dataesr/scientific-paragraphs-categorization huggingface dataset:
Extraction des données La ressource principale de notre corpus est une collection de brevets du domaine de la pharmacologie auxquels sont associés des citations bibliographiques. La répartition des brevets dans les classes du domaine suit le code de la classification CIB (Classification Internationale des Brevets) qui est un système hiérarchique de symboles indépendants de la langue pour le classement propres à chaque domaine technologique. Le but de la CIB est de faciliter la recherche d'information en matière de brevets et d'aider à recenser les technologies existantes et nouvelles. La CIB se base, dans la tâche de la classification, sur un système de codage hiérarchique qui comporte plus de 70 000 catégories, avec des couches de plus en plus détaillées. Un exemple extrait de ce système de codage est le suivant : Nous avons interrogé la base de données Medline 2  qui est spécialisée dans le domaine de la médecine et qui, d'autre part, bénéficie de mises à jour régulières.
Volume 16 -n° 1/2013 de l'algorithme Apriori  (Agrawal et Srikant, 1994). Le fonctionnement de cet algorithme peut être décomposé en deux phases : 1) Recherche des tous les « patrons » ou itemsets fréquents, qui apparaissent dans la base de données avec une fréquence supérieure ou égale à un seuil défini par l'utilisateur, appelé Min_Sup. "## $ = (% ∪ ') , ( ) *+),-= (%|') où (% ∪ ') indique la probabilité qu'une transaction contienne à la fois X et Y, et ( 2) Génération, à partir de ces patrons fréquents, de l'ensemble des règles d'association ayant une mesure de confiance supérieure ou égale à un seuil défini par l'utilisateur, appelé Min_Conf.
A -NECESSITES COURANTES DE LA VIE A-61-SCIENCES MEDICALE OU VETERINAIRE ; HYGIENNE A-61-K-PREPARATIONS A USAGE MEDICAL, DENTAIRE OU POUR LA TOILETTE A-61-K-6-PREPARATIONS POUR LA TECHNIQUE DENTAIRE …. 6DN. Volume 16 -n° 1/2013 Dans notre cas, les notices brevet au format XML sont au nombre de 6387 réparties dans les 15 classes de la catégorie A61K (préparations à usage médical…). Comme l'illustre la figure 1, nous commençons par l'extraction des références à partir des brevets. Grace à des robots web, nous interrogeons une base de données de publications pour extraire les notices relatives aux références collectées. Chaque notice est ensuite étiquetée par la classe du brevet citant. L'ensemble des notices étiquetées représente finalement notre corpus d'apprentissage.
, 2009 ; Artus, 2009). Il ressort que l'équilibre du marché de la transmission nécessite de développer le rachat d'entreprises de petite ou moyenne taille. Le rachat de PME recouvre deux types de pratiques : la reprise par une personne physique et la croissance externe d'entreprise. Dans la continuité des travaux qui soulignent la proximité des processus de croissance interne et de création d'entreprises d'une part, et de croissance externe et de reprise par une personne physique d'autre part  (Bruyat, 1993 ; Paturel, 2007) , notre recherche traite des deux configurations, le terme « rachat d'entreprises » désignant dans cet article reprise par une personne physique et croissance externe d'entreprise. Notons que les bases de données nationales qui recensent les opérations de rachat d'entreprisesle fichier SIRENE de l'INSEE par exemple -ne permettent pas de distinguer les opérations de croissance externe des créations d'entreprises holdings dont l'objectif est de financer et de réduire le risque des reprises par des personnes physiques.
Cela supposait d'examiner, dans la durée, toutes les déclarations et actions de Jacques Glassmann. À cette fin, nous avons constitué une base de données à partir de tous les verbatim de l'ancien footballeur que nous avions repérés dans la presse sur la période 1993-2011. Cela nous a permis d'identifier quatre périodes bien distinctes dans la trajectoire post-alerte de Jacques Glassmann et de repérer les éventuels signes de sa résilience individuelle. Pour ce faire, nous avons mobilisé la grille construite grâce à notre revue de littérature. Le tableau N°3 ci-dessous apporte des précisions complémentaires sur ce travail de codage : « Je suis révolté de la façon dont l'honnêteté de ce sportif a été sanctionnée».
