dataesr/scientific-paragraphs-categorization huggingface dataset:
Reassuringly, we recover TrmBL2, a known constituent of chromatin in T. kodakarensis where it is unusually abundant compared to TrmB homologs in other archaea  (Fig S6). For some species (e. g. , Methanothermobacter marburgensis and Archaeoglobus fulgidus) we identified no additional candidates, suggesting that our pipeline is not excessively greedy  (Fig 3D). For others (e. g. , Sulfolobus acidocaldarius), we only retrieved candidates that are much less abundant than known NAPs in the same organism. In contrast, we also observed species where novel candidates make up a substantial portion of the overall investment in NAPs, rivalling or even dwarfing the abundance of known NAPs. Notably, this list includes the model archaeon Haloferax volcanii, where the two candidate NAPs (HVO_1577, HVO_2029) are considerably more abundant than either histones or MC1 (Fig 3D ), a finding we confirm in an independently generated proteomics dataset  (Fig S7).
Processing of public proteomics data We only included proteomes that were derived a) from whole cell extract, b) without size   (2018). For each dataset, measurements that did not correspond to the Genbank complete genome of the strain/species were discarded. Correspondence between Uniprot and Genbank ID was established using the Uniprot Retrieve/ID mapping tool. For each dataset, normalized intensity (in %) was computed as the ratio of each protein intensity over the total intensity for the species of interest.
Habitat and phenotypes Phenotypic data was obtained from  Madin et al. (2020)  and habitat data from ProGenome  Mende et al. (2020). When multiple strains per species or multiple sources per strain were available, optimal growth temperature was averaged, with the exception of H. volcanii for which the average was seemingly too low (38°C) and was thus set at 45°C, based on the description of the original isolate. Growth rates for M. luminyensis and M. alvus (absent from the database) were added manually.
ACKNOWLEDGEMENTS We thank  Holger Kramer  and the  LMS Proteomics Facility  for generating mass spectrometry data and the  Molecular Systems Group  for discussions. This work was funded by  Medical Research Council core funding (TW) , an  EMBO   Short-Term Fellowship 8472  (AH) and the  French National Agency for Research (SG ; Grant ArchEvol  ANR-16-CE02-0005-01 ). A. fulgidus A. profundus F. placidus H. salinarium H. volcanii M. jannaschii M. luminyensis M. alvus M. kandleri M. barkeri M. marburgensis M. thermautotrophicus N. magadii N. maritimus P. torridus S. acidocaldarius T. kodakarensis T. acidophilum T. volcanium A. fulgidus A. profundus F. placidus H. salinarium H. volcanii M. jannaschii M. luminyensis M. alvus M. kandleri M. barkeri M. marburgensis M. thermautotrophicus N. magadii N.
Automatic lidar and ceilometer (ALC) measurements of attenuated backscatter allow the mixed layer height to be determined from observations. Here Vaisala CL31 observations are used. A background correction 50 is undertaken prior to using the CABAM (characterising the atmospheric boundary layer based on ALC measurements) algorithm. 35 The algorithm considers precipitation, cloud cover and cloud type, all obtained from the ALC measurements. Instruments installed in Beijing,  38  very close to the TB cluster (Fig. 2b ), and in suburban Paris 39 (Section 2. 1; https://www. lmd. polytechnique. fr/sirta/mld/) are used to obtain the attenuated backscatter data. The APHH-Beijing 38 data available for analysis are from November 2016 to June 2017. Although observations occur in all four seasons, both summer and autumn are incomplete.
