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Methodology article | Open | Published: 17 March 2005
Combining Affymetrix microarray results
John R Stevens1 &
RW Doerge1,2
As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.
We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis.
The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest.
Microarray technology allows simultaneous assessment of transcript abundance for thousands of genes. This exciting research tool permits the identification of genes which are significantly differentially expressed between conditions. With the use of microarrays becoming more commonplace, it is not unusual for several different laboratories to investigate the genetic implications of the same condition(s). Each lab may produce its own list of candidate genes which they believe to be related to the condition of interest. As a result of sound statistical approaches, each lab will also have for each candidate gene some quantitative measure that serves as the basis for the claim of statistical significance.
Of interest in this paper are the methods by which these quantitative measures may be combined across labs to arrive at a more comprehensive understanding of the effects of the different candidate genes. Where the term "analysis" is used to describe the quantitative approaches to draw useful information from raw data, the term "meta-analysis" [1] refers to the approaches used to draw useful information from the results of previous analyses. Meta-analysis has been predominantly used in the medical and social sciences, in situations where several studies may have been conducted to investigate the effect of the same treatment, and the researcher seeks to combine the results of the different studies in a meaningful way in order to arrive at a single estimate of the true effect of the treatment. For the current application, meta-analytic approaches can be employed to combine the results from several different labs without having access to the original raw data that yielded the initial results. Such approaches have particular utility with the results of Affymetrix GeneChip® microarrays and other fabricated arrays, where results are given in a uniform format that readily lends itself to comparison between labs and combination across labs.
A measure of the degree or magnitude of differential expression provides more information regarding a gene's relation to a disease or condition of interest than does a statement regarding its significance or nonsignificance. This information is useful because it allows for greater precision of estimation of the gene's effect with respect to the condition of interest. That is, to arrive at a clearer understanding of a gene's true effect relating to the condition of interest, it is most helpful to have a quantitative measure of the magnitude of differential expression rather than a simple declaration of significance.
Prior applications of meta-analysis to microarray data have either sought to combine P-values or to combine results across platforms (i.e., combining Affymetrix and cDNA array results) [2–6]. Combining only P-values, while useful in obtaining more precise estimates of significance, does not provide information that is easily interpretable by a biologist, may not indicate the direction of significance (e.g., up- or down-regulation), and most importantly, gives no information regarding the magnitude of the estimated expression change. Similarly, while a "vote-counting" approach based on P-values [6] addresses differences in lists of significant genes from separate experiments, it gives no information regarding the magnitude of the estimated expression change. While an "integrative correlation" approach [5] will help identify genes with reproducible expression patterns, it also does not provide any information regarding the magnitude of the estimated expression change
Previous attempts to combine results across microarray platforms (i.e., technologies) assume that spot intensities or signal values for a given gene can be directly compared even though they represent different segments of the gene. That is, a spot for a given gene on a cDNA array represents the entire gene, while each spot for the same gene on an Affymetrix array represents a specific small section of the gene. Thus, combining results across technologies using only spot intensities is problematic from a biological perspective because the measurements represent different physical quantities. Even if the average spot intensity on an Affymetrix array is used, it is not certain that this average spot intensity value is at all comparable to the spot intensity value of the gene on a cDNA array.
Moreau et al. [4] report that 'after appropriate filtering, ratio and intensity data from different platforms can be compared and are amenable to' be used in a meta-analysis. However, "filtering", or "averaging out" outliers or non-reproducible spots, requires some subjectivity in the method of choice and may force agreement between platforms where no agreement should exist due to fundamental technological differences. Parmigiani et al. [5] attempt to address this problem of cross-platform consistency by identifying a set of genes whose expression patterns are essentially reproducible across platforms. However, even for these "reproducible" genes, there remains the question of how to systematically combine their corresponding results from the several laboratories to arrive at a single quantitative measure of differential expression. At the very least, if results are to be combined across platforms in a meta-analysis, the use of covariates [7] should be employed to account for the underlying differences between oligonucleotide (e.g., Affymetrix) and cDNA platforms. The focus of the current application is restricted to standard Affymetrix microarray results, and a method to combine results across laboratories is proposed and evaluated.
Affymetrix technology
The Affymetrix GeneChip® microarray [8] represents individual genes by 25-mer segments (probes) fixed to the chip, and also makes use of mismatch probes differing at position 13. Each gene on the chip is typically represented by the same number of probe pairs on the chip (usually 14–20), although exceptions exist. It is now possible for some organisms' entire genomes to be represented on a single microarray (e.g., Arabidopsis). Appropriately prepared tissue sample is hybridized to the array and the array is scanned, producing raw data consisting of the intensities of the individual spots on the array. These intensities come in pairs, with PM denoting the intensity of a perfect-match probe and MM denoting the intensity of the corresponding mismatch probe.
Affymetrix algorithms
Affymetrix has developed statistical algorithms [9] that employ these individual spot intensities for the purpose of estimating the true expression levels of individual genes in single samples. Furthermore, the Affymetrix approach compares gene expression levels in two different tissues (samples or treatment conditions) and reports a "signal log ratio" (SLR) with 95 percent confidence bounds. The signal log ratio is the signed Iog2 of the signed fold change (FC) familiar to biologists [9]. That is, FC = 2SLRif SLR ≥ 0 and FC = (-1)2-SLRif SLR < 0. The algorithm used by Affymetrix to compute the SLR is based on Tukey's biweight algorithm [10] and for each gene takes a weighted average of the log2 of the ratio of PM - MM between treatments or conditions, with weights related to the deviations from the median log2 ratio for the gene, and with adjustments made when PM <MM. The resulting weighted average is the SLR.
Between the two conditions of interest, each gene either changes its level of expression or the level remains the same. A declaration of significant differential expression results from sufficient evidence that the gene is not expressed the same in the two conditions (i.e., that the SLR differs significantly from zero). Tukey's biweight algorithm provides an estimate for the variability of the SLR and an approximate distribution for the SLR estimate. The Affymetrix software (Microarray Suite Version 5.0, or MAS 5.0) reports a 95 percent confidence interval for the SLR [11], from which the estimated standard error can be computed. Individual laboratories can use this information to make a declaration of significant differential expression.
It should be noted that this SLR estimate represents a measure of differential expression between two chips (generically referred to as a base sample chip and an experimental sample chip). In practice, of course, it is recommended that experiments involve more than two chips, but the current MAS 5.0 algorithm is designed to represent differential expression only between two chips at a time. Other approaches exist to measure differential expression (dChip [12] and RMA [13], for example), and future work will evaluate their performance in meta-analyses. However, for the purposes of this current work, the focus of differential expression will rely on the SLR estimate of differential expression between two chips, because these are the estimates provided automatically by the commercial MAS 5.0 software.
If we let
denote the estimate of the SLR θi,kfor gene k in lab i, and be the upper bound for the 95 percent confidence interval for the same, then both and are reported by the Affymetrix software. The Affymetrix documentation [9] gives the estimated standard error of as si,k= ( - ) / ti,k(.975), where ti,k(.975) is the upper .025 critical value of the t distribution with dfi,kdegrees of freedom, where dfi,k= max(0.7(ni,k- 1), 1), with ni,krepresenting the number of probe pairs representing gene k on each array in study i. The estimated variance of the SLR estimate is vi,k= .
Accordingly, lab i could then test for significant differential expression of gene k (i.e., test the hypothesis : θi,k= 0) by use of the test statistic Ai,k= /si,k. Under , Ai,kapproximately follows the t distribution with dfi,kdegrees of freedom. The significance P-value (Pi,k) for gene k in lab i is the value such that |Ai,k| is the upper Pi,k/2 critical value of the t distribution with dfi,kdegrees of freedom, and is rejected at the αi,klevel if Pi,k<αi,k. That is, if Pi,kis sufficiently small, then lab i would declare gene k significantly differentially expressed.
The general meta-analytic framework [7] assumes that a measurable relationship exists between certain quantities of interest, and n independent studies have been conducted to examine this relationship. In turn, this relationship can be quantified so that each study produces an estimate of the relationship. If the estimates are appropriately standardized, then each study's estimate can be termed an "effect size" estimate. An effect size is essentially a standardized quantitative expression of the relationship of interest. For example, several different laboratories may investigate which of two drugs are better at treating a particular disease. In this case, the relationship of interest is the difference between the drugs' effects. If each laboratory produces an estimate standardized such that estimates from all laboratories address the same quantity and are on the same scale, then these estimates are effect size estimates.
There are three main classes of effect size estimates [14]. The first and perhaps most common is the standardized difference estimate, such as Hedges's g, similar to the t-statistic in a two sample study: . The second is the standardized relation estimate, such as the sample correlation coefficient r. The third is the measure of significance, such as the P-value from a particular hypothesis test. (Although not an effect size in the traditional sense, the measure of significance approach is mentioned here for the sake of completeness.)
In order to be combined across studies, effect size estimates must address the same measure or quantity, be standardized, and (with the exception of P-values, which are combined differently [15]) include some measure of variability of the effect size estimate [16]. Once each study i has provided its effect size estimate and its measure of variability v i , a meta-analysis can be performed. Three main meta-analytic approaches exist: fixed effects, random effects, and hierarchical Bayes. The first two approaches are summarized here in order of increasing complexity, and the third is the subject of Choi et al. [3] and a future research interest. The three approaches are discussed more fully in Cooper and Hedges [14] and DuMouchel and Normand [17].
Fixed effects meta-analysis model
Assume that n independent studies have provided effect size estimates and measures of variability v i , i = 1, ..., n. The most general meta-analytic approach assumes that
with sampling error ε i ~ N(0, ). That is, each is an estimate of a true fixed underlying effect size θ i , and it is assumed that θ1 = ... = θ n with the common value θ. This is referred to as the homogeneity assumption and can be interpreted as assuming that all studies examined and provided estimates of the same parameter θ, and any differences between the estimates are attributable to sampling error alone. This common value parameter θ is estimated as a weighted average of the effect size estimates:
The weights w i are chosen to minimize the variance of , and this is achieved by , where v i is the estimated variance of . The variance of is .
The underlying assumption of homogeneity
: θ1 = ... = θ n can be tested by use of the test statistic
, Q is approximately distributed as . Then this χ2 distribution can serve as the basis for an approximate test of homogeneity. If Q is larger than the upper α Q critical value of the distribution, is rejected at the α Q level. Alternatively, the homogeneity P-value P Q is the value such that Q is the upper P Q critical value of the distribution, and is rejected at the α Q level if P Q <α Q .
The test of significance
: θ = 0 can be considered by use of the test statistic . Under , Z is distributed as N(0, 1), and if |Z| is larger than the upper α Z /2 critical value of the N(0,1) distribution, is rejected at the α Z level. Alternatively, the significance P-value P Z is the value such that |Z| is the upper P Z /2 critical value of the N(0, 1) distribution, and is rejected at the α Z level if P Z <α Z .
Meta-analysis in the context of a microarray experiment assumes that several laboratories have provided quantitative measurements of differential expression (the effect size) for a number of genes along with variability estimates. For the fixed effects model, the homogeneity assumption (
) provides that for each laboratory the gene is expressed the same, and differences between laboratories are due to sampling error only. On the other hand, the hypothesis has the biological interpretation that there is no change in gene expression between the conditions of interest. This test of significance identifies genes that are significantly differentially expressed between the two conditions, using information from multiple laboratories.
Random effects meta-analysis model
In practice, the homogeneity assumption (and the resulting fixed effects model) tends to be overly simplistic but is presented in this paper for the sake of completeness. This assumption can be relaxed to make the meta-analysis model more appropriate. The basic random effects model [18] assumes n independent studies have provided effect size estimates and measures of variability v i , i = 1, ..., n. In addition, the model assumes that
In this framework, θ is the population mean effect size, and there are two error components, δ and ε, corresponding to between-study and within-study variability, respectively. Each study seeks to make statements regarding this quantity θ, and so takes a sample of individuals from a certain population in order to study the underlying effect size θ. However, due to differences between studies such as time, location, equipment, and other uncontrollable (and possibly unknown) factors, each study will in fact be estimating a slightly different quantity. That is, due to differences between studies, study i is estimating θ i , a random effect size from the population of all possible effect sizes. The error component δ i ~ N(0, Δ2) is the random deviation of θ i from θ (representing variability between studies). In this basic model, Δ2 represents the random variation between studies. Within study i, the actual estimate will vary from the "true" effect size θ i based on which random sample is selected. That is, replicates within a study will result in slightly different estimates of the effect size due to sampling error. Here, ε i ~ N(0, ) is sampling error (representing variability within study i).
Q is calculated as in the fixed effects model. (Note that the fixed effects model assumes that Δ2 = 0.) The random effects model uses this Q value to calculate new weights , where
Then the meta-analysis estimate for the population mean effect size θ is
The variance of
is . The test of significance : θ = 0 can be considered by use of the test statistic . Under , Z w is distributed as N(0, 1), and the significance P-value is calculated in the same manner as in the fixed effects model.
When the random effects meta-analysis is applied in the context of a microarray experiment, again it is assumed that several laboratories have provided quantitative measurements of differential expression (the effect size) for a given gene along with variability estimates. The random effects model assumes that there is some true degree of differential expression for the gene, and each lab is actually estimating a slightly different true degree of differential expression. That is, each laboratory has a slightly different "true" degree of differential expression. In addition, the estimate from each laboratory varies randomly about its true degree of differential expression due to sampling error. Then Δ2 is a measure of the amount of variation between the laboratories' true degrees of differential expression, and the test of significance is used to identify differentially expressed genes by using information across multiple laboratories.
Meta-analysis with Affymetrix data
