text:
\section{Introduction}

Meta-analysis is a common approach used to combine quantitative evidence across studies to generate a global exposure/treatment effect that is more precise than estimates obtainable from individual studies alone.  Traditionally, this has been achieved by obtaining summary statistics from published studies and then combining those estimates using methods of meta-analyses. Although it is cost effective and easy to implement, meta-analyses tend to be underpowered and prone to ecological and confounding bias \citep{doi:10.1002/sim.2768, doi:10.1002/jrsm.4}. Individual patient data (IPD) meta-analysis provides a potential solution to avoid such biases, along with modeling flexibilities such as accounting for the correlation between endpoints and dealing with missing data at the patient level \citep{Riley2010MetaanalysisOI}. If one has access to the original patient level data, there may be a choice between a fully specified multivariate IPD  and a two-stage IPD approach. The full multivariate approach generally uses mixed-effects multilevel regressions to model between and within heterogeneity and quantify the effect of interest in a single model. Although this approach is considered flexible, it may be challenging for conducting and communicating the findings, especially regarding visualization with the hallmark forest plot. The alternative IPD approach involves modeling the data in two stages. In the first step, study-level estimates are obtained using separate regression models. In the second step, standard methods of meta-analysis are used to obtain an overall estimate. An important disadvantage of standard methods of meta-analysis is the assumption that effect size estimates being combined are independent. This assumption is violated when multiple outcomes are interest for synthesis across studies. To avoid the dependence of the effect sizes, several ad-hoc methods have been proposed including averaging the effect sizes and selecting one effect size per study. These ad-hoc approaches may lead to missed opportunities to utilize all available data to address the relevant research questions \citep{cheung-NR2019}.

More principled approaches have been proposed to deal with correlated effects when conducting IPD meta-analysis.  These advances include multivariate meta-analysis which has been used to jointly synthesize the outcomes observed across studies to estimate multiple pooled effects simultaneously \citep{cite-key}. Another approach is  the three-level meta analytic model \citep{cheung2013, konstantopoulos-RSM2011,van-den-noortgate-BRM2013}, which has been used to account for dependence of effect sizes within clusters. This approach considers participants to contribute to only one effect size, so the non-independence is primarily introduced due to the nested structure of the effects \citep{cheung-NR2019}.
An additional approach is based on a two-stage meta-analysis that uses summary measures. In this approach, dependency among effect sizes is handled via robust variance estimation (RVE) in which the dependence between the endpoints is not explicitly modeled, but instead the standard errors for the overall treatment effect or meta-regression coefficients are adjusted \citep{hedges-RSM2010}. This approach may require making a reasonable guess about the between outcome correlation to estimate the between-study variance and to approximate the optimal weights.

In this paper, we propose an innovative approach: a hierarchical meta-analysis for the settings in which each cohort study provides multiple endpoints,  resulting in correlated sampling errors of the estimated effects. The work is motivated by a project that involves the integration of data from six longitudinal cohorts, each one using using multiple inter-related tests and assessment tools to measure child cognition. We assume that all endpoints are measuring an effect of exposure on a common underlying construct  A major strength of our approach is that it will enable us to pool data from diverse endpoints within each cohort and then assess the degree to which consistent patterns emerge across cohorts.

In the proposed approach, we first derive the estimates and the standard errors by fitting univariate regression models for each separate outcome of interest. In the second stage, we  combine the summary measures  using a random-effects model. In this stage, unlike the existing methods of two-stage IPD analysis, we account for the correlated effect sizes within each cohort. First, some of the endpoints within a cohort were highly correlated causing unstable results. To overcome this problem, we obtain robust covariance estimates for effect size estimates within cohorts which we use at the second synthesis stage of the hierarchical meta-analytic approach which not all endpoints was observed for all children within a cohort. To address this issue, we derived a formula for the pairwise correlation between estimated effects using an adjustment, reflecting the fact that not all endpoints are observed for all children. To facilitate the creation of forest plots, downloading the results, and view information about the endpoints included in the analysis in an efficient way, we developed a web-based application which is written using the Shiny library and is hosted on a server using the open-source Shiny Server software (Rstudio and Inc., 2014).  In the last stage of our hierarchical meta analytic approach, we combine the independent effect size estimates obtained for each cohort in a random-effects model to obtain a global measure of the effect size across cohorts \citep{whitehead-book2002,10.1093/biomet/asq006}.

We compare and contrast the findings from proposed approach and a full multivariate analysis to answer a question that naturally arises when results of these two models coincide. Previous studies evaluated this question in different settings. Olkin and Simpsin \citep{olkin-sampson-BIOMETRICS1998} showed that in the case of comparing multiple treatments and a control with respect to a continuous outcome, the traditional meta-analysis based on estimated treatment contrasts is equivalent to the least squares regression analysis of individual patient data if there are no study-by-treatment interactions and the error variances are constant across trials. Mathew and Nordstorm \citep{mathew-nordstrom-BIOMETRICS1999} claimed that the equivalence holds even if the error variances are different across trials. Empirically, meta-analysis using original data has been found to be generally similar but not identical to meta-analysis using summary statistics. Whitehead and Lin and Zeng \citep{whitehead-book2002,10.1093/biomet/asq006}, showed that for all commonly used parametric and semi-parametric models, there is no asymptotic efficiency gain by analyzing original data if the parameter of main interest has a common value across studies, the nuisance parameters have distinct values among studies, and the summary statistics are based on maximum likelihood. More recently Kontopantelis \citep{doi:10.1002/jrsm.1303} conducted a comprehensive simulation study to compare one-stage and two-stage IPD analysis and concluded that a fully specified one-stage model is preferable especially when investigating interactions. We extend the results from these existing studies to the setting where there are correlated endpoints across multiple cohorts.

The remainder of the article is organized as follows. In Section 2, we introduce our motivating application based on a meta-analysis of correlated endpoints measures the effect of prenatal alcohol exposure (PAE) on cognition in six large cohort studies. In Section 3, we introduce notation and describe the two-stage analysis and modeling framework used to combine multiple correlated endpoints within a single cohort. In Section 4, we present the modeling framework used to combine pooled effect size estimates across cohorts. In section 5, we compare/contrast the proposed approach with the corresponding one-stage approach via simulation studies. In section 6, we illustrate our method using data from our motivating application.  Finally, in section 7, we discuss the strengths and limitations of our method.

\section{Motivating Application}
Evidence from animal models and epidemiological studies has linked PAE to a broad range of cognitive and behavioral deficits, growth impairment, and physical anomalies, which are known collectively as fetal alcohol syndrome disorders (FASD) \citep{doi:10.1097/01.ALC.0000145691.81233.FA,doi:10.1111/j.1530-0277.2007.00585.x,Cartere20160775,doi:10.1111/acer.14040}. Fetal alcohol syndrome (FAS), the most severe of the FASD, is characterized by distinctive craniofacial dysmorphology (small palpebral fissures, flat philtrum, thin vermillion), small head circumference, and growth retardation \citep{stratton-book1996}; \citep{hoyme-PEDIATRICS2005} while partial FAS (PFAS) is diagnosed in the presence of facial dysmorphology, a history of PAE and growth retardation, microcephaly, or central nervous system (CNS) impairment. Individuals with PAE who lack the characteristic pattern of dysmorphic features but exhibit cognitive and/or behavioral impairment are often given diagnosis of alcohol-related neurodevelopmental disorder (ARND), which is the most prevalent FASD.  Although the diagnosis of ARND requires a confirmed history of maternal alcohol consumption during pregnancy, there is virtually no information in the scientific literature regarding the levels of PAE associated with an increased risk of clinically significant adverse effects. In a recently updated set of clinical guidelines \citep{hoyme-PEDIATRICS2005}, risky drinking during pregnancy was defined as 6 or more drinks/week for a 2-week period or 3 or more drinks per occassion consumed on at least 2 occasions. However, in human studies, there is little empirical evidence of adverse effects at these levels of exposure.

Between 1975 and 1993, the National Institutes of Health (NIH) funded six longitudinal cohort studies in four U.S. cities (Seattle, Atlanta (two cohorts), Pittsburgh (two cohorts), and Detroit); these are described briefly in Section 1.2. In all but one of these studies, mothers were recruited and interviewed prospectively about their alcohol use during pregnancy and their children were followed longitudinally from infancy through young adulthood; the Atlanta 2 study \citep{doi:10.1002/dev.20215} recruited the mothers shortly following delivery, interviewed them about their drinking during pregnancy, and followed the children as in the other studies. All six studies had measured a variety of neurodevelopmental endpoints that assess five specific measures of cognitive function: IQ, executive function, academic achievement, and learning and memory.

\subsection{The Six Cohort Studies}

\subsubsection{Seattle Cohort\normalfont{\citep{Streissguthetal1981}}  \hfill birth years: 1975-76}
All women who were enrolled in prenatal care by the 5th month of pregnancy at two large Seattle hospitals were eligible to participate. To  ascertain PAE,  participating mothers (N = 1529) were administered a Quantity-Frequency-Variability interview \citep{doi:10.15288/qjsa.1968.29.130} regarding alcohol, tobacco, and drug use for two time periods:  during pregnancy and just prior to pregnancy recognition. 462 newborns were selected based on an algorithm derived from maternal absolute alcohol (AA)/day, alcohol use/occasion, volume variability, and frequency of intoxication constructed to over-represent infants born to heavier drinkers. Controls included both abstainers and light drinkers. Infants were followed up at 8 and 18 months and 4,  7,  11,  14, 21,  25,  and 30 years. Although cohort retention was high (e.g., 82\% at 14 years), other children not initially selected whose mothers had been interviewed during pregnancy were added at  follow-
up assessments to keep the sample size close to 500 at each examination.

\subsubsection{Atlanta Cohort 1\normalfont{\citep{article}} \hfill birth years: 1980-86}
527 low socioeconomic status (SES), pregnant women were recruited at their first prenatal  visit at an urban Atlanta hospital serving a primarily African American, low income population. Women who reported drinking at least 1 oz AA/week during pregnancy were recruited. Nondrinkers, who were similar in demographic background, were recruited at the same time to serve as controls. Women were interviewed at recruitment about their alcohol and drug use; the majority reported drinking on weekends in a 'binge' pattern. Infants were evaluated following birth. Sub-samples were followed up at 6 and 12 months and 7, 14, and 22 years.

