dataesr/scientific-paragraphs-categorization huggingface dataset:
7 mm. Raw data (CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip Command Console (AGCC) version 3. 2. CEL files were further processed with Affymetrix Expression Console software version 1. 1 to calculate probe set signal intensities using Robust Multi-array Average (RMA) algorithms with default settings. Raw data were submitted to GEO datasets. Genes were considered to be downregulated if expression was r0. 8-fold and upregulated if it was Z1. 2-fold compared with the respective control. Data sets were uploaded on the Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology analyses. Motif analysis. The motif discovery programme MEME (ref. 57) was used to identify motifs that are enriched with high frequency in an unbiased manner.
The input consisted of 5,000 bp (3,000 bp upstream of transcriptional start site and 2,000 downstream of transcriptional start site) of the 80 most downregulated genes in PGC-1b (i)skm À / À muscle. The motifs from MEME were queried against the JASPAR and UNIPROBE database using Tomtom  70  to identify the best-matching known motifs. Data analysis. Data were collected blindly from mice, as investigators did not know their genotype. A minimum of six animals was used for inter-individual comparison, and four for intra-individual comparison. No inclusion/exclusion criteria, and no method of randomization were used for this study. Data are represented as mean ± s. e. m. (s. e. of the mean). Statistical comparisons of data from two experimental groups were made by using a two-tailed Student's test and were considered to be statistically significant if Po0.
Acknowledgements We thank the staff of the mouse, histopathology, imaging, biochemistry and microarray facilities from  Institut de Genetique et de Biologie Moleculaire et Cellulaire and Institut Clinique de la Souris ,  Fabienne Goupilleau ,  Isabelle Bentz ,  Yi Luo ,  Melanie Schuh  and  Vanessa Ueberschlag-Pitiot  for excellent technical assistance. We thank  Doulaye Dembele  for his assistance in analysing microarray data, and  Tao Ye  and  Mohamed Aschick Mohamed Saleem  for their help in motif search analysis. Transcriptomic analyses were performed by the  IGBMC Microarray and Sequencing platform , which is supported by the ' France Genomique' National Infrastructure, funded as part of the 'Investissements d'Avenir ' programme managed by the  Agence Nationale pour la Recherche  ( ANR-10-INBS-0009 ).
This work was supported by funds from the  Centre National de la Recherche Scientifique , the  Institut National de la Santeet de la Recherche Medicale , the  College de France , the  Universitede Strasbourg , the  Agence Nationale de la Recherche  ( 05-PCOD-032 ), the ADIRAL and by  French  state funds through the  Agence Nationale de la Recherche   ANR-10-LABX-0030-INRT  under the frame programme  Investissements d'Avenir labelled   ANR-10-IDEX-0002-02. T. G. R. was supported by the  Fondation pour la Recherche Medicale  and the  Association pour la recherche al'IGBMC (ARI) , G. L. by the  Agence Nationale de la Recherche  ( 2010BLAN1108-01 ), A. I. S. by the  Universitede Strasbourg  and by the  Hopital civil de Strasbourg and S. P. by the  European Social Fund  operational programme of the Calabria region.
Flash floods represent one of the most common natural hazards in mountain catchments, and are frequent in Mediterranean environments. As a result of the widespread lack of reliable data on past events, the understanding of their spatio-temporal occurrence and their climatic triggers remains rather limited. Here, we present a dendrogeomorphic reconstruction of past flash flood activity in the Arroyo de los Puentes stream (Sierra de Guadarrama, Spanish Central System). We analyze a total of 287 increment cores from 178 disturbed Scots pine trees (Pinus sylvestris L. ) which yielded indications on 212 growth disturbances related to past flash flood impact. In combination with local archives, meteorological data, annual forest management records and highly-resolved terrestrial data (i. e. , LiDAR data and aerial imagery), the dendrogeomorphic time series allowed dating 25 flash floods over the last three centuries, with a major event leaving an intense geomorphic footprint throughout the catchment in 1936.