Our motivation for applying meta-analytic techniques to microarray data is threefold. First, standard platforms (e.g., Affymetrix) make combining results across labs straightforward and eliminate the usual criticism of meta-analyses that "apples and oranges" are being mixed [16] because the estimates being combined across labs have each been standardized by the same algorithms [9] in such a way that they are in fact estimates of the same underlying effect. Furthermore, any known differences between laboratories such as sample tissue type can be incorporated into the meta-analysis by use of covariates [7]. Second, combining raw data may provide more information than combining results, but raw data are not always easy to obtain, and it is conceivable that raw data may become unavailable while published (or unpublished) results are available. Third, if it can be shown that meta-analysis produces similar results to the pooling of raw data, then it can be argued that meta-analytic approaches are more efficient in the sense that they only require easily obtainable results rather than the raw data.
The uniformity of chip design and data acquisition from Affymetrix oligonucleotide microarray experiments readily lends itself to a meta-analysis. Given n studies examining the differences in gene expression between two treatments (e.g., healthy vs. diseased), a meta-analysis can combine each study's signal log ratio (SLR) estimates in a meaningful way by taking the SLR as the effect size estimate. The SLR satisfies the criteria for an effect size (i.e., comparability of estimates, standardization to the same scale, and availability of a variance estimate). The SLR for a given gene represents the degree of differential expression between two conditions, and is directly comparable between labs since it estimates the same physical quantity. The SLR from Affymetrix is standardized in the sense that a SLR of zero means no differential expression is observed, and the algorithms used to produce the SLR place all SLR estimates on the same scale. Finally, a variance for the SLR estimate is provided by the Affymetrix algorithms [9, 10].
A general fixed effects model can be employed to perform a meta-analysis to estimate the true effect size (signal log ratio, SLR) θ k of gene k. In addition, the test of homogeneity can be evaluated to determine whether the n studies are in fact estimating the same true underlying value of θ k , i.e., whether θ1,k= ... = θn,k. If this homogeneity assumption is found to be reasonable, then a test of significance can be considered to determine whether the true signal log ratio θ k is significantly different from zero (i.e., whether gene k is significantly differentially expressed between the two conditions). If the homogeneity assumption is deemed unreasonable, then the random effects model can be employed to account for inter-study variability.
Simulation example
In order to evaluate the usefulness of this meta-analytic approach, a simulation study was conducted. The purpose of this simulation study was to illustrate how the results of the meta-analysis compare with the actual ("truth") simulation setting. A simple simulation model was developed with the sole purpose of generating "raw" probe-level data with certain genes "known" to be differentially expressed. While this model may not account for all sources of possible variability, it is nonetheless adequate for the purposes of the current work.
Simulation model
"Raw" probe-level data were generated from a model assuming that mismatch intensities (MM) are random background noise, which is an underlying assumption of the Affymetrix approach [9]. Our investigation of real data indicated that mismatch intensities appear to follow a long-tailed Gamma distribution. Based on this, a random mismatch intensity is simulated for each probe l of each gene k such that MM kl ~ Gamma(α, β), with mean and variance [19].
In this simulation, larger values of the shape parameter α indicate more signal being detected by mismatch probes, with the peak of the distribution of MM intensities being moved away from zero. Larger values of the scale parameter β make high MM intensities less likely by pulling in the tail of the distribution. For the purposes of this simulation, it was assumed that mismatch intensities did not vary across labs or treatments.
Once the background mismatch intensities were obtained, the perfect match (PM) intensities were generated via the model
Y ijkl = μ + L i + G k + P(G)(k)l+ LG ik + ρ k (T j + LT ij + TG jk + LTG ijk + TP(G)j(k)l) + ε(ijk)l (7)
where Y ijkl is the log2 of the PM - MM difference for probe l of gene k under treatment j in lab i. N labs were considered with each lab using the same two treatments. The term ρ k ~ Bernoulli(p) is 1 if gene k is differentially expressed between conditions j = 1 and j = 2, and is 0 otherwise. The parameter p corresponds to the percentage of genes that are differentially expressed, with higher values resulting in more differentially expressed genes. In this model, L i is the effect of lab i, T j is the effect of treatment j, G k is the effect of gene k, P(G)(k)lis the effect of probe l of gene k, ε(ijk)lis a random error term, and the other terms are interaction effects. To introduce more between-lab variability, the error variance was allowed to be different in each lab. That is, ε(ijk)l~ N(0, ) for the error terms in lab i. Each term (X) in the model is assumed to be a random effect from a N(0, ) distribution, except for the constant μ, the fixed effect T j , and the ρ k term. The parameters p, μ, T j , σ1, ..., σ N , and σ X for X = L, G, P(G), LG, LT, TG, LTG, and TP(G) can be adjusted to introduce various sources of variability in the "observed" simulated data.
These simulated data can be used to generate "observed" SLR estimates for each gene in each lab. These "observed" SLR estimates can then be combined systematically in a meta-analysis. Note that the "true" SLR value for each gene can be obtained by using the same parameter values as in the simulation model but dropping all lab and error terms. Then the adaptive nature of the meta-analytic approach can be illustrated by comparing the "true" SLR values with the estimates from each lab and from the meta-analysis models.
Simulated data
The simulation was conducted in the R environment [20] with code requiring the use of the affy package [21] from the Bioconductor project [22, 23]. While not the purpose of this investigation, the simulation was performed based upon the Affymetrix rat neuro chip RN_U34 with model parameter settings N = 6, α = 0.1, β = 0.0003, p = 0.05, μ = 2.5, σ L = 0, σ G = 0.5, σP(G)= 0.3, σ LG = 0.1, T1 = -0.2, T2 = 0.2, σ LT = 0.1, σ TG = 1.0, σ LTG = 0.13, σTP(G)= 0.5, σ1 = .48, σ2 = .60, σ3 = .72, σ4 = .84, σ5 = .96, and σ6 = 1.08. These parameter settings were selected to produce a distribution of MM intensities similar to that observed in real data (Figure 1a,b) and to force the distribution of signal log ratio (SLR) estimates to fall within a reasonable range with some variation between laboratories (Figure 1c,d).
Summary of real and simulated RN_U34 Affymetrix chip data. (a) The real mismatch (MM) intensities are from a RN_U34 Affymetrix chip, and their histogram closely resembles a Gamma distribution with a long tail. (b) The simulated MM intensities are drawn from a Gamma distribution to resemble the real MM intensities. (c) The relationship between the SLR from a simulated lab and the standard error of the SLR for each gene on the RN_U34 Affymetrix chip. Large absolute SLR's with small standard errors indicate significant differential expression. (d) A comparison of SLR estimates from two simulated labs shows general agreement, with some differences between labs.
Most SLR estimates were near zero (Figure 1c), indicating nondifferential expression, while some genes had larger absolute SLR's with smaller standard errors, an indication of significant differential expression. The data were simulated such that there were similar patterns between labs while allowing for lab differences, as evidenced by a comparison of the SLR's from two simulated labs (Figure 1d). While the estimates from the two simulated labs were clearly similar, there were obvious differences between the labs, although not as different as could be observed in real data. As a result, these two labs might produce slightly different lists of significantly differentially expressed genes. The simulation parameters can be adjusted to introduce varying degrees of difference between experiments, and this will affect the final claim made by the meta-analysis regarding statistical significance of differential expression.
Fixed effects meta-analysis results
The results from the six simulated labs were combined using the fixed effects meta-analysis model (Figure 2). The test of homogeneity : θ1,k= ... = θ6,kwas performed for each gene k, k = 1, ..., 1322 (the RN_U34 chip has 1322 features or reference sequences), and the P-values for each were summarized in a histogram of homogeneity P-values (Figure 2a). Clearly there was widespread violation of the homogeneity assumption, as evidenced by the abundance of smaller homogeneity P-values.
Fixed effects meta-analysis of simulated results. (a) The abundance of smaller homogeneity P-values indicates widespread violation of the homogeneity assumption. (b) The plot of homogeneity P-values versus fixed effects SLR estimates shows that this lack of homogeneity exists across a large range of SLR estimates.
When the False Discovery Rate (FDR) [24] was controlled at 0.05, 88 of the 1322 genes failed the homogeneity test. That is, there appeared to be significant interlaboratory differences, such that the laboratories did not appear to provide estimates of the same true degree of differential expression for all genes. This appeared to be true for genes across a wide range of fixed effects meta-analysis SLR estimates, as evidenced by the lack of a clear relationship between fixed-effects SLR estimates and homogeneity P-values (Figure 2b). As a result, the random effects meta-analysis model was deemed more appropriate to adjust for the lack of homogeneity.
Random effects meta-analysis results
The same data from the six simulated labs were used in a random effects meta-analysis, and the resulting SLR estimates were similar to those from the fixed effects meta-analysis (Figure 3a). The test of significance : θ k = 0 was performed for k = 1, ..., 1322 (i.e., for all 1322 genes), and the P-values for each value of k were summarized in a histogram of significance P-values (Figure 3b). As a result of the parameter selections for the simulation, an abundance of small P-values was observed, indicating a large number of significantly differentially expressed genes. A comparison of the meta-analysis SLR estimates with the significance P-values (Figure 3c) showed a trend of smaller P-values for larger absolute SLR. Similar to the results from a single lab (Figure 1c), most meta-analysis SLR estimates were close to zero (Figure 3d), but the standard errors were slightly lower overall for the meta-analysis estimates, after combining the SLR estimates across labs.
Random effects meta-analysis of simulated results. (a) A comparison of the fixed effects and random effects meta-analysis estimates of SLR shows general agreement between the models. (b) The histogram of significance P-values shows an abundance of significantly differentially expressed genes, as evidenced by the large number of smaller significance P-values. (c) The smallest significance P-values tended to occur for genes whose random effects meta-analysis SLR estimates were large in absolute value. The reference line is the P-value cut-off used to control the False Discovery Rate at 0.05. Using this cut-off, the random effects meta-analysis declared 72 of the 1322 genes significantly differentially expressed. (d) Demonstration of how the SLR's estimated from the meta-analysis relate to their standard errors.
The differences between the fixed effects and random effects models can be summarized by considering bubble plots for a single gene (Figure 4a,b), with bubble area proportional to weights used in the meta-analysis. For this particular gene, laboratory 2 estimated a SLR considerably smaller than the SLR's from the other labs with very small variance and hence very large weight in the fixed effects meta-analysis. As a result, the fixed effects meta-analysis estimated the true SLR for this gene to be closest to the SLR from lab 2. Such a result would call into question the SLR estimate from lab 2 for this gene. The random effects model took this lack of homogeneity into account and appropriately down-weighted the SLR estimates for this gene from all six labs. In particular, the weight for this gene in lab 2 was reduced from 88.5 in the fixed effects model to 1.8 in the random effects model. For comparison, the weights for this gene in the other five labs ranged from 3.0 to 11.9 in the fixed effects model and from 1.2 to 1.6 in the random effects model. Whereas the fixed effects model declared this gene significantly differentially expressed, the random effects model did not (controlling the FDR at 0.05 in both models). Thus, the random effects model was not overly influenced by any single lab's SLR estimate.
Bubble plots from the simulation example. Bubble area is proportional to weights used in the meta-analysis. Dashed lines represent the 95 percent confidence interval for the true value of the SLR, adjusted to control the FDR for all 1322 genes at 0.05. The red dotted line represents the true SLR value. The green bubbles represent labs which claimed significant differential expression for the gene. When zero lies outside the confidence interval, the meta-analysis declares the gene significantly differentially expressed. (a) SLR estimates from the six labs for a particular gene, with fixed effects weights. (b) Plot for the same gene as in (a), but with random effects weights. (c) Plot for one of the twenty-one Integration-Driven Discovery (IDD) genes declared significant by none of the six simulated labs but significant by the random effects meta-analysis. (d) Plot for one of the four Integration-Driven Revision (IDR) genes declared significant by multiple labs but not significant by the random effects meta-analysis.
Comparing simulated results and "truth"
When the FDR was controlled at 0.05, 72 of the 1322 genes were declared by the random effects meta-analysis to be significantly differentially expressed based on the results of the test of significance
. Individually, the six labs identified between 44 and 58 significantly differentially expressed genes (controlling the FDR at 0.05 for each lab) (Table 1). For each lab, most of its significant genes were declared significant by both the fixed effects and random effects meta-analyses.
Table 1 Comparison of results from the simulated data. Comparison of numbers of genes in common declared significant (i.e., significantly differentially expressed) by simulated labs 1 through 6, the SLR-based fixed effexts and random effects meta-analyses, and the previously proposed P-value-based meta-analysis [2]. The (i, j)thelement of this table is the number of genes declared significant by both lab i and lab j, with F here representing the fixed effects meta-analysis, R the random effects meta-analysis, and T the "truth" behind the simulation model. P represents the meta-analysis based on P-values [2]. Each lab (and meta-analysis) had the False Discovery Rate (FDR) controlled at 0.05.
These results demonstrate how a meta-analysis handles discrepancies between labs. A meta-analysis can be useful in finding genes that are statistically significantly differentially expressed and not just declared significant by one or more labs due to random variation between labs. For example, lab 1 declared 46 genes significant and lab 2 declared 49 genes significant, but these two labs declared only 33 of the same genes significant (Table 1). These 33 are not necessarily the most significant in either lab. That is, the 33 are not necessarily the genes with the smallest lab 1 P-values or smallest lab 2 P-values, but are those genes with the smallest P-values from both labs. Alternatively, rather than considering all genes declared significant by any of the labs, the random effects meta-analysis combines information across all six labs in a well-structured manner and declares 72 genes significantly differentially expressed.
While the numbers of correctly identified differentially expressed genes do not vary drastically between the individual labs (Table 1), the meta-analyses tend to correctly identify a higher number of differentially expressed genes. A comparison of the results from this SLR-based meta-analysis with the results from a previously-proposed meta-analysis approach based on combining P-values [2] is also summarized in Table 1. A slight modification to this P-value approach was necessary to account for differences in experimental design. Where the previous approach implicitly required multiple control and experimental sample arrays from each lab, this simulation data (as well as the real data presented subsequently in this work) did not satisfy this requirement in all labs. To modify the previous approach for the current data, probe-specific perfect match (PM) intensity differences between the experimental and control conditions were used to obtain a paired t statistic. Then the same permutation approach [2] was used to obtain a significance P-value for each gene in each laboratory based on this paired t statistic, and these P-values were combined across laboratories as previously proposed. In general, the SLR-based approach presented here tended to result in more genes found significantly differentially expressed by the meta-analysis than this previously proposed P-value approach (Table 1). In addition, the P-value approach does not provide a final quantitative estimate of the degree of differential expression for each gene, as does the currently proposed SLR-based approach. The meta-analysis SLR estimates tend to be much closer to the true SLR values than do the estimates from individual labs (Figure 5).
Comparison of SLR estimates with true values from simulation example. Green squares represent type I errors, genes incorrectly claimed as differentially expressed. Red triangles represent type II errors, genes incorrectly claimed to be not significantly differentially expressed. The SLR estimates from the random effects meta-analysis tend to approximate the true values much better than does any single lab.
Integration-Driven Discovery (IDD)
