text:
\section{Introduction}

The study of genomic variation and its association with gene expression
is critical for elucidating the genetic basis of complex traits,
including diseases. The advent of next-generation sequencing (NGS) made
possible the detailed investigation of this relationship (also known as
\emph{eQTL mapping}) in large cohorts, but it also gave rise to novel
statistical challenges\cite{GTEx_Consortium2013-jf,Lappalainen2013-ex}.\
eQTL mapping can be examined in the context of
sparse Bayesian multivariate/multiple regression, where we typically
propose a number of candidate statistical models followed by benchmarking
them against each other in terms of computational and statistical efficiency\cite{Vavoulis2017-em}.
The most laborious aspect of this process is the development and
software implementation of statistical inference algorithms for each
model under consideration, a task that can be impeded by the potential
absence of favourable mathematical properties (e.g.~conjugacy) in any of
these models.

The aim of this chapter is to demonstrate the utility of a popular
probabilistic programming language (PPL), \texttt{Stan}, in the prototyping and
testing phases of the data modelling process for eQTL mapping\cite{Carpenter2017-gn}.
PPLs make it possible
to describe and perform inference in hierarchical probabilistic models in a
matter of minutes using a small amount of high-level code. Besides \texttt{Stan}, another
popular PPL is \texttt{PyMC}, a Python-based software offering excellent performance,
a wide range of inference algorithms, and the ability to mix these algorithms in the same
inferential cycle for estimating different parts of a given model\cite{Salvatier2016-ic}.
The reason for choosing
\texttt{Stan} in this chapter is its simplicity, which stems from the fact that its syntax is
very close to the mathematical notation already familiar to statisticians.

The practical part of this chapter covers: (a) the acquisition of genomic variation
and gene expression data from online sources, (b) the use of these to simulate artificial
eQTL datasets with known properties, (c) the implementation of statistical models in \texttt{Stan}
for eQTL mapping, and (d) the estimation of unknown model parameters using the previously simulated
datasets. In the remaining part of this introduction, we outline the statistical theory,
which underpins the practical part of this chapter.

\subsection{Theory}

We assume an \(N\times M\) matrix \(Z=\{z_{ij}\} \) of read counts
quantifying the expression of \(N\) transcripts in \(M\) samples, an
\(N\times M\) matrix \(C=\{c_{ij}\} \) of transcript- and sample-specific
normalization factors, and an \(M\times K\) matrix \(X=\{x_{jk}\} \) of
genotypes indicating the number of minor alleles (0, 1 or 2) in each of
\(K\) bi-allelic genomic loci in \(M\) samples. A matrix
\(\tilde{X}=\{\tilde{x}_{jk}\} \) is derived by standardizing each column of
\(X\).

We introduce an \(N\times K\) sparse matrix \(B\) of regression
coefficients with elements \(\beta_{ik}\), which measure the effect of
variant \(k\) on the expression of transcript \(i\). Estimating \(B\) is
the main focus of subsequent analysis. Typically, sparsity is induced on
\(B\) by imposing appropriate priors. A common sparsity-inducing prior is
a two-component mixture of the following form:
\[
  \beta_{ik} \sim (1-\pi_{ik})\delta_0 + \pi_{ik}\mathcal{N}(0, \sigma_i^2)
\]
\noindent where \(\delta_0\) is a point-mass distribution centred at 0. This prior
can set \(\beta_{ik}\) exactly at 0, but posterior estimation requires
stochastic exploration of a \(2^{NK}\)-dimensional discrete space. An alternative
approach with obvious computational advantages would be to adopt a
continuous shrinkage prior; for example, the important class of global-local
shrinkage priors\cite{PolsonScott2012}:
\[
  \beta_{ik} \sim \mathcal{N}\left(0, \eta^2\zeta_{ik}^2\right) \quad
  \eta^2 \sim \mathrm{p}_\eta(\eta^2) \quad
  \zeta^2_{ik} \sim \mathrm{p}_\zeta(\zeta^2_{ik})
\]

\noindent where \(\zeta_{ik}\) are local (i.e.~gene- and variant-specific)
shrinkage scales, while \(\eta \) is a global scale controlling the
overall shrinkage of \(B\). The shrinkage profile of \(B\) depends on
the form of \(\mathrm{p}_\eta \) and \(\mathrm{p}_\zeta \). Different
choices of these distributions give rise to different shrinkage priors,
but here we shall use the \emph{horseshoe prior}\cite{Carvalho2010-ad} for which
\(\zeta_{ik}\) and \(\eta \) follow standard and scaled half-Cauchy distributions,
\(\mathcal{C}^+(0, 1)\) and \(\mathcal{C}^+(0, \alpha)\), respectively.

\subsubsection{Normal model}

For the Normal model, we assume that read counts have been normalized,
\(\tilde{z}_{ij}=\frac{z_{ij}}{c_{ij}}\), and log-transformed, \(y_{ij}=\log(\tilde{z}_{ij} + 1)\).
The model takes the following form:
\[
  y_{ij} \sim \mathcal{N}\left(\beta_{0i}+\sum_k\beta_{ik}\tilde{x}_{jk}, \sigma_i^2\right) \quad
  \beta_{0i} \sim 1 \quad
  \log\sigma^2_i \sim 1
\]
\[
  \beta_{ik} \sim \mathcal{N}\left(0, \frac{\bar\sigma^2}{NK}\eta^2\zeta_{ik}^2\right) \quad
  \eta \sim \mathcal{C}^+(0, 1) \quad
  \zeta_{ik} \sim \mathcal{C}^+(0, 1)
\]
\noindent where \(\sigma^2_i\) is the variance of gene \(i\) and \(\sum_k\beta_{ik}\tilde{x}_{jk}\)
is the effect of genomic variation on the baseline expression \(\beta_{0i}\) of gene \(i\).
We assume that \(M\) is large, so we can afford to impose a flat prior distribution on
\(\beta_{0i}\) and \(\log\sigma_i\) over the interval (\(-\infty,+\infty \)).
A more complicated model would include correlations between
different \(y_{ij}\) variables, additional (e.g.~clinical, environmental
and population) co-variates influencing the baseline gene expression
\(\beta_{0i}\), as well as hierarchical priors on \(\beta_{0i}\) and
\(\sigma^2_i\). Notice that the variance of \(\beta_{ik}\) is proportional to
\(\bar\sigma^2=\left(\frac{\sum_i\sigma_i}{N}\right){^2}\)
and inversely proportional to the total number of coefficients. The implicit
assumption under this formulation is that the true global scale parameter is \(\xi \sim \mathcal{C}^+\left(0, \frac{\bar\sigma}{\sqrt{NK}}\right) \).
Finally, in this and the subsequent models, we assume that we can ignore any gene-specific factors
(e.g.~length) affecting \(c_{ij}\), thus \(c_{ij}\equiv c_j\).

\subsubsection{Negative Binomial model}

The Negative Binomial distribution is immensely popular for modelling over-dispersed RNA-seq
data\cite{Love2014-yt,McCarthy2012-bh,Vavoulis2015-ot}, but
the mathematical complexities associated with inference in this model might explain (at least
partially) the popularity of transformation-based methods, such as \texttt{voom}\cite{Law2014-uc}.
Here, we examine the following model:
\[
  z_{ij} \sim \mathcal{NB}(m_{ij}, \phi_i) \quad
  \log m_{ij} = \log c_{ij} + \log L_j + \beta_{0i}+\sum_k\beta_{ik}\tilde{x}_{jk} \quad
  \beta_{0i} \sim 1 \quad \log\phi_i \sim 1
\]
\[
  \beta_{ik} \sim \mathcal{N}\left(0, \frac{\eta^2\zeta_{ik}^2}{NK}\right) \quad
  \eta \sim \mathcal{C}^+(0, 1) \quad
  \zeta_{ik} \sim \mathcal{C}^+(0, 1)
\]
\noindent where \(m_{ij}\) is the gene- and sample-specific mean of the Negative Binomial distribution,
and \(\phi_i\) is a gene-specific dispersion parameter, such that \(\mathrm{Var}[z_{ij}]=m_{ij}+m^2_{ij}\phi_i\);
\(L_j=\sum_i z_{ij}\) is the total number of reads in sample \(j\).

\subsubsection{Poisson-LogNormal model}

An alternative approach to work with non-transformed data is to impose a Poisson observational model on top
of the Normal:
\[
z_{ij} \sim \mathcal{P}(m_{ij}) \quad \log m_{ij} = \log c_{ij} + \log L_j+ y_{ij}
\]
\noindent where \(y_{ij}\) serve as latent variables following the Normal model. The Poisson-LogNormal
model is motivated by (a) the fact that the Negative Binomial model can be thought of as a
Poisson-Gamma mixture, and (b) replacing the Gamma distribution in the
previous mixture with LogNormal. By invoking the law of total expectation and the law of total variance,
we can see that \(E[z_{ij}]=c_{ij}L_j e^{\beta_{0i}+\sum_k\beta_{ik}\tilde{x}_{jk} + \sigma_i^2/2}\)
and \(\mathrm{Var}[z_{ij}]=E[z_{ij}] + E[z_{ij}]{^2}\phi_i\). Hence, the variance has the same form
as in the Negative Binomial model, with dispersion parameter \(\phi_i=e^{\sigma^2_i}-1\). When
\(\sigma^2_i=0\), the above model reduces to Poisson.

