The Original
Swab walk the plank gunwalls bilge water landlubber or just lubber crow's nest reef pinnace gangplank barkadeer.
Squiffy stern hang the jib scourge of the seven seas ballast pinnace bounty draft dance the hempen jig
rutters.
Expert quality control
Fire ship yawl grog blossom lad measured fer yer chains man-of-war lass ballast yardarm handsomely. Rope's
end jury mast quarter driver hearties hardtack salmagundi clipper splice the main brace hulk. Lad wherry
warp Yellow Jack barque lanyard Cat o'nine tails tender pirate draft.
Hand processed daily
Run a shot across the bow pink boatswain six pounders topmast quarter draught doubloon mizzen reef sails.
Pink lee cable Blimey jury mast reef hands lookout clipper Cat o'nine tails. Port Davy Jones' Locker
Barbary Coast measured fer yer chains trysail bowsprit jolly boat Sink me Gold Road killick.


Newcastle fans react on Twitter to Jarrod Bowen’s brace
by Jordan HarrisA number of Newcastle United fans have implored the Magpies to make sure that they land Jarrod Bowen in January after he bagged a brace to help Hull come from 2-o down to draw with Middlesbrough.
The Sunday Mirror (24/11; page 72) reports that Newcastle boss Steve Bruce wants to sign Bowen. However, the Premier League side could delay a move for the 22-year-old until next summer.
Given Bowen’s form this season, supporters would have been forgiven for being frustrated by the reports. Bowen went into the weekend having scored nine goals in 16 league appearances for Hull this season.
And it appears that his latest performance has left the Toon Army feeling even more impatient. But for some time, it seemed that Hull were about to suffer a defeat against struggling Boro.
Jonathan Woodgate’s men were 2-0 up inside 26 minutes at the Riverside Stadium. But Marvin Johnson’s red card for the home side seemed to open the door for a potential fightback.
And it was Bowen who made the most of the opportunity. The youngster scored twice in four minutes during the latter stages of the contest to secure a share of the points. The first goal in particular saw the former Hereford man produce a stunning strike from distance
The brace has taken Bowen into joint-second in the Championship goalscoring charts. Only Aleksandar Mitrovic has scored more goals this season.
And it has also prompted a number of Newcastle fans insist that Bruce needs to make sure that he signs the forward in the coming window. They believe that he could be a special talent, and the supporters are clearly worried that another club could make a move if the Magpies dawdle.
Here is what some of the Newcastle fans were saying after Bowen’s brace…
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Mario Kart Tour online multiplayer beta coming next month for Gold Pass subscribers
by Aatif SumarThe much-anticipated release of Mario Kart Tour this September (after an extended period of testing) was a bit of a letdown. The game was riddled with excessive in-app-purchases, had a questionable loot box mechanism, and lacked a multiplayer component — arguably the selling point of the entire Mario Kart series. At the time, the in-game-menu said that multiplayer was "inbound", and we now know more details.
Come December 2019, Nintendo will begin beta testing real-time multiplayer in Mario Kart Tour.
We don't know if multiplayer will remain Gold Pass exclusive after the beta, but I want to believe that won't be the case — it could be hard to find enough humans to play with if you were limited this way.
That's all we really know at this point, but an online multiplayer mechanism may be the shot in the arm Mario Kart Tours needs to remain relevant. And now, for no good reason, here's a GIF of Jane from Breaking Bad that Stephen stumbled upon.
Source:
Mario Kart Tour on Twitter


Your triangle is an equilateral triangle!
Your triangle is an iscosceles triangle!
Your triangle is an scalene triangle!
This is NOT a triangle!


THE STORY OF US (Universal) Starring: Bruce Willis, Michelle Pfeiffer, Rob Reiner, Rita Wilston, Paul Reiser, Tim Matheson. Screenplay: Alan Zweibel and Jessie Nelson. Producers: Rob Reiner, Alan Zweibel and Jessie Nelson. Director: Rob Reiner.
As THE STORY OF US nears its conclusion, Katie Jordan (Michelle Pfeiffer) finally breaks down. Katie, a detail-oriented crossword puzzle creator, and her husband Ben (Bruce Willis), a free-spirited writer, have spent the summer in a trial separation while their two kids are away at camp, trying to determine whether their 15-year marriage is worth saving. Finally, the tense meetings and what's-best-for-the-children rationalizations dissolve into tears as Katie rants for several minutes about everything that has been right and wrong with their relationship, and about the history they share. It is a wonderfully affecting moment, beautifully performed by Pfeiffer with all the love and frustration that makes up a marriage. It's a moment that can't help but bring mist to the eyes of anyone who has known that love and those frustrations.
It's also a moment that is a complete cheat. Theoretically, THE STORY OF US is the story of how easy it is, in the middle of all the white noise and tension of everyday life, to lose track of why you fell in love with someone in the first place. Unfortunately, it's such a fragmented narrative that it's just as easy for the _audience_ to lose track of why they fell in love in the first place. The script by Alan Zweibel and Jessie Nelson sprinkles flashbacks through the summer of the Jordan's separation -- a peek at their meeting and first date here, an interrupted dalliance on the butcher block there -- but there aren't nearly enough of them. THE STORY OF US spends virtually all its time showing us the relationship already in trouble, making it incredibly difficult to appreciate what made the relationship good in the first place. We're asked to invest ourselves emotionally in the success or failure of the Jordans' marriage without getting enough evidence that it should succeed.
It's even more frustrating to realize that THE STORY OF US is so solid at its core that it could have been wonderful. The film is not always a pleasant experience, showing off some extremely unfair fighting between its protagonists and the wrenching moments when emotional damage seems beyond repair. Willis and Pfeiffer deftly turn their post-separation scenes into awkward shuffles between longing and despair; where most films treat divorce either as all-acrimony or all-indifference, this one captures how much people want it all to work out. But at only 92 minutes, THE STORY OF US feels bereft of context. Somewhere on the cutting room floor is the arc of a relationshp from affection to simple coexistence to a blur of petty bickering.
If only more of the so-called humor had found its way to the same floor. Along for comic relief are some usually reliable performers -- director Rob Reiner as Stan, Ben's best friend; Rita Wilson as Rachel, Katie's best friend and Stan's wife; Paul Reiser as Ben's agent -- who wind up stuck with painfully un-funny material. Wilson rants about how men never change the toilet paper roll, a gag that hasn't gotten any fresher since it was generated by bad stand-up comics 20 years ago.  Reiser spends five minutes degrading an idea for a book about Ben's grandparents, inspiring only stony silence from the audience. Reiner and his writers keep trying so hard to lighten the mood they only end up breaking it, with little to show for their efforts but a few half-hearted chuckles.
It's easy to see the comedy as part of Reiner's attempt to create a companion piece to his 1989 gem WHEN HARRY MET SALLY. With its direct addresses to the camera and hairdo-change time lapses, THE STORY OF US does sometimes feel like WHEN HARRY LEFT SALLY. Yet while this film was unlikely to be as purely entertaining, it could have been far more resonant. Some of the individual scenes in THE STORY OF US are heartbreaking, and a few are truly charming. They just never quite add up to the story of them in a way that provides real catharsis. Just before Katie's big emotional scene, we get a 60-second montage of hights and lows from a 15-year marriage. It's easy to understand why those snippets would move Katie to tears; after all, she lived those moments. THE STORY OF US could have moved us all if only we'd been allowed to live them, too.
On the Renshaw scale of 0 to 10 separation anxieties: 6.
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Title
Or Create an Artist Artist Name
Date of Birth
Date of Death
Medium
Location
Date Seen
Select Movement(s)
Or Add New Movement


Live Example
Table of contents, auto-generated from the content on this page:
Usage
You can download the latest release, or install with
Twitter's Bower:
bower install jquery.toc
.
Include jQuery (>= 1.6) and jquery.toc.js/jquery.toc.min.js on your page. The plugin can then be used either via HTML5 data attributes, or via the programmatic API. See below for the available options.
Via data attributes
Minimal example:

With options:

Via the JavaScript programmatic API
Minimal example:

 ...
With options:

 ...
Options

$(...).toc({content: "body", headings: "h1,h2,h3"});
The plugin has two options:
-
content
is a selector where the plugin will look for headings to build up the TOC. The default value is"body"
. -
headings
is a string with a comma-separated list of selectors to be used as headings, in the order which defines their relative hierarchy level. The default value of"h1,h2,h3"
will select allh1
,h2
, andh3
elements to build the TOC, withh1
being a level 1,h2
a level 2, and so on. You can use any valid list of jQuery selectors; for example, if you just wanth1
tags with a specific class, and noh3
tags, you could use"h1.title,h2"
for this parameter.
In addition, the plugin will create nested lists of the same type (ul
or
ol
) as the element that it is called on.
Automatic ID generation
The plugin generates hash-links to the headings on the page, to allow users to jump to the heading by clicking in the generated table of contents. This feature requires that the headings have IDs assigned; if they do not, the plugin will generate and assign IDs automatically.
The generated IDs are based on the text inside the headings, and uses two simple rules:
- Space characters are converted to underscores. Multiple spaces are replaced with a single underscore.
- If the ID already exists, a suffix like "_1", "_2", etc. is tried till we get a unique ID.
For example, a heading like
Heading 2.1

will get the ID
Heading_2.1
.
Alternatives
If you're looking for a jQuery plugin that does more than just generate a minimal table of contents, the project wiki lists some more plugins.
License
Licensed under the Apache License, version 2.0.
Credits
Created by Nikhil Dabas.


Method of checking the long running task status by a recursive queue.
Long running task can either finish in SUCCESS or FAILURE (manual input needed).
Long running task completes in 3 seconds, either in SUCCESS or FAILURE.
Upon finishing, Long running task signals the recursive queue to stop checking (longTaskFinishedFlag)
Task completing can be interrupted. (Manual INTERRUPT)


Applications for the inaugural cohort of PennApps Incubator are now closed!
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Advanced Concepts
Unit Tests - Testing pipes
Finally, we have to write our tests
-
First question, Where?
Basically where you want, but put the unit tests files
just next to the files to test makes sense ( easier to find,
remember to update, move them together... )
-
Below is a first example to test a pipe
-
The main method is not a jasmine/karma
constraint, it's our own convention
import {Pipe, PipeTransform} from '@angular/core';
@Pipe({name:'identity'})
export class IdentityPipe implements PipeTransform{
constructor(){}
transform(input: any, args: any){
return input;
}
}
import {IdentityPipe} from './identityPipe';
export function main() {
describe('UpdateDataPipe', function() {
it('Update data', function() {
let pipe = new IdentityPipe();
let data: string = 'data content';
let result: string= pipe.transform(data, []);
expect(result).toEqual(data);
});
});
}