One of the benefits of a meta-analysis is also one of the benefits of pooling raw data, that is the increased power to detect significant differences. It is possible that while a given gene is not declared significantly differentially expressed by any one lab, the combination of results across labs in a meta-analysis provides sufficient evidence to declare significant differential expression. Choi et al. [3] use the term "Integration-Driven Discovery" (IDD) to refer to a gene identified as differentially expressed by the results of a meta-analysis, but not identified as differentially expressed by any of the individual studies or labs. In this case, the term "integration" is used in the unification sense rather than the mathematical, since the results of several different studies are being integrated into a single meta-analysis.
As shown in Table 2, our particular simulation study produced 21 IDD's (i.e., 21 of the 72 genes declared significant by the random effects meta-analysis were not declared significant by any of the six labs). Of these 21 IDD's, 6 were truly differentially expressed; that is, our simulation study produced 6 true IDD's and 15 false IDD's. An examination of the SLR estimates for these IDD genes (Figure 4c) indicated that IDD's will tend to occur when 'small but consistent' [3] effect size estimates are combined. In addition, high variability of each lab's estimate may cause individual labs to not declare a gene significant while the meta-analysis estimate will have a lower variance, making a declaration of significance more likely.
Table 2 Summary of results for the simulated data. Numbers of genes declared significant (i.e., significantly differentially expressed) by different numbers of labs and the fixed and random effects meta-analyses in the simulation example. The False Discovery Rate (FDR) for the meta-analyses and each lab separately was controlled at 0.05. There were 21 Integration-Driven Discoveries (IDD's) and 4 Integration-Driven Revisions (IDR's).
Integration-Driven Revision (IDR)
In the simulation results presented here, there were 4 genes declared significant by at least two of the simulated labs that were not declared significant by the random effects meta-analysis (Table 2). A closer examination of the SLR estimates for these particular genes (Figure 4d) revealed that while at least two of the labs individually declared the gene significant, the SLR estimates between the six labs differed sufficiently to make the variance of the meta-analysis SLR estimate large. This increased variance of the meta-analysis SLR estimate caused the meta-analysis to declare these genes not significant. In addition, some labs' variability estimates may be artificially low due to chance, thus forcing a false declaration of differential expression at the individual lab level. The random effects meta-analysis is able to account for this possibility by down-weighting overly influential results.
We introduce the term "Integration-Driven Revision" (IDR) to describe a gene identified as differentially expressed by multiple studies or labs, but determined by the results of a meta-analysis to be not differentially expressed. While multiple laboratories might promote such a gene for further study because of its large and significant effect size, the meta-analysis would conclude that, due to the inconsistencies in effect sizes across labs, the gene is not significantly differentially expressed. Whereas Integration-Driven Discoveries (IDD's) will tend to occur when 'small but consistent' [3] effect size estimates are combined, Integration-Driven Revisions (IDR's) will tend to occur when large but inconsistent effect size estimates are combined. Of the 4 IDR's made in this simulated study, 3 were not truly differentially expressed; that is, our simulation study made 3 true IDR's and 1 false IDR's.
As noted previously, the simulation parameters can be adjusted to introduce varying degrees of difference between experiments. Increased inter-laboratory variability, or greater inconsistency among effect size estimates, will tend to affect the numbers of IDD's and IDR's made by the meta-analysis. Because IDD's occur when effect size estimates are small but consistent and IDR's occur when effect size estimates are large but inconsistent, greater inter-laboratory variability will tend to result in fewer IDD's and more IDR's being made.
Real data example
Several laboratories have investigated the genetic basis for EAE (experimental autoimmune encephalomyelitis, the mouse model for human multiple sclerosis) by use of Affymetrix technology, and have reported their findings in published papers [25–29]. In each of these published papers, mention is made of appropriate care of the mice following the ethics guidelines at the respective institutions. The three laboratories providing data are Offner [26, 29], Carmody [28], and Ibrahim [25, 27]. Each laboratory measured gene expression in a base (naive or control) sample and in an experimental (EAE-induced) sample, with some laboratories using multiple experiments. For the current purposes, an "experiment" is a single array-to-array comparison to study differential expression. Seven total experiments from the three laboratories are summarized in Table 3. While not all labs used the same measure of differential expression in their publications, here the Affymetrix MAS 5.0 algorithm [11] provides the SLR estimates for each lab from the respective raw data sets.
Table 3 Summary of observed data for the EAE example. A summary of the seven observed experiments involving EAE data to be combined in a meta-analysis.
The use of different Affymetrix chip versions presents a non-trivial challenge in comparing and combining results across laboratories. The same gene may be represented on two different chip versions, and yet the names reported by the two chips may differ. Also, different sets of probes may represent the same gene on different chip versions, resulting in different probe set names on different chip versions. For example, the gene 1200011I18Rik on chip Mu11KsubA is identified by Probe Set ID AA000151_at, while on chip MG_U74Av2 it is Probe Set ID 104759_at. Furthermore, different chip versions may have different sets of genes represented on them. In order to combine the results across labs (and consequently, across chip versions), each gene must have a "name" recognized by all chips in the meta-analysis.
Previous meta-analyses of microarray results ([2, 6], for example) have relied on Unigene cluster numbers to essentially achieve a uniform gene naming scheme across chip versions and platform types. Other recent work [30] proposed combining raw data from common probes into new probesets based on Unigene clusters. Because the focus of the current work is on combining the results of the Affymetrix algorithms, SLR estimates corresponding to the same Unigene cluster numbers are combined across all experiments. This approach will allow a gene to have multiple SLR estimates (corresponding to different original probe set names) from the same experiment. The Unigene number corresponding to each probe set on an array is available through the NetAffx feature [31] of the Affymetrix website [8].
There were considerable differences in the SLR estimates from the different labs, as represented in Figure 6. Some of these differences may be due to the use of different mouse strains, tissue types, and chip versions in the different laboratories. However, even experiments from the same laboratory tended to show disagreement, highlighting the need for biological replicates to provide more precise estimates of the degree of differential expression for each gene. This inter-laboratory variability also illustrates the need for methods to systematically combine results across laboratories. The fixed effects and random effects meta-analysis models were employed to combine these estimates across all laboratories for each Unigene number.
Comparison of SLR estimates from two experiments in the EAE example. This plot illustrates the variation between experiments and the consequent need for a method of systematically combining results from different experiments.
Similar to Table 1 for the simulated example, Table 4 summarizes the overlap in the numbers of genes declared significantly differentially expressed by each experiment in this observed data example. Based on the results of the random effects model, 3,671 genes are identified as statistically significantly differentially expressed. There were 12,775 unique Unigene numbers represented by the genes across all arrays in this meta-analysis, so approximately 28.7% of the genes represented were declared significantly differentially expressed. This may support the prediction made by Carmody et al. [28] that about 28.9% of the genes in the mouse genome 'may be relevant for autoimmune inflammation', or affected by EAE. Similar to the simulated data (Table 1), a comparison of the results from this SLR-based meta-analysis with the results from an implementation of the previously proposed P-value approach [2] indicates that even with real data, the SLR-based approach tends to identify more genes as significantly differentially expressed (Table 4).
Table 4 Comparison of results from the EAE example. Comparison of numbers of genes in common declared significant (i.e., significantly differentially expressed) by the observed experiments, the SLR-based fixed effects and random effects meta-analyses, and the previously proposed P-value-based meta-analysis [2]. Each experiment (and meta-analysis) had the False Discovery Rate (FDR) controlled at 0.05.
As in the simulated data, the meta-analysis of these observed data produced Integration-Driven Discoveries (IDD's) and Integration-Driven Revision (IDR's). Similar to Table 2 for the simulated data, Table 5 summarizes these findings for the observed data. There were 65 IDD's and 5518 IDR's made. Of the IDR's, 32 were made for genes declared significantly differentially expressed by all seven experiments but not by the random effects meta-analysis. Figure 7 presents bubble plots for representative IDD and IDR genes from the observed data, similar to Figure 4 for the simulated data. As in the simulated data, IDD's tend to occur when small but consistent effect sizes are combined, and IDR's tend to occur when large but inconsistent effect sizes are combined. (See Additional file 1 : EAE.Random.Effects.Results.csv for the final estimates for all 12,775 genes.)
Table 5 Summary of results from the EAE example. Numbers of genes declared significantly differentially expressed by different numbers of experiments and the fixed and random effects meta-analyses in the observed data example. The False Discovery Rate (FDR) for the meta-analyses and each experiment separately was controlled at 0.05. There were 65 Integration-Driven Discoveries (IDD's) and 5518 Integration-Driven Revisions (IDR's). There were 32 IDR's that had been declared significantly differentially expressed by all seven experiments.
Bubble plots from the EAE example. Bubble area is proportional to weights used in the meta-analysis. Dashed lines represent the 95 percent confidence interval for the true value of the SLR, adjusted to control the FDR for all 1322 genes at 0.05. The green bubbles represent experiments which claimed significant differential expression for the gene. When zero lies outside the confidence interval, the meta-analysis declares the gene significantly differentially expressed. (a) Bubble plot for one of the sixty-five Integration-Driven Discovery (IDD) genes declared significant by none of the seven observed experiments but significant by the random effects meta-analysis. (b) Bubble plot for one of the thirty-two Integration-Driven Revision (IDR) genes declared significant by all seven observed experiments but not significant by the random effects meta-analysis.
Before any clinical decision is made based on the results of a meta-analysis, a biological validation of the results should be performed. Microarray technology is well-suited for hypothesis generation, and a meta-analysis can be used to effectively combine results across multiple laboratories to refine the list of candidate genes deserving biological validation. This approach will tend to yield more informative results when each lab has used biological and technical replicates in their experimental design [32]. The use of replicates at the laboratory level provides both added power to detect differential expression and more precise estimation of the true degree of differential expression for each gene under consideration.
The model used to generate data for the simulation example can be adjusted to account for various sources of variation and relationships between genes. It is of great interest to investigate how such relationships affect the outcome of a meta-analytic approach. An extension of the fixed effects and random effects models to the hierarchical Bayes approach is also being investigated with the hope of improving the meta-analysis approach as applied to microarrays and to incorporate prior knowledge. Included in this extension is the use of covariates in the meta-analysis framework to account for known differences between labs and the appropriate modeling of possible dependence among effect size estimates. We feel the use of covariates will provide insight into the effects of different labs, tissues, and microarray platforms on the observed differential expressions of genes. Separating out the effects of these covariates will facilitate the identification of those genes which are differentially expressed between two conditions rather than appearing differentially expressed due to external influences such as lab, tissue, or platform. For example, the differences observed between experiments from the same laboratory (Table 4) may be explained by differences in mouse strain or tissue sample, and the inclusion of covariate information in the model would adjust for this.
In the examples presented here, all studies involved used the Affymetrix platform and the data were summarized using the same normalization strategy with the MAS 5.0 algorithm [11]. When multiple studies have employed different platforms (such as cDNA and other oligonucleotide arrays) or normalization strategies, then some adjustments to the approach presented here will be necessary. In particular, a readily-available quantitative measure of differential expression common to all platforms involved is needed. In addition, it will be of great interest to consider the effect of a platform covariate in the extended meta-analysis model.
Although we have demonstrated our approach using both simulated rat data and real observed data from essentially genetically homogeneous mice, its utility with human data is of great interest. Along with the increased variability in human data comes an increase in the information about each individual subject and subpopulation. Therefore the incorporation of such covariate information is an important subject of our future work. We anticipate that the use of covariate information with human data will be particularly informative in identifying biologically significant subpopulations - for example, in identifying genes that are related to a disease in one subpopulation but not in another.
The signal log ratio (SLR), automatically reported by MAS 5.0 [11], is naturally suited to serve as an effect size estimate in a meta-analysis of results from multiple laboratories. In order to perform a meta-analysis of microarray results as presented here, the following components are needed for each probe set from each experiment: the corresponding Unigene ID, the SLR estimate, and the estimated variance of the SLR estimate. The random effects meta-analysis model is better suited than the fixed effects model for the analysis of microarray results because of the lack of homogeneity of effects from different laboratories. Genes not declared significantly differentially expressed by any single lab but then declared significantly differentially expressed by the meta-analysis are referred to as Integration-Driven Discoveries, or IDD's [3]. In addition to the identification of IDD's, our meta-analysis method identified genes declared significantly differentially expressed by multiple (and possibly all) laboratories but not significantly differentially expressed by the meta-analysis. These genes are referred to as Integration-Driven Revisions, or IDR's. The simulation example demonstrated how the final SLR estimates from the meta-analysis models tend to be much closer to the "true" SLR values than do the SLR estimates from any single lab. These meta-analytic approaches to microarray results provide a systematic method to combine results from different laboratories with the purpose of gaining clearer insight into the true degree of differential expression for each gene.
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We thank Drs. Robert Meisel (Purdue University) and Paul Mermelstein (University of Minnesota) for use of their RN_U34 Affymetrix data that provided our simulation parameters. We also thank Drs. Halina Offner (Oregon Health Sciences University), Ruaidhrí J. Carmody (University of Pennsylvania School of Medicine), and Saleh M. Ibrahim (University of Rostock), as well as their colleagues, for providing access to their raw Affymetrix data. We also thank two anonymous reviewers for their helpful suggestions to improve this work.
Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, Indiana, 47907-2067, USA
John R Stevens
& RW Doerge
Department of Agronomy, Purdue University, Lilly Hall of Sciences, 915 W. State Street, West Lafayette, Indiana, 47907-2054, USA
RW Doerge
Search for John R Stevens in:
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Correspondence to RW Doerge.
For this research RWD initiated the underlying concept of meta-analysis as applied to microarray technology, and coordinated the focus. RWD is the Ph.D. advisor of JRS. JRS developed and evaluated the simulation model, wrote the R code for the respective analyses and graphical displays, and drafted the manuscript. Both authors read and approved the final manuscript.
Additional File 1: EAE.Random.Effects.Results.csv Summary of random effects meta-analysis model results for all 12,775 genes, including SLR estimates, declaration of statistically significant differential expression, and status as an Integration-Driven Discovery (IDD) or Integration-Driven Revision (IDR). Note that the Gene column refers to Unigene cluster number unless the entry has an extension such as _at, in which case it refers to an Affymetrix probe set name for which no Unigene number was available. This file is comma-delimited and can be opened in Microsoft Excel. (CSV 699 KB)
Random Effect Model
Fixed Effect Model
Effect Size Estimate
Significant Differential Expression
True Degree


