diff --git a/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.Rmd b/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.Rmd index cc2cb6a..bc2a3f8 100644 --- a/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.Rmd +++ b/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.Rmd @@ -46,7 +46,7 @@ set.seed(42) n_species <- 10 n_samples <- 50 -species <- str_c("Species ", seq(n_species)) +species <- str_c("Sp. ", seq(n_species)) ``` ## Actual abundances @@ -167,7 +167,6 @@ species_params %>% Question: Is it true that the first species is driving the association? - The measured (i.e. estimated) proportions and abundances are given by perturbing the actual abundances by the efficiencies, and normalizing to proportions or to the original (correct) total. ```{r} @@ -216,12 +215,12 @@ abun <- bind_rows( ```{r} p_species_all <- abun %>% ggplot(aes(x, log2_abundance, color = type)) + - labs(y = "Log abundance ") + + labs(y = "Log efficiency", x = "Condition (x)") + facet_grid(.otu~type, scales = "fixed") + geom_quasirandom(alpha = 0.3, groupOnX = TRUE) + # stat_summary(fun.data = "mean_cl_boot", geom = "pointrange") + - stat_summary(geom = "point") + - stat_smooth(aes(x = as.integer(x)), + stat_summary(geom = "point", fun.data = mean_se) + + stat_smooth(aes(x = as.integer(x)), formula = y~x, method = "lm", size = 0.9, fill = 'grey', se = FALSE ) + theme(legend.position = "none") @@ -231,11 +230,11 @@ p_species_all ```{r} p_mean_eff <- mean_eff %>% ggplot(aes(x, log2(mean_efficiency))) + - labs(y = "Log efficiency ") + + labs(y = "Log efficiency", x = "Condition (x)") + geom_quasirandom(alpha = 0.3, groupOnX = TRUE) + # stat_summary(fun.data = "mean_cl_boot", geom = "pointrange") + - stat_summary(geom = "point") + - stat_smooth(aes(x = as.integer(x)), + stat_summary(geom = "point", fun.data = mean_se) + + stat_smooth(aes(x = as.integer(x)), formula = y~x, method = "lm", size = 0.9, color = 'black', fill = 'grey', se = FALSE ) p_mean_eff @@ -284,21 +283,28 @@ lm_results_slope <- lm_results %>% ```{r, fig.dim = c(5.5, 7)} # params for arrows showing error delta <- lm_results_mean_eff %>% filter(term == "x1") %>% pull(estimate) -start <- lm_results_slope %>% filter(type == 'Actual', .otu == 'Species 9') %>% +start <- lm_results_slope %>% filter(type == 'Actual', .otu == 'Sp. 9') %>% pull(estimate) p_coef_ci <- lm_results_slope %>% ggplot(aes(y = .otu, x = estimate, color = type)) + - labs(x = "Log fold difference", y = NULL) + + labs(x = "Mean LFD", y = NULL, color = "Type") + geom_vline(xintercept = 0, color = "grey") + geom_pointinterval(aes(xmin = conf.low, xmax = conf.high)) + theme(legend.position = 'top') + guides(color = guide_legend(reverse = TRUE)) + annotate( - geom = 'segment', color = "darkgrey", + geom = 'segment', color = "darkred", arrow = grid::arrow(length = unit(0.1, "inches")), x = start, xend = start - delta, - y = 10.5, yend = 10.5 + y = 10.5, yend = 10.5, + size = .7 + ) + + annotate( + geom = 'text', color = "darkred", + label = 'Effect of