dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by  EURL ECVAM, Systems Toxicology Unit at the Institute for Health and Consumer Protection of the European Commission Joint Research Centre  and by  Cosmetics Europe , The Personal Care Association (known as Colipa before 2012) through Collaboration Agreement N. CCR. IHCP. C433251. X0. The authors acknowledge  BfR  ( Dr. M. Liebsch  and  Dr. H. Spielmann ) for providing detailed in vivo Draize eye test data for all chemicals in the ZEBET database, and  Dr. P. Vanparys  for providing detailed in vivo Draize eye test data for all chemicals in the LNS database, which were obtained by  Dr. J. Giroux  at the  Agence du Medicament (Montpelier, France ) in 1992. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
0 19. 6 61. 8 65. 2 10. 7 15. 3 2. 9 3. 6 5. 3 2. 7. 1. 3. 1 2. 1 0. 4 4. 4 4. 4 10. 6 16. 3 64. 4 50. 5 9. 7 13. 0 7. 3 6. 8 4. 3 3. 6. 4. 0 5. 3. 0. 7. 0. 67 1. 8 2. 9 7. 8 9. 6 61. 5 59. 3 10. 1 8. 2 4. 5 2. 2 3. 7. 0. 5. 1. 8. 0. 33 7. 0 8. 8 71. 6 67. 6 16. 9 12. 8 8. 0 8. 2 4. 1 2. 2 2. 7. 1. 1. 1. 8. 0 88. 9 87. 9 14. 3 17. 6 6. 0 8. 1 2. 0 1. 0 0. 4. 1. 0. 0. 5. Tot animals 1 605 239 1395 136 603 86 651 97 267 46 300 59 375 112 57 7 5 7 5 2 6 150 79 Above box 1 0 4 0 2 0 2 1 3 20 2 5 1 1 6 - - - - - - - - Within box 1604 239 1391 136 601 86 630 94 247 44 249 43 308 96 50 66 71 26 147 79 Below box - - - - - - - - - - - - 6 7 1 6 7 9 4 0 3 0 Tot chemicals 5 35 66 465 36 201 22 217 26 8 9 1 4 100 17 125 28 1 9 2 1 2 5 8 50 25 Proportions that fall in the light-grey area (No Cat range) correspond to animals with mean CR < 2 that were tested with chemicals with CR Maj < 2; Proportions that fall in the grey area (Cat 2 range) correspond to animals with mean CR ≥ 2 that were tested with chemicals with CR Maj ≥ 2; Proportions in bold (diagonal) correspond to animals with mean CR scores equal to the CR Maj of the chemicals tested in those animals CR conjunctiva redness, NCD European New Chemicals Database, RCD Reference Chemicals Databases.
Statistical Analysis The Fischer's exact test has been used to determine if there were nonrandom associations between two categorical variables and the outcome of the patients; the association between the number of antigens targeted by specific T cells and the status of the subjects (HS or patients with IA). Chi-squared test has been used to compare the rate of samples with more than 100 SFCs/10 ' 6 PBMCs to all the antigens between HS and patients with IA. The paired T test has been used to compare the rates of fungal hyphae lysis between the single cell fractions and their associations. P values below. 05 were considered significant. The results were obtained using the Stata Software (11. 0, College Station, Texas, USA).
Artificial waterholes however maintain water availability during the dry season through pumping of underground water (see Figure  S1 ). Dry-season water availability is recorded yearly in September/ October by the Wildlife Environment Zimbabwe association, and we used these data here. duration. Note that in the wet season when elephants are not constrained by water availability mean speed is 0. 3360. 52 s. d. km/h. All panels show differences between commuting trips (when different waterholes are visited at the beginning and at the end of the trip; white-filled symbols, dotted lines) and looping trips (when the same waterhole is visited at the beginning and at the end of the trip; blackfilled symbols, solid lines). Note that the difference between commuting and looping trips in panel (C) is not significant (see Table  1 ).
Movement data The movement data were collected using GPS collars with Inmarsat transmission from the Africa Wildlife Tracking company. Ten collars were deployed in August/September 2009 on elephant cows from 10 different family herds. Due to the limited amount of locations that could be transmitted we used a hierarchical location sampling design to obtain both a long-term, large-scale description of elephant space use and fine-scale daily patterns during selected times of the year: a baseline rate of one location per day was increased to one location per hour during two consecutive weeks in February (wet season) and three consecutive weeks in September/October (dry season). All collars worked for at least two years. When evaluated at a fixed site the GPS relative error was within 30 m ninety-five percent of the time (  Chamaille-Jammes, unpublished data).