Amanda Beckers Sowden focuses her practice on estate planning, estate and trust administration, business formation and transactions, real estate acquisitions and sales, and general commercial transactions. Amanda has experience working on a wide variety of legal issues which has enabled her to develop a high level of competency in addressing the diverse business and personal legal needs of her clients.
Amanda obtained her law degree with high honors from the University of Montana in 2012, and has practiced in Billings, Montana since graduating. She is also a CPA and graduated with honors in 2008 from the University of Montana with a Bachelor of Science in Business Administration in Accountancy.
As a Montana native, Amanda enjoys spending time with her husband, son, and daughter in the great outdoors.


The Boston Bruins (42-19-9, 93 points, 2nd in the Atlantic Division) enter Thursday night on a two-game losing streak as they take on the Winnipeg Jets (40-25-4, 84 points, 1st in the Central Division) coming off a, 7-4, loss to the Columbus Blue Jackets on Tuesday.
Winnipeg visited Boston on Jan. 29th and won, 4-3, in a shootout, thereby leading the season series over the Bruins, 1-0-0.
The Jets also enter Thursday night on a two-game losing skid, as well as a 4-6-0 record in their last ten games, while the B's enter Bell MTS Place with a 7-2-1 record in their last ten outings.
Torey Krug (upper body injury) is day-to-day and will be out of the action against the Jets. As a result, Connor Clifton was recalled from the Providence Bruins (AHL) and will likely pair up with Steven Kampfer on the third defensive pairing.
Bruce Cassidy is expected to bump up John Moore to the second defensive pairing in Krug's place alongside Brandon Carlo, while Cassidy will also utilize Paul Carey– making his Bruins debut– on the second line with David Krejci and Charlie Coyle.
Carey was called up from Providence in place of Jake DeBrusk (lower body) who will remain out of the lineup against Winnipeg, but resumed skating on Thursday back at Warrior Ice Arena in Boston.
The 30-year-old forward has 14-6–20 totals in 21 games with Providence this season and was acquired in a trade with the Ottawa Senators in January in exchange for Cody Goloubef.
He will wear No. 34 for the black-and-gold.
To fit Carey on the current roster, Peter Cehlarik was reassigned to Providence. Cassidy indicated the decision to send down Cehlarik was primarily a playing-time based decision, with Cehlarik having appeared in a season-low 8:15 time on ice in Tuesday's loss to Columbus.
Instead of another low ice-time outing, Cehlarik will rejoin the minor-league Bruins for a matchup on Friday and Saturday, whereas Boston is getting back to the Commonwealth of Massachusetts late on Friday before a game on Saturday and another off day on Sunday.
Cassidy also provided updates to reporters on Thursday, indicating the team would have an update on Marcus Johansson (lung contusion) next week, Matt Grzelcyk (upper body) is nearing a return (likely on the next road trip) and Kevan Miller (upper body) is still week-to-week.
David Pastrnak (left thumb) remains skating on his own at practice, but was not given an update to his return.
In goal for the Bruins on Thursday, Tuukka Rask (24-9-5 record, 2.41 goals against average, .918 save percentage in 39 games played) looks to avenge his early dismissal against the Blue Jackets on Tuesday, in which he stopped 19 out of 24 shots faced in 28:09 TOI.
Connor Hellebuyck (28-20-2, 3.01 GAA, .909 SV% in 52 GP) was the first goalie off the ice at morning skate for the Jets on Thursday and is likely to be Paul Maurice's starter against Boston for the 2nd time this season.
Hellebuyck notched the shootout win for Winnipeg in January.
The Jets are still without the services of Dustin Byfuglien, Josh Morrissey and Joe Morrow against the Bruins.
Winnipeg is 3-3-0 in the month of March so far and 18-16-0 at home this season. Boston is 4-2-0 this month and 15-12-6 on the road this season.
Nick and Connor talk Alex Tuch's extension with the Vegas Golden Knights, superstars Auston Matthews, Sidney Crosby and Connor McDavid, as well as Charlie McAvoy extension options, the New York Rangers, Boston's first line vs. Colorado's top line and the week's biggest matchup.


