This is analysis of a public dataset (PRIDE PXD002875) from Paulo, O'Connell, Gaun, and Gygi:
Paulo, J.A., O’Connell, J.D., Gaun, A. and Gygi, S.P., 2015. Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae. Molecular biology of the cell, 26(22), pp.4063-4074.
There were 24 RAW files of yeast grown in three different carbon sources. It was a 3x3 (9-plex) TMT experiment done with the SPS MS3 (MultiNotch) method (ref-1).
Here are some ballpark numbers from the published paper. There were about 4700 quantifiable proteins at a low FDR. The 3 by 3 study design was tested for differential expression significance using a one-way ANOVA test with a Bonferroni multiple testing correction. 1003 proteins were found to be significant at a corrected p-value cutoff of 0.01.
An analysis using the PAW pipeline was also done (repo here or notebook HTML file here).
The RAW files were downloaded and reprocessed with MaxQuant (ref-2) <IP_ADDRESS>. SPS MS3 data type was selected and 10-plex TMT reagents used. The protein database (without contaminants or decoys) was the same as used for the PAW analysis. Most parameters were kept as defaults (parameter file is included).
References
1. McAlister, G.C., Nusinow, D.P., Jedrychowski, M.P., Wühr, M., Huttlin, E.L., Erickson, B.K., Rad, R., Haas, W. and Gygi, S.P., 2014. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Analytical chemistry, 86(14), pp.7150-7158.
2. Cox, J. and Mann, M., 2008. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nature biotechnology, 26(12), p.1367.
MaxQuant (MQ) summarizes (sums) reporter ions into protein totals using the specified protein inference and shared peptide settings. I used the defaults of razor+unique. This is an area where MQ and PAW differ. MQ also applies a protein ranking function for protein FDR and usually reports single peptide per protein identifications. I kept the default setting of 1% PSM, 1% protein, and a minimum of one peptide per protein.
Before we do any visualizations and work with the data in edgeR, we need to do a little data prep. Any contaminant proteins (common contaminants like keratins, trypsin, and BSA, and any biological contaminants (proteins from adjacent tissue, blood, etc.) should be removed from the table. Any decoy identifications should also be dropped. There can also be proteins that do not have any reporter ion intensities (quantification is typically stricter than identification) that can be dropped from the table.
MQ output tables are extensive, so a streamlined table with just the information for the TMT data was exported from the proteinGroups.txt file using Excel. My approach is to open the main results file and do some data filtering and table reorganization using Excel's interactivity. I find it easier to so some data checking in Excel compared to R (I have been using Excel longer). The sheet has a "Log" tab with step by step descriptions of the data prep. I do not worry about the potential non-reproducibility of this because the Excel spreadsheet will be my main results summary file. I want the results for the R analysis to get added back to the spreadsheet so that everything is in one place for publication. Preparing the sheet layout to facilitate the data export also prepares the sheet to receive the edgeR results. These steps can also be done in R (if you are careful). The cleaned-up reporter ion data is what we will import here.
Reporter ions within a single TMT plex should be normalized to a common level. The same total amount of protein digest is labeled for each channel so the total reporter ion signal for each channel should also be the same total amount (the reporter ion signals are proxies for protein abundances). We will do a grand total of reporter ion intensities normalization first to see if any channels have issues. We should have normalization factors within a factor of two (or less) of the average grand total.
# load libraries first
library("psych")
library("tidyverse")
library("stringr")
library("gridExtra")
library("scales")
library("edgeR")
library("limma")
# load the data file into a data tibble
raw_data <- read_tsv(file = "R-input.txt")
# separate row identifiers from the data
accession <- raw_data$Accession
tmt_raw <- raw_data %>% select(-c(Accession))
# let's use more descriptive column names
colnames(tmt_raw) <- c("gal-1", "gal-2", "gal-3",
"glu-1", "glu-2", "glu-3",
"raf-1", "raf-2", "raf-3")
nrow(tmt_raw)
We have the first 3 channels (in mass order) from galactose, the next 3 from glucose, and the last three from raffinose.
# let's see what the starting data look like
# set a 3 by 3 color vector
color_vector <- rep(c("red", "blue", "green"), each = 3)
# boxplots of RAW log intensities
boxplot(log10(tmt_raw), col = color_vector,
notch = TRUE, main = "RAW data: Gal (red), Glu (blue), and Raf(green)",
xlab = "TMT Samples", ylab = "log of Intensity")
The boxplots above already look excellent and we have not done any normalizations. We can compute normalization factors to make each column of reporter ion intensities sum to the same value. We loaded equal amounts of labeled protein digests, so we should have equal sums of reporter ions (in a perfect world). Large correction factors might imply a problem with the labeling for that channel.
SL_Norm <- function(df) {
# This makes each channel sum to the average grand total
# df - data frame of TMT intensities
# returns a new data frame with normalized values
norm_facs <- mean(c(colSums(df))) / colSums(df)
cat("SL Factors:\n", sprintf("%-5s -> %f\n", colnames(df), norm_facs))
df_sl <- sweep(df, 2, norm_facs, FUN = "*")
}
# normalize the raw data
tmt_sl <- SL_Norm(tmt_raw)
# let's see what the SL normalized data look like
boxplot(log10(tmt_sl), col = color_vector,
notch = TRUE, main = "SL Norm data: Gal (red), Glu (blue), and Raf(green)",
xlab = "TMT Samples", ylab = "log2 of Intensity")
# and check clustering
plotMDS(log10(tmt_sl), col = color_vector, main = "Clustering of SL Norm data")
Boxplots are nicely aligned after the SL Norm step. All of the normalization factors were close to 1.0. The cluster plot shows tight grouping by condition and large separations between conditions. There should be some very clear differential expression candidates between conditions.
# load data, study design, and row labels into edgeR object
group <- rep(c("gal", "glu", "raf"), each = 3)
y <- DGEList(counts = tmt_raw, group = group, genes = accession)
# run TMM normalization
y <- calcNormFactors(y)
y$samples
TMM normalization factors were all very close to 1.0. We can get the norm factors from the DGEList object and create the normalized reporter ion intensities. EdgeR does not return adjusted intensities; the factors are used internally in the modeling. We can compute the adjusted values and save them. We can repeat the cluster and boxplot visualizations on the TMM normalized data.
apply_tmm_factors <- function(y) {
# computes the tmm normalized data from the DGEList object
# y - DGEList object
# returns a dataframe with normalized intensities
# compute grand total (library size) scalings
lib_facs <- mean(y$samples$lib.size) / y$samples$lib.size
# the TMM factors are library adjustment factors (so divide by them)
norm_facs <- lib_facs / y$samples$norm.factors
# compute the normalized data as a new data frame
tmt_tmm <- as.data.frame(sweep(y$counts, 2, norm_facs, FUN = "*"))
colnames(tmt_tmm) <- str_c(colnames(y$counts), "_tmm")
# return the data frame
tmt_tmm
}
tmt_tmm <- apply_tmm_factors(y)
# look at intensity distributions across samples after TMM
boxplot(log10(tmt_tmm),
col = color_vector,
xlab = "Samples", ylab = "Reporter Intensity",
main = "After TMM Normalization", notch = TRUE)
# check clustering after TMM with MDS plot
plotMDS(y, col = color_vector, main = "Clustering after TMM normalization")
Boxplots and clustering look pretty similar between SL Norm and TMM Norm. About all I can see is that the MDS plot axes ranges are a bit larger after TMM, indicating a little bigger difference between groups after TMM compared to SL norm.
We can see how similar samples are to each other (within and between groups) using a multiple panel scatter plot visualization. The first three are the galactose carbon source, the next three are glucose, and the last three are raffinose.
# compare all samples to each other
pairs.panels(log10(tmt_tmm), method = "spearman",
lm = TRUE, main = "All versus all (normalized)")
The scatter plots indicate superb sample-to-sample reproducibility with the same condition (carbon source), and very significant differences between groups. The correlation coefficients within conditions are all 1.00 and coefficients between groups in the 0.92 to 0.96 range. Although the plots look quite different between conditions, the correlation coefficients are still very close to 1. Correlation coefficients are not a very sensitive metric.
# save the indexes of each condition
gal <- 1:3
glu <- 4:6
raf <- 7:9
We can use distributions of protein coefficients of variance (CV) to quantify how similar the samples within groups are. Using a little helper function, we can make boxplots of various sample groups. I will look at the CVs for each group before and after TMM normalization. The CVs are in percent.
CV <- function(df) {
# Computes CVs of data frame rows
# df - data frame,
# returns vector of CVs (%)
ave <- rowMeans(df) # compute averages
sd <- apply(df, 1, sd) # compute standard deviations
cv <- 100 * sd / ave # compute CVs in percent (last thing gets returned)
}
labeled_boxplot <- function(df, ylim, title) {
# Makes a box plot with the median value labeled
# df - data frame with data to compute CVs of
# ylim - upper limit for y-axis
# title - plot title
cv = CV(df)
boxplot(cv, ylim = c(0, ylim), notch = TRUE, main = title)
text(x = 0.65, y = boxplot.stats(cv)$stats[3],
labels = round(boxplot.stats(cv)$stats[3], 1))
}
# make CV distributions for each condition before and after normalization
par(mfrow = c(2, 3))
labeled_boxplot(tmt_raw[gal], 30, "Galactose RAW")
labeled_boxplot(tmt_raw[glu], 30, "Glucose RAW")
labeled_boxplot(tmt_raw[raf], 30, "Raffinose RAW")
labeled_boxplot(tmt_tmm[gal], 30, "Galactose TMM")
labeled_boxplot(tmt_tmm[glu], 30, "Glucose TMM")
labeled_boxplot(tmt_tmm[raf], 30, "Raffinose TMM")
par(mfrow = c(1, 1))
Median CVs were improved by TMM normalization for all three conditions. The sizes of the boxes (inter-quartile ranges) are also pretty small. It might be useful to see the full distributions.
# print CV cutoffs that cature 90% of the proteins
cat("Gal - 90% of CVs less than:", round(quantile(CV(tmt_tmm[gal]), probs = 0.9), 2), "%\n")
cat("Glu - 90% of CVs less than:", round(quantile(CV(tmt_tmm[glu]), probs = 0.9), 2), "%\n")
cat("Raf - 90% of CVs less than:", round(quantile(CV(tmt_tmm[raf]), probs = 0.9), 2), "%\n")
# make a tidy data frame for plotting
cv_gal <- data.frame(cv = CV(tmt_tmm[gal]), sugar = "gal", stringsAsFactors = FALSE)
cv_glu <- data.frame(cv = CV(tmt_tmm[glu]), sugar = "glu", stringsAsFactors = FALSE)
cv_raf <- data.frame(cv = CV(tmt_tmm[raf]), sugar = "raf", stringsAsFactors = FALSE)
cv_long <- rbind(cv_gal, cv_glu, cv_raf)
# density plots
ggplot(cv_long, aes(x = cv, fill = sugar)) +
geom_density(alpha = 0.3) +
coord_cartesian(xlim = c(0, 25)) +
ggtitle("CV distributions (TMM Norm)")
We will do pairwise exact tests in edgeR. We have already loaded data into a DGEList object (above) and ran the TMM normalization. We need to get the global dispersion estimates before doing the pairwise exact statistical testing.
# compute the shared variance estimates and plot variance trends
y <- estimateDisp(y)
plotBCV(y, main = "Carbon Sources")
We will save some average condition intensity vectors (for plotting purposes), compute the statistical models, save the results, and see what the top differential candidates are.
collect_results <- function(df, tt, x, xlab, y, ylab) {
# Computes new columns and extracts some columns to make results frame
# df - data in data.frame
# tt - top tags from edgeR test
# x - columns for first condition
# xlab - label for x
# y - columns for second condition
# ylab - label for y
# returns a new dataframe
# condition average vectors
ave_x <- rowMeans(df[x])
ave_y <- rowMeans(df[y])
# FC, direction, candidates
fc <- ifelse(ave_y > ave_x, (ave_y / ave_x), (-1 * ave_x / ave_y))
direction <- ifelse(ave_y > ave_x, "up", "down")
candidate = cut(tt$FDR, breaks = c(-Inf, 0.01, 0.05, 0.10, 1.0),
labels = c("high", "med", "low", "no"))
# make data frame
temp <- cbind(df[c(x, y)], data.frame(logFC = tt$logFC, FC = fc,
PValue = tt$PValue, FDR = tt$FDR,
ave_x = ave_x, ave_y = ave_y,
direction = direction, candidate = candidate,
Acc = tt$genes))
# fix column headers for averages
names(temp)[names(temp) %in% c("ave_x", "ave_y")] <- str_c("ave_", c(xlab, ylab))
temp # return the data frame
}
# compute the exact test models, p-values, FC, etc.
et <- exactTest(y, pair = c("gal", "glu"))
# see which proteins have the smallest p-values
topTags(et)$table
# make the results table
tt <- topTags(et, n = Inf, sort.by = "none")$table
