dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank staff at the  Centre de Diagnostic et de Traitement de l'Ulcère de Buruli (CDTUB), Pobè, Bénin ; staff of the  Laboratoire de Bacteriologie, CHU, Angers, France ; and staff from the  INSERM U1163  for helpful discussions; and acknowledge support from la  Fondation Raoul Follereau. QV acknowledges support from the  Fondation Imagine. LM and AAl acknowledge support from the  Agence Nationale de la Recherche (ANR). AAl acknowledges support from the  Fondation pour la Recherche Médicale  (FRM, grant number  DMI20091117308 ). AAl, LA, and LM acknowledge support from the  Institut National de la Recherche et la Recherche Médicale.
RNA extraction, labelling and microarray experiments Two different pools of seminiferous tubules were exposed to two concentrations of BPA (1 nM and 10 nM) or to complete medium with vehicle (control cells) for 8, 14, and 21 days. Total RNA was extracted using the RNeasy Mini kit (Qiagen). RNAs were quantified with the Nanodrop 1000 spectrophotometer; their qualities were assessed with the Agilent 2100 Bioanalyzer. RNA samples were amplified and labeled with the cyanine-3 fluorophore using a Low Input QuickAmp Labeling Kit (Agilent). Hybridization was performed using Agilent Oligo Microarrays (Rat V3 4644K). Fluorescence was scanned and signal data were extracted with Feature Extraction Software (Agilent).
Statistical analysis Transcriptomic analysis. In this experimental design, six independent analyses were conducted versus each specific control for the considered time point: a) 1 nM BPA-exposed cells for 8 days, b) 1 nM BPA-exposed cells for 14 days, c) 1 nM BPAexposed cells for 21 days, d) 10 nM BPA-exposed cells for 8 days, e) 10 nM BPA-exposed cells for 14 days, f) 10 nM BPA-exposed cells for 21 days. For each analysis, eight raw fluorescence data files (four controls and four tests) were submitted to GeneSpring Software GX11 (Agilent Technologies) using a widely used method for determining the significance change of gene expression  [38, 47]. The fold change cutoff between control and exposed samples was set to 1. 5. Genes significantly up-or downregulated were determined by an unpaired t-test, with a p-value ,0.
Biological analysis Lists of genes significantly induced or repressed after exposure to BPA were uploaded into Ingenuity Pathway Analysis Software (IPA, Ingenuity Systems, www. ingenuity. com) for biological analysis by comparison with the Ingenuity Knowledge Database. These lists of altered genes were then processed to investigate the functional distribution of these genes, as defined by Gene Ontology. Datasets and known canonical pathways associations were measured by IPA by using a ratio (R) of the number of genes from a dataset that map to a specific pathway divided by the total number of genes that map to this canonical pathway. A Fisher's exact test was used to determine a p-value representing the significance of these associations.
2. For instance, at D21/1 nM BPA, the numbers of genes related to cancer, cell death and cellular development were 2927, 2422 and 1833, respectively. For reproductive system disease and DNA replication and repair, the numbers of genes were 746 and 511, respectively. For other dose and time points, the numbers of genes per altered function are indicated in Figure  2. Figure  3  shows the common canonical pathways disturbed by BPA (1 nM at D8, D14, and D21). Each canonical pathway is constituted of a finite number of genes. For each time point, we calculated a ratio indicating the percentage of altered genes in our dataset belonging to a given canonical pathway (for precise calculation, see Material and Methods). We selected nine canonical pathways on the basis of the most significant p-values.
All have been shown to be interconnected in a network (Fig. 5 ) obtained by IPA. qRT-PCR validation of the microarray data focused on meiosis. Quantitative RT-PCR was performed using the same batches of RNA as those evaluated by microarrays. Validation of the microarray data was investigated on five genes belonging to BPA-1 nM/D21, and on 7 genes belonging to BPA-10 nM/D21. Table  1  reports the compared fold changes obtained with microarray and qRT-PCR (p-value ,0. 05).