text:
Fire crews report smoke rolling out of house as they arrive
Updated: 11:08 AM CDT May 31, 2016
Marcus McIntosh
KCCI Anchor, Reporter
Multiple fire crews were called to an Urbandale home Tuesday on a report of a fire.They found smoke rolling out of the home when they arrived at 3009 90th Street, which is located at the intersection of 90th and Urbandale Avenue.Firefighters reported the fire was located in the basement after a passerby called 911 to report the fire.Clive, Johnston, and Windsor Heights were called in to assist Urbandale. No injuries have been reported.Firefighters said the fire may have been caused by a candle burning in the basement.The cause of the fire remains under investigation.
URBANDALE, Iowa —
Multiple fire crews were called to an Urbandale home Tuesday on a report of a fire.
They found smoke rolling out of the home when they arrived at 3009 90th Street, which is located at the intersection of 90th and Urbandale Avenue.
Firefighters reported the fire was located in the basement after a passerby called 911 to report the fire.
Clive, Johnston, and Windsor Heights were called in to assist Urbandale. No injuries have been reported.
Firefighters said the fire may have been caused by a candle burning in the basement.

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Monday is the first day, Tuesday is the second day, and so on. Then, what do you need the money for? We couldn't help feeling sorry for her. They made speeches in turn. I shall be delighted to see you again. It's deserted. Hunter and Vincenzo instantly recognized one another. Can you excuse us a minute? Your nose is bleeding. We love going on picnics.
It worked out OK. Olof says he saw nothing. Lock the safe. How's it going over there? He is living with his friend for the time being. They've located her. He had a kind word and a pleasant smile for everyone. Dion spent all night proofreading his thesis. Sid loves chocolate ice cream. Some things are never forgotten.
I prayed that my mother would forgive me. The particles in the rings closer to the planet orbit the planet at a faster speed than the particles in the rings farther from the planet. It's not going to snow today. He came up with a party of hikers. Noemi is fairly well known. I definitely have room for improvement. Do you make friends with these boys? In the American continent, the consumption of fast-food has tripled between 1977 and 1995. The monks are meditating. Glen didn't want to argue with Dana.
He is less diligent than his brother. Jacobson never told me that Darin was his sister. What were you saying? Izumi thinks that Tanya is beautiful. I never expected Bradley to get out of prison. She was in tears. Please give me a little more tea. He had plenty of fight left in him. The father does not impugn her capacity as a good mother. I don't care for alcoholic drinks.
Can I reserve a seat to Hawaii for Christmas? He took off his hat. Pandora put on his glasses to read the contract. Let's go by taxi. Randolph wanted to ask many questions. She was indignant at the way she had been treated. I'm an old man. Ravindranath gave me a dozen cookies in a plastic bag. Joe works for the public defender's office. Get to work. These sandbags won't fill themselves.
The teacher approved his plan. Why did you let Ralph go? Cindy clenched the shovel. You might meet Mechael if you go to the library. The question is why not. Should I take this? You're just a little too frank. He had been walking for hours. Will I make it through? He amused us with a funny story.
I need to go. At last he yielded. Bill resisted the temptation. There was nothing for it but to obey. The salary is fixed according to age and experience. Jackye doesn't remember me. Is the tap water in Australia drinkable? There is no charge. This rice is contaminated by arsenic. When I opened the door, Dean was standing there wearing a suit.


