Figure 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROS ALE Figure 6\n",
    "\n",
    "Generates Figure 6 from the manuscript \"Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance\" by Kevin Rychel et al., 2022."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Set-Up\" data-toc-modified-id=\"Set-Up-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Set Up</a></span></li><li><span><a href=\"#Write-Special-Functions\" data-toc-modified-id=\"Write-Special-Functions-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Write Special Functions</a></span></li><li><span><a href=\"#Generate-Figure\" data-toc-modified-id=\"Generate-Figure-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Generate Figure</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use py file to set up workspace\n",
    "from src.ros_ale_functions import *\n",
    "\n",
    "# load data\n",
    "ros_meta = pd.read_csv('input/ros_ale_p1k_metadata.csv', index_col = 0)\n",
    "hplc_rates = pd.read_csv('input/hplc_files/hplc_rates_kr.csv')\n",
    "lit_rates = pd.read_csv('input/hplc_files/chen_2021_plos_comp_bio_aerotypes.csv',\n",
    "                        index_col = 0)\n",
    "strain_fluxes = pd.read_csv('input/me_model/strain_models_fluxes.csv',\n",
    "                            index_col = 0)\n",
    "pq_sweep = pd.read_csv('input/me_model/0_0__1_0__PQ_sweep_TCA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write Special Functions\n",
    "\n",
    "All plots in this figure use their own functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>T</th>\n",
       "      <th>sample</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>GUR</th>\n",
       "      <th>OUR</th>\n",
       "      <th>APR</th>\n",
       "      <th>biomass_yield</th>\n",
       "      <th>Nexpressed</th>\n",
       "      <th>peptide_per_complex</th>\n",
       "      <th>cofactor_per_complex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>simID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T25_S000</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
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       "      <td>5.945514</td>\n",
       "      <td>9.392770</td>\n",
       "      <td>0.401742</td>\n",
       "      <td>0.536170</td>\n",
       "      <td>587</td>\n",
       "      <td>2.966507</td>\n",
       "      <td>4.002392</td>\n",
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       "    <tr>\n",
       "      <th>T25_S001</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.177788</td>\n",
       "      <td>7.380906</td>\n",
       "      <td>5.994829</td>\n",
       "      <td>11.964246</td>\n",
       "      <td>0.133772</td>\n",
       "      <td>548</td>\n",
       "      <td>2.695444</td>\n",
       "      <td>3.721823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T25_S002</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0.213196</td>\n",
       "      <td>2.994341</td>\n",
       "      <td>7.894971</td>\n",
       "      <td>0.158807</td>\n",
       "      <td>0.395414</td>\n",
       "      <td>573</td>\n",
       "      <td>2.830508</td>\n",
       "      <td>3.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T25_S003</th>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>0.287010</td>\n",
       "      <td>3.473529</td>\n",
       "      <td>7.394752</td>\n",
       "      <td>0.208662</td>\n",
       "      <td>0.458882</td>\n",
       "      <td>582</td>\n",
       "      <td>2.848558</td>\n",
       "      <td>3.879808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T25_S004</th>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0.281136</td>\n",
       "      <td>4.742294</td>\n",
       "      <td>6.986329</td>\n",
       "      <td>4.352681</td>\n",
       "      <td>0.329232</td>\n",
       "      <td>561</td>\n",
       "      <td>2.728155</td>\n",
       "      <td>3.735437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T46_S096</th>\n",
       "      <td>46</td>\n",
       "      <td>96</td>\n",
       "      <td>0.268196</td>\n",
       "      <td>3.870958</td>\n",
       "      <td>6.923432</td>\n",
       "      <td>2.097786</td>\n",
       "      <td>0.384777</td>\n",
       "      <td>582</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>3.908654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T46_S097</th>\n",
       "      <td>46</td>\n",
       "      <td>97</td>\n",
       "      <td>0.251381</td>\n",
       "      <td>3.865182</td>\n",
       "      <td>5.839358</td>\n",
       "      <td>2.988292</td>\n",
       "      <td>0.361191</td>\n",
       "      <td>586</td>\n",
       "      <td>3.002387</td>\n",
       "      <td>4.040573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T46_S098</th>\n",
       "      <td>46</td>\n",
       "      <td>98</td>\n",
       "      <td>0.275719</td>\n",
       "      <td>4.075945</td>\n",
       "      <td>6.080340</td>\n",
       "      <td>2.964405</td>\n",
       "      <td>0.375675</td>\n",
       "      <td>583</td>\n",
       "      <td>3.036058</td>\n",
       "      <td>4.067308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T46_S099</th>\n",
       "      <td>46</td>\n",
       "      <td>99</td>\n",
       "      <td>0.270981</td>\n",
       "      <td>4.028386</td>\n",
       "      <td>6.020999</td>\n",
       "      <td>2.959513</td>\n",
       "      <td>0.373579</td>\n",
       "      <td>581</td>\n",
       "      <td>3.016787</td>\n",
       "      <td>4.055156</td>\n",
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       "    <tr>\n",
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       "      <td>0.274239</td>\n",
       "      <td>3.999775</td>\n",
       "      <td>5.939324</td>\n",
       "      <td>2.840674</td>\n",
       "      <td>0.380774</td>\n",
       "      <td>582</td>\n",
       "      <td>3.021635</td>\n",
       "      <td>4.069712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2222 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           T  sample  growth_rate       GUR       OUR        APR  \\\n",
       "simID                                                              \n",
       "T25_S000  25       0     0.574007  5.945514  9.392770   0.401742   \n",
       "T25_S001  25       1     0.177788  7.380906  5.994829  11.964246   \n",
       "T25_S002  25       2     0.213196  2.994341  7.894971   0.158807   \n",
       "T25_S003  25       3     0.287010  3.473529  7.394752   0.208662   \n",
       "T25_S004  25       4     0.281136  4.742294  6.986329   4.352681   \n",
       "...       ..     ...          ...       ...       ...        ...   \n",
       "T46_S096  46      96     0.268196  3.870958  6.923432   2.097786   \n",
       "T46_S097  46      97     0.251381  3.865182  5.839358   2.988292   \n",
       "T46_S098  46      98     0.275719  4.075945  6.080340   2.964405   \n",
       "T46_S099  46      99     0.270981  4.028386  6.020999   2.959513   \n",
       "T46_S100  46     100     0.274239  3.999775  5.939324   2.840674   \n",
       "\n",
       "          biomass_yield  Nexpressed  peptide_per_complex  cofactor_per_complex  \n",
       "simID                                                                           \n",
       "T25_S000       0.536170         587             2.966507              4.002392  \n",
       "T25_S001       0.133772         548             2.695444              3.721823  \n",
       "T25_S002       0.395414         573             2.830508              3.857143  \n",
       "T25_S003       0.458882         582             2.848558              3.879808  \n",
       "T25_S004       0.329232         561             2.728155              3.735437  \n",
       "...                 ...         ...                  ...                   ...  \n",
       "T46_S096       0.384777         582             2.875000              3.908654  \n",
       "T46_S097       0.361191         586             3.002387              4.040573  \n",
       "T46_S098       0.375675         583             3.036058              4.067308  \n",
       "T46_S099       0.373579         581             3.016787              4.055156  \n",
       "T46_S100       0.380774         582             3.021635              4.069712  \n",
       "\n",
       "[2222 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lit_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aerotype_plot(hplc_rates, ax, lit_rates = None):\n",
    "    \"\"\"\n",
    "    Draws the aerotype plot for fig 6d\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    hplc_rates: table of hplc data and growth rates\n",
    "    ax: axes to plot to\n",
    "    lit_rates: table of similar data from Chen 2021's simulations\n",
    "        if 'None', don't display\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    ax: axes with plot\n",
    "    \"\"\"\n",
    "\n",
    "    # plot literature data\n",
    "    if not(lit_rates is None):\n",
    "        ax.scatter(lit_rates.GUR, lit_rates.biomass_yield, color = 'lightgray',\n",
    "                   alpha = 0.5, zorder = 2, s = 2)\n",
    "\n",
    "    # plot the new data\n",
    "    ev_label = {True:'Evolved', False: 'Start'}\n",
    "    pq_shape = {0:'o', 125: 'D', 250: 's', 750: '^'}\n",
    "    ev_color = {True:'tab:green', False:'k'}\n",
    "\n",
    "    for (pq, name), group in hplc_rates.groupby(['pq', 'evolved']):\n",
    "\n",
    "        mean_gluc_rate = -group.glucose_rate.mean()\n",
    "        mean_biomass_gg = group.biomass_gg.mean()\n",
    "        std_gluc_rate = group.glucose_rate.sem()\n",
    "        std_biomass_gg = group.biomass_gg.sem()\n",
    "\n",
    "        ax.errorbar(mean_gluc_rate, mean_biomass_gg, \n",
    "                    xerr = std_gluc_rate, yerr = std_biomass_gg,\n",
    "                   color = ev_color[name], marker = pq_shape[pq],\n",
    "                   label = '%s, %i µM PQ'%(ev_label[name], pq),\n",
    "                   markersize = 6, alpha = 0.7, markeredgecolor = 'k',\n",
    "                   elinewidth = 1, zorder = 3)        \n",
    "\n",
    "    # add the isoclines & labels\n",
    "    xlims = ax.get_xlim()\n",
    "    ylims = ax.get_ylim()\n",
    "    x = np.linspace(xlims[0], xlims[1], 100)\n",
    "    grs = [1.2, 1, 0.8, 0.6, 0.4]\n",
    "    y_dict = {}\n",
    "    for g in grs:\n",
    "        y_dict[g] = g**2/((180.156/1000)*x)\n",
    "\n",
    "    for gr, y in y_dict.items():\n",
    "        ax.plot(x, y, '--', c = 'dimgray', alpha = 0.8, zorder = 1)\n",
    "\n",
    "    ax.set_ylim(ylims)\n",
    "    ax.set_xlim(xlims)\n",
    "\n",
    "    ax.tick_params(labelsize = 7)\n",
    "    ax.set_xlabel('Glucose uptake rate (mmol/gDCW/h)', fontsize = 7)\n",
    "    ax.set_ylabel('Biomass Yield (gDCW/g glc)', fontsize = 7)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TCA_flux_bar(strain_fluxes, ax, subsystem = 'Citric Acid Cycle'):\n",
    "    \"\"\"\n",
    "    Draws a bar graph of fluxes for reactions in TCA for fig 6E\n",
    "    Can be used with other subsystems in strain_fluxes too\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    strain_fluxes: output of ME model, in which rows are reactions,\n",
    "        one column 'subsystem' categorizes the reaction,\n",
