R/functions.R
In mchiapello/Ramf: Arbuscular Mycorrhizal colonization calculation
Defines functions
.grid_statime
.trouvelot_stat
.grid_stat
trouvelot_summary
grid_summary
## SUMMARY
# Grid
grid_summary <- function(x){
Arbuscule <- Hyphopodia <- IntrHyphae <- Total <- Vesicle <- comp <- NULL
features <- Replicates <- Samples <- values <- NULL
# Create summary table
y <- x %>%
group_by(Samples, Replicates) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
ungroup
y <- y %>% arrange(factor(Samples, levels = unique(x$Samples)), Replicates)
return(y)
}
# Trouvelot
trouvelot_summary <- function(x){
A <- Abundance <- Colonization <- M <- M1 <- a <- cbPalette <- feature <- features <- final_a <- m <- NULL
mA <- n_myc <- nn <- num <- perc <- Replicates <- Samples <- Scoring <- tmpa <- tot <- tot2 <- value <- n <- NULL
values <- NULL
y <- x %>%
group_by(Scoring, Replicates, Samples) %>%
tally %>%
ungroup
# Prepare complete dataset
theo_df <- tibble(Scoring = rep(c("0A0", "1A3", "2A3", "3A3", "4A3", "5A3",
"1A2", "2A2", "3A2", "4A2", "5A2", "1A1",
"2A1", "3A1", "4A1", "5A1", "1A0", "2A0",
"3A0", "4A0", "5A0"), dim(table(y$Replicates, y$Samples))[1] * dim(table(y$Replicates, y$Samples))[2]),
Replicates = rep(rep(rownames(table(y$Replicates, y$Samples)), each = 21), dim(table(y$Replicates, y$Samples))[2]),
Samples = rep(names(table(y$Samples)), each = 21 * dim(table(y$Replicates, y$Samples))[1])
)
# Merge complete dataset with user data
complete_df <- left_join(theo_df, y, by = c("Scoring", "Replicates", "Samples"))
### Compute F
N <- complete_df %>%
group_by(Samples, Replicates) %>%
summarise(N = sum(n, na.rm = TRUE))
n0 <- complete_df %>%
group_by(Samples, Replicates) %>%
dplyr::filter(Scoring == "0A0") %>%
summarise(n0 = sum(n, na.rm = TRUE))
z <- inner_join(N, n0, by = c("Samples", "Replicates"))
z <- z %>%
mutate(F = round(100 * (N -n0) / N, 2))
# Compute M
yy <- complete_df %>%
mutate(Colonization = substring(Scoring, 1, 1),
Abundance = substring(Scoring, 2, 3)
)
yy2 <- yy %>%
group_by(Samples, Replicates, Colonization) %>%
summarize(nn = sum(n, na.rm = TRUE)) %>%
mutate(perc = c(0, 1, 5, 30, 70, 95))
yy2 <- yy2 %>%
mutate(new = nn * perc) %>%
ungroup %>%
group_by(Samples, Replicates) %>%
mutate(tot = sum(nn, na.rm = TRUE)) %>%
summarise(M1 = sum(new, na.rm = TRUE),
tot2 = mean(tot, na.rm = TRUE)) %>%
mutate(M = M1 / tot2)
# Compute m
yy3 <- yy %>%
group_by(Samples, Replicates) %>%
dplyr::filter(Abundance != "A0") %>%
summarize(n_myc = sum(n, na.rm = TRUE))
yy4 <- inner_join(yy2, yy3, by = c("Samples", "Replicates"))
yy4 <- yy4 %>%
mutate(m = M * tot2 / n_myc)
# Compute a
yy5 <- yy %>%
dplyr::filter(Abundance != "A0") %>%
group_by(Samples, Replicates, Abundance, Colonization) %>%
summarise(mA = sum(n, na.rm = TRUE)) %>%
mutate(perc = c(1, 5, 30, 70, 95)) %>%
mutate(tmpa = mA * perc) %>%
ungroup %>%
group_by(Samples, Replicates, Abundance) %>%
