dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This review is part of the  FACCE-JPI  " Global Network " project. Authors acknowledge national funding from the  Department for Environment, Food and Rural Affairs (UK) , from  INIA (Spain) , the  Ministry of Economic Affairs  (The  Netherlands ; project  Global Research Alliance on Agricultural Greenhouses Gases ,  BO-20-007-006 ),  USDA-NIFA (USA) ,  French National Research Agency  through the program  FACCE-JPI program ,  Agricultural GHG Research Initiative for Ireland (AGRI-I) , and  BLW (Switzerland).
Statistics. To compare the behaviours of sows in the different situations, as data (i. e. proportions of occurrence of each behavioural reaction) were not distributed normally, we ran non-parametric tests. Statistics were done with StatView ® 5. 0 (SAS Institute Inc, USA). The significance level was set at p < 0. 05, and data in the text are presented as medians and quartiles. Mann-Whitney tests were used to compare the two independent groups constituted of the Experimental (V) and the Control (C) sows. Wilcoxon tests for dependent data were used to compare positive and negative handling sessions for the same individuals. In addition, Fisher's exact tests allowed us to compare the number of sows expressing aggression between negative and positive sessions. To analyze the variations in piglets' distress calling, we ran ANOVA Mixed Model analysis (NLME package in R) with mother and offspring identities as random factors.
All meaningful factors were first included as fixed factors in the model (i. e. according to the experimental test: group, voice, voice emotional valence, phase -for SIT2 only). As there was no significant voice effect in any model tested (p > 0. 05), data of V1+ and V2+ piglets were pooled and then called group V. To control for litter effects, the litter was added as a random effect in all models. We thus compared V and C piglets in all situations and we compared Phase 1 and Phase 2 in SIT2 situation.
Acknowledgements We thank  Caroline Coste  for assistance in collection of the data,  Daniel Chevrel ,  Yannick Surel ,  Henri Renoult  and the staff of the experimental farm for assistance in organizing the experiment. The study was supported by an  INRA   grant  " Crédit incitatif Phase " 2013, an incentive action from the  University of Rennes 1  ("Défis émergents") and benefitted from discussions from the  GIS Cerveau-Cognition-Comportement. We are grateful to  Dr Annick Jouanjean  who created the text read by the experimenters, and to  Prof. Adrian Craig  and  Dr Jodi Pawluski  who reviewed the English of the manuscript.
2 ). Relevant information about DNA methylation, RNA and miRNA expression studies and chromatin structure and modification data can already start to be derived from the literature, together with analyses to identify markers for detection of NGTxC with epigenetic activity. This information can be used in combination with the key event assay blocks identified in Table  1 , specific examples being endocrine receptor  (Zhang and Ho, 2011, reviewed in Greally and Jacobs, 2013)  and xenobiotic receptor activation  (Lempiainen et al. , 2011). One of the major challenges when studying epigenetic changes inferred by NGTxC is to link these epigenetic changes to adverse outcomes. Preliminary review suggests that association of NGTxC exposure with miRNA's may contribute to the WoE for downstream adverse outcome pathway (AOP) linkages, but specific epigenetic machinery may differ between adverse outcomes.
The underlying blue lines with connecting nodes are symbolic of the NGTxC AOP causality network, where key events (KE) and key event relationships (KER) can be identified on the basis of the mechanisms and MoA identified in Table  1. As work on the IATA progresses, these nodes will be completed as KE, KER and relevant in vitro assays for both the IATA and as potential OECD TGs are identified and assessed for relevance and readiness. ALTEX 33(  4 ), 2016 370 studies were actually performed, as from the 2010 deadline. As they are rarely performed, few NGTxC might be identified via the assessment of these in vivo studies. Published final decisions of ECHA can be found at http:// echa. europa. eu/information-on-chemicals/dossier-evaluationdecisions. The REACH regulation permits registrants to adapt the standard testing regime to fill data gaps using existing data, weight of evidence, (Quantitative) Structure Activity Relationships ((Q)SAR), in vitro methods, and grouping of substances and read-across  (ECHA, 2008 (ECHA, , 2014)).