rouge, les perceptions/actions négatives :Pour affiner ce travail de codage, il nous a semblé opportun de distinguer différentes catégories d'acteurs (protagonistes de l'affaire VA-OM, dirigeants de l'US Valenciennes, dirigeants du football français, dirigeants de clubs de football professionnels français, footballeurs professionnels, supporters de football (VA et hors VA), dirigeants de l'UEFA et de la FIFA, monde du football amateur, Procureur de la République, célébrités extérieures au monde du sport, journalistes et opinion publique), car il nous est très vite apparu que les discours et actions relatifs à Jacques Glassmann dépendent du type de parties prenantes. Sur cette base, nous avons constitué une base de données sous EXCEL, qui nous a permis d'observer des récurrences, période par période, puis de repérer des tendances de fond dans l'évolution des discours de chaque catégorie d'acteurs.
Deux autres initiatives sont notables dans la mobilisation et la structuration d'une arène internationale dans les années 1990 : BioNET et le Global Biodiversity Information Facility (GBIF). T. Jones, taxonomiste anglais, lance dès 1991 l'idée d'un réseau international de taxonomistes qui se concrétisera en 1993 avec la création à Londres de BioNet International 25. Son idée est de compenser le manque de compétences des pays du sud en la matière par la mise en réseau et le partage des informations aux échelles régionales sur la base de coopérations nord-sud et sud-sud  (Jones, 1995). Par ailleurs, le GBIF, organisation indépendante basée à Copenhague est créé en 2001 à l'issue des recommandations faites à l'OCDE par son Biodiversity Informatics sub-group, afin de remédier à l'éparpillement et l'hétérogénéité des bases de données 26.
Une systématique sans systématicien ? Première limite à ce renouveau : très peu de chercheurs se sentent et se présentent comme « systématiciens », car les recrutements et évaluations dévalorisent cette identité ; même la majorité des chercheurs de la SFS ne 60 Le MNHN participe aussi à d'autres projets européens sans en être le leader, comme SYNTHESYS (Synthesis of Systematics Ressources), pour faciliter l'accès aux collections, BIOCISE (Biological Collection Information Service in Europe, 1997-99) devenu BIOCASE (The Biological Collection Access Service for  Europe, 2001 Europe, -2004) )  pour donner seulement certains projets financés par l'Union européenne en faveur de l'informatisation et l'interopérabilité des bases de données. Le MNHN participe aussi aux projets européens : Species 2000  Europa (2003 Europa ( -2007)) , et il est aussi coordonnateur du projet européen CRESCENDO.
Une mobilisation toujours plus européenne et mondiale Troisièmement, les grands programmes financés par les Américains à partir du Systematic Agenda 2000 ont pris tout de suite une échelle globale et ont été suivis dans la plupart des pays, en phylogénétique (par exemple Assembling the Tree of Life), pour les descriptions d'espèces et les inventaires de biodiversité et de collections (Planetary Biodiversity Inventory), les bases de données (Species 2000) et pour la formation de jeunes taxonomistes (Partnerships in Enhancing Expertise in Taxonomy). Avec les grandes initiatives de type GBIF, et autres programmes liés aux bases de données et au barcode, les initiatives de mobilisation de la systématique s'organisent de plus en plus au niveau global, moins au niveau national. Enfin, la géopolitique de la systématique a beaucoup changé, en particulier avec l'émergence des BRICS, même si d'autres pays du Sud restent totalement marginalisés (à l'exception de l'Afrique du Sud).
) et 210 -Mais pas pour tous = activité principale ni « professionnels » ; adhésion volontaire avec certains biais parisiens… (min. ) -Donne un état de la mobilisation, de l'auto-inscription disciplinaire. -À partir enquête SFS de 1994 avec 143 réponses dont 42 du MNHN (jugé représentatif d'une population évaluée à 350)… Enquête Sigogneau et puis nouvelles enquêtes en 1998 et 2000 : 317 réponses). Barriel (1998) et Étude Académie des Sciences 317 (Tillier, De Wever, Gros et al. , 2000) Étude Malécot pour Ministère 2007 250* -Mais une simple évaluation approximative à partir du croisement des différents chiffres déjà existants et de sa connaissance du milieu. International World Taxonomist Database de la société ETI2 145* français -Mais seulement ceux qui sont rattachés soit au MNHN soit à l'INRA (les deux institutions françaises partenaires).