Acknowledgements SG thanks the  Royal Society of Chemistry  and the conference organizers for the invitation to contribute this paper. The research reported here has been funded by  NERC   NE/N00700X/1 ,  Newton Fund/Met Office CSSP-China (SG, DH, SK) ,  EPSRC UK Fluids Network visiting scientist award, ERC Synergy urbisphere  ( 855005 ), and  ACTRIS-2  (SK). Facilities at the  University of Southampton  are supported by  UKRI , and at the  University of Surrey  are supported by  NERC. The  EPSRC   MAGIC  project allowed use of the London hardware model. For the Beijing cluster model work we acknowledge the help of George Meachim (UR),  Matthew Coburn  (Southampton),  Christoph Kent  (UR) and  Shiguang Miao  ( IUM, CMA ). For the Paris MLH processing we thank  Marc-Antoine Drouin  for ongoing support.
ALC measurements at SIRTA are performed by  Météo-France. SIRTA is supported by  CNES ,  Ecole Polytechnique ,  INSU-CNRS , and IPSL. London observations are funded with grants from  NERC ,  EPSRC , the  Met Office ,  EUf7 ,  H2020  and  ERC  with support from  KCL  and the  University of Reading. We thank the European.
Fig. 1  | InC binding to MBLs mimics that of intact β-lactam substrates and/or products. a, Clinically used β-lactam classes. b, Outline of B1 MBL mechanism exemplified with a carbapenem (note that the protonated form of the enamine tautomer of the product is shown in the enzyme-product (EP) complex  15, 30  ). c, InC binding mimics that of carbapenem or carbapenem-derived products to B1 MBLs, with maintenance of the di-Zn(II) complex hydroxide and conservation of the Zn-Zn distance observed in unligated MBLs (Zn:Zn distance 3. 5 Å, Protein Data Bank (PDB) ID 4BZ3  43  ). Most MBL inhibitors displace the Zn-hydroxyl, as observed for bi(cyclic) boronates (Zn:Zn distance 4. 3 Å, PDB ID 5FQC  38  , which mimic the tetrahedral transition state), and thiazole-carboxylates (Zn:Zn distance 4. 15 Å, PDB ID 6ZGM  17  , the binding of which is related to that of hydrolysed penems and penicillins).
2g ). At least one of the methyl groups of the C7 isopropyl group points towards the surface of the active site where the C6 or C7 groups of the penicillins or cephalosporins, respectively, bind (Fig. 2a-d ). Thus, the binding of C7, C3 alkyl or aryl and C2 carboxylate substituted InCs mimics those of β-lactams and the structures rationalize the SAR observations that both the InC C3 and C7 positions are amenable to substitution with relatively large groups (Fig. 2e, f ). Note the presence of a di-Zn(II) ion-bridging hydroxide, the position of the C7 alkyl group relative to that of the bridging water, and that the C2 carboxylate is positioned to interact with the different types of binding motifs employed in MBL substrate carboxylate binding. e,f, Overlays comparing binding modes of InCs, hydrolysed β-lactams and analogues of the tetrahedral transition state, with two MBLs-hydrolysed cefuroxime with NDM-1 (PDB ID 4RL0) (e) and cyclic boronate 2 with VIM-2 (PDB ID 5FQC) (f).
Acknowledgements We thank the ELF Screening partners for efforts leading to the initial hits, the current and past ENABLE Portfolio Management Committee (PMC) members and EFPIA partners for guidance, and the  Diamond Light Source  synchrotron for access to the IO3, IO4 and IO4-1 beamlines. J. U. H. thanks  N. Sipari  from the  Viikki Metabolomics Unit  ( Helsinki Institute of Life Science, University of Helsinki and Biocenter Finland ) for her expertise with the liquid chromatography-mass spectroscopy analyses. L. E. thanks  Y. Zhou  for help in the laboratory, and  H. Saif ,  R. Farzana ,  E. Portal ,  K. Sands ,  K. Thomson  and  B. Hassan  for providing strains for the Enterobacterales collection. The ELF work and the  ENABLE  project that led to these results were supported by the  Innovative Medicines Initiative Joint Undertaking  (grant agreements no.
