dataesr/scientific-paragraphs-categorization huggingface dataset:
Epidemiological survey in slaughterhouses The sample size of the studied animals was determined considering a total of 3,206,821 heads of Sardinian dairy sheep (National Data Bank of the Italian Ministry of Health; https ://www. vetin fo. it) with an expected Dicrocoelium spp. prevalence of 15%, and confidence level of 95% (http://www. raoso ft. com/sampl esize. html). From 2013 to 2014, 770 Sarda sheep slaughtered in six different abattoirs in Sardinia, were submitted to anatomo-pathological examination to detect the liver parasites and to evaluate the typical thickened terminal bile duct (TTBD) lesions  [33, 34] , following the EEC Inspection Regulation No. 854 of 2004 (Annex 1, Section IV, Chapter II, point 5)  [35]  and the methods illustrated by Marcato  [36]. To perform the anatomo-pathological examination, seven hepatic areas were selected: right lobe (RL) and left lobe (LL) of both the diaphragmatic face (DF) and visceral face (VF), quadrate lobe (QL), caudate lobe (CL) and finally the cut surface.
Genetic analysis No intraspecific variations were observed for the ITS2 gene sequence (GenBank: MG004688) among the D. dendriticum isolates. In addition, the same isolates showed a homology of 99%, with the Iranian ITS-B haplotype isolate (GenBank: JQ966973)  [40] , and a homology of 99% and 98% with the Italian isolates DQ379986. 2 [41]  and EF547132. 1  [6] , respectively. The sequence alignment of ITS showed a T/A substitution at the 153 codon with an index diversity of 0. 002, compared with the sequence of the above mentioned Iranian isolate.
The degree of compatibility was quantified by the proportion of infected snails and the infection intensity was established according to the number of SpI by infected snails (15). The results were analyzed using ANOVA and Tukey post-hoc test (R version 3. 4. 4) and are presented in graphs. Differences were considered statistically significant when P < 0. 05. The R Script (Data Sheet 1) and the data analysis are provided in the Supplementary Table  1.
ACKNOWLEDGMENTS The authors thank to  Paulo Cesar  and  Marta Chagas  from  LRNM  for providing technical support. We thank to  Eduardo Chinilla  for the support with the images (B. glabrata photography and formatting the figures for publication) and  Ana Carolina Ceolin  ( Serviço de Capacitação de Métodos Quantitativos do Instituto René Rachou)  for the support with the statistical analysis.
Efficacy parameters and statistics The protocol of this study was in accordance with the recently published guideline on evaluation of efficacy of anticoccidials in piglets and other mammalian species  (Joachim et al. , 2018a). Statistical calculations were performed using SAS 1 version 9. 3 (SAS Institute Inc. , Cary, USA) for all included, randomised and treated piglets, that were exposed to coccidiosis. A quantitative determination of oocyst numbers (oocysts per gram faeces, OPG) was performed for each piglet six times during the study (Fig. 1 ) by using a modified McMaster counting method  (Mundt et al. , 2006). At each sampling date, descriptive statistics of the OPG counts (including arithmetic and geometric mean, median and number of samples with positive OPG counts) were applied for each group.
Discussion In a controlled, randomised, blinded study, the efficacy of the test product to control coccidiosis in suckling piglets exposed to natural infection with C. suis was assessed. The study was conducted in three major pig producing countries in the European Union: Spain, Germany and France (Eurostat, 2017), including farms with different management and organisation of production and recently confirmed history of coccidiosis. In all chosen countries coccidiosis is considered as a frequent and major cause of suckling piglet diarrhoea  (Torres, 2004; Mundt et al. , 2005a,b; Ruiz et al. , 2016). In total, the study population consisted of 122 litters with 1508 piglets fulfilling the inclusion criteria, and thus we believe that the data are representative for conventional piglet rearing facilities in Europe.
