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CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness
Michael D. Morgan1 &
John C. Marioni1,2,3
Genome Biology volume 19, Article number: 81 (2018) Cite this article
Population phenotypic variation can arise from genetic differences between individuals, or from cellular heterogeneity in an isogenic group of cells or organisms. The emergence of gene expression differences between genetically identical cells is referred to as gene expression noise, the sources of which are not well understood.
In this work, by studying gene expression noise between multiple cell lineages and mammalian species, we find consistent evidence of a role for CpG islands as sources of gene expression noise. Variation in noise among CpG island promoters can be partially attributed to differences in island size, in which short islands have noisier gene expression. Building on these findings, we investigate the potential for short CpG islands to act as fast response elements to environmental stimuli. Specifically, we find that these islands are enriched amongst primary response genes in SWI/SNF-independent stimuli, suggesting that expression noise is an indicator of promoter responsiveness.
Thus, through the integration of single-cell RNA expression profiling, chromatin landscape and temporal gene expression dynamics, we have uncovered a role for short CpG island promoters as fast response elements.
Variability in gene expression across an isogenic population of cells has garnered significant interest over the past decade and a half. Initial studies of variability in gene expression, henceforth referred to as gene expression noise, from single promoters [1, 2] and simple gene regulatory circuits [3] have motivated work in pro- and eukaryote systems to characterise and investigate both the sources and consequences of this noise. The extent of noise within vertebrate, and more specifically mammalian, systems has received less attention [4]. In part this is due to the simplicity and elegance of organisms that exist primarily in a single-cell state for much of their natural lives, but also the ease with which these organisms and systems can be manipulated to alter gene expression noise. This is especially important for understanding the drivers of expression noise, as well as its consequences at the biochemical, cellular and physiological levels.
In yeast, the presence of a TATA-box motif in the core promoter is linked with greater noise, which is associated with differences in nucleosome occupancy and pre-initiation complex dynamics [5, 6]. Examination of mammalian promoter features that influence individual-to-individual variability have highlighted a number of chromatin modifications linked to differential variability [7]. Likewise, the integration of single-cell expression profiling with bulk chromatin modification data in embryonic cells has highlighted a role for differences in chromatin modifications between genes as a possible source of gene expression noise [8–10]. Specifically, both Kar et al. [8] and Faure et al. [10] show that promoter chromatin modifications are related to gene expression noise differences between promoters, whilst Wu et al. find that gene body chromatin methylation, specifically H3K79me3, is associated with differential noise [9]. Additionally, both [8] and [10] demonstrate that promoters that are simultaneously marked with opposing chromatin states (repressive H3K27me3 and activation H3K4me3), so-called bivalent promoters [11], are associated with greater gene expression noise in bulk populations of embryonic stem cells. This may arise due to heterogeneity in the chromatin state across the populations of cells studied or due to opposing chromatin modification-associated activity at these promoters [10].
We hypothesise that there are a number of promoter features that may influence the expression noise of a particular gene. To understand the shared features of promoters, we investigated potential sources and causes of gene expression noise at a genome-wide scale. We find that CpG island promoters are associated with less gene expression noise than their non-CpG island counterparts, and that the characteristic features of CpG islands, e.g. polycomb repressor complex (PRC) and trithorax group (TrxG) chromatin modifications and CpG island size, contribute to differential noise between promoters. Within CpG island promoters, there remain extensive differences in gene expression noise, which is anti-correlated with CpG island size. We corroborate recent findings showing that bivalent promoters in mouse embryonic stem cells (mESCs) are the noisiest promoters [8, 10]. Expanding on the work by Faure et al. [10], we propose that the increased noise from short CpG islands is an indication of promoter dynamics. Specifically, we investigate whether short CpG islands act as agile response elements for switch/sucrose non-fermentable complex (SWI/SNF)-independent primary response genes. Analysis of time-series expression profiles from human and mouse studies suggests that under different stimuli, genes with short CpG islands respond earlier than those with longer CpG islands and that highly variable genes in unstimulated cells form part of the early response.
Genomic sources of gene expression noise
We considered different categories of genomic features involved in gene expression regulation, to capture generic features of gene promoter architecture that influence expression dynamics (Additional file 1: Table S1). These features can be sub-divided into static and dynamic types. Static features are invariant between cells, whilst dynamic features can vary between cells depending upon their lineage, type or state.
To screen for genomic elements that influence gene expression noise at the global level, we model the relative noise of each gene as the squared coefficient of variation (CV2). There is an inherent relationship between the mean and the variance for data generated by a time-interval counting process, e.g. a Poisson process. This must be accounted for so that any correlations with gene expression noise can be disentangled from correlations with the average expression across cells. Thus, we fit a reciprocal relationship between the mean log2 expression values and CV2, parametrised as in Brennecke et al. [12] (Fig. 1a). We call this mean-adjusted measure the residual CV2 (rCV2). To find the influence of each genomic feature on the mean-adjusted noise, we regress each feature on the rCV2 (across all genes), as illustrated in Fig. 1a.
Scan for promoter features associated with gene expression noise. a Gene expression noise is measured using the squared coefficient of variation (CV2) for each gene (subscript i). The relationship between the mean and CV2, illustrated by the smoothed scatter plot (blue cloud), is accounted for by calculating an expected value for the gene expression noise for each gene (E[CV2]), shown by the orange line. The residual coefficient of variation rCV2, or the mean-adjusted gene expression noise, is calculated as the absolute deviation of the observed CV2 from its expected value, shown in the kernel density plot (right panel). The influence of genomic features is tested by fitting a robust linear model to prevent outlier points biasing our results, using the rCV2 as the dependent variable. Each feature is regressed on rCV2 individually (univariate model: X is a vector of values representing the genomic feature; multivariate model: X denotes a matrix where each column is a genomic feature and the rows are genes). The statistical significance is determined by testing the null hypothesis that the genomic feature regression coefficient, β, is equal to 0 using a t-test. Univariate and multivariate model fitting results are visualised side-by-side for all genomic features, as in the toy example (bottom right panel). Points above 0 (orange) are associated with greater noise, whilst those below the line are associated with lower noise (purple). b Static promoter features were regressed on expression noise (rCV2) in mESCs and mouse Cd4+ T cells (Additional file 2: Figure S1). Each point represents a genomic element in either a univariate (diamond) or multivariate (circle) robust linear regression model. Grey points denote features in which there is insufficient statistical evidence to reject the null hypothesis of no association with expression noise (p<0.05). c Examples of how gene expression noise differs between CGI and non-CGI promoters in both mouse embryonic stem cells and Cd4+ T cells. The y-axes in the plot of rCV2 are truncated for clarity. d Genomic features across multiple cell-type lineages and between mouse and human that are consistently associated with transcriptional noise in a multivariate robust linear model. Cell types are denoted by different symbols; human- and mouse-derived cells are delineated by colour (blue for human and red for mouse). Transparent points are those where there is insufficient statistical evidence to reject the null hypothesis (p<0.05). The y-axis ranges in (b) and (d) are truncated for clarity. mESC mouse embryonic stem cell
Using single-cell RNA-seq measurements from mESCs [13], we first explored which genomic features could underpin differences in (mean independent) expression noise across cells. Using the approach described ('Methods'; Fig. 1a), we evaluated the effect of each promoter feature on gene expression noise (Fig. 1b–d). This included a multivariate robust linear model with all of the genomic features to test for linear independence between possible related features (Fig. 1b). To replicate the observed effects independently and to test their generalisability, we performed the same analysis using data derived from mouse Cd4+ naive T cells [14] (Additional file 2: Figure S1).
Unlike previous reports on Saccharomyces cerevisiae and mESCs, we do not observe a consistent correlation between predicted TATA box binding protein (TBP) motifs and differences in expression noise [10, 15] (Fig. 1b). In this study, we consider that the promoter encompasses a 1.5-kb region, whilst previous studies on TATA boxes and TBP binding have restricted their analysis to core promoter regions (∼200 bp) centred on the transcriptional start site. Using the same definition of TATA-box promoters as in [10, 16], we find that TATA-box promoters are associated with greater gene expression noise in our univariate, but not the multivariate, robust regression model (Additional file 2: Figure S2). Thus, this discrepancy arises due to differences between relying solely on predicted TBP motifs and more comprehensive promoter classifications, rather than the size of the promoter region per se.
We find in the univariate case that gene structure (i.e. transcript length, number of exons and mean exon length) has a relatively large influence on noise (Fig. 1b, circles). With the exception of mean exon length, these effects are consistently captured by other variables related to gene structure in both mESCs and Cd4+ T cells. Interestingly, we find that promoters with an overlapping CpG island are on average less variable than their non-CpG island counterparts (Fig. 1 and Additional file 2: Figure S1), concordant with a recent report by Faure et al. [10].
As we wish to understand the general features of mammalian promoters that influence their noise, we extended our analysis to several human cell types (Additional file 3: Table S2). In accordance with our observations in mouse, we observe that CpG islands are consistently associated with lower gene expression noise (Fig. 1d). The extent to which CpG islands are correlated with gene expression noise varies between cell types and between species. This may represent biological differences between developmental and evolutionary lineages or technical and experimental differences between studies. The data sets used in our analysis are all generated using the SMART-seq(2) chemistry [17, 18], and thus, may be susceptible to technical noise arising from fragment duplication. To test whether our results are affected by this potential bias, we also performed the same analysis using single-cell expression profiles from mESCs cultured in serum + leukaemia inhibitory factor, generated using unique molecular identifiers [19]. We find that CpG islands remain associated with lower expression noise, suggesting that this correlation does not arise due to shared technical sources of variation in single-cell RNA-seq experiments (Additional file 2: Figure S3). Subsequently, we can confidently conclude that the relationship between differential noise and CpG island and non-CpG island promoters is a feature of mammalian genomes separated by ∼80 million years of evolution.
Characteristics of CpG islands associated with expression noise
Although genes with CpG island promoters are systematically less noisy than genes without a CpG island, there is still considerable variability in expression levels among CpG island genes (Fig. 1c, black outlier points). This raises the question of whether the characteristics of specific CpG islands also contribute to gene expression noise, which to our knowledge has not been previously addressed. We selected features of CpG islands to test for association with gene expression noise, such as CpG island size and the number of predicted SP1 binding motifs. Several features of CpG islands are highly correlated, e.g. CpG island size and CpG dinucleotide ratio (Additional file 2: Figure S4). For this reason, we selected CpG island size as a characteristic measure of CpG islands, as it has a more intuitive interpretation and has been linked with potential functional differences between genes [20].
We tested each feature of CpG islands individually across cell types. This univariate analysis found that in both human and mESCs, and murine Cd4 + T cells, CpG island size and the number of SP1 binding sites are individually associated with lower transcriptional noise (Fig. 2a). However, in human pancreatic α- and β-islet cells, CpG island size is generally not associated with differences in expression noise (Fig. 2a). To test whether the partial correlation between SP1 binding motifs and CpG island size influences gene expression noise independently, we fitted a multivariate robust linear model that included both features. We find that CpG island size and the number of predicted SP1 binding motifs are linearly independent in both mouse cell types, but not in human pancreas cells. The discordance between pancreatic islet cells and other cell types may be indicative of lineage-specific effects that alter the relationship between genomic features and gene expression noise. Applying the same model and analysis to additional single-cell pancreatic islet expression profiles gives similar results (Additional file 2: Figure S5). Overall, these results indicate that variation in CpG island size is consistently correlated with gene expression noise amongst different gene promoters independent of species.
Robust linear regression analysis of CpG island features across cell types. a Univariate robust linear regression analysis of CpG island features across cell types associated with expression noise. b Multivariate robust linear regression analysis of CpG island features across cell types associated with noise. For both human and mouse cell types, robust linear models were fitted to rCV2 for all genes with an associated CpG island. Points that fall above the horizontal line are associated with greater noise, whilst those below are associated with lower. Features for which there is insufficient statistical evidence to reject the null hypothesis of no association with gene expression noise have a lighter shading. Symbols represent the different cell types (circles for α-islets, squares for β-islets, triangles for naive Cd4+ T cells and diamonds for embryonic stem cells). Human and mouse cell types are denoted by colours (blue for human and red for mouse)
The epigenetic landscape is correlated with expression noise