gal_glu <- collect_results(tmt_tmm, tt, gal, "gal", glu, "glu")
The p-value distribution is way to check that the data and the statistical test are compatible (not necessarily correct). We should have a flat distribution from non-differentially expressed candidates at larger p-values and an increase at small p-values from true candidates.
pvalue_plots <- function(results, ylim, title) {
# Makes p-value distribution plots
# results - results data frame
# ylim - ymax for expanded view
# title - plot title
p_plot <- ggplot(results, aes(PValue)) +
geom_histogram(bins = 100, fill = "white", color = "black") +
geom_hline(yintercept = mean(hist(results$PValue, breaks = 100,
plot = FALSE)$counts[26:100]))
# we will need an expanded plot
p1 <- p_plot + ggtitle(str_c(title, " p-value distribution"))
p2 <- p_plot + coord_cartesian(xlim = c(0, 1.0), ylim = c(0, 50)) + ggtitle("p-values expanded")
grid.arrange(p1, p2, nrow = 2) # from gridExtra package
}
# check the p-value distribution
pvalue_plots(gal_glu, 50, "Gal vs Glu")
The peak at small p-values from true differential expression candidates is dominant. We sort of have a flat distribution of larger p-values, but we had to expand the scale to see them.
edgeR has some built-in functions for some quick checks of the statistical testing. We can get the number of up and down regulated proteins and make an MA plot of candidates. Up is over-expression in second condition (glu), down is over-expression in first condition (gal).
# see how many up and down candidates (10% FDR)
summary(decideTests(et, p.value = 0.10))
# use function from limma for MD plot
plotMD(et, main = "Gal vs Glu TMM Normalized", p.value = 0.10)
abline(h = c(-1, 1), col = "black")
ggplot2 can make some nicer looking plots with modern colors and design elements, and gives us some options for exploring categories (color and symbols by category, and multi-panel facet plots).
# see how many candidates are in each category
gal_glu %>% count(candidate)
log2FC_plots <- function(results, range, title) {
# Makes faceted log2FC plots by candidate
# results - results data frame
# range - plus/minus log2 x-axis limits
# title - plot title
ggplot(results, aes(x = logFC, fill = candidate)) +
geom_histogram(binwidth=0.1, color = "black") +
facet_wrap(~candidate) +
ggtitle(title) +
coord_cartesian(xlim = c(-range, range))
}
# can also look at log2FC distributions
log2FC_plots(gal_glu, 3, "LogFC by candidate for Gal vs Glu")
transform <- function(results, x, y) {
# Make data frame with some transformed columns
# results - results data frame
# x - columns for x condition
# y - columns for y condition
# return new data frame
df <- data.frame(log10((results[x] + results[y])/2),
log2(results[y] / results[x]),
results$candidate,
-log10(results$FDR))
colnames(df) <- c("A", "M", "candidate", "P")
df # return the data frame
}
MA_plots <- function(results, x, y, title) {
# makes MA-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# returns a list of plots
# uses transformed data
temp <- transform(results, x, y)
# 2-fold change lines
ma_lines <- list(geom_hline(yintercept = 0.0, color = "black"),
geom_hline(yintercept = 1.0, color = "black", linetype = "dotted"),
geom_hline(yintercept = -1.0, color = "black", linetype = "dotted"))
# make main MA plot
ma <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("logFC (", y, "/", x, ")")) +
scale_x_continuous("Ave_intensity") +
ggtitle(title) +
ma_lines
# make separate MA plots
ma_facet <- ggplot(temp, aes(x = A, y = M)) +
geom_point(aes(color = candidate, shape = candidate)) +
scale_y_continuous(paste0("log2 FC (", y, "/", x, ")")) +
scale_x_continuous("log10 Ave_intensity") +
ma_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
# make the plots visible
print(ma)
print(ma_facet)
}
scatter_plots <- function(results, x, y, title) {
# makes scatter-plot DE candidate ggplots
# results - data frame with edgeR results and some condition average columns
# x - string for x-axis column
# y - string for y-axis column
# title - title string to use in plots
# returns a list of plots
# 2-fold change lines
scatter_lines <- list(geom_abline(intercept = 0.0, slope = 1.0, color = "black"),
geom_abline(intercept = 0.301, slope = 1.0, color = "black", linetype = "dotted"),
geom_abline(intercept = -0.301, slope = 1.0, color = "black", linetype = "dotted"),
scale_y_log10(),
scale_x_log10())
# make main scatter plot
scatter <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
ggtitle(title) +
scatter_lines
# make separate scatter plots
scatter_facet <- ggplot(results, aes_string(x, y)) +
geom_point(aes(color = candidate, shape = candidate)) +
scatter_lines +
facet_wrap(~ candidate) +
ggtitle(str_c(title, " (separated)"))
# make the plots visible
print(scatter)
print(scatter_facet)
}
volcano_plot <- function(results, x, y, title) {
# makes a volcano plot
# results - a data frame with edgeR results
# x - string for the x-axis column
# y - string for y-axis column
# title - plot title string
# uses transformed data
temp <- transform(results, x, y)
# build the plot
ggplot(temp, aes(x = M, y = P)) +
geom_point(aes(color = candidate, shape = candidate)) +
xlab("log2 FC") +
ylab("-log10 FDR") +
ggtitle(str_c(title, " Volcano Plot"))
}
MA plots are commonly used in genomics studies to show expression levels and highlight DE candidates.
# do the gal vs glu MA visualizations
MA_plots(gal_glu, "ave_gal", "ave_glu", "Galactose vs Glucose")
Scatter plots and MA plots are very closely related. One thing I like about scatter plots is that both axes are in natural scales. Both axes are transformations of intensities for MA plots. The y-axis in MA plots will usually be auto scaled so that datasets with low or high variability often look similar. The scatter plots display dispersion in a more absolute way.
# same comparison with scatter plots
scatter_plots(gal_glu, "ave_gal", "ave_glu", "Galactose vs Glucose")
I find volcano plots a less informative way to visualize DE data. That said, they are so commonly used that we will include them without further comments.
# the ubiquitous volcano plot
volcano_plot(gal_glu, "ave_gal", "ave_glu", "Galactose vs Glucose")
The cluster plot gave us a heads up that there would be a lot of DE candidates. The CVs per condition are so low that even modest fold-changes have very low p-values. There are only 13.6% non-differentially expressed proteins (10% FDR with a BH multiple test correction). There are 80% of the proteins in the high category!
Just to double check that the results make sense, we can do a multi-panel scatter plot grid of Gal versus Glu samples.
# compare the conditions to each other
pairs.panels(log10(tmt_tmm[c(gal, glu)]), method = "spearman",
lm = TRUE, main = "Galactose versus Glucose")
# compute the exact test models, p-values, FC, etc.
et <- exactTest(y, pair = c("gal", "raf"))
# see which proteins have the smallest p-values
topTags(et)$table
# get the results table
tt <- topTags(et, n = Inf, sort.by = "none")$table
gal_raf <- collect_results(tmt_tmm, tt, gal, "gal", raf, "raf")
# check the p-value distribution
pvalue_plots(gal_raf, 50, "Gal vs Raf")
# see how many up and down candidates (10% FDR)
summary(decideTests(et, p.value = 0.10))
# use function from limma for MD plot
plotMD(et, main = "Gal vs Raf TMM Normalized", p.value = 0.10)
abline(h = c(-1, 1), col = "black")
# see how many candidates are in each category
gal_raf %>% count(candidate)
# can also look at log2FC distributions
log2FC_plots(gal_raf, 3, "LogFC by candidate for Gal vs Raf")
# make the MA plots
MA_plots(gal_raf, "ave_gal", "ave_raf", "Galactose vs Raffinose")
# scatter plots
scatter_plots(gal_raf, "ave_gal", "ave_raf", "Galactose vs Raffinose")
volcano_plot(gal_raf, "ave_gal", "ave_raf", "Galactose vs Raffinose")
We had a few more non-differentially expressed proteins in Gal vs Raf than we saw in the Gal vs Glu comparison. We still have an overwhelmingly large number of DE candidates.
# compare the conditions to each other
pairs.panels(log10(tmt_tmm[c(gal, raf)]), method = "spearman",
lm = TRUE, main = "Galactose versus Raffinose")
# compute the exact test models, p-values, FC, etc.
et <- exactTest(y, pair = c("glu", "raf"))
# see which proteins have the smallest p-values
topTags(et)$table
# get the results table
tt <- topTags(et, n = Inf, sort.by = "none")$table
glu_raf <- collect_results(tmt_tmm, tt, glu, "glu", raf, "raf")
# check the p-value distribution
pvalue_plots(glu_raf, 50, "Glu vs Raf")
# see how many up and down candidates (10% FDR)
summary(decideTests(et, p.value = 0.10))
# use function from limma for MD plot
plotMD(et, main = "Glu vs Raf TMM Normalized", p.value = 0.10)
abline(h = c(-1, 1), col = "black")
# see how many candidates are in each category
glu_raf %>% count(candidate)
# can also look at log2FC distributions
log2FC_plots(glu_raf, 3, "LogFC by candidate for Glu vs Raf")
# MA plots for glu vs raf
MA_plots(glu_raf, "ave_glu", "ave_raf", "Glucose vs Raffinose")
# scatter plots
scatter_plots(glu_raf, "ave_glu", "ave_raf", "Glucose vs Raffinose")
volcano_plot(glu_raf, "ave_glu", "ave_raf", "Glucose vs Raffinose")
# compare the conditions to each other
pairs.panels(log10(tmt_tmm[c(glu, raf)]), method = "spearman",
lm = TRUE, main = "Glucose versus Raffinose")
Changing the carbon source seems to have a dramatic effect of the yeast protein expression levels.
In all three comparisons, we had:
The numbers of candidates are around 75% of the proteins with a basic 5% FDR cutoff. Can quantitative proteomics data be too good? Does changing the carbon source change the expression levels of every protein in yeast? Or is the reproducibility between biological replicates for yeast in the Gygi lab hands so good that biologically insignificant expression changes are statistically significant changes? The answer probably lies somewhere in the middle. My previous experiences with edgeR testing of TMT data suggests that the modeling is pretty conservative, so I suspect that changing the energy source for yeast is a pretty big perturbation.
This raises the open question of how to proceed when some perturbation causes wholesale protein expression changes and you have a precise method for measuring protein expression. The statistical testing will indicate that practically all proteins are changing. How to rank the candidates for hypotheses and follow up studies is an open problem in modern genomics and now in proteomics.
We will explore what different statistical tests and test options have on the numbers DE results in the next notebook. More advanced features of edgeR allow minimum fold-change criteria to be incorporated into the modeling. Different multiple testing corrections also vary from less conservative to overly conservative and might be important factors.
| Pipeline | MS2 Scans | Accepted Scans | FDR | ID rate |
|---|---|---|---|---|
| PAW | 744,875 | 179,146 | 1.03% | 24.1% |
| MaxQuant | 746,852 | 150,613 | 1% | 20.2% |
There are fewer PSMs identified with MaxQuant compared to PAW. There are several likely factors: SEQUEST-like searches are generally more sensitive than Mascot-like searches; a discriminant function or classifier like Percolator improves sensitivity over primary search engine scores; and wider parent ion tolerance searches can better distinguish signal from noise.
Differences at the protein level can be harder to assess. This is especially true when we are essentially identifying the entire yeast proteome. In fact, once you identify in your sample a significant fraction of the proteins in FASTA file, estimating protein FDR using decoy protein matches becomes inaccurate. I kept the default MQ setting of a 1% protein FDR. That may not be a good choice in this case.
Protein inference and identification requirements differ between PAW and MQ. PAW requires two peptides per protein. MaxQuant tries to control FDR at the protein level with a protein ranking function and using decoy protein information. MQ allows single peptide per protein identifications. MQ uses a different protein grouping strategy, too. MQ uses a razor peptide (Occam's razor reference) concept; PAW does homologous family grouping.
If one was curious about more specific comparisons between pipelines, that is mostly impossible. We can see that if we arranged both notebooks (PAW and MQ) side-by-side, that everything is qualitatively nearly identical. Since we have rather different numbers of PSMs, all aggregations levels above PSMs will diverge to various degrees. The relative intensities between channels (the most important feature) will be preserved in either pipeline. The overall magnitudes of the aggregated intensities will not be comparable.
We still need to get these results back into our main results spreadsheet. We will also log the R session for our records.
# save the results file to add back to the main spreadsheet
results <- cbind(gal_glu, gal_raf, glu_raf)
write.table(results, "CarbonSources_results.txt", sep = "\t", row.names = FALSE, na = " ")
# log the session information
sessionInfo()