g. ,  Ballesteros et al. , 2011; Gottesfeld and Gottesfeld, 1990; Gottesfeld, 1996; Ruiz-Villanueva et al. , 2010; Schraml et al. , 2013; St. George and Nielsen, 2003). Further research has focused on changes in the spatial geomorphic patterns of processes  (Arbellay et al. , 2010; Bollschweiler et al. , 2008; Stoffel et al. , 2008)  and on the seasonality and related climatic drivers of hydrogeomorphic processes  (Schneuwly-Bollschweiler and Stoffel, 2012; Stoffel et al. , 2011 Stoffel et al. , , 2014)). Here, we present a case study focusing on the spatio-temporal reconstruction of past flash flood activity in the Arroyo de los Puentes stream (Sierra de Guadarrama National Park, Spanish Central System). We analyze 178 Scots pine trees (Pinus sylvestris L. ) disturbed by past flash flood events and couple this data with a large local historical forest management and climatic dataset of the study site to (i) report on the flash flood history of the stream during the last 212 years, and to (ii) identify local meteorological conditions which most likely acted as triggers of flash flood events during the past 83 years for which meteorological records exist locally.
Geomorphic mapping and sampling strategy A geomorphic characterization of all of the features related to hydrogeomorphic processes in the studied catchment was carried out by combining aerial imagery (cell size: 0. 25 m), LiDAR data (cell size: 1 m) and field surveys. All geomorphic features were digitized using ArcGIS TM version 9. 3 (ESRI, Redlands, CA, USA, 2009). Disturbed trees -namely wounded, tilted, decapitated or buried trees -located along the channel banks and/or on the fan were sampled following standard procedures in dendrogeomophic studies as described in  Stoffel and Corona (2014). Trees with possible disturbances by any process other than hydrogeomorphic (such as rockfall or human activities) were not been included in analysis. Trees were sampled with increment borers and two increment cores per tree were extracted with sampling positions being chosen according to the nature of the disturbance  (Stoffel et al.
, 2005a). Samples were taken at the contact between the scar edge and the intact wood tissue to make sure that the entire tree-ring record was obtained  (Schneuwly et al. , 2009a (Schneuwly et al. , , 2009b)). In parallel, undisturbed trees were also sampled in the upper and lower parts of the catchment to build a reference chronology and to identify pointer years for a reliable and precise crossdating with disturbed trees  (Touchan et al. 2013). Tree-ring widths were converted into width indices by standardizing raw data using ARSTAN software  (Cook, 1985).
Flash flood reconstruction A total of 212 GDs were identified in 287 increment cores, most of them in the form of scars (n=91, 42%). Among the other types of GDs, we observed 50 occurrences of reaction wood (23%), 42 trees exhibited an abrupt growth suppression (n=42, 19%), whereas growth releases (n=18, 8%) and callus tissues (n=11, 5%) were much less frequent. From all GDs, 43% were classified as very strong, 34 % as intermediate and 23% as weak reactions. Based on the number and intensity of reactions as well as the sample depth available for each year of the reconstruction, Fig. 3  shows the computed W it index, which ranks between 0. 04 and 36. 3. In 28 years, the W it index exceeded the combined threshold of W it >0. 5 and GDs ≥3 (Table  1 ). The historical forest management database indicated that the sampled area, divided into five forest management units, was subjected to periodically forestry interventions, starting in the early 1940s.
For the 6 remaining cases, neither direct nor indirect references to floods or intense rainfalls could be found, so that the analysis of potential triggers was extended to the entire year. As a result of the proximity of the Navacerrada station (only 900 m away from the source area of flash floods and located at a similar altitude) and the good quality of the data, the climate dataset analyzed in this study is highly reliable. Because the resolution of the precipitation time series was daily (and not hourly), we could not perform an intensityduration-frequency (IDF) analysis for each event, but still believe that the obtained 1-, 3-, and 5-day rainfall thresholds presented in this study are reliable enough to understand average rainfall thresholds involved in the triggering of flash floods at the study site  (Kundzewicz et al.
