dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , searching databases, applying predictive models and alerts, performing an expert review/ assessment, documenting and communicating the results and associated uncertainties). The use of traditional experimental evidence coupled with in silico information to support hazard identification and risk assessment also varies both across, and often within, organizations. Although not always, such ad hoc approaches may be time-consuming and the results poorly accepted. Standardization of protocols will enhance the acceptability of the methods and their results by end users. Additionally, there are misconceptions about when in silico predictions are appropriate to use as well as a lack of defined consensus processes for interpreting the result(s) of such predictions  (Bower et al. , 2017; SCCS 2016).
For each toxicological effect/mechanism, relevant information (as defined in the IST protocol) is collected, including any available experimental data as well as in silico predictions. The experimental data and/or in silico results are then analyzed and an overall assessment of the toxicological effect or mechanism is generated alongside a reliability score (defined in Section 2. 6. 2) that reflects the quality of the results. The assessment results and reliability scores for a range of relevant toxicological effects/mechanisms are then used to support a hazard assessment within the hazard assessment framework.
5. Supporting an expert review. For QSAR models, tools to help the expert review (see Section 2. 4) include the ability to allow examination of the descriptors and weightings used in the model, underlying training set data, and how the applicability domain assessment was defined. For expert rule-based systems, this could include how the alert was defined (including any factors that activate or deactivate the alert), any mechanistic understanding associated with the alert, citations, and any relevant known examples of alerting chemicals. Read across may be used when there are experimental data from high quality databases for one or more substances which are similar enough to the target chemical of interest. The Read-Across Assessment Framework (RAAF), or similar published and established frameworks, may be used to document the read-across assessment and to support its scientific plausibility  (ECHA 2017b; Patlewicz et al.
2. 3. 3 Running the in silico models-All in silico systems require an electronic representation of the chemical structure and any errors in this representation will result in invalid predictions. Therefore, it is important to ensure that the chemical structure is properly curated and entered following conventions set out by the model's developer, including appropriate representations for tautomers, aromaticity, salt forms, stereochemistry, charges, and specific functional groups (e. g. , nitro or carboxylic acid groups). It is possible that different formats (i. e. , SMILES vs. MOL files) may be processed differently. It is also important to verify that the software correctly interprets the structural representation during processing, particularly for complex molecules. For some types of chemicals, in silico models may not be applicable due to the structural representation or the unsuitability of the experiment assay for the specific chemical class.
Assessment of available experimental data Experimental data may have been previously generated and reported for a chemical being assessed, for example, in the literature or through a public or proprietary database. To support the identification of experimental data, each IST protocol will identify a series of relevant study types and specific result(s) corresponding to the identified toxicological effects or mechanisms, as discussed in Section 2. 2. To illustrate, in the assessment of the toxicological effect/mechanism bacterial gene mutation (part of the genetic toxicity IST protocol), the overall mutagenic or non-mutagenic results from a bacterial reverse mutation assay may be used. A more complex example is in the assessment of the toxicological effect/ mechanism of sperm morphology (part of the reproductive IST protocol).
• The structural diversity of the underlying chemicals for each significant descriptor may be reviewed as part of an expert review. Structural features that map to a large number of structurally diverse compounds would provide additional evidence that the toxicological effects or mechanisms associated with the descriptor could be extrapolated across different chemical classes (increasing reliability in the prediction), whereas a structural feature whose underlying data constitutes a congeneric series might not, especially if the query compound is structurally distant (decreasing reliability in the prediction). C. Analysis of physicochemical descriptors used by model (see Note B) • Is there any supporting information from the literature or elsewhere to support any correlation between the physicochemical properties identified as significant by the model and the toxicological effect/mechanism?.
Acknowledgments Research reported in this publication was supported by the  National Institute of Environmental Health Sciences  of the  National Institutes of Health  under Award Number  R43ES026909. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to thank  Sachin Bhusari  and  George Pugh  from the  Coca-Cola Company  for their input into the manuscript.
