dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS AJS, SB, TT, and CDF acknowledge support by  NASA  through  STSCI grant   HST-GO-15320 , and by the  Packard Foundation  through a  Packard Fellowship  to TT. Support for Program HST-GO-15320 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. TA acknowledges support by the  Ministry for the Economy, Development, and Tourism's Programa Inicativa Científica Milenio  through grant  IC 12009 , awarded to  The Millennium Institute of Astrophysics (MAS). FC and GM acknowledge support from the  Swiss National Science Foundation  # 200020 172712. IK is supported by  JSPS KAKENHI  Grant Number  JP15H05896. PJM is supported by the  U.
Using this approach, we estimated substitution and recombination rates individually. The substitution rate of 1. 9 × 10 -4 substitutions per site per year estimated for lactococcal phages is within the range of estimates for eukaryotic dsDNA viruses (∼10 -5 ) and eukaryotic RNA viruses (∼10 -3 )  [10]. The reconstruction of recombination events revealed a rate of five recombination events per year and 4. 5 × 10 -3 nucleotide alterations due to recombination per site per year. Thus, the recombination rate exceeded the substitution rate, resulting in a relative effect of recombination to mutation (r/m) of ∼24, which was homogenous over time. The analysis of bacterial genome-wide data resulted in generally low r/m values, for example, 0. 283 in Staphylococcus aureus  [11]  and 7. 2 for a Streptococcus pneumoniae lineage isolated over 20 years  [12].
We used early blood microarray transcriptome profiles collected from 39 hospitalized infants (collected within 24 h of hospitalization) that were followed until recovery and for whom the level of disease severity was determined retrospectively. Applying support vector machine learning on age-by-sex standardized transcriptomic data, an 84-gene signature was identified that discriminated hospitalized infants with eventually less severe RSV infection from infants that suffered from the most severe RSV disease. This signature yielded an area under the receiver operating characteristic curve (AUC) of 0. 966 using leave-one-out cross-validation on the experimental data and an AUC of 0. 858 on an independent validation cohort consisting of 53 infants  [22]. The presented signature may serve as the basis on which to develop a prognostic test to support clinical management of RSV patients.
Using simulations, we demonstrated that RTCR can easily adapt to the error profiles of different types of sequencers and exhibits a consistently high recall and high precision even at low coverages at which other pipelines perform poorly  [23]. Using the RTCR pipeline, we analyzed a longitudinal dataset obtained from a healthy donor. The results showed that a healthy repertoire is highly dynamic: T-cell memory clone sizes appeared to undergo major shifts in abundance, demonstrating that small snapshots of the full repertoire nevertheless contained a wealth of information. Pattern analysis of paired (longitudinal) datasets will allow detailed studies of infection and, for example, vaccination-induced host responses.
Here we report the development and implementation of TABAJARA (Figure  2 ), a tool for the rational design of profile HMMs. Starting from a multiple sequence alignment (MSA), TABAJARA is able to find blocks that are either (1) conserved across all sequences or (2) discriminative for two or more specific groups of sequences. The program uses different metrics to ascribe position-specific scores along the whole alignment and then uses a sliding window to find top-scoring regions. Selected alignment blocks are then extracted and used to build profile HMMs. To validate such models, we used sequence data from phages of the Microviridae family and members of the Flavivirus genus. In both viral groups, we were able to obtain wide-range seeds (generic for all members of Microviridae or Flavivirus) and narrow-range seeds, exclusive to specific Microviridae subfamilies (Alpavirinae and Gokushovirinae) or to particular Flavivirus species (e.
g. , DENV, ZIKV, or YFV). Additionally, using TABAJARA's generated profile HMMs as seeds, we succeeded to reconstruct specific viral sequences with the GenSeed-HMM program using different metagenomic datasets. Finally, we also report the development of e-Finder, a program that uses profile HMMs to identify clusters of genes in a specified syntenic context. This tool allows the detection of viral and nonviral elements such as prophages, transposons, CRISPR-Cas systems, and operons in assembled genomes. These programs constitute a toolbox and can be used alone or in combination to detect and classify known viruses as well as distantly related viruses that may represent potentially emerging pathogens. This work was done by the team composed of Liliane S. Oliveira, Dolores U. Mehnert, Paolo M.
