dataesr/scientific-paragraphs-categorization huggingface dataset:
Hydrolytic Enzyme Potential Prediction of hydrolytic enzymes potentially present during retting was performed by using PICRUSt  (Langille et al. , 2013). This software successfully predicts bacterial enzymatic activities represented in different databases (e. g. , KEGG Ortholog, COGs, or CAZy). Overall, a large collection of enzyme activities targeting both the main backbones and side chains of the major polysaccharide polymers were identified. Based on OTU counts, ∼38, 43, and 19 percent of the total hydrolytic enzyme potential targeted pectins, hemicelluloses, and cellulose, respectively. Despite the clear dynamics and significant changes in the straw microbiome these values remained constant throughout the retting period. Similar software does not exist for predicting fungal enzyme potential.
ACKNOWLEDGMENTS The authors would like to thank the following people/organizations: The flax farmer C. A. L. I. R. A ( Coopérative Agricole LInière de la Région d'Abbeville ) for growing and retting flax plants, in particular  Vincent DELAPORTE  for his valuable assistance. The  Genomic and Transcriptomic platform of Genopole Occitanie-Toulouse  ( INRA, GeT-Plage , http://get. genotoul. fr/)where sequencing was performed and more particularly Catherine ZHANCHETTA and  Olivier BOUCHEZ. The  Plateau d'Ecologie moléculaire et biochimie Evo-Eco-Paléo  ( CNRS-UMR 8198 ,  Evolution, Ecologie et Paléontologie ) for use of the LightCycler 480 and epMotion and more particularly Cécile GODÉ and  Anne-Catherine HOLL  for their technical support. Antoine PORTELETTE ,  Julien LE ROY ,  Sandrine ARRIBAT , and CHABBERT for their valuable assistance during the sampling.
2016 ). Therefore, it seems plausible that, following 3. Phylogenomic analysis of HSV-2. This maximum likelihood tree was generated from an alignment of 135,445 positions comprising 60 sequences (including 42 publicly available genomes). Branch leaves are annotated with the accession number and country of origin of the virus; for sequences generated during this study isolate number and country name are in bold. The color code refers to the region of origin: orange for subSaharan Africa, blue for Europe, red for the Americas, and green for Asia. Isolate v15 was obtained from a patient from Martinique (West Indies) and was therefore colored as originating in the Americas. Recombination profiles appear on the right side; filled circles indicate the presence of a HSV-1 fragment, empty circles their absence.
Note that (1) for two isolates (v4 and v11) we missed the data at the recombinant loci; (2) given the very short length of their putative recombinant regions we did not mark KR135311, KR135312, and KR135313 as recombinant in UL39. Branch robustness was assessed using Shimodaira-Hasegawa-like approximate likelihood ratio tests (SH-like aLRT); branches supported by SH-like aLRT values < 0. 95 are gray. The scale is in substitutions per site. DRC, Democratic Republic of the Congo. Recombination Events between Human HSV. doi:10. 1093/molbev/msx113 MBE a putative transmission from chimpanzees to the human lineage  (Wertheim et al. 2014) , HSV-2 diversified in African populations before a single lineage characterized by recombinant HSV-1 fragments (the worldwide lineage) accompanied the migration of Homo sapiens out of Africa.
In Silico Analyses We removed low quality bases from raw reads using Trimmomatic (LEADING: 30, TRAILING 30, SLIDINGWINDOW: 4:30, MINLEN: 40;  Bolger et al. 2014). We then mapped trimmed reads to a HSV-2 reference genome from which we had removed the TRL and TRS regions (NC_001798) using BWA-MEM  (Li and Durbin 2009). We finally sorted mapping files and removed duplicates using the SortSam and MarkDuplicates tools from Picard (http:// broadinstitute. github. io/picard). Coverage plots were generated with Geneious  (Kearse et al. 2012). Coverage was uneven and usually lower in fast evolving regions, e. g. , IRL and IRS (supplementary fig. S7 , Supplementary Material online, p. 21). We generated consensus sequences using Geneious by calling bases at positions covered by more than 20 reads and for which more than 95% of the reads agreed.