bias', + x = start - delta/2, + y = 10.8, ) + coord_cartesian(clip = 'off') @@ -315,19 +321,20 @@ p_coef_ci_mean_eff <- lm_results_mean_eff %>% min(lm_results_slope$conf.low), max(lm_results_slope$conf.high) )) + - labs(x = "Log fold difference", y = NULL) + + labs(x = "Mean LFD", y = NULL) + geom_vline(xintercept = 0, color = "grey") + geom_point() + annotate( - geom = 'segment', color = "darkgrey", + geom = 'segment', color = "darkred", arrow = grid::arrow(length = unit(0.1, "inches")), x = 0, xend = delta, - y = 1.0, yend = 1.0 + y = 1.0, yend = 1.0, + size = .7 ) p_coef_dot <- lm_results_slope %>% ggplot(aes(y = type, x = estimate, fill = type)) + - labs(x = "Log fold difference", y = "Type") + + labs(x = "Mean LFD", y = "Type") + geom_vline(xintercept = 0, color = "grey") + stat_dots() @@ -353,7 +360,7 @@ Chose species that show the full range of qualitative behaviors in terms of the ```{r} -species_to_plot <- str_c("Species ", c('9', '10', '5', '7', '2')) +species_to_plot <- str_c("Sp. ", c('9', '10', '5', '7', '2')) p_species_focal <- p_species_all p_species_focal$data <- p_species_focal$data %>% filter(.otu %in% species_to_plot) @@ -380,7 +387,7 @@ p_mean_eff1 <- p_mean_eff + (p_mean_eff1 + ggtitle("Mean efficiency")) + (p_coef_ci_mean_eff + ggtitle("LFD in mean efficiency")) + (p_species_focal + ggtitle("Actual and measured\nabundances of select species")) + - (p_coef_ci1 + ggtitle("Estimated LFDs of all species") + + (p_coef_ci1 + ggtitle("Mean LFD estimated from\nactual or measured abundances") + theme(legend.box.margin = margin(b = -15)) ) + plot_layout(ncol = 2, heights = c(0.2, 1)) + @@ -465,3 +472,66 @@ The prediction for the squared standard error from the theoretical calculation a TODO: Sort out why it is this version, and not the other, that gives agreement. Perhaps the standard errors being returned by R are using the MLE estimate of sigma instead of the OLS estimate? + +## 2022-10-08 Modified main figure + +New panel showing simulated efficiencies and mean LFDs, to replace the panel showing the change in mean efficiency (former panel B). + +```{r} +p_params <- species_params %>% + ggplot(aes(x1, log2_efficiency)) + + theme(axis.line = element_blank()) + + # panel_border(remove = TRUE) + + # theme_minimal_grid() + + coord_cartesian(clip = 'off') + + geom_vline(xintercept = 0, color = 'grey') + + geom_hline(yintercept = 0, color = 'grey') + + geom_text( + aes(label = str_c('Sp. ', str_extract(.otu, '[0-9]+'))), + size = 4) + + labs( + x = 'Expected LFD', + y = 'Log efficiency' + ) +p_params +``` + +Annotate mean efficiency plot, + +```{r} +# params for arrow showing change +arrow_params <- mean_eff %>% + with_groups(x, summarize, across(log2_mean_efficiency, mean)) %>% + pull(log2_mean_efficiency) + +p_mean_eff2 <- p_mean_eff + + annotate( + geom = 'segment', color = "darkred", + arrow = grid::arrow(length = unit(0.1, "inches")), + x = 2.5, xend = 2.5, + y = arrow_params[1], + yend = arrow_params[2], + size = .7 + ) +p_mean_eff2 +``` + +Assemble