115489  and no. 115583 ), which have financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ), and the  EFPIA  companies' in-kind contributions. The  ENABLE  project is financially supported by contributions from  Academic  and SME partners. The Oxford work was also supported by  Cancer Research UK  ( C9047/ A24759 ), the  Medical Research Council , the  Biotechnology and Biological Research Council  ( BB/S50676X/1 ) and the  Ineos Oxford Institute for Antimicrobial Research (C. J. S. ). This research was funded in whole, or in part, by the  Wellcome Trust  (grant no. 106244/Z/14/Z  and no. 099141/Z/12/Z ). The Bristol work was also supported by the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH)  grant  R01AI100560  (J.
Methods Methods, full experimental section and all data supporting the findings of this study are available within the paper and its supplementary information files. The atomic coordinates and structure factors are deposited in the Protein Data Bank, accession codes: PDB ID 7AEZ (48 with NDM-7), PDB ID 7AEX (49 with VIM-1), PDB ID 7AFY (11 with VIM-2) and PDB ID 7AFZ (49 with L1). The raw data for the InC 58 and meropenem microbial activity dataset can be accessed in Zenodo at https://doi. org/10. 5281/zenodo. 4438867. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data sources The meteorological data used for the historical period (i. e. baseline: 1990-2007) was generated using the Weather Research Forecasting (WRF) model at a 5 km x 5 km resolution  (Bannister et al. 2019). For future climate projections, outputs from a coupled climate model developed at the NOAA Geophysical Fluid Dynamics Laboratory Climate Model version 3 (GFDL-CM3) was downloaded from the KNMI Climate Explorer  (Trouet and Van Oldenborgh 2013)   given by the Land and Water Development Division -FAO  (Vargas 2007)  was also used to determine soil moisture conditions for irrigation lands. Other physical datasets such as elevation, slope, roads, and river were reanalysed using the ASTER Global Digital Elevation Model (USGS 2018) data at 30 m resolution. Population in the baseline was obtained from the Census of India of 2000  (MHA 2011)  and the future projections in 10-year intervals from 2010 to 2100 were obtained from the Climate and Global Dynamics Laboratory for the SSP1 scenario (CGD 2020).
Acknowledgements The work reported here was funded by the  UK-NERC  (grant numbers  NE/N016394/1  and  NE/N015541/1 ) -" Sustaining Himalaya Water Resources in a Changing Climate (SusHi-Wat )" -as part of the  UK-India Newton-Bhabha Sustainable Water Resources (SWR) thematic Programme. We also thank the BBMB for making the data available and Sanjay Jain at  NIH-Roorkee  for facilitating the link with the BBMB.
Study sample Study participants were selected from four previously conducted GWAS from the PanScan and the PanC4. Details of these studies have been published previously  (8) (9) (10) (11) (12). Each participating study obtained written informed consent from participants and approval from their local Institutional Review Board. The studies were conducted in accordance with recognized ethical guidelines (e. g. , Declaration of Helsinki, CIOMS, Belmont Report, U. S. Common Rule). Both the Johns Hopkins School of Medicine (Baltimore, MD) Institutional Review Board and the NCI (Bethesda, MD) Special Studies Institutional Review Board approved the consortia study. Our study was based on 9,038 primary PDAC cases (ICD-O-3 code C250-C259) and 12,389 controls free of known PDAC. Participants with nonexocrine pancreatic tumors were excluded (histology types 8150, 8151, 8153, 8155, and 8240).
GWAS genotype data Genotyping was conducted in four phases, PanScan I, PanScan II, PanScan III, and PanC4. The PanScan studies were genotyped at the NCI Cancer Genomics Research Laboratory at the NIH (Bethesda, MD) and genotyped on the Illumina HumanHap series arrays [Illumina HumanHap550 Infinium II  (8) , Human 610-Quad  (11)  for PanScan I and II, respectively, and the Illumina Omni series arrays (OmniExpress, Omni1M, Omni2. 5, and Omni5M) for PanScan III  (12) ]. PanC4 was genotyped on the Illumina HumanOmniExpres-sExome-8v1 array at the Johns Hopkins Center for Inherited Disease Research  (9). The data from the PanScan and PanC4 GWAS are available through dbGAP (accession numbers phs000206. v5. p3 and phs000648. v1. p1, respectively). Details on imputation and quality controls have been described previously  (10).