\subsubsection{Atlanta Cohort 2\normalfont{\citep{Brownarticle}} \hfill birth years: 1992-1994}
306 mothers and their infants were recruited shortly after delivery at an urban Atlanta hospital.  111 reported having drunk alcohol during pregnancy, 71 of whom also had used cocaine (based on self-report or urine screen); 44 used cocaine but no alcohol; 151 did not drink alcohol or use cocaine. All participants were English speaking, 19 years or older, and had singleton births; most were African American and low SES. The infants were assessed at 2 and 8 years.

\subsubsection{Pittsburgh Cohort 1\normalfont{\citep{DAY1991329}}  \hfill birth years: 1983-86}
Participants were recruited from the prenatal clinic at a maternity hospital if they were English-speaking, age 18 or older,  and in their 4th  or 5th gestational month. A total of 1360 women were interviewed regarding their usual, maximum, and minimum consumption of beer, wine, liquor, tobacco, marijuana, and other illicit drugs prior to pregnancy and during the first trimester. Two cohorts were selected: (1) women who drank 3 or more drinks/week in the first trimester and a random sample of women who drank less or abstained, and (2) women who used marijuana at least twice/month in the first trimester and a random sample of those who used less or abstained.  Because women could be selected for either or both cohorts, there was a 48\% overlap. The cohorts were combined for this analysis: The birth sample consisted of 763 live singleton infants. The alcohol, tobacco, and drug use interview was repeated in the 7th gestational month and at delivery, when second and third trimester substance use information was obtained. The cohort consisted of women who were predominantly low income and of fairly equal numbers of Caucasian and African American women. Participants were followed up at 8 and 18 months, and 3, 6, 10, 14, 16, and 22 years.

\subsubsection{Pittsburgh Cohort 2\normalfont{\citep{Galearticle}} \hfill birth years: 1988-93}
English-speaking women in their 4th or 5th month of pregnancy attending the prenatal clinic at a large inner-city hospital who were 18 years old or older were interviewed regarding their usual, maximum, and minimum consumption of cocaine, alcohol, marijuana, tobacco, and other drugs prior to pregnancy and during the 1st trimester. Every woman who reported any cocaine/crack use during the first trimester was enrolled in the study cohort, as was the next woman interviewed who reported  no cocaine or crack use during both the year prior to pregnancy and the first trimester. Although crack/cocaine use was the criterion for recruitment, a large proportion of these women also drank moderate-to-heavy levels of alcohol; some of the adverse effects subsequently seen on development were independently attributable to the alcohol exposure. The alcohol and drug use interview was repeated at the  end of the second and third trimesters, and offspring were assessed at 1, 3, 7, 10, 15, and 21 years.   The birth cohort consisted of 295 women and infants; the women were predominantly of low socioeconomic status and were roughly equally divided by Caucasian and African American race.

\subsubsection{Detroit Cohort\normalfont{\citep{doi:10.1111/j.1530-0277.1993.tb00744.x}} \hfill birth years: 1986-89}
All women (N > 6400) enrolling in the antenatal maternity clinic at a large, inner-city hospital were interviewed regarding their alcohol use at their first prenatal visit (M = 23.4 wk gestation; SD=7.9), using a timeline follow-back interview \citep{jacobson-PREDIATRICS2002}. The mother provided detailed information about her alcohol consumption on a day-by-day basis during the previous 2 weeks with recall linked to specific times of day and activities and was also asked to recall her day-by-day drinking during a typical week around time of conception. Moderate and heavy drinking women were overrepresented in the sample by including all women reporting at least 0.5 oz AA at conception and a random sample of approximately 5\% of the lower level drinkers and abstainers. The 2-week timeline follow-back interview was repeated at each prenatal clinic visit (M = 5.4 visits). To reduce the risk that alcohol might be confounded with cocaine exposure, 78 heavy cocaine (<2 days/wk), light alcohol (<7 drinks/wk) users were also included in the final sample, which consisted of 480 pregnant women and their children. Participants were followed up at 6.5, 12, and 13 months and 7, 14, and 19 years.

\section{Notation and Model Formulation}

Let $Y_{ijk}$ be the random variable representing response $k$ for individual $j$ in cohort $i$,
$k = 1, \ldots, K$, $j = 1, \ldots, J_i$, where $J_i$ is the number of individuals in cohort $i$, $i = 1, \ldots, I$.
Let $A_{ij}$ be the exposure of interest (i.e. prenatal alcohol exposure) for individual $j$ in study $i$
and $S_{ij}$ be their corresponding propensity score.

\begin{equation} \label{eq-Y-linear-model-separate}
Y_{ijk} = \alpha_{ik} + B_{ik} \, A_{ij} + \gamma_{ik} \, S_{ij} + E_{ijk} \,,
\end{equation}
\noindent
where $B_{ik}$ is the effect of a one-unit increase in $A_{ij}$ (alcohol volume) on the mean for response $k$ in cohort $i$
given the propensity score $S_{ij}$, $j = 1, \ldots, J$, $k = 1, \ldots, K$, $i=1, \dots, I$.
The propensity score is calculated separately for each cohort in our setting
since the sets of covariates measured differ between cohorts \citep{Hwang2020}.
We assume here that conditioning on the propensity score renders the exposure variable independent of all confounders and so that it is sufficient to condition on $S_{ij}$ rather than the confounders themselves \citep{doi:10.1198/016214504000001187}.

The parameter $\gamma_{ik}$ characterizes the effect of the propensity score for a given level of alcohol exposure
and $E_{ijk}$ is the error term which has mean zero and variance $\sigma^2_{ik}$, $k = 1, \ldots, K$, $i=1, \dots,I$.

We suppose that the effects $B_{ik}$ vary about some average exposure effect, with,
\begin{equation} \label{eq-average-exposure-Bk}
B_{ik} \sim N(\beta_{i}, \phi_{i}) \,,
\end{equation}
\noindent
where $\beta_i$ is the exposure effect for cohort $i$ and $\phi_i$ represents the heterogeneity of the response-specific exposure effects for cohort $i$.

Across cohorts, we suppose that the average exposure effects $\beta_{i}$ vary about some average exposure effect, with,
\begin{equation} \label{eq-average-exposure-Bk_2}
\beta_i \sim N(\beta_{o}, \eta^2) \,,
\end{equation}
\noindent
where  $\beta_{o}$ represents the ``average effect'' of a one-unit increase in the exposure across all cohorts, and is our parameter of ultimate interest.
The variance $\eta^2$ in (\ref{eq-average-exposure-Bk_2}) reflects the extent of heterogeneity of the cohort-specific exposure effects.

In the next two subsection, we describe a two-stage approach to estimation and inference with data from a single cohort and in Section 4, we show how to synthesize cohort-specific exposure effects to obtain an estimate for the average effect of a one-unit increase in the exposure across all cohorts.

\subsection{Stage I Estimation for a Single Cohort}
In this section, we temporarily omit the subscript $i$ and describe a two-stage approach to estimate the average exposure effect for a single cohort where the effects are correlated.
Before model fitting we standardize the responses so that they have the same first two moments as the Full-Scale IQ variable
which has a mean of 100 and a standard deviation of 15.
By conducting this standardization the exposure effects can be expressed
in terms of the decrement in IQ  associated with a one-unit increase
in prenatal alcohol exposure \citep{axelrad-EHP2007}.

For the first stage, we fit separate linear models for each response, assuming
\begin{equation}
Y_{jk} = \alpha_{k} + B_{k} \, A_{j} + \gamma_{k} \, S_{j} + E_{jk} \,,
\end{equation}
\noindent
where $B_{k}$ is the effect of a one-unit increase in $A_{j}$ (alcohol volume) on the mean for response $k$,
given the propensity score $S_{j}$, $j = 1, \ldots, J$, $k = 1, \ldots, K$.
The parameter $\gamma_{k}$ characterizes the effect of the propensity score for a given level of alcohol exposure
and $E_{jk}$ is the error term which has mean zero and variance $\sigma^2_{k}$, $k = 1, \ldots, K$.

We suppose that the effects $B_{k}$, $k = 1, \ldots, K$
vary about some average exposure effect, with
\begin{equation}
B_{k} \sim N(\beta, \phi),
\end{equation}
\noindent
independently and identically distributed and $\beta$ is the average exposure effect. The variance $\phi$ reflects the extent of heterogeneity of the response-specific exposure effects for a single cohort.

If we let $X_{jk} = (1, A_{j}, S_{j})'$ be the covariate vector, we can be write
\begin{equation} \label{eq-Y-giv-BE-linear-model-compact}
Y_{jk} = X'_{jk} \, \theta_{k} + E_{jk}
\end{equation}
where $\theta_{k} = (\alpha_{k}, B_{k}, \gamma_{k})'$.
We assume $E_{jk}\independent (A_{j}, B_{k},S_{j})$ with $E_{jk} \sim N(0, \sigma_{k}^2)$ are i.i.d. for $j = 1, \ldots, J$, $k=1,2,\ldots, K$.
Note that $X_{jk}$ does not vary by response type
since the exposure variable $A_{j}$ and the propensity score $S_{j}$ are individual level covariates,
but we retain this notation for generality.

We next define $K \times 1$ vectors $Y_j = (Y_{j1}, Y_{j2}, \ldots, Y_{jK})'$,
$\alpha = (\alpha_1, \ldots, \alpha_K)'$,
$B = (B_1, \ldots, B_K)'$ and $\gamma = (\gamma_1, \ldots, \gamma_K)'$ and a $K \times 3K$ covariate matrix
\begin{equation}
X'_j = \left[
\begin{array}{ccccc}
X'_{j1} & 0          & 0 & \cdots & 0 \\
0          & X'_{j2} & 0 & \cdots & 0 \\
\vdots   &            & \ddots &   & 0 \\
0          & \cdots  & 0         & 0 & X'_{jK} \\
\end{array}
\right] \,,
\end{equation}
\noindent
where the $0$s in this matrix refer to $3\times 1$ vectors of $0$s. The model given by (\ref{eq-Y-giv-BE-linear-model-compact}) can then be represented in a unifying model
\begin{equation}
Y_j = X'_j \, \theta + E_j
\end{equation}
\noindent
where
$\theta = (\theta'_1, \ldots, \theta'_K)'$ is a $3 K \times 1$ vector of parameters,
$E_j = (E_{j1}, \ldots, E_{jK})'$ and $E_j \sim N(0, \Sigma)$,
where $\Sigma$ is a $K \times K$ covariance matrix with diagonal entries
$\Sigma_{kk} = \sigma_k^2 = {\rm var}(E_{jk})$.
The off-diagonal entries
$\Sigma_{kl} = \sigma_{kl}={\rm cov}(E_{jk}, E_{j \ell})$
accommodate a conditional dependence (given $X_{jk}$, $X_{jl}$, $B$) between the responses from the same individual.