Last update: 2018-01-18
Code version: 5996c19850dc5fc65723f30bc33ab1c8d083a7e6
Also loading important libraries and custom functions for analysis.
seq_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/data"
file_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/output"
Rdata_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/data"
Script_dir <- "/Volumes/PAULHOOK/sc-da-parkinsons/code"
source(file.path(Script_dir,'init.R'))
source(file.path(Script_dir,"tools_R.r"))
library(AnnotationDbi)
library(impute)
library(GO.db)
library(preprocessCore)
library(knitr)
library(rgl)
library(WGCNA)
## ==========================================================================
## *
## * Package WGCNA 1.61 loaded.
## *
## * Important note: It appears that your system supports multi-threading,
## * but it is not enabled within WGCNA in R.
## * To allow multi-threading within WGCNA with all available cores, use
## *
## * allowWGCNAThreads()
## *
## * within R. Use disableWGCNAThreads() to disable threading if necessary.
## * Alternatively, set the following environment variable on your system:
## *
## * ALLOW_WGCNA_THREADS=<number_of_processors>
## *
## * for example
## *
## * ALLOW_WGCNA_THREADS=4
## *
## * To set the environment variable in linux bash shell, type
## *
## * export ALLOW_WGCNA_THREADS=4
## *
## * before running R. Other operating systems or shells will
## * have a similar command to achieve the same aim.
## *
## ==========================================================================
library(SC3)
library(clusterProfiler)
library(org.Mm.eg.db)
library(ReactomePA)
library(cummeRbund)
library(foreach)
library(doParallel)
library(splines)
library(flashClust)
P7.dat.filter <- readRDS(file.path(Rdata_dir,"P7.dat.filter.final.Rds"))
dim(P7.dat.filter)
## Features Samples
## 13746 224
Preparing the data for WGCNA analysis. The genes used in the analysis were filtered to only include genes that were determined to be expressed in at least 20 cells (9% of the total cells in the data set).
# Identifying genes only expressed in at least 20 cells
P7.expressed_genes.WGCNA<-row.names(subset(fData(P7.dat.filter),num_cells_expressed >= 20))
length(P7.expressed_genes.WGCNA)
## [1] 12628
# Keeping only those genes for analysis. Ended up being 12,628 cells.
P7.dat.WGCNA <-P7.dat.filter[P7.expressed_genes.WGCNA,]
# Converting data to log2 + 1 expression
mydata <- log2(exprs(P7.dat.WGCNA)+1)
# Converting data to a matrix which is needed for WGCNA analysis.
mydata <- as.matrix(mydata)
# Checking the dimensions of the data
dim(mydata)
## [1] 12628 224
# Transposing the matrix so it is in the orientation needed for WGCNA analysis and saving the matrix under a better variable name.
mydata.trans=t(mydata)
datExpr <- mydata.trans
Next, heirarchial clutering was used to cluster the data and look for any outliers. There seems to be only one glaring outlier.
# Clustering the data by using heirarchial clustering
sampleTree <- flashClust(dist(datExpr), method = "average")
# Plotting the heirarchial clustering in order to manually identify and removce outliers
par(cex = 0.6, mar = c(0,4,2,0))
plot(sampleTree, main = "Sample clustering to detect outliers",
sub="", xlab="", cex.lab = 1.5, cex.axis = 1.5, cex.main = 2)
cutHeight=120 # choosing a cut height of 120 in order to remove the one high level outlier
abline(h = cutHeight, col = "red")
# Cutting the tree at the cut height and seeing how many outliers are removed with the cut
clust = cutreeStatic(sampleTree, cutHeight = cutHeight, minSize = 10)
table(clust)
## clust
## 0 1
## 1 223
# Keeping only the samples that are not removed by the cut height used
keepSamples <- (clust==1)
datExpr <- datExpr[keepSamples,]
# Saving the number of genes and the number of samples that will move in to WGCNA
nGenes <- ncol(datExpr)
nSamples <- nrow(datExpr)
Making a data frame that can be filled with meta-data about the cells and can be used to annotate the heirarchial clustering
# subsetting out trait data from the initial cds
datTraits <- pData(P7.dat.WGCNA[,keepSamples])
datTraits<-datTraits[,c("region","subset.cluster","num_genes_expressed")]
# Setting the colors to match the regional colors
region_labels <- RColorBrewer::brewer.pal(6, "Set1")[1:3]
# Making sure the regional colors match what has been used througout and making a column with the hex codes.
datTraits <- datTraits %>%
mutate(cell.names = row.names(datTraits)) %>%
mutate(x = ifelse(datTraits$region == "FB", region_labels[1],
ifelse(datTraits$region == "MB", region_labels[2],
ifelse(datTraits$region == "OB", region_labels[3], NA))))
# Re-performing heirarchial clustering
sampleTree2 <- hclust(dist(datExpr), method = "average")
# Writing out that dendrogram to a PDF
pdf(file = file.path(file_dir, "sample.dendrogram_region.heatmap.pdf"), height = 8, width = 12)
plotDendroAndColors(sampleTree2, datTraits$x,
groupLabels = names(datTraits),
main = "Sample dendrogram and regional identity")
dev.off()
## quartz_off_screen
## 2
# Plotting the dendrogram with regional identity colored beneath each cell
plotDendroAndColors(sampleTree2, datTraits$x,
groupLabels = names(datTraits),
main = "Sample dendrogram and regional identity")
In order to now perform WGCNA on our data, a “soft threshold” needs to be calculated. This produces some NAs (warning message suppressed)
# Setting what soft thresholds (powers) should be tested for potential use as a soft threshold
powers <- c(c(1:10), seq(from = 12, to=20, by=2))
# Calculating soft thresholds for a signed network
sft <- pickSoftThreshold(datExpr, powerVector = powers,
verbose = 5,
networkType ="signed",
moreNetworkConcepts = F)
## pickSoftThreshold: will use block size 3542.
## pickSoftThreshold: calculating connectivity for given powers...
## ..working on genes 1 through 3542 of 12628
## ..working on genes 3543 through 7084 of 12628
## ..working on genes 7085 through 10626 of 12628
## ..working on genes 10627 through 12628 of 12628
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.50600 10.80 0.842 6580.000 6.56e+03 7440.0
## 2 2 0.74000 13.30 0.776 3460.000 3.43e+03 4490.0
## 3 3 0.74700 12.90 0.667 1830.000 1.80e+03 2770.0
## 4 4 0.65000 10.70 0.775 981.000 9.49e+02 1750.0
## 5 5 0.39700 6.56 0.965 529.000 5.03e+02 1130.0
## 6 6 0.00729 -0.43 0.744 288.000 2.68e+02 745.0
## 7 7 0.62400 -3.07 0.891 158.000 1.44e+02 502.0
## 8 8 0.92100 -3.91 0.988 87.900 7.74e+01 345.0
## 9 9 0.41500 -1.93 0.797 49.300 4.19e+01 242.0
## 10 10 0.85700 -2.81 0.952 28.000 2.28e+01 173.0
## 11 12 0.96100 -2.91 0.992 9.370 6.87e+00 93.2
## 12 14 0.97800 -2.60 0.994 3.320 2.12e+00 53.5
## 13 16 0.97500 -2.30 0.988 1.250 6.69e-01 32.5
## 14 18 0.97700 -2.04 0.991 0.508 2.17e-01 20.9
## 15 20 0.97300 -1.81 0.988 0.223 7.21e-02 14.3
# Plotting the soft threshold diagnostics in order to choose an optimal soft threshold.
# Setting display margins
par(mfrow = c(1,1))
cex1 = 0.9
# Scale-free topology fit index as a function of the soft-thresholding power. We are looking to choose a soft threshold where the plot plateus out
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=cex1,col="red")
abline(h=0.8,col="red") # this line corresponds to using an R^2 cut-off of h that could be used as a guide to picking a soft threshold
# Mean connectivity as a function of the soft-thresholding power. Again, we are looking at when the plot begins leveling out.
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
# Writing out these plots
pdf(file = file.path(file_dir, "WGCNA.softpower.pdf"), width = 6, height = 6)
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=cex1,col="red")
abline(h=0.8,col="red")
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
dev.off()
## quartz_off_screen
## 2
A soft threshold of 10 was chosen based on the scale independence plot beginning to finally level out at that point for good. The dip from 8 to 9 prevented me from choosing 8. We choose a topology cut-off of 0.80, which is arbitary but similar to the number suggested in WGCNA tutorials (see “A General Framework for Weighted Gene CoExpression Network Analysis”, 2005).
# Choosing a power of 10 based on Scale independence plot and 0.8 threshold
softPower <- 10
adjacency <- adjacency(datExpr, power = softPower, type = "signed")
# Turn adjacency into topological overlap
TOM <- TOMsimilarity(adjacency, TOMType = "signed")
## ..connectivity..
## ..matrix multiplication..
## ..normalization..
## ..done.
dissTOM <- 1-TOM
# Call the hierarchical clustering function
geneTree <- flashClust(as.dist(dissTOM), method = "average")
# Plot the resulting clustering tree (dendrogram)
par(mfrow=c(1,1))
plot(geneTree, xlab="", sub="", main = "Gene clustering on TOM-based dissimilarity",
labels = FALSE, hang = 0.01)
# We like large modules, so we set the minimum module size relatively high:
minModuleSize <- 20
# Module identification using dynamic tree cut:
dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM,
deepSplit = T, pamRespectsDendro = FALSE,
minClusterSize = minModuleSize)
## ..cutHeight not given, setting it to 1 ===> 99% of the (truncated) height range in dendro.
## ..done.
table(dynamicMods)
## dynamicMods
## 0 1 2 3 4 5 6 7 8 9 10 11
## 4 11567 438 158 70 57 41 35 35 31 29 29
## 12 13 14 15 16 17
## 24 24 23 22 21 20
# Convert numeric lables into colors
dynamicColors <- labels2colors(dynamicMods)
table(dynamicColors)
## dynamicColors
## black blue brown cyan green
## 35 438 158 23 57
## greenyellow grey grey60 lightcyan magenta
## 29 4 20 21 31
## midnightblue pink purple red salmon
## 22 35 29 41 24
## tan turquoise yellow
## 24 11567 70
# And plot the gene dendrogram with module colors annotated underneath
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
dendroLabels = FALSE, hang = 0.03, addGuide = TRUE,
guideHang = 0.04, main = "Gene dendrogram and module colors")
Now that we have identified modules in our data, we want to explore the relationships between those modules a little more. Note that grey and turquoise modules were removed in these analyses because turquoise contained all genes that did not get slotted in to a specific module and grey had only 4 genes (below our set module size)
# Calculate eigengenes for each module
MEList <- moduleEigengenes(datExpr, colors = dynamicColors)
MEs <- MEList$eigengenes
# Remove grey and turquoise for ahead of attempting to look at dissimilarity between eigengenes
MEs <- MEs[,c(1:6,8:16,18)]
# Calculate dissimilarity of module eigengenes
MEDiss <- 1-cor(MEs)
# Cluster module eigengenes
METree <- flashClust(as.dist(MEDiss), method = "average")
# Plot the result
par(mfrow=c(1,1))
plot(METree, main = "Clustering of module eigengenes",xlab = "", sub = "")
MEDissThres = 0.2
abline(h=MEDissThres, col = "red") # Plot the cut line into the dendrogram. Branches falling below this line will be merged. It does not look like any will get merged, but we run it anyways.
# Call an automatic merging function
merge <- mergeCloseModules(datExpr, dynamicColors, cutHeight = MEDissThres, verbose = 3)
## mergeCloseModules: Merging modules whose distance is less than 0.2
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 18 module eigengenes in given set.
## Calculating new MEs...
## multiSetMEs: Calculating module MEs.
## Working on set 1 ...
## moduleEigengenes: Calculating 18 module eigengenes in given set.
## ..principal component calculation for module grey failed with the following error:
## Error in svd(datModule, nu = min(n, p, nPC), nv = min(n, p, nPC)) :
## infinite or missing values in 'x'
## ..hub genes will be used instead of principal components.
# Extracting the merged module colors
moduleColors <- merge$colors
# Extracting eigengenes of the new merged modules:
mergedMEs <- merge$newMEs
# Constructing numerical labels corresponding to the colors
colorOrder = c("grey", standardColors(50));
moduleLabels = match(moduleColors, colorOrder)-1;
MEs <- mergedMEs
MEs <- MEs[,c(1:6,8:17)] #remove turquoise and grey again, since they for some reason are not gone
# Calculate dissimilarity of module eigengenes
MEDiss <- 1-cor(MEs)
# Cluster module eigengenes
METree <- flashClust(as.dist(MEDiss), method = "average")
# Plot the result
par(mfrow=c(1,1))
plot(METree, main = "Clustering of module eigengenes",xlab = "", sub = "")
# write out the final plot to a pdf
pdf(file = file.path(file_dir,"Module_Eigengene_Tree.pdf"), height = 6, width = 8)
plot(METree, main = "Clustering of module eigengenes",xlab = "", sub = "")
dev.off()
## quartz_off_screen
## 2
Now that we had explored the modules relationship to each other, we wanted to see if the genes within each module were enriched for gene ontology, KEGG, and Reactome terms. Not all genes map through ‘bitr’ so warnings were supressed
# Making a list of module names
modNames <- substring(names(MEs), 3)
# Correlating each genes expression profile with the module eigengenes in order to create module gene sets
geneModuleMembership <- as.data.frame(cor(datExpr, MEs, use = "p"))
# "For each module, we also define a quantitative measure of module membership MM as the correlation of the module eigengene and the gene expression profile." - WGCNA tutorial
# Iteratively creating a list of module genesets to test. These are in ensembl ids
moduleGeneSets<-lapply(modNames,function(module){
column = match(module, modNames)
moduleGenes = moduleColors==module
rownames(geneModuleMembership[moduleGenes,])
})
names(moduleGeneSets)<-modNames
# Trimming the module gene sets so that the final two digits after the "." are removed
moduleGeneSets.trimmed<-lapply(moduleGeneSets,function(x){
str_split_fixed(x,"\\.",2)[,1]
})
# Looking up the ENTREZ id for each gene
moduleGeneSets.Entrez<-lapply(moduleGeneSets.trimmed,function(x){
bitr(x,fromType="ENSEMBL",toType="ENTREZID",OrgDb="org.Mm.eg.db")$ENTREZID
})
# Looking at KEGG Geneset Enrichment
ck<-compareCluster(geneCluster=moduleGeneSets.Entrez,
fun="enrichKEGG",organism="mmu",pvalueCutoff = 0.1)
plot(ck,showCategory=10)+ggtitle("KEGG geneset enrichment by module") #width = 0.5
# Looking at molecular function geneset enrichment
cg.mf<-compareCluster(geneCluster=moduleGeneSets.Entrez,fun="enrichGO",
OrgDb = "org.Mm.eg.db",pvalueCutoff = 0.01,ont="MF", readable = T)
plot(cg.mf,showCategory=20)+ggtitle("GO MF geneset enrichment by module")
# Looking at biological process geneset enrichment
cg.bp<-compareCluster(geneCluster=moduleGeneSets.Entrez,fun="enrichGO",
OrgDb = "org.Mm.eg.db", pvalueCutoff = 0.01,ont="BP", readable = T)
plot(cg.bp,showCategory=15)+ggtitle("GO BP geneset enrichment by module")
cg.cc<-compareCluster(geneCluster=moduleGeneSets.Entrez,fun="enrichGO",
OrgDb = "org.Mm.eg.db", pvalueCutoff = 0.01,ont="CC", readable = T)
plot(cg.cc,showCategory=15)+ggtitle("GO CC geneset enrichment by module")
# Looking at reactome enrichment
cr<-compareCluster(geneCluster=moduleGeneSets.Entrez,fun="enrichPathway",
organism="mouse",pvalueCutoff = 0.1, readable = T)
plot(cr,showCategory=10)+ggtitle("Reactome geneset enrichment by module")
# Printing out the cluster enrichment diagrams. These will be useful for annotating the
pdf(file.path(file_dir,"P7-all-module_geneset_enrichments.pdf"),width=18,height=10, useDingbats = F)
plot(ck,showCategory=10)+ggtitle("KEGG geneset enrichment by module")
plot(cr,showCategory=10)+ggtitle("Reactome geneset enrichment by module")
plot(cg.bp,showCategory=10)+ggtitle("GO BP geneset enrichment by module")
plot(cg.mf,showCategory=10)+ggtitle("GO MF geneset enrichment by module")
plot(cg.cc,showCategory = 10) + ggtitle("GO CC geneset enrichment by module")
dev.off()
## quartz_off_screen
## 2
In order to output all of our GO data in a relatively simple way, all gene enrichment results were written out to a tables
# Gene enrichment tables are found in 'ck','cg.mf','cg.bp','cg.cc', 'cr'
# summary() allows for the S4 objects created by clusterProfiler to be converted in to dataframes
kegg.table <- summary(ck)
# Had to make my own KEGG conversion function
kegg.table.clone <- kegg.table
for(i in 1:nrow(kegg.table.clone)){
gene.str <- kegg.table.clone[i,9]
gene.split <- unlist(strsplit(gene.str,split = "/"))
genes.symbol <- bitr(gene.split, fromType = "ENTREZID", toType = "SYMBOL", OrgDb = 'org.Mm.eg.db')
converted.gene.string <- paste0(genes.symbol$SYMBOL,collapse = "/")
kegg.table.clone[i,9] <- converted.gene.string
}
# Easy enough to do for the others when you can set it at 'readable' in the functions above
go.molecular.function.table <- summary(cg.mf)
go.bio.process.table <- summary(cg.bp)
go.cell.comp.table <- summary(cg.cc)
reactome.table <- summary(cr)
# writing out tables
write.table(x = kegg.table.clone, file = file.path(file_dir,"WGCNA.KEGG.table.txt"), quote = F, sep = "\t", row.names = F)
write.table(x = go.molecular.function.table, file = file.path(file_dir,"GO.MF.table.txt"), quote = F, sep = "\t", row.names = F)
write.table(x = go.bio.process.table, file = file.path(file_dir,"GO.BP.table.txt"), quote = F, sep = "\t", row.names = F)
write.table(x = go.cell.comp.table, file = file.path(file_dir,"GO.CC.table.txt"), quote = F, sep = "\t", row.names = F)
write.table(x = reactome.table, file = file.path(file_dir,"Reactome.table.txt"), quote = F, sep = "\t", row.names = F)
In order to use them in downstream analyses, a table was written out in order to show all the genes in each module identified in our analysis (Table S6)
library(reshape2)
# Turn the 'moduleGeneSets' list in to a data.frame, copy the columns and rename them 'module' and 'gene', select only the 'module' and 'gene' columns
module.df <- ldply(moduleGeneSets, data.frame) %>%
mutate(module = .id, gene = X..i..) %>%
dplyr::select(module, gene)
# Add id numbers in in order to unmelt the data and make sure the row lengths are the same for all columns
module.df$id = numeric(nrow(module.df))
for (i in 1:nrow(module.df)){
dat_temp <- module.df[1:i,]
module.df[i,]$id <- nrow(dat_temp[dat_temp$module == module.df[i,]$module,])
}
# Unmelt the data using "dcast" and then remove the 'id' column that helped you unmelt the data
module.genes.df <- dcast(module.df, id~module, value.var = "gene") %>%
dplyr::select(-id)
# Replace all gene IDs with the mouse gene symbols
for(i in 1:ncol(module.genes.df)){
geneIDs <- module.genes.df[,i][!is.na(module.genes.df[,i])]
geneSymbols <- as.character(paste0("",lookupGeneName(eset = P7.dat.filter, geneIDs)))
n <- 438 - length(geneSymbols)
toAdd <- rep("",n)
coltoAdd <- c(geneSymbols,toAdd)
module.genes.df[,i] <- coltoAdd # adding a ' to gene names so they are not changed in Excel
}
# Write out the table
write.table(module.genes.df, file = file.path(file_dir,"WGCNA.module.gene.lists.txt"), quote = F, row.names = F, sep = "\t")
Now that we have looked at enrichment for GO terms in our modules, we want to correlate the modules with cellular traitis, specifically subset cluster in order to find modules that differentiation between DA populations at P7
# The first step to find these correlations is to make a trait table
# Copying over the datTraits dataframe
datTraits.new <- datTraits
datTraits.new$region <- as.factor(datTraits.new$region)
datTraits.new$subset.cluster <- as.factor(datTraits.new$subset.cluster)
# Using these for loops to create additional columns for each level of a trait, which will read "True" or "False" for each trait in each cell
for (l in levels(datTraits.new$subset.cluster)) {
datTraits.new[[l]]<-datTraits.new$subset.cluster == l
}
for (l in levels(datTraits.new$region)){
datTraits.new[[l]] <- datTraits.new$region == l
}
# Factoring the T/F columns in to a binary 0/1 classification system
datTraitsFactor<-datTraits.new
datTraitsFactor$region<-as.numeric(as.factor(datTraits.new$region))
datTraitsFactor$subset.cluster<-as.numeric(as.factor(datTraits.new$subset.cluster))
for (l in levels(datTraits.new$subset.cluster)){
datTraitsFactor[[l]]<-as.numeric(datTraits.new[[l]])
}
for (l in levels(datTraits.new$region)){
datTraitsFactor[[l]]<-as.numeric(datTraits.new[[l]])
}
# Keeping only the columns for subset clusters
datTraitsFactorCor <- datTraitsFactor[,c(6:14)]
# Correlating the module eigengenes with subset cluster identity
moduleTraitCor <- cor(MEs, datTraitsFactorCor)
# Getting a p-value for each correlation
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
# Plotting the relationship between learned gene co-expression modules and parameterizations
pdf(file = file.path(file_dir,"P7_module-trait_relationships-all.pdf"), width = 10, height = 8)
par(mfrow=c(1,1))
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3))
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = names(MEs), yLabels = names(datTraitsFactorCor),
ySymbols = names(datTraitsFactorCor),
colorLabels = FALSE,
colors = blueWhiteRed(50),
textMatrix = t(textMatrix),
setStdMargins = T, cex.text = 0.5, zlim = c(-1,1),
main = paste("Module-trait relationships"))
dev.off()
## quartz_off_screen
## 2
par(mfrow=c(1,1))
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3))
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = names(MEs), yLabels = names(datTraitsFactorCor),
ySymbols = names(datTraitsFactorCor),
colorLabels = FALSE,
colors = blueWhiteRed(50),
textMatrix = t(textMatrix),
setStdMargins = T, cex.text = 0.5, zlim = c(-1,1),
main = paste("Module-trait relationships"))
We then wanted to correlate the modules eigengenes with tSNE position and plot 7 that were found to be significantly correlated with a P7 subset cluster
# Correlating tSNE position with eigenvectors
eigentSNE<-cbind(pData(P7.dat.WGCNA[,keepSamples])$tSNE1_pos,pData(P7.dat.WGCNA[,keepSamples,])$tSNE2_pos,MEs)
colnames(eigentSNE)<-c("tSNE1_pos","tSNE2_pos",modNames)
eigentSNE.melt<-melt(eigentSNE,id.vars=c("tSNE1_pos","tSNE2_pos"))
# Melting the data and selecting only the modules we want to plot
eigentSNE.melt.subset <- eigentSNE.melt %>%
filter(variable == "yellow" | variable == "green" | variable == "salmon" | variable == "brown" | variable ==
"midnightblue" | variable == "blue" | variable == "lightcyan")
p2.subset<-ggplot(eigentSNE.melt.subset) + geom_point(aes(x=tSNE1_pos,y=tSNE2_pos,color=value),size=1) + monocle:::monocle_theme_opts() + scale_color_gradient2(low="blue",mid="white",high="red",limits=c(-0.1,0.1),oob=squish)
p2.subset.all <- p2.subset + facet_wrap("variable", nrow = 2) + ggtitle("WGCNA module eigengene expression across single P7 DA neurons") + coord_fixed(ratio = 1) + xlab("t-SNE 1") + ylab("t-SNE 2") + theme(plot.title = element_text(hjust = 0.5))
pdf(file = file.path(file_dir,"Eigen-tSNEplots-subset.pdf"), width = 10, height = 4)
p2.subset.all
dev.off()
## quartz_off_screen
## 2
p2.subset.all
Save modules that were enriched for PD genesets
saveRDS(moduleGeneSets$green, file = file.path(file_dir,"WGCNA.ParkGeneSet.Green.rds"))
saveRDS(moduleGeneSets$brown, file = file.path(file_dir,"WGCNA.ParkGeneSet.Brown.rds"))
sessionInfo()
## R version 3.3.0 (2016-05-03)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.6 (El Capitan)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid splines stats4 parallel stats graphics grDevices
## [8] utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2 flashClust_1.01-2 doParallel_1.0.11
## [4] iterators_1.0.8 foreach_1.4.3 cummeRbund_2.14.0
## [7] Gviz_1.16.5 rtracklayer_1.32.2 GenomicRanges_1.24.3
## [10] GenomeInfoDb_1.8.7 RSQLite_2.0 ReactomePA_1.16.2
## [13] org.Mm.eg.db_3.3.0 clusterProfiler_3.0.5 DOSE_2.10.7
## [16] WGCNA_1.61 fastcluster_1.1.24 dynamicTreeCut_1.63-1
## [19] rgl_0.98.1 knitr_1.17 preprocessCore_1.34.0
## [22] GO.db_3.3.0 impute_1.46.0 AnnotationDbi_1.34.4
## [25] IRanges_2.6.1 S4Vectors_0.10.3 ggbiplot_0.55
## [28] scales_0.5.0 SC3_1.1.4 ROCR_1.0-7
## [31] jackstraw_1.1.1 lfa_1.2.2 tsne_0.1-3
## [34] gridExtra_2.3 slackr_1.4.2 vegan_2.4-4
## [37] permute_0.9-4 MASS_7.3-47 gplots_3.0.1
## [40] RColorBrewer_1.1-2 Hmisc_4.0-3 Formula_1.2-2
## [43] survival_2.41-3 lattice_0.20-35 Heatplus_2.18.0
## [46] Rtsne_0.13 pheatmap_1.0.8 tidyr_0.7.1
## [49] dplyr_0.7.4 plyr_1.8.4 heatmap.plus_1.3
## [52] stringr_1.2.0 marray_1.50.0 limma_3.28.21
## [55] reshape2_1.4.3 monocle_2.2.0 DDRTree_0.1.5
## [58] irlba_2.2.1 VGAM_1.0-2 ggplot2_2.2.1
## [61] Biobase_2.32.0 BiocGenerics_0.18.0 Matrix_1.2-11
##
## loaded via a namespace (and not attached):
## [1] robust_0.4-18 htmlwidgets_0.9
## [3] combinat_0.0-8 BiocParallel_1.6.6
## [5] munsell_0.4.3 codetools_0.2-15
## [7] colorspace_1.3-2 GOSemSim_1.30.3
## [9] fastICA_1.2-1 BiocInstaller_1.22.3
## [11] robustbase_0.92-7 labeling_0.3
## [13] slam_0.1-37 topGO_2.24.0
## [15] bit64_0.9-7 rprojroot_1.2
## [17] RSelenium_1.7.1 wdman_0.2.2
## [19] biovizBase_1.20.0 R6_2.2.2
## [21] bitops_1.0-6 assertthat_0.2.0
## [23] nnet_7.3-12 gtable_0.2.0
## [25] ensembldb_1.4.7 rlang_0.1.6
## [27] lazyeval_0.2.1 acepack_1.4.1
## [29] dichromat_2.0-0 checkmate_1.8.4
## [31] yaml_2.1.15 semver_0.2.0
## [33] GenomicFeatures_1.24.5 backports_1.1.1
## [35] httpuv_1.3.5 qvalue_2.4.2
## [37] tools_3.3.0 Rcpp_0.12.14
## [39] base64enc_0.1-3 zlibbioc_1.18.0
## [41] purrr_0.2.4 RCurl_1.95-4.9
## [43] rpart_4.1-11 openssl_0.9.7
## [45] SummarizedExperiment_1.2.3 cluster_2.0.6
## [47] magrittr_1.5 data.table_1.10.4
## [49] DO.db_2.9 SparseM_1.77
## [51] reactome.db_1.55.0 mvtnorm_1.0-6
## [53] matrixStats_0.52.2 mime_0.5
## [55] evaluate_0.10.1 xtable_1.8-2
## [57] XML_3.98-1.9 compiler_3.3.0
## [59] HSMMSingleCell_0.106.2 biomaRt_2.28.0
## [61] tibble_1.3.4 KernSmooth_2.23-15
## [63] htmltools_0.3.6 mgcv_1.8-22
## [65] corpcor_1.6.9 pcaPP_1.9-72
## [67] rrcov_1.4-3 binman_0.1.0
## [69] DBI_0.7 WriteXLS_4.0.0
## [71] rappdirs_0.3.1 gdata_2.18.0
## [73] bindr_0.1 igraph_1.1.2
## [75] pkgconfig_2.0.1 fit.models_0.5-14
## [77] GenomicAlignments_1.8.4 registry_0.3
## [79] foreign_0.8-69 annotate_1.50.1
## [81] rngtools_1.2.4 pkgmaker_0.22
## [83] XVector_0.12.1 doRNG_1.6.6
## [85] VariantAnnotation_1.18.7 digest_0.6.12
## [87] graph_1.50.0 Biostrings_2.40.2
## [89] rmarkdown_1.8 htmlTable_1.9
## [91] GSEABase_1.34.1 shiny_1.0.5
## [93] Rsamtools_1.24.0 gtools_3.5.0
## [95] graphite_1.18.1 nlme_3.1-131
## [97] jsonlite_1.5 BSgenome_1.40.1
## [99] httr_1.3.1 DEoptimR_1.0-8
## [101] interactiveDisplayBase_1.10.3 glue_1.1.1
## [103] qlcMatrix_0.9.5 bit_1.1-12
## [105] class_7.3-14 stringi_1.1.5
## [107] blob_1.1.0 AnnotationHub_2.4.2
## [109] latticeExtra_0.6-28 caTools_1.17.1
## [111] memoise_1.1.0 e1071_1.6-8
This R Markdown site was created with workflowr