with improved titles, + +```{r main-figure-2, fig.dim = c(8, 9)} + +(p_params + ggtitle("Simulated log efficiencies\nand expected LFDs")) + + (p_mean_eff2 + ggtitle("Mean efficiency across\nsamples in each condition")) + + # (p_coef_ci_mean_eff + ggtitle("LFD in mean efficiency")) + + (p_species_focal + ggtitle("Actual and measured\nabundances of select species")) + + (p_coef_ci1 + ggtitle("Mean LFD estimated from\nactual or measured abundances") + + theme(legend.box.margin = margin(b = -15)) + ) + + plot_layout(ncol = 2, heights = c(0.4, 1)) + + plot_annotation(tag_levels = 'A') & + colorblindr::scale_color_OkabeIto() & + colorblindr::scale_fill_OkabeIto() & + theme( + plot.title = element_text(face = "plain") + ) +``` diff --git a/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.html b/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.html index c6ae5d9..3999bda 100644 --- a/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.html +++ b/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example.html @@ -118,7 +118,7 @@ @@ -1517,21 +1517,29 @@
n_species <- 10
n_samples <- 50
-species <- str_c("Species ", seq(n_species))
+species <- str_c("Sp. ", seq(n_species))
@@ -1613,28 +1621,28 @@ #> # A tibble: 20 × 3
-#> term .otu truth
-#> <chr> <chr> <dbl>
-#> 1 (Intercept) Species 1 -0.0905
-#> 2 (Intercept) Species 2 2.91
-#> 3 (Intercept) Species 3 -0.137
-#> 4 (Intercept) Species 4 2.18
-#> 5 (Intercept) Species 5 -0.153
-#> 6 (Intercept) Species 6 0.583
-#> 7 (Intercept) Species 7 0.913
-#> 8 (Intercept) Species 8 0.524
-#> 9 (Intercept) Species 9 -0.815
-#> 10 (Intercept) Species 10 1.98
-#> 11 x1 Species 1 1.90
-#> 12 x1 Species 2 -3.52
-#> 13 x1 Species 3 -3.83
-#> 14 x1 Species 4 -0.410
-#> 15 x1 Species 5 0.917
-#> 16 x1 Species 6 -0.192
-#> 17 x1 Species 7 -0.402
-#> 18 x1 Species 8 -2.00
-#> 19 x1 Species 9 3.30
-#> 20 x1 Species 10 1.88
+#> term .otu truth
+#> <chr> <chr> <dbl>
+#> 1 (Intercept) Sp. 1 -0.0905
+#> 2 (Intercept) Sp. 2 2.91
+#> 3 (Intercept) Sp. 3 -0.137
+#> 4 (Intercept) Sp. 4 2.18
+#> 5 (Intercept) Sp. 5 -0.153
+#> 6 (Intercept) Sp. 6 0.583
+#> 7 (Intercept) Sp. 7 0.913
+#> 8 (Intercept) Sp. 8 0.524
+#> 9 (Intercept) Sp. 9 -0.815
+#> 10 (Intercept) Sp. 10 1.98
+#> 11 x1 Sp. 1 1.90
+#> 12 x1 Sp. 2 -3.52
+#> 13 x1 Sp. 3 -3.83
+#> 14 x1 Sp. 4 -0.410
+#> 15 x1 Sp. 5 0.917
+#> 16 x1 Sp. 6 -0.192
+#> 17 x1 Sp. 7 -0.402
+#> 18 x1 Sp. 8 -2.00
+#> 19 x1 Sp. 9 3.30
+#> 20 x1 Sp. 10 1.88
#> # A tibble: 10 × 5
-#> .otu intercept x1 efficiency log2_efficiency
-#> <chr> <dbl> <dbl> <dbl> <dbl>
-#> 1 Species 1 -0.0905 1.90 0.0225 -5.48
-#> 2 Species 2 2.91 -3.52 1.15 0.200
-#> 3 Species 3 -0.137 -3.83 0.213 -2.23
-#> 4 Species 4 2.18 -0.410 0.0748 -3.74
-#> 5 Species 5 -0.153 0.917 2.98 1.58
-#> 6 Species 6 0.583 -0.192 0.339 -1.56
-#> 7 Species 7 0.913 -0.402 0.892 -0.165
-#> 8 Species 8 0.524 -2.00 1.73 0.787
-#> 9 Species 9 -0.815 3.30 162. 7.34
-#> 10 Species 10 1.98 1.88 9.62 3.27
+#> .otu intercept x1 efficiency log2_efficiency