We also annotated putative coding sequences with Geneious using a conservative similarity threshold of 60% to coding sequences annotated in the HSV-2 reference genome (NC_001798). We collected all complete and near complete HSV-2 and HSV-1 genome sequences for which geographic location was known as well as the chimpanzee alpha-1 herpesvirus (JQ360576) and cercopithecine herpesvirus 2 (NC_006560) genome sequences. We discarded 7 HSV-2 (KU310662-8) and 5 HSV-1 (KU310657-61) genome sequences which originated from a single unpublished study and exhibited considerable variation in many coding sequence lengths; the final data set comprised 42 HSV-2 and 42 HSV-1 genome sequences (supplementary table S1, Supplementary Material online, p. 2-5). We also annotated 34 unannotated HSV-2 genomes using Geneious, as abovementioned.
2010) , and finally excluded the interspecific recombinant regions as well as the TRL and TRS regions. The resulting alignment included 61 sequences and 135,445 positions (Whole genome 2; see Data Availability). We performed phylogenetic analyses on Whole genome 2 and on a reduced version thereof excluding the chimpanzee alpha-1 herpesvirus (to avoid any long branch attraction effect; Whole genome 3) in both ML and Bayesian frameworks. We also performed ML analyses on the ten longest nonrecombinant subalignments that we could identify by running a breakpoint distribution analysis with RDP4 (Nonrecombinant 1-10, 4,654-12,640 bp; see Data availability). These ML analyses were all performed as described above, using GTR þ IþG as a model of nucleotide substitution. Bayesian Markov chain Monte Carlo analyses were run with BEAST v1.
2014 ) (Whole genome 2); or using the substitution rate derived from the analysis of Whole genome 2 (7 Â 10 À2 substitutions site À1 My À1 ; 95% highest posterior density: 6-8 Â 10 À2 substitutions site À1 My À1 ) to inform the clock rate with a normal distribution of mean 7 Â 10 À2 substitutions site À1 My À1 and standard deviation 7 Â 10 À3 substitutions site À1 My À1 (95% CI: 6-8 Â 10 À2 substitutions site À1 My À1 ). In both cases, the overall model also included a tree shape component, for which we used a coalescent model assuming a constant population size. We used Tracer v1. 6 to check that runs had converged and that the posterior was adequately sampled. We then used LogCombiner and TreeAnnotator to combine trees and generate a maximum credibility tree summarizing the posterior tree sample (both softwares are distributed with BEAST).
Data Availability UL30 sequences have been deposited in GenBank under the accession numbers JX905315-8, KF588390-7, KM068900-1, and KY363355-8. Raw sequences (ERR1380254-71) and genome assemblies (LT797622-36, LT797682, LT797786, and LT7993806) are available at the European Nucleotide Archive under study number PRJEB13741 (http://www. ebi. ac. uk/ena/data/view/PRJEB13741). The following documents are available from the Dryad Digital Repository (http://dx. doi. org/10. 5061/dryad. 9t21c): (1) alignments: Whole genome 1, 2, and 3; Whole genome de novo 2 and 3; Non-recombinant 1 to 10; UL15 nonrecombinant and recombinant, UL29 nonrecombinant and recombinant, UL30 nonrecombinant and recombinant, UL39 nonrecombinant and recombinant; (2) annotated HSV-2v genomes. All trees generated in this study can be viewed in and exported from iTol (connexion: calvignacs, project: Ancient recombination events between human herpes simplex viruses).
INTRODUCTION The rapid progress in sequencing technologies correlates with a dramatic increase in genome information availability in all domains of life. The volume of genome data produced allows new kinds of evolutionary genomics questions to be tackled, but require the data to be well integrated in a comprehensive and intuitive framework. Whole genome comparative genomics especially requires both high-resolution measures (precise breakpoint positions, gene-level evolutionary rates, etc. ) as well as largescale observations (genome karyotype comparisons, chromosome synteny maps, etc) New tools are needed to meet the computational challenge raised by the volume and complexity of this data. Towards this goal, we continue to develop new graphical tools in the Genomicus server, to offer easy access to evolutionary analyses with vertebrate genomes.
DATA SOURCES AND ANCESTRAL GENOME INFER-ENCE The data available in Genomicus Vertebrates on extant species is downloaded from the Ensembl database (4) and information on the ∼1 200 000 genes of the 70 extant species (Protein sequence, gene location, gene family and gene tree, percentage of identity between pairs of proteins and dN/dS ratio) is stored in a local MariaDB database. From this information, we infer and compute ancestral genes content and order with the AGORA (Algorithm for Gene Order Reconstruction in Ancestor) method  (5)  for each Ensembl release. The ancestral genomes are reconstructed as ordered and oriented sets of genes with a range of quality, from highly contiguous chromosomelength scaffolds holding the expected number of genes to highly fragmented assemblies with few reconstructed adjacent genes, depending on the age of the ancestor and the Nucleic Acids Research, 2018, Vol.
