dataesr/scientific-paragraphs-categorization huggingface dataset:
Using Drosophila Kc167 cell Hi-C data at 1 kb resolution, we assessed the effects of insulator binding proteins, cofactors, gene transcription and functional elements on TAD borders. Although TADs were computed from 1 kb resolution Hi-C data, genomic features were binned at an even higher resolution of 50 bp in order to better discriminate between genomic features that influence TAD borders and those that do not, and to reduce standard errors of model parameters (see Subsection Materials and Methods, Binned data matrix). In this subsection, we first focused on the effects of insulator binding proteins in driving TAD borders  [35]. In Drosophila, there are five subclasses of insulator sequences  [36]. Each subclass is bound by a particular type of insulator binding protein (IBP): suppressor of hairy wing (Su(Hw)), Drosophila CTCF (dCTCF), boundary-element-associated factor of 32 kDa (BEAF-32), GAGA binding factor (GAF), and Zeste-White 5 (ZW5)  [10].
Large-scale analysis of DNA motifs in human In the previous subsection, analyses of DNA-binding proteins were limited by available ChIPseq data. Here we alleviated this limitation by analyzing transcription factor binding site (TFBS) motifs available from the large MotifMap database  [47]. Given the large number of TFBS motifs (544 motifs), we used L1-regularization for parameter estimation. We identified 213 positive drivers (all coefficients b > 1) and 75 negative drivers (all coefficients b < 1), meaning that a large number of TFBSs actually play a role in TAD border establishment or maintenance. CTCF motifs ranked first ( b ¼ 45:34) in complete agreement with recent studies  [8, 31]. But our model also uncovered other TFBSs whose roles in TAD borders are less well known such as EGR-1 ( b ¼ 34:04), p53 ( b ¼ 25:55), MIZF ( b ¼ 22:46), GABP ( b ¼ 21:94) and many others (for a complete list, see S4 Table ).
Hi-C data and topologically associating domains For Drosophila 3D domain analysis, we used publicly available high-throughput chromatin conformation capture (Hi-C) data from Gene Expression Omnibus (GEO) accession GSE63515  [13]. Hi-C experiments were done for wild-type Drosophila melanogaster Kc167 cells with DpnII restriction enzyme. Hi-C data were binned at 1 kb resolution. Contact matrices were normalized using ICE method  [15]  implemented in the R package HiTC (http://www. bioconductor. org/packages//2. 11/bioc/html/HiTC. html). From the normalized contact matrices, TAD genomic coordinates were identified using HiCseg method  [19]. For human 3D domain analysis, we used publicly available 3D domains of GM12878 cells identified by the Arrowhead algorithm from Gene Expression Omnibus (GEO) accession GSE63525  [8].
ChIP-seq data For Drosophila analysis, we used publicly available binding profiles of chromatin proteins of Drosophila melanogaster wild-type embryonic Kc167 cells. ChIP-seq data for CP190, Su(Hw), dCTCF and BEAF-32 were obtained from GEO accession GSE30740  [60]. ChIP-seq data for Barren (condensin I), Cap-H2 (condensin II), Chromator, Rad21 (cohesin), GAF and dTFIIIC were obtained from GEO accession GSE54529  [11]. ChIP-seq data for Fs(1)h-L and Fs(1)h-LS were obtained from GEO accession GSE42086  [38]. ChIP-seq peaks were called using MACS 1. 4. 2 (https://github. com/taoliu/MACS). Fs(1)h-S peaks were defined as peaks from Fs(1)h-LS did not overlap any Fs(1)h-L peak. For human analysis, we used publicly available ChIP-seq peaks of 69 chromatin proteins (ATF2, ATF3, BATF, BCL11A, BCL3, BCLAF1, BHLHE40, BRCA1, CEBPB, CHD1, CHD2, CTCF, E2F4, EBF1, EGR1, ELF1, ELK1, ETS1, EZH2, FOS, FOXM1, IKZF1, IRF3, IRF4, JUND, MAFK, MAX, MAZ, MEF2A, MEF2C, MTA3, MXI1, MYC, NFATC1, NFE2, NFIC, NFYA, NFYB, NRF1, P300, PAX5, PBX3, PIGG, PML, POU2F2, RAD21, REST, RFX5, RUNX3, RXRA, SIN3A, SIX5, SP1, SRF, STAT1, STAT3, STAT5A, TAF1, TCF12, TCF3, USF1, USF2, YY1, ZBTB33, ZEB1, ZNF143, ZNF274, ZNF384 and ZZZ3) of GM12878 cells from ENCODE  [61].