We wish to understand the mechanism that leads to differences in gene expression noise among CpG islands. The principal difference between CpG island and non-CpG island promoters is how their transcription is repressed or modulated. Non-CpG island promoters maintain transcription repression by cytosine methylation at CpG dinucleotides [21], though recent evidence demonstrates that CpG methylation alone cannot induce repression [22]. CpG islands are constitutively unmethylated regions, despite a high CpG density that would attract active DNA methyltransferases [21]. These unmethylated CpG dinucleotides provide a platform for binding by proteins that form part of the PRC and TrxG, via their CxxC domains [23, 24]. Thus, transcription from CpG island promoters is primarily regulated by chromatin histone modifications, in the absence of direct modification of DNA, such as cytosine methylation.
To understand how CpG islands contribute to noise in gene expression, we combined chromatin histone modification data associated with transcriptional activation and repression (H3K4me3 and H3K27me3 ChIP-seq; see 'Methods') on bulk mESCs with single-cell RNA-seq expression profiling. Previous work has highlighted the enrichment of genes involved in developmental processes amongst promoters with long CpG islands [20, 25]. One explanation for the observed relationship between CpG island size and decreasing expression noise (Fig. 2a), therefore, might be that developmental genes need to have more highly regulated expression. However, after removing >900 developmentally-associated genes, we find that the anti-correlation between CpG island features and expression noise remains, suggesting there is an alternative explanation (Additional file 2: Figure S6).
If CpG islands directly represent a platform for chromatin modifiers that bind to unmethylated CpGs, the size of a CpG island may influence the amount of bound PRC or chromatin modification laid down at a locus. If this is the case, then the amount of H3K4me3 and H3K27me3 would be anti-correlated with gene expression noise. Indeed, we find in mESCs that CGI size and H3K4me3 are individually correlated with lower expression noise as expected (Fig. 3a), when considering only CpG island promoters. However, the opposite pattern is observed for H3K27me3. The promoters at which transcription is truly repressed would not produce any transcripts; thus, the observed association of greater noise with H3K27me3 may represent bivalent promoters [11]. Bivalent promoters are actively transcribed genes whose promoters are marked by both repressive and active chromatin modifications. The observed relationships between histone tail modifications and expression noise demonstrates that the greatest variability in gene expression noise between different promoters lies within regions with the lowest amount of modification (Fig. 3b). This may arise due to consistent differences between loci across all cells, i.e. all cells have a similar amount of H3 modifications at a given locus, or due to differences in the chromatin state between cells. This represents a limitation of integrating bulk-level chromatin modification with single-cell expression profiling. To resolve which mechanism explains the observed correlation would require per-locus chromatin modification information at single-cell resolution.
Promoter chromatin modifications influence expression noise in mESCs. a Robust linear regression model results, modelling the influence of promoter chromatin landscape and CpG island size on expression noise. Points below the horizontal dotted line are correlated with lower noise (purple), whilst those above are associated with greater noise (orange). b Promoter chromatin modifications (fold enrichment for ChIP/input) for repressive (H3K27me3; top panel) and active (H3K4me3; bottom panel) expression are negatively correlated with expression noise. c Bivalent promoters are the noisiest promoters. Robust linear regression model results of expression noise, jointly fitting promoter category relative to the repressed promoters, with CpG island size. d The relationship between promoter chromatin modification category and rCV2. Overlaid box plots represent the median rCV2 for each category, with lower and upper quartiles. Individual points lying outside the interquartile range are plotted as black points. The y-axis is truncated at [0, 15] for clarity. mESC mouse embryonic stem cell
To investigate the potential for bivalency to explain the correlation between gene expression noise and CGI size, we categorised promoters as repressed, active or bivalent based on the combined H3K27me3 and H3K4me3 ChIP signal (Additional file 2: Figure S7). Consistent with recent reports [8, 10], our analysis reveals that bivalent promoters are highly variable, an effect that is not dependent on CpG island size (Fig. 3c).
Short CpG islands provide response agility to stimulus
Our observations that CpG island size is associated with expression noise is not explained by the tendency for developmental genes to be associated with longer CpG islands (Additional file 2: Figure S6), nor by any correlation between CpG island size and bivalency. The necessity for tight regulation (i.e. a larger buffer against inappropriate stochastic activation) for genes involved in coordinated developmental programmes is well described. Whilst the gene expression noise of individual pluripotency and differentiation factors is associated with cellular plasticity and cell fate choice [26, 27], the execution of any particular developmental programme is highly coordinated and consistent, e.g. gastrulation, limb bud formation, etc. Mammalian CpG islands range in size from ∼200 bp to 10's of kilobases and CpG island size is proportional between human and mouse orthologous genes (Spearman rank correlation = 0.51; Additional file 2: Figure S8). Whilst large islands may lead to tighter transcriptional regulation, it is not immediately clear why there is also concordance in the size of short CpG islands between homologous mouse and human genes.
Whilst we consider gene expression noise to arise due to influences from both cis and trans factors, the utility of gene expression noise remains unresolved. Previous work has described the benefits of gene expression noise in eukaryote systems [28], but often in relation to a single gene [28] or small gene regulatory network. If fluctuations in gene expression (i.e. noise) are detrimental to fitness, then buffering mechanisms may have evolved to reduce the impact of expression noise [29] or there could be some degree of tolerance to noise due to a cost/benefit trade-off. Thus, is the observed greater expression noise in small CpG islands indicative of a cost/benefit trade-off?
In aeronautical engineering, there is a trade-off between flight stability and agility, i.e. greater instability leads to greater flight responsiveness. We hypothesised that short CpG islands may act as fast response elements to environmental stimuli, i.e. they are enriched for immediate early genes (IEGs). To test this hypothesis, we collected publicly available time-series gene expression data for a number of different stimulation conditions and cell types (Additional file 4: Table S3). Under our hypothesis, we would expect there to be an enrichment of short CpG islands amongst the primary early response genes within the first hour of induction. Delayed and secondary response genes tend to lag by 2–4 h in induction [30]. In seven different cell types and stimuli, across both human and mouse tissues, we observe an enrichment for genes with short CpG islands being up-regulated immediately post-stimulation, compared to later time points (Fig. 4a). In mouse bone marrow-derived dendritic cells (BMDCs), early response genes are more likely to overlap or lie proximal to a short CpG island (binomial one-tailed p=6.05×10−3). To verify that this observed enrichment was not specific to LPS-stimulated dendritic cells, or to mouse, we tested for short CpG island enrichment in a time course of human breast adenocarcinoma cells stimulated with an ErbB3/4 ligand, heregulin. We found the same pattern of short CpG island enrichment immediately post-stimulation (binomial one-tailed p=2.66×10−9). Enrichment for short CpG islands in early response genes was observed in five additional human and mouse innate immune cells and human cell-line data sets with different stimuli (Additional file 2: Figure S9 and Additional file 4: Table S3).
Short CpG islands are enriched in early response genes. a Murine LPS-stimulated BMDCs (left panel) and heregulin-stimulated human breast adenocarcinoma cells (right panel) demonstrate an enrichment for short CpG islands immediately post-stimulation compared to later time points. b Stimulation-specific enrichment for longer CpG islands post-stimulation in MCF7 breast adenocarcinoma (left) and retinal pigment epithelial cells (right). Plotted are the top 250 up-regulated genes with the largest log2-fold changes between time points (early, 0 vs 1 hr and late, 1 vs 2 hr). CpG island size is truncated at 3 kb for clarity. Vertical dashed lines denote the median CpG island length for the most up-regulated genes in the relevant time-point comparison (early is orange and late is yellow). c Highly variable genes in unstimulated BMDCs mark early response genes. Gene expression noise (CV2, y-axis) in unstimulated BMDCs across CpG island genes binned into 0.5-kb intervals (x-axis). Points are coloured by the direction of differential expression between unstimulated cells and 1-h post-stimulation (blue for down-regulated, yellow for no change and red for up-regulated). Filled grey circles represent the mean CV2 within each size category for differential expression. BMDC bone marrow-derived dendritic cell, ER α oestrogen receptor α, HRG heregulin, LPS lipopolysaccharide, SWI/SNF switch/sucrose non-fermentable complex
Whilst we see consistent enrichment of short CpG islands amongst early response genes across a number of different cell types and stimuli, we also observed several exceptions to this pattern. For example, oestrogen receptor α (ER α) stimulated breast adenocarcinoma cells, and retinal pigment epithelial cells induced to an epithelial-to-mesenchymal transition with TGF β + TNF α displayed the opposite pattern of enrichment, i.e. longer CpG islands are enriched in early response genes (Fig. 4b). This opposing pattern of enrichment was also observed in mouse Cd8+ cytotoxic T cells stimulated with interferon β (IFN- β), amongst others (Additional file 2: Figure S10 and Additional file 4: Table S3). That we observe opposing patterns of enrichment within the same cell type but using different stimuli, i.e. MCF7 cells, indicates that there is a stimulus-specific mechanism. Previous work on stimuli-specific differences in IEG induction in mouse macrophages found a bias towards a SWI/SNF complex-dependence for IFN- β but not LPS [31]. The SWI/SNF complex in both mammals and yeast plays a role as an ATP-dependent nucleosome re-modeller [32, 33]. Steroid-hormone nuclear receptors, including ER α, require SWI/SNF complex proteins in the induction of target genes [34–37]. Likewise, induction of TGF- β responsive genes via Smad 2/3 in epithelial cells is dependent on the SWI/SNF ATPase BRG1 [38, 39]. Thus, the SWI/SNF-(in)dependence between different stimuli reconciles the discordance in CpG island size usage between stimuli, even within cells of the same type and origin (Fig. 4).
If short CpG islands are fast response elements and gene expression noise diminishes with CpG island size, we would expect to observe that both short and noisy promoters are up-regulated immediately post-stimulation. To corroborate our hypothesis that short and noisy CpG island promoters are responsive promoters, we used single-cell RNA-seq expression data from stimulated BMDCs [40]. Genes that are highly variable in unstimulated cells should be those genes most likely to respond. We performed differential expression testing between unstimulated BMDCs and those cells 1 hour post-LPS stimulation. Comparing genes that are up-regulated with their noise in unstimulated cells revealed that these genes are consistently more variable than those genes that are either down-regulated (one-tailed Kolmogorov–Smirnov test p=7.89×10−123) or for which expression does not change (one-tailed Kolmogorov–Smirnov test p=6.97×10−10). This effect is consistent for CpG island promoters up to 1.5 kb, but was still dependent on CpG island size (Fig. 4c). This relationship was maintained after adjusting for the mean dependence using rCV2 (Additional file 2: Figure S11b). Notably, we did not observe the same tendency for noisy genes with non-CpG island promoters to be immediately up-regulated (Additional file 2: Figure S11).
In this study, we investigated a number of promoter features and their association with differences in gene expression noise. In concordance with a recent systematic analysis in mESCs [10], we find that CpG island promoters are less variable than their non-CpG island counterparts. This observation, and its generalisability to different cell types and mammalian species, implicates transcription regulatory mechanisms as important sources of expression noise [7–9, 41]. Further, the defining characteristics of CpG islands are associated with differences in noise. In particular, our finding that CpG island size is negatively correlated with expression noise indicates that variation in CpG island composition explains a proportion of the differential noise between promoters.
Based on these observations, we propose that short CpG islands, as noisy promoters, may be indicative of transcriptional dynamism. Our observation of short CpG island enrichment amongst early response genes indicates the potential role for promoter-associated short CpG islands to act as rapid transcriptional response elements. These findings, supported by evidence that highly variable genes in unstimulated BMDCs are up-regulated immediately upon stimulation, suggest a potential cost/benefit trade-off between stochastic transcriptional activation (noise) and how quickly a promoter is able to respond to an external stimulus. Interestingly, Antolovic et al. [42] recently showed that noisier genes in undifferentiated Dictyostelium were more likely to be up-regulated upon induction of a differentiation signal. These findings in a less complex eukaryote organism, which are concordant with our observations in BMDCs, highlight the potential for gene expression noise to mark genes primed to respond as a general feature of transcriptional regulation. Dictyostelium species lack experimentally validated CpG islands, which suggests that the exact mechanism by which gene expression noise arises may differ between phylogenetic clades or species. This is important, as we do not observe the same responsiveness for noisy non-CGI promoters (Additional file 2: Figure S11c). Transcriptional dynamics and promoter sensitivity to a modulating stimulus (repressive or activatory) are influenced by the chromatin landscape [43, 44] and the presence of paused RNA polymerase [45, 46]. The class of genes associated with immediate response (IEGs) are rapidly induced within a few minutes of stimulation, without the need for prior protein synthesis [30]. IEGs are associated with specific promoter architectures (high affinity TATA boxes and CpG islands) and encode shorter mRNA transcripts [30]. Induction of IEGs is associated with post-translational modification of histone proteins, such as lysine acetylation and histone H3 serine phosphorylation [44]. These modifications facilitate the binding of 14-3-3 and the SWI/SNF ATPase BRG1 [44]. Our observation that specific stimuli do not appear to utilise short CpG islands as IEGs is potentially reconciled by the stimulus-specific recruitment of SWI/SNF complex proteins to these IEGs [34–37, 44]. Moreover, the current lack of data on SWI/SNF complex dynamics in early response to stimuli suggests that there is the potential to discover the molecular mechanisms underlying these observations. Thus, our observation that short CpG islands are enriched amongst early response genes indicates a potentially novel mechanism for mammalian IEG induction.