Malawi and Zambia government officials on Friday engaged in a blame game on who was promoting intermarriages of girls below the age of 18 years.
The finger-pointing ensued during an interdepartmental meeting between officials from the two districts of Isoka in Zambia and Chitipa in Malawi.
Chitipa first grade magistrate Billy Ngosi sparked the discussion into a debate when he informed officials in the sister districts that the marriage law for girls in Malawi has now changed from 16 to 18 years.
He said following the change, Zambian men should be watchful to avoid facing a jail term in Malawi.
But Isoka district commissioner (DC) Evenwell Mtambo expressed surprise at the warning, arguing that many men from Chitipa are intermarrying underage girls in Zambia.
"In our country, a girl child is below the age of 20. Therefore, do not attempt to cross over your borders in search for such girls; you will not like us," he said.
It was also observed that Zambians, especially from Katabwanga and Chindilo villages in Mafinga District, are cultivating in Malawi's Jembya Forest.
The Malawi delegates, therefore, advised their counterparts to civic educate their citizens to stop the encroachment.
At the end of the gathering, the delegates went for a tree planting exercise around Isoka Boma.


text:
Home > Panasonic Lumix DMC-TS3 brings GPS integration outdoors
Panasonic Lumix DMC-TS3 brings GPS integration outdoors
January 25, 2011 by Allison Johnson Reads (49)
Panasonic updates its rugged Lumix T series today with the TS3. As usual, it's waterproof, shockproof, dustproof, freezeproof, and now it has built-in GPS. Like other Lumix cameras launched today, it offers a new 3D mode.
The 12.1 megapixel TS3 is designed to withstand a drop of 6.6 feet, temperatures down to 14 degrees Fahrenheit and underwater use up to 40 feet. A built-in GPS receiver and compass, altimeter and barometer make it friendly to the wild outdoors.
Also available is a newly-developed 3D mode. Much like Sony's 3D Sweep Panorama, the TS3 will record 20 consecutive images as the user pans across a scene and overlay the two best to create a 3D image file. Photos can be viewed in 3D on compatible 3D HDTVs, not on the camera itself.
The TS3 takes a slight detour from the TS2's design. A slightly curved handgrip has been added, and the shutter button on top is round rather than rectangular. The TS3 has the same industrial styling as its predecessors. The camera's ports, battery housing and memory card slot have been moved under one weather-sealed door.
Like the TS2, the TS3's 28mm wide angle 4.6x optical zoom lens is contained internally, keeping the front of the camera clean. On the back is a 2.7-inch LCD with 260k-dot resolution. Even the menus have been given a slight facelift with a smoother interface. The TS3 will also offer full 1080 HD video recording and optical image stabilization.
The Panasonic Lumix DMC-TS3 will be available in March. Pricing will be announced 30 days beforehand.
All content posted on TechnologyGuide is granted to TechnologyGuide with electronic publishing rights in perpetuity, as all content posted on this site becomes a part of the community.