Their interconnections were visualized in a literature-based network showing the high level of connections between differentially expressed genes linked to DNA DSB repair in our datasets (Fig. 5 ). Thus, the phenotypes observed with ICC could result from the action of several of these interconnected genes and probably other genes expressed in germ cells and whose functions are not yet clearly defined. Expression changes of genes revealed in our study could also explain some published data. We saw above that the increased number of Rad51 foci observed by Allard and Colaiacovo  [65]   might be consistent with transcriptional changes of the Rec/ Rad51 family. The increased number and modified distribution of Mlh1 foci reported in pachytene oocytes following BPA exposure  [25, 30]  might be consistent with the strong downregulation of Prdm9, a major player in hotspot specification  [67].
Acknowledgments We thank the  Research Consortium ECCOREV nu 3098 (Ecosystemes Continentaux et Risques Environnementaux) CNRS / Aix-Marseille Universite  ´who funded this study ( AOI 2010 , grant number  7 ). We acknowledge  Marc Fraterno  for his help in creation of figure 8 and  Thierry Orsie  `re for fruitful discussions.
m. ) and progressively increased to 14 °C with increasing air temperature (generally reached at 11 a. m. ). The above protocol was followed for 11 weeks in the case of F. hepatica according to the length of the patent period. In experiment A, the Petri dishes subjected to thermal shock were placed outdoors and protected from direct sunshine by putting them under a sloping roof (TC snails, 3000 lux) or in the shade if necessary (TC snails, natural light) The natural light level was measured each hour during the time of thermal shock (3 h). Sky nebulosity (this parameter expresses the extent of the cloud covering, going from 0 octa for a clear sky to 8 octas for a completely overcast sky) was also taken into account from data furnished by the meteorological station of Limoges-Bellegarde. The light level was measured using a MT0 001 Light Lux Meter (Velleman components) just above the Petri dishes.
Data analysis As the aim of the present study was to determine the influence of the light level on cercarial emergence, the differences noted in experiment A between the numbers of cercariae or time intervals (TC snails only) were analysed for each snail population considered separately. In experiment B, a similar protocol was used for the values noted for the numbers of cercariae and time intervals. In the two snail subgroups subjected to natural light during thermal shock (experiment A), the influence of the light level and sky nebulosity on cercarial emergence was analysed by comparing differences between the numbers of CR snails or the quantities of emerged cercariae for each class of light level and each class of sky nebulosity. The influence of each climatic factor on these parameters was analysed separately because the classes of light level did not correspond to those defined for nebulosity (two or three close light classes were noted for the same class of nebulosity during the experiment).
Non-significant differences among these numbers of cercariae were noted. The natural light level and sky nebulosity, associated with thermal shock, seem to have a significant influence on cercarial emergence by stimulating this process. High numbers of cercariae, ranging from 157 to 341 per TC snail and day with a thermal shock, were frequently noted in the lowest classes of light level (from 1 to 1200 lux) and the upper classes of sky nebulosity (data not shown).
Statistical analyses Data were analysed using the PROC Mixed procedure of SAS for repeated measurements. Mare liveweight time course was analysed separately from early June to early September (which includes the intake measurement period), and from early to late September using linear models. Foal growth was analysed at each grazing cycle. For liveweight and BCS, the model included the main effects of treatment, date, and the interaction between treatment and date (treatment × date). Initial liveweight or BCS was taken as a covariate. Height at withers and cannon bone width were analysed in a model including the main effects of treatment, age and the interaction between treatment and age. Faecal egg excretion by mares was analysed in a model including the main effects of treatment, date, and the interaction between treatment and date (treatment × date).
Acknowledgements The PhD thesis of C. Collas is funded by the  French Horse and Riding Institute (IFCE)  and the  Animal Physiology and Livestock Farming System (Phase) division of INRA. The authors thank the  Carnot Institute for Animal Health (ICSA)  for complementary funding. They are also grateful to the staff of the experimental farm, particularly  P. Dupuy ,  J. Bellonie ,  J. L. Larry ,  C. Dubois , to students  C. Ceglowski  and  M. Duclouet  for their technical assistance, and to  R. Delagarde ,  J. Agabriel  and  N. Rossignol  for methodological and statistical advice.