In the second stage of estimation these estimates are pooled over
to obtain a single estimate of the global measure of the causal effect denoted by $\beta$ in (\ref{eq-average-exposure-Bk}). We begin the second stage by estimating the covariance between the errors  ${\rm cov}(E_{jk}, E_{jl}) = \sigma_{kl}$, $l \ne k = 1, \ldots, K$, characterizing the dependence between the stage I estimators $\widehat{\theta}_1, \ldots, \widehat{\theta}_K$

The challenge in estimating the covariance between the errors  ${\rm cov}(E_{jk}, E_{jl}) = \sigma_{kl}$, $l \ne k = 1, \ldots, K$ is
that not all individuals contribute data for all responses. To accommodate this fact, we introduce the indicators $R_{jk} = I(Y_{jk}~\mbox{is observed})$, $k = 1, \ldots, K$, and assume that the responses are missing at random (MAR) according to Little and Rubin (2019).

If $S_{jk}(\theta_{k})=X_{jk}(Y_{jk}-X_{jk}' \, \theta_{k})$
is the desired contribution from individual $j$ to the score function for $\theta_{k}$
given $B$,
the observed data score equation for estimating $\theta_{k}$ at stage I can be written as
\begin{equation} \label{eq-stageI-Sk-thetak}
S_{k}(\theta_{k})= \sum_{j=1}^{J} R_{jk} S_{jk}(\theta_{k}) = 0 \,,
\end{equation}
the solutions to which are
\begin{equation}
\widehat{\theta}_{k}=\sum_{j=1}^{J} R_{jk} \, (X_{jk} \, X'_{jk})^{-1} \, X_{jk} \, Y_{jk} \,, \quad k = 1, \ldots, K \,.
\end{equation}
\noindent
The maximum likelihood estimate of $\sigma^2_{k}$ is
\begin{equation} \label{eq-mle-sigma2k}
\widehat{\rm var}(E_{jk})
= \widehat{\sigma}^{2}_{k}
=\dfrac{\sum_{j=1}^{J} R_{jk} \, (Y_{jk}-X'_{jk} \, \widehat{\theta}_{k})^2}{n_k}
\end{equation}
where $n_k=\sum_{j=1}^{J} R_{jk}$
is the number of individuals contributing to the estimation of $\theta_{k}$, $k=1,2,\dots, K$.
We also obtain the maximum likelihood covariance estimate as
\begin{equation} \label{eq-mle-sigmakl}
\widehat{\rm cov}(E_{jk}, E_{jl})
=\widehat{\sigma}_{kl}
= \dfrac{\sum_{j=1}^{J} R_{jk} \, R_{jl}  \,(Y_{jk}- X'_{jk} \, \widehat{\theta}_{k}) \,  (Y_{jl}- X'_{jl} \, \widehat{\theta}_{l})}
            {n_{kl}}
\end{equation}
\noindent
where $n_{kl} = \sum_{j=1}^J R_{jk} \, R_{jl}$,
which is consistent
under a missing at random assumption \citep{little-rubin-book2019}.
We then let $\widehat{\Sigma}$ denote the estimated covariance matrix for the errors
where $\sigma_{kl}$ are adjusted for missing data as

\begin{equation}
\widehat{\rm cov}(E_{jk},E_{jl})_{adjusted}
=\dfrac{\widehat{\sigma}_{kl} \,n_{kl}}
{n_{k}\,n_{l}}
\end{equation}
\noindent

where $n_k=\sum_{j=1}^{J} R_{jk}$ and  $n_l=\sum_{j=1}^{J} R_{jl}$
are the number of individuals contributing to the estimation of $\theta_{k}$ and $\theta_{l}$ respectively.

More details regarding the derivation of the dependence between the estimates $\widehat{\theta}_{k}$ and $\widehat{\theta}_{l}$
are provided in the appendix.

\subsection{Stage II: Synthesis across Responses within a Cohort}
\label{SEC3.2}

To consider the synthesis of estimators across all responses
we note that
\begin{equation*}
E(\widehat{B}_k - \beta) = E \{ (\widehat{B}_k - B_k) + (B_k - \beta) \} = 0
\end{equation*}
\noindent
so $\widehat{B}$ is comprised of $K$ dependent unbiased estimators of $\beta$.
Thus
\begin{equation} \label{eq-Bhat-MVN}
\widehat{B} \sim {\rm MVN}(\mu(\beta), \Psi(\phi))
\end{equation}
\noindent
asymptotically,
where $\mu(\beta)$ is a $K \times 1$ vector with each element equal to $\beta$.
If we let $\widehat{\Psi}(\phi) = J^{-1} \, \widehat{\Gamma} + \Delta \, \phi$
then based on (\ref{eq-Bhat-MVN}) we may specify a pseudo-likelihood $PL(\beta, \phi)$ for $(\beta, \phi)$ given by
\begin{equation}\label{eq-PL-beta-tau2}
PL(\beta, \phi)
\propto \dfrac{1}{ (2 \pi)^{K/2} \, \sqrt{\widehat{\Psi}(\phi)|}}
\exp \left(-\frac{1}{2} \, (\widehat{B}- \mu(\beta))'  \, \widehat{\Psi}^{-1}(\phi)  \, (\widehat{B} - \mu(\beta))  \right) \,.
\end{equation}

Note that (\ref{eq-PL-beta-tau2}) could be maximized with respect to $(\beta, \phi)$,
but we proceed in a computationally convenient iterative approach based on (\ref{eq-PL-beta-tau2}).
Given an estimate $\phi^{(r)}$
we compute an estimate $\beta^{(r)}$ based on a linear combination of
$\widehat{B}_1, \ldots, \widehat{B}_K$.
The most efficient linear estimator of $\beta$ has the form
\begin{equation} \label{eq-linear-estimator-beta-hat}
\widehat{\beta} = [\mathbbm{1}' \, [\Psi(\phi)]^{-1} \, \widehat{B}] / [ \mathbbm{1}' \, [\Psi(\phi)]^{-1}  \, \mathbbm{1} ] \,,
\end{equation}
\noindent

so we replace $\Psi(\phi)$ with an estimator
$\widehat{\Psi}(\phi^{(r)}) = J^{-1} \, \widehat{\Gamma} + \Delta \, \phi^{(r)}$.
We could invert $\widehat{\Psi}(\phi^{(r)})$ but in practice it may be difficult and while a generalized inverse could be used,
the weights resulting from this approach were often found to vary greatly in magnitude and even in sign.
We therefore adopted an alternative more stable linear estimate using inverse variance weights,
whereby we replace $\Psi(\phi)$ with
$diag(J^{-1}  \, \widehat{\Gamma}_{kk} + \phi^{(r)} \,,~k = 1, \ldots, K)$ in (\ref{eq-linear-estimator-beta-hat}) to obtain $\widehat{\beta}^{(r)}$.
We then maximize $PL(\widehat{\beta}^r, \phi)$ with respect to $\phi$ to obtain $\phi^{(r+1)}$, with which we recompute $\widehat{\beta}^{(r+1)}$
and repeat iteratively until convergence;
we let $(\widehat{\beta}, \widehat{\phi})$ denote the estimates upon convergence.
A robust variance estimate is then obtained for $\widehat{\beta}$ based on
$\widehat{\Psi}(\widehat{\phi})$ which is given by
$\widehat{\rm var}(\widehat{\beta}) = [ \mathbb{I}' \, \widehat{\Psi}(\widehat{\phi}) \, \mathbb{I}]^{-1}$.
When we consider data from multiple cohorts we reintroduce the subscript $i$ and write the corresponding estimates for cohort $i$ as $\widehat{\beta}_i$ and
\begin{equation}
\widehat{V}_i(\widehat{\beta}_i) = \widehat{\rm var}(\widehat{\beta}_i) = [ \mathbb{I}' \, \widehat{\Psi}_i(\widehat{\phi_i}) \, \mathbb{I}]^{-1}
\end{equation}
respectively, $i=1,\ldots, I$.

\subsection{An Alternative One-Stage (Fully Specified Multivariate) Approach}
Note that the parameters estimated according to the hierarchical meta-analytic approach can alternatively be fitted in one-step via software for fitting hierarchical mixed effect linear models.
To do so we define $K-1$ covariates $T_{jkr} = I(k = r)$, $r = 2, \ldots, K$ which indicates the outcomes is for response $k$, $k = 2, \ldots, K$.
We may put these $K-1$ indicators in vector format and define the $(K-1)\times 1$ vector $T_{jk} = (T_{jk2}, \ldots, T_{jkK})'$.
We consider the response for verbal IQ as the reference type and let $T_{ik2} = 1$ for performance IQ,
$T_{ik3} = 1$ for freedom from distractibility (Table 2).
Then we fit the model
\begin{equation*}
Y_{jk}  = \alpha_1 + B_k \, A_j + \gamma_1 \, S_j + \tau' \, T_{jk} + \zeta' \, S_j \, T_{jk} + E_{jk} \,, \quad k = 1, \ldots, K \,.
\end{equation*}
where $\tau=(\tau_2, \ldots, \tau_K)'$ and
$\zeta = (\zeta_2,\ldots, \zeta_K)'$ are $(K-1) \times 1$ vectors with
$\tau_r = \alpha_r - \alpha_1$ and
$\zeta_r = \gamma_r - \gamma_1$, $r=2,\ldots, K$.
We assume $B_k \sim N(\beta, \phi)$ as specified in (\ref{eq-average-exposure-Bk}) where $B_k$ is the effect of a one-unit increase in $A_j$ on the
mean of response $k$ given the propensity score $S_j$,
$\beta$ is the parameter of ultimate interest representing the ``average causal effect'' of a one-unit increase in the exposure across
all responses within the cohort, and
$\phi$ characterizes the degree of heterogeneity in the effect across responses.
We also assume $E_j = (E_{j1}, \ldots, E_{jK})'$ is a $K \times 1$ error term with
$E_j \sim {\rm MVN}(0, \Sigma)$
with $\Sigma$ a $K \times K$ covariance matrix as in Section 3.1.
The one-step approach involves simultaneous estimation of all fixed effects, $\beta$, $\phi$ and $\Sigma$ at once.
This can be fitted using software for fitting hierarchical linear mixed effects models.
We let
$\tilde{\beta}_i$ denote the estimate of $\beta_i$ obtained from fitting the hierarchical model to the data from cohort $i$ and
$\tilde{V}_i(\tilde{\beta}_i)$ denote the correspondign variance estimate based on the observed information matrix, $i=1,\ldots, I$.

