dataesr/scientific-paragraphs-categorization huggingface dataset:
The residential consumers use the BENEFFICE mobile application, create a profile providing some information related to their energy consumption and are able to continuously monitor their energy consumption. In addition, they receive recommended actions and challenges for improving consumption based on their personal preferences. Successfully adopting them in everyday life results in earning CO2 credits, which can be exchanged to Euros or spent within the BENEFFICE ecosystem, by using the CO2 Credit Management System. The BENEFFICE eco-system unifies different actors and interdisciplinary modules which will be presented in Section 3 and converges into an overall framework that aims to incentivise voluntarily actions to the residential consumers related to energy efficiency by providing a friendly and useful mobile application.
BENEFFICE platform backend (Esthes. is): This is a multi service platform which handles the data collection of the sensing components and the external systems through the gateway device and is responsible to store the data, to manage the deployed residential gateways and to provide APIs access to all the other Data Processing Applications. In addition, the platform backend is responsible to store the consumer's input (i. e. profile, preferences, energy consumption related behaviour etc. ) and also sent the outcomes of the Data Processing Applications (i. e. disagreggation results, recommendations, challenges, notifications etc. ) for display to the BENEFFICE mobile application. 3. Data Processing Applications: these include all BENEFFICE modules which consume /process data that are stored in the platform back end (obtained from the sensing components, the external systems or provided by the consumer).
These are the Energy Disaggregation algorithm, the Data Analytics, the Behavioural model, the Recommendation Engine and the Green energy scheduling. 4. The BENEFFICE mobile application is the main interface that the residential consumers use in order to interact with the BENEFFICE platform. It collects the consumers' input (i. e. information about their energy consumption behaviour, preferences or actions) and send it to the platform backend to be stored. It also displays energy measurements, disaggregation results, recommendations, challenges and earned CO2 credits to the consumers. The CO2 credit management system enables the circulation of the CO2 currency within the BENEFFICE ecosystem. The overall BENEFFICE functional architecture is presented in Figure  1.
BENEFFICE Components Description As presented in the previous Section, the BENEFFICE platform is formulated by various modules. A functionality overview of each of these modules is provided below. The Sensing Components (Sensing hardware and software gateway) are based on existing, inexpensive commercial sensors and technologies integrated within the Esthes. is modular platform. The hardware kit that is easy to install and directly interconnected with the platform backend, is low cost suitable for wide-scale adoption. The Energy disaggregation algorithm disaggregates the total consumed power, providing information about energy usage per cluster of devices (9 categories in total) in each residence. The disaggregation approach allows consumers to identify the most energy consuming devices and activities which are targeted by the recommendations and challenges  [15].
It is implemented by adopting the concept of sources reallocation using a variety of algorithms as greedy algorithms and convex programming. Given a set of tasks that has to be done during a day (e. g. cooking, laundry, vacuum, etc. ), the Green Energy Scheduling aims to minimize operational overlaps, which result in consumption peaks, ensuring that the energy demand is covered using the existing renewable energy sources. Additional constraints imposed by the consumer explicitly or implicitly are considered, exploiting the outputs of the Data Analytics. The Platform Backend (Esthes. is) is a collection of services, databases and front-end clients that provides access to the stored measurement and consumer related data as well as to the deployed gateway devices. The access is provided by REST APIs specifically tailored to the needs of the modules that call them.
Case lines however, showed premature cell cycle exit of OPCs, a finding consistent with reports for cortical neurons derived from DISC1 exon 8 interrupted iPSC lines  [26]  and supported also by the observed increased expression of NKX2. 2, ZNF488, and SLC8A3 genes. Of these, NKX2. 2 and ZNF488 are described to be specific to the oligodendroglial lineage as well as having a pro-differentiation effect on OPCs  [43] [44] [45]. Thus, their increased expression in our dataset is consistent with the observed phenotype of precocious differentiation. Our analysis also highlights genes identified via GWAS studies and implicated in psychosis (ZNF804A)  [46]  and suicidal behavior (SKA2)  [47]. In addition, several genes (UGT8, GAL3ST1, ZNF488, CTTNBP2, SLC8A3, GRIA4, and KCND2) identified in our analysis agree with a recent study of glial progenitors from childhood onset SZ patient-derived iPSC lines  [23].
