dataesr/scientific-paragraphs-categorization huggingface dataset:
Library preparation and MiSeq sequencing. The sequences were aligned with reference sequences from bacteria of the same genus available from the SILVA SSU Ref NR database v119, using SeaView v4  [92]. We used a neighbor-joining method (bioNJ) to produce phylogenetic trees with a Kimura 2-Parameter model using SeaView software, and species were identified on the basis of the "closest phylogenetic species". We also used our sequences for blast analyses of GenBank (blastn against nucleotide collection (nr/nt) performed in january 2016) to identify the reference sequences to which they displayed the highest percentage identity. The raw abundance table, the mothur command lines, the mothur output files, the Perl script and the FASTA files used for the phylogenetic analyses are available in the Dryad Digital Repository http://dx.
doi. org/10. 5061/dryad. m3p7d [42]. were performed with 96-well microplate 9, positions A1 and E1 for run 1 and B1 and F1 for run 2, and the four PCRs for PC Mycoplasma_m were performed with 96-well microplate 9, positions C1, D1, G1 and H1 for the two runs. The numbers of sequences for the other wells correspond to indexing mistakes due to false index-pairing due to mixed clusters during the sequencing (see Table  1 ).
% of rodents positive for both PCR replicates Run 1 Run 2 Table  S7. Number of mismatches between PCR forward and reverse primers and 41,113 bacterial 16S rRNA V4 sequences of 79 zoonotic genera. Bacterial genera were selected according to the inventory of Taylor et al  [1]  and sequences were extracted from the Silva SSU database v119. Numbers of mismatches > 3 correspond to sequences of bad quality from diverse taxa. The number of primer mismatches in the 10 bases of the 3' side was ≤ 2 for 99. 93% of the reference sequences.
Acknowledgments This study was funded by the  French National Institute for Agricultural Research (INRA) Meta -omics and microbial ecosystems metaprogram ( Patho-ID  project: Rodent and tick pathobiomes), the  ANR ENEMI  ( ANR-11-JSV7-0006 ) and supported by the  COST Action TD1303  (EurNegVec). We would like to thank  Virginie Dupuy  for extracting DNA from bacterial cultures as well as  Julie Sappa  from  Alex Edelman & Associates ,  Jessie L Abbate  and  Petra Villette  for improving the English writing. Analyses were performed on the CBGP HPC computational platform. The funders had no role in study design, data collection and analysis, the decision to publish, or preparation of the manuscript.
Thresholds T CC : corrects for cross-contamination T FA: : corrects for false index-pairing bacterial. The remaining reads were grouped into operational taxonomic units (OTUs) with a divergence threshold of 3%. Bioinformatics analysis identified 13,296 OTUs, corresponding to a total of 7,960,533 sequences in run 1 and 6,687,060 sequences in run 2. Data filtering Taxonomic assignment of sequences. We used the Bayesian classifier (bootstrap cutoff = 80%) implemented in mothur with the Silva SSU Ref database v119 [43] as a reference, for the taxonomic assignment of OTUs. The 50 most abundant OTUs accounted for 89% (min: 15,284 sequences; max: 2,206,731.
are intracellular fastidious hemotropic gram-negative organisms identified in a wide range of domestic and wild mammals and transmitted by arthropods. Several rodent-borne Bartonella species have emerged as zoonotic agents, and various clinical manifestations are reported, including fever, bacteremia and neurological symptoms [84]. Borrelia crocidurae (100%) 21 0 8 6 duttonii (100%) recurrentis (100%) Ehrlichia khabarensis (100%) 40 0 12 8 Mycoplasma haemomuris (96%) 28 42 30 1 OTU_1 Mycoplasma sp. novo (100%) 0 0 0 90 OTU_2 GenBank AB752303 Mycoplasma haemomuris (93%) 93 40 1 1 OTU_3 Mycoplasma coccoides (96%) 0 0 0 18 OTU_4 Mycoplasma coccoides (95%) 3 8 0 0 OTU_5 Mycoplasma coccoides (97%) 3 13 0 0 OTU_6 Orientia chuto (100%) 0 2 46 0 Orientia is a genus of obligate intracellular gram-negative bacteria tsutsugamushi (98%) found in mites and rodents.