Report all findings of experimental studies Still, even the most robust systematic review method cannot alone prevent reporting biases, particularly publication bias or outcome reporting bias  (Hopewell et al. , 2009). There are many potential causes of missing data; whole studies may never have been published or made accessible to risk assessors, studies may have been inappropriately indexed in databases making them difficult to locate, and specific endpoints or data points may have been excluded from reports and/or summary analyses  (Higgins and Green, 2011). Missing data are problematic since decisions may then be based on a dataset that does not reflect the totality of the body of knowledge. This is particularly alarming if data are deliberately suppressed or censored. Publication and outcome reporting biases refer to the publication or non-publication of research findings or the selective reporting of some outcomes but not others, respectively, depending on the nature and direction of the results.
Make ecotoxicity studies publicly accessible Access to scientific literature and databases can be costly and not all regulatory agencies, interest groups, or other stakeholders (e. g. , small and medium sized enterprises) can afford it. Peer-reviewed ecotoxicity Evaluating the inherent quality of a test report or publication relating to preferably standardized methodology and the way the experimental procedure and results are described to give evidence of the clarity and plausibility of the findings. Reliability of data is closely linked to the reliability of the test method used to generate the data.
EFSA, 2011 O. V. Martin, et al. Environment International 128 (2019) 210-217  studies should therefore preferably be published as open access, or reasonable efforts should also be made to make research data available in any other way. Access to raw data can be crucial for the understanding of study results, as well as for recalculations of data to fit the user's own needs. To facilitate access, supplemental information can be used to share raw data. Today, numerous general-purpose data repositories, at scales ranging from the institutional (for example, a single university), to open globally-scoped repositories such as FigShare (http://figshare. com), Mendeley Data (https://data. mendeley. com/), or Zenodo (http:// zenodo. org/), to cite a few, are available and accept a wide range of data types in a wide variety of formats.
This diversity has prompted the formulation of the so-called FAIR Data Principles  (Kilkenny et al. , 2010)  (namely that data should be findable, accessible, interoperable, and reusable), and large-scale initiatives such as the European Open Science Cloud (https://ec. europa. eu/research/openscience/index. cfm?pg= open-science-cloud). The recommendation to make ecotoxicity studies publicly accessible does not only apply to academic researchers but is equally valid for regulatory and industry scientists. Grey literature (e. g. , reports, working papers, and evaluations from government agencies, non-governmental organizations, research institutes, and consultants), are often publicly available, but can be difficult to track and access since they are not included in literature databases in a systematic way.
, 2018)  (this aspect is partially addressed by recommendations 7 and 8). At a minimum, a critical aspect is that the WoE approach be transparent, documented, and fit-for-purpose  (Schreider et al. , 2010). This should include the development and publication of a priori WoE protocols appropriate for the assessment or application under consideration according to relevant guidelines  (Shamseer et al. , 2015) , preferably in a peer-reviewed journal or alternatively on open platforms (e. g. , Zenodo). It should also be noted that WoE approaches ought to be sufficiently calibrated by ring-testing or by experimental, epidemiological and/or modelling results  (Brock et al. , 2016; van Wijngaarden et al. , 2015). Verifying the consistency of environmental risk assessment decision schemes should be a basic requirement for every guidance document.
A number of topic-focused search engines are now available and can help identify grey literature not listed in databases. Examples include the search engines Environar (https://environar. com/environar/desktop/ en/search. html), open access bibliographical databases such as Open-Grey (http://www. opengrey. eu/), which searches grey literature across Europe, or CORE (http://www. core. ac. uk), to search open access items in institutional repositories.
Acknowledgements These recommendations were formulated during a  Society of Environmental Toxicology and Chemistry (SETAC)  Pellston Workshop™ titled "Improving the usability of ecotoxicology in regulatory decisionmaking", Aug 30-Sept 4, 2015. The authors would like to thank  SETAC  for practical and financial support before, during, and after the workshop. The authors would like to thank three anonymous reviewers for valuable comments on an earlier version of the manuscript.
The helium carrier gas flow was maintained constant at 1 ml/min during the analysis. The transfer line and ionization chamber temperatures were 330°C and 180°C, respectively. Electron impact ionization was used for mass spectrometry with ionization energy of 70 eV. GC-MS/MS signals were evaluated using a computer workstation by means of the software Excalibur ® , release 3. 0 (Thermoscientific, USA). Identification of steroids was supported by their retention time and according two or three transitions. The precise retention time and the MS/MS parameters of the 35 targeted steroids in gilt saliva are reported in Table  1. Quantification was performed according to the transition giving the more abundant product ion with a previously established calibration curve. The calibration curves were prepared by using 100 µl of gilt saliva by adding increasing concentrations of pure targeted steroids (5 calibration points with steroids concentrations ranging from 0.
