dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic characterization. Strain FB-527 16S rRNA gene sequence (GenBank Accession Number: LT935784) was 99. 6% similar to that of strain FI-09026 (FJ786255). A 16S rRNA gene sequence-based phylogenetic tree showed that strain FB-527 was a member of the Mycobacterium szulgai complex closely related to Mycobacterium riyadhense (Fig. 5 ). We also sequenced a 733-bp rpoB gene fragment in FB-527 strain (LT935785)  11. This sequence's highest similarity rate was of 99. 3% with strain FI-09026 (FJ786254) which enforced the identity of strain FB-527 at the species level. Genome sequencing of strains FB-527 and FI-09026 yielded 49 and 105 contigs indicative of one 6,251,405 bp-long chromosome (64. 5% GC content) and one 6,192,024 bp-long chromosome (64. 6% GC content), respectively, without any evidence of extra-chromosomal replicon.
FB-527 genome encodes for 5,684 proteins and 51 RNAs including 48 tRNA, 2 rRNA and 1 tmRNA. A total of 3,563 (62. 1%) genes were assigned with a putative function. The remaining genes were annotated as hypothetical proteins 2,172 (37. 9%). FI-09026 genome encodes for 5,570 proteins and 54 RNAs including 50 tRNA, 3 rRNA and 1 tmRNA. A total of 3,490 (62%) genes were assigned a putative function while 2,134 (38%) genes were annotated as hypothetical proteins. Annotated genome sequences of strains FB-527 and FI-09026 have been deposited (GenBank accession number: OCTY01000001-OCTY01000049 and OCVX01000001-OCVX01000105, respectively). Comparing whole genome sequences, strain FB-527 exhibits an average nucleotide identity (ANI)  12  of 97. 88% with strain FI-09026. In silico DNA-DNA hybridization analysis (DDH)  13  comparing strains FB-527 and FI-09026 yielded a value of 79.
Samples were analyzed in the Sensitivity Negative ionization mode using a Vion IMS QTof high resolution mass spectrometer (Waters, Guyancourt, France). Samples were infused at 10 μL/min, after fluidics wash with chloroform/methanol (50/50), and monitored from 500 to 2000 m/z during 2 minutes. Ionization parameters were set as follows: capillary voltage of 2. 5 kV, cone voltage of 50 V, source and desolvation temperatures comprised between 120/650 °C. Mass calibration was adjusted automatically during analysis using a Leucine Enkephalin solution at 50 pg/μL (554. 2620 m/z). Mass spectra were combined between 1000 and 1400 m/z for subsequent data interpretation. Mycolic acids were described according to previously detailed structures  23. Here, keto, epoxy and ω-1 mycolic acid subclasses could not be distinguished.
11 nmol/l. The libraries were normalized at 2 nM and pooled. After a denaturation step and dilution at 20 pM, the pool of libraries was loaded onto the reagent cartridge and then onto the instrument along with the flow cell. Automated cluster generation and sequencing run were performed in a single 39-hour run in a 2 × 151-bp. Total information of 4. 7 Gb was obtained from a 461 K/ mm 2 cluster density with a cluster passing quality control filters of 97. 8% (9,187,000 passing filter paired reads). Within this run, the index representation for strain FB-527 was determined to be of 7. 48%. The 687,466 paired reads were trimmed then assembled with the paired-end reads. For both strains, reads were assembled using the SPAdes software (http://bioinf. spbau. ru/spades)  24 and gaps were closed if possible by GapFiller  25.
Phylogenetic analysis. The phylogenetic tree based on 16S rRNA sequences was constructed using the MEGA7 software. The evolutionary history was inferred by using the Maximum Likelihood method based on the Hasegawa-Kishino-Yano model. Initial trees for the heuristic search were obtained automatically by applying the neighbor-joining and BIONJ algorithms to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. Statistical support for internal branches of the trees was evaluated by bootstrapping with 1000 iterations.
