dataesr/scientific-paragraphs-categorization huggingface dataset:
rattus. Similarly, only rrs2 was amplified from six out of the seven RT-PCR-positive dog samples and from one out of the two RT-PCR-positive P. seychellensis comorensis samples. Lastly, no sequence could be obtained from the single zebu sample testing positive by RT-PCR. In addition, for seven PCR-positive R. rattus samples failing to produce any data using the MLST scheme, we successfully amplified and sequenced the rrs2 gene using an alternative primer set (LA-LB). Altogether, we could identify leptospiral diversity at the species level for 21 R. rattus, eight T. ecaudatus, six dogs and one frugivorous bat from Mayotte (see Table  1 ). Rattus rattus-borne Leptospira spp. appeared genetically diverse as L. borgpetersenii, L. kirschneri and L. interrogans were identified for thirteen, 5 and 3 samples, respectively.
Acknowledgments Permits were issued by  Direction de l'Environnement, de l'Aménagement et du Logement (DEAL) of Mayotte. We thank  Office National des Forêts, Conseil Général of Mayotte and Association Naturalistes Environnement et Patrimoine de Mayotte  for allowing animal capture on conservation sites. For Madagascar samples, permits were provided by  Malagasy national authorities: Ministère de l'Environnement et des Forêts, Madagascar National Parks  and the  Département de Biologie Animale of the Université d'Antananarivo  ( N°238/14/MEEF/SG/ DGF/DCB. SAP/SCB ). We are also grateful to  Catherine Dioniso  in helping to locate some bat roost sites. COOPADEM provided the field group laboratory space on Mayotte and we would.
for t-tests only, whether tests were one-or two-tailed and justification for the use of one-tailed tests; 6. description of the α level (e. g. , 0. 05) that defined statistical significance; 7. the name of the statistical package or program used in the analyses. In this situation we considered reporting complete even if only the program, rather than the package, was reported, that is, both SAS ® and SAS ® PROC MIXED were considered 'complete reporting. ' If at least one but not all of the above were reported, then we considered statistical analyses 'partially reported. ' The presence or absence of each REFLECT checklist item was independently evaluated by two reviewers. Disagreements were initially resolved by one of the reviewers. Where there was disagreement between reviewers about the presence of a checklist item, one reviewer would re-evaluate the article.
Acknowledgements The authors would like to acknowledge support from the  French Ministry of Agriculture  in funding our review of the literature and carrying out interviews (study  SSP-2014-061 ). They also thank all the breeders and representatives of breeding programmes in the ruminant, pig, and poultry sectors for the time they gave for our interviews. Thanks also go to staff from  Institut de l'Elevage, Institut de la Filière Porcine, and Institut de l'Aviculture , who helped with the interviewing.
g. the mammalian order Artiodactyla  14  ). In addition, these studies have led to conflicting results because they both failed  12, 13  and succeeded  14  to detect the expected lower actuarial senescence rate (i. e. the rate of decrease in annual survival with increasing age) in zoos. Lastly, none of these studies included survival metrics other than senescence rate, such as longevity or age at the onset of senescence. Therefore, whether the common belief that mammals in zoos outlive their wild counterparts holds true remains unknown. To address this question, we compared a set of survival metrics derived from life tables available from the literature for males and females of free-ranging populations of 59 mammalian species (including eight different orders, see Supplementary Figure  S1 ) to those derived from the data on captive specimens of the same species from the Species360 database  13, 15  (formerly named International Species Information System database, ISIS).
Methods Life tables. Zoo and wild population life tables were compiled from the Species360 database and literature, respectively (see Lemaître et al. 14  for more details). Concerning free-ranging populations, publications containing life-tables from semi-captive populations were excluded to allow a strict comparison between captive and free-ranging populations. For 25 species, we collected several life tables from the same or different populations. When available, we gave preference to life tables obtained from longitudinal data. When several life tables of a given quality were available, we averaged them. When life tables were given in months or not with an integer of years, a standardization was made to obtain the survival at each integer age. For 9 species, the total number of individuals followed or considered was not given in the focal study.
As in captivity the sex ratio could be biased due to the culling of some young males during the first year for management issues (mostly in ruminants), we only computed parameters when at least 25 individuals for each sex of each species were alive at 1 year of age to get accurate estimates of age-specific survival. We finally obtained a dataset of 52 species for which data for both females and males were available, with one additional species with male-only data (leading to 53 species in males) and 6 additional species with femaleonly data (leading to 58 species in females) (Supplementary Table  S1 ). For both captive and wild populations, we made the same calculations to obtain exactly comparable life tables. For visual comparison only, we included data from females of the two elephant species  26, 27  and sex-combined data from the common hippopotamus  21  in the resulting data plots but did not include them in the analyses, as their data did not correspond to the data selection criteria stated above.