We performed runs of 2 x 251 bp paired-end sequencing, which yielded high-quality sequencing through the reading of each nucleotide of the V4 fragments twice after the assembly of reads 1 and reads 2. The raw sequence reads (. fastq format) are available in the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. m3p7d [42]. Two Illumina MiSeq runs were conducted. Run 1 included the PCR products (two or three replicates per sample) from wild rodents collected in north Senegal (148 Mastomys erythroleucus and 207 Mus musculus) plus the positive controls and the negative controls. Run 2 included the PCR products (two replicates per samples) from wild rodents collected in south Senegal (73 total of 864 PCR products per run. PCR products were pooled by volume for each 96-well PCR microplate: 4 µL for rodents and controls, and 1.
EST sequencing, EST-SSR identification, and primer design In this study, pulp and seed of the cupuassu genotype (Coari 174) (Theobroma grandiflorum [Willd. ex Spreng. ] Schumm. ; see also Plant material) grown at the experimental station of Embrapa Amazonia Oriental (Belém, Pará, Brazil) were sequenced using the 454 platform / Roche Applied Sciences. The raw sequences were trimmed and assembled using the est2assembly  [39]  and Mira  [40]  software resulting on 8,330 contig sequences. The sequences are available on the cupuassu restricted databases at http://lbi. cenargen. embrapa. br/cupuacu/. The ESTs were screened for the presence of SSRs using the MISA software  [41]  according to the following criteria: i) nucleotide motif/minimum number of repeats of 1/10, 2/6, 3/4, 4/2, 4/3, 5/3, 6/5, 7/2, 8/2 and 9/2; and ii) maximum difference between two SSRs of 100 bp.
For putative function determination and annotation, EST sequences containing SSRs were compared with the public sequence database using BLASTX against the non-redundant (NR) protein database (http://www. ncbi. nih. gov/BLAST/; [42] ) and with the cacao protein-coding sequence database (http://cocoagendb. cirad. fr; [19] ). Alignments showing similarity with an expected value (e-value) 1. 10 -7 were considered significant. The GO annotation for the ESTs containing SSRs were performed using Gene Ontology Consortium tools (http://www. geneontology. org/) [43]  and then manually inspected and classified as previously described  [44]. The primers were designed using the Primer3 software (http://primer3. wi. mit. edu/) according to the following criteria: i) amplicon size of 100-300 bp; ii) primer length of 17-23 bases; iii) melting temperature of 56-60°C; and iv) GC content of 40%-60%.
Genetic diversity and statistical analysis The amplified SSR DNA bands representing different alleles were scored on the different genotypes. The genetic diversity parameters were assessed in terms of observed number of alleles (Na), observed heterozygosity (Ho), and expected heterozygosity (He) using the Genetic Data Analysis software  [46]. Polymorphic information content (PIC) was obtained for each locus as previously described  [47]  and null alleles were examined using Micro-checker software, v. 2. 2. 3  [48]. Factorial Component Analysis (FCA) was made with the GENETIX software  [49]. Correlation test between molecular data and pulp quality or resistance to witches' broom disease was realized using the SAS program  [50]. In silico comparison of Theobroma grandiflorum loci with Theobroma cacao var.
Transferability of EST-SSRs The transferability of the cupuassu EST-SSRs to T. cacao was analyzed by cross-species amplification. From the 22 pre-selected EST-SSRs (polymorphic or not in cupuassu; Fig  1 ), 17 amplified cacao DNA, which corresponds to a transferability rate of 77% (Table  4 ). The amplifications were within the expected size, and 14 of the 17 cupuassu SSRs were polymorphic in cacao (Table  4 ). From the 11 EST-SSRs polymorphic in cupuassu, 8 were transferable to cacao and 6 were also polymorphic in this species (Tables  3  and 4 ). The 11 polymorphic locus of cupuassu were also compared to the cacao genome database (cacao var. Criollo) and several homolog sequences were encountered (Table  5 ). Eight cupuassu loci presented polymorphism when compared to cacao: six of them presented the same repeat motif, but with less repeats (c2723, c5718, c70, c180, c193B, c203B) for at least one homolog sequence, and 2 of them did not present the repeat motif (c3202/3202B, c733; Table  5 ).
It is important to highlight that we produced the first EST database from cupuassu as well as the first EST-SSRs for this species. In cacao, more than 200,000 ESTs from different plant genotypes and organs submitted or not to different biotic and abiotic stresses  [18, 44, [51] [52] [53] [54] , and more than 2,000 SSRs (whose 1631 [81%] were EST-SSRs) were already obtained (S3 Table ) whereas in cupuassu, only genomic SSR were previously found (unpublished data, R. M. Alves). Furthermore, ESTs for use in molecular studies related to pulp or bean quality from the Theobroma genus are rare  [18, 53]. Under these conditions, our results are highly relevant due to the large amount of ESTs generated  (8, 330)  as well as the functional data associated to some of the EST-SSR identified (Fig  3 ).
