFpb2Vq0lRU5K9ApdYwOGI0gCMwbHsqPd/RngHE7to9TYOGN0FIFV70DDxyBwXdLrT7tUPr54aR2wNxai6n9K7Z1IqizqPTsS2G+mYHTQvnxeCmlDW28NCgU04lanIb7dbQlnYZeoyfWPZadJb0bzByqOES0PrpjUuE0Dq9ZjkKpImHcpB5fK8hkqKOiaDt5YbV2h4q6spXWblrp+ng6Ua8RsLdYabFKA53RyjMDtfMR1GH9BuLq7cPBlb/1TmmQ/jMc+gqrJhhFaALM/BRGPCoN7j36gFxJ/tFqDqzKJ2qIN7HDu7dwaEmtpPL9NGwNJtxvjcH1qlCJ+O3ms2si3DDcEoP3EwNxGh2AuaQJp8MVTHRTUvRzFqa6S7MhOhd2m53cNfmM1quQbynCVm/CdXo43o8NQDfYt+v2RYyXJgyNZ5TMgkxAf1MkSk8tVd+fICejirmDg6Tz0hWCs8GBmY/1IyjOnR2Lstj2Yya2c8ITBUFgRsQMshuPkhxm5rPtOZis51fJW8pbqP4iHUEhk5TTF6nuPRvLj5RS02LmbymSouP49gxEeythEW5Qc+qS1dMVLRXsLtnNDxk/8NLel5i3bh5jF49l8I+DuXHVjTy14ym+PPolWXVZ+Gn9mB09m+eHPs83k75h+w3bSUhMIcSo54VhL3BH3B2MDhxNsEvwJSmnz0ZKuIGKRhM5VWdUm5oIN/TX9cFabcQhRo/XI/2pjtPTYrPTx+vSvZIHeA+g3lTfkc3QE5TeWlwmBtN2ogbjocouzwuCgNuc2bQdPUprenqX5621zYimJtShYZS3t0F6hfYcimaymThUcYgvjn7BPRvvIeWnFGYtn8VL+17iQPkBEgwJPDnwSX69+lcCnC6vNpC2JRyZXE5ZVu86Xv7C/00Exrojkwnkt3cY+bk6kODvwtre2nwc+Qna6mHwPZ0ermmtIe+1F3EwQ/yrHyAIAqIo0ripkIaVuTjEuWO4JabDnqe4zkiEgzSh5+p9ZVWmF4vTtmMHyw+ibyeofWWKHhXUAE4pfsj1GupX5p7JUVBpscRdjyZ7Bf4qI+/ckIRcJqCOkCaq2jJ7PznU16AmsTaPDU5h7Mi+chPLlU1tbM+qYkZfv05dSqnrC9G6qPC159O8ddtlvUffiUGYW60c39HZMmBgiJ5QDy0/Heh5AtjWYEZQy5GpJbKj7GQ1dkseCHKy5D48MPb3VU8DLDi2gBP1h7DrJwBQGx5Ca1oavn1cKS2Rrlln188ANJZitkrkqUbfB4NCRqW+kW+yvqWg8eJtw7qDxWbheM3xLiKP2tJiCtJTSRw3uVuRh9xFjSrACVH0Arsdc97lZZI4Oqtw0CmpskYSaZWz15TONY//i8aqCpa9+TI2a1fircpYxQt7XyDOPY47E+686Pe0lBSj9PfvniQ21sK30+HkKpj8Box7rleiNGNaJQ0rc1EFOqGfE43vvwZf0LbjciEoZehviMR5XCDGw5VUfXEUWy+tHnANkALRU7+X1OIXwO5T1fx8qJi/jwiVFKrnwY1RN9LXsy+vH3idKuPFnW8EQeC16XG8otDSYrVjnBCIIL/yvtNd3lcux7FvX1p2S4Khk2oD/YLcqK8w0ma04aPMgKDOHuep61aiddN3O5FzOUicMIXWpkay93YdD/uEuVKe10iinysAGQ3SdaU3Fh+r8lbRYmk5fzhib6APgWs+hPsPQty1sO/TDqL6hw37cVTJeWHMrQQ6BfJB2gfdCjrkOi0e737E8fi/s3ObEYOnghueGUhE/ysTAiupkqV9orQWdmvvIdrtpK1biU9EJF6h4VfkfbuDu38AieMnk75xDTXFnUWmf3lQ/x9HW1bWJfs1nw9yuYwJd8SSMMKTI7lhbLC/gk15/tl2URSpX7qUvBkz0VSV8cqgufwyMl5S9i09RcshqS3v9EXktKfS0ZIGYgQlDk7KToEiMkdHXKZNI3DBl4Rv2YzHIw9ja2ig/LnnyE4ZTvGDD9G0ZQviWTNt5uLii0sn7gZxMQaaZSJBdXFk1UpKDXWoK5ZyI01bzxSE9UYzx0sbGRrW7sulUMPIx6H08HkDGOSurgiOjl0sPmyNjdibmi7ZPxuguq2KPuEZ7Gh4hxGLRvDhiX8iU1UzL/Y+Nl63kbCmBDwCnNDouhIjbdl1tGXU4jQ6oJMFxdk4dHgP1U6ODI5OJCS5Pxu//IiMnVsveXvPhiATiOgv+eUmjw+kZstmLAo5hpiLUwici75effFy9GJJ9hI2FGxgfNB4NAoN2Kxw5CcGrZ3Ka/a3cFbYeNh8Fy+F/oAl+Tbp++wG+sBgrNjxi9JgEcF6lkdgU2Ye2ZE34hnkhD7JnU+35zIt0Rf/43XIHBXoUrqffEjxTyG9Kp36tvrzfhaTzcTRqqNd7D2MjQ1k7NhKzIgxHanjPUETFYUp4ySi/cKKA4c4AwpPRxo3FXVRUctkAu6B0vFaWSaCTImv2US0PppAp0DcHXr2DBRkMvpNmU75qSxKMy9A9FRlwYoHIHAoNrszCkPX9TZUGdn41QkMATpG3hTZpfi2m23U/pxF3aJMlL5aPB/oi0N07zwNFa5qXCYE4/PkQPSzo9B4aQkXRSpfP0DtTycx5TdckuWHKIq0ZdVR/M5h4k1WdGp5OzHdH91gn26JcwDCx4Nol2xozoJMrcD9thhaRZHXcOTmhCurFgNQaRRMuSuevhODOL6jlBXvptF2jjf/tLBpyAU5ocEnqGg08euhrp0ip2GpaKHq86Mgl2G4THJaFEUW7MwjytuJoWHSd3tkwwEA4pU7QOct+U9f5Do/PfIp434Zx/yN83l1/6uszl1Nq7WVwb6DeaDvA/x31H9Zds0yDsw5wMoZK3l/7Ps80v8RZkbMpK9XX9w0bviEt9tCdNPmdjkY1n7t2XWqutPjjkme+P57KO6zo1G4qsmqkAKForwvnaAe6D0Q4II2HwC6FD9UIc7Ur8jBWtu1PnG5ZjoyrZa6H7oqmGwNRkRzC6rQECpys9G6uqFzO3OsNpga2Fa0jXcOvcMta25hyI9DuG3tbbx7+F1KmkuYEDSBl1NeZvXM1Wy+bjNvjXqLOdFzCHC+fHIaQKlS4xEU2hGU+Bf+30Z5SzlmW/d2NOeD2kGBbx9X8o6cOfYmxnqTVlRPecMFanK7HfZ9Ig0cA86ENYmiyGc/PExKahvK2bPQRkYhiiINa/Jo3FCAY19P9DdFd7o2FNe14ieXyLQ/0uIDIMItAhe1CwcrDuLgpESjVRIgV/SooAap68J1SgjWCiMt+8/UxP+tH44KK18kZOLpJBHwchcXFN7eFxVQZ9y7B5nNSklUMo//kn7J4b3nYnlaKXYRZpzVpVRV1ETxyToSxgTgPHwYzTt3IlovfQDuGeSMX6QbRzYVYrOcqdcEQeCmAYEcKqjrOLefC1uDCbmLVMPaGhupMTpgt+ZR6uDL6Dj/3109nVqZyodpHzI5eDKPT72HBoUTuXI5puxsfAI0WEUQlbIOpXcHGkswtWoREfBUeOAiFwhOjEQtV/PGgTeuyLadqD2ByWbqEpCYunYlcoXivCIPTaw79mY5goP+sm0+BEHA3V9Hgy2IgS2tnKg5gVOIPxPveoCSk8fZ+u2XnZYXRZFndz+LyWriP8P/c0kT3Obiku7tPRqKJUvD0sNw3dcwqKvtUHcw5TdQ+3MWqhBnDLfG4hhv6CLC+b0gCALO44LQ3xSJubiJyo/SsFT20n6y7y1SIGfO+Tue2yw2nvrtKMHujr2a1JEJMp4f+jxmm5mHtz6M0XJxdpiWzcUEWwXeVZn5+7J0Wkz/GwLPceCZzsJCJy/6BrlRml0PgK+2ELwTOp6vKy8lP+0QCWMn9dite6kIikvE1duHtG7Ebz7hLlhNNrxFGUq5QGqV1IVhbzn/PhJFkcWZi4l0izyvcOqioA+F6WeIanHfpzyVfQMLfH7Dg2buTrqbrLos1uev7/LS0ux6fvnwFFVOkUSUryduw7/QmLpmkl0q7K1WRKuU+6AUS7oNSCw4doS6shKSJ/5+6unTGHLtbFQaB7Z+1/l8JnNQStt6hS0zrxT+IqgvA6LNRvFdd3Nq5CgqXn+jw3/5SkGQCaREHGCw7juyy4JY9VE65rbuTwS25mZKH3ucsiefQhMTQ9iypdhTRrM+sxL3OdGow1yo+yUL49EqSUEtExDa2xiOFdfjb5IREK1H6KF1RuntjeHOOwlduYLgX37B9YYbMO7bR/Hd95A9ajQVr7xCa1oa1oqKy1ZQy+UyGt0U+NdHkVUjFSC64X44JnnQuK6gg2jfm1uLKJ4JqwIkvy59KGx+WRqEdANBEFD6+GA5R0HdEfB4EQpqs83MvrJ9vH3obWYtn8XYn8eS2vopaHKJdB5CX82DqMqe4R/9/o6DXUt5XiMBMV1bPUSbSP3KXOR6DU49kKjZ+3ZzYM1yApvaCLUruPrhp/CPjmXNh29z6uDFpU/3hP5TQ7jmoWQ8Ap0o3yYVDl79B17WOmWCjCkhU9hXvg+j1cjVwZOk1toP+kn+xTIlXPsVnk+m4TLkFr7YXcTsz/dS2dj9INMjWVIJeWqKaLOLNJ6VJn2kLgiL3IHRc6N5dd1JZILAk/F+mLLqcBoVgEzT/cU8xS8FEZHdpee3vDhadRSz3dwlIPHopnVYLeYewxHPhjo6CntLS0ea9/lwRkVtpPVodZfnY2LdsSOSl10n2U8Yq3k55WVeH/H6BdcdO3IsGp0TB1f+1vNCZqPUbqh0gGu/xFpTg1zfmVi2mG2s+eQYAJPnx6M4RzVsKW+h8oNUjIcrcBoTIIXvuXQ/+XA+CHIZjgke+D/Yl1OBzuSb7BhP1FL1STqV76bSvLcMey+KydPEdNXHRyRv4Jo2MmUCfk8OOD8xfRp+fcFB38mH+jSalDIet7fgKchgac7FtT32EoJMYMiMMMbdHkN5biM/v3qgU3CkwcHACP8RHKrdQEKAEx9vO4XV1nU7OshpmYDHnfEoDZdOTgPszqnhZHkTd6SEIAgC1cXN1BSfQq5Q4VG1GQbMA0X3E2/dQRRF/nv4v3yQ9gGTQyazYOICtly/hV037eKHqT/wcsrLzIufx9igsYS6hqI8TxusT7jUmt7bAKTeItDdkQC9QxeCGugUgHiyvAmZAOGel5607qvzxU/nx/6ynoMST0OQCeivkz5z7c+ZXSa35DotLtOn07h6DdaazoGK9hYzorkZdVgYFbmncAn0Z1XeKl7Y8wIzls0g5acU7tt8H98e/xabaGNO9BzeHf0u227YxrLpy/j30H8zLWwaAU4Bv0vqPYBvnyjKcrIuzkP/L/zpYLaZuXP9ndy5/s4LTg53h+AEA3XlRurbSZFJcVL33blB1V2QuxmqsyT19Fm/0dWnVpD8zX5MBiciHnoK0S5Sv/QUzdtL0A7xwe3aPl0UdUV1RtztMrQuqstuDb5cyAQZfT37cqjikNQp4eOIwSY7r4IaJNJPHepC44YC7EYLvx4q5sPjSoqdkwjOX9ypllZH9um1xQdA87ZtyHQ67r53JhWNbby44sQlf76z8evhEhIDXDudU9M2FqJUy4kd7otu5EjsjY20pqZe1vv0nRBIS4OZzP2df1Oz+vmjkstYuL9760Nro7nD3qM1LY1anQeirZYcTQAPXIr39KlN8EkKrHhQEnecBw2mBp7Y/gQ+Wh+eHfIsg8MMmLzCqW9rxC6KuDTlIVfIsMhl2M8OSbTbobGM2mqBOrdIPNo7PN0ifZifMJ9txdt63Wl4PqRVpgF0IqhNRiPHt20icugIHF1ce3ytQ6xUfyr8+2HKvjJBiSaLgTEtdYiI7CvbR3TKKPpdNYO0dSs5vm1Tx7I/Z/3MzpKdPNTvIUJcLt73VxRFLEVFXW0kK07AF+OlgMqbl0Ds9F6tz1LdSs23J1C4aTDMjblw7fo7wTHRE4+/JyCabFR+dIS2U70g+yKngKM77PsYSg5BZYbkSd1cJdkvtZ9z3t2UTUGNkf/MiEej7F1HYrBLMP9J+Q/p1ek8sOUBTLbeWcG0ZtbSvFM61988N4lTlc08+vOR/wmB59i/3YfayQ2FqwuhBi1lOQ04yJtxCQ2WvLvbcWT9KmRy+Xknci4VgkxG4rjJlGaeoKowv9Nzp32Za/ObiPF1YX9pPQhSbsn5kF6dzsnak1wfef2Vrwnbier3oxeyShzKoMqf4d1EJmftJtw5mA/TPsRqP3O+zD9azfL30lCq5Vz75ABGvHobYnMThfPuxFp3ZUhqu9GK2NYECjkKjV2yhTwHaetW4ujiSsTglCvynueDo7MLg2fdSH7aIfLSDnU8LnNQgF1ENF/5ceqVwF8E9eVAJsPnP//BcdAgar/5hpwJEymcP5+mrVsRr8TASRQRDi2gX1gWY+ZGUXyyjmXvpGI8J3W5NS2NvOkzaFyzBo8H/kHgN1+j9PFhbLQnOVUtFDS04n5LLKpAZ2oXZtKaUYNMq0QQBJraLDRXtKK0igR2Q5yeC0EQcIiLxfufTxOxfRv+H32EY//+1P24kPwbbwJRRBVw+b6ruhAnVHY1NbnSLJQgE3C7tg/qcFfqfs2mLauOPTnVOCjlJPq7nnmhXAGjnobK43B8SY/r746gNrcThhciqIsai1h4ciH3b7qflJ9SmLd+Ht+d+A5XtSsP9XuIn6b8jKz4WdyNc6mviiTa2xVBECjJqkO0iwREd93PLfvLsFYYcZ0S0m2RUVNcxJqP3sEnPJJkJz3mwkKUKjXTH3sWr5AwVv73VQqOpp13uztBFKGlGgp2S2Txun/CD9eh/qwf/j9HI74aTNXBXQDodz0JCybD97Ng0c2wZL4U4LHun7DlP7DzHdj3mdSudWwJZK6FvO1QfKbomOottSf5KJzot2iepMh10MONC+GunRA3E6VSyXNXx/LujUkcK2lk6vs7OZDfNRDOe4SUBG/PPYBVIcNYKf1G8g+XUuoUR7ShmixTG2uOlXPPyFAUO8uQOanQDu7ZqiTOPQ5XtesFi+9DFYcQEOjr1bfjMZvVQtq6lQQlJOPuf2G/Mk2UlMTcdqJ3LeoO8QYUng40burqRT0owoMquUhephSUSEsNEW4RXYJnuoNSoyFx/GROHdxLfXkPXoqrH4WqkzDrc0RHT2z19SjczxDUoiiy9YeT1JQ2M/5vsTifRXKKokjzvjIqPkjD3mrF8Lc4XCYEX5GWuf43RXLCKnLUS4vrzHAQoH7pKcr+s5+6paewlHcNyhJFkbbMWqo+kohpW6MZhvqyrs6M18Rg5OpekgsyudSeeGpjl0mwH/YVkGazYpschDm/kbol2b9bcRs5yJvpDydjMdv55fWD5J81gTEjfAY1bTWMTqqmqLaV5Uc6tyhbKo0SOS2Ax9/jUXpcuvKv1Wxj7bEyXl6VgUGnYlqiZO+SvrkI0VaOt16BTKGG/rf3ep120c6r+19lwbEFXN/nel4d/ioDvAdgcDBcUoHrFRqOIJNRdurKq26HhRnYk1vT7STAaWSVNxHsru31AKsnDPAewMGKg+f1oT4NhV6D69VhmPMaad7ZdTLMbc5sRIuF+p9/7njMZrdhM4vYBDNP7X+B6uICVht38NSOp1iTtwYvrRf3J9/PgokL2D17Nz9M+YFH+j/CmMAx6DUXrh+uFHwiIrGaTFQXXZmW87/wx0AlV3F34t0cqz7GnNVzyG/Iv6jXhyRIHQz56dK5L8xDR7in7sI2H3s/lkK1Y2d0PFRprOTA+/8mqAqCn30RQeNA3eJMWvaV4zQqANdpYV0EHA2tFprarDi22f9we4/T6O/Vn6KmIipaKtD7aHFos1NUazzvdUgQBFyuDsPeaqVwxSmeWXqMQSF6fMfeC3V5kLe1Y1lNnz6YcnM7dU72BFEUad62He2wYSSFGLh7VBg/HypmU8b5QxZFUSStMo1ndj7DfZvu6zJ5caK0kYyyRmadFY7YVNvGqQOVxKT4onZUoh02FJTKjiCsS0VAjB5DgI7U9Z3rL71WxYRYL5YcLqHN0nW8Z28wdQQkNh1IpVEt7S//hL7E+l6Eerq+UKq7v58JTRWSN/Gvd4C1+64DURR5dtezVLVW8ebIN9GpJAJ/+MgU5KKNOq0G89EjeIc6Y7TYO1t8GKvBbqGuwkS+7zA81XIEpQyVvxNzoucQ6BTI6wdex2K/PBV8WmUa/jp/DA6GjseOb9uEpa2123DEs6H0cETh6YgqeNAVC0oURTn+RgNauQN7yvYAMGL2bQTGJbDh8w+oyD1FQWMBbx58kyE+Q7gx6sZLei9rRQX25ubOXbr5u+CrSYAIt6+BkN4FidpaLNR8fRwEMNwW+z9TTfcEdaAznvckIXdRUb3gOM37exhTnIZCJXlRn9oIn4+BjwbDuwnwZjj8xxdecMP+ohd37hlLqtNDDF07GT4dec5Y9O/ShM3ap2HzS7DjLdj7CRz6hglNjbyQeD97y/by2LbHLvibtTWaqVuchdJbi+uUUFIiDDw5OYo1x8r5aGvOldtRPUATE4Pg4ECRsxd9A92QyQTKsmvxURxDCDnjP21pa+PY1o1EDByKzu33qbliR41DrlRyZH3nTnSdmwYndw1lOfUkB7hypKQRmaPyggT14szFOCocmRo69XfZ3ppmEx8dtbM3/kWE+w5A7Azk+z7mvtwj5Dfms+KE5L+cc7iSNZ8cRe+jZdZj/fAIdEITFYX/Rx9iKSqi+K67sRsvTnF/LkRRxN5qxd5Sj9JNiyCjS0BiQ2UFOYf2kzB2Igrl/+a4TZ50Fa7ePmz77ssOYcfpgFR765XparrS+GOn+/8fhyAIaAcPQjt4EJaKCuoXLabu58UU33U3Sn9/3G66EZeZM1G4uV3aG5QehvJ0mPoW0QN8cXBSse7zYyx54xBX/yMJZ72Kms+/oOr991F6eRH03Xc49j0zUzM2yovnV5xg88lK7kgJwXB7LFWfH8VS0oyy3Z/uRGkjIe3+0wExvWu57/j8SiVOY0bjNGY0tvp6Gteuxbj/ANphwy784gsgIt5AyaEanMp9aDA14KJ2QVDIcL85mqpP0qn5PoNirYUBIXpU5xK6cbNg59uw9RWImS6R1udA6eND28nOQXWn05VV57RfGS1GDlYcZGfJTnaV7OoIbvTX+TMtbBopfikM9B6Io/IMyTM2qpWNGRW0WmzMHhgEQNGJWhQqGT6hnYtTu9FC44YC1KEuaGK7fgcmo5Flb72MUq3m6oefovHV12k9JM2CqR0dmfnU8yx+/imWvfES1z7zEr7tIVKApLSoL5DUQh1/2dJt61mzhQoNuEeAb19w9sVUVE2jcAwZIi4GT7C1Sv5ollawtEjKWkv7Xy/QBxjnaWBway0ytwSY9r5E8nVDNl2T5EeUtzN3fX+Imz7by9NTorl9WHAHMeUWFIwA1ORk4zXMEUtpMyVZdWz9MQvHljLip+qZs/wE/m4O3Oqrp3FjBq7XhHVSNJ4LuUzOUN+h7CrdhV20d/EGP42DFQc72mhPI2vvLprrahk///5e7Qt1RDjI5bSdzMB54oQLLn9aRV27MJPWY9U4JpwJV4n1deZTpYhHuRExwB3B2FXJeT4kTbyKA8uXcGj1MsbecVfnJ1O/l0JMRj4JYWOwlpSAKCI/y+Lj6NYSsvZVMPDqEILO+u3a26zULcmmNb0adYQr+usje7StuRTo3DT0nxLM3qW5xI7yJ+AfyZiLmmjZW0bLwXJa9pahCnZGN8QHh1gDppx6GjcWYi5qQu6qxnVmONq+Xqz8KB2Nk5KoIRfpsx4xHo79AuVHOmbHTVYb3+wpYEQfD8JGBNJoFmncWIjC0xHnUVfG3uBceIe6cN2T/Vn9cTqrPkpn6MxwksYFMNx/OAYHAzltm4nyvp6PtuYwPUny6rRUGan6XPIe9rgz4ZLIaaPZypaTVaw+VsaWk5UYzTb0WhXPXhWDRimntclM5r4SRFsVfmIxxF/XEeR5IdjsNl7Y+wJLspdwS8wtPNr/0ctWXSg1GgyBwb+LLcSwcAM/HSjiWGkjSQGu3S6TWdFE5GX4T5/GQO+BLD21lKy6LKL0URdc3rGfJ60namhYl48qwoUmVxMVLRVUGCuoMFcQEOdHyzef8lTgAcpNlVS2VPILr1Mtryc36wheqBk94Br+OeJqIlwjkMsuj2C/Ujh9jSvNOolncOgfvDV/4XIwJXQKvjpfHtjyAHNWz+G/o//LAO/eBak6GxzQ+2rJP1pN0jhpcnhSrDcfb8uhtsWMXtvNNacqUyJFRj/T0dEhiiJvrX6aG7a2Ihs6AOfR46j54SRtJ2pwnhiM8+juz99FtUYQQWiy4hb3x9p7nEY/73Yf6oqD+PskILOIyM12qppNHVYd3UHlo0XdzwvbwXIiHBS8e2MyMm1fWPckHFwg1WuAuk8kWCyY8vLQ9Olz3m0xZWZiraxEN2IEAP8YG8GmjEqeXHKU9Q+64XbO99NgamBFzgp+zf6VU/WncFQ4YrVbuX3d7Xw2/jM8HKXa57fUYpRygasSzmRdpG8pRgQSxkjkn1ynw7F/P5q2bsXz0UcvbieeBUEQSJ4QyIYvT5CXXk1o0pn666aBgaxML2PtsXKmJ58ZN4g2EVvTGQV1eXohNpsFo9yZu6f10jPW0gq73pPGMoIMxj4LQ+6D/Z/D+n9KKtPrv+uU0QLwU+ZPbC7azKP9H+0kVBg7dignf/yAPIMn/qlp+F4zmcbNTejrz1KXNkpjoJZqEw2RcfhoFagCnRAUMlSoeHzA49y3+T5+OvkTc2PmXuyulPaNKJJamcoQ3zP7QfJjXYFPRCTeYRdWlzvEumOtCKDtyIW7EC8Ed3/pulxnCSZGpWFv6V4AZHI5Ux94gu+fepClb77E7nEmFDIFLw57scfxwflgPHCAkkcfQ1AqcRzY3pV6Yjn8Og/cguDmX7uEtfYE0Wqn5rsTWOvb8JgXj+Iyu9+uFBR6DZ53J1Lz40nql5zCWtmKy5SQHjuzGf1PiJgg/ZZPjyctrWBuwW42svRANlZ7C9dEuoG9rX38aZTGrg0lXV4DnSfhrgFaghJ4pWgLz2x/kv+MeL3bGka0i9QuzkQ029DPjkJQSt/vncNDOVbSyJvrM4nxcWZ0lOcV3mNnICiVOD/6ON9uKmVskBst9SYaa8zEO2VA0D86lsvYtRVTSwtJE38fshfAwcmZyMEpnNixhRFzbkPlcOYc4xPuQnFGHUkDgvl6dz4WnQz1eYL2GkwNrMtfxzVh13TJbbpS+Hp3Piarnfkjw8BdBzM+hhGPMmbb68RWb+WTfa/SZ08d21Lj8Qp24ar7E1Gf1e2kHTgQ37fepOSBByl+8EECPvwQ4RKJY9FsA7uIraEapRNSQKJDZw7wyIbVCIJAwrjJl/GpLw5yhZIRN9/B8jdfJn3jWpImTu1wUbAbreD6P9uUXuMvgvoKQenlhcc/7sdw9100bdxI3Q8/UvnGm1S9+x7OU6bgNmc2DvHxF7fSg1+BUgvx1wNSO+M1DyWz8sMj/PraAfpX/4Zy71qcp0zG+9//Ru7c2fs20N2RCE8dm05WcEdKCDKNAsMdcVR/fhSFp3TCOVbaSIhFhqufFkfnSyeQ5K6uuN14I243XtrM8rlIDHZjn6qFwLoYsuqyOgYs0meIpeyDNO6vs3AkoRslgkwmXfgWzYH0nyD55i6LKH19sFVX01jRSEFGI/5RblhKSpBptQjOzmTVZbG7ZDc7S3dyuOIwFrsFB4UDA7wHMCd6Dil+KQQ691xMTIj1ZmmapFqM9pEKoKKTdfhGuCFXdi5uGjcWYm+14nJ1WBciRrTbWfvR29SXl3Ldv17Gyd1AW2AgjStXYjebkalUODg5M+vRx1n0wr9Y8vLTXD+1D55isURE1+TA2TPHOi+JiI6ZDoY+7X8RUrik7Mx2NX/2OS3qTFy8fJHN/bznL8puB2ubVCScLjLOJq/bHxPMRt6xGMF/QJc04u4Q6e3EsvuG8ejiI7yw8gSHC+t4bVYCWrUCmVyOk4OWxrIKnH1UtJTDqo/SsbTZ6Jf5A5vMT5NZ0cAnc/rSurkIuasabXvw5vmQ4pfC6rzVZNRkdKtAttgtHKk6wozwM6orURQ5vHoZbj5+hCT26/Ka7iDTaFCHhmDKOHnhhdvhEO+BYmMhjZsKcYgzdBR8SrkMB28HZPlmGgjBtWVrr9cJoHPTE50ykmNbNzDs+pvR6NrbZcuPwapHIGQk4ojHaFyxgorXXwdBwCFW2jdlOQ3s+jmb4AQD/ScHd6zTXNxEzY8nsdW34TwpGKcR/j0XqJeBpLGBZOwuY8fibG7810DUgc6oA51xmRqK8VAFzXvLqF2YiaDMRrTYOxHTgkJGVWEThSdqGTw9tIstyQURNla6zd7YQVAvTyulqsnE29dLrZ9OYwOxVLXSuDYfpcEBh7jeEbQXCye9hpmP9mPTNyfY/espakubGTU7imlh0/jm+Dc8mjKfZ34pZN3xcsa6O1H91XEQkWw9PHtPqjSbrGw+Wcnq9DK2ZlXSZrFj0KmYkezHlHgfBoXoUbSH8RzfUYrVVI4o2vBR18Lguy6wdgkWu4Vndj7D6rzVzE+Yz71J916xlkCf8D5k7t6BaLcjyC5+gNkTTvtt7zpV3S1B3WaxkV/T0qEsvxycvg7uL9vfiaA22UxUGiupaKmg0lgp3TdKRHSLcyP/EGaR+8lKHgx+HYvszGBiYJTAo8csBB4pw2lIFGMMIxCOyfDyDeKpoCFs5XOuH/m3302pc6lw9vDC0cWVsuyTJE2Y8kdvzl+4TCR5JvH9lO+5b9N9/H3D3/n3kH9zTXjv/OpDEgwcXl9IW4sFjVbJpDhvPthyio0ZFVzfvxtied8nIO/c0bEsZxmR3+9GjYLgZ16k5tsTmLLrcb0mDN2Qno/b4rpWtCLYzXbcvP8cBHWUWxQ6pY5DFYdIaCcB3W0yimqN5yWoAT6yGbkeeEOvx9ulfdmkObDnQ8l+wNkXdTspbcrKviBB3bxtOwC6EZIqVK2Q89b1iVzzwS6eW36c925KRhRFDlYc5NfsX9mQvwGz3Uy8IZ7nhz7PpOBJHK0+yv2b7+fWtbfyxYQv8HSQauvRkZ4dExCmVivHd5QQ3s8T57NyFHQjR1L56muYi0u6CE8uBuF9pcD61PUFhCSe6eIZEupOkLsjC/cXdiKo7c1mEKVQP9FsprzCgt2lEEtgX2Iv5D0tipC5BtY+KYlLYmfAhJfApV11O/Q+UOsk5ej3s2D2ItBIY7+TtSd548AbDPcb3oVAdtA54RQQRqU1n6bDqfg96kLmRolIF+2iVKM1SmOWNiEApVyF2mRDHerasY4R/iMY5juMj9M+Zmro1EvqmiluKqamraaTvUd+eip1ZaVMub93EwkOse40bSlCtHtgNxqROV76sefm7YgghxprMJGmRg7YsyhqKiLAKQBHZxeueeSffP+vh/HYYOT6h5/GS3txQWqizUb1J59Q/eFHqAIC8P9poVRD7/8cVj8mjYlmLwLH3u1LURSp/SULc34j+puiUAf/vl7mFwuZRoHh1lgaVuXSvLMEa00r+hujkKm7qbGVmh4V4wt25PJSXQbv35SMuje1kyiC1XRm7NnWAFlrmZ32I0ZrPe+yHoefjvDcoGcQQkZ0Gu82bSvGdKoet1kRnWpiQRB4bVYCpyqb+cdPqSy/L4UQw+/XKZPZbwyHjx7gsUA3ynKkvBQfh9wOD2NRFElbtwqPwGD8oi7cJXs5SJwwhRM7tpCxcxuJ488QqT5hrmTtq2CITtpPjXJQZNfTtL0Y7SDvjlDY01h2ahkmm4nrI6//Xbaz2WTlm935TIzx7myh5x6GMPNT7s/4hXdXrGdrbix+6mNMiatBZQsEPDqtx3n8eGzPPUf5c89R9swz+LzyykWPE0RRpKSqCAGw1pbjoGoCn86T7RaziaOb1xMxYAhO7r/PmLAnhPcfTEBMPLt+/oGolJHIHE8rqP+cQYl/EdRXGIJSifPkyThPnkxbZhZ1Py2kYdlyGpYuRRMXh9vs2ThPmYxMc/5CkfoiOParpDzTnCGevUNdmJhiYf2KBnYrxzPqkdH4zru6x0H8mGhPvtyRR1ObBSeNErlWief9ydC++PGCekJsMkJ/J/LkUhHqoaNAJTCyyYuMvJxOihq5s5rjwzwJXFPEqCMN2EZYOgIfOxA1VSKOtr4m7cOzAvdEUaRG7c/RmL+x5d8HEUVJoOBtcsDDXcs9v4ynsrUSgHDXcOZEz2GY3zD6evZFJe8diT+ijwcqhQyz1U60jzONNa3UVxiJHd75QmupNNK8txTtQG9UPl0vfPuX/cKpA3sZdcudBMTEg92GykUAUcTy/X2oZaVQnY2uuZxrXdX81JDIr0sPc0NSI/rAcIicfIaIdg8HB9debX/L9u0YnXR4BQadf0GZTFJwqBx7rZDsLZw1Sj65uR+fbM/hzXWZZJY38cncfoR56ND7B1BTW4tYX4ZC0GBvsxLuVImLqZRX0psZFu7OCEFBbXEzbtdG9MqbbZjfMAQEdpTs6JagPlFzglZrayf/6bLsk5TnZDP2jrsv6mKmjorGeOBAr5fvUFH/lEnr8Woc489cXEP6uEF+BcXGIFyNNedZS/foN3U6x7dt4sjGNQyafh2YmuDnW0HjQlvi01TcejvGgwfRxMUR8NFHOMTH09JgYu1nR9G5axh3WzSCTJDaeXeW0rA2D7mTCo/5iaiDzh8YeTmQK2WkXBfBqg/TObK5iL4TpN+qXKvEaYQ/uhQ/TKfqMaZXoQp06iCmT+PwugJUGjlxIy/BkkjnIZ1fMpZB0BBErScLd+QS5aUjJVw6DgRBQH9tH6rq2qhdlImHqxqV/+WraLuDUi1n4rw4DqzK48CqfBoqW5kyexoLji2gTbWfMPcwcpafIt4oIHNUSuS014UL7cY2C5syKlh9tJxtWVWYrXY8ndTc0D+AyfE+DAjWIz9n8sFms3NsWzHO+nqqm8AnIga8Lzw5a7aZeWzbY2wu2swDfR9gXvy8S94f3cEnPJL0jWupLSvB3e/KKdrddWqivJ3Ydaqae0d3TePOrmhGFKVJt8uFt9abAKcAfsr8ib1lezvI6LpuAl4cFA54OXrh5ejFzqRMph7sx4eql2gdLj3u6eiJm9KF/N2TuSXDg6Cn3qY1s5ga8tD6eJORdwqdm/5PR06DdGz5RERRlt37Sb6/8OdGgFMA3035joe3Pswzu56hoLGA+5Lvu6BaMTjBwKG1BRQer6HPQG9ifZ3xc3Vg/fHyrgS1sRbSFkLC9R31SnlLOSt/fIlHToq433cvDevqMRc24nZdH7T9zk9GFdcZ0dvafXp7cT79X0Auk5PsmczBioM8EiNtk7tdoLDWSL+gno/lZWklfJVaQlKYD/1yWmg9WYtDlF4i8ne/B4e/g1FPoA4JBoUCU2YmXHV+FV/ztm1oYmNReJzd9eXC/WMi+O+Wwzhu3MHxpg3kN+bjpHRiZsRMru1zLZH6yI7lB/kM4vMJn3P3xru5Zc0t/D3iNaqaTMzse+a6fWJnKZY2G0njOn/fTqNGUfnqazRv24p+zpyL2Y2dIJPLSBoXyI5FWZSdasA3wlV6XCZww4AAXl+bSW5VM6EeEkFyOrhb7qKm7cQJKnU6wMq4yaPP/0Y1ObDmCTi1ATyi4JblEDqy63L9bgOVTspw+XYa3LwEo1LDY9sew03txkspL3V73MQNHMCeolPY24zobDWYBAFBBHuLRepwayzFahKoMyTjrZJspNRndXwKgsDjAx5n1vJZvJ/6Ps8Nee6i92VqleQJnuSZdOaxtSvQurrRZ3Dvum+VfjoEjYjSN4nab7/Dfd7fEC4xLE6ukKH31lJWFU504x7QwZ7SPQRESr+lGhczu2KrGXZEj3ZPGVyEfbilooLSRx/DeOAAztOuxvvZ55BrHWHTi7DjTegzGa5d0EUFfz40biigNa0K54nBOCZ6XPgFfwAEuYDrtDAUHg7UL8+h6pMjuN8ai8K1d9kzRbVG3lqfxZgoT65K6GV3oyBIhLdSA+ilCR2vWEh5mHnFB2jZ/QJftBWgXXUrj9qcEBJvhMSbMDV70LghH4cEA479u57vHVRyPp3bj2kf7OTv3x7kt3uHoeutHeBF4lBBHXKZQGKACwcP5qKQmTGEekJ7vkppZgZVBXmMv/O+3y3f4zR8IqLwCArhyIbVJIyb1PF+PuHS+UCsasOgU7HSoGCeqwMNq/No3FyEbogPumG+yHUqRFHk56yfSfJI6nROv5JYuK+QxjYrd40K6/Z5bclARubqKdNncUvScVQHFkPqZzDoLhjxWKdjz+2G67HWVFP93vvI3Q14Pf5Yr7Yhpz6HNXlrWJe/DlmllQ95GntNBel+bdS5BxBlt3Uo9zN3baetuYmkSb9/OOK5EASBUbfeyXdPPsDeJYsYOvYGAMS/COr//0ET2Qef557D85FHaFi6jLqFCyl7+mkqX3sNl2tn4XbjjajODhRsa4STKyF9keThK8ilYKl22NvaqHjtNZoX/sSQuAGkRd3JliNW5IcqiejmxAqSzcen23LZnlXN1PYT/dkesFU5DYQhEBj75xqEymUCCl89ZFooOd4A53TFbaluIl9l4s1mGTXfnsBjXhzC2f6eggBjnpEUBoe/hYF3YjHZyNpfztGtJdSUaFG4ReIZUsemgJ3IMwyIFYMoDwpjeFMeCSlBDPUfgrf2wsrb7qBTK0gJN7Atq4pwTx25+yTPvXMDEutX5iKo5DiP70oE56cdYuei74hKiqWvUzZ8fy0U7UdZYgQ8MB/cgLpvGISPBUMELoY+XGt1YdG7H/FLvh833vEazoaLb0myNTbSnJpKS3wIbr6X7yd+OZDJBO4ZFU6ivyv3L0zlmg928eZ1ibhHRFKYmYE5PwtIIGGwN97bF1Fq8KXJYue5qTE0/pSNwsMBx+TeKR70Gj2x7rHsKNnBXYldFZ+HKiRblbMJ6kOrl6N21BIzcsxFfS5NVBSNK1Zgra1Foe/dseeQ4IFiUyGNGwtxiD2jou6f4MXe9eXk1RqIszRKKgJF7wpBAI+gEALjk0hbu4L+U69BvuIBbBV5VNvmUDtnHnKdDu/nn8f12lkIcjk2m531XxzHbLRy9f1JqB2V2Fos1P2cRdvJWjQx7uivjfifeOIFxxsIjnfn4Kp8Igd6oz2rABZkApo+bmj6dLVYqq8wknO4kuQJQZ1avS4KUVfB5hfh66kIwBLAKndAeM8LdN6g80Rw8sY9wo/K6kSqF6TidZMTcm8f0Hp0Cj25EhBkAgOvDsXNR8vmbzLY+2EbwxLGsu/Ydj6wx+HUZKYpREefm2O7TuidhQajhQ0ZFaw5WsaO7GrMNjvezhrmDApkSrwP/dr98XpCzuFKWhrMGJyzcVa2oR3xjx6XPY1WaysPbX2IXSW7eHLgk8yJvnQyoSf4RJwJSrySBDVASriBb/cW0GaxdfGZzqyQQlyvBEENMC1sGgtPLqSqtQpvrTcJHgkdhLOXoxdeWum+TqnrNIipJYuAQ+AxLAG14Qzp4Db7JirffIu2rCzMOdIEl8rfk4od6/EM7Uq4/1ng2yeKnIN7MTY24Oj851KR/YVLg7PKmY/HfczLe1/m86OfU9RUxIvDXkSj6FnQ4RXsjIOTkvz0avoM9EYQBCbGevP9vgKaTdbOZMLhb8DaCoPvBiSxwgvb/sWctUaEkAhE2wAsZU3oZ0fjGH/hCffiulZ8ZdL6Xf8kCmqA/t792XFoB0ZlIyoHBQablaLa1h6Xz61q5uklR+kf5MaUWxKoeT+NhlW5aCJcEfShkr3H4W9g+CMIKhXq0NALBiXa6utpTUvDcNf8jsfsop19ZfsoUPyCLmITq0psxLkn8nLKy4wPGo+DonurgkSPRBZMXMD8DfN59cg/cHG5k9FREjlns9lJ31yEX6QrnudMiKuCg1EFBdG8ddtlEdQA0cN8OLAqj9T1BR0ENcC1/fx5e30WPx0o4ukpUr7I6eBBubOKiq0HaFRbEW0KhqT0EDhuapYIyz0fSnZ7E1+BgXd2EFPdIv5aiaRefAt8NYWXY4ZS0FjAlxO/7FHZHJLUl72/LqRG50D56q04eCZCgwlbo1kiqBuKqW/UU6OPIVavQLDJUPl3DvYNdQ3lxqgb+SHjB26IvKFXVlNnI7UyFSelE+Gu0rWlrryUvLRDDJl1I3JF72pGQRBwTPSiuTWOqg8epGn9eryf//fFdym3w+DvRH5ZMIMafsPby5u9ZXu5PvJ62qxtPLXjKZrDHYj1msihVcvwCgknevgFJhqApq1bKXvyKexmMz6vvoLr9OmS5eLy+yT7vL63wNR3urWg7Akthypo2lyEY38vnEZ1HZt1KOH/JNAN8UWh11Dz40kqP0zDcGvMBUUaoijyzNJjyAR4cXrc5ROxggABA/nH9Sto2fsi32b9jE7Qc/eOt7Bv+4ha26fIHbS4TfHq8b0C9I58MLsvc7/cxyOL0/h4Tr/z1sDnwmKz09BqoaHVQmP7bdf7VrZnVxHj44yjSkFZVg1eipPIQ86QH6nrVqJ21BKdMury9kkvIAgCieOnsPGLDynLPolvH+ncpvfWonZUUJ7TQFKAGxuqG3nqkVGYi5po2lok/e0oQTvAi5yoavIb8/lPyn9+l200WW18sTOXoWHu3XYvHlydz77lubhFK/jM+RPCYx7ktjGPwbbXJOuk47/BtPcgZETHawx3342tuobaBQtQuLvj/rc7un3vgsYC1uatZW3+Wk7Vn0ImyBjgNYAZEZMgD0SLkeXeDuwoXobL4q0M9B7IYJ/BNK3egiEgCP/ouN9ln1wInsGhxI0aR+qaFcQPGgf8paD+/zXkOh36m+fgNmc2xn37qfvxR2q//obaBV+hHZ6CfmQUWkU6QvYayS7BNQiGPwoJN4BBuoi3ZWVR+sgjmLJPob/9djweepBQi8Dqj9JZ/+VxWpssJIzuesHqG+iKq6OSTScrOgjq0zCarWiqzYgKJd6hf74BXnioG7X5JxHyuw5OdufUEBnuhj45iNofT1LzUybuc6I7X5zDxkLgUBo2fc2x/BQy9lZhMlpx99cRNtKK/4v/5FNXG3lhAVw94yr63/df8vrNQzyQgKVch/16zUXNlp+LxyZGMiXeB41STlFGLVoXFfqzVNKtJ2sxZdXhMjUUua5dmd1aD0X7aTi2hVW/puOhNjKh9TOEzXZJURE3A1VKImx8g+rqAbQ2DkHtEYrKJQx1QAjuWi3X/tOXxc8/xc8v/pMbn38drevFeaC37N5Dq1zALorofS69LfJKYli4gZX3p3D3D4e56/tD3OOtQC4TaDqZhlNUAv1G+5Pz2XFSdSHMHRyEf0UbtRVG9DdFXVQoX4p/Cp8e+ZT6tnpcNa6dnjtYfpAQlxDcHaSW/sbqKrL37aLf1OmoNBfnAefYTwpZzJt1Lfpbb8H12uuQ686vwOqsoq7pGEAnB7mxXCHiVOsCToCxBpwvzk6g/1UzWPLKc5z84WX8t66mIiMYW+MmXK+7Do+HHuzko79nSQ6l2fWMuz1GSkDPa6D2p5PYmi24Xh2Kdqjv7z67fzaGXRfBwhf2sfu3U4y/vXdtb6nrC5ApZCSOvQyiMuVhiL4amsr5eNVuzHWl3DvACVoqoblC8jrN24a8rQGDPYhK8xtUL8jBQzURmcwihVo6nSazvdrvn/XnFQOaiz8vR/T3wsXDgdUfpzNj3zX0UYNMY+ZtRwuFdiM/O3a97Ne1mNlwooJVR8vYdaoaq13Ez9WBW4YEMTneh+QA114X5Ombi3HxdKCh4BS+zjapm+U8aLG0cP/m+zlYfpDnhz7PzIiZF/2ZewO9rz8qB0fKsk8SO3LsFV33sHADX+zM42B+HSkRnYmtzPJGVAoZQforQ2DdlXhXtxNoF4Lr1aGYchuoXZyF1wPJHa2YLrNmUfX+B9T98CMKnwTAA8HPQG1pMVFDR5x/pX8gzp5wCO3bO8/iv/Dnh1Km5LkhzxHkHMQ7h96htKWUd0e/2ylQ7WwIMoHgeAM5qVXYbHbkchmT4rxZsCuPrZmVZ3yKbRaprT5khKSsA37J/gWvJbvxMbrgPOUxLFVtGG6JQRPZu0njoloj/goFCrUMXS/Vgf8LnJ5EP1x5GL2PAZ8SC4W13eeFtFls3PtjKkqFjPduSkapVuAyJYSab0/QvKcMpxQ/6H+HFEqWvR6ipqDu0wdjew5KT2jetQvsdnQjRlDdWs3SU0v5NetXipuLcVG7cFXwtSzZ5o+LLJ6rQ/tesGaI0kfxwejPuWn5Hah8PyGzri8JHgmcOlhJc52JUXO6J0p1o0ZSt/Cny7aCUKrkJIz2Z/+KPGpKmnH3k4hbTycN46K9+OVQMY9M6INaIe+koD6+JRWLrAFnnz4oVef8RkRRCnNf9ww0lULibBj3b6kW6A0iJ8HNv7Bi6S0sL9zAXX1uOq9/u3dYH9SOWspdnWnavpcBNw2HI1W0VRpR+ekQG0s53joaUaXAVaFCFeCMIO+qxL476W5W5a7ilX2v8PWkry+q3kurTCPBM6FD4Z22bhUymeyi/Vgd+/nQsq8S15v+S/PmN8m//gbcbroRjwcf7GJ3eSG4++vI3OeCc1sDg32msblwMza7jXcPv0tuQy6fjvuUQV4DaSgqYf1nH+AeENRj9oFoNlP51tvUfvMN6qgo/N5+G3VoiOST/MsdkLkaRj4Bo57qNoOnJ7Tl1FO3JBt1uCtuM8I77XOLycb6L49TU9zMlHsSMJwzqfBHQhOpx/OeRKq/Pk7Vp+m4XR953sm/5UdK2ZZVxXNXx+DneuW8tQVB4MnBz2C0mfkoZxmOU1/gql2B2Mqc8JA9huyDIoi+CpJmQ8jILgKSYeEGnp4SzUurMnht7UkGheolctlooaHV2kE0N7RaaGzrTD4bzV1DVM+GRinDxUGJs0bJzYMDMbdaqSk10s8xA4JuAaC5rpbsfbtImngVygt14F8hRA8fxfYfFnBk/eoOglqQCXiHuVCW00DySAMbMyqoN5pxDXDCfW4MlkojTduLadlfjudeG0+73skYTcrvsn2/HS6hotHEm9d1DiEURZG9y3I5vLaAPoO8GHtLNNs2D+LLY19ybZ9r0c36HPrOheX/gG+uhr63wvgXwMEVQRDw+ufTWOtqqXzjDeTuemlyCShpLmFd/jrW5q0lozYDgL6efXlq4FNMCJ6AwcGA8Wg1tWQgmlt4xlRF6qAP2FOdzt6yvaSlbWNqoQ8n+tnI3/sCg30GM8h7UBeu4ffGsBvmkrl7B7uXLiSZYX8R1P9XIYpiry/OHaGKgwZiSVtH/YIPqdu5naLtO1Dq7LiNGorL7fejiBndceESRZG6H3+k8rXXkTk7E/D55+iGSwe7RgXTHkhi3RfH2bEoC2OjiUHTQjttj0IuY1QfD7ZmVmGzi53asY+XNBBslaEL0iHvhQXC/xpxvi6sdawiqSYMo7ENR0fppFxUa6Sw1sjtw4JxjPfAdpWZhhW51K/IkZLWBQHRLlKUUcvRmifIL7AiFJYS1tcL5742vq39hD1FO/hBtDHTaRQp099DaGwmu+ZDxvdtoDZ2CLuXnOK3tw4T3t+ToTPDcdJf/AUh2seZaB9n7HaRopO1hMSf8a4TrXYaVuWi0CvROe2BVXuhcA9UHMdiF1hWkISII9Ou7o8y7jEIGARaiRiViyJus4tp2buPmgULwHrm5KLw9kYdGspInxA252ew+J+PcN3jz6INDOr177R5+3aMbq4Af7iC+mz4ujqweP5gXlhxgmVbS5gJNNaX4QSYy2oRamsoD0rhsdHhNH6SjtJbi0MvVFBnY7jfcD458gm7S3czJfSMt6nNbiO1MpXJIWeK6LT1q0CE5IkX36rjkJREwOefUfPZ51S++hrVH32M2w034Db3ZpSePaveT6uomzYV4BDrLqmElXLseiVtVY7YdArkLVUXTVAHJ/bFTe/G3uW7GHLCDYe4UAKee66LIiX7QAVHNhURP9qfPgO8aNxUSOPGAikc5Z4kVH7/+8LY1dOR5HGBHFpbQNxwP3zCXc+7fHOdiZN7y4lJ8b0s331kMvCIJNPmy2slzTw28ToU3Vg8YGlD2VyB/lgZNasdqHX/GvfYVISWcmiuhKZyqDgukdriWYWsIJPSn0NGQPAICBwseU/2Anqdkkn+WqzFzZRZ7OwMyCI+Zij7lu5g9YomJl81jjqjlXXHK1hzrIzdOTXY7CIBegf+NjyEKXE+JPi7XPREQ3leAxV5jQwcaWd7poDPgL7nVYo3mhu5e+PdHK8+zivDX/ndUr4BBJkM7/A+lGWfX/l3KRgYokchE9iVU92VoK5oJsJT1+HP/UdBplagv74PVZ+m07AyD7dZ0uyrws0N56lTaVi+HN14Pcg8aLS3gijiFfbnVVB7h0YgyGSUZp38i6D+PwZBELg97nYCnAJ4asdT3Lz6Zj4Y8wHhbt3/HoMTDGTsLqMsux7/KD39gtww6FSsPVZ+hqDOWCGFwE19C5AGm9+ue51X093RTXwa0SLH447YTpYGF0JxXSvhogw3L8c/lXIxxj0GB4UDhyoOMcRnBm4FDZzsgaB+adUJMsoa+fLW/vi2E0KaaD3qCFcaNxbimOyJvM8kcPKRwhLbCerGlSuxNTb2SAY2bduG3UXH0zVfsfXn7VhFKwO8B3Bf8n2MCxqHWq4mSMjh1TUnWZZW2snDuSdkFDjQUjAfr7jvuXP9nbw/5n1yN4Cbj7bHLlDdqFHUfvMtLXv34TTmwsrX8yF+pD+H1xWQuqGQcbfFdDx+48AA1h4vZ8OJCq5K8MXWYAaFQINoo7XWiKhvIHrQOROvFSdgzeOQvwO8E+C6ryFw0EVvU74+kBc9DPQ1tjB/93cQfi14dO8NLpPLCYxLpNC4l9DcHHKUIp5AbU4DzsmeGKsLKeRaXForockP9bDujwVnlTP3972fF/a8wLqCdUwKntSrbW00N3Kq/lTH8ua2Vo5t2UCfwSkXbSWlDnRGPyeKup+z0Y78J4iHqfvpIxrXb8DriSdwvmpqr+uX04SuzOJEgksiS81L+ebEN3yf8T03Rt7IUL+hAFz1oBSauOzNl7n5lXdwcOr82zcXFFDy8CO0HT+O25w5eD7+GDK1WvJDXngTFOyGKW9K6viLgKXSSM13GSjcHSQh1lm1RGuzmVUfplOZ34haq+S3Nw8xaX48AdF/nq5opZcWz3uTqPn2BLU/ZGCdGIzTKP8u309di5kXVpwgKcCVW4YEn3edNpsdS6sNc5sVc5t0aznnVq6QodEq0WiVqLUKNFolTyU/g9Fi5NjO44wtT8J5UjDq8E+kQPhjv8DRn8HZTxIIJs2WMpra8beUEI6VNPDp9lw+3Z7baXu0KrlEMrf/BegdiXNQ4nLOn7OD4qz70q1a0blGLjxRgygK+GpOgZ802Xh00zrsNtv/NHNDpXEgevgYjm1Zz6hb7+z4vfuEuVBwtIZ4d+m4SSuqZ1SkNG5Vejqiv7YP1hQdi779nKsaRlL3/jFao/Q4jfK/Yp7pNrvIp9tzifdz6bBVBIkz27k4m/QtxcQM92XUTZEIMoH7k+/nplU38V3Gd9ydeLc0rrp7N2z9j9S5kr1eqg2ipiLI5fi+9hpF9fWU/fOfbG9MZZHhFOlVUrh8vCGeR/s/ysTgiV267E/bZYgWIx7ePkyNup6pXC/Znbz9PMWaI+gSg1mbt5Zfsn5BQCBKH8UQ3yEM9hlMsmfyeTvGrgR0bnoGTr+OXYu+Iyn0L4L6/yxWvvMqTgYD/a+edeELbHU2pC+Go4tR1uXj4aXB8MQkmlqiqNtynMqVh6ja8LAUqjj7JpT+/pT98xmaN29GO2I4vq+8gsLdvdMqFSo5k+fHsW1hFofWFGBsNDNqdiSysy5gY6O9WJpWSlpRXScPuqMna3C1y4j4k/pYxfo585HKTD9RQdrhbIamSGTZnlypDXlomHRSchrmh63BRPP2EnBUUiiXcWxbCfUVRhyclPT334uzYjFfho1g3bFNOKuc+cfAh1F6fE2kRY9SrqS1WEqDVvn7ETHAi+BEA6nrCji8vpD8I9UkTwwieUIgyosNUwOqi5owtVjxj3aTVJWFe2jeV4e1qj/uyn8jLD0ohWEGDEQc+SQb9tZSZcpg5hPP4Zrcv8v6BEHA+9lnAWm23lxUhCk3F3NOLqbcHMy5eajS0ugrEzkYYmXRvfMYXNWMNiQEVVgo6tBQVKGhqMPCUPr5IcjPfCZRFGnesR1LVBg0VqP3/XMoqE9DrZDz8ox4FrqKlH6ynEZa8QMy92ehBQaNHYgyo47mmjbcb4m56EFjrHssrmpXdpbs7ERQZ9Zl0mxp7lAmWdraOLpxLREDh+DscWnJzrrhw9ENH05rejo1Xy6g5ssvqf36a5yvmYb7HXegDu2q0BBkAs5jAqldlEnbiZqO4D2vYGeEyjqqrcF4tVRf1HbYmpupfuct/I9kcjTAE+bfRvA/Hu30u7CYbRzdUsyBVXn4hLkweEIg1QuOYTpVj0OSB24zwruEY/wv0W9yMJn7ytm+KIvrnhpwXrXvkU2FiCIkj+9dYvqF8OXOXDRKGbMH9rA+pQbcgnAYHoSLUELDylwa+8zGZVpI5+Xsdkn93lwBTWVQfECyetrzEex6F2QKqVgNHi4FywQMAmVnhYkoirTsK6NhVR6iINIUY2L73pUIu+qp3b6UmYic/AEOrF7OL04jaZZrCXZ3ZP6IUKbE+xDr63xZ6vf0zcWoNHJ0lT8A4DPyhh6XrWurY/6G+WTXZ/PWyLcYG3RlVc3dwSc8kv3LfsZiakOpvnJFoFatIDnQlV2nuh57meWNDAv7c2Q8qINdcBrhT9O2YjTRehxipHrCbc5sGpYsoe1ENpq4ZKrKCwDwCvnzEtRKjQaPwJC/fKj/D2Nc0Dh8tD7ct/k+5q6Zy1uj3mKo79AuywVE65ErZeSlV+MfJfnij4/xZnlayRmbj70fg1sIREzELtp5due/+PsWV5yG/gPBwQmPv8WjCui9DY8oihTXGdGaNLh6/XnsPUBSoSd5JHGw4iBTfeeiskFlVVeCemV6Kd/vLeTO4SGMjT6j2hUEAderQql49zCNGwpwmx4uWRJsex3q8tFEtgclZmfj2K9zOHR5Szm/ZS2h/8ZVHA4ROVydxtyYucyMmEmwS3CnZe8cHsr64+U8u+wYQ8Lc8XI+/zn518PFhLgE8ONV3zJ/w3xe/PVtJhX/nTG3RPV43XLs1w+ZVkvz1q2XTVBrdEpihvlybFsJg6aFdghXhkd44OfqwE/7iySCutGE3FnNot9246iVlokb1f67bWuAra/Cvk+lfKGr3pEUfJdg+WW2mXl8++OoFBpeG/8Wil/mwVeTYO5v0uR2NwhKSCZ7/27czXV8cCKPwaKOpmLJhqq+qoVGVRjJbScBv/NO1swMn8nizMW8dfAtRvqP7NGe5WwcqTwCnPGfPrF9C+ZWI8mX6MfqGO+B0ktLzfcnsFYl4PXcNzT88jqljz1G/ZJf8X72WdQhIRdcz2k1fI01BB+jRMK9c+gdgp2Debj/wx3LaV3dmPbI0yx67glWvvs6s556Hll7ndywYiXlzz0HSiX+H7yP0zipfZ7mSvh+JlRmwKwvJGuWi4Ct2Uz118cRFAKG22KRnWVJ11jTyor3jtBU08akv8fjEeTEyg+OsPL9I4yeG0XUkF76N/8PINep8Lgzgdpfsmhcl4+1yojuqlAq8pswtVoxt1n5eW8hUbV2bgxyZcfCzC7Es7nNhqXNirnVhs1qv+RtSVJcxQitghJ7G2u3n8TnuCdq7Tw0wXeiactDXbUfzcYDqDdtQuMdiCZhAurEqajc9Lx5XSKzBwWhUshw1ig6iGblFRQglJ1qQMCOV4gOlBpsVivpG9dIQqL/cVdz0vjJHFm/imNbNzLgammS7bQIyGAWEARILTxDUJ/GbxUr+MzrF6bdOhfno3Kad5dS9Uk6qmBnnEYFoIl0u6yxxrrj5eRVt/DRnDPdN3a7yLYfTnJiVxmJYwIYdt2ZToM4QxyjA0bz7fFvmR01Gxe1i+Q/PeEliJ0Jy+6Dn2ZD7AxqRj/FhupUNl9jZVqeSODri/GaH8oDox5gYvBEApx67ry1t1oAEK0tKMKTOh43NtRTciiN5IlTeXjinVjtVo7XHGdv6V72lO3h2xPfsuDYAtRyNcmeyQz2Gcxg38FE66MvmMNxKeh31XTSN63FYjdhazFf8fVfCfxFUF8G7DYbSo2Gw2tWkLZ+NfFjJjBg2rU4G84ifJsrpbDD9EVQmiqp4kJGSC0+UVchaJxxBpz/Jtl41C1cSOOy5TT89huCRgM2G15PPYnb3Lk9hrDJ5DJGzYnE0VnFwdX5tDZZmDAvtoNMHdHHA7lMYFNGZSeCuuhELQYgtu+lEWy/N/p4OVEh6DDJjZxKLz9DUOfU4K5V0cfrjJrQluRJc3o1bCokv8WK2t+JcbfHoI208fmBn1hWYkZVvI35CfO5NfZWnFRO5Pusx1pWBoClpBgApb+kGFaq5Ay8OpSooT7sWZLDgZV5ZOwqZeiscML7efbuxGq3QWkahWuzAC8CNk2GNXnYRGcazV+gdipCM3oWBL0hKSjkClLXrCAj7VOGXj+HkG7I6XMhqFSow8JQh4XB+DOPi6JIaHk5rhvWsmH1ElI9PBhqVdK8bTsNvy7p9HpVcLBEXIeEItPpsFVV05oyEI1o7qIQ+LPgxpFxvLtATbnGQpRopzCjhGhgwuSh1H1biDLACc0lKAjkMjlDfYeyq3QXdtHecWE413/6xI7NtLU0kzxl2mV/FoeEBPzf/S/mggJqvv6ahiW/0fDLr+jGjMF93t9w7Nu38/KJZ7yoNTGSijou3oPs/XVUmiPw6mVQoiiKNK5cRcVrr2GrriYmvI1TjkFkmtuIaS+6bTY7J3eXcWBlHi0NZoLi3ElJ8aH6wzREkw23WRE49u/Zu+1/BaVaztBZ4az/4jgndpT0GHzY1mLh2I5SIvp74my4/PbByqY2lqaWcsOAANy0F1Zj64b5Yq0y0rS1GIXBEe3Z2QEymRS+qPMA7ziIGA+jnwZzCxTtg7wdEmG98x3Jr1KuAv+BEDKcVo9+lNU4YtvRgkOTAxWmAvZVrKI1swmFg4ZaRxlulkHI3QLZbKpgcP0ubm/9lf5z72HE2BFX5PtrrjORc6iS+GF6qnanIpP54hmZ1O2yVcYq7lx/J8XNxbw/5n1S/H6fFsBz4RPRB9FupyIvB/8rnII+LNzAu5uyaTBacGn3X683mqloNF0x/+krAefxQbRl1lG3JBtVoBNynQqH2FgckpOxtTmCKFJWkIXO3XDR9lD/a/j0ieLE9s3Y7TZkV9jT/S/8ORBriGXh1IXcu+le7tl4D/8c/E+u63Ndp2WUajn+UW7kp1eTcl0EgiAwI9mPhfsLmfD2Nt4YamFY8X6Y/DrIZPyU8SM+W0tJDHoImaMDnnclofS+uJDDOqMFk8mGvNWO25/If/o0+nv35/3U91EFigDY682YrLYOtV5BTQtP/XqUpABXHp/U1R5D6aVFO8iHlr1l6Ab7oOx7K2x/Aw59jTpW8pVuy8zEsV8/rHYrO4p38Gv2r+wo2UFosY1RRjsxV81l47WPoezBS1kuE3jzukSmvLeDJ39NZ8FtA3q8FhXVGtmXV8ujE/rgpfXiq0lf8cHLyzAqGynwPEo03XeNCSoV2qFDad6+/aI6XntC4rgAjm4r4cimIlKui+j4HDcMCODtDVkU1hjRNJgQdUpOrNtBhMaMUmXAzdMLUn+Ajc9BS7UUPjnmX+B46UrXtw+9TUZtBu+Nfg/vwJFw+xr49hr4+mqYs1jqujoHwYnJANQ6OaDJP4nJcwCmaqljpqResk3ydtUiqOWo/Hq+bsllcp4Y8AS3r7udr499zd1Jd19we1MrU5ELcuIN8YiiSNq6lXiFhuMTcXE+1mdD6emI573J1P2ahTG1GqernsN5WjrV775N3rRrcL/zTtzn/11SMvcAR2cVGkeosQShKC8l0i2SU/Wn+E/Kf7oQ7z7hkYyddw/rP3mPnT99S8qMGyh/+WUafl2CQ9+++L35Bkrf9t9iXT58N0PqkrtpEUSMu6jPJlps1Hx7AnuTGY+/J6A4q5O3uriZFe+nYbPYmfZAUocv+szH+rHmk6Ns+iaDpto2+k8J/sPr89MQlDL0N0bS5OFA48ZCSlOr2FNvxiydojAABpSUHqhC6aBApZajclCgVMvRuWlQaeQoNQpUGvlZ90//r0DZfqtykKNUy7FZRdqaLbQZLZhaLLS1WGlrNOG6rwzMNnaoSqltaQWbHJVFQ1uLBYtJA4xo/wPqgAxg0REEwY5GI6B1dyK8vydeg33Q6q68tVNZdg0GRR6qUMmz/tSBvTTX1TLuznuv+HtdCIbAYPyiYkjfsIb+U6cjyGR4BjkhUwjUFzQR6eXEsrQSrk70IdxTOl/Y7DZ+yf6FIT5DCPIKBS/QjfCn5UA5zdtLqPn6OEpvR5xGBuCQ4HFRNpwgjV0/3ppDiEHLxFhJwWy32dn4dQbZByroPyWYgVeHdPnd35d8H9cuv5avjn3Fg/0ePPOEX18abl3Gxi3/ZG3xVvavnIVdEAh1CaX8hb8R/sJq5n9fQ/DkUajPQ05Du5+zaEepakHwS+54PH3TWuw2a4cCXiFTkOiRSKJHIvMT52O0GDlUcYg9ZXvYW7aX/x7+LxwGF7ULg7wHMdh3MMP9hl9yLtq5UKrUDJ99G6Yl9dQXlqKn+66bPxJ/EdSXAZlczqR7HmLwrJvYv3Qx6RvXkr5xHXEjRjIw2gmXojWQs0Vq2/ZOgAkvQ9wscO5+VlPT56xQxWXLaE1Nw/2O29HExHS7/NkQBIFB00JxdFaxfVEWy/+bxtR7E9BopRaSAcFubMqo7FSI2kpbaVMLuHr++YprAKVcRrghlCLzYVSn4hBF6Sq2O6eaIWHuiCLkHani6NZiik/WoVAIjPLU0E8moLrKg69avmHRikUA3CQ3MK80F/drZ4NKOokqfH0wnZB8hCwlkoJa6dd5dtLZ3YGJd8YRP6qOHYuzWf/FcY5uLWb49X3wCOyheBNFqV1kw3NQlUFx7Qu4q604Rg+BoIdpzIxFPGrEdV4KwlnJ78UZx9j23ReE9R/E4Bk9Kw97A0EQUPr4EH/L7chDglnzwVsc6TeQaV8tgJYWSXGdm4spR7ptO36CpnXrJRWnQkGzjD+N/3R3EGQy9D6+yJS1iKZmPMwW7M4uWPJs2BpMuF0bcclFWYpfCqvzVnOi5gRxBinI4GD5Qfx1/nhrvRHtdg6vXo5XaDh+kRc+NnsLVVAQPs89h8f991P3w4/U/fADBbM345CUhP5vd+A0ZgyCXI4gE3AaE0Dd4qwOFfXgOE+OCBlUWCKI74WCui0ri4oXX8J44ACaYE8CkqtwuOnfJJXo2fPLj9QUFVJboWbf8lwaKlvxDnVh/G0xqHLqaP4pE7mnI4Z5cai8/zxed+H9PDm+vYS9y3MJ7+eFRtd1UHx0azFWk42+E7uGkl4KvttTgMVu546UC6t0oF2ZNi0Ma00bdb9lo3DXoA65QMubSiuFVIVJQZy25lqqD6ym7MgOytLyKNu8ARdVPcnu41Ag56R5K2KglRFX34ZPZAxu3r7MWnktflUtxB8NYpIyEFVAJI3lSzn4+RtkLt2Be+B4FCoVcoWs/U9A1nFfhkwhdHpO3s1zhcdrsIsi8a5bWGt0xDMgEIWqK2lf1lzGvPXzqGqt4uNxH5/XM/NKwydcGoCXZWf+LgT1fzdmsye3mklx0jU+s1xSpvX5ExHUgkIaJFa8n0rdklO4z41GEATc5syh5tvDILdRkZ+D9584IPE0fCMiObJ+FTXFRXgEBv/Rm/MXfid4a735dvK3PLrtUV7Y8wIFDQU81O8h5GdNSoQkGCg4WkNtWQvuvjoGhuhZPH8Izy47RvXGNzEqHCn1nYaqsZDNm3/jWetDCDIrXvf3v2hyGqC4zojeJtUYbpfw+t8bpyfTCxTZgIDeJlBS10qohw6T1cZ9P6YiCPD+Tck9qv+cxwVhTKuifmUuhr/FIfSZDIe/QzHyKWTOzpiysthUsInXDrxGWUsZBgcDd8TdwdTKZiyyHxky/W4U5wv6A0I9dDw+MYoXVp7g54PFXD+gewJgaapUo5+2ArFVK9FXBVIUfYgndn9Pq2hkRsSMbl+rGzWKpg0bMGVmoom6dDIUpDFBxABPju8spf+UYDTtgcPX9ffnvxuz+OlAIXMbzeTKRUKr87G41ODrPwAWTJC6ovwHwpxfwDfpsrZjS+EWfsj4gZujb2Z0YLsy3BAOd6yVSOrvZsCNP3TUDafh4umNq5c3VY0tjDCXU4eIrNVKTUkpOW0pODfmoXLzRx3sfEHiqL93fyYGT2TBsQVMD5+Oj+78it20qjQi9ZE4Kh0pOJpGTXEhk+556LIJVJlajv6mKJoDS2lYnYdCH0Hgt0uoXfAe1R99RMOqlXj/61l0KcN6XIdHgBPVOcG4VWTzxNVPUNdWR7xH96GL8aMnUJFzigPLf4VFv+CRnYf73Xfhce+9CIp2WqXihPQdWNvglmUQ0ENAZg8Q7SK1i7MwFzXhPie6U3dHSVYdqz8+ilItZ8YjfSnDxisLUzlZ1kiQuyOhEVo8rE7sX5FHVUULE26JRqH4c0zgCoJAiaOK7DY7iWoZk7wdkMUZePREEbUqgd8eGI6j5spRU+fa+NX9lk1LqxXDHXHcHZLMnevvZG3tZ3w49kOG+I7EZrVjMlppa7HQ1mLB1GyhrTQHU84h2opzaDMrqK2OYO/SMPYtyyUwzp2Yob4ExbtfEatUm9VORV4jMaoTECQJoNLWr8TZw6tXorXfA4njp7D6/TcpOJpGcGJfFEo5XkHOlOXU88yMGO5feJip7+3kyclR3DokmB0lOyhvKeeJAU90rEOmkuM0zA/dYB+MaVU0bSumdlEm8vX5OI3wR9vfC0HZu9/ozlPVHC1p4NWZ8chlAjaLnfVfHic3rYrB00PpNym429f1cevDpJBJ/HjyR26OuRm1XM2Woi2szVvLntI9WEUrge5B/K25mUkl2UQI4QijrsP89Q3k33QThfPuJHjhjyh9ej7P2Y1WRFsrSq0NfCWC2ma1kr5hDcFJ/XpUwDsqHRnuP5zh/sMBqG6tZl/ZPvaUSoT1+oL1KAQFc6LnMD9xPk6qyx9TRA0dQcaK5dQXlWJua73oLK3fG38R1FcArl7eTJh3D4OT/di/dBHHtm7g6BaBGI8WBo25C7eUW8Cz90WRXKeTEqcvIXU6fpQ/Dk4qNnx1nCVvHubq+xNx0kshHi+tyqCo1kiA3pEWowV9ix0h9M9DMHWHOF8DWc0lhNcmU13UTKODQEO9ib5GOd8/s4em2jZ0bmoGXRNKbIovZnsLxR8dQPlDFnuDtzMtbhrzE+bj01ILHw+D3e/DuOcAUPr40rxpM6IoYikpQebigtyp+4PeN8KN654aQMauUvYuy2XxKweIGebbMSnQgZLDsOFZyVdOH4pl6ieUfe1FwphAmDEXc1kLLT8fRjfEF+VZ5HRTbTUr3nkVF08vJt/7cI9q+UtBzPDRmFtb2fTlR6z96B0m3/cwjsnJOCYnd1rObjJhzi8ARLa+9eIfdjHsLdx8/Kior0M0NRBgtyJEx9K0tQh1qAvqC/gQnw/D/IYhILCjZAdxhjjsop3DlYcZ6T8SgIL0VGpLi5l83yO/izJBodfjcf99uM/7G/VLllD71deU3P8PVEFB6O+4A5fp1+CY6EnT5iIaN0kqamcHFc1aBRWtEWDM73HdtuZmqj/4kNrvvkOu0+H94O24lr+GEDkRhtxLYmMD+377mZ//8wU2cRR6Xy3j5sVQUVhH3Q/HcbXCcsy8W9mI5d1y1Ao5aqUMTfutWiFDo5R3ulV3PCdH037b6fn21yvkAqIIdlHEZhc77tvbb8Wz7ttF2v+X7kvLi9iD1ciy6/nsw8NYkl07nlfKBNSCAOtLkfs7srG0FlVlPWqFHJVc2gbpVtomlUJ21m37Y3JZJ+uQVrON7/cWMC7aixBD7wkKQS7DfXYUlR8doea7E3jem4TCvefCoKm2mrLszPa/k1TknMJqkVqy3FyDGRg8CVeLB6JDJQbnrwhp2AGNwN4voHQIhAxnhscA3qhfyB3zb6YhVY7V5IEl5D7KslZTX7oXs7EQQ/C1yBRu2Kx2bFY7dqvYcf/0/3a7eN7PFprojtOJjyg39SE+pmuLcWFjIfPWz6PZ3Mxn4z/raPX9X8HRxRUXTy/KszOv+LqTAlzRquTsOlXTQVBnVUgEddSfiKAGUHprcZkYTMPqPIyHKtH298J5wnhaDlhAo6Yup5iY/0FS/OXCp49UV5VlnfyLoP4/Dq1Sy/tj3uf1A6/zzYlvKGwq5NXhr+KolAQWwfEGIJP89GrcfaW6dmCInpW3hiJ7bz/fiZN48eNDTAraxDMF86G1DrfZYSgv8dgsqm1Fb5fqtD+jgjreEI9aribdeAhn9UDcbTIKa42Eeuh4ZfVJjpY08OncfgScJ7xVrlXiPC6QhhW5tJ2oxaH/HZC5CiFzJbKwYDIOrOPBkF+JdIvkiQFPMCJgBEqZkrz/XIciMbFTuPL5cNvQYNYdL+eFlScYFmHoEo4miiJLUksYHKrH303a3rSNhSjUcp64dT51B07x7O5nMVqNzInuOm7SjZAG/c1bt142QQ2QPD6IrH0VHNtWTP8p0uS0j4sDoyM9+flgMbNbNewXTQTKWinATljLdklNO/1jSLhR6pa6DJS3lPOv3f8iWh/NQ/0e6vyka0A7ST0dfrwBrv1KCn87C8FJ/ThaXk6KpYxiu40ImZxtKzKptQYRU7McAvugDnXt1bY80u8RthZt5e1Db/PGyDd6XM5it3C06iiz+swCIHXtShycXYgcMvwiPnnPEAQBpxQ/VP46an7IoHZhIW5zHsN15gzKn3+BonnzcJ4yGc8nn+w258UQ5MaRzADc6zZfcNJcFEWSHJwpNJo4rFEx841X8bzqrG7Kwn3w43WgdJS+C8/oi/48DevyaT1ajcuUkA4rP4Ccw5VsWHACJ4MG/2uCeGjdCbZnVaFTKxgYoqeotpXt2dWYLXaGqRWwv5Kn08rJDFUR6OVEiEFLqEFLiEFLiIcWZ835J5CuJCxmG9sXZnJyTzl+ka64TgzCvLME04FyXkOB2U0HmXWIMe4Iyitva2A8Wk3LvnJ0I/zR9JHOTR+P+5jb193OA1se4NPxn5LsmYyjs6rzuD7JAxgMVhNkrYODX1KflUWGeSInsyey5mgNDk5K+gzyJnqIT4dlzKWgqqgJq1XAxykLAgZSVZhP8YljDJ992x/WJRYxaBgO33zOkQ2rCU6UOnq9w1w4sqmIa4LcWPfQCJ74JZ3nV5xgU0YlKv+FeDp4MjJgZJd1CXIZ2n5eOCZ70naylqatRdQvy6FxYyG6FF90g3072dh0h4+35uDlrGZGXz+sZhtrPj1K4fFaUq6PIHHM+RXO9ybdy/r89dy+9nZKm0sx2834aH2YGzOXiSETidHHIIgiHPgCNv4bPhyMatxzBH76CQW33Ebh3+YR9MP3PV7b7K1WRFMzSq21w2bptAJ+/N/v78XelmBwMDA1dCpTQ6ciiiJ5jXl8e/xbvj3xLStyV/Bg3we5Jvyay7L/EAQBZz8vLLkFHFj2C8NumHvJ6/o98BdBfTkQRYmQPLoYjv2Kc0sV45xdGTTjag5UeHB0/xFOLD5GVOkqBs3Q4u53/gPnSiG8nycOOiWrP05nyRuHuOr+RMZEefLSqgy2ZFZyy5Bg9h8oQ4mA958oSKE7xPk5sya/idGIHN1azKmqZu5q1NB6sAbfCFeGXRtOSKIBk2jix4zvWHBsASoPGR+Z/sV7lU/jE90PhU4DOh+Imwn7PoHBd4POE6WvL6LZjK22FnNxMUq/8wfLyWQCscP9CO/nyYFV+RzdUsypgxUMuCqE+EQ78m0vSiELju5SEEa/2yg50YDdlk5gtB5RFGlYmYPMQYHzuDN+tVaLhRVvv4KlrY3r/vUyascrr8ZJmjAFc6uRHT9+jVKjYfyd93UhV2VqNZrIPpiMLRgb6tH/iQISu4Objy+nDuzBrmlEptSh9B6BvdmC89zLa2nTa/TEGeLYWbKTuxPvJrc+l3pTPf29JcL+0JrlaF3diBzy+9oSyBwc0M+Zg9sNN9C0YQM1Xy6g/LnnqHrvPfRzb0Y7aCINq4ppy6jBIdaAzk9LfaY/xro0zh1ynrbzqHz9dazV1bhedx0ed92G4qerwNUPpn9IeX4je5fmIsijaKlLJ2LWTAobbZT/mkmYKCNHbudQkhsqPx33W2yYrHZMVjttFhsmi502q3Rrstpos9hpMVmpaZb+l5Y7c998Gf5xF8IYpZK+ec38XF1DtVLKm7XYRPqa5Iw1qfi2vo7SX3tng3IuVPIz5LUgSG3edw7vPs39fJA5KnG/LZaqj9Ko/vo4nvckdRRlNquFY1s2Ung0jdJTmTTXSIp4uUKBZ0gYCeMn4xMRiYfNl7bNVYgWOy5XBaMbmoIgmyW1D+fvlOxA8nfAhg1cLZPxTqAfGw/dy+OhM0EfCq6B4Hof2cfGsv7jd6nM+Zzx8+4henjPPp12u4i9g7TuSmC7Vq2l+udmrLYzauXTyKnP4c71d2KxW/hi4hfEuF+57oOLgXd4JKWZGVd8vUq5jIEh+k4+1CfLm3DSKPC+gLfqHwFdih+tGTXUr8hBHeqCQq9BGRBGW4NEqnuFRVxgDX88XL188OkTdUUndP/CnxcKmYKnBz1NkHMQrx94ndvW3sYHYz/A09ETrasazyAn8o5Ud1JQKQ4vAOxMm/cc+Su3ckve1QhN5ZTJjhEw9OL8YM9Gh4JaABfPP5fyCEAlV5HgkcDBioPM8BqFodRCUV0ra4+V8/XufG4bGtzRHn0+6Ab70LKvjPrVuWgeGIXdNZCfDr5LnbyeIcUWHu77GDfHzkUpk4gua3U1bceO4fHgg73eVlm71cek/27niV/S+faOgZ0mg1OL6smrbuHuUWEANNe1kb2/griRfri5OvP+mPd5fPvjvLr/VYwWI3cmdA6hU3h4oImLo3nrNgx33dXr7eoJBn8dgbHupG8pJmlcIIp2O8WbBgZy4OQhQE2g7RgNKhmgInZUMkz+BTSXHxBmtVt5YvsTWGwW3hj5Bip5N9ZiOk+4bSX8cB0svkUixhPPdGUGJfQlbd0qTKW5tETL0BihLN2OgIi32AKAOqx32+qj8+GOuDv4+MjH3BB5Q0edfC4yazNps7WR5JlEQ2U5OYf2MWj69d12WV0O1MEueN3fl5ofM6hdlIl2iA/BS36j9usF1Hz6Gc3bd+Dx4IO43XRjp5wVg78OO0qUjW3nXb+toYGyZ/5F04YNDBs2lC1yE+vXL+fmUWPQ6HSQvQEWzZWCyuf+Bm4X363XvK+M5m3FaAf7oBt+RnF5bFsx237KQuPlwFI3KwcWHcagU/HYxEhuHhyEi4N0DNrsImUNreRVt5C5u4zAvdUYCu1sMNaxMr0U8SydgUGn7kRYnyawA90du4T3XQ5qy1pY9/kxasta6D81mAFTQ5DJBI65KJj/wW4e8XFnSLOV2oUnERwUOCZ5oO3vjdJXe0WEQNa6Nup+zUbpr8NlwpnvxEXtwmfjP+O2tbdxz8Z7+HLilz3Xpgo1xEyDmGm4lqUzZM8HDDp6C4VtCZxU3szRzRaObCzCM9iZ6KE+RAzwQn0BsvVclJ1qAMAnQAkqLUfWf41CqSJ+zIRL/uyXC4VSSdzo8RxcvoSmmmqc3A34hLuSur6QyoJGfCPcWHDbAH7cX8hLa3ehUOxhtPfsjmtCdxBkAg4x7mii9ZjzGmnaVkTjugKathajHeSDU4ovcueu9ilpRfXszqnhn1OiEawiKz9KpyS7ntFzo4gZdn4OByDIOYg50XNYX7Ce6yOvZ2LwRBI9Ejv/xgQBBv0dIifByodgzeNo/AcS8MpTFD7yPEV33UXQV18hc+w6uWtrMSO2NaM0OIODq7TN61bi4uVNcFLfLsv3BkK73ci/h/6b6yKv49V9r/Ls7mdZlLmIJwc+eVlCHweDKy41bQQM//N1TP5FUF8ORFG6+LdUQeRkSLgewsfjpFAxBhhUX8fBlb+Rtn4VGTu30mdwCoNn3vA/Ufr4Rbox49G+rHjvCL+9eZip9yYSatCyMUMiqE+lV2FDpP/AP0+IQneI83WhxepGpa4AYbeAXQb5OnjmwQEY/J2w2Cwszl7Mp+mfUt1azXC/4fxj4j8IswRQ+fERqr86huddicgclTDqaTi+FHa8DZNfRekrfXZLaRmWklLUob1r01c7Kkm5LoLY4b7s/OkEu345xfGlJaS4FBM05lEY9oAUfgIUZ9QhV8jwCXeh7UQNppwGXKeFSdvTjq3ffE5ZdiZXPfgkhoArYz3QHQZecy0mYwv7l/6MysGRkTff0e2Fv7ZU8uN2+5MFJJ4LNx8/7DYbdq0cucIdu02HJtINddDl+2an+KXwyZFPqG+r52DFQUBqma0pLiI/7RDDrr8ZueJ/ozwQFAqcJ0/GadIkjPsPUPPlF1T9910E7ZfoJrxMw5ocNDHuhEXqKctspqDIztl6DVN2NuUvvCjZecTF4f/hBzjExcHCG6ClktqrV7Pvm2Jy06pQaRUISaNwTatAu7eA6Uo9DSo5NcN9SRkdzMgr0MIGEtFptrWT21Y7Josdi92OTBCQCUi3sjP3hdOPtT8vnL1c+/NymXTfbLTw47/38binKzMelQI0rBYb3z+7F52vmuV3x2NuJ9elW1uX/02d/u9+GbPVjq+rAwOCL82nV2lwwP3maKq+PEbNjxm43xpLbup+tn3/JfXlZTh7eOEfFYtPRKRESAeFolAqsbVYqF96itajZagCnHC7vg9Kj7MKJa0BYqdLfwBN5bjl72T00Y9YaarioQ3/4uxfboTGBa+EIFZn6Vn9wVsUbF7EmOmTUHmFSST2WYNqmUxAppJ3DMi7YN3HlMkkEuFsX8mTtSf5+/q/I5fJ+WriV4S7/XHFkE94JJm7t9NcW4NO737hF1wEhoUb2JKZQWl9K76uDmRVSB59fxYPyLMhyAT010VS8e5han/OxOPOBOwtFkxWKUzN6/8Biw9BEJj94pt/9Gb8hf8x5kTPIcApgMe2PcZNq27iw7EfEqWPIjjBwP6VeRgbzZICzmyEg19B5BQaCs3clt8HU0sxjQc/Z97we4n8ZA/PXxNLrO/FE4dFdUa8BTnO7hoUvWxN/l+jn1c/Pkv/DDcfDYaiJvbkVLMzu5p4PxeemtI7JbEgl+E6NZTqr46Tu/koz3gbOGaqZn6wH44HC7hZP7ETEdG8fQcAupEjLmpbA/SOPD01mn/+dowf9hUwd0hwx3NLDhejUcqYHCcR6ulbihFFkcSxkuhHJVfx5sg3+deuf/Fe6nu0WFp4oO8Dnc67ulGjqP7wQ6y1tSj0ly/M6TsxkKVvp3JyT1lH5sUoXxvzNKnQNoJ+xi0sU8lROwTgOP0FifS4AvjkyCccrjzMf1L+Q5DzecYLjnq4ZSksvAl+mw/mZhjwNwACYuIRBIFKOUR7KVAX2JGJMrytqajdghFUAkqf3qtAb4+7nd9O/cZrB17jp6k/dbLeOY20yjQAkjySSPttNYIgkDh+8sV89F5D7qzC4854Gtbk07yzBEtJM/qb5+Fy1VWUP/8CFS+9RMNvv+H973/jEC/Z+Ln7S59XZdZ0ypE4G8bDqZQ8+gjWyio8H3sM/e234XQqk0X/forV77/B9MkxyJbfA54xcPMSKU/kItGWVUf9slNoIt1wvToMQRAQRZE9y3JJXVtAmRZ+aq3Fx9GBF6fHcV0/fzTnnH/kMgF/N0f83RwZHuFBQb8a1n5+jJub1Uy8pz9NKoHc6hbyqlvIq5JuN52spPqgqWMdMgH83BwIMegIcXfsCAJUKWQdtyq5cOZ/uQylQoa6/VYlP7Nc5dEa0pfmoVDJGXdXHEHt4cxWm50nl6Rj0ioZOy8ZZ40CU049LQcraDlQTsueMpQ+WhzbFbdybe/GXA2V5ez7bTGiCN5h4XgGhSHbKPmsu98UhXDOOMbgYODz8Z9z69pbuWvDXXw16SvCXMPO/yY+CTDzM2RjnyV43ycEH/wXrQaBLIfbONE0im0/NrLz52zC+noQPdQXvwhXhPOEt59GWXYNzvJytBGJmIwtnNi+hcihI/7wPKjEcZM4sPxX0jetY9j1c/BpD1Aty2nAN0IKPJwzKIhsy2J+PQUrdgYi1qXy0jVx3R5LpyEIgtT1HOqCubSZpm3FNO8opnlXCdq+0veuCjpjN/TJ1hycNQpmxfuy/N00KguaGH97DH0G9t6b+bEBj/HYgMcuvKBroGTHlL4I1j6JY9l8/P52A8WfbqH4wQcJ+PBDBGXnz2ZvbEW0GFEFSufmqoI8ijOOMfLmO66IAj7WPZZvJ3/L6rzVvH3wbeaumcvVoVfzYL8H8XS8+Dw5mYMClUz9pxQk/kVQXw5kMrjhO3AP63ZmXOvqxsib72DAtFkcWrWU1LUrydqzg/ABQxg860a8Qi5wArxMGPydmPV4P5a/l8ay/6YyMdGZL3MqaDFZaS5opkolEuj55/POOxuR3k4IZl92Bf/KAwFP8XKqkZQYL9x8HVmRs4IP0z6kpLmEvp59eWvkW/T1OjNDZbglhqovj1H97Qk8/haPYAiHpJvg4Jcw9D5wdyff4MzBz/6Lk6mJvl5e59mSc2Bpwy3nS65ue5N8t3B2mR9gZc0TBGW4MyxWjlv7ubIwoxafcBfkMoGq1XkovBzRDjozKXBsywaObFhN/6tn/u6KXICUG2/B3Grk0MrfUDs6MmTWTV2WqSuVvP7+jCess+HmI82W2lw1qC3OYAPnCcFXZN0pfil8fORjdpfu5lDFIbwcvfDX+bNp0UfIlUoSfqfC+nwQBAHtoIFoBw2kLTOL2gULaEldjEPSrZQ89hb9Z4xlBVBerSWabuw8nn8e12tnSaqRne/QdPIQB1w/5+SnTaBoocBHwW5jM7dVqZngextm0YR6vD8x/x97Zx0exdW28d+sxN2DJQR3d6/ghZa6u7u9dX3r7u5vXahRWuoFqri7W0IEiNvufH88Z7OTEGB3o/Q798VcmZldnjl75sh9nvPIqLT9CF1dYbMZhNjs+xHr+kBoRBBDjm3HL++uZu0/WXQalMK6ebsp2lPGmNM7k9AASU0CRXBGDLHHtmfPZ+tYePf7/LbuQ+JatmbazXfXGmKnZEUuez5fh7ukkqjx6USOaHXoBCORKdDjBKbFpfDDj5fyy6lvMDaiLezdWnVE7d3KSY6t/LmmnL9Wmuxc/wyTW64mKaQIQmKUtXUbiEmznKtDbcaxbR7sWMCukNMIjconOknG06XZS7nkx0sId4bz2tjXDr6obgSkdpBkILvWr6HDwKH1KntYe3HF/X19Dif0a8XqzAKm9Dq0VUdTwREXQswxGez5dB2Fc3fgLq6kqHwfkQmJhEXV3dpPQ6OhMLLVSN6Z8A6X/3Q5Z317Fo+Neoyuvfrwz9eb2LwsR6ypln0MJXnkR16M7as8dpobif7lGdrdfQd3pA3koe9Wc8yzczl9UBrXj+1ITJjv1pzb95TQCVuzjD/tQf/k/rxkvkRRZB5hpsEvSzJxhDp4/rS+fllHujNC2Jmyl+jZJRR2CeHh7D2MSh3MVrZQunZttdwthbNn40hMJDiAUBqnDWzDd8szeWDmakZ2TCQtPpyyShdfL9nFuG4pRIY4KS+pZMXsHbTrVz3RscPm4P7h9xPmCOP15a9TWFHIrYNurXKBjhg1ipznnqNozhyip071u2w10aJDDElpkSz6dh1dy99g88bvmF62g61xg2DnSH4uMXEZZeyOhN7/602YI4wwZ1itf8Od4YQ6QqvdD3eGy7m6DnWEsr1gO68sfYWp7aZyTLtjDl3I4EhRsHxyNnxzHZQVwPBrCA4LIzUtg5yiUoaxh3KiCbZBq6K/cSSeRHDbGL8Sl4U6Qrm+3/XcOPtGpq+fvl8SU5AEianhqcQ7Ylj+8/d0GDiUyPiEWqTVDwy7jZjJGQS1iWTPp+vY/cwi4k7rTOvXX6Pg22/JfPBBNp90ErGnnkritdcQkxyOYbhxVSawfmcO/dp712mm203uq6+R/cwzOFNTSX//PUJ79gSgRccuHHHuxfz42vP8se1zhg8YDKd+4OVFfqAis4jc91bhTAon7rTOGHaDwpIK3n1+Ea71hSwNqmRrq2AeH9ObST1ScRwgdnxNpHWPZ9r1fZnx3BK+fmIxEy/pUav3RH5pBZuV4nqjUlxvyili0ZY9FJZXVrO89gUOE44ocdKr3ME2u4uv7UXc98G8qs/tNgOX2+SF0/tWKTFDOsQS0iEWd3EFxUuzKZqfxb4ZG9n37SZCu8QR1j+FkA6xtbbPitJS/v7iE+bPmI5hs+EICmb5L9/TPWYE3WKHssL8mxWfLSC5bXtS2nUgvnUaDqVgTI1I5bWxr3H2d2dz0fcX8daEt2h9iIR4AES3grH3wcgbCV34Dr3+epGepc+Q3e5IVgWfw9qlBmv/ziIqIYTOQ1LpPCSVyLjaPepM02TXuj2kO1dC+ihW/PYTFWWl9Bk/udbvNyaik1Jo26svy36exeBpJxMS4SQ2NbzK4hug3FXOLztmMLr1aDq2HsDTP61j3qY8HjuxF8M7HLqvB7WIIP7UzlSOTaNg9naKFshGhS3MQUjnOPa2CGf2ikzOH9aWH15YSt7OIsZf2J2MPv5vBPkMw4Bep0gs/29vInLF/0gZ3Z7Mn+ew6/bbSX3wwWoefO7CMsyKIpztJX794lnf4AgKptuYo+uxSAaTMiYxpvUYXlv2Gm+teIsft/7IRT0v4qyuZ9XuVXMA2MIcEpbEbfq0gdKY0ArquqLloU32w6KiGXHq2fQ/ZhoLZ37Fom+/Yv28P8noO4DBx5+ynyt0fSIqIZTjb+zHjOeW4Fqwl04hBr8s2kVQoYvKVsHN0rLLihCnnfSoDuyKfJ01SavIKk0lNnENJ3x9N+v3rqdLXBduP+p2hrUYtt9vCc6IIe7kTuS9v5q8j1YTd1oXjJH/oWj+dBY9czOLN1ZQ1jKRqPx8dsdFsHXVfPp9/B79Jk0lJPwA1gNut4Tx+Om/sG8rtD+a9HPvoXV8F5b+sp3532ziw3v/occRreg6tAV7dhXReUgKhb/vwJVbKolm1MSauWEdP77+Am2692LEqWc3dFUCMrAdcc7FlJeU8MfH7xEcGkbfidXJet7OHRg2GzHJ9ZMttqEQkyKKn4rkaNgOId3iCKpD7C8rusV3IyY4hjk75jA/az4DUwZSWlTIitk/02X46CZX3oR06kiLhx+ifMdOdj+/AldxS4rPP42QIXeRl5vAvhnfsPvhh73hPK69pipmVumqucz/fAvLil/CleNgSYiLxc4SzjcjuNweid0wMDs7+Wbm04wxLibR4ZtnQXNCl6GprJizgz+mrye9ZwKLvt9CQusI2nRrXiGNivbu4fdFn2DsK6NL9CAmHn0lHc85Eruj+tTsLqlk79cbKF64G2dqOAnn9yAo1T/FyJDUIaSEpzB96w8cfdSLGDXmLhswzDRpvWAu3778Au9vjWDU8A70bmNi7NsGuethw89QUVxdcGisKKrLCiA4ml1ZLlI7dMIwDOZnzufyny4nLiSO18e9TouIplfWJqW3w2Z3sGv92npXUHdKjiQhIog/NuQyvEMCBaWVdGpm8adrIqxfMiUr89g3azOYkF+WTUpG8w/voaHRKa4TH0z6gCt+voIrf76SG/v/h4jYdDYvzaHr0FT460UKQi8jf47BotBltPv2HUJ7diNm2nGcZLMxrlsKT/64lnf+3MyMpTv5z/jOnNS/NXYfFmrbcovoVw4xzTD+tAc9E3visDnY5liHk3bEuW3cenxP2sT7Xua5O+Zy31/3QVgFr5h38lrIYySnP45r5SwgnLI1a4kcI2GhzIoKin7/nchxYwNaWxiGwcPH92Tck7O58ZOlfHjRYH5ZvZt9JRVM6ysGEyt/30l5qYs+R7fZ7//bDBu3D76dMGcYb614i5LKEu4Zeg8Om4OQbl2xJyRQ+NtvdVNQF+6G9T9irPuebiX7+GXvlVyzYBW/pBYQEZTE7fvEFT++KJq95NG6bx8u6NGe4opiiiuLq/3NKcmRa8+9ymLc5sHDn6VHpXProFt9L68zBE5+V6yof7xL5ukjbqftoCH8vnkDRVtW42QQsaFrSNi2E1taMsHtYvyulnHp4/hg9Qc8u/BZxqWPIyrIq6A1TZPFuxfTL6Ufq+b+SmlRYaMp3cJ6JuJMCSf33ZXkvLaM6PHpRE6YQPiIEWQ//Qx73n+f/B++J/nmm4mOtZNXmIZ9y2pQCurK7Gx23nQTRX/8SdTECaTcc0/1XEWmSU/7QrJidvF3bhuSO91AhwCU0678cnLeXIERbCf+3G7sq3Tz9i+r2fnddtJKbWxJdnDyKd0Y0zkpoL6V2EYM1mY8t4Svnl3MkWd12c/qNCrESc9WMfRsFVOrjEqXmwqXSXmlm3KXHBWe88rq14XZJWz/Zhvl5WWE946jZ+9YurhNKlziiVih/k96fHiVZ4QVtjAnEYMlJnFFZhFF87MoXpRFyfJcbFFBhPdNIqxfMs7EMEzTZPXcX5n9/lsU5uXSZfhoRpx+DhGx8exZuIWiT7ZREFtAQeU+sv5ez7KfZskz7A4S09JJbtue5Aw5XhrzAuf/dCEXfn8hb49/m+RwHw3XQqJh6JUw6BKMFZ+T9PszJGWdybDUVmxMuY5V2T345+tN/DNjE627xNFlaCpteyVU877Zm1VMaQmkRq/GbHkDi5+/idT2nZqNN1uvsRP54pH/smHB33QcNIzU9tGsn7+7Srn509afyCvN45TOJzOsZQdGdUrk2o8Wc8brf3PO0HRuntDZJ6MkR3woscd1IHpCW0rX7qF0ZS4lK/MIWribb4gk/888dpS4GHxOF9IaUjltRUQSnPgm9DiR2G+uw5VdSPaXX2GPjiL51tuqvuYuc2GWF+HsOojSwkJWzv2FLsNHERpR/+uAMGcYV/W9iuM6HMdj8x7j6YVPM33ddG7sfyOjW4/2aZywhTrABLPchVGPyUnrA82rNP9yhEZEMuyk0+k3aSqLv5vBgplf8v5t15Peqy+Dp51Cy84NE5MzNDKIqdf24duXlzF+1R5WfbGJMCCxQ0yDPK++0Su1FbsKIvhx64+Epefz0dbtpEel8/ioxzkq7aiDBokP65mIa185+77ZSOZHS1i2bzYr1/fH5d5D+169SJzxMy3atGX32u1sn3AEf332AYu++4r+k46j78QpBIVaiPzG3+CHO2DXEkjpCVOfhYzRANiBPke3odOgFP7+cgNLftrGsl8kVEartEjy319NSJc4QjqIkrA4fx9fPf4AYdExTLr6P9jsjeciathsjLvkaspLSvjl7VcJCg2ju2V3b8/O7cQkpzRaCItAERoZRUh4BPvce4gJTiC6nqynAew2O0NbDOWHLT9Q5iqjf0p/lv00i8qyMvpOmHJoAY2EoJYtiJ1mZ8+na4m/9E6i52whj47suOEGQj3hPJSlR3lpJbOnL2fjnL1UmJNZFVTBkkiTy1vEccfuCmxlLsL6JBE1Ng17dDAxy1qw4Jsv6D7m6Ga/kVUThs1gxCkd+ezhBXz19GL2ZBYz9oJuzeZ3VJaXs/Dbr/j784+oLC+n99jJBLmjiVwH5RvyCe3kVaSXrt3Dnk/X4iosJ/LINkSNaR2QRbvdZufY9sfy0pKXGP3xaLrFd6NbQje6x3enW0I3EkITwDBo038EZ3bsyawXn+Ln3+axpf9gxl1yt7gYmiYU58LeLbBnSzUrbCrLKB18LXkv/USXkUfy+47fueaXa2gR0YJXx74akPtZQ8ARFERSetsGSZRosxkMaZfA3PU5TMkUZXyn5OatoDYMg9hp7cl6aiHuwgoKCnJIzujY1MXS0PAJiWGJvDnuTW6ZcwsPz3uIcxNuoHRFSypenUzxzr4UuCYyJ2ohIZu+xV5QSMpdd1ZZPEWHObl7SjdOHtCau75cwS3Tl/HBP1u5Z0o3+rQ5cOgm0zTZl1uKzR1MbHLzVVCHOkLpkdCDJYXz6E87zuvekkk9fQvrl1OSwyPzHuHbTd+SHpXOXVPuI2peLIV/7KT8+HMJWv4ZzsQWFKxexZ6//yChTRrB23fiLiggYtT+ybF8RYuYUO6a0o0bPlnCG79v4u9NeSRFBjOsXTwul5slP29T1su1KwENw+C6ftcR4YzgucXPUVxRzMMjHybIHkTEqJEUfP8DZkXFfq7ZB4TbBdvnw/ofYN0PmLsWszg4iOmxiXyfEcSU/Cza5E3l2fRTaL8mEUpchHSLJXNNAUZoEieNPcHnpGmmaVLmKqOooqhKiV1SWUJxRTFFlUWUVpYypMWQqsSgPsPuhGmvQlA4zHkMygpI73E+v3/0LpkbV9A6YxDj0jawe4OM+4EoqA3D4OaBN3PyjJN5cfGL3DTwpqrPdhbtZHfJbnon9mbRGzNITGtLy87d/H5GoHAmhZF0eW/2fLqOfd9upmxrAXEndiTl9tuInjqVzLvvZuf1NxAx9ApygtMJ27kGGEPh3N/ZedNNuIuKSPnvvcSccEJ1Dul2w7c3Ysx7jSPGnk7OwhC+ffl54tpkEN9q/w2UA8Fd7iLn7RW4SyowTu/Mg3M2MP2vrUzYY6eNy07a0S25/Pi6G7JFJYQy7cZ+fPvSMn54YyUFeaX0HZfmMy922G047BB6oBBvCmvnZbLqo03YHTYmX9mLtG51C6XmTAknZnIG0ePTKV2dR9H8LAp+207Br9sh2cnanH9YtuFX4tumMfmam2nZSYIcuooqKJ2VhTMxlE5XDqVL0ETJx5OdRdbG9WRuXE/WxvWs+WsOS3/6DpBcL2e36M4i2zru2HwBN0y6h3bte+5nNHJA2J0S7rXHibDpNxx/PEvH9dfR0RlG/pEXsZqprFpUxPevrSA4zEHHgSl0GZpKYptIb/zpFm62rN/Enl07mHDF9XWqu/pE2z79iUxIZMn3M+k4aBgt2kWzcs5O8nYVEd8ygo/WfESriFYMaTEEgJ6tYvjmqhE89O1q3vpjM3PX5/DkSb3p0co3Ay9biIOwnomE9UxkR24Rtz46l3NdobR0u+kZbIPP1pH19y5Cu8QT0jUeZ0pYw6/xOk+E9GHEd7iDylc/J++dd3E4i4m/8X5MtwmmE7OiCEe3kSz97Ucqy8roPa5hN+NaR7bm6SOe5s+df/LwPw9z1S9XMbTFUG4acBMZMQfPkeTJfeQursSmFdQaIeERDD7+FPpOnMLi72cyf8bnfHjXf2jdrSdDjj+FVio+WH0iKMTB5Mt78fC9vxO9u4JCw6R7l/qNv9lQ6N4ymhmLUtlpX4kzKJa7h97LMe2OwWE7dPM1TZN98XvIDNlKypI2VO4toPuIkfTb9SyxnVux4fcESpYvJ7KykonnXso+p50/P32f3z9+lwXffsWAY6bRp097nHPuh3XfQ3RrOO4VmXxqScwUFhXEmDO70G1kS+Z+so6y4kocy3Ior3QTPUkGCrfLxTdPP0Jx/l5OueeRJrHGtdntTLr6P3zxyL18//KzOENCq0KM5O3aQWxq844/DUqxktqSHfvWMuTOs/xySfQFI1qNYOammQD0ie/NT7Pup033niSmNS+L4rA+SeT/vBXT1pmoAXlkbYqm5Lq76XL+CRh2O3sLy/j80zXsnZ9DUCWkBS/nn7QMTmrXi1tX7cO9rZTgjrFEj08nqIV3IdV/8nF8+/wTbFmykPTe/ZruBwaIlLbRdB6ayuo/dhGVGEq7xtppPwhM02Td378z+7032bc7i4x+Axl1xnnEtWiFu8xF9ktLyHt/NUmX9sIeG8y+mZso+jsTR1IoSWf1JqhV3ZSdF/W4iMTQRJZmL2VF7gp+3/l7lcVWclgy3RO6Vymux1x9NW1++o3Z773JOzddxaQrbqBV1+4S4zo8AVru3yYylywEfiIruoR7fr6SjOgMXhn7CnEhzctyPaV9J1b8+iNut6veM6MPaxfP10t28s3SXQDN3oIawB4RROy0DuS+s5JiVwFdm4nFjoaGLwgryOLJoLY86Qplju17LrRdyK7Nl2Bzx/B30gq+d33Mf+buIe7sswnptL+ip0tqFB9dPJivluzkgZmrOO6FPzipfyv+M772kFA5heVElMt5cw7xARKH+s3dbzI4+DQ6hB46WavbdPP5us95fMHjlFaWclmvyzi/x/kE2YNwH1lB8aLd7J0fRqHRg4WJQezYuR73Ew8AEB8eSWJyHC06102Zdnzflny3fBePzlqDy21y3vC2OOw21s7LpDCvjFGnHFy+YRhc3OtiwpxhPDLvEa765SqeHP0kEaNGse+z6RQvWkT4wIEHFlCYDRt+Es6/4Wco2UOO3cHXLTszvWN3NlfkE+YI45hWkxhf2obYjS6CFhuEdIoi8sg2FG9ZTJGzgqDgtsT54elkGAYhjhBCHCHEU8/rM5sdjnkGgqPgz+dIKi0g2OFkR+5OWgOuQhdmWHswKnH66Z3lQZf4Lhzf8Xg+XP0hJ3Y8sUo5smj3IgDS9kXz19bNjL34qkY3FrAFO4g7rTOFc3ey79uN7H5uMfFndCG0R3fSP/6IPR9+yPb35rG9TRdazfqE3buLyH31NYI7tKflW28S3KGGV1FlOXxxCSz/DIZehePoezlmTC7v3nwNXz52H6c/8KRPCe9Nt0neB6sp31nI1xmhPPnO34RVwjkVYQRjMvbCbrTvV3+b+yHhTqZc1Zuf3l7JX19spDCvjBEnd8DmY7iQg6GywsXcT9azYvYOUttFM/aCbkTE1l+CaMNhI7R7AqHdEyjYtpu17/9M6PYQOjr70KFdb8J7JxMelIKpYpHs+WQt7qIKEs7phk0p1Q3DIDopheikFDoOljWvaZrs251F1sZ1ZG1cT9bGdXTcEItrcylf/3MndqeTxDbpJGd0IDmjPSntO5LQ+hCKfcMQA7aM0ZC1Av54jqhlzzPQfIYBvaawPfUyVq2LYOXcnSz7dTsJrSMwMAmx5RPTqQu/zZpBaFR0VRnrArfpJrMok+Sw5Frjw/sKm81OzyPH8/tH/yNv5w5S2wuv37V+L3vDs1iQtYBr+11bzWgwxGnn7indOLJLEjd8soTjXvida47qwCWj2vkcogbgrR830CXfwbogk85X9iIhwknJylxKV+aR/8MW8n/Ygj02uEpZHdw2CqMe2nStCImGiU8R1/J4Kh97kT2/FVCx53lsrUUxb7OVQFgsi2d9Q8vOXUlKP7iSuL4wpMUQPpnyCR+t/ogXFr/A8V8dz6ldTuWSXpdU82ixwqYSq7pLKhuljP5AK6ibEEGhYQycegJ9xk1myY/fMv/r6Xx876207NyVwcefSlqP3vU6idsdNroc25YP3llBkc3k1JYx9Sa7IdG9ZRRl34+nIr8Xx3c6huM6HDqsitvtYt3ffzJ/xnQy168lJDKKozqcRU9GEje0E2G798Efz+FMmEj5BumYQS1bkhwezrE33kHm+rX88cEbzHn/LRZ8VM7AlBx6Tr0b59BLxWXuEEhKi2LaDf0o31HI7ucWETG8JU4VL2/uh++wdfk2asQKAACGQklEQVQSxl1yNSntms6V2uF0MvX62/j0gTuZ+exjBIWEkN6rL3t37SStZ58mK5c/iEltwfZVy+tdOQ1I2BgMYkNiqVyzi8LcHI4879J6f05dYdgNoo5ozZ5P19GhQwLrNsHS4FYUbcjl+5kbCVtbRLTbICR4N8dFPY5z0LUM2NqBij93Y08NJ+74DlWW/VZ0GjqCOe+/xfxvvjgsFdQAQ45tx861exg0pW29EPC6IGvjen55+1V2rF5BQus0TrjtPtJ69q763BZsJ/7sbux+fhE5b60Am4FrTykRI1sSfXQ6hrPu5XfanZzU6SRO6nQSAMUVxazKW8WKnBUsz13OipwV/LT1p6rvt45sTe/j2pH4czYf3XsLA6adyPATTj+gUnfXujVgwH2bn6ZzShdePOpFooObXyzj1A6dWDxrBrnbttb7hpMnDvVXi3eSHBXsV1zbpkRo13iye+ey7fNVTNIKao3mjuy1sOpLWPklZC7D5U7jvOCzOKGoL/YwO1k4WddvDQ8VPM/bnyXjSHKScMUVBxRnGAZTe7fkyC7JPPvzOl6fs4lvl2dy3dEdOXNwWrXF9LY9xcS5hXPENuMQHyBxqF9b9hpB8SY71+5l05JsIuNDiIwLIbhG8qqNezdyz5/3sHD3Qvol9+POIXeSES2La9M0ydm9jcyo7SRtTmFe1kB2O1bSOi+f/o8+Sc72LSx55w1Wp8Sy+sbLad21B52HjaTDoGF+uzcbhsED03ow9snZ7C2uYFrflpimyaLvtxKbEkZad9+Ut2d2PZNwZzh3/3E3l/54Kc8MehicTgp/+626gtrtgh0LlZX097BzMWBSGZ7I3LYD+TzYZPa+dVSa+fSO6c39bY5n6I7ulP2yG7PURbZhkBMfytHnSsK9ZW/9BAYkpnVrXnE9DUPi5QZHYfv1AVpFjGR7iQE2k4p8B/a4TtgjK+pU5iv7XMmsTbN4ZN4jvHjUixiGweLdiwl3hpPz+2JCIiLpPDxwC/u6wDAMIke0JKhlBLkfrGL384uJPb4DYb2TiDv9dDq2G8LatzYTtqGM3PmvEXPyySTffBO20NDqgsqL4KMzZRPj6Hth2NUARMYlcMy1N/PJf29j5nOPc+wNt1eLT1sb1n+0itBVeTxFCTO2FHJmlxa0XFqI2+