In brief, SNPs were excluded if they had call rates ≤ 98%, MAF ≤ 0. 05, and Hardy-Weinberg equilibrium P values, < 1. 0 Â 10 -6 in the controls. SNPs were prephased using SHAPEIT2 software  (25). Genotype imputation was conducted using IMPUTE2  (26)  with 1000 genomes phase 3  (27). Imputation was conducted separately for the PanScan I/II, PanScan III, and PanC4 GWAS. After imputation, we retained only SNPs with imputation quality scores >0. 5 and MAFs >0. 05. Our final analysis included 6,769,447 variants.
The EB is an intermediate between the two methods and allows for a data-adaptive relaxation of the geneenvironment independence assumption. Because the EB provides a good compromise between bias and variance  (30, 31) , we used this as the primary method for evaluating interaction and the other two methods for sensitivity analysis. The association analysis was conducted using CGEN software (Version 3. 5. 0; https://dceg. cancer. gov/tools/analysis/cgen), an R package for logistic regression analyses of SNP-environment interactions  (32) , using the "snp. score" option to incorporate the genotype probabilities from the imputed data in the analysis. This option calculates a score test (JScore), which tests for the joint effect of SNP and SNP-by-environment interaction under a logistic regression model.
The analyses were first conducted separately for the PanScan and PanC4 datasets and results were combined using meta-analysis. Smoking was included as categorical dummy variable with never smokers as reference. Association magnitude of each SNP was modeled according to additive SNP dosage. Imputed SNPs were incorporated through expected dosage using the snp. score function of the CGEN package  (32). Interaction between SNP and smoking was modeled using two parameters (current, never) and (former, never). Each SNP genotype was coded using an underlying dosage model, coded in terms of observed/impute allele counts. The analysis was adjusted for age in decade, sex, and top eigenvectors (five for PanScan and nine for PanC4) from principal component analysis to control for ancestry  (10). PanScan analyses were also adjusted for study and geographic region of the parent studies  (12).
Expression quantitative trait locus analysis Using the NIH Genotype-Tissue Expression (GTEx) v8 (dbGaP Accession phs000424. v8. p2, https://gtexportal. org/home/; ref. 35) , we examined expression quantitative trait locus (eQTL) to assess the cis effects of the SNPs in the 2q21. 3 region on gene expression across multiple tissues. In addition, we created regional plots of the 2q21. 3 region using the SNP2GENE function of FUMA  (36). The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from: (https://gtexportal. org/home/) the GTEx Project Portal on June 30, 2019 and July 30, 2020 and/or dbGaP accession number phs000424. vN. pN on June 30, 2019.
Acknowledgments This work was supported by  RO1CA154823  and  P50CA062924  (to principal investigator A. P. Klein),  R01 HG010480-01  (to principal investigator N. Chatterjee),  Patient-Centered Outcomes Research   Institute Award  ( ME-1602-34530 ), and the  Intramural Research Program, Division of Cancer Epidemiology and Genetics, NCI  (to principal investigator  R. Stolzenberg-Solomon ). Funding for the parent studies are listed below. The  IARC/Central Europe study  was supported by a grant from the  U. S. NCI at the NIH  ( R03 CA123546-02 ) and grants from the  Ministry of Health of the Czech Republic  ( NR 9029-4/2006 ,  NR9422-3 ,  NR9998-3 ,  MH CZ-DRO-MMCI 00209805 ). The work at  Johns Hopkins University (Baltimore, MD)  was supported by the  NCI  grants  P50CA062924  and  R01CA97075.
