dataesr/scientific-paragraphs-categorization huggingface dataset:
The purified cDNA (1 mg) was used to generate each of the paired-end Illumina sequencing libraries. The libraries were prepared using Illumina adapter and PCR primers, according to previously published protocols  [42, 43]. Libraries with an average insert size of 400-500 bp were isolated, and the concentration was adjusted to 10 nM. The samples (7 pM per sample) were loaded into separate channels of an Illumina GAIIx sequencer. The sequencing was performed at the Oregon State University Center for Genome Research and Biocomputing. The raw data (untreated reads) for all treatments are publicly available (http://www. ncbi. nlm. nih. gov/sra study accession number SRP029998).
The sophisticated ancestral immune toolkit responds to experimental bacterial exposure The immunity of scleractinian corals is an expanding field in basic research because of the basal position of cnidarians in the eumetazoan tree of life, and its relevance to understanding the evolution of defense systems against pathogens. It is also crucial to understand the immune capabilities of corals, because of the increase of coral epizootics. In this context, most past studies have focused on the identification of immune pathways and effectors, mainly through searches for orthologous genes in the expanding genome and transcriptome databases. These studies have revealed that the common ancestor of animal species contained several immune genes and pathways that are also present in higher invertebrate and vertebrate species (Table  5 ).
This study was supported by the  Agence Nationale de la Recherche  through the  Program BIOADAPT  ( ADACNI ANR-12-ADAP-0016-03 ) and the  French-Israeli High Council for Science and Technology  ( P2R  n  u29702YG ). Work in the Freitag laboratory was supported by start-up funds from the  OSU Computational and Genome Biology Initiative and Oregon State University. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors confirm that all data underlying the findings are fully available without restriction. The reference transcriptome used is accessible at http://2ei. univ-perp. fr/telechargement/transcriptomes/blast2go_fasta_Pdamv2. zip. The raw data (untreated reads) for all treatments are publicly available at http://www. ncbi. nlm. nih. gov/sra with the accession number SRP029998.
, 2004). Furthermore, long polar fimbriae (LPF) (lpfA) closely related to LPF of Salmonella enterica serovar Typhimurium, were proposed to be novel adhesion factors  (Doughty et al. , 2002; Torres et al. , 2002). Due to the recent emergence of STEC O178 strains in cattle and food of bovine origin on both the South American and the European continent, we became interested in studying the virulence potential of such strains. For this, we compared E. coli O178 isolates from Argentina and Germany originating from animals, food and humans in regard to their virulence properties, including toxins, adhesins, and serine proteases. Furthermore, we analyzed the genetic relationships among these strains using data obtained by pulsed-field gel electrophoresis. Our results indicate that STEC O178:H19 belong to the growing group of LEEnegative strains that are able to cause severe illness in humans  (Newton et al.
, 2007). Controls used were C600(H19) stx1a (GenBank accession no. M16625), DG131/3 stx1c (Z36901), and MHI813 stx1d (AY17085), C600(W34) stx2a (X07865), CB2851 stx2v-ha (AJ605767), CB7753 stx2v-hb (AF479829), NV206 stx2-NV206 (AF329817), RL481/06 stx2-EC1586 (AM498375), C165-02 stx2d-O73 (DQ059012), 12422 stx2g (AY286000), EH250 stx2b (AF 043627), 3615-99 stx2e (AJ313016), and T4/97 stx2f (AJ0110730).
The resulting 890 bp-amplicons and 1334 bp-amplicons, respectively, were purified and sequenced at a DNA sequencing service unit (Eurofins MWG Operon, Ebersberg, Germany). The sequences were analyzed using Lasergene software (DNASTAR, Madison, WI) and Accelrys Gene software (Accelrys Inc. , San Diego, CA). Using the Blast algorithmus, the sequences were compared with sequences deposited at NCBI (http://www. ncbi. nlm. nih. gov/blast/). The following sequences were used as references: stx2a (accession no X07865), stx2b (AF043627), stx2c (M59432), and stx2d (AF479829).
