dataesr/scientific-paragraphs-categorization huggingface dataset:
We undertake independent replication in large international consortia and other cohorts, providing robust validation of our findings and new biological insights into BP regulation. Our study design is summarized in Fig. 1. Briefly, data are available for 152,249 UK Biobank participants genotyped using a customised array (including GWAS and exome content) and with genome-wide imputation based on 1000 Genomes and UK10K sequencing data14. (Further details on the UK Biobank imputation are available at the UK Biobank website. ) After quality measures and exclusions (see Online Methods), we study 140,886 unrelated individuals of European ancestry with two seated clinic BP measurements using the Omron HEM-7015IT device (Supplementary Table  1 ). We carry out GWAS analyses of systolic (SBP), diastolic (DBP) and pulse pressure (PP) using single-variant linear regression under an additive model, based on ˜9.
Discussion A key attribute of this study is the combination of a large, single discovery sample with standardized BP measurement and dense 1000 Genomes/UK10K imputation, yielding a high quality dataset with ˜9. 8 million variants14, taking advantage of major international consortia for parallel replication of common and low-frequency variants. In total we include GWAS data from 330,956 individuals and exonic SNVs from a total of 422,604 individuals. This strategy resulted in 107 robustly validated loci for BP traits, including 32 loci that have not previously been reported, and at least 53 further loci validated for the first time. Despite its size, our study is still under-powered to find low-frequency variants. Our findings are mostly common variants, with similarly modest effect sizes as variants previously reported at the time of analysis (Supplementary Fig.
14 ). The lack of rare variant discovery could also be due to the challenge of detecting rare variants from imputed data. There may be greater potential for identifying rare variants from the future release of genetic data for all 500,000 UK Biobank participants. Our findings point to new biology as well as highlighting gene regions in systems that have previously been implicated in the genetics of BP. Several of our validated loci affect atherosclerosis or vascular remodelling (ADAMTS7, THBS2, CFDP1) and exhibit locus pleiotropy in prior genome-wide association studies for coronary artery disease or carotid intimal-media thickness24-26 (Supplementary Fig. 5a  and Fig. 4 ). In previous work we have shown that expression of ADAMTS7 is upregulated and increases vascular smooth muscle cell migration in response to vascular injury in relation to a distinct coronary artery variant (rs3825807, not in LD with our sentinel SNV; r 2 = 0.
As the above data are observational, the extent to which adherence to lifestyle recommendations amongst high genetic risk individuals might result in favorable outcomes remains uncertain; given the substantial effect of GRS on BP by middle-age, the potential for adopting early lifestyle intervention amongst individuals at high genetic risk, along with population-wide measures to lower BP, warrants further study. Since the completion of our study, another BP GWAS using UK Biobank data has been published15, as part of a larger single-stage combined meta-analysis without replication; it reported a total of 316 loci, including 241 loci identified from the meta-analysis involving UK Biobank that were not tested for validation. Of the 107 validated loci reported in our study, 32 are discovered and validated for the first time in our analysis of UK Biobank.
UK Biobank data Our GWAS analysis is performed using data from the interim release of the first ˜150k UK Biobank (UKB) participants (Supplementary Note): ˜100k individuals from UK Biobank genotyped at ˜800,000 single nucleotide variants (SNVs) with a custom Affymetrix UK Biobank Axiom Array chip and ˜50k individuals genotyped with a custom Affymetrix UK BiLEVE Axiom Array chip from the UK BiLEVE study63, a subset of UKB. SNVs were imputed centrally by UKB using a merged UK10K sequencing + 1000G imputation reference panel. UK Biobank array design and protocols are available on the UK Biobank website.
Statistical methods Statistical approaches used for the discovery and replication of loci are reported in detail below. We also describe methods used for: identification of secondary signals; lookups in non-European populations and for monogenic BP genes; functional and experimental methods; construction of a genetic risk score for analysis with BP traits and cardiovascular outcomes. All P-values are from two-sided tests. Analysis models-For the GWAS, we perform linear regression analyses of the three (untransformed) continuous, medication-adjusted BP traits (SBP, DBP, PP) for all measured and imputed genetic variants in dosage format using SNPTEST software65 under an additive genetic model. We carry out a similar analysis for the exome content. Quantile-quantile plots are shown in Supplementary Fig.