summarise(a = sum(tmpa, na.rm = TRUE))
# Merge to add n_myc
tmp1 <- left_join(yy5, yy3, by = c("Samples", "Replicates"))
# Merge to add m
tmp2 <- left_join(tmp1, yy4[,c(1,2,7)], by = c("Samples", "Replicates"))
# Final "a" computation
yy6 <- tmp2 %>%
group_by(Samples, Replicates) %>%
mutate(final_a = (a / n_myc) * 100 / m,
perc = c(10, 50, 100)) %>%
summarise(a = sum(final_a * perc, na.rm = TRUE) / 100)
# Compute A
tmp <- inner_join(yy4, yy6, by = c("Samples", "Replicates")) %>%
mutate(A = a * (M / 100))
tmp <- inner_join(z, tmp, by = c("Samples", "Replicates")) %>%
select(Samples, Replicates, F, M, a, A)
tmp <- tmp %>% dplyr::filter(!is.na(F))
tmp[, c("F", "M", "a", "A")] <- round(tmp[, c("F", "M", "a", "A")], 2)
tmp <- tmp %>% arrange(factor(Samples, levels = unique(x$Samples)), Replicates)
return(tmp)
}
###############################################################################
## REGULAR
.grid_stat <- function(x, group = FALSE, method = method, alpha = 0.05, ...){
V1 <- NULL
sls <- am_summary(x)
sls[[1]]$Samples <- paste0(rep(LETTERS[1:length(unique(sls[[1]]$Samples))],
rep(as.numeric(tapply(sls[[1]]$Replicates, factor(sls[[1]]$Samples,
levels = unique(sls[[1]]$Samples)), length)),1)), "_", sls[[1]]$Samples)
stat <- list()
num <- ncol(sls[[1]])
if (group == FALSE){
for(i in 3:num){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$comparison
tmp2$V1 <- rownames(tmp2)
stat_tmp <- tmp2 %>% separate(V1, c("group1", "group2"), " - ") %>%
select("group1", "group2", "pvalue")
rownames(stat_tmp) <- NULL
stat[[c(1, 1, 1:5)[i]]] <- stat_tmp
}
stat <- do.call(cbind, stat)[-c(4, 5, 7, 8, 10, 11, 13, 14)]
names(stat) <- c("group1", "group2", paste0(names(sls[[1]])[3:num], ".pval"))
stat$group1 <- gsub("^\\w_", "", stat$group1)
stat$group2 <- gsub("^\\w_", "", stat$group2)
class(stat) <- c("am_stat", class(stat))
return(stat)
} else {
for(i in 3:num){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$groups[, 1:2]
tmp2$trt <- rownames(tmp2)
tmp2 <- tmp2[order(tmp2$trt), ]
stat[[c(1, 1, 1:5)[i]]] <- tmp2[2]
}
stat <- do.call(cbind, stat)
stat$sample <- rownames(stat)
stat <- stat[, c(ncol(stat), 1:(num-2))]
names(stat) <- c("sample", paste0(names(sls[[1]])[3:num], ".group"))
stat$sample <- gsub("^\\w_", "", stat$sample)
return(stat)
}
}
.trouvelot_stat <- function(x, group = FALSE, method = method, alpha = 0.05, ...){
V1 <- NULL
sls <- am_summary(x)
# sls[[1]]$Samples <- paste0(rep(1:length(unique(sls[[1]]$Samples)),
# rep(as.numeric(tapply(sls[[1]]$Replicates, factor(sls[[1]]$Samples,
# levels = unique(sls[[1]]$Samples)), length)),1)), "_", sls[[1]]$Samples)
sls[[1]]$Samples <- paste0(rep(LETTERS[1:length(unique(sls[[1]]$Samples))],
rep(as.numeric(tapply(sls[[1]]$Replicates, factor(sls[[1]]$Samples,
levels = unique(sls[[1]]$Samples)), length)),1)), "_", sls[[1]]$Samples)
stat <- list()
if (group == FALSE){