ACCESSORY VIRULENCE GENES The presence of accessory virulence genes was determined by a real-time PCR microarray as previously described  (Bugarel et al. , 2010a)  Furthermore, all strains were tested by PCR for the presence of the espI gene encoding a serine protease, termed EsPI, that is located in the chromosome on the locus of protease and adherence (LPA). Primers espI-I and espI-II and cycling conditions are used as previously described  (Schmidt et al. , 2001). All subA-positive strains were tested by PCR for the presence of the tia gene (Genbank accession no. U20318) using primers tia_lo and tia_sense as described by  Tozzoli et al. (2010). tia encodes for an invasion determinant described in enterotoxigenic E. coli.
DETECTION OF MUCUS-ACTIVATABLE stx2d BY PCR AND NUCLEOTIDE SEQUENCING By PCR eight of the STEC O178:H19 strains (12. 1%) were identified as stx2d-positive. The STEC O178:H7 and O178:H10 strains were found negative for stx2d (Table  1 ). To confirm the results, we determined the nucleotide sequences of the stx2AB genes of all stx2d-positive and 8 selected stx2d-negative STEC O178:H19 strains. The sequences were analyzed and compared with the published stx2-reference sequences in GenBank. It is noteworthy, that chromatograms derived from STEC strains carrying both stx2a and stx2d genes showed overlapping peaks at nucleotide positions which differ between stx2a and stx2d (data not shown). The sequences were translated into putative amino acid sequences of the A and B subunits and analyzed for the activatable type of a Stx2 toxin.
PFGE GENOTYPING OF STEC O178:H19 STRAINS The genetic relatedness between the different E. coli O178 strains was investigated by XbaI-PFGE genotyping. E. coli O178 strains carrying other H-antigens/fliC types than H19 revealed individual PFGE patterns and proved to be genetically more distant (data not shown). The 66 O178:H19 strains revealed 47 different PFGE patterns. A dendrogram based on similarities of the PFGE patterns was created with the BioNumerics software v. 6. 6 using the band-based Dice coefficient (Figure  2 ). Sixty-one of the O178:H19 strains were assigned to two major PFGE clusters designated A (35 strains) and B (26 strains) (Figure  2 ). The five remaining strains were assigned to clusters C (one strain), D (three strains), and E (one strain). Clusters A to E were defined using a cut-off value of 80% similarity (Figure  2 ).
, 2013). Urease helps STEC to survive in the stomach and amends its ability to colonize the calf and human intestinal tracts. The role of tellurite resistance genes is not completely clear. However, it was postulated that ter genes might encode an adhesin-related gene product and/or might offer a selective advantage in the host environment and under stress conditions  (Yin et al. , 2009). No O178 strain in this study was ureD-positive, 24. 3% of the strains were terE-positive. Tellurite resistance of these strains was evidenced by their ability to grow on CT-SMAC agar and CHROMagar STEC (data not shown). Among other toxins a novel subtilase cytotoxin (SubAB) has been reported to contribute to the virulence and pathogenicity of LEE-negative STEC for humans. SubAB is the prototype of a new AB 5 toxin family first detected in the LEE-negative O113:H21 STEC strain 98NK2 that caused an outbreak of HUS in Australia in 1998  (Paton et al.
study design This was a post-hoc analysis of an international, multicenter, double-blind, randomized, active-controlled, parallelgroup, factorial study conducted in patients with primary hypercholesterolemia or mixed hyperlipidemia. 27 Complete details regarding study design and patient entry criteria are published elsewhere (Protocol 022, ClinicalTrials. gov: NCT00269217). 27 In brief, eligible patients included men and women aged 18-85 years with primary hypercholesterolemia or mixed hyperlipidemia and TG levels #3. 95 mmol/L prior to randomization. Additionally, patients were required to meet one of the following two NCEP (National Cholesterol Education Program) Adult Treatment Program III categories for coronary heart disease risk: 1) LDL-C levels $3. 36 and #4. 91 mmol/L (for patients with 0-1 risk factors) or 2) $3.
