Structural order in vinyl chloride polymers

Structural ordering in a commercial suspension poly(vinyl chloride), a low temperature synthesized vinyl chloride polymer and a vinyl chloride/vinyl acetate copolymer has been investigated. The polymers were characterized as regards their molecular weight using gel permeation chromatography and their chain tacticity using 13 carbon nuclear magnetic resonance and infrared spectroscopy. [Continues.

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease - Fig 3

<p><b>(a) Enrichment (y axis, observed <i>SNPs per Mb</i>: expected <i>SNPs per Mb</i>) at increasing search window sizes (x axis) upstream and downstream from the transcription start site (TSS) for increasingly strong GWAS signals (z axis, −<i>log</i></b>_<b>10</b><b><i>p</i>).</b> (b) Change in coexpression signal using different subsets of the FANTOM5 dataset, using the Crohn’s disease GWAS as the input set. Q:Q plots of observed:expected NDA scores obtained using a given subset of samples (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005934#sec002" target="_blank">methods</a> for full description of each subset). Rows indicate the subset of regulatory regions used in each analysis. Percentage of significantly coexpressed entities (hits, <i>FDR</i> < 0.05) and <i>p</i>-value (Kolmogorov-Smirnov test) comparing observed (blue) and expected (red) distributions are shown below each plot.</p

Examples of detail of chromosomal regions surrounding regulatory regions significantly coexpressed in ulcerative colitis (TSS+/-150Mb).

<p>(a) Region surrounding IL10 (b) Region surrounding C1orf106. Top panel: Coloured rectangles show genomic location of individual regulatory regions (promoters or enhancers). Height of regulatory regions on y-axis depicts the coexpression score assigned to this regulatory region; groups of regulatory regions considered as a single unit (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005934#sec002" target="_blank">Methods</a>) share the same colour. Black circles show GWAS p-values for individual SNPs. Red circles show causative probabilities estimated by Huang <i>et al</i> for specific variants, where available. Bottom panel: genomic locations of known protein coding transcripts in sense (green) and antisense (purple).</p

Optimisation of GWAS p-value threshold.

<p>Coexpression signals are shown for six different –log₁₀(p) bins for GWAS p values from a single study of Crohn’s disease. From each bin, 800 SNPs were selected at random. No signal for coexpression is detected at weak p-values.</p

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease - Fig 4

<p><b>(Top panels) Circular plots of coexpression links between different locations on the genome, illustrating the spatial separation of highly-correlated regulatory regions.</b> The coloured outer circle shows an end-to-end concatenated view of the human chromosomes. The black inner circle shows –<i>log</i>₁₀ GWAS p-values for included SNPs. Links depict an association between two regulatory regions containing these SNPs and are coloured according to –<i>log</i>₁₀(<i>p</i>) (line colour indicates –<i>log</i>₁₀(<i>p</i>): red>3, blue> 2, green> 1.5). (Bottom panels) Quantile-quantile plots showing observed and expected coexpression scores. Expected coexpression scores are derived from circular permuted subsets of regulatory regions (post-mapping permutations; black circles) or SNPs chosen by circular permutations against the background of all SNPs genotyped in each study. Data are shown for high-density lipoprotein (HDL), low-density lipoprotein (LDL), and total cholesterol. See supplementary results for full results of all analyses.</p

Examples of detail of chromosomal regions surrounding regulatory regions significantly coexpressed in ulcerative colitis (TSS+/−150MbTSS+/-150Mb).

<p>(a) Region surrounding IL10 (b) Region surrounding C1orf106. Top panel: Coloured ectangles show genomic location of individual regulatory regions (promoters or enhancers). Height of regulatory regions on y-axis depicts the coexpression score assigned to this regulatory region; groups of regulatory regions considered as a single unit (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005934#sec002" target="_blank">Methods</a>) share the same colour. Black circles show GWAS $p$-values for individual SNPs. Red circles show causative probabilities estimated by Huang \emph{et al} for specific variants, where available. Bottom panel: genomic locations of known protein coding transcripts in sense (green) and antisense (purple).</p

Results of coexpression analysis for a range of human traits for which high-quality data are available: Crohn's disease, ulcerative colitis, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, height, systolic blood pressure (SBP) and diastolic blood pressure (DBP).

<p>Results of coexpression analysis for a range of human traits for which high-quality data are available: Crohn's disease, ulcerative colitis, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, height, systolic blood pressure (SBP) and diastolic blood pressure (DBP).</p

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease - Fig 5

<p><b>(a) Relationship between GWAS p-value for a SNP, and coexpression scores of individual promoters assigned to that SNP for all phenotypes for which significant coexpression was detected.</b> Top panel: GWAS p-values (log scale) vs corrected coexpression scores. Bottom panel: linear regression lines for data in top panel; Spearman's r and associated p-values are shown for each trait. Only significantly coexpressed (FDR<0.05) promoters are included. (b) Rank comparison of named genes compared with gene-level burden of significance in original GWAS studies (PASCAL sum genescore). Log rank is shown on each axis (Rank 1 = highest scoring gene) for the subset of coexpression scores obtained for promoters of named genes. Open squares indicate significant coexpression (FDR<0.05).</p

Use of NDA to detect coexpression.

<p>(a) A subset of regulatory elements is identified containing disease-associated SNPs. (b) The strength of the links between pairs of these regulatory regions is quantified, first as the Spearman correlation, then as the –<i>log</i>₁₀ p-value quantifying the probability, specific to this regulatory region, of a Spearman correlation of at least this strength arising by chance. This is determined from the empirical distribution of correlations between this regulatory region and all other regulatory regions in the entire network of all regulatory regions in the genome. (c) The subset of regulatory regions containing disease-associated SNPs form an unexpectedly dense grouping in the network, but this may not be visible in a two-dimensional representation (for illustration, this network shows all correlations between regulatory regions with Spearman <i>r</i> > 0.7, layout generated by the FMMM algorithm). The NDA score assigned to any one node is the sum of the links it shares with other nodes in the chosen subset (see Supplementary Methods for a full explanation). d) NDA scores from the input subset of regulatory elements are compared with NDA scores from permuted subsets of regulatory elements in order to quantify the false discovery rate (FDR).</p