Acknowledgments We thank  Raner José Santana Silva  ( Embrapa Cenargen ) for technical support in RNA extraction prior NGS,  Dr. Fernanda Amato Gaiotto  and  Horley Vitória Ribeiro (UESC)  for supplying reagents for SSR analysis,  Didier Clement  ( Cirad ) for kindly providing cacao DNA, and  Dr. Claudia Fortes Ferreira  ( Embrapa CNPMF, Brazil ) for English language revision.
Illumina sequencing and data processing Total RNAs (including small RNAs) were extracted using an RNeasy Plant Mini Kit with RLT and RWT buffers (Qiagen, France). DNase treatments were performed using the RNAeasy-free DNase set (Qiagen, France). Two bulks of total RNAs corresponding to a mix of total RNAs from the 10 accessions of the two species were used for sequencing. Purified small RNA sequencing was performed by Eurofins/MWG Operon (Germany) on an Illumina Hi-seq 2000 using the Tru-Seq™ SBS v5 sequencing kit. The complete raw dataset is available in NCBI Gene Expression Omnibus repository [GEO: GSE48346]. The raw data were trimmed by removing adapter sequences and low quality sequences using CutAdapt  (Martin 2011). All the trimmed reads ranging from 18 to 28 nucleotides were clustered and mapped to O.
sativa ssp japonica cv Nipponbare genome (MSU release version 7; http://rice. plantbiology. msu. edu/) using BLAST  (Altschul et al. 1990 ) (Additional file 4) in order to evaluate library quality. The 18-28 nucleotide reads were them annotated through a filtering process using successive hierarchical BLAST (Additional file 4) versus (in order) miRBase v17. 0 (Kozomara and Griffiths-Jones 2011), Rfam v7, a home-made repeat database (successive curated concatenation of RetrOryza, RepBase, TREP and TIGRRepeats), CDS then gene features (i. e. introns and UTRs) from Oryza sativa ssp japonica var Nipponbare MSU v7. 0 annotation, and finally the MSU v7. 0 rice genome for the sequences not similar to miRNAs, non coding RNAs, Repeat-associated sequences and genes. BLAST filtering was done using Oryza genus data form the different databases (i.
e. mainly O. sativa). Mapping of the sequences to the O. sativa MSU v7. 0 reference genome was done for the last three steps of our annotation pipeline. This genome was used as a reference because (i) of its equal divergence to the two species we analyzed (avoiding favoring one species in mapping analyses rather than the other one), and (ii) because of the larger resources in terms of annotation, expression data and genomic information available for this species. The BLAST and post-filter parameters used were probability of 85 %, e-value of 10 -3 , on a size of 85 % of the reads (minimum size of 16). The same BLAST parameters were used throughout the analysis. Mapping from O. glaberrima and O. barthii small RNA sequences was then compared and filtered using a series of homemade Perl scripts (available on demand).
8. 1  (Bailey and Elkan 1994). Then, the loci sequences were used as a BLAST database to add other 21-nt sequences that were not identified originally as phased (in order to by-pass the genome divergence), in two pass: first we select only new sequences with 100 % of identity on 85 % of their length (minimum HSP length of 16), then we change the threshold to 85 % of identity on 85 % of their length (min HSP length of 16). Finally, statistical tests of all the processed data were performed using g-test and a fixed p-value of 10 -3. Depending on the experiment, the degree of freedom was adjusted but was generally 1. All the calculations were performed using homemade Perl scripts and CPAN statistical modules.
The resulting DNA fragments were used directly as templates for synthesizing antisense ribo-probes incorporating UTP-digoxigenin (Roche) as the label in conjunction with a T7 Maxi Script kit (Ambion). For miR2118, PH12 precursor and phasiPH12-1 detection, 0. 02 μM of a 5′ digoxigenin-labeled LNA probe complementary to the target (see Additional file 1: Table  S3  for primer sequences) was used. In situ hybridization experiments were carried out as described by  Adam et al. (2011). Detection was performed using the Vector Blue Alkaline Phosphatase Substrate Kit III (Vector Laboratories). Slides were observed and photographed by Evolution MP5. 0 colour Media Cybernetics camera in conjunction with a Leica DMRB microscope and images were processed using Photoshop CS6.