[1]. In Madagascar, the first isolation of B. pseudomallei occurred in Antananarivo in 1932 when Girard, a former Director of the Pasteur Institute of Madagascar, examined a dead guineapig inoculated with a submaxillary node of a slaughtered pig  [5]. The presence of this bacterium in Madagascar was also reported later  (1977)  by Galimand and Dodin working in the 'Whitmore bacillus laboratory' in the Pasteur Institute in Paris. They isolated and identified the bacterium from soil samples originating from the zoo of Antananarivo and from a pig farm in the same city  [6]. There are two isolates (770429-id6-and Soil1977-id7-) in the B. pseudomallei multilocus sequencing typing (MLST) database (https://pubmlst. org/bpseudomallei/) originating from soil collected from the Antananarivo region in 1977.
Blood cultures and respiratory samples were positive for a Burkholderia identified as B. thailandensis by a MALDI-TOF mass spectrometer (Bruker Biotyper, Bruker Daltonics, Bremen, Germany) but confirmed later as B. pseudomallei by qPCR targeting three genetic markers of the type III secretion system (orf1, orf13, and BsSCU2). It is noteworthy that B. pseudomallei was not at that time included in the IVD MALDI Biotyper database but in a separate database (SR: security-relevant) which was not available at the hospital. The strain was susceptible in vitro to ticarcillin/clavulanic acid, trimethoprim/sulfamethoxazole, and ceftazidime, and was resistant to meropenem and levofloxacin. The meropenem resistance (MIC = 12 mL/L) was unusual, and the presence of a possible metallo β-lactamase (MBL) was investigated with the RUO Etest MBL MP/MPI US 8/2 (bioMérieux, La Balme-Les-Grottes, France), but failed.
Sandy soil samples A4, A5, and A7-9 had been augmented with the dark soil A12. The soil samples were processed as described in Appendix B, and subsequent cultures were spread on Ashdown's agar plates (supplemented with colistin 50 mg/L) which were incubated at 37 • C. The plates were examined on a daily basis. Colonies with morphology typical for B. pseudomallei (pink/purple, flat, slightly dry, wrinkled-smooth but with surface roughness in the outer half of the colonies) were subcultured onto chocolate agar plates. Only samples A4, A8, and A12 gave rise to B. pseudomallei-like colonies on Ashdown medium. Identification of those isolates was confirmed by MALDI-TOF mass spectrometry (Bruker Biotyper, Bruker Daltonics, Germany) using the security-relevant database (B. pseudomallei with scores >2) and by PCR amplification targeting a genetic marker of the type III secretion system1 (TTS-1; 115-base-pair region within orf2)  [14].
Genetic and Genomic Relatedness between Strains Isolated in the Western Indian Ocean In recent years, whole-genome sequencing (WGS) has become a cost-effective method for strain genotyping, including for MLST. Using WGS data for phylogenomic analyses has also been instrumental in unravelling the early origins of B. pseudomallei in Australia, and its subsequent dissemination to tropical regions across the globe, including the western Indian Ocean region. Three studies to date have used WGS to investigate B. pseudomallei strains from western Indian Ocean islands  [19] [20] [21]. Sarovich and colleagues  [19]  were the first to identify an Asian origin for the Madagascan B. pseudomallei strains included in the phylogenetic analysis. Their study showed that the introduction of this bacterium into Madagascar was likely associated with the migration of Austronesian peoples (or their animals) from Indonesian Borneo approximately 2000 years ago  [19].
For each locus, sequences obtained were analyzed using the MLST database containing the sequences of alleles, allelic profiles, and information about the Burkholderia isolates, with analysis tools. This tool is stored on a publicly-available web-based database, allowing comparisons to be made between isolates from different laboratories (https://pubmlst. org/bpseudomallei/). MLST is the gold standard for comparison of isolates from different geographical locations due to high inter-laboratory reproducibility.