c) Type I error rates for MLR and ET computed from 100 simulated datasets. RF were not included because no p-values were available. The significance threshold α was set to 10 -5. Simulated data were the same as in b). d) Comparison of MLR with ET and RF to detect known or suspected architectural proteins in human using GM12878 cell ChIP-seq data. Receiver operating characteristic (ROC) curves were computed from Wald's statistics for ET, from beta parameters for MLR, and from variable importances for random forests. Computations were carried out at 1 kb resolution.
Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.
However, these variants may be identified by testing for interactions between SNP and environmental risk factors (genome-wide interaction analysis)  [14, 15]. These findings may provide etiologic insight into CRC and identify potentially susceptible subpopulations  [14, 15]. There is compelling evidence from epidemiologic studies that alcohol consumption and cigarette smoking are associated with risk of CRC  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]. Both alcohol consumption and cigarette smoking influence disease risk through pathways involving multiple gene products and regulatory elements, providing potential for biological interactions  [26] [27] [28]. Accordingly, alcohol consumption and smoking are important lifestyle factors to study interactions with genetic variants. In this study, we performed a genome-wide interaction analysis using the large datasets from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO)  [3]  to identify SNPs that modify the effects of alcohol and smoking on CRC risk.
03). Similarly, we were able to reproduce this observation in 50 independent paired colorectal adenocarcinoma and adjacent normal samples from The Cancer Genome Atlas (TCGA) (paired student t test, P = 0. 02, S2 Fig). Furthermore, we observed that    sun-exposed skin) with p values ranging from 7x10 -138 to 4x10 -6 (S8 Table ). In contrast, evaluation of eQTL in both normal (GTEx) and cancer colorectal tissue from TCGA for the rs9409565 locus (r 2 > = 0. 2 in Phase 3 1000 genomes EUR data) did not show any significant eQTL. The inability to detect an eQTL is likely because the enhancer tagged by the locus is active in some but not all cancer cell lines and the current reference cancer transcriptome data may not be large enough or molecularly representative of our study population S5 Fig). Furthermore, we investigated whether any of the tagging SNPs are located in variant enhancer loci (VEL)reported by Akhtar-Zaidi et al.
Alcohol consumption and smoking information. All information on basic demographics and environmental risk factors were collected through interviews or through self-administered questionnaires. Data for all studies were centrally harmonized at the data coordinating center. We used the risk-factor information at the reference time, which varied across studies (S1 Text). A multi-step data-harmonization procedure which is described in detail in Hutter et al. [29]  was applied to reconcile differences in individual study questionnaires. We converted consumption of alcoholic beverages into grams of alcohol per day (g/day) by summing the alcohol content of each beverage consumed per day. To test if the categorical or continuous variable fitted the association between alcohol intake and CRC risk better we used Akaike Information Criterion (AIC) to compare both models.
We calculated the heterogeneity p-values using Woolf 's test  [75]. Quantile-quantile (Q-Q) plots were assessed to determine whether the distribution of the p-values was consistent with the null distribution (except for the extreme tail). Subjects with missing data for SNPs or environmental factors were excluded from the relevant analyses. Considering the potential male-female difference in alcohol metabolism  [76, 77]  and the different levels of alcohol consumption between sexes, we conducted the genome-wide interaction analysis for alcohol separately for men and women and used fixed effects meta-analysis to combine their results. All analyses were conducted using the R software (Version 3. 0. 1). Two statistical methods that leverage SNPs and environmental factors interaction (G×E interaction) were used to detect potential disease associated loci.