Technically, the graphics are rendered in the form of an image in PNG format inserted in an SVG container that allows javascript operability on the client side. As an example, Figure  2B  represents the Multi-KaryoView of the inferred Catarrhini reconstruction (here as reference genome), the selected 4 descendants species (Rhesus, Orangutan, Chimpanzee and Human), and the intermediate ancestor Hominidae (common ancestor to Human, Chimpanzee and Orangutan). The top menu of the Multi-KaryoView page gives access to representation parameters such as the number of chromosomes or ancestral blocks to be displayed, or the minimum length of chromosomes (in number of genes). Clicking on 'Compute karyotype comparison' generates the representation in Figure  2B , showing all the macro rearrangements that occurred in genomes through evolution, such as chromosome fission or chromosome fusion.
Here, the human PGAM4 gene is requested. One can see its location on the human X chromosome, but that its orthologs in Gorilla, Orangutan and Rhesus are on autosomes. The first interpretation of this situation could be the possible translocation of PGAM4 in Human. Figure  1  reflects that this gene has been specifically duplicated in human genome, and its paralog PGAM1 is indeed located on the X chromosome. D822 Nucleic Acids Research, 2018, Vol. 46, Database issue of the page. In this table, clicking on a block will highlight it in the matrix. The full list of blocks can also be downloaded as text file for further analyses.
GENOMICUS SOFTWARE IMPLEMENTATION Genomicus is composed of Perl (version 5. 22) scripts and modules, along side with Python (version 2. 7) libraries from other projects of Dyogen (LibsDyogen, PhylDiag) for new functionalities, executed with mod perl on an Apache2 (version 2. 4) server and querying a MariaDB (version 10. 0. 31) database. The web server is running on an Ubuntu server 16. 04. The pages embed inline-SVG drawings in XHTML while the JavaScript (version 1. 9. 1) usage is limited to an information panel retrieved with AJAX calls. The interface is optimized for Firefox and Chrome navigators but it also runs on Safari and Internet Explorer. The source codes of Genomicus and the MariaDB schema can be obtained upon request by email.
FUNDING French Government and implemented by  ANR  [ ANR-10-BINF-01-03 ,  ANR-10-LABX-54   MEMOLIFE ,  ANR-10-IDEX-0001-02 PSL* Research University ];  RENABI-IFB  programme [ ANR-11-INSB-0013 ]. Funding for open access charge:  CNRS [UMR8197 ].
A Cad Model for Motion Simulation In this paper a new model is proposed for the lumbar part of a spine. In this model we consider that all solicitations of the spine are vertically applied at the center of the vertebra. The 3D model has been elaborated in ADAMS-software with the aim to run a dynamic simulation for computing motion properties and reaction forces (Fig. 6 ). In ADAMS-software the discus has been modeled as a body that is attached to the assembled vertebrae. Joint between all discs is spherical. This joint is very near to the real type of link between vertebrae. First simulation consists of tilting the entire body to the right and left and calculating the forces applied to the column in the thoracic and lumbar region. Fig. 7  shows the simulation with ADAMS-software and the result of forces applied in vertebrae for the left bending motion.
Simulations and Result The prototype was simulated with ADAMS-software and trajectory planning is designed with Matlab-SIMULINK, which is used to control the ADAMS model (M. Souissi, 2012b), minimization of torque was done in (M. Souissi, 2016). Prototype is used to correct the posture of the spine in two planes: sagittal and coronal plane. For all experiences done with ADAMS, the sagital plane is presented by zy-plane, the axial plane is presented by xy-plane and the coronal plane is presented by xz-plane as shown in figure (Fig. 2 ).
Introduction PANTHERE is a radiation protection code used for 3D simulation of gamma dose rates and fluence for complex environment with distributed radioactive sources like nuclear facilities. PANTHERE is intensively used by EDF engineering units and its industrial partners. It was originally used to prepare maintenance and modification operations in nuclear power plants, especially on the primary circuit like pressurizer heaters replacement and steam generator replacement. It is now extended to other fields like: -shield design and zoning for EPR reactors -dose calculations for the qualification of PWR equipments during severe accidents. -decontamination and dismantling operations -radiological characterization for applications associated to transport and nuclear waste management PANTHERE is verified and validated by comparison with the reference Monte Carlo code TRIPOLI-4 of the CEA and gamma dose rates experimental measurements.