Data structure and description of farming systems The study covered one conventional and three differentiated categories of pig farming systems in Europe (Table  1 ). In total, 15 farming systems were studied: five Conventional (C), five Adapted Conventional (AC), three Traditional (T) and two Organic (O). For a general description of these categories of farming systems, see  Bonneau et al. (2011). To maintain anonymity the systems are labelled with codes in the present paper. Countries and farming systems were chosen to cover a wide spectrum of different conditions and practices in pig production. The Conventional systems are all common, and indeed standard, in the countries in which they operate, but as a consequence of those countries' differing laws, markets, policies and climate, they are configured in different ways.
Most of the data were collected from the breeding organisations. Some data were also collected from the 135 farms included in the Q-PorkChains project. The farm data were collected during farm visits when data for the other themes studied in the project were obtained. On average, five farmers per farming system (range 0 to 11) provided data for this study on breeding activities; 70 farmers in total. Participating breeding organisations were contacted by e-mail or telephone; the nature and purpose of Q-PorkChains were explained. A questionnaire (Supplementary Table  S1 ) was sent to the breeding organisation (five cases), or used as the basis of an interview conducted over the telephone (two cases) or during an arranged visit (three cases). Those who collected the information were instructed to collect the answers given by the breeding organisation without evaluating them.
The people answering the questions at the breeding organisation were geneticists and breeders with an active role in the breeding work. For one farming system, data were collected separately for sire and dam lines. The questionnaire was presented in English (for half of the studied farming systems) or translated into the local language. Likewise, the answers were sometimes given in English and sometimes translated into English by those collecting the information. The information collectors were all researchers involved in Q-PorkChains. Evaluation of qualitative data Breeding activities performed in the 15 farming systems were evaluated based on qualitative data from breeding organisations and farmers. The breeding organisations, some of which were competitors, were not expected to reveal details of their breeding work.
The method of evaluation The evaluation revealed substantial differences in the farming systems with a clear bearing on their long-term sustainability. In the Q-PorkChains project the 15 farming systems included in this study have been evaluated separately against seven other themes of sustainability  (Bonneau et al. , 2014a and 2014b). Most of the data collected for those themes were recorded quantitatively, for example, frequency of pigs with skin lesions. By contrast, the data in this assessment were verbal. The interpretation of responses to the questionnaire and the following transformation of notes to the scores that formed the basis of our analysis, are in this sense both subjective and capable of being questioned. Notwithstanding this, the group analysis revealed informative contrasts between three groups of systems.
The present study shows that evaluation of the sustainability of breeding activities across several countries and on a large scale is feasible. Using a questionnaire, we were able to collect qualitative data about breeding activities from 15 farming systems and 9 breeding organisations, which provided these systems with animals and semen. The responses we obtained depended on the knowledge of the person answering the questionnaire, and also, of course, the knowledge of the person who asked the questions and translated the answers. Ideally, all data should be collected in interviews performed by just one geneticist who is fluent in all of the national languages involved, but in practice this will no doubt prove impossible. By making some of the questions more specific, and providing multiple-choice answers to others, it would be possible to improve the comparability of the raw data collected in the survey.
Acknowledgements The authors gratefully acknowledge the  European Community 's financial participation under the  Sixth Framework Programme for Research, Technological Development and Demonstration Activities , for this Integrated Project  Q-PorkChains FOOD-CT-2007-036245. The authors also wish to thank the breeding organisations that participated in this study and the geneticists and farmers who took time to respond to the questionnaires, and  Albrecht Weber ,  Joel Gonzàlez ,  Julie Ryschawy ,  Monica Hansson ,  Per Eidnes Sorensen  and  Wim Houwers  for collecting and editing data. Disclaimer: The views expressed in this study are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of the information.