Definition of foraging trips We studied dry season foraging trips using data collected during the 1-hour interval sampling period, and pooled data from 2009 and 2010 to increase sample size as preliminary analyses revealed no significant year effect. Individual movement trajectories were segmented by cutting the trajectory at each visit to a waterhole retaining water, and defining foraging trips as segments of the trajectory that occurred between two consecutive visits at waterholes. We classified each trip as either a looping trip (the elephant drank at the same waterhole at the start and the end of the trip) or a commuting trip (the elephant drank at two different waterholes). Hereafter the waterhole(s) visited at the beginning and at the end of the trips are collectively referred to as start/end waterholes.
doi:10. 1371/journal. pone. 0059164. t002 Several aspects of our research could be improved upon in the future: first, higher sampling frequency would increase the certainty of waterhole visits, although similarity of results obtained with various distance threshold and visual analyses of the trips helped us build confidence in our results. The limitations imposed by collar battery life have now been removed in new-generation collars. Second, field data of elephant activity should be gathered simultaneously to the acquisition of tracking data. This would for instance ascertain the local elephant abundance at waterholes at the time of visits, or the resource availability at feeding sites at various distance to water. Only with the simultaneous observation of local conditions and movement trajectory before/after observations will one be able to fully grasp with confidence the environmental determinants of elephant movements.
Data Inoculum Viral suspensions were prepared from the liver of a pig experimentally infected with hepatitis E virus (genotype 3, Genebank accession number JQ953665), homogenized in PBS (pH 7. 2) and centrifuged at 1000 g at 4 °C for 20 min. The resulting clear supernatant was purified by passage through microfilters with a pore size of 0. 45 μm (Millex-GV; Millipore SAS, Molsheim, France). The amount of HEV was determined by real-time quantitative RT-PCR (qRT-PCR) as described below.
Latency and infectious periods The distributions of the durations of the latent and infectious periods for HEV infection in pigs were examined. For each animal, the first positive fecal sample for HEV RNA was considered as the beginning of the infectious period and corresponded to the latency duration in inoculated animals. Thus, data from inoculated animals was used to determine the distribution of the latent period. The pigs were then considered infectious until the last positive fecal sample. Gamma-distributions were assumed for both durations, from which the shape and rate parameters were estimated by maximum log-likelihood method. A nonparametric bootstrap procedure was used to determine the 95% confidence intervals of the parameter estimates. One thousand datasets were generated by resampling the original dataset.
Acknowledgements This study was funded by the  French Research Agency (ANR)  as part of the research project called "HEVECODYN". We thank  André Keranflec 'h,  Yann Bailly ,  Nadège Prioux ,  Angélique Moro , and  Jean-Marie Guionnet  for their excellent technical management of the experiment. We acknowledge  Dr Niel Hens  from  Hasselt University, Belgium , and  Dr Stéphanie Bougeard  from  Anses, France , for fruitful discussions on statistical methods. We also thank the two anonymous referees for their comments which permitted to greatly improve the manuscript.
These differences were in line with biochemical tests such as measurement of nitrate reductase and urease activities, performed to differentiate these two species. 11. 6% CDS (633 out of 5477) of N. farcinica and N. cyriacigeorgica genomes were not recorded in other actinobacterial sequenced genomes (Figure  3 ). These two Nocardia strains have an equivalent genome size which is longer than the ones of primary actinobacterial pathogens (Table  1 ). 16. 6% (777 kb) of N. cyriacigeorgica CDS content was not recorded in any other organisms referenced in the databases. These CDS were sometimes related to mobile and extrachromosomal elements but most of them were of "unknown function". A search for amplified CDS among N. cyriacigeorgica genome revealed 161 occurrences, of which 132 are duplicated, 21 are triplicated and 8 are quadruplicated.
This analysis showed that the number of CDS per COG could differentiate primary pathogens from non-pathogens. This "pathogen pattern" was more significant than the COG organization bias inferred from species belonging to a same genus. On the other hand, all non-pathogens had closer COG patterns even though some were part of different genus or part of a genus showing pathogenic species. COG patterns of the Nocardia genomes were found similar to those of non-pathogens (Figure  5 , but also see the Additional file 4). A correspondence analysis on the functional domains inferred from the CDS of the "transcription" COG was performed (Additional file 5 and Additional file 6). This analysis did not segregate the dataset according to the pathogenic nature of the species regardless of their genus. Instead, the numbers of CDS per sub-division were found similar between the Nocardia genomes and similar to those observed among M.