Acknowledgments: Authors would like to address a special thank you to all the doctors and medical assistants from the  CHU de Mahajanga. We also specially thank  Jay Gee ,  CDC, USA  for important information about the sequence types. A part of this work (which took place in Madagascar) was financed by  Institut Pasteur à Paris :  ACIP2011. We also thank  Le Dispositif Prioritaire de Recherche  et d'Enseignement en partenariat  One Health-Océan Indien  for allowing some of the authors to meet and discuss this manuscript. Acknowledgments: Authors would like to address a special thank you to all the doctors and medical assistants from the  CHU de Mahajanga. We also specially thank  Jay Gee ,  CDC, USA  for important information about the sequence types. A part of this work (which took place in Madagascar) was financed by  Institut Pasteur à Paris :  ACIP2011.
Glucose effectiveness (Sg), acute insulin response to glucose (AIRg), insulin sensitivity (SI) and the disposition index (DI) were calculated using the Bergman's minimal model  [50, 52, 53]  on the MinMod Millennium software (Ver 6. 02, MINMOD Inc. , 2001)  [54]. A schematic representation of the Bergman's minimal model has been described previously  [51]. The goodness of fit r 2 of models for all mares was 91. 5% (range  74. 6-98. 22 ) and all the parameters were estimated with a fractional standard deviation (FSD) <0. 5. Briefly, glucose and insulin are produced and released by the liver and the pancreas respectively in a basal state (basal plasma glucose concentration, Gb; basal plasma insulin concentration, Ib). After a meal, or an intra-venous injection of glucose, plasma glucose concentrations increase.
Statistical analysis Results are expressed as median [quartile 1 -quartile 3] and presented as curves (median and interquartile range IQR) or boxplots (minimum to maximum). Statistical analyses were performed using the R software (http://www. r-project. org, version 3. 4. 0. ). The longitudinal data (mare weight and BCS, plasma concentrations in mares and foals and foals' metabolic tests) were analysed using a type III ANOVA (car package  [63] ) on a mixed linear model (nlme package  [64] ) taking into account group (N or O), time and the interaction group:time as fixed effects and the individual as random effect. Age of the mare and sex of the foal were included in the model when statistically significant. When time and/or group:time interaction were statistically significant, a post-hoc Tukey test was applied using the lsmeans package  [65]  Foal and yearling measurements were analysed using a mixed quadratic model considering the group (N or O), the age of the foal (from birth to 18 months of age) and the interaction group:age of the foal as fixed effects and the foal as random effect.
When age and/or group:age interaction were statistically significant, a post-hoc Tukey test was applied using the lsmeans package. Age of the mare and sex of the foal were not statistically significant at any time and were subsequently not included in the model. Metabolic tests performed in mares in late gestation were analysed using a type III ANOVA (car package) on a fixed linear model considering the group (N or O) and age of the mare. Testicular data were analysed using a Principal Component Analysis of the FactoMineR package  [66]. The age and weight of the foal at gelding did not affect any of the results and were therefore not included in the final analysis. Radiographic results were analysed using a chi-squared test. Effects were considered significant when p-values<0. 05.
Acknowledgments This work was funded by grants from the " Institut Francais du Cheval et de l'Equitation " and by the " Fonds Eperon" (FOETALIM project ). The funders had no role in study design, data collection and analysis, decision to publish, nor preparation of the manuscript. The authors are grateful to the staff of the  Institut Francais du Cheval et de l'Equitation (IFCE) experimental  farm ( Domaine de la Valade, Chamberet, France ) for care and management of animals.
ACKNOWLEDGEMENTS The authors thank  Stéphane Barete  for her kind support providing the picture of an ISM patient. This work was supported by the  Austrian Science Fund : projects P-25937-B13, SFB  F4701-B20 ,  SFB F4704-B20 ,  SFB F6101 , and SFB F6106. M. A. received research grants from  Blueprint  and from  Deciphera , as well as honorarium from Deciphera. P. V. received a research grant from  Novartis , from  Blueprint , and from  Deciphera , received honoraria from  Novartis ,  Celgene ,  Pfizer , and  Deciphera , and served as a consultant in a global Novartis trial evaluating the efficacy of midostaurin in advanced SM. O. -H. received research grants from  Novartis ,  Celgene , AB science, and honorarium from AB science.