Acknowledgements We thank the individuals and their families who agreed to participate in this study. We are grateful to  Dr Amanda Boyd ,  Mrs Carolyn Manson , and  Mr John Agnew  for help with animal care and to  Mrs Nicola Clements ,  Mrs Karen Gladstone , and  Mrs Rinku Rajan  for generous help with tissue culture and  Ms Sophie Glen  for help with preparing RNA. We thank  Dr Greg Polites  and  Dr Michel Didier  for providing access to the Der1 mice. We are also grateful to  Roslin Cells (UK)  for excellent support with reprogramming of fibroblasts. This work was supported by a  Medical Research Council  grant to SC, JKM, and AMM, an  EU   7th Framework Programme Grant  ( 607616FP7 ) to JKM and generous funding from the  MS Society  to SC. NAV was supported by a  Department of Biotechnology, Government of India  fellowship.
Methods Following an institutional consensus conference run in June 2010, we decided to discuss indication of surgical fixation of multiple rib fractures in any patient older than 45 years and/or with more than 4 rib fractures with the senior thoracic surgeon on call. All operated patients following this consensus were entered into a prospective cohort database and were informed accordingly. Follow-up of the patients was maintained until discharge of the patients. The study has been submitted to the Ethical Committee of the French Society for Thoracic and Cardiovascular Surgery (registration number: 2017-1-9-21-46-30-OlAn).
Data collection For each operated patient, following information and acceptance from the patient, we recorded demographic data, prior medical history, the number of fractures, and the associated traumatisms. Regarding the surgical technique, we recorded the number of clips used for each patient, if needed the use of struts, the type of incision, if possible, the use of videoassisted technique. As for the outcomes, the ICU and hospital stay, the postoperative delay for extubation and mechanical ventilation weaning, and the postoperative complications were recorded. We especially focused on the onset of pneumonia.
Overall results and risk factors for post-operative complications From July 1 st , 2010 to March 31 st , 2015, we recorded 132 patients who underwent thoracic osteosynthesis. Preoperative demographic and medical data are summarized in Table  1. Mean age at the time of surgery was 60 (15. 2) years. Patients presented with a mean number of 11 (5. 2) fractures and 63% had flail chest. Prior to surgery, 28% of patients had active tobacco consumption at the time of traumatism, 12% had chronic obstructive pulmonary disease (COPD), 12% had a prior cardiac disease, and 15% had prior chronic atrial fibrillation. The overall mean Values are expressed as mean (SD) or as number n (%). COPD, chronic obstructive pulmonary disease; mv, missing value; yrs, years. duration of mechanical ventilation was 3. 6 (6.
M any colorectal cancers (CRC) develop in genetically susceptible individuals  1  and genome-wide association studies (GWAS) of CRC have thus far reported 43 SNPs mapping to 40 risk loci in European populations  2, 3. In Asians, 18 SNPs mapping to 16 risk loci have been identified  4, 5  , a number of which overlap with those reported in Europeans. Collectively across ethnicities GWAS has provided evidence for 53 unique CRC susceptibility loci. While much of the heritable risk of CRC remains unexplained, statistical modelling indicates that further common risk variants remain to be discovered  6. To gain a more comprehensive insight into CRC aetiology, we conducted a GWAS meta-analysis that includes additional, unreported datasets. We examine the possible gene regulatory mechanisms underlying all GWAS risk loci by analysing in situ promoter Capture Hi-C (CHi-C) to characterise chromatin interactions between predisposition loci and target genes, examine gene expression data and integrate these data with chromatin immunoprecipitation-sequencing (ChIP-seq) data.