\section{Materials}

\subsection{Operating system}

\begin{enumerate}
\item
  A working UNIX environment (e.g.~Linux or MacOS X) with a terminal
  emulator running the command shell \texttt{bash}. The work presented in
  this chapter was tested on Ubuntu Linux v18.04.
\end{enumerate}

\subsection{Software}

\begin{enumerate}
\item
  A recent version of the command line tool and library \texttt{curl} for
  transferring data with URLs.
\item
  A recent version of \texttt{vcftools}, a set of tools written in Perl
  and C++ for working with VCF files\cite{Danecek2011-eu}.
\item
  A recent version of \texttt{R}, the free software environment for
  statistical computing\cite{R}.
\item
  A recent version of \texttt{rstudio}, an integrated development
  environment for working with \texttt{R} and the command line\cite{RStudio}.
\item
  A recent version of \texttt{rstan}, an \texttt{R} interface for \texttt{stan}.
\item
  A recent version of \texttt{plyr}, a set of \texttt{R} tools for splitting,
  modifying and combining data\cite{plyr}.
\item
  A recent version of \texttt{doMC}, an \texttt{R} package providing a parallel backend for multicore computation.
\item
  A recent version of \texttt{cowplot}, an \texttt{R} package for plotting.
\item
  A recent version of \texttt{tidyverse}, a collection of \texttt{R} packages
  for data wrangling and plotting.
  \item
  A recent version of \texttt{reshape2}, an \texttt{R} package for restructuring and aggregating data\cite{reshape2}.
\end{enumerate}
The above \texttt{R} packages can be installed either through the graphical interface provided by \texttt{rstudio}, or through
the \texttt{R} console; for example \texttt{install.packages('tidyverse')}

\section{Methods}

\subsection{Data acquisition}

\begin{enumerate}
\item
  Start \texttt{rstudio}

\item
  Create a working directory tree by typing the following commands at
  the \texttt{bash} command prompt\footnote{
    You can access the command prompt through a terminal emulator. From within \texttt{rstudio},
    you can create a terminal emulator by going to \texttt{Tools\textgreater{}Terminal\textgreater{}New Terminal}.
  }:
\begin{lstlisting}[language=bash]
mkdir -p eQTLchapter/{data,R,stan}
\end{lstlisting}

\item
  Make the root of the tree you just created your working directory by
  typing the following at the \texttt{R} console\footnote{
    Instead of steps 2 and 3, you can just create a new \texttt{rstudio} project (\texttt{File\textgreater{}New
    Project\ldots{}}) with equivalent results
  }:
\begin{lstlisting}
setwd('eQTLchapter')
\end{lstlisting}

\item
  Download genomic variation data from the 1000 Genomes project\cite{1000_Genomes_Project_Consortium2015-qu}. At the
  \texttt{bash} command prompt, type the following\footnote{
    This chunk of code uses \texttt{curl} to stream data from the 1000 Genomes Project and pipe them
    (in gzipped VCF format) to \texttt{vcftools} for further processing. Only variants in chromosome 7
    between positions 100K and 200K are retained. In addition, all variants with FILTER other than PASS are
    removed; indels are removed; variants in non-bi-allelic sites are removed; only variants with allele
    frequency higher than 5\% are retained (i.e. 0.05\textless{}MAF\textless{}0.95). Filtered data are stored as a matrix
    with elements 0, 1 and 2 indicating the number of alleles in 496 variants across 2504 samples. If \texttt{vcftools}
    throws an error about not being able to open temporary files, you need to increase the maximum number
    of open files allowed by your system to more than double the number of samples in the VCF file. At the
    \texttt{bash} command prompt, type: \texttt{ulimit\ -n\ 5100}. You may need root permissions to run
    this command.
  }:
\begin{lstlisting}[language=bash,escapechar=;]
cd eQTLchapter
curl -o - ;\href{ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr7.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz}{link}; \
    | vcftools --gzvcf - \
        --chr 7 --from-bp 100000 --to-bp 200000 \
        --remove-filtered-all --remove-indels \
        --min-alleles 2 --max-alleles 2 \
        --maf 0.05 --max-maf 0.95 \
        --012 --out data/chr7
\end{lstlisting}

\item
  Download RNA-seq read count data from the ReCount project\cite{Frazee2011-ko}.
  At the \texttt{bash} prompt, type the following:
\begin{lstlisting}[language=bash,escapechar=|]
curl -o data/montpick_count_table.txt |\href{http://bowtie-bio.sourceforge.net/recount/countTables/montpick_count_table.txt}{link}|
curl -o data/montpick_phenodata.txt |\href{http://bowtie-bio.sourceforge.net/recount/phenotypeTables/montpick_phenodata.txt}{link}|
\end{lstlisting}
\end{enumerate}

\subsection{Data importing}

\begin{enumerate}
\item
  Create the following \texttt{R} scripts. At the \texttt{bash}
  prompt, type:\footnote{
    Alternatively, you can create these files through
    \texttt{rstudio} (\texttt{File\textgreater{}New File\textgreater{}R Script}).
  }:
\begin{lstlisting}[language=bash]
touch analysis.R R/{utils,viz}.R
\end{lstlisting}

\item
  Using the code editor provided by \texttt{rstudio}, add the line
  \texttt{library(tidyverse)} at the top of \texttt{utils.R} and
  \texttt{viz.R}, and the following lines at the top of
  \texttt{analysis.R}\footnote{Whenever the code in \texttt{utils.R} or
    \texttt{viz.R} changes, these files have to be sourced again to make
    the changes visible to \texttt{analysis.R} by executing
    lines 2 and 3 of this code chunk. A more structured approach would be to develop an
    \texttt{R} package, which is however beyond the scope of this chapter. The function
    \texttt{doMC::registerDoMC(cores\ =\ 8)}
    registers a parallel backend with 8 cores. This is required for multicore
    functionality in the \texttt{plyr} package.}:
\begin{lstlisting}
source('R/utils.R')
source('R/viz.R')

doMC::registerDoMC(cores=8)
\end{lstlisting}

\item
  Create a function in \texttt{utils.R} for importing the genotypic data
  into \texttt{R}\footnote{
    In this and all subsequent code listings, we make heavy use of the pipe operator \texttt{\%>\%} to form
    long linear chains of functions, where the output of each function becomes the input of the next. This allows for the
    generation of readable, easy-to-understand workflows. In this particular function, we read genotypic data from disk files
    in the form of an \(M\times K\) matrix. Before returning, the matrix is filtered by removing all variants (i.e.~columns) that
    may have zero variance across all samples (lines 24,25). Notice that file names are constructed in a device-independent
    manner using the function \texttt{file.path}. Finally, we prefer the operator \texttt{=} instead of the traditionally used
    \texttt{<-} for indicating assignment.
  }:
\begin{lstlisting}
load_genotypes = function() {
  # read sample names
  samples =
    file.path('data', 'chr7.012.indv')
    read_tsv(col_names = 'SAMPLE')
    pull(SAMPLE)

  # read loci
  pos =
    file.path('data', 'chr7.012.pos')
    read_tsv(col_names = c('CHROM', 'POS'))
    unite('POS', CHROM, POS, sep = ':')
    pull(POS)

  # read genotypes
  geno =
    file.path('data', 'chr7.012')
    read_tsv(na = '-1', col_names = c('SAMPLE', pos))
    select(-1)
    as.matrix()
  dimnames(geno) = list(samples = samples, variants = pos)

  # remove loci with the same genotype across all samples
  sds = apply(geno, 2, sd, na.rm = T)
  geno[,sds > 0]
}
\end{lstlisting}

\item
  Source \texttt{utils.R}, add the following line to \texttt{analysis.R}
  and execute it in order to import the genotypic data into \texttt{R}:
\begin{lstlisting}
obs_geno = load_genotypes()
\end{lstlisting}

\item
  Create the following function in \texttt{viz.R} for visualizing the correlation structure of the
  matrix of genotypes:
\begin{lstlisting}
plot_genotypes = function(geno) {
  geno
    cor()
    reshape2::melt()
    ggplot() +
    geom_raster(aes(x = Var1, y = Var2, fill = value)) +
    theme(legend.position = 'none',
          axis.text.x = element_blank(),
          axis.text.y = element_blank(),
          axis.ticks.x = element_blank(),
          axis.ticks.y = element_blank()) +
    scale_fill_gradient(low = 'black', high = 'white') +
    labs(x = 'genomic position', y = 'genomic position')
}
\end{lstlisting}

\item
  Source \texttt{viz.R}, add the following line to \texttt{analysis.R}
  and execute it in order to visualize the genotypic data (Figure~\ref{fig:geno}):
\begin{lstlisting}
plot_genotypes(obs_geno)
\end{lstlisting}

\item
  Create a function in \texttt{utils.R} for importing the read counts data
  into \texttt{R}\footnote{
    Data are loaded in the form of an \(N\times M\) matrix of read counts. By default,
    only data for the CEU population are read. If YRI data are preferred, set the function
    argument to \texttt{pop='YRI'}, instead. As for the genotypic data, genes with constant
    expression across all samples are removed (lines 18,19).
  }:
\begin{lstlisting}
load_counts = function(pop = 'CEU') {
  # load samples
  samples =
    read_delim(file.path('data', 'montpick_phenodata.txt'), delim = ' ')
    filter(population == pop)
    pull(sample.id)

  # load read counts
  counts =
    read_tsv(file.path('data', 'montpick_count_table.txt'))
    select(GENE = gene, samples)
    as.data.frame()
    column_to_rownames('GENE')
    as.matrix()
  dimnames(counts) = list(genes = rownames(counts), samples = samples)

  # remove genes with the same number of counts across all samples
  sds = apply(counts, 1, sd, na.rm = T)
  counts[sds > 0,]
}
\end{lstlisting}

\item
  Source \texttt{utils.R}, add the following line to \texttt{analysis.R}
  and execute it in order to import the read counts data into \texttt{R}:
\begin{lstlisting}
obs_counts = load_counts()
\end{lstlisting}
\end{enumerate}

\begin{figure}[ht]
  \centering
  \includegraphics{figs/geno}.
  \caption{\label{fig:geno} Correlation structure of the matrix of genotypes. Blocks
    of highly correlated variants appear in white}
\end{figure}

\subsection{Estimation of gene expression statistics}

\begin{enumerate}
\item
  Create a function in \texttt{utils.R} for calculating sample-specific normalization factors
  given a matrix of count data\footnote{
    This function implements the \emph{median-of-ratios} method for normalizing a matrix of count data,
    as presented in~\cite{Love2014-yt}. Calculations are performed on the logarithmic scale.
  }:
\begin{lstlisting}
calculate_norm_factors = function(counts) {
  lcounts = log(counts)
  lgm = rowSums(lcounts) / ncol(lcounts)
  idxs = is.finite(lgm)
  lratios = sweep(lcounts[idxs,], 1, lgm[idxs], '-')
  apply(exp(lratios), 2, median)
}
\end{lstlisting}