The expert group discussed the studies in detail to come to a consensus conclusion for each of them. In order to align their judgment and to guide data evaluation, the expert group reviewed data from other comet assays available at the expert companies and determined the evaluation criteria described below to conclude the outcome of each study: -A clear increase in DNA damage (i. e. tail intensity and/or tail moment) in dose group(s) versus concurrent vehicle control group, e. g. at least one of the dose levels exhibits a statistically significant increase compared to the concurrent vehicle control (in case statistical analysis is performed). -A dose-related increase in DNA damage. -The DNA damage measured for the dose group(s) is outside the distribution of the historical vehicle control range (in case historical vehicle control data are available and considered appropriate).
For 12 out of 14 studies, histopathological evaluation has been performed. Organ toxicity was assessed only by histopathology in solid tissues demonstrating positive comet assay results. None of the positive comet assay results were clearly associated with organ toxicity. No histopathological changes were observed in 13 out of 18 positive tissues. Minimal histopathological findings observed in 2 tissues (study no. 1 of company 1 and study no. 2 of company 4) were considered not to be the primary cause for the positive comet assay results. For 2 tissues (study no. 2 of company 1) no histopathological evaluation was conducted and for 1 tissue (study no. 1 of company 17) no histopathological data were provided, but the number of hedgehogs (also called 'clouds' or 'ghost cells'), that have been used as a marker of toxicity, were not increased.
This suggests that comet assay is not over-sensitive. -For only 2 out of 12 the compounds showing positive comet assay results, the in vivo micronucleus assay was positive as well. This indicates that the comet assay (mainly conducted in liver) can be used as an appropriate second in vivo assay and efficiently complements the micronucleus assay performed in bone marrow or peripheral blood. -In cases where positive comet assay results were obtained, the findings were not related to tissue toxicity. -Data interpretation requires historical data distribution for the evaluated organ, and scoring more cells and/or repeating experiments could help reaching a clear conclusion in case of inconclusive data.
The data were generated in compliance with the Minimum Information About a Microarray Experiment (MIAME) guidelines and were deposited in the Gene Expression Omnibus of the National Center for Biotechnology Information (accession number: GSE49016). Image analysis and intra-array signal corrections were performed using Agilent Feature Extractor Software A. 9. 1. 3. The microarray data analysis was performed using R (v. 3. 0. 1) and the Bioconductor software suite, and the raw data were filtered and normalized using the Agi4x44PreProcess library. Unsupervised and supervised analyses were performed using hierarchical clustering and a principal component analysis (PCA) (made4 library  [28]  and Linear Models for Microarray Analysis (limma library)  [29] ). Genes were considered to be differentially expressed if the False Discovery Rate (FDR)  [30]  was below 0.
1% and the absolute fold change (FC) was above 2. 0. The core program of the moDCs induced by O. tsutsugamushi, C. burnetii and B. abortus was annotated using the DAVID annotation tool  [31]  and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways  [32]. The integration and rendering of the pathway-based data were performed using the library pathview  [33].
burnetii and B. abortus (Figure  3B ). Using Venn diagrams, we identified a core response to LPS and pathogens: this core response included 62 genes (55 up-modulated and 7 down-modulated). We also identified specific signatures for LPS and each bacterium (Figure  3C  and 3D ). We then analyzed the gene pathways stimulated by LPS and bacterial pathogens. Among the 2,609 genes modulated, we found that 469 genes (20%) were implicated in several canonical immune response pathways (extracted from the KEGG database, Table  1 ), such as recognition pathways (the TLR signaling pathway, NODlike signaling pathway, RIG-I-like signaling pathway and cytosolic DNA-sensing pathway), signal processing (the Jak-STAT and MAPK signaling pathways) or functions (cytokine-cytokine receptor interaction, chemokine signaling pathway and apoptosis) (Figure  3E ).
burnetii and B. abortus to downmodulate TLR4/TLR3, STAT1 and type I IFN-responsive genes. We recently explored the transcriptional datasets that are deposited in public repertories, such as the Gene Expression Omnibus, and we focused the main transductional pathways that lead to the type I IFN response during bacterial infections. We found that the type I IFN pattern induced by vacuolar bacteria is completely different from the signatures induced by viruses and cytosolic bacteria  [41]. The IFN response pathway has been described in transcriptomics analysis of tuberculosis in which it remains silent in latent tuberculosis and was fully active in patients with active tuberculosis  [42]. As C. burnetii and B. abortus share the ability to cause a chronic evolution of disease and bacterial persistence, such a strategy may be operative for their persistence.