+#> <chr> <dbl> <dbl> <dbl> <dbl>
+#> 1 Sp. 1 -0.0905 1.90 0.0225 -5.48
+#> 2 Sp. 2 2.91 -3.52 1.15 0.200
+#> 3 Sp. 3 -0.137 -3.83 0.213 -2.23
+#> 4 Sp. 4 2.18 -0.410 0.0748 -3.74
+#> 5 Sp. 5 -0.153 0.917 2.98 1.58
+#> 6 Sp. 6 0.583 -0.192 0.339 -1.56
+#> 7 Sp. 7 0.913 -0.402 0.892 -0.165
+#> 8 Sp. 8 0.524 -2.00 1.73 0.787
+#> 9 Sp. 9 -0.815 3.30 162. 7.34
+#> 10 Sp. 10 1.98 1.88 9.62 3.27
p_species_all <- abun %>%
ggplot(aes(x, log2_abundance, color = type)) +
- labs(y = "Log abundance ") +
+ labs(y = "Log efficiency", x = "Condition (x)") +
facet_grid(.otu~type, scales = "fixed") +
geom_quasirandom(alpha = 0.3, groupOnX = TRUE) +
# stat_summary(fun.data = "mean_cl_boot", geom = "pointrange") +
- stat_summary(geom = "point") +
- stat_smooth(aes(x = as.integer(x)),
+ stat_summary(geom = "point", fun.data = mean_se) +
+ stat_smooth(aes(x = as.integer(x)), formula = y~x,
method = "lm", size = 0.9, fill = 'grey', se = FALSE
) +
theme(legend.position = "none")
@@ -1818,11 +1826,11 @@ Estimated and actual
p_mean_eff <- mean_eff %>%
ggplot(aes(x, log2(mean_efficiency))) +
- labs(y = "Log efficiency ") +
+ labs(y = "Log efficiency", x = "Condition (x)") +
geom_quasirandom(alpha = 0.3, groupOnX = TRUE) +
# stat_summary(fun.data = "mean_cl_boot", geom = "pointrange") +
- stat_summary(geom = "point") +
- stat_smooth(aes(x = as.integer(x)),
+ stat_summary(geom = "point", fun.data = mean_se) +
+ stat_smooth(aes(x = as.integer(x)), formula = y~x,
method = "lm", size = 0.9, color = 'black', fill = 'grey', se = FALSE
)
p_mean_eff
@@ -1880,21 +1888,28 @@ Regression coefficients
# params for arrows showing error
delta <- lm_results_mean_eff %>% filter(term == "x1") %>% pull(estimate)
-start <- lm_results_slope %>% filter(type == 'Actual', .otu == 'Species 9') %>%
+start <- lm_results_slope %>% filter(type == 'Actual', .otu == 'Sp. 9') %>%
pull(estimate)
p_coef_ci <- lm_results_slope %>%
ggplot(aes(y = .otu, x = estimate, color = type)) +
- labs(x = "Log fold difference", y = NULL) +
+ labs(x = "Mean LFD", y = NULL, color = "Type") +
geom_vline(xintercept = 0, color = "grey") +
geom_pointinterval(aes(xmin = conf.low, xmax = conf.high)) +
theme(legend.position = 'top') +
guides(color = guide_legend(reverse = TRUE)) +
annotate(
- geom = 'segment', color = "darkgrey",
+ geom = 'segment', color = "darkred",
arrow = grid::arrow(length = unit(0.1, "inches")),
x = start, xend = start - delta,
- y = 10.5, yend = 10.5
+ y = 10.5, yend = 10.5,
+ size = .7
+ ) +
+ annotate(
+ geom = 'text', color = "darkred",
+ label = 'Effect of bias',
+ x = start - delta/2,
+ y = 10.8,
) +
coord_cartesian(clip = 'off')
@@ -1911,19 +1926,20 @@ Regression coefficients
min(lm_results_slope$conf.low),
max(lm_results_slope$conf.high)
)) +
- labs(x = "Log fold difference", y = NULL) +
+ labs(x = "Mean LFD", y = NULL) +
geom_vline(xintercept = 0, color = "grey") +
geom_point() +
annotate(
- geom = 'segment', color = "darkgrey",
+ geom = 'segment', color = "darkred",
arrow = grid::arrow(length = unit(0.1, "inches")),