A cladogram was built using the RGP distribution patterns based on the positive and negative PCR results (Figure  7 ). Dataset of inner RGP and RGP-ends DNA targets were analyzed separately or together, and gave similar cladograms. All strains harboring 5 or more RGP were grouped in the "GUH-2 complex". Other strains belonged to a "type strain complex". In fact, RGP patterns of 30 strains were found to be in the "GUH-2 complex", while patterns of 56 strains were allocated to the "type strain complex". Among the "GUH-2 complex", some of the selected RGP were highly prevalent: RGP-Cy1, RGP-Cy6, RGP-Cy11, RGP-Cy15, RGP-Cy16, RGP-Cy18 and RGP-Cy21 were found in 23, 18, 25, 29, 23, 20, and 26 strains respectively. Strain N7 harbored the highest number of RGP found in the N. cyriacigeorgica GUH-2 genome (15/22 positive PCR products).
Among the type strain complex, RGP-Cy2, -Cy5, -Cy6, -Cy7, -Cy9, -Cy10, -Cy16, -Cy18, -Cy19, -Cy20, and -Cy21 were not recorded. No RGP seemed to be representative of this latter complex, and RGP-Cy3, RGP-Cy15 and RGP-Cy17 were the most prevalent. RGP-Cy3 was the most broadly distributed (69 positive strains) among the N. cyriacigeorgica species. PCR screenings targeting the left and right ends of RGP were defined to estimate the level of conservation of their respective proximal DNA region. The left (L) and right (R) ends of RGP-Cy9, -Cy15, and -Cy16 were broadly detected whereas only one end could be detected for RGP-Cy2 (R), -Cy3 (L), -Cy4 (R), -Cy7 (R), -Cy11 (R), -Cy17 (R), -Cy18 (L), -Cy19 (R), -Cy20 (L), -Cy21 (L), and -Cy22 (R) among all the strains tested. The L and R ends of the RGP-Cy5, Cy6, Cy10, and Cy11 were detected among the "GUH-2 complex", while only RGP-Cy1 (L), -Cy3 (R), -Cy7 (L), -Cy11 (L), -Cy13 (L), -Cy20 (R), -Cy21 (R) could be detected among the "GUH-2 complex" (data not shown).
Genetic potentials and metabolic profiling Phenotypic microarray datasets including antibiotic resistances were compared with the functional predictions made from the annotated N. cyriacigeorgica GUH-2 genome. Phenotypic profiling was performed by testing N. cyriacigeorgica ability to grow with various carbon and nitrogen sources. KEGG pathways were used to find the CDS involved in these pathways but also as a reference for explaining an absence of growth under certain conditions. (Additional file 11). Only seven amino acids (L-asparagine, L-aspartic acid, L-cysteine, L-glutamic acid, L-glutamine, L-histidine, L-lyxose, L-tyrosine) could be used as carbon and/or nitrogen sources. The absence of some CDS previously shown in other bacteria to be involved in the substrate catabolism or transport could explain several negative results.
However, the lack of growth with Laspartic acid, L-lysine and L-proline could not be explained by the absence of particular CDS. The osmoadaptation capacity N. cyriacigeorgica GUH-2 was also tested and found to be high under high salt concentrations. This property could involve CDS encoding transport proteins and/or osmoprotectants like ectoine and betaine (Additional file 11). Growth tests were also performed under various pH. N. cyriacigeorgica GUH-2 was shown to grow at pH 7 and 8 but not pH 5. 2. This dataset was completed by an analysis of the antibiotics resistance pattern of N. cyriacigeorgica GUH-2 using plate assays. N. cyriacigeorgica GUH-2 was confirmed to have a type VI pattern with resistance against several sulfonamides, aminoglycosides, tetracyclines and penicillins. It was found sensitive to cefamandole, cefotaxime, amikacin, and imipenem.
farcinica. These data support the hypothesis of a good match between IS acquisitions and infra-specific diversifications. Interestingly, distribution analysis indicated similar RGP/IS repartition profiles among N. cyriacigeorgica strains. These profiles divided N. cyriacigeorgica into two clusters: (1) those with patterns similar to strain GUH-2, and (2) those with patterns similar to the type strain. These similarities in the evolutionary patterns of these elements suggest a strong association. RGP could have been the genetic shuttles for some of these IS elements. In fact, even though most IS elements were found distributed over the genome without particular insertion site preferences, some e. g. ISNcy5, ISNcy2-b, ISNCy4, & ISNcy1-a4, were found harbored by RGP. For example, ISNcy2-b could have been acquired with RGP-Cy13.