François Enault Introduced the Study of Viral Communities Using Metagenomics Although viruses are considered as major players in natural ecosystems, their comprehensive study has been hampered by the necessity of isolating and cultivating their host and by the absence of a single gene common to all viral genomes. Viral metagenomics, based on culture-independent sequencing, is a well-fitted approach to provide insights into the composition, structure, and dynamics of environmental viral communities. However, specific computational methods are required to analyze these viral metagenomes (or viromes). First, we developed a method to estimate the proportion of cellular sequences in viromes. A quarter of the published datasets were found to contain a substantial number of sequences originating from cellular genomes, either as a result of shortcomings in experimental protocols, as virus-like particles (VLPs) are described as difficult to purify from some matrices (soil, sediments, and animal-associated samples), or because of VLPs containing cellular DNA (membrane vesicles, gene transfer agents, or due to phage transduction).
Considering the fact that viromes can contain a substantial number of sequences originating from cellular genomes, a tool was developed to identify viral sequences in such datasets. Using both homology to reference viral sequences and viral genome characteristics, VirSorter complements existing prophage prediction software and was proved to be able to identify viral signals in assembled sequence (contigs) as short as 3 kb  [29]. Once the viral contigs are identified, determining the identity of their hosts is crucial for deciphering the interactions and dynamics between microbes and their viruses and thus for our ecological understanding of the microbiome. To determine the hosts of the newly sequenced viruses, we used the fact that viruses tend to have a genomic signature similar to their prokaryotic hosts.
Using homogeneous Markov models, our tool WisH was found to predict hosts for short phage genomic fragments with a better accuracy than existing tools such as VirHostMatcher  [30]. Furthermore, WisH runs several hundred times faster than this software and scales up to large datasets  [31]. Using bioinformatic methods and software, we studied viral communities in aquatic systems such as freshwater lakes and hypersalin ponds, and in the context of the Virus-X EU project (Horizon 2020 Project No. 685778), we mostly focused on Icelandic hot springs. The analysis of viromes informs us about the composition, diversity, and dynamics of the viral communities in these environments and allowed us to analyze the main viral families retrieved in these datasets, mostly Caudovirales and ssDNA viruses such as Microviridae.
Brett Pickett Discussed Genome Sequencing and Serodiagnostics for Zika Virus Zika virus (ZIKV) is responsible for an ongoing outbreak concentrated in South and Central America and the Caribbean, with travel-based cases being reported around the globe. In addition, new outbreak clusters located in the southeastern United States have been attributed to autochthonous transmission. As multi-disciplinary collaborations are forming to combat the ZIKV outbreak, it is imperative to authenticate and sequence-verify existing reagents prior to performing infection experiments to better understand and combat increasing virulence. Here, we report high-quality sequence data for multiple ZIKV strains available from the Biodefense and Emerging Infections Research Resources Repository (BEI Resources). These sequence data include the historical reference strain from the African lineage (MR-766), the Asian lineage derived American reference strain (PRVABC59), and several other strains collected from around the world during the 2015 outbreak with documented passage histories.
We used next-generation sequencing coupled with 3' rapid amplification of cDNA ends (RACE) and viral genome annotation pipelines to generate consensus sequence records in GenBank. We applied a deep-sequencing analysis method to identify minor variants, which exist as natural quasi-species within viral stocks. We detected minor variants that consistently resulted in nonsynonymous substitutions at multiple codons among a subset of the Asian strains. Interestingly, the nonstructural protein 1 (NS1) and membrane glycoprotein (prM) coding regions had the highest number of minor variants in the Asian and African lineages, respectively. The evolutionary relationships for these strains were determined using phylogenetic methods, and a single recombinant sequence was identified. These bioinformatics analyses of the sequencing results confirm that the BEI Resources virus stocks available to the scientific research community adequately represent the viral population diversity of ZIKV and justify additional wet-lab experiments.