-Qualitative: where AOPs / MoAs have qualitative understanding of critical components of the AOP / MoA. Pathways are often based on one or a few well-studied stressors where there is experimental evidence for the most critical KEs and the AO. The level of confidence in the AOP is moderate. -Semi-Quantitative: where AOPs / MoAs have, in addition to qualitative understanding of the entire AOP / MoA, semi-quantitative understanding of some of the KEs. Pathways are based on multiple compounds and/or stressors evaluated at several KEs and the AO. The level of confidence in the AOP is moderate to high. -Quantitative: where AOPs / MoAs have, in addition to quantitative understanding of critical components of the AOP, empirical data across the spectrum of KEs and AO. These pathways are based on many compounds evaluated for all KEs and the AO, so in vitro effects can be scaled to in vivo effects for risk assessment.
Inflammation is a major early stage, since these are outside the scope of the intrinsic hazard profile. The reader is referred to more authoritative sources for the conduct of an exposure assessment, e. g. ,  Embry et al. (2014). The governance of IATA activities is in development under the auspices of the OECD. The intention is that at least core elements of IATAs can be developed that will also fall under the Mutual Acceptance of Data (MAD) principle that underpins the OECD Test Guideline Programme (Section 3), such that they are mutually accepted in member countries. Here, we explore the basis for the development of an IATA for NGTxC, aiming first to address hazard identification and how this can start to be approached internationally in a harmonized manner (and under the auspices of the OECD).
SAR models only use (sub)structure information and can therefore be seen as a more formal way of performing read-across with a given reference set of data, as the property of one or more substances is directly used to predict the property of the substance of interest. It is noted that conceptually (Q)SARs should be developed utilizing a large, good quality database using robust scientific and statistical concepts and as such should represent the most formalized non-testing approach. In contrast, read-across has the disadvantage of representing a much less formalized and therefore more subjective non-testing approach, but it may provide more specific information. In the future a combination of (Q)SARs with read-across, i. e. , a local validity analysis of the models, may be of greater reliability for decision making.
However, there were several false negatives of concern, and the data used to build this model is noted to be limited, but the phase 2 study, may overcome some of these data quality problems. For both predictions (positive and negative), the similarity of the substance of interest to the substances used in the training datasets of the models (the so-called applicability domain of the models) is crucial. Although a (Q)SAR model might be able to give a prediction for any (organic) substance, if this prediction is far outside of the applicability domain, the reliability of the prediction will be low. Amongst the model builders there are also different interpretations of the definition of the term in risk assessment and will not be delivered by the use of, e. g. , alert models such as DEREK-Nexus 3  , or ToxTree 4   (Patlewicz et al.
A further distinction should be made between models that are "only" alert models, such as the DEREK-Nexus expert system, ToxTree, or the profilers in the OECD QSAR Toolbox, versus models that also try to correlate the absence of genotoxicity or carcinogenicity to specific chemical and/or physicochemical properties. The first are considered to give only a valid positive prediction, i. e. , the absence of an alert is not a prediction of the absence of genotoxicity and/or carcinogenicity. In contrast, models such as VEGA, TOPKAT and MultiCASE give valid predictions of the presence and absence of genotoxicity or carcinogenicity, the latter being based on the similarity of a substance with the negative (non-carcinogenic) substances in their training datasets. With the large diversity of chemicals that might interfere with the NGTxC mechanisms and the many potential molecular targets, the establishment of a single (Q)SAR for NGTxC is an unrealistic goal and the diversity makes development of (Q)SAR models for identifying NGTxC a challenging process.
Otherwise regulatory acceptance will be limited due to concern for false negatives on one hand or concern for false positives on the other hand, such that gains in the 3Rs as well as gains in regulatory testing and assessment throughput will be minimal. Expectations for the time frame necessary to develop such an IATA vary between experts and in the end depend upon resources that are made available. The time frame will also be influenced by how far the IATA can accommodate defined testing approaches for specified chemical spaces (the "chemical applicability domains" referred to in the previous section), for more robustly characterized mechanisms of action compared to less well characterized mechanisms, and for categorization and read-across based on a larger database including other animal data or for specific regulatory areas, e.