However, this difference in tendency to specialize by union type should vary by context, with greater differences between cohabiting and married individuals in contexts where cohabitation is less widespread and more distinct from marriage. Countries where marriages are less stable (i. e. those with higher divorce rates) may also be characterized by fewer differences between cohabiting and married individuals. In this paper, we use data from time diaries to assess time allocation to market and nonmarket work for cohabiting versus married individuals in three distinct country contexts: France where cohabitation is widespread and treated much like marriage, Italy where cohabitation is still confined to specific groups of individuals and not a legally recognized family form, and the United States which fits somewhere between France and Italy.
By choosing three very distinct countries we can assess whether the size of the cohabitation-marriage differential in market and nonmarket time allocation is associated with supports and stability for marriage. The organization of the paper is as follows. In Section 2, we further develop expectations and review previous literature on variation in time use between men and women in cohabitation and marriage and on selection out of cohabiting and into marital unions. In Section 3, we briefly review the French, Italian, and U. S. contexts for marriage and cohabitation. In Section 4, we describe the French, Italian, and American time use data collections and lay out our analysis plan. In Section 5, we present results and in Section 6 we summarize our findings and draw conclusions.
French time use survey The French data come from the 2009-2010 French Time Use Survey that was conducted by the French National Statistical Institute (Institut national de la statistique et des études économiques -Insee). Face-to-face interviews were carried out among 12,053 households and 18,380 individuals aged 11 years old and above 11. The response rate 12 at the household level was of 68%. In addition to household and individual questionnaires, one household member aged 11+ was randomly selected and had to respond to an individual questionnaire. She also had to fill a paper diary on which she described her activities for a predetermined day in ten minute intervals.
American Time Use Survey (ATUS) The U. S. data come from the 2003-2007 American Time Use Survey (ATUS), a nationally representative cross-sectional time-use survey launched in 2003 by the U. S. Bureau of Labor Statistics (BLS). The ATUS interviews a randomly selected individual age 15 and older from a subset of the households that have completed their eighth and final interview for the Current Population Survey (CPS), the U. S. monthly labor force survey. Using a stratified sampling design, approximately 1,780 interviews were conducted per month in 2003 and 1,160 per month in 2004 and subsequent years, yielding an estimated total of 77,000 interviews over the 2003-2007 period. Interviews for the ATUS typically take place between two and five months after the household's last CPS interview.
Using computerassisted telephone interviews, respondents provide a detailed account of what they were doing between 4:00 a. m. of the previous day and 4:00 a. m. of the interview day (a 24-hour period). ATUS interviewing occurs continuously over the course of the year, with each ATUS respondent interviewed once. ATUS data files are released annually. We combine five years of data to increase sample sizes. The response rate was 57. 8% in  2003, 57. 3% in 2004, 56. 6% in 2005, 55. 1% in 2006, and 52. 5% in 2007. We apply weights that adjust for nonresponse and oversampling of some groups. Evidence suggests that weights help correct for sources of nonresponse bias  (Abraham et al. 2006).
The IntAct molecular interaction database has created a new, free, open-source, manually curated resource, the Complex Portal (www. ebi. ac. uk/intact/ complex), through which protein complexes from major model organisms are being collated and made available for search, viewing and download. It has been built in close collaboration with other bioinformatics services and populated with data from ChEMBL, MatrixDB, PDBe, Reactome and UniPro-tKB. Each entry contains information about the participating molecules (including small molecules and nucleic acids), their stoichiometry, topology and structural assembly. Complexes are annotated with details about their function, properties and complex-specific Gene Ontology (GO) terms. Consistent nomenclature is used throughout the resource with systematic names, recommended names and a list of synonyms all provided.
The use of the Evidence Code Ontology allows us to indicate for which entries direct experimental evidence is available or if the complex has been inferred based on homology or orthology. The data are searchable using standard identifiers, such as UniProt, ChEBI and GO IDs, protein, gene and complex names or synonyms. This reference resource will be maintained and grow to encompass an increasing number of organisms. In-put from groups and individuals with specific areas of expertise is welcome.