Whilst we use CpG island size as a definition for these rapid response elements, there are most likely additional influences from static and dynamic promoter features. For example, recent work in Drosophila has highlighted the importance of promoter shape in transcriptional dynamics and the importance of noise [47]. CpG islands have been proposed as the vertebrate equivalent of polycomb response elements (PREs) in Drosophila, which is supported by evidence of PRC chromatin modification and PcG binding enrichment over these genomic features (reviewed in [48]). Thus, the link between CpG island size and response agility is potentially linked to differential dynamics in chromatin histone modifications or the binding of histone modification readers and writers.
Modelling of chromatin dynamics suggests that slow changes in chromatin modifications are required to induce transcriptional changes [49], a finding supported by the response model put forward by Klose et al. [50]. Berry et al. model the impact of robustness to trans-activation, supportive of a buffer in the responsive model, and note that the width of the cis memory window can have a drastic impact on responsiveness to noise in trans activator levels [49]. Thus, modelling of chromatin dynamics is concordant with the following: (a) CpG island size influences expression noise and (b) the smallest CpG islands provide the least buffering against stochastic fluctuations in trans activator levels.
The exact nature and source of gene expression noise within short CpG island promoters is not immediately clear. Recent evidence indicates that one common emerging source of gene expression noise is related to chromatin accessibility and dynamics [8–10]. It has been proposed that CpG islands generally have a more open or accessible conformation based on their lower affinity for histone proteins [31, 51]. However, more accessible chromatin is also associated with more promiscuous expression, which would not explain the increased noise observed at short CpG islands. Whether differences in noise arise due to the presence of paused or actively transcribing RNA polymerases, or the constitutive presence of trans activating factors requires further study.
Note that our measures of single-cell noise capture both technical and biological sources of variability. We do not expect technical sources of variation to distort systematically relationships between promoter architecture and gene expression noise, across multiple single-cell RNA-seq data sets. One possible source of confounding between our genomic features and noise is the sequence content of genic regions (e.g. coding sequence G + C content). However, exon G+C content is not related to CpG island composition or genomic position, indicating that our findings cannot be explained by technical sources of variation.
In this work, we used a mean-adjusted measure of noise, rCV2. We derive this measure by fitting a reciprocal relationship between CV2 and the mean expression for all genes, using a gamma distributed generalised linear model [12]. The extent to which this regression removes any relationship between mean expression and the features and contexts in which we test gene expression noise is dependent on the quality of this model fit. CV2 is a real-valued positive variable whose sampling distribution is likely to be similar to other variance-like statistics that are usually described by a χ2 distribution. The χ2 distribution is a special case of a gamma distribution; thus, CV2 can be appropriately described by a gamma distribution. Therefore, we suggest that modelling the relationship between the mean and gene expression noise using a gamma generalised linear model provides the most appropriate model in this context.
Our findings indicate that noisy genes, independent of mean expression, tend to be the most rapidly up-regulated. This suggests that many genes are poised to react to environmental stimuli which may have consequences for understanding how, as well as the speed with which a cell is primed to react to its environment. For instance, stress response genes have been identified as particularly noisy [9, 52] and the expression of stress response genes diverges between yeast species [53]. This indicates that highly responsive genes might evolve at different rates compared to more stably expressed and less noisy genes. Indeed, mutation accumulation experiments in yeast identified a correlation between the degree to which a gene promoter evolves and expression noise [54]. Fast-evolving genes, i.e. those whose expression is not constrained by stabilising selection, may also represent a mechanism for generating phenotypic heterogeneity in a population [53, 55]. This may further provide a way to either promote or buffer against multiple different perturbations, e.g. environmental or mutational. Whether these principles generalise to more complex eukaryote organisms remains to be seen, and provides an exciting possible avenue of research.
In conclusion, we have shown that short and noisy CpG islands may act as rapid response elements to external stimuli. These findings raise interesting questions about what role transcriptional variation has to play in the cellular and physiological response of organisms to their environment, and how these mechanisms have evolved.
Single-cell RNA-seq data processing
Where available, gene-by-cell-expression count matrices were downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) or ArrayExpress (https://www.ebi.ac.uk/arrayexpress/; see Additional file 3: Table S2 for a list of accessions) [14, 19, 40, 56, 57]. Cells with low count numbers (<100 000) or that had been flagged as poor quality by the study meta-data (where available) were removed prior to normalisation. Within each data set separately, genes that were expressed in <1% of cells were removed, prior to cell size factor normalisation using the deconvolution approach [58] and log2 transformation.
Gene expression noise
Gene expression noise was calculated as the absolute residual squared coefficient of variation (rCV2) from the fit between the mean log2 expression and squared coefficient of variation (CV2), parametrised as in Brennecke et al. [12]. Specifically, CV2 was fitted using a gamma generalised linear model:
$$ E\left[\text{CV}_{i}^{2}\right] = 1 + \frac{1}{\mu_{i}}, $$
$$\mu_{i} = \log_{2} \left(\frac{1}{m} \sum^{m}_{j=1}{\frac{c_{ij}}{s_{j}}} \right), \quad i \in \{1, 2, \dots, m\}, $$
is the average expression of gene i, c ij is the read count for gene i in cell j and s j is the deconvolution size factor. The squared coefficient of variation is
$$\text{CV}_{i}^{2} = \frac{\hat{\sigma}_{i}^{2}}{\mu_{i}^{2}} $$
and \(\hat {\sigma _{i}^{2}}\) is the sample variance for gene i. The gene expression noise, \(r\text {CV}_{i}^{2}\), for gene i, is, therefore, calculated as the deviation of the expected value from the observed \(\text {CV}_{i}^{2}\), i.e. \(r\text {CV}_{i}^{2} = \left |\text {CV}_{i}^{2} - E\left [\text {CV}_{i}^{2}\right ]\right |\).
To test the influence of genomic features on gene expression noise formally, we fitted a linear model to rCV2 in each data set and tested the slope of the fitted line, i.e. the regression coefficients. We observed that rCV2 had a long-tailed distribution (Additional file 2: Figure S1c). Ordinary least squares regression compares the mean difference between values of a predictor variable on the response variable. The breakdown point of the mean is 0; thus, only a single extreme value is required to bias its estimate. The median has a breakdown of 0.5, that is, it requires more than half of the values to be outliers to bias its estimation. Thus, ordinary least squares regression would be inappropriate in this context where there are many extreme rCV2 values, whilst a robust linear regression that uses the median would not be susceptible to the same extreme outlier values.
Gene promoters were defined for each mm10 and hg19 gene based on the Ensembl build v86 annotations (https://www.ensembl.org/index.html). Promoter regions encompassed −1 kb and + 500 bp centred on the transcriptional start site (or beginning of the first annotated exon), accounting for strand. Total transcript lengths, exon length variation and number of exons were calculated from the Ensembl v86 General Transfer Format (GTF) file.
CpG island data were downloaded from the UCSC Genome browser [59] for hg19 and mm10 using the table browser tool [60]. Promoter sequence GC content was calculated for each promoter using bed2fasta from CGAT tools [61].
Transcription factor binding motifs were predicted over the length of all promoters using the MEME suite tool FIMO, with motif positional weight matrices provided from the JASPAR CORE vertebrate TF motif database (2016) [62, 63].
ChIP sequencing data processing
ChIP-seq libraries derived from mESCs were downloaded from GEO accession GSE36114 [64]. Sequences were downloaded from the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena). Known Illumina sequencing adaptors were removed using trimmomatic prior to alignment to mm10 using bwa-mem [65]. Aligned sequences were quantified over non-overlapping 200-nt windows on the mm10 genome in all ChIP and input libraries. log2-fold enrichment was calculated between each chromatin modification ChIP library and its matched input for each window. Signals over promoter intervals were calculated as the average log2-fold enrichment across overlapping windows for each replicate, then averaged across all replicates for the respective chromatin modification immunoprecipitation.
Bivalent promoters were calculated based on overlapping H3K27me3 and H3K4me3 ChIP signals. The H3K4me3 chromatin modification signal was dichotimised based on the local minimum of the kernel density estimate over all gene promoters. The H3K37me3 ChIP signals displayed a long tail without any noticeable minimum in density. Thus, to dichotomise the H3K27me3 signal, a threshold was set as the mean value across all gene promoters. Gene promoters were assigned to either active (H3K4me3) or repressed (H3K27me3) if the promoter signal exceeded 1.5 times the threshold for the relevant chromatin modification. Genes that exceeded both thresholds were assigned to the bivalent category.
Defining mouse embryonic development genes
Mouse embryonic development genes for Additional file 2: Figure S6 were defined from J1 embryonic stem cells (ESCs) differentiated over 14 days and measured at 11 time points [66]. Gene expression data were downloaded from the StemBase data base (April 2018, http://www.stembase.ca) [67] and were based on measurements with an Affymetrix Mouse Expression 430a array. Embryonic genes were defined based on log2-fold change >0 over the differentiation time course, tested in a generalised linear model with a moderated t-test [68], at a false discovery rate of 1%.
CAGE processing and time-series analysis
Peak annotations for a human time series from a cap analysis of gene expression with sequencing (CAGE-seq) were acquired from the FANTOM5 consortium website (http://fantom.gsc.riken.jp/5/data/) [69, 70]. Tag counts were downloaded and split by time-series experiment. CpG islands were assigned to peak annotations within 500 bp centred on each CAGE peak. Peaks expressed in ≤75% of samples were removed prior to analysis for each time-series data set separately. Moderated log2-fold changes between time points were calculated and tag counts were modelled using a negative binomial generalised linear model implemented in the Bioconductor package edgeR [71, 72].
RNA-seq processing and time-series analysis
Gene-by-sample count matrices were downloaded from GEO for the relevant accessions [73, 74]. Genes containing on average >5 read counts across all samples were retained for analysis. Moderated log2-fold changes were estimated for each time-point comparison using the Bioconductor package DESeq2 [75].
CpG island size enrichment testing
For each data set, we compared the proportion of genes, ranked by log2-fold change in expression between time points, that had a smaller CpG island overlapping the defined promoter region in the earliest time-point comparison. We then applied a one-tailed binomial test against a null hypothesis of a 50:50 relationship between rank and CpG island size. This process is analogous to gene set enrichment testing, using the ranked test statistics from a differential expression test, or equivalent to a paired sample sign test (Additional file 2: Figure S12).
The code used to perform the analyses and the process data are available from https://github.com/MarioniLab/CpGisland2017.
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We wish to thank Wolf Reik, Ferdinand von Meyenn and Aaron T. Lun for kindly donating single-cell gene expression data from human ESCs. The authors also wish to thank Sarah Thorpe and Christina Ernst for critical reading of the manuscript.
MDM was supported by the Wellcome Trust (grant 105045/Z/14/Z). JCM was supported by core funding from the European Molecular Biology Laboratory and from Cancer Research UK (award number 17197).
All data sets can be obtained via the appropriate accession number for each study or website where relevant:
Mus musculus naive Cd4+ T cells: Stubbington MJT, et al. T cell fate and clonality inference from single-cell transcriptomes. Array Express. E-MTAB-3857.
Mus musculus embryonic stem cells: Grun D, et al. Validation of noise models for single-cell transcriptomics. GEO. GSE54695.
Mus musculus embryonic stem cells: Kolodziejczyk AA, et al. Single-cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Array Express. E-MTAB-2600.
Homo sapiens embryonic stem cells: von Meyenn F, Lun AT, Marioni JC, Reik W. Array Express. E-MTAB-6819.
Homo sapiens pancreatic islet cells: Lawlor N, et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes. GEO. GSE86473.
Mus musculus bone marrow-derived dendritic cells: Shalek AK, et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. GEO. GSE48968.
Homo sapiens MCF-7 breast adenocarcinoma cells: Baran-Gale J, et al. An integrative transcriptomics approach identifies miR-503 as a candidate master regulator of the oestrogen response in MCF-7 breast cancer cells. GEO. GSE78169.
Homo sapiens monocyte-derived dendritic cells: Diehl WE, et al. Ebola virus glycoprotein with increased infectivity dominated the 2013–2016 epidemic. GEO. GSE84865.
Homo sapiens CAGE-seq data sets: Forrest ARR, et al. FANTOM5. http://fantom.gsc.riken.jp/5/datafiles/latest/extra/CAGE_peaks/.
MDM conceived the study, performed all analyses and wrote the manuscript. JCM conceived and supervised the study and wrote the manuscript. Both authors read and approved the final manuscript.
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
Michael D. Morgan & John C. Marioni
Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
John C. Marioni
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
Michael D. Morgan
Correspondence to Michael D. Morgan or John C. Marioni.
Additional file 1
Supplementary Table 1. CSV file describing genomic data sources and acquisition dates. (CSV 1 kb)
Supplementary Figures. PDF of all supplementary figures S1–11. (PDF 3588 kb)
Supplementary Table 2. CSV file describing single cell RNA-sequencing data sets used in this study, with cell numbers and accession numbers to publicly available data. (CSV 1 kb)
Supplementary Table 3. CSV file of time-series gene expression data sets and CpG island size enrichment testing results. (CSV 1 kb)
Morgan, M., Marioni, J. CpG island composition differences are a source of gene expression noise indicative of promoter responsiveness. Genome Biol 19, 81 (2018). https://doi.org/10.1186/s13059-018-1461-x
Promoter response