Expression analyses We used different types of gene expression data to examine putative expression of genes identified in our genome-wide interaction analysis, and to determine biological plausibility that the variants identified might impact CRC risk. First, we searched the Genotype-Tissue Expression project (GTEx) portal (http://www. broadinstitute. org/gtex/searchGenes) [34]  and the Human Protein Atlas (http://www. proteinatlas. org) [35]  to establish whether the implicated genes and corresponding proteins are expressed in human colon/rectal tissues. Second, we used several eQTL databases including the Browser at University of Chicago (http://eqtl. uchicago. edu/ Home. html),the Genevar (GENe Expression VARiation) at the Wellcome Trust Sanger Institute (http://www. sanger. ac. uk/resources/software/genevar)  [42] , HaploReg (http://www.
broadinstitute. org/mammals/haploreg/haploreg. php) (PMID:22064851), and the GTEx Portal Version 4(http://gtexportal. org/home/) (PMID: 26484569) to investigate whether any of the implicated SNPs may impact the expression of the nearby genes. A cis-eQTL analysis was also performed in TCGA COAD data in 356 Caucasian samples that have demographic and clinical data for 15,008 genes (S1 Text). Third, we analyzed expression data for the implicated genes from 35 pairs of colorectal tumor-normal tissue samples included in the ColoCare Cohort (S2 Text) as well as expression data from the Cancer Genome Atlas (TCGA; http://cancergenome. nih. gov) in 50 pairs of colorectal adenocarcinoma-normal tissue samples. We searched the UCSC Cancer Genomics Browser (https://genome-cancer. ucsc. edu) [37] [38] [39]  to examine whether the implicated genes showed evidence of differential expression in colorectal tumor tissue and normal tissue.
Bioinformatics analysis We explored potential functional annotations for the SNPs that showed evidence for interactions with either smoking or alcohol in our genome-wide interaction analyses. As detailed in S1 Text, we queried multiple bioinformatics databases using the UCSC genome browser (http://genome. ucsc. edu), HaploReg (http://www. broadinstitute. org/mammals/haploreg/ haploreg. php), and literature review of published enhancer signatures of colon cancer. cases in TCGA (c,d). The 4 probes for HIATL1 all showed that HIATL1 expression was significantly higher in tumor tissue than in normal tissue (Paired t test, P = 4. 4×10 -9 to 7. 2×10 -5 ); the results from two probes that uniquely match HIATL1 transcript were shown in a (P = 7. 2×10.
:645-9), Colon CGEMS pancreatic cancer scan (PanScan) (Amundadottir, L et al. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet. 2009 Sep;41(9):986-90, and Petersen, GM et al. A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22. 1, 1q32. 1 and 5p15. 33. Nat Genet. 2010 Mar;42 (3):224-8), and the Lung Cancer and Smoking study (Landi MT, et al. A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma. Am J Hum Genet. 2009 Nov;85 (5):679-91). The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource (http://cgems. cancer. gov/data/) accession numbers phs000207.
v1. p1 and phs000206. v3. p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www. ncbi. nlm. nih. gov/gap) through accession number phs000093. v2. p2. Funding for the Lung Cancer and Smoking study was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. PMH is supported by National Institutes of Health (R01 CA076366 to P. A. Newcomb). VITAL is supported byNational Institutes of Health (K05 CA154337). WHI is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.
Acknowledgments We thank  Dr. Wei Sun  for providing eQTL results on TCGA data. We thank  Dr. Peter Scacheri  for his guidance on the variant enhancer loci analysis. ASTERISK: We are very grateful to  Dr. Bruno Buecher  without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. DACHS: We thank all participants and cooperating clinicians, and  Ute Handte-Daub ,  Renate Hettler-Jensen ,  Utz Benscheid ,  Muhabbet Celik  and  Ursula Eilber  for excellent technical assistance. GECCO: The authors would like to thank all those at the  GECCO Coordinating Center  for helping bring together the data and people that made this project possible.
Physical and biochemical parameters showed that the ripening rate of CS berries is faster when grafted onto M4. A multifactorial analysis performed on mRNA-Seq data obtained from skin and pulp of berries grown in both graft combinations revealed that genes controlling auxin action (ARF and Aux/IAA) represent one of main categories affected by the rootstock genotype. Considering that the level of auxin tightly regulates the transcription of these genes, we investigated the behavior of the main gene families involved in auxin biosynthesis and conjugation. Molecular and biochemical analyses confirmed a link between the rate of berry development and the modulation of auxin metabolism. Moreover, the data indicate that this phenomenon appears to be particularly pronounced in skin tissue in comparison to the flesh.