Acknowledgements This study was financially supported by  URMITE ,  IHU Méditerranée Infection, Marseille, France ; and by the A* MIDEX  project (n°  ANR-11-IDEX-0001-02 ) funded by the ≪Investissements d' Avenir≫, a  French Government program , managed by the  French National Research Agency (ANR). FB benefits from a PhD grant by the  IHU Méditerranée Infection, Marseille, France. This study was funded by the  Méditerranée-Infection Foundation.
The assays represented in this HTS could include consideration of stress response pathways (e. g. oxidative stress) as well as specific protein/receptor interactions (e. g. oestrogen receptor activity). Any available animal data should be used with care at this step of the safety evaluation. If relevant in vivo data are sufficient to complete a risk assessment using traditional methodologies, the expectation is that a risk assessment based on the in vivo data will be performed. However, where there are significant data gaps in the existing in vivo dataset, existing animal data should only be used if it can help to establish or refine a hypothesised MoA. Furthermore, because the goal is to produce a humanrelevant safety assessment, it is important that the hypothesis is not focussed on predicting or confirming reported adverse effects in the available limited animal data.
These uncertainty factors (also referred to as safety or assessment factors) are intended to allow for possible interspecies and inter-individual differences in response to test chemicals (both toxicokinetics and toxicodynamics) as well as other considerations such as duration of study and overall quality of the database. Therefore, although not always explicit, uncertainty has always been a feature of toxicological risk assessment and has been addressed in a variety of ways. In NGRA, all sources of uncertainties should be identified and characterised to provide transparency for the decision-making process, ideally leading to a future where default 'uncertainty factors' are redundant. Variability and uncertainty should be distinguished and all different sources should be considered (e. g. measurement or method uncertainties;  [10, 11].
Data collection Snort production and immediate context. The snort rates produced by each individual were measured by one single experimenter (M. L. ), blind to the data on welfare assessment, using 5 min-Focal behavioural samples  [72]. Horses from riding schools were observed both in stalls and in pastures while the leisure horses were only observed in pastures. The context (stall/pasture) and the assumed internal state (as expressed by behaviour/posture) of the animal at the precise time of production were examined. In the stables, the experimenter performed 5 min-focal behavioural sampling, simultaneously on two neighbouring horses, focusing on the snort production. The experimenter followed a standardized route along the corridor separating the two rows of stalls and stopped at each pair of stalls encountered.
Two sessions (1 in the morning, 1 in the afternoon) of 30 minutes per individual were conducted on one day. This operation was repeated one day after. Thus in total 62 scans of activity and ears positions per context were obtained per horse. These data allowed us to obtain a percentage of time spent doing each activity along with a percentage of each ears position expressed. Welfare assessment. Welfare assessment was based on an array of validated behavioural, postural and health-related indicators. One observer (M. S. ), blind to the data on the snorts, performed all the observations.
Data recorded were the number of stereotypic behaviour and abnormal repetitive behaviours exhibited per individual (for 60 min). In the present study, the observer stood motionless at one point in the middle of the corridor at equal distance between the two rows of stalls so that she could see six horses at the same time. In stalls, horses generally performed SB/ARB only when their head was at the box door. The sampling was on an all-occurrence basis  [53, 71]  which means that the behaviours concerned were scored each time they occurred together with the horse identity. SB and ARB were identified based on the list described by Lesimple et al  [88]  and Mills et al  [69]. For a behaviour to be considered as SB/ARB, the behavioural sequence had to be repeated at least 3 times successively and observed 5 times, independently of the period of observation.
The stalls were equipped with Dutch wooden-doors with the top and bottom divided, the bottom being solid and the top with wire grids. The horse's first reaction was assessed as non-aggressive (i. e. looks at the experimenter with upright ears and approaches or does not show any evidence of directed attention toward the experimenter) or aggressive (i. e. looks at the experimenter with backward ears, and/or moves towards the experimenter with ears backward). 2. A motionless person test (e. g. [92, 96] ), during which the experimenter entered the box and stood motionless with her back against the closed door, facing inwards and looking at the ground. The test lasted 1 minute. Data recorded were the total number of aggressive behaviours displayed by horses, including threats (threats always included ears backwards and could vary from simple threats, i.