Paleo, Vegan, Pegan, Keto, Atkins, Gluten Free, Non-GMO, Alkaline, Kosher, Low Sodium… so many terms!
What are we supposed to eat now-a days?
Should I just eat vegetables and fruit?
How much is too much fruit?
Kale causes issues with female hormones if consumed too often.
Carrots cause carrageenan poisoning? Am I a Flamingo?
Raw or Cooked? Blackened or grilled may cause cancer? Aluminum foil may cause Alzheimer's?
Dairy should we have it? Maybe Ghee is better? Isn't it dairy at the end of the day?
I was recommended to try an immunology test recently, which tests my blood with over 54 different chemicals and food ingredients. They then analyze the reactions and conclude what you may or may not be "allergic" too. I was pretty aware of a few of them, but was definitely surprised by a lot of them! Every since moving to a more vegan plant based diet, and replacing my meals with plant based protein, I could tell something was off. It has been a month of me being vegan, and as much I enjoyed it, it's clearly not working with my body. I am beginning to be more creative with the meals I can eat.
The results show that I am reactive to: Almonds, Barley, Lima Beans, Cashew Nuts, Catfish, Coconut, Eggs (!!!), Flounder, Garlic, Ginger, Lobster, Cow's Milk, Filbert Nuts, Peas, Peanuts, Perch, Pork, Rice (my life), Rye, Sesame, Soybean, Tuna, Walnuts, and Wheat.
I am going to eat consciously around this for 6 months, and see the impacts on my body. Let's see! If you're curious to know more about this, leave a comment below!
and now with my focus in sustainability, I have become an advocate for choosing responsibly what you are putting in your mouth, and buying from suppliers.
Do you know where your avocados are coming from? Do you know what the chickens are fed to create the perfect egg? Do we even know what is being sprayed on our Spinach and Apples that we love so dearly?
What can You, and We do right now?
Follow organizations and individuals in your community that are making a difference. Share their content, quotes, tidbits of knowledge.
Learn how to improve your household, your habits, and inspire those around you.
Share this article with friends and family and start the conversation on what nutrition and eating ethically means to you.