\item
  Create a function in \texttt{utils.R} for calculating the log-likelihood of a vector of counts, assuming each element is sampled
  from the Negative Binomial distribution\footnote{
    The probability mass function of the Negative Binomial distribution assumed here is parameterized in terms of
    gene-specific mean \(\mu_i\) and inverse dispersion \(\alpha_i\) parameters, as follows:
    \[
      z_{ij} \sim \frac{\Gamma(z_{ij} + \alpha_i)}{\Gamma(z_{ij}+1)\Gamma(\alpha_i)}
        {\left(\frac{c_j\mu_i}{c_j\mu_i+\alpha_i}\right)}^{z_{ij}}
        {\left(\frac{\alpha_i}{c_j\mu_i+\alpha_i}\right)}^{\alpha_i}
    \]
    \noindent where \(c_j\) are sample-specific normalisation factors.
  }:
\begin{lstlisting}
lognbinom = function(pars, z, cc) {
  mu = pars[1]
  phi = pars[2]

  m = cc * mu
  alpha = 1 / phi
  ll = lgamma(z + alpha) - lgamma(alpha) - lgamma(z + 1) +
    z * log(m) + alpha * log(alpha) - (z + alpha) * log(m + alpha)

  sum(ll)
}
\end{lstlisting}

\item
  Create a function in \texttt{utils.R} for calculating gene-specific mean and dispersion maximum likelihood
  estimates for the Negative Binomial distribution given a matrix of count data\footnote{
    Given an \(N\times M\) matrix of counts, we fit the Negative Binomial distribution to each gene/row, and we estimate
    gene-specific maximum likelihood estimates of mean and dispersion parameters. For parameter estimation, we use the function
    \texttt{optim}. The code \texttt{possibly(optim, otherwise = NULL)} on line 3 returns a version of the \texttt{optim} function,
    which returns \texttt{NULL} on error instead of halting execution, thus allowing processing of the whole set of available genes.
    The parameter \texttt{fnscale=-1} on line 11 signals \texttt{optim} to perform maximization of the log-likelihood
    \texttt{lognbinom}, instead of minimization. Iterating over the rows of the data matrix is done using the \texttt{plyr}
    function \texttt{alply}, and it is performed in parallel (argument \texttt{.parallel=T} on line 15). After all rows have
    been processed, genes for which estimation failed or did not converge are discarded
    (lines 16,17), results are extracted and properly formatted (lines 18--21), and gene-specific variance values are calculated
    given the estimated values for mean and dispersion parameters (line 22).
  }:
\begin{lstlisting}
calculate_gene_stats = function(counts) {
  sizes = calculate_norm_factors(counts)
  optim = possibly(optim, otherwise = NULL)
  stats =
    counts
    plyr::alply(1, function(cnts) {
      optim(par = c(1, 1),
            fn = lognbinom,
            z = cnts,
            cc = sizes,
            control = list(fnscale = -1),
            method = 'L-BFGS-B',
            hessian = T,
            lower = 1e-12)
    }, .parallel = T, .dims = T)
    compact()
    discard(~.x$convergence > 0)
    map('par')
    enframe()
    mutate(value = map(value, str_c, collapse = ','))
    separate(value, into = c('MEAN', 'PHI'), sep = ',', convert = T)
    mutate(VAR = MEAN + PHI * MEAN^2)
    rename(genes = name)
    as.data.frame()
    column_to_rownames('genes')
    as.matrix()
  dimnames(stats) = list(genes = rownames(stats), statistics = colnames(stats))
  stats
}
\end{lstlisting}

\item
  Source \texttt{utils.R}, add the following line to \texttt{analysis.R}
  and execute it in order to calculate basic gene-wise statistics from the read counts data:
\begin{lstlisting}
obs_counts_stats = calculate_gene_stats(obs_counts)
\end{lstlisting}

\item
  Create a function in \texttt{viz.R} for visualising empirical and estimated gene-wise statistics\footnote{
    An inner function \texttt{plot\_fn} is defined at the top of \texttt{plot\_mean\_variance}, in order to
    avoid code repetition when generating plots \texttt{gg1} and \texttt{gg2} below.
  }:
\begin{lstlisting}
plot_mean_variance = function(counts, stats) {
  plot_fn = function(df, xlabel, ylabel) {
    df
      ggplot() +
      geom_point(aes(x = MEAN, y = VAR), size = 0.1) +
      geom_smooth(aes(x = MEAN, y = VAR), size = 0.5,
                  color = 'red', method = 'lm', formula = y ~ poly(x, 2)) +
      scale_x_continuous(trans = 'log10') +
      scale_y_continuous(trans = 'log10') +
      labs(x = xlabel, y = ylabel)
  }

  # normalise count data
  sizes = calculate_norm_factors(counts)
  counts = sweep(counts, 2, sizes, '/')

  # plot of estimated stats
  gg1 =
    stats
    as.data.frame()
    plot_fn(xlabel = 'estimated mean', ylabel = 'estimated variance')

  # plot of observed stats
  gg2 =
    data.frame(
      MEAN = rowMeans(counts),
      VAR = apply(counts, 1, var)
    )
    plot_fn(xlabel = 'observed mean', ylabel = 'observed variance')

  # combine plots
  cowplot::plot_grid(gg1, gg2, align = 'vh', labels = 'AUTO')
}
\end{lstlisting}

\item
  Source \texttt{viz.R}, add the following line to \texttt{analysis.R}
  and execute it in order to visualise estimated and observed gene-wise
  statistics (Figure~\ref{fig:mean_var}):
\begin{lstlisting}
plot_mean_variance(obs_counts, obs_counts_stats)
\end{lstlisting}
\end{enumerate}

\begin{figure}[ht]
  \centering
  \includegraphics[width=0.9\textwidth]{figs/mean_var}.
  \caption{\label{fig:mean_var} Estimated and observed mean-variance relationship
    in the gene expression data}
\end{figure}

\subsection{Data simulation}
\begin{enumerate}
\item
  Create a function in \texttt{utils.R} for simulating genotypic and count gene expression data
  given observed data of the same kind and their estimated statistics\footnote{
    This function should be treated as a starting point for more sophisticated data simulation methods.
    Genotypic data are generated by randomly sampling without replacement \texttt{nsamples} rows and \texttt{nvars}
    columns from the observed matrix of genotypes. This approach breaks the spatial correlation between variants,
    so the variants/columns in the generated matrix \(X\) can be treated as being mostly uncorrelated. The matrix \(B\)
    of coefficients is initially generated as a matrix of zeros. In a second stage, \texttt{nhits} elements are randomly
    chosen, and set to non-zero values sampled randomly from a shifted exponential distribution. Half of the \texttt{nhits}
    elements are asigned positive values and the remaining half are assigned negative values. Given matrices \(X\) and
    \(B\) and the top \texttt{ngenes} estimated mean and dispersion parameters (where parameter values are first ranked
    in order of decreasing mean values; see line 20), a matrix \(Z\) of read counts is generated with elements sampled
    from the Negative Binomial distribution. Alternatively, estimated mean and dispersion parameters can be randomly
    sampled by uncommenting line 19 (and commenting out line 20).
  }:
\begin{lstlisting}
simulate_data = function(count_stats, genotypes, nsamples = 1000, ngenes = 100,
                         nvars = 50, nhits = 10, rate = 4) {
  # fetch genotypes
  X = genotypes[sample(1:nrow(genotypes), nsamples),
                sample(1:ncol(genotypes), nvars)]
  X = X[,apply(X, 2, sd, na.rm = T) > 0]
  dimnames(X) = list(samples = str_c('S', 1:nrow(X)),
                     variants = str_c('V', 1:ncol(X)))

  # simulate matrix of coefficients
  B = matrix(0, nrow = ngenes, ncol = ncol(X),
              dimnames = list(genes = str_c('G', 1:ngenes),
                              variants = str_c('V', 1:ncol(X))))
  hits = c(1 + rexp(0.5 * nhits, rate = rate),
          -1 - rexp(0.5 * nhits, rate = rate))
  B[sample(length(B), length(hits))] = hits

  # simulate counts
  # df = count_stats[sample(1:nrow(count_stats), ngenes),]
  df = count_stats[order(count_stats[,'MEAN'], decreasing = T),][1:ngenes,]

  X0 = scale(X, center = T, scale = T)
  m = sweep(exp(B
  alpha = 1 / df[,'PHI']
  Z = matrix(rnbinom(length(m), mu = m, size = alpha), nrow = nrow(m))
  dimnames(Z) = list(genes = str_c('G', 1:nrow(Z)),
                     samples = str_c('S', 1:ncol(Z)))

  # output
  lst(B, X, Z, stats = df)
}
\end{lstlisting}

\item
  Source \texttt{utils.R} to make \texttt{simulate\_data} visible to the global environment.
\end{enumerate}

\subsection{Model implementation}
\begin{enumerate}
\item
  Copy the files implementing the Normal, Poisson and Negative Binomial models
  to the \texttt{stan/} directory (\texttt{normal.stan}, \texttt{poissonln.stan} and \texttt{negbinom.stan},
  respectively; see end of chapter for code listings)\footnote{
    The \texttt{stan} code presented here is divided in a number of blocks. The \texttt{data\{\ldots \}}
    block is read exactly once at the beginning of the inference procedure, and it declares required model
    data. The \texttt{transformed data\{\ldots \}} block in \texttt{negbinom.stan} and \texttt{poissonln.stan}
    defines transformations
    of previously declared data, and it is also executed only once at initialisation. The
    \texttt{parameters\{\ldots \}} block declares model parameters, an unconstrained version of which
    will be the target of inference procedures. The
    \texttt{transformed parameters\{\ldots \}} block defines variables in terms of previously defined
    parameters. All variables that appear in these last two blocks will be returned at the output after
    inference completes. Finally,
    the \texttt{model\{\ldots \}} block is where the joint log probability function is defined, using
    a syntax very close to the actual mathematical notation. Sampling statements (priors) for all variables
    in the \texttt{parameters\{\ldots \}} block must appear here, otherwise a uniform, possibly unconstrained, prior
    is assumed.
  }.
\item
  Compile the models by adding the following lines in \texttt{analysis.R} and executing them\footnote{
    The argument \texttt{auto\_write=T} ensures that unecessary re-compilations of \texttt{stan} code
    are avoided. For this to work, the variables \texttt{negbinom}, \texttt{poissonln} and \texttt{normal}
    must reside in the global environment, as in this code listing.
  }:
\begin{lstlisting}
normal =
  file.path('stan', 'normal.stan')
  rstan::stan_model(auto_write = T)
poissonln =
  file.path('stan', 'poissonln.stan')
  rstan::stan_model(auto_write = T)
negbinom =
  file.path('stan', 'negbinom.stan')
  rstan::stan_model(auto_write = T)
\end{lstlisting}
\end{enumerate}