Synteny group computation Sequence data for comparative analyses were obtained from the NCBI database (RefSeq section). Putative orthologies were defined as gene pairs satisfying either the BBH criterion or an alignment threshold (at least 40% sequence identity over at least 80% of the length of the smallest protein)  [64]. These relationships were subsequently used to search for synteny groups (i. e. conservation of the chromosomal co-localization between pairs of orthologous genes from different genomes) among several bacterial genomes using an algorithm based on an exact graph-theoretical approach  [65]. These results were used to draw a LinePlot using the MaGe MicroScope platform (https://www. genoscope. cns. fr/agc/microscope/home/index. php).
Moreover, the tool integrates the results of Alien Hunter  [66]  and SIRGP-HMM  [67] , two methods that analyses compositional biases to detect atypical sequences (i. e. sequences potentially acquired by horizontal gene transfer). The whole genome of the N. cyriacigeorgica and N. farcinica were also aligned using ACT, a program for comparing two or more DNA sequences  [68]. A region of five or more CDS and of more than 5 kb not retrieved in N. farcinica was also considered a RGP.
Global comparative study We compared genomes of A. mediterranei (CP002896. 1), C. diphtheriae (BX248353), C. glutamicum (BA000036), M. tuberculosis (AL123456), M. smegmatis (CP000480), N. farcinica (AP006618), N. cyriacigeorgica (FO082843), R. equi (ADNW00000000) and R. jostii (CP000431) using various graphic tools implemented on the MaGe Microscope platform and ACT. The phylogenetic tree of life of the Actinobacteria was built from a MLSA data set on the basis of  [13]. We used Clusters of Orthologous Groups (COGs) automatic annotation and correspondence analysis (CA) with R software (http://www. R-project. org) [69]  to graphically infer global trends between the above genomes. Nocardia IS families were identified using BLAST analyses against an IS database at https:// www-is. biotoul. fr/.
Acknowledgements Thanks are expressed to  D. Abrouk  and  S. Bouchet  for help with treatment of large data sets. Thanks are expressed to  L. Loiseau  and  E. Bergeron  of the PARMIC and EML -biological resource center that made available the Omnilog Combo system and some of the strains used in this study. Thanks to  BL. Beaman  ( University of California, Davis, Davis, California ) for help with animal experiment procedure. This project was funded by a  Genoscope research program , by a  CNRS   MIE interdisciplinary program  and a  CNRS-USA  exchange program. Part of this work was funded by  ANR SEST  ( 2005-2009 ) project  56 05  and  ANR Contaminants et Environnements : Métrologie, Santé, Adaptabilité, Comportements et Usages (CESA)  ( 2012-2016 ) project " CABRRES ". The CNRS, University Lyon 1 and VetAgroSup institutions also contributed financially to this work.
Data analysis Data are expressed as median (range) except where indicated. Statistical analyses were performed with computer software (Stats Direct version 2. 6. 8, Stats Direct Ltd. ), with the level of significance set at P < 0. 05 for two-sided analyses. Given that there were no known previous studies examining changes in nutrient biomarkers in obese dogs undergoing weight loss, a meaningful power calculation was not possible. Instead, the number of dogs enrolled was based upon the prior experience from previous studies of plasma biomarkers from the same clinic  [25, 32, 33]. The Shapiro-Wilk test was first used to assess all data sets, and parametric and non-parametric tests were used as appropriate. These included simple and multiple regression analysis, the signed ranks test, the Mann-Whitney U test, and Kendall's rank correlation.
Paired sampled from 20 dogs were available for urine selenium analysis. Those 20 samples were selected from the original 31 dogs in order of percentage weight lost (i. e. , the 20 dogs with the greatest percentage of weight loss had their samples analysed). Weight loss characteristics for dogs included in the study did not differ from those also completing but not ultimately being included in the study (data not shown). In the 31 dogs finally included in the study, percentage weight loss was 28. 3% (16. 0-40. 1%) starting body weight (SBW), over a period of 250 days (91-674 days); therefore, median rate of weight loss was 0. 8% (0. 3-1. 4%) SBW/week (Table  2 ). Neither the signalment nor weight loss outcomes differed between the 26 dogs having choline and amino acid analysis, and those where it was not measured (P > 0.