Animals Data were collected for animals of the AGP77 maternal rabbit line  (Hypharm, Roussay, France). Records from 23,773 purebred rabbits born between 2012 and 2016 were analyzed. This line was created from rabbits of the New Zealand breed in 1975. Animals have been selected for litter size and weaning weight (direct and maternal effects) since 2002. In our dataset, does were inseminated every 42 days and kits were weaned at 32 days of age. All rabbits were born and weaned on a nucleus farm, i. e. , in a highly bio-secure and controlled environment, where all candidates for selection were further tested. This farm is hereafter referred to as the selection environment (S). After being weighed at weaning, these rabbits were reared in two different environments: (1) the S environment or (2) sibtesting farms, which were 3 farms with less favorable sanitary conditions than the nucleus farm.
Genetic Parameters Analyses All traits were analyzed using a restricted maximum likelihood method, with the ASReml 3. 0 software  (Gilmour et al. , 2009). Variance components and heritabilities were estimated using single-trait animal threshold models with a logit link function for the binary disease traits and multiple trait linear animal models for the production traits. Genetic and phenotypic correlations between traits were estimated using multiple trait linear animal models. The models included a random additive polygenic effect for all traits, a random common litter effect for the disease resistance traits and WW, a random maternal environmental effect and a random maternal genetic effect for WW, and a permanent environmental effect to account for the repeated measurement of NBA for does.
General Parameters We used the deterministic simulation program SelAction 2. 2  (Rutten et al. , 2002)  to compare the expected selection responses for breeding schemes including resistance to non-specific disease. SelAction predicts responses to selection on pseudo-BLUP estimated breeding values. In our study, we used the option of discrete generations and 1-stage selection. We simulated a selection nucleus of 140 dams and 35 sires. We assumed 7 progeny per litter with a sex ratio of ½. The selection intensity was 15% for males and 25% for females. Rabbits were selected at 70 days of age. The genetic parameters used for the simulation were those obtained in the first part of the study and the variance components obtained with a linear model for Resist_S and Resist_Ch.
, 2015). Nonetheless, contrary to the previous study in which only the main disease syndrome was recorded, two disease syndromes could be recorded for each animal in the present dataset. This reduced the bias caused by the limited recording of multiple disease symptoms for the same animal. However, if the two main syndromes were from the same kind of disease (either digestive or respiratory), a third syndrome from another kind of disease would not be registered. The absence of genetic correlation could depend partially from this recording method. The composite trait Resist was genetically correlated with Resp and Dig, and the correlations were similar in the Ch and S environments. Resist could therefore be a good indicator for improving general disease resistance and reducing the sensitivity of rabbits to digestive and respiratory diseases.
Statistical analysis Data analysis was carried out using SAS 9. 4 software Copyright © 2002-12 by SAS Institute Inc. , Cary, NC, USA. The significance threshold was classically fixed at 5%. Homogeneity of the two groups at baseline (T 0 the 1 st week) for oxytocin and serotonin levels was analysed according to Student's t test using the TTEST procedure. For serotonin levels, t-test was involved because normality and homogeneity of variances were established. For oxytocin levels, and because of non-normality of this variable, the nonparametric two-sample Wilcoxon's test was employed using the NPAR1WAY procedure. For the analysis of short-term effects of straw provision on oxytocin concentration, because of a substantial individual variability between pigs and heterogeneity between the two groups at T 0 , differences in oxytocin levels between T 1 and T 0 were computed and analysed.
For serotonin, because of a substantial individual variability between pigs, differences in serotonin levels between T 1 and T 0 were computed and analysed. Differences between groups for T 1 -T 0 were verified according to Student's t test if normality and homogeneity of variances were established. If conditions were not established, a two-sample Wilcoxon's test was performed. The long-term effect (2 weeks) of straw provision on oxytocin was evaluated by examining the change of oxytocin levels from T 2 to T 3. For serotonin, it was evaluated by examining the change of serotonin levels between T 0 , T 2 and T 3. Data were tested from the assumptions of normality using the residual diagnostics plots and the UNIVARIATE procedure. Homoscedasticity was tested by Levene test using the GLM procedure.