\subsection{Model testing}
\begin{enumerate}
\item
  Create an auxilliary function in \texttt{utils.R} for fitting a compiled model and extracting the estimated
  regression coefficients \(B\)\footnote{
    \texttt{Stan} provides three different inference methodologies\cite{StanManual}: (a) Full Bayesian inference using a self-adjusting Hamiltonian
    Monte Carlo approach, (b) approximate Bayesian inference using Automatic Differentiation Variational Inference (ADVI), and (c)
    point parameter estimates by maximizing the joint posterior. Here, for reasons of computational efficiency, we use the third
    approach (through function \texttt{rstan::optimizing}), which however does not provide any measure of uncertainty of the estimates.
    In principle, such estimates can be derived at a second stage by approximating locally the posterior distribution using the
    Laplace approximation. \texttt{rstan::optimizing} takes as input a compiled model and input data in the form of a list of variables,
    as they appear in the \texttt{data\{\ldots \}} block of the model definition (variables not in this block are ignored). Function
    \texttt{fit\_model} returns the matrices of true and estimated coefficients in vector format, and a vector of the corresponding
    indices.

  }:
\begin{lstlisting}
fit_model = function(data, model, fcts = calculate_norm_factors(data$Z), ...) {
  Z_tilde = sweep(data$Z, 2, fcts, '/')          # normalise count data
  Y = log(Z_tilde + 1)                           # transform normalised count data
  X0 = scale(data$X, center = T, scale = T)      # standardise genotypes

  # MAP estimation
  fit = rstan::optimizing(object = model,
                          data = list(Z = data$Z,
                                      Y = Y,
                                      X = X0,
                                      c = fcts,
                                      s = colSums(data$Z),
                                      N = nrow(Y),
                                      M = ncol(Y),
                                      K = ncol(X0)),
                          seed = 42,
                          ...)

  # extract estimated matrix of regression coefficients B in vector format
  tibble(EST =
           fit
           pluck('par')
           enframe()
           filter(str_detect(name, '^B\\['))
           pull(value),
         TRU = as.vector(data$B),
         IDX = 1:length(TRU))
}
\end{lstlisting}

\item
  Source \texttt{utils.R} to make \texttt{fit\_model} visible to the global environment.

\item
  Make the function \texttt{cpp\_object\_initializer} visible to the global environment by adding
  the following line to \texttt{analysis.R} and executing it\footnote{
    This is necessary for successfully calling \texttt{rstan::optimizing}. Alternatively, we could have
    imported the whole of \texttt{rstan} using \texttt{library(rstan)}, and called \texttt{optimizing}
    directly (i.e.~without the \texttt{::} operator).
  }:
\begin{lstlisting}
cpp_object_initializer = rstan::cpp_object_initializer
\end{lstlisting}

\item
  Set the initial seed of \texttt{R}'s random number generator by adding
  the following line in \texttt{analysis.R} and executing it\footnote{
    This code facilitates reproducibility, since for the same seed,
    the same sequence of pseudo-random numbers is generated.
  }:
\begin{lstlisting}
set.seed(42)
\end{lstlisting}

\item
  Test all three models on simulated data of various sizes (312, 625, 1250, 2500) by adding the
  following lines in \texttt{analysis.R} and executing them\footnote{
    This code chunk includes two nested loops: the outer loop iterates over different sample sizes, while the inner
    loop iterates over models, and it is executed in parallel. At each iteration of the outer loop, a dataset of appropriate
    size is simulated and then passed to each tested model in the inner loop.
  }:
\begin{lstlisting}
fitted_models =
  plyr::ldply(lst(312, 625, 1250, 2500), function(N) {
    sim = simulate_data(obs_counts_stats, obs_geno, nsamples = N)
    plyr::ldply(lst(normal, poissonln, negbinom), function(mdl) {
      fit_model(sim, mdl)
    }, .parallel = T, .id = 'MODEL')
  }, .progress = 'text', .id = 'NSAMPLES')
  as_tibble()
\end{lstlisting}

\item
  Create a function in \texttt{viz.R} for visualising the results\footnote{
    This function takes a \texttt{thr} argument: estimated coefficient values below this threshold
    are considered effectively 0 and set to NA (line 4).\ When plotting, NA values will be dropped
    with a warning.
  }:
\begin{lstlisting}
plot_fitted_models = function(df, thr = 0.1 * max(abs(df$EST))) {
  df
    mutate(TRU = na_if(TRU, 0),
           EST = if_else(abs(EST) < thr, NA_real_, EST))
    ggplot() +
    geom_ribbon(aes(x = IDX, ymin = -thr, ymax = thr), fill = 'grey85') +
    geom_hline(yintercept = 0, linetype = 'dashed', size = 0.2) +
    geom_point(aes(x = IDX, y = TRU), color = 'red') +
    geom_point(aes(x = IDX, y = EST), size = 0.5) +
    facet_grid(NSAMPLES~MODEL) +
    scale_x_continuous(expand = c(0.02, 0.02)) +
    scale_y_continuous(expand = c(0.02, 0.02)) +
    labs(x = 'index of regression coefficients (genes x variants)',
         y = 'value of regression coefficients')
}
\end{lstlisting}

\item
  Source \texttt{viz.R} to make \texttt{plot\_fitted\_models.R} visible to the global environment.

\item
  Add the following line in \texttt{analysis.R} and execute it to visualise the estimated coefficients
  of the fitted models (Figure~\ref{fig:fitted_models}):
\begin{lstlisting}
plot_fitted_models(fitted_models)
\end{lstlisting}
\end{enumerate}

\begin{figure}[ht]
  \centering
  \includegraphics[width=0.9\textwidth]{figs/fitted_models}.
  \caption{\label{fig:fitted_models} True (red dots) and estimated (small black dots)
    regression coefficients for the Normal, Poisson-LogNormal and Negative Binomial models at
    four different sample sizes. Regression coefficients within the gray band around zero are
    considered of negligible size and they are omitted from the plot.}
\end{figure}

\section{Conclusion}

Although it is not prudent to make general statements based on a single set of simulations, we
may observe from Figure~\ref{fig:fitted_models} that all three models perform similarly.
At small samples (N=312), the Normal, Poisson-LogNormal and Negative Binomial models
correctly identify 7, 8 and 7 eQTLs, respectively, but the Negative Binomial model throws in addition
a false positive. None of these models estimates sufficiently well the size of the eQTLs they
identify correctly. At higher samples, all models estimate sufficiently well the true eQTLs in the data,
although they do not avoid the ocassional false positive (see Normal and Poisson-LogNormal models at N=1250
and the Negative Binomial model at N=2500). The Normal model is the fastest to estimate at all sample sizes,
followed by the Poisson-LogNormal model. Proper parameterisation can greatly facilitate inference in
a hierarchical model, and reduce its associated computational cost (see the Stan User's Guide for
 more information).

Subsequently, we may wish to experiment with additional sparse-inducing priors, and/or
more systematic and extensive benchmarks, which would require implementing a
custom inference algorithm for increased performance. This initial analysis using
a fully automated inference system provides a first assessment of the efficiency of each model,
and it can serve as a baseline for the subsequent development of novel statistical methods.

\section{Acknowledgments}

This work was supported by the National Institute for Health Research (NIHR) Oxford
Biomedical Research Centre Program. The views expressed in this manuscript are not
necessarily those of the Department of Health. We also acknowledge the Wellcome Trust
Centre for Human Genetics Wellcome Trust Core Award Grant Number 090532/Z/09/Z. The
funders of the study had no role in the study design, data collection, data analysis,
data interpretation, or writing of the paper.