Results Association analysis. Thus far, studies have identified 61 SNPs that are associated with CRC risk in European and Asian populations (Supplementary Data 1). To identify additional CRC risk loci, we conducted five new CRC GWAS, followed by a metaanalysis with 10 published GWAS totalling 34,627 cases and 71,379 controls of European ancestry under the auspices of the COGENT (COlorectal cancer GENeTics) consortium  7  (Fig. 1 , Supplementary Data 2). Following established quality control measures for each dataset  8  (Supplementary Data 3), the genotypes of over 10 million SNPs in each study were imputed, primarily using 1000 Genomes and UK10K data as reference (see Methods). After filtering out SNPs with a minor allele frequency <0. 5% and imputation quality score <0. 8, we assessed associations between CRC status and SNP genotype in each study using logistic regression.
Risk estimates were combined through an inverse-variance weighted fixed-effects meta-analysis. We found little evidence of genomic inflation in any of the GWAS datasets (individual λ GC values 1. 01-1. 11; meta-analysis λ 1000 = 1. 01, Supplementary Figure  1 ). Excluding flanking regions of 500 kb around each previously identified CRC risk SNP, we identified 623 SNPs associated with CRC at genome-wide significance (logistic regression, P < 5 × 10 -8 ). After implementing a stepwise model selection, these SNPs were resolved into 31 novel risk loci, with 27 exhibiting Bayesian False Discovery Probabilities (BFDPs)  9  <0. 1 (Table  1 , Fig. 2 , Supplementary Figure  2 ). The association at 20q13. 13 (rs6066825) had only been previously identified as significant in a multi-ethnic study  10. Two new associations (rs3131043 and rs9271770) were identified within the 6p21.
We sought to gain further insight into the target genes at each locus, and hence the biological mechanisms for the associations, ). However, while multiple nominally significant cis-eQTLs were present, nearly half of all loci had no evidence of cis-eQTLs in the sample sets used. In addition to eQTL analysis, we performed Summary-databased Mendelian Randomization (SMR) analysis  28  as a more stringent test for causal differences in gene transcription (Supplementary Data 8). There was support for the 11q23. 1 locus SNP influencing CRC risk through differential expression of one or more of COLCA1, COLCA2 and C11orf53 transcripts (P SMR < 10 -10 ). There was also evidence that the 3p21. 1 and 19q13. 33 SNPs acted through SFMBT1 and FUT2, respectively, (P SMR < 10 -5 ), as well as the 6p21. 31 SNP acted through class II HLA expression (P SMR < 5 × 10 -4 ).
Discussion Here we report a comprehensive analysis that sheds new light on the molecular basis of genetic risk for a common cancer, and greatly increases the number of known CRC risk SNPs. To identify the most credible target genes at each site, we have performed detailed annotation using public databases, and have also acquired our own disease-specific data from ChIP-seq, promoter capture Hi-C and gene expression analyses. Given that there remains significant missing common heritability for CRC, additional GWAS meta-analyses are likely to lead to discovery of more risk loci. Such an assertion is directly supported a contemporaneous study  42  , which has reported the identification of 40 independent signals; 30 novel loci and 10 conditionally independent association signals at previously and newly identified CRC risk loci.