text:
Honorees Announced for the 67th Annual Advertising Hall of Fame
The Ad Council, TBWA\Worldwide, Leo Burnett Worldwide, Sedelmaier Films, GSD&M, Condé Nast, Wieden+Kennedy Among Those Honored
WASHINGTON, D.C., Nov. 20, 2015—Elected by the Advertising Hall of Fame's distinguished Council of Judges, the American Advertising Federation announced this year's "magnificent seven" to be inducted into the 2016 Class of the Advertising Hall of Fame.
The Advertising Hall of Fame recognizes the most accomplished and legendary figures in advertising, bringing together the industry to celebrate at one of the biggest nights in advertising. The 67th Advertising Hall of Fame Induction Ceremonies & Gala Dinner will be held the evening of Monday, April 11, 2016, in the Grand Ballroom of New York's historic Waldorf=Astoria hotel.
"Our Hall of Fame Council of Judges, composed of leaders of advertising and media, advertisers and distinguished Hall of Fame members, was deeply committed to choosing men and women of legendary stature and achievement," said John B. Osborn,President and CEO of BBDO New York and Chairman of the Advertising Hall of Fame.
This year's honorees include: Peggy Conlon, Former President and CEO, The Ad Council; Jean-Marie Dru, Chairman, TBWA Agency Network; Carla R. Michelotti, Former Executive Vice President, Chief Legal, Government, Corporate Affairs Officer, Leo Burnett Worldwide; Joe Sedelmaier, Director/Producer/President, Sedelmaier Film Productions; Roy Spence, Co-Founder and Chairman, GSD&M; Charles H. Townsend, CEO, Condé Nast; Dan Wieden, Co-Founder + Chairman, Wieden+Kennedy.
Peggy Conlon will also be presented with the distinguished David Bell Award for Industry Service, awarded to recognize extraordinary and unique contributions and service to the advertising community and industry as a whole. The award is named in honor of David Bell (Hall of Fame Class of 2007), a visionary leader and mentor to several generations of advertising professionals.
Wenda Harris Millard, President and COO, MediaLink, and Vice Chair of the Advertising Hall of Fame added, "This extraordinary Class of 2016 epitomizes advertising's lasting impact over time continuing through today, inspiring many more generations to come."
For more information on the Advertising Hall of Fame, please contact the AAF at lrubin@aaf.org or (202) 898-0089.