Acknowledgments We thank members of the lab for their help with plant care and RNA extractions. We thank members of  IRD-Montpellier bioinformatics platform  for their technical support. We also thank colleagues from  IRD-Montpellier  for critical reading of the manuscript. This project was jointly supported by  Agropolis Fondation  (through the  « Investissements d'avenir » programme  ( ANR-10-LABX-0001-01 )) and  Fondazione Cariplo  under the reference ID EVOREPRICE  1201-004. This work was also supported by the  Institut de Recherche pour le Développement (IRD) institutional funding, Agropolis foundation  ( ARCAD  project for post-doctoral fellowship of SDM),  Vietnamese government  (program 322 to TKN) and the  Global Rice Science Partnership (GRiSP)  scholarship program (TKN).
We used CodonCode Aligner software (CodonCode Corporation, Dedham, MA, USA) to edit sequences, compared them to the GenBank database content with BLAST, and deposited them in GenBank under accession numbers KX255747 -KX255751 (Table  1 ). Species identification was confirmed with the BOLD identification engine  [18]. Details about molecular identification of host fish are provided briefly in the description of the monogenean species.
COI sequence of monogeneans We used a QIAmp DNA Micro Kit (Qiagen) to extract DNA from whole monogenean specimens. The specific primers COI-ASmit1 (forward 5 0 -TTTTTTGGGCATCCTGAGGTTTAT-3 0 ) and COI-ASmit2 (reverse 5 0 -TAAAGAAAGAACATAATGAAAATG-3 0 ) were used to amplify a fragment of 424 bp of the COI gene  [21]. The PCR reaction was performed in 20 μl, containing 1 ng of DNA, 1× CoralLoad PCR buffer, 3 mM MgCl 2 , 0. 25 mM dNTP, 0. 15 μM of each primer, and 0. 5 units of Taq DNA polymerase (Qiagen). Thermocycles consisted of an initial denaturation step at 94°C for 2 min, followed by 37 cycles of denaturation at 94°C for 30 sec, annealing at 48°C for 40 sec, and extension at 72°C for 50 sec. The final extension was conducted at 72°C for 5 min. Sequences were edited with CodonCode Aligner software (Codon-Code Corporation, Dedham, MA, USA), compared to the GenBank database content with BLAST, and deposited in GenBank under accession numbers KX255741 -KX255746 (Table  1 ).
Differential diagnosis. Pseudorhabdosynochus oliveri is close to P. beverleyburtonae, P. sosia and P. hayet n. sp. in terms of general morphology of body, presence of two circular innermost rows of rodlets in squamodiscs and general morphology of the sclerotised vagina. It is separated from the three others by the development of its primary canal, which is wide, vs. thin, and the small primary chamber, vs. large in the other three species. Molecular identification of fish via DNA barcoding: Four specimens from Tunisia were sequenced (Table  1 ), including three new and one already sequenced as KT805240  [31]. All sequences had 99-100% similarity with sequences already identified as M. costae in GenBank, confirming the morphological identification of the host. Site of infection: Gills Type-locality: Off Sfax, Tunisia.
5%). The genetic distance between P. sosia and P. hayet was 13-14%, thus lower than the differences observed between other Pseudorhabdosynochus species in our dataset (19-26%), but much higher than the differences between specimens of P. cyanopodus found on two different hosts  [44]. The literature on monogenean COI sequences and their interspecific distances is scarce. Vanhove et al. (  2013 ) have emphasized the difficulties encountered in using COI for the differentiation of species in various groups of flatworms, including monogeneans; however, they cite a case in which two valid diplectanid species were divergent by only 3. 2%  [45]. In other flatworms (geoplanid triclads), such as Microplana spp. , intraspecific variation was up to 4%, and interspecific variation was 19%  [46] , and a difference of 4.
Introduction Product Lifecycle Management (PLM) is a combination of solutions and techniques which enable the efficient management of information through various stages of product lifecycle. These solutions have also tackled the heterogeneity and complexity of data and the challenges in tracking the evolution and the modification of information. Nowadays, with the support of ICT (Information and Communication Technology), PLM databases are becoming more and more complex: the amount of data, the diversity of data types, and especially the dependencies among technical information  [1]. Furthermore, new data are always generated and added into database by users of PLM system during their quotidian activities. Normally, these data are related to an individual and created for a concrete purpose.