Primary GWAS. We analysed data from five primary GWAS (Supplementary Data 2 and Supplementary Data 3): (1) The NSCCG-OncoArray GWAS comprised 6240 cases ascertained through the National Study of Colorectal Cancer Genetics (NSCCG)  43   44. Controls comprised 4349 cancer-free individuals from The Heinz Nixdorf Recall study  45. Both cases and controls were genotyped using the Illumina Global Screening Array. []-FHL3 PARS2-[TTC22]-C1orf177 COX5B-[ACTR1B]-ZAP70 PLCL1-[]-SATB2 SFMBT1-[ENSG00000272305]-RFT1 CXXC4-[]-TET2 GYPA----[HHIP]--ANAPC10 HIVEP1--[EDN1]---PHACTR1 IER3-[]-DDR1 HLA-DRB5-[]-HLA-DRB1 PREP-[]-PRDM1 ENSG00000233539----[TNS3]-C7orf65 CDKN2B-[]-DMRTA1 SWAP70-[SBF2]-ADM STAT6-[LRP1]-NXPH4 RFXAP-[SMAD9]-ALG5 PIBF1-[KLF5]----KLF12 EDNRB--[]--POU4F1 COL4A1-[COL4A2]-RAB20 ENSG00000259471-[SMAD6]--SMAD3 SKOR1-[]-PIAS1 IQGAP1-[CRTC3]-BLM MAF----[]----DYNLRB2 TMEM220-[]-PIRT FUT2-[MAMSTR]-RASIP1 GTSF1L--[TOX2]-JPH2 PITX1-[C5orf66]-H2AFY MDFI-[TFEB]-ENSG00000268275 SFTPD-[] ZDHHC6-[VTI1A]--TCF7L2 CD9-[]-PLEKHG6 RNMTL1-[NXN]-ENSG00000262003 CCDC97-[ENSG00000255730]-[TMEM91] BMP2-[]-HAO1 PLCL1-[]-SATB2 SLC6A19-[SLC6A18]-TERT ENSG00000233539----[]----TNS3 POLD3-[CHRDL2]-RNF169 PARP11-[]-CCND2 GREM1-[FMN1]--RYR3 GREM1-[FMN1]--RYR3 FOXL1-[]-C16orf95 PARD6B-[]-BCAS4 CEBPB-[]-PTPN1 WNT4-[]-ZBTB40 SHCBP1L-[LAMC1]-LAMC2 DUSP10----[]----HHIPL2 AAMP-[PNKD]-TMBIM1 ZNF621-[]-CTNNB1 TERT-[]-CLPTM1L DAB2-[]-PTGER4 TULP1-[FKBP5]-ARMC12 SRSF3-[]-CDKN1A HMGCLL1--[BMP5]--COL21A1 TRPS1-[]-EIF3H FAM84B-[]-POU5F1B GATA3----[] NKX2-3--[]--SLC25A28 LIPT2-[POLD3]-CHRDL2 ARHGAP20----[C11orf53]-COLCA1 ATF1-[]-TMPRSS12 SH2B3-[ATXN2]-BRAP ENSG00000257726----[]----MED13L STARD13-[]-RFC3 DDHD1----[BMP4]---CDKN3 SCG5-[]-GREM1 CTIF-[SMAD7]-DYM C19orf40-[RHPN2]-GPATCH1 FERMT1-[]-BMP2 SULF2----[PREX1]-ARFGEF2 CEBPB-[]-PTPN1 ADRM1-[LAMA5]-RPS21 GPR143-[SHROOM2]-ENSG00000234469 BMP4----[]----CDKN3 SCG5-[GREM1]-FMN1 FERMT1-[]-BMP2 C3orf17-[]-BOC STMN3-[RTEL1-TNFRSF6B] AP1M1-[]-KLF2 -log 10 (P)    55  based on 1105 cases and 700 controls and (10) Croatia consisted of 764 cases and 460 populationbased controls  56.
1 were clumped using PLINK. The NSCCG-Oncoarray data were used to provide the LD reference data. Fidelity of imputation. The reliability of imputation of the novel risk SNPs identified (all with an IMPUTE2 r 2 > 0. 8) was assessed for 51 SNPs (comprising all new signals not directly genotyped) by examining the concordance between imputed and whole-genome sequenced genotypes in a subset of 201 samples from the CORGI and NSCCG studies. More than 98% concordance was found between the directly sequenced and imputed SNPs (Supplementary Data 14). eQTL analysis. In the INTERMPHEN study, biopsies of normal colorectal mucosa (trios of rectum, proximal colon and distal colon) were obtained from 131 UK individuals with self-reported European ancestry without CRC. Genotyping was performed using the Illumina Infinium Human Core Exome array, with quality control and imputation as above.