    "        and remaining columns represent models\n",
    "    ax: axes to plot to\n",
    "    subsystem: which subsystem to show in the plot\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    ax: axes with plot\n",
    "    \"\"\"\n",
    "\n",
    "    # get just the fluxes for the subsystem of interest\n",
    "    rel_rxns = strain_fluxes.loc[strain_fluxes.subsystem == subsystem]\\\n",
    "        .drop('subsystem', axis = 1).abs()\n",
    "\n",
    "    # remove reactions that are never used by any strain\n",
    "    rel_rxns = rel_rxns.loc[(rel_rxns > 1e-20).T.any()]\n",
    "\n",
    "    # organize for plotting\n",
    "    to_plot = rel_rxns.stack().reset_index().rename(columns = \n",
    "                        {'level_0':'reaction', 'level_1':'model', 0:'flux'})\n",
    "    to_plot['evolved'] = to_plot.model != '0_0'\n",
    "\n",
    "    palette = {0:'tab:gray', 1:'tab:green'}\n",
    "\n",
    "    # plot\n",
    "    sns.barplot(x = 'evolved', y = 'flux', data = to_plot,\n",
    "                palette = palette, errwidth = 1.5,\n",
    "                linewidth = 0.5, edgecolor = 'k', ax = ax)\n",
    "    sns.stripplot(x = 'evolved', y = 'flux', data = to_plot, \n",
    "                  color = 'k', jitter = 0.2, s = 1.5, ax = ax)\n",
    "\n",
    "    # clean up\n",
    "    ax.set_xticklabels(['Start', 'Evolved'], rotation = 90)\n",
    "    ax.tick_params(labelsize = 7)\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('Optimal Flux in TCA', fontsize = 7)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pq_sweep_plot(pq_sweep, axs):\n",
    "    \"\"\"\n",
    "    Draws two plots showing the effect of evolution on the TCA cycle\n",
    "    for fig 6F-G\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    pq_sweep: table of data from ME modeling\n",
    "    axs: list of axes [panel_f, panel_g]\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    axs: axes with plots\n",
    "    \"\"\"\n",
    "    \n",
    "    # loop through the strains\n",
    "    for strain, color in zip(['0_0', '1_0'], ['tab:gray', 'tab:green']):\n",
    "        \n",
    "        # get the right columns\n",
    "        tot_col = 'total_tca_' + strain\n",
    "        dam_col = 'undamaged_tca_' + strain\n",
    "        nadh_col = 'nadh_' + strain\n",
    "        \n",
    "        # panel f\n",
    "        axs[0].fill_between(pq_sweep.pq_flux, pq_sweep[dam_col]*100,\n",
    "                            pq_sweep[tot_col]*100, color = color)\n",
    "        axs[0].plot(pq_sweep.pq_flux, pq_sweep[dam_col]*100, color = 'k',\n",
    "                    linewidth = 0.75)\n",
    "        axs[0].plot(pq_sweep.pq_flux, pq_sweep[tot_col]*100, color = 'k',\n",
    "                    linewidth = 0.75)\n",
    "        \n",
    "        # panel g\n",
    "        axs[1].plot(pq_sweep.pq_flux, pq_sweep[nadh_col], \n",
    "                    color = color, label = strain)\n",
    "    \n",
    "    # clean up\n",
    "    axs[0].set_ylim(0, axs[0].get_ylim()[1])\n",
    "    axs[1].set_ylim(0, axs[1].get_ylim()[1])\n",
    "\n",
    "    fontsize = 7\n",
    "    axs[0].set_ylabel('Proteome Percentage', fontsize = fontsize)\n",
    "    axs[1].set_ylabel('NADH\\nProduced', fontsize = fontsize)\n",
    "\n",
    "    axs[0].tick_params(labelsize = fontsize)\n",
    "    axs[1].tick_params(labelsize = fontsize)\n",
    "\n",
    "    axs[1].legend(fontsize = fontsize, ncol = 2)\n",
    "    axs[1].set_xlabel('PQ Cycle Flux', fontsize = fontsize)\n",
    "    \n",
    "    return axs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Figure\n",
    "\n",
    "Panels A, B, and D are added in Illustrator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AxesSubplot:ylabel='Proteome Percentage'>,\n",
       " <AxesSubplot:xlabel='PQ Cycle Flux', ylabel='NADH\\nProduced'>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p9r0ajYacnBygdD27jY1NtRPqmsQoHl6STAshhBBCCHGHxMREPv74Y9zd3cu0m5iY6CiiujV//nweeeQRjh8/zvnz50lMTKSwsLBWzl1UVMSlS5c4ffp0td6vr69Pu3bt6N69Oz169MDS0lJ7LCsri5MnTxIZGUlKSgoqlareY9TT08PR0REnJyecnJywtrau9HsNDQ2xsbFBqazajsU3b97k+vXrVYz0f0xMTLC0tJQHBjUkybQQQgghhBB3GDZsGH///TcvvfSSrkOpF8bGxowbN45x48ZRUFBAUlIS6enptXJujUZDZmYmYWFhnDlzpkrnbdKkCR06dKBnz55069YNOzu7cn2mT59OTk4OCQkJ3Lhxo8Yxnj59moyMjEq/t7CwkMTEREJDQ6u1B7mBgYF2er2FhcVdE9yioiKSkpJISEioUSJ9i7GxsfYBwO2zEJRKJXZ2djg4OGBgYFDj6zzIJJkWQgghhBDiDi1btmTevHls2rQJDw8P7fThW/tQP8iMjIxwd3cvNypfU8OGDUOlUpGRkUFxcXGl32djY4ORkdE9+zRt2rRc5fXqqE6MGo2GjIwM4uPjCQkJISoqCrVaXan35ufnk5CQQEJCAqdOnaKoqOie/Zs1a4abmxtOTk7Y2NhUe2T51sOHhIQEAgMDKxzRVyqV2Nvb06xZs3uOnFtbW+Ps7Ezz5s1p0qRJteJprCSZFkIIIYQQ4g4ZGRk88cQT2te3EqvKJkmiYrfWJjdk1YnR2dmZDh06MHLkyCq9T61Wk5aWRkJCAnFxccTHx9/1Z0ypVGJtba0dTXZycrrvQ4a7KSkpITk5mYSEBGJjY0lNTdVWrC8uLiY8PJyoqCgSEhJISkrSHqso/tDQUO1xKysr9PX/l2IaGRlpR92dnJwwNzfXHlMoFGX6NkaNO3ohhBBCCCHqwK2tse4k+0yL2nRr9Nfe3p7OnTvX23X19fVxdnbG2dmZ7t27lzuu0WjIzs4mPj6epKSke46YZ2ZmcvLkSS5fvlwmKQe4cePGPae/W1paake1nZ2dG93UckmmhRBCCCGEqMDhw4dZunQpkZGR/PXXX6xbt45r167h4uKi69CEqFMKhQJLS0ssLS1p167dffv7+/tz48YNEhISyM/P17arVCqSk5MJCQnh7Nmz5OXllTmWkpJCQkIC58+fB/63XtvJyQkrK6sy09itrKxwcnKqlen8tUWSaSGEEEIIIe6wadMmXnjhBebMmcPOnTspLi7G1NSU4ODgWqtyLcSDxMzMDG9v7wqPjRw5kpKSErKyssqMXOfm5pKQkMDly5c5efIkV69eJSEhgUuXLnHz5s0Kz2Vubo6jo2OZEezbi7jZ29ujp6dXux/uLiSZFkIIIYQQ4g4ff/wxP/zwA5MmTeLrr78G4OWXX2bDhg2cO3dOt8EJ0Qjp6+vTrFmzMm12dnZ4eHjQt29fZs6cSXZ2NgkJCdrp5bcKo6nVamJjYwkLCyM+Pp6UlJQya8vz8vI4e/YsUH6rMmdnZ6ysrOrmM9XJWYUQQgghhGjEoqKi6Nq1a7l2S0tLCgoKdBCREA+226eWt23btsI+t28Pdvs2aGq1mqtXr3Ly5Emio6PLbVXm7+9fJzFLMi2EEEIIIcQdunTpwsaNG3n77bcBtGs3r1y5UmejXEKIezM0NMTV1RVXV9cKj7/wwgtkZmZqR7dDQkIIDw/Hw8MDCwuLWo9HkmkhhBBCCCHusHTpUkaOHMlff/1FQUEBc+fO5fLly8TFxdGnTx9dhyeEqIBSqcTW1hZbW1vtVmUlJSUAdbINlyTTQgghhBBC3KFTp05ERkayfv16/Pz8UKlUjBw5kj179jSqrXuEeNjV5V7Wyjo7sxBCCCHEA+L8+fP069cPU1NTvLy8+PXXXyvsp1KpMDIyQqFQaL8cHBzqOVpRW/bu3Uvz5s358ssvWbx4MWFhYaSlpek6LCFEAyHJtBBCCCHEPRQVFTFixAjatWvHuXPn+OCDD3j22We1hW1uFxUVhUajITExkaSkJJKSkggLC9NB1KKmvvzyS5555hmuX7+ubbOysuLEiRNcuXJFd4EJIRoMmeYthBBCCHEPiYmJ9OrVi6+//hoDAwM8PDz46quvOHr0KN27dy/T91ahG0dHRx1FW3V6enqo1ep625e1sViyZAmbN29m0KBB2rYFCxZw+PBhTp8+rcPIhBANhYxMCyGEEELcg5ubGxs2bMDAwACNRsPOnTuJiIiosAhVeHg4Xl5eOoiy+lxdXTEyMrprddyHVXZ2doUPRUxMTCgqKtJBREKIhkZGpoUQQgghKkGj0WBqakp+fj4vvPACvr6+5fqEh4eTkZGBn58fMTEx9OnTh6+//vq+I9U9e/assD07O5v27dvXSvx34+jo2KhG0uvLkCFDePXVV1mzZo32zyc5OZlz585hZ2en4+iEEA2BjEwLIYQQQlSCWq3m8OHDbNy4kY0bN/LVV1+V63Pp0iWysrL49NNP+f3330lJSWH06NGo1WodRCxqYvny5eTm5tKiRQvtAwdnZ2dUKhVdunSp0bkzMzOxs7MjPj7+rn3WrFmDq6srZmZmTJ8+nby8vBpdUwhR+2RkWgghhBCiEvT09OjatStdu3YlPj6eZcuW8dprr5XpExAQgFqtxsTEBIDff/8de3t7goODy62vvl1QUFCF7VOmTKm9DyCqJCkpiaNHj3LhwgUiIiIwMDCgVatW/Oc//6nRebOzsxk3btw9q4IHBQUxZ84cVq1ahaenJ9OnT+eNN97g22+/rdG1hRC1S5JpIYQQQoh7SExMJCQkhJEjR2rbWrduTXp6erm+RkZGZV7b2tpiY2NDYmJinccpategQYPYs2cPnTp1qrWp9qdOnWLq1Knlfk7u9N133+Hv78/kyZMB+P7773n00UdZuHCh9kGNqB8ajYb4+HgyMzN1HUqVFBUVEXwmmL1H9nIq+BQFxQWs37+ewe6DdR3aA0WSaSGEEEKIewgPD2fChAmkpKRgaWkJQEhICG3atCnTT61W4+7uzqJFi5g4cSIACQkJpKen4+3tXd9hixpq1aoV586do1OnTrV2zgMHDvD444/j7+9/z0J1x48f57333tO+7tatG8XFxZw7d44ePXrUWjwV0Wg0xMTEEHQiiPj0eNDU6eUarJSUFI6dOMb5M+e5kXlD1+FUm761PibuJph3NGdF2ApJpmuZJNNCCCGEEPfQt29fPD09mTlzJp988glnz57l888/Z+3atahUKtLS0rC2tsbQ0JChQ4fy7rvv4uTkhJGREXPnzmX48OHlEm/R8Dk4ODBjxgzee+893NzctKPJ586dq/Y533jjDQBiYmLu2S8pKYnmzZtrX+vp6VVqhkNNCtkdCzrGa/9+jfMh57mZdfOefR8KemDkbIRJRxOc3J3Qt2pcaZNCoaBJ8yYYWBlo21SodBjRg6lx/VQIIYQQQtQzAwMDduzYwYsvvkjXrl2xsbFh0aJFjBkzhpiYGNzd3Tl48CD9+/dn8eLFzJ8/n7Fjx5Kfn8/YsWP55ptvdP0RRDV06NCBDh06lGuvjyn7eXl5GBoalmkzNDSksLCwzq654+QOzl07h3FnYyxbWmLS0gT9pg9vqqAwVKA0kFrN4t4e3v9DhBBCCCEqyd3dnV27dpVrd3NzQ6P53zxYU1NTli9fzvLly+szvGrZtWsXp06donfv3gwYMEDX4TQ4H3zwQYXtFy9erPNrGxkZldvLuqioCFNT03u+ryaF7HxH+OJh6VH5IIUQsjWWEEIIIcTDKCQkBLVazdmzZ3UdSoP1yy+/0KVLFywtLYmKiuKVV14hPDy8zq/r5OREcnKy9nVJSQkZGRmyH7gQDYwk00IIIYQQD6E+ffpgaWlJr169dB1Kg7R06VLee+89XnzxRe0osZ+fH1euXCEsLKxOr929e3eOHTumfX3y5EkMDQ155JFH6vS6QoiqkWneQgghhBAPoT59+tCnTx9dh9Fgffvtt/z4448MGTKEV155BYDJkyezfPlyTp48WavXurOQ3ezZsxk8eDB+fn60bt2aF198kZkzZ9KkSZNava4QomZkZFoIIYQQQog7JCQk4OFRfg1xReuZayouLg5HR0ftaHTv3r1ZsmQJb7zxBgMGDKBjx458/vnntXpNIUTNyci0EEIIIYQQd+jXrx9Llixh6dKlQOlWQ3l5eVy4cIFmzZrV6Nx3Fq678zXArFmzmDVrVo2uI4SoWzIyLYQQQgghxB2WL1/O4cOHcXZ2Jj8/nwkTJuDs7ExOTg6dO3fWdXhCiAZARqaFEEIIIYS4g5ubGyEhIezbt4+IiAhUKhVeXl78/PPPKBQKXYcnhGgAJJkWQgghhBDi/2VnZxMQEECTJk3w8/Nj8ODBDB48WHt89erVugtOCNGgSDIthBBCCCEEEBQUxIgRI8jOzgbA1taW9evXM2jQIB1HJoRoiGTNtBBCCCGEEMAHH3zAiBEjSElJIS0tjdGjR/PCCy/oOiwhRAMlI9NCCCGEEEIAx44dIzQ0VFut+8svv8TGxoaMjAxsbGx0HJ0QoqGRkWkhhBBCCCGAvLw8zM3Nta+trKwwMTEhJydHh1EJIRoqSaaFEEIIIYS4C4VCUW4PaCGEAJnmLYQQQgghBFCaOCcnJ1NSUlKmPSUlBSMjIwDy8/N1EZoQogGSZFoIIYQQQghAo9HQqVOncm29evXS7i2tVqtln2khBCDJtBBCCCGEEABER0fft8+cOXPqIRIhRGMgyfRthg0bRnZ2Ni4uLroORYiHwrVr17CwsOCff/7RdShCCCEErq6u9+1jampaD5EIIRoDSaZvk52dTUFBga7DKKewsBBDQ0OZUlQBtVpNcXExTZo00XUoohoa4v9vQgghxMPo+J7jXP3qaqX7K5soMXY1xtjDGOOWxhhYG8i9qnjoSDJ9m1sj0ps2bdJxJKWKiorYv38/oaGh9O3bFz8/P12H1KAUFRXx22+/kZSUxIQJE/Dw8NB1SKKKpkyZousQhBAPkbCwMH755Re++OILXYfS4MTExLB582asra3x9/fH0NBQ1yGJetbMuhkG1gaV7q+6qSLzUCbqf9T/a7wtlza0N8SkpQnGLY0xcjVCaXD3TYQMrAzQt5C0RDQ+8lPbgEVGRhIaGgpAYGAgHh4e2Nvb6ziqhuPAgQPEx8cDsGXLFkmohRBClJOUlMT69ev59ddfOXfuHPb29tVKps+fP8+cOXM4deoUTk5OvPfee/j7+1fYd82aNbz//vtkZGQwadIkli1bhomJSU0/Sp0KCwsjLy+PvLw8kpKSKjXdWTxY/jXlX7Tp3obsouxK9Vdr1FzJvMLxs8e5ePYiRdlF2mMalYbCxELyIvO4fux6pc5nYGOAcUtjjN2N0TPSu2s/PTM9jN2NMbCVkXChe5JMN2CtW7cmPDycK1euoFar+euvv3j66acxMKj8U8MHWb9+/UhKSiIlJQWVSsWWLVsYP348np6eug5NCCGEDuXl5bFlyxZ+/fVXDhw4gFqtpnfv3mzcuJHx48dX+XxFRUWMGDGC0aNHs2rVKo4fP86MGTPw8vKie/fuZfoGBQUxZ84cVq1ahaenJ9OnT+eNN97g22+/ra2PVyc6depEdHQ0NjY2NG/eXNfhNBgqlYpdu3Zx5coVpk+fTkREBEVFRQ/kyL2B0oARLUdU/Y29IL8kn+jsaFRqlbY5qzCL8+nnORl1ktNnT3Oj8MZdT1GUWkTe1Tzyo/PJOZUDldjWW6+pHibuJug1vXvifSd9C31M3E0w9jDGwFLup0XNSTLdgCkUCoYNG0ZCQgJ5eXmkp6dz8OBBhgwZouvQGgRjY2Mef/xxNm7cWC6hbtWqla7DE0IIUY80Gg379u3j119/ZevWrRQUFNC3b18WLVrE66+/znfffUebNm2qde7ExER69erF119/jYGBAR4eHnz11VccPXq0XDL93Xff4e/vz+TJkwH4/vvvefTRR1m4cGGDHp12dnZm7ty5ug6jQYmNjWXo0KHa0fqxY8eyaNEi/vnnH/r27avr8BoUY31j2tiU//+rr3NfXuz4Iky49/tvFN3gYsZFzqWfIyQhhOSc5Lv2zU7L5nLoZW5evUl+dD6FKYWVjrM4s5j0onQA9K300TOtfCJeV5RG/7/2vKUxJh4mpVPtKzngrtBToFBWrrNGU4knFKLKJJlu4ExNTRkxYgS///47AGfOnMHDw0OmM/+/OxNqtVrN1q1bGTNmDD4+ProOTwghRD1xdnZGpVLx6KOP8t133zFy5EisrKwAeP3112t0bjc3NzZs2ACU3pDu2rWLiIgI+vTpU67v8ePHee+997Svu3XrRnFxMefOnaNHjx41ikPUrzlz5jB06FC+/vprmjZtCsCGDRvw8vLizJkzOo7uwWJmaEY3x250c+wG7e/fv6CkgEuZlwhLDyM2J7bS17lZcJMToSe4fPYyeTF5qAvV939THVPdVJF9MpvM/ZlVfq/CUIGxW+nUeJOWJuhb/i+106g0FFwrID8qn7zoPFBDq5Uy2FTbJJluBDw9PenUqRMhISEA/P3338ycOVO2Zvh/txLqTZs2kZycjFqtZtu2bahUKtq2bavr8IQQQtSDZs2aERMTQ1ZWFvHx8SQkJGiT6dqi0WgwNTUlPz+fF154AV9f33J9kpKSykyT1tPTw8bGhsTExHueu2fPnhW2Z2dn0759JbILUeuOHDnC4sWLy6zL1dPTw8fHhz179ugwMmGkb0RHu450tOtY9Tf3h5vFN7mYcZH43PjaDq3KVBoVkVmRnLhwgtDToRRkV36nk5LsEvKi88g6nEXG7owK+zRp3gRTL1PMO5rj0lS2/61tDTaZzsvL4/nnn2fbtm1YWlry8ccfM23atHL9VCoVpqamFBb+b4qHvb09ycl3nx7SGA0cOJBr166RkZFBXl4eO3bsYNKkSSiVd6+M+DC5lVBv3ryZhIQENBoNf/31F8XFxXTs2FHX4QkhhKhjZ8+e5fLly2zatIm1a9fyzjvv4ObmxujRowFqpVCRWq3m8OHDXL16lRdeeAFPT09ee+21Mn3y8vLKrac1NDQsc58iGgdLS0tiYmLKLR3Lysp6INdMP0xMDUzxdfDF16H8AzGd6Q7FzxQTlxtXZu35vRSri7mSdYXQ1FCCzgURmxyLWlM62q5QKLB1s6WjS0fa27annW07utp3rctP8FBqsMn066+/TkREBIcPHyY0NJTZs2fj4+NDt27dyvSLiopCo9GQmJio/YdST0/36x9qm4GBAWPGjOGXX35BpVIRHR1NcHBwubVaDzMjIyOmTJnC5s2biYuLA6CkpETHUQkhhKgvXl5evPfee7z33nuEhYWxadMmfvvtN1QqFYMGDWLatGlMnz4dLy+vap1fT0+Prl270rVrV+Lj41m2bFm5ZNrIyIiioqIybUVFRfedTRYUFFRhu2whqDuvv/46M2fO5N///rf2QcqGDRs4depUtdffC3EvBkoDWlq0rNJ72ti0YaznWJAddHWiQSbTN2/eZPXq1QQEBNChQwc6dOjA0aNHWb58eblkOjw8HA8PDxwdHXUUbf2xt7dnwIAB7Nu3D4CMjAw0Go1sC3AbQ0NDJk+ezNatW2nRogVdu/7vCdytCq5bt27VVXhCCCHqSfv27Wnfvj0LFizgzJkzbNy4kY0bN7Jw4UJUqsqN+tySmJhISEgII0eO1La1bt2a9PT0cn2dnJzKzI4rKSkhIyPjobhPedDMnTsXJycnFi9ejLGxMa+++ipeXl506dIFFxeZLiuEgAY5Rzg0NBSVSlUmEfLz8+P48ePl+oaHh1f7CXNj1KVLF3x8fBg+fDjDhw+XRLoCBgYGTJw48a7rz4QQQjxcOnfuzMKFC4mOjubYsWNVfn94eDgTJkzg+vXr2raQkJAKRye7d+9e5honT57E0NCQRx55pFqxC9167LHHCAwMJD09naysLE6cOCGJtBBCq0GOTCclJWFvb19mura9vX2FxTvCw8PJyMjAz8+PmJgY+vTpw9dff33PJ8CNuciHQqFg7NixkkTfR0VryYuLi7l+/ToqleqBXAoghBAPuytXrvDRRx+xYMECXF1dte0zZswgPz+fTz75pFrLo/r27YunpyczZ87kk08+4ezZs3z++eesXbsWlUpFWloa1tbWGBoaMnv2bAYPHoyfnx+tW7fmxRdfZObMmTRp0qQ2P6qoBzk5OaxYsYKIiIgya95PnDihw6iEEA1JgxyZrkrxjkuXLpGVlcWnn37K77//TkpKCqNHj0at1n2p+7pSUSIte8fd282bN4mPjycrK4utW7dSXFys65CEEELUogsXLtCjRw9iYmLKrVkeNWoU8fHx+Pr6EhERUeVzGxgYsGPHDvLy8ujatStvvvkmixYtYsyYMcTFxeHo6Kgdje7duzdLlizhjTfeYMCAAXTs2JHPP/+8Vj6jqF+TJ0/mq6++QqPRYGFhof0yMDDAwMBA1+EJIRqAGo9MHzx4kN27d3PmzBlSU1NRKpXY29vTqVMnhg4dSr9+/ap8zqoU7wgICECtVmNiYgLA77//jr29/T2Lcz1oRT5ycnL466+/6N+/P05OTroOp0G6cuWKNoGOjIxk48aNTJw4EWNjYx1HJoQQoja88847jB07llWrVpU7NmHCBMaPH8+UKVN4++232bJlS5XP7+7uzq5du8q1u7m5lXugPWvWLGbNmlXla4iGJTAwkEOHDtGlS5cy7Y31flEIUfuqPTL9888/4+3tzbRp00hOTmbgwIG8+OKLzJo1i969exMVFcW0adPw8fFh5cqVVTq3k5MTaWlpZUaXU1JSKpy6bWRkpE2kAWxtbSu1n+ODIikpiZ9//pm4uDj+/PNP8vLydB1Sg9SxY0fs7Oy0rxMSEli3bh05OTk6jEoIIURtOXr0KPPmzbvrcYVCwZtvvklgYGA9RiUaM19f34fmflIIUT3VGpnu27cvbdq0YcOGDXTu3PmefY8dO8bKlSv5+eefOXr0aKXO36FDBwBOnz6Nr2/p/m9Hjx6lR48eZfqp1Wrc3d1ZtGgREydOBEqTpPT0dLy9vav6sRolS0tLDAwMyM/PJzc3l23btjFlyhTZf7oClpaW6Onpoaenh0qlIj09nV9//ZVJkyaVSbSFEEI0Pmq1+r71RMzNzWWZj6i0n376iX79+rF161ZcXFy091YXLlzQcWRCiIaiWhnXr7/+yvfff3/fRBpKq3CvWrWKdevWVfr8pqam+Pv7M3v2bEJCQvjll19Yu3Ytzz//PCqViuTkZIqKilAqlQwdOpR3332XoKAgQkJCePzxxxk+fPhDs/+fsbEx48eP1/6Cj42N5ciRIzqOquEyNzdn8uTJ2jX5ubm5rFu3jtjYWB1HJoQQoia6d+/OH3/8cc8+mzZtavCFRkXD8c4775CRkcHly5c5evQoR44c4ciRI6Snp1e4LVpl5OXlMW3aNCwsLHB1deWXX36psJ9KpcLIyAiFQqH9cnBwqMnHEULUgWqNTN9eIRPgzJkzFBYWaqtkf/755wwZMoROnTpp+7i5uVXpGosXL+bZZ5+lV69e2NnZsWLFCnx9fYmJicHd3Z2DBw/Sv39/Fi9ezPz58xk7diz5+fmMHTuWb775pjofq9FydHRk8ODB7N69GyhdE+7g4PDQjM5XlaurK08++SSbN2/mxo0bFBYWsmnTJkaOHEnbtm11HZ4QQohqePvttxk6dChGRkbMmTOnTE2Mmzdvsnz5chYsWFCt9dLi4bR9+3b27duHn59fmfaarJl+/fXXiYiI4PDhw4SGhjJ79mx8fHzo1q1bmX5RUVFoNBoSExO1My5kJxIhGp4azwVeu3YtvXv35vTp09q2ixcv0rt3bzZv3lzt85qZmbFx40by8vKIiYnhqaeeAv5X6KN///5A6Sj28uXLSU1NJTc3l7Vr12JlZVWjz9QYdezYscweln///Xe1n5o+DOzt7fH398fGxgYonR74zz//cOPGDR1HJoQQojr69u3L6tWr+eSTT7CysqJjx4707duX9u3bY21tzRdffMHKlSsZOXKkrkMVjYS3t3et7pZy8+ZNVq9ezdKlS+nQoQPTpk1j2rRpLF++vFzf8PBwPDw8cHR0xMHBAQcHB5o1a1ZrsQghakeNq3l/+OGH/Pzzz2We0q1Zs4Zhw4bx73//m0mTJtX0EqISFAoFQ4YMITU1VTsN/o8//uDpp5/GyMhI1+E1SBYWFjz11FNs2bKF+Ph4xowZg5mZma7DEkIIUU1Tpkxh2LBhbN++nbCwMLKzs7GxsaFLly4MGzaswl1BhLibl19+malTpzJjxgzc3d3R1y+9ba7u0rDQ0FBUKhVdu3bVtvn5+fHpp5+W6xseHo6Xl1f1AhdC1JsaJ9PJycl07NixXHuXLl2Ii4ur6elFFejr6zNhwgRWr15NXl4eWVlZbN++nYkTJ0pBsrswNjZmypQpxMXF4e7urutwhBBCVNNHH33EvHnzsLCwwN/fX9fhiAfAxx9/jFKpZPXq1WXa09LSqnW+pKQk7O3ty0zXtre3r7BieHh4OBkZGfj5+RETE0OfPn34+uuvK9zZ5na3llzeKTs7W+oFCFEHapxh9erVi//+978UFhZq2woLC/n888/vus+zqDtNmzYtV5AsOTlZx1E1bPr6+hUm0tHR0WRmZuogIiGEEFX1n//8R5bqiFoVHR1d4dfIkSOrtVwgLy9PWwD1FkNDwzL30LdcunSJrKwsPv30U37//XdSUlIYPXp0mW1jhRC6V+OR6WXLljFkyBAcHR21Ba8uX76MjY0N27Ztq3GAoupatGjBoEGDCAwMZMKECTRv3lzXITU6KSkpbNmyBT09PcaNG1flAnpCCCHqV22ubRXilvj4eJYtW0ZERAQlJSV4e3uTk5ND06ZNq3wuIyMjioqKyrQVFRVVuPwgICAAtVqNiYkJAL///jv29vYEBwffc7AqKCiowvaaFE0TQtxdjZNpT09PwsPD2b17N5cvX8bAwIBWrVoxdOhQqTqoQ507d6Z169ZlqpmKytFoNOzcuZPi4mKKi4vZtGkTgwcPrtRWcEIIIXQnODi4UkVI76zOLERF9u/fz+jRo+nWrRvdu3dHrVZz/PhxgoKC6Nu3b5XP5+TkRFpaGmq1WjuDMCUlpcKp23fWu7G1tcXGxqbCKeFCCN2pcTIN0KRJE8aMGVMbpxK1qKJEWqPRaLdYEBVTKBSMGzeO33//nYyMDDQaDXv27CE1NZXBgwfLQyIhhGigRo8efd8+CoUClUpVD9GIuhQbG8sXX3xB3759GTlyJAsWLMDKyor58+ejUChYtmwZUVFRdO7cmaNHj/Lqq6/SqlWrKl1j/vz5vPnmm3zwwQdl2tu2bUtoaGiVY+7QoQMAp0+fxtfXF4CjR4/So0ePMv3UajXu7u4sWrSIiRMnApCQkEB6erpseypEA1OtZNrAwKDSCdmd01mE7sTExHDkyBEmTpwoI9b3YWVlhb+/P9u3bycqKgqAs2fPkpGRwbhx46QirBBCNEDJycnY2dnpOgxRD+bOncv27dv57rvvePPNN/nss88AaN26Nc2aNeOll14CSh+eaDQaIiIi2L9/f5WucenSJX777bdy7a6urkRERFQ5ZlNTU/z9/Zk9ezY//fQTYWFhrF27lkOHDqFSqUhLS8Pa2hpDQ0OGDh3Ku+++i5OTE0ZGRsydO5fhw4fTpk2bKl9XCFF3qpVM79u3r7bjEHUsLCyMXbt2oVar2bp1K1OmTJER1vswMjJi4sSJBAQEcPLkSQDi4uJYvXo1EyZMuG9FTSGEEPVHZl09XFq2bAmAnZ2ddrTWwMCAFi1aYGFhgYmJCXl5eVhZWZGZmantXxWtWrVi//79eHp6lmlPSUmp9qDE4sWLefbZZ+nVqxd2dnasWLECX19fYmJicHd35+DBg/Tv35/Fixczf/58xo4dS35+PmPHjuWbb76p1jWFEHWnWsl0v379ajsOUccsLCy031+7do1du3YxcuRIufm4D6VSycCBA7Gzs2PXrl2oVCpyc3NZu3Ytw4cPp127droOUQghBFKA7GGzaNEixo0bh4+PD/b29rRr1w5zc3Pt3sznz58nJSUFT09Pzp8/T+/evat8jY8++ogpU6Zw/PhxbdGvEydOcPbsWe007aoyMzNj48aN5drd3NzK/AybmpqyfPlyli9fXq3rCCHqR423xlIqlejp6VX4ZWxsTMuWLXnzzTdlureOubi4MHz4cO3r8+fPc/ToUR1G1Li0a9eOp556CnNzcwBUKhXHjx+npKREx5EJIYQAOHjwINbW1roOQ9QTpVJJv379sLe3B6BLly7aRBrA3d2dHj16YGtrS//+/dHXr/r40dixY9mxYwfZ2dksXbqUFStWkJ+fT//+/XF1da21zyKEaLxqnEwvW7YMBwcHfvjhB86ePcvZs2f56aefcHZ2Zv78+XzxxRfs2bOHt956qzbiFTXQvn37Mk9mAwMDOXv2rO4CamQcHR155plnaNGiBYaGhowfP75a/zgLIYSoff369ZPfyaLWDRo0iC1btnDx4kXOnDnDxo0bsbW11XVYQogGosb/6nzxxResWbOGQYMGadvat2+Pq6srzz77LB999BHOzs6MHTuWr776qqaXEzXUq1cvsrOzCQsLA2D37t2YmZmVWw8kKmZqasrjjz9Oeno6NjY2ug5HCCGEEHXol19+YcmSJVy9epUzZ86wdOlSwsPDad26ta5DE0I0ADUemc7IyKgwqbCwsCAlJQUoLQ5x8+bNml5K1AKFQsGwYcNwd3cHSteY/fnnn8THx+s4ssZDT09PO63sdgcPHuT48eOo1WodRCWEEEKI2rR06VLee+89XnzxRe1yRT8/P65cuaIdlBBCPNxqnEyPHj2aGTNmEBQURGFhIQUFBRw/fpxZs2YxevRoCgoKWLRoEV26dKmNeEUt0NPTY/z48Tg4OABQUlLC77//Tnp6uo4ja7wuXbrEiRMnCAgIYMuWLeTn5+s6JCGEEOKhl5OTw0cffcTmzZu1bUFBQcyfP5+LFy/e873ffvstP/74IzNnztTugDJ58mS6d+9OTExMXYYthGgkapxMr1ixgg4dOtCvXz9MTEwwNTVlwIABdOnShRUrVrB7924CAwP59ttvayNeUUsMDQ2ZNGkSVlZW2tdKZY1/HB5akZGRZb7/+eefSUhI0GFEQgjx8IqKiuLJJ5/Ex8cHDw8PWrZsWearOqKjoxk1ahSWlpa4ubnx6aefVlhBXKVSYWRkhEKh0H7dengt6t+///1vPvjgAyZPnsyFCxcAGDVqFF9++SWPP/74Pd+bkJCAh4dHuXYjIyMprCuEAGphzbSJiQmrV6/m22+/JSIiAn19fTw8PDAzMwNKKyGOHTu2xoGK2mdqasqUKVPYvXs3I0aM0FaqFlU3cuRI7O3tOXjwIGq1mpycHNatW0e/fv3w9fWVBxVCCFGPnnrqKW7cuMG//vUvmjZtWuPzlZSUMHbsWNq0acPx48eJiYlh2rRpWFtbM3v27DJ9o6Ki0Gg0JCYmarefvDWqKerfrWJhRkZG2vscR0dHMjMzcXR0vOd7+/Xrx5IlS1i6dClQulQuLy+PCxcu0KxZs7oNXAjRKNRa2UszMzOZyt0IWVpaMmXKFF2HUeeKiorIyclBpVJx4cIFWrVqhaGhYa2dX6FQ4Ovri6OjI9u2bSM3Nxe1Ws3BgweJjY1l1KhRmJiY1Nr1hBBC3N2ZM2c4e/YsPj4+tXK+kJAQLl26xLFjxzAzM8PHx4dXX32V9evXl0umw8PD8fDwuG+iJurHv//9bzp37kzLli1xcXEBICAggKCgIAYMGHDP9y5btozx48fj7OxMfn4+EyZMIDY2FpVKVa19q4UQDx7ZQ0JUKCIiAnd391pNOHUhIyODbdu2sWfPHkJDQ9FoNMyZMwcHBweGDBnCuHHjanVfUmdnZ2bMmMHff/+tnfodFRXFqlWrGDVqFG5ubrV2LSGEEBVr164dMTExtZZMt2zZkp07d2pn3UHpQ9Ts7OxyfcPDw8vsdyx0S6lUMmrUqDJttra2jB49+r7vdXd3JyQkhH379hEREYFKpcLLy4uff/5ZO+tACPFwk2RalHPy5EkOHDhAixYtmDx5MgYGBroOqVqioqJ4//33CQ8PJzU1lfz8fJRKJXFxcVy9epUrV65w4MABPvroo2qvoauIsbExjz32GKdOndJO+75x4wYbN27k6aefltEKIYSoYzNnzmTGjBk8++yztGzZstyD4alTp1bpfDY2NmW2AC0sLGTlypUVLmMLDw8nIyMDPz8/YmJi6NOnD19//fV9f/f37Nmzwvbs7Gzat29fpXhF7Rg4cCBbt25l8ODBDB48WNv+/fffc+TIER1GJoRoKCSZFmVkZGRw8OBBAOLi4tiyZQuPPfYY+vqN60clIyOD999/n+DgYBQKBd7e3pw/fx6ANm3akJ+fT3x8PMHBwbz//vssXbq0Vkeob037dnZ2Ztu2bVy/fh1vb28pQiOEEPVg4cKFNGnShF9//bXcMYVCUeVk+nYajYZnn32W3Nxc3nzzzXLHL126xI0bN1i2bBkGBga88847jB49mpMnT0r9jEZgy5Yt/PPPPwAcOnSIl156CWNj4zJ9Tpw4Ibt2CCGAWkimp0+fXuFUF4VCgaGhIY6OjowfP16eqjYSNjY2jBo1ir/++gsorV66detWJkyY0KgKqGzbto3w8HAUCgWenp7lfkaNjY3x9PQkMjKS8PBw/vzzT2bMmFHrcTg6OjJjxgyOHDmCn5+fTAsTQoh6EB0dXSfnVavVzJo1iy1btrBv3z7s7OzK9QkICECtVmvrZPz+++/Y29sTHBxM9+7d73ruoKCgCtsfhromDUnv3r3ZsWMHGo0GjUZDcXFxmQEFhUKBubk57dq102GUQoiGosbJtJmZGcuXL6dnz57afyTOnDnDoUOHGDduHPHx8Xz++eesW7eOcePG1fRyoh60bduWkpISdu3aBcDVq1f5888/GTduXKNIqIuKitizZw+pqal4e3vfNYFVKBQ4OzsTERHB3r17eeqpp+pkjbihoSGPPvpoufbc3FwOHTrEwIEDpTiZEELU0Pr165k4cSKGhoasX7/+rv0UCgVPPPFElc+vVqt5+umn2bJlCzt27LjrtGwjI6Myr21tbbGxsSExMbHK1xT1z87OjlWrVgHg5ubGvHnzMDU1LdNHHnAIIW6pcTJ9+fJlPvzwQ957770y7QsXLuTQoUP8/fffrFmzhvfff1+S6UakQ4cOqNVqdu/eDcCVK1eqlFCPHz++rkO8q5ycHEJDQ8nPz9dO7Qa4fv06AMHBwWX65+bmsnfvXoYPH14rW6hUhkajISEhgby8PPT19fnjjz/w9PSsl2sLIcSD6N1332Xo0KHY2Njw7rvv3rVfdZPpN998kz/++IMdO3bctQq0Wq3G3d2dRYsWMXHiRKB0r+L09HS8vb2rfE2hWx988AHx8fEsWLCAiIgISkpK8PLyIicnp97uF4QQDVuNF+8EBgZW+IRu/PjxHDhwAIABAwZoKxs3dBqNhosXL+o6jAahU6dOZQpuXLlyha1bt1JSUqLDqO5PpVKh0WgqvTZNqVSi0WhQqVR1HNn/FBYWatdblZSU8Pvvv/PPP/9QVFRUbzEIIcSDJDo6GhsbG+33d/uKioqq8rnPnj3LokWLWLBgAW3atCE5OZnk5GTS09NRqVQkJydTVFSEUqlk6NChvPvuuwQFBRESEsLjjz/O8OHDadOmTW1/ZFHH9u/fj5eXF0FBQbRq1Qpvb29OnDjB3r17SUtL03V4QogGoMYj061bt2blypUsXLiwTPuqVau0I21nz56lefPmNb1UvUhNTWX79u3o6enJU2TQ7h2+d+9eACIjI9m6dSvjx4+/Z1GyrVu31kt8Fblw4QJz5swhLi6uzM3LrRFpX1/fMv0vXrxIixYtWL58eb3e7CQlJfHXX3+RmZkJlP5/Eh0dzciRI7V7YQohhNC9P//8E41Gw+uvv87rr7+ubffw8GDfvn24u7tz8OBB+vfvz+LFi5k/fz5jx44lPz+fsWPH8s033+gwelFd8+fP58033+SDDz4o0962bVtCQ0N1FJUQoiGpcTL9zTffMGrUKLZv307nzp1Rq9WEhoaSlpbGtm3bOH78OE888QRfffVVbcRb526Nuu7ZswcXF5dyFRwfRncm1AUFBajVal2GdE+tWrXCwcGBq1evkp+ff8+/w/z8fIqKinB0dKz3adaOjo5Mnz6dgIAATp8+DZRugbJ+/Xq6du1Kv379Gu22ZEII8SD58MMP+fDDD+96XKPRaL83NTVl+fLlLF++vB4iE3Xp0qVL/Pbbb+XaXV1diYiI0EFEQoiGpsbTvHv27MnVq1d55ZVXsLGxwdHRkddee42YmBh69eqFg4MDgYGBzJ49uzbirXO3Rltv3rzJ/v37dRxNw9GlSxeGDh1K8+bNmTx5cp0U6qothoaGDBkyBDs7O+Lj48vc5NxOo9EQHx+PnZ0dgwcP1slnMjAwYPDgwTz++ONl1l+dOnWKVatWERcXV+8xCSGEEKL04XxF94IpKSky2CKEAKo5Mu3n58ejjz7KgAED6NWrF5aWljz//PMV9nVzc8PNza0mMdYrKysr7ffnz5/H29ubVq1a6TCihqNTp0506NChUeyTOW7cOA4cOEBwcDCRkZE4OzuXOX5rn2mNRkObNm10XhzPzc2NGTNmcODAAc6dOwdAVlYWO3fu5LnnnmsUf+ZCCNFQqNXqu/7eTE1NrXBLKyHu9NFHHzFlyhSOHz+u3bHmxIkTnD17ttySMSHEw6lad+grVqygWbNmLF26FFdXVwYMGMDHH39MYGBggy9OdT+GhoZlfkHu2rVLWyhKUO7mRK1WExgYyM2bN3UUUcWsra356KOP8PX1xdTUlIiICHJzc7l58yYXL14kIiICU1NTunXrxkcffYS1tbWuQ8bIyIgRI0YwadIkzM3NARg+fLgk0kIIUUW+vr7aB5O3++GHH2jdurUOIhKN0dixY9mxYwfZ2dksXbqUFStWkJ+fT//+/XF1ddV1eEKIBqBad+nt2rVj7ty5/PnnnyQmJvLFF19gZGTEf//7X5ydnRk6dCiff/45J0+erO1460Xfvn21FUHz8vLYs2ePjiNqmDQaDfv37ycwMJC1a9dqt55qKFq2bMnSpUuZM2cOvXv3xtjYGENDQ1q0aEHv3r2ZM2cOS5Yswd3dXdehluHh4cHMmTMZPXp0uUJkJSUl8nBHCCHuw9fXl27duvHee+9RXFzMhQsX6NWrF++++y4ff/yxrsMTjcQzzzyDm5sbW7Zs4eLFi5w5c4aNGzdia2ur69CEEA1EjYe8lEolXbt2Zf78+ezatYv4+Hjef/99ioqKePvtt2sjxnpnYGDAqFGjUCgUAISHh8t2WRXIysoqMyV53bp1pKen6ziqsqytrZkxYwZr1qyhQ4cOtG/fnuXLl7NmzRpmzJjRIEakK2JkZETbtm3LtR8/fpwff/yRS5cu3XUtuBBCPOy+//57Dhw4wPbt2/Hy8sLX15dOnTpx+fJlXnzxRV2HJxqJv/76S2aHCSHuqVq/Idq3b89LL73Eb7/9RnJycplj+vr69OrVi/fee69RF/BydHSkZ8+e2te7d+8mOztbhxE1PNbW1kyZMoUmTZoAkJuby9q1a4mPj9dxZOUZGhrStGlTrKysaNOmTYMuoHY36enpHDt2jLy8PP7880/++OMP+ZkUQoi7uHHjBiqVCo1Gg1KpJCcnh+LiYl2HJRqRF154gSeeeIKff/6ZAwcOcOzYMY4dO0Z6enqDGzwQQuhGtZLpp59+mtTUVObNm4eTkxNeXl4899xz/Prrr8TGxtZ2jDpzqxo5gEqlIjU1VccRNTzOzs48+eSTmJqaAqXbZm3cuJHIyEgdR/bgUalUZQrkRUZGsnLlSoKDgxv0VmVCCFHfRo0axZgxYxg3bhyXLl3i3LlzpKWl4eXlxZdffqnr8EQj8cknnxAcHMzMmTMZNGgQvXv3pnfv3hw4cICDBw/qOjwhRANQrWR63rx5/Pbbb1y7do34+HgWLlyIjY0Nq1aton379ri6uuLv78/KlStrO956paenx+jRo2nRogXTp0+Xqt53YWdnh7+/P5aWlkDput4//viDs2fP6jSuB429vT3Tp0+nd+/e6OnpAVBcXMz+/ftZvXo1iYmJOo5QCCEahpycHM6cOcOCBQswMjKiZcuW7Nq1i++//55FixbpOjzRSKjV6gq/Jk+ezKRJk3QdnhCiAajxQhBHR0fGjRvHZ599xm+//caaNWvo1asXW7duvet2WZWRl5fHtGnTsLCwwNXVlV9++eW+79m8ebN2n+jaYmNjw5NPPqktSCYqZmlpib+/v3YkX6PR8M8//3DkyBFZ21uL9PX16d27NzNmzKBFixba9tTUVH755Rf++ecfKVAmhHjoHT58mDZt2pRrf+KJJ7h06ZIOIhKNSWRkJEuWLOGHH34gOjpa1+EIIRqwGmWeCQkJBAQEcPDgQQ4dOkRMTAzt27enb9++/Prrr/Tt27fa53799deJiIjg8OHDhIaGMnv2bHx8fOjWrVuF/a9fv87cuXOrfT1Rc6ampkydOpWtW7dq//E5evQoNjY2Fd7UiOqzsbFh6tSphIWFcfDgQW0CffbsWaKjo5k1a5Z29FoIIR42M2bMuOfxVatW1VMkorH5+++/GT9+PMbGxgC88sor/PTTT0ydOlXHkQkhGqJqjUzPmjULLy8v3N3d+frrr7G0tOSrr74iLS2NM2fO8PXXXzN+/Phqj+bevHmT1atXs3TpUjp06MC0adOYNm0ay5cvv+t75s+fj4eHR7WuV1VXrlxh9+7dMuJaAUNDQyZOnEj79u0BaN26NT4+PjqO6sGkUCh45JFHeO655+jQoYO2vVOnTpJICyEeanp6emW+NBoNMTExbNq0iebNm+s6PFFN586dIzc3t8JjxcXFhIaGUlhYWOFxjUZDcHAwx48fR6VS3fUa//3vf3n55ZfJzMwkOzubefPmMX/+/FqJXwjx4KnWyPTKlStp3rw5H3/8MWPGjKF169a1GlRoaCgqlYquXbtq2/z8/Pj0008r7H/o0CECAgJYunQpo0ePvu/5b6/Sfbvs7GxtEliRW/sqnzp1CihdK9ypU6f7Xu9ho6enx4gRI2jRogVt2rSRbSXqmImJCcOHD6d9+/acOnUKX1/fMsc1Gg2FhYUYGRnpKEIhhKhfP/74Y4Xty5Yt48iRI/UcjagN8+fP58svv8TLy4uwsLByu3JMmTKFrVu3MmjQIPbu3Vvu/XPnzuXbb78FYOrUqaxbt67C64SEhLBhwwbtQ+m33nqL//73v6SmpmJnZ1fjz5GXl8fzzz/Ptm3bsLS05OOPP2batGkV9l2zZg3vv/8+GRkZTJo0iWXLlmFiYlLjGIQQtadaWU5ISAjz58/n+PHj9O7dG3t7eyZNmsS3335LWFhYjYNKSkrC3t6+zOiavb19hQWWCgsLmTVrFsuWLdNOyakrCoWiTEKyb98+UlJS6vSajdWtUdM717Dn5+c3yK2zHgTOzs6MGzeu3Kh0ZGQk33//PWfOnJGq30KIh9rIkSP566+/dB2GqIYzZ84ApbMDKxqdPn36dJl+d7o1EHJ734oUFhaWuZ80NTXF1NSUmzdvVivuO92+jPHjjz9m9uzZnDx5sly/oKAg5syZwxdffMHhw4c5c+YMb7zxRq3EIISoPdUame7QoQMdOnTgX//6FxqNhtDQUA4dOsT+/fv58MMPAejTpw/9+vXjlVdeqfL58/Lyyj1xNDQ0rHDqzscff0znzp0ZMmQIAQEBlTp/UFBQhe1Tpky573v9/PyIi4sjNjYWlUrF1q1beeaZZ2TUrxJUKhVbtmwhMTGRYcOG3XMWgKgdJSUl7N+/n4KCAvbs2UNISAiPPvoobm5uug5NCCHqTEUP32/cuMHHH3+Ms7OzDiISNfX111/zySefMGTIkAqXEf7888/88MMPdx3lXbZsGXPnzqWkpKRa26PVxtK+W8sYAwICtPfSR48eZfny5eVqAn333Xf4+/szefJkAL7//nseffRRFi5cKKPTQjQgNS59rVAo6NixIx07duTpp58mODiYzZs3s2HDBrZv316tZNrIyIiioqIybUVFRdq9jG85f/48K1asqJXR8MpSKpWMGTOGVatWcfPmTa5fv86OHTuYMGGCTGe+j8DAQOLi4oDSAh9paWn0799f/tzq0M2bN8s86ElLS2Pjxo20atWKgQMHltm3WgghHhTOzs4oFIoybRqNBicnJ3766ScdRSVqon379mzYsOGuxwcOHMjAgQPverxz584EBgZW6lrBwcFl/n3UaDScOXOG5ORkANLT0ysZdVlVWcZ4/Phx3nvvPe3rbt26UVxczLlz5+jRo8ddr3G3pYyZmZkAsjxRiLvIy8urVk5S7WQ6Ly+P06dPc+rUKYKDgwkODiYmJoa2bdvSq1cvVqxYQe/evat1bicnJ9LS0lCr1doPlZKSgqOjY5l+f/zxB5mZmdrCYyqVCpVKhZmZGbt27aJPnz7V/Xj3ZGpqytixY9mwYQMajYbIyEhOnDhx119golS3bt1ISEjg2rVrAJw8eZL09HTGjBkjI/t1xMLCgmnTpnH+/HkCAgLIy8sDSqfJXb16la5du+Ln5yd//kKIB8qd2xkpFAoMDQ2xt7cvl2SLB8fq1av58MMPmTp1Kp988km1z1NR/Z1bI8RQmlxX5+eoKssYk5KSyhTL09PTw8bGpsK+lWViYoKXl1e13y/Eg+zy5csUFBRU+X3VSqbbtm3L5cuXMTAwwNfXl969e+Pv74+fnx8WFhbVOWUZtyoTnz59WltM6ejRo+WexL388ss8+eST2tcnTpzg6aef5uzZszg5OdU4jntxcXGhf//+HDx4ECjd09LBwQF3d/c6vW5jZmxszJQpU9i7dy9nz54FICoqijVr1vDYY49ha2ur2wAfUEqlkkceeQRvb2+OHTvGqVOnUKlUqNVqTp48SVhYGL1796Zjx45SBVwI8UBwdXXVdQhCBxYuXEhsbCyfffYZH330Ubm6LZVRmdoilVkWWJGqLGOsSt/b3W8p46ZNm+75/uo+KBCisavu/9fVSqafeeYZevfuTdeuXTEwMKjWhe/F1NQUf39/Zs+ezU8//URYWBhr167l0KFDqFQq0tLSsLa21n7dcquwlaenZ63HVJFu3bqRmJhIREQEGo2Gbdu28cwzz2BpaVkv12+M9PT0GDZsGHZ2duzbtw+1Wk1WVha//PILo0aNkiemdahJkyYMGDCAjh07cvDgQS5fvgyUFoXbu3cvdnZ2tGjRQsdRCiFE9RgYGFQ6CbhzKZl4MMyYMYN///vfTJ06tVqJdF2r7DLGqvatDUVFRaxZs4bk5GSsrKywsbHB1tYWe3t7XFxcsLCwkCRbiApU6zfNrf329u/fT8+ePcsUQti0aRMWFhYMGzasRoEtXryYZ599ll69emFnZ8eKFSvw9fUlJiYGd3d3Dh48SP/+/Wt0jZpSKBSMHDmSjIwM0tPTKSgo4ODBg4wfP16ncTUGnTt3xtbWlj///JO8vDyKiorYsmULPXv2pE+fPrKOug5ZWVkxYcIErl27xoEDB0hOTsbT01MSaSFEo7Zv3z5dhyB0bN68ecybN0/XYdxVZZcx3up7a402lBYUzcjIqLBvbYiLiyMhIQEoXROenp5ORESE9ri5uTktWrTA3t4eGxsb7dedo+dCPGyqlUzn5+czefJkdu3axeHDh/Hz89MeO3DgAKtWrWLMmDGsX7+eJk2aVCswMzMzNm7cWK7dzc3trhUV+/fvT0lJSbWuV12GhoZMmDCBNWvW0KJFC0aMGFGv12/MXFxcePrpp9myZYt2i7Hjx4/j7e2Ng4ODjqN78Lm4uDBt2jQuXrxY4T/O58+fx9HRscKqqUII0dCoVCr69u1bZyOS0dHRvPzyywQGBmJpacns2bN56623Khytk/2BdSs2NpYJEyZgZGTE1q1by+wPvWHDBl577TVGjRp11/3I60pllzECdO/enWPHjmmXM548eRJDQ0MeeeSROomtRYsWtG/fnvDw8ArvpXNzc7l48SIXL17UtikUCuzt7WnRogUtWrTAyckJS0tLWTImHirV+hfnk08+ITIykgsXLuDt7V3m2A8//MDcuXMZM2YMn332GR988EGtBNqQWVtbM23aNKysrGREtYosLCx46qmn2LNnD2FhYfTv318S6XqkVCpp165dufbs7Gx27dqFWq3mkUceoXfv3pibm+sgQiGEqJzBgweTlJRUJnF6++23mT9/fpklYdVRUlLC2LFjadOmDcePHycmJoZp06ZhbW3N7Nmzy/S9tT/wqlWr8PT0ZPr06bzxxht8++23NYpBVN6mTZu0+03v2LGDGTNmaI998803JCcns3LlSj7//PMa/2xURWWXMRoaGjJ79mwGDx6Mn58frVu35sUXX2TmzJnVHqS6H0NDQx577DHUajW5ubna0en4+Hji4uK4fv16ufdoNBqSk5NJTk4mODgYKL2vsLa2xtbWFgcHB1q0aIGzs3OdxS2ErlUrmd6wYQPfffdduUT6lrZt27Jo0SLeeOONhyKZBmT0rgYMDAwYMWIEPj4+tGzZUtfhCEqflKtUKqB0K4/z58/TpUsXevbsibGxsY6jE0KI8iqatbZs2TKee+65GidMISEhXLp0iWPHjmFmZoaPjw+vvvoq69evL5dMy/7Aujd69GiWLl2KkZERgwYNKnNs1qxZhIeHM2LECJ1sD1nZZYy9e/dmyZIlvPHGG9y4cYPx48fz+eef13l8SqUSCwsLLCws8PDwoHv37gDk5OSQkJCgXdqYkZFBSkpKuXXdarVam4hfunQJKD+CLWuwxYOkWsl0UlISrVq1umefjh07atdePIzUajUHDx7E29sbZ2dnXYfT4CkUCu0WZ7eLj4/n9OnTDB06VLZvqkd+fn6oVCouXLgAlE6fPHnyJGfPnqVbt274+vrKU2YhRIN3t2VhVdWyZUt27tyJmZmZtk2hUJCdnV2ub3X3Bxa1p3Xr1tqitHd65plneOaZZ+o3oNtUZRnjrFmzmDVrVn2Fdk9NmzaladOmZdrUajUpKSnExcURFxdHamoqGRkZ5aaJVzSCbWpqSrNmzcoUOnNycpJ7C9HoVCuZdnd35+zZs7i5ud21z9mzZx/aJLKoqIg///yTqKgoLly4wLRp06TCdzXk5eWxbds2cnNzSUpKYuzYsXVWeEOUZWlpyejRo+nevTuHDx8mMjISKP3ZDgwM5NSpU/To0YPOnTtL8REhxAPPxsamzAhnYWEhK1euZOzYseX6Vnd/4J49e1bYnp2dTfv27asZuRB1R6lU4ujoiKOjI926dQNKE+ycnBzS0tK0U8Tj4+PLjWDfvHmTmzdvEhMTo22TEWzRGFUrmb61/qdLly4VVgCOi4vjjTfe4IknnqhxgI1RSUkJmZmZQGlCuHnzZp566imZHltFsbGx3LhxA4Dr16/z66+/0r9/f7p27Spr0+uJnZ0dEydOJD4+nkOHDhEXFwdAQUEBAQEBhIWFMXPmTPn7EELonEKhKHfTXVFbTWk0Gp599llyc3N58803yx2v7v7AQjwIlEollpaWWFpaamex3jmCfe3atQpndVQ0gm1ubo6zszN2dnbaUWxbW1t5kC8ajGol06+++iqHDh3Cx8eHCRMm0KVLF5o2bUpWVhanT59m27Zt9O7dm3feeae2420UTExMmDRpEr/88guFhYVkZGSwZcsWpkyZ0iD3PWyoWrdujampKdu3b+fGjRuo1WoOHDhATEwMI0eOrLO9FkV5zs7OTJ06lZiYGA4dOqTdrqNt27aSSAshGgSNRoOTk1OZtpKSkgrru1R3n2m1Ws2sWbPYsmUL+/btK1Ps7Jbq7g8cFBRUYfuUKVOqFasQDUVFI9gFBQXa9dfp6ekkJCRUOIKdm5tLeHg44eHh2jYZwRYNSbUyO6VSyfbt21mzZg2//vorn3zyCdnZ2djY2NClSxd++uknHn/88dqOtVGxsbHhscceY9OmTahUKuLi4tixYwdjxoyR5KMKXFxcmDFjBjt27CAqKgqAqKgofvrpJ0aOHFnhOmtRNxQKBe7u7ri5uXHlyhVOnz5N165dy/TRaDScP38eHx8fDAwMdBSpEOJhdPDgwTo9v1qt1m7nuGPHjrtOy67v/YEfNtu2bcPc3JyBAwdq2woKCti8eTOdOnUqs0PF8ePHiY2NZeLEidrtmsLDwwkODmbixIlSEE6HjIyMcHJyKvMA7M4R7KpUEb+1D/at5NrBwUG26BL1okbDpE8//TRPP/10bcXywHFxcWHkyJFs374dgEuXLmFqasqgQYPk6VkV3BrpP3XqFAcPHkStVmunz3ft2pV+/fpJ4laPFAoFXl5eeHl5lTt25coV/v77bwICAujevTsdO3aUqVhCiHrRr1+/Oj3/m2++yR9//MGOHTsYMGDAXfvV9/7AD5Off/5Zu81VQECA9u987ty5/Pjjj5iZmREbG4u1tTWXLl2id+/eqFQqFixYwLvvvktOTg49e/YkOzub/fv3s2bNGl1+HHGHikawc3JySEpKKjOKnZycXOEI9u37YOvr6+Pk5KRNrp2dneXhiagTNUqmk5OT2blzJxcvXiQ3N5emTZvSvn17RowYga2tbW3F2Ki1adOGnJwcAgICADh9+jRmZmZ3faItKqZQKPD19cXFxYVt27Zp16SfOnUKlUrF0KFDdRyh0Gg0HD16FCgtLHLgwAGCgoLo1q0bnTt3lgqdQohG6+zZsyxatIgvv/ySNm3aaEee9fX1sbKy0un+wA+TvLw87ff5+fnlvi8qKtJWki4sLEStVpd5n0ql0q5dv/1couGqTBXxitZgl5SUEBsbS2xsrLbN1tZWm1y3aNECGxsbGdwSNVatZFqj0fD++++zcOFCDA0NcXd3x8LCgpycHL799lsUCgXvvPMO//73v2s73kape/fu3Lx5UzsV5dChQxgZGdGpUycdR9b42Nvb88wzz3DgwAHOnj2LkZERfn5+ug5LUPp7oWfPngQGBpKeng6U3uAcOnSI48eP06VLF7p27SpPhoUQjc6ff/6JRqPh9ddf5/XXX9e2e3h4sG/fvgaxP/DD4Pnnn0dfXx9zc3OGDRumbf/mm2945JFH6N69u3Yde4cOHdi2bRtRUVHavcCtrKzYt28fR48eZebMmTr5DNU1cuRIHB0dWblypa5D0bm7jWDfPj08KSlJ+zDlllsj2yEhIQAYGxuXSa6bN28uMx1FlVUrmf7qq69YunQp33//PVOnTi3ztLWoqIj169fzyiuv0Lx5c+10nIeZQqFgwIAB3LhxQ1tAIS0tTcdRNV6GhoYMGzZMu17a3Ny8zHGNRlPhk8atW7fWS3wPK6VSiY+PD15eXly5coVjx46RkpIClI4QHDt2jODgYDp27Ei3bt3K/b0JIURD9eGHH/Lhhx/e9XhD3h/4QVBUVMTbb7/NoUOH8PX15csvv9QeS01N5d1336Vly5bl9vEePXp0mdcajYZ9+/aRlJTUqEYkN27cyM6dOxvdA4D61LRpU9q2bUvbtm2B0p+ZxMTEMgn27bMZoPSB/+XLl7l8+TLwvyT99gRb7lXE/VQrmf7xxx/5+uuvmT59erljhoaGPPPMMxQUFLB8+XJJpv+fUqlk1KhRFBYWYm9vT9++fXUdUqN3a8uF22k0GrZv346TkxOdO3eWYm86oFQq8fb2xsvLi6ioKI4dO0ZCQgIAxcXFBAcHk56eLhVqhRBCVMrGjRv56quvgNLlcq1ateK1114D4JNPPtGO1r733nt06dKFw4cPVzitfs+ePdqHIjY2Nvz3v/+tnw9QA5mZmcyfPx9fX19dh9KoGBoa4ubmhpubG1B6f5ienl5manhGRkaZ96jVahISEkhISOD48eMAWFpalkmu7ezs5N5SlFGtZDomJob+/fvfs8+QIUN46623qnP6B5aenh6PPfaYVBesQ2FhYdotFCIiIhgxYgRWVla6DuuhpFAo8PDwoGXLlsTFxREUFER0dDRQuvRBCCFqU6tWrSo92nhrJEo0Dj4+Pujr61NSUoJCodCOPgLawm63jp88eZKYmJgKt0Tz8PDA1NSUmzdv0r59+3qLvybmzZuHv78/iYmJug6lUVMoFDRr1oxmzZrRuXNnoLS+S3x8vDbBTkhI0K65v+X69etcv36dsLAwoDRJd3Z21lYOd3Z2xsjIqN4/j2g4qpVMV2a/RFNTU3Jzc6sV1IOsokS6sLCQzMxM2TajFkRGRmq/j4uLY9WqVfTt25cuXbrIk0QdUSgUuLi44OLiQlJSEpcuXcLV1VXXYQkhHjBSp+XB1a1bNyIjI7l27RqOjo54enqSk5NDcXExM2bMoHv37ly9epUPP/yQnj17ltttIjU1FQMDAzw9Pbl8+TI5OTn4+Pjo6NNU3oEDBzh8+DBhYWG88MILlXrP3QrcZmdnN5oHCPXF1NQUb29v7YOXkpISkpOTtSPXcXFx3Lhxo8x7ioqKiIqK0m7XCpTb89rS0rJRLSMQNVOtZFqhUMgPSS3Jz8/nt99+IyMjg8mTJ+Ps7KzrkBq1cePGERISQkBAAMXFxRQXF7N//34uXbrE8OHDpcq8jt0qGCKEELWtslt13jnyJBoHV1dXXF1dCQgI4K233mL37t0UFxeze/du+vXrR9u2bRkzZoy2f05ODsOGDSM7O5vIyEiaNGnCk08+ibm5Of/5z390+Ekqp6CggNmzZ7Ns2TKMjY11Hc5DQV9fH2dnZ5ydnenZsycajYbr16+XWXedkpJSrkZCSkoKKSkpnDp1CgAzM7Nye17r69doAyXRgFW7mvekSZPuuX/sra0HxL3t3r2bpKQkAH777TemTJlSZgN7UTVKpZIuXbrg4eHBzp07uXbtGgAJCQn8/PPP+Pn50aNHD5lqL4QQD7CkpCQ+/fRTwsPDUalUQOm9S2FhIeHh4WRlZek4QlFdU6ZMITU1Vfv66NGjFe4x/uKLLxIUFKR9XVRUxPfffw+As7Mzc+fOrftga+A///kPXbt2rfLWn7d/5ttJnZKqUygUWFlZYWVlpV1OUFBQQEJCQpkE+849r28VHL5VdFhfXx8XFxdtPRlZfvhgqVYy/cEHH1Sq38CBA6tz+ofKo48+SkpKCllZWRQVFbFp0yYZoa4FlpaWPP7444SGhnLw4EGKiopQqVQcOXKE8PBwRowYQfPmzXUdphBCiDowffp04uPjmTRpEp988glvvPEGMTExZQpZicbJx8eH1NRUnJyc6Nmz512rpnfu3Jl169Zpv3dxceGvv/5Co9FUuJ66odm4cSPJycmYmZkB/xukCgoK4sKFC7oM7aFmZGSEh4eHdkcZtVpNamqqdmp4fHx8uYd1JSUl2qnhu3btwt7eHnd3d+3o9Z37aIvGpU6TaXF/5ubmTJ06lXXr1nH9+nWKior47bffmDRpEi1atNB1eI2aUqmkU6dOeHp6snv3bu166vT0dJnmJ4QQD7AjR45w8OBBunXrxq5duxgyZAh9+vShS5cubN++nZdfflnXIYpq2rVrFyEhIXTp0uWehZ9ee+01WrduDcDw4cOB0roqxcXF2vaG7NZytVvefPNN9PT0+OKLL3QYlbiTUqnEwcEBBwcHbcX13NzcMiPXiYmJZfa8vjUt/M6K4be+7OzsZAZlI1LjCfx32/pKoVBgaGiIo6Mj48ePl6IH93Arod6wYYN2hPq3337jscce05b0F9Vnbm7OY489RkREBHv27MHT0xMXFxddhyWEEKKOKJVK7O3tAWjTpg2nT5+mT58+jBs3jo8++kjH0YmaMDExoVevXtrXx48fZ82aNfj7++Pn51em760k+hZPT08ATp48yYYNG/D399dWdm5o7izUaW5ujr6+vhTwbATMzc1p06YNbdq0AUpnFURFRREREcHly5fJy8sr0//OiuFGRka0atUKb29vPD09pVp4A1fjZNrU1JTly5fTs2dP7XY3Z86c4dChQ4wbN474+Hg+//xz1q1bx7hx42p6uQdW06ZNmTp1KuvXrycrK4vi4mI2b97M+PHjtb/8RfUpFAp8fHxwc3MrVzgC4Ny5czRv3lwKlAkhxAOgR48eLFmyhM8//5zOnTuzdetWXnnlFUJDQzEwMNB1eKIWPf7448TGxrJ9+3YSEhIq9Z7x48eTmJjI9u3buXr1ah1HKB52TZo0oXXr1rRu3Rq1Wk1SUlKZ/a7v3P2ooKCAsLAwwsLCUCqVODk5afe5btGixX13VBL1q8bJ9OXLl/nwww957733yrQvXLiQQ4cO8ffff7NmzRref/99Sabvw9zcnCeffJKNGzeSnp6OSqViy5YtjBkzplFs4dAYVPR0Lz09nX/++QcAX19fevXqdc/iekIIIRq2r776irFjx+Lk5MSsWbNYunQpzZo1Izs7m7ffflvX4Yla1LJlS2JjY2nZsmWl3+Ps7ExiYmKjWk63evVqXYcgasGt5NjJyYkePXoAlKkYfu3aNZKTk7X91Wq19tgtjo6O2i29HBwcZIclHatxMh0YGMg333xTrn38+PHatdUDBgyo9P54DzszMzOmTp3Kpk2bSElJQa1Wl6sSKGpXQECAdi3LiRMnuHDhAgMHDqR169byC0oIIRqh9u3bc/XqVfLz8zExMdHOmLO2tr7rPryicfrrr784ceIE3bp1q/R79uzZw7Fjx+jdu3cdRiZE5VhaWmJpaaldEpuTk8Ply5eJiIggKipKuyPBLUlJSSQlJREQEIC5uTkuLi7akWt7e3tZb13PapxMt27dmpUrV7Jw4cIy7atWrdJOTz579qxUTq4CExMTnnjiCTZv3oyPj4+2HL+oG0OHDkVPT4+IiAigdEuD7du3ExISwqBBg7Tr7oQQQjQeqampREZGaqsgGxsbk5+fz4EDB2S3kQeAWq1GqVRiampa5b9PCwuLcuuphWgomjZtSteuXenatSvFxcXaaeHXrl0jNjaWgoICbd/c3FwuXLigrfCuVCqxsrLC1tYWGxubMv81MTGRQaI6UONk+ptvvmHUqFFs376dzp07o1arCQ0NJS0tjW3btnH8+HGeeOIJ2YqiioyMjJg6dao8XaoH5ubmjB8/nujoaPbu3UtmZiYAcXFxrF69mg4dOtC3b19MTEx0HKkQQojK+PLLL3n77bdRKpXllu0oFApycnJ0FJmoDYcPH2bUqFE4Ojpy9OjRcvVOUlNT6d+/P2lpaezatYuuXbvqKFIhasbAwEA78tyrVy9UKhXXrl0jIiKCiIiIcttwqdVqMjIyyMjIKHcuExMTWrZsqS1sZmxsXF8f44FW42S6Z8+eXL16lU2bNnH+/Hn09fV59NFHeeKJJzAzMyMmJobAwEA6depUG/E+VCpKpDMzMzl58iSDBg1CX7/Gf33iNu7u7sycOZPg4GCOHTtGUVERGo2Gs2fPEh4eTv/+/eXnWAghGoHPPvuML7/8krlz58pIzAPozz//JDc3l9zcXE6ePMmIESPKHD9y5Ajh4eEAbN26VZJp8cDQ09PD3d0dd3d3hg0bRnZ2dpltuNLS0spsqXa7vLw8zp8/z/nz51Eqlbi6umrXXltZWdXzJ3lwVCsbS0hIwMnJSfva0tKS2bNnV9jXzc0NNzc34uPjcXZ2rl6UAiidyrFx40ZycnLIyMhgwoQJ8lSplunp6dGjRw/atWtHQEAA58+fB0q3NZC9qYUQonEwMTFh8ODBkkg/oJ599lkOHTqEs7Mz/fv3L3d80KBBDBw4kPT0dPz9/es/QCHqiYWFBRYWFrRr1w4oHZnOzc0lPT2djIyMMv/Nzs7Wvk+tVhMdHU10dDT//PMPdnZ22sS6efPmKJVKXX2kRqdayfTkyZPp1KkTL7zwAm3btr1n39OnT/Pjjz8SGhpKUFBQtYIUpaKjo7VT0+Li4li7di2TJk3C0tJSt4E9gMzMzBg1ahRdunRh37595OXlNdi9KIUQQpS1aNEiXn75ZT788ENatGhR7sbQxcVFR5GJ2nBr7/C