For the riding school horses, observations were made in the stables in calm conditions (outside pellets feeding times, day off, 2 to 5 pm, no wind). For the leisure horses, observations were made outdoors when horses were grazing, with the observer standing motionless outside the pasture (after at least 10min habituation). Ears position while feeding were scored using an instantaneous scan sampling method  [72] , with 2 min intervals. Two sessions (1 in the morning, 1 in the afternoon) of 30 minutes per context (stalls and pastures) and per individual were conducted on one day. This operation was repeated one day after. Thus in total 62 ears positions per context were obtained per horse. These data allowed us to obtain a percentage of each ears position during feeding for each horse. • The orientation towards the wall in the stall reflects a lack of interest towards the environment associated with potential health (e.
g. anaemia  [74] ) or "psychological"  [99]. In the present study we were only interested in orientations outside feeding time when animals were inactive. Therefore, horse's orientation towards either a full wall (back wall, partitions) or outside (window or door top) was noted using instantaneous scan sampling method  [72] , with 2 min intervals. Two sessions (1 in the morning, 1 in the afternoon) of 30 minutes per individual were conducted on one day. This operation was repeated one day after. Thus in total 62 scans of orientation in the stall context were obtained per horse. These data allowed us to obtain a proportion of time (percentage of scans) spent with an orientation towards a full wall by individual outside feeding time. Given their living conditions all day long in pasture, this sampling could be not conducted for the leisure population.
Study areas, animals and samples We used 2115 archived blood samples from wild boar from 16 study areas and five European countries, i. e. , Switzerland, France, Italy, Sweden, and Spain (Additional file 1 and Fig. 1 ). The countries other than Switzerland were selected based on the availability of samples and the existence of a wild boar health monitoring program providing an insight into the mange status of the local population. Study areas were considered as epidemiological units because they were either located in distinct wild boar populations and/or distant from each other (> 60 km) and/or separated by major geographical barriers such as lakes and highways. Target sample sizes were estimated per study area, using the free software WinEpiscope 2. 0 (http://www. winepi. net/uk/index. htm), with an accepted error of 5% and level of confidence of 95%.
Samples were collected post mortem from wild boar hunted (n = 1955; all countries) or found dead (n = 49; Switzerland only), and from live animals (n = 111; Spain only). The age of the wild boar was determined according to body weight and coat color  [20, 21]  or based on the tooth wear of the inferior jaw  [22] [23] [24] [25]. We harmonized the dataset using three age classes according to the criteria of  Hebeisen et al. (2008)   [21] : 1) piglets (striped, < 6 months old, < 20 kg) and juveniles (reddish, 6-12 months old, between 20 and 40 kg), which were merged as one juvenile age class for our study; 2) subadults (black coat, 12-24 months old, > 40 kg and ≤ 60 kg); and 3) adults (black to silver coat, ≥ 2 years, > 60 kg). Sampling was performed in the field by game-wardens, hunters, veterinarians or trained field technicians.
Statistical analyses Thirty-nine samples which remained doubtful despite retesting and were distributed among all animal categories (sex, age, area's mange status) in seven study areas were considered as negative in the data analysis. All statistical analyses were performed using the R software version 3. 3. 2 (the R Project for statistical computing, available at https://www. R-project. org/; R Core Team 2016, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria). Confidence intervals of apparent seroprevalences were estimated using the binom. test function and were therefore automatically computed according to Clopper and Pearson. We used generalized linear mixed models (glmm) with a logit link to test the effects of age (juvenile, subadult, adult), sex (male, female) and the area's mange status (N, S, C) on the probability for a wild boar to be seropositive (dependent variable).
We considered the serological status of wild boar as a bimodal variable (0/1): seronegative or doubtful results were encoded 0 and seropositive results were encoded 1. The effect of the study area (n = 16) was considered as a cluster random effect. Glmm models were computed using the function glmer from the package lme4  [27]  and the dredge function of the package MuMIn (MuMIn, B. K. 2016, multi-model inference, R package version 1. 15. 6. 2016. ). We started with a "complete model" comprising the three explanatory variables and tested all simpler models. Model selection was based on the Akaike's Information Criterion (AIC) and the number of parameters: among the best models, i. e. , with the lowest AIC and delta-AIC less than 2 (models fitting best the observed data), we retained the most parsimonious model (with less parameters)  [28].