This list includes all published SNVs which have been identified and validated from previous GWAS, CardioMetabochip and exome chip projects7-11. We augment this list to include all 34,459 SNVs in Linkage Disequilibrium (LD) with these previously reported SNVs, according to a threshold of r 2 ≥ 0. 2. Results for all these variants are extracted for each of the three BP traits, to check previously reported BP associations in the UKB data, according to whether the sentinel SNV or a variant at the locus in LD (r 2 ≥ 0. 2) with it showed evidence of support (P < 0. 01) for association with at least one of the three BP traits. Replication strategy-We use three independent external data sets for replication (Supplementary Note). First, for the GWAS analysis based on advanced 1000G imputation enhanced by UK10K data we consider SNVs with MAF ≥ 1% and perform a reciprocal replication exchange with the International Consortium of Blood Pressure (ICBP) 1000G meta-analysis (max N = 150,134).
The imputation strategy for ICBP 1000G meta-analysis is based on an earlier imputation grid for the 1000G project. In addition, we recruit further cohorts with 1000G data which had not contributed to the ICBP-1000G discovery metaanalysis: ASCOT-UK (N = 3,803), ASCOT-SC (N = 2,462), BRIGHT (N = 1,791), Generation Scotland (GS) (N = 9,749), EGCUT (N = 5,468), Lifelines (N = 13,292) and PREVEND (N = 3,619). This gives a total of N = 190,318 independent replication samples for the GWAS analysis. Second, because the UK Biobank and UK BiLEVE genotyping chips contain exome content, we sought replication from two BP exome consortia (European exome consortium and the Cohorts for Heart and Ageing research in Genome Epidemiology -CHARGE BP exome consortium), to allow validation of coding variants and variants with lower frequency.
For the exome discovery, by following up SNVs with MAF ≥ 0. 01%, INFO > 0. 1 and P < 1x10 -5 across the three BP traits, we carry forward for replication sentinel SNVs at 22 unvalidated loci, and potential secondary SNVs at three previously reported loci at the time of analysis. We produce locus zoom plots for each of the lookup variants. Replication meta-analyses-The replication and combined meta-analyses are performed within METAL software66 using fixed effects inverse variance weighted metaanalysis (Supplementary Note). The combined meta-analysis of both the UKB discovery (N = 140,886) and GWAS replication meta-analysis (max N = 190,070) include a total maximum sample size of N = 330,956. For the exome combined meta-analysis, we synthesize data from the UKB discovery exome content (max N=140,866), with the replication dataset from both exome consortia (total max N=281,718), giving a maximum sample size of N=422,604.
Validation Criteria-In our study a signal is declared validated if it satisfies ALL of the following three criteria: (i) the sentinel SNV is genome-wide significant (P < 5×10 -8 ) in the combined metaanalysis for any of the three BP traits; (ii) the sentinel SNV shows evidence of support (P < 0. 01) in the replication metaanalysis alone for association with the most significantly associated BP trait from the combined meta-analysis (NB: P < 0. 01 is more stringent than a range of thresholds calculated according to False Discovery Rate (FDR), see Supplementary Methods); (iii) the sentinel SNV has concordant direction of effect between the UKB discovery and the replication meta-analysis for the most significantly associated BP trait from the combined meta-analysis. Secondary signals-By conditional analysis within UKB data we assess all validated secondary signals from our validated and previously reported loci at the time of analysis for independence from the sentinel or previously reported SNV, respectively (Supplementary Note).
We declare a secondary signal to be independent of the previously reported SNV if there is less than a 1. 5 fold difference between the main association and conditional association P-values on a -log10 scale, i. e. if -log10(P) / -log10(P_cond) < 1. 5. Note that the lookup criteria already ensure that the secondary variant is not in LD (r 2 < 0. 2) with the previously reported SNV. If more than one SNV in a region is found to be independent we undertake further rounds of iterative conditional analysis. Monogenic blood pressure gene lookups-The UKB arrays include some rare coding variants for monogenic disorders. We collate a list of all specific mutation variants within genes known to be associated with monogenic BP disorders19. Results from the UKB association analyses for all three BP traits are extracted for any of these SNVs directly covered within the UKB dataset (Supplementary Table  14 ).