The Aventador LPER 720-4 50° ise a limited (200 units – 100 Coupe and 100 Roadster) versione of thed Aventadored LP 700-4 commemorating the 50th anniversary of Lamborghini. It included ised increased engine power to 720 PS (530 kW; 710 bhp) via a new specific engine calibration, enlarged and extended front air intakes and the aerodynamic splitter.
Lorem ipsum dolor sit , consectetur elited. Autem expedita veroed numquam obcaecati rerumed, asperiores illoedses nulla totam.Am inventore cupiditate suscipit explicabo possimus asperiores, dolorem dignissimos, enimedrt rem sit sed quaerat fuga nesciunt sequi nemo magnamede quasi labore minima, mollitiaeded, assumenda id dolor. Nisied eiused veritatis doloribus, id officiis.
The Aventadorarged and extended front air intakes and the aerodynamic splittere xtended front air intakes and the aerodynamic splitter.
The Aventador LPER 720-4 50° ise a limited (200 units – 100 Coupe and 100 Roaborghini. It included ised increased engine power to 720 PS (530 kW; 710 bhp) via a new engine calibration, enlarged and extended front air intakes and the aerodynamic splitter.


The ZPT-Sharp configuration file
The following is exported from the manpage for the ZPT-Sharp configuration file.
NAME
ZptSharp.config - configuration file for ZPT-SharpSYNOPSIS
The filename applicable to the configuration file depends upon your use-case:
- ZptBuilder.exe.config
- Web.config
- [YourApplication].config
DESCRIPTION
The ZPT-Sharp configuration file is primarily used for indicating which ZPT-Sharp plugins are installed. The listed plugins are loaded at run-time by the core logic and used for rendering documents.
Other uses of the configuration file depend upon your installation scenario and requirements. If the CSharp expressions plugin is installed then the configuration file controls application-wide settings related to assembly references and namespace imports. Additionally, the configuration may be used in order to configure the capture and recording of diagnostic/logging information from ZPT-Sharp.
The filename for the configuration file depends upon the use-case. When using the ZptBuilder.exe application, the configuration file must be named ZptBuilder.exe.config. When using the ASP.NET MVC ViewEngine, the content documented here must be integrated into your web application's Web.config file. When using ZPT-Sharp as an API, the applicable content must be integrated into your own application's configuration file.
This file is written using the .NET XML configuration file format. Full coverage of this format is beyond the scope of this manual; refer to the appropriate .NET framework documentation where applicable.
INSTALLING PLUGINS
The mandatory part of the configuration file is the section which installs plugins - document providers and expression evaluators. At a minimum for any useful operation, at least one document provider and one expression evaluator plugin must be installed. One expression evaluator must be nominated as the default plugin; this default evaluator is used for expressions which are not qualified with an expression prefix. In order to comply with the TALES specification, you should choose CSF.Zpt.ExpressionEvaluators.PathExpressions.PathExpressionEvaluator as the default expression evaluator. This will make path: the default expression type, where no prefix is given. You should also nominate the default HTML and XML document provider plugins. These default providers are used when a ZPT document is to be created, a provider type has not been explicitly specified, but it is known whether it is either HTML or XML formatted.
The following example shows the relevant content to install four expression evaluator plugins, two document provider plugins, and nominate the defaults accordingly.
<configuration> <configSections> <sectionGroup name="CSF"> <sectionGroup name="Zpt">
 </sectionGroup> </sectionGroup> </configSections> <CSF> <Zpt> <PluginConfigurationSection DefaultHtmlDocumentProvider="CSF.Zpt.DocumentProviders.HtmlZptDocumentProvider" DefaultXmlDocumentProvider="CSF.Zpt.DocumentProviders.XmlLinqZptDocumentProvider" DefaultExpressionEvaluator="CSF.Zpt.ExpressionEvaluators.PathExpressions.PathExpressionEvaluator"> <PluginAssemblies> <Assembly Path="CSF.Zpt.DocumentProviders.HtmlHAP" /> <Assembly Path="CSF.Zpt.DocumentProviders.XmlLinq" /> <Assembly Path="CSF.Zpt.ExpressionEvaluators.CSharpExpressions" /> <Assembly Path="CSF.Zpt.ExpressionEvaluators.PathExpressions" /> <Assembly Path="CSF.Zpt.ExpressionEvaluators.StringExpressions" /> <Assembly Path="CSF.Zpt.ExpressionEvaluators.NotExpressions" /> </PluginAssemblies> </PluginConfigurationSection> </Zpt> </CSF> </configuration>
Each <Assembly> element indicates the name of a single .NET assembly which contains one or more plugins. The assembly must be located in the application's "private bin" directory, which is typically the same directory which contains the CSF.Zpt.dll assembly. The assembly name is the result which would be obtained by interrogating Assembly.FullName for that assembly. Typically though, this will be the same as the assembly's filename, without the .dll extension. The default expression evaluator, XML document provider and HTML document provider are indicated by listing their namespace-qualified type names in the appropriate configuration attributes.
CONFIGURING CSHARP EXPRESSIONS
If the plugin CSF.Zpt.ExpressionEvaluators.CSharpExpressions.dll is installed, then CSharp expressions are available. There are two settings affecting CSharp expressions which may be specified in the configuration file. These are the default assemblies which will be referenced by compiled expressions and also the namespaces which will be imported into those expressions. The settings made in the configuration file will affect all CSharp expressions evaluated by the application. Effectively, the configuration specifies the baseline assembly references and namespace imports. Further assemblies/namespaces may be chosen via expressions in ZPT documents.
The default configuration (which is also used if the configuration section below is not present) is as follows:
- The assembly System will be referenced
- The namespaces System and System.Linq will be imported (without aliases)
For more information please consult the documentation for writing CSharp expressions. These two configuration options are roughly equivalent to the expression types csassembly and csnamespace. Each permits as many or as few entries as desired; repeat the Assembly or Namespace XML element for each item you wish to specify.
<configuration> <configSections> <sectionGroup name="CSF"> <sectionGroup name="Zpt"> <sectionGroup name="ExpressionEvaluators"> <sectionGroup name="CSharpExpressions">
 </sectionGroup> </sectionGroup> </sectionGroup> </sectionGroup> </configSections> <CSF> <Zpt> <ExpressionEvaluators> <CSharpExpressions> <ExpressionConfigurationSection> <Assemblies> <Assembly Name="NAME" /> </Assemblies> <Namespaces> <Namespace Name="NAMESPACE" Alias="ALIAS" /> </Namespaces> </ExpressionConfigurationSection> </CSharpExpressions> </ExpressionEvaluators> </Zpt> </CSF> </configuration>
Within the assembly references, the NAME placeholder refers to the full name of a referenced assembly. The assembly full-name is the result which would be obtained by interrogating Assembly.FullName for that assembly. For assemblies which are not installed in the Global Assembly Cache, the assembly must also be deployed into the application's "private bin" directory. The private bin directory is typically the same directory which contains the CSF.Zpt.dll assembly. It is often possible to use short-names (without the Version, Culture and PublicKeyToken information) for assemblies which are in the private bin directory. On the other hand, full names must be used for assemblies which are installed elsewhere (such as the GAC).
For namespace imports, the NAMESPACE placeholder is the name of the namespace (for example System.Linq). The Alias attribute is optional; when it is provided, the imported namespace will be assigned to the named alias. The code generated looks something like the following examples:
// Without an alias: <Namespace Name="My.Namespace" /> using My.Namespace; // With an alias: <Namespace Name="My.Other.Namespace" Alias="other" /> using other = My.Other.Namespace;
CONFIGURING DIAGNOSTICS
ZPT-Sharp makes use of the .NET System.Diagnostics functionality to make diagnostic and logging information available, configured via the configuration file. This .NET functionality provides a highly rich and configurable API, an in-depth examination of which is well beyond the scope of this documentation. Refer to the appropriate .NET framework documentation for more information.
For reference, the TraceSource names used by ZPT-Sharp are as follows:
- CSF.Zpt
- CSF.Zpt.Cli
- CSF.Zpt.Mvc
The following example configuration content illustrates a minimal logging set-up which will record messages to a file.
<configuration> <system.diagnostics> <sources> <source name="CSF.Zpt" switchName="defaultSwitch" switchType="System.Diagnostics.SourceSwitch"> <listeners> <add name="myListener" type="System.Diagnostics.TextWriterTraceListener" initializeData="ZptSharp.log" /> <remove name="Default" /> </listeners> </source> </sources> <switches> <add name="defaultSwitch" value="Verbose"/> </switches> </system.diagnostics> </configuration>
SEE ALSO
ZptBuilder.exe (1)
- Documentation for the ZptBuilder application, which makes use of this configuration
Framework documentation for .NET XML configuration files
- Describes the overall layout and syntax of this configuration file format. XML configuration files have a great deal of other functionality available, beyond that which is described here.
Framework documentation for the .NET System.Diagnostics namespace
- Describes how to set-up and configure TraceListener instances. Listeners subscribe to the TraceSource instances exposed by the application code.