They thank all the CLUE participants. Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414 , and  1074383  and by infrastructure provided by  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry  and the  Australian Institute of Health and Welfare , including the  National Death Index  and the Australian Cancer Database. The  NYU study  ( A. Zeleniuch-Jacquotte  and  A. A. Arslan ) was funded by  NIH   R01 CA098661 ,  UM1 CA182934  and center grants  P30 CA016087  and  P30 ES000260. The  PANKRAS II Study in Spain  was supported by research grants from  Instituto de Salud Carlos III-FEDER, Spain: Fondo de Investigaciones Sanitarias  (FIS; # PI13/ 00082  and # PI15/01573 ) and  Red Tem atica de Investigaci on Cooperativa en C ancer, Spain  (# RD12/0036/0050 ); and  European Cooperation in Science and Technology  ( COST Action  # BM1204 : EU_Pancreas),  Ministerio de Ciencia y Tecnología  ( CICYT SAF 2000-0097 ),  Fondo de Investigaci on Sanitaria  ( 95/0017 ),  Madrid, Spain ;  Generalitat de Catalunya (CIRIT -SGR) ; " Red tem atica de investigaci on cooperativa de centros en C ancer " ( C03/10 ), " Red tem atica de investigaci on cooperativa de centros en Epidemiología y salud p ublica " ( C03/09 ), and  CIBER de Epidemiología (CIBER-ESP) ,  Madrid.
The Physicians'  Health Study  was supported by research grants  CA-097193 ,  CA-34944 ,  CA-40360 ,  HL-26490 , and  HL-34595  from the  NIH, Bethesda, MD. The Women's Health Study was supported by research grants  CA182913 ,  CA-047988 ,  HL-043851 ,  HL-080467 , and  HL-099355  from the  National Institutes of Health , Bethesda, MD. Health Professionals Follow-up Study is supported by  NIH  grant  UM1 CA167552. from the  NCI, Bethesda, MD. Nurses' Health Study  is supported by  NIH  grants  UM1 CA186107 ,  P01 CA87969 , and  R01 CA49449  from the  NCI, Bethesda, MD. Additional support from the  Hale Center for Pancreatic Cancer Research ,  U01 CA21017  from the  NCI ,  Bethesda, MD , and the  United States Department of Defense   CA130288 ,  Lustgarten Foundation ,  Pancreatic Cancer Action Network, Noble Effort Fund, Peter R.
62E-76) (Fig. 4C-E , Supplemental Fig. S12  and Supplemental Table  S16 ). To confirm this, we assessed the proportion of ref6-5 hyper-methylated genes associated to repressive loops and found that 40% were associated with reSLs (Fig. 4C-D  and Fig. 4E ). These data suggest that a gain in H3K27me3 triggers the establishment of novel chromatin interactions (Fig. 4D ). Using publicly available transcriptomic data (Antunez-Sanchez et al. 2020) we assessed whether gene pairs connected through reSLs showed similar transcriptional shifts between ref6-5 and WT. We found a significant enrichment of co-repressed reSL-paired genes loci among the 546 plotted couples (p-value = 4E-10) (Fig. 4F , Supplemental Fig. S12  and Supplemental Table  S17 ). This result indicates that genes displaying specific physical contact in ref6-5 are globally repressed, likely linked to the ectopic accumulation.
Altogether, our results suggest that a clf-dependent decrease in H3K27me3 leads to a re-configuration of chromatin architecture and the destabilization of H3K27me3-mediated repressive loops (Fig. 5B  and 5F ). This phenomenon can be associated to the transcriptional de-repression of hundreds of genes in PRC2-related mutants. To better understand the association between genome topology reconfiguration, H3K27me3 deposition and gene expression, we compared epigenomic and transcriptional features found in clf-29 and WT, and integrated these datasets with chromatin interaction positioning (Supplemental Table  S21 ). Our analysis revealed that interactions occur more frequently between genes with concordant transcriptional status, independently of whether they are up-or down-regulated in clf-29 compared to the WT (Fig.