KEGG pathways identified in stimulated moDCs. were stimulated with E. coli LPS or bacterial pathogens for 6 hours. RNAs were extracted, and microarrays were performed. The modulated genes were analyzed using the KEGG database. The KEGG pathways, the number of modulated genes and P values are shown. doi:10. 1371/journal. pone. 0099420. t001.
Mapping and spatial analysis After having geo-referenced each capture site, an information system was incremented using the SavGIS (freeware developed by the IRD) programs to link the database of the field results obtained at each site. The distributions of the sampling sites (50 selected at the beginning of the study) and those positive for nymphs (they can be equated to natural colonies of T. infestans) were analyzed using the coordinates of each point and the vertex of the determined quadrat, by means of the nearest neighbor distance function G(r)  [19]  in the Spatstat package of R software "Development Core Team"  [20]. This function represents the correlation of the cumulative function G of the r distance from a random point to the closest point. Following Baddeley et al. [21] , we also used three different estimators of G(r) to take into account the influence of the edge of the sampling area: "rs", the reduced sample estimator, "km", the threedimensional version of the Kaplan-Meier estimator, and "Hanisch", the three-dimensional generalization of the "Hanisch" estimator.
Acknowledgments We are particularly grateful to the Director of the  Inlasa (Instituto de Laboratorios de Salud, La Paz Bolivia ),  Dr. Walter Agreda , and  Dr. Tamara Chavez , director of the  Department of Entomology , for having hosted this work. We also thank  Marie-France Bosseno  retired engineer at the IRD for advice to beginning this study. The study was funded by the  National Agency for Research (ANR No 3624, France)  and the  IRD Institute.
Demographic history DnaSP v5. 10. 1 was used to perform Tajima's and Fu's neutrality tests in order to detect violations of mutationdrift equilibrium caused by selection or changes in population size. Within B. truncatus, the two tests were significantly negative using cox1 sequences, while no significant results were obtained when samples from individual countries were analyzed, perhaps in part because of the smaller sample sizes. Tajima's D was significantly negative in the 28S dataset. Although these two statistics were originally introduced as tests of neutrality, Fu's test is considered a powerful test to detect past population expansion. Therefore, significant negative values, such as those seen here, can indicate population expansion especially in Cameroonian and Senegalese B. truncatus populations.
Data analyses The obtained sequences were used to perform BLAST searches  [30]  via the National Center for Biotechnology Information (NCBI) GenBank to assure that the sequences matched the sequenced genes. Sequence chromatograms were checked using SeqScape v2. 5. Nucleotide sequences were multiple aligned using CLUSTAL X  [31] , and submitted to GenBank database under the accession numbers [GenBank: KJ135287-KJ13508 and KJ157326-KJ157508].
Population genetic analysis DnaSP v5. 10. 1  [32]  was used to estimate global and regional levels of haplotype and nucleotide diversity for the seven species, and to conduct Tajima's and Fu's neutrality tests for patterns on non-neutral evolution within each species. We also used DnaSP to estimate the diversification index (Fst) between each pair of species. MEGA v5  [33]  was used to estimate the average evolutionary divergence over sequence pairs within species for each gene. The optimal model of sequence evolution that best fits the data was evaluated using FindModel: for cox1-Folmer region (GTR + G), Cox Asmit-region (GTR + G), 18S (GTR + I), 28S (K81 + G) and combined data matrix (GTR + I + G). Standard error estimates were obtained by a bootstrap procedure (500 replicates).