To determine the amplification kinetics of each product, the fluorescence derived from the incorporation of EvaGreen into the double-stranded PCR products was measured at the end of each cycle using a SsoFast EvaGreen Supermix 2X Kit (Bio-Rad). The results were analyzed using Bio-Rad CFX Manager Software 3. 0 (Bio-Rad). The uvrD gene was used as a reference for normalization, in particular because transcription of uvrD is fairly stable in bacteria exposed to antibiotics even at relatively high concentrations  [47].
SdB and CB are supported by the  French Cystic Fibrosis Foundation (VLM) ,  GDR3171 ,  ANR  grants:  TWOCOMPNET ANR-05-MIIM-040-01 ,  REGALAD ANR-14-CE09-0005-02  and  ERA-NET ADHRES 27481. GC was supported by  Aix-Marseille University. LR was supported by the  French Cystic Fibrosis Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All these results indicate the clinical utility and application of NGS profiling of tumours in the clinic. The following guidelines aim to establish consensus standards for somatic diagnostic testing rather than germline testing, specifically for identifying and reporting mutations in solid tumours. Consensus standards will include the testing strategy, implementation of testing within clinical service, sample requirements, data analysis and reporting of results. In addition, laboratories providing NGS clinical diagnostic testing must adhere to recognised International Standards  [8, 9]  and staff must be appropriately qualified, trained and competent.
Furthermore, a recent data suggests that using EDTA rather than hydrogen chloride (HCL) to decalcify bone samples prior to embedding may improve the yield of usable nucleic acid for downstream NGS profiling  [34]. Whatever system is selected, it is important to recognise that DNA and RNA yield and quality can vary between sample types. Nucleic acids can be quantified spectrophotometrically (e. g. NanoDrop), fluorometrically (e. g. Qubit) or by.
Limit of detection (LOD) The lowest allele frequency to which the assay can detect with an acceptable quality to enable confidence in a result i. e. the LOD is within the reporting range, (establishes the detection limit for sequence variants) Adapted from  [21] ,  [22]  and  [49]  quantitative PCR (qPCR). Combined quantification and fragment analysis can be performed by capillary electrophoresis of fluorescently labelled nucleic acids (e. g. BioAnalyzer). Assessments using PCR have the advantage of better prediction of the general amplifiability of the material, the key factor for downstream NGS analysis. Fluorometric assessments correlate fairly well with qPCR data and are generally recommended for use prior to NGS analyses along with qPCR, while spectrophotometric assays measure all nucleic acids and therefore overestimate double-stranded DNA concentration.
Sample misidentification The verification of sample identity is essential in order to ensure data integrity and validity of conclusions and to assign results to the correct patient. The NGS workflow is a multistep process with inherent risks of sample mix-up, therefore procedures must minimise the risk of the misidentification, from the operating theatre to the reporting of the result. To this end, barcoding may be used and, if so, should be introduced into the pathway at the earliest opportunity. Panels can be designed to incorporate SNPs that allow molecular identification of patients and allow laboratories to monitor the incidence of sample switching by detecting chromosome X-and Yspecific sequences  [35]. With different options available, ultimately it is important to perform a full end-to-end analysis of the risk of misidentification of the samples prior to introducing NGS and also at regular intervals during operation.
Next-generation sequencing quality scores NGS quality scores will differ between sequencing platforms. Most sequencing platforms use DNA sequencing quality scores based on the Phred quality scoring system  [36, 37]. Phred quality scores (Q scores) are logarithmically related to the probability of base calling errors. Historically, Phred scores have been used to determine the quality of Sanger sequencing; after initial base calling, Phred calculates parameters related to peak shape and resolution in relation to the base call, and a quality score is then assigned. Quality (Q) scores can range from 4 to 60, with higher values corresponding to higher quality (Table  3 ). NGS quality metrics vary from Sanger sequencing methods as there are no electropherogram peak heights to refer to. Parameters relevant to the particular sequencing platform are analysed for accuracy; using quality score tables, a quality score is calculated for NGS data which possesses an equivalent meaning to the Phred scores produced when using Sanger sequencing platforms.