Statistical analysis Coding and classification of variables. Raw data were entered into an Access database (Microsoft Corp. , Redmond, WA). They were then transferred into SAS 9. 2 (SAS Institute Inc. , Cary, NC) to build the variables of interest and carry out statistical analyses. The treatment was coded as described by  Ravinet et al. (2014)   [25] : a combined variable between treatment and time was created ('week-trt') and divided into 17 categories: for a treated cow, 'week-trt' took the values -1 (reference point before treatment), 0, 1, 2, 3. up to 14 (after treatment period, week 0 being the week of treatment); and for a control cow, 'weektrt' only took the value 99 whatever the week. The 3 production-based indicators were categorized. Three classes were constructed for parity (1, 2, 3 and more), and days in milk at the time of treatment (DIMt 100, 100<DIMt 200, DIMt>200 days).
Anthelmintic treatment was administered between 2012-04-16 and 2012-05-29, that is to say 41 to 68 days after turn out. In two herds (n°4 and 10), the actual time spent on pasture between turn out and treatment was shorter (37 and 34 days, respectively) because cows returned to the barn for 4 and 1 weeks respectively, because of adverse weather conditions. 47 cows were excluded from the dataset for the following reasons: insufficient number of MP data recovered before treatment to calculate the pre-treatment production level (10 cows), days in milk at the time of treatment exceeding 516 days (11 cows), daily MP data not correctly recorded on farm (1 cow), duration of the post-treatment follow-up shorter than 4 weeks or health issue indicated by the farmer (25 cows). Finally, 578 cows were included to carry out our statistical analysis: 295 treated cows and 283 control cows, 31 to 72 cows per herd.
The final dataset is described in Tables  2 3 4. Among these 578 cows, 556 were present on the two visits, and 22 were sampled only on the second visit. Thus, serum pepsinogen levels and anti-Ostertagia antibody levels measured on the first visit were not available for these 22 cows. Several cows were dried off or sold during the period of post-treatment follow-up. The number of cows therefore decreased gradually from one week to another, similarly in treated cows and control cows. On week 14 , there were still 428 cows in the dataset (226 treated cows and 202 control cows). For each indicator, there was no difference in the proportion of treated cows between categories (p>0. 05) (Tables  2 3 4 ).
Acknowledgments The authors thank all the farmers for their participation, their help and co-operation. They are very grateful for all the assistance provided on the field and at the laboratory by  Jean-Yves Audiart ,  Marie-Astrid Malard ,  Françoise Leray ,  Emmanuelle Blandin ,  Didier Billon ,  Manon Joly  and  Melissa Girard  (ONIRIS). The authors would also like to thank all the veterinary practitioners who provided their support to the recruitment and on the field. The study was conducted within the  UMT Cattle Herd Health Management.
The study was funded by the  CASDAR  project  N°1127  ( Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt ). CASDAR:  Compte d'Affectation Spécial Développement Agricole et Rural. Authors who received the funding: NR, AC, CC,  N. Bareille. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The sequencing was performed on a HiSeq2000 in single read 50nt mode. Concerning ChIP libraries, the TruSeq ChIP sample preparation kit (Illumina Inc. , USA) was used according to the manufacturer's recommendations on 30 ng of DNA per condition. Briefly, DNAs were blunt ended and adenylated on their 3' ends. Illumina's indexed adapters were ligated to both ends. Ligated DNA were enriched by PCR and sizes separated by electrophoresis. Size selection was performed at 400 base pairs (bp). The quantitative analysis of the DNA library was carried on Agilent High Sensitivity chip and qPCR (Applied Biosystems 7500, SYBR Green). The sequencing was performed on a HiSeq2000 and HiSeq2500 in single read 50nt mode. RNA-Seq and ChIP-Seq reads are available at the NCBI-SRA under the accession numbers SRP071285 (RNAseq on both sexes and ChIPseq on males) and PRJNA236156 (ChIPseq on females).
In order to be consistent in the further differential analyses between the different samples, all datasets were adjusted to the smallest by randomly picking 65,844,021 reads from each file. Exon-intron structure was then reconstructed with Cufflinks (v2. 1. 1) [58]  without any correction parameter: neither quartile normalization, nor bias corrections were applied. All the obtained data were joined with Cuffmerge (v1. 0. 0)  [58]  without any genome reference nor guide, in order to create a S. mansoni de novo reference transcriptome containing the exon-intron structures of the five developmental stages. The GTF file of this transcriptome is available at the IHPE laboratory webpage  [59]  and the sequences of all the assembled unique transcripts (TCONS) in the S5 Table (sheet1). We quantified each sample read abundance by mapping each condition to this de novo reference transcriptome with HTseq (v0.