\theendnotes{}

redpajama_set_name: RedPajamaArXiv


text:
From a Sister Organization
W.E.M.A.D
We are Energized to March, Act, Demonstrate
Newsletter #7 June 7, 2017
Calendar Updates….
Our Immigrant Community:
Education about Rights and
St. Alban's Church
301 Caldwell Ln, Davidson, NC 28036
with Police Reps from Davidson, Huntersville, Mooresville and Cornelius
(full write-up below)
The Equality March Charlotte and Worldwide,
12 pm-2:00 pm.
First Ward Park.
The Equality March for Unity and Pride is a global movement that will be brought to Charlotte, NC- the epicenter of HB2. We will use the Equality March- Charlotte, along with our National and Sister marches, to give voice to our concerns, and to support, uplift, and bring attention to those in our communities who are targeted due to immigration status, ethnicity, religion, skin color, gender, sexual orientation, age and disability. We affirm and celebrate that we are a mix of diverse communities. Family friendly march through uptown Charlotte.
All marchers should meet at First
Ward Park just before noon.
Speaker at Progressives of North Carolina at the Pearl Restaurant
19501 W. Catawba Avenue in Cornelius.
A speaker from the Climate Reality Project. Dinner ($15) begins at 6:30 pm but if you just want to come for the talk, be there at 7:00 p.m. For dinner, call Michelle at 704-608-1329 to reserve a space. Make sure to tell her it is for the June 13th meeting!
Jon Ossoff's Georgia District 6 Run-Off Race
Women's March Charlotte Summer Social 5pm – 8pm
Immigration Education Rights Night Details…… (thanks to Candy Gipe and Barbara Randolph)
After many hours of work, meetings, coordination, discussion and collaboration, we are pleased to come one step closer to helping our immigrant community in a tangible way, by helping those who HELP them.
On Monday, June 12, we are gathering representatives from area agencies that serve the immigrant communities of Mooresville, Davidson, Cornelius and Huntersville with representation of the four police departments to review immigrant rights . Thank you to St. Alban's for allowing us to use their space for this important meeting.
6:30 pm Start Time
• Welcoming Remarks by a Deacon Rebecca Yarbrough of St. Alban's Church
• Presubmitted questions answered by officers of each of the 4 precincts– Davidson, Mooresville, Cornelius and Huntersville
• Additional Questions from Audience answered by Police Reps
• Wrap Up and Next Steps
8:00 pm Closing
If you can help at this meeting, we would love to have you. Do keep us in your thoughts and intentions for a successful outcome to this important endeavor.
We'll keep you posted!
Candy (candaceassociates@att.net)
Barbara (brandolphk@gmail.com)
NC SB 145 Immigration meets the "Bathroom Bill…." (forward by Candy Gipe and thanks to Barbara Randolph)
In getting ready for our Immigration Education About Rights & Law Enforcement Night , I came across an active bill in the NC house (SB145) that would again make NC famous for all the wrong reasons. My co-chair, Barbara Randolph, dived "right in" and after doing more research, just felt she had to reach out to someone with a greater bully pulpit than our own: Rachel Maddow.
Needless to say, this is still an important bill to mention to your representative (Jeff Tarte), the sponsors (#s below) and how you feel it will not only hurt immigrants but hurt the State of NC. BTW, Jeff Tarte said at his town hall (last wemad newsletter edition writeup) that "There is a 'correct way' to enter this country" and thus, he is all for SB 145 because it punishes those who did not use the 'correct' way….
To read the whole bill or any nc bill, check out ncleg.net
To check the status of any bill, check out openstates.org
Primary: Norman W. Sanderson, District 2, 919-733-5706 Cosponsors: David L. Curtis, District 44, 919-715-3038 Andrew C. Brock, District 34, 919-715-0690 Chuck Edwards, District 48, 919-733-5745 Bill Cook, District 1, 919-715=8293 Ralph Hise, District 47, 919-733-3460 And our Rep, Jeff Tarte, District 41, 919-715-3050
Here is Barbara's email to Rachel Maddow.
From: Barbara Randolph
Date: 2017-05-20 15:11 GMT-04:00
Subject: House Bill 145 of NC targeting immigrants
To: Rachel@msnbc.com
Dear Rachel, I am a true fan of your show! I reach out to you however for another reason. I reside in North Carolina, having relocated here recently after living outside of the US for many years. I co-chair an Immigration Committee that formed from the movement which began after the Women´s March on January 21st. Our committee, comprised of all voluntary members, has been working on matters related to immigration in our area since that time. We organized specifically to fight the Executive Order regarding Deportation, signed January 25th by Donald Trump, as well as advocate for the immigrant community with local law enforcement in regards to policies and practices on this matter. We recognize the need for balance between federal and local interests in ways that do not diminish public safety but we are concerned about overreach and abuse of civil liberties. We are proponents for their rights to seek basic services available to them including education, medical services, and vocation, which in today´s environment are on the decline as they recess further to the shadows.
The immigrant community is now more vulnerable to exploitation, including wage theft, sex trafficking, sexual assault, and this trend is something you are aware of and have spoken about in your programming. We are grateful you continue to cast a spotlight on these realities. To the point of my email; Once again NC is on its way to making another name for itself as a "regressive state", introducing punitive bills that isolate and target a specific population. You are aware, I am sure, of NC´s famous "Bathroom Bill" which targeted specifically the transgender population National attention to this matter was the only motivator, along with loss of sponsors for highly profitable events, that has caused the needle to move somewhat to reconsideration of this senseless and unnecessary bill regarding transgender rights. I am requesting consideration to casting a spotlight on the following; A bill called Senate Bill 145 has passed the State Senate and is now on its way for approval within the House, in North Carolina — a bill that is one of the most restrictive against immigration in the US to date. The bill´s provisions can be seen in this link – http://www.ncga.state.nc.us/Sessions/2017/Bills/Senate/HTML/S145v1.html
1. The entire NC state university system will be restricted from limiting federal immigration enforcement on its campus and funding from the state will be cut off if they do so.They will not be able to stop law enforcement officers from gathering information regarding a student´s status, or prohibit information from being passed on to federal agents. In addition, violation of the law will lead to constraints on the system´s spending authority.
2. The State Police force must now agree to an MOU re 287(g) which makes all state police officers collaborators and enforcers along with ICE.
3. An anonymous complaint process for any citizen will be opened allowing for reporting to the Attorney General´s Office a suspicion that a local government is not following immigration laws.
4. It repeals a current law that allows community or Faith based IDs as a form of identification, such identifications are customarily needed to receive many forms of aid and food relief.
This bill is, to put it bluntly, mean spirited. This does indeed put NC into regression. Over the last decade, the national trend has gone in the other direction, with states introducing and passing legislation that allows undocumented immigrants to attend public colleges and even be eligible for in-state tuition. I can say that even the state of Texas has done this! By barring access to the NC state university system we are reducing the number of capable qualified people that could earn degrees, contribute to the economy as opposed to becoming reliant upon it, and help build our state. State officials across the country say that in order to be competitive, they need all the education young people – documented or undocumented — can get. By 2020, 65% of jobs in the US will require some sort of postsecondary degree or training beyond high school, according to the Georgetown Public Policy Institute.
We began our activism knowing our voice would not have an effect at the national level but addressing disconcerting trends and the impact locally of President Trump´s agenda was where we needed to focus our efforts and energy. This bill is noteworthy in the length it goes to eradicating immigration altogether and further isolates the existing community, eliminating their hope to survive the new measures the Republican dominated NC General Assembly is close to making law. Can I implore upon you to take a look at this bill? I am grateful and recognize you are terribly busy!
My warmest regards, Barbara
Barbara Randolph Senior Advisor,
Fundación México en Harvard, A.C. Davidson, North Carolina
Barbara Randolph, passionate about immigration issues!
A PRACTICAL WAY TO HELP…….
(Barbara Randolph and I have networked into other organizations that are embracing the Immigrant Rights fight and here is a very practical way you can help the community. Court Observers will in no way be asked to do anything except be present and do a small write-up on what transpired during the court session)
The ACLU's Charlotte chapter is looking for volunteers to serve as local court observers in Charlotte's Immigration Court. In a jurisdiction with some of the lowest asylum grant rates in the nation, the presence of volunteers would foster fairness and consistency in the courtroom. Volunteers will:
• Receive hands-on training about immigration court proceedings
• Experience our justice system in action
• Gain perspective on the needs of our local immigrant communities
• Help immigrants in the Charlotte and surrounding area have a more consistent court experience.
Observers provide much-needed oversight in cases involving unrepresented respondents, particularly minors and non-English speakers. Make a difference in your community: the first training for people interested in this project is this Friday, June 9th at 5:30pm. Go to https://goo.gl/forms/owXUtU644Qmerzq43 now to register for Friday's training and/or to receive information about subsequent training events and times.
Please note that due to space limitations, Friday's training will be capped at the first 35 people to sign up
Sincerely, Lisa Wielunski Team Leader ACLU Charlotte Immigrant Rights Team
Indivisible LKN Meeting June 3, 2017 (thanks to Ann Mead)
( I know this is a special edition on Immigration but there is so much going on and this happened on Saturday. Our next edition will focus on gerrymandering. This is a reprint of the notes Ann Mead sent to her Gerrymandering Huddle)
"Where have all the representatives gone, possible summer dreaming? Well whatever it was they (Jeff Tarte, Chaz Beasley, John Bradford, Tedd Budd, Richard Burr, John Fraley, Thom Tillis, ) were a NO-SHOW. Yes, our representatives who live just a few miles away, did not show-up or even have the courtesy to respond to Lake Norman's Indivisible's invitation.
*** Now, Where I come from that is just not right….
But we did have one Member from our State Legislature Show-Up. It was Jeff Jackson!!! Link to Jeff's website: http://www.jeffjacksonnc.com/
Yes, Jeff drove up all the way from Charlotte, through congested 77 construction, on a hot Saturday afternoon to meet with the Lake Norman Indivisible Group, and he was happy to do so. What a truly dedicated public representative, "This is what Democracy Sounds & Looks Like"! Jeff Jackson, (unlike Jeff Tarte who showed up at a recent town hall with a 48 page power point presentation, planning on answering as few questions as possible), was engaging, genuine, and refreshingly honest. Jeff even stayed longer than expected to make sure everyone's questions were addressed.
Jeff, described for us what it's like working in our state capital, the struggles, the challenges and the rewards. He encouraged us not to lose hope, stay united and engaged. If we want a representative government that works to enrich the lives of "All the People", from health care to education to environment to social programs, we are going to have to work together to make it happen. And believe it or not, achieving our goals are within reach and closer then they may seem Meeting Q&A Highlights (and Yes…nothing was off the table):
• Redistricting: Jeff updated us on recent progress, House Bill 200 and upcoming events. We are looking to the Supreme Court to unblock re-drawing of 28 districts in NC, however the plan to hold elections in 2017, does not look promising. He is a link to some additional information. https://ballotpedia.org/North_Carolina_state_legislative_special_elections,_2017
• Game Plan: We discussed 2018 elections and learned about the concept of having a "full-bench" of representatives that learn and grow through our democratic process. Basically, an elected official, that align to our progressive agenda, representing us from the school board level all the way up through NC Senator in Washington. The GOP is very good at this.
• Fake News/Phony Politicians: We discussed Fake News and phony politicians, I believe Ted Cruz was mentioned as an example. And I don't mind calling him out since he was the ring leader in the government shut-down in 2013 that cost me a trip to the National Parks.
• The Work: Jeff was honest with us. Breaking the GOP super majority in 2018 is very doable – 3 house seats and 6 senate, but we have to get involved. Yes, this means phone calls, knocking on doors, mailings etc… Which really sounds like "No Fun" but with a good strategic plan (leveraging our limited resource pool) and quality voter data, flipping 9 seats is doable.
• The Country is Awake: Jeff said he has never seen anything like the energy and engagement at the grass roots level. He attended the Women's March, shaking hands and meeting people who want to learn more about running for office. GOP candidates running unopposed in 2018 will hopefully be a thing of the past.
• Bang-for-the-Buck: Jeff recommended donating to the State Senate and House Democratic Caucus.
Link to Caucus – http://www.ncsenate.org/
After Jeff's Q&A, as you can imagine the group was eager to get started. We broke out into groups by area of interest. We got to know each other, shared information, discussed possible direction and next steps. Speaking of Next Steps: WEMAD and Lake Norman Indivisible will be joining forces to host a Community Information and Engagement Event on "Gerrymandering"/ "Redistricting" sometime in July…stay tuned….
• I had a fantastic meeting with Tom Ross, who has been heavily involved in working with an independent, nonpartisan redistricting panel working on a special task force program to research and simulate independent redistricting. The panel consisted of current/former NC judges and Duke University Research students. The goal of the program is to develop new maps for NC congressional districts and increase public understanding of how independent political redistricting might function in North Carolina. Tom also developed a TED Video on this topic that I will be sharing the group in a future newsletter. See Link for details:
https://today.duke.edu/2016/04 /redistrictingsimulation .
Websites for Taking Action–100 Days and Beyond …….
https://www.thenation.com/article/your-guide-to-the-sprawling-new-anti-trump-resistance-movement/
https://www.indivisibleguide.com/resourceshttps://knockeverydoor.org/
https://swingleft.org/https://www.runforsomething.net/
https://www.pussyhatproject.com/
http://jointhemovementtoday.weebly.com/
http://peoplespowerassemblies.org/
If you are a Facebook user, operationfortyfive has a great page with tons of info on events and actions.
If you want an app for your device, countable has a thread and reminders of bills and actions that matter to your specific state and county.
Gender Equality, Gerrymandering, Health Care, Immigration, LGBTQ