redpajama_set_name: RedPajamaCommonCrawl


Published: Oct. 11, 2013 at 03:41 p.m.
Updated: Oct. 12, 2013 at 12:12 a.m.
Our first four graduates are Harrison Smith, Jordan Cameron, Cameron Jordan and Jurrell Casey, all of whom have established themselves as Pro Bowl-caliber players. They've made the leap.
Previous lists: Week 1, Week 2, Week 3, Week 4, Week 5.
It's hard to a recall a player who has improved more than Pryor over a six-week span. Defenses are having difficulty adjusting to such an athletic running threat that keeps his eyes downfield and making pass rushers pay with better than advertised improvisational skills as a passer. If Pryor's sandlot style continues to put defenses in a bind, the Raiders will have to start viewing him as the quarterback of the future.
Two rookies dominating the trenches. We've outlined Damon "Big Snacks" Harrison's brilliance as an elite run-stuffer. Richardson has been nearly as dominant versus the run while adding a pass-rushing element as a book-end to franchise cornerstone Muhammad Wilkerson.
The Cowboys offensive line is wildly outpacing expectations, currently holding the No. 3 spot in Pro Football Focus' grades. Frederick has been among the league's most effective run blockers over the past three weeks.
Jeffery entered Thursday's game fourth among all receivers in yards from scrimmage, which led the Giants to send double teams his way on several occasions. Teammate Brandon Marshall believes Jeffery has the potential to become the best receiver in franchise history.
Moore is finally making good on his potential as a poor man's Mike Wallace. Only seven receivers have more yards over the past three weeks.
Allen has looked like a third-round steal over the past two weeks, showing signs that he can win with physicality at the point of the catch and still make plays down the field.
Blackmon followed up an impressive preseason with 136 yards a touchdown in his season debut.
It's rare to see a rookie safety take on as much responsibility as Vaccaro has, lining up as a deep safety, an in-the-box safety, a nickel back in the slot, a cornerback on the outside, an inside linebacker and an outside linebacker in Rob Ryan's defense. The first-round draft pick is one of the primary reasons the Saints are arguably the most complete team in the NFL this season.
Two picks away from being this year's Mr. Irrelevant, Cooper was claimed off waivers from the San Francisco 49ers in early September. After showing impressive cover skills over the first four games, he intercepted a pass and recovered a block punt in the end zone for a touchdown at Tennessee in Week 5. "He can be as good as he wants to be. He has a lot of ceiling," general manager John Dorsey said earlier this week. "He can turn and track the vertical throws and catch the ball. He's got ideal length ... that's the ideal press corner."


Specialty CME Credits: Included as part of the 4 credits, this CME activity is eligible for 4 Infectious Disease CME and 2 Pharmacology CME credits, subject to your state and institutional approval.
This was a very thorough review with good clinical application.
As usual, an excellent presentation on a very broad topic. You have outdone yourselves again! As always, many thanks!
Great article discussing the physiology of meningitis as well as the diagnosis and treatment.
Good information on antimicrobial coverage; I have previously given ceftriaxone and ampicillin in neonates, so good to learn this update.
Excellent points in the management of pediatric meningitis.
I will continue to emphasize early appropriate antibiotic administration based on age and risk factors to optimize empiric choices.
I will run through red flag symptoms of somnolence, fever, neck stiffness, meningismus, ill appearance, severe headache more routinely. I will also add the bacterial meningitis score to my practice.
This course helped improved decision making based on the strongest clinical evidence, improved diagnosis of bacterial meningitis, improved treatment, and management of meningitis.
I was not aware of the bacterial meningitis score for children and feel this will greatly impact the way I approach bacterial meningitis. Also, the procalcitonin as an adjunct in the differential between viral bacterial meningitis was also enlightening.
Better evaluation and treatment of pediatric patients with possible meningitis.
After reading this article, I will consider doing more LP's in borderline cases. Consider the prior use of antibiotics as an increased risk factor.
I now have a better understanding of the use of procalcitonin.
I will start using the bacterial meningitis score in febrile patients with high WBC with suspicion of meningitis.


text:
Вильгельм Юстин Кролл или Гийом Жюстен Кроль (; 24 ноября 1889, Эш-сюр-Альзетт — 30 марта 1973, Брюссель) — люксембуржский металлург. Наиболее известен изобретением в 1940 году , который используется для извлечения металлического титана из руды

Биография
В 1909 году окончил Люксембургский Атенеум, в 1910 году поступил на металлургический факультет в Берлинский технический университет, который окончил в 1914 году. Следующие четыре года, писал докторскую диссертацию под руководством профессора К. А. Гофмана.

Работая в Германии в 1918 году, Кролл изобрел подшипниковый сплав на основе свинца, получивший коммерческое название «Лург», и получил несколько патентов, связанных с металлургией. В Австрии в 1922 году он разработал сплавы, которые использовались в основном для поршней из литого алюминия.

В 1923 году Кролл вернулся в Люксембург и создал частную лабораторию. Кролл изобрел дисперсионное твердение (PH) нержавеющей стали в 1929 году, добавив небольшое количество титана в нержавеющую сталь и усилил ее способом термического осаждения частиц карбида титана. В 1930-х годах он сосредоточился на изучении титана и его сплавов. До Кролла титан как металл был лабораторной редкостью. В 1938 году титан был впервые обработан на заводе Cerametal в Берелданже, Люксембург. Кролл посетил США, прибыв в Нью-Йорк на борту корабля «Королева Мария» из Шербура, 18 октября 1938 года. Интереса к титану в то время не было и, разочарованный, он вернулся назад в Люксембург. В 1940 году Кролл решил эмигрировать в Соединенные Штаты опасаясь прихода к власти нацистов. Он прибыл в Нью-Йорк 22 февраля 1940 года, отплыв из Роттердама, 10 февраля 1940 года. 10 мая 1940 года немецкие войска вторглись в Люксембург, оккупировав страну на следующие четыре года.