The Engineer is just recently becoming aware that other people are different than he is – and he's starting to question why. His thoughtful question "why isn't my skin brown, mommy?" opened a door to a multitude of other questions.
It's a tough subject. I'm hyper aware of my privileged position as a white, middle class, educated woman, and how that frames any conversation that I have with my kids. I'm also highly aware how pointing out our differences and defining races can be a bad thing. I don't want my kids to be color blind: I want them to value a person for who they are.
Two incidents this week got me thinking about how I want to handle the subject with my kids. One story isn't mine to tell in detail: a member of one of my homeschooling groups told us her son is in the hospital. He was attacked because of his relationship with his girlfriend – they are a bi-racial couple.
The other incident happened yesterday at lunch, and it made me realize how much words can hurt even when they're the truth.
The kids and I were eating lunch at a fast food restaurant. The kids had been playing in the indoor playground for a while, and I had gotten them ice cream cones. As they happily slurped their ice cream, one of the little girls they had been playing with came back out of the play area.
Her grandfather was sitting at the table next to us: he looked like a hardworking person used to getting the job done, and he was white. His granddaughter was bi-racial. They had interacted a little while we were there, enough for me to see just how much he loved her. It made me smile – this hardworking, crusty looking, tough guy clearly loved this little girl so much.
As she walked past the Engineer to her chair, he paused, and then asked me "mommy, why is her hair so curly?" She was basically sitting right next to him and clearly heard him. I had a breath of a moment before I answered him, and I considered what I said very carefully. Why? Because I saw her react.
Her shoulders tensed up, she looked away, and her body language looked like she was bracing for a hit. Her grandfather paused what he was doing and waited to see how I would respond.
I smiled at the Engineer and said "because that's how her hair grows, bud." He said "but mine isn't…" as he searched for the right word. I told him "no, yours is straight like mine. You got my hair." And then I quickly told him a little story about how when I was his age, my sister had beautiful curly hair and I was so sad because mine was straight.
Her shoulders relaxed. She finished her juice box and hopped back down to go play. Crisis averted, her grandfather went back to what he was doing.
I wanted to cry. For a child to react like that meant that she had already heard mean, ugly comments. She was beautiful. She was adorable. She was perfect just the way she was. And I wanted the Engineer to know that, but I didn't know how to tell him that because to him, she already was.
If I had told him that the little girl's hair was curly because she was biracial, it would have been the truth. But it would have hurt her and confused him. He doesn't understand what that means and why some people don't like it.
I feel like I didn't handle the situation well. I didn't have the right words to say, the right way to say it. I was caught off guard and not prepared. And while I probably made the little girl feel like she had beautiful hair, I inadvertently made the Engineer think that I didn't like straight hair – and he has straight hair.
Still, I can talk to my son and clear things up. I only had one chance with the little girl and I didn't want to mess it up. I didn't want to add to the hurt she's already experienced.
I'm dreading the discussion I need to have with the Engineer, because I feel like I'm going to destroy his innocence. He lives in a world where we judge people on their actions, not their skin color, the way they look, or the car they drive. I wish I could let him stay there forever – but I need to tell him how hurtful his words could have been to the little girl, and why he shouldn't say things like that.
I realize that agonizing over this discussion is, in itself, a sign of my privilege. He's clueless about race relations because he's white. If we weren't white, chances are we would have already discussed this because he would have experienced discrimination already.
It's a tough subject. But if we want change in our country, our kids are where we start. Our kids need to grow up valuing diversity. Our kids need to grow up being sensitive to other's needs. Tough or not, it's something we need to discuss. It's something we need to live, to show them in action.
Maybe if we all do this, teenagers won't end up in the hospital because someone didn't like their girlfriend. I can hope.
Maybe you could discuss it without specifically mentioning race? Like, 'People often don't like it when we make comments about the way they look. So you should ask me later when they can't hear.' Or something? Because there's all kinds of things that people could be sensitive about. He could just as easily have asked 'Why is that girl fat?' for example!
Good suggestions! We deal with unfiltered comments a lot, so this would cover all angles. Thanks!


text:
BUILD=DEBUG
PLATFORM=WIN32

all: autogenerated
	$(MAKE) -f ./XLL.mak BUILD=$(BUILD) PLATFORM=$(PLATFORM)

clean:
	$(MAKE) -f ./XLL.mak BUILD=$(BUILD) PLATFORM=$(PLATFORM) clean
	@echo Removing ../common_source/xlwTest.cpp
	@$(RM) ../common_source/xlwTest.cpp

autogenerated :../common_source/xlwTest.cpp

../common_source/xlwTest.cpp:  ../common_source/Test.h
		../../../build/gcc-make/$(BUILD)/BIN/InterfaceGenerator  ../common_source/Test.h ../common_source/xlwTest.cpp

rebuild: clean all


text:
Classiconorroena è un'associazione culturale italiana che studia i rapporti tra civiltà classica e Europa del Nord. Dal 1991 pubblica la rivista Classiconorroena, attualmente anche in versione on line nel sito dell'associazione.

La costituzione di un'associazione con tali scopi era negli auspici unanimi dei partecipanti al congresso internazionale Cultura classica e cultura germanica settentrionale, tenutosi nel 1985 presso l'ateneo di Macerata per conto di P. Janni, D. Poli, C. Santini, i cui atti sono editi sotto lo stesso titolo (Roma 1987, Editrice Herder).

Classiconorroena ha contribuito in prima persona ad organizzare i convegni su figure significative di questo rapporto tra mondo classico e germanico del Nord Europa quali Saxo Grammaticus (Saxo Grammaticus. Tra storiografia e letteratura, a cura di C. Santini, Roma 1992, il Calamo), Giovanni e Olao Magno (I fratelli Giovanni e Olao Magno. Opera e cultura tra due mondi, a cura di C. Santini, Roma 1999, il Calamo), la regina Cristina di Svezia (Cristina di Svezia e la cultura delle Accademie, a cura di D. Poli, Roma 2002, il Calamo), Adamo di Brema (Devotionis munus. La cultura e l'opera di Adamo di Brema, a cura di R. Scarcia e F. Stok, Pisa 2010, Edizioni ETS), dei cui Gesta Hammaburgensis Ecclesiae Pontificum l'associazione ha anche promosso la concordanza pubblicata a cura di L. Cardinali e M. P. Segoloni (2010) presso l'Editore Olms. Altre occasioni di studio e di comunicazioni scientifiche da parte degli aderenti alla società sono state rappresentate dal volume Tra testo e contesto. Studi di scandinavistica medievale, a cura di C. Santini, Roma 1994, il Calamo, e dagli atti del convegno Einar Löfstedt nei percorsi della linguistica e della filologia latina, a  cura di P. Poccetti, Pisa – Roma 2007, F. Serra Editore.

Ogni anno in primavera l'associazione tiene l'assemblea dei soci annuale presso l'Istituto svedese di studi classici a Roma.

Associazioni culturali

redpajama_set_name: RedPajamaWikipedia


text:
Q: Where's hashHistory in React Router v4? I'm trying to use a router for my React application. I tried something I'd been using a while back, but can't seem to get it going.  Has hashHistory been removed/reformatted in React Router v4?
<Router history={hashHistory}>
  <Route path='/' component={MainContainer} />
</Router>

A: Use a HashRouter. They got rid of individual histories such as browserHistory and hashHistory and instead replaced them with BrowserRouter and HashRouter components respectively in React Router v4:
import { HashRouter } from 'react-router-dom';

<HashRouter>
  …
</HashRouter>

Note that HashRouter comes from react-router-dom, not the core react-router package.

redpajama_set_name: RedPajamaStackExchange


text:
GRB 790305b — гамма-всплеск, обнаруженный 5 марта 1979 года. Экстремально яркий гамма-всплеск, который удалось сопоставить с остатком от вспышки сверхновой N49 в Большом Магеллановом Облаке. В настоящее время считается, что явление было вызвано гигантской вспышкой магнетара и в большей степени связано с источниками мягких повторяющихся гамма-всплесков нежели с настоящими гамма-всплесками. Это первая наблюдавшаяся сверхвспышка подобного рода коротких гамма-всплесков. Также её связывают с пульсаром PSR B0525-66.

Событие
5 марта 1979 года советские космические аппараты Венера-11 и Венера-12, двигавшиеся по Солнечной системе, подверглись воздействию фронта гамма-излучения приблизительно в 10:51 EST. На долю миллисекунды количество отсчётов на детекторах спутников возросло от 100 до более чем  в секунду.

Вспышка гамма-излучения быстро продолжила расширяться. Спустя 11 секунд спутник NASA Helios 2, находившийся на орбите вокруг Солнца, подвергся воздействию гамма-излучения. Затем всплеск достиг Венеры и аппарата Пионер-Венера-1. Секундой спустя гамма-излучение достигло Земли, при этом заполнив датчики трёх спутников Министерства обороны США Vela, советского спутника Прогноз-7 и HEAO-2. Вспышка гамма-излучения оказалась наиболее энергетической среди всех известных, она в 100 раз превосходила по интенсивности все предыдущие вспышки. Поскольку гамма-излучение движется со скоростью света, а момент импульса был зарегистрирован несколькими космическими аппаратами, то направление на источник может быть установлено с точностью около 2 секунд дуги. Направление на источник соответствует остаткам звезды, вспыхнувшей в виде сверхновой около 3000 года до нашей эры. Scientific American. Вспышка произошла в Большом Магеллановом Облаке, источник вспышки получил название SGR 0525-66, а само явление вспышки было названо GRB 790305b.