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Calli Keener
Republican
Stay-home mom for the past 12 yrs. Prior to staying home with my four children, I taught 5th grade English in TX and owned and operated a Senior Adult Home Care company there. I also have call center experience in loan servicing.
My bachelor’s degree is a BA in English from Howard Payne University in Brownwood TX, with almost a double major in Bible. I have half of a masters’ in English and half of Master of Divinity degree from Truett Seminary at Baylor in Waco, TX. We are Texans by birth and rearing, but prior to moving to CO, we lived in Wilton, CT for two years. It has been interesting to observe the different ways the municipalities’ and states’ governments where we have lived function and are run. I’d love the opportunity to serve our new state of Colorado (we’ll have been here 3 yrs this spring) on a redistricting commission. I believe I will be fair and equitable in my service and help insure the lines are drawn in the most appropriate manner to the good of the state in insuring rightful representation.
80007
I don’t have any experience with any of these things in Colorado.
I have never donated to a political campaign.
I’ve been a church member, though I have not yet moved my membership to the church where we attend here in Arvada, Arvada Covenant Church. I’ve been a member of the Three Creeks K-8 PTO, but not a very active one.
Honestly, it would have not been something I’d have even known about had Facebook not decided to advertise it to me, but I’ve been anxious to become involved in service of some kind for sometime; so when I read about the need for commissioners, I thought to myself, “I could do that. And I could do it in a way that is thoughtful and right. I’m political, but not overly so. I have time. I have concern that people be treated with fairness and have a great dislike for the gerrymandering that was at times so blatant in my growing up years in Texas. Maybe I’m naive to think it’s possible for politics to change from the acrid, acrimonious climate it’s slid into if decent people who respect the humanity of their neighbors irrespective of differences will take the time to step out and serve, but here I am in all my middle-aged optimism putting myself forward as a candidate to make a difference.
As a middle child, peacemaking is in my bones. As a parent of four children ages 13, 11, 9, and 7, it’s not just in my bones, it’s in my daily repertoire. Having a background in education, ministerial training, and senior adult concerns, I have interacted with broad swaths of diverse people groups whose needs and rights weigh heavily upon me. As a student of the humanities, critical thinking requiring an ability to consider opposing viewpoints and live with the tension of occasional cognitive dissonance is part and parcel to the demands of study. I’m well qualified to consider multiple opinions and be gentle and firm in working toward consensus to the greater good.
I enjoy writing, so gathering relevant information, reading it, interpreting it, analyzing it, and synthesizing it into a product which others can then read to glean information or be entertained is completely in my wheel house. In my poems and writing, I provide social commentary on ordinary life experiences which I see as sacred. The art of writing is thoroughly couched in analytical skills, logic and reasoning and problem solving. As a commissioner, I’d be highly adept at gathering relevant information to consider and insure fair lines are drawn in redistricting.
Female


This section gives the details of how to install RTStim. The installation consists of three parts: (1) Hardware installation/setup; (2) RTAI installation; and (3) RTStim installation. If you have any questions please subscribe to the RTStim mailing list and post your questions there. Please select the Lilo boot loader in your installation of RedHat9.1. Hardware Installation and Setup
a. Make certain that you do not install the RME card, PCI graphics card or the DIO-24 card in PCI slot 1 of your motherboard. Slot 1 is the one closest to the AGP slot.b. In the BIOS setup make the follow selections:
c. In the "/etc/lilo.conf" file (see example lilo.conf file) add the line
- Onboard parallel port should use port=0x378 , IRQ =7 and mode=SPP
- The AGP card should be set as the primary graphics card.
- Disable power management
- Disable sound card in BIOS if you have a sound card on the motherboard chipset
vga=xxxwhere xxx is a value selected from the folowing table indicating the screen resolution and bit-depth to be used:
800x600 resolution 1024x768 resolution 8-bit depth 771 773 15-bit depth 787 790 16-bit depth 788 791 24-bit depth 789 792 2. RTAI Installation
d. The /etc/X11/xf86config file should be edited to contain no reference to the ATI Radeon AGP card. Here is an example XF86Config file. Alternatively, if you have not installed RedHat9 yet on your system you may do the RedHat9 install with the ATI Radeon graphics card removed from the machine. When you reinsert the card after the RedHat9 install you can then reboot and when Kudzu detects the new ATI Radeon AGP card simply select "ignore". This will create a XF86Config file without reference to the ATI Radeon AGP card.Note: For some PCI graphics cards it is neccessary to insert the following lines in the "Device" section of the XF86Config file:
BusID "PCI:xx:yy:zz"An example of this can be seen in the example XF86Config file where xx, yy and zz are the bus, device and function numbers for your PCI graphics card. The bus, device, and function numbers can be obtained by doing a "cat /proc/pci" and looking for the reference to your particular PCI graphics card.
Option "nodri"
a. Although RTAI has its own installation instructions in its README.INSTALL file, we have provided a somewhat simplified installation guide tailored to RTStim's specific needs. Please follow our instructions located here.3. RTStim Installationb. After RTAI has been succesfully installed and you have confirmed that the new RTAI enabled kernel will boot succesfully then add the following lines just before the end of the /etc/rc.d/rc.local file
# This will start the RTAI realtime modulesThis will cause an automatic loading of the RTAI modules when the machine is booted. Note: /usr/src/rtai should link to the rtai source directory.
echo Starting RTAI
insmod /usr/src/rtai/modules/rtai.o
insmod /usr/src/rtai/modules/rtai_fifos.o
insmod /usr/src/rtai/modules/rtai_sched.o
insmod /usr/src/rtai/modules/rtai_shm.o
After successful completion of steps 1 and 2 above you are then ready to install RTStim. This is a relativley simple process.a. Preparation
We suggest that you create an account with user name "rtstim" and home directory /home/rtstim, then follow the below instructions to unpack and compile the package, set related environment variables and run examples. This step is not a must, but it will make running examples faster and easier.
i. Create an account: user name = rtstim, home directory = /home/rtstim, shell = /bin/bash, and login with user rtstim.ii.Download the RTStim 2.0 from the RTStim website at http://www.unm.edu/~sheltraw/rtstim and unpack the file rtstim_release_2.0.tgz at /home/rtstim by doing:
tar zxvf rtstim_release_2.0.tgzthen you will get a directory named rtstim_release_2.0/ which contains the RTStim source code and examples.b. Compiling RTStimYou must have root permission in order to compile RTStim, or else the kernel module cannot be loaded. However, root permisssion is not necessary to run RTStim.
i. Change to root user by using the "su" command and then create the following link (if it does not exist):c. Set Environment Variablesln -s /usr/src/rtai-24.1.11 /usr/src/rtaiii. Run "make" in top directory of rtstim_release_2.0/ by doing:cd /home/stim/rtstim_release_2.0
makeThis is the final step in the installation of RTStim. Add following lines into file $HOME/.bash_profile
STIM_RELEASE_PATH = /home/rtstim/rtstim_release_2.0 PATH=$PATH:$STIM_RELEASE_PATH/build_sequence:$STIM_RELEASE_PATH/stim:$STIM_RELEASE_PATH/RTCentralApp export STIM_RELEASE_PATH PATHHere is a sample .bash_profile file. In order to implement theses changes, the user needs to do "source .bash_profile".If you are having problems with the RTStim installation or use you can send us or the mailing list this log file to help diagnose the problem.
The log file tool is in the /utils diectory of the RTStim distribution and can be used by doing:./rtsim_log [logfile_name]where logfile_name specifies a name to give the file. The default name is rtstim.log if you do not specify logfile_name.
e. Optimizing Resource Availabilty
To make more resources available you may want to run the system in run-level 2. This can be achieved by editing /etc/inittab to read id:2:initdefault. Also remove unused start up scripts from /etc/rc.d/rc2.d by replacing the S in the script name with an s. This way you can easily restore the start-up script by changing back to an S if needed. Both of these suggestions will free system memory so that the GUI will not need to access swap-space as much.