25 июня 1940 года Ведомство США по патентам и товарным знакам выдало патент на метод Кролла по производству титана и его сплавов. 4 декабря 1940 года Кролл подал заявление на получение гражданства США. Он работал инженером-консультантом в исследовательской лаборатории Union Carbide в Ниагара-Фолс. Когда США объявили войну странам Оси в декабре 1941 года, патент Кролла был конфискован правительством США, как подпадающий под действие Закона о хранителе имущества иностранцев, что привело к семилетнему судебному разбирательству, закончившемуся в пользу Кролла. Судебные издержки в размере более 1 миллиона долларов съели присужденную Кроллу компенсацию. Постепенно правительство США и промышленность начали осознавать потенциал титана. Правительство США создало исследовательский центр в Боулдере в 1944 году, а в конце 1948 года компания DuPont начала коммерческое производство титана с использованием процесса Кролла. Со временем внимание Кролла переключилось на цирконий. В 1945 году он стал металлургом-консультантом в Министерстве внутренних дел США Горнорудное управление в их исследовательском центре в Олбани, Орегон. Первая полоса циркония была выпущена там в августе 1946 года. В 1951 году Уильям Кролл начал преподавать в Университете штата Орегон, учредил некоммерческий фонд исследований металлов, предоставляющий стипендии и гранты в США и Европе.

Кролл вернулся в Европу в 1961 году, где и умер в Брюсселе 30 марта 1973 года.

Институт добывающей металлургии Кролла (KIEM) был основан в 1974 году по завещанию Кролла при Горной школе Колорадо, Голден, Колорадо. Финансовые средства Кролла предназначались для создания Центра передового опыта в области добывающей металлургии. С момента своего основания Институт Кролла оказывал финансовую поддержку как студентам, так и аспирантам, многие из которых впоследствии, внесли важный вклад на национальном и международном уровнях в области добычи полезных ископаемых, металлов и передовых материалов.

Признание и награды
Кролл был посмертно занесен в Национальный зал славы изобретателей в 2000 году.

Среди других его наград были Медаль Бенджамина Франклина в 1954 году, Премия Альберта Совера за достижения от Американского общества металлов в 1955 году, Медаль Кастнера в 1960 году и Медаль Перкина в 1958 году. Он также был награжден Премией Ачесона от Электрохимическое общество в 1958 году.

В честь Кролла названа улица в городе Люксембург, в деловом районе Гасперих и в Эш-сюр-Альзетт.

В сентябре 2018 года профессиональная средняя школа в Эш-сюр-Альзетт в Люксембурге, Lycée Technic d'Esch-sur-Alzette, изменила свое название на «Lycée Guillaume-Kroll».

Примечания

Изобретатели XX века
Иммигрировавшие в США из Люксембурга

redpajama_set_name: RedPajamaWikipedia


Monday – Sunday 6:00 a.m. – 10:00 p.m.
Weekends 9:00 a.m. – 5:30 p.m.
The Recreation Complex is typically closed for all statutory holidays, please call (403) 652-4041 for 24-hour info.
The Bob Snodgrass Recreation Complex is located at 228 12 Avenue S.E. and consists of a 25 meter swimming pool with hot tub and sauna, two ice arenas, concession, and is home of the Highwood Curling Club. The Curling Club runs four sheets of ice at the back of the facility.
The Cargill Field House addition, which opened in early 2014, adds over 33,000 sq. ft. of new space to the recreation complex. The new facility includes a two-lane walking track, spectator seating for 200 people, meeting room, and a playing surface designed to accommodate a variety of sports such as soccer, lacrosse, basketball, volleyball and badminton.


Lucknow, March 19: Congress General Secretary Priyanka Gandhi Vadra, who is undertaking a boat ride from Prayagraj to Varanasi in the Ganga, on Tuesday made a scathing attack on Prime Minister Narendra Modi led government at the Centre and said they can no longer hide behind 'What did they do in 70 years?' argument. Priyanka Gandhi said it is high time the Modi government tells people what they have done in the last five years.
"'What did they do in 70 years?' argument also has an expiry date. Now they (BJP) should tell what they have done in their five years when they are in power," Priyanka said. The Congress leader also took on Chief Minister Yogi Adityanath led government in Uttar Pradesh said every section of the society is in distress under the current regime. She said the ground reality is far from what the Bharatiya Janata Party (BJP) is showing in promotional ads. Priyanka Gandhi Vadra's Reply When Asked if She Would Contest 2019 Lok Sabha Election.
"The report card, promotions, all of it sounds good but there's nothing on the ground. I'm meeting people every day. All the people are in distress," Priyanka said. Earlier today, she offered prayers at Sita Samahit Sthal in Bhadohi. On Monday, Priyanka also took a dig at PM Modi's 'Main Bhi Chowkidar' campaign. "It is up to him what prefix he uses in his name. Yesterday, I met a group of farmers. One of them told me, chowkidars are for the rich. We farmers are our own chowkidars," she said. Priyanka Gandhi Not Be Allowed to Enter Kashi Vishwanath Temple: Lawyers to DM in Letter.
The BJP, however, downplayed Priyanka's 'Ganga Yatra' equating it with a picnic. "There are some families, in the past, they were called 'Rajgharana'. They come in the election season to celebrate picnic and return only after 5 years. It is obvious, that this 'boat yatra' is for the vote only," Deputy Chief Minister of Uttar Pradesh Dinesh Sharma said.
Uttar Pradesh will go to polls during all seven phases of the Lok Sabha election to be held on April 11, April 18, April 23, April 29, May 6, May 12 and May 19. Counting of votes will be done on May 23.


text:
Patricia Suzanne | Conservative Activism: Part of Your Routine
Signal Contributor
As you're subjected to the day-to-day news of escalating federal power grabs, what is your response? Do you wring your hands, yell at the TV screen, complain to your friends and neighbors? While there may be some catharsis in all that, freedom-loving patriots must get off the couch to save the republic and protect individual liberty. Our founders knew this in their bones, and we must learn from their example.
Make no mistake, every Joe Biden executive order, every piece of Democrat legislation introduced in Congress is aimed squarely at removing any control you have over your own life and giving it to government bureaucrats. Not to mention allocating trillions of dollars to illegal border crossers, pet projects, foreign countries and wasteful blue states without a single concern for mounting debt landing on the backs of U.S. citizens for generations to come.
"Equity" is their stated cause, a term that carries a certain emotional appeal, I suppose. Who doesn't want everyone to experience an equal outcome? Well, I for one, because it's an impossible goal and invariably leads to mediocrity for all. Think about it. Can we truly guarantee the same outcome for every person, regardless of talent, IQ, determination, physique, health, family values, etc.?
Rhetorical question.
While our society hasn't yet reached the point of regulatory insistence on drafting 4-foot-10 clumsy students onto the high school basketball team, just wait. That time is on the horizon, urged along by a million legislative knife cuts to meritocracy promoted exuberantly by fake news media and corporate giants working in tandem to intimidate the masses and cancel public expression that falls out of line with their left-leaning controlling party line.
Don't be disheartened. Do NOT give up. Take courage and take action. Yes, we're overwhelmed, hit from all fronts – unchecked illegal border crossings, the shredding of election legitimacy, looming tax increases, continuing freedom suppression in the name of COVID, institutionalization of racial hatred, the drip-drip-drip of laws that shred our Second Amendment rights. And more.
Americans who don't see 1930s Germany in what's happening in the U.S. today simply have no knowledge of the past…and how we're repeating a sinister pattern HERE. Perhaps this is by design by global elites, as classroom texts at all levels have been systemically altered or set aside to dumb-down our youth. You need only watch (or conduct!) "man on the street" interviews to realize how many people don't know one whit of civics or history, who don't understand how unique and precious our system of government was designed to be.
So…what to do in the face of creeping Marxism? How can you protect individualism in the face of growing cries for collectivism (which clearly leads to totalitarianism)?
Here are 10 concrete steps you can take. No doubt you can think of more.
1. Call or write to your elected representatives. Be specific with your support or criticism. You can find a lot of contact information here – http://bit.ly/SCV-Contacts.
2. Join, volunteer for and donate money to local conservative organizations.
3. Sign and circulate petitions. (Later this month, the petition to recall LA County District Attorney George Gascón will hit the streets. Visit the official website: recallgeorgegascon.com.)
4. Stop buying from Amazon and other corporate giants. Support local small businesses instead.
5. Get armed and practice at the shooting range.
6. Stop sending money to your alma mater. Let that university know why! (Why? Because predominantly leftist educators are convincing an entire generation that this country is racist, misogynist and imperialist. Watch Ami Horowitz interviewing students at Yale University, where 65% of those encountered want to get rid of the U.S. Constitution: bit.ly/GetRidAtYale).
7. Attend local school board meetings. You might be shocked! Here's a list for Santa Clarita: bit.ly/SCVSchoolBoards.
8. Study the Constitution for yourself. It's a small document. Free copies are available at Rep. Mike Garcia's office here in town and the language is, of course, online. Quick quiz: What does it say in Article I, Section 4, that every Democrat in the House of Representatives blithely ignored in passing HR1 last month?
9. Educate young people in your life about founding principles, civil rights and individual liberty. Short, free videos on a variety of topics may be found at Prageru.com.
10. Comment constructively on left-leaning social media pages and give supportive praise on those platforms to elected officials who make constructive choices.
With regard to No. 1 above, I once heard a Democrat activist address a meeting of retired teachers. She articulated her morning routine to the group: "I make my bed, brush my teeth, call my congressman, eat breakfast, shower, get dressed…"
In other words, being politically active was part of her everyday schedule.
Make it part of yours!
Patricia Suzanne is a professional writer, retired small business owner, and conservative activist. "Right Here, Right Now" appears Saturdays and rotates among local Republicans.
Jim de Bree | Are There Any Deficit Hawks?
Thomas Oatway | More Late-Night Comedy Fodder
Rick Barker | Finding Common Ground
Gary Horton | America, a Nation of Shoulder Shruggers
Judy Reinsma | Too Much Written Combat
Rob Kerchner | The President's Authority