Macintosh font formats
The mac stores fonts in resources. It use to store resources in resource forks (which have no representation on unix) but now (Mac OS/X) it also stores them in data forks.
Apple (and Adobe) provide some documentation on the mac's formats:
- Mac Resource Fork
- Mac NFNT/FONT resource
- Mac FOND resource
- Mac sfnt resource
- This is basically a truetype font file stuffed into a resource
- The POST resource
- The connection between postscript file and FOND is described in the Style Mapping Table of the FOND
- Type1 postscript fontsare described by Adobe
- Adobe also describes how they are wrapped up on the mac.
- Macintosh scripts
- Carbon (Mac OS/X) docs
I have not yet found a description of a data fork resource file. I have determined empirically that they look almost the same as resource fork resource files except that the map table begins with 16 bytes of zeros rather than a copy of the first 16 bytes of the file. To date I have only seen sfnt (and FOND) resources in a data fork resource file (often called a .dfont file).
FontForge does not support the old 'fbit' font format for CJK encodings (but then neither does the Mac any more so that's probably not an issue).
When an 'sfnt' resource contains a font with a multibyte encoding (CJK or unicode) then the 'FOND' does not have entries for all the characters. The 'sfnt' will (always?) have a MacRoman encoding as well as the multibyte encoding and the 'FOND' will contain information on just that subset of the font. (I have determined this empirically, I have seen no documentation on the subject)
Currently bitmap fonts for multibyte encodings are stored inside an sfnt (truetype) resource in the 'bloc' and 'bdat' tables. When this happens there are a series of dummy 'NFNT' resources in the resource file, one for each strike. Each resource is 26 bytes long (which means they contain the FontRec structure but no data tables) and are flagged by having rowWords set to 0. (I have determined this empirically, I have seen no documentation on the subject)
When a 'sfnt' resource contains only bitmaps (and no glyph outlines) it is sometimes called an 'sbit'.
Resource forks on Mac OS/X
The unix filename for the resource fork of the file "foo" is "foo/rsrc". (I was told this and it appears to be correct, I have seen no documentation on the subject)