Until 2017N, performance was evaluated by retrospective testing, in which data from after the time of the WHO vaccine strain meeting for a particular season was excluded from the analysis. From 2017S onwards, predictions were made for the future and can be monitored live at https://github. com/hzi-bifo/SDplots_VaccineUpdates. 2. 13. Susana Posada Céspedes Spoke on the Bioinformatics Pipeline V-pipe for Inferring Viral Diversity from High-Throughput Sequencing Data RNA viruses are responsible for various infectious diseases in humans, animals, and crops, representing a burden to human health as well as causing substantial economic losses. One major limitation when treating viral infections is the great genetic diversity featured by RNA viruses. The existence of a heterogeneous mixture of viral strains has implications on viral pathogenesis, virulence, and disease progression.
Philippe Lemey Described Bayesian Phylogenetic and Phylodynamic Data Integration Using BEAST The field of phylodynamics has witnessed a rich development of statistical inference tools with increasing levels of sophistication, but these tools have traditionally focused on sequences as their sole data source. Integrating various sources of information with genomic data promises to deliver more precise insights into infectious diseases and to increase opportunities for statistical hypothesis testing. Emerging concepts of data integration are now stimulating new advances in Bayesian evolutionary inference methodology. We have worked on such methodology in the BEAST software, which represents a coherent statistical framework for integrated analyses of sequences and traits using Bayesian inference techniques.
Seasons with sweep changes not associated with antigenicity-altering or avidity-changing sites are marked in grey; (Third row) Sweep-related changes in antigenicity-altering or avidity-changing sites. Using these as a criterion for vaccine strain updates results in a simultaneous or earlier detection of newly emerging antigenic types than with the procedure utilized by the WHO; (Fourth row) Detected sweep-related sites known to change neither the avidity nor the antigenicity; (Fifth row) WHO recommendations. Until 2017N, performance was evaluated by retrospective testing, in which data from after the time of the WHO vaccine strain meeting for a particular season was excluded from the analysis. From 2017S onwards, predictions were made for the future and can be monitored live at https://github.
The Wheat@URGI portal has been developed to provide the international community of researchers and breeders with access to the bread wheat reference genome sequence produced by the International Wheat Genome Sequencing Consortium. Genome browsers, BLAST, and InterMine tools have been established for in-depth exploration of the genome sequence together with additional linked datasets including physical maps, sequence variations, gene expression, and genetic and phenomic data from other international collaborative projects already stored in the GnpIS information system. The portal provides enhanced search and browser features that will facilitate the deployment of the latest genomics resources in wheat improvement.
Background The International Wheat Genome Sequencing Consortium (IWGSC)  [1]  is an international collaborative group of growers, academic scientists, and public and private breeders that was established to generate a high-quality reference genome sequence of the hexaploid bread wheat, and to provide breeders with state-of-the-art tools for wheat improvement. The vision of the consortium is that the high-quality, annotated ordered genome sequence integrated with physical maps will serve as a foundation for the accelerated development of improved varieties and will empower all aspects of basic and applied wheat science to address the important challenge of food security. A first analysis of the reference sequence produced by the consortium (IWGSC RefSeq v1. 0) was recently published  [2]. To ensure that wheat breeding and research programs can make the most of this extensive genomic resource, the IWGSC endorsed the establishment of a data repository at URGI (Unité de Recherche Génomique Info/research unit in genomics and bioinformatics) from INRA (Institut National de la Recherche Agronomique/French national institute for agricultural research) to develop databases and browsers with relevant links to public data available worldwide.
The IWGSC data repository is thus hosted by URGI to support public and private parties in data management as well as analysis and usage of the sequence data. Wheat functional genomics (expression, methylation, etc. ), genetic, and phenomic data have increased concurrently, requiring the development of additional tools and resources to integrate different data for biologists and breeders. To manage this escalation of data, URGI has built this data repository for the wheat community with the following specific aims:  (1)  to store resources for which no public archive exists (e. g. physical maps, phenotype information);  (2)  to enable pre-publication access to specific datasets (e. g. sequence assemblies and annotations, physical maps, markers); and (3) to enable rapid release of integrated resources upon publication.
A wealth of data is available throughout the Wheat@URGI portal The data hosted by the Wheat@URGI portal are available through flat files stored in the IWGSC data repository and through the GnpIS information system  [4]. GnpIS encompasses a set of integrated databases to manage genomic data using well-known tools such as the Basic Local Alignment Search Tool (BLAST), JBrowse, GBrowse, and Inter-Mine, and an in-house database called GnpIS-coreDB developed by URGI to manage genetic and phenomic data.