text:
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{% load i18n %}
{% load pagination_tags %}
{% load order_by %}
{% load humanize %}
{% load project_tags %}

{% block head_title %}Tasks for {{ project.name }}{% endblock %}

{% block body %}

{% trans "Tasks for Project" %} {{ project.name }}

Group by:
        {% ifequal group_by "state" %}
            last modified
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                last modified
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            {% else %}
                last modified
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{{ section.grouper }}

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{{ section.grouper }}

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{{ section.grouper|naturalday:_("MONTH_DAY_FORMAT")|capfirst }}

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New Task

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redpajama_set_name: RedPajamaGithub


text:
Q: Accessing CONFIG_ macros in my source code I'm creating a Linux kernel module using buildroot to perform the cross-compile. I've created a Config.in file to create some optional bits like so:
config BR2_PACKAGE_MYPACKAGE_OPTION1
    bool "Option 1"

In my source code I would have:
#ifdef CONFIG_BR2_PACKAGE_MYPACKAGE_OPTION1
#error Testing if symbol defined
#endif

This doesn't work, the symbol is not defined at compile time when the option is checked from menuconfig. The symbol does appear in the top-level .config file.
I guessed that mypackage.mk would have access to the symbol and it does. I've tried adding these to the .mk file, but although both TARGET_CFLAGS and EXTRA_CFLAGS get altered by the statements (confirmed with an @echo command), they don't get passed to the compiler.
ifeq ($(BR2_PACKAGE_MYPACKAGE_OPTION1),y)
    TARGET_CFLAGS += -DCONFIG_BR2_PACKAGE_MYPACKAGE_OPTION1
endif

ifeq ($(BR2_PACKAGE_MYPACKAGE_OPTION1),y)
    EXTRA_CFLAGS += -DCONFIG_BR2_PACKAGE_MYPACKAGE_OPTION1
endif

How does one get access to the configuration symbols in your source code?

A: I figured out one possible answer:
Pass the ones you are interested in to the $(MAKE) invocation in your mypackage.mk file, then you can pass them on to the compiler in your package Makefile/Kbuild file.

redpajama_set_name: RedPajamaStackExchange


Serving all of the beautiful El Dorado County foothills and surrounding communities, I work hard to locate, research and negotiate the best price for properties and to market homes or land in order to target the right buyers. I have over eighteen years experience and know how to get the job done right.
Earning my Senior Real Estate Specialist designation & Graduate of Real Estate Institute designation are just part of an ongoing goal that I have. It is of utmost importance that I continue my education in this changing market in order to best meet my clients needs.


Question: You have attacked the teachings of Calvin and Augustine. We'll see which lasts the longest and stands the test of time—the writings of Augustine and Calvin and their contribution to orthodox theology or those of the writers and staff of TBC. Sorry, but history shows that in the world of art, music, literature, and theology God has given some to be head and shoulders above the rest of us. If I was sick I would seek the best, most brilliant surgeons possible. When studying Scripture I will continue to trust and rely on the great theologians, flawed as they may be.
Response: It is good counsel to "seek the best, most brilliant surgeons possible." However, regarding spiritual matters, the intent to lean upon "the experts" clearly avoids the scriptural admonition to "prove all things; hold fast that which is good" (1 Thes:5:21Prove all things; hold fast that which is good.
See All...). Who are the experts when it comes to the Scriptures? Should we not test them? We are all flawed. More important, do Augustine and Calvin consistently and accurately "rightly divide the word of truth" (2 Tm 2:15)? We have simply pointed out some of their obvious contradictions and errors.
The Lord notes on a number of occasions that "Cursed be the man that trusteth in man, and maketh flesh his arm, and whose heart departeth from the Lord" (Jer:17:5Thus saith the LORD; Cursed be the man that trusteth in man, and maketh flesh his arm, and whose heart departeth from the LORD.
See All...). Are we to place people upon pedestals? Even the Old Testament priests were told to not "go up by steps unto my altar, lest thy nakedness be discovered." We are living in a time when socially, politically, and most important, spiritually, men are turning from their responsibilities and surrendering their thinking to "the experts." The failure of this approach is becoming only too obvious.
If people regard us as the "answer men," then we have failed in the task the Lord has given us. We seek to point individuals back to the Scriptures, following the example of the Bereans (Acts:17:11These were more noble than those in Thessalonica, in that they received the word with all readiness of mind, and searched the scriptures daily, whether those things were so.
See All...). What you advocate is contrary to the Lord's admonition and is detrimental to the health of the Body. Those who accept the pronouncements of "big names" such as Calvin or Augustine without "searching the Scriptures daily" are insubordinate to the commands of the Lord.
When Christ rebuked individuals, it wasn't because they failed to listen to Gamaliel or other prominent teachers. He went to the heart of the matter. The Scriptures were sufficient for the Lord Jesus Christ when He confronted the two disciples who were on the road to Emmaus (Lk 24:13-27). He told them, "O fools, and slow of heart to believe all that the prophets [not the commentators] have spoken." And, "...beginning at Moses and all the prophets, he expounded unto them in all the scriptures the things concerning himself" (v. 27).
It is He who warned against the traditions of men. Now, let's be obedient to the commands of Christ and although we may find the comments of "great theologians" helpful at times, like all commentaries, the inspiration ceases when we move from the biblical text to the comments of men. We would greatly encourage a prayerful and scriptural reexamination of the position that you have advocated.
As another writer notes, "The reformers had proclaimed the priesthood of all believers, but the Anabaptists, their contemporaries, were not impressed with what they found in the reformers' churches. The monopoly of the Catholic priest seemed to have been replaced by the monopoly of the reformed preacher. Experts were still disempowering the congregation and hindering it from becoming mature" (Stuart Williams, "Interactive Preaching," 3/3/08, The Anabaptist Network).


In 1932, this house stood among 8 others in this block of Leslie Street between 12th and University Streets. By 1966, there were twice as many residences on the 1100 block of Leslie Street listed in the City Directory. Today it is alone, the others having been moved or demolished. The blocks around it on three sides are medical offices and the expanded Salem Hospital. The fourth side is the busy 12th Street with its ramp heading east onto Mission Street.
Our earliest record, for 1932, shows it occupied by Mrs Grace Burns, followed by Arthur Denison two years later, and from 1936 to 1947 by L. C. Ramage and wife Lillian. The family who lived there the longest period of years was that of Richard Schmidt. There were many children in this spacious bungalow and altered bedrooms accommodated the growing family. (Remodeling of the basement revealed that there had been a fire, that this might have been the second structure on this lot.) Research continues in order to learn more about the house in the residential years.
The latest owner discovered that the local builder used a cement-based stucco that was exceptionally tough and durable, although not suitable for moving. And, as the other residences disappeared, this one found an owner in the 1990s who put it another practical use: storage space for a nearby medical facility.
This house is a reminder that the downtown University neighborhood was once one for families and the small businesses that served them. We are grateful that this former residence can still echo the small-town Salem that no longer exists.


Time Mesh is a motion based Android watchface. If you are calm, time moves in an orderly fashion. If not, ripples will propagate through your perception of time. Built using a physics simulation connected by springs, rendered using OpenGL in Java.