BACKGROUND: This study implemented a holistic approach based on the farm-to-fork data at the four levels of the continuum (farm -slaughterhouse -muscle -meat) to study the inter-individual cluster variability of beef tenderness. For that, 171 young bulls were selected on a large database of 480 animals according to the industrial expectations based on animal and carcass characteristics. The targeted factors were age at slaughter (14; 20 months), carcass weight (370; 470 kg), EUROP conformation (7; 15) and fatness (2. 5; 5) scores of the carcasses. Multivariate analyses and unsupervised learning tools were performed. RESULTS: Principal component analysis combined to agglomerative hierarchical clustering allowed ten clusters to be identified that differed (P < 0. 0001) for the four targeted factors.
Implementation of a holistic approach by considering the farm-to-fork data at the four levels of the continuum (farm -slaughterhouse -muscle -meat) of the animals was relevant to characterize sufficiently those factors in relation to the desirable beef qualities. This would further allow better understanding of the inter-individual variability within animals produced under similar conditions or that share similar characteristics. Thus, this study on a homogeneous dataset of 171 French young bulls from a large INRA (Institut national de la recherche agronomique) database aimed to investigate sensory tenderness variability only among homogenous animal population chosen according to the industrial expectations based on animal and carcass characteristics considered in the EUROP grid system. In this study, we performed multivariate analyses and unsupervised learning tools to approach the animal variability of individuals in link with the continuum data.
Animals handling This study was carried out as part of a research program conducted in the context of suitability of French beef market to respond to the queries identified by the beef sector namely those of the six biggest slaughterers of the Auvergne-Rhône-Alpes (ARA) region, France. Thus, from an INRA-ARA database of 480 young bulls,  9  171 animals of three breeds, i. e. 124 Charolais, 24 Limousin and 23 Salers were extracted. Data extraction was based on four factors that were defined thanks to a field survey within the industrial sector (butchers) conducted to identify and to fix the range limits of the desirable animal and carcass characteristics. The four factors and their fixed ranges were age at slaughter (14; 20 months), carcass weight (370; 470 kg), conformation score (CS) (7; 15, on a 1-15 scale from the EUROP grid) and fatness score (FS) (2.
5; 5, on a 1-5 scale from the EUROP grid) of the carcasses. The respondents (managers of the slaughterhouses) were face-to-face interviewed using a written semi-structured questionnaire consisting of simple open-ended questions for 29 items including the four considered criteria in this study. The 171 young bulls belong to the major suckled breeds slaughtered in France. The dataset belong to previous animal studies conducted according to procedures approved by the regional ethical committee and in accordance with applicable French and European guidelines. 9 The data were of no breed effect concerning the four criteria factors (Supporting Information, Table  S1 ). The animals were raised and managed under similar experimental conditions in the INRA research center. 9, 14 They had, ad libitum, the similar feeding but at different proportions consisting of forages (including straw, hay, grass silage, corn silage, and beet pulp silage) and concentrate (containing dehydrated alfalfa, grain corn, soybean, urea, wheat, and rapeseed).
Statistical analyses A total of 23 variables including tenderness were used in this integrative study by considering the four levels of the continuum from the farm-to-fork (Tables  1  and 2 ). The SAS statistical software (SAS 9. 3, SAS Institute INC, Cary, NC, USA) and XLSTAT 2018. 2 (AddinSoft, Paris, France) were used for data analyses. Normal distribution and homogeneity of the dataset was first tested by the Shapiro-Wilk test (P > 0. 05). Then, a general description analysis was realized by computing means, standard deviations, minimum and maximum ranges (Tables  1  and 2 ). To study the inter-individual variability among the 171 animals, the four factors, i. e. age at slaughter, carcass weight, CS and FS of the carcasses were centered and scaled by computing z-scores using PROC STANDARD of SAS.