Data analysis of sequence reads The combination of bioinformatics tools used for processing, aligning and detecting variants in NGS data is commonly referred to as the data analysis or the bioinformatics pipeline. Currently, no single algorithm can analyse all types of sequence variants; therefore, different software tools often are applied to NGS data in order to answer the clinical question. The difficulty in designing uniform and transferable pipelines ultimately requires each laboratory to validate the bioinformatics pipeline in accordance with the types of variants to be reported. Commercially available software packages especially designed for the purpose of analysing amplicon-based NGS data have a significant advantage over individual and homemade bioinformatics solutions.
Since these variants are usually present at very low frequency, such artefacts are unlikely to interfere with data analysis if enough unique DNA molecules are available during NGS library enrichment. However, if the number of unique DNA molecules is limited or if the aim is to detect low-level variants, these artefacts will interfere with data analysis. Since deaminations are introduced during processing of the sample ex vivo, these variants are not copied into the DNA, meaning that the variant is present on only one sense or antisense DNA strand. To facilitate detection of fixation artefacts, several techniques independently enrich and/or sequence both the sense and antisense DNA strands e. g. molecular inversion probes and HaloPlex and Duplex sequencing. Treatment with uracil-DNA glycosylase prior to PCR amplification markedly reduces these artefacts without effecting true mutational sequence changes  [41, 42].
Multiple sources of information are required to describe variants of uncertain significance (VUS). When sequencing DNA from tumour tissue, an effective way to discriminate somatic variants from germline variants is to sequence, in parallel, reference DNA from non-neoplastic tissue from the same person. Otherwise, the first step is to retrieve the population frequency of the VUS from the dbSNP database to discriminate common variant polymorphisms from other unknown variants (UV). Although there is no clear cut-off, UVs with a population frequency >1% are usually considered to be polymorphisms. Other databases e. g. Catalogue of Somatic Mutations in Cancer (COSMIC) or ClinVar (which aggregates information about genomic variation and its relationship to human health) can provide information regarding frequency and biological function of variants in disease.
Further classification of VUS can be accomplished using algorithms that consider nucleotide conservation throughout evolution e. g. phastCons and phyloP and physicochemical difference between wild-type and mutated amino acids i. e. Grantham distance, or predict the possible impact of mutations on protein structure and function e. g. PolyPhen, SIFT, and Align GVGD. Examples of software solutions, databases and tools that facilitate variant classification and interpretation are presented in Supplementary Table  2.
(ST2). Based on the available data, e. g. variant frequency, predictions, knowledge bases and functional studies, a variant can be described using standard terminology e. g. benign, likely benign, of uncertain significance, likely pathogenic or pathogenic  [46]  or activating, neutral/VUS and inactivating  [47]. Overall, the advantage and limitations of existing tools must be objectively evaluated by clinical or pathological laboratories according to their specific sequencing needs. For NGS data analysis, the acceptable thresholds for data quality and depth of coverage should be determined during the assay development and validation process. Quality thresholds should include metrics such as base calling quality, coverage, allelic read percentages, strand bias and alignment quality. Importantly, the thresholds validated for the detection of germline alterations cannot be transposed for the identification of somatic alterations in tumour samples  [48].
The mainstay of assessing the ability to action a variant should be via the usual literature searches. Due to the fast pace of cancer research and therapy development, constant vigilance and regular update of searches is required to ensure the advice given on reports is up to date. There is an increasing number of online resources such as mycancergenome. org which contain lists of tumour types, genes and variants and include assessments of clinical impact. These resources can be very useful, but before such a source is used, it is important to ensure that it is properly curated, referenced and regularly updated. In this regard, recent guidance from the FDA has suggested considerations for determining whether a genetic variant database is a valid source of scientific evidence for supporting the clinical validity of genotype-phenotype relationships that may assist laboratories in selecting appropriate data sources  [49].