for(i in 3:6){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$comparison
tmp2$V1 <- rownames(tmp2)
stat_tmp <- tmp2 %>% separate(V1, c("group1", "group2"), " - ") %>%
select("group1", "group2", "pvalue")
rownames(stat_tmp) <- NULL
stat[[c(1, 1, 1:5)[i]]] <- stat_tmp
}
stat <- do.call(cbind, stat)[-c(4, 5, 7, 8, 10, 11)]
names(stat) <- c("group1", "group2", paste0(names(sls[[1]])[3:6], ".pval"))
stat$group1 <- gsub("^\\w_", "", stat$group1)
stat$group2 <- gsub("^\\w_", "", stat$group2)
class(stat) <- c("am_stat", class(stat))
return(stat)
} else {
for(i in 3:6){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$groups[, 1:2]
tmp2$trt <- rownames(tmp2)
tmp2 <- tmp2[order(tmp2$trt), ]
stat[[c(1, 1, 1:5)[i]]] <- tmp2[2]
}
stat <- do.call(cbind, stat)
stat$sample <- rownames(stat)
stat <- stat[, c(5, 1:4)]
names(stat) <- c("sample", paste0(names(sls[[1]])[3:6], ".group"))
stat$sample <- gsub("^\\w_", "", stat$sample)
return(stat)
}
}
###############################################################################
## TIME
.grid_statime <- function(x, group = FALSE, method = method, alpha = 0.05, ...){
V1 <- Samples <- comb <- NULL
sls <- am_summary(x)[[1]]
num <- ncol(sls)
sls$comb <- paste(pull(sls, Samples), pull(sls, time), sep = "_")
# sls$Samples <- paste0(rep(1:length(unique(sls$Samples)),
# rep(as.numeric(tapply(factor(sls$Samples,
# levels = unique(sls$Samples)), sls$comb, length)),1)), "_", sls$Samples)
stat <- list()
if (group == FALSE){
for(i in 4:num){
tmp <- kruskal(pull(sls, i),
pull(sls, comb),
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$comparison
tmp2$V1 <- rownames(tmp2)
stat_tmp <- tmp2 %>% separate(V1, c("group1", "group2"), " - ") %>%
select("group1", "group2", "pvalue")
rownames(stat_tmp) <- NULL
stat[[c(1, 1, 1, 1:5)[i]]] <- stat_tmp
}
stat <- do.call(cbind, stat)[,-c(4, 5, 7, 8, 10, 11, 13, 14)]
names(stat) <- c("group1", "group2", paste0(names(sls)[4:num], ".pval"))
# Split comparison: Replicates / time
tf <- list()
tf[[1]] <- stat[which(gsub("_\\d", "", stat$group1) ==
gsub("_\\d", "", stat$group2)), ]
tf[[2]] <- stat[which(gsub(".*(_\\d)", "\\1", stat$group1) ==
gsub(".*(_\\d)", "\\1", stat$group2)), ]
tf[[2]] <- tf[[2]][order(gsub(".*(_\\d)", "\\1", tf[[2]]$group1)), ]
names(tf) <- c("Stat per sample", "Stat per time point")
class(tf) <- c("am_statime", "list")
return(tf)
} else {
for(i in 4:num){
tmp <- kruskal(pull(sls, i),
pull(sls, comb),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$groups[, 1:2]
tmp2$trt <- rownames(tmp2)
tmp2 <- tmp2[order(rownames(tmp2)), ]
stat[[c(1, 1, 1, 1:5)[i]]] <- tmp2[2]
}
stat <- do.call(cbind, stat)
stat$sample <- rownames(stat)
stat <- stat[, c(ncol(stat), 1:(num-3))]
names(stat) <- c("sample", paste0(names(sls)[4:num], ".group"))
class(stat) <- c("am_statime", class(stat))
return(stat)
}
}
mchiapello/Ramf documentation built on April 9, 2022, 5:07 p.m.