To assess whether the patterns we report held when accounting for size differences among species, we included log-transformed body mass as a covariate in our models in a secondary analysis. We collected information about sex-specific mean adult body mass from the literature for each species analyzed (Supplementary Table  S2 ). Therefore, for each of the four survival or senescence metrics in zoos and for a given sex, the full model included the corresponding wild metric and mean adult body mass as covariates, and the two-way interaction between the wild metric and data quality (as a fixed factor using longitudinal data as the reference). We then reduced the model by testing nested models by likelihood-ratio tests (LRT) so that the final model only included variables with statistically significant effects.
Acknowledgements Data reported in this paper originate from Species360 for captive life tables and from literature for free-ranging life tables, and are available in the Supplementary Materials (Tables S1 andS2). M. T. is funded by the  French Ministry of Education and Research. We thank  Jeanne Peter Zocher  of the  Vetsuisse Faculty (Zurich)  for the permission to use her images of hippopotamus, elephants and lion. We also thank  Jean-Michel Hatt ,  Sandra Wenger  and  Stamos Tahas  for comments on the manuscript.
e. dominance and heterosis)  [4, 5]. In this study, we developed and tested on a real pig dataset a single-step terminal-cross model to estimate the genetic parameters of several production and quality traits. This model is based on the model of Wei and van der Werf  [6] , with an extension to include PB genotypes. The theoretical accuracies of the estimated breeding values from the mixed model equations, a pedigree-based terminal cross model, and two univariate single-step models for PB and CB performances are provided. Predictive ability of the models is also evaluated in cross-validation.
Animals and data The experiment was conducted according to the French guidelines for animal care and use (http://ethique. ipbs. fr/ sdv/charteexpeanimale. pdf ). Animals were produced by the four French breeding companies of the Bioporc group (ADN, Choice Genetics France, Gène+, Nucléus) involved in the UtOpIGe project ANR-10-GENOM_BTV-015, which aims at investigating the feasibility of implementing genomic selection in pyramidal breeding schemes. A large number of traits that are difficult to measure under field conditions were recorded in several PB and CB populations that were raised in the same environment. In a first step, PB Piétrain and CB Piétrain × Large White animals were produced on selection and multiplication farms and tested at a single test station. In a second step, PB Piétrain sires were mated with sows of various CB and PB populations in order to test pigs that represent usual commercial production crosses at the same test station and to validate the results obtained from the first step.
Data were obtained in accordance with the national regulations on the welfare of animals used in research. At the slaughterhouse, carcasses were chilled in a cooling room at 4 °C for 24 h and right half-carcasses were cut  [7]. LM was estimated from a linear combination of the weights of cuts that were expressed as a percentage of the cold half-carcass weight for ham, loin and backfat  [8]. Ultimate pH of the longissimus dorsi muscle was measured using a Xerolyt electrode (Mettler-Toledo, Australia) and a Sydel pH meter (Sydel, France). DL was measured on a sample of loin of about 130 g (at the 13th lumbar vertebra). After weighing the samples, they were placed directly in a polystyrene tray, covered with polyethylene film and stored at 4 °C for 48 h, such that each slice formed a 40-degree angle with the horizontal plane.
Then, the samples were wiped gently and weighed again. The DL was quantified as the difference between the two weights, expressed as a percentage of the initial weight. After DL measurement, the samples were frozen until IMF was measured by magnetic resonance imaging  [9]. A separate pedigree file was constructed for each PB line. Piétrain and Large White pedigrees were constructed up to five generations back from the Piétrain boars for which data were available as PB or sires of CB offspring and from the Large White sows for which data were available as dams of CB offspring, respectively. The 90 Pietrain boars and their PB descendants were genotyped using the Illumina Porcine SNP60 BeadChip (Illumina, Inc. , San Diego). SNPs with a call rate lower than 0. 90 and a minor allele frequency lower than 0.
Parameter inference A Bayesian framework was adopted for inference to express uncertainty about the unknowns through the use of probability density functions. Flat prior distributions were assumed for the parameters of the systematic effects and the (co)variance components. The Gibbs sampler algorithm was used to estimate the marginal posterior distributions of the systematic effects and the (co)variance components using the GIBBS1f90 software developed by Misztal et al. [18]. Single chains of 250,000, 500,000 and 250,000 iterations were run by discarding the first 25,000, 50,000 and 25,000 iterations of each chain for the PED, GEN and GEN_UNI models, respectively, for each analyzed trait. Longer chains were run for the GEN models due to the less sparse structure of the single-step equations, which may preclude good mixing of the chains.