Functional annotation was achieved using Prodigal  (6)  and BLASTp searches against the National Center for Biotechnology Information (NCBI) nonredundant (NR), UniProt (http://www. uniprot. org/), and COG databases  (7). tRNA and rRNA genes were predicted using Aragorn and RNAmmer, respectively  (8, 9). M. septicum strain DSM 44393 T comprises 173 contigs (including 159 contigs of Ͼ1,500 bp) in 7 scaffolds. The draft genome size is 6,879,294 bp, with 66. 73% GϩC content. There are 56 RNAs. Of the 6,748 predicted genes, 6,692 (92. 63%) are protein-coding genes, including 4,752 (71. 01%) genes assigned to a putative function, 1,381 genes (20. 64%) annotated as hypothetical proteins, and 199 (2. 97%) genes identified as ORFans. We found 178 genes encoding resistance to aminoglycosides, ␤-lactamases, fosfomycin, fucidic acid, fluoroquinolones, macrolide-lincosamidestreptogramin B, phenicol, rifampin, tetracycline, trimethoprim, and glycopeptides.
A total of 119 type VII secretion system proteins were annotated by comparison with reannotated Mycobacterium tuberculosis strain H37Rv. The M. septicum genome encodes 487 prophages and phage proteins and 11 clustered regularly interspaced short palindromic repeats (CRISPRs), according to the fast phage search tool (Phast)  (10)  and the CRISPERfinder online software program (http://crispr. u-psud. fr/Server/). M. fortuitum members were determined to be the closest spe-cies to M. septicum based on the 16S rRNA gene sequence similarity. Accordingly, M. septicum presents 83. 32% nucleotide sequence identity of core proteins with M. fortuitum subsp. fortuitum, confirming that these are two distinct species within the same group of mycobacteria  (11). We identified 5,977 orthologous genes shared between M.
fortuitum subsp. fortuitum and M. septicum. Also, 715 genes present in M. septicum are not found in M. fortuitum. These data could serve to set up new molecular tools for the refined identification of M. septicum in both environmental and clinical specimens to identify precise epidemiological and clinical features associated with this emerging opportunistic pathogen. Nucleotide sequence accession number. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. CBMO00000000. 1. The version described in this paper is the first version.
Each observer noted (PsionWorkabout, Psion PLC, London, UK) the occurrence of each mounting behaviour on a hand-held PC and identified the performer of the behaviour and the receiver. As the outdoor area of the enriched housing was not visible, the pens were videotaped using cameras (SONY PC25-2230 P 1/3, Tokyo, Japan) linked to a multiplex (Advanced Technology Video DPX9 PAL, Washington, USA) and to a recorder (Panasonic TL 500, Osaka, Japan). The videotapes were then analysed for sexual behaviour with the same time schedule. Data were transferred to The Observer XT9 (Noldus, The Netherlands) to calculate the total number of actions performed by each animal for each series of observation. Taking into account that the mounting behaviour is a rare event, all data (i. e. 9 h of observation per pen) were summed up for the statistical analysis.
Statistical analyses For all analyses, the animal was considered as the statistical unit. Normality of the data was checked with the Shapiro-Wilk test using the SAS ® software (SAS 8). When necessary, a log transformation was applied to normalize the data (P → log(P + 1)) for mounting behaviour and skin lesions, (P → log(P)) for androstenone, testosterone and oestradiol at most ages. The influence of age on the time spent outdoors for E pigs was analysed within the season using the Mixed procedure of SAS ® , with animal as a random effect and age as a fixed effect. The influence of season (influence of the trial = fixed effect) on the time spent outdoors was analysed within the ages using the GLM procedure of SAS ®. Live weight, testicular weight, mounting behaviour, fat level of androstenone, number of skin lesions within each month of age and plasma levels of hormones within each month were analysed by analysis of variance using the GLM procedure of SAS ®.
The model included the fixed effects of season and housing and the season × housing interaction. Whenever possible, live weight was introduced as a covariate. When the season × housing interaction was not significant (P > 0. 1), it was removed from the statistical model. A similar procedure was applied to live weight. As it was not possible to normalize the skatole levels for analysing the housing or season effects, a non-parametric test was applied (Mann-Whitney test using the Anastat software). For the same reason, a non-parametric test for a related series (Friedman test using the Anastat software) was applied to analyse the influence of stage at sampling on hormones. To test the existence of relationships between parameters, the Pearson correlations were calculated using the Corr procedure of SAS ®.