Gateway
BOT
19-Sep-18 11:12 AM
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1 write to _inputStream
System.Management.Automation (1)
engine\hostifaces\PowerShell.cs (1)
5397
_inputStream
= inputStream;
2 references to _inputStream
System.Management.Automation (2)
engine\hostifaces\PowerShell.cs (2)
5597
_inputStream
,
5705
_inputStream
.Close();


a
Measurements of pH were conducted at room temperature, where the nucleation of the Li-carbonate phase was inhibited. Saturation index calculation obtained from PHREEQC simulations of the initial solution at 90 °C when Ca or Mg were present in the initial solution.
b
n.d. = not determined due to immediate precipitate formation.


Before Change
I2R = prev["I"] - curr["I"] + S2I
// Condition on flows between compartments.
prob_s = -(rate_s * prev["I"]).expm1()
pyro.sample("S2I_{}".format(t),
dist.ExtendedBinomial(prev["S"], prob_s),
obs=S2I)
pyro.sample("I2R_{}".format(t),
After Change
// Condition on flows between compartments.
pyro.sample("S2I_{}".format(t),
infection_dist(individual_rate=R0 / tau,
num_susceptible=prev["S"],
num_infectious=prev["I"],
population=self.population),
obs=S2I)
pyro.sample("I2R_{}".format(t),
dist.ExtendedBinomial(prev["I"], 1 / tau),


| drake-package | drake: A pipeline toolkit for reproducible computation at scale. |
| bind_plans | Row-bind together drake plans |
| build_times | List the time it took to build each target. |
| cached | List targets in the cache. |
| clean | Remove targets/imports from the cache. |
| clean_mtcars_example | Clean the mtcars example from 'drake_example("mtcars")' |
| code_to_plan | Turn an R script file or knitr / R Markdown report into a 'drake' workflow plan data frame. |
| deps_code | List the dependencies of a function or command |
| deps_knitr | Find the drake dependencies of a dynamic knitr report target. |
| deps_profile | Find out why a target is out of date. |
| deps_target | List the dependencies of a target |
| diagnose | Get diagnostic metadata on a target. |
| drake | drake: A pipeline toolkit for reproducible computation at scale. |
| drake_build | Build/process a single target or import. |
| drake_cache_log | Get a table that represents the state of the cache. |
| drake_config | Create the internal runtime parameter list used internally in 'make()'. |
| drake_debug | Run a single target's command in debug mode. |
| drake_envir | Get the environment where drake builds targets |
| drake_example | Download and save the code and data files of an example 'drake'-powered project. |
| drake_examples | List the names of all the drake examples. |
| drake_gc | Do garbage collection on the drake cache. |
| drake_get_session_info | Return the 'sessionInfo()' of the last call to 'make()'. |
| drake_ggraph | Show a 'ggraph'/'ggplot2' representation of your drake project. |
| drake_graph_info | Create the underlying node and edge data frames behind 'vis_drake_graph()'. |
| drake_hpc_template_file | Write a template file for deploying work to a cluster / job scheduler. |
| drake_hpc_template_files | List the available example template files for deploying work to a cluster / job scheduler. |
| drake_plan | Create a workflow plan data frame for the 'plan' argument of 'make()'. |
| drake_plan_source | Show the code required to produce a given workflow plan data frame |
| expose_imports | Expose all the imports in a package so 'make()' can detect all the package's nested functions. |
| failed | List failed targets. to 'make()'. |
| file_in | Declare input files and directories. |
| file_out | Declare output files and directories. |
| file_store | Tell 'drake' that you want information on a _file_ (target or import), not an ordinary object. |
| find_cache | Search up the file system for the nearest drake cache. |
| get_cache | Get the default cache of a 'drake' project. |
| ignore | Ignore components of commands and imported functions. |
| knitr_in | Declare 'knitr'/'rmarkdown' source files as dependencies. |
| legend_nodes | Create the nodes data frame used in the legend of the graph visualizations. |
| loadd | Read and return a drake target/import from the cache. |
| load_mtcars_example | Load the mtcars example. |
| make | Run your project (build the outdated targets). |
| missed | Report any import objects required by your drake_plan plan but missing from your workspace or file system. |
| new_cache | Make a new 'drake' cache. |
| outdated | List the targets that are out of date. |
| plan_to_code | Turn a 'drake' workflow plan data frame into a plain R script file. |
| plan_to_notebook | Turn a 'drake' workflow plan data frame into an R notebook, |
| predict_runtime | Predict the elapsed runtime of the next call to 'make()' for non-staged parallel backends. |
| predict_workers | Predict the load balancing of the next call to 'make()' for non-staged parallel backends. |
| progress | Get the build progress of your targets during a 'make()'. |
| readd | Read and return a drake target/import from the cache. |
| read_drake_seed | Read the pseudo-random number generator seed of the project. |
| render_drake_ggraph | Render a static 'ggplot2'/'ggraph' visualization from 'drake_graph_info()' output. |
| render_drake_graph | Render a visualization using the data frames generated by 'drake_graph_info()'. |
| render_sankey_drake_graph | Render a Sankey diagram from'drake_graph_info()'. |
| render_text_drake_graph | Render a text-based visualization of drake's dependency graph using the data frames generated by 'drake_graph_info()'. |
| rescue_cache | Try to repair a drake cache that is prone to throwing 'storr'-related errors. |
| running | List running targets. |
| r_drake_build | Reproducible R session management for drake functions |
| r_drake_ggraph | Reproducible R session management for drake functions |
| r_drake_graph_info | Reproducible R session management for drake functions |
| r_make | Reproducible R session management for drake functions |
| r_missed | Reproducible R session management for drake functions |
| r_outdated | Reproducible R session management for drake functions |
| r_predict_runtime | Reproducible R session management for drake functions |
| r_predict_workers | Reproducible R session management for drake functions |
| r_sankey_drake_graph | Reproducible R session management for drake functions |
| r_text_drake_graph | Reproducible R session management for drake functions |
| r_vis_drake_graph | Reproducible R session management for drake functions |
| sankey_drake_graph | Show a Sankey graph of your drake project. |
| show_source | Show how a target/import was produced. |
| text_drake_graph | Use text art to show a visual representation of your workflow's dependency graph in your terminal window. |
| tracked | List the targets and imports that are reproducibly tracked. |
| trigger | Customize the decision rules for rebuilding targets |
| use_drake | Use drake in a project |
| vis_drake_graph | Show an interactive visual network representation of your drake project. |