\section{Synthesis across Cohorts}
In the previous section, we described methods for synthesizing data across multiple outcomes to obtain estimates of the global causal effect based on a
two-stage approach and based on fitting a hierchical mixed effect model.
These methods were based on analyzing data from a single cohort. Here, we describe how to combine cohort-specific estimates to obtain a an overall estimate
of a causal effect while accommodating possible heterogeneity.
The approach described in Section 3.2 is an extension of the approach described by Viechtbauer and implemented in the metafor package \citep{metaforR} which deals with
independent estimates; Section 3.2 adapted the methods to deal with dependent effect estimates so what follows is a simplification of the approach for the
last stage of the data synthesis.
We describe it briefly as follows.

We consider $\beta_i$ as the global causal effect of exposure in cohort $i$ reflecting the impact of an increment in the volume of  prenatal alcohol exposure
on the common underlying construct; we let $\hat\beta_i$ be the corresponding estimate.
Note that the studies draw individuals from different populations and so the composition of the samples varies across cohorts.
Moreover, the methods used to measure exposure along with the precise nature of the outcomes differ between studies, even though they were measuring the same latent attributes regarding cognition.
We therefore wish to accommodate a component of variation between studies (heterogeneity) for the true effects which we accomplished by use of a random effects model of the form
\begin{eqnarray}
\hat{\beta}_{i} & = & \beta_{i}+\epsilon_{i}\\
\beta_{i} & = & \beta_\circ + u_{i}
\end{eqnarray}
where
we let $\epsilon_i \sim N(0, \widehat{V}_i(\widehat{\beta}_i))$ reflect the sampling variation of the estimator from cohort $i$ about the true effect $\beta_i$,
and $u_{i}\sim N(0,\eta^2)$ reflects the heterogeneity of the global cohort-specific causal effects across studies.
The parameter $\beta_\circ$ represents the overall global effect which is the parameter of ultimate interest.
Through this variance decomposition then upon introducing the heterogeneity between studies we have
$\widehat{\rm var}(\widehat{\beta}_i)=
\widehat{V}_i(\widehat{\beta}_i) + \eta^2$.
The synthesis is achieved in a simlar spirit to Section 3.2 whereby we consider a pseudo-likelihood of the form
\begin{equation}
\label{eq-PL-beta-eta2}
PL(\beta_\circ, \eta)
\propto
\prod_{i=1}^I \left\{ \dfrac{1}{ (2 \pi)^{I/2} \, \sqrt{(\widehat{V}_i(\widehat{\beta}_i) + \eta^2)}}
\exp \left( - \frac{(\widehat{\beta}_i- \beta_\circ)^2}{2 ( \widehat{V}_i(\widehat{\beta}_i) + \eta^2)} \right) \, \right\}.
\end{equation}
The pooled exposure effect estimate $\widehat{\beta}_\circ$ is obtained as a weighted average of the $\widehat\beta_i$ terms
with cohort weights equal to the inverse of $\widehat{V}(\widehat{\beta}_i)+\hat\eta^2$ where $\hat{\eta}^2$ is obtained as the solution to iteratively
maximizing (\ref{eq-PL-beta-eta2}).
The R package `metafor' can be used to carry out this final stage of the data synthesis.
If the linear model of Section 3.3 is used for simultaneous estimation of the overall causal effect then $\tilde{\beta}_i$ and
$\tilde{V}_i(\tilde{\beta}_i)$  can be used in a similar fashion to obtain the estimator $\tilde{\beta}_o$.

\section{Simulation Studies}
For the simulation studies, we consider $k$ correlated continuous endpoints from a single study. We generated outcomes from the following linear regression model:

\begin{equation}
Y_{jk} = \alpha_k + \beta_k \,X_j + \gamma_k \, Z_j + E_{jk} \,,
\end{equation}

where $Y_{jk}$ be the random variable representing response k for individual j, k = 1,...,K, j = 1,...,J, and  $\beta_k$ is the effect of a one-unit increase in $X_j$ on the mean for response $k$ given the covariate $Z_j$. We let $\beta_k$ vary about some average exposure effect within a study, with $\beta_k \sim N(\beta, \tau^2)$. The parameter $\gamma_k$ characterizes the effect of the covariate for a given level of exposure, $X_{j}$. We also assume $E_{j} = (E_{j1}, \dots, E_{jK} )$ is a $K \times 1$ error term with $E_{j}\sim MVN(0, \Sigma)$ with $\Sigma$ a $K \times K$ covariance matrix. The simulations were performed in different scenarios. We generated the effect size for the exposure, $\beta_k$ from normal distribution with mean 3 and variance $\tau^2$.  Scenarios were created by manipulating the number of outcomes (k) and, varying between-study heterogeneity ($\tau^2$). We consider the scenarios where the number of outcomes is equal to 3, 5, and 10 and $\tau^2$ takes the values 0.10,0.25 and 0.50.
For each combination of the simulation parameters, we generated 1000 datasets with the sample size of 500 for each endpoint. For each dataset, we performed the two types of meta-analysis, i.e. the one based on the proposed approach versus the full multivariate analysis.

We evaluated the performance of the proposed approach in simulation settings previously described, over 1000 iterations. The estimates of interest were the average exposure effect $\beta$. To allow for a comprehensive comparison, performance was assessed on a range of metrics: empirical mean bias (EBIAS), average model based standard error (ASE), empirical standard error (ESE) and coverage probability (CP).  The results are summarized in Table 1. Patterns of empirical mean bias were very low and comparable for the two methods, with the exception of larger between heterogeneity and smaller number of outcomes. In those particular scenarios, the proposed approach was the best performer. Coverage probability for the proposed method was about to the nominal 0.95 for all scenarios considered in this paper .

\section{Prenatal Alcohol Exposure and Cognitive Function in Children}
For this paper we used data from six large cohort studies to assess the effects of PAE on IQ, which is a measure of cognitive function. The proposed hierarchical meta-analytic approach is well-suited to assess the effect of PAE on IQ measure since it enables us to pool data from diverse, correlated endpoints across cohorts. Table 2 lists the endpoints by cohort considered in this paper. To yield sufficiently precise estimates of effect size, we considered a broad set of potential confounders when fitting separate linear models for each endpoint. Since each cohort provided a somewhat different set of control variables, we employed a propensity score approach to adjust for potential confounders (\citealp{AkkayaHocagil_2020}). We estimated the propensity score for each cohort separately and included the propensity score in the linear model as an additional covariate as in model (3.1).

For each outcome k, $\beta_{k}$ was estimated from model (3.1). Table 3 lists the estimated effect size and standard errors from the first stage of the hierarchical meta-analytic approach. The aim of the second stage of the proposed methods is to pool the estimates of the PAE and estimate the cohort specific overall true mean effect $\beta_{i}$ while adjusting for the fact that endpoints are correlated within a cohort and accommodating incomplete information on some endpoints. Table 4 shows the estimated effect sizes and standard errors for each cohort. To compare and contrast the results obtained from our method, we conducted a fully specified multivariate analysis to estimate a pooled effect size for each cohort study using SAS procedure `proc mixed` . Table 4 shows the estimated effect sizes for each cohort obtained from the fully specified multivariate model. Both methods provided impressively similar estimates for the effect sizes and the standard errors. Although the difference was not substantial, these two methods provided slightly different estimates for the between endpoints heterogeneity.

To combine the independent effect size estimates across cohorts and obtain a global effect size estimate of PAE on IQ at age 7 years, we used the R package ``metafor" to pool the estimates resulting from our hierarchical meta-analysis and the fully specified multivariate model. Table 5 provides the estimated effect size of the PAE on childhood IQ across cohorts. The resulting global effect sizes from the two methods were almost identical.

\section{Discussion}

	In this paper, we have proposed an extension of the standard procedure for two-stage IPD meta-analysis. Our procedure follows the same steps as conventional methods for two-stage IPD analysis for making inference about the effect size but extends these analyses by accounting for the correlation between endpoints and by accommodating incomplete data on some endpoints.

	Our approach has several advantages over the one-stage IPD meta-analysis. Firstly, it builds upon the two-stage IPD meta-analysis that practitioners are already familiar with. Secondly with our approach, one can create forest plots to visualize the estimated effect sizes for each endpoint. Thirdly, our approach is less likely to encounter convergence problems compared to the one-stage IPD meta-analysis. Finally, our approach uses the known within study variances, which helps obtain more precise estimates.

	We evaluated and compared our approach with a fully specified multivariate analysis. In simulation scenarios considered in this paper, we observed that the proposed approach can successfully reduce bias relative to the fully specified multivariate approach. Our simulation results suggest that, when the number of endpoints is small and the between endpoints variance is large, our proposed approach outperforms the multivariate analysis.

    We illustrated our approach using data on childhood IQ from six cohorts. We analyzed 18 outcomes from the six cohorts. For our data application, it was important to adjust for the fact that endpoints within a cohort are correlated and some of the endpoints have incomplete data. To obtain a global effect size estimate, we conducted a hierarchical meta-analysis. In the first stage, we obtained effect size estimates for each endpoint separately. In the second stage, we employed our proposed approach to obtain cohort-specific pooled effect size estimates while adjusting for between-endpoint correlation and incomplete data. In the last stage, we combined effect sizes across the cohorts employing a random-effects model. We compared the results from our approach with the results from the fully specified multivariate approach. In this comparison, our method performed well and thus provides an useful innovative tool for performing and interpreting meta-analyses with the correlated effect sizes.

redpajama_set_name: RedPajamaArXiv


For foreign people of living in Japan.
We have received orders for electrical work and communication work.
If you are going to start a business in Japan, we have a lot of things and give you all the assistance you need.
Copyright (c) Work Creation Research Institute LLP All Rights Reserved.


text:
Jankovce – wieś (obec) na Słowacji położona w kraju preszowskim, w powiecie Humenné. Pierwsze wzmianki o miejscowości pochodzą z roku 1317.

Według danych z dnia 31 grudnia 2010, wieś zamieszkiwało 265 osób, w tym 126 kobiet i 139 mężczyzn.

W 2001 roku względem narodowości i przynależności etnicznej wszyscy mieszkańcy deklarowali się jako Słowacy.

Przypisy

Miejscowości w powiecie Humenné

redpajama_set_name: RedPajamaWikipedia


The new GDS disposable panel filter is available in 1" and 2" depths in many standard sizes with progressively dense fiberglass to maximize dust holding capacity. The media and strut retainers are glued and sealed inside a one piece, craftboard frame made from 100% recycled materials. The filter frame is clearly marked with size, UL classification and air flow direction. GTA metal grill fiberglass series is available for nonstandard sizes only.


then usc is only the second worst team in the Pac. they lost to the then worst pac south team yesterday on their home field.
Being 11th out of 12 teams is plenty good for IQnegative. Funny, because you were actually alive when USC earned the monicker of "trojans". Of course, you were too ignorant to actually understand why it was applied, and too mentally lazy to care all these years later. You do epitomize usc. LAZY.