6. 1p1) with the overlap resolution mode union  [60]. Finally, the differential gene expression levels between sexes were analyzed with the DESeq package (v1. 12. 1)  [61]. We carried out five comparisons: male vs female cercariae, male vs female schistosomula s#1, male vs female schistosomula s#2, male vs female schistosomula s#3, male vs female adult worms (Fig  1 ). Considering one stage and one transcript, significant difference in expression between sexes was evaluated according to the adjusted P-value (Padj) for multiple testing with the Benjamini-Hochberg procedure which controls false discovery rate (FDR). Genes that were significantly (Padj <0. 05) overexpressed in one sex compared to the opposite sex, were defined as "sex-biased genes".
Functional annotation The de novo assembled transcriptome was entirely and automatically annotated (S5, sheet2). Blastx searches against the non-redundant database of the NCBI (14-oct-2013) were performed on a local server using BLAST 2. 2. 26+ version  [62]. XML files were loaded onto Blast2GO for gene ontologies (GO), mapping and annotation with version b2g_sep13 of the Blast2GO database  [63]. InterProScan 5-44. 0 version  [64]  was then used and Interproscan GO were merged to Blast2GO. Using BLAT (v34)  [65]  we aligned the de novo transcriptome to the S. mansoni coding sequences of the reference genome v5. 2 (ASM23792v2. 30) got at the Ensemble Genomes resource  [66] , setting the minimum score to 50. In the case of alignment of a Cufflinks gene (XLOC_ID) to multiple reference genes (Smp_IDs), the hit with the higher identity was considered as the correct alignment.
Reciprocally, only the higher coverage for a reference transcript was conserved. We then use Cuffcompare, an associate utility program of Cufflinks (v2. 2. 1) in order to characterize the type of matches between the Cufflinks transcripts and the reference transcripts (v5. 2). Functional analysis introduced in this paper mainly consider the three stages cercariae, schistosomula s#2 and adults because these three stages display the highest number of significant sex-biased genes, due to the quality of the biological replicates  [61]. Concerning the two other schistosomulum stages s#1 and s#3, we detected less than 100 significant sex-biased genes (Padj<0. 05). Thus, only the 100 best adjusted P-values were analyzed (S2 Table ) and heatmaps revealed the consistence of replicates in these stages (S1 supporting information, slide5).
Gene Ontology sex-comparative enrichment. To identify GO terms that were significantly up-or down-regulated between males and females, a Blast2GO enrichment analysis was performed (version 2. 6. 4) (S3 Table)  [63]. Six test sets were used, corresponding to the sexbiased transcripts of three stages: cercariae, schistosomula s#2 and adults. Increases in GO terms were considered statistically significant at P < 0. 02 (Fisher exact test). We focused our analysis on biological processes only. The number of GO terms in each category was normalized towards the total number of enriched GO terms for each dataset. Functional clusters of sex-biased genes. For each gene (XLOC), we considered only the annotation of the longest unique transcript (TCONS) for manually sorting into 16 functional categories based on their sequence homology (S4 Table ).
In these 16 functional categories, we manually picked sex-biased transcripts depending on the function of their orthologs and sorted them into six functional representative under-categories related to: "homeotic genes", "growthfactor pathways", "steroid pathway", "mobile genetic elements", "splicing" and "chromatin modifications". The transcripts of each category were then clustered according to the developmental pattern of their sex-biased expression (represented by the log10 fold-change) using Gene Cluster 3. 0  [67]  software with the complete linkage method and hierarchical parameters. Graphic representations were obtained with Java treeView software 1. 1. 6r4 version  [68]. Functional de novo annotation of the 100 best sex-biased genes for each stage. For the five developmental stages, the top 100 of the sex-biased genes (100 best Padj) were de novo manually and separately annotated using Blastx (v2.