I‘m a technical writer based in Hamburg, Germany. Before I started my professional career, I obtained a Master‘s degree in translation and interpreting from the Johannes Gutenberg University Mainz. I enjoy working with people to produce user-friendly and intuitive software that helps users get their work done efficiently.
If you also think documentation is not just a necessity, but a way to ensure great user experience, feel free to reach out to me.
As a technical writer, I‘ve worked on different software projects putting the user‘s needs first. To provide the right help at the right place, I dig deep to fully understand what the code is supposed to achieve. Not even complicated tech speak can scare me off.
As a trained translator and interpreter, I‘ve learned how important it is to be accurate when conveying a message in a foreign language. My linguistic and intercultural skills allow me to find the right tone of voice and terminology.
I enjoy interacting with people from diverse backgrounds and using different types of media, be it blog posts, videos or interactive graphics. This way, I ensure that documentation is tailored to the needs of different user groups and reaches a broader audience.


WITNESS LIST Finance Committee February 8, 1999-9:00A Adjutant General's Department On: Cargile, Eugene D. (Adjutant General's Department), Austin James, Daniel L. (Adjutant General's Department), Austin Sanchez, Rosario (Texas National Guard), Austin Registering, but not testifying: On: Goodwin, Bill (Texas National Guard), Austin Marty, Wayne D. (Adjutant General's Department), Austin Smith, Michael J. (Adjutant General's Department), Austin Alcoholic Beverage Commission On: Bailey, Doyne (Texas Alcoholic Beverage Commission (TABC)), Austin Fox, Jeannene (TABC), Austin Garza, Rolando (TABC), Austin Hamilton, Gregory (TABC), Austin Steen, John (TABC), San Antonio Criminal Justice Policy Council On: Fabelo, Tony (Criminal Justice Policy Council), Austin Department of Public Safety On: Davis, Tommy (Texas Department of Public Safety (DPS)), Austin Francis, Jr., James B. (DPS), Dallas Haas, Tom (DPS), Austin Hawthorne, Brian (Department of Public Safety Officers Association), Beaumont Kyser, Ben (DPS), Austin McHugh, M. Colleen (DPS), not given Thomas, Dudley M. (DPS), Austin Wiggins, Mike (Department of Public Safety Officers Association), Seguin Registering, but not testifying: On: Holt, Robert B. (DPS), not given Waller, Frankie (DPS), Austin Military Facilities Commission On: Cruz, Lydia (Texas Military Facilities Commission), Austin Malcolm, Jerry D. (Texas Military Facilities Commission), Austin McClure, Gary (Texas Military Facilities Commission), San Angelo Registering, but not testifying: On: Ahrendt, David (Texas Military Facilities Commission), Austin Gilbert, Sherry L. (Texas Military Facilities Commission), Austin Mangum, James E. (Texas Military Facilities Commission), Austin Wright, Julie (Texas Military Facilities Commission), Austin Office of State-Federal Relations On: Rich, Laurie M. (Office of State-Federal Relations), Washington, D.C River Compact Commissions On: Cable, Lowell (Red River Compact Commission), Sulphur Springs Cox, Roger (Canadian River Compact Commission), Amarillo Newton, Brad (The Pecos River Compact Commission), Ft. Stockton Settemeyer, Herman (River Compact Commissions), Austin Youth Commission On: Fabelo, Tony (Criminal Justice Policy Council), Austin Graham, Terry (Texas Youth Commission (TYC)), Austin Jeffords, Dr. Charles (TYC), Austin Lawrence, Leonard E. (TYC), San Antonio Reyes, Linda S. (TYC), Austin Robinson, Steve (TYC), Austin Registering, but not testifying: On: McCullough, Don (TYC), Austin


The server is up and running. Replace this file if you like, it's located in the public/ folder. If you're looking for the interactive swagger docs, they're right here.


Open DevTools
Go to Performance tab
Press Start profiling and reload page
Look for time labels in the Console timeline
Look for Timestamp labels in the Main timeline
You may need to check Disable JavaScript samples


This is sample Fixlet which will show to read library from embeded Fixlet Enter the required information below and click on take action.


module.exports =
This is the annotated source code for Pow, a zero-configuration Rack server for Mac OS X. See the user’s manual for information on installation and usage.
The annotated source HTML is generated by Docco.
module.exports =
The Configuration class stores settings for a Pow daemon and is responsible for mapping hostnames to Rack applications.
Configuration: require "./configuration"
Daemon: require "./daemon"
DnsServer: require "./dns_server"
HttpServer handles incoming HTTP requests.
HttpServer: require "./http_server"
Installer: require "./installer"
Logger: require "./logger"
RackApplication represents a single running application.
RackApplication: require "./rack_application"
util: require "./util"


So you want to tether your iPhone 3G running the shiny new firmware 3.0, but your carrier doesnt support it? Don't worry! There's an easy way to fix it. Simply choose your carrier from the list below (or enter the APN manually if you know what that means) and hit download (if you're on your phone now), or add your email address, hit send, and we'll mail you a configuration file right away that you can install on your phone that should get you tethering in no time!. If it doesn't work for you please let me know, I don't have access to many of these networks.
Disclaimer: While this enables tethering, I don't know for sure if your carrier will be able to detect that you are using it. I assume not, however I may be wrong. Keep an eye on your bill
Want to get rid of it? Simple, Just open Settings, and look in General -> Profiles. Select the Tether profile, and remove. Easy as!
problems? suggestions? mail me. Created by Lincoln Stoll


Multigenic traits can have single genes with large impacts.
Genotype: -/- means double-null for the gene (from both mother and father). +/- means one copy ok (+) and one broken (-) is sufficient to have a positive impact. The rest require very specific sequences to see the positive effect. For example E6V means that at position 6 an "E" (Glutamate amino acid codon GAG) is mutated into a "V" (Valine codon GTG).
| Gene | Genotype | Protective, resilient or extreme effects |
Potential Negatives |
| LRP5 | G171V/+ | Extra-strong bones | Low Buoyancy |
| MSTN | -/- | Large, lean muscles, low atherosclerosis | |
| SCN9A | -/- | Insensitivity to pain | Unnoticed harm |
| FAAH-OUT | del/del | Insensitivity to pain | Unnoticed harm |
| HSD17B13 | +/TA(lof) | Low Chronic Liver Disease | |
| IFNL4 | dG/TT | High Hepatitis C virus clearance | |
| ABCC11 | -/- | Low odor production | |
| CCR5 | -/- | HIV resistance | W.Nile Virus & flu |
| FUT2 | -/- | Norovirus resistance | Crohn's disease |
| HBB | E6V/+ | Malaria resistance | Exertional rhabdomyolysis |
| PKU | +/- | Ochratoxin resistance | |
| CFTR | +/- | TB or other | |
| HEXA | +/- | TB resistance | |
| APOL1 | +/- | Trypanosoma brucei | |
| PCSK9 | -/- | Low coronary disease | Diabetes |
| GHR,GH | -/- | Low cancer, stature | |
| SLC30A8 | -/+ | Low T2 Diabetes | |
| IFIH1 = MDA5 | E627X/+ | Low T1 Diabetes | |
| ANGPTL3 | -/+ | Lipid and cardiac health | |
| BDKRB2 | a/g expression | Deep Diving | |
| PDE10A | c/t incr thyroxine | Breath-hold diving | |
| EGLN1 | a/g | High altitude | |
| EPAS1 | 3.4kb del | High altitude | |
| MTHFR | A222V | High altitude | |
| EPOR | W439X | High oxygen transport | |
| BHLHE41 = DEC2 | P385R/+ | Less sleep | |
| APOE | E2/E2 | Low Alzheimer's (E2=R112C, R158C) | |
| APP | A673T/+ | Low Alzheimer's | |
| GLUK4 | del/+ | Low bipolar, high cognitive | |
| Transgenic & Knock-in mice |
| SOST | -/- | Bone loss resistance | |
| NPC1 | +/- | Ebola resistance | |
| CTNNB1 | overprod. | Radiation resistance | |
| TERT | overprod. | Low Aging | |
| CDKN2A | overprod. | Low cancer | |
| TP53 | overprod. | Low cancer | |
| GRIN2B | overprod. | High learning & memory | |
| PDE4B | inhib. | Low anxiety, high problem solving | |
| FOXP2 | humanized | Learn stimulus-response associations faster | |
| CCR5 | -/- & +/- | Enhanced learning, low atherosclerosis, low hepatotoxicity | Flavi- & Orthomyxo- viruses |
| NLGN3 | R451C/Y | Enhanced spatial learning abilities (Autism) | |
| Cognition-related gene therapies |
| NGF | overprod. | (human) Low Alzheimer's | |
| NEU1 | overprod. | (mice) Low Alzheimer's | |
| NGFR | overprod. | (mice) Low Alzheimer's | |
| miR-29b | overprod. | (human cells) Low Alzheimer's | |
| BACE1 siRNAs | overprod. | (mice) Low Alzheimer's | |
| anti-amyloid antibodies | overprod. | (mice) Low Alzheimer's | |
| APP-s-alpha | overprod. | (mice) Low Alzheimer's | |
Note: Some of the above studies need independent reproduction.
Lower case a,c,g,t are nucleotide variants (typically regulatory), while uppercase indicated amino acid variants.
See also:
Longevity:
GenAge Database of Ageing-Related Genes
Height:
~9,500 SNPs explain ~29% of phenotypic variance
Resistance to typhoid, cholera, mycotoxin abortions, enveloped viruses via MOGS, filoviruses, rabies via myasthenia gravis
If you know of additional published or unpublished examples, or anyone who is exceptionally resistance to any disease, please let us know.
Bay Area Lyme Foundation
,
American Lyme Disease Foundation,
Lymedisease.org
Thanks to Jason Bobe and David Thompson for suggested additions.