Home Properties, Inc. is an independent real estate investment trust. The firm invests in the real estate markets of the United States. It is engaged in the ownership, management, acquisition, rehabilitation and development of residential apartment communities. The company has a P/E ratio of 36.65.
The average volume for Home Properties has been 263,900 shares per day over the past 30 days. Home Properties has a market cap of $3.5 billion and is part of the real estate industry. Shares are up 14.8% year-to-date as of the close of trading on Friday.
TheStreet Ratings rates Home Properties as a hold. The company's strengths can be seen in multiple areas, such as its revenue growth, reasonable valuation levels and good cash flow from operations. However, as a counter to these strengths, we also find weaknesses including deteriorating net income and poor profit margins.
Net operating cash flow has slightly increased to $78.48 million or 3.73% when compared to the same quarter last year. Despite an increase in cash flow, HOME PROPERTIES INC's cash flow growth rate is still lower than the industry average growth rate of 30.47%.
Cheniere Energy Partners (AMEX: CQP) shares currently have a dividend yield of 5.50%.
Cheniere Energy Partners, L.P., through its subsidiary, Sabine Pass LNG, L.P., owns and operates the Sabine Pass liquefied natural gas (LNG) terminal located on the Sabine Pass deep water shipping channel, Louisiana.
The average volume for Cheniere Energy Partners has been 325,000 shares per day over the past 30 days. Cheniere Energy Partners has a market cap of $1.8 billion and is part of the energy industry. Shares are up 12.2% year-to-date as of the close of trading on Friday.
TheStreet Ratings rates Cheniere Energy Partners as a hold. The company's strengths can be seen in multiple areas, such as its revenue growth, expanding profit margins and increase in stock price during the past year. However, as a counter to these strengths, we also find weaknesses including feeble growth in the company's earnings per share, deteriorating net income and disappointing return on equity.
CQP's revenue growth has slightly outpaced the industry average of 3.1%. Since the same quarter one year prior, revenues slightly increased by 1.7%. This growth in revenue does not appear to have trickled down to the company's bottom line, displayed by a decline in earnings per share.
The gross profit margin for CHENIERE ENERGY PARTNERS LP is currently very high, coming in at 79.69%. It has increased from the same quarter the previous year. Regardless of the strong results of the gross profit margin, the net profit margin of -103.73% is in-line with the industry average.
The company, on the basis of change in net income from the same quarter one year ago, has significantly underperformed against the S&P 500 and did not exceed that of the Oil, Gas & Consumable Fuels industry. The net income has significantly decreased by 34.8% when compared to the same quarter one year ago, falling from -$51.73 million to -$69.73 million.
Net operating cash flow has significantly decreased to -$0.46 million or 105.90% when compared to the same quarter last year. In addition, when comparing to the industry average, the firm's growth rate is much lower.
You can view the full Cheniere Energy Partners Ratings Report.
LinnCo (NASDAQ: LNCO) shares currently have a dividend yield of 10.00%.
LinnCo, LLC, through its limited liability company interests in Linn Energy, LLC, focuses on the acquisition and development of oil and natural gas properties in the United States. The company was founded in 2012 and is headquartered in Houston, Texas.
The average volume for LinnCo has been 1,190,300 shares per day over the past 30 days. LinnCo has a market cap of $3.7 billion and is part of the energy industry. Shares are down 3.6% year-to-date as of the close of trading on Friday.
TheStreet Ratings rates LinnCo as a hold. The company's strengths can be seen in multiple areas, such as its largely solid financial position with reasonable debt levels by most measures, good cash flow from operations and expanding profit margins. However, as a counter to these strengths, we find that the stock has had a generally disappointing performance in the past year.
LNCO has no debt to speak of therefore resulting in a debt-to-equity ratio of zero, which we consider to be a relatively favorable sign. Along with this, the company maintains a quick ratio of 18.09, which clearly demonstrates the ability to cover short-term cash needs.
Net operating cash flow has significantly increased by 269.64% to $93.23 million when compared to the same quarter last year. In addition, LINNCO LLC has also vastly surpassed the industry average cash flow growth rate of 17.38%.
Compared to other companies in the Oil, Gas & Consumable Fuels industry and the overall market, LINNCO LLC's return on equity significantly trails that of both the industry average and the S&P 500.
The change in net income from the same quarter one year ago has significantly exceeded that of the Oil, Gas & Consumable Fuels industry average, but is less than that of the S&P 500. The net income has decreased by 6.9% when compared to the same quarter one year ago, dropping from -$18.22 million to -$19.48 million.
LNCO has underperformed the S&P 500 Index, declining 21.94% from its price level of one year ago. Turning toward the future, the fact that the stock has come down in price over the past year should not necessarily be interpreted as a negative; it could be one of the factors that may help make the stock attractive down the road. Right now, however, we believe that it is too soon to buy.
You can view the full LinnCo Ratings Report.


Food Not Lawns London Canada (FNL) is a collective of gardeners committed to ecological stewardship, through cultivating our relationship to the earth and to each other. We are enthusiastic about creating integrated solutions to heal a broken food system. We advocate for food sovereignty because we believe that our communities have the right to participate in and define our own food systems.
Respect: We stand for the dignity and rights of all persons, across all communities, by supporting a diversity of gardening and food growing interests.
Education: We commit to creating positive social interactions through workshops and community events. We cultivate the sharing of knowledge and interest in growing food, respectfully redefining health, wealth, and beauty.
Cooperation: We support cooperation between FNL and like-minded organizations to increase community awareness about growing food. We encourage and foster relationships with a focus on environmental stewardship and ecological awareness.
Nature: We cultivate respect for nature and the intricate systems that result in Earth's biodiversity. We support this abundance through responsible and varied gardening practices. We individually recognize our connection to the soil, water, air, and other creatures.
Responsibility: We act on the earth in an ecologically responsible manner, promoting the ongoing restoration, proliferation, and overall health of our micro and macro environments. We act as stewards of the Earth, positively impacting our selves, our communities, and our future generations through the growing of food.


They're only whispers as of now, but this news literally has us four five seconds from wildin'. Rihanna makes as big of an impact on the Billboard charts as she does in the beauty world, and with a catalog of 7 fragrances and a few successful collaborations with MAC already under her belt, she's already plotting to expand her empire. At the launch of her brand-new scent RiRi yesterday at Macy's in Brooklyn, the star hinted at potential plans to launch a beauty range of her own. "I would love to do a makeup line," she told WWD. "It's a part of my life, it's a part of my career, it's a part of every video, every visual, every red carpet, so it's something I've gotten to know quite well. I ask a lot of questions and I spend a lot of time in the makeup stores." The versatile looks Rihanna constantly serves up stay giving us inspiration for weeks on end, so if our beauty prayers are answered, we expect to see lush lashes, glow-enhancing highlighters, and unconventional lip colors galore.


text:
If giving up pasta is the hardest part of making changes to help lose weight, zucchini noodles are a healthier alternative, Michalczyk says. "I like to think of them as pasta's lower-carb and higher-in-nutrition cousin because zucchini is a good source of vitamins A and C, fiber and potassium for only about 33 calories per zucchini," says Michalczyk. You can use in zoodles, much on as is with some dip, roast them, or add to salads.
And right up there on the FF list—weight loss. Sure, slow and steady may win the race, but who wants to plod along like a tortoise, especially when a warm weather getaway is right around the corner? Add these super weight loss foods to your day to get your weight-loss goals on hyperspeed. All of them have been scientifically proven to fry flab in 6 weeks or less! Tighten your seatbelt—in fact, you'll soon be tightening every belt!
From a psychological and cultural perspective, a healthier diet may be difficult to achieve for people with poor eating habits.[48] This may be due to tastes acquired in childhood and preferences for sugary, salty and/or fatty foods.[49] In the UK, the chief medical officer of the government recommended in December 2018 that sugar and salt be taxed to discourage consumption.[50]
Steel-cut and rolled varieties of oats have up to 5 grams of fiber per serving, making them the most filling choice. (Instant oats contain 3 to 4 grams per serving—if you're opting for that kind, make sure to skip the versions with added sugar.) And as a complex carb, they can help with weight loss. "Your body needs to break down complex carbohydrates because they're a complicated mesh of molecules," Lauren Harris-Pincus, M.S., R.D.N., and owner of Nutrition Starring You, told SELF in a previous article.
Hello, hydration. Watermelon, which gets its pretty color from the antioxidant lycopene, is a juicy, delicious way to make sure you're staying hydrated—a key factor in weight loss for various reasons. When you're drinking enough water, all your systems (including your metabolism) are better able to work at their maximum capacity, Shawn Talbott, Ph.D., a nutritional biochemist, told SELF in a previous article. Plus, proper hydration prevents you from confusing thirst for hunger, which can lead you to overeat throughout the day. And don't buy into the hype about watermelon being a calorie bomb—1 cup only contains 46 calories.
Fresh, Frozen, or Canned Vegetables ― try something new. You may find that you love grilled vegetables or steamed vegetables with an herb you haven't tried like rosemary. You can sauté (panfry) vegetables in a non-stick pan with a small amount of cooking spray. Or try frozen or canned vegetables for a quick side dish—just microwave and serve. When trying canned vegetables, look for vegetables without added salt, butter, or cream sauces. Commit to going to the produce department and trying a new vegetable each week.
^ Volta U, Caio G, De Giorgio R, Henriksen C, Skodje G, Lundin KE (Jun 2015). "Non-celiac gluten sensitivity: a work-in-progress entity in the spectrum of wheat-related disorders". Best Pract Res Clin Gastroenterol. 29 (3): 477–91. doi:10.1016/j.bpg.2015.04.006. PMID 26060112. A recently proposed approach to NCGS diagnosis is an objective improvement of gastrointestinal symptoms and extra-intestinal manifestations assessed through a rating scale before and after GFD. Although a standardized symptom rating scale is not yet applied worldwide, a recent study indicated that a decrease of the global symptom score higher than 50% after GFD can be regarded as confirmatory of NCGS (Table 1) [53]. (…) After the confirmation of NCGS diagnosis, according to the previously mentioned work-up, patients are advized to start with a GFD [49].
Think smaller portions. Serving sizes have ballooned recently. When dining out, choose a starter instead of an entree, split a dish with a friend, and don't order supersized anything. At home, visual cues can help with portion sizes. Your serving of meat, fish, or chicken should be the size of a deck of cards and half a cup of mashed potato, rice, or pasta is about the size of a traditional light bulb. By serving your meals on smaller plates or in bowls, you can trick your brain into thinking it's a larger portion. If you don't feel satisfied at the end of a meal, add more leafy greens or round off the meal with fruit.