Physical maps Physical maps assembled by IWGSC scientists for the 21 bread wheat chromosomes, based on high-information-content (fluorescence) fingerprinting (HICF)  [6]  or Whole Genome Profiling (WGP™)  [7]  of flow-sorted chromosome or chromosome-arm specific bacterial artificial chromosome (BAC) libraries, are stored and displayed. The positions of individual BAC clones, markers, and deletion bins are mapped onto physical contigs. The database maintains all released versions of each physical map with the software used to produce the BAC clone assemblies (FingerPrinted Contig (FPC)  [8]  or Linear Topological Contig (LTC)  [9] ), information from the group that produced the map, and a link to order the BAC clones from the French plant genomic resource centre  [10].
Sequence assemblies and annotations The IWGSC wheat genome sequence assemblies available for download, BLAST  [11] , and display in genome browsers include the draft survey sequence assemblies released in 2014 (IWGSC Chromosome Survey Sequencing (CSS) v1) and two improved versions (CSS v2 and v3)  [12]  and the chromosome 3B reference sequence (the first reference-quality chromosome sequence obtained by the consortium)  [13]. Associated with these assemblies are the virtual gene order map generated for the CSS (Genome Zipper), the population sequencing (POPSEQ) data used to order sequence contigs on chromosomes  [14] , and mapped marker sets. The reference sequence of the bread wheat genome (IWGSC RefSeq v1. 0, 14. 5 Gb assembly with super scaffold N50 of 22. 8 Mb) was obtained by integrating whole genome shotgun Illumina short reads assembled with NRGene's DeNovoMAGIC™ software with the wealth of IWGSC map and sequence resources  [2].
Genetic and phenomic data In addition to sequence data, the Wheat@URGI portal hosts, within GnpIS-coreDB, several sets of genetic and phenomic wheat data  [20]  that have been produced from French, European, and international projects since 2000  [21]. A significant amount of these data is available without restriction. However, access to restricted data can be obtained through a material transfer or intellectual property agreement. Table  2  presents the types and number of genetic and phenomic data hosted in the GnpIS-coreDB database. Genetic information corresponds to genetically mapped markers, quantitative trait loci (QTLs), genetic resources (germplasms), and genetic studies (genome-wide association studies (GWASs)). Genomic information consists of variations from SNP discovery experiments, genotyping, comparative genomics (synteny), and expression data (microarray, RNA-Seq).
Genome browser and BLAST IWGSC RefSeq v1. 0 is displayed in a dedicated JBrowse  [28, 29]. The "markers track" provides links to additional genetic information stored in GnpIS-coreDB which includes access to the position of the marker in centimorgans (cM) on genetic maps and to the overlapping QTLs. The most popular tool of the IWGSC data repository is the BLAST search tool (476,000 BLAST searches launched in 2017, Additional file 1: Table  S1 , Additional file 1: Figure  S1 ). All of the wheat sequences available on the Wheat@URGI portal are indexed for BLAST search (see  [30]  for the complete list). A set of databanks can be selected, e. g. IWGSC RefSeq v1. 0 and IWGSC CSS v3 for a given chromosome. The result is presented in a classical tabular format with (1) links to download the data (matching contigs and high scoring pairs (HSP)), (2) links on the genome browsers directly zooming in on the matching region, and (3) external links to Ensembl Plants  [31].
Mining and data discovery tools To complete these already rich integrated datasets, a gene-centric data warehouse, the WheatMine, has been set up using the well-established InterMine tool  [24]. The gene card displays gene function, gene ontology terms, and overlapping genomic features. WheatMine  [36]  provides access to the IWGSC RefSeq v1. 0 and v1. 1 annotation data (genes, messenger RNA, polypeptides, transposable elements), markers and, through pivotal objects, to genetic data (QTL, metaQTL). It is also possible to navigate from a gene card to its position on the wheat genome browser or to relevant marker details in GnpIS-coreDB. Figure  1  summarises the concept and the tools to navigate through the key data in GnpIS. Finally, to facilitate data search and access to this wealth of data, we developed a data discovery tool, which, similar to a google search, allows the user to enter keywords or terms to find all the matching information in the various data warehouses.