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Defines functions .grid_statime .trouvelot_stat .grid_stat trouvelot_summary grid_summary
## SUMMARY
# Grid
grid_summary <- function(x){
Arbuscule <- Hyphopodia <- IntrHyphae <- Total <- Vesicle <- comp <- NULL
features <- Replicates <- Samples <- values <- NULL
# Create summary table
y <- x %>%
group_by(Samples, Replicates) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
ungroup
y <- y %>% arrange(factor(Samples, levels = unique(x$Samples)), Replicates)
return(y)
}
# Trouvelot
trouvelot_summary <- function(x){
A <- Abundance <- Colonization <- M <- M1 <- a <- cbPalette <- feature <- features <- final_a <- m <- NULL
mA <- n_myc <- nn <- num <- perc <- Replicates <- Samples <- Scoring <- tmpa <- tot <- tot2 <- value <- n <- NULL
values <- NULL
y <- x %>%
group_by(Scoring, Replicates, Samples) %>%
tally %>%
ungroup
# Prepare complete dataset
theo_df <- tibble(Scoring = rep(c("0A0", "1A3", "2A3", "3A3", "4A3", "5A3",
"1A2", "2A2", "3A2", "4A2", "5A2", "1A1",
"2A1", "3A1", "4A1", "5A1", "1A0", "2A0",
"3A0", "4A0", "5A0"), dim(table(y$Replicates, y$Samples))[1] * dim(table(y$Replicates, y$Samples))[2]),
Replicates = rep(rep(rownames(table(y$Replicates, y$Samples)), each = 21), dim(table(y$Replicates, y$Samples))[2]),
Samples = rep(names(table(y$Samples)), each = 21 * dim(table(y$Replicates, y$Samples))[1])
)
# Merge complete dataset with user data
complete_df <- left_join(theo_df, y, by = c("Scoring", "Replicates", "Samples"))
### Compute F
N <- complete_df %>%
group_by(Samples, Replicates) %>%
summarise(N = sum(n, na.rm = TRUE))
n0 <- complete_df %>%
group_by(Samples, Replicates) %>%
dplyr::filter(Scoring == "0A0") %>%
summarise(n0 = sum(n, na.rm = TRUE))
z <- inner_join(N, n0, by = c("Samples", "Replicates"))
z <- z %>%
mutate(F = round(100 * (N -n0) / N, 2))
# Compute M
yy <- complete_df %>%
mutate(Colonization = substring(Scoring, 1, 1),
Abundance = substring(Scoring, 2, 3)
)
yy2 <- yy %>%
group_by(Samples, Replicates, Colonization) %>%
summarize(nn = sum(n, na.rm = TRUE)) %>%
mutate(perc = c(0, 1, 5, 30, 70, 95))
yy2 <- yy2 %>%
mutate(new = nn * perc) %>%
ungroup %>%
group_by(Samples, Replicates) %>%
mutate(tot = sum(nn, na.rm = TRUE)) %>%
summarise(M1 = sum(new, na.rm = TRUE),
tot2 = mean(tot, na.rm = TRUE)) %>%
mutate(M = M1 / tot2)
# Compute m
yy3 <- yy %>%
group_by(Samples, Replicates) %>%
dplyr::filter(Abundance != "A0") %>%
summarize(n_myc = sum(n, na.rm = TRUE))
yy4 <- inner_join(yy2, yy3, by = c("Samples", "Replicates"))
yy4 <- yy4 %>%
mutate(m = M * tot2 / n_myc)
# Compute a
yy5 <- yy %>%
dplyr::filter(Abundance != "A0") %>%
group_by(Samples, Replicates, Abundance, Colonization) %>%
summarise(mA = sum(n, na.rm = TRUE)) %>%
mutate(perc = c(1, 5, 30, 70, 95)) %>%
mutate(tmpa = mA * perc) %>%
ungroup %>%
group_by(Samples, Replicates, Abundance) %>%
summarise(a = sum(tmpa, na.rm = TRUE))