Post-processing in the GUI The results of the computation are displayed in form of source/point or nuclide/point matrix and can be exported in csv format or opened in a spreadsheet editor. Dedicated SALOME [3] modules allow for radiological zoning and isodose mapping. PANTHERE results, combined with NPP maintenance program can be used as an input of CADOR, an optimization code for the installation of protection on NPP contaminated pipes. This code perform an optimization of the collective dosimetry by scheduling the task in order to reduce both painfulness of team in charge of installing protection and the dosimetry of team in charge of maintenance.
7% of these read pairs that were not overlapping with any gene feature (Table  1 ). Close investigation of these unassigned read pairs highlighted genes that seemed to be absent or partially covered by the UCSC and Ensembl reference annotations (Fig. 1A, B ), as well as transcripts with missing or partial exon features (Fig. 1C ). In order to improve the read assignment rate, we first performed a genome-guided transcript discovery by using Cufflinks  (Trapnell et al. , 2010). This approach was intended to determine more accurately exon-intron junctions, to correct or to complete existing annotated genes, and to identify unannotated gene candidates from the UCSC/Ensembl gene annotation model (Fig. 1D, E ). Following this approach, 77. 9% of the sequencing read pairs were assigned to gene features, corresponding to 89.
8% of the read pairs that were mapped against the genome (Table  1 ). Therefore, the genome-guided transcript discovery enabled us to raise the read assignment rate by 15. 7% as compared to when using both UCSC and Ensembl reference annotations (Table  1 ). In contrast to genome-guided transcript prediction, de novo transcriptome reconstruction relies on overlaps between the sequencing reads to build consensus transcripts, independently of the genome sequence. We therefore applied a genome-independent strategy by using Trinity  (Grabherr et al. , 2011) , in combination with the genome-guided approach, in order to detect transcripts or regions that were not recovered from the genome sequence, such as those located within gaps or uncharacterized chromosomes (Fig. 1D, E ). Reconstructed transcripts thus generated were then compared to the gene candidates obtained with the genome-guided approach in order to remove redundant sequences.
Given that 62. 2% of the sequencing read pairs were assigned to gene features by using both UCSC and Ensembl reference annotations, our transcript reconstruction model enabled us to assign 19. 3% more read pairs to gene features (Fig. 2A , Table  1 ). The genome-independent transcript assembly also enabled us to correct for gene fragmentation by gathering gene regions located on multiple chromosomes and contigs together (Fig. 1D, E ). In contrast to genome-guided transcript discovery, de novo reconstruction of transcripts was not limited by the quality of the reference genome sequence. By comparing transcripts generated from both reconstruction approaches, we were able to group dispersed gene features belonging to a same gene candidate together. Although 19,376 (90. 8%) gene candidates were found exclusively on a single chromosome or unplaced contig, 1971 (9.
2%) gene candidates were identified as being fragmented (Fig. 2B ). These fragmented gene candidates included 478 (2. 2%) gene candidates that were located on multiple ordered chromosomes, 462 (2. 2%) gene candidates split among multiple unplaced contigs, and 1031 (4. 8%) gene candidates with regions located on an ordered chromosome and additional unplaced contigs (Fig. 2B ). Transcript prediction and reconstruction approaches did not provide any information on gene name and function. Therefore, gene candidates identified by the dual transcript-discovery approach were then annotated by database comparison and protein domain prediction (Fig. 1E ). Gene candidates were first compared to bird gene sequences, taking advantage of the recent increase of available genomic data within avian species and their high DNA sequence conservation  (Dalloul et al.
, 2010; Huang et al. , 2013; Jarvis et al. , 2014; Schmid et al. , 2015; Shapiro et al. , 2013; Warren et al. , 2010; Zhan et al. , 2013; Zhang et al. , 2014). Undefined gene candidates were then compared at the protein level to mouse and human databases. Finally, prediction of open reading frames (ORFs) and protein domains was performed on remaining unannotated gene candidates by using homology search against SwissProt and Pfam databases, and sequence analysis tools to identify transmembrane domains and signal peptides. Overall, the computed gene annotation model was mostly constituted of protein-coding gene candidates (16,716, 78. 3%) (Fig. 2C ). However, 672 (3. 1%) gene candidates were only partly annotated (putative proteins having at least one protein domain detected), while 1410 (6.