Theoretical accuracies of estimated breeding values The posterior mean (co)variance components obtained with the Gibbs sampler for the GEN model were used as the true values to obtain best linear unbiased prediction (BLUP) estimates for the breeding values in the different models using the BLUPf90 software  [18]. The theoretical accuracy of the estimated breeding value for the ith individual for the kth (k = A, AC) performance with the mth y C = X C b C + W C p C + Z AC u AC + Z BC u BC + e C. model (m = GEN , PED, GEN _UNI) (EBV i,k,m ) was calcu- lated as  [13] : where PEV i,k,m is the prediction error variance of its EBV i,k,m , F i is the inbreeding coefficient of individual i, which was computed from the pedigree using the INBUPGf90 software  [21] , and σ k 2 is the additive genetic variance of PB or CB performance.
Assessment of predictive ability The ability of the models to predict yet-to-be observed phenotypes was compared using sixfold cross-validation. First, BLUP solution estimates for the systematic effects obtained with the GEN model were used to obtain adjusted phenotype records, separately for each trait. Second, sires were randomly split into six approximately equal subsets. All records of the offspring of a sire were assigned to its respective subset. BLUP parameters were estimated based on five of the six data subsets, referred to as the training set, and the predictive ability was assessed in the remaining dataset, which was used as the testing set and considered as the yet-to-be observed phenotypes of the candidate sires. The training-testing cycle was repeated six times by rotating the six subsets used for training and testing, with each subset used only once for testing and five times for training.
Acknowledgements The authors acknowledge funding from the  UtOpIGe   ANR-10-GENOM_BTV-015  project and the  INRA SelGen metaprogram projects (SelDir  and  X-Gen ) and  BIOPORC. The authors are grateful to the  French breeding companies of the Bioporc group (ADN, Choice Genetics France, Gene+, Nucléus)  for providing the animals and to the staff of the test station of  Le Rheu  for collecting the data. We are also grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing and storage resources, and to  Ignacy Misztal  and coworkers for the blupf90 suite of programs.
This particular guideline requires, in order to use QSAR models for regulatory purposes, a 'mechanistic interpretation, if possible'. The 'mechanistic interpretation' refers to the imputation of a directed causality between a substance and its toxic activity. However, the 'if possible' points to a specific tension around the very possibility of carrying out such a causality stricto sensu, and it calls to enlarge the scope to other, larger forms of causality. This question matters when it comes to the use of modelling for the regulation of chemicals, because depending on which sort of causality will be enforced from a regulatory viewpoint, the requirements regarding the data generation and processing will diverge. Hence, 'the data' will not be the same according to whether a 'mechanistic interpretation' turns out to be possible.
They cannot be expected to 'speak truth to power', as science used to  (Hoppe, 1999). At least, QSAR models further challenge some previous achievements in boundary making between science and policy by reshaping the kind of evidence generated for regulating  (Worth et al. , 2014). In this respect, QSAR models open up a space for political negotiation: it all depends on which model you use for studying which material to what ends and purposes. Commenting on guidelines produced by the OECD, we explore the kind of causality enacted in scientific and regulatory practices with QSAR modelling. In so doing, this study will contribute to the growing field of critical data studies, which aim mostly at not taking 'data' as given but instead as inserted at the locus of social, technical, and political configurations, considering the differences data make or can make in such configurations  (Dalton and Thatcher, 2014).
Conversely, it will also analyse the notion of causality as it is at play with QSAR models and regulation processes, hence providing insights into the philosophy of computing and information  (Floridi, 2011; Illari and Russo, 2014). This paper relies on fieldwork I conducted between January and June 2015 within the framework of an experimental research project aiming to question the relevance of QSAR models for nanomaterials. 2 As part of this project, I attended several scientific meetings during which the construction of relevant data to feed a QSAR simulation was discussed (i. e. choice of reference substances, of physicochemical descriptors, and of relevant parameters). I conducted 12 semistructured qualitative interviews between February and May 2015, with scientists developing experimental QSAR models for nanomaterials (Ecole des Mines Saint-Etienne) and a scientist from the Joint Research Center (JRC) of the European Commission; researchers working in the French industry (Sanofi in pharmaceuticals, Arkema in chemistry and materials); national regulators from the French regulatory authority (Agence nationale de se´curite´sanitaire de l'alimentation (ANSES)), as well as international regulators from the European Chemical Agency (ECHA), from the OECD.
The practitioner who wishes to predict the toxicity of a compound will have to choose a set of reference substances that share some descriptors, such as some features of the molecular structure, or its form. On this set of references, existing data must be provided, namely firsthand datasets issued from experimental protocols on the chosen particular substances. In the case of QSAR, datasets are physically distributed over a wide range of research centres and institutions, although they are often integrated (imported and duplicated) in widespread QSAR interfaces such as the OECD Toolbox or REACH (see below). 4 The second step is to characterise the compounds about which the prediction will be formulated -these would have this or that shape, this or that feature, and could henceforth be linked with the set of references under several shared aspects.