Примечания

Гамма-всплески
Золотая Рыба (созвездие)


Top 10 ways to say NO!
I found this message many years ago on the very active discussion boards at www.naifa.com.. Not much has changed!!
If they send you an order, without verbal correspondence first, say No.
If they can't pronounce your city name, say No.
If they tell you they'll be doing a lot of business in your area, say No.
If they answer the phone saying "ABC Mortgage, formerly XYZ Mortgage," say NO.
If they send you an order to appraise a property several states and thousands of miles away, say No.
If they don't have a toll free phone number, say No.
If they say they don't know who to call, tell them to check out www.asc.gov's roster.
This message was posted by subscriber Richard Poole, who gave me permission to reprint it. Thanks for a little humor!


text:
Surveillance video shows incident involving 3-year-old at Sunshine House daycare
A mother speaking out about a lawsuit on file following an incident at Sunshine House daycare in Aiken. "It's a nightmare," mother Ashana Odom said about an incident involving her son at daycare. Odom says that the nightmare began for
James Swartz2022-12-21T09:11:08-04:00December 3, 2021|
Broken bones, loosened teeth among student injuries lawsuit alleges against DCPS
Parents at three separate DC Public Schools have filed a lawsuit alleging their children with special needs suffered unexplained injuries while in DCPS care. WASHINGTON — Multiple parents of children at three different DC Public Schools have joined a lawsuit alleging their
James Swartz2021-04-15T17:25:00-04:00February 3, 2021|
Childcare services claims – David Faraci
Too often we hear about the abuse and neglect many children are subjected to while in a nursery or daycare setting. Children are frequently neglected or physically abused by staff members who are inadequately trained or screened. Many
James Swartz2021-04-16T13:17:42-04:00January 24, 2021|
CPSC Approves New Federal Safety Standard for Infant Swings
On Tuesday, November 13, the U.S. Consumer Product Safety Commission (CPSC) voted collectively (3-0) to support a new federal mandatory safety standard for infant swings to prevent injuries and deaths to young children. Over the course of a year, CPSC has received 351 reports
James Swartz2021-04-16T06:50:42-04:00November 20, 2012|
CPSC Warns of Children Drowning in Bathtubs, Bath Seats and Buckets
The U.S. Consumer Product Safety Commission (CPSC) urges parents and caregivers of young children to look for and protect against drowning in bathtubs risks inside and around their homes. Drowning is the leading cause of unintentional death among children ages 1 to 4 years
James Swartz2021-04-15T17:16:03-04:00October 16, 2012|
Toddler Found Dead in School Van
The family and friends of a 17-month-old boy, Gabriel Josh-Cazir Pierre, are grieving over his tragic death. During Monday's high temperatures, the toddler was left unattended and unnoticed in school transportation for close to seven hours. The facts revealed thus far suggest a number of


East and West, Laura Ritland's astonishing debut, is a book of visions. These are roving poems drawn to defamiliarizing points of view, and are exquisitely attentive to the way the world exceeds our senses ("Cloud deduced cloud / after cloud and cloud.") Beckoningly tender, lucid and intelligent, elegaic without being maudlin, East and West explores the thresholds--or "middle ground"--of childhood and family, diaspora and migration, and how new cultural ideas can disrupt traditional perspectives. "My bedroom window an escape hatch / to endless sights of coastal stars." Ritland takes the measure of herself"I'm an integer of my own society"--in one of the most distinctive and beautifully turned styles in Canadian poetry.
Laura Ritland's poems have appeared in magazines across Canada, including The Fiddlehead, CNQ, The Walrus, Maisonneuve, Arc Poetry Magazine, and The Malahat Review. A recipient of the 2014 Malahat Far Horizons Award for Poetry, she currently divides her time between Vancouver and the California Bay Area, where she is a PhD student in English at UC Berkeley.


text:
/ dilaudid
Dilaudid Abuse and Addiction in the US
Signs of Dilaudid Abuse
Withdrawing from Dilaudid
Dilaudid Overdose
Getting Treatment to End Dilaudid Addiction
The drug Dilaudid is a brand name for the opioid analgesic hydromorphone. The Food and Drug Administration (FDA) approved hydromorphone in 1984 for around-the-clock use in long-term pain relief. Unlike some less potent opioid painkillers, Dilaudid is not intended for moderate or severe pain that is expected to go away with time. In fact, the drug's potency is designed to help those suffering from chronic pain who have developed a physical tolerance to other opioids.
Unfortunately, because Dilaudid is so potent, it has become one of the opioid drugs that is often abused for recreational reasons. Overcoming opioid addiction, especially if the person has started to abuse Dilaudid or equally potent narcotics, is extremely important to avoid serious health issues and overdose.
Like most other prescription narcotics, Dilaudid is listed as a Schedule II drug, according to the Drug Enforcement Administration (DEA). This means that there are important medical uses for this medication, but it is also dangerous and could trigger addiction in some people. The DEA reported that, in 2012, there were 3.9 million prescriptions for Dilaudid issued, and in the first quarter of 2013, close to 1 million of those were actually dispensed. Several of these prescriptions involved pill, capsule, and tablet versions of Dilaudid; people with a prescription may take the drug home and consume it as prescribed. Unfortunately, prescription drug abuse often involves theft of prescriptions from others. The DEA reported that low-dose, immediate-release, and extended-release versions of Dilaudid were the leading versions abused and diverted for illicit sale and abuse.
The National Survey on Drug Use and Health (NSDUH) for 2011 reported that 1 million people ages 12 and older had abused Dilaudid for nonmedical reasons at some point in their lifetime. While doctor shopping was once the main source of opioid drugs for those struggling with prescription narcotics addiction, as more states adopt prescription monitoring plans and doctors more closely monitor their patients, these individuals are beginning to turn to heroin, which is cheaper and easier to find. People struggling with Dilaudid addiction may turn to other illicit narcotics, like illegal fentanyl, which can put them at a greater risk of overdose.
An FDA report from 2008 showed that Dilaudid has a high potential for addiction and abuse because the correlation between how long the individual enjoyed the drug and how long the drug was active in the body was high. According to the Department of Justice (DOJ), Dilaudid was once one of the most diverted prescription opioids on the market. The Centers for Disease Control and Prevention (CDC) does not list Dilaudid as one of the most abused opioids anymore – the list includes hydrocodone, oxycodone, and methadone – but this potent, long-lasting narcotic is still available and may still be abused.
While a person taking Dilaudid for legitimate medical reasons may experience side effects, these are more likely to present in people who abuse these kinds of drugs because they take large amounts of the opioid, often very frequently. This increases the risk of side effects developing. Some of these side effects include:
Trouble sleeping or staying asleep
Skin flushing
Heavy sweating
Muscle, back, or joint aches and pains
A person who is intoxicated on Dilaudid will likely be very relaxed and sleepy. They may nod off or act like they are drunk. Too much Dilaudid can lead to an overdose, but people who abuse this drug because of addiction will develop a physical tolerance to opioids over time or they may have switched to Dilaudid abuse after developing a tolerance to Vicodin or Percocet. An opioid tolerance means the person will need to take more of the drug over time to achieve the same, original effects.
Abusing Dilaudid for a long time can result in serious harm to the brain and body. Dilaudid especially has been found to damage hormonal production by harming the endocrine system. Too much Dilaudid is also likely to result in kidney disease and damage; while this is more likely to occur in those who have underlying problems with kidney function, it can still occur in otherwise healthy people.
Although Dilaudid is a potent opioid, withdrawing from the drug is not life-threatening; however, like other opioid drugs, it is important to detox with the help of medical professionals. Withdrawal symptoms associated with Dilaudid can be uncomfortable, and that discomfort may cause a person to relapse if they do not have medical intervention and social support.
Symptoms of withdrawal from opioid drugs like Dilaudid include:
Muscle aches and joint pain
Excessive yawning
Cold chills
Other stomach cramps or discomfort
Irregular or fast breathing
Because Dilaudid is so potent, it is easy to take too much of this opioid and overdose. Symptoms of an opioid overdose include:
Pinpoint, unresponsive pupils
Unconsciousness and unresponsiveness
Extreme drowsiness and confusion if awake
Depressed, irregular, shallow, or stopped breathing
The most dangerous and fatal symptom of an opioid overdose involves breathing. Opioid receptors affect breathing regulation, as well as mood and pain relief, so when a person takes too much of an opioid drug, the brain will be less able to regulate breathing. This leads to oxygen deprivation, which harms organ systems and can cause brain death.
If a person is overdosing on opioid drugs like Dilaudid, they need immediate medical attention. Call 911.
For a strong narcotic like Dilaudid, a person will likely need a combination of medication-assisted therapy, a tapering regimen, and psychotherapy. This combination is the most effective for treating all kinds of substance abuse, including opioid addiction, according to the National Institute on Drug Abuse (NIDA). Their Principles of Effective Treatment list replacement medications, psychotherapy, and social support as vital aspects of rehabilitation and recovery.
The leading prescription treatment for opioid addiction in the US is currently buprenorphine. The Substance Abuse and Mental Health Services Administration (SAMHSA) recommends this for medication-assisted therapy (MAT). Buprenorphine is a partial opioid agonist, meaning it replaces the drug in opioid receptors in the brain to reduce the intensity of withdrawal symptoms, especially cravings. It has a much longer half-life than most narcotics, so a person using buprenorphine to detox from Dilaudid abuse can also work with a therapist or counselor to change their compulsive behaviors around taking drugs. A physician will also work with the person on tapering buprenorphine, so they can slowly ease the body off dependence on the drug to feel normal.
Cognitive Behavioral Therapy works well to help people change their behaviors in response to emotional reactions, like cravings, stress, or environmental triggers. Staying in a rehabilitation program for three months, or 90 days, according to NIDA, will offer the best results with this kind of therapy, along with support groups.
Treating an Addiction to Percodan
Opiate Detox Programs for Managing Withdrawal