List of ID numbers for genes mentioned in the text Protein coding genes mentioned in the text are accessible on GeneDB (  [70] , http://www. genedb. org/) under the following accession numbers: Smp_093230: actin protein 10 (Sm-arp10) / Smp_197220: subunit of the oligosaccharyltransferase (Sm-RLP35) / Smp_089880: FAD dependent oxidoreductase domain containing protein (Sm-fad oxidoreductase) / Smp_196410: dachshund / Smp_000530: zinc finger transcription factor gli2 / Smp_141030: EGF receptor kinase substrate 8-like / Smp_035260: EGF receptor kinase substrate 8-like / Smp_134550: Neuropeptide (Sma-npp-27) / Smp_212730: tyrosine kinase, TK group, Src family (SmTK3) / Smp_174880: FOG / Smp_103470: protein mago nashi / Smp_045950: transformer 2 protein / Smp_009600: serine: threonine protein kinase PLK1 (polo-like kinase 1) / Smp_159800: MEG-2 (ESP15) family / Smp_159810: MEG-2 (ESP15) family / Smp_010550: MEG 15 / Smp_163630: MEG-4 (10.
3) family / Smp_033600: Dicer 2 / Smp_118190: staphylococcal nuclease domain-containing protein / Smp_165220: polycomb protein EED / Smp_006250: polycomb protein Scm1 (Sex comb on midleg homolog, Scm1). miRNA precursors mentioned in the text are accessible on miRBase (  [71, 145] , http://www. mirbase. org/index. shtml) under the following accession numbers: MI0027256: sma-mir-1a / MI0027255: sma-mir-8458 / MI0027196: sma-mir-125c / MI0027285: sma-mir-8483 / MI002 7247: sma-mir-8451 / MI0027258: sma-mir-8459 / MI0027222: sma-mir-8429.
They were defined as "sex-biased genes" and represent 20. 62% of the total number of expressed genes. For cercariae, schistosomula s#2 and adults, we identified respectively 5,264 (2,353 male-biased and 2,911 female-biased); 1,534 (1,040 male-biased and 494 female-biased) and 1,645 (1,043 male-biased and 602 female-biased) sex-biased genes (Fig 2 ). Ninety-eight male-biased and 26 female-biased genes were consistent through all the stages (Fig  2 ). Interestingly, several genes are alternatively more expressed in females or in males, they were qualified as "sex-switching biased genes" (Fig  2D ). To further support our transcriptomic analysis, qPCR experiments were performed to validate the expression patterns obtained from the DEseq analysis. Forty-three transcripts were randomly tested and a Spearman Rank Gene Ontology analysis: Disparity in perception of environmental factors, metabolism and chromatin structure between sexes Among the 7,168 "sex-biased" genes, 2,468 corresponded to known genes (S4 Table, sheet20) and 1,598 other could be de novo functionally annotated by the blast2GO analysis (S4 Table, Sheet19), totalizing 4,065 functionally annotated sex-biased genes.
To gain insight into major biological processes that would be enriched during development in either male or female individuals, we performed an exact Fisher test using the blast2GO software, focusing on the "biological processes" ontology. Enriched GO terms are presented in the S3 Table. To obtain a more synthetic overview, we manually sorted the GO terms into more general categories and considered the GO term enrichment for each of them (Fig  3A ). Of particular interest in the context of host-parasite interaction, we detected that the response to "environmental stimulus" occurred in cercariae at the same level for both males and females (Fig  3B ). Nonetheless, GO terms were different and indicated a distinct perception of environmental factors (S3 Table ). Female cercariae displayed a better "response to light" and "mechanical stimulus" than males.
The region  Languedoc Roussillon  ( Chercheur d'avenir Schistosex , recipient: JB) provided the financial support for sequencing data and materials. The  Burroughs Wellcome Schistosome Toolbox Collaborative Research provided a Travel Award to MP for collaborative Conclusions/ Significance MP 's salary was funded by a grant from the "ecole doctorale energie environnement ED305" of  Perpignan University Via Domitia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Subjects Przewalski (E. ferus przewalskii) and domestic (E. caballus) horses were used in the study. For the Przewalski horses, the data obtained in the context of a former study  (Bourjade 2007)  have been reanalysed in order to look more precisely at yawning behaviour. The Przewalski horses (PHs, N = 19) had been observed during two periods (on May and June 2004 and on April and May 2005) at 'Le Villaret' (France), where they are kept in a large 280-ha enclosure. Because they are involved in a reintroduction program, the horses are left with a minimum of human interference (only some hay in harsh winters); groups have formed naturally and were moving freely for vital resources within this large enclosure. Behavioural data were available for adult males (N = 8) from two bachelor groups (composed of four to six non-reproductive adult males) and for 1-and 2-year-old male (N = 5) and female (N = 6) horses from five independent harems.