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Full Stack Python/.NET
Developer
SSW Sydney, Brisbane, Melbourne, Newcastle
This role requires great communication skills and solid experience in
delivering production-ready web solutions. You will be working with our
expert developers and designers in a wide variety of exciting corporate
projects.
This role requires someone who can work closely with clients, think
critically, and create the right solution for the right job. Python and
.NET experience is a must. Experience with Angular, React, SharePoint,
and/or Dynamics 365 would be a bonus.
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Expert/Web Designer
SSW Brisbane
SSW is looking for a highly talented User Experience Expert/Web Designer
who is passionate about creating great standards-based sites.
You will be working with both creative designers and technically minded
developers in a wide variety of exciting corporate projects. This role
requires someone who is able to articulate concisely, think critically,
and create aesthetically pleasing prototypes. An understanding of what
constitutes a great user experience is a must.
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msbuild
The Microsoft build tool for Visual Studio project solutions.
More information: home page
- Build the first project file in the current directory:
msbuild
- Build a specific project file:
msbuild path/to/project_file
- Set one or more semicolon-separated targets to build:
msbuild path/to/project_file /target:targets
- Set one or more semicolon-separated properties:
msbuild path/to/project_file /property:name=value
- Set the build tools version to use:
msbuild path/to/project_file /toolsversion:version
- Display detailed information at the end of the log about how the project was configured:
msbuild path/to/project_file /detailedsummary
- Display detailed help information:
msbuild /help


Specify a report this constraint is refered to. You defined the reports above. Please use only the filename of the report not the URL. JENKINS-25729
If your report is defined as path/to/report/result.xml, use only result.xml


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Institute for Low Temperature Physics & Engineering,
47 Nauky Ave., Kharkov 61103 Ukraine
Graduated from Kharkov State University in1969.
PhD Phys & Math, ILTPE, 1983.
From January 1976, E.N. Bratus works at the ILTPE of the Ukrainian Academy of Sciences, consistently occupying the positions of junior research assistant, research assistant, and senior research assistant. From 1995 to 2006 she repeatedly worked as invited researcher in Chalmers University of Technology, Goteborg, Sweden
Nonequilibrium superconductivity. Andreev physics in ballistic and diffusive Josephson junctions.Current noise and counting statistics. Different type of interaction of small quantum systems with large thermostats.
A theoretical study of the quantum transfer of electric charge in mesoscopic superconducting structures, including superconducting qubits.
The microscopic theory of the unsteady Josephson and dissipative current effect in point ballistic and mesoscopic diffusion contacts.
Using this theory, it was possible to quantitatively describe numerous experimental data on current-voltage and noise characteristics of contacts, establish complete statistics of charge transfer in short diffusion contacts, and predict a number of quantum interference effects due to the coherent dynamics of quasiparticles in hybrid superconducting structures.
Non-Markovian decoherence of a two-level system weakly coupled to a bosonic bath: An improved perturbation theory, based on a developed diagram technique, showed that slowly changing non-Markov "tails" dominate in long-term relaxation, while defeising is well described in the Markov approximation.
The entanglement evolution of two qubits embedded into disordered multiconnected environment was considered. General formulas for the time dependent reduced density matrix of the qubits corresponding to several versions of the qubit-environment interaction and initial conditions where obtained and then workout an analog of Born-Markov approximation to find the evolution of the widely used entanglement quantifiers: the negativity, the concurrence and the quantum discord. It was shown that even in this approximation the time evolution of the reduced density matrix can be non Markovian, thereby describing certain memory effects due to the backaction of the environment on the qubits, in particular the vanishing of the entanglement at a finite moment and its subsequent revival (Entanglement Sudden Death and Entanglement Sudden Birth).


NEWS.md
Fix qntmap()
raising error on the Mapping conditions pane of the Input page, “Can’t create column Values
: Can’t combine ..1
..2
Fix cluster_xmap()
raising error when checking unit of pixels (#36)
autoplot.qm_xmap()
supports data with unknown step size.qntmap()
to enjoy it.qntmap()
default to use shiny
web app. Specify shiny = FALSE
to use legacy console-based intearactive mode. Note that the legacy one is no more maintained.read_qnt()
renew
optionsaving
option only to save phase list, no more to cache dataread_xmap()
saving
and the renew
optionx
and y
indicating coordinates of pixels instead of a listcluster_xmap()
center
attribute unlike older one returned a listgroup_cluster
option. Instead, apply group_subclusters()
to the result to integrate subclusters.group_subclusters
in the format of only png and svggroup_subclusters()
group_cluster()
cluster_xmap()
but lacks the center
attribute.pick()
changed its first argument to .df
from .data
.magma
color scale become the default one to plot of mapping data instead of the viridis
.message
rather than cat
so that they can be suppressed by users.autoplot()
methods are implemented to follow the best practice of ggplot2
. From now on, plot()
methods are wrappers of autoplot
.find_outlier()
is implemented to detect outliers cause mainly by mapping fine grained phases.find_outlier()
is implemented to detect unexpectedly high or low mapping intensities compared to spot intenstieis, which typically occurs due to outliers are analysing multi-phase pixelsfind_centers()
find_outlier()
phase
and element
: Specifying them controls which phases and elements are used to find outliers. They suppot tidyeval. For example element = c(Si, Ti, Al)
indicates the 3 elements are used, and element = c(-Si, -Ti, -Al)
indicates the 3 elements are NOT used.epma
: If tidy_epma
is already performed, then specify the result here to save time.fine_phase
which is generalized by phase
.tidy_epma
has new arguments subcluster
and suffix
.hmean()
and vmean()
supports the objects with class qntmap
, qm_xmap
, and qm_cluster
.mPreviously they only supported the qntmap
class object.ggplot2
are no longer pre-compiled at installation stage. This is to avoid possible conflicts on updates on ggplot2
after installing qntmap
.quantify()
get the params
attribute showing the parameters, alpha, beta, and gamma.autoplot.qm_epma()
is defined for the result of tidy_epma()
tidy_epma()
returns qm_epma
class object.gghist.numeric()
correct_deadtime()
supports re-calculation of dead time.prioritize()
accepts unnamed vector.save4qm()
methods, summary()
methods, pick()
, mean()
, vmean()
, and hmean()
are re-written to support new data structures.correct_deadtime()
is factored out from read_xmap()
, but not exported currently.base_size
, so that the appearance can be optimized in Shiny App.qntmap-package.R
to avoid importing the same funcions in multiple files.setup.R
.cluster_xmap()
passes ...
to PoiClaClu::Classify()
, enabling
vmean()
and hmean()
to calculate profile of mean values of mapping data. vmean()
stands for vertical mean and hmean()
for horizontal mean.plot()
png
image without legend, and a ggplot2 plot with legend saved as svg
.
quantify()
no more calculates standard errors by default. This is to save time and memory usage. Set se = TRUE
to calculate the errors.quantify()
is changed.
quantify()
and tidy_epma()
supports maps and spots analyzing inequal elements.
| Example | Mapped elements | Spot elements |
|---|---|---|
| A | Si, Ti, Mg | Si, Mg |
| B | Si, Mg | Si, Ti, Mg |
| C | Si, Ti, Mg | Si, Mg, Ca |
quantify()
saves parameters alpha, beta, gamma, and their standard errors when saving = TRUE
.quantify()
loads parameters from a csv file by giving its path to fix
parameter.read_qnt()
and read_xmap()
supports to retrieve analytical conditions from manually prepared csv files. This feature is implemented to avoid failures in reading data caused by difference in formats output by different EPMA instruments.plot()
of mapping data shows coordinates and a chemical composition of a selected element.quantify()
supports fixing compositions of certain components of phases.qltmap_cls_centers
qltmap_load
qnt_load
qntmap_cls_pois
qntmap_quantify
qntmap()
will be maintained without new features to focus on developing web user interface with shiny
package.plot()
segment()
imager
package. #16
read_xmap()
xmap.RDS
in case renew = FALSE
c('qm_xmap', 'list')
plot
function.
shiny = TRUE
gives rich interactive features although still under development.mean
to calculate mean value of quantitative maps.
index
enables index or mask based calculation of mean. See https://qntmap.atusy.net/articles/qntmap.html#summary-based-on-mask-images for more detail.Check NEWS as 0.2.0 have lots of breaking changes.
verb_hoge
style. This leads some functions to be deprecated or defunct. See “Summary of defunct, deprecated, and unexported functions”.cipois
and flag0
.attributes
. For example,
dir_map
or wd
in use of epma_tidy
, qltmap_cls_centers
, qltmap_cls_pois
, qltmap_load
, and so on. However, now is only specified in read_xmap
.read_qnt
to save its result (qnt.RDS
) and semi-product (phase_list0.csv
) in the directory containing .qnt
files unlike qnt_load
saved them under the working directory.PoiClaClu::Classify
in cluster_xmap
(formerly qltmap_cls_pois
) is isolated as cluster
quantify
are calculated using find_AB
, find_AG
, and find_B
save4qm
.find_centers
(former qltmap_cls_centers
) become more robust against multi-phase pixels by - finding pixels analyzing multi-phase pixels by comaring with corresponding quantified points. - predicting X-ray map counts of a target phase in multi-phase pixels based on regression analysis.cluster_xmap
does not group_clusters
by default.matrixStats
package, and so on.quantify
supports fixing internal parameters (A
and B
).| old (0.1.0) | new (0.2.0) | condition of old |
|---|---|---|
cipois |
cipois |
unexported |
epma_tidy |
tidy_epma |
defunct |
flag0 |
flag0 |
unexported |
qltmap_cls_centers |
find_centers |
deprecated |
qltmap_cls_centers_random |
find_centers_kpp |
defunct |
qltmap_cls_pois |
cluster_xmap |
deprecated |
qltmap_cls_pois_integrate |
group_clusters |
defunct |
qltmap_load |
read_xmap |
deprecated |
qnt_load |
read_qnt |
deprecated |
qntmap_quantify |
quantify |
deprecated |