redpajama_set_name: RedPajamaCommonCrawl


text:
About Graduate Admission
The University of Texas at Dallas is a comprehensive, state supported institution of higher learning, offering a variety of programs at the undergraduate, masters, and doctoral levels. UT Dallas is committed to providing quality education to a diverse student body and offers programs designed for both full-time and part-time students. The University of Texas at Dallas accepts applications for admission to graduate degree programs for the fall, spring and summer semesters.
Admission to UT Dallas is open to all candidates on the basis of academic preparation, ability, and availability of space without regard to race, color, religion, national origin, gender, age, disability, citizenship, veteran status, or sexual orientation.
For more detailed information, contact the Graduate department or program to which you are applying since each program has specific admission requirements listed at http://www.utdallas.edu/admissions/graduate/degrees.
Graduate application deadlines and available terms of entry may vary by program. Please refer to the application section in this catalog. International applicants may have different deadlines; please refer to both the application section in this catalog and the International Students graduate admissions website for more information.
As with all state institutions of higher education, the procedures and criteria for admission used by UT Dallas are effective as of the publication date of this catalog but are subject to change by actions of the Texas Legislature or the Board of Regents.
Each program has specific admission requirements listed at www.utdallas.edu/admissions/graduate/degrees.
At a minimum, all applicants must meet the following admissions requirements:
The minimum requirement for admission to any master's degree program at UT Dallas is an earned UT Dallas baccalaureate degree or its equivalent from an accredited institution with a grade average of B or better in upper-division (junior and senior level) work in the student's major field and related fields.
Applicants in their final year of undergraduate study may be admitted to a master's degree program at UT Dallas. Evidence for the conferral of the baccalaureate degree must be presented before enrollment the master's degree program is permitted.
Students who have completed a relevant and acceptable master's degree and have submitted official degree conferral documentation are only required to submit unofficial copies of their baccalaureate degree transcripts and degree conferral.
The minimum requirement for admission to any doctoral program is an appropriate earned UT Dallas master's degree or its equivalent from an accredited graduate institution, with an average of at least B+. Applicants currently enrolled in post baccalaureate study may be admitted on the condition that official documentation confirming the conferral of their master's degree is presented before enrollment at UT Dallas is permitted.
Some departments admit directly to the doctoral program from a bachelor's degree for highly qualified candidates.
Graduate Certificate Programs
UT Dallas offers a number of graduate level certificate programs that typically involve 9 to 15 semester credit hours of graduate coursework in a focused area of study. Each certificate at UT Dallas consists of a subset of the courses from a current master's degree program offering. For a full list of our current certificate offerings please refer to www.utdallas.edu/academics/certificates.html.
Each certificate provides specialized training to help expand a student's areas of expertise, teaches them about new developments in their field, augments their professional skills and provides credentials that help advance their careers. In addition the certificate offering enables students to test the waters before deciding to pursue a master's degree in that area. Students who are later accepted to the master's program may be allowed to count some or all of their completed certificate courses with grades of B or better toward their masters degrees. A student may pursue graduate certificate and master's degree concurrently.
Admission to graduate-level certificate programs requires a bachelor's degree and an undergraduate record indicative of readiness for graduate work. Many certificate programs do not require admissions tests such as the Graduate Record Examination (GRE) or Graduate Management Admission Test (GMAT) unless or until a student seeks admission to a related master's program.
The term "Official Transcript" is understood to refer to the official recorded results of the student's academic work in a sealed envelope signed and stamped by the registrar or by an authorized official of the issuing college or university. It is the responsibility of the applicant to provide English translations of transcripts and documents that are in a language other than English. This documentation should be sent to:
Office of Admission and Enrollment
800 West Campbell Road
Richardson, Texas 75080-3021
All materials submitted in the process of applying to the University become the property of the University and will not be returned to the applicant. Falsifying or omitting information may result in withdrawal of any offer of acceptance, cancellation of enrollment, and/or disciplinary actions.
Admission to a master's degree requires that an official transcript demonstrating the completion of a bachelor's degree with a grade average of B or better in upper-division (junior and senior level) work in the student's major field and related fields from UT Dallas or another accredited institution be submitted at the time of enrollment. An applicant who has earned a baccalaureate degree under the "academic fresh start statute" (Texas Education Code, Section 51.931) will be evaluated only on the grade point average (GPA) of the coursework completed for that baccalaureate degree and the other criteria stated in this catalog.
Students who have completed a relevant master's degree and have submitted official master's degree conferral documentation are only required to submit unofficial copies of their baccalaureate degree transcripts and degree conferral.
An official transcript demonstrating the completion of a master's degree, submitted at the time of enrollment, is required for admission to a doctoral degree. Some departments admit highly qualified candidates directly to the doctoral program from a bachelor's degree.
Graduate Admission Examination Scores (GRE, GMAT)
Standardized test scores must be official and reported directly by the Educational Testing Service (ETS) should be sent to The University of Texas at Dallas, Code 6897. The Graduate Management Admissions Test (GMAT) is required if applying to the Naveen Jindal School of Management and the Graduate Record Examination (GRE) revised General Test is required if applying to all other schools except the School of Arts and Humanities. Each degree program sets its own criteria for what constitutes a satisfactory score for degree-seeking admission (see http://www.utdallas.edu/admissions/graduate/degrees).
The information about the GRE and GMAT examinations given below was current at the time this catalog was published. Applicants should be advised that both examinations are undergoing changes in format and design.
Graduate Record Examination (GRE)
The GRE revised general test is offered on a year-round basis at regional testing centers in a computer-based testing (CBT) format. Information on regional CBT testing may be obtained directly from Graduate Record Examination, Educational Testing Service, P.O. Box 6000; Princeton, NJ 08541-6000; by phone 1-610-771-7670 or 1-866-473-4373, via email through its email form or direct email, or go to http://www.ets.org/gre. Applicants should specify by both institution and code that the test score be sent to The University of Texas at Dallas, Code 6897.
Graduate Management Admission Test (GMAT)
The GMAT is offered on a year-round basis at regional testing centers in a computer-based testing (CBT) format. Information on regional CBT testing sites may be obtained directly from the GMAT website; by phone 1-800-717-GMAT (4628); via email; or go to http://www.mba.com. Applicants should specify by both institution and code that the test score be sent to The University of Texas at Dallas, Code 6897.
A narrative is submitted by the applicant outlining academic interests in the UT Dallas degree program of interest, current or long-range interests in research, teaching, or other professional objectives; describing publications or other scholarly endeavors; listing of academic and professional organizations and fellowships, scholarships, or other honors received (for additional information see http://www.utdallas.edu/admissions/graduate/degrees).
Request for Recommendation Forms
Applicants must ask three individuals (employers, community leaders, teachers, etc.) who are able to judge their ability to complete the graduate study program and their probable success in graduate school by completing recommendation forms. These letters of recommendation may be sent directly to the contact listed for the degree program. The form is located at: http://www.utdallas.edu/admissions/graduate/documents/GradReqRecommendation.pdf. Letters of recommendation can also be requested online as a part of the Graduate Application for Admission.
International applicants without Permanent Resident Visas must submit evidence of financial support (financial affidavit and original bank statement) before they can receive the I-20 or other required documents needed for visa application.
In accordance with Chapter 51, of the Texas Education Code, decisions on admission to degree-granting graduate programs at UT Dallas are based on holistic considerations of all submitted information regarding the academic, career, and personal histories of the applicants. Standardized test scores and coursework GPA levels cited in the catalog descriptions of some degree programs are listed for advisory purposes only, to indicate the typical achievement levels of students enrolled and succeeding in the various programs. No single quantitative or qualitative measure, or any specific combination thereof, constitutes a definitive standard for admission. Rather, each application will be considered individually and each applicant's complete profile of strengths and prospects for successful completion of the program will be evaluated by the admissions committee. Applicants are encouraged to contact the graduate advisor in the degree program in which they expect to enroll to discuss specific admission requirements.
Applicants who satisfy all of the above criteria qualify for regular admission to the degree program.
Students denied the regular admission status may qualify for admission under one of the following special admission requirements:
Conditional Degree-Seeking Graduate Student
Upon review of the credentials of an applicant seeking regular admission to a UT Dallas degree program, the graduate studies committee of that degree program may recommend, and seek concurrence of the Dean of Graduate Studies, that the applicant being admitted be subjected to specific conditions being satisfied over a specified time period. Such conditions might include requiring additional semester credit hours to be taken, and/or a specific GPA to be maintained. A student satisfying the conditional requirements within the specified time period will then qualify for regular admission. The graduate advisor in the academic program will monitor compliance with the admissions conditions. A student who does not fulfill the specified conditions within the time period specified at the time of admission will be barred from continued registration in the degree program.
Normally a student cannot remain in conditional status for more than one calendar year. Exceptions to the one-year limitation can be granted only by the Dean of Graduate Studies upon recommendation of the graduate program. Under no circumstances will the student be allowed to remain enrolled under Conditional Status for more than 15 semester credit hours or two consecutive years, whichever comes first. Within these limits, specified graduate level coursework taken as a conditionally admitted student can be applied to the degree program.
Non-Degree Seeking Graduate Student
A student wishing to take graduate level coursework without becoming a candidate for a graduate degree may apply for admission to UT Dallas as a non-degree seeking graduate student. The non-degree student seeking admission to the master's degree program must satisfy the condition of having an earned baccalaureate degree for admission to a master's degree program at UT Dallas. The applicant should consult with the department or program offering the graduate level coursework to determine GRE/GMAT and letters of recommendation requirements.
The applicant should consult with the graduate advisor in the department or program offering the graduate level coursework. The graduate advisor in the degree program will define specific eligibility requirements and admit students to the courses open to non-degree enrollment each semester. Enrollment as a non-degree student is restricted to the regular registration period each semester. Please refer to the graduate catalog in each school for additional information on prerequisite requirements for each course.
Enrollment as a non-degree seeking graduate student is subject to review and approval by the Associate Dean of Graduate Studies in the specific school. No more than 15 semester credit hours taken as a non-degree enrolled student at UT Dallas may be transferred to satisfy the requirements of a graduate degree program, except with the permission of the Dean of Graduate Studies. Students admitted as non-degree seeking may not be eligible for financial aid and should consult the UT Dallas Financial Aid office regarding their status prior to submission of their application for admission.
NOTE: International students are not eligible to maintain F or J immigration status by participating in a non-degree seeking program. Exceptions include those enrolling in pre-established international exchange mobility programs, and transient or visiting F-1 and J-1 students whose immigration documents are issued by another U.S. college or university.
Graduate Student Taking Only Undergraduate Courses
Upon review of the academic background leading to the award of a Bachelor's degree by the academic advisor in the graduate program, a student may elect to take or be restricted to taking only undergraduate level courses. The Associate Dean of Graduate Studies and/or Associate Dean for Undergraduate Studies in the specific school must approve enrollment in the undergraduate courses and the student will be required to maintain the same scholastic standards as regularly admitted undergraduates. In addition the student will receive academic guidance from the advisor in the school. Students restricted to taking undergraduate courses may not take graduate courses in a degree program at the same time. Consultation with the UT Dallas Office of Financial Aid regarding aid eligibility is strongly advised before enrolling.
English Proficiency Requirements for International Applicants
International applicants expecting to hold a non-immigrant visa type, from non-English speaking countries, whose primary language is not English and who graduated from a non-U.S. university where the language of instruction and examination was not English, must demonstrate English proficiency.
English proficiency requirements can be met by:
Achieving a minimum score of 550 on the Test of English as a Foreign Language (TOEFL) PBT (paper-based test),
Achieving a minimum score of 80 on the TOEFL IBT (Internet-based test),
A minimum score of 6.5 on the International English Language Testing System (IELTS) test,
A minimum score of 67 on the Pearson's Test of English Academic (PTE), or
A successful completion in level 112 of English from the ELS Language Centers, http://www.els.edu.
This requirement should be met at the time the admission application is submitted. Applicants with lower scores will be considered but are advised to improve their test scores and reapply.
Applicants native to a country where the primary language is English and have earned a baccalaureate degree from an accredited institution of higher education where the language of instruction and examination was in English are excused from this requirement. Scores must not be more than two years old, and an official copy must be sent from the testing agency to:
Higher scores may be required if the applicant is to succeed in the competition for Teaching Assistant openings.
English Requirements for Teaching Assistants
Students are required to be able to speak and write English clearly and well. Texas state law and regulations, Texas Education Code, Section 51.917, require that international students appointed as Teaching Assistants (TA's) be proficient in the use of the English language. An English Proficiency Interview conducted under the auspices of the office of the Dean of Graduate Studies will be used to screen for students requiring remedial help in the form of English as a Second Language (ESL) course. International students must satisfy the proficiency requirement upon appointment or pass the ESL course within two semesters to be eligible for consideration of continued appointment as a TA. Regardless of test scores, students must meet the language requirements of their programs.
Updated: 2019-08-09 13:11:11 v12.6b3970
Arts and Technology Program
Arts and Technology (MA)
Arts and Technology (MFA)
Arts and Technology (PhD)
EMAC
Emerging Media and Communication Program
Emerging Media and Communication (MA)
Criminology Online (MS)
Sociology Graduate Programs
Healthcare Management (MS)
Healthcare Management (MS) - Professional Track
Healthcare Management (MS)- Executive Track
Healthcare Management - Certificate Programs
Management and Administrative Sciences (MS)
Certificates in Systems Engineering and Management
Healthcare Management for Physicians (MS)
Healthcare Management for Physicians (MS/EMBA)
Systems Engineering Certificate
Applied Mathematics Specialization (MS)
Engineering Mathematics Specialization (MS)
Mathematics Specialization (MS)
Statistics Specialization (MS)
Applied Mathematics Specialization (PhD)
Statistics Specialization (PhD)
Science/Mathematics Education Program
Teaching in Science Education (MAT)
Teaching in Mathematics Education (MAT)
Taken in Fast Track Options
Catalog Appendices
Appendix I - Rules, Regulations, and Statutory Requirements
Appendix II - Respective Responsibilities of Students and Faculty
'); $('.cat-dateversion').hide(); var viewport_height = $(window).height(); editor.resize('100%', viewport_height-250); }); // hide workflow $('#workflow-status').slideUp(); // CKEDITOR.replace('catml', { contentsCss: '/css/amtor2-ckedit.css', height: '1500px', allowedContent: 'p' } ) } // save html function edit_save() { var editor_data = editor.getData(); $('#out').load( 'https://catalog.utdallas.edu/clips/clip-nekobukku-actions.zog', { bukku_id:'2015-graduate', page_address:'admission', action:'save', editor_data:editor_data }); return(true); } var ace_editor; function edit_catml() { $('#bukku-page').load( 'https://catalog.utdallas.edu/clips/clip-nekobukku-actions.zog', { bukku_id:'2015-graduate', page_address:'admission', page_version:'12', action:'get_catml' }); } // save catml (text) function save_catml() { var editor_data = ace_editor.getSession().getValue(); $('#out').load( 'https://catalog.utdallas.edu/clips/clip-nekobukku-actions.zog', { bukku_id:'2015-graduate', page_address:'admission', action:'save', aceeditor_data:editor_data }); return(true); } // diff function neko_diff(diff_type) //function neko_diff(diff_type, bukku_id, page_address) { switch (diff_type) { case 'year': $('#bukku-page').load('https://catalog.utdallas.edu/clips/clip-nekobukku-actions.zog', { bukku_id:'2015-graduate', page_address:'admission', action:'get_diff_year' } ); break; } } function narrative_selected() { var pid = $('#narr_select').val(); window.location = 'http://sacscoc.utdallas.edu/5yrnav/'+pid; } // get selected text var last_selected_text; function get_selected_text() { var t = ''; if(window.getSelection){ t = window.getSelection(); } else if(document.getSelection){ t = document.getSelection(); } else if(document.selection){ t = document.selection.createRange().text; } last_selected_text = t.toString(); return(last_selected_text); console.log(last_selected_text); } // google search - enable searching if this is a search page if (typeof google == 'object') { google.load('search', '1', { language: 'en', style: google.loader.themes.V2_DEFAULT }); google.setOnLoadCallback(function() { var customSearchOptions = {}; customSearchOptions['adoptions'] = { 'layout': 'noTop' }; customSearchOptions[google.search.Search.RESTRICT_EXTENDED_ARGS] = { 'as_sitesearch': 'catalog.utdallas.edu/2015/graduate' }; var customSearchControl = new google.search.CustomSearchControl('xxx', customSearchOptions); customSearchControl.setResultSetSize(google.search.Search.FILTERED_CSE_RESULTSET); var options = new google.search.DrawOptions(); options.enableSearchResultsOnly(); customSearchControl.draw('cse', options); function parseParamsFromUrl() { var params = {}; var parts = window.location.search.substr(1).split('&'); for (var i = 0; i < parts.length; i++) { var keyValuePair = parts[i].split('='); var key = decodeURIComponent(keyValuePair[0]); params[key] = keyValuePair[1] ? decodeURIComponent(keyValuePair[1].replace(/\+/g, ' ')) : keyValuePair[1]; } return params; } var urlParams = parseParamsFromUrl(); var queryParamName = "q"; if (urlParams[queryParamName]) { customSearchControl.execute(urlParams[queryParamName]); } }, true); } (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)})(window,document,'script','https://www.google-analytics.com/analytics.js','ga'); ga('create', 'UA-19668321-7', 'auto'); ga('send', 'pageview');