Lookups in non- Functional analyses-In order to prioritize associated SNVs, we use an integrative bioinformatics approach to collate functional annotation (Supplementary Table  30 ) at both the variant and gene level for each SNV within the BP loci (all SNVs in LD r 2 ≥ 0. 8 with the BP-associated SNVs). At the variant level we use ANNOVAR67 to obtain comprehensive functional characterization of variants, including gene location, conservation and amino acid substitution impact based on a range of prediction tools including SIFT and polyphen2. All nonsynonymous variants were predicted damaging by two or more methods. We use the University of California Santa Cruz (UCSC) genome browser to review sequence specific context of SNVs in relation to function, particularly in the Encyclopedia of DNA Elements (ENCODE) dataset68.
We use the UCSC table browser to annotate SNVs in ENCODE regulatory regions. We evaluate SNVs for impact on putative micro RNA target sites in the 3' un-translated regions (3'UTR) of transcripts by a query of the miRNASNP database69. We evaluate all SNVs in LD (r 2 ≥ 0. 8) with our validated sentinel SNVs for evidence of mediation of expression quantitative trait loci (eQTL) in all 44 tissues using the Genotype-Tissue Expression (GTEx) database, in order to identify validated loci which are highly expressed, and to highlight specific tissue types which show eQTLs for a large proportion of validated loci. We further seek to identify validated loci with the strongest evidence of eQTL associations in arterial tissue, in particular. At the gene level, we use Ingenuity Pathway Analysis (IPA) software (IPA ® ,QIAGEN Redwood City) to review genes with prior links to BP, based on annotation with the "Blood Pressure" Medline Subject Heading (MESH) term which is annotated to 684 genes.
We also use IPA to identify genes which interact with BP MESH annotated genes, and evaluate genes for evidence of small molecule druggability based on queries of Chembl and Drug Gene Interaction database. We then perform overall enrichment testing across all loci. Firstly, we use DEPICT70 (Datadriven Expression Prioritized Integration for Complex Traits) to identify highly expressed tissues and cells within the BP loci. DEPICT uses a large number of microarrays (˜37k) to identify cells and tissues where the genes are highly expressed and uses precomputed GWAS phenotypes to adjust for co-founding sources. DEPICT provides a P-value of enrichment and false discovery rates adjusted P-values for each tissue/cells tested. Furthermore, to investigate regulatory regions, we employ a two tiered approach to investigate cell type specific enrichment within DNase I sites using FORGE, which tests for enrichment of SNVs within DNase I sites in 123 cell types from the Epigenomics Roadmap Project and ENCODE71 (Supplementary Note).
Validated sentinel SNVs from our study are analysed along with previously reported SNVs at the time of analysis and secondary signals (with P-value < 1×10 -4 ) to evaluate the overall tissue specific enrichment of BP associated variants. In a second analysis we use FORGE (with no LD filter) to investigate directly our curated candidate regulatory SNVs for overlap with cell-specific DNase I signals. GenomeRunner72 is used to search for enrichment of validated and previously reported sentinel SNVs with histone modification mark genomic features (Supplementary Note). Relevant cardiovascular tissue expression is investigated using Fantom5 reference transcript expression data (fantom. gsc. riken. jp/5) (Supplementary Note). We use IPA (IPA ® ,QIAGEN Redwood City) to identify biological pathways and transcriptional upstream regulators enriched for genes within the BP loci.
We carry out metabolomics analysis using two sets of data. First we use 1 H NMR lipidomics data on plasma from a subset of 2,000 participants of the Airwave Health Monitoring Study74,75 (Supplementary Note). For each validated BP-associated SNV we ran association tests with the lipidomics data using linear regression analyses, adjusted for age and sex. We computed significance thresholds using a permutation derived family wise error rate (5%) to account for the high correlation structure of these data (ENT=35)76. We also test each validated SNV against published genome-wide vs metabolome-wide associations in plasma and urine using publicly available data from the "Metabolomics GWAS Server" to identify metabolites that have been associated with variants of interest at P < 3. 0 x 10 -4 (Bonferroni corrected P for validated signals)22,23.