redpajama_set_name: RedPajamaCommonCrawl


This is a clear building lot in the heart of Cherry Hill. This lot is free standing and requires NO sub-division. Its location is amazing close to everything and a rare find in Locust Woods Cherry Hill. Also available with the adjoining property 915 Fulton creating a 183 x 183 lot listed for 229,900. Property can also be packaged with 905 Fulton which is 113 x 183 blending all 3 sites together would create the opportunity to develop a lot that is 296 x 183.


text:
Sgt. Gregory Ames honored at 4th annual softball tournament
Mina Corpuz The Enterprise mlcorpuz
The fourth Sgt. Gregory Ames Memorial Scholarship softball tournament brought together family, friends and community members to play in his honor Sunday.
WEST BRIDGEWATER — The fourth Sgt. Gregory Ames Memorial Scholarship softball tournament brought together family, friends and community members to play in his honor Sunday.
"Greg was a much loved member of the community and he loved the community back," said Lisa Ames, the wife of Greg's older brother, Jon.
Fourteen teams with about 200 players spent the day at the West Bridgewater Youth Athletic Association complex.
The town's fire and police departments faced off during the tournaments.
Students from West Bridgewater Middle-Senior High School including Ames's son, family who are also alumni, and graduates from the Class of 1987 -- Ames's class -- had teams in the tournament, Jon said.
The event, which started out as a way for family to get together, has grown over the years, he said.
Ames, who was known to have a competitive spirit, died in 2013 at the age of 43.
He played varsity baseball and football while a student at the middle-senior high school, according to his obituary.
Ames followed in his father's footsteps by joining the Police Department and was promoted to sergeant in 2003.
The tournament raised money for the scholarship named in his honor that is awarded annually to two or three graduating seniors from the middle-senior high school.
Scholarship recipients are chosen based on their community service and how they serve the town, Jon said.
Staff writer Mina Corpuz can be reached at mcorpuz@enterprisenews.com. Follow her on Twitter @mlcorpuz


text:
Q: What does <$> mean in Haskell? While reading a piece of Haskell code I came upon this: <$>. What does it mean in Haskell? After some google searches I remain in the dark.

A: Google is not the best search engine for Haskell. Try Hoogle or Hayoo†, both will point you right away to this:
(<$>) :: Functor f => (a->b) -> f a -> f b

It's merely an infix synonym for fmap, so you can write e.g.
Prelude> (*2) <$> [1..3]
[2,4,6]
Prelude> show <$> Just 11
Just "11"

Like most infix functions, it is not built-in syntax, just a function definition. But functors are such a fundamental tool that <$> is found pretty much everywhere.

†Hayoo has been offline for quite a while now.

redpajama_set_name: RedPajamaStackExchange


Tomorrow I board an Amtrak train bound for beautiful Back Bay Boston, where I'll have the honor and pleasure of delivering a keynote address for Web Design World. I have a long-standing fondness for this conference and especially for its content director, Jim Heid. I've learned a great deal over the years, watching the way Jim understands his audience, educates his speakers to deliver what his audience needs, and structures multiple days of content into an enjoyable learning experience. He's also a heckuva nice guy.


text:
Wanderlei Silva Wants To Get Revenge On A Few Fighters
David Tees May 8, 2017 7:30AM
Wanderlei Silva will be returning to action for the first time in a four years when he headlines Bellator 180 against longtime rival Chael Sonnen.
Once his battle with Sonnen comes to an end, the Brazilian slugger has a few other fighters he wants to get revenge on.
"I would like a rematch against Vitor Belfort, Quinton Jackson, and Dan Henderson. There's a lot of classic guys that would make a cool fight against me. That's because there's too many fights going on these days, but no classics where everyone stops to watch them. A cool rivalry, with good marketing. There's been some good fights, obviously, but classics? With big name guys? It just doesn't happen anymore. They're all retiring, quitting. We need to bring back those guys so that through those classic fights, we can promote new names and make them become legends and fighting classic fights in the future like us," Silva said to Fox Sports.
Of the three names on the list, the only one Silva has defeated before is Quinton Jackson, whom Silva defeated twice in PRIDE.
Silva will be making his Bellator MMA debut at Bellator 180 and the fighter is debuting with a company that he has high hopes for.
"Bellator is growing a lot. They have a much bigger number of shows now. They're really starting to build their champions, so kudos to them. The company growing non-stop, no doubt about it. Nowadays, Bellator is the number two company, but on June 24, everyone's saying it'll be number one. That's the day we'll beat the UFC, who cancelled their event and changed it to a different day out of respect for us. They're afraid of this show we're putting on, that will be huge, no doubt about it," says Silva.
Bellator 180 takes place on Saturday, June 24 from Madison Square Garden in New York, New York with Chael Sonnen and Wanderlei Silva headlining. Fightful is providing live coverage of the event, with a post-show podcast to follow.


The frequency and magnitude of extreme weather events such as drought are expected to increase in the future. At present, plant responses to recurrent extreme events have been sparsely examined and the role of stress history on subsequent stress response has been widely neglected. In a long-term field experiment, we investigated the response of grassland and heath communities to a very severe drought event, which exceeded the duration of projected drought scenarios. During the preceding 6 years, the plant communities experienced scenarios of varying water supply, including annually recurring drought, heavy rain, regular watering, and natural drought periods. Single species and plant communities that were regularly watered in the preceding years revealed the highest tissue die-back under a very severe drought when compared to plants that experienced mild or severe drought stress before. Contrary to expectations, the root to shoot ratio did not increase due to previous recurrent drought occurrences. Furthermore, pre-exposure effects on Vaccinium myrtillus and Plantago lanceolata tissue die-back and reproductive biomass (P. lanceolata) were altered by community composition. Recurrent mild drought stress seems to improve drought resistance of plant communities and species. Potential reasons could be epigenetic changes or soil biotic legacies. Morphological legacies such as altered root to shoot ratio did not play a role in our study. Imprinting events which trigger this ecological stress memory do not have to be extreme themselves. Thresholds, longevity of effects, and the role of biodiversity shown by the importance of community composition require further attention.
SB performed the experiment, analyzed the data, and wrote the manuscript. JK, CB, JW, and AJ conceived and designed the experiment. KG performed the abiotic data collection. JK, KG, CB, JW, and AJ revised the manuscript.
The online version of this article (doi: 10.1007/s10021-014-9781-5) contains supplementary material, which is available to authorized users.
This study was funded by the Bavarian State Ministry of the Environment and Public Health (ZKL01Abt7 18456). We thank Jordan Vani for language editing, Elke Koenig, Stefan Koenig, Christine Pilsl, and numerous student workers and interns for their outstanding help during the field work.
R Development Core Team. 2011. R: a language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing. ISBN3-900051-07-0. http://www.r-project.org.