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text:
REDISCOVER Nina Simone
The iconic Ms. Simone bares her heart & soul in each & every one of her inspired, passionate songs.
Sinnerman
Fodder On My Wings
To Love Somebody (Expanded Edition) (Expanded Edition)
I Loves You Porgy (Live At Carnegie Hall, New York, 1964)
Four Women: The Nina Simone Philips Recordings
Mississippi Goddam (Live At Carnegie Hall, New York, 1964)
The House Of The Rising Sun
Nina Simone Sings The Blues (Expanded Edition)
Here Comes The Sun (Expanded Edition)
Take Care Of Business
Beautiful Land
I Wish I Knew How It Would Feel to Be Free
I Want A Little Sugar In My Bowl
My Baby Just Cares for Me
Don't Explain
I Shall Be Released
The Very Best Of Nina Simone 1967-1972 - Sugar In My Bowl
Ne Me Quitte Pas
To Be Young, Gifted And Black
Do What You Gotta Do (Live at Westbury Music Fair, Westbury, NY - April 1968)
'Nuff Said (Expanded Edition)
Silk & Soul (Expanded Edition)
I Think It's Going To Rain Today
Bye Bye Blackbird
At the Village Gate
See-Line Woman
Gin House Blues
Dambala (Live at Philharmonic Hall, NYC - July 1973)
It Is Finished (Expanded Edition)
Be My Husband
Summertime (Vocal) (Remastered) (From "Nina Simone At Town Hall")
Four Classic Albums (The Amazing Nina Simone / Nina Simone at Town Hall / Forbidden Fruit / Nina Simone at Newport) (Remastered)
Le peuple en Suisse
O-o-h Child
Do I Move You?
Mood Indigo
Little Girl Blue (Remastered 2013)
Black Is The Color Of My True Love's Hair (live)
One By One
Backlash Blues
Why? (The King of Love Is Dead) (Live)
Children Go Where I Send You
Since I Fell For You (Original Mix)
Mr. Bojangles
I Think It's Going to Rain Today (Live in Munich, Germany - April 1969)
Nina Simone & Piano (Expanded Edition)
Just Like a Woman
Let It Be Me
Blues For Mama
The Times They Are A-Changin'
Ain't Got No - I Got Life (From the Musical Production "Hair") (Live)
Marriage Is For Old Folks


Manta Ray (Manta birostris) at Hin Daeng, Thailand.
Manta Rays are a large genus of flattened fish.
Manta Rays are closely related to cartilaginous fish such as sharks.
Some manta rays reach up to 7 meters wide.
There are two species of Manta Rays.
Both species of Manta Rays are considered vulnerable.
Manta rays are the biggest of all rays.
Manta Rays eat by filtering plankton from the water.
Manta Rays evolved from bottom feeding rays.