CONCLUSIONS Flash flood activity in the Arroyo de los Puentes catchment (northern slope of Sierra Guadarrama) was reconstructed during the last 212 years using GDs from a large number of disturbed trees (P. sylvestris L. ). The reliability of the presented flash-flood chronology was, therefore, supported by a large climatic and historical forest dataset. Despite the potential limitation related with the use of indirect proxies, this study is supporting the usefulness of the dendrogeomorphic approach for the understanding of past climate-floods in managed mountain areas. Our results describe the complexity of processes in the catchment, as both snowmelt and soil humidity have a major role as triggers of past geomorphic events. In addition, we detected that periods with more intense and frequent flash flood activity were related to negative NAO phases.
Acknowledgments This study has been funded by the projects IDEA-GesPPNN-163/2010 ( OAPN -Organismo Autónomo de Parques Nacionales ,  Ministerio de Agricultura, Alimentación y Medio Ambiente)  and  CGL2010-19274  (projects  MAS Dendro-Avenidas )  of the  Spanish Ministry of Economy and Competitiveness. The authors sincerely acknowledge CENEAM,  Montes de Valsaín  and  OAPN  for providing information for this study. Specifically, they want to thank  Javier Donés  (Director of  Montes de Valsaín ),  Andres Díez-Herrero ,  Ramzi Touchan  and  Dave Meko  for their support during the study, as well as all participants of the Tree-ring summer school carried out in 2012 in Valsaín (Touchan et al. 2013). R. Sánchez-Salguero  thanks the financial support from  University of Córdoba-Campus de Excelencia Internacional-ceiA3.
Spectral clustering is then used to partition the genes, thereby generating functional modules. The gene space can range from a targeted list of genes, like a specific pathway, all the way to an ensemble of genomes. Given a collection of gene sets and a count matrix of transcriptomic features (e. g. expression, polymorphisms), the gene sets produced by htsint can be tested for 'enrichment' or conditional differences using one of a number of commonly available packages.
The distinction between the other two approaches is made between methods that compare values among genes in the set against all other genes in the experiment and those that use the values to test for differences between two phenotypes  [7]. There is a lack of consensus among methodologies and suggested 'best practices' for GSA methods that require gene sets a priori. A number of reviews discuss the topic  [8] [9] [10]. Resources are abundant for carrying out the functional analyses of gene sets and among them the software DAVID  [11]  is one of the most frequently used. DAVID is a web-based platform that is accessible to a broad range of users and carries out a number of tasks useful for RNAseq pipelines including the following: namespace mapping, functional annotation, enrichment analyses, and importantly a variety of ways to navigate the results.
In the past decade, the use of functional annotations has experienced unprecedented growth, be it statistical, algorithmic or tool specific  [12] , but a disproportionately small amount of effort has been placed on the critical step of gene set generation, perhaps helping to explain why singular enrichment methods are more frequently used than a priori based ones, despite the perceived advantages of GSA. htsint is a software library that enables the creation of genes sets independently of the method employed by the user to test these sets for significance. Because gene sets are produced using an unsupervised approach and because it is well-known that high-throughput sequencing results are difficult to interpret, visualization tools were included as part of htsint.
Implementation Conceptually, the aim of htsint is to use compiled annotation information from one or more genomes in order to calculate distances among all genes in a specified gene space. This implies a need for efficient annotation querying at the level of taxa. To address this along with name-space mapping, and related organizational tasks, a database is included as an integral part of the library. A common task in bioinformatics is to map gene names to protein space or vice versa and this capacity is facilitated through the database. In addition to the database component there are Gene Ontology, blast mapping, and pipeline components. These three components comprise the core of the library and additional tools are present in a support role. Python is an efficient programming language for bioinformatics because it is object-oriented, flexible, syntactically clean and there is a growing ecosystem of packages  [13].
Recently, the package HTSeq was released to provide a Python-centric environment for high-throughput sequencing pipelines  [14]. HTSeq has a developed set of tools for managing reads, assemblies, performing quality assurance, and generation of count matrices. htsint is complementary to HTSeq as it aggregates annotation data specifically for hypothesis testing, while HTSeq and other environments, like Bioconductor  [15] , facilitate the rest of the pipeline. Convenient packages that include SeqGSEA  [16]  and GSA  [17]  exist to carry out the significance testing portion of the pipeline.