HG was funded by the  NIHR Imperial College Health Care NHS Trust and Imperial College London Biomedical Research Centre. MR was a recipient from  China Scholarship Council  (No. 2011632047 ). BM holds an  MRC eMedLab Medical Bioinformatics Career Development Fellowship , funded from award  MR/ L016311/1. JMMH was funded by the  UK Medical Research Council  ( G0800270 ),  British Heart Foundation  ( SP/09/002 ),  UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council  ( 268834 ),  European Commission   Framework Programme 7  ( HEALTH-F2-2012-279233 ). BK holds a  British Heart Foundation Personal Chair  ( CH/13/2/30154 ). NJS holds a chair funded by the  British Heart Foundation  and is a NIHR Senior Investigator. FD was funded by the  MRC Unit at the University of Bristol  ( MC_UU_12013/1-9 ).
PSu was funded by the  UK Medical Research Council  ( G0800270 ). CL and AK were funded by the  NHLBI  intramural funding. et al. CNC was funded by the  National Institutes of Health  ( HL113933 ,  HL124262 ). PVDH was funded by the  ZonMw  grant  90. 700. 441 , Marie Sklodowska-Curie GF  (call:  H2020-MSCA-IF-2014 , Project ID:  661395 ). NV was supported by  Marie Sklodowska-Curie GF grant  ( 661395 ) and  ICIN-NHI. NP received funding from the  UK National Institute for Health Research Biomedical Research Centre at Imperial College Healthcare NHS Trust  and  Imperial College London  and also from his  Senior Investigator Award. PS was supported by the  NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust  and  Imperial College London. ST was supported by the  NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust  and  Imperial College London.
PFO received funding from the  UK Medical Research Council  ( MR/N015746/1 ) and the  Wellcome Trust  ( 109863/Z/15/Z ). IK was supported by the  EU   PhenoMeNal  project ( Horizon 2020 ,  654241 ). AC was funded by the  National Institutes of Health  ( HL128782 ,  HL086694 ). MF was supported by the  Wellcome Trust   core award  ( 090532/Z/09/Z ) and the  BHF Centre of Research Excellence, Oxford  ( RE/13/1/30181 ). CH was funded by an  MRC  core grant for QTL in Health and Disease programme. Some of this work used the  ALICE and SPECTRE High Performance Computing Facilities  at the  University of Leicester. MJC is a  National Institute for Health Research (NIHR)  senior investigator. PE is a  National Institute for Health Research (NIHR ) senior investigator and acknowledges support from the  NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust  and  Imperial College London , and the  NIHR Health Protection Research Unit in Health Impact of Environmental Hazards  ( HPRU-2012-10141 ).
As director of the  MRC-PHE Centre for Environment and Health , PE acknowledges support from the  Medical Research Council and Public Health England  ( MR/L01341X/1 ). This work used the computing resources of the UK MEDical BIOinformatics partnership -aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO) which is supported by the  Medical Research Council  ( MR/L01632X/1 ). This research was supported by the  British Heart Foundation  (grant  SP/13/2/30111 ). Project title: Large-scale comprehensive genotyping of  UK Biobank  for cardiometabolic traits and diseases:  UK CardioMetabolic Consortium (UKCMC). This research has been conducted using the UK Biobank Resource under Application Number  236.
Data Availability Statement The data generated during the current study are available from the UK Biobank data repository (http://biota. osc. ox. ac. uk), which can be accessed by researchers upon application. This includes the derived GWAS analysis results summary data from our UK Biobank discovery data for all three BP traits. The genetic and phenotypic UK Biobank data are also available upon application to the UK Biobank (https://www. ukbiobank. ac. uk). All replication data generated during this study are included in the published article. For.
Microarray and Experimental Design A DNA microarray was designed based on the only published genome sequence for Salmonella Enteritidis strain NCTC13349 (http://www. ncbi. nlm. nih. gov/nuccore/AM933172. 1). The microarray, containing 3971 ORFs (representing 94. 4% of S. Enteritidis gene composition), was designed using Agilent's e-array software (https://earray. chem. agilent. com/earray/). Note that 235 ORFs were not included due to restricted design parameters of the e-array program. The custom oligonucleotide microarray was manufactured by Agilent Technologies using an 8 × 15K format and included each probe in duplicate. Microarray hybridization and data analysis were performed by the Pasteur Institute (Transcriptome and Epigenome Platform PF2, Paris, France). S. Enteritidis, exposed to egg white model medium for 7, 25, and 45 min at 45 • C, was compared to a reference control (0 min of incubation) consisting of S.