Conclusion and future directions The Wheat@URGI portal hosts and gives access to essential, high-quality wheat data from the IWGSC, European, and international projects. Furthermore, its added value is that it integrates different data types altogether (genomics, genetics, and phenomics) and provides dedicated tools to explore them. As new wheat resources such as GWASs, genomic selection, and pan-genome data are generated in the frame of ongoing projects, GnpIS will allow their management and integration with other data already available in the information system, linking new upcoming data to this central IWGSC genomic resource. At a wider scale, an expert working group (EWG) of the international Wheat Initiative has built an international wheat information system, called WheatIS, with the aim of providing a single-access web-based system to all available wheat data resources and bioinformatics tools  [37].
The Wheat@URGI portal is a major node of the WheatIS federation that exposes genomic, genetic, and phenomic integrated data to the community. The WheatIS data discovery tool allows a one-stop search in GnpIS  [4]  (including IWGSC browsers, InterMine and GnpIS-coreDB), from URGI; Ensembl Plants, from the European Bioinformatics institute (EMBL-EBI)  [31] ; CrowsNest  [38] , at the Plant Genome and Systems Biology (PGSB) group; CR-EST  [39] , GBIS  [40]  and Meta-Crop  [41] , from the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); The Triticeae Toolbox (Triticeae Coordinated Agricultural Product); CIMMYT DSpace and Dataverse (International Maize and Wheat Improvement Center (CIMMYT)); Gramene  [42] , from Cold Spring Harbor Laboratory (CSH), Ohio State University (OSU), and EMBL-EBI; Cropnet, from the Institute of Plant Genetics of the Polish Academy of Sciences (IPGPAS); WheatPan  [43] , from the University of Western Australia (UWA); and GrainGenes  [44] , US Department of Agriculture (USDA).
Acknowledgements The authors would like to thank for their help or advice at various stages of the project, the following people from  INRA-URGI :  Véronique Jamilloux ,  Joëlle Amselem ,  Dorothée Charruaud ,  Guillaume Cornut ,  Laura Burlot ,  Florian Philippe ,  Nicolas Francillonne ,  Loïc Couderc ,  Daphné Verdelet ,  Baptiste Brault ,  Kirsley Chennen ; from  INRA-GDEC :  Jacques Le Gouis ,  Gilles Charmet ,  François Balfourier ,  Pierre Sourdille ,  Catherine Ravel ,  François-Xavier Oury ,  Audrey Didier ; from  INRA-DIST :  Esther Dzale ,  Sophie Aubin ,  Odile Hologne ; and from  INRA-Agronomie :  Arnaud Gauffreteau. Thanks to Isabelle Caugant (IWGSC),  Hélène Lucas  ( Wheat Initiative ), the  International Wheat Genome Sequencing Consortium  and its sponsors, the  WheatIS expert working group , the  URGI platform , and all the data submitters.
Funding The development of the information system and the integration of wheat data were supported by  INRA  and several projects:  BreedWheat  ( ANR-10-BTBR-03 ,  France Agrimer , FSOV),  Whealbi  ( EU FP7-613556 ),  TriticeaeGenome  ( EU FP7-KBBE-212019 ),  3BSEQ  ( ANR-09-GENM-025 , FranceAgrimer), and  TransPLANT  ( EU FP7-283496 ).
Decomposition of peat carbon controlled by water saturation In the standard version of ORCHIDEE, plant litter carbon is added to two litter pools: the metabolic and the structural pool. Decomposed litter carbon from these two pools is then distributed into three soil carbon pools: the active, slow, and passive pools, similar to the CENTURY model  (Parton et al. , 1988). Both temperature and moisture functions are used to control soil carbon decomposition rates (Text S1 in the Supplement). In ORCHIDEE-PEAT, these standard processes are kept the same as in  Krinner et al. (2005)  for non-peatland vegetation (Fig. S2 , black dashed box). For the peatland vegetation, we added a peat carbon module, in which the three soil carbon pools (active, slow, and passive) are replaced by two pools forming distinct layers, following  Kleinen et al.