INTRODUCTION Biological processes are driven by the interactions of proteins with other molecules in an organism. These interactions may be transient (e. g. signalling receptor-ligand interactions) or lead to the formation of stable biological complexes (e. g. minichromosome maintenance complex, EBI-913604). Many molecules exist only in an obligate complex formation (e. g. collagen type I, EBI-2325312). Many databases deal with protein, small molecule and polysaccharide functions, for example, UniProtKB (1), Gene Ontology (GO)  (2) , ChEBI (3), ChEMBL  (4)  and their interactions (e. g. IntAct  (5) , MINT (Molecular INteraction Database  (5, 6) ), MatrixDB  (7)  or DIP (Database of Interacting Proteins (  8 )) but no central, integrated resource has so far been available to hold this type of information for stable, macromolecular complexes.
The Complex Portal, hosted by the IntAct team at the European Bioinformatics Institute (EBI) (www. ebi. ac. uk/intact/complex), provides a framework for the capture of such data, and the existence of web-based editorial tools not only enables collaborating groups to capture standards-compliant experimental data  (5)  but also to contribute to the curation of the growing collection of manually curated protein complexes. Several databases (such as CORUM  (9) ) exist that have previously collated information on protein complexes but they provide limited data integration across resources and have tended to concentrate on a more limited species D480 Nucleic Acids Research, 2015, Vol. 43, Database issue range. UniProtKB/Swiss-Prot contains a wealth of data pertaining to the behaviour of individual proteins within larger complexes in the form of free-text statements.
Curators from the UniProt Consortium collaborate with the Complex Portal to help extract and structure these data. The Protein Ontology (PRO) provides an ontological representation of protein-related entities by explicitly defining them and describing their relationship  (10). This includes the existence of proteins within larger macromolecular complexes. The PRO consortium, by collaborating with the In-tAct group, will contribute to both extend the number of complexes which are available as objects within the PRO hierarchy while making more richly annotated data on these entities available in a broader range of formats through the Complex Portal. Model organism groups, such as Saccharomyces Genome Database  (11)  and WormBase  (12) , are contributing expertise and curation effort within their organism of interest, while the MINT database is augmenting the resource with additional expertise on the composition of protein complexes.
Curators at the EBI ensure that entries relating to conserved complexes remain consistent across multiple species, or that differences that have evolved across a taxonomic range are suitably highlighted. Long-term data maintenance, such as regular updates of controlled vocabulary terms and underlying protein sequence data, are also provided by the IntAct team. Entries combine biological information about the molecular function, biological processes, cellular location and pathways of a complex with information about its constituents (proteins, nucleic acids, small molecules and polysaccharides), topology, stoichiometry and structural assembly. Structured information about complex ligands, diseases they are involved in and drugs they act as targets for is also added to appropriate entries. The use of the Evidence Code Ontology (13) allows us to indicate for which entries direct experimental evidence is available in a molecular interaction databases, such as IntAct (www.
Quality control As with IntAct and many other manually curated databases, each entry is checked by a second curator before it is released, enabling the provision of highly accurate and upto-date information. However, no data set can claim to be totally error-free and new information is constantly being published. Therefore, the user is encouraged to feedback, or request curation of a specific complex via the <Feedback> button on the website or by directly emailing intact-help@ebi. ac. uk.
g. <'nuclear pore'> is more precise than <nuclear AND pore> which is more precise than <nuclear pore> as the latter equals <nuclear OR pore>). More refined searches can be performed using the Complex Query Language, CQL. CQL has been specifically designed for searching complexes but has retained those fields that overlap with the Molecular Interactions Query Language, MIQL, such as ID, alias, species, biological role and feature type. More detail on the search options can be found at http://www. ebi. ac. uk/intact/complex/help/. The search results can either be filtered or a particular entry can be selected in order to view its details. Currently, it is possible to filter by species, and by biological role (e. g. enzyme) and interactor type (e. g. protein) as defined using the Proteomics Standards Initiative -Molecular Interactions Controlled Vocabulary (PSI-MI CV)  (14).