Then, there is diverse QSAR software that allows for running analysis on these datasets to gather insights into the toxic activity of those new compounds. Some of the software is developed by private companies to fit their R&D purposes. Others are developed by a lively developer community that spans way beyond industry, including non-profit organisations such as Lhasa (which develops Derek, a widely used Open Source QSAR software specifically directed towards predicting toxicity). 5 Finally, regulators draft some specifications that integrate specific requirements for operating QSAR models for regulatory purposes and outsource the actual development of the software to private firms through public market procedures. For example, the OECD Toolbox -arguably the main, if not the only big enterprise of its kind -delegates the technical work to the Laboratory of Mathematical Chemistry in Bulgaria.
Regulators somehow have to keep pace with such evolutions, and it is no surprise that regulatory bodies also use QSAR models to orient the decision-making processes. The American Environmental Protection Agency (EPA) developed and strengthened the first applications of QSAR models for regulatory purposes. It would carry this work out in cases in which data gathered from animal experimentation were already available to accurately delimit the restrictive conditions under which models could be used for regulation  (Demortain and Boullier, 2015). At the EU level, the European Commission took up the EPA's approach to models in 2007 through the enforcement of the REACH regulation. REACH frames QSAR models as an alternative to animal testing and expensive in vitro experimentations  (European Commission, 2007).
Operation for regulation purposes At least three specific uses of modelling for regulatory purposes can be distinguished. The first and main one results from the development and adoption by the OECD of a 'QSAR Toolbox'. This tool centralises multiple datasets and seeks to provide means for standardising the conditions under which QSAR models can be used and evaluated. One of its main purposes, as one member of the QSAR management group states, is to 'fill data gaps'. QSAR models are here useful to provide a panoramic, integrated view of the chemical's landscape and to distinguish categories about which toxicity information is lacking. This toolbox is crucial because it standardises the means of QSAR evaluation by using simple and widely used training sets, algorithms, and validation groups, whereas scientific expertise used to be over-specialised and too narrowly focused.
It is a matter of defining the physicochemical descriptors that will characterise both current substances, for which data has already been generated, as well as compounds that do not exist yet. Therefore, this 'domain of validity' expands from existing data to QSAR projection and knowledge about future compounds. As such, the choice of descriptors defines the boundaries of a 'space', a set of coordinates that will match various substances within the same ensemble, hence delimiting a line inside the broad domain constituted by all chemicals. As  Barry (2005: 62)  points out: 'the quality of the models depends on the volume of chemical space they are able to operate within with some degree of reliability' (our emphasis). He adds that 'chemical space is not Euclidian or Newtonian but instead a relational space'.
To do that, it is necessary to rely on an extensive literature review and multiple datasets and to fill gaps in the existing knowledge. According to one ECHA official: [OECD is] trying to put together all the mechanistic data that we know from pharma investigations, from different small in vitro assays that are really, you know, just designed to detect certain features, and they are trying to build full pathways [i. e. ] full chains of reactions that lead from something small at the molecular level to something big -that show some adversity at the organism level.
Along the way, QSAR remodels not only the very idea of 'causality' but also the way decision-making needs to adjust to such tentative realities crafted by models. Models adopt a sort of incremental approach that tends towards the truth by approximations  (Hacking, 1983) ; it takes going back and forth from the models to its effects, and therein lies the potential truthiness of a model. 'If there is any truth around, it lies in the approximation, not in the background theory'  (Hacking, 1983: 219). And here it appears clearly that it is a form of experimental endeavour rather than the causal application of a theory. This experimental endeavour is well visible in the many choices at stake in the definition of endpoints, datasets, and other technical features of QSAR. That these choices are connected to regulatory choices is a reason the OECD guidelines state that they have to be made explicit, without attempting to pre-determine them.
'The chemist is interested in the fact that the properties of atoms and molecules vary considerably depending on the form and circumstances of their association with others'  (Barry, 2005: 56). Just like Barry's chemist, the 'modelling regulator' would learn to deal with the uncertainties inherent to multi-variables analysis and would be sensitive to the adjustments and caution required by a 'variation rationale', thus rendering the notion of 'evidence' as in 'evidence-based decisions' problematic. Such an evolution would have ontological consequences. Not all of that is indifferent from the data viewpoint; depending on how far regulatory processes can follow that path, different sets of demands and requirements and, eventually, different datasets will be generated, asking different questions to different families of molecules.
Since the spectrum of covered realities is potentially unlimited, it would not make sense to talk about 'the data' but about 'some data' or 'datasets', understood as partial and limited. There would be consequences for the molecules themselves, since their informational environment would be dramatically affected by models. As for the regulatory authorities themselves, they would hereafter embrace uncertainties about the material, not deny them; they would not take physicochemical descriptions as invariant but instead as transformed through association with other political stakes.