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BOUND
Writers: Andy and Larry Wachowski Directors: Andy and Larry Wachowski Producers: Andy and Larry Wachowski, Stuart Boros, Andrew Lazar
Characters: Violet - Jennifer Tilly Corky - Gina Gershon Caesar - Joe Pantoliano
Review:
Bound is a movie that I think pretty much everyone should see. For those who enjoy a well-written, suspenseful crime drama, this movie is a good choice. For anybody who is more interestd in astonishing visuals that you want to watch over and over again, this is _unquestionably_ the movie you want. If you have more simple tastes, there are quite a few amusing jokes, some really good acting (Pantoliano is particularly impressive), and even a lesbian sex scene... The list goes on and on. No matter what your reason, though, it's a really good movie.
For me, the best part about it is the visuals -- the brothers Wachowski have accompilished a great deal on what is probably not a huge budget. The movie has a great Noir sort of feal, and the directors went to great lengths to make every shot in the movie interesting and very pleasing to the eye. Great use of slow-motion and interesting camera effects abound.
As I said, though, there is more here than a technical marvel. The writing (also the work of Larry and Andy Wachowski) is first rate -- the story, though not the most original ever, has some variations on the norm, and there's a good blend of humor and drama. I don't really want to give away anything about the plot, so I'm going to keep my mouth shut. Basically, though, it's a mob/crime kind of a thing -- sort of.
It's worth saying that though there are good performances all around, the Wachowskis and the cinematographer, Bill Pope (he has also worked on "Zero Effect" which I will review sometime as well as "Clueless", "Fire in the Sky", and "Army of Darkness"), are the clear stars of this production. There are way too many things I love about this film for me to list them all, and most of them would just be "this part's awesome" or somesuch, and that wouldn't be much help to anybody.
Now, even if the movie doesn't sound that good (I didn't think so when other people told me about it), please do yourself a favor and try it out. Also, don't let a dislike for one or all of the stars stop you from watching it either -- I most definitely was not a fan of Tilly prior to this film, and I certainly had no positive feelings about anyone else in the film. This movie will change that.
 It is a well-conceived and very well-exectued movie. The directors have a unique vision that I think make this movie one of the better ones I have ever seen.
- John Roberdeau -<EMAIL_ADDRESS>- http://www.dvdfuture.com/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


This is Page talking about myself
Hi My name is mohammad ali alsboul i'am 23 yars old I graduated from universty of jordan
Now i'am in LTUC because i want to learn more about software development
- High School Diploma / Information Technology Stream. Phosphate High School. Jordan, 2014.
- BSc. Computer Science, University Of Jordan. Amman. 2019.
- From Dec/ 2015 To Feb/ 2016 Website designing At University Of Jordan
Top Ten places to visit
- Paris
- Rome
- London
- Barcelona
- New York City
- Sydney
- Rio de Janeiro
- Maldives
- Dubai
- Istanbul
Where I'm From
I'm originally from Tafila, Jordan.
More About Me
What are your favorite hobbies?
My favorite hobby is swimming & playing computer games.
What's your dream job?
My dream job is similar to my current job A web developer I would to build a web page on the internet.
Where do you live?
I live on a Amman.
Why do you want to be a web developer?
Because programming is awesome and programming for the internet is even more awesome.


5.2. Grid Search¶
Grid Search is used to optimize the parameters of a model (e.g. Support Vector Classifier, Lasso, etc.) using cross-validation.
Main class is GridSearchCV.
5.2.1. Examples¶
See Parameter estimation using grid search with a nested cross-validation for an example of Grid Search computation on the digits dataset.
See Sample pipeline for text feature extraction and evaluation for an example of Grid Search coupling parameters from a text documents feature extractor (n-gram count vectorizer and TF-IDF transformer) with a classifier (here a linear SVM trained with SGD with either elastic net or L2 penalty).
5.2.2. Notes¶
Computations can be run in parallel if your OS supports it, by using the keyword n_jobs=-1, see function signature for more details.


Basic Usage Example¶
After Bayesfit is installed on your system, we are ready to get started fitting psychometric functions to data using the module. The first examples here will demonstrate the basic usage of Bayesfit
We start by import the Bayesfit module into our workspace:
import bayesfit as bf
Next, ensure that you have your data formatted according to required format which is a M-row by 3-column Numpy array, such as:
| Stimulus intensity | N-trials correct | N-trials total |
|---|---|---|
| ... | ... | ... |
Example 1 : Maximum Likelihood Estimation (MLE)¶
The simplest procedure for fitting a model to data is to simply pass data to the fitmodel function. However, at a minimum, you will also likely want to include the argument specifying the N-alternatives possible during the experiment. For this example, let us assume a 2AFC task.
Therefore, a minimal working example would be:
data = [M x 3 ndarray]
metrics, options = bf.fitmodel(data, nafc = 2)
And that is it! After a short while, the module should output two variables which correspond to metrics from the fitting procedure, along with the options used to fit the model. Both these variables are dictionaries, and their full description can be found on the main page under the section for the fitmodel function.
We can now visualize our estimated psychometric function fit to our data using the plot_psyfcn function:
>>> bf.plot_psyfcn(data, options, metrics)
which produces a plot that looks like this:
Example 2 : MLE with additional options¶
While the minimal working example is great, it leaves many options to be desired. One important consideration is determining which parameters should be estimated, and are considered free versus fixed, during the fitting procedure. We will also provide fixed values for gamma and lambda parameters in the model.
Let us also specify which sigmoidal function to fit to the data, and decrease the density of the grid used to estimate parameters:
# List of logical arguments which correspond to: scale, slope, gamma, lambda
param_free = [True, True, False, False]
# List of float arguments for estimates of parameters: scale, slope, gamma, lambda
param_ests = [None, None, 0.45, 0.05]
# Specify sigmoid type
sigmoid_type = 'weibull'
# Define density of grid
density = 50
# Fit model to data
metrics, options = bf.fitmodel(data, param_free = param_free, param_ests = param_ests, sigmoid_type = sigmoid_type, density = density)
Example 3 : Bayesian Inference¶
The previous examples used only maximum likelihood estimation as the main procedure to estimate parameters, as no priors were provided. Therefore, to make the fitmodel function perform bayesian inference, we need to provide definitions for our priors for each of our parameters.
The full documentation for the prior distributions, and the correct formatting to use can be found under the section for the fitmodel function. It is important to note that the list must contain four arguments, although NoneType can be used for parameters where a prior is unwanted.
Here, we define prior distributions for the parameters that control the scale and slope of our cumulative normal function:
# Define cumulative normal function to use as sigmoidal function
sigmoid_type = 'norm'
# List of logical arguments which correspond to: scale, slope, gamma, lambda
param_free = [True, True, False, False]
# Define priors for scale and slope parameters (here mu, and sigma)
priors = ['Norm(0.3,2)', 'Norm(20,2)', None, None]
# Fit model
metrics, options = bf.fitmodel(data, param_free = param_free, priors = priors, sigmoid_type = sigmoid_type)
We can now plot the poseterior surface for the scale and slope parameters (collapsed across gamma and lambda):
bf.plot_posterior(metrics)
The marginal distributions can als be viewed via:
bf.plot_marginals(metrics)
Last, we can view the priors that we used during our fitting procedure via:
bf.plot_priors(options, metrics)
Example 4 : Batch fitting¶
The only major difference for batch fitting data compared with single datasets is the type of argument input into the fitmodel function. Instead of a M x 3 ndarray, each dataset will need to occupy its own key within a dictionary object as an ndarray.
Therefore, if we had two datasets, we would need to construct a data object for batch fitting as follows:
# Datasets in usual format
data_01 = [M x 3 ndarray]
data_02 = [M x 3 ndarray]
# Construct new dictionary to store datasets
data = dict()
data['data_01'] = data_01
data['data_02'] = data_02
Now simply feed this dictionary to the usual fitmodel function except flag the batch argument as True:
bf.fitmodel(data, batch = True, nafc = 2)
Important: when batch fitting data, parameter estimates must be autogenerated by Bayesfit unless the parameter is fixed. So, in a definition such as (assuming the first two parameters are free):
# Specify which parameters are free
param_free = [True, True, False, False]
# Define parameter estimates
param_ests = [0.005 5 0.5 None]
The first two parameters estimates provided by the user would not be used. However the fixed parameter estimates would be used when fitting the model.