Therefore, they cause the difficulties in data management and knowledge sharing because of their heterogeneity and personality. As consequence, in the context of complex, heterogeneous, and intertwined data resource, a major requirement for an efficient PLM system is to provide users the ability to query data from database and then to share them in community. In recent years, ontology has been widely used in the scientific community as a promising solution for knowledge sharing. By definition, ontology allows expressing a conceptualization not only in natural language but also in a format that can be interpreted and used by software agents. Therefore, it enables the sharing and the reuse of knowledge. Our aim is to develop an ontology-based knowledge sharing platform, where the understanding of changes and evolutions in the dependencies and linkages among data will be assimilated to all users (nontechnical, coming from different areas…).
The main objective of this platform is to enhance the data exploitation: in data querying, in data visualization, in technical information sharing and furthermore, in data mining. This article presents our first results on an ontology graph-based query interface which allows performing queries. A case of study will be illustrated in Bio-Imaging domain in which researchers need to have a good understanding of data model as well as the dependencies among data in order to interrogate the database. The rest of paper is organized as follows: In section 2, we present the literature in PLM systems as well as some techniques in the knowledge sharing. Next, in section 3, we propose an approach for ontological model construction. Section 4 describes an ontology graph-based query interface as an application in the context of Bio-Imaging.
[4]  adopted PLM concepts to handle the complexity, heterogeneity and characteristics of Bio-Medical Imaging (BMI) data. However, traditional PLM systems are not flexible as requires by research practices, a requirement of actual works is to enable nontechnical users (Bio-Imaging scientists) to query data from the database. In fact, to query database, users need to understand the data model and the dependencies among data in the database. Furthermore, the complexity of the dependencies increases gradually because of new added data. These new data are usually related to a predefined context and are the work results of a group of individuals, therefore, the others don't understand the nature of these data as well as relationships with existing ones. As consequence, it is required to assimilate the understanding about data dependencies to all users of system for the purpose of database exploitation.
Ontology-based knowledge sharing in PLM Ontology has been used in to share knowledge in various domains  [8] ,  [9]. In the domain of PLM, many authors have also used it as a solution to tackle the issues in technical information interoperability, knowledge sharing and knowledge reuse. A knowledge layer has been added to commercial PLM systems to solve semantic interoperability problem of heterogeneous data and to fully utilize all available information  [10]. In that approach, ontology has been used as a common language across several domains and information sources in manufacturing industries. An ontology model has been built to explicitly define relationships among products, processes and resources, and make this information accessible through a web services. In the same way, MEMORAe  [11]  has been integrated in PLM system in order to enable the knowledge sharing  [12].
Methodology We initiated by interviewing the scientists at GIN (Neurofunctional Imaging Group) -a laboratory in domain of Bio-Imaging, where the growth and heterogeneity of data resources have been handled by using PLM solutions in Teamcenter (Siemens). During the interview, the scientists have been provided a set of questions related to their quotidian activities. The purpose of these questions were to identify the difficulties of users in manipulating with the information system as well as their need and requirements for the new PLM platform. The interview showed that each researcher has his own individual studies which requires different dataset. Therefore, it is important to enable users to query database themselves. Furthermore, scientists generate data dynamically by themselves and they want to store them in database.
However, this task demands a deep understanding of complex dependencies among data in database, and concepts in the data model. To assimilate this understanding, we decided to use ontology as solution because of its formal expression as well as extensibility and customizability capacity. Based on the approach presented in  [13] , the construction of ontological model initiated by the data model analysis. The classification (Fig. 5 ) has been built based on the data model. BMI data have been classified into branch, classes and subclasses. The classification allows a specific class to be added to a generic item (object in the data model). In comparison with the data model, the classification and its attributes are easier to modify for user than objects attributes, it therefore adapts the flexibility requirement of database.
Query making and query execution process The query generated from the query interface will then be sent to and executed in server. By now, we use TCXquery  [14]  as a Query Processor that makes PLM content in the database as usable as XML document. Therefore, the query defined by users is transformed into XQuery language. An extract of query in xQuery language is cited as bellow. The xPath (through all objects) is generated automatically by using a graph pathfinding algorithm. return Teamcenter:Query($teamcenter,$query)[. /F_IMAN_master_fo rm/GIN4_StudySubMaster/@gin4_gender = "M" and. /F_IMAN_ master_form/GIN4_StudySubMaster/@gin4_datebirth >= "1970"]/F_GIN4_rel_ExamResIMA/GIN4_ExamRes/F_GIN4_rel_A cquisition/GIN4_Acquisition[. /@name = "AcqName" and. /@examDate <= 01. 12. 2014]/F_GIN4_rel_ProcessingRes/ GIN4_ProcessRes[.