25 The ten clusters were compared using PROC GLM procedure of SAS for the whole variables of the continuum after the projection on a new PCA to perform a between-animal cluster analysis. Therefore, we used both simple linear regression and random regression methods to model systematic changes in tenderness within clusters by considering homogeneity of variance across individuals. To take in to account the inter-individual cluster variability, univariate associations based on regression curve analysis was performed. The curve (linear, quadratic, cubic, logarithmic and power) that explained the greatest proportion of the variance in tenderness scores (± standard deviation) with each mean cluster variable from the four levels of the continuum was taken as the model of best fit. The significant differences were considered at a significance level of P < 0.
05). It is worth emphasizing that individual values that were > 5. 8 or > 6. 0 counted for 2. 3% and 0. 58% of cattle in our dataset, respectively. For the sensory attributes, the cluster 9 grouped the steaks that were tender, juicy and more flavorful compared to the other clusters. Finally, IMF content was not found the highest in clusters 9 and 10 that were tender, juicy and flavorful but in the clusters 3 and 5 that were intermediate for these sensory traits. The differences and similarities among the ten clusters for the whole variables of the continuum data are further summarized in Fig. 2  based on an unsupervised hierarchical clustering heatmap.
Inter-individual cluster variability and associations between mean tenderness scores with rearing factors The rearing factors were regressed with the mean tenderness scores to study the inter-individual cluster variability and the type and direction of the associations (Fig. 3 ). Sensory meat quality scores were the only complete data of the dataset of the 171 young bulls, therefore, Warner-Bratzler shear force measurements were not considered in this report to work on full balanced design. The plots highlighted that seven of eight rearing factors were significantly correlated to meat tenderness in linear or curvilinear direction. Strong inter-individual variability exists among clusters for each rearing factor. In agreement with the non-significant effect observed for initial body weight, no correlation was found with tenderness (Table  3 ).
9, 30 If we refer to earlier reports, some authors attributed differences in tenderness to a potential increase in connective tissue by collagen content with increasing age at slaughter as well as animal/carcass maturity. 31, 32 Tenderness in lean meat is usually associated with the collagen of muscles. 33 Of course, this was partly observed in this report as insoluble but not total collagen was among the tenderness discriminators of the clusters. A previous study on www. soci. org M Gagaoua, B Picard, V Monteils a large dataset showed that insoluble collagen was negatively correlated to tenderness. 34 However, it is worthwhile to note that in this study an inconsistent effect of age on insoluble collagen content was found for the intermediate clusters. This is difficult to interpret, but was already observed.
ACKNOWLEDGEMENTS The authors acknowledge the financial support given to M. G. from project  S3-23000846  funded by  Région Auvergne-Rhône-Alpes  and  Fonds Européens de Développement Régional (FEDER). This study was designed to support the  FBEA  project ( Filière Bovins Engraissement Auvergne-Rhône-Alpes) in collaboration with ARIA-ARA (Association Régionale des Industries Agro-Alimentaires d'Auvergne-Rhône-Alpes ) and  Chambre Régionale d'Agriculture d'Auvergne-Rhône-Alpes. The authors convey special thanks to all the persons from the experimental units  INRA-UE232 (Bourges)  and  INRA-UE 1414 Herbipôle  for their help in animal management, handling, slaughtering, sampling and data collection. The authors gratefully acknowledge all the coordinators of the  INRA projects namely Gilles Renand  ( UMR 1313  GABI, INRA),  Jean-François Hocquette , and  Didier Micol  ( UMR 1213 Herbivores, INRA ) for their help and data collection.
Isothermal Titration Calorimetry Assays Thermodynamic parameters of the molecular interaction between HsrA and its DHP-class inhibitors were analyzed by isothermal titration calorimetry (ITC) using an AutoiTC200 calorimeter (MicroCal, Malvern Instruments, Malvern, Worcestershire, UK). A solution of 20 µM of the protein in 50 mM Tris-HCl [pH 8], 150 mM NaCl, 10% glycerol, 1% DMSO was titrated with 200 µM of the corresponding ligand (DHP) dissolved in the same buffer at 25 • C. A total of 19 injections of 2 µL was added sequentially to the sample cell after 150 s spacing to ensure that the thermal power returned to the baseline before the next injection  [26]. Dissociation constants and binding enthalpies were calculated by non-linear least squares regression data analysis using the software package Origin 7.