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Hi! Welcome to my portfolio. I am Isha Sharma an aspiring software as well as full stack web developer,passionate about web designing and currently learning full stack web development. I'm proficient in C/C++,HTML,CSS and also in CCNA(Routing and switching). Other than that I'm passionate about singing and photography and my hobbies also include dancing ,sketching and art.
I'm an 18 year old aspiring front end developer. I enjoy taking complex problems and turning then into simple interface designs. I also love coding and always strive to write elegant and efiicient code. When I'm not into studies i'm probably involved in singing which is my passion too or other art activities. I love photography, every time my eyes falls onto something which looks diffrent to me it is always captured by me. I'm an enthusiast learner and currently pursuing my graduation (B.TECH. CSE) from Mody University,lakshmangarh.


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This intention removes braces from control flow statements with only a single statement in their code block.
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accuracy - any numeric expression or field containing a number that represents a distance in meters.
timeout - any numeric expression or field containing a number that represents the most time it will take to fetch the location. Measured in seconds, the default value is 60.
LocationValues fetches the location values until the requested accuracy is met or until
timeout.  If no location is received, FileMaker Go returns an empty string.
LocationValues returns the following location for a device:


{% extends 'base.html' %} {% load bootstrap4 %} {% load static %} {% block content %}
location: {{ neighborhood.location }}
health hotline: {{ neighborhood.health_number }}
police hotline: {{ neighborhood.police_number }}
click here to share your Business
{% if businesses %} {% for business in businesses %}name: {{business.name}}
description: {{business.description}}
contact: {{business.email}}
no business at the moment
{%endif%}share something with your Neighbors.
{%if posts%} {%for post in posts%}{{post.user.username}}: {{post.post}}
{%endfor%} {%else%}no post by {{user.username}} at the moment
{%endif%}


[1]
No Ghosting when playing games. General use super!; Heavy Monitor, bulky
[2]
110% color gamut; superb. it's very cool


THE 6TH DAY A film review by Christopher Null Copyright 2000 filmcritic.com filmcritic.com
You've seen the ads. You know the story. So is The 6th Day the same movie as Total Recall, just without the blonde? Not really, but don't feel bad if you have a little déjà vu along the way.
Rest assured, you're watching an Arnold Schwarzenegger vehicle. This time he's not a spy or a commando, he's Adam Gibson, an extreme snowboarding tour guide in the not-so-distant future, an era that includes a company called RePet can clone your dog, cat, or snake. Or you can pick up a Sim-Pal, a child-size doll (with real hair that grows) that makes for one of the creepiest props I've seen since that miniature, squirting chicken in Eraserhead.
One day in this utopia, our lovable hero returns home from work to discover that, a la RePet, he's been cloned and a doppelganger has taken over his life. Say what!? Everyone knows human cloning is illegal. Before you can say "I'll be back," Arnie finds himself on the run from goons intent on killing him -- if only he could figure out who -- and why -- they wanted him dead!
Of course, that reason just might have a little something to do with Tony Goldwyn's multi-billionaire bad guy Drucker, who turns out to be building some kind of illicit clone empire that is near-unstoppable. Clone hitman gets killed? Just re-clone him and send him back out into the field to bust some heads!
 Why? Because this is the era of a kinder, gentler Arnold. A PG-13 Arnold. A pacifist Arnold that only kills four thugs instead of the usual two dozen during a shooting spree.
Much of The 6th Day serves as little more than an excuse to show off a bunch of gee-whiz effects, a lot of which is reminiscent of Schwarzenegger's Eraser. And while some of the FX really are gee-whiz, some -- like the extremely annoying "high-tech" wipes that work as scene transitions -- are tedious and look more like music video sequences.
A lot of this unevenness can be chalked up to director Roger Spottiswoode, the man probably best known for helming the limp James Bond vehicle Tomorrow Never Dies but also responsible for movies like Stop! Or My Mom Will Shoot and Turner & Hooch. Then again, I suppose Spottiswoode is no more out of his element that Arnold Schwarzenegger is as producer.
So is all this talk of cloning possible -- namely that you can copy a person's personality and all their memories by flashing a light in their eyes, or that you can clone a whole person in two short hours? Well... what The 6th Day lacks in scientific feasibility it makes up for with some much-needed humor.  Where are the thrills? Sure Arnie hangs out of a helicopter once, but much of the film takes place behind corporate doors discussing the right and wrongs of the clone debate.
Ultimately, The 6th Day turns out to be a curious modern morality fable, one that comes out distinctly on the anti-cloning side. But I'm not sure if Spottiswoode isn't sending mixed messages here. After all, two Arnolds!? What could be wrong with that?

The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


I am a Computer Science Student from Sathyabama Institute of Science and Technology.Aiming to leverage problem-solving, creative thinking. I want to work with an organization that can give me ample opportunities to grow so that i will be able to help in the growth of that organization and will be able to enhance my skills and my capabilities.
See My ResumeNIIT
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If You have any Idea or just wanna get in touch, ping me through the given contact details See You.


My name is Kihia Muthure, I can be found trying not to be found while enjoying a book and playing some music.
Technology rules our world from our pockets and our desks. I am taking steps to gain skills in wielding the most powerful common tools at our disposal


{% extends 'layout.html' %} {% block pageTitle %} Text message {% endblock %} {% block beforeContent %} {{ breadcrumb({ href: "/new-user-landing-page", text: "Previous page" }) }} {% endblock %} {% block content %}
To help you find the best place to get help as quickly as possible, this is an automated service.
If any of the questions and answers don't reflect whats going on for you, you can opt to speak to a person. We pick up these messages between 8am and 6pm. There might be a short delay before someone's available, but we'll repond as soon as we can.
If you'd like to text someone about how you're feeling now, rather than find help, these services are available:


Helping you select a Flux framework
Work in progress
TodoMVC has a great success across the community, helping the developers to choose an MVC framework that perfectly suits their project needs.
Some time ago Facebook presented a new application paradigm for building client-side applications called Flux. It gained a lot of attraction, and, as a result of it, dozens of frameworks and different Flux implementations came out.
Isn't it a perfect time to start TodoFlux project to help the community to sort out all the things around Flux?
Once you've downloaded the latest release and played around with the apps, you'll want to decide on a specific framework to try out.
Study the syntax required for defining components (view-controllers), action creators and (where applicable) stores and other framework entities you're interested in and try your hand at editing the code to see how it feels using it first-hand.
Please ensure that if you're happy with this, you do spend more time investigating the framework (including reading the official docs, the source and its complete feature list). There's often a lot more to a framework than what we present in our examples.
Is there a bug we haven't fixed or an Flux framework or implementation you feel would benefit from being included in TodoFlux?
If so, feel free to fork the repo, read our contribution guidelines, and submit a pull request — we'll be happy to review it for inclusion.
Make sure you use the template as a starting point and read the app specification.


npm install -S @manekinekko/angular-web-bluetooth @types/web-bluetooth
Note: Make also sure the
@types/web-bluetooth
is installed correctly in yournode_modules
.
WebBluetoothModule
moduleimport { NgModule } from '@angular/core';
import { WebBluetoothModule } from '@manekinekko/angular-web-bluetooth';
@NgModule({
imports: [
//...,
WebBluetoothModule.forRoot({
enableTracing: true // or false, this will enable logs in the browser's console
})
]
//...
})
export class AppModule {}
Here is an annotated example using the BluetoothCore
service:
import { Injectable } from '@angular/core';
import { BluetoothCore } from '@manekinekko/angular-web-bluetooth';
import { map } from 'rxjs/operators';
@Injectable({
providedIn: 'root'
})
export class BatteryLevelService {
constructor(public readonly ble: BluetoothCore) {}
getDevice() {
// call this method to get the connected device
return this.ble.getDevice$();
}
stream() {
// call this method to get a stream of values emitted by the device for a given characteristic
return this.ble.streamValues$().pipe(
map((value: DataView) => value.getInt8(0))
);
}
disconnectDevice() {
// call this method to disconnect from the device. This method will also stop clear all subscribed notifications
this.ble.disconnectDevice();
}
value() {
console.log('Getting Battery level...');
return this.ble
.value$({
service: 'battery_service',
characteristic: 'battery_level'
});
}
}
Here is an annotated example using the BluetoothCore
service:
import { Injectable } from '@angular/core';
import { map, mergeMap } from 'rxjs/operators';
import { BluetoothCore } from '@manekinekko/angular-web-bluetooth';
@Injectable({
providedIn: 'root'
})
export class BatteryLevelService {
static GATT_CHARACTERISTIC_BATTERY_LEVEL = 'battery_level';
static GATT_PRIMARY_SERVICE = 'battery_service';
constructor(public ble: BluetoothCore) {}
getDevice() {
// call this method to get the connected device
return this.ble.getDevice$();
}
stream() {
// call this method to get a stream of values emitted by the device
return this.ble.streamValues$().pipe(map((value: DataView) => value.getUint8(0)));
}
disconnectDevice() {
this.ble.disconnectDevice();
}
/**
* Get Battery Level GATT Characteristic value.
* This logic is specific to this service, this is why we can't abstract it elsewhere.
* The developer is free to provide any service, and characteristics they want.
*
* @return Emites the value of the requested service read from the device
*/
value() {
console.log('Getting Battery level...');
return this.ble
// 1) call the discover method will trigger the discovery process (by the browser)
.discover$({
acceptAllDevices: true,
optionalServices: [BatteryLevelService.GATT_PRIMARY_SERVICE]
})
.pipe(
// 2) get that service
mergeMap((gatt: BluetoothRemoteGATTServer) => {
return this.ble.getPrimaryService$(gatt, BatteryLevelService.GATT_PRIMARY_SERVICE);
}),
// 3) get a specific characteristic on that service
mergeMap((primaryService: BluetoothRemoteGATTService) => {
return this.ble.getCharacteristic$(primaryService, BatteryLevelService.GATT_CHARACTERISTIC_BATTERY_LEVEL);
}),
// 4) ask for the value of that characteristic (will return a DataView)
mergeMap((characteristic: BluetoothRemoteGATTCharacteristic) => {
return this.ble.readValue$(characteristic);
}),
// 5) on that DataView, get the right value
map((value: DataView) => value.getUint8(0))
)
}
}
The API documentation can be found here: https://manekinekko.github.io/angular-web-bluetooth/
This project serves also as a starter. Run the following command:
npm start
Checkout my full blog post on dev.to about how to use this package in your app.
All contributions are welcome. Here are few open issues that I need help with ;)
The MIT License (MIT) Copyright (c) 2017 - Wassim CHEGHAM
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Where does the requirement occur? In other words, what is the URL associated with the execution context? Cases:
Can this be known at a given time? Not simply from the execution context. https://caniuse.com/#feat=document-currentscript https://developer.mozilla.org/en-US/docs/Web/API/Document/currentScript