text:
The Fashion Match
Your Daily Fashion And Beauty News
British Model Stella Tennant Has Died
Stella Tennant arrives at The Fashion Awards 2019. (Photo by Jeff Spicer/BFC/Getty Images)
Victoria DiPlacido
British model Stella Tennant, 50, has died. A statement from her family, as per the BBC, reads: "It is with great sadness we announce the sudden death of Stella Tennant on 22nd December 2020. Stella was a wonderful woman and an inspiration to us all. She will be greatly missed."
The cause of death has not been announced, but police say there are "no suspicious circumstances."
Tennant was a favourite of fashion designers, including the late Karl Lagerfeld, who likened Tennant to Coco Chanel, and made her a face of the French luxury brand in the '90s. She also appeared in campaigns for Hermes, Burberry and Calvin Klein, among others. As of 2019, she was still walking the runway for brands like Alexander McQueen, Saint Laurent and Valentino haute couture.
Stella Tennant walks the runway during the Alexander McQueen runway during Paris Fashion Week Womenswear Fall/Winter 2019/2020 in March 2019. (Photo: Imaxtree)
During the closing ceremony of the 2012 London Olympics, Tennant walked alongside other British supermodels Lily Cole, Karen Elson, Kate Moss, Naomi Campbell, Jourdon Dunn and David Gandyand, and wore an outfit by British designer Christopher Kane.
When she appeared on the December 2018 cover of British Vogue, the magazine noted that "few models have had as sizeable an impact on British fashion [as Tennant]."
Stella Tennant walks the runway during the Valentino Haute Couture Spring/Summer 2020 show as part of Paris Fashion Week in January 2019. (Photo: Imaxtree)
The model was a supporter of slow fashion and partnered with Oxfam for Second Hand September, an initiative to draw awareness to the environmental impact of fast fashion. "At my age I think it's probably quite normal you're not that interested in consuming, [and not] loving shopping as much as when you're much younger," she told The Guardian. "It's going to take us a long time to change our habits, but I think that this is so obviously a step in the right direction," she added.
Stella Tennant walks the runway during the Saint Laurent Womenswear Spring/Summer 2020 show as part of Paris Fashion Week in 2019. (Photo: Imaxtree)
Tennant leaves behind her husband, photographer David Lasnet, and four children, Marcel, 21, Cecily, 19, Jasmine, 17, and Iris, 15.
This article was originally published by Fashionmagazine.com. Read the original article here.
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text:
https://www.healthcentral.com/encyclopedia/post-polio-syndrome
Encyclopedia /P /Post-Polio Syndrome
Post-Polio Syndrome or Sequelae (PPS) is a condition that can strike polio survivors anywhere from 10 to 40 years after recovery from an initial attack of polio. PPS is characterized by a further weakening of the muscles that were previously injured by polio. PPS is usually a very slow progressing condition and often occurs after a physical or emotional trauma, illness or accident.
Polio, also known as poliomyelitis or infantile paralysis, is caused by three viruses that enter the mouth, grow in the intestines and pass along the nerves into the brain and then the spinal column causing the nerves to stop working normally. The loss of mobility caused by the damaged nerve(s) is called paralysis and can affect the muscles in the arms, legs, chest, diaphragm and throat.
Polio was eradicated in the 1950s with the introduction of the polio vaccine developed by Jonas Salk. Mandatory immunization for polio is recommended at 2 months of age, 4 months of age, 6 to 18 months age, and 4 to 6 years of age.
It is estimated that between 40 and 80 percent of polio survivors will get PPS after 20 to 40 years of initial onset of polio.
To date, doctors are not certain what causes PPS but researchers hypothesize that it may be one of the three possibilities.
The first possibility is that the polio virus becomes active again after decades of lying dormant in the victim's cells. What activates it is still uncertain and is under investigation. British scientists have found polio-type antibodies in the spinal fluid of recently diagnosed PPS sufferers.
The second possibility is the evidence of impairment in the production of various hormones and neurotransmitters in brain.
Lastly, and the most promising theory of PPS, is the nerve cells (neurons) that were undamaged or partly damaged took over the functions of the dead cells. These nerve cells not only had to do their job, but the job of the damaged cells. Over time, these overworked cells began to show "wear and tear," thus the re-emergence of polio-like symptoms.
The symptoms of PPS mimic those of the original disease, polio, and can occur in previously affected muscles and/or in what were previously thought to be muscles that were not affected at onset. The symptoms may include:
Muscle weakness
Muscle atrophy (stiffening)
Painful joints and muscles
Sensitivity to cold and heat
Difficult breathing and/or swallowing
Muscle twitching
The extent to which polio survivors will suffer from PPS depends on how seriously they were affected by the first polio attack. Patients who had mild symptoms of polio will have mild symptoms of PPS. However, post-polio survivors who had hospital care, or were older than 10, or had paralysis in all four limbs, or needed mechanical assistance to breathe will have a more severe case of PPS.
Diagnosis of PPS is made after observing the patient, asking about the symptoms and reviewing their medical records for previous polio infection. If needed, an electromyographic study (EMG) will be done to determine which extremities have evidence of old polio. EMG is a test in which electrical activity in muscle in analyzed.
To relieve the pain of PPS, doctors may recommend nonsteroidal anti-inflammatory medications (NSAIDs) such as aspirin and ibuprofen (Advil, Motrin IB or Nuprin), and heat applications on the affected areas. Otherwise, the treatment therapy for PPS is to "take it easy." Things to do that may help the PPS sufferer cope with this condition are as follows:
Take time to nap
If the person is experiencing increasing muscle weakness, exercise may be needed. Please consult your doctor.
Eat a well-balanced diet
Be alert to the changes in the body
Take note of any new symptoms or any clear or gradual changes
Pace yourself
Use medical equipment such as canes or crutches or other adaptive medical equipment to ensure that you do not fall
Minimize alcohol use
Take common colds very seriously
Get regular checkups with emphasis on the major areas affected by the polio: neuromuscular, circulatory and respiratory, as well as, muscle strength evaluations
Discontinue any exercise that causes pain, weakness or muscle fatigue
Know your own strengths and limits
Get involved or continue with physical therapy
Consider possible adaptations to your lifestyle
Is the pain, fatigue, and muscle weakness due to the original polio?
Should the muscles be rested or exercised?
What exercises are recommended to strengthen the muscles?
What changes in lifestyle are called for?
Can muscle be added surgically?
What assistive devices can be used?
Will the problem get worse?


On behalf of Law Office of Ann A. Thomson (Seal Beach family law attorney), A Professional Law Corporation posted on Friday, October 25, 2013.
A child support check has resulted in bank fees for a California mother received a child support payment that cost her more than the actual check was worth. The woman's account with the Pleasanton Child Support Services office had a balance of three cents that had not been distributed to her. To close the account, the office sent the mother a check.
Unsure whether the check was real or a joke, the woman didn't cash it. When she received another child support check for three cents, she contacted Child Support Services to find out why they were sending her checks in such small amounts. She was reportedly told that she needed to cash the checks to close out her account.
When she deposited the checks, she found out that she was only due three cents and Child Support Services didn't pay the second child support check. The woman was charged a $15 fee by her bank for bouncing a check. When she called the child support office for assistance, she was reportedly told that she was just going to have to pay the fee. After contacting her local news station, the woman tried to get the bank to remove the charge from her account.
Many divorced parents rely on the child support system to collect and distribute money to custodial parents. The system allows parents who no longer live with their children to continue providing financial support. Family courts often establish child support orders when a couple gets divorced. A lawyer with experience handling divorce and child support issues may be able to help a parent get a fair child support order, so they can adequately care for their children.


text:
Art History and Modern Languages BA (Hons)
( 4.2) 989 reviews
Other courses you might like
UCAS CODE VR39
This course allows students study the history and theory of art combined with French (post-A Level), German, Italian, Spanish or Portuguese (post-A Level or ab initio), and includes the chance to spend a year abroad studying at one of the partner institutions, such as the Ecole du Louvre in Paris. Students receive a distinctive curriculum which covers the whole history of art, enabling them to choose from a variety of specialist options, while simultaneously becoming proficient in one or more... READ MORE
This course allows students study the history and theory of art combined with French (post-A Level), German, Italian, Spanish or Portuguese (post-A Level or ab initio), and includes the chance to spend a year abroad studying at one of the partner institutions, such as the Ecole du Louvre in Paris. Students receive a distinctive curriculum which covers the whole history of art, enabling them to choose from a variety of specialist options, while simultaneously becoming proficient in one or more languages.
Assessment methods include coursework, for example essays, analysis of source material, exhibition reviews and virtual portfolios, coursework reports, oral presentations; Languages assessed through role-plays and translations; Written examinations are also taken for the majority of modules at the end of each academic year. As the graduate job market becomes increasingly global the ability to speak a modern language will place them in a competitive position. Language skills are in scare supply and can be used in almost any job. Graduates from our BA Art History and Modern Languages are prepared for roles as curators, teachers, translators and journalists.
57th / 131
History Of Art, Architecture and Design
DATA SOURCE: UNISTATS
Drop-out rate
The percentage of students who didn't finish the course.
Calculated by 100% - (% of students continuing course + % of students completed course)
100 - 85 = 15% Drop-out rate
The percentage of students who graduated with a 2:1 degree or better.
The percentage of students in full-time work 6 months after finishing their course.
After 6 months
After 6 months After 40 months
History Of Art, Architecture and Design graduate salary at this uni
UK History Of Art, Architecture and Design graduate salary
Others in Language and Area Studies
Others in Language and Area Studies graduate salary at this uni
UK Others in Language and Area Studies graduate salary
A level Access to HE Diploma International Baccalaureate Diploma Programme Pearson BTEC Level 3 National Extended Diploma (first teaching from September 2016)
Grades / Points required BBB Grades / Points required M:45 Grades / Points required 30 Grades / Points required Not currently available, please contact university for up to date information.
Not currently available, please contact university for up to date information. Not currently available, please contact university for up to date information. We are happy to consider a combination of separate IB Diploma Programmes at both Higher and Standard Level. Exact offer levels will vary depending on the range of subjects being taken at higher and standard level, and the course applied for. Please contact the Undergraduate Admissions Office for more information. Entry requirements for students studying BTEC qualifications are dependent on subjects studied. Advice can be provided on an individual basis. The standard required is generally at Distinction level.
The most common A-levels taken by students who end up studying this subject at uni.
Source: HESA, 2018
DATA SOURCE: HESA
Top 5 A-levels taken by students who study this subject at uni
England Scotland Wales Northern Ireland EU Rest of World
This is the fee you pay if you live within Channel Islands.
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University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ
CO4 3SQ United Kingdom Nearest train station: Hythe 0.6 miles away
Modern Languages (Italian and Russian) BA (Hons)
University of Central Lancashire
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Modern Languages BA (Hons)
History of Art with Gallery & Museum Studies BA (Hons)
Compare Art History and Modern Languages BA (Hons) at University of Essex against all the unis listed above.
Uncompare Art History and Modern Languages BA (Hons) at University of Essex against all the unis listed above.