# Merge to add n_myc
tmp1 <- left_join(yy5, yy3, by = c("Samples", "Replicates"))
# Merge to add m
tmp2 <- left_join(tmp1, yy4[,c(1,2,7)], by = c("Samples", "Replicates"))
# Final "a" computation
yy6 <- tmp2 %>%
group_by(Samples, Replicates) %>%
mutate(final_a = (a / n_myc) * 100 / m,
perc = c(10, 50, 100)) %>%
summarise(a = sum(final_a * perc, na.rm = TRUE) / 100)
# Compute A
tmp <- inner_join(yy4, yy6, by = c("Samples", "Replicates")) %>%
mutate(A = a * (M / 100))
tmp <- inner_join(z, tmp, by = c("Samples", "Replicates")) %>%
select(Samples, Replicates, F, M, a, A)
tmp <- tmp %>% dplyr::filter(!is.na(F))
tmp[, c("F", "M", "a", "A")] <- round(tmp[, c("F", "M", "a", "A")], 2)
tmp <- tmp %>% arrange(factor(Samples, levels = unique(x$Samples)), Replicates)
return(tmp)
}
###############################################################################
## REGULAR
.grid_stat <- function(x, group = FALSE, method = method, alpha = 0.05, ...){
V1 <- NULL
sls <- am_summary(x)
sls[[1]]$Samples <- paste0(rep(LETTERS[1:length(unique(sls[[1]]$Samples))],
rep(as.numeric(tapply(sls[[1]]$Replicates, factor(sls[[1]]$Samples,
levels = unique(sls[[1]]$Samples)), length)),1)), "_", sls[[1]]$Samples)
stat <- list()
num <- ncol(sls[[1]])
if (group == FALSE){
for(i in 3:num){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$comparison
tmp2$V1 <- rownames(tmp2)
stat_tmp <- tmp2 %>% separate(V1, c("group1", "group2"), " - ") %>%
select("group1", "group2", "pvalue")
rownames(stat_tmp) <- NULL
stat[[c(1, 1, 1:5)[i]]] <- stat_tmp
}
stat <- do.call(cbind, stat)[-c(4, 5, 7, 8, 10, 11, 13, 14)]
names(stat) <- c("group1", "group2", paste0(names(sls[[1]])[3:num], ".pval"))
stat$group1 <- gsub("^\\w_", "", stat$group1)
stat$group2 <- gsub("^\\w_", "", stat$group2)
class(stat) <- c("am_stat", class(stat))
return(stat)
} else {
for(i in 3:num){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$groups[, 1:2]
tmp2$trt <- rownames(tmp2)
tmp2 <- tmp2[order(tmp2$trt), ]
stat[[c(1, 1, 1:5)[i]]] <- tmp2[2]
}
stat <- do.call(cbind, stat)
stat$sample <- rownames(stat)
stat <- stat[, c(ncol(stat), 1:(num-2))]
names(stat) <- c("sample", paste0(names(sls[[1]])[3:num], ".group"))
stat$sample <- gsub("^\\w_", "", stat$sample)
return(stat)
}
}
.trouvelot_stat <- function(x, group = FALSE, method = method, alpha = 0.05, ...){
V1 <- NULL
sls <- am_summary(x)
# sls[[1]]$Samples <- paste0(rep(1:length(unique(sls[[1]]$Samples)),
# rep(as.numeric(tapply(sls[[1]]$Replicates, factor(sls[[1]]$Samples,
# levels = unique(sls[[1]]$Samples)), length)),1)), "_", sls[[1]]$Samples)
sls[[1]]$Samples <- paste0(rep(LETTERS[1:length(unique(sls[[1]]$Samples))],
rep(as.numeric(tapply(sls[[1]]$Replicates, factor(sls[[1]]$Samples,
levels = unique(sls[[1]]$Samples)), length)),1)), "_", sls[[1]]$Samples)
stat <- list()
if (group == FALSE){
for(i in 3:6){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$comparison