x = 0, xend = delta,
- y = 1.0, yend = 1.0
+ y = 1.0, yend = 1.0,
+ size = .7
)
p_coef_dot <- lm_results_slope %>%
ggplot(aes(y = type, x = estimate, fill = type)) +
- labs(x = "Log fold difference", y = "Type") +
+ labs(x = "Mean LFD", y = "Type") +
geom_vline(xintercept = 0, color = "grey") +
stat_dots()
@@ -1960,7 +1976,7 @@ Manuscript figure
-species_to_plot <- str_c("Species ", c('9', '10', '5', '7', '2'))
+species_to_plot <- str_c("Sp. ", c('9', '10', '5', '7', '2'))
p_species_focal <- p_species_all
p_species_focal$data <- p_species_focal$data %>%
filter(.otu %in% species_to_plot)
@@ -1989,7 +2005,7 @@ Manuscript figure
(p_mean_eff1 + ggtitle("Mean efficiency")) +
(p_coef_ci_mean_eff + ggtitle("LFD in mean efficiency")) +
(p_species_focal + ggtitle("Actual and measured\nabundances of select species")) +
- (p_coef_ci1 + ggtitle("Estimated LFDs of all species") +
+ (p_coef_ci1 + ggtitle("Mean LFD estimated from\nactual or measured abundances") +
theme(legend.box.margin = margin(b = -15))
) +
plot_layout(ncol = 2, heights = c(0.2, 1)) +
@@ -2053,18 +2069,18 @@ Standard errors
#> # A tibble: 10 × 2
-#> .otu cov
-#> <fct> <dbl>
-#> 1 Species 9 0.565
-#> 2 Species 1 -0.111
-#> 3 Species 10 -0.163
-#> 4 Species 5 -0.0326
-#> 5 Species 6 -0.0115
-#> 6 Species 7 -0.0672
-#> 7 Species 4 -0.172
-#> 8 Species 8 -0.105
-#> 9 Species 2 0.0449
-#> 10 Species 3 0.0338
+#> .otu cov
+#> <fct> <dbl>
+#> 1 Sp. 9 0.565
+#> 2 Sp. 1 -0.111
+#> 3 Sp. 10 -0.163
+#> 4 Sp. 5 -0.0326
+#> 5 Sp. 6 -0.0115
+#> 6 Sp. 7 -0.0672
+#> 7 Sp. 4 -0.172
+#> 8 Sp. 8 -0.105
+#> 9 Sp. 2 0.0449
+#> 10 Sp. 3 0.0338
@@ -2079,18 +2095,18 @@ Standard errors
#> # A tibble: 10 × 4
-#> .otu se_Actual se_Measured cov
-#> <fct> <dbl> <dbl> <dbl>
-#> 1 Species 3 0.292 0.341 0.0338
-#> 2 Species 2 0.311 0.354 0.0449
-#> 3 Species 8 0.244 0.336 -0.105
-#> 4 Species 4 0.252 0.357 -0.172
-#> 5 Species 6 0.278 0.340 -0.0115
-#> 6 Species 7 0.290 0.363 -0.0672
-#> 7 Species 5 0.269 0.337 -0.0326
-#> 8 Species 1 0.283 0.367 -0.111
-#> 9 Species 10 0.289 0.383 -0.163
-#> 10 Species 9 0.270 0.130 0.565
+#> .otu se_Actual se_Measured cov
+#> <fct> <dbl> <dbl> <dbl>
+#> 1 Sp. 3 0.292 0.341 0.0338
+#> 2 Sp. 2 0.311 0.354 0.0449
+#> 3 Sp. 8 0.244 0.336 -0.105
+#> 4 Sp. 4 0.252 0.357 -0.172
+#> 5 Sp. 6 0.278 0.340 -0.0115
+#> 6 Sp. 7 0.290 0.363 -0.0672
+#> 7 Sp. 5 0.269 0.337 -0.0326
+#> 8 Sp. 1 0.283 0.367 -0.111
+#> 9 Sp. 10 0.289 0.383 -0.163
+#> 10 Sp. 9 0.270 0.130 0.565
@@ -2104,24 +2120,93 @@ Standard errors
#> # A tibble: 10 × 6
-#> .otu se_Actual se_Measured cov se2_Measured pred
-#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 Species 3 0.292 0.341 0.0338 0.116 0.116
-#> 2 Species 2 0.311 0.354 0.0449 0.125 0.125
-#> 3 Species 8 0.244 0.336 -0.105 0.113 0.113
-#> 4 Species 4 0.252 0.357 -0.172 0.128 0.128
-#> 5 Species 6 0.278 0.340 -0.0115 0.115 0.115
-#> 6 Species 7 0.290 0.363 -0.0672 0.132 0.132
-#> 7 Species 5 0.269 0.337 -0.0326 0.114 0.114
-#> 8 Species 1 0.283 0.367 -0.111 0.135 0.135