The following three (3) Local Councils are participating in Healthy Neighborhoods: Wilmington/Claymont, Kent/Smyrna, and Sussex County Health Coalition (SCHC). The Wilmington/Claymont has two task forces called Maternal and Child Health AND Behavioral Health. Kent/Smyrna Council has two task forces called Behavioral health and Lifestyles/Chronic Disease. Sussex County Health Coalition has two task forces called Behavioral health and Lifestyles/Chronic Disease.
"Play Streets" is an initiative that was the result of research conducted by the Delaware Center of Health Innovation's Healthy Neighborhoods Healthy Lifestyles and Chronic Disease Task Force. Their objective was to learn about activities or events that could help stimulate physical activity and potentially lower obesity rates in areas where there were limited opportunities to get out and get active. Play Streets is launching in Wilmington this summer, thanks to funding by Christiana Care Health System, under the Wilmington Parks and Recreation umbrella. Beginning in early June, a total of 32 Play Streets neighborhood events will take place throughout the summer in eight different neighborhoods. Rysheema Dixon, of RD Innovative Planning, is the consultant working with Christiana Care to manage the events. She gave a bit of the history: "Play Streets is an activity modeled after others that have been successful in Philadelphia, Baton Route, New York, and Chicago." She explained that the goal is to "Increase physical activity among children but also create a safe environment for kids to play and the community to get together." The blocks in Wilmington were chosen because of need — those that had been hit by crime or violence.
The Wilmington/Claymont Behavioral Health Task Force is developing an internship program for Peer Specialists— individuals who have lived an experience with mental illness, addiction, co-occurring conditions, and recovery. Peers who have been trained to help link individuals with behavioral health needs to appropriate services and supports, become certified in this profession. They hope to create more Certified Peer Specialists through the internship program and remove barriers that currently exist for those who would like to become certified.
A similar model is being used by the Behavioral Health Task Force to tackle domestic violence — referring victims to resources beyond those that are medical and clinical. The goal is to use the Peer Specialist and Community Health Worker model to connect victims of domestic violence with other services they may need for their children and themselves. Peer Specialists are also getting additional training to identify domestic violence victims in a different way.
The Healthy Lifestyles and Chronic Diseases Task Force is partnering with the National Council on Agricultural Life and Labor Research Fund Inc. (NCALL) to implement "Open Streets." By closing streets to cars, they become places where people of all ages, abilities, and backgrounds can improve their health, by using public streets for recreation and active living. The launch of Open Streets took place in Dover on Saturday, April 21, where nearly 200 people participated. NCALL is implementing six Open Streets events from April through October 2018. The next event is scheduled to take place on June 30th from 10am to 2pm in Dover. The idea for Open Streets was piloted successfully in Wilmington last year and the Task Force plans to expand these events to Smyrna starting in the Fall.
The Behavioral Health Task Force is partnering with Dover Interfaith Mission for Housing (DIMH) to implement an engagement initiative for homeless individuals using Community Health Workers (CHW) and lockers. Through this proven proposed initiative, DIMH will work to expand and enhance access to services and support for homeless individuals living with behavioral health conditions in our community, by offering access to a locker in exchange for engagement in a one-on-one consultation with the DIMH CHW, with the goal of developing a person-centered action plan. As a part of their role, DIMH's CHW will work to strengthen collaborations with community partners through behavioral health 101 training, as well as the development of a Resource Guide that will be made available to those who serve this population.
The Dover/Smyrna Behavioral Health Task Force is also working with several local providers to support access to care for individuals and families who are experiencing domestic violence through a Community Health Worker and High Risk Advocate, as well as implement a pilot project that will bring together first responders (e.g., police, hospitals, etc.) and treatment providers on behalf of individuals who are experiencing acute behavioral health crises and their families.
The Sussex County Health Coalition (SCHC) announced its timeline to launch a robust Drug Free Awareness Campaign — "Seaford Goes Purple" — at its Quarterly Meeting in April. The campaign will officially launch at two events in September: A community event on the 11th, followed by a school-based launch event on the 12th. The campaign is working in conjunction with the Chris Herren Project, an organization dedicated to substance abuse education and public awareness. Steered by the Drug Free Community Committee, the campaign hopes to join forces with Southern Maryland's existing awareness campaign for a regional, community-driven initiative. To maximize the impact of this campaign, funded in part by Highmark Blueprint for the community, SCHC is developing a proposal to implement expanded school-based prevention strategies, utilizing Botvin Life Skills Curriculum for students in the Seaford School District. Additional campaign initiatives will address how to help those in need, advocate for increased and better service arrays and support families devasted by this disease.
Additionally, SCHC is proposing a mental health access initiative, couched in an integrated care model, for youth with mental health needs. This project, modeled in Sussex County initially by a pilot group of school districts and initially funded through Highmark, will be expanded to greatly increase mental health access in schools and decrease wait time and barriers to care.


Parents who have young children with access to an iPad might be familiar with YouTube videos which show toys being unboxed or watching little ones engage in pretend play.
For everyone else, a seven-year-old boy is a prime example of why making these kinds of videos might just be a lucrative career change.
Ryan ToysReview has 17 million subscribers on YouTube and according to Forbes has topped a list of Youtube's highest earners.
Forbes estimates the young boy, said to be in grade one, and his family have raked in about $30million (US$22 million) from June 1 2017 to June 1 this year through the channel.
And it seems like a pretty simple concept.
"Ryan loves Toys. Toys Review for kids by a kid!" the main channel's description reads.
Ryan's main channel was launched in 2015 and in the past few years the videos have amassed close to 26 billion views.
Ryan ToysReview and a secondary page, Ryan's Family Review, make most of their money from pre-roll advertising, Forbes reported.
It is reported that the remainder of the money comes from posts which are sponsored.
The founder and CEO of Bottle Rocket Management, which represents some digital influencers, Chas Lacaillade explained to Forbes why unboxing videos are so popular.
"Unboxing videos provide the proxy for actually experiencing the joy of receiving and opening something you really desire; this is especially true for items that are out of reach or unattainable.
A toy and apparel collection named Ryan's World was also launched in August this year, which will contribute to earnings for the next financial year.


BRUCE PAUL studied creative writing at Ohio University. After graduating with honors in 1967, he traveled to San Francisco and lived in the Haight/Ashbury district. Bruce has been a singer-songwriter in a folk-rock band, an elementary school teacher in the Virgin Islands, a taxi driver in Denver, a social worker in a maximum-security prison, an investment advisor, a real estate developer, and a riverboat bartender. Bruce is the author of many songs and The Artama Legend series of books. He now lives (and writes) among the ponderosa pines in the mountains of central Arizona.
Best Short Stories, an anthology of Arizona writers, has been published, and I am pleased to write that a memoir of mine was included. "The River of Life" is a true, humorous account of my ill-fated, white-water introduction to kayaking.
I am also pleased to write that the lyrics of three of my songs have been included in the recently published Best Poetry (A collection by AZ Writers). These are lyrics from the days when I had a band.


Russian Jewish composer (name also spelled Veprik). Born in Balta, near Odessa, June 1899. Died, Moscow, 13 October 1958. Born in the Ukraine, but grew up in Poland. Studied piano at Warsaw Conservatory until age 10. Studied composition in Leipzig with Janacek and Reger between 1909-1914. Studied piano with Dubasov in St. Petersburg Conservatory. Studied composition with Zhitomirsky from 1917-1920. Became a teacher 1923. Very active in the founding of the Society for Jewish Folk Music. In mid 1920s, composed several Jewish works, including Songs and Dances of the Ghetto, Hebrew Songs for Orchestra, and Kaddish. In March 1933, "Dances and Songs of the Ghetto" was performed in Carnegie Hall, New York conducted by Toscanini. During the Stalin regime, Weprik was sent to a Gulag. He died a few years after release, in 1958. Website has brief biography and photo. The book Jüdische Musik in Sowjetrussland edited by Henny van de Groep, has a chapter on Weprik.


text:
La playa de Brazomar está situada en el municipio de Castro-Urdiales, en la Comunidad Autónoma de Cantabria, España.

Referencias
 Ministerio de Medio Ambiente, playas de Castro-Urdiales
 Cantabria Turística