Plant Material, Experimental Design and Meteorological Data Sampling was performed in 2011 and 2012 on V. vinifera cv Cabernet Sauvignon plants grafted onto 1103P (V. berlandieri × V. rupestris) and M4 [(V. vinifera × V. berlandieri) × V. berlandieri × cv Resseguier n. 1] rootstocks located in Verona, Italy (Novaglie, 45  00 ′ 48. 54 ′′ E, 156 m a. s. l. ) and collected by the Regional Agency for the Environmental Protection of Veneto (ARPAV), Italy. The dataset consisted of meteorological time series from January 1992 to December 2012, a series of 20 years that enabled climatological study to be performed. Meteorological data used for the purposes of the study consisted of precipitations (mm) and air temperature ( • C) measured at 2 m. During both the growing seasons (2011 and 2012), berries grown on CS/1103P and CS/M4 graft combinations were sampled at five developmental stages following the modified Eichhorn and Lorenz (E-L) developmental scale proposed by  Coombe (1995).
The spliced reads were identified using the procedure described in PASS manual (http://pass. cribi. unipd. it). Forward and reverse reads were aligned independently on the reference genome. PASS-pair was used from the PASS package to perform the pairing between forward and reverse reads and to select only those sequences that are uniquely aligned. The version 1 (V1) of grape gene prediction (http://genomes. cribi. unipd. it/grape) was used as a reference genome, whereas htseq-counts program (http://www-huber. embl. de/users/anders/HTSeq/doc/count. html) was adopted to quantify gene transcripts abundance. Gene expression data have been submitted to Gene Expression Omnibus (GEO) (accession no. SRA110619) at the NCBI (https://www. ncbi. nlm. nih. gov/geo/). Quantitative RT-PCRs (qPCR) were performed as described in  Ziliotto et al.
Statistical and Bioinformatics Analysis DEseq R package (http://www. r-project. org) was used to perform the statistical analyses for discovering differentially expressed genes (DEGs). In order to evaluate the single effects of the rootstock (R: 1103P and M4), tissue (T: whole berries, skin and pulp), and phenological phase (PP: E-L31, pre-véraison; E-L36, mid/late véraison; E-L38, ripening) on gene expression, a multifactorial analysis was conducted using the multi-factor designs method of DEseq (Anders and Huber, 2010) (http://bioconductor. org/packages/release/ bioc/html/DESeq. html). This method evaluates the weight of each factor considered in the analysis (R, T, and P) and its impact on DEGs, according to a false discovery rate (FDR) corrected p-value lower than 0. 05. Enrichment analysis was performed for each set of DEGs (R, T, and P) by using BiNGO tool  (Maere et al.
, 2005)  with the built-in Fisher's exact test function and an FDR corrected p-value lower than 0. 05. Hierarchical clustering analysis on mRNA-Seq data was carried out using Multi Experiment Viewer software (MeV; http://www. tm4. org; Saeed et al. , 2006). Expression values used for the analysis were filtered based on the 5% of their median. Principal Component Analysis (PCA) and related graphs were carried out using "prcomp" and "scatterplot3d" R packages, respectively.
The singular (R, T, P) impact of each component on genes expression was calculated according to a FDR corrected p-value lower than 0. 05. A complete list of DEGs whose expression is influenced by these factors is reported in Supplementary Table  S3 , while Figure  3  provides a graphical representation of the total amount of DEGs influenced by each single component. Amongst these, 2358 genes were differentially expressed due to different rootstocks. Expanding the comparison to include different tissue types revealed 4297 genes showed differential expression. The majority of DEGs were influenced by the phenological phase, with 5743 transcripts showing altered expression. In order to identify specific metabolic pathways differentially regulated by M4 and 1103P rootstocks in CS berries, DEGs resulting from multifactorial analysis were associated to their respective GO terms, and a GO enrichment analysis was carried out for each dataset (Figure  3  and Supplementary Table  S4 ).