The guidance suggests users seek databases that implement decision matrices with published details supporting each variant's interpretation as well as having documented standard operating procedures for outlining the process for curation of evidence and that demonstrate that multiple sources for evidence are used. When assigning variants to domains, it is essential that they are interpreted in the context of the tumour/tissue type, as the are used. For reference purposed, the HUGO Gene Nomenclature Committee (HGNC) approved that gene symbols should be used at least once. In addition, it is strongly recommended to include genomic coordinates in order to ensure uniform bioinformatics analysis and consistent documentation of identified variants. If genomic coordinates are used, then the appropriate genome build must be stated.
Conflict of interest Deans, ZC has received financial support for educational programmes from Astra Zeneca, Roche and Qiagen and is a member of advisory boards for Amgen, Astra Zeneca, Pfizer, Merck Serono and Roche. Cree, I has received grant income from Novartis and speaker fees from Welcome Trust and Novartis. He is on the Advisory Board for Life Technologies/Thermofisher and Novartis. Dequeker, E has received research grants from Pfizer and Amgen. Edsjö, A has received research grants and speaker Honoraria from Astra Zeneca and Amgen. Hall, JA owns stock in Vivactiv Ltd. Ligtenberg, M has received research grants from: research project MLDS 13-19 and research project KWF KUN2015-7740 and has Honoraria for speaking at symposia; consultation and research grant with Astra Zeneca and is a member of the onconetwork consortium of Life Technologies.
This strain contained no plasmids; thus, the gene(s) responsible for PDI is chromosomally located. In addition to the effector molecule, a transporter protein would be necessary to secrete/transport the effector to the target cell. We examined the genome for transporters and detected 274, with most belonging to the ABC superfamily of transport proteins. Nucleotide sequence accession number. The genome sequence has been deposited at GenBank under accession no. JACI00000000.
Biological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, the Atlas of Cancer Signaling Network (ACSN) being an example. All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe ACSNMineR for calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSN molecular pathways gene sets, but can use any gene set encoded as a GMT file, for instance sets of genes available in the Molecular Signatures Database (MSigDB). We also present RNaviCell, that can be used in conjunction with ACSNMineR to visualize different data types on web-based, interactive ACSN maps.
We illustrate the functionalities of the two packages with biological data taken from large-scale cancer datasets. We have recently released the Atlas of Cancer Signaling Network (ACSN), a web-based database which describes signaling and regulatory molecular processes that occur in a healthy mammalian cell but that are frequently deregulated during cancerogenesis  (Kuperstein et al. , 2015). The ACSN atlas aims to be a comprehensive description of cancer-related mechanisms retrieved from the most recent literature. The web interface for ACSN is using the NaviCell technology, a software framework dedicated to web-based visualization and navigation for biological pathway maps  (Kuperstein et al. , 2013). This environment is providing an easy navigation of maps through the use of the Google Maps JavaScript library, a community interface with a web blog system, and a comprehensive module for visualization and analysis of high-throughput data  (Bonnet et al.
, 2015). In this article, we describe two packages related to ACSN analysis and data visualization. The package ACSNMineR is designed for the calculation of gene enrichment and depletion in ACSN maps (or any user-defined gene set via the import function), while RNaviCell is dedicated to data visualization on ACSN maps. Both packages are available on the Comprehensive R Archive Network (https://cran. r-project. org/web/packages/ACSNMineR/ and https://cran. r-project. org/web/packages/RNaviCell/), and on the GitHub repository (https://github. com/sysbio-curie/ ACSNMineR and https://github. com/sysbio-curie/RNaviCell). For the remainder of this article, we describe the organization of each package and illustrate their capacities with several concrete examples demonstrating their capabilities.
Introduction Biological pathways and networks comprise sets of interactions or functional relationships, occurring at the molecular level in living cells  (Adriaens et al. , 2008; Barillot et al. , 2012). A large body of knowledge on cellular biochemistry is organized in publicly available repositories such as the KEGG database  (Kanehisa et al. , 2011) , Reactome  (Croft et al. , 2014)  and MINT  (Zanzoni et al. , 2002). All these biological databases facilitate a large spectrum of analyses, improving our understanding of cellular systems. For instance, it is a very common practice to cross the output of high-throughput experiments, such as mRNA or protein expression levels, with curated biological pathways in order to visualize the changes, analyze their impact on a network and formulate new hypotheses about biological processes.