Database The database considers by default some of the most commonly studied taxa and for each corresponding gene and protein, GO information is populated either at database initialization or afterwards. The content of the database reflects a list of taxa that are specified in a configuration file. It is possible to populate the database with all available taxa; however, this is not necessary in most cases. The module gathers data, from the National Center for Biotechnology Information (NCBI) ftp://ftp. ncbi. nlm. nih. gov, Uniprot  [18]  and the GO. The database tables are genes, taxa, uniprot, go_terms, and go_annotations. They are shown as a database schema in Fig. 1. We used the object relational mapper available as part of the Python package SQLAlchemy http://www. sqlalchemy. org to keep the database accessible, extensible, and flexible.
Gene ontology The GeneOntology class exists for organizing annotations and building GO graphs. Several helper functions are also available to fetch GO annotations from the database and these are described with examples, in the documentation. The Python package NetworkX  [19]  is Fig. 1  Database entity diagram. Data collected from NCBI, the Gene Ontology, and UniProt are organized for efficient taxa related queries. The database tables or entities are shown along with their attributes. The relationships among tables are designated with edges that connect specific attributes central to the GeneOntology class, because the library is mature enough to handle all graph manipulations natively. GeneOntology is a container for a NetworkX Graph class, which removes much of the burden associated with the graph representation and makes it easier for the general community to develop new algorithms.
BLAST The clustering of genes, based on distances estimated from annotation data, does not directly require homology mapping, but in the context of RNA-Seq pipelines, sequence alignment becomes a necessity. The command line tool, BLAST+,  [22]  is used either directly or through BioPython  [23]  to produce a mapping of transcripts against a relevant database like the SwissProt portion of UniProt  [18]. The htsint class BlastMapper interfaces these results with the database and pipeline components of the library. The class can be used broadly to summarize the taxa that are associated with an assembled transcriptome or to map between the transcripts and multiple taxa. If multiple taxa are used to create a collection of gene sets, then identifying orthologous and paralogous genes is a required step before GSEA methods may be employed.
biological_process). Then all annotations corresponding to the genes (x) and taxa (t) are appended to the graph. Next, the GO graph is trimmed and the edges are weighted by estimating all term-term distances-a computationally intensive step. The distance is based on information theory, where differences in the Information Content (IC) of semantic entities are employed as a measure of the semantic distance  [24, 25]. IC(t) = -ln P(t), such that P(t) is the number of annotation instances of the term divided by total number of annotation instances from the annotation database. We can then define the semantic distance between a parent-child pair of GO terms as dist(t p , t c ) = |IC(t p ) -IC(t c )|. GO co-mentioning is included as an optional way to augment the GO graph with edges representing shared gene products  [26].
Additional methodological details and discussion are provided therein where several experiments with pathways and molecular interaction data provide rationale. In particular, there is evidence that multiple sources of annotation information can be combined at the level of affinity matrices to improve the quality of functional modules. The integration of arbitrary annotation information (chromosomal location, cis-regulatory information, phenotype data etc. ) using the scaffolding of the GO is the future of htsint. However, it was necessary to first provide a flexible library with expandable database capabilities before the integration aspects of the package were investigated and implemented. The gene sets produced under this scheme may be referred to as functional modules.
The literate programming style of documentation embeds functional code within the prose of the tutorial. In the tutorial example, a de novo assembly of X. tropicalis provides context and serves as a working example of htsint. The produced gene sets are tested for significance using GSA  [17]  and the most significant gene set is visualized. The gene set (shown in Fig. 3 ) is dominated by glycosylation and other post-translational modifications providing a point of entry for further investigation into genes associated with transcript differential expression.