$def with (form,left_form,right_form,foward_form,backward_form,start_form,stop_form,lastpress_form)
menu
camera
last button pressed: $lastpress_form
sdrtyhdftykjfgvylod6tjuty
dsrtnyrtunsrtundrsurdtudrtuhrturdu


Thanks for installing Bookmark Search this version!
Please also check the options page.
Here are the changes from old version to this version!
Please also check the options page for any new options.
This version is this version.
Version <IP_ADDRESS>
- Pressing arrow keys or switching windows could cause automatic matches (first match item) to be forgotten. Some code has been modified to prevent this from happening. (Thanks northtea for pointing it out)
Version 1.4.2
- JavaScript bookmarklets now requires a lesser permission. If you had enabled JavaScript bookmarklets before, you'll need to re-enable it.
- You may now choose where bookmarks are opened with the keyboard. The option is available in the options page. With this option, you can open bookmarks on a new foreground tab by pressing Alt + Enter, or on a new background tab by clicking on an item with the middle mouse button.
- Fixed a minor bug which sometimes causes the wrong page to be opened.
Version 1.4.1
- Made the searching algorithm selection option more clearer.
- Added a page to test different searching algorithm.
Version 1.4
- Implemented a new searching and sorting algorithm which should give better and more useful results in general.
Version 1.3.4
- Removed an optional permission which is no longer needed.
- Changed the way of informing version updated.
Version 1.3.3
- Changed the bookmark manager's page back to chrome://bookmarks/ (which I forgot to change in 1.3.2).
- Changed the way of invoking JavaScript bookmarklet due to some security concern.
Version 1.3.2
- Slightly improved the options page.
- The extension will now ask you to enable JavaScript bookmarklet support when attempt to use one.
- Minor code changes produces slight unnoticeable performance increase.
- Chrome will now unload this extension when not being used, so it will not consume any system resources when idle.
Version 1.3.1 is not released.
Version 1.3
- (internal) Switched to manifest version 2.
- The persist permission to "your data on all websites" and "your tabs and browsing activity" is removed.
Instead an option is added for enabling support for JavaScript bookmarklet.
The related permissions will be requested only when the option is enabled.
- A bug that prevents automatically navigating to the only match when the option "Automatically selects full match on name" is disabled is fixed.
Version 1.2.2
- Javascript bookmarks had never worked before due to lack of permission. This version should have fixed it.
Version 1.2.1
- Due to my carelessness, a full match will pops out both the selected page and the bookmark search page. This is likely fixed.
- Automatically selects if there is only one search result.
- Allow setting the maximum items to show.
- The option page should work fine.
- Some minor improvements.
Version 1.2
- Bookmark Search can now automatically select the full match. (see Options)
- Display is improved. It now shows both name and address.
- Some minor improvements.
- The icons are updated, although they are still...
Version 1.1
- The search keyword has changed to "bm"!
Version 1.0


Why the All Blacks job is 'not in Warren Gatland's intermediate future'
by Online EditorsBy Christopher Reive, NZ Herald
Chiefs’ chief executive Michael Collins believes Warren Gatland has no interest in becoming the new All Blacks coach in his short-term future.
Gatland will take the reins as the Chiefs head coach next season in the first year of a contract that will see him with the club until the end of 2023.
However, he will take time away from the team in 2021 to coach the British and Irish Lions, before returning to the helm.
Continue reading below…
Speaking to the Radio Sport Breakfast, Collins said Gatland was committed to coaching the Lions which in turn would make him unavailable for the All Blacks, at least until 2022.
When asked if Collins believed Gatland would have mentioned his interest in applying for the All Blacks job, Collins said: “I’m pretty sure he would have.”
“The dealings I’ve had with him, he’s pretty straight up and down and comes across that way. Our vision is that we want all our coaches to aspire to be the All Blacks coach at some stage.
“I just think with what Warren’s got ahead of him, I just don’t think that’s on his radar in the intermediate future, but I haven’t had those conversations with him so I’m speculating.”
The Chiefs announced the rest of their coaching staff on Friday morning, with familiar faces returning to the coaching ranks, albeit with some in different roles.
Tabai Matson (defence), Neil Barnes (forwards), Nick White (scrum), Roger Randle (attack) and Andrew Strawbridge (resource) will all return to the coaching ranks, with one-time All Blacks No10 David Hill joining the fray as an assistant coach.
“Warren had a certain vision of the way he wanted to play when he had a look at the players assembled and said this is what I’m looking for.
“He was highly involved in putting that team together and watching them work the other day they seem to be a cohesive unit already.”
Collins remained coy on the team’s plans around who would step into Gatland’s role when he was away on Lions duty, and whether that would be one of the current team or an outside hire. He said they were allowing their new coaching staff to settle in before moving on to such decisions.
“We go through that appointment process in conjunction with New Zealand Rugby so we know what the process looks like and roughly when it’s going to happen. So we’ll be going through that in the early stages of 2020.”
This article first appeared on nzherald.co.nz and was republished with permission.
In other news:


Get => Loading...
POST => Loading...
PUT => Loading...
DELETE => Loading...
PUT id 3 => Loading...
DELETE id 3 => Loading...


 IT IS IN NO WAY ENDORSED BY TWITCH.TV
What is It?
Twitch Player is a library that helps you embed Twitch Streams, Clips, Videos, and Collections into your application easily. You can embed directly from the Storyboard or programmatically.
Example Usage
From the storyboard, drag a WKWebView onto a ViewController. Set it’s subclass to TwitchPlayer
(for Stream, Video, or Collection playing) or TwitchClipPlayer
(for Clip playing)
You can then modify the variables directly from the Storyboard.
Example Project
An example project is provided with this project that shows the storyboard-creation of a Clip Player, Stream Player, and Video Player
To run the example project, clone the repo, and run pod install
from the Example directory. After that, open the resulting .xcworkspace
file and go nuts!
Documentation
- If the documentation is not loading, clone this repository and open
docs/index.html
.
Installation
- Install CocoaPods
Add this repo to your
Podfile
target 'Example' do # IMPORTANT: Make sure use_frameworks! is included at the top of the file use_frameworks! pod 'TwitchPlayer' end
Run
pod install
in the podfile directory from your terminalOpen up the
.xcworkspace
that CocoaPods createdDone!
License
TwitchPlayer is available under the MIT license. See the LICENSE file for more info.


Experts Have Declared Koalas “Functionally Extinct” After The Devastating NSW Bushfires
by By Alasdair DuncanWildlife campaigners have declared that koalas are “functionally extinct” in Australia, after the destruction of 80% of their habitat in the recent, devastating bushfires on the east coast.
Deborah Tabart of the Australian Koala Foundation said that there are an estimated 18,000 koalas in New South Wales, and that the fires likely had a “massive impact” on their population.
She told the Daily Mail that over the past two months, an estimated 1000 of them have been killed across the state, as a result of fires and deforestation.
While there have been significant rescue and rehabilitation efforts, it is feared that this may be too little, too late for the survival of the species in the long term.
Functional extinction refers to a population that is no longer viable, or that no longer plays a significant role in the functioning of its ecosystem.
Eucalyptus leaves are the main source of nutrition for koalas, and Tabart said that even those that survived the fires will now struggle thanks to the devastation:
“Because of deforestation and now the bushfires, there is so little habitat left and trees with eucalyptus take months to grow back.”
Tabart has urged the federal government to pass the Koala Protection Act, which is modeled after the Bald Eagle Protection Act from the U.S., and aims to safeguard their habitats. She said:
“[Koalas] are equivalent to the Great Barrier Reef. Everyone wants to touch a koala, so you would think the government would want to do something to save them. The plight of the koala now falls on the Prime Minister’s shoulders.”
Image: Getty Images / Tao Shelan


Our first task was to choose a topology which would be able to accurately identify thoracic pathologies in chest x-rays. Experiments on distributed TensorFlow framework conducted by Dell have found that ResNet-50 to scale effectively
on distributed training when compared to DenseNet which consists of many repeating blocks of convolutional and batch normalization layers. It is determined that the prevalence of batch normalization layers may be the limiting factor
in scaling DenseNet topology for large scale distributed training.
As noted above ResNet yields the best results. We choose ResNet-50, pretrained with ImageNet dataset, has the primary model for our training and to perform the following hyper parameter tuning.


{% extends "admin/base_site.html" %} {% block content %}
Select tournament to register participation:
The tournament participation will be applied to:


WITNESS LIST Veteran Affairs & Military Installations Committee May 7, 2003 - 1:30P HB 405 Registering, but not testifying: On: Caldwell, Jane Director Grants/Special Programs (Texas Higher Education Coordinating) HB 545 FOR: Cook, John Attorney (Title Data, Inc.), Houston, TX Rister, Nancy County Clerk (Texas County and District Clerks Association), Georgetown, TX Registering, but not testifying: For: Mitchell, Seth Intergovernmental Services Manager (Bexar County Commissioners Court), San Antonio, TX HB 1221 ON: Broussard, Allen UI Planner/Analyst (Texas Workforce Commission), Austin, TX


{% extends "email/template.html" %} {% block content %}
Your payment has been processed and your tapiriik account is now set up for automatic synchronization. Of course, you can still visit tapiriik.com at any time to trigger an immediate synchronization.
{% endblock %} {% block plaintextcontent %} Your payment has been processed and your tapiriik account is now set up for automatic synchronization. Of course, you can still visit {{ url|safe }} at any time to trigger an immediate synchronization. {% endblock %}


Cultural Blog Post # 8
Conflict
Conflict in my life
Sunday, March 8th
The last conflict I can remember that had an impact on my family was the purchasing of my current car. I wanted to get a stick shift car to go to the track but my family did not think it made sense to buy a car that only I could drive. We fought over the reasons why it did and didn't make sense to buy the car. Ultimately, we decided to get the car for two years. At first I was angry that my parents did not understand why I wanted the car and why it made sense to get it. Then, I felt guilty that I made them buy a car that did not make sense for our family. In the end, I decided to sell the car and get something more practical to help the family.