Playas de Castro-Urdiales

redpajama_set_name: RedPajamaWikipedia


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And so it came to pass that Mrs May delivered her statement to the House of Commons on her adventure in Brussels.
But her statement and the subsequent debate did nothing to shed any light on our predicament as a nation. If anything, it further muddied the waters, providing testimony – to those who are capable of understanding it (which did not seem to include many in the House) - of their ignorance.
If we cut to the chase, the crucial issue was the customs union, making 23 guest appearances in the debate, with "customs" in further combinations adding nine more mentions of the word.
Oddly enough, the customs union first popped out, not from the lips of Mrs May but from the man with ambitions to replace her – Jeremy Corbyn. In his response to Mrs May's statement, he expressed his "disappointment" in reading Liam Fox's letter which he had penned earlier in the week, ruling out a customs union as the "worst of all possible worlds".
When Corbyn then averred that this seemed to be an attempt to scupper meaningful talks by all but ruling out Labour's customs union proposal, he positioned the customs union at the centre of the talks on which Mrs May was relying to get her Withdrawal Agreement through the Commons.
Mrs May did not answer this point directly, so it took Kenneth Clarke to suggest to her that, in order to reach a settlement with the "principal Opposition party" would require at the minimum the, "sort of customs arrangement and sufficient regulatory alignment at least to keep our trade as open and free as it has been across the channel and in the Republic of Ireland".
This time, the prime minister took the bait, responding that she was looking at the customs arrangement that "would be in place in that future relationship". This, she said, was reflected in the political declaration, that we want to retain the benefits of a customs union - no tariffs, no quotas and no rules of origin checks.
For Yvette Cooper (the very same), that wasn't good enough, so she goaded Mrs May by asking whether she was now willing to consider a common external tariff with the EU - which is a key part of any customs union – or did she still rule that out?
Yet it was the response to this that really started to get us somewhere. The different language used, the prime minister implied, tended to conceal that there was more agreement on the customs union than is often given credit for. We have been clear, she said, that we want to obtain the benefits of a customs union, while being able to operate our own independent trade policy.
The Labour party wanted a say in trade policy, she said, so the question was, "how we can provide for this country to be in charge of its trade policy in the future".
Moments later, she was to repeat this in a response to Conservative MP Andrew Murrison – the issue was about having the benefits of a customs union and an independent trade policy, "the freedom to make those trade deals around the rest of the world that we want to make as an independent country".
There we have it, except that the prime minister is setting up a horribly flawed paradigm in asserting that there is any immediate difference between her position and Labour's. For sure, she can have the benefits of a customs union – and specifically no rules of origin – without a customs union. All she needs is a no-tariff free trade agreement, and then unilaterally to adopt the EU's WTO tariff schedules.
Where the problem comes is with the independent trade policy. In terms of substance, if Mrs May wants to keep a tariff-free arrangement with the EU, and without rules of origin, then she must keep in place the EU's WTO schedules, which have the effect of a common external tariff. But that places no more – and no less – restrictions on formulating trade policy with other third countries.
Unless she is thinking of the common commercial policy – which would be the wrong thing to do - the fact of a customs union or its equivalent places no restrictions on formulating trade policies with other countries other than in limiting the scope to conclude tariff agreements.
But, since tariffs these days are only a very small part of what are now called comprehensive free trade agreements, the limitation is hardly of any significance. On that basis, there are no grounds to go to war with Labour, or refuse an agreement with it. If the freedom to make trade agreements is the only issue, there is nothing to argue about.
Sadly, the real issue is something else – the fact that, when it comes to securing frictionless trade, the customs union is an irrelevance. We have the political equivalent of two bald men fighting over a comb.
Stephen Kinnock raised this after a fashion. "A stand-alone customs union simply does not cut it", he said, adding: "In the options that will be presented to us if the talks do not work, can she guarantee that full membership of the Single Market through the European Economic Area will be on offer?"
Unfortunately, as he so often does, he confused the issue, by asserting that "full membership of the single market is the only way we can guarantee workers' rights and the integrity of the Union and do something for the services sector".
This gave Mrs May her "get out of jail free" card, as she pointed out that full membership of the single market was not necessary to deal with the City of London and its financial services, nor was it needed to ensure workers' rights.
You can see what Kinnock has done here. He has oversold CM2 to Labour in order to get his party's support, but in so doing opened himself to being slapped down by the prime minister. If only he had said that the EEA was a vital step towards securing frictionless trade, this little bit of history might have been different.
Looking at it another way, if all we wanted in life was no tariffs, no rules of origin checks and no quotas, then all Mrs May has to do is offer is a free trade agreement with the WTO schedules, and we are home and dry. In this sense, the two parties are as bad as each other.
But, for all that, we don't want a customs union because that does impose unnecessary restrictions on our long-term flexibility. This is the difference between the rigidity of a treaty and a unilateral commitment which can be altered in the future, with considerably less formality.
Yet, even then, this is down the line. As both Mrs May and Donald Tusk point out, there is already plenty of scope to deal with such matters in the political declaration. But since this doesn't kick in until the Withdrawal Agreement is ratified, there is nothing to gain by arguing the toss right now.
What happened in the Commons yesterday, therefore, was a complete waste of time, taking us no further forward. If we want to tie up frictionless trade, to the extent that we can dispense with the Irish backstop, then the way forward is to ratify the Withdrawal Agreement.
Then – undoubtedly with the aid of an extended transitional period – we need a comprehensive deal on the lines of a much-amended EEA Agreement, complete with its institutional architecture, and a bundle of bilateral agreements which take in such things as climate change, VAT, data protection, civil aviation, agriculture and fisheries, scientific cooperation – and much more besides.
Even with six months to go before we are due to face the cliff-edge again, we haven't got time to play these vacuous games, inside or outside of parliament. We need to get our priorities right.
And in this, we are not helped by the idiot pair, Nick Boles and Lucy Powell, writing in the Evening Standard that "it is not currently possible to maintain frictionless trade and avoid a hard border on the island of Ireland without the UK being in the same customs territory as the Republic of Ireland and the rest of the EU".
That claim is so manifestly untrue that one wonders how the same pair can, with straight faces, also promote their CM2 plan which would have us take up the Efta/EEA option – but without the vital provision of the multiple bilateral agreements that are also needed to secure frictionless trade.
Largely though, confusion reigns throughout. For Open Democracy, we have Ruth Bergan and Laura Bannister - another pair of self-appointed pundits – writing on customs unions. They seek to convince us that a customs union would still allow the UK to negotiate new trade deals covering large chunks of the economy.
This much they get right, but then spoil it all by asserting that the customs union "doesn't necessarily cover regulations and standards" – when it doesn't ever cover either.
They then compound the error by grandly declaring that the benefit of a customs union for the UK "would be to retain at least some degree of frictionless trade which goes some way to resolving issues at the Northern Ireland border" – when it would have no impact at all on securing frictionless trade. All you need to prove that is to go to the Turkish border and watch the lorry queues.
The sort of moronic interventions that we are getting here are entirely typical of this debate, constantly dragging us away from the essential priorities, and muddling the issues.
At this rate, six months down the line will have done nothing for us. Our politicians and pundits will still be prattling about the wrong things, with no end in sight. As the MPs disperse to the constituencies for their Easter break, they might as well stay there and not bother coming back.


text:
Marijuana Use in Past Month, by Level of School Connectedness
View Marijuana Use in Past Month by Grade Level by Gender and Grade Level by Parent Education Level by Race/Ethnicity by Sexual Orientation Choose a Different Indicator
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Level of School Connectedness High Medium Low
Number of Days 0 Days 1 Day 2 Days 3-9 Days 10-19 Days 20-30 Days
Select All Counties
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Amador County Office of Education
Butte County Office of Education
Calaveras County Office of Education
Colusa County Office of Education
Contra Costa County Office of Education
Del Norte County Office of Education
El Dorado County Office of Education
Fresno County Office of Education
Glenn County Office of Education
Humboldt County Office of Education
Imperial County Office of Education
Inyo County Office of Education
Kern County Office of Education
Kings County Office of Education
Lake County Office of Education
Los Angeles County Office of Education
Madera County Superintendent of Schools
Marin County Office of Education
Mariposa County Office of Education
Mendocino County Office of Education
Merced County Office of Education
Mono County Office of Education
Monterey County Office of Education
Napa County Office of Education
Nevada County Office of Education
Orange County Department of Education
Placer County Office Of Education
San Benito County Office of Education
San Diego County Office of Education
San Francisco County Office of Education
San Joaquin County Office of Education
Santa Barbara County Office of Education
Santa Clara County Office of Education
Santa Cruz County Office of Education
Shasta County Office of Education
Siskiyou County Office of Education
Solano County Office of Education
Sonoma County Office of Education
Stanislaus County Office of Education
Sutter County Office of Education
Tehama County Department of Education
Tulare County Office of Education
Tuolumne County Superintendent of Schools
Ventura County Office of Education
Yolo County Office of Education
Yuba County Office of Education
Definition: Estimated percentage of public school students in grades 7, 9, 11, and non-traditional programs who have used marijuana in the previous 30 days, by level of school connectedness and frequency (e.g., in 2017-2019, an estimated 5.4% of California students in grades 7, 9, 11, and non-traditional programs with low levels of school connectedness had used marijuana on 20-30 days in the previous month).
Footnote: Years presented comprise two school years (e.g., 2017-18 and 2018-19 school years are shown as 2017-2019). County- and state-level data are weighted estimates; school district-level data are unweighted. Levels of school connectedness are based on a scale created from responses to five questions about feeling safe, close to people, and a part of school, being happy at school, and about teachers treating students fairly. Students in non-traditional programs are those enrolled in community day schools or continuation education. The notation S refers to (a) data for school districts that have been suppressed because there were fewer than 10 respondents in that group, and (b) data for counties that have been suppressed because the sample was too small to be representative. N/A means that data are not available.


Industrial policy refers to organized government involvement in guiding the economy by encouraging investment in targeted industries. Such policy serves to allocate capital across manufacturing industries by a system of taxes, subsidies, and investment incentives designed to move the economy along a specific pathway. Although an industrial policy was in place since the 1950s in many developed countries including Japan, Germany, France, and Sweden, it continued to be hotly debated in the United States toward the end of the twentieth century.
Japan's successful industrial policy drew a great deal of attention. In Japan the Ministry of International Trade and Industry (MITI) orchestrated directed development of selected industries and products which it deemed necessary for Japan to compete in the international market. MITI only assisted the private sector in targeted industries. It generally financed no more than 50 percent of a project, leaving the rest to the private sector and market influences. The MITI is not autonomous but is overseen by various government agencies. Japan's automobile industry served as a highly successful model where a specific industry was targeted to assume an expanded role in world markets.
Advocates for an industrial policy in the United States pointed to four major issues. First, since the law of supply and demand in world markets is greatly skewed by various measures employed by nations to help particular industries, the U.S. needed to do likewise to compete. Second, an industrial policy could help ease the social costs when industries lost their competitive advantage by assisting in retraining workers for new jobs. Third, industrial policy could ease "boom and bust" cycles by planning tax incentives to stabilize regions. Finally, the United States already protected certain industries and special interest groups but in a haphazard manner with no organized social goals; an organized program was believed to be more effective.
Opponents cited the market as the most efficient allocation of resources, insisting the government could not do a better job. Interfering in the market process was generally less accepted in the United States than in other countries.
"Industrial Policy." Gale Encyclopedia of U.S. Economic History. . Encyclopedia.com. 12 Apr. 2019 <https://www.encyclopedia.com>.
industrial policy, government-sponsored economic program in which the public and private sectors coordinate their efforts to develop new technologies and industries. Government provides the financial support and capital to the private sector by direct subsidies, tax credits, or government-run developmental banks. Industrial policy emphasizes cooperation between government, banks, private enterprise, and employees to strengthen the national economy. In the 1980s and 90s, industrial policies enjoyed some success in Germany and Japan. In the United States, such direct government involvement in business planning has traditionally been viewed with suspicion. However, the Advanced Technology Program, begun in 1990, has been instrumental in the research and development of promising, high-risk technologies. U.S. government subsidies can also be found in such areas as defense, energy, transportation, and home construction.
See R. Reich, The Work of Nations (1991).
"industrial policy." The Columbia Encyclopedia, 6th ed.. . Encyclopedia.com. 12 Apr. 2019 <https://www.encyclopedia.com>.


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Aiming at expanding market to cities and provinces in Lai Chau on agricultural products for export. Our current market of agricultural products for export includes: Pepercorns, Vietnamese chillies, Indian chillies, Betel nuts, Dried betel nuts, Cinnamon, Illicium verum, Garlic, Onion, etc.
Besides, we have Bank Guarantee service, Export-Import Transportation Guarantee to limit potential risks during trading AGRICULTURAL PRODUCTS FOR EXPORT.
We provide enterprises with the newest and most accurate export-import news of importers and exporters about buying and selling demands so that they are able to promote products for export and import on their own.
- Should the enterprise need a consulting expert from KHANH VUA who are of international standards of training to give advice and follow closely transactions. Using export skills and experience, giving advice on strengths and weaknesses of enterprises, giving orientation on customer service, especially prodiving export-import services such as consulting about export billing, quoting foreign customers, welcoming customers in Vietnam, consulting about air-way transport (express service with trial delivery), consulting about Logistics, international payment to ensure customers' money is received in the fastest way.