Additionally, the full name for each term is provided in the legend. Terms are connected by edges representing their semantic distance, which is scaled and shown only for a percentile cutoff (default is 25 th ) for visualization purposes. Genes are represented as circular nodes with NCBI gene symbols overlaid as labels. The gene nodes are connected through annotations and the species from which the gene belongs to is indicated by the color and specified in the legend based on experimental context-that is based on what the domain expert feels is best. We feel that a form of phylogenetic distance is the most biologically meaningful way to select species, but from a practical perspective the annotation coverage will play a more important role in the selection process. Annotation coverage can be calculated using the database and a phylogenetic distance calculator will be soon included as part of the library.
Acknowledgements We would like to thank  Michel Baguette  and  Hervé Philippe  for providing valuable resources and discussion. Support for this work was provided by the  Agence Nationale de la Recherche  ( ANR; France) MOBIGEN  [ ANR-09-PEXT-003 ] and by a  NERC  research grant ( NE/M00256X/1 ; UK). We also thank the anonymous reviewers for their comments and suggestions to improve the software and manuscript.
As part of the follow-up, participants are asked to complete the same set of questionnaires each year. Moreover, each month, participants are invited by e-mail to fill in optional questionnaires related to dietary intake, determinants of eating behaviors, nutritional and health status. This study is conducted in accordance with the Declaration of Helsinki, and all procedures were approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm n°0000388FWA00005831) and the Commission Nationale de l'Informatique et des Libertés (CNIL n°908450 and n°909216). All participants provided informed consent with an electronic signature. This study is registered in EudraCT (n°2013-000929-31).
Thus, a total of 25 different versions of the questionnaire were used. For example, one participant was shown the 5-CNL on frozen prepared fish dishes, and MTL on fresh pizzas, while another participant was shown the 5-CNL on fresh pizzas and MTL on dairy products, etc. In addition, one respondent would be shown the 5-CNL first, while another participant would be shown the MTL first, etc. Sociodemographic data. At baseline and annually thereafter, participants in the Nutri-Net-Santé Study, are asked to provide socio-demographic data including sex, age (18-30, 30-50, 50-65, >65), educational level (up to secondary, some college or university degree) and occupational category (blue-collar, manual workers; intermediate profession/office work; selfemployed, farmer; managerial staff; without any professional activity (student, unemployed); retired).
Statistical analyses We performed analyses using data from participants of the NutriNet-Santé cohort who had completed the questionnaire on FOP labels. We excluded participants who had responded "I don't know" to more than two thirds of the cases. We compared included and excluded participants using Student's t-tests or chi-square tests, as appropriate. To assess label acceptability, the percentage of support for each label was calculated for all variables related to liking, attractiveness and perceived cognitive workload. To compare label performance with respect to the participants' understanding of nutritional quality, the percentage of correct answers was calculated and chi-square tests were performed. Differences across product categories and across subgroups (e. g. sex, age) were also assessed by using chi-square tests.
The data were weighted according to the French population socio-demographic distribution. Weighting was calculated separately for each sex using an iterative proportional fitting procedure and the 2009 national Census data on age, educational level, area of residence and whether or not the household included any children  [32]. All tests of significance were two-sided, and a P value <0. 05 was considered significant. Statistical analyses were performed using SAS software (version 9. 3; SAS Institute Inc. ).
Characteristics of the sample A total of 15,002 participants completed the questionnaire. A total of 772 participants were excluded because in more than two thirds of the cases they had responded "I don't know"; and 652 participants were excluded because of missing data required for the weighted analyses. This left 13,578 participants available for analyses. Characteristics of included participants are presented in Table  1. Compared with excluded participants, included participants were more often women (p<0. 0001), younger (p<0. 0001), and had a higher educational level (p<0. 0001).
Acknowledgments We thank all scientists, dietitians, technicians, and assistants who help carry out the  NutriNet-Santé study. We especially thank  Valentina Andreeva, Ph. D. , epidemiologist;  Younes Esseddik ,  Yasmina Chelghoum ,  Mohand Ait Oufella ,  Paul Flanzy , and  Thi Hong Van Duong  computer scientists;  Veronique Gourlet ,  Charlie Menard ,  Fabien Szabo ,  Nathalie Arnault ,  Laurent Bourhis , and  Stephen Besseau , statisticians; and the dietitians. We are grateful to the volunteers of the  NutriNet-Santé study.