RNA-seq was performed and data analysed as per the GTEx Project pipeline v7 using the 1000 Genomes and UK10K data as reference. Gene-level expression quantification was based on the GENCODE 19 annotation, collapsed to a single transcript model for each gene using a custom isoform procedure. Gene-level quantification (read counts and TPM values) was performed with RNA-SeQC v1. 1. 8. Gene expression was normalised using the TMM algorithm, implemented in edgeR, with inverse normal transformation, based on gene expression thresholds of >0. 1 Transcripts Per Million (TPM) in ≥20% of samples and ≥6 reads in ≥20% of samples. cis-eQTL mapping was performed separately for proximal colon, distal colon and rectum samples using FastQTL. Principal components for the SNP data and additional covariate factors were identified using Probabilistic Estimation of Expression Residuals (PEER).
P-values were generated for each variant-gene pair testing alternative hypothesis that the slope of a linear regression model between genotype and expression deviates from 0. The mapping window was defined as 1 Mb either side of the transcription start site. Beta distribution-adjusted empirical P-values from FastQTL were used to calculate Q-values, and FDR threshold of ≤0. 05 was applied to identify genes with a significant eQTL. The normalised effect size of the eQTLs was defined as the slope of the linear regression, and computed as the effect of the alternative allele relative to the reference allele in the human genome reference GRCh37/ hg19). MetaTissue was used to generate a "pan-colonic" eQTL measure from the three individual RNA-seq datasets per patient. To supplement this analysis, we performed SMR analysis  28  including all eQTLs with nominally significant associations (P < 0.
05). We additionally examined for heterogeneity using the heterogeneity in dependent instruments (HEIDI) test, where P HEIDI < 0. 05 were considered as reflective of heterogeneity and were excluded. Promoter capture Hi-C. In situ promoter capture Hi-C (CHi-C) on LoVo and HT29 cell lines was performed as previously described  63. Hi-C and CHi-C libraries were sequenced using HiSeq 2000 (Illumina). Reads were aligned to the GRCh37 build using bowtie2 v2. 2. 6 and identification of valid di-tags was performed using HiCUP v0. 5. 9. To declare significant contacts, HiCUP output was processed using CHiCAGO v1. 1. 8. For each cell line, data from three independent biological replicates were combined to obtain a definitive set of contacts. As advocated, interactions with a score ≥5. 0 were considered to be statistically significant  64.
Chromatin state annotation. Colorectal cancer risk loci and SNPs in LD (r 2 > 0. 8) were annotated for putative functional effect based upon ChIP-seq H3K4me1 (C15410194), H3K9me3 (C15410193), H3K27me3 (C15410195) and H3K36me3 (C15410192) for LoVo, and H3K4me1 and H3K9me3 for HT29. ChIP libraries were sequenced using HiSeq 2000 (Illumina) with 100 bp single-ended reads. Generated raw reads were filtered for quality (Phred33 ≥ 30) and length (n ≥ 32), and adapter sequences were removed using Trimmomatic v0. 22. Reads passing filters were then aligned to the human reference (hg19) using BWA v0. 6. 1. Peak calls are obtained using MACS2 v 2. 0. 10. 07132012. Histone mark and transcription factor enrichment analysis. ChIP-seq data from colon crypt and tumour samples was obtained for H3K27ac and H3K4me1  65.
Multiple samples of the same tissue type or tumour stage were merged together. Additional ChIP-seq data from the Roadmap Epigenomics project  21  was obtained for H3K4me3, H3K27ac, H3K4me1, H3K27me3, H3K9ac, H3K9me3 and H3K36me3 marks in up to 114 tissues. Overlap enrichment analysis of CRC risk SNPs with these peaks was performed using EPIGWAS, as described by Trynka et al. 20. Briefly, we evaluated if CRC risk SNPs and SNPs in LD (r 2 > 0. 8) with the sentinel SNP, were enriched at ChIP-seq peaks in tissues by a permutation procedure with 10 5 iterations. To examine enrichment in specific TF binding across risk loci, we adapted the variant set enrichment method of Cowper-Sal lari et al. 22. Briefly, for each risk locus, a region of strong LD (defined as r 2 > 0. 8 and D′ > 0. 8) was determined, and these SNPs were termed the associated variant set (AVS).