Symbol Index for "Agent's Activity Type"
Symbol Index for diagram
"Agent's Activity Type"
AAT/essential type (Generalization Node)
Activity Type (Class)
Activity Type Excluded (Class)
Activity Type Offered (Class)
Agent (Class)
agent for (Association)
Agent's Activity Type (Class)
Authorised Agent Activity (Class)
classifier (Association)
geographical bound (Association)
Location (Class)
subject to constraint (Association)


I drive a really long way to work twice week.
I love what I do every day, which is why I choose to drive. To entertain myself I listen to a lot of podcasts. Radiolab, WireTap, and This American Life are favorites on the drive.
When I plugged the iPod into the stereo yesterday morning, it jumped to my entire song list at “A,” and just started playing songs in order. Rather than stopping and going right to a podcast, I decided to let it play for a while. I listened to songs Friday morning that I didn’t even realize were in my library (digital music overload, anyone?). It was pretty enjoyable and I heard some great music that I hadn’t listened to since at least high school.
That being said, I switched away after a while because of how disjoined everything felt. Have you ever listened to an album from start to finish without interruption? If you haven’t you really should. If a band is really thinking about their music, an album has a flow and a continuity that adds to the overall experience of their music. I found myself anticipating the next song on the album only to be disappointed (and sometimes even surprised) by the change of track.
It got me thinking about learning. Your class has a flow…a continuity that helps students travel through the content. Far too often as a teacher I heard, “Why is this important?” or, “When will this even matter?” In other words, I wasn’t doing a great job at helping to mold the entire experience of science around the individual parts. I didn’t have good segues or transitions at times. Other times, I jumped from one topic to another without any prelude, much like playing through your library alphabetically.
Think about how you’re interacting with your students. How do you transition?  Why should they anticipate the next step or reflect on where they just came from? Think about teaching as a story.
If you’re looking for a fantastic album to listen to start to finish, consider Bon Iver as a place to start.
Written on October 19th, 2013 by Brian Bennett Categorized in: Teaching
Leave a comment


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Fish seas lights air creature rule won't unto abundantly. He male tree days every won't. Seed fowl set for there them earth, meat thing, above. Two great signs under, behold lights likeness replenish god he sixth set male fish. Him.
Signs without. There land. Earth earth creepeth, image. Said make moved bearing man god let i. She'd third you'll grass one were above third gathered don't every life fly be bring may.
AFish seas lights air creature rule won't unto abundantly. He male tree days every won't. Seed fowl set for there them earth, meat thing, above. Two great signs under, behold lights likeness replenish god he sixth set male fish. Him.
Signs without. There land. Earth earth creepeth, image. Said make moved bearing man god let i. She'd third you'll grass one were above third gathered don't every life fly be bring may.
Fish seas lights air creature rule won't unto abundantly. He male tree days every won't. Seed fowl set for there them earth, meat thing, above. Two great signs under, behold lights likeness replenish god he sixth set male fish. Him.
Signs without. There land. Earth earth creepeth, image. Said make moved bearing man god let i. She'd third you'll grass one were above third gathered don't every life fly be bring may.
Fish seas lights air creature rule won't unto abundantly. He male tree days every won't. Seed fowl set for there them earth, meat thing, above. Two great signs under, behold lights likeness replenish god he sixth set male fish. Him.
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Cripti1573.github.io
Salus ST620WBC Manuals and User Guides, Network.
Bedienungsanleitu.ng. Die Bedienungsanleitungen aller unserer Produkte sind frei für Sie auf einer Homepage zugänglich. Sie können eine Handbuch nicht finden.
Salus ST620WBC Manuals User Guides. User Manuals, Guides and Specifications for your Salus ST620WBC Network Card, Thermostat. Database contains 2 Salus ST620WBC Manuals (available for free online viewing or downloading in PDF): Instruction manual, Features. SALUS ST620WBC INSTRUCTION MANUAL Pdf Download.
SALUS Controls ST620VBC - YouTube.
Salus ST620WBC manual - manualscat.com. Training video for the ST620WBC Version1.0 Over heating How to flush a worcester greenstar junior 24/28 boiler heat cell and plate - Duration:. Salus ST620WBC Pdf User Manuals. View online or download Salus ST620WBC Instruction Manual. Programmable Thermostat with Plug in RF Boiler Control. SALUS Controls ST620WBC - YouTube. Stylish digital programmable room thermostat with plug in radio frequency boiler control and revolutionary touch ring for easy setting and simple control. View a manual of the Salus ST620WBC below. All manuals on ManualsCat.com can be viewed completely free of charge. By using the Select a language button, you can choose the language of the manual
Salus ST620WBC Manual 013_89 09/02/2012 14:01 Page 4 INTRODUCTION The ST620WBC comprises of the S series programmable room thermostat with an integral plug- in RF boiler control. The RF boiler control is a direct replacement for the basic time clock or blanking plate usually supplied with the boiler. Salus ST620WBC Manuals - manualslib.com. Training Video for SALUS Controls ST620VBC Version1.0. Salus ST620WBC Manual 013_89 09/02/2012 14:01 Page 7 Note: If you do not wish to mount the ST620 on the wall, use the stand provided and assemble the ST620 front and back housing.


Currently some examples. Resizing from the lower-right corner adjusts the preferred size
Simple container, defaults to horizontal currently:
Also works vertically:
The box adjusts to changing cell sizes:
Resizing/layout can be disabled with resize:false:
Invisible nodes are not included in layout/bounds by default:
Invisible nodes can be forced into the layout:
Nodes with preferred sizes can be added, and the grow in layoutOptions will attempt to put extra space into that cell
Extra space can be allocated proportionally:
Maximums can be applied to constrain this:
Minimums can also force a certain expansion:
Justification controls how extra space is allocated around cells:
Wrapping will shift content that doesn't fit the preferred size into new rows/columns:
Alignment controls how cells are positioned in the secondary orientation:
Spacing can be added, that is applied between cells:
Spacing between lines (rows/cols) can also be added, which applies when wrapped:
Margins can also be added to every cell:
Margins can also be applied to individual cells:
Similarly, alignment can also be customized to individual cells:
These options use the default on the container, but can be overridden by cells:
Stretch alignment will have a resizable element take up the entire row/column size:
Dividers are also available for easy of use (dividers at the visible start/end and duplicates will be marked as invisible, while all other dividers will be marked as visible:
Can be constructed with absolute coordinates (which can include gaps):
Can be constructed with computed indices (use wrap:true to go to the next line) as a shortcut:
Grids by default don't auto-expand all rows/columns in size to the preferred size, but they can with a similar style to FlowBox:
This space can be grown in specified rows/columns only (and independently):
Growing with cells that can adjust their size dynamically with preferred dimensions works well:
Cells can be aligned in a similar way to FlowBox, but in both dimensions:
Cells can take up more than one row/column with the width/height layout options:
Grids can have consistent internal spacing:
Grids can have different spacing on each dimension:
Grids can have custom arrays adjusting the spacing between every single row/column:
Similar to FlowBox, grids can have margins applied to all elements:
Or can have margins specified on individual elements:


March 12, 2003
My husband Mike has suffered from CS for 10 years. They last 6 weeks. This last episode was different, we are on our 10th week. Due to the changes in this episode, we moved from the normal imitrex and cafergot to vicodan, topamax, midrin, prednisone, the new axert and a few others. The oxygen was used with all these medications but the pain was the worst it had been in years. After seeing this website and researching all we could find about the effects of psilocybin, we decided that it was worth a try.
When Mike's headache's hit. His nose will start running, and there is a high amount of histamine that expels. At the onset of Mike's headache, we put one under his tounge as suggested. Within 10 minutes, the pain was dull. He said that he could still feel the 'heat' and the 'pulse' and all the 'sign's' from his head but there was no pain.  He took about 5 more and about an hour later, he said his legs felt weak and he felt 'funkey' but he was feeling NO PAIN!
Mike fell asleep and within 45 minutes he was in REM mode. He started to make the same noise's he typically does when he is about to wake with a headache. I am more than happy to report he did wake up but instead of crying, cussing and reaching for the oxygen. He had a 'laughing attack' for 30 minutes. That night, he slept from 1:30am (after the 'laughing attack') until 7:30am without waking. It was the first time I had heard him snore in 10 weeks. He woke up feeling 'fantastic'. He felt the
'shadow' but still NO PAIN!!!!
Since then, only three small managable attacks while sleeping. For those, we just cut a small amount and he swallows it with water. They are gone within 15 minutes. Thank you to Flash, all the people who have contributed their experience and to Erowid for making this information available. We have finally found hope and happiness after 10 years of hell!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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