Complex details Each complex has as a minimum a unique accession number, a recommended name, a list of participants, a functional description and a list of cross-references (Figure  2 ). Complexes are curated based either on direct evidence in the literature, or inferred from closely related species (e. g. mouse PCNA, EBI-1208675) or homologous complexes in the same species, for which experimental evidence exists (rat hemoglobin variants, EBI-9105417 and EBI-9105480), or based on background scientific knowledge of an expert submitter (e. g. existence based on pharmacological evidence, e. g. GABA receptors, EBI-9008426). The type of evidence that exists for each complex entry is identified by using Ev-idence Code Ontology (ECO) codes. For ECO codes currently in use please refer to the online documentation (http: //www.
ebi. ac. uk/intact/complex/documentation/). In cases where the existence of a complex has been inferred by sequence homology of the components, rather than from direct experimental evidence, the details of the experimentally verified complex, on which it has been modelled, are given. These entries will be updated when experimental evidence becomes available. Complex members are identified via unique accession numbers with direct links to the respective databases (UniProtKB for proteins, ChEBI for small molecules), name and description of the molecule (imported from the above reference databases), stoichiometry (when known), interactor type and the biological role of that molecule within the complex. Known protein binding regions are identified by their start and end residue numbers and crossreferenced to InterPro (16) if they match a functional domain (EBI-9519429), whereas unknown regions are identified by range '?-?' (EBI-706546).
43, Database issue D483 indicated, e. g. , 2xHBA1:2xHBB for haemoglobin HbA. For further details of the Reactome nomenclature rules, see  (19). Further information on the complex is added by the curator providing a detailed description of its function, and any processes it is involved in, as well as information about properties, such as molecular weight, complex size or internal topology of the complex, where available. Complexes with complete stoichiometry are also annotated with the relevant assembly terms, such as homodimer or heterotrimer. Where applicable, important ligands are listed and short descriptions are given about diseases with which the complex is associated. To aid data integration and to provide maximum additional information to the user, related classes or instances of the complex in other databases are provided as crossreferenced links.
These may include links to ChEMBL, EM-DataBank  (20) , the GO, MatrixDB, PDBe  (21)  and Reactome. Links to experimental evidence proving the existence and content of a particular complex are provided as IMEx (International Molecular Exchange Consortium (  22 )), In-tAct, MINT, MatrixDB or DIP cross-references and, as indicated, ECO codes are used to further indicate the level of evidence available for the complex. For enzymes we provide a link to the Integrated relational Enzyme database (IntEnz (  23 )). When a complex is involved in a disease we provide cross-references to Online Mendelian Inheritance in Man (OMIM, http://omim. org/), Experimental Factor Ontology (EFO) and/or Orphanet (http://www. ebi. ac. uk/ efo/about. html and http://www. orphadata. org/cgi-bin/inc/ ordo orphanet. inc.
COLLABORATIONS AND COMMUNITY INVOLVE-MENT The Complex Portal has been designed to provide a framework in which groups are able to contribute their expert knowledge by annotating complexes within a particular defined area. The IntAct molecular interaction database is responsible for training, data quality, data consistency and long-term data maintenance. While coverage is limited at this early stage of the Portal development, the involvement of multiple contributing groups ensures that this will grow rapidly. We hope that further groups will add to the community effort in the near future, in particular, participation of the plant community would assist in filing this gap in the taxonomic coverage. For suggestions for new complexes to be added to the database and direct submissions please contact us via the <Feedback> link on the homepage or by directly emailing at intact-help@ebi.
The complex viewer will be wrapped in a BioJs (25) component which will be available in the BioJs registry so it can be re-used by other developers wishing to import and display some or all of these protein complexes, or indeed other data presented in the PSI-MI XML format. Other BioJS components, such as a structural viewer, for those complexes which have been fully, or partially, crystallized and patterns of co-expression data for complex components, will also be incorporated. Ongoing activities include improvements to associated controlled vocabularies and further cross-referencing in addition to increasing the content of the Complex Portal. We constantly try to improve our databases and services in terms of accuracy and representation and actively encourage user feedback. Please contact the IntAct group if you have any questions via the <Feedback> link on the homepage or email us directly at intact-help@ebi.