Acknowledgements The authors thank the Sanitary Management of IRSEA for taking good care of the mini-pigs, particularly the veterinarian,  Elisa Codecasa , and the animal-keeper,  Hugo Tetelin. The authors also thank the entire IRSEA team for helping us to develop the project, including all the work that was done before the trial: the preparation for the arrival of the animals, the construction of the halls and pens, the habituation and socialisation period of our mini-pigs, and so on. Finally, the authors also thank  IRSEA's Board of Directors  for giving us the opportunity to create and carry out this project.
After four weeks of infection, the spleen and bone marrow presented similar inflammation scores, though the granulomatous inflammation decreased in both tissues afterward (Figure  2(b) ). The cellular changes in the BM of infected mice were estimated by flow cytometry. At 8 days of postinfection, we observed changes in the hematopoietic cell population. At day 8 of postinfection, the percentage megakaryocyte- erythrocyte progenitor (MEP) decreased compared to the BM of noninfected mice (data not shown). Contrarily, the percentage of granulocyte-monocyte progenitors (GMP) significantly increased. Likewise, neutrophils (PMN) and CD4 + lymphocyte populations significantly increased at 8 days of postinfection (Figure  3 (a)). The increase of CD8 + cells was evident, but not significant (p < 0 05).
Acknowledgments We would like to thank  Gerardo Ávalos  for his professional support and discussions of statistical data. This project was supported by the  International Centre for Genetic Engineering and Biotechnology  ( CRP/16/005 ),  Fondo UCREA, University of Costa Rica  ( 803-B8-762 ), and  Fondo Institucional de Desarrollo Académico de la UNA, FIDA  ( 0087-17 ). CG-J and RM-C received a fellowship from  Ministerio de Ciencia Tecnología y Telecomunicaciones ,  MICITT  ( PNM-001-2015-1  and  PND-014-2015-1 , respectively). This work was also supported by the  INSERM , the  CNRS , and the  Agence Nationale de Recherche  ( ANR-11-LABX-0054 ) ( Investissements d'Avenir-Labex INFORM)  and ( ANR-11-IDEX-0001-02 ) ( Investissements d'Avenir-A * MIDEX ).
e. a target weight, a fattening duration or the total amount distributed). Each feed rationing plan defines the amount of feed or energy provided to pigs as a percentage of ad libitum intake or an amount per pig and per day for each period of the plan. The model shifts from one period to another in the feed rationing plan according to one of several criteria: fattening duration, age, weight, cumulative quantity of feed consumed or quantity of feed distributed. These rules are applied at the specified level: individual, pen (average value for pigs in the same pen) or room (average value for pigs in the same room). The feed rationing plans, feed sequence plans and feed composition are determined with the InraPorc software tool  (InraPorc®, 2006)  and are sent as inputs to the model.
Environmental indicators using Life Cycle Assessment The environmental indicators are environmental impacts calculated by LCA. The model calculates environmental impacts of the fattening of each slaughtered pig, while environmental impacts of producing piglets and breeding sows in upstream farrowing and post-weaning units are calculated according to average performance (IFIP, 2016) and feed composition (Supplementary Material Table  S2 ). Impacts of feed ingredients came from the ECOALIM data set (Wilfart et al. , 2016) and those of their transport by road to the feed factory were based on distances assumed for pig production in Western France  (Garcia-Launay et al. , 2016). The amounts of electricity and natural gas required to grind and pelletise the feeds at the feed factory came from Garcia-Launay et al.
(2014). By default, transport distance of the feed from the factory to the pig farm was assumed to be 30 km. Background data (i. e. impacts of road transport; electricity, light fuel oil and natural gas used in the pig unit) came from the ecoinvent database V3. 1  (Weidema et al. , 2013). Environmental impacts of manure use were estimated using a system expansion approach (Garcia-Launay et al. , 2014) in which we assumed that the manure produced replaced a certain percentage of mineral fertilisers, indicated as a mineral fertiliser equivalent (MFE, %). We assumed that MFE was 75% for manure N, which was assumed to lose 5% more N as nitrate (NO 3 ) than mineral fertilisers  (Nguyen et al. , 2010) , and that MFE was 100% for manure P  (Sommer et al. , 2008). Processes for manure transport and spreading came from AGRIBALYSE V3.