7sbCwYP/+/fUYkRANg1Kp1CbYHh4eZY7dq7BZamoqqampHDlyBDMzM7y8vPD29sbd3V12uLmPaiXTgYGB/Pjjj4wZMwaFQsGjjz6Kt7c31tbW2rn658+f5+jRowDMmzeP5cuX12rgD6NHHnkEPT09/v77b+2f8y+//MLEiROlwFsdcXR05KmnnuLmzZvlpt1fuHCBK1eu0LdvX6ytrXUUoRBCiDvl5+dz4sQJ+vXrV2Z0WqPRoFAoylXHFUKIB93thc2Kioq4evUqERERXL58mby8PG2/GzducObMGc6cOYO+vj4eHh54e3vTunVrmRFbgWol0wqFglmzZvHcc8+xZ88edu/ezfbt20lLS0OhUGBvb0+nTp34/vvvGThwoEwVqEVt27bFzMyMLVu2UFhYSF5eHuvWrWPkyJG0adNG1+E9kBQKBWZmZmXaSkpKOHTokPYpX8eOHenVqxempqY6ilIIIcQtb7zxBi+99BLPPvus3PwJIcQdDA0Nad26Na1bt0atVhMfH68tapaenq7tV1JSom3fuXMnrVu3plOnTri5uUl+9/9qVMFKoVAwdOhQhg4dWlvxiEpwdXXF39+fzZs3k52djUqlYvv27WRmZuLn5yc/3PUgISGBGzduAKXrTs6cOUNYWBi+vr5069YNIyMjHUcohBAPL5VKxXPPPVdumqMQQoiylEqltmL44MGDycjI0CbQ165dQ6PRAKWJdVhYGGFhYTRt2lS7xtrNze2hLoosWVcjZWtry7Rp08oUggsMDOTPP/+kqKhIh5E9HFxdXXn22Wfx8fHRthUXF3Ps2DG+//57jh8/ftdqikIIIerWxx9/zFtvvUVsbGytnzszMxM7Ozvi4+MrPK5SqTAyMkKhUGi/HBwcaj0OIYSoCzY2Nvj5+TF9+nTmz5/P+PHjK1x/HRwczNq1a1m4cCF//PEHUVFRqNVqHUWtOw/vY4QHgKmpKU888QS7du3SbtYeGxvLjRs3ZA1vPbC2tmbcuHEkJSUREBCgvWkrKCggICCAU6dO0bNnTzp06PBQP7ETQoj6tmjRIq5du8aWLVtQKBTlZmxV96FzdnY248aNIy0t7a59oqKi0Gg0JCYmatdrV7TVpRBCNHQmJiZ06NCBDh06kJ2dTWhoKGfPniUzM1Pbp6ioSDtibWFhQceOHWnbti3NmjV7KHZUkDv8Rk5fX59Ro0bRrFkzDh8+zJgxYySRrmeOjo488cQTxMTEEBAQQHJyMlBawGHv3r04Oztjb2+v4yiFEOLhsXLlylo/56lTp5g6dep9l/GEh4fj4eGBo6Njrccg7i0mJoYLFy4wdOhQeYgtRC2zsLCgb9++9OnTh7S0NO1U8Ntn6WRnZ3Po0CEOHTqElZUV3t7e+Pj44OLi8sAuQ22Qv2ny8vJ4/vnn2bZtG5aWlnz88cdMmzatXL+ioiJef/11Nm7cSElJCZMmTWLx4sUPXREohUJBjx49aN26NRYWFuWO36peKuqWm5sbTz/9NJcvX+bIkSOkp6fj5eUlibQQQtSzfv361fo5Dxw4wOOPP46/vz9eXl537RceHn7P46JuXL9+nfbt23Pjxg1Gjx7N9u3bdR2SEA8khUKBnZ0ddnZ29OnTRztiHRISQlZWlrZfVlYWx48f5/jx49oR644dO2JlZaXD6GtftZLpjz76qNJ933///Sqf//XXXyciIoLDhw8TGhrK7Nmz8fHxoVu3buXOvX//fv7++28MDAy0c/sf1m24Kkqkw8LCuHz5MiNHjpSiWPVAoVDg7e1Nq1atCA8PrzCRPnz4ME2bNqV9+/Yy9U8IIWqJoaEhCQkJNGvWDAMDg3s+RK7ONO833ngDKB39vJfw8HAyMjLw8/MjJiaGPn368PXXX993pLpnz54VtmdnZ9O+ffsqx/uwyc/P5+bNmwCcOXNGx9EI8fC4fcQ6NjaWCxcuEBERQU5OjrbP7SPWLVu2xM/PDw8PjwdisK9ayfSRI0e039/aIsjFxYVOnTphYGDAuXPnuHr1KqNHj67yuW/evMnq1asJCAjQztE/evQoy5cvL5dMAyxZskTbPnPmTH788cfqfKQHUkpKCv/88w8qlYo1a9Ywbtw4GSWtJ0qlkrZt25Zrv379OkFBQWg0Go4ePUqPHj1kTbUQQtSCvXv3akc89u3bp7M4Ll26xI0bN1i2bBkGBga88847jB49mpMnTz6w0xwbAkdHRzp37kx6ejqenp71dt26HmASorFQKBS4ubnh5ubGiBEjSE5O5tKlS5w7d67MiHVUVBRRUVHY2dnh5+dHu3btGvV9cLUi37t3r/b72bNn88gjj7B48eIy/0i8/fbbXL16tcrnDg0NRaVS0bVrV22bn58fn376abm+n332mfb7a9eusX79evr371/laz6oUlJStFX1srKy+OWXXxg0aBAdO3Z8IJ4ENUYhISHaLQZyc3PZu3cvQUFB+Pr60qlTJwwNDXUcoRBCNE63T+1es2YNS5YswdzcvEyfzMxMnn322TqZBn5LQEAAarUaExMTAH7//Xfs7e0JDg6me/fud31fUFBQhe1TpkypkzgfRPb29lhYWNCkSZN6u2ZdDjAJ0VgpFAocHR1xdHSkX79+XLt2jZCQEC5evKjd7SY1NZU///yT/fv306NHD7p06dIoZ9HW+DHAunXrOHPmTLmnrdOnT6dTp05VPl9SUhL29vZlpr/a29uTmJh41/e88MILfP/997i5ufHuu+/e9xoPy1SqRx55BEtLS7Zt28bNmzdRqVTs3r2ba9euMWzYsHr9x0aU6tevH7a2thw7dkz7lO7GjRscPHiQoKAgunbtSpcuXTA2NtZxpEII0bjs2bOHU6dOAaXJtIODA2ZmZmX6REZGcvDgwTqN486bQVtbW2xsbO55HyPuLzg4mODgYFq1asVzzz2HQqEgLy+PDz/8kBdeeKFS58jPz6dNmzZcu3aNV155hUWLFtU4rrocYBLiQaBUKrUj1sOGDSM4OJgTJ05ol2XcGlw6fPgwXbp0oU+fPo3qPrjG841atmzJhg0byrWvXLmS1q1bV/l8eXl55UbnDA0NKSwsvOt7/vWvf3H8+HFatmzJsGHDHso9zu7GxcWF6dOn4+Liom0LDw/n559/JikpSYeRPZyUSiXt27fnueeeY8yYMdja2mqPFRQUEBgYyPLly9m/fz8FBQU6jFQIIRoXT09P9u3bx969e9FoNBw6dIi9e/dqv/bt20daWhqrVq2qsxjUajWurq78/vvv2raEhATS09Px9vaus+s+6NLT0+nbty9z5sxhzpw5xMbGEhMTQ2pqKl9//XWlz3PixAliYmJQq9V1UvF93bp1zJkzp8IBpr///rvWrydEY2NsbEzfvn155ZVXGD16NDY2NtpjhYWFHDt2jJUrV3L9+nXdBVlFNR6ZXrJkCWPHjuW3337jkUceQaPRaKu57dq1q8rnMzIyKlcYpKio6J4Vun18fADYuHEjDg4OHD16lD59+ty1/8M2lcrMzIzHH3+cY8eOcfToUTQaDdevX+fXX3+lb9++dOvWTdZx1TOlUkmbNm3w8fEhMjKSoKAg7cON4uJiLly4UKfTEIUQ4kHTsmVLDhw4AJQmL0uWLKFp06Z1fl2VSkVaWhrW1tYYGhoydOhQ3n33XZycnDAyMmLu3LkMHz6cNm3a1HksD7Jby9M8PDzIzs5GX1+f3Nxcnn/++Uqfw9fXl+bNm5OUlMSTTz5Z6zHeGmD64IMPyrRXd4BJiAeVgYEBXbp0oVOnTly5coWjR49y7do1ADIyMvjpp5/w9/fHzs5Ox5HeX42T6QEDBnDlyhU2bdpEREQECoWCV199lSlTplSr9LmTkxNpaWmo1WptgpeSklKuCqZGo2H79u306tVLO7rXrFkzbGxsSE9Pr+nHeuAolUp69+6Ni4sL27dv58aNG6jVagICArQ/0KL+KZVKvLy8aNWqFbGxsQQFBREbG4uvr2+5Ygy5ubmYmZnJenchhLiPn3/+mYyMDJYvX054eDhKpZIOHTowceLEWk+w4+LicHd35+DBg/Tv35/Fixczf/58xo4dS35+PmPHjuWbb76p1Ws+bGxtbTl8+DDBwcH4+/uXm75fWaampiQkJNRydP9T2wNMQjzolEol3t7eeHt7c/XqVX777TcKCwvJzc1l1apVPPnkk7Ro0ULXYd5TrQxH2tvbM3fuXJYtW8a3337L888/X+09xDp06ADA6dOntW23qh7fTqFQ8NJLL7F582ZtW1xcHOnp6fL09x5cXFyYMWOGttKlra2t9s9c6M6tCohPPPEETz/9dLl6A2q1mvXr1/Pzzz9z7tw5SkpKdBSpEEI0fEeOHKFly5YsXbqU9PR0kpKS+PTTT2nVqhWhoaE1OrebmxsajQZnZ+cyr28VQDU1NWX58uWkpqaSm5vL2rVrH7h9VXUhJyeHl19+GQsLi7tOmT5z5gwHDx4kOjq63LEZM2ZgYGDAf//73zqLccCAAURGRjJ79mysra2xtbXl1VdfJSIiQgYthLgPDw8Ppk+frn1YVlBQwJo1a9i9ezfZ2dk6ju7uqjUy3apVq0qPjl2+fLlK5zY1NcXf35/Zs2fz008/ERYWxtq1azl06FC5qVTPP/88H330Ed7e3lhbW/PSSy8xbtw4WZd0HyYmJjz22GOEhITg7OzcqMvRP4gq2os0MjJSW7Bs586dBAQE0LlzZzp16nTPJRBCCPEwevnll3nxxRfL7ASi0WiYN28ezz///F2Xe4mG68cff0SlUmm/HzlyZJnjN27cIDk5GaDc6LNGo2HNmjWo1Wp++eWXShWrrS47OzueeeYZrl69SuvWrSkqKqqX5QZCPAgcHByYMWMGv/76K1lZWZSUlBAUFMSJEydo164dvXr1anDb/FYri/r3v/9d23GUsXjxYp599ll69eqFnZ0dK1aswNfXl5iYmDJTqd58803y8/Px9/cnJyeH8ePH8+2339ZpbA8KhUJB586dy7WXlJSwefNmfH1963WfRnFvxcXFmJiYkJeXB5QW6gsMDCQoKIjWrVvj6+vb4H65CCGErtxafnY7hULB7Nmz+e6773QUlaiIa0tXEhPuX+n89uKyf/39FwZNDMr1cXZ2JiMjAycnp/LH/38MKPJqZIXvvZfmTs2JjYq9b7+8vDzmzJnDL7/8gkKh4PLly7z++uvcvHmTDRs2lCm2JISomLW1NTNnzmTr1q3aKvhqtZpz584RFhZGt27dGDBgQIPZRqtayfTTTz9dYXtWVhYWFhYoFIoares0MzNj48aN5dpvTaW6RV9fnwULFrBgwYJqX0uUdfjwYWJjY4mNjaVdu3Y8+uijjao8/YOqbdu2eHt7Ex4eTnBwMKmpqUBp4Zvz589z/vx5nJyc6NKlC97e3mW2lhNCiIfNhAkTWLJkCd98802Z34dr165l3LhxugtMlJOYkEi3L7tV+X05UTnE7YrDqq0VGrWG6xevk5+Zj7m5OTZ2Nli9cPep9XG748iNysV1jCumTvef3XVy3slKxTRv3jxiYmKIiIjQLtf67LPPmDFjBi+//DLr16+v3IcT4iFnZmaGv78/CQkJHDt2jIsXL6LRaNBoNJw4cYILFy4wZMgQ2rdvr/NaQjWe36tWq1mwYAFLliwhOzuby5cv895772FmZsaSJUsazFMDcX/FxcVl9kE8f/48MTExDBkyBC8vLx1GJqD04VH79u1p164d165dIzg4mMjISO3xhIQEEhISeO655+TptxDioaZWq/npp5/YsWOHtqDjuXPnuHz5Mn5+fgwZMkTbd8+ePTqMVFRGTlQO0ZujMXM1o+XkliiUCiJ+iqA4t5jsiPJrKc2amuGAQ4XnKkgrIO7vOAAUegpaz6q9Kttbt25l165dZWb2eXt7891332nX1DdkRxOOEpYehkUTCyybWGLZxBIHUweczZ0xUFZtNF+I2uDk5MSkSZPIzMwkICCAc+fOAaXLOrZs2UJISAgjRoygWbNmOouxxsn0Bx98wLZt2/jtt98YO3YsALNmzWL27Nm89tprLF++vMZBivphYGDAM888w5EjRzh5svQp7K0f1tatWzNo0CBZn9sAKBQKXF1dcXV1JSsrizNnzhAaGkpRURFubm7lEumioiIMDAx0/uROCCHqi5eXF++8806ZNilO2nglHkzkZsJNbibcxLGfI3pN9CjOLQZA2USJkY0ReYl5KJQKFCiwsLS467kMLAwwtjcmPyUfi1YWRK6LvGvfW1QlKmbMmHHfPcoLCwsr/Lc2Nze3wf8bHJ4RzvP7Kt5mTF+hj7O5M65NXbE1tsWyiSVWRlZYNLHAqomVNvm2M7HDxMCkniMXDwNra2smTJhA586d+fvvv0lLSwMgOjqa7777Dj8/P/r27YuhoWG9x1bjZPrXX39l/fr1+Pn5aX9R9OvXj9WrVzNmzBhJphsZAwMDBg4ciJeXFzt37iQzMxOA8PBwoqOjGThwYIOYUiFKWVlZ8eijj9KnTx8uXLiAtbV1uT67d+8mKSmJTp060a5dO5m2L4R44N25z69o3Gw62JAVloWpsylGtkYolAosvC3IiczBbYIbdr52FOcWY3LQBFSgb6RPIYUVnkvPUI8Ob3SgJK8EQwtDIjeUT6bTTqZh/Yg1ekb/WyJQmeVTU6ZM4V//+hcrVqxAoVBQWFjI0aNHmT17NpMmTar+H0A9MDEwQYECDZpyx0o0JcTkxBCTE3Pf89iZ2OHe1B03CzfsTOy0I9yWTSyxNPrf94Z69Z/0iMbPzc2N559/nuPHjxMQEEBxcTFqtZrAwEDCwsIYPXp0vdd8qnEynZGRUeGU0iZNmlBYWPEvMtHwOTs7M2PGDAIDAzlx4gQajYaCggJ27tzJ+fPnGTFiBJaWlroOU/w/Q0PDcttpQWkxlEuXLqFSqdi/fz8BAQH4+PjQoUMHWrRoIQ9FhBAPnJ07d7Jy5UpOnjypvUfx9fXlueeeY8SIEboOT1RDs67NsOlkg1Lvfzu6tp3TFo1Go/13rIlVE/QN9FEoK04Ib6c0UGJoUZrMeT5R/sY7/Uw6rqNdMbItXaqYeTqTH3/88b5x3tpj/JFHHqGoqIh27dqhVCqZOXMmX331VaU/ry64NnVl69itBCUGcb3wOtcLr5NZkEl8bjwxOTHkl+RX6jypeamk5qVyIvnEPfs1NWyKa1NX3Jq64WbhhqOpo3bE+1bCbWpgKvcpohw9PT169epF27Zt2b17N+Hh4QBkZ2ezdu1afH19GTx4cL2NUtc4mR45ciTvv/8+q1evBkqnoMbHx/Ovf/2L4cOH1/T0Qof09fXp378/rVu3ZteuXdotJ5KSkuSXWyORmZmJiYkJubm5QGnBsgsXLmhHsTt06EC7du1k+r4Q4oEwc+ZM1qxZw8iRI5k7dy5WVlYkJiZy+vRpRo8ezYwZMyqVFImGR6mnpCC9gKubrtLEugkekz1Q6FV8L1JcXEz4D+FoNBpaPdUKA7P6We9rZGTEN998w+eff05UVBQqlYqWLVtibm5eL9evKQ9LDzwsPcq1azQaUvJSiMuN43rhdbIKssguzCar8P//W5DF9cLrJNxIILMgs1LXyinKISw9jLD0sLv20VfqY9nEEmsja1zMXXCzcMOtqRvO5s7ahNuiiQX6Stni9WFkaWnJlClTuHLlCjt37tRuIRscHMzVq1cZN24cLi4udR5HjX/6li9fztNPP03Tpk1RqVR0796dzMxMBg0aJNtUPSDs7e2ZNm0ap0+f5vDhw/Tp0wcLi7uvRxINh7OzMy+88AJXr17l7NmzZQrMZWZmcvDgQQ4dOoSnpyfdunXD2dlZh9EKIUT1rVixgh07dnDq1Ck6duxY7viZM2cYNWoUK1eu5Nlnn63/AEWNJR1K0hYcs+1ki6WPZdkO+oAKUlNTybpYemOdfjodx36O9RJfWloas2bNokOHDnz44YdAaQGlHj168OOPP1a4FKsxUCgUOJg64GBacVG32+UU5RCTHUNsTiyZBZnaUe7rBdf/9/3/j3qrNep7nqtEXUJ6fjrp+elczrp8137mhualo9pNrLA0ssTZzFmbeLs0dcGqiRXG+sYyEPSAatWqFS+88AJ79+4lODgYKL3H/fnnn3nqqafw8Cj/gKg21TiZtra25q+//iIyMlI7ndTLy4vWrWuvOqLQPaVSia+vLz4+PuVGMTUaDUFBQbRp00amfjdASqWSVq1a0apVK7Kzszl37hznzp3Tjlar1WouX75Mq1atJJkWQjRa33//PV9++WWFiTRA586d+eKLL/jqq68kmW6kLLwsSA5MxsDcAJPm5QtdZZhnEHkkkmJ1MUpDJQo9BU09m9ZbfLNnzyYvLw9/f39t2/79+/nXv/7FnDlz2LBhQ73FoitNDZvySLNHeKTZI/fsV6Qq4lrONe1a7LS8NK4XXi834p1Xknffa+YW5ZJblEtcbtxd+zTRa1Lh2u3mZs21U81bmLXAQE+qljdGhoaGjBw5Em9vb7Zt20Zubi4ajYY///yTF198sU7rBVUrmT527Bjdu3dHT0+PY8eOadtvPXHLysrStvv5+dVCmKKhqGiqUmRkJIcPH+bo0aN069aNnj176qSanrg/CwsL+vTpQ69evYiKiiI0NJSrV6+ir6+Pt7d3mb75+flcunSJ1q1byxZ3QogGLyIigj59+tyzT58+fZg9e3Y9RSRqU1F2Eebu5vh+4otSX4nSQFnmuEaj4fy286gLS0c7lUZKfBf4lut3p5yonPKNGrgRd4OinKLSl2oNx44du+897b59+zh16lSZkTAfHx++/vprevbsWZmP+dAw1DPE08oTT6t7F4sqUhWRmpdKbE4sMTkxRGdHk5qXWmbEO7so+76j3IWqQlLyUkjJS7lrH6VCSVPDpuWS7lsVy+1N7bXF1UwNZHlcQ+Tp6cmLL77Ijz/+SGZmJrm5ufz9999MnDixzq5ZrWS6d+/eJCcnY2dnR+/eve/aT6FQoFKpqh2caPjUajUBAQFA6XrcoKAgzp8/T79+/WjTpg1K5b3/ERO6oVQq8fT0xNPTkxs3bpCamlruAcilS5fYvXs3+/btw8vLi3bt2uHu7i5/p0KIBqlp06akpKTg5uZ21z6JiYmNdqrtw+z6peuEfx+OwkDBI68/golD+VFphUJRZhqvsa3xfRNpgPNLzlfYfnn1bdOKNaUPYu53T2tlZcWlS5fw8vIq0x4dHY2JiWwZVR2GeoY4mzvjbO5ML6deFfZRa9TkFuVyvfA66fnpXMu5RnRONDHZMSTeSNSOeBeoCu55LbVGrU3S76eZcTNsjW3LbBF2Z/J9+xZixvqyk0p9MTY2Zvz48axatar0Idv58/j4+NCuXbs6uV61kung4GDs7OyA0mRKPLyUSiWPPfYYBw4cIDKydHuJ3NxcduzYwZkzZxg4cKBMHW7gzMzMMDMzK9ceFlZaFESlUhEeHk54eDgmJia0adOGtm3b4uDgIOuPhBANxvDhw/nPf/7Dzp07Kzyu0WhYsGABo0ePrufIRE3diL2BRq1BU6ghLzGvwmQawMjOiJvXbgLQZk7l9hX3W3L/GZQn552kuLD4vv1efvllZsyYwbx587Q7bISGhrJo0SLmzp1bqXhE1SkVSiyaWGDRxALXpq50se9SYb/8knyyC7O1RdQyCzK5lnuNmOzSqebxufHkFFUwU6ECaflppOWnVTpGyyaWZaqX2xrbapPuWxXMzQ3NUSpkwKI2tGjRgt69e3PkyBEAduzYgYuLC02b1v6yj2ol0927d+ell17i448/bjQVCkXdsba2ZuLEiVy9epX9+/dr96ZOTExk7dq1+Pj40K9fP6ysrHQcqagsjUZD165dCQsLIyYmBo2mdJuRvLw8Tp06xalTp7C2ttYm1vJ3K4TQtQULFtCtWzcGDRrEG2+8QdeuXbG0tCQ5OZmQkBAWLFhAcnIyP/30k65DFVVk39ue/NR89Iz1sH7k7jMLPCZ7kLAvAat2VhiYVm/tq6pQhapAhZ6RHnpN7r+39O3mzZuHubk5K1asYMGCBRgYGNCqVSsWLlzI008/Xa14RO0x1jfGWN/4noXUStQl5BTlcL3gOlmFWf9bx12QRfyNeG3inZ6fXqVrXy+8zvW064Smhd61j1KhxLKJJc2Mm5Um3v9fRM3exB5Lo/8vsNbEUtZ1V1K/fv24cuUKycnJFBQUsH37dp566qlav061kunt27fz2muvsXnzZhYvXszkyZNrOy7RCHl4eODm5saZM2cIDAzU7jN+6dIlLl++zMCBA+natauOoxSVoVAoaNOmDW3atCE3N5eLFy9y4cIFUlNTtX0yMzMJDAzk5MmT/Otf/0JPr2o3HUIIUZuaN29OUFAQc+bMKbc1p1KpZNy4cfz+++/Y29vrKEJRXQamBrTyb3XffmYuZnjP8C7Xfj38OqpCFTYdbSp8X1F2EfF748kMy6ToepG2vYlVE2w62mgfKFfG7NmzZV1+I6av1MfayBpro3svB8krziOzIFNbMO32pPvOCuaZBZmVGsVWa9RkFmSSWZBJRFbEXfuZ6Jtop4/bGttqR7zdLdxxa1o66i0zB0u3+J0wYQI//PADKpWKyMhICgoKar0OULWS6REjRjBkyBCWLFnC7NmzWblyJd99912dlx4XDZ+enh6+vr60a9eOo0ePcubMGdRqNWq1GltbW12HJ6rB/P/au++oKM72b+Df3YWlSoelSpEmIFgoithLjInGEjXRqGhQjAZj1McUu0lM4k9j9DGa2CLGaDRP7N0oqICoAQFFEKRL751ly7x/8DJxXZQqS7k+53AOe889s9cMqzvX3K1HD3h5ecHLywu5ubl4/PgxHj9+zM4G7uDgIJdI5+XlQVNTk8aIEULalYWFBc6ePYu8vDxERkaiqKgIenp6cHd3p++gbqo0oRSP9zwGAPSa2QuCgbIPU6pyqxC7MxY8NR4E3gKoG6uDp8qDpEaCqqwq5EfkQyKSICUlBdbW1q98r02bNr1y+7p161p3MqTDUFdWh7qyOsx7NG0oY7W4mh3LnVGWIbdGd/1PeW15o8eqElehqqIKmRWZDW7XVNZkW7YN1QzZMd0WPSzgYezRrRJtIyMjTJo0CRcvXoStrS1UVFTa/D1avDSWkpISVqxYAV9fX2zatAl9+/bFpEmT5KYe37t3b6uDJJ2PmpoaRo8ejf79++PmzZsQiURyk8LU1taCx+NRi2YnIhAIIBAIMGzYMGRkZODx48dwdnaWq3fx4kV2IqDevXvDzs7utS5LQAghzzMyMsK4ceMUHQbpAKTif+f2YcTyLcxpp9OgaVnXos1Vkh2vqu+mD7OxZri7/C42btyIQ4cOvfK96sdn1hOLxUhLS0Nubi5mzpzZ8pMgnZ6akhoc9BzgoCffc+J5IqkIORU57ARq6eXpKKopYpPu4pq6JFzMiF96jApRBWILYxFbGCu37UOXD7FswLLWnk6n0qdPH7i4uLy2hwitXmf62bNnePr0KUQiEUQiEZSUWn1I0oXo6elh8uTJDc6AGRoaivj4eAwePBguLi40S3QnwuVyYWlpCUtLS7ltxcXFyMnJAVA3g2lKSgq4XC6srKzg4OAAe3t7SqwJIYS0mlQslUmAX3zNSBnoOOrA3tceEqEEhh6GYCQMOLx/b6rLU8rhtNhJLpGux+VxweVxERQU1Gg8165da7B8zZo1KC4ubuppkW5MmasMCy0LWGhZYKj50AbrMAyDSlElioXFyKnMqVur+/+P5U4tTUVmRSYkTMMzz2dVZr3O8Dus19ka3+LMNzc3F19++SUCAwMxZswYxMTEyC0FQEi9F1ufKyoqEBERAbFYjIsXL+LOnTvw8fFB7969Kanu5CQSCezs7JCcnMw+RJFKpUhOTkZycjIuX76Mnj17wtHRERKJhHomEEIIaba4X+JQHFsMy4mWMBtthswbmUg7nQYdRx30/qg3hAVCPPzxIaRiKVwCXKCkoYTITZEQV4vhvNgZPazrJtAV14jB1+a/+s04dZOqtpSfnx9cXV3x008/tfgYhNTjcDjQ5GtCk6/Jdt1+nkgiQkZ5BtLL01FcU8yO6VbjqWGK3RQFRd11tSiZ/v7777F582bo6+vjf//7HyZNmtTGYZGurqysDJqamigpKQFQ15p57tw5hIWFYfDgwXB0dKSkupMyMDDA1KlTIRQKkZiYiPj4eKSkpLCJNcMwSEtLQ1paGsrLy6Gjo6PYgAkhpBmKiorg6OiIyMjIly79GBgYiHXr1qGwsBDTpk3DTz/9RHNItCGJUILi2LqW3oIHBTAbbYbCB4UA6takllRLUPq0FKJyEVumoqfCTi5W/LiYTabBAGjC7UZLl4Ktra3Fnj176LuOtBtlnjJsdGxgo2Oj6FC6hRYl0xs3bsSqVavw+eeft/mMaB1BeXk5Lfn1mpmammLBggV4+PAhQkND2cmsCgsLcfbsWYSGhmLQoEFwcnKipLqTUlFRgYuLC1xcXFBTU4OkpCTEx8fLtFhTd29CSGdSWlqKSZMmIT//5TPz1s8ofvDgQdja2mLevHlYtWoVdu3a1Y6Rdk2pZ1ORdT0LPFUeBD4ClCeXw2KcBapyqyAsEoKjxIHxYGMoqStB300fhdGFkNZKYehhiLSzaeBwOeDr8WE00EjmuBXpFVBSe/ktcVNn81ZWVpbrTiqRSKCiokJzCBHSRbUomX78+LHcZFJdRVVVFX755RdMmDABDg6vniSAtA6Px0Pfvn3h4uKCmJgY3LlzRyapPn/+PEJCQvDOO+/AxMREwdGS1lBVVYWzszOcnZ1RW1uLp0+fIiUlBZGRkYoOjRBCmuSff/7BzJkzG21E2LNnD2bPns0uG/rzzz9j1KhR2LJlC7VOt1JuSC7AAJJqCVR0VdDr87pVZJ5deca2QmtYaAAAlNSV4LTICUBdS3b+/boHIEqqSlDVl/0bxu+Nf/UbM00bc3nt2jWZehwOB3w+H87OztRIQ0gX1aJkuqsm0kBdd2OxWIxTp05h+PDh8PLy6lZTyCuCkpIS+vfvD1dXV7mkuqqqirpGdTF8Pp9dw/rw4cOKDocQQprkxo0beO+99zB79uxXzhETHh6OtWvXsq89PT0hEokQExODgQMHtkeoXZbJMBM8u/YMSmpKMmtG6/fTR/4/+eDyudDprSO3H0+FB8FgAYpiimA81Fhmm/cO70bf997KexAJRY3WGz58eKN1CCFdC029/QI+/99JKIKDg5Gfn48333yTZilvB88n1Q8fPkR4eDgcHR3lugIXFBRAXV293Z/wf3LjEwDAjpE72vV9CSGEKN6qVasAAKmpqa+sl52dDVNTU/Y1j8eDvr5+oxNYDRo0qMHy0tJS9OnTp3nBdlE93+qJnm/1lCtXM1JDv9X9Xrlvrxm90GtGr2a/p6hCBKlECk9PT9y7d09uu7W1dZMbXZKTk5v9/oSQjo0yxBcYGBjA0dER8fF1XX5iY2NRXFyMyZMnUxeddqKkpIR+/frB1dVVbkkthmFw8eJF5OXloU+fPvDw8ICent5rj6m2thaFqYUQ14gRK4iFnZ2dzIMXQgghBKjrUfXi9wOfz4dQKFRQRKS5pCIpih4WIf9+PkriS8BIGJiZmTVY9+uvv2Z/T0hIwI4dOxAQEAB3d3coKyvjwYMH2LlzJwICApodR0pKCgICAhASEgIdHR34+/vj888/px6ThHQgbZJMx8XFwdTUFNra2rhy5QrOnj2L/v3748MPP2yLw7crDoeDiRMnwsDAACEhIQDqlkMIDAzEpEmTXjpzJ2l7PB5PbtmkjIwM9un+gwcP8ODBA9jb28PT0xNmZmZt/gVTWFiIM2fO4OrVqwhLCQMDBgFnA2CkY4SxY8di0qRJ7ZLME0II6RxUVVVRW1srU1ZbWwsNDY1X7nfnzp0Gy2fMmNFmsXVXFekVyLyeCWMfY2jbab+0XmliKfLv56MwuhCSmrqH+aYjTJF7OxenTp1qcJ9Zs2axv/ft2xeBgYF455132LLx48ejX79+WL58OdasWdPkmMViMd555x04OTkhPDwcqampmDNnDvT09ODv79/k4xBCXq9WT5O8e/du9O3bFw8fPkRERAQmTpyIjIwMrF+/HqtXr26LGNsdl8uFj48PJk2aBGVlZQB16yIfPXoUkZGRTZ7VkbQ9FRUV2NjITvWfkJCAI0eOIDAwEI8fP5ZrzW6p5ORkfPLJJ/jl918QkREBRsBA2VYZJdoliMiIwC+//4KlS5dSty1CCCEsMzMz5OTksK/FYjEKCwtpIk0FerTjEQofFCL2p1i5e7iq3CqknU/DP+v/QeyuWFQ+q4TpCFO4rXIDh8OBkZdRkx/UJyUloVcv+a7kxsbGyM7OblbMDx48QHx8PPbv3w9HR0eMGzcOn376KY4ePdqs4xBCXq9Wt0xv3boVf/zxB3x8fPDRRx/B3d0dZ8+eRVhYGKZMmYJvvvmmLeJUCEdHR+jq6uLkyZMoLS2FVCrF1atXwePx4ObmpujwuiWBQIDp06cjLy8P9+/fR2xsLLv2Y05ODs6ePQtNTU3069cP/fr1a/G46sLCQqxbtw7RGdHQsNHA4HGDkYpUAICLgQsq8isQfyUeMckxWLduHXbu3Ekt1IQQQuDl5YWwsDC2xfLevXvg8/lwdXVVcGTdh7haDHG1GKp6jS/fGrU5CqoGqjAfbQ5dF12o6Kq0+H3feOMNLFiwADt37oSrqysYhkFERAQCAgIwZcqUZh3LxsYGFy9ehKamJlvG4XBQWlra4vgIIW2v1cl0Tk4O3N3dAQBnz57FsmXLAABGRkaorq5u7eEVTiAQwNfXF2fPnkVKSgoEAgGcnJwUHVa3Z2RkhLfeegvDhg1DREQEoqKi2M9bRUUFbt++DUtLyxYn02fOnEFidiI0bDQwYOYAcLgcoODf7ZqGmhgwcwAijkbgafpTnD59GvPnz2+LUyOEENKJSCQS5OfnQ09PD3w+H/7+/hgzZgy8vb3Ru3dvLF68GB9++CFUVFqepJGmqy6oRtQ3UWAkDMzHmaPn+J5wCnBC1vUsGPsYI+NSBvLu5sFstBlMhpjAZKgJCqMKkXYuDUUPi6Drogs9Fz2o6DX/73XgwAH4+flh0KBBbAu4kpISPvjgg2avM66vr4/Ro0ezr4VCIfbv3y/ThbwhNJEdIe2r1cm0m5sbtm/fDgMDA2RnZ2Pq1KnIy8vD+vXr4eXl1RYxKpyamhqmTZuG8PBwODk5sV2/ieJpampi2LBh8Pb2xqNHjxAREYGCggIIBAK5yUKqq6uhpKTU6N+vtrYWV69eRVFNEQaPG1yXSDeAw+XA8Q1HhP43FNf+voYPPviAJiUjhJBuJiMjA9bW1ggKCsLw4cPh4+ODHTt2YNWqVaioqMDkyZPx/fffKzrMbqMopgiMpC6RLYouQs/xPaFlpQWtD7UAAHE/x4GRMMi6kQWTISawnmoNqylWKEsqQ2FkIZ5deYaUv1KgbqIOBgxqS2pf9XYytLW18eeff6K0tBQJCQngcDhwcHBo9QS2DMPAz88P5eXl+Oyzz1p1LEJI22p1Mr1nzx7Mnj0b6enp2L59O2xsbLBs2TLEx8fj+PHjbRFjh8DlcuHtLb8WoUgkQkhICAYNGgRV1ca7E5HXQ1lZGf369UPfvn2RlpYGAHJjnMLCwhATEwMXFxeclJx8aeJbmFpYN9mYgKnr2v3/W6TLassAAI8KHv1bmQPU6NcgNCkU84/Mh76VvvwBodjltGhJL0IIaTtWVlYy425ffA0ACxcuxMKFC9s7NAJAMFCArBtZEFeJG1xGS+AtQF54HgQDBWwZh8OBtq02tG21Yf2uNUoTS1EQWYDa0lo8/vkxAGDfvn1YsGBBo+//7Nkz/PTTT3jy5AnEYjEcHBzw4YcfwtHRsUXnI5VKsXDhQpw8eRJ///03jIyMXlmfJrIjpH21Opmun3zseVu2bOk2LXRBQUGIjIxEXFwcJkyYAAsLC0WH1K1xOBxYWVnJlYtEIsTExEAoFCIiIgKpuqlQU1WDto42emj2kEm8xTViMGCg3KNpPRCUNZUhyhVBXCNuq9MghBBCSAsoqSvB42uPl263mWYDm2k2L93O4XKg46ADHQcdMNMZlMSXIH5fPD777LNGk+nr169jwoQJ8PT0hJeXF6RSKcLDw7Fr1y5cuXIFQ4cObda5SKVSzJ07FydPnsT58+df2oWbEKI4rU6mxWIx9u3bh/Hjx8PS0hLr1q3DX3/9hf79+2PHjh1delKmoqIiPHjwAABQVlaG33//HQMHDsSQIUPklnQiilVRUQEDAwNkZmYCAIYVD6vbkF03Q7izszPc3NwgEAgQK4hFwNkAlIhL4GLgwh6jvkX6+TIAKJOUQUdXB+uGr1PoePpp06ZBLJZP6GPyYwAA6f9Nl9umpKSEP//887XHRgghhHQ2HB4Hus664CnxZGZof5n//Oc/+Oyzz7B+/XqZ8g0bNmDVqlUIDw9v1vt/9tln+Ouvv3D+/HmMGDGiWfsSQtpHq5PpTz75BBcvXoSPjw9OnTqFLVu2YPPmzbhw4QIWL16MP/74oy3i7JD09PQwc+ZMnDt3DmVldV2Aw8PDkZycjLfeegsCgaCRI5D2oquri9mzZyM3NxfR0dF49OgRuw6oUChEZGQkIiMjIRAI0K9fPxjpGCEjIwMVBRXQNNB86XEr8itQ/qwcDpYOsLW1ba/TaZBYLEZUThQ4PNnu7fXd06Pzo2XKGQmDvsZ92ys8QgghRGFqy2qReS0TGuYaMPL6t6t07E+xje4rEUkwYcIEXLly5ZX14uPjceLECbnyWbNmYcuWLc2KNyoqCtu2bcPWrVvh5OTEJvNKSkowMDBo1rEIIa9Pq5PpEydO4PLly+jTpw82bdqEN998E8uXL8ebb77ZZSYgexULCwvMnz8f165dQ2xs3X/IeXl5CAwMhLe3NwYNGkSt1B2IQCDA2LFjMXz4cMTHxyM6OpptrQaA3NxcCIVCjB07Fim/pyD+cvy/s3m/gJEyiL8SD31VfYwZPaZDDG3g8DgYv3G8TNnLWtQvrr/YbnERQgghrSURSsDhcsBV5gIApCIpGCkDnsq/91lSsRRPAp+gNL4U1lOsIRgkgEQoQdqZNOTfzwcA9LDqATWBGgBAy0brpe/HMAzy7+WDYRgkJyc3Gp+dnR2uX78u93D9+vXrMDc3b9a5nj59GgzDYMWKFVixYgVb3qtXLzx9+rRZxyKEvD6tTqZFIhG0tLRQU1ODy5cvY+fOnQCAqqqqbjMhl6qqKiZMmAA7OztcuXIF1dXVkEqlCAkJQUJCArVSd0D1a366urqioKCAba2uqamBi4sL7OzscOPGDcQkx+Cfo/9A4CGAVFcKLrfuC7x+nemq5Cq49XTDpEmTFHtChBBCSBdWllSGx7sfg6fKQ5/lfQAO8HDbQ0hqJHBa7AStXlqQCCWI2RqD6ty6pTLz7uSBr8NH/N54NgHnqfGgpP7v7a/Fmw3PdVOeWo7kP5MhqhCBy+Pi0aNHDdZ73qZNmzBjxgyEh4ezDUp3797FsWPHcPDgwWad74YNG7Bhw4Zm7UMIaX/c1h5gxIgRWLhwIaZPnw4Oh4PJkyfj5s2b+PDDDzFhwoQWHbOqqgpz5syBtrY2LC0tcfjw4QbrSSQSrFu3Dj179oSOjg4mT56MrKys1pxOqzg6OsLPzw92dnZsWV5eHv744w+IRCKFxUVezcDAAKNGjcLHH3+MWbNmQUNDA3p6eti0aRNcLVwhTBTi3p57SDicgKT/JeH6zusI2RECpANuPd2wadOmLj03ACGEEKJoJU9KIBVJISoXoSKtAhWpFRCViyAVSVHypAQAUFNYwybSXGUuTIaboCS+BIyEgaRGAsuJluj7ed9XTjAqqhTh6bGnePjjQ6joqqDvl33B5XGbtE74O++8g/Pnz6O0tBQ7d+7E3r17UV1djaCgIMycObNNrgMhpGNpdcv0r7/+ijVr1iAjIwMXL16Ejo4OHjx4gFGjRuGrr75q0TFXrFiBJ0+e4NatW4iOjoa/vz8cHR3h6ekpU2/btm04dOgQfv31V5iammLVqlV49913ERoaKrcsUnvR0NDAlClTEBcXh2vXrqG6uhrDhg2jtak7AR6PJ9MNy8bGBjt37sSaNWsQGhaKrMwsiCGGilgFGnwNuDi7YNasWdTrgBBCCHnNjAcboyK1AkrqStB10QUAGPQ3gLhKDIF33fewuok6TIaboCK9AtZTraFpoQlNS01UZVaBr8OHyXATcJVe3o6UE5qD9HPpUNJQQu+FvaHrpNusGH19fbFmzRqcPHmy5SdKCOlUWp1M6+joYNeuXTJly5Yta/HxKisrcejQIQQHB8PNzQ1ubm4IDQ3F7t275ZLpwMBArFmzBqNGjQIA7N27F6ampkhNTYW1tXWLY2gtDocDJycnWFpaIjo6Gm5ubjLbGYZBVVUVNDQ0FBQhaSo9PT2sX78eUVFR+Pbut6itroVXjRf09fWhpKSEu3fv4t69e7CyskLv3r3h6OjY6Njp+nWf21pMfgzKastk18HGS9bH/v/lMfkxry0eWteaEEJIW+Fr8+G0WHbFDHtfe5nXHA4H1lNk7/9U9VXh/LHzK49dnlbXpbs6pxrmY8xhOsr0lUn3y5w7dw7r1q1r9n6EkM6r1cl0RUUF9u7di7i4OEgkEgB1yaJQKER0dDQ7KVdTRUdHQyKRwN3dnS3z9vbGt99+K1f3p59+Qu/evdnX9a3RpaWlLTmVNqehoQFvb2+58kePHuHatWsYPHgw3N3daYKyDk4gEOCNN97AmDFjkJaWhtjYWCQkJLCzgTMMg5SUFKSlpcl08SeEEEKIYjFSBgmHElCaUAqbaTYwGCA7E3bSH0nIDc+Fiq4Ker3fCyq6KqhIr5A7RlhYWIP3dM/76KOP8P7772PRokWwtLSUmzuosf0JIZ1Pq5PpDz/8ECEhIRg7diyOHDmCWbNmITk5GSEhIS2aOCE7OxsCgUAmwRQIBA2OhR4+fLjM6127dsHIyAguLi5ydZ/3skXvS0tL0adPn2bH3Bw1NTUICgpCbW0tgoKCEBMTg9GjRyu0JZ00DZfLhbW1NaytrSESiZCUlITHjx8jKSkJEokEVlZWUFNTk9knNTUVZWVlsLOzY7e9rhbb9P+mIzo/Wm7W7pfN5p3OT4eroSu1IBNCCOmyastqURhVCADICcuRS6Zz7+QCAIRFQiQeTmz4IAwwZMgQttHoZTZv3gwAuH//vtw2DofT6P6EkM6n1cn05cuXcfr0aYwYMQJRUVFYsmQJPDw8sHr1akRFRTX7eFVVVXLdZPl8PoRC4Sv3u3TpEr7//nvs27cPSkqtPq3XRigUQl9fH1VVVQCAwsJCHD9+HLa2thg5ciRNZNVJKCsrw9HREY6OjhAKhUhMTISmpvx61Pfv30dSUhK4XC569uwJBwcH2NnZNViXEEIIIW2Lr82HobshShJKYOxjLLfde0fjrcX3Vt6DSNj4RLJSqbRFMRJCOq82WRqrfj09Z2dn3L9/Hx4eHpg/f/5LW4BfRVVVle0+W6+2tvaV44uvXLmCqVOnIiAgAL6+vo2+x507dxosnzFjRrNibQltbW3MnDkTjx8/RlBQECoq6roSPX36FMnJyRgwYAC8vb3lWjhJx6WiotJgb4iamhqkpKQAqPuCTU1NRWpqKq5cuQIzMzPY2dnB3t6eHqAQQgghjWCkDBJ/S0RpYl13bX03fbk69ROI6bnpwfb9untTDocDuzmvdwjW06dPceHCBaiqqmLs2LHU25CQbqTVS2P17dsXx48fBwC4ubnh77//BgBkZmZCLBY3+3hmZmbIz8+XebqXm5sLExOTBuufP38eEydOhK+vL3744YcWnEH743A4cHZ2xsKFCzFo0CC2S7tUKsX9+/fx888/Izw8nJbT6uR4PB7efPNN2Nrayo2Lz8zMRHBwMPbu3Yt9+/YhLCxMQVESQgghHV9taS0KIgogKhMh53ZOg3VyQnIgrhIj704eJLXt06X6woULcHJywrp167Bq1So4OTnh6NGj7fLehBDFa3XL9HfffYeJEyeCz+dj7ty52Lp1K/r374/U1FRMmzat2cern/k6IiICHh4eAIDQ0FAMHDhQru4///yDadOmYf78+fjpp59adyIKwOfzMWzYMLi5uSE4OBjx8fEA6rqCBwcHo1evXjA0NFRwlKSllJWV0adPH/Tp0we1tbV4+vQpEhMTkZSUJNP7orCwEHl5eXL7S6VScLnNe97FSBhcXH9Rpqx+Nu90frpcXUIIIaQz4Gvzod9XH6UJpexSWC8yGWqCtHNp0O+rDx6/fSZ3/eabbxAQEIAtW7aAx+Nh7dq1+M9//kPrShPSTbQ6mR46dCjS09NRXV0NIyMjREZG4vTp09DT02tRt2kNDQ3Mnj0b/v7+OHDgAB4+fIgjR47g5s2bkEgkyM/Ph56eHvh8PhYuXIi+ffti3bp1yM3NZY+hr6/fqdZ11tHRwaRJk/Ds2TMEBQUhMzMTLi4ulEh3IXw+H05OTnBycoJYLEZaWhoSExORkJCAqqqqBmcBP3bsGHg8Hnr16oVevXo12h1cSUkJfY37ypXH5McAAFwNXRvchxBCCOnoOFwOHOY7vHR72tk0ZN3IgvEQY1hPbb9u1g8ePGC/rwHg888/xzfffIO8vDwYGRm1WxyEEMVokztpTU1NlJeXIzk5GQDw5ptvAqibydjGxqbZx9u+fTv8/PwwePBgGBkZYe/evfDw8GDXjw4KCoKVlRUePHgAADA1NZXZ//bt2/Dx8WnlWbU/c3NzfPDBB0hISICxsfwkGSEhITAyMoKdnR27DBjpfJSUlNgEeezYscjOzoaBgezsopWVlcjIyABQ9+/o+vXr0NXVhY2NDWxsbNCzZ0+5B0Z//vlng+9Xv440zdpNCCGkq8q7lwdGyiDvbl67JtNCoVBmnhsNDQ1oaGigsrKy3WIghChOq5Ppo0ePYvHixSgvLwfD1HUb5XA4YBimxcsAaGpq4o8//pArt7KyYt8DgMzvXQWHw4GDg/yT14KCAoSEhACoWyps8ODBlFR3AVwuF2ZmZnLlubm54HK5MnMHFBcXIyIiAhEREeDxeOjZsydsbGwabbWmJJoQQkhXZz7GHFnBWQ3O2K0IXfEelRAir9XJ9IoVK7Bw4UIsXbqUZqB+je7du8f+npubi5MnT8LIyAje3t6wt7dv9tha0rHZ2Nhg2bJlSElJQVJSEpKTk9mZ3wFAIpEgJSUFKSkpKC4uxtixYxUYLSGEEKJYJsNMYDKs4clqX7f79+9DV1eXfc0wDCIjI5GTIztRmrd348twEUI6l1Yn0xKJBH5+fjA3N2+LeMhLjB49Gnp6erh79y6qq6sBAHl5eez49IEDB8LZ2Vlu1mjSefH5fDg4OMDBwQEMwyAvLw/JyclITk5GZmYm22rd0BIc//vf/6Cvrw8rKyuYm5t3qjkECCGEkM5kwoQJcmXTp0+Xed3S3pqEkI6t1cn02rVrsXr1avzwww+wsLBoi5hIA/h8PgYOHIj+/fvjwYMHuHv3LqqqqgAARUVFuHjxIm7fvg0PDw/079+fJpbqYjgcDgQCAQQCAQYNGgShUIi0tDSkpKTA0tJSpm5xcTGePn2Kp0+f4u7du+DxeDA1NYWlpSUsLS1hampKD10IIaSZqqqqsGjRIpw5cwY6Ojr46quvMGfOHLl6EokEGhoaEAqFbJlAIJBrpSRdw/PDsQgh3U+rMy4HBwesW7cOVlZWDW6np3Bti8/nw8vLC/3790d0dDTu3r2L8vJyAEB5eTkiIiLg7u6u4Ci7F6lUikuXLgEAxo8f3y7j2FVUVGBvbw97e3u5bampqTKvJRIJMjIykJGRgZCQECgrK8Pc3Bw9e/aEnZ2d3ORnhBBC5K1YsQJPnjzBrVu3EB0dDX9/fzg6OsLT01OmXnJyMhiGQVZWFvt9QA8wW6+2vBZ54XnQttVGD+sebHnBgwIUxRTBdIQpNHtqKjBCQkh31OpkeuHChZgwYQLmzJlDY6bbkbKyMtzd3dGvXz/Exsbi7t27KCwshIeHh9z46crKSmhoaCgo0q4vNDQUH236CABwVPuowmeSd3Nzg0AgQGpqKlJTU5GZmSnzUEskErHjraVSqVwy3ZL1rQkhpCurrKzEoUOHEBwcDDc3N7i5uSE0NBS7d++WS6bj4uLQq1cvmJgoZvxuV5V0LAnFj4rB5XPh8Y0HeCo8SMVSJB5OBCNhUJ1XDbf/uCk6TEJIN9PqZLq4uBgbN25scNwmef14PB5cXV3h4uKCpKQkuS6/EokEv/76K7S0tDBgwAA4OjrSE/I2JJVKcezYMQgldd35jh49isGDByt0lnUulwtTU1OYmprC29sbIpEImZmZSE1NRXp6OrKzs9lZRnv27Cmzr1Qqxa5du2BgYABzc3NYWFjAzMwMfD5fEadCCCEdQnR0NCQSiUzPL29vb3z77bdydePi4hrsNURah8uve8jLVeIC//8rlqvEhZqxGqoyq6BhRo0GLcUwDHJzc1FWVkbd1km74XA44PP5MDIy6tSNfq1Opj/55BNs374d27Zto0mOFIjL5cLOzk6uPD4+HhUVFaioqEBWVhauX7+Ovn37om/fvtDS0lJApF1LaGgo4pPjoaRX908pPjkeoaGhCm+dfp6ysjKsrKzYoRhCoRDPnj1Denq6XMtJbm4uqqqqkJ6ejvT0dAD/jte2sLCAubk5zMzMoKlJXekIId1HdnY2BAKBzMNogUCArKwsubpxcXEoLCyEt7c3UlNTMWTIEPz444+NtlQPGjSowfLS0lL06dOndSfQBdi+bwvd3rroYdUDPP6/f4c+y/qgOq+akulWyM3NRXFxMQDQkquk3UilUtTU1CA9PR0GBgYwNDRUdEgt0upk+s6dO7h9+zb27dsHExMTuYmvEhISWvsWpBVqa2uhqqqKmpoaAHUTqISFheHOnTuwtbVF3759YW1tTd16W6C+VTq3KhdGo4zqnuzeyO0QrdOvoqKigl69eqFXr15y24qKiuTWt2YYBjk5OcjJycH9+/cBADo6OvDy8kK/fv3aLW5CCFGUqqoquR46fD5fZpKxevUPsX/66ScoKyvjyy+/xIQJE3Dv3j36rm0FngoPRl5GDZZrWtAD3tYoKysDAFhaWkJdXV3B0ZDuQiqVori4GHl5eSgpKem+yfQHH3yADz74oC1iIa9Bv3794OLigtjYWERERCA/Px9AXYKUmJiIxMREaGlpwc3NDf3796dx781Q3yrN1eFCy0ELYICiyKIO2TrdVM7OzrC3t0dmZiaePXuGjIwMZGVlQSQSydQrKSlhu4o/LyIiAtra2jA1NaUvZEJIl6Gqqora2lqZstra2ga7JgYHB0MqlbL/B/7vf/+DQCDA/fv34eXl9dL3uHPnToPlM2bMaEXk3VNtaS24fC6U1Ghlk6aQSqXgcDj0vU3aFZfLhb6+PvLz8zv1hNWt/l9m7ty5AOom50hKSoJYLIaNjQ10dHRae2jSRpSVldG3b1+4ubnh2bNniIyMxJMnT9jWx7KyMoSEhMDNrXtN3PHJjU9avC8jZRD832DkFuTCdIIpykV1M6rz+vMQez4Wi/9vMYYLh7eqdXrHyB0t3rc1XuwWLpFIkJubi8zMTGRkZCAzMxOVlZUwMzOT2a+2thZ///03m2Tr6uqyY7dNTU1hZGRE4/UJIZ2SmZkZ8vPzZSZozM3NbbDrtqqqqsxrAwMD6OvrN9glnLS9wphCPDnwBErqSnD7jxtU9FRktgtLhHi04xEkNRI4LXaiVm1CSKu0OpkWCoVYsWIF9u7dC4lEAoZhoKSkhPfeew/79u2DiopK4wch7YLD4cDCwgIWFhaorKzEw4cPERUVhZKSEtja2sqNg83NzYVUKoWxsXGH7bKsKFmPslCSUwIlPSVoOWixybS2gzbyw/JRklOC7EfZMO1jquBIW69+nWpTU1N4eHiAYRiUlpbKjbnPysqSaa0uLi5GcXExYmNj2eMIBAIYGxu/tJs5IYR0RPUPmyMiIuDh4QGgrnfSwIEDZepJpVJYW1tj27ZtePfddwEAmZmZKCgogIODQ/sG3U2Vp5QDDCCuFKMqp0oumS5LKoOwsK57fvGjYkqmCSGt0upketmyZQgODsbVq1fh5eUFqVSKO3fuICAgAF988QV++OGHtoiTtDENDQ0MHDgQnp6eyMjIaHC25tu3b+Pp06cwMDCAi4sLnJycutSkZS1t+ZVKpfj4fx+jh0oPOIx3gKmhKR4VPAIAuBi4QH+8Pp6cfALDJ4b4cemPXe5BBIfDabDniY6ODoYOHYqsrCxkZWWhqqpKZrtEImG3cTgcSqYJIZ2GhoYGZs+eDX9/fxw4cAAPHz7EkSNHcPPmTUgkEuTn50NPTw98Ph9vvPEGVq9eDTMzM6iqqmLp0qV488034eTkpOjT6BZMh5tCWCgEX4cPHUcdue26vXWhba8NcbUYhu6dc4wmIaTjaPVMGCdOnMD+/fsxfPhwqKmpQUNDA6NHj8a+ffvw+++/t0WM5DXicrmwtLSU66pWUVGBpKQkAEBBQQGCg4Oxe/du/P7774iKikJ1dbUiwu0Qnh8rbeIi38XPxNkEXB0uO3a6u9DR0YG3tzfeffddBAQEYNGiRZg4cSLc3d1hamoq08Wb1l8lhHQ227dvh729PQYPHox169Zh79698PDwQEZGBkxMTBAWFsbWGzVqFN555x0MHToUlpaWOHLkiIKj7z742nw4zHeA9RRrcLjyD7OV1JXg/LEz3P7jBlVD1QaOQDqLqqoqzJkzB9ra2rC0tMThw4ebvO/3338PY2Nj6OjoYOXKlbQk2CsUFRXByMgIz549a/I+3en6trplmsfjNbhMjqamZrdOuLoCDw8PxMbGorKyki3LyMhARkYGrl69ChsbG/Tu3Rt2dnbdZh3i52fwdhjn0GCrM4fLgd1IOzw5+aTDz+z9utS3Xuvo6LCtMRKJBHl5ecjOzpZbD50QQjo6TU1N/PHHH3LlVlZWMkNcNDQ0sHv3buzevbs9wyOk21mxYgWePHmCW7duITo6Gv7+/nB0dISnp+cr9zt+/Di2bt2Ko0ePQlVVFTNnzoSxsTFWrlzZTpF3HqWlpZg0aRI7gXFTdLfr2+qW6bfeegsBAQHsmrQAkJqaiqVLl2L8+PGtPTxREE1NTYwcORJLlizB9OnT4ezsLLOOuFQqxdOnT3Hu3DlcunRJgZG2r8Zapet119bpV+HxeDAxMUH//v3Ro0cPRYdDCCGEkE6qsrIShw4dws6dO+Hm5oY5c+Zgzpw5TXqI9dNPP2HlypUYM2YMhgwZgi1btmDXrl3tEHXn8s8//8DDwwMlJSXN2q+7Xd9Wt0z/8MMPeOedd2Btbc2uD5afn48xY8Z06QvXXXC5XNjY2MDGxga1tbV4+vQpHj9+jOTkZLbLRkOTqiQmJsLc3LxLLbX1fKt0VVkVLm349yFCWW3dGo3p/H8fKjFSBlIlabdtnSaEEEIIeR2io6MhkUjg7u7Olnl7e+Pbb7995X4SiQT379/HN998I7NfWloacnJyYGxs/Npi7mxu3LiB9957D7Nnz4a9vX2T9umO17fVybSuri5u3bqFmJgYxMfHQ1VVFfb29nB0dGyL+EgHwufz4eTkBCcnJ1RXVyMhIQGJiYlyE0mVl5fjr7/+AofDQc+ePWFvbw87O7tOP3lZeHg4srOzYa/ftP9Q6mVnZ+POnTvw9vZ+TZERQggh3UtteS3if4mHpFYCxwWOUDPsOg/vO4J//vkHQUFBcuurvy58Ph8jRoyQSY5fJTs7GwKBQGY+FoFA0OgSdCUlJaipqYGp6b+rrQgEAgB1q5K0V7J34skJ7I7ajSpxVeOV24i6kjoW912M6Q7Tm1R/1apVAOp6HDdVR7m+7alFyXRYWBi8vLzA4/HYCTcAwNzcHEDdQPX6ckoguiY1NTW4ubk1uDZ1YmIiAIBhGKSlpSEtLQ3Xrl2DQCCAnZ0dbG1tIRAIOl1Lrbe3N06dOqXoMAghhJBur+RxCSrSKwAAhQ8KYT7WXMERdS1hYWEyc+a8biKRCGFhYU1OpquqquTm6+Hz+RAKhY3uV1/3+f0ANLpvWwqMDURhTWG7vR8AVIurERgb2ORkuiU6yvVtTy1Kpn18fJCTkwMjIyP4+Pi8tB6Hw4FEImlxcF1JeXndOsTdYayorq4ubG1tkZKSIvP3z83NRW5uLkJCQtCjRw/Y2NhgwIABMDIyUmC0hBBCCOlsdBx1oGasBnGVGMWxxeAocaBtq43kP5Oh2VMT1u9ad7qH9h2Jt7d3u7dMN6cBTlVVVS622tpaaGhoNLpffd3n9wPQ6L5taa7zXIW0TPu6+L7W9+go17c9tSiZfn5686481XlbKS8vx5o1awAAX3/9dZdPqK2trWFtbY3a2lokJycjMTERSUlJqKmpYeuUl5cjOjoavXv3ltm3fkZU+gIkhBBCyMvwtfno92U/xO6KRWlCKcpTyqHroouKtApUpFVAMFgADdOuefPeHtzd3ZvcSqwIZmZmyM/Ph1QqBZdbN59ybm5uo0tv6uvrQ1VVFTk5OewwxdzcXADtu2zndIfpr7WFWFE6yvVtT62ezft5tbW1uHDhAs6fP4+Kioq2PHSnVZ9I3759G7dv38aaNWvYVuqujs/nw9HRERMmTEBAQABmzpwJDw8P6OrqststLCxk9snLy8OePXtw+fJlPHnyRCYBJ4QQQgh5npZd3Xws6mbq0O+rD3ABdVN1qBrQGtJdWf0ww4iICLYsNDQUAwcOfOV+XC4X7u7uMsNUQ0NDYWNjw06kTFquO17fFrdMb926FSdOnADDMJg1axZmz54NHx8fdrysgYEBrly5gn79+rVpwJ3J84l0cXExALAJdXdooX4ej8dDz5490bNnT4waNQpFRUUoKCiQmTgCAFJSUlBWVoaoqChERUWBw+HAxMQEVlZWsLKygpmZmdw+hBBCCOmeLN6wgJGnEZQ1lcFV5kK/rz64SlxwuNTDrSvT0NDA7Nmz4e/vjwMHDuDhw4c4cuQIbt682ei+ixYtQkBAAFxcXNCjRw98/vnnWLZs2esPupvobte3Rcn0qlWrcOrUKcyfPx8GBgbYv38/du/eDXt7e9y6dQsSiQQBAQFYvXo1Ll682NYxdwovJtL1s5vHx8d324T6eXp6etDT05Mrr+8KUo9hGGRlZSErKwthYWFQVlaGubk5LC0t4eLiAk1NzfYKmRBCCCEdkIquCvs7j08P3LuL7du3w8/PD4MHD4aRkRH27t0LDw+PRvebNWsWkpKSMHv2bDAMg3nz5nXpZK+9dbfr26Jk+ujRozhx4gQ7+diECRNgbm6Ow4cPs9Off/311412teiqGkqklZTqLrWjoyMl1K8wceJE+Pj4ICUlBampqUhLS4NIJGK3i0QipKSkICUlBba2tjLJdP34/fqxM4QQQgghpGvS1NTEH3/80aJ9161bh3Xr1rVxRF2TlZUVO6dRU3Wn69uiZDo3Nxc2Njbsa1NTU6ipqcnMyqynp9ctx02/KpEGACUlJUqoX4HD4UBfXx/6+vpwd3eHRCJBZmYmm1hnZWWBYRioq6tDX19fZt+UlBScOXMG5ubm6NmzJ8zNzWFiYkLdwgkhhBBCCCFtrkXJNMMwMgkiUNca2N1bBBtLpOtRQt10z4+1BurWqMvIyEB1dbXcjN8ZGRnsDOLJyckA6q61iYkJzM3NYW5uDjMzM3bafkIIIYR0DaIKEZJPJIOnyoPNNBtwlbv3PWl3tWLFCvzyyy8v3X7gwAHMmDGjHSPqWuj6ymtRMg0A9+/fZ2dlBuoS7MjISOTk5AAAioqKWh9dJ9LURLoeJdQto6KiAltb2wa3NdQTQiwWIyMjAxkZGWyZgYEBJkyYwA5JIIQQQkjnlhuWi8KoQgCAtr02DN275szB5NW++OILfPTRRy/dbmxs3I7RdD10feW1OJmeMGGCXNn06bLrpXXltYInT57M/i4SiRAXF4fCwkLU1tZCU1MTDx48kKlfUlICANDR0ZEpl0qlyM3NRXJyMs6fP4/evXtDWVmZ3X7q1KnXdg5dzdtvv43Ro0fj2bNnyMjIwLNnz5CdnS23FnpBQYHcwvH1616bmprCxMQEampq7Rk6IYQQQlqhh3UPcJQ44CpzoWlBk5N2VwYGBjAwMFB0GF0WXV95LV4ai5COSFVVFba2tmzrtUgkQnZ2Np49e4Znz54hMzMTampqcrOAp6enIyQkhH2tq6vLJtbGxsYQCAQyDzkIIYQQ0nFo22nD/St3cLgcKKm1uK2oW+JwOJBKpZBKpd1+yCZpXxKJBAzDdOr5jeh/mxZ6scW4ud28gbouyPHx8TA3N8eQIUOom/droKysLDPmWiqVorKyUq5eZmamzOvi4mIUFxcjNjYWQN0XjaGhIUxMTNC/f3/qIk4IIYR0MMoa9NC7Jfh8PmpqalBcXCw3uSshr0v9JMNA3TDOzoqS6TbSo0cPfP3112xCHR8f/8qEuj6R1tXVpUS6HXG53Aavs7OzM9TV1dk1rWtqamS2MwyDvLw85OXlwdnZWWabWCxGdHQ0BAIBjIyMwOfzX+s5EEIIIYS0FSMjI6SnpyMvLw/5+fmKDod0E/XLbSkrK8PExETB0bQcJdNtqKkJNSXSHY+ZmRnMzMwA1P3jLi0tRVZWFrKzs5GdnY3c3Fx2vesXW6Xz8/Nx7do19rWuri4EAgGbXBsZGUFTU7NLzyFACCGEkM5JQ0MDBgYGKCkpgUQiUXQ4pJvg8XhQUVGBiYlJp26IomS6jTWWUFMi3fFxOBzo6OhAR0cHTk5OAOq6hxcVFaGgoECuK0pubq7M6/ou4vHx8WxZ/Trs/fv3h4ODw+s/CUIIIYSwGCmD1NOpqC2rhfUUa/C1Ou/N++tgaGgIQ0OaAZ2Q5qJZBl4gkUjw7NkztutBS9Qn1EOGDIGuri7i4+MhFospke7EuFwuDAwM4OjoKLfNyMgIHh4e6Nmz50vHfFRXVyMtLQ1VVVVy2y5fvsw+eCkoKKAJ/gghpAOqqqrCnDlzoK2tDUtLSxw+fPildQMDA2FpaQlNTU3Mmzevwf/7SfsqjitGdnA2CiMLkR2crehwmqQ5nzlCiGJQy/QLqqqqcOTIEejp6aFPnz5wcXFpUcLbUAs1AEqkuyBTU1OYmpoC+LeLeP346tzcXOTl5aG0tBQA5J761tbWIioqSqaMy+VCT08PhoaG0NfXZ5ch0NXV7dSzHRJCSGe2YsUKPHnyBLdu3UJ0dDT8/f3h6OgIT09PmXp37tzBkiVLcPDgQdja2mLevHlYtWoVdu3apaDICQCoG6tDSV0J4hoxelh3jvuvpn7mCCGKQ8n0C+qfHhcVFeHmzZu4efMmLC0t4ezsDHt7e6iqqjb5WC8m1AAoke7inu8ibm9vz5YLhULk5+fLjbcuKCiQO4ZUKkVBQYHcNmtra8yYMUOmrLy8HHw+v1PPgkgIIR1dZWUlDh06hODgYLi5ucHNzQ2hoaHYvXu3XGKzZ88ezJ49G9OnTwcA/Pzzzxg1ahS2bNkCdXV1RYRPAKjqq6L/uv6QiqTga3f8Lt7N+cwRQhSHkukXqKmpoUePHigvL2fL0tLSkJaWhitXrsDGxga9e/eGra1tkwbLP59QA6BEuptSUVGBubm5XLm+vj6mTZuG/Px8FBQUID8/H4WFhRCLxXJ19fT05MouXbqE5ORkaGpqQk9PD3p6etDX14eenh50dXWho6NDa0YSQkgrRUdHQyKRwN3dnS3z9vbGt99+K1c3PDwca9euZV97enpCJBIhJiYGAwcObJd4ScOU1DvPbW9zPnOEEMXpPP+rtIP09HTU1NTg5s2bEAqFqK6uRnV1dYPjp5WUlGBkZNTkGZrrZ4L28/Nr05hJ18MwDCQSCUQiETvWXiQSISQkBPv375epm5OT0+jMm0pKSlBSUoKenl6Hm1E8ISGhWb09CCFEEbKzsyEQCGSG2ggEAmRlZTVYt37oD1A3Y62+vn6DdZ83aNCgBsuLi4uhpqYm1zPJxMQEV69ebc5ptCsTExO5mBtiamyKkj9LXn9ArWBqbCpzLu3x3dWcz9zzmvs5IoTUaem/a0qmn6OtrQ2grquuqqoqVFVVoaur2ybHVlZWlnn98OFDAECfPn3a5PjdQXe5ZhwOh02AG2NsbPzK7R39mqmqqrL/7gghpKOqqqqS643G5/MhFApbVbcp6u9JXtSWrdyv47vCwsKiSfU6+nkAAF5YArc9vrva63MEdPx7hc6CrmPbUNR1bOm/a0qmn3P58uV2e6/6J4fHjx9vt/fs7OiaNR9dM0IIaT1VVVXU1tbKlNXW1kJDQ6NVdZ93586d1gfaQl3lu6KrnAfQvp+jrnTdFImuY9vobNeRBlMSQgghhLyCmZkZ8vPzZZYuzM3NhYmJSYN1c3Jy2NdisRiFhYUN1iXkZZrzmSOEKA4l04QQQgghr+Dm5gYAiIiIYMtCQ0Mb7KLs5eWFsLAw9vW9e/fA5/Ph6ur6+gMlXUZzPnOEEMWhbt6EEEIIIa+goaGB2bNnw9/fHwcOHMDDhw9x5MgR3Lx5ExKJBPn5+dDT0wOfz4e/vz/GjBkDb29v9O7dG4sXL8aHH35ISxiSZnnVZ44Q0nFQMk0IIYQQ0ojt27fDz88PgwcPhpGREfbu3QsPDw+kpqbC2toaQUFBGD58OHx8fLBjxw6sWrUKFRUVmDx5Mr7//ntFh086oZd95gghHQcl04QQQgghjdDU1MQff/whV25lZSW3hObChQuxcOHC9gqNdFEv+8wRQjoODtPQIsqEEEIIIYQQQgh5KZqAjBBCCCGEEEIIaSZKpgkhhBBCCCGEkGaiZJoQQgghhBBCCGkmSqYJIYQQQgghhJBmomSaEEIIIYQQQghpJkqmCSGEEEIIIYSQZqJkuh0VFRXByMgIz549Y8vu3buHAQMGQE1NDcOGDUNSUpICI+x4Grpmo0ePBofDkfmJj49XYJSKl5KSgrfffhs6OjqwsrLCt99+y657Sp8xQgghpOurqqrCnDlzoK2tDUtLSxw+fPildQMDA2FpaQlNTU3MmzcPVVVVTT5OV7+vaK/reOLECTg7O0NTUxNeXl64c+fOazsnRWiv61gvNTUVmpqaCAkJafNzeSWGtIuSkhJmyJAhDAAmIyODYRiGKSsrY4yMjJi1a9cysbGxzKxZs5g+ffowUqlUwdF2DA1dM4ZhGFNTU+bs2bNMdnY2+yMWixUYqWKJRCKmT58+zIwZM5i4uDjm0qVLjKGhIfPzzz/TZ4wQQgjpJhYtWsR4enoyUVFRTGBgIKOqqsrcvXtXrl5YWBijoaHBHD9+nImIiGBcXV2ZJUuWNOk43eG+oj2u4507dxgVFRXm4MGDTGJiIrN27VpGW1ubycnJabfzfN3a4zo+b9y4cQwA5vbt26/1vF5EyXQ7uH//PmNnZ8f06dNHJjE8cOAA4+joyNarrq5mtLS0mKCgIAVF2nG87JqVlpYyAJjc3FwFR9hx3Lt3j1FWVmbKy8vZss2bNzNDhw6lzxghhJAGbdy4kamsrJQrLy0tZT799FMFRERao6KiglFVVWXCw8PZsoULFzJz586Vqzt79mxm0aJF7OuwsDBGTU2NqaysbPQ4Xf2+or2u4+LFi5n3339f5nj29vbMwYMH2/aEFKS9rmO9I0eOMIMHD1ZIMk3dvNvBjRs38N577+Gvv/6SKQ8PD4e3tzf7WlVVFf3790d4eHh7h9jhvOyaxcXFQVtbG0ZGRgqKrOOxsbHBxYsXoampyZZxOByUlpbSZ4wQQgjr7t27OHr0KI4ePYqNGzfi8OHD7Ov6n61bt2Lfvn2KDpU0U3R0NCQSCdzd3dkyb2/vBr/vX7w38PT0hEgkQkxMTKPH6er3Fe11HRctWoQ1a9bIHK/+3q0raK/rCACFhYVYtWoVfvnll9d0Nq+mpJB37WZWrVoFoK4v//Oys7PRt29fmTKBQICsrKx2iqzjetk1i4uLQ48ePTB58mSEh4fDzs4OW7duhaenpwKi7Bj09fUxevRo9rVQKMT+/fvxzjvvICEhgT5jhBBCAAB8Ph9r1qwBU9czEZs3bwaPx2O3czgcaGho4Ntvv1VglE1jbW0NDofTpLrJycmvORrFy87OhkAgkPl7vuz7Pjs7G6ampuxrHo8HfX19ZGVlgWGYVx6nq9+7ttd17NOnj8yx/v77bzx58gQjR45s61NSiPa6jgCwfPlyzJ07F87Ozq/pbF6NkmkFqqqqAp/Plynj8/kQCoUKiqjji4+PR1lZGd577z2sW7cOBw8exKhRoxAXFwdzc3NFh6dwDMPAz88P5eXl+Oyzz/D+++/TZ4wQQggAoF+/fmxi6eTkhLCwMOjo6Cg2qBb6+uuv2d8TEhKwY8cOBAQEwN3dHcrKynjw4AF27tyJgIAABUbZfppzT/mqumKx+JXH6er3ru11HZ+XlpaGuXPnYu7cuXB1dW2Ds1C89rqOf//9N0JCQvDo0aM2PoOmo2RagVRVVVFbWytTVltbCwMDAwVF1PF98803+OKLL6CtrQ0A+O9//4tbt27h999/x2effabg6BRLKpVi4cKFOHnyJP7++28YGRnRZ4wQQkiDiouLkZKSgn79+ik6lBaZNWsW+3vfvn0RGBiId955hy0bP348+vXrh+XLl8t1p+2KXvZ9r6Gh0ay6QqHwlcfp6vcV7XUd66WmpmLkyJGwsrLCnj172ugsFK89rmN1dTX8/f2xe/duqKmptf1JNBGNmVYgMzMz5OTkyJTl5ubCxMREQRF1fDwej02k6zk4OHSZ7kUtJZVKMXfuXBw7dgznz5/HoEGDANBnjBBCSMP09PRQUFCg6DDaRFJSEnr16iVXbmxsjOzsbAVE1P7MzMyQn58PqVTKlr3s+/7FewOxWIzCwkKYmJg0epyufl/RXtcRqPvcDh06FEZGRrh06ZJCE8K21h7X8d69e0hOTsbUqVOhqanJzh00duxYbN68+TWenSxKphXIy8sLYWFh7OuamhpERkZi4MCBCoyqY5s1axaWLl3KvpZKpYiJiYGDg4MCo1K8zz77DH/99RfOnTuHESNGsOX0GSOEENIQLy8vvPXWWxg6dCjmzJmDhQsXyvx0Jm+88QYWLFiA+/fvQygUoqamBqGhofDz88OUKVMUHV67cHNzAwBERESwZaGhoQ1+3794b3Dv3j3w+Xy4uro2epyufl/RXtextLQU48aNg4mJCa5cuQItLa3Xcj6K0h7X0dPTE4mJiYiKimJ/AODXX3/FokWLXsdpNaxd5w7v5lJSUmSWeSopKWH09fWZzz77jF2rz9XVtUut1ddaL16z33//nVFTU2P+/PNP5smTJ8zixYsZgUDAlJWVKThSxXnw4AHD4XCYbdu2yay9nZ+fT58xQgghDfL19X3lT2dSUlLCvPvuuwyPx2O4XC7D5XIZPp/PzJ8/n6mqqlJ0eO3Gz8+P6devHxMZGckEBgYyampqzL179xixWMxkZ2czQqGQYRiGuX37NqOqqsocOXKEiYiIYNzc3JiAgIBGj8Mw3ePetT2u47JlyxgdHR3m4cOHMvduFRUVCjnn16E9ruOLQOtMd20vJoYMwzChoaGMi4sLo6amxgwfPpx5+vSpAiPseBq6Zjt27GCsra0ZVVVVZsiQIUxMTIwCI1S89evXMwDkfnr16sUwDH3GCCGEdA8lJSXMvXv3mPv373fLh+zl5eXMjBkzGDU1NcbS0pL57bffGIb5917q+bWgf/nlF8bU1JTR0tJi5s6dK/PQ4WXHqdfV7yva4zpaWlo2eO/21Vdftdt5vm7t9Xl8niKSac7/f2NCCCGEENJNSKVSnD59GnFxcZBIJADqVoQQCoWIjo7GhQsXFBxh85SUlODIkSNITEzE2rVrER4eDkdHR9ja2io6NEJIF0azeRNCCCGEdDNLlizBb7/9Bg8PD9y+fRtDhw5FSkoK0tPTsWTJEkWH1yzR0dEYPXo0HB