ChIP-seq uniform peak data were obtained for LoVo and HT29 cell lines (198 and 29 experiments, respectively)  66  and the above described histone marks. For each of these marks, the overlap of the SNPs in the AVS and the binding sites was determined to produce a mapping tally. A null distribution was produced by randomly selecting SNPs with the same characteristics as the risk-associated SNPs, and the null mapping tally calculated. This process was repeated 10 5 times, and P-values calculated as the proportion of permutations where the null mapping tally was greater or equal to the AVS mapping tally. An enrichment score was calculated by normalising the tallies to the median of the null distribution. Thus, the enrichment score is the number of standard deviations of the AVS mapping tally from the median of the null distribution tallies.
Functional annotation. For the integrated functional annotation of risk loci, LD blocks were defined as all SNPs in r 2 > 0. 8 with the sentinel SNP. Risk loci were then annotated with five types of functional data: (i) presence of a CHi-C contact linking to a gene promoter, (ii) presence of an association from eQTL, (iii) presence of a regulatory state, (iv) evidence of TF binding, and (v) presence of a nonsynonymous coding change. Candidate causal genes were then assigned to CRC risk loci using the target genes implicated in annotation tracks (i), (ii), (iiii) and (iv). If the data supported multiple gene candidates, the gene with the highest number of individual functional data points was considered as the candidate. Where multiple genes had the same number of data points, all genes were listed.
Direct nonsynonymous coding variants were allocated additional weighting. Competing mechanisms for the same gene (e. g. both coding and promoter variants) were allowed for. Finally, if no evidence was provided by these criteria, if the lead SNP was intronic we assigned candidacy on this basis, or if intergenic the nearest gene neighbour. Chromatin data were obtained from HaploReg v4 and regulatory regions from Ensembl. Regional plots were created using visPIG 67 , using the data described above. We used ChromHMM to integrate DNAse, H3K4me3, H3K4me1, H3K27ac, Pol2 and CTCF states from the CRC cell line HCT116 using a multivariate Hidden Markov Model  68. Chromatin annotation tracks for colonic mucosa (E075), rectal mucosa (E101) and sigmoid colon (E106) were obtained from the Roadmap Epigenomics project  21  , using the core 15-state model data based on H3K4me3, H3K4me1, H3K36me3, H3K27me3 and H3K9me3 marks.
Transcription factor binding disruption analysis. To determine if the risk variants or their proxies were disrupting motif binding sites, we used the motifbreakR package  69. This tool predicts the effects of variants on TF binding motifs, using position probability matrices to determine the likelihood of observing a particular nucleotide at a specific position within a TF binding site. We tested the SNPs by estimating their effects on over 2,800 binding motifs as characterised by ENCODE, FactorBook, HOCOMOCO and HOMER. Scores were calculated using the relative entropy algorithm. Heritability analysis. We used LDAK  35  to estimate the polygenic variance (i. e. heritability) ascribable to SNPs from summary statistic data for the GWAS datasets which were based on unselected cases (i. e. CORSA, COIN, Croatia, DACHS, FIN, SCOT, Scotland1, SOCCS/GS, SOCCS/LBC, UKBB and VQ58).
LD score regression  39  was used to determine if any traits were correlated with CRC risk. GWAS summary data were obtained for allergy, asthma, coronary artery disease, fatty acids, lipids (total cholesterol, high density lipoprotein, low-density lipoprotein, triglycerides), auto-immune diseases (Crohn's disease, rheumatoid arthritis, atopic dermatitis, celiac disease, multiple sclerosis, primary biliary cirrhosis, inflammatory bowel disease, ulcerative colitis, systemic lupus erythematosus), anthropometric measures (BMI, height, body fat), glucose sensitivity (fasting glucose, fasting insulin, HbA1c), childhood measures (birth weight, birth length, childhood obesity, childhood BMI), eGFR and type 2 diabetes. All data were obtained for European populations. Summary statistics were reformatted to be consistent, and constrained to HapMap3 SNPs as these have been found to generally impute well.