Create an awesome design portfolio
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Please see below for all calulations used in this project related to the attached Excel sheet
$$ Sunk Cost = Q * P $$ Where \(Q \) is the Quantity of the product and \(P\) is the price of the product Sample Calculation Vehicles to drive: $$ 5 * -$30,000 = -$150,000 $$
Operating costs are the sum of all the monthtly expenses and yearly costs added together. Note that all monthly costs are multiplied by 12 to represent an annual cost.
$$ OC = Be + B + Ad + GI + LR + W $$
Where:
\(Be\) is the cost of beer to purchase
\(B\) is the cost of packaging each set of beers
\(Ad\) is the cost of advertising
\(GI\) is the cost of gas and insurance
\(LR\) is the cost to renew liquor licenses
\(W\) is the cost of wages to pay
The operating revenue ( OR) is the monthly cost of delivering beer multiplied by 12
$$ OR = Q * P *12 $$
Where:
\(Q\) is the number of beer packages to sell
\(P\) is the price at which beer is sold
Sample Calculation Operating Revenue year 1:
$$ OR = $31*4,227*12 = $1,572,444 $$
The before tax cash flow is the sum of the operating revenue less the operating costs
$$ BTCF = OR - OC $$
Where:
\(BTCF\) is the before tax cash flow
Sample Calculation BTCF year 1 :
$$ BTCF = 1,572,444-1,280,606 = $291,838 $$
The loan repayment is an ordinary annualized payments. The loan term is given in payback periods of months.
$$ A = P\bigg{[} {i(i+1)^n \over {(1+i)^{n-1}}} \bigg{]} $$
Where:
\(A\) is the annualized payment to pay each year
\(P\) is the amount loaned
\(i\) is the loan interest rate
\(n\) is the number of time periods correlary to the interest rate
Sample Calculation: monthly loan repayment over 60 months at monthly interest rate of 0.8%:
$$ A = 200,000 \bigg{[} {0.008(1.008)^{60} \over {(1.008)^{59}}} \bigg{]} = $4,238 $$
To determine the annual loan repayment, the annualiazed payment is multiplied by 12 ($50,856).
Loan interest to pay per month is the remaining amount of the loan to pay back multiplied by the loan interest rate.
$$ LI = L * i $$
Where:
\(LI\) is the amount of the loan repayment towards interest
\(i\) is the loan interest rate
\(L\) is the remaining amount to pay off the loan
Sample Calculation: Year 1 loan interest paid:
$$ LI = 200,000*0.008 = $1,647.42 $$
The annual interest paid off is the sum of every 12 months of loan interest. E.g. Year 1 \(LI = $18,321.2\)
Loan principle is the amount put towards the principle loan repayment
$$ LP = A - LI $$
Where:
\(LP\) is the amount of the loan repayment towards principle
\(i\) is the loan interest rate
\(L\) is the remaining amount to pay off the loan
Sample Calculation: Year 1 loan interest paid:
$$ LI = 4,238 -1,647 = $2,591 $$
The annual principle paid off is the sum of every 12 months of principle repayment. E.g. Year 1 \(LI = $32,535\)
Following Canada's tax depreciation schedule, the initial purchases of capital's depreciation is multiplied by half the CCA rate for the class
$$ D_p = {CCA \over{2}} * BV $$
Where:
\(D_p\) is the depreciation in the year of the purchase of capital
\(CCA\) is the depreciation rate specified by the Canadian government
\(BV\) is the book value of capital investment
In subsequent years, the Depreciation schedule follows the full CCA rate
$$ D_n = CCA * BV_{n-1} $$
\(D_n\) is the depreciation in the \(n^{th} \) year
\(CCA\) is the depreciation rate specified by the Canadian government
\(BV_{n-1}\) is the book value of capital investment in the year prior to the \(n^{th} \) year
Sample calculation: Year 1 and 2 Depreciation in CCA class 10 where capital is bought in year 1:
$$ D_1 = 15,300*0.30/2 = $22,950 $$
$$ D_2 = 130,050*0.30 = $39,015 $$
The book value in the \(n^{th} \) year \(BV_n\) is calculated by the book value of the previous year minus the depreciation $$ BV_n = BV_{n-1} - D_n $$ Sample calculation: Year 2 book value: $$ BV_2 = 150,000-22,950 = $13,0050 $$
Taxable income in the \(n^{th} \) year is the income which can be taxed by the government. Drat $$ TI_n = IC_n - D_n - LI$$ Where \(IC_n\) is the income in the \(n^{th} \) year Sample Calculation: Year 1 taxable income with a class 10 and class 8 group $$ TI = 291,838 - 22,950 - 1,851 = $248,716 $$
Tax due is the amount of tax owed to the government in the \(n^{th} \) year $$ T_n = TI_n * Tr $$ Where Tr is the compounded provincial and government tax rate Sample Calculation: Year 1 tax due $$ T_1 = 248,716*0.12 = 29,846 $$ Note: No marginal tax rates apply for these calculations.
Tax due on salvage is the tax due after salvaging an item.
$$ TS = \sum^N_{k=1} [(SV_k - BV_{nk})*CCA_k] $$
if \(SV > SV_1\) then capital gains are taxed:
$$ TS = \sum^N_{k=1}(SSV_k - BV_{nk}) * CCA_k + (SV_k - (SV_k - BV_{nk}))*CCA_k/2 $$
Where:
\(TS\) is the tax due on salvage
\(BV_{nk}\) is the book value of the \(k^{th}\) class in the \(n^{th}\) year that the class is closed
\(SV_k\) is the salvage value of the class of the \(k^{th}\) class
Sample Calculation: Tax due on Salvage for Class 10 & 8 items
$$ T_S = (62,500 - 6,000)*0.30 + (6,273 - 2,536)*0.20 = $17,561 $$
Total money recovered after closing the book of k CCA classes less their respective taxes due $$ NSV = \sum^N_{k=1} [SV_k-TS] $$ Where \(NSV\) is the net salvage value Sample Calculation Net salvage value of class 10 & 8 $$ NSV = 62,500+6,273 - 17,561 = $50,939 $$
The after tax cash flow is the sum of the before tax cash flow less the tax due $$ ATCF = BTCF - TI - TS $$ Where ATCF is the after tax cash flow Sample Calculation ATCF year 1 (no tax on salvage) $$ ATCF = 291,838 - 29,846 = $261,993 $$
The net profit is the taxable amount (including salvage) less the income tax $$ NP = TI - T - Ts $$ Where NP is the net profit Sample Calculation ATCF year 1 $$ NP = 248,716 - 29,846 = $21,8871 $$
The net operating funds is the ATCF less the dividends or charities, working capital, and loan repayments
$$ NOF = ATCF - WC - Di - A $$
Where
\(NOF\) is the net operating funds
\(WC\) is the working capital (taken as a negative value)
\(Di\) are dividends paid out or charitible funds donated to
note: We do not assume tax credits on charitable funds.
Sample Calculation NOF year 1
$$ NOF = 26,1993 - 0 -15,2172 - 50,857 = $58,964 $$
The net present worth is the sum of present worth money over the sum of the lifespan of the project at the minimum acceptable interest rate (or rate of return)
$$ PW = F(1+i)^{-n} $$
Where
\(PW\) is the present worth
\(i\) is the market interest rate
\(F\) is the future value
\(n\) is the number of years in the future
Sample Calculation PW year 1 at MARR 0.09 (where MARR is the minimum accetable interest rate)
$$ PW = 58,964*(1.09)^{-1} = $54,095 $$
The present worth is the value of money today you would need to invest for \(n\) years into the future at interest rate \(i\) to obtain the future value
$$ NPW = \sum^LS_{k=1} [PW_k] $$
Where
\(NPW\) is the net present worth
\(LS\) is the lifespan of the project (as an integer)
Sample Calculation NPW at MARR 0.09 (where MARR is the minimum acceptable interest rate)
$$ NPW = -67,108-54,095+56,033+52,807... = $87,3476 $$
A positve NPW indicates that the project will return more money over its lifespan than a bond/stock at the same MARR
The rate of return is interest rate at which the net present worth is equal to 0
$$ ROR = NPW(i) = 0 $$
Where
\(ROR\) is the rate of return
Sample Calculation: ROR
The ROR is the NPW with the objective value set to 0 by varying the interest rate. using the the solver tool, the ROR for a sale price of $31 is 97.75%


A brute-force attack is an attack in which every possible key is tried in turn in an attempt to discover the key with which the document has been encrypted.
The CPU time required increases very drastically as the key length is increased.
CrypTool offers a brute-force attack to be carried out. The key range to be searched through is entered in the Key range dialog.
To perform an attack successfully in an appropriate time, some conditions must be fulfilled:
The document is a text file containing normal language texts and is not too short (>1000 characters long).
The first 128 characters at the beginning of the according cleartext document (this number can be adjusted in the dialog Analysis Options) are not too uniquely distributed (see histogram and entropy). This applies to most documents which are not compressed.
The key range, which has 2^128 and more possibilities with modern symmetric algorithms, can be cut down. So we make the assumption: Parts of the key are known, e.g. because the user did not choose the key by random, or because the attacker somehow got the knowledge, or because the random number generator does (by will) not use the full key range.
The key will always be found if the document fullfills the above requirements.


-
clearPendingRequests( [error])
-
Clears all pending requests by failing them
Parameters:
| Name |
Type |
Argument |
Description |
error |
Error
|
<optional>
|
Error to reject pending requests with |
- Inherited From:
-
Returns:
- Self for chaining
-
Type
-
JSONBottleInterface
-
createMessage(path, body)
-
Create new message bound to this interface
Parameters:
| Name |
Type |
Description |
path |
string
|
Message path |
body |
Object
|
Message body |
- Inherited From:
-
Returns:
- New message bound to this interface
-
Type
-
JSONBottleMessage
-
destroyAllSockets()
-
Destroy all sockets
- Inherited From:
-
Returns:
- Self for chaining
-
Type
-
JSONBottleInterface
-
destroySocket(id)
-
Destroy socket
Parameters:
| Name |
Type |
Description |
id |
string
|
Socket id |
- Inherited From:
-
Returns:
- Socket with the passed id or undefined
-
Type
-
external:Socket
|
undefined
-
getConfig(key)
-
Get config key
Parameters:
| Name |
Type |
Description |
key |
string
|
Key to get the config for |
- Inherited From:
-
Returns:
- Value for the key or undefined
-
Type
-
mixed
-
getSocket(id)
-
Get socket by ID
Parameters:
| Name |
Type |
Description |
id |
string
|
Socket id |
- Inherited From:
-
Returns:
- Socket with the passed id or undefined
-
Type
-
external:Socket
|
undefined
-
getSockets()
-
Get all sockets as array
- Inherited From:
-
Returns:
- All sockets
-
Type
-
Array.<external:Socket>
-
handleIncomingData(data [, socket])
-
Create message from input data.
Mainly intended for internal use, but can be used from outside
to inject message into the handling cycle.
Parameters:
| Name |
Type |
Argument |
Description |
data |
string
|
JSONBottleMessage~data
|
|
Either raw JSON string or a message data object (if object, id can be passed as well) |
socket |
external:Socket
|
<optional>
|
If present, represents the socket this message came from, otherwise the message returned will be treated as locally injected |
- Inherited From:
-
Returns:
- New message object
-
Type
-
JSONBottleMessage
-
handleIncomingMessage(message)
-
Process message through the next registered handler
Parameters:
- Inherited From:
-
Returns:
- Self for chaining
-
Type
-
JSONBottleInterface
-
request(path, body [, socketId] [, timeout])
-
Issue request
Parameters:
| Name |
Type |
Argument |
Description |
path |
JSONBottleMessage~data.path
|
|
Send on specified path |
body |
JSONBottleMessage~data.body
|
|
Body to send |
socketId |
string
|
<optional>
|
Id of the socket to issue the request to (request will be sent to the first found socket if not provided) |
timeout |
number
|
<optional>
|
Response timeout in milliseconds, 10000 by default |
- Inherited From:
-
Returns:
- Promise representing the request
-
Type
-
Promise
-
send(path, body [, socketId])
-
Send body on path to all sockets
Parameters:
| Name |
Type |
Argument |
Description |
path |
JSONBottleMessage~data.path
|
|
Send on specified path |
body |
JSONBottleMessage~data.body
|
|
Body to send |
socketId |
string
|
Array.<string>
|
<optional>
|
If provided, sends to socket id(s) specified, otherwise sends to all socket |
- Inherited From:
-
Returns:
- Freshly sent message
-
Type
-
JSONBottleMessage
-
sendMessage(message, sockets [, callback])
-
Send message
Parameters:
- Inherited From:
-
Returns:
- Self for chaining
-
Type
-
JSONBottleInterface
-
sendMessageRequest(message, socket, timeout)
-
Send message as a request (await response)
Parameters:
| Name |
Type |
Description |
message |
JSONBottleMessage
|
Message that is issuing a request |
socket |
external:Socket
|
Socket to send the request to |
timeout |
number
|
Response timeout in milliseconds |
- Inherited From:
-
Returns:
- Promise representing the request
-
Type
-
Promise
-
setupSocket(socket)
-
Setup socket
Parameters:
- Inherited From:
-
Returns:
- Passed socket
-
Type
-
external:Socket
-
start(port, host)
-
Start JSONBottleClient instance
Parameters:
| Name |
Type |
Description |
port |
number
|
Port to listen to |
host |
string
|
Host to bind to |
Returns:
- Self instance for chaining
-
Type
-
JSONBottleClient
-
stop( [cb])
-
Stop JSONBottleClient instance
Parameters:
| Name |
Type |
Argument |
Description |
cb |
function
|
<optional>
|
Callback invoked once the client has stopped |
Returns:
- Self instance for chaining
-
Type
-
JSONBottleClient
-
use(callback)
-
Use handler for all incoming messages
Parameters:
- Inherited From:
-
Returns:
- Self for chaining
-
Type
-
JSONBottleInterface
-
useOnce(callback)
-
Use handler for any incoming message only once
Parameters:
- Inherited From:
-
Returns:
- Self for chaining
-
Type
-
JSONBottleInterface