0dce/ePXzyySc4f/483nvvPbm5RAghpC3RBGSEEEIIId3Mn3/+iaNHjyIoKAj29vbYsWMHkpOT4evri/LyckWH1yxLly7F8uXLcfv2bXZN2p9//hkrV67EypUrFRwdIaQro2SaEEIIIaSbqaioQN++fQEAffr0wd27d8HhcLBy5UpcuXJFscE1U0REBKZPny5XPmfOHMTFxSkgIkJId0HJNCGEEEJIN+Pg4IBbt24BqEumQ0JCAADV1dWorKxUZGjNZmZmxi6L87xr167BwsKi/QMihHQbNGaaEEIIIaSbWb9+PWbOnAmRSIQPPvgALi4uKCsrQ3R0NMaMGaPo8Jpl48aN8PPzQ0REBMRiMY4ePYq0tDQcOXIEBw4cUHR4hJAujGbzJoQQQgjphpKSksAwDGxtbREVFYXff/8denp6WLp0KTQ0NBQdXrP8888/+PHHHxEfHw+JRAJ7e3ssW7YMgwYNUnRohJAujJJpQgghhJBuZv78+dixYwd69OghU15UVAQ/Pz+cPHlSQZE1X2hoKAYPHqzoMAgh3RB18yaEEEII6QauXr2Kf/75BwAQGBgIY2NjaGpqytR5+vQpgoKCFBFei40bNw46OjqYOnUqZsyYQa3RhJB2Q8k0IYQQQkg3YGtri++++w4Mw4BhGNy8eZNdSgoAOBwONDQ0cPDgQQVG2XwFBQW4fPkyTp06hbfffhvq6up49913MW3aNHh7eys6PEJIF0bdvAkhhBBCuhGGYTBv3jzs3LkTWlpayM7ORlhYGAwNDTF06FBFh9cqEokEwcHBOHPmDAIDA6Gjo4O0tDRFh0UI6aJoaawGlJeX48svv4S9vT1UVVUhEAgwe/ZspKamsnU2bNiA0aNHKy7IdhQZGYnw8PAm1R0+fDi+/vrr1xxRw44fP47CwsIW7x8ZGYkJEya0YUTN19DnasWKFfjll18a3dfKygocDgccDgdcLhdaWloYOXIkwsLC5OqmpqZi3rx5MDU1haamJgYOHIhz586x252cnLBlyxaZfZYtWwYOh8Mun1LPxcUFW7dulYvX19cXfn5+DcYqkUjg5eWFnJycRs+LEEJI2xCLxVi6dCl69OiBL7/8ElpaWrh48SJsbW0xb948TJo0CV5eXigqKlJ0qC0iEolw7do1nDhxAidPnoS2tjamTp2q6LBIO6u/F6r/UVVVxaBBg3D+/HmZemVlZfj8889hY2MDdXV19O7dG9999x1EIlGj73Hw4EG4u7tDQ0MDFhYWWLJkSavuQYG6ezMOh4Nnz541e99Dhw7JnXf9z9OnT3Ho0CHY2tq2Kj7SMEqmX1BeXg5vb29cuXIFP/74I548eYKzZ8+isrISgwcPRnFxsaJDbHdTp07F06dPFR3GK6WlpeG9995DdXV1i4/x8ccfY+3atW0YVdu4ceMGRo4c2aS6u3btQnZ2Np49e4bbt2/D2toao0ePRnR0NFsnPj4eHh4eqKqqwunTpxEVFYXp06dj6tSpOH78OADAx8eHHVdXLzg4GCYmJggODmbLysvLERcXJ9OS0ZR4eTwePvnkE6xatapJ50UIIaT1vvrqK5w/fx6HDx+GlZUVamtrMW/ePNjZ2SEzMxO5ubmwsbHBF198oehQm+XEiROYOXMmDA0N4efnBzU1NZw4cQLp6en44YcfFB0eUYAzZ84gOzsb2dnZiIuLw6BBgzB58mQkJCQAqLt/8fHxwc2bN7F//348fvwYX3/9Nfbt24e33noLEonkpcf++OOP8cUXX+Djjz9GdHQ0jh07hoiICLzxxhuoqalpr1OUY2VlxZ7z8z/W1tYKi6k7oGT6BZs2bUJRURGCg4Mxfvx4WFpawsvLC3/++Sd0dHQ63TiittAZRgK0NsagoCAIhUJ4enq2UURto6ioCEVFRbCzs2tSfW1tbRgbG8PU1BRubm7Yv38/PDw8sHr1arbORx99BA8PD/zxxx/w9PSEra0tli9fjpUrV2LVqlWQSCTw8fHB/fv3ZeJ4/PgxAgICZCamuXfvHtTV1dG/f/9mxzt9+nRcunQJ6enpTb0chBBCWuHo0aPYvXs3pkyZAj6fj2vXriE/Px/Lly9Hjx49oKysjI8//hhnzpxRdKjN8umnn8LAwADnz5/Hs2fP8OOPP9JY6W5OT08PxsbGMDY2hrW1NbZs2QI1NTVcuHABQN066yKRiG0AsLKywtSpU3Hz5k2Eh4djz549DR735s2b2L17N86ePQtfX1/Y2trCx8cHFy5cwJMnT/Dbb7+152nK4PF47Dk//8Pj8RQWU3dAyfRzpFIpAgMD8emnn8otFcHj8XD58mUsXbpUbr+Guk7Y2tri0KFDAOq6VX3++ecQCATQ1dWFr68vqqqqANQlH/PmzYOhoSH09PSwaNEitnW1trYW8+fPh56eHrS1tfH++++jpKSEfY+dO3eiZ8+e0NLSwvjx42W6oT8vODgYSkqyc82NHj0aGzZsAAD4+vpi2bJlGDduHNTU1DBo0CA8evQIQF237bS0NMyePZut//PPP8Pe3h58Ph/GxsYvbc19+PAhtLW18fPPPwMAqqur8dFHH0FXVxcCgQAfffQRKisrG9x3w4YNmDx5MgYNGgR9fX08ePAADx8+xOjRo6GpqQl1dXW88cYbyMzMBAD2qZuFhQXbctrU6wMAe/fuxcSJE9nXvr6++OKLLzBp0iSoqanB3d0diYmJ8PPzg4aGBpydnREVFQWg7u8/evRorF27FlpaWujZsyeuXLmCnTt3wtDQEMbGxvj999/ZY6enp2PKlCnQ0dGBsbExVq9e/dInoEFBQRg2bBj7+ttvv4WxsTF0dXWxceNG2NrayrQUv4jD4cDPzw9Xr15FdXU1MjIyEBwcjE8//RQcDkem7vLly/HXX3+By+XCx8cHqampbJelW7duwc3NDRMmTMCdO3dQW1sLALh79y4GDRrEfr5ejLekpARTp06Fmpoa7OzscO3aNXabkpISxo4di3379r00fkIIIW0nIyMDTk5O7OurV6+Cy+Vi/PjxbJm5uTlKS0sVEV6LPXv2DDt37oSPj4+iQyEdVH1CqaKiArFYjMDAQCxbtgxqamoy9czNzeHr64v9+/c3eJzDhw9j0KBB8PLykinX19fH9evXMXXqVISEhEBZWVmmN+uFCxdgYWEBhmFQUVGBBQsWQFdXF0ZGRli5ciWkUqncexUWFuK9996DpqYmevbsiTVr1kAsFrf2UrwyL6ipqYGtrS2WLFnCbhs3bhzefvvtVr9vV0XJ9HOSkpKQn5+PIUOGNLjdwsICysrKzT7u2rVrcezYMRw9ehQ3b95EdHQ024Vq0qRJSExMxJUrV3DmzBkEBwdjxYoVAOqSwfDwcNy4cQOhoaFISEjAN998AwD466+/sGXLFuzduxf3799Hr169MHbs2CaN82jI7t27MXDgQERGRsLe3h5vv/02hEIhTp48CXNzc+zatQsrV65EUFAQVq5ciW3btiEhIQGbN2/GN998I9clODMzE+PHj8enn36KRYsWAQBWrVqF2NhYXL9+HZcuXcLjx49l/rG+6PTp01iwYAGuXr0KV1dXTJw4Ec7OzoiJicGNGzeQkZGBb7/9FkBdCylQN+7Z29u7WdeHYRhcvXpVbqzy9u3b8c477+DBgweoqamBh4cHrK2t8c8//8DExARffvklW/f27dsoKirCgwcPMGzYMMyYMQM3b97E7du3MXv2bHz00UcQi8Wora1lu0CHhobi4MGDOHjwILZt29bgNbhx4wZGjBgBoC5prz+n4OBgXL16FcnJya/8uwJ1459FIhESExMRExMDABgwYIBcPQMDA7i7u4PD4cDGxgampqZs63RwcDCGDRsGZ2dnqKurs2Po7969K9fFuz5eADh58iSGDx+OR48eYciQIZgzZ45ML4LRo0fjypUrjZ4DIYSQ1jMxMZGZjOv8+fPw9PSEgYEBW3b//n2Ym5srIrxmGTNmDMaOHYuxY8fijTfeYH9v6Id0b5WVlVi3bh2EQiHGjh2LpKQkFBUVwcPDo8H6Pj4+iI6OhlAolNsWExPT4D0UAHh6ekJPTw+DBw+GsbExzp49y277888/MX36dHA4HCxYsAD379/H5cuXcfbsWZw4cQI7duyQO56vry/EYjHu3buHo0eP4vTp0699XiJVVVXs2bMHe/fuxaNHj3Ds2DHcuXOHbRgjDWAIKywsjAHAJCQksGWnTp1iNDQ02J9x48YxDMMw69evZ0aNGsUwDMP8+uuvTK9evWSO1atXL+bXX39lpFIpo6+vzxw5coTddufOHWb79u1MVFQUA4BJTk5mt924cYPh8XhMWVkZExAQwPTv358pLi5mGIZhnjx5wjx+/JhhGIbx9vZm9uzZw+4nlUoZc3Nz5syZM3LnFRQUxPB4PJmyUaNGMevXr2cYhmHmzp3LeHp6stsqKysZbW1t5ty5cwzDMIylpSXz22+/MQzDMP/88w9z4sQJmWNZWloyhw8fZhiGYYYNG8asWrWKcXNzY/z9/dk6FRUVDJ/PZ+Li4tiyxMREhsPhsOf3vPXr1zNmZmYy+2/ZsoWpqamRqTNy5EiGYRgmJSWFAcBkZGQ0+/okJyczAJj8/Hy2bO7cucyQIUPY119++SVjaWnJvj58+DBjY2PDMEzd35/P5zPV1dUMwzDM1atXGQBMSkoKwzAMk56ezgBg0tPTmdOnTzM9evRgSktL2WMdPHiQMTIyYs+p/nPFMAzTu3dvJjU1lWEYhvHy8mK++uordltCQgIDgAkKCmIYRvbv9LzExEQGABMSEsL8/vvvDABGJBLJ1XvRtGnTmE2bNjEMwzBubm7M2bNnGYZhmMmTJ7PlAoGAuXXrVoPxzp07lxk+fDi77dGjRwwAJicnhy0LCwtjlJSUGKlU2mg8hBBCWmfNmjVM//79mYsXLzL/+c9/GA6HI/O98ejRI8bGxob5/PPPFRhl02zYsKHJP6R7AcCoqamx9+4AGGNjY/ZeNTQ0VO5+/3n193GZmZly2+zs7JjVq1c3GsPy5cuZCRMmMAzDMEKhkNHR0WHCw8OZoqIihsfjMSEhIWzd8+fPM4GBgTL3sgkJCQyPx2MqKirYeteuXWN0dXUbfL9ff/2V4XA4MjmLhoYG89NPP7Hb63OVxvIChmGY999/nxk2bBhjbGzM/PLLL42eb3dG60w/R1dXFwBkulKPGTOG7c67e/duREZGNuuYBQUFKCwsZMeUAsDAgQMxcOBAHD9+HIaGhjITA3h5eUEikSApKQmLFy/GiRMnYGxsjJEjR2L69OmYPXs2gLpJpD799FOsXLmS3be6uhqJiYnNPW0AkBlbpK6uDkdHR8TFxcl16xgwYACUlZWxZs0axMbGIioqCmlpaTLdlH/88UeIRCJMmzaNLUtOTkZtbS3c3d1ljscwDJKSkhp8ymdlZcX+rqGhAX9/f+zduxeRkZF4/PgxHjx48NIxUc25Pvn5+QD+/fvXs7GxYX9XU1NDz549ZV4//8TSxMQEqqqq7DYAbP3610KhEPHx8XB0dISWlha7r5eXF/Ly8mQ+dwCQk5MDoVAIS0tLAMCjR4/YrvYAYGdnJxdzQ8rKygAAWlpa0NfXBwAUFxfD0NDwlfv5+Pjg+vXrKCoqQmxsLNtjY9iwYbh8+TJSU1NRWlrKjjN/MV5A9m+ora0NADKTc+jp6UEsFqO4uBh6enqNngshhJCWW7duHSoqKvDBBx+Ay+Viw4YN+OCDDwAAn332Gf7v//4P48aNw7p16xQcaePWr1/fYHlZWRnEYjF9p3Rzv/76KwYMGMCunW5sbMxuq78XyszMbHCOl/phDvX3Lc/T19dv0mTE7733HoYMGYLy8nLcvn0bOjo68PLywv379yGRSGTygrfeegsAZIYjxsfHQyKRQCAQsGUMw6CqqgqFhYXsOTzPwsIC169flylr7F7vZbZv3w5bW1s4OztjwYIFLTpGd0HJ9HN69eoFXV1d3L17l+36oaGhwY6Hftl/zC+OPQXAjml4Vbfw+uTrefVjJsRiMRwdHfH06VOcO3cOZ8+exZIlS/D333/jyJEjkEgk2LVrl8z41JfF+Kr46r04dkIikYDLlR8FcOnSJUyZMoVdQuP777/HuHHjZOp4eHhg4cKF+OijjzBnzhxYWFiwyXZ4eLjceZuZmcm9DyB7fcrLy+Hu7g4zMzNMmDABvr6+uHbtmtwyTc/H39zrw7wwidmL16Sh6/Gyui+r39jf/HkvdplWUlJqcExNY2JiYsDn82FnZwdjY2NwOBxERETI/d3S09OxcOFC7N+/H+bm5vDx8cH333+PkJAQuLi4QEdHB0BdMr1x40bcvXsXnp6eUFFRaTBeAA1OevH8da4/n1ddW0IIIW1DWVkZ27dvx/bt2+W2zZo1C9OnT39pF9aOjGEYbNu2Ddu2bUNeXh6AuqRn0aJF2LhxY4P3QaRrMzMze+lSUL169YKenh4iIyMxfPhwue3h4eFwcHCAhoaG3LYBAwbIDW2st3btWujr62PZsmXw8PCAmZkZLly4gGvXrrENTE0dLiqRSKCvr9/g0rQNJfn1x27K8ldNyQuSkpLYRqDc3FyZhxFEFt3BPkdZWRm+vr7Yvn07Kioq5LZnZWU1uB+fz5epLxQKkZubCwDQ0dGBgYGBzNJEV65cgaurK+zt7ZGfny8z7jU8PBw8Hg82NjY4fPgwrly5gvfffx/Hjh3D4cOH8ddffwEA7O3tkZWVBVtbW9ja2sLKygpffPEFnjx50mB8EolEpkXw+TFTAGTiq6iowJMnT+Dq6gpA9h/dgQMH4O/vj927d2Pu3LkwMzNDdna2TII0btw4zJkzB/3792dbhnv16gUej4fi4mI2ZolEgpUrVzY4JuVFN2/eRGFhIa5fv47ly5dj2LBhSEtLY9/3xf8YmnN96p/6tXZ9wKawt7dHfHw821oM1P3N9fX15Z4yvpicOjk5sb0kgLonmE15OhoYGIjx48dDVVUVhoaGGDNmDHbu3ClXb8+ePYiJiYGJiQkAwM3NDeXl5Th37pzMQwlXV1cwDIPTp0+/crx0UxQWFoLP57OJOiGEEMVwdXXtlIk0UJfE/PDDD/j222/x6NEjxMTEYPPmzdi/fz82b96s6PBIB6OkpIS5c+fihx9+YCfCDQwMxOjRoxEcHIxff/0V8+bNa3DfWbNm4d69e3JJbk5ODnbt2gU+n8+WzZgxAxcvXsTly5cxY8YMAHW9Hrlcrsx99969e+V6gtrb26OoqAh8Pp+9l83MzMS6deta/XCosbxAJBLB398fq1atgre3N5YtW9aq9+vqKJl+wVdffcVOHnD69GmkpqYiLCwMs2bNwp49exqcKXLAgAHIzc3Fjh078PTpUyxevFhm+5IlS7B69WrcunULUVFR+OKLLzBy5Ej07t0bY8eOxZw5c/DgwQOEhITg448/xowZM6Cnp4eSkhIsW7YMt27dQlJSEv766y/069cPQN0yEFu3bsXx48fx9OlTLFmyBDdv3oSDg4NcfM7OzlBVVcXatWuRkpKCNWvWICcnR6bO33//jV27diE+Ph7+/v4wMzNjEyMNDQ3Ex8ejtLQUenp6CA0NRWxsLKKjo/H++++jpqamwYR4+/bt+N///ofg4GD06NEDH374IRYtWoSQkBDExMRg3rx5qKioaFIipaenh9LSUpw9exYpKSn48ccfcezYMfZ9658exsTEoKamplnXx8LCAgYGBuwM5q/T2LFjYWFhAV9fX8TGxuLSpUtYu3Yt/P395f5zDAoKklmvecmSJdi2bRvOnz+Phw8fYv78+QBkHySUlpYiJycH2dnZiIqKwpw5c3Dv3j124joA2LZtG8LCwvD+++8jIiIC8fHx2LhxI7Zu3Yr//ve/bGsyj8fDwIEDcezYMZlkmsvlYsiQITh79qxMMv1ivE3x8OFD9jNNCCGEtET9ZJ6+vr7o3bs3nJ2d4efnhwMHDtDESaRBGzduhIGBAUaOHIkbN27A09MTHA4HI0aMgKam5ksTyEGDBmHevHmYOHEifvvtNyQnJ+PatWt48803YWdnx96bAXVdvf/880+oqamxD6q0tLQwe/ZsBAQEICIiAqGhodi8ebPcJLhOTk4YM2YMZs2ahcjISISHh2PhwoVQV1dv9VJXjeUFW7duRWlpKb788kts374dp06dwuXLl1v1nl0ZJdMv0NDQwO3btzFjxgysXr0avXv3xsSJE1FVVYWgoKAGZ9Gzt7fHd999h6+//hqenp4wMzOTST6+/PJLvP3225g8eTJGjhwJDw8Pdhbq3377DaamphgyZAgmT56MN954g52Ovz6xnjFjBlxcXFBQUIAjR44AAN5//32sW7cOK1euhKurK2JjY3H58uUGuzFraWnhl19+wbFjx9CnTx8UFhZi5syZMnXeeecdnDlzBv3790dOTg4uXrzIdl329/fH9u3bsX79emzYsAHa2trw9PTE22+/DRsbG7z77rsyT9jqubu744MPPkBAQADEYjF++OEHDB48GBMmTMDQoUNhYWGBY8eONenv4u3tjS+++AILFixAv379cO7cOWzbtg1xcXGora2FgYEB3nvvPUyePBkXLlxo1vXhcDgYO3bsS7uMtyUej4czZ86gsrIS7u7u8PPzw0cffYRNmzbJ1EtLS4OysjLbSgzUPQ39+OOP4evri2HDhmHs2LFQUlKSeQr68ccfw8TEBGZmZnjzzTdRXl6OkJAQmaVQXFxcEBISAqFQiPHjx8PDw4OdTX7q1Kkycfj4+KCqqkpuhvthw4ahtraWHbPeULxNERYWhjfeeKNZ+xBCCCHPq66ulpmjo56lpSWKioraPyDS4fXo0QO3bt3CsGHD8OGHH6Jfv35ITU3FypUroaKiggkTJiA7O7vBffft24fPPvsM3333HVxcXODn5wcfHx9cvnxZZjifq6srLC0tMX36dJn9d+zYAUdHRwwfPhxTpkzBzJkzG1x697fffoOJiQmGDRuGt99+G8OHD2+wZ2FzvSovSElJwVdffYX/+7//g7q6OhwcHLB06VIsXryYXbqXyOIwLw4UJd2Or68vlJSUXrqmXndw48YNLF26tF1ap1vq5s2bsLW1ZceYFxQUwNDQECkpKQ3eRHR0NTU1MDU1RWRkZKeMnxBCSMcwZcoU8Hg8HDp0iO2pVlFRgblz56KqqgqXLl1ScISkM6mqqsLPP/8Mf3//BsdNN5VIJIKJiQmuX78ONze3NoyQdCQ0ARkhAEaOHAl1dXXcuXMHgwYNUnQ4DTp58iQiIyOxe/duKCsrY9OmTXB3d++0iejx48cxbty4Ths/IYSQjmHHjh0YMWIEzMzM4OjoCKBuNmQLCwucOXNGwdGRzkZdXR3Lly9v1TEuXLiAs2fPwtramhLpLo6SaUL+v59++gkbNmzAhQsXFB1KgzZu3IjFixdj2LBhEIlEGDFiBE6ePKnosFpEIpHgv//9L93kEEIIaTULCwvExcXh4sWLePLkCVRVVWFvb4+xY8fSahFEIbZs2YLk5GScOnVK0aGQ14y6eRNCCCGEkE5rwYIFmDFjBkaOHEnJMyGkXdH/OIQQQgghpNOSSqWYOXMmBAIBFi5ciL///htSqVTRYRFCugFqmSaEEEIIIZ2aVCrFzZs3cerUKZw+fRo1NTWYPHkypk+fjlGjRik6PEJIF0XJNCGEEEII6TKEQiG2b9+OzZs3o7KyEhKJRNEhEUK6KJqAjBBCCCGEdGoVFRW4cOECTp06hUuXLsHExARLly6VW+OXEELaEiXThBBCCCGk03rzzTcRHBwMc3NzTJs2DTdv3kTfvn0VHRYhpBugCcgIIYQQQkinkpWVxf7u5uaG0NBQJCYmYvPmzWwiLRQKceLECQVFSAjpDmjMNCGEEEII6VR4PB6ys7NhZGTElr311lvYv38/TExMAAC5ubkwNTWlMdOEkNeGWqYJIYQQQkin0lBb0K1bt1BdXd1oPUIIaSuUTBNCCCGEkC6Jw+EoOgRCSBdGyTQhhBBCCCGEENJMlEwTQgghhBBCCCHNREtjEUIIIYSQTufkyZPQ0tJiX0skEpw9e5adlKy0tFRRoRFCugmazZsQQgghhHQqVlZWTR4PnZKS8pqjIYR0V5RME0IIIYQQQgghzURjpgkhhBBCCCGEkGaiZJoQQgghhBBCCGkmSqYJIYQQQgghhJBmomSaEEIIIYQQQghpJkqmCSGEEEIIIYSQZqJkmhBCCCGEEEIIaSZKpgkhhBBCCCGEkGaiZJoQQgghhBBCCGkmSqYJIYQQQgghhJBmomSaEEIIIYQQQghpJkqmCSGEEEIIIYSQZvp/dJhKgHY9lj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 959x350 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# layout\n",
    "fig = plt.figure(figsize = (6.85, 2.5), dpi = 140)\n",
    "subfigs = fig.subfigures(1, 4, width_ratios = [1.1, 1.1, 0.5, 1])\n",
    "skip, panel_c = subfigs[0].subplots(2, 1, \n",
    "                        gridspec_kw = {'height_ratios':[1, 4]})\n",
    "panel_e = subfigs[2].subplots()\n",
    "panel_f, panel_g = subfigs[3].subplots(2, 1, sharex = True,\n",
    "                        gridspec_kw = {'hspace':0})\n",
    "skip.axis('off')\n",
    "\n",
    "# Panel C - Aero-types\n",
    "aerotype_plot(hplc_rates, panel_c)\n",
    "\n",
    "# Panel E - TCA flux in strain models\n",
    "TCA_flux_bar(strain_fluxes, ax = panel_e)\n",
    "\n",
    "# Panel F & G - TCA proteome fraction & NADH production\n",
    "pq_sweep_plot(pq_sweep, [panel_f, panel_g])\n",
    "\n",
    "#plt.savefig('output/fig6_raw.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}