Biochemical and colorimetric data showed that the differences in the rate of berry development between the two graft combinations were limited to the onset of ripening. For this reason we focused our attention on those Aux/IAA and ARF genes belonging to cluster Cl. 3-II, characterized by a higher expression at pre-véraison stages and whose differential behavior could be associated to the different ripening rate observed in the two graft combinations. In order to validate and expand results obtained from the mRNA-Seq data, we performed a quantitative RT-PCRs on VviIAA15b and VviARF25, representing those Cl. 3-II members showing the highest difference in fold change between CS/1103P and CS/M4, at E-L31, 32, 34, 36, and 38. Both genes were showing the highest expression in CS/M4 at the prevéraison stages (E-L31 and E-L32) (Supplementary Figure  S3 ).
Later on, the two graft combinations showed similar level of IAA. As for IAA-Asp, CS/M4 showed a quantity two-fold higher than 1103P at E-L34 stage. During véraison CS/1103P berries appeared to accumulate more IAA-Asp than CS/M4 while at harvest no significant differences were observed. The relative mRNA-Seq expression of genes involved in auxin biosynthesis and conjugation is graphically represented in Figures  6A, 7A , regardless whether they were or not included amongst those DEGs obtained by the multifactorial analysis. Considering genes involved in auxin biosynthesis, the hierarchical clustering on mRNA-Seq data split auxinbiosynthetic genes into two sub-groups (Figure  6A ). In this regard, considered genes can be divided into early-expressed (E-L31, Cl. 2) in whole berries and late-expressed (E-L36 and E-L38, Cl.
Construction of fragility curves In order to generate fragility functions, the capacity spectrum assessment is repeated for each structural model subjected to ground motions of increasing intensity, either by scaling each earthquake record or adopting a range of earthquake records with increasing intensity. A statistical curve fitting method is then adopted to fit a fragility curve shape from the IM -EDP cloud generated. Within FRACAS either a Least Squares (LS) approach or a Generalized Linear Model (GLM) can be used for the curve fitting, and confidence bounds derived using a bootstrap analysis of the data points. The former approach (LS) is more commonly used in the fragility literature but the method assumptions may be violated by the data, as shown by Rossetto et al. [17]. FRACAS has been developed into a Matlab-based automated tool, which is freely available at https://www.
3. The considered frames are regular (both in plan and in elevation). Details regarding the design of the buildings are available in De Luca et al. [21]. In the case of the Pre-Code building, concrete with characteristic compressive strength f ck = 19 MPa and reinforcement of characteristic yield stress f yk = 360 MPa are used. In the case of the Special-Code building the characteristic compressive strength of concrete and the characteristic yield stress of steel reinforcement used are f ck = 29 MPa and f yk = 450 MPa, respectively. Both frames are modelled using the finite element platform SeismoStruct  [22]. The effect of confinement is taken into account by implementing the confinement model proposed by Mander et al. [23]. An insufficient level of confinement is observed in all sections of the Pre-Code building: the confinement factor, k, is defined as the confined-unconfined concrete compressive stress ratio and ranges from 1.
7% higher than its TRI-PO counterpart, with damage predicted to be better distributed along the structure's height (again consistently with the available literature on the topic). These pushover analyses are adopted in the FRACAS assessment and are also used in Section 3. 3 to define the structural response parameter thresholds of the damage limit states used in the fragility assessment. A set of 150 unscaled ground motion records from the SIMBAD database (Selected Input Motions for displacement-Based Assessment and Design;  [28] ), is used to compare FRACAS with NLTHA and to test some of the model assumptions in FRACAS. SIMBAD includes a total of 467 tri-axial accelerograms, consisting of two horizontal (X-Y) and one vertical (Z) components, generated by 130 worldwide seismic events (including main shocks and aftershocks).
In particular, the database includes shallow crustal earthquakes with moment magnitudes (M w ) ranging from 5 to 7. 3 and epicentral distances R 6 35 km. A subset of 150 records is considered here to provide a statistically significant number of strong-motion records of engineering relevance for the applications presented in this paper. These records are selected by first ranking the 467 records in terms of their PGA values (by using the geometric mean of the two horizontal components) and then keeping the component with the largest PGA value (for the 150 stations with highest mean PGA).