See: Description
| Interface | Description |
|---|---|
| Element |
Interface to represent a XML element.
|
| ExtensionElement |
Interface to represent extension elements.
|
| NamedElement |
Interface to represent a XML element.
|
| Nonza |
A Nonza, i.e everything that is not a stanza as defined
RFC 6120 8.
|
| Packet | Deprecated
use
Stanza instead |
| PacketExtension | Deprecated
use
ExtensionElement instead. |
| TopLevelStreamElement |
A XMPP top level stream element.
|
| Class | Description |
|---|---|
| AbstractError | |
| AbstractError.Builder<B extends AbstractError.Builder<B>> | |
| Bind |
IQ stanza(/packet) used by Smack to bind a resource and to obtain the jid assigned by the server.
|
| Bind.Feature | |
| DefaultExtensionElement | Deprecated
use
StandardExtensionElement instead. |
| EmptyResultIQ | |
| ErrorIQ | |
| IQ |
The base IQ (Info/Query) packet.
|
| IQ.IQChildElementXmlStringBuilder | |
| Mechanisms | |
| Message |
Represents XMPP message packets.
|
| Message.Body |
Represents a message body, its language and the content of the message.
|
| Message.Subject |
Represents a message subject, its language and the content of the subject.
|
| Presence |
Represents XMPP presence packets.
|
| Session |
IQ stanza(/packet) that will be sent to the server to establish a session.
|
| Session.Feature | |
| SimpleIQ |
A simple IQ.
|
| StandardExtensionElement |
An
ExtensionElement modeling the often required and used XML features when using XMPP. |
| StandardExtensionElement.Builder | |
| Stanza |
Base class for XMPP Stanzas, which are called Stanza(/Packet) in older versions of Smack (i.e.
|
| StartTls | |
| StreamError |
Represents a stream error packet.
|
| StreamOpen |
The stream open tag.
|
| UnparsedIQ |
An IQ stanzas that could not be parsed because no provider was found.
|
| XMPPError |
Represents an XMPP error sub-packet.
|
| XMPPError.Builder |
| Enum | Description |
|---|---|
| IQ.Type |
A enum to represent the type of the IQ stanza.
|
| Message.Type |
Represents the type of a message.
|
| Presence.Mode |
An enum to represent the presence mode.
|
| Presence.Type |
An enum to represent the presence type.
|
| StreamError.Condition |
The defined stream error conditions, see RFC 6120 § 4.9.3.
|
| StreamOpen.StreamContentNamespace | |
| XMPPError.Condition | |
| XMPPError.Type |
A class to represent the type of the Error.
|


Service Enablement is one of the common categories of patterns for connectivity solutions.
The Service Enablement pattern encapsulates functionality that does not have a service interface, and presents this functionality through a service-oriented interface. This pattern represents the transition from traditional enterprise application integration into service-oriented architectures, allowing existing assets to be reused in the new style without requiring radical change. This pattern is used to implement an Enterprise Service Bus that delivers access to services that are derived from functionality that is implemented by using diverse technologies across the enterprise and to present a single, consistent view of these services to the service consumers.
This pattern also uses WebSphere Enterprise Service Bus to provide access to services that some applications cannot access directly.
The following diagram illustrates an example of a Service Enablement scenario:


--- title: View a training run type: docs repo: https://github.com/rstudio/tfruns menu: main: name: View a training run - tfruns parent: tfruns-reference aliases: - /reference/tfruns/view_run.html - /tfruns/reference/view_run.html - /guide/tfruns/reference/view_run.html - /tools/tools/tfruns/reference/view_run.html - /installation/tfruns/reference/view_run.html - /tutorials/tfruns/reference/view_run.html - /guide/tools/tfruns/reference/view_run.html - /deploy/tfruns/reference/view_run.html - /tools/tfruns/reference/view_run.html - /tutorials/tools/tfruns/reference/view_run.html ---
View metrics and other attributes of a training run.
view_run(run_dir = latest_run(), viewer = getOption("tfruns.viewer"))
| run_dir | Training run directory or data frame returned from
|
| viewer | Viewer to display training run information within (default to an internal page viewer if available, otherwise to the R session default web browser). |


ECS 153 (Spring 2019)
Computer Security
General information
| Information | Details |
|---|---|
| Lecture time and location | Monday, Wednesday, and Friday from 11am - 11:50am in Olson 206 |
| Instructor | Sam King<EMAIL_ADDRESS>|
| Sam's Office Hours | Monday and Wednesday noon - 1pm in 3061 Kemper |
| TA | Kurt Schneider<EMAIL_ADDRESS>|
| TA Office Hours | TBD |
| Final | No final exam! |
| Canvas | https://canvas.ucdavis.edu/courses/332866 |
| Piazza | https://piazza.com/class/ju0e98z8dcq1fy |
Overview
This class focuses on computer security. Our goal is to train you how to think like an attacker, learn from past mistakes, and understand the primitives available to modern system builders to defend the computer systems of today and tomorrow.
The prerequisite for this class is undergraduate operating systems.
Lectures
You are responsible for knowing about all announcements made in lecture. We will discuss expectations about the project, suggestions for how to succeed, and grading guidelines in class, and general class policy issues, so make sure you don't miss any lectures.


{% extends "base.html" %} {% block title %}Page not available{% endblock %} {% block left_panel %}
Unfortunately, the page you're looking for is unavailable. Let us know if you think there is a problem with the site. We'll take a look.
Return to homepage. {% endblock %}


What is your first name?
What is your last name?
Name goes here.
What is your favorite book?
Who is the author?
Book and author goes here.
What is your first name?
What is your last name?
Name goes here.
What is your favorite book?
Who is the author?
Book and author goes here.


| 017:021 Khan | : |
See how We prefer one above another (in this world) and verily, the Hereafter will be greater in degrees and greater in preference. |
| 017:021 Maulana | : |
See how We have made some of them to excel others. And certainly the Hereafter is greater in degrees and greater in excellence. |
| 017:021 Pickthal | : |
See how We prefer one of them above another, and verily the Hereafter will be greater in degrees and greater in preferment. |
| 017:021 Rashad | : |
Note how we preferred some people above others (in this life). The differences in the Hereafter are far greater and far more significant. |
| 017:021 Sarwar | : |
Consider how We have given preference to some people above others, yet the life to come has more honor and respect. |
| 017:021 Shakir | : |
See how We have made some of them to excel others, and certainly the hereafter is much superior in respect of excellence. |
| 017:021 Sherali | : |
Behold, how WE have exalted some of them over others in the present life; and surely the Hereafter shall be greater in degrees of rank and greater in excellence. |
| 017:021 Yusufali | : |
See how We have bestowed more on some than on others; but verily the Hereafter is more in rank and gradation and more in excellence. |
Concepts/Themes Covered: الإنسان والعلاقات الاجتماعية > المجتمعات > التفاضل بينها Man and The Social Relations>The Societies>Preference Among them
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Extensibility and reuse are cornerstones of high-value software engineering. With those goals in mind, the Workstation is built on top of the NetBeans Rich Client Platform, and provides a plug-in architecture for extending functionality.


LGraphNode Class
Base Class for all the node type classes
Item Index
Methods
- addConnection
- addInput
- addInputs
- addOutput
- addOutputs
- collapse
- computeSize
- configure
- connect
- disconnectInput
- disconnectOutput
- findInputSlot
- findOutputSlot
- getBounding
- getConnectionPos
- getInputData
- getInputInfo
- getOutputInfo
- getOutputNodes
- getSlotInPosition
- getTitle
- isInputConnected
- isOutputConnected
- isPointInsideNode
- pin
- removeInput
- removeOutput
- serialize
- setOutputData
- toString
Methods
addConnection
-
name
-
type
-
pos
-
direction
add an special connection to this node (used for special kinds of graphs)
Parameters:
-
name
String -
type
Stringstring defining the input type ("vec3","number",...)
-
pos
x,yposition of the connection inside the node
-
direction
Stringif is input or output
addInput
-
name
-
type
-
extra_info
add a new input slot to use in this node
Parameters:
-
name
String -
type
Stringstring defining the input type ("vec3","number",...), it its a generic one use 0
-
extra_info
Objectthis can be used to have special properties of an input (label, color, position, etc)
addInputs
-
array
add several new input slots in this node
Parameters:
-
array
Arrayof triplets like [[name,type,extra_info],[...]]
addOutput
-
name
-
type
-
extra_info
add a new output slot to use in this node
Parameters:
-
name
String -
type
Stringstring defining the output type ("vec3","number",...)
-
extra_info
Objectthis can be used to have special properties of an output (label, special color, position, etc)
addOutputs
-
array
add a new output slot to use in this node
Parameters:
-
array
Arrayof triplets like [[name,type,extra_info],[...]]
collapse
()
Collapse the node to make it smaller on the canvas
computeSize
-
minHeight
computes the size of a node according to its inputs and output slots
Parameters:
-
minHeight
Number
Returns:
the total size
configure
()
configure a node from an object containing the serialized info
connect
-
slot
-
node
-
target_slot
connect this node output to the input of another node
Parameters:
-
slot
Number_or_string(could be the number of the slot or the string with the name of the slot)
-
node
LGraphNodethe target node
-
target_slot
Number_or_stringthe input slot of the target node (could be the number of the slot or the string with the name of the slot)
Returns:
if it was connected succesfully
disconnectInput
-
slot
disconnect one input
Parameters:
-
slot
Number_or_string(could be the number of the slot or the string with the name of the slot)
Returns:
if it was disconnected succesfully
disconnectOutput
-
slot
-
target_node
disconnect one output to an specific node
Parameters:
-
slot
Number_or_string(could be the number of the slot or the string with the name of the slot)
-
target_node
LGraphNodethe target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected]
Returns:
if it was disconnected succesfully
findInputSlot
-
name
returns the input slot with a given name (used for dynamic slots), -1 if not found
Parameters:
-
name
Stringthe name of the slot
Returns:
the slot (-1 if not found)
findOutputSlot
-
name
returns the output slot with a given name (used for dynamic slots), -1 if not found
Parameters:
-
name
Stringthe name of the slot
Returns:
the slot (-1 if not found)
getBounding
()
Float32Array4
returns the bounding of the object, used for rendering purposes
Returns:
the total size
getConnectionPos
-
is_input
-
slot
returns the center of a connection point in canvas coords
Parameters:
-
is_input
Booleantrue if if a input slot, false if it is an output
-
slot
Number_or_string(could be the number of the slot or the string with the name of the slot)
Returns:
the position
getInputData
-
slot
retrieves the input data (data traveling through the connection) from one slot
Parameters:
-
slot
Number
Returns:
data or if it is not connected returns undefined
getInputInfo
-
slot
tells you info about an input connection (which node, type, etc)
Parameters:
-
slot
Number
Returns:
object or null
getOutputInfo
-
slot
tells you info about an output connection (which node, type, etc)
Parameters:
-
slot
Number
Returns:
object or null
getOutputNodes
-
slot
retrieves all the nodes connected to this output slot
Parameters:
-
slot
Number
Returns:
getSlotInPosition
-
x
-
y
checks if a point is inside a node slot, and returns info about which slot
Parameters:
-
x
Number -
y
Number
Returns:
if found the object contains { input|output: slot object, slot: number, link_pos: [x,y] }
getTitle
()
get the title string
isInputConnected
-
slot
tells you if there is a connection in one input slot
Parameters:
-
slot
Number
Returns:
isOutputConnected
-
slot
tells you if there is a connection in one output slot
Parameters:
-
slot
Number
Returns:
isPointInsideNode
-
x
-
y
checks if a point is inside the shape of a node
Parameters:
-
x
Number -
y
Number
Returns:
pin
()
Forces the node to do not move or realign on Z
removeInput
-
slot
remove an existing input slot
Parameters:
-
slot
Number
removeOutput
-
slot
remove an existing output slot
Parameters:
-
slot
Number
serialize
()
serialize the content
setOutputData
-
slot
-
data
sets the output data
Parameters:
-
slot
Number -
data
toString
()
serialize and stringify


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