1i4lW9aNkpsCTcB4PdaePo87oRGR/CwllbKdxTytgLuuMMDlx5uXd3MbNeXU7OtkL6jG3DoKkZ2BuAc7sqK1j47df89dkHVJZX0HfCFNoMHErF8n0UL8mmeMFuHAmhOFuEU7o6j5gp7aoZ3tQGwzCISU4hJjmFTkNGADLe/bxkBs/OepCOZakkm0GsmvsrS34Qo6WIuHja9ulPRp8BtOnRi6CQ0AM/ILkbHPciHHkH/P0yxvw3ab3qC1q3GUrphVexLq87q/7MJHtrIe1ClpEf3Z2NCz5g4LEnVMXIDgTr96xnxsYZzNw0k11Fu4h0RtI7qTd9k/vSL7kf3eK7+RWvGKDHEWP589P3Wfrjt4w683zCooNY9tsOdq1cS7fCYQxyHcnerGIi4oKrhS8Z0SGRWdeM5I4vV/DY92v5efVunjipN+kJh97E2bRhD87fsgmx2zjuur4ktpH5xJkcTtSYNrjyyylZLcrqwn92UfjHTowQByGdY0Vh3Sk2IOtg023i2ltGZW4JlTklVGaXUJlbQkVOCa49peC2QdrlhADlJQU4Ny7BnZOJrXItm5csZG/WLoadcqbfz60LnDYnZ3Q9g4kZE3l20bO8u/Jdvtn4DVf1uYpj2x+73wZFlQV1SUWjltMXaAV1M4AzJIT+k4+j19iJLPvpe+Z99Smf3X8HSW3bkdq+I9FJKcQkpxKdLLt+wWGBE+LRnZK4PnQJkSFOWscdZED1E263i+J9+yjJ30dMcirOkPrbNe2SGoVZ1pLK0paMaH/wuMgVpaUs//UHFsz8kn1ZmcSkpHLk+ZfRbdQROOxBZL++nLxP1mI79SJCgt7E6ZIwHPbYWGzhaqAszSdl68dMM95iR9sQ/igbzK87gpj3wSIGlfxAjyPH+zRpmKbJ3q83YAtzEnWkuHut+XMu8776jF5HT6wWVqOp4AwJYdrNd/HxPbfy1eMPcPRFV1BZUU5ci+ZvQQ2SKHHVnF+oKC/DGVS/ye9iQ2IZ1nIYLcJbsGjm18Qkp5LRd//Edc0BYkW9DWduEjbK2Ty/kM1/l5DqtlER5aTfoEL6LH6AgvBbKfmzJfboCmJP6khY76QDLkbsDie9xx/D3A/eJnvrZhLbpDfuj6oHhEUFceZ99Rtn2F8U5uUy98P/sWL2T4RGRnHUBZfT44ixtYb1ccQEk3BWN7JfWYotKojEi3sSnN5wCt4wZxj9kvvRL9m7AbGvbB8rc1eyIncFK3JWMC93OTn9sxiyPA4++5gf5nyMOakrXdN60z2hO53iOhFsl763aMlv7Ikop2uLnjx/5PNEBNVPTPj6RmoHscDbtX5NvSuoW8eF0SYujK15xXRs5uE9amJ75hoiEhObPGO8hsZ+ME2xRFv1lSils1djmnZKYs+gMPw2ynMjwG0Q2TuRuVt2sWZbKe8UPc11WzoRtGEFyU89hT3i0IvhiGAHt0zowon9WnPP1yu45+uVfDRvG3dP6cbgDFGMbt9TQrzLhjPUTkhE8w6F1jupN3bDTmHiboqXJDLzxWVVnwWF2ImICyE8Loht5kb+Kfyd8rAQbut9H5N6jiUiMoTCvFxW/f4bK2f/TM7WzdjtTia1vZgh6VOJ2PolWUuDSAkNp2W/wYTfeBvBl19KZko8q3//jR9eeY6fXn+J9N596TJsFO36DfJ5bZAUGcLTp/Thjw05dE6JYvvqPHK2FTLmjM5+KVCndZhGmCOMW+bcwsW/X8N9fXtT+OtvJF92Lqz/SZTS63+CkjwwbNCyP1uGX8HnThdfZf1Ndskq4sw4zuh6Jse1nEL8MieFn+yktGwXId3iiTqiDbuX57L28w303lpAYptINq5dDUYIbfv28vt9NTgMA0bfBMGRZLz/NBucHXCbJZRXtsMWkURQm7p5FMWFxHFJr0t4dP6jzN4+m1GtR7F492L6hnRjw/y/6T/5uHrn6v4iOCOa5Cv7kvv+KvI+XEP51gKiJ7YluXtrYDM5PTPoeOKVJB11xP7/uTgP3jsRdi6EKc9B3+oKqFZdujP6rAv4+c2X+fOzDxl64mn7iTBNk7nrc1jy5VqOzXHzub2CFqPS+CI1jt/fXIXNaWPqDX1JqKPH3MFg2AyGHNeeiNgQ5ny0li+eWMiky3sRFuX/hvr6Bbv5+X+rsNkMJl3Wk/Se9Z/80jRNNi2az6/vvMqeXTvJ6DuAUWde4F2rdkoi+pgMSpblUDQ/i5KlOYR0jSd8iG8hjWrCMAyO7H0MZmwoN/x2A3nJsTx369uU5OSxY/VKNi6ax5o/ZrPsp1nYHQ5ade1BRt+BZPQdcOD8TVEt4Oh7YOQNsPB/8NcLhHx+Cj3iO9Bj3BXs2ZpD6KKX+Gft7QD0PGq83+XOKsri203fMmPjDNbsWYPdsDO4xWDO6noWG/ZtYEHWAubsmANAsD2YHgk9qhTWvRN7HzJ8UHhMLO0HDmXFrz8y7JQz6TmmFct+245zSSojOIlfX9wAbABk/RUZH1K1IRoZF8L13VszKjmaB35dx8Rn5nDH5K6cMqD1AY0xs7cW8M3TSzBNGH5RtyrltBX2qCAiBqYSMTAVd7mLsnV7KFmZR+nqXEoWZ4PdIDgjWllXx+GI8c5BpmniLqwQ5XNOCRW5JVXnlXklUOnNDGoE2XEkhBDUMgJHr0QcCaE4EkKxhZpsP/9UijduwsBN5OCu/DVrBuGxcXQYOMTfV1gviAuJ464hd3FSx5N46J+HuPvPu/lozUfcMugW+iR5DRBtYd4QH80NWkHdjOAMCqbvhGPoedR4Vvz6I8t//YE1f86ltLCg2vdCI6OqlNUxySlEJ6cQkyR/I+LiD+qyHBsexMiOiQQ7bD5bZ3uUzwW52RTm5lKQm01BXi4FOfK3MC+Hwrxc3C4XAIZhI75Va3GHaSfZchPbtMURFJglWViQg3aJEazfXVi1QKiJor17WDxrBou/n0lpYQGpHTox6vTzaDdgULX6SDirK7tfWkLuJ9tJ7HsjzkWPAlGShdxVAQvegl8fguIc6H4CLY+8gxNj09m2chl/fPweP7/5Mv989RmDjzuZ7mOOqhan2XSbuPLLqcwppjK7hPJtBZRvzifmuPbYQhzkbNvCrBefIrVDJ8acc2FAddEQCA4L5/jb7uWju27i2+efACAutVUTl8o3eEKR7M3c1SAK1BePepFd69fw/trrGXPORfUeDqC+YNhtRI1pzZ7P1tE+ZCfu0lQc0U5GTGtPxxYV5L/6P7IrnsUodRI9oQ0RQ1v4ZJHb86jx/DX9QxbM+ILxl13T8D/kX4SK8jIWfP05/3z5Ka7KSvpPPo7B004+pNtnUOtIUm7sjy3UgeFDQpH6RnRwNENaDKmKIwcSj3Rl7koW//IdUd8to+K95fyvx69sTy7BYTjoENuB1hGtiNu0BVtGDC8d/RKhjvrbAK1vxCSnEhIZxa51a+l5pP+LgENhWPsEtv6zlc6HQXgPK7I2riNFW09rNBeYJuxcJArpVV9B3kbAwNVyLEXp/6FweyruTBf22GCiJ6QS0ieRnN1bsGVuxJG7gHN+7oB7Xz4r+3WnoKyA+AX/EN+yNVFJSYecy9snRfDOeQOZtSKL/85YySmv/MWUXi24dWIXtuWJBXVsy/Bmk9fgQAh3htM1vivzE2by4umvUpBbSkGe99i2M5MVWzYRXBzBQJfEysxZWcGbb72Bq2Il7oqtgElYTBvaDTyJtr0HE2qEwPdbMDtcAb+/QtnyxbgrpR5aTpxERrt2DDnhNHZv2sCqub+y5o/ZbFzwD87gENr1H0SX4aNJ69nnkHFdR3VMZFRHCc216IdthEYF0XGQj0nLLBjfdjwhtiCun30906NtTP67mPK7OhEUUQlhCdBhLMUZo/gxCKZv/Z4FO77EZtgY0XIEx3U4juGxQyj9fTeFX+2koNxFaI8EIse0rrLM7BYXwvxvN7Pw+y0cfU5nMsuKsQV3JLV9jN9lbTQMuYz0Mhu8OIPSwixs4RITOrRn3Y1TTu1yKp+u+5RH5j1Cz8SerNu7jhF5o3GZ2fQeO6nO8usD9qggEi/swb5vN1M4dwfl2wuIP70LIc4iDHs82zv2Yb/AGvk74X/HQd4mOOl/0KX22LK9x00ma+N6/vz0fZLatqN9/0Hkl1awc28Jq3bl8/rcTUTtKOZBQslKCuWcSweTvWYv37+ygsi4EI65shdRCY3Dn3qMbkVEbDDfv7aCzx6Zz+QrevkctshV4eb3z9az7NftJLeNYtyF3YmMqz/jNA9yd2zj13deY/PiBcS2aMW0W+6hbS1enbYgO+H9kgnvl4wrvwxbmLPO4/NRaUfx32H/5da5t3Lj7P/wxJgn6J7aku5jjsZVWSHK6oXz2LhoPr+89TK/vPUycS1a0bbvADL6DKBl5677j3PBkTDkMhh4Eaz8Av54Br6+mligMqkby+bMoV3/QUQl+BbaJb88nx+3/Mg3G79hXuY8TEx6JPTg5oE3My59nOSXsSCvNI9FWYuYnzWfhbsX8tqy13hl6SvYDTud4zrTL7kffZP70jepL7Eh+1vw9z56Amv/nMPaP+fSb/yRbGm3kCfn3sOLQ14jzdZe5pbc0qq5JntLARsXZ+O2KHvPx0mFHVa+vZYHPt/MgO6JJKdEeJXZ8SHszSrm62cXU+hysbVXJH17HLo+bEF2QrslENotAdNtUr41X5TVq3LZ+9UG+GoDztRwHImhVOaWUpldglnu8gqwGzjiRfEc0jkOZ0IojoQQHAlh2CIP3J5av/k/tpx2OuVbtlDSYQCbFv3JkONPbfJcXl3iu/DW+Lf4bvN3PDb/Mc769iwmZUzi2r7XkhyejFFlQd38FNSGJ1bP4Y7+/fub8+fPb+piNAhKiwrZl5XJvt2Z7M3KZF9WJnt3y3V+9m5Mtzfrs93hICox+YAK7KCQUMoqXRgYBDlsfiufPXA4g4iIjycyLoHI+AQi4hOIjE8kJCKCvB3byNywjswN6yjJl6D/NrudhNbporDO6EByuw4ktE7zOfHAfTNWsnznPj68qPpuVN7O7cyf8TkrZ/+Mq7KS9v0H0X/ytIMmnKzcW0b2C4sxTTehW68k87ciIof2pFWf9ZC7HtKGw9h794uxapomW5ct4e+PPqRoWy5Jcel07DqE2LAUXLmlVOaUYFZ434URZCOkUxxxp3amvLSY9269lrLiYs546Cki4+p/h7muKMjL4cM7byI/O4tLXv4f4TH171pW38jauJ53b7mGKdfdSodBDWMp+80zj7Jx4TwufvGt6kkzmxlMl5vMR/7EtW8zhX0jaTdpPMWzt1M4ezOmCRH9ooic2At7uH8T5k9vvMjSH2dx4fNvEBFbf7GEXZWV5GzdTOaGtWRuWEfRnjyCwyMIDgsnODyc4LBwQsIjqs6Dwz3XEQSHhTX5xH8gmKbJmj9mM/v9tyjIyabDwKGMPP1cYlICs+RobsjbuZ0ZTz9C9uaNJAzvTd7AOFbuW8W2rWs5+odIjrjwUvoc1TwWoQfD9AfvoiA3h7Mfe77OstxuF3szd5GzbQs5WzezbPkaNm/YTGp0CLERodgcdmw2u/y1O7Db7dgcDmz2A1yr78m199yuvoNh4K6spLKiAndlJa7KSlyVnvMKdS2Hu7ICV0WF5dr7fc93PfdKCwsYfurZDDr2xHqo4X8/DMNYYJpm83Sr8RPNhkO73bB9nlJKfw37toJhx0wfSXnSSRRmd6VkdSG4TYI7xOBqZ2N7wVq2rVzK9lUrKC8pBsCwxxPjDMWWu57SlGSKCvZVPcLhDCK2RUviW7UhrmUr4lu1Ib5la2JSUmudV0rKXbz42wZe+m0DDptBanQI4ze46De4BUec1aXRqiZQPD7/cd5b9R5/nPoHIQ5RIO0r28cTC55g+rrptIxoye0Db6dFdjjLfv6RzUv+prK8lOCwWKKS+xAU1pXSogiK8stBLRkHhttJdBjMycnDEVxKlLuAiKyV9HzxPmKSqyercrtd7Fi1glW//8a6v36ntKiQkMgoOg4aSpdho2nZuetBQyHk7ijkw//+w6ApGfSfmO77Dy/Os1hJ/8jf7kL+60zg/jcM3BO7kjT1NDY67Py6YzZ/7fyL0spSUkKTGdFyOENbDCXGjIIVZRjrKqASzDZ2XF2dEG1gYoKJindrsur3XWxaspteAwzm/fIlzrDxXPzCJQSHNm87sFdPm0rfqHEkx/TALC+i5SNHY3PUfWN87o65XPrjpQxrMYy/tv3BuXM7kd61F1NvuL0eSl2/KF6azZ5P12IE2VlZmkX23lycZ4zkhMGWzdqc9aKcLtkDp74PbUdWfVTpcpNVUMbOvSXs3FvCjr0l7MjOJ/T7F3EU5fJ1m5PYgXezenRMOPcUOAhODiPpkl6s+juT395fQ2JaFJOv6EloROOHBcvalM83LyzB7TaZeGlPWhxic2Vfdgnfv7ac3VsK6HVUa4Yc2w57AMm7D4bSokL++uwDFn03A0dQMENPPI3e4yb7nrCwHvHh6g+5/+/7mZA+gQdHPFhrLOc9mTvZpJTV21cuw1VZSVBoGOm9+pLRdwBte/cjLDpmf+GmCZvnwrxXWVHSnu9m/sMJt99HWo/eByxPuaucOdvn8M2mb/ht22+Uu8tJi0pjUttJTMyYSFpUms+/raiiiCW7l1QprJdlL6PcLUkWMqIzqhTW/ZL6kRqRimmavHXdpQSHhXPa/Y9z8oyTKXeVM33K9AMqcE23SXF+eZXyOj+3hPzcUlZv2EN2VhFRbgOnuf//NSIdvGgU8tblQ+iXVjedREV2MaUr8yhZmYursBxHfKhSQHsPe0xwwCGOyrdvZ+d/bmJ9324sW/g3Fz7/Zr2umeuK4opiXl/+Om8tfwu7zc6FPS7kzA6nk3PPAqLGpxM1unWjl+lgHForqA9zuCorKcjNsSiwd3kV2FmZlBUXVft+WHQM0UnJGDa738rnyPh4IuMTiYiLJzQy6pA7k6ZpUpCbQ9ZGUVZnbVxP1oZ1lBYVAkjigbS2JGd0IKVdB1Iy2hPXsnWtru815e5Ys5L5X09nw/y/sTuddBt1JP0mHUtcC98sfysyi9j94hIMcw/7Pr+TuHa7ST4qVeKJdRyH6TIl7lC2xBuqij+UU4K7yBurx226KTELCUqOIKZjS5yJsjPnTAjFFhWEYRiYbjdfPn4/mxbN58Q77qdVl+4+lbEpkJ+zmx2rVtBlxJimLopPKC8p5tlzTmowpUpBXg6vXXE+vcdNZszZzcfq/UAomruePTN2EZ6eR0l2Eu6iSkJtvxI9qQuOYScEJHNP5k7euOZiBh17EsMDjKdlut3sydxF1oa17Nqwlsz1a9m9eSOuCulLoZFRRCUmUVZcRFlREWXFRfuNSzXhCA4mJCy8mlLbq9COsCi5w4mIiycyIZHwmNgGtYLftW4Nv7zzKrvWriYpvR2jz76A1l17NNjzmgqV5eXMfv9NFn37NUnp7Zh8zX/YtW4N3z7/BGc/+hwJh0E4mD8+eZ8/P/uAK974yOewWaZpUpiXW6WIlr9byNuxjcoKlTVNJeHZGxRDelIUdtON2+XC7arEVSl/3S4X7spK3G53rfdd6vvuShem6T54oQDDZsPucIoC2+HA7pRzu92h7jm99zzfcTjU//H+P2dICP0mTj0sNiebA7SCuu4oXbsW9759mNsW4t4wF3PzPNyFezFNJ2ZMB1yx3XE5++EqScF0hWGa5ewrW8eGfQvZWrKLcrWGCTchscIkvrSc+IISlqadQyVOJg4pJPGKyyktKiRvxzZyt28jd8c28rZvJXfHdvKzs6rKYrPbiUlOJa5la6W0bkVcqzbEtWiJMziErbnF3DtjJXNWZHFVfihDprWj71jfFQFNhd+2/cYVP1/B62NfZ0DKAGZumskj8x5hX9k+zkw5nr45LVg3dy4FudkEhYbScfAIuo4cQ6vO3aopjl0Vbgr3ioKhcGsB4b9sJTc/k1WFeewLao3LLsrv4HAHyenRJLeNIqVtFEnpUYSojXFXZQWblyxk9e+zWT//LyrLyoiIT6Dz0JF0HjaKpPSM/dYWP72zivXzszj7gWEHD6niduPesZCiJTPJXz2H/J2byS8PIt+MpsCRSn5lCHvzi3GVHzzeZog9nM7RA2kX2QebYWdb0SpW7P2Tgopc3yrctJHc/grOeGCsb99vQnx3x00EZSfRMWYg7vy1tHnh/HqTfcVPV/Db9t/osD2SYUvjOPGOB2jTvWe9ya9PVGQVkfvuKiqyi1lVUsmGISVceopstBdtnk/whyfhNk1+7PsCy8y2VcronXtLycwvxeWurkuJCXOSEVrBoKVvY4SEE33yDbRMiqWNw0HC55swbAaJl/Vi4ZydzJuxiTbd4hl/Ud3iQNcV+7JLmPHcEgpySznq3K4HTM64cVE2P72zCsOAI87qQkbv+k1A7na7WP7LD8z98H+UFOTT44ixDD/5zNqVu42IN5a/wZMLnmRSxiSmtJtCelQ6KeEp1ZICelBeWsKWZYurFNZFe/LAMEhp14GMPgPI6DtAxroaG3Pv3Xot5SUlnPPEi/uNg27TzYKsBXyz8Ru+3/I9BeUFxIXEMaHtBCa1nUT3hO714tFT7ipnRe4KFmQtYEHWAhbvXkxhhehtWoS3oG9yX9pvDGHvzHkMuflKLl56A7cOupVTO58a0PPWZRVw7UeL2LC9gGmdUji1ewsqCyooKargtqWbSUgK4+OLmyZUhr+oKC3l5cvOJr1XPyZf/Z+mLk6t2F6wncfnP86PW3+kVXgrnl97C4kjMogc0fie81pB/f8UpmlWt77O3FVlhY1p1kn5XJcyebLlZm5YR9aGdWRtWk95SQkgSqek9HakZEhokOR2HYhNaYFhE2vv9fP+Yv5X09m1fg0hkVH0HjuJPuMmBTRxlW7YS87ry6jcvZawtGKCeo6nIreMSk8AfEvXsEU6JfFCYph3py0hhK2bl/HHp++RvWUTcS1aMeTE0+g0eHi1SefPzz7gj4/fY8w5F9N3wjF1rUKNGnjpkrNI79WX8ZdeUy/yXJWVFOblkJ+9m+W//MDKub9y/tOvHjiuWDOCWVFJ5p1f4DKTCU51EZ17HUH9h8OUZ+ok98vH7mP7qhVc9MKbOIMP7cJXuCePzPVrq6yjMzespaxINsscwcEkt5VM2KntO5LSrgNRicnVxhzTNKksK6O0uFAU1kppXVZUSKlSYpcWqc883/HcV9+zepZ4YLPbiYhLICohkciERPkbn1jtOhAr+YLcHOZ88Dar5vxCeEwsw045k26jjmy2IWHqC+vn/82sF5/CVVlJXIuW7Nm1g8vf+PCw+N2bFy/gswfv4sQ77qdN9/1jhZYWFpKzbTM5W7eIInqbKKQ97RggPDaOhNZpcrRJJ6F1GvGtWvvUR3yF6XbjdrtwV7pwKSU2pond6axSNB8O9f1vhFZQ1xGmyYYR/SnPKd7vI1tECo62o3C2GYrNGUphyS7WFSxiQ/EqXGYlYW5IxE6iI5jk4DDCw8IxgoOwBYdghASzrjSdZXvb0KZrLNHJ4UQnhBKVGEpUQghRCaE4g6TPVJSWkrdzuyivLQrsvZk7vXOIYRCVkER8q9bEtWzN7pJwMv8uZcQpPWnXpzWhkZH12ufrG/nl+Qz/YDjHdTiOrKIs5m/+k2H5HeiRlci+LdswDBvpvfrQdeQRtOs/yOffsnfmJgpnb6fo1/tx7d1K5KWnUdBqEFl5UWTuNMnLLK7i0bEpYSSnR5GcIYrr+BbhVFaUsWH+36z+/Tc2L1mI2+UirkUrOg8bRedhI4lJaUHR3jL+d/ufdBvRkpGndKSyooKCnN3kZ2eTn7Ob/F1bKNi8nPzM7eTvLaCg3IGb6kqfkIhIohKTiEpIJCohiZIFf+Ocv4QvTk9jYPpwBqYOJNQZBoWVGIsLsW2zgwkVQdkUla+gcs82KnNzce3Jw713n1g8Aob6bbawMBzx8WxMHMluowU2Rxidh3dhzBmd6+0dNhTWfvcNaz9bQO/4MVC+mFZPXFlvsrfkb+HYL47luD9akRbRhrMefa5Zh8Rxl1Wy7bm/sGeb7KWUfEcwDlcxrcnEhcF6M5USZIPE4bDhdNgIctgJctoIdtrVYSMkyC4JAg2DkoJ9bF+9gvDYOFp26kLFzkJchRUkXNyTP3/Zzoo5O+k8OIXRZ3ZukKSC/qK0sIJvXlhK5qZ9DDu+Pb2O9MYHdlW6+WP6epb+vJ2ktEjGXdi93kORbF+1nF/eepXdmzfQsnNXxpxzMclt29XrM+qC5xc/z0tLXqq6DrIF0SaqDW0i25AWlUZaVBptotqQHpVOQmiCGKmZJrs3bWDjonlsWjifXRvWgmkSHhtH2979yejbn7QevcndsY33b7ueI869mD7jvbqCtXvW8s3Gb5i5aSaZRZmEOkI5ss2RTMqYxODUwThsDWtR7nK7WLtnLQt3L6xSWhcW7OGkn1qxqWUJC/sU8fOJP9cp50x5pZtnflrHC7+up0VMKE+c1JstuUXc+OlS3jx3AGM6+RbupKmx9Kfv+OGV5zjlnkcO6snfHPDXrr94+J+HWb93Ped3P59r+l3T6GXQCmqNZg2xrtxJ1oZ1ZG5cT+aGdezevIHKsjIAgkJDSW7bnoLcHPZm7SImOZV+k46l2+gj67woKF6ym7wP1gAqAH6i19XDaTk/WAZY0+1m3bw/+ePj98jdvpWE1mkMPfF02g8YzKYlC/j84XvpMnw0Ey6/rlmTs8MVH919M6bp5pR7HvHp+67KCgpyctiXnUV+9m5Z5OzOYp86L8zNrWa12G3UkYy/7NqGKn69o+LBEbji+hO853OM6FZwwQ/grBuJ3L56BR/ddRNHnn8ZvcdOrPZZWXERWRvXs2u9WEZnblxHYW4OIJadiW3aiodE+46ktO9IvA9eEnWFaZpUlJWKwrqwgMK8XPJzsinIzZa/OfK3MC9nP0vt4PBwouJFYR2ZkORVXqt7EbFxVeWvKC1l3tefMe+r6Zimm/6Tj2Pg1BOadSiY+kZBbg7fPPMoO1avoE33Xpx4x/1NXSSfUFpYyPPnn8Lg40+lff9BSgnttYwuzPNaywWHhRPfOo3ENmnyt3U68a3b6GSC/8/RVApqwzDGA08DduA10zQfqvF5MPAO0A/IBU42TXPzwWQ2FYcuemAypuHE1n4kZAyjYJuLopVFBOUH4zZdbCtazbr8RZSGldCmW09ad+tJm+49iU46+IZxcX45cz5ey96sYvZll1BRWn2cD4sOsiitQ4lOCCEqMYyohBDCooJwuyrZs2vnflbXebt2VHn+WOFwBhESGUloRCQhkVHqbyQhEZZ7kZGERHj+RhISEdEgm0um213lheEJ4XPxdxeyZ8tWOu2MpuXuEHCbJKZn0HXEGLoMHx2Q14S7tJKd/51D5e7NlP7xMB2OzQVHCKYZikko5SHt2GPvyR5XWwpKEygqCsVdacNhQJDdICIyiLAwB6HBdhyGm/KCQioKS6ACHDYnDpuEOih1lVJhK6W4Yi8FpXmUVBZS4iqguLKQUlc+BrmEB5UQFR9PVKv2RLXrS1TLDKISk4hMSCQopDr/KfrrL7aecy6RY8XCuSK7AMPZGXtyfzBsVG77i7K1MzGLsrGFh+NITcGZkoojJRlnSirO1BQcySlVfz3JN/dkFvH+PX+DKZalXYY2/7Be5SUlTL/0ZoYlTyMofiXJ/7m0XuVP/+VNNr30GUddcDm9jp5Qr7IbAnkbd/DnU+tICt+HGWKQXraeSnso+XG9cAaFEmK34bQbGKbapzAl1Ivnr1njGtOkJD+for17CYuKISwmiohx6fz26w42Lcmh77g0Bh+7v9dAU6Ky3MWPb65kw6Jseo5pxdDj21G0t5xZr61g9+Z8uTetPXYf8tj4ivyc3cx+7y3W/DGbyPhERp5xLp2GjGhW9eJBdnE2m/M3syV/C1vzt7I5fzNb87eytWArFW7v3BDmCKNNlCiu20S2IT06nTaRbUgihj2rNrBx4Tw2L1lIeUkxNruDsKgoykpKuPjFt9lj5jNz00y+2fgNa/esxW7YGdpiKJMzJjO69ehDJjNsSJimyZb8Lcx66WkKlm6g1X9O4pReZ9SL7AVb8rju4yVszSsmKsRJi5hQZl41vFm2g4ryMkry8ynJ30dJgfz9a/pH2IOCOPOhp5tlmWui0l3Jx2s+pn9KfzrGdmz05zeZgrouJNowjFuA8wEXcJVpmrMO9iytoP53we1ySSzrjeurrK0dziD6TDiG9gMG1yupr9xbhmEDW2RQnQYU0+1mzZ9z+OPTD9izczuJ6RnkZ2cRlZDEqf99tFlb2BzO+P7lZ9iw4B8ufeVdQMIP5Oco6xqPEjp7tyigs7Mo3JNXZQEDktQzIi6eqMQkohOTxNImMVn9TSImKeWg8RGbHZ4fBNmrISgSLv4N4utufWCaJu/fdh1lxUVMuOJ6ZR29jsz1a8nbub3qe7GpLUjO6EBq+44kt+tIUtuMJs/YfjC43S6K9u6pUlhX/bUosmsmqTVsqr0kJLIvK5PCPXl0GjKCEaedQ3SS/wmc/g1wu1ws/WkWSeltadGx+cdk9eCNay5mz64dVdd2p5O4lq1JbK0U0W3SiW+dRmR8wmFBNjUaF02hoDYMww6sBY4GtgPzgFNN01xp+c5lQE/TNC8xDOMU4DjTNE8+mNwmU1DvyWPbomUUz8siMjeKUFs4xZX5bClbRWUapPbsSutuPYhr0SrgPijehBXkZ5eyL6eY/OxS8nNK2JddQn5OCYV7y6p5zDmCbEQlKMW1UmBHJYQQnRhKRGwQcz5awMo5azn6vAzKigopKcin1PO3sICSggJKC/IpKSygtLCgVm8eD4LDwwmNiKpSbgeHR2Capgr1I8plj6JZlM0uiTfvCQ1UWSHKaM/nHi+LAyAkOpruI4+k64gxJKa1Dag+rch9bw4ly8A03Ri1uLrXBtN048ZFJQYVbhsVJlSagAOcYXYcEXbKK/ewL3crxfvyCQ+OIyYsghBCcJqh2I39lTNGsB17dBD2qGDsUUHYo4P3u7aFOzFsBmZ5ORsmTMRdYhLcbQq2uF4YhoEtbA/BGW6C05JwpiTjSE3FHuGfVeDMF5eyaUkOp909yOdkc02N/51+PJTaOOa0U4medhyVFeVUlpVRWV5ORVkZleVyVKh71c7LSqko95yXVTuvLC8jd8d2yooLufiFt3GGNP81kNtt8srls+ge9zfDg56SXESnfQxhgceSNU2Tmc8+xuo/ZjP56ttY8buTXRv2MfzEDvQ6ova4r263S+qzZp2req0oL/PWd1kZFeXlVJaXy3hgGQuq552orP65NX+FZYzxfFZRVoHprkQGRzuGLZiw6Egi46IICgsnJCycoKqcMWESWi8srCpPTHCYN39MUGhorev2irJS5n01nXlffQamyYCpxzNgyvGH5XrZ5XaRWZzJlvwt+ymvdxTuwGV6x+WooCixuA5vQ8v8CMK3llK+IRNHl1R+T9vB/Mz5mJj0SuzFpIxJjEsfR1xI84lnDJC5YR3v3XotfSdOpWWnLtJ2Kqw5UCx5UWq97/l+Bd68KJVUlJeTuaeIguIyUuOjSIyLJigsjKDQMIJCQgkKCyM4NIyg0FC5p/4Gh4bhDA2Vz8LCcAaH+MwZTLe7ag4vtiicS/LzKSmQv8UF+dWuK8pKa5U14Yrr6XqYhEltajSJgrouJNowjK7AB8BAoAXwI9DRNM0Dsi6toNZoLnC7XKya+yt/fvYBZcXFnH7/E4dFeIjDFfO++ozZ771JaodO5OdkS6wvCwybjcj4RKV8TiYqMVESiSoFdERcQpMk3WgwvDkJtsyFE9+CbsfVm9jVf8zmm6e9VurhMbFiFd2uY1U4ntCIyINIODxRXlpCQU6OuBXnZldTZtscDoYcf2qzd+XSqB2bFi9g17rVEpqjdRqxKS0a3Lpf49+DJlJQDwHuNk1znLq+BcA0zQct35mlvvOnYRgOIBNINA9C+JuCQ7tdbv658S1SnRnYDTv5zj24OzhIGtGZxLT0RtsYdlW4JWlTTnXFtedvZblFwWyAzW4QnRjGaXcNOqRs0zQpLymurrRWf0sKRIEtSm1RbpcWFWKz2SQxqieEj93hjR9vt2N3qPA+drvEl3c4sDns3njyB/hOVEISrbv1qNcxriI7h60XPUzEqCMI7d0dW5AdI8iOEWzHFmzHCLJhBDuw2SowSnZiK9oK+Zsx9m6GPZuozN1Ozm7IKk0ns6IjWeWdKHBXd+c+Pv4WUpyrISQG2h2Bu+04XEnDcVVE4M4vx5VfhmtfOa59Zbg81wXlUHNfwGaIsjoqCCPEQdmGvQCE908mclRrHHF1V4rl55SwcXF2tdAIzR2/3HIjCzeuwuFwUll58PjcB4Jhs+EMDsERFIQzOBhHkOcIouvII+h55Lh6LnXD4ZPr3mVvSTSRYcUYMWlgs1W9y6pXahhV54bn3HJd9V0DwMB0l7N9+atUlO7B5kgjvlUwwaFGDSWz2gwoK8NVWRlQ2auNFVV5Jqx5JxxV48H++Sj2H3NythWzbfVeQiNstOwYimmWSzg9FVKvvLiIsuLiAyrrrAgKDbUosUV5nb11MwU52XQcMoJRp59LVOLhEcrBX1S4K9hRsIOtBVvZvG8zWwu2VimyM4syJemqQnpUOpMyJjGp7SRaRzV+4jp/8P7t17Nr3ZpDfq9au7PkQ6map6ruO6vuV5pguCooKy6mvLSE8pJiyouLKS8p9q1/GMYBFdqAUjaLQrq0oOCA+V4cwcGERUUTGhlFqPobFhVFaGQ0oVFR1e6HR8cS4uem5v9nNJWCOmASDdxs/a71ewd6nlZQazQ3uF0uKivK93Mv1Khf7N68ke+ef4LQqOgqq+foxGSiEpKISkoiIjb+/5fSafl0KMiEIZfVq1i328XSH2cRHh1DSvuORMTFHzYLMA0NDY36RhMpqE8AxpumeYG6PhMYZJrmFZbvLFff2a6uN6jv5NSQdRFwEUCbNm36bdmypZF+hRdbXvgdR2QICUd3JDil+W1wmqZJSUFFlbLao7hu3TmWToObfwiHxkBlTg72+DrwAbcbCrNgz2bYs5minTvI2lpEVqYDh62MAcOc0GGsWLPafTMmMN0m7sJyUVznK8X1vjLvdWEFwW2jiRzdCkfM4WetWZ/Yt2Y1c598hPDBg3GGheEICsYZFIQjOBinR9FcdS73HUHBFkV00L/KyGP9xx+wdoWJmdQVDFtV2A5Rl5ief2CaVc6Y1T6ruuc5kXsVZXnkbJ5OcKib0Miwasp8+Wupc899T917Pq9W7+pc/R+709kgnHxfdgkRscHYHQfeMHRVVlJeUuzNB2NRYpcVF0uumOJir2K7pIjSoiKCQkMZeuLp/8pk4r6itLKU7QXb2VKwhZTwFLrGdT1s1laePGcHVEA7ndjsjnr/PZUVFaKwLlGKa3Ve5lFiWxTaZZbPPX9N07QonUXZHOZRNnsUzur6cLTmP1zQVArqgEk0cDfwl2ma76r7rwPfmqb5aY1nNDm51tDQ0NDQ0NDQ+P+Fw11BbYU28tDQ0NDQ0NDQ0GgMHIxDH0aBVfeHaZqvmKbZ3zTN/omJiU1dHA0NDQ0NDQ0NDY2Gwg7A6vfbSt2r9TvKOzEayfOioaGhoaGhoaGh0WzRkArqupBoX/6vhoaGhoaGhoaGxv8XzAM6GIbR1jCMIOAU4Ksa3/kKOFudnwD8fLD40xoaGhoaGhoaGhrNAQ2poK4Lif4KOMUwjGDDMNoCHYB/GrCsGhoaGhoaGhoaGs0WpmlWAlcAs4BVwMemaa4wDONewzCmqK+9DsQbhrEeuA6V10VDQ0NDQ0NDQ0OjOaPBshqYpllpGIaHRNuBNzwkGphvmuZXCIn+nyLReYgSG/W9j4GVQCVwuWmaroYqq4aGhoaGhoaGhkZzh2maM4GZNe7daTkvBU5s7HJpaGhoaGhoaGho1AUNmna3LiTaNM37gfsbsnwaGhoaGhoaGhoaGhoaGhoaGhoaGhpNh8M6SaKGhoaGhoaGhoaGhoaGhoaGhoaGhsbhC62g1tDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQaBJoBbWGhoaGhoaGhoaGhoaGhoaGhoaGhkaTQCuoNTQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0mgRaQa2hoaGhoaGhoaGhoaGhoaGhoaGhodEk0ApqDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ2NJoFWUGtoaGhoaGhoaGhoaGhoaGhoaGhoaDQJtIJaQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0OjSaAV1BoaGhoaGhoaGhoaGhoaGhoaGhoaGk0CraDW0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NBoEhimaTZ1GeoFhmFkA1ua6PEJQM5hJPdwla3L3DiydZkbR/bhWOaGlK3L3DiydZkPf9m6zI0n+2BIM00zsQmeW+/QHPr/hWxd5saRrct8+MvWZW4c2brMjSP7cCxzQ8rWZW482QfDATn0v0ZB3ZQwDGO+aZr9Dxe5h6tsXebGka3L3DiyD8cyN6RsXebGka3LfPjL1mVuPNkaDQ/d5hpHti5z48jWZT78ZesyN45sXebGkX04lrkhZesyN57sQKFDfGhoaGhoaGhoaGhoaGhoaGhoaGhoaDQJtIJaQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0OjSaAV1PWDVw4zuYerbF3mxpGty9w4sg/HMjekbF3mxpGty3z4y9ZlbjzZGg0P3eYaR7Yuc+PI1mU+/GXrMjeObF3mxpF9OJa5IWXrMjee7ICgY1BraGhoaGhoaGhoaGhoaGhoaGhoaGg0CbQFtYaGhoaGhoaGhoaGhoaGhoaGhoaGRpNAK6g1NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDSaBFpB7SMMwzCaugwaGhoaGo0DwzAc6m+9j/0emXpe0dDQ+P8APdZpaGho/P+B5tAaGhqBQiuoDwHDMDobhpFhmqZ5OA6Eh0OZayvj4Vju+ipzY/x2wzDi61leg5bZQkbqfcwyFOpbbmOhPuvkcKuHhiqvYRidgYsNw4gz6zlRg2EYhkVm9/qU3Vioz3o3DKOPYRg960teDdmG5fyw4DuGYdgt59FNWRYNjbpCc+iGh+bQB5fbENAcurrsw6G9HQiaQzeIXM2hDwLNoRsOmkP/O3BYNLamgmEYkcAZwH8Mw2hbXwTbQhSC6yrrILJTDMOIrM+JwSI7rD6JpKeMhmG08uy4NuRipr7eoaXcnQzDSKiPuq4hN6yB2kgacJ1hGM76mHBqlLmrYRhBhmEEeT6rq3yoag8DgKlKbn2OXbYGIFANPrYahjHGMIyepmm660umqueRhmFMqw95DUnWa7S7qw3DuLUen9cL6AmcZBhGbD3JBKSOAQzDOAv42DCMiPpemBuGkVAf8mqRmwze31APcoOBo4DHDcPoVh8yLbKt7eNM4Nh6kttgfVsR6wmGYRyh2scphmE4G+p59YXmPFdrNB00hz6gbM2hNYe2ytMcugY0hxZoDr0/NIeuJldz6OqyNYduBLmNAa2gPghM0ywAvgJ2AVcbdbQCMQyjg6F2Ew3DuAh41TCM8wzDCKnHMpuGYUwGvgB+NgxjgmEYEXWV6xmoDMMYBDwFtK8HmTbr5Ah8DzxhGMYlUD8E2zIpDDQMY6JhGL3qOjHUnNSB94HZhmEcZxhGVD3JvR74GHjLMIz+dSlvLYgAjgH61JWY1Sjz5cDXwJvAeZa2Xl8DZFvgAcMwkuqLUBqGcSHwsmEYNxuGMb4+ZAJ4ymcYxjjDMIapBU19oxdwp2EYofUsNwGYqBZJAc8RNdrGyYZhXG8YRsf6WjBaZF8KnAJ8Xl+kzzTNj4CfgC7AqfVNsA3DOAK4HDjGNM1CwH6I/+ITVH+bCHxlGMbraqFU5/ZhkfuNYRgfq3Yd8FgHVe2jDHgemAvcZxhGx7qW1QNL+xgGnIPML/Uh19O3TzYM41jDMEbVh1yFIKAceBy4G/jMNM2K+lKCeP5az+tDrmofow3DOMcwjJPqKtMDJfdowzCuMgzj5PqSq9E40BzaC82hq8vUHFqgOXTt0Bxac+iDQXNozaEPAM2hLTicObRWUNcCa4MzTfMf4EugGLgqUIKtBudLgJsNwzgXOBP4AbgUuNwwjJR6KnsfZNA+F3hOyZ9YV4KtfvM44CZgMvCKYRgd6ijTM0iNQcj6WcCfQB/DMK6zPDfgAcBS7reBDGC+miTqUm7PoD0BOAIYBNyFDOCTDbEaqovcYcB44D6kPt5Xi5o6wTCMeMMwgkzTXIG0jQvqOkFaynws0AMYBXwDtKOOBLvm/zFN82Nk0ThEfV6n8cswjIuB04DXgQmq7HWCtcyGYZyNtLuTgQ+M+nfB+grIBULV8+prPF8PJAPxpmm6A+1/lrZxOnAj0B+4BzimLuORYRhtDcNoYbnVF7jUNM1VRh2sjg7Q3n4BumIh2HWVbYiFWxiQjox5mKZZWU9EZwAyz9wGZAEnAuPrSrCV3AuBK4D5iCXFpLqMH5aF0LnI2NwaeNgwjB51KasVaix9GlipFjH1Jfck4GFgGDKnn2f5rC7zVQmwHQgH5gGD1f16UYIYhjEJeAN4zjCMSfWxGPUQYGSRlIOMdZfWpR4s5L8X8ALQChk3nq9reTUaHppD7w/NoavLVOXWHFpz6JoyNYdW0By6dtmaQws0h94fmkN7y6v+Hr4c2jRNfVgOwLCc9wU6AsHIQHg/YvnQNkDZRyKE6TvgaHWvN0IYrgda1LHsycCrwK+Weycr+acDkXWQ3QlYgcR7sgOvAR8B7eog0wZ0A9zAQ+peHDAJeBm4rY71YQMS1e/viAyEy4Ck2t63n7IzgA+Bny33piELsXMDrWtgLDKh32C5dzGwGhheh7rog0y47wMtEaLzOEKgAq4HS7tbB0y33DsOeBS4E4itg+yRwH+A49X1ecC7dWkXFtm3A/HABcAswKGOlADlWceOFsgCOk1dXwMsAnrUscxHIATH895eAF6qh7roBzwLhKvrW4F3AEcd5R4NzAai1PXlwIvA8YH0ESAauFf99ZT1Z+DaGt8bVYd3dzLilj5MXU9GFqOXeOq9DrKt5Z4IzEAWBvt9N4DnJKt2/JZHFnCteq8nAqEByk1ArLq+sNy7SLW9s4DoOtTHELyLuWHIPPgF0CnAsu5Xf8iC7iegc13askXeqcBDQIa6Hgv8CJxbD7JT1N8gZAHzBnC6uteNOnAExA10HjIX/A/4FXDWsbwGslB8B+EyQ4EFQGqA8qIsY8UohAeMV9cdgLeA5+rjPeqjYQ40hz6QbM2hq8vWHLp6u9McWnPo2uRqDq059MHqQ3Po6rI1h/6XcGhtQV0DpqfFiKvVm8hA/jfgQhp6IXCbYRjpvsirsQMyG9mxLQQuMcTNajFCQCYDJxiW4O4ByN8HzAQqDMO4Rv2ej4DPkMki3B/ZNVAMrAVyTdN0maZ5AZCCWIGk1FKWQ5bXNE23KZYIVyCWNd1N08xD6uk7INkwjLhAC6zkZyMWFGcCTwDHmqa52zCMswzDaOd53/6UW2ErsrNfaBjGtep50xHCPQ4ZdAKR+zey+zfYU6+mab6MEJJnDD9cWS07aEchg+ksYDNSDxOB84Fb1DN8qgclr9q4YZpmFrIA6GsYxmXq3ufILnEwPtZFLc/xEMlQZAf7a2QiHuqvBY8hrnbt1Pk0wzDCkUXifOA40zTHmaZZidTJeH8tKTy7q+r8SmTheT0w0DAMh2maTyETw1dGgHHCDMMYjrStKYhb4uMIwYnx13rMEFh/YzaQCjxpGMYzyCJ0LzLZ+byjXYuVQwJCCs4BME3zeWApsvg60t8dYtM09yEKivZIDMgoZNE43jCM09RzTwde9KdOLO/uWuAyZAF6l2EYV5qmOQOxaBoMHOtv27DIvg5pA9MNw5hmmuZMZMf8aMsY4nM/rAX5wOdI/zjBFDwJ7EAUO4FaauwD3gM6GuLOi2marwArgREIufIZlvrohChT/jFNM8s0zd8RYu0AXjAkyY7PqNEHTzAM42LDMI4zTfMuYA5wr3qmX7CMo573fgSiQPG0r1+BR4DLDMM4w1/5lufcAHxtGMZXwEmmaX4B/AYcZRjGR0hfr/RDXqIhlo8eZCCLolaIdd7Zprg+JgdaZtXGipE+fQ7wmCr7LsMwLlBzj6/ljQJuQFmzIXzlZGQxALAB6ftJhmG8GmiZNRoWmkMfEJpDV4fm0AqaQ2sObZVrOdccGs2ha0Jz6AM+R3PofxOHDlSz/W87qL4j1RnZqW2pru8GFgKxiKvKrUCyn/KvQxqeA9kheQR4EEhUn3cDWgVSZmA0QgouRMjMCchu5ZWW7/q1G2ORHYzsRIUgBG0qarcPmSCXAe8HUMdTkY50lLo+G9gD9FbXkahd0gDfZx/gP+r8MWALymoH2aVaAQwNoNznAjerNhCFWHw8B1xj+Y5Pu9o15E5WdTIQIX3vAM9g2e0DYgKsh1nAwBr3RiE7oh/52zYsck5C3DJPR4jZAGR30bqbHRGg7C7IpNXWcu9K1Way8VoL+bRbrvrtH8BLCKGOR+LxTQfusbTBFdRhlxghvu8jlmOvIC5Mgyz96TICsB5DrJdmoyyXEFeupxHrgQrgPD9khVrOh6k2nKqu26p6/hsoAu72Q661PSeirH4Qy4MvgDMsn1+AH1Y2SBIeapT7Q2QB0xnZgd+g+s1ioGsAddwX+ESd34HEdHsTuFrdG+tPmWvIvhSx6gpXfc4FnKM+m6rajF/929KmBqh3OBgZN09ElDjTLN/1uc1Z5A5B+vZ4ZKybpPrLeZbvtg6wPk4H3gXaIHPtJZbPHkUsVvyaDy3//2rgL0Qx9iHwsbp/D7JI6hBgm25lOb9PtY84dR2MLGDaBFjmQYgSrLOSs8bSPoYCTwLd/JBnRxQTryAxGkE4yELEWsrT3yeqegkJoH2kIYsjA+EfO/HO3z2B5cAYP+shCRnbzlTXE4CNeC1ADMQKpFcg9ayPhjvQHPpAsjWH1hz6QLI1h97/OZpDm5pD1yJbc+jqz9AcuvpzNIc2/10cuskL0ByOGh1ogmo0b3oajrr/OnCzOve5IarvX4RYIHSx3BuJuDg8AyQEUGab+nsEsiN+iRqkHkIm4inIRHON9ft+PmMKMim+jJCd4WpwuhshOX+pjj8dtUjwUe7VqpM/qsr4PDLpnIe4Knavy3tU5XwWcek7G1nQzECsd15FJt8pAdT1Oer/HgP8rsrfDpl03gMuq9mefJR/FfAP4vr6HTJw25Gd4tcIfEJ3qnZRWtvvRVzovgFODkD2hWrwvFy1kQcQcjIY2AScH0iZlewkxNplMWqBW+PzYYgVS28/5d6j6uJsdR2BLEy/VPU+Fz8msJrvGtld3Y2X+CciljaPUjfX0g4IsX7rAO/wTGThcEjXJYQkzUYWmF0Qt9KZqp2daulDbZEF3yfIbvFB23SNergRWdCtUu2jP0IgPwcuCLQelOyjkPEtSP19ByENMYjyIxU/FR9KbrTqL20QovorMibdptr5NXUos4G48SWrsn6IuG2W4yURfikSALv6O071hUeRheEV6t2ejJD5EwKUe7SSeyeiQLkBcWGdjCzKA36PCLF+Dhikro9U7eVJvPPYfv3ex7bnVG2is+f3qLZ9t7p+2B/ZFrmXIqT0XWThZVOyfiOA+buG7CGIS98TNe6tBK6qo+w7kEX4YNVnfgY+tPSl1cBYf+sa4UnbkDnqadXGH0Lmgg8RBcZUP+TaLOdnImPyaer6WMTy1Oc5Wx+Ne6A59IGeoTl09bo+B82hPf9Xc2hzv7FDc2hTc+ja6gbNoa3P0By6umzNof+FHLrJC9CcDmRy+RPZHf8BOMvy2U1YYpr5KfcxlKUBEKb+GsjEfh/+EdN0vFYpdoT4XuSRjZC8p9X1CQROVDshE8zJCBleg0zGnRAy+CoyiI9CiHLMQWS1Q1lE4LUiaaWuuyIWBJer68sIMHaS+v+jEdfBE5CJYTpCBO3IIH4CMMDzDg4haxje+GdOVe5j1XUQ8AHwvLqehI8kWL1Dz65hjGprbdR1HEL0zkZ2ct/GD7KAd/ALVr/ZQCbGWXjjgRl4J9H7kIn5oIsvZGJ1qvNQZMIarK47IDutHmuboajYUv6W23I9FK+LX7Kl3J6FzovAJD/k90AmhHOoMVBb6syveG5Un9RbqL+XAJnAEeo6FtmBvQ8/F+U1nnUrsqDrW+O5nrK/DaT7KOs6ZPL7DLXgV33kadTCQ92zq+/4s+s8Gll0hyCL3OeQ2IERCKn6ECH4fseJU+9uG2Ip8aTqRx0RRcg9BL7zPhBRcvRQ1xcD16vzyxAS5ZOFlLWNqusgy3kq0tfT1PUMxHXQ53ZHdQuEcGS8n6CuByHjsoewnwn091FumuU8BFFETFPXXVV/u1JdH+er3Bpt1PP3KcSCy7Oj70DcTV9ErCJ7+itbnZ+n2vHPWBQGyLj/rJ9tIthyPhZZYLVDxpAHUTHckDnhGwJQXtV43s2q3F3wjnEjkX4aG2B/OQqJ67cUIaujlawfkEXzXGCij7LslvNuyJwxWvW/F1S7s6n3OMTSlw5Zbku7SLDcm4rMr2eo6xOQvu8zV9JH4x9oDm19jubQmkNb5WoOfZAyozm0tf9pDu291hxac2hfnqc59L+MQzd5AZrLgRDFvzyNDYkLtABJgvEEsiPll8sLXlLwMYrwWj47CiEpwX7KPB/ZTQ1S1zcjO5QR6jpadZoIfzo8suPu2Y3rrgaMu2s8dzUwxHJvLEK6a01aAVXB359HSKSH7H0P3Gn53umoHal6eI/n4bV4iQLGIDvjFwYg6xxk59uzMLgXISaeuo4BvkWIt69ucskI2Y1GJjE7Etupp+U7pwL3qfNArXbeR3ZpJyLE8nxkZ26k5XuhyMLvkAsw1Q9SLNdPI8TJk6xiCDI5RAVQXs/gOk7V8SPIJDAKseK5ssaz05BJw6dFmKrvr/EuYE5GLFRGI5ZZryATg88TGNUn9esQouRZMJ2NTGieJE4xBGaRMFS9t4mqfNepd9izxvP7Ijv/6QeRFWI5j0csmIrxEqho9ayXUYkqkEl0JQdZKCGE7iXL9RTgS8t1H8TCZJDqJ/4QSetvTEYW4amIlc1NiKtpOuLS9aKn/v2Ra2m7DyIJvDqpsroQ0rAV6OhHmSMs59eo9vuBKnMUQkJOREh7NfdjH+W/iWW8RcbWW1DzCLLI/xMZW/wZ/58C+lquH0EWhJ7+PQghehF16CeJlvP/IhZvdUpqZpE3BpXYTPWXjXhd389D5rMwX8oO9EJcrz0KiKnAo+rcY3X0AdBe3QvUvfs8pE9fgsyL9yBcoZvl2QEtyPG6fXZUfec21Rb7WX6HT2MSYmF2nGpTUcjY+Z2SYUfmyOcQwh5QSAHEqmgWQto9Y9IxyOLlPHWdFIhsfTTOgebQmkPvL+scNIf2fFdz6FrKrM41h67e9jSHNjWHruUdag5d/TmaQ3uf8a/j0E1egCb74TLBdFTng4FTkB2SL/CS4m4I0bkOP2LuqP87TA1YnZFdo8/x7o6fjpDSQOMDxSIWF2nIxPA5QkSiEWL8F35kylUd5Hxk58YTL+9/CGGock1Sg8BWvCSiHQeZeCwDRJAq58NK/nBk4vJ0mhPUoBJwvDzLM89G3KI8mVxD1ED4OSqLtZ/y2iBxudqqd/olsgOYjLgxzfG33MiubXdkYWRDsgQvQ8WiQqxrPkDIiF/kGlkkLkNial2CuNWei7jKXYcsBmIs7/RQVh/WneyBiDtOkJL/GF6XzGMQAhtovLzRCCE9EyEg85FBfShiaXItXusTJz6SeITI/I6KGYfEZnIgbr3fIS5cvfwsq7VOzkcWb54xI1aVbxqSqGd0gPUxEXHPfBJxifpayb0emYR6Wb4byUEmeGTiPkfVYX/EoiNSXVfFkUTGj4uo7kZ9SIsm9Z5GeL6PjB2jUcQAWYiN9/P3W0nZNYg1wnK8hKYrQrDfRcZBvzMpo8iXOh+AuF0/ioxR3ZBx2p9Ya1OA19X5GchiMwwZMz0uq9ciJGcZAWajV793ljqfqtrIaEu9fEUAYymS1Ga+Oh+DEKZx6roTohSJDrDMVyHWXM+iFAmIO9sfBBCDr0b7OFLJfs9y71TV/15SbdxnxZjqI0nIgihV1ek6axtGiOQxNcvixzOuRpQfE5D56gJ1/wFVz138lVlDfgdkDPXEyUtGuM1PKMs3X8uNzHtd8c77AxFF2zmW77RBFrh9AijraGAJwj8+Q+bUq9VnxyPcoF4WYfqovwPNoa3yNIc+sDzNob3nmkPvXyeaQ2sO7ZGhOfSBZWsOXf0ZmkN7/+9o/oUcuskL0CQ/WiabHsik/YZ6eTHIDtejWBKv1OEZ7ZDdvfsQgtAPSX7xsWr0/rj8hKN26JGdqXRkl/w3xJ3tBLwE8h8ko7K/5XUiBOwZhAh7CPazWOIN4XUrPBQpi0MRfFXmNgiRuVF1omlIEonP1eDi90SDlyD2U53QQ6jvUJ20lXr2LIQIXu2DzESU6w1i3ZKO7Jz9rj6bjMRn+gqJQ+aTKw1CXDx11xZZBLyExLuKQCwUNiMT5QoCHFxVPXxouZ6A7AK2RwhVmr/1q849brXfqLYWicQ1eke917/wI54d++/A3wLcZrn+r+ondoRAdK3t//nwnJNVGz4F2V2djUxeKUh2bL+sVZBF0QuW62uRBcEE9Q7nI1YK8cBp+OmmaZH7GjBZnTsQCzSPS9QjqJ16f+oDyEWSKHW13LsQsXIbpa7t1r8He39UX2T8g3f3/UrEKuERxMphIz66TtbynGMRktoHb0Iij/tWT2TB6KtbsLU9hyBuWh9Y7g1BXLJfxX9lSjxikdQZGeueQkja5ciCNrjG96P9kB2BN6mWx510KfA/dX49Yu31JbIg80mJgPRhz5jpIbw/Ad+p8wuQOeBb1Ub8Vk5Y5PyGzLErEKWNxzXxWfXMg7a3GvKs7e5chJD+V/3+UXgX4V0QkulTvDyquyQmIWT0emScOB4Z5y5BFl3z8WMsrfkc4EV1/h9VH9Zn307gyd4i8fbh+1X/8LhOnw18ih/WTBb5Uci4f42lr2ykeggHny1Za/TFS9S7mqTq9VJkzr4GGf8PK6uP/w8HmkPX9gzNoU3NoWurX3WuObSpObT1/aE5tFW25tAHfo7m0DWeg+bQ/3oO3eQFaLIfLhPWO0Ae3vhzNmQn4mGEZPrk7lJD7jjUhIoQs8dU509XDSXKX7nIjvWnyG7cr3iD19+Fl/SFIDuWfpGQGo28pRqknkAmhmC8SUb87ewTEZe2/wJr1L2OqtNcrwaBSFVmv123LM+ZjAzYTyK7RiciO5V3I4uZ+cjO1bnqd9gPVjfIxPgtEnfvO7wLhFsRAtnC8j2fOz3ijnoFshP+EUJOpqp2dpX6Tn9kIRZItuBO6m83ZKIdbPnsZdQurr9y1fm1wIOW66/Ub/Ds7nfCP2ujELwWB12QxdFlqB1yy/c+8KeOa/xfj7tWK4To/KraSlekf/cNRK6SmYrsiIYjpPpDZOI9WbW/16jDbqVqox/gjSlpR6x63ghQnkP9fR6xRHiwxucXI26IsfhgbVSjbVjdXX9AuSYisb/uRchToAvFHghhvcdyb6Zqe1WWZQG05+GqDSYhi/C3LJ89i4wlfilXkLHsO/XepiPE5nt17iF7d6HcsvFvUTQIWRSei1hLJKj7f+Ml2ElIpvCevspHxop/kP79J97F/7d4rUvCEFf9bv6WW33fhig7WiEWD9/jze4+SX3Hn/ixTsv5UNXmPOPc/er9jfS1XVhkRSNJbVoiY9FIZMx+CyF77RBu8CEynvoc46+WZ4UjxP1LZF73jFUXY3H/D0DuMap8MxCF21VIDNkv1G9YiR/WaDXfNcJt/gdcqq4HIwmtzg2wvMMRN9Cxqt5fwBtb8jtV9+mB1oc+GvZAc2hqfg/NoUFz6NrahebQ1WVrDu091xza1Bz6IM/QHHr/Z2kObf77OXSTF6BRf2yNiQOZbK+jhusaQnpuxs+JXXWax1RjTlf32qkB8QNU7JoAy34tUAg8oq4NZNK9E3Hb8Wu3sobsfsjkHa9+w60I6euHEOz38TMzs5I7E8gHjrTc64hYEDxEgK5sFlmdkZ3DlgiZ34EkujhZfe4h8GOQpB4HdFGh+uR7N7AXZY2Aiq2GWCisx48ENAhh7opMLr8hyT+mWT4/DiF/NxOgxRFC8DYgRDUaIY/3Irtqw5BdOr/dRpTsS5CJsGWN+++o3+MIQGYMMth/hrjpJql3uRghECkIoVhBANYTqk+/gLiuRal7HoJ5POIalu6nzEHA15br9xGiEIQlLpx6F/MIgFyr/tZV9bkB6r0dpz4bhSwek/HRZRUv6UjG4q6m2rAnMVEPJP7eIRe47D/ZXqHqMsNy70dghuXaZ7fBWuS3Un3xZyxZklV7fDtAmVcg46XHFThRtcPvVVv/h8ATxfwHGaNvVHJXI26KqYj10WICX2i8hcT0O7nG/T9QRNgPWa3xkvBHldyLanxnBrAgkLJaZEwATrQ8c6blsxXq2T67USJjxFXIvJeGKGp+xbsoCELGvTdRi3c/ZIciVn5zkTEpTd0/AhnrLrf0cb/dEdX/G4VSnCDcYxveGJtnI+Q3PUDZPVS5B6qy3ososTxu6g/hR6Zxi9zxyILlOMQKaQjCZS5Wnw8Fxvghz7D8v1Wqbt9EFqGbEUuhbuo99KpL+9NH/R9oDn0g2ZpDaw59INmaQ5uaQ9fsI+pac+jqsjWHri5Dc+jqz9Ac+v8Rh27yAjTJj5YJ9gy8VhTnIoTvCGTX50p8cFuiOiHzJFxJQNwLPrJ0pDvU4OjzDnkt8gcju0P/eBq2uh+OEO/hgchGdvZ2IvGA3kB2ZEKR2FSvqM7ptyWJuh6P7Di/qTq/h+C0V/WT4E+Za8i2IaSmq+rwCxAScSdCSs5ALHySkAnaV2LdGVlcTVF1fbnls2gkKL9P1hnq+Scgk2uEkvkCMoiPsHzvdMSNNRBro77IoOoJ3B+MxH26RL2/6Sg3Nx/lHQ2c7qkX1W6PQ6yXLqd6wP138SP2FbIIekCdnw+4UbvX6t5QZFJ/A7Ha8bncFhkXI6Q/GhmsZyPuoDZV/8vwIaHNAWQvBT6zXL+MTPARlt+0NBD5yLizXb2v5xB3vGHq3vPqHfuccd0id6qqgx/wxu+MQaxAPkQI4CgfZbW2nI9D3F491lF9LOd/Al/V7FuHkG3tgxMR9+V+yHh6larro2orSx3K3AMh8A68Fn8BxbRT8tIQi4HViGvqCGSc+xCxqPCrXdSokxNU+XaiYghaPvsVGOiH3LNV3YYic+FdiOvq4Brf+wQYFkh5LWV+WLW3FIRQT0WsFL7Ff4vC3ojbexfEKmU0ohg7DaUIQ8a/2wnMZdUTF+4VRCnmmdNHI5YaFxKAMkHJuBBZ1H6HKGkGIJYPW5AYpwsJQIFlkX8M8JHl+ihVxx6rQJ/dPy0yOiGLtwcRgv0YYiE2GFEgWudGf6yZBiLWcoPVdQbCuT5V9fAXFgWUPprfgebQVbLRHLpmPWsOrTn0gWRrDu091xx6/2dpDl39WnNor2zNoavX87+eQzd5ARr9B0uig/XIztNevFm3z1QvdQ1+xpdB3C7eQojBYDUI3IwM5Hchu8GBxtEarWRNRAjaUCXvVDUAPIyKbRaA7JEIqeuFWH5cALyHEOwwNUj5FBi/xiB1KtUXLw8jE0xLZPf1+EA6u0V+H8RCJdHy7l621Ncsz7PVPZ/i+iCLlLl4XRBHq85+NrJQeAQ/E0kgBDUD2bnvj9c64wE1eB2hnuN35m4lfwiyeBiP7D4vRwbrXurzmJrv5xDyMhAy6kl+dLmqzx9VO7wOeCrAsgapd5eEEJopSEy7h/AmIuiIDOBpvpRbyfS45I5X7TdFlfMzZKJcrvpKGgFkC8YyoSIuYV9Zrl9GrJxCVT9KD0B+FLJTOwgvmXwNWTgmISTQZ7czi9wxyAIxQdVzDnCH+iwSccPyiTwhpOYDvNY0g5TMWxG3sJVIH/fEd0sLsI1cot7XfYhF0ynIgvdq9fzRfsg6VJlXqc89dRsQcarluf2QOeZUxFLBSeCJUYYi45snpufViAVZhGoXFwQoNxkhjJ7EPBcjrvoZSIzIG+vw+6ciC4soZHd/grp/GrIQ+Ac/FjFUj5cXg5fohSHz4nuqrn0i1BZZ1jkrAlmAJ6u28QRqwaL6yigCdDlW7fBJZFzrh4z9dyLzYUtEceFvJnqPcszj2piGkOkJlu+8jbJqxc9FLuJ+fzpwproeqPrkY+r3DMWPeKk1nnE0YnF0q7p2qjbzgHoPSf6UWR+Ne6A5tFW25tDV5WoOrTl0bc/QHFpzaH9+g+bQmkPXbIeaQ3uf8f+CQzd5ARr1x0oHeQNvFtvzqE6wW+NjMHiLzImI20m66pTPq4ZiQ3Z8HiFwl5RRyATwX2T39n5k12gosou9kgB2hJXsFGS3pRKvlUprZAd7OkL4/Mp+rWScqMr2AULyPO4pD6tBcE2gndLyjF6qPm5QA1ZHZNfuZWSH8Sj1PZ/Lrzr8X9RwSUUWS/8gCxp/kvJYB+5IJFj9l8jCK1G9y0+RibK9v3JRbj3IZPAhQvDOQCxrXgVOq1mOQ8nFmzgjArHMuEJdd8Ybs+tEZHc/xg/ZbYEnLNdfAJ+r897IxPNfxMrkGdSCwEfZ7RHXsvdQmWoR0jHb8p1dyOLXL/JE9Uk9zHI+h+quih8AHwfYlqcgY9IilJsP3t3Q94Aj6tBPxiFjxTHILu4E1U+e9qeOa+knFyDE5l7EAmg4sjh/wdPuApBrIGPSj3gX5aMQMjYe6eeXEtji6FBlPiXQOj7IM3shi5nL6iBjFDLGv4tYYjyJWDfcjGTEXorKgu1rHde4vhMZNzyxLD3JeBYDUwMscwJifbUWWczcjIz5njkmlgCt/lRfGav6xjWIVUKoah9fI2NTIHPWpchYfDMyv4YhJPIRxBJrAYErP65FxrRFeJP0DEMUCo9hUQIFIHusar+XIlag16k68bglriWwjOAT1P/9B5hjud9flfkZ6rgQRTjSeuBUS1tfEGjb0EfjHGgObZWtOXR1mZpDaw5dU7bm0LX3E82hD/1MzaE1hwbNoQ/0jH89h27yAjToj/MSEY8r21PqBZ6B1/XgXIRI+BRvB9mN9QS7H6lk/tfy+cUI+fBkbA7IygEhDd/h3b3pgri13WIph79Zcj31MQTZ0bcjg/Z8y3faABcRAAFG3C1m4I0zdC6y2PD8hhbUIbM7kk3WY/HRE3FNuQkZsHsjljYBkRFkMfGYOo+o8VkEPrqWWgdhhNxMQXbmwlTbmIF3t7kbAcTqUjLnIiRpnLrnac+dkIHc5/hRVF8IeMrWHSH+16rrEGQxuhI/3WiQgT8TeFZdhyELgvcs7/JBhDBMCaA+HkPiNHoWA+nIAnE4knTlJfxImlOL/HORSfYBoJ269ytqgaCuAyF9/RHXwaMRAvUrXmuddgiB8GeX3NO/o2vcf9vSTh5GkkIF0u56q/e0EjUpWj47FlmA+r1QrHHvTSS2mGdX+0zgE3Xul+VVQ5TZz2d397SXAP/vHLyKn37I+Haluh6EdxHij0XQYMSaLU5dX43MBUPUdSe8yid/k7m0Q6y6jkEsdx5Q/W8bMk9G+ynPOi6dAmQhFgj/IFY816nrMNWH/FKMKbkXqXrug7jMfavKH4JYij5JgMlclJzfEZ4wG5hr+WyU+g2BLjQ6qzbtcYm+TrX1qYiS4X0CWCCpdveNku9ErN2esXw+kAAymB+kfvKRhc3HBDD266NhDzSHPlB9aA69v+zRaA4NmkPXJl9zaFNz6ACerTm05tCaQx+8fv61HLrJC9BgP6x6x/SYu9sQgvo0sivqyWJ7Bj4m7UAInme3+TPgLMTKweoO9wU+uvUd5DnjkR3b6UCoutcfmQz8nsgtco9SZT/Gcm868Ifl2ld3vpq7iechCxUPmY5QA9Rb1JjYAih3pHpvT+Al2L2QncVH8WPgrlludW8kYrUSYbl3BnC2H3I7IzuGnREivQ5xj1uFEIZkZKduDgFmmkUmsE8Rd5zTECuY89VnR6v2cWyAsq9BJoF0dd0N2cH2ZEg/3d+BFS/p74lYWj2qrkPV73jH8t2UA72fQzyjvXpXC/HG/rsKmSQWEqD1leU3L1d1+yYy2Y5Un61GLRACkNsSsRqx/v43kIRFHuITGoDcychO+OfIRBuHWEl9hLgF/xBI20OsM/6D7Db3QQjOJeqzKcjCwGcSQvXxuQveRctNqo973BxPV/UeyK5+vZa5MQ9gkup7D1nunQxMr4PMUchY9L1qD+PV/SuQMcnnWHm1vMMMxG3wecS67WQkbmwGYvX2B35YUNSQ3UbJ87SRkxBCeScyn9/ph9wgy3k8YvERg1iU/IxYCX2LN7FSoMlcBqk+eLPl3kzgN8u1X/0b7+K5M2LdcaG6bqdk34R3cR7ub/mROfZ2JLbmMHUvVr271xqonU9BrI1u9JQ30DrXR72/G82ha5erObTm0AeSfQ2aQ9eUrTm0qTl0Yx9oDq059AHqBM2hD4ujyQvQ4D9QXC5mI1YOnon9HmQ3alSAg7Znt9mThfNV1ShPQlwkVuJ/9nJPx0nDmx13mBqs7laNrj0yQfi9y2V5zmkIAa6ZcfZbYKG1LL6UV523x+t6cRZCRjyuVpHIBHnIDMcHeZbH2qY/8Dji2uchYrcjO2xp/tSzOj9bDa7T1PXdCCE+BckivAofiRlCYqKQhdajyG5+G/XZSQhp6o8s8M7Dj8QonnIjJGQdcLe6F4JYmHyHLGKcBBBnTX1/POKe6Vm4eBaeXVR7ubAO7+84ZHfvESAbb6zDUMQa5nN17XdfrPGcY5BJd5Sq8+d8bRcHqG9P4g8PYY9F3I1etXwvIKsS1V6uVm33ZMv9DxHLnkAyuw9Q/3co4qL0KLIrPBxxy/sLP9zZLHI9lmwOJf9CZBf4H9Q4gh/jXY0+eI1q01+g4jIiffxDZPE1jwAIcH2XuaEPvON/Mt4ENJMR0nSpuu6vfovf5UYUEd/hTfhxPWIV5SHY1+BfkhjrO2yh6rkLojxYiSQxecryHZ9j29WQfRVigbASIb4h6v5JiFXJLfiYGEv131EImT4XsYSJRazFZqrvxCBk8nUCjHmo5HRElEGfYlm0qP7+Xc3f6Yfco5AxdBaSHKaHup+OLBbvxw+L05plQJKRPQq8iDdpWCx1TEBziDKMRRJZHfZJXf6NB5pD13yO5tDec82hvfI1h96/vjWHNjWHbowDzaEPJFtz6OpyNYc+TI4mL0ADvChrx5yATLZdVOP7A3hFffasaoiB7LC2R9xmFiHx8xIQcvMNYulQF3eGhciu8EyEaI9HEV8kttS4QOoDscTwZAA/Gyihhgsb0DeAMt+gfvcHCCGNVXWxEK9LVCCDiKfcHZEJ9h113QtZ3LyN7Aj+SGC72deqweg8ZPH1KDJBnIm4vLyKj7GNkLiDd6nzeIQcLEEWFZ46vxl4X537E9ev5uD3JLJ48UzAQcgkPBs/ExzUkHs08LCnrai/nrJ3wkfrqBoybYhr4i8oixTVPtai4ukhBNvvGE8HeeYExIpnYYBlrlnflyALA89CKRKJDxZowqYRiOXOGMSt6kIk7uPxlu8EQiY91iSfWu5NQkhruqfua/uNh5A7BlFGTFbXY5FFqB1Z2M2hbsmJ3kCygCcglinPq8/SkbEvEDfKBitzQx6Iu+QvwFeIK2J79Q43IIvQ7zy/yd+2jFhH7cO7sDCQMfBtf2TW8ozrVHlfQyWbQayO3kIW5Q/UsT7eQeaARxCyOhrvuDTNn36ILGjvU+1sLd6kUt3UmBGFVxEQkBs9YmE1DbH+iEJcNO/DMkfhp2LF8v96IUrB4ci4egtiudldfd4WGOBv+0DGzIcQS5pEZIF3F8KRPElu6iX50UHKcrQ/71IfDfouNIeupT7QHLqmfM2hq8vVHLr2+tYcWnPoRjnQHLq2+tAc2vv/NIc+jI4mL0A9v6Ca7hJDscS2U/fnAH2xZLqsw/OmqE45Rk0E96AmsABkdVUDq8e1YzVet8SRiBXIA5bv+2yhoQa795DJt7e6dyayizSqDr9/HPCjOp+hBlTPMy9CdirDfCnrAeRPRVxGHkYWRp54a21UXfs82dRoG11VfTqQXdDZyMD9OF7XMH8IcLwaSPupdhCsZD2EN8PvuciOoj/ExlOXI5CdzyPU9ZMIwbMSbJ/jMNUsAzLRDkUWok7L/TOAM+rQPjwx0N5AxQFT1xOQifepQGUf4rlJ+Dk5Aq0s5ycgC8XByGR+G2Jl1BlZyMwJpJ8jJG8lYl3kRizFWiAxsN4FTvJTnrVNxyM7+Iupbk3ymefanzZt+f8Zqu2tRMjY5cii1BPXLSwAmTbEzXspsjvuyRAegexqB+yC11BlbugDcd+dgyzePJY6oYhF1xTExe9Oy/d9Ta50BN5d/AsRRc2xls9vRJGzANrc0Sh3O2QMfQXvmJWAkMyAEpggi8WtwOvqOgRJAvWseq7PZK9GmScilgZPYpn/EUuYP1Sb7BVgmS9VMk5V/bsHQtz/i5Bin61rapFtR+bvRXjnlGTE/XYWgSv0JgPzkbFpJjKvtEDGkwdVvUQFMnbo4/A70By61vpAc+iabUNz6BrXaA6tOXTtz9AcuhEONIeu+RzNoavL1hz6MDuavAAN8qOkkX+NWCGsxuIah+y0HlmPz/JYmCwKdCBRctKQOEaXqYE1Q933ZIcdi5CU63wdWNX/G4cktWmlOuEiYKL67HygCNmR94WsO2tcj0Um9KsRoushU13V3+g61EcQQtjHquskJObTm5bvRKu/By17jcH1JDXAtkYmnl/VwHUakr38cYR0++uiGYK40ryLuMCEqwH1F2SH9c9ABm1kYp2nZM/B66r0AmLh4FPimQPIvkL93ldVG3gQWSxOQxYdqwkw9hxCnl4AolUbsS4WhyG7uUcHWvb6PJDM168iVlFnIgT1KbwJhC5GyPVvqg/19lO+gUxQHyIT7lD1DM/uczRiZeJzMheLbI81iWeX9mLV3m5EEj6spQ6TuuU5HZF4aPchxOEjX/tJzb5iuTdWtelj8I4dkciOdsAu2PVV5kZug4MRS7qzEILmGf87IouRCchYdZavdY0k65gBlOMl2Gcj1honBlBG63h3EaKgOFu1uVl4Y2UGnEypxvOmATvxZql2ILEVH8PHBVKNMoepowOSRftevO7cwYjFUSCLZhui8Plc9eWLEXd0j4t3OyTWX8DJzZScKMQapmphgIxdt3ner5/yIhClUifE6sWjZPoHcVNMxM8kcvr4dxxoDm2Vqzm05tAHkq05tKk5tI/P0Ry6Ydug5tD7P09z6OrP0Rz6MDqavAD1/oOEjCzF6050D7AJcXW4GiGX9WoGrxqhv7vNngFwJGKNkK4GqPl4J92RiJtbK4RsHoWPcehQuzXI5DIYbzbUe5C4cMeqz1v4KC9SDRoRqi7PQaxo5iKkw/O8a5BJLKSOdRqCTAKehYADcdVZBbwYoMzBSHIDj/vd8cBL6vxMNbAEEpvKE6vL4272JmI1EILEIXrR13qupQ7eRNxqpiFxpKxWCi/jh2smsqvnKevliGtnhmpjHnfB6xCXmjcIgFhb2kF/hKA+hpDL+xFC+Riy+zrc2g+a8kAmxLPV+/8ZRQ4Q4vogioggCzy/3drwEsfzEHLwN95kFecT+G7zAMSC6wFgF3CKun8ZsB4ZT0apez7H1DrY71Bt8nYCzEKs6vkp4FZkzBupxo+peGOj1VubqI8yN1Cb84z//dS40Q0hZ/MsbWMSsviIQxbrY639/xDyxyHWL8cicST34rV+uUC1jWQCswjyuPCdjoz/v1g+uw6xAvE7W/wBnjUJmc+tBNunudb625B56Sdk/uuJzNmvIPPjU6rfRwRYxlBEQfMAoqyYgVeRcCUy7tap/+El6tFILODH8MaQDfJDTk3Lv3hksTEPmQtCEEXIEn/k6uPfc6A5tEe+5tD7y9QcWnPo2sqsObSPvwPNoeurXJpD+/YszaFNzaEPx6PJC1DvP0h2UW9V53bLvXsQwtAgQcoDLOtEVHZjdX0aYqlxoRqkluNnbCPLoB1suReNWGd4EsfMRYhrQs3/dwiZFwC7EQsBm+qIjyFueFeoci/GD3eXWp7REW+crxMQktBfXR+hBsLPUC4afsgfBWShkqOoeynAHmSi2IqPWeOROEaetnU9QtifQXb1DWQR8hoywYXiR3IbSz3YkYnkDcRFZDbeSXcyfhIyxN3nWVU2J7LTnKjK/416l3a8sakCmgywLFwR4vAwsltpIIub8Shi3dQH1XeGY1X/Ww08aLl/vuorgdbHsYjbzzOIJdCfeONd9UQI0KgA2kcL1d48MSpHIPHRPAT7YmR3PuDYaAerrwD//yXIzvK5yG74UmTn+QgsVmkN9Y6b26H6wgbL+HY3YrlzDmKltoIAEvIoWbcD11uuLwTy8LqnBxrDrSWSWMXjNvg6YkVxIrJwWkQA4/8hnjlBPdNvixX1/4ciCp9jEauo/yGxGxMQhdub/o6nnraFzCu/q/PXgFzL5ycjpNXnuj5Ye8WruIhGxuxnCIAAI+EUzkBZEql3+hqiQPMoFPyOpauPf8eB5tCaQ9cuX3NozaFrrWt1rjm0H/UV4P/XHLp62TSH9u2ZmkObmkMfbkeTF6Def5B0xKpMq+retPqcZOqpnHGqUw6ucf94JCbOw3jjpRn+TBLILuEzCOHti5C0T5Ed//5ILKbBPsqKtJwfixDzJXhjt6Uj8cCeVs/0e/GClzCMRSx1ZiLB61OU7F2IO9tOJIvws8AEX2TWuPeoKnuc5V4CEo/J50QSqn19gZDFr1S5L0V2EEep93U1Qm58jtVlqYejgdPU+anIAuN4dT0ScTkb5KtcSxs6B1kMnYe4Uf6GuMx5CPUV6nfY/GlvlmeEIIuv+yz3Bqg6eo46ZKGv74P9ibVn4joZIQxXqOvjVX8JJE5cjGonp6ljIWKt8TjeWFhTAmgfE4A1iKXYq6hFMpL4wY3sykcjLq1PYenDjVzH9hrXDwJjLNfnAp+o8xMIMFv84Xggi6Ol1FASqP73ELKg9jlBVs3vIHEDX/V8hswBPwAbqSP5RebT3YjyIBZxVfxAtcV6JdaWZwaUAASxmCy3jKfpiJLoHUv9+p28pMb48RHeGJV/InPtm8hC0meXY0QR41GgdKEWV0nLOBWDHwTYMnYMQRRJtyKLt+fxugS/rsanel/k6uPwOdAcGjSHrjbGWe5pDq059H7tA82hG6qONYc+cN1oDu3fMzWHNjWHPpyOJi9Avf8giTHzXzWQT1YTzTyaSRwYSyNvgcRJilbXHneG4ADlegjSaMSFbzhiPfKU6rTT1AC4Ft+TojiQ+GHnIC4pryNxda5RddpZfc8TgyjgIPAIUX0ZiS82CnFnuw8hCT2AIxHLkKEIKWl3qDpW56chi4qp6vpZZBc+kDhJVrmfI5YuF6rrCMTF8UfgKHUvJoBnjEeI0yh1nYK4my1ECOoqYFKAbe48ZOHyLWINlQNcpj47B7FE8KufWGS3RxaMGWrwvtnynacQMt9sLK8sZbsKibX5Jl7LiVNUG5uj3mcgGcEHIUTpDsu9KYh11wOqvvrUbFc+yn0c2aU9U9XtRXgTE43ESxqSAmmD9VSvsShigVi5tUASQL1m+U66ahd1cmU+3A7E4qU38IHlnsdt2Kn++jwPUJ04HYMQyiDESu8RS7t4Wh031MNvOEbJP8Zyr8nd2WrrSwj5XW+5boUoEl4mcJfEFMv58Sj3bnU9Qb2DdD9ltkMyf9+HKJlqXUxgWbT6OXYMQOaQkzxtDFkM3InM9d0J0FVaH/+eA82hR6M5tObQtbc5zaH3L7/m0A1Tr5pDH7huNIduuLrVHPrA8jWHbsSjyQvQID9KCOAlCJH4IJDJsQHK5BkAoy33nkJ2+jwD6xGI20E0PrpDAW3xEvRgZFdnDOIaNh+vS2KyGnAzrOXxobwJQBmy2+dxyQtCrDNWqOf9QN0SjQQju+RrLfeGIwT7MVTcK8Sd6ztfBwDExfMnhFQuxLtD9yJiBRLj7/urce9DxILCU1ee+HlV8ZP8kY/swv0CjFf3RiPucUcgE/IIvO5FfllnIIvMRcik/pwaUJ9AFlsvIpZIPrln1iL7GFWfX6n3NRnZRbwNWRT92Rz6YC3lvgRx+2wJvI9YF3kWG2chRMRvNy5kAbhG9eV5qj96SNOZyAIpPAC5BrAFmGu5dybi/nmVtQ/62z4aoG57IBZs73j6NbJjPR94TF2fiixgAh47DpfDMkYMU+NlZ2ShdY3lO0cju/E2/FRUIIvyZYiV3J+IEiFYjX//Q6w+eiPx3O6vp9/kcRs8oanr11rH6vxYxAWvt7r+CJhn+bwFASQgU++mC2KReIOqA6ca/84MsNxJnnagxswK4JaDfN8zD0eqsfagXMHS9q5A3GHvQlmEqbFvViDjkT7+vQeaQ2sObWoOXUO+5tD7l1tz6IarW82ha7w79Vdz6AauY3V+LJpD12x7mkM34tHkBWjQHycksMl3pCzlmYDEfnsXIWEnIAHmZyMEcCX+x8s7CokB54k5d4aasBagsvgisY0u90OmdZCKVwP+TuDKGt87E1m8+E3KqDH5IxlcF2JJ3oJYgTyMl1zHYnEtPJBMvET1TXX9H4ToBFu+8wQ+ukPVqI+JauD2DHBfIO4onutQAkgCYpF/u5oIPlft5E3UDm4d2969wI3qPAix4PkYiZ+XQIBWAojVzjw1QZyOZLS/FyHcXyLJCI6va/nr48Cy4FG/+WTVvq9W73EEsuvqIdiBJHMZhJAnj+XDfxGiMwovwfY5u7alvQ5SdX0KUAxcZ/nO+ciCyWcX2wasY2tfuQUoAa623GuBuG39T/X3ZmcR1IB1MwBx4fbELRuKKICeR+aCxSgrNT/l2tSYMc1y7y9kAW1XbT0RWeguJsBF9AGeHZDbYAPX8w2IkuIOZKHhcfP/EIsVSICyPUmI2iPutbPVmH2zkh8dgMwxapy/EXG/vw54AbG4jFHf8YwdnnkmBrEerTXzOJaQBgiB9vy/4xGLtjFI0qAhqh8ecF7Vx//fA82hNYc2NYdWcjWH1hy6MepYc+gD143m0I1Tz5pDaw7d5EeTF+D/y4FkmN2kJvBLkV2eJxCXhGvVvSPVd/3d2R+P7O7FAGnIbs5lamDtg+wKHjTenEWWdXK8AiG3N6ny7wb+Y3lmCoFlsPV0+iMQYuCxymilyv6s5bs+uRFSPSaeJ4HGdHV8iVpkIYsYnyZ0NUBZs9iei7h8zkYmRI9b5qdIkhd/d2w99dATmaTSkAn4FGCg+mwC4k7od/y2Gs86FiGQ3Sz35iG7xXVZDLRSZR6LkKb2yI7zp0isRo9lU1NbI4Qj7oGDEauDs5DFWoqqX4+V1Keqv8QE+JyxQCWK/CI7w57kUmMClDkVWSw/hCQAuQ+xrrnF8h2fCXsj1fdwxNJhGpI84iwgSX0Wgyx+/XYRPpwPNZ6uRxYdHnfytojb992oMdqXvmIZO0YjypNnUVZj6n4XhLB54q2lIG6tvZq6HhqgXmMs5y2A99X5TcjixZrs7C2gbYDPuQqxcPsVb0b0SGTB9IXqkzEByHUgC59nUG7dSLKcd5B5YaoaQzwhDGKQBfwhk5wh8/SfyGL2fUSZcCoy9n+MKHGObep3qA99HOpAc+j9noHm0JpDN9KB5tCNXd+aQ+9fJ5pDN0y9xljONYeuLltz6KZql01dgH/zYRkAU5Dd8Fcsnw0G3kPFoKuHZ01EXKLCVYd6HnE5m01gO4qXIVYkrRDrkoeQ3ai9qtOvJrCA+w5LeVeqgaMYb9b4VkgSmZf9lHsG4k52BbIrZiDEfTneWHTnqGf6tEuOJfA/MAmJs2YghOk+xL3Us8v/LtAqgPqYgCyMXkd24c7DS0JGIUkg/IqXd4DnxCDunvergXwysgvYop7a3/2oXX6ESP1DM4lZqcoUrn7zQlXfMep+KmLBNArJ2v0ykFjHZ01FXHc9SSUcSMy8QJIfxajyRSE7t0vU/c5I4orbmrpuaylzIqI4eEH1lQlqrDsR2ZF/mgCSahxuB97xPwPvInOa6nfDa6sD/FiEInPKIsQy6HJgM16Lv6MQC4goy/cDis3aXA81FrdGiG2wupeg2tr7iJu4R6lyumdcDfBZ04C/kRiunszxHisxu+qfAY2lKCtERPH2EmIFYiAE+0VkXp+mvhOBEOuRB5CViMSMDUOsPtYquR2RxcZcJeN0hB9MC6TM+tBHYxxoDl2bXM2hqz9Dc+hGONAcujHrWnNoU3PoxqhfNIeuKUtz6GZyNHkB/q0H1Xfn3kd2Qhdjye6p7tdb7CGEAK5EBa1HdhZbWMvjo5woZMc2FdnxmqXK+ipCii/DT2JN9Th/LdXA3xXZLV+CxBR7VH3eGmX94OczliLk3xMjMAOJF7VW/Z5l+G75kYjs9Hmy994F5KMyf6vP71N14hdpsrSNcMQyZYS6noiQ7IlIDMUHUO6q/ry/gzy3BbL4+BmxVqm3mHaIxcoviGvQH8Cw+pJdx3JZrZn6IfG+PqJ6Juw7EVK9lHraHVfvcAFwTh3lhKuyPaUmRk+G4k6oZB5NXccHKPcAJMnW08hEPwGxPvi9vur4cDjU756PuKl+o8aSqxHiN5oAFxkIQfocGGq5dxeiTHicABJBHW6HZRyNRqwkxqjrW5HFfW917bHa89st2HJ9MvCg5XqAGkt8zgJ+kPf4BfCwuh6BEOrrENIegiWGJ+K+2OdAZUZI8+uIgqMHyt0frxXQ88Dp6vwq1R+H1Mf8og991OeB5tA1ZWoOvX/b0By6gQ80h26qetcc2tQcuoHrVnPoGmVGc+hmczR5Af7NB0KoH8ZLkC5QDf8SJNj+cg4Q/6YOz5wAZFHHeDhIrLlewC/q2kCI680EkDGY/eP8tVLy56vrboAb5f7oo8yaA+BFCGl8l+ouKe2VfL+sMxBScDReS4H71YTmcUtMRohZso/ybHgzRXdU168DF1m+czHwozqPqO131kMbCSfAzLsHkRmF7FZ+RjOZ1KlOrFMRS4QINVG+ilrYIgu/tg1QJ1OQxW4LfEzYdAA5Vyg5R6vrUcjuc+eGaB91KOe5WJKHAH0Rovcwygqhvuu4OR+I8mAhssi/AbEe8PTpaxErkIBcNFUfnguMtbYB1Qe7o+aV5tI2GqBurX27PaJE2YaQzw6I4uMvhKiuIMA4jYhb91hE0fEMEh/VU9cvUMfYsWoO6I24Ct6r7g1HXEvvIDD3/+tV2a5F4t6ea/msKo6qur6UZhB3Ux/6qO1Ac2irPM2hNYdu1APNoRu7vjWHrl4fmkM3XN1qDn1g+ZpDN4OjyQvwbzyovtOy10M2EEI5EYkv9i5wXAM9fxIBxuqqIacD4qLYA3Ht+qguHRFvnD8PwR4BfKLO+yr5h4wJpL5vHaAmIS53HheVL4DP1PlZ1CE+kBpctyM7jAays/gZatfPn8FP1ePHiCvRt6p+L0ZiI3msSvogljaHpSsRXvfTZjOpI8RmBhJH6hqE7FyILGzeRRZkCQ307Dq5OioZyQhZ+AHJ8L6aZrCAqfmOkQVKDtXj+p2LEMyHqcMC43A5aoxL7RBLuQmINYLHcmeo+lsnUoMQyruBLup6iBpXfFrs/xsOZKHsUdBchLjy9VVj9TAkkUlA9YxYtO1CCOnfyFz+sJpvLkYWvK0DlD3cMubbkAXRdOAmdW8UgbkzjwN+QxZe7yNWY9vUvDUNsUAd3dTvTR/6ONiB5tAHkqc5tObQTVEmzaEb5rdpDn2QOkFz6Maob82hq8vVHLqZHE1egH/TgXdXKMVy77+IC0CM5Z4TbybTBiMhdZWNWIDcpCb1FdRD5lq88eJikdhgbymCsxFvnLsDlpsDJ12Zg+z09VL3v0Ti3a1BJXypQ5nHA+vwEuz7kBhNwQcr6wFkvYLECzxXXacBj6qB8D3EhfKwjWnUkO05wPIcC3ynzt8BvlDn8ciC7JW6to9G+h3hasI9FhjQ1HVNdRJ5BRL363Ikllu2hSScoib4lKYoZxPVzTBkUX+yqotVeK0+RiJuiqn18JyWCPGbg7iCbqAZLLoasZ6nIolKelnuXazmqjopl4A26v15FkTHIG70f+NN9BLwfIjXMsOTyMup5rJ1wB0BykxCSHVXdX25ah/3qvnlPpQlqj700RwPNIf2Rabm0JpDN2Z5jkVz6AZ9z2gOXbNuNIdunHrWHLq6TM2hm9HhIYMa9QTDMCYgAdr/BCpN07zLMIxXkZ3Rk0zTzGvSAvoJwzCcSIIat2maO+pJ5gQkrlYfxF1sGJBrmuZvPvxfh2malep8EuLqOQVvjLsI4HXTNJcZhtEf2Gma5s56KPNExNVqiGmaew3DiDdNMzcAORchGccHAJeYprnIMIwgxJUpFcg2TXO+YRiGqTun3zAMw2aapttyfTSyiOuM7LgeY5pmuWEYHU3TXNtExfzXwDCMyxAScjoSf/BlxNLhOSRT8whgnGmaq5qqjI0BT381DGMoYlW0ALEcOAIIQqx2SpDs43ebpvllPT03HBlLkoHNpmn+XR9ymyOsfdswjDDEmu574BHTNO+3fO8qxE1zOFDq7ziq/v/pSHbxJ4B3TdMsNQzjWCTT+0nAPM885KNMT/uI83AANRfcAJxhmuY/hmEchSifPjJN8x9/yqzk/V97dxokV1lGcfx/YiQiQQEDlKiULBaIUMpqKLFEESMigRJRjBsiUIqyiayylqiQUtxABTGiFiCouIIsYoJaFEtRZQQxiqwGFxQF1Agk5PjhuVNMUmRmetI9t2fm/L7NTE/3Oz3d955+7/s+z7rU6p9jbf+iOXefS2WPS6nzonNuiX6WDD2i+0yGTobuiWTosZUMXZKhey8ZetjHSIbuI5mg7iJJ21OrGfajrohuQZ1Ylku6kNqO9ppO3pQTVROwv0ZdeR/RBw5J6ze/szdV/P5jVK2g3W3f1Pz8COoq2AWjOUAN8/j7UDWNdhjpwWnQQXWr5luLmtfDMdSV8b2av2UP2+d3c7yTWfO/WkJ9cHs5tWpnX9vLJB1GbeN5G/C/nGhGR9JzqPBxMnXM2xN4iAqRF1PP/59sL25tkGNI0k7U9rUTbN8oaTNqW/fOVKOOPwLX2f5pAs7oSfoAMBO4D7ifqul6hu1vDLrNOrYfHsV970Nt4zuDqnc7jaqZ+qvm2LE/cLPtu0dx37OpFR4PARfZni/pfVQzl6uoQP+OkUwyDfEYH6EmmC63fbukWVSNvONtLxrt/UaMhWTokUuGTobupWTo3kuGXlEy9NhIhh7yMZKh+0QmqLtI0qupYvN3Uiedt9u+R9JLbN8paWvbt7c7yv7RrN5YYnt+B7/zbCow3dKswvgEtXLiVNu/kbQhtUXlK7Yf7MGYp9v+T4e/sye1cuTn1BXJ2bbvlXRsM9bHgaNsX93t8U4Wg8NKcwL8LNXEZRZ1Zfy7VGf0FwMHUCex37Yy2AlE0jRqZc3nbL9WkoB/Us//WbYfb3WAY6hZaXQVtb3sk83V9zdRNUE/Ouh2CdajJGlfqt7ou4ADgXupMPkmatXEZ5vbdfwcS3oBtWrzWtvvl/QsagJnHWo74vwOV3y8GHie7Vsl7UatAJpNbeVdD/iS7W81K4Y2Av5he0EnY36ax3whtSJyJ2rr5luBD9n+2ercb8RYSIbuTDJ0MnS3JEO3Ixn6KcnQvZcMPexjJkP3iSltD2C8kvRs1ZY1JO3WvOn/Tr3x51ErEu6R9AbgRElrJ1ivyPYVzRUwdfA7S6haYrdLei5wEnUQOVXSdrb/Rl0J7Hqwbh6/02C9LdW9dhZ14t0EuFS1PW4uMAeYk2A9eisF640BA7vYPoV67h8BdqBWBW1FfehNsO6CJjwvAaZK2oZaAXINcOFkCtYAtq+lVg4cKOkdtpdSTUFeI2nDgeNcgvVq2YJ6bf2a2tq3hKrFeiQwW9I6o/3w4tp+fySwR/P/e4w6ny+ljt9rjPS+muPQ9dRKKKgPoAdTW9NnUCtKDpV0IPBb299d3WDd/A2LqRVIn6ZWmRySYB39KBl69SVDJ0N3QzJ0e5Khn5IMPSaSoYf+G5Kh+0RWUK8GSedStYvWAA61fYNq29mO1BUeqKugJ9v+UUvDnJAkvZGqZbQD8CjVSGcT6orgE/1yApM0nepE/DzgTGr1x1epulq7OTXcVstKwXq4uldvsX1La4OdoJoVIEcCr6euYu9n+45WB9UiSXtRzZquAZZTr8Mc/7ugeS+/j9oCekfzvflUcF3cBOLVfYw9qYY5n7J9iaSpwLq2/97BfWxL1XP9PrAp1WTlOcAFwGG2/yzpOuCB5m/pSm3aiPEkGbo9ydABydD9IBl6RcnQvZMMHePF1LYHMB4NOqGfDfwMuM/2Dc2Pvwc8CJwGLAZOsv3jbEnpLttXSToCuJFqunKSqulKq1ecB/7Pkp4PTLN9L7BQ0gnA1a7mIt+nmhNMb3OsE8GgYL0P9UHr3VTdq22AmZJ+ZfsHzVajjhvyxPBsPy7pbKpmXtcaQY1XzfH+XVSgusj2j7Lyo2sWUO/zd0paAKxJHUcf7kawhlqVKGk5cL6kZba/Q63s7MRtwH+oVRin235S0hJqq+N7JF1B1fT84mR/v8TkkwzdvmTogGTofpAMvaJk6J5aQDJ0jANZQd2hQeFpOrX1YCNqhccUatvTUknTmhPOM5o3VYJ1j2gUTVd6rRnTadRr4mLbZ0p6C9VQ5PfUVpfDshKhO9TlulcR3dBsTZ8HHG778rbHM1FI2ojaBjqbCrCn217Yg8fZHbjLo2vmIqpm6rpUPcnLXE3IdqHOV+tT4+5KJ/qI8SIZur8kQ0cydPSjZOjeSIaO8SAT1KOg6p59BNUw4nzbf5X0AypsX0JtbdiD6r6bJ7jHNIqmKz0Yw8CHrinUqqB51IH/Smqr6jeoJgS7Aj+0/ZO2xjoRNR9ezgGOHrSlaC61PewUV93FiDG1OgEthqZq9iXb/217LLDCOWBL4L/Ak8DfqA/6zwfOs/3rZjvvhrbvz8RbTEbJ0P0lGTqSoaMfJUP3TjJ09LNMUI+QpKm2l0naCTgD+DoVlh4Evml7YbNFZ0PqSk+u6kwyqoY/uwMbA8favkvSVsBl1Gtk7qDb5qDaZd2oexURMVrNxNtc4FLgvcDOVO3UvYGXAl+2fXN7I4xoRzJ0DCcZul3J0BHRpmToGDCl7QH0O0mbq7qHL5P0ImrLwS9sXwIcAzxG1cPZ0fZHgINt/3CgXlJMbAP/Z0kvpQr6P0rVRfqQpE1dTQj2Bw5pXkupo9Ujtq8AjgPmStrP9rIE64joFUkbSJojaa2mZurpwD7A3VQ2WG7791Rd3bupjukRk0YydAwlGbp/JENHxFhKho5VyQrqYUh6FWCqPtfA1rNZVK28hZLWB05ofna67X+1NthohaRXAmcB82x/U9JM4M3ANGpLyh8lrdUv22gmumwJi4heayZK5gCvA64Hfkllg38CHwXmNMf+NwLzqbzVlSY0EeNFMnQMJxm6vyRDR0SvJUPHUDJBPQKS1gYWAtvb/pekj1FdUE+1/ZsmYK/XXOWJSUbSDOAa4B7b+zbfeyXV0GUqcDzwhO0n2xtlRER0m6Sjgc2BRcDhwDOBTZsVozOpLdMH2b6rxWFGtCYZOoaSDB0RMTklQ8fTyQT1CEnaGzgTmEltQTuOuupzTC+6n0b/GlTIfytgTeBOqpj/tcBNto9qbrcz8JDtP7Q32oiI6AVJs4ATqdWfDwD3AwdQNfQeA94PnJZ6ujHZJUPHgGToiIhIho5VyQR1B5oGHp+nVn48CpwEXGn71lYHFmNO0mzgZOAvVE2kq6mi/lcDt9k+tMXhRURED0naALgcOMT2HZI+DMyg6qduQE263Gb72jT0ikiGjqckQ0dETF7J0DGUqW0PYDyxfaWk5dQ2hC1tf7ztMcXYa7arHgUcDPwOeAVwNPAnqrj/fElb2l7U1hgjIqKnllIZakbz9XnAucBmwEW25w3cMME6Ihk6SjJ0RMSklwwdqzSl7QGMN7avAg4Ctm17LDF2Vuo0vgV1UH3Y9uPA7cBNwC62HwJ2SLCOiJi4mmZulwG7Stra9lKq0/i/gRtaHVxEn0qGnpySoSMiYkAydAwlE9SjYPsK2wsGAldMfE29vL2Ai4HFVMfZz0ia0XQW/yuwiaRpVC29iIiY2C4D1gDOlvQJ4AvAOZlciVi1ZOjJJxk6IiJWkgwdTys1qCNGQNIrgAuB/W0vkvQy4DBgO+BrwLHAB21f09ogIyJiTDXb1XcGtgZutX19y0OKiOgrydAREbGyZOh4OpmgjhiBZlviccCNwHpU9/kHgBdS4frPthe0NsCIiIiIiD6TDB0REREjkQnqiBGQNB04AJgDfJpq8vNq4BHb325xaBERERERfSkZOiIiIkYiE9QRHZC0hu0nJO0IfB04wvZ1bY8rIiIiIqJfJUNHRETEUNIkMaIzT0raHjgHODHBOiIiIiJiWMnQERERsUpZQR3RIUlrARvYvkeSnDdRRERERMSQkqEjIiJiVTJBHRERERERERERERGtSImPiIiIiIiIiIiIiGhFJqgjIiIiIiIiIiIiohWZoI6IiIiIiIiIiIiIVmSCOiIiIiIiIiIiIiJakQnqiIiIiIiIiIiIiGhFJqgjIiIiIiIiIiIiohWZoI6IiIiIiIiIiIiIVvwfIwpVdLy1dZIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# just for fun: lets visualize to which extent each of our maps is explained by the predictors\n",
    "fig, axes = plt.subplots(2,2, figsize=(20,12), constrained_layout=True, sharex=True)\n",
    "axes = axes.ravel()\n",
    "for i, dom in enumerate([\"dominance_full_r2\", \"dominance_relative\", \n",
    "                         \"dominance_total\", \"dominance_individual\"]):\n",
    "    ax = axes[i]\n",
    "    data = juspyce_vol.predictions[dom].reset_index()\n",
    "    sns.lineplot(\n",
    "        data=data.melt(\n",
    "            id_vars=\"index\",\n",
    "            var_name=\"Predictor\", \n",
    "            value_name=\"Explained Variance\"),\n",
    "        x=\"index\",\n",
    "        y=\"Explained Variance\",\n",
    "        hue=\"Predictor\",\n",
    "        ax=ax)\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_xticks(range(data.shape[0]))\n",
    "    _ = ax.set_xticklabels(data[\"index\"], rotation=45, ha=\"right\")\n",
    "    ax.set_title(dom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run prediction on modified dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:juspyce.api:Subtracting parcelwise mean of B from mean of A: new Y = mean(Y[A]) - mean(Y[B]).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "# groups\n",
    "n_Y = juspyce_vol.Y.shape[0]\n",
    "groups = [0] * int(n_Y/2) + [1] * int(n_Y/2)\n",
    "print(n_Y, groups)\n",
    "\n",
    "# run comparison function\n",
    "juspyce_vol.compare(\n",
    "    comparison=\"diff(mean(A),mean(B))\", \n",
    "    groups=groups,\n",
    "    store=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd2a78a32d6648bca5affe7760f351f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting (spearman, 1 proc):   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5HT2a-cimbi36-29-beliveau2017</th>\n",
       "      <th>NMDA-ge179-29-galovic2021</th>\n",
       "      <th>mGluR5-abp688-73-smart2019</th>\n",
       "      <th>MU-carfentanil-204-kantonen2020</th>\n",
       "      <th>GABAa-flumazenil-6-dukart2018</th>\n",
       "      <th>5HT1b-p943-65-gallezot2010</th>\n",
       "      <th>D2-raclopride-156-malen2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diff(mean(A),mean(B))</th>\n",
       "      <td>-0.293576</td>\n",
       "      <td>0.127329</td>\n",
       "      <td>0.061984</td>\n",
       "      <td>0.246023</td>\n",
       "      <td>-0.291639</td>\n",
       "      <td>0.068489</td>\n",
       "      <td>0.085834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       5HT2a-cimbi36-29-beliveau2017  \\\n",
       "diff(mean(A),mean(B))                      -0.293576   \n",
       "\n",
       "                       NMDA-ge179-29-galovic2021  mGluR5-abp688-73-smart2019  \\\n",
       "diff(mean(A),mean(B))                   0.127329                    0.061984   \n",
       "\n",
       "                       MU-carfentanil-204-kantonen2020  \\\n",
       "diff(mean(A),mean(B))                         0.246023   \n",
       "\n",
       "                       GABAa-flumazenil-6-dukart2018  \\\n",
       "diff(mean(A),mean(B))                      -0.291639   \n",
       "\n",
       "                       5HT1b-p943-65-gallezot2010  D2-raclopride-156-malen2022  \n",
       "diff(mean(A),mean(B))                    0.068489                     0.085834  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict compared data\n",
    "juspyce_vol.predict(\n",
    "    method=\"spearman\", \n",
    "    comparison=\"diff(mean(A),mean(B))\",\n",
    "    r_to_z=False, # apply r to z transformation\n",
    "    n_proc=1,\n",
    "    store=True)\n",
    "display(juspyce_vol.predictions[\"diff(mean(A),mean(B))-spearman\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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