Molecular Docking Molecular docking analyses were performed using the program AutoDock Vina 1. 1. 2 [27]  in order to estimate the conformation of the HsrA-DHP complexes. The chemical structures of ligands (Nifedipine, Nicardipine, Nisoldipine, Nimodipine, Nitrendipine, and Lercanidipine) were retrieved from the PubChem database (https://pubchem. ncbi. nlm. nih. gov/). The structure of the H. pylori HsrA response regulator (2HQR, model 1, chain A) was downloaded from the Protein Data Bank (https://www. rcsb. org/). The protein and the ligands were prepared using the AutoDock Tools program. Rotatable bonds were defined as free for the ligands and rigid for the protein. The AutoGrid algorithm was used to estimate the interaction energy of each ligand pose. The pose that exhibited the lowest free energy of interaction (∆G, kcal/mol) for each ligand was considered as its predicted model of binding to the target protein.
Statistical Analysis Bacterial counts (CFU/mg) and qPCR results (Helicobacter/10,000 murine cells) were compared between each DHP-treated group and the infected non-treated control group. Statistical analyses were carried out using the non-parametric Mann-Whitney test. A p value smaller than 0. 05 was considered significant. All statistics were performed using GraphPad Prism 6. 0 (GraphPad Software, Inc. , La Jolla, CA, USA).
No relevant differences were observed in the inhibitory capacities of Nifedipine, Nicardipine, * Median lethal dose (LD50) data from PubChem. Lercanidipine LD50 information from  [31]. HsrA functions as a transcriptional activator of gene expression; hence, it interacts with DNA and binds to specific sequences located in target promoters, thereby modulating gene expression. EMSA analyses of HsrA in the presence of its target promoter PporGDAB demonstrated a high affinity of HsrA by its target DNA in a concentration-dependent manner (Figure  1a ). Under the experimental conditions used in EMSAs, 120 ng of target DNA were completely and specifically complexed to HsrA from 6 µM of the recombinant protein. Hence, this concentration was subsequently used for EMSA inhibition assays in the presence of 100 µM to 2 mM of each DHPs.
Analysis of the Molecular Interaction between HsrA and its DHP-Class Inhibitors In order to determine the affinity, the enthalpy and the stoichiometry of binding as well as to unravel the structural basis of the interaction between HsrA and its DHP-class inhibitors, ITC measurements and molecular docking analyses were carried out. As shown in Table  2 , the ITC data provided dissociation constants in the micromolar range in all cases, while complexes of a 1:1 stoichiometry were observed for all DHPs (Figure  S1 , Supplementary Materials), indicating that each HsrA monomer of the dimeric protein binds one molecule of DHP. Molecular docking suggested that DHP inhibitors bind to the native conformation of HsrA preferably at the C-terminal effector domain, interacting with amino acid residues directly involved in the structure of the helix-turn-helix (HTH) DNA binding motif, but also with other residues presumably essential for the domain stabilization (Table  2 , Figure  2 ).
• Video recording and behaviour scanning All pens were video recorded (by QVIS HDAP400 CCTV cameras and Pioneer-16 digital recorder case, Hampshire, UK) continuously on one day every week, except for immediately postweaning and in the week after moving to the finisher house. Behavioural observations were carried out using scan sampling. A static image from the video recording was obtained every 20 min between 07:00 h and 20:00 h (n = 39 data points). The number of pigs interacting with each of the 8 enrichment items during that instant was counted. The identity or sex of the pigs was not recorded.
Statistical Analysis The data were analysed using Statistical Analyses System (SAS, version 9. 4, 1989, SAS Institute Inc. , Cary, NC, USA). All residuals were checked for normality after analysis, and an appropriate transformation was applied (square root transformation) to non-normally distributed frequency data. For continuous and normal data such as the weight of the pigs, feed intake, the weight of loose materials, and behavioural frequency data, the MIXED procedure was used to fit mixed models. The stage (weaner/finisher), week within stage, treatment (SAME/SWITCH), and the enrichment combination within each stage were included as fixed effects in all behaviour analyses. When analysing the level of interaction with different enrichment items, category and the interaction between category and treatment were also included as additional fixed effects to investigate how often pigs interacted with each category.