This result is consistent with previous ChIP-based studies in Arabidopsis, which have shown that the distribution of facultative heterochromatic histone modifications such as H3K27me3 on the linear genome is anti-correlated to euchromatic histone modifications  (Ha et al. 2011 ). Hi-C data is useful for the depiction of the genome-wide 3D organization of chromatin in the nucleus; however, to understand which proteins or covalent histone modifications have a role in structuring this organization, it is necessary to integrate it with external datasets (e. g. ChIP-seq) allowing only correlative analyses. To circumvent this limitation and to address whether and how genomic regions associated to histones bearing a specific covalent modification interact through long-range chromatin looping in a genome-wide and unbiased manner, we performed HiChIP experiments, confirming that transcriptionally active and inactive genes localize in different nuclear compartments.
Analysis of ChIP-seq data Single-end sequencing of ChIP samples was performed using Illumina NextSeq 500 with a read length of 76 bp. Trimmomatic-0. 38  (Martin 2011)  was used for quality trimming. Parameters for read quality filtering were set as follows: Minimum length of 36 bp; Mean Phred quality score greater than 30; Leading and trailing bases removal with base quality <5. The reads were mapped onto the TAIR10 assembly using Bowtie 2 (Langmead and Salzberg 2012) with mismatch permission of 1 bp. To identify significantly enriched regions, we used MACS2  (Gaspar 2018 ) Parameters for peaks detection were set as follows: Number of duplicate reads at a location:1; mfold of 5:50; q-value cutoff: 0. 05; extsize 200; broad peak. To extract the average scores across the genomic regions, multiBigwigSummary command from the deepTools package  (Ramírez et al.
Analysis of HiChIP and Capture Hi-C data Raw FASTQ files were preprocessed with Trimmomatic-0. 38 to remove Illumina sequencing adapters. The ′ and 3′ ends with a q ality score below Phred+33 were trimmed and reads shorter than 3 bp after trimming were dropped. The trimmed files were then processed with HiC-Pro v2. 11. 1  (Servant et al. 2015). The reads were aligned using Bowtie 2 onto the TAIR10 assembly with default settings, except for the parameter "--score-min L, -0. 6, -8" Invalid ligation prod cts s ch as dangling ends, fragments ligated on themselves, and ligations of juxtaposed fragments) were discarded. Valid pairs were used to prod ce raw interaction matrixes at vario s resol tions Finally, " hic" files were generated with the software Juicer Tools and visualized with the tool Juicebox  (Durand et al.
The focus of this study is to examine the impact that complex flow conditions, such as profiled velocity gradients or surface waves, can have on the individual turbine component loadings and performance of a HATT. The research methods used include experimental lab-scale testing at the French Research Institute for Exploitation of the Sea (IFREMER) wave-current flume as well as the use of numerical CFD modelling using ANSYS CFX software. This paper presents the methodology used to develop a free surface, multiphase model capable of simulating turbine rotation in uniform and profiled current flows, as well as in regular wave fields. All numerical models are initially validated using lab-scale experimental data allowing further investigation into the transient turbine loadings which were unattainable through the experimental test campaign.
Current flow and wave generation For uniform flow models (U), the flow characteristics were added to the inlet of the model mathematically, to reach a streamwise velocity of 1. 0 m/s input using cartesian coordinates, as shown in Table  9. For models using a profiled current flow (P1 or P2), average, steady state data obtained experimentally was input to the model using a 'Profile Method' boundary condition at the inlet. This boundary condition used interpolated values from the flow profiles obtained during experimental testing. CFX Expression Language (CEL) expressions were then used to refer to the imported data using interpolation functions. The aim was to directly compare CFD simulations and experimental data, therefore it was important to replicate the current profile generated at the experimental facilities into the models.