The new annotation model is composed of 21,347 gene candidates, accounting for 5989 additional gene candidates as compared to the UCSC and Ensembl reference annotations associated with the galGal4 genome version. Of these gene candidates, 1971 (9. 2%) have parts spread on multiple locations, while 3340 (15. 6%) are identified among the 16,000 unplaced contigs that are not assigned to any ordered chromosome. In addition, the resulting gene annotation model increased the assignment rate of RNA-seq read pairs by 19. 3% as compared to when using both galGal4 reference annotations (UCSC and Ensembl), thus contributing to a more accurate estimation of transcript abundance. It is noteworthy to take into consideration that de novo assembly of short reads is prone to cause artefacts and to generate false chimeric transcripts  (Yang and Smith, 2013).
Such errors can be corrected for instance by comparing reconstructed transcripts with transcripts/ proteins of the same organism, closely related organisms, or more accurately annotated organisms. In addition, transcriptome assemblers tend to create multiple transcript sequences per gene, which would cause reads to map at multiple locations and be subsequently ignored during read counting. Several programs have been developed in order to cluster transcript sequences into genes and to remove redundancy. TGICL  (Pertea et al. , 2003)  and CD-HIT-EST  (Fu et al. , 2012) , which were originally designed for clustering of expressed sequence tags (EST), can be used to create consensus gene sequences. However, since both programs perform their clustering based on all transcript sequences, paralogous genes may be erroneously merged.
In contrast, Corset  (Davidson and Oshlack, 2014)  identifies sequence similarity between transcripts by identifying multi-mapped reads resulting from re-mapping of reads against the reconstructed transcriptome. Although this program accurately clusters transcripts into genes, it falls short of building consensus genes from transcript sequences. To overcome these limitations, we applied a strategy that consists in a pairwise comparison of transcript sequences belonging to the same gene candidates followed by incremental concatenation of identical and unique transcript sequences to build full-length gene candidates. Very recently, a similar approach has been reported under the name of superTranscripts  (Davidson et al. , 2017). We observed that 99. 95% of consensus gene sequences generated by superTranscripts were identical to our results.
Genome-guided transcript discovery RNA-seq data obtained from both biological replicates of micromass cultures were processed independently. Strand-specific read pairs were mapped against the chicken genome galGal4  (Hillier et al. , 2004 ) by using TopHat2 v0. 14  (Kim et al. , 2013)  ( parameters: -r 150; -N 3; -read-edit-dist 3; -library-type fr-firststrand; -i 50; -G). UCSC (galGal4) and Ensembl (release 75) annotations were downloaded from Illumina iGenomes (http:// support. illumina. com/sequencing/sequencing_software/igenome. html) and compared by using Cuffcompare from the Cufflinks suite v2. 1. 1  (Trapnell et al. , 2010). Identical genes were retrieved only once and merged with the unique genes from each annotation. In case of discordant genes, the gene annotation with the best coverage was selected.
The resulting gene annotation model composed of 17,318 genes was used as input for TopHat2 mapping. Transcript discovery was performed for each replicate by using Cufflinks v2. 1. 1  (Trapnell et al. , 2010)  ( parameters: -b; -u; -librarytype, fr-firststrand; -g) and the combined gene annotation model as guide. Resulting annotations were merged into a single model by using the Cufflinks tool Cuffmerge v2. 1. 1  (Trapnell et al. , 2010).
De novo transcript discovery A second transcript-discovery approach was led independently of the genome sequence. Low-quality RNA-seq reads from each replicate of micromass cultures were first filtered out by using the FASTX-Toolkit v0. 0. 13 (http:// hannonlab. cshl. edu/fastx_toolkit/). Reads with a median quality value lower than 28 were discarded. Filtered read pairs were then trimmed by using Trimmomatic v0. 32  (Bolger et al. , 2014)  (parameters: ILLUMINACLIP TruSeq3 paired-end for HiSeq, seedMismatches 2, palindromeClipThreshold 30, simpleClipThreshold 10; LEADING 5; TRAILING 5; MINLEN 36). Complete read pairs were then assembled by using Trinity r20140717  (Grabherr et al. , 2011)  (default parameters except for the strand-specific library orientation set at RF).