Enteritidis grown overnight in TSB at 37 • C, washed three times in egg white filtrate and finally resuspended in egg white filtrate at ambient temperature. For each time point, three arrays were hybridized with three independent biological replicates in duplicate (giving three biological replicates and two technical replicates). Cy3 and Cy5 dye-swap design was included in order to reduce dyespecific effects. The data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE92545 (http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE92545).
Data Acquisition and Preprocessing Images of the microarrays were scanned using an Axon 4000a scanner (Axon, Instruments, CA, USA) and intensity data were extracted using the GenepixPro 6. 1 software. Raw microarray data were first normalized using the LOWESS (Locally Weighted Scatter Plot Smoother) regression option in the R software suite, version 2. 10. 1 (http://www. r-project. org/), to correct for dye-bias within the array, followed by median normalization to normalize across all arrays.
Differential Expression Analysis Changes in gene expression upon incubation in EWMM compared to the control were recorded as fold changes (ratio of 7, 25, or 45-0 min). Statistical significance was estimated with a moderated t-test using the LIMMA package  (Smyth, 2004)  of the R software suite (version 2. 10. 1). The adjustment of raw p-values was then performed to account for multiple comparisons, using the method of  Benjamini and Yekutieli (2001). Genes that exhibited ≥2. 0 fold changes in expression (with respect to the control) and P ≤ 0. 05 were considered as differentially regulated. Differentially expressed genes were initially categorized by function according to the Clusters of Orthologous Groups (http://www. ncbi. nlm. nih. gov/COG) designations and categorization was subsequently optimized manually.
A total of two RNA extractions were performed using distinct S. Enteritidis cultures grown on different batches of EWMM. Each of the two RNA extracts thus obtained was subject to qRT-PCR, in triplicate, for each selected gene. Standard curves were generated to calculate the copy number of each gene in each sample. The most stable control genes in our conditions were determined by geNorm among five potential genes. qRT-PCR data were normalized by geometric averaging of three internal control genes (asmA, emrA, orf32; primers in Table  1 ). Gene expression is thus provided as relative expression with respect to the normalization factor calculated by geNorm.
The melt-mixing process was carried out using a twin-screw recirculating extruder HAAKE Mini-Lab II Rheomex CTW 5 fitted with corotating conical screws. The mixing was carried out at 200 °C at 50 rpm for 15 min. Molar mass distributions were measured using size exclusion chromatography (SEC) using an Agilent system with a Wyatt Dawn multiangle light scattering detector using tetrahydrofuran (THF) as the eluent. Averages of the molar mass distributions were computed using Matlab. Molar mass distributions for melt-mixed blends were obtained by calculation from their constituents. Further details of the polymers and averages of the distributions are reported in Table  1 , and the normalized molar mass distributions are presented in Figure  1. In order to interpret the chain lengths in terms of the number of entanglements, linear viscoelastic rheological measurements on materials BF, BG, and BH were fitted to the theory of Likhtman and McLeish  6  to give M e = 15.
Using constants derived from the corrected literature data  11  of M c = 39. 6 kDa, M ∞ = 154. 2 kDa, and σ ∞ = 19. 85 MPa, a Matlab code was written to compute the crazing stress for distributions R, BX, and BY using a θ value of 2. 59, and the results are given in Table  4. The application of the model produces reasonable predictions of crazing stress for materials R and BY but overpredicts the crazing stress for material BX by a factor of 6 (Table  4 , without DTD). The molar mass distributions, shown in Figure  1c , for materials R and BY lie predominantly in the disentanglement and chain scission regimes, while that for material BX shows a distinct bimodal distribution that spans across all three regimes, with a significant fraction in the unentangled dilution region. This suggests that the current model is incorrectly dealing with the contribution of the unentangled fraction that is so dominant in polymer BX.