tmp2$V1 <- rownames(tmp2)
stat_tmp <- tmp2 %>% separate(V1, c("group1", "group2"), " - ") %>%
select("group1", "group2", "pvalue")
rownames(stat_tmp) <- NULL
stat[[c(1, 1, 1:5)[i]]] <- stat_tmp
}
stat <- do.call(cbind, stat)[-c(4, 5, 7, 8, 10, 11)]
names(stat) <- c("group1", "group2", paste0(names(sls[[1]])[3:6], ".pval"))
stat$group1 <- gsub("^\\w_", "", stat$group1)
stat$group2 <- gsub("^\\w_", "", stat$group2)
class(stat) <- c("am_stat", class(stat))
return(stat)
} else {
for(i in 3:6){
tmp <- kruskal(pull(sls[[1]], i),
pull(sls[[1]], 1),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$groups[, 1:2]
tmp2$trt <- rownames(tmp2)
tmp2 <- tmp2[order(tmp2$trt), ]
stat[[c(1, 1, 1:5)[i]]] <- tmp2[2]
}
stat <- do.call(cbind, stat)
stat$sample <- rownames(stat)
stat <- stat[, c(5, 1:4)]
names(stat) <- c("sample", paste0(names(sls[[1]])[3:6], ".group"))
stat$sample <- gsub("^\\w_", "", stat$sample)
return(stat)
}
}
###############################################################################
## TIME
.grid_statime <- function(x, group = FALSE, method = method, alpha = 0.05, ...){
V1 <- Samples <- comb <- NULL
sls <- am_summary(x)[[1]]
num <- ncol(sls)
sls$comb <- paste(pull(sls, Samples), pull(sls, time), sep = "_")
# sls$Samples <- paste0(rep(1:length(unique(sls$Samples)),
# rep(as.numeric(tapply(factor(sls$Samples,
# levels = unique(sls$Samples)), sls$comb, length)),1)), "_", sls$Samples)
stat <- list()
if (group == FALSE){
for(i in 4:num){
tmp <- kruskal(pull(sls, i),
pull(sls, comb),
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$comparison
tmp2$V1 <- rownames(tmp2)
stat_tmp <- tmp2 %>% separate(V1, c("group1", "group2"), " - ") %>%
select("group1", "group2", "pvalue")
rownames(stat_tmp) <- NULL
stat[[c(1, 1, 1, 1:5)[i]]] <- stat_tmp
}
stat <- do.call(cbind, stat)[,-c(4, 5, 7, 8, 10, 11, 13, 14)]
names(stat) <- c("group1", "group2", paste0(names(sls)[4:num], ".pval"))
# Split comparison: Replicates / time
tf <- list()
tf[[1]] <- stat[which(gsub("_\\d", "", stat$group1) ==
gsub("_\\d", "", stat$group2)), ]
tf[[2]] <- stat[which(gsub(".*(_\\d)", "\\1", stat$group1) ==
gsub(".*(_\\d)", "\\1", stat$group2)), ]
tf[[2]] <- tf[[2]][order(gsub(".*(_\\d)", "\\1", tf[[2]]$group1)), ]
names(tf) <- c("Stat per sample", "Stat per time point")
class(tf) <- c("am_statime", "list")
return(tf)
} else {
for(i in 4:num){
tmp <- kruskal(pull(sls, i),
pull(sls, comb),
alpha = alpha,
p.adj = method,
group = group,
console = FALSE)
tmp2 <- tmp$groups[, 1:2]
tmp2$trt <- rownames(tmp2)
tmp2 <- tmp2[order(rownames(tmp2)), ]
stat[[c(1, 1, 1, 1:5)[i]]] <- tmp2[2]
}
stat <- do.call(cbind, stat)
stat$sample <- rownames(stat)
stat <- stat[, c(ncol(stat), 1:(num-3))]
names(stat) <- c("sample", paste0(names(sls)[4:num], ".group"))
class(stat) <- c("am_statime", class(stat))
return(stat)
}
}
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