-#> 9 Species 10 0.289 0.383 -0.163 0.147 0.147
-#> 10 Species 9 0.270 0.130 0.565 0.0170 0.0170
+#> .otu se_Actual se_Measured cov se2_Measured pred
+#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 Sp. 3 0.292 0.341 0.0338 0.116 0.116
+#> 2 Sp. 2 0.311 0.354 0.0449 0.125 0.125
+#> 3 Sp. 8 0.244 0.336 -0.105 0.113 0.113
+#> 4 Sp. 4 0.252 0.357 -0.172 0.128 0.128
+#> 5 Sp. 6 0.278 0.340 -0.0115 0.115 0.115
+#> 6 Sp. 7 0.290 0.363 -0.0672 0.132 0.132
+#> 7 Sp. 5 0.269 0.337 -0.0326 0.114 0.114
+#> 8 Sp. 1 0.283 0.367 -0.111 0.135 0.135
+#> 9 Sp. 10 0.289 0.383 -0.163 0.147 0.147
+#> 10 Sp. 9 0.270 0.130 0.565 0.0170 0.0170
The prediction for the squared standard error from the theoretical
calculation agrees with what we observe.
TODO: Sort out why it is this version, and not the other, that gives
agreement. Perhaps the standard errors being returned by R are using the
MLE estimate of sigma instead of the OLS estimate?
+2022-10-08 Modified main figure
+New panel showing simulated efficiencies and mean LFDs, to replace
+the panel showing the change in mean efficiency (former panel B).
+
+
+p_params <- species_params %>%
+ ggplot(aes(x1, log2_efficiency)) +
+ theme(axis.line = element_blank()) +
+ # panel_border(remove = TRUE) +
+ # theme_minimal_grid() +
+ coord_cartesian(clip = 'off') +
+ geom_vline(xintercept = 0, color = 'grey') +
+ geom_hline(yintercept = 0, color = 'grey') +
+ geom_text(
+ aes(label = str_c('Sp. ', str_extract(.otu, '[0-9]+'))),
+ size = 4) +
+ labs(
+ x = 'Expected LFD',
+ y = 'Log efficiency'
+ )
+p_params
+
+
+
+
+Annotate mean efficiency plot,
+
+
+# params for arrow showing change
+arrow_params <- mean_eff %>%
+ with_groups(x, summarize, across(log2_mean_efficiency, mean)) %>%
+ pull(log2_mean_efficiency)
+
+p_mean_eff2 <- p_mean_eff +
+ annotate(
+ geom = 'segment', color = "darkred",
+ arrow = grid::arrow(length = unit(0.1, "inches")),
+ x = 2.5, xend = 2.5,
+ y = arrow_params[1],
+ yend = arrow_params[2],
+ size = .7
+ )
+p_mean_eff2
+
+
+
+
+Assemble with improved titles,
+
+
+
+(p_params + ggtitle("Simulated log efficiencies\nand expected LFDs")) +
+ (p_mean_eff2 + ggtitle("Mean efficiency across\nsamples in each condition")) +
+ # (p_coef_ci_mean_eff + ggtitle("LFD in mean efficiency")) +
+ (p_species_focal + ggtitle("Actual and measured\nabundances of select species")) +
+ (p_coef_ci1 + ggtitle("Mean LFD estimated from\nactual or measured abundances") +
+ theme(legend.box.margin = margin(b = -15))
+ ) +
+ plot_layout(ncol = 2, heights = c(0.4, 1)) +
+ plot_annotation(tag_levels = 'A') &
+ colorblindr::scale_color_OkabeIto() &
+ colorblindr::scale_fill_OkabeIto() &
+ theme(
+ plot.title = element_text(face = "plain")
+ )
+
+
+
+
diff --git a/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example_files/figure-html5/main-figure-1.svg b/notebook/_posts/2021-08-03-simulate-regression-example/simulate-regression-example_files/figure-html5/main-figure-1.svg
index 79da006..e419082 100644
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