Acknowledgments Sampling in Switzerland was performed with the support of cantonal hunting inspectors, professional game-wardens, hunters and veterinarians (in particular  Natacha Wu ,  Janne Schöning ,  Mainity Batista Linhares ,  Olivia Beerli ,  Sohvi Blatter ,  Roman Meier  and  Nelson Marreros ,  FIWI ). Access to samples from other countries was possible thank to contributions of  Walter Mignone  ( IZSTO, Piedmont, Liguria and Aosta Valley ),  Gen. Flavio Vertui , Commanding Officer of Forest Regional Corps,  Paolo Oreiller ,  Mauro Ruffier  and  RAVA Officers (Italy) ,  Bertil Malmsten  and  Erik Ågren  ( SVA, Uppsala; Sweden ), the hunting societies of the  Controlled Hunting Zone of Collserola , the  Barcelona City Hall  (contracts 13002279 and successive renewals; Spain),  Dominique Gauthier  ( Laboratoire départemental des Hautes-Alpes, Gap ),  Fondation François Sommer, National Domain of Chambord and French Ministry of Agriculture  (veterinary services), and the hunters from the  Vosges du Nord (France).
Funding Sampling in Switzerland was carried out within the framework of projects funded by the  Federal Food Safety and Veterinary Office  (Project Nos. 1. 07. 19 , 1. 10. 07,1. 11. 16,1. 12. 16  and  1. 14. 01 ). Financial support for sampling in France was provided by the  Fondation François Sommer , the  National Domain of Chambord  and the  French Ministry of Agriculture  (veterinary services). In Spain, the  Barcelona City Hall (Spain)  supported sample collection. In Italy, the Assessorato sanità, salute e politiche sociali supported sampling in the Aosta Valley region. Serological analyses were funded with a grant of the  Fondation Galli-Valerio, Lausanne, Switzerland. Raquel Castillo-Contreras  received a researcher formation grant from the  Agència de Gestió d'Ajuts Universitaris i de Recerca.
Acknowledgments The authors thank  Etienne Canoui  and  Alviset Sophie  for their expertise on the topic,  Yohann Rouscoff  and  Elie Saad  for the surgical management of the patient,  Emelyne Hamelin-Canny  and  Elena Garelli  for performing lung biopsies, and  Marie-Françoise Triller  for her anatomopathology skills. The patient approved the use of his medical record for this work and provided written informed consent.
Horses Kinematic data were collected from six horses (four trotters, one Lipizzaner, one French saddle pony, three females and three geldings, age 8e19 years, body mass 430e720 kg, and height at withers 151e172 cm). All horses were clinically sound and had no history of recent lameness during the last 6 months. All lived in a paddock and were not in work for at least 1 year before the study as they were used as blood donors. They were unshod but were regularly trimmed at 8-week intervals.
Data Collection The same experienced handler, aiming at leading the horse at constant forward velocity, led each horse along the runway. No significant differences in speed were observed between shoeing conditions. The mean ± SD speed for REF at walk and trot were, respectively, 1. 53 ± 0. 05 m/s and 3. 51 ± 0. 17 m/s. A trial was considered successful if the horse moved regularly and perpendicularly to the camera. For each condition, six successful trials (one stride per trial) were recorded in walk and trot and averaged for subsequent calculations. Only trials within 5% of the mean speed at walk and trot were retained for further analysis to negate the influence of speed on kinematics.
Data Processing Joint angles were measured from the videos using Image J (http://imagej/nih. gov/il/). Joint angles were measured at 0%, 25%, 50%, 75%, and 100% of the stance phase and at 25%, 50%, and 75% of the swing phase. Zero percent of the stance phase was defined as the time point when the foot stops to move forward and 100% of the stance phase as the time point when the toe take off and 25%, 50%, and 75% were calculated, respectively, from these. This is different from other kinematic studies in horses; however, the majority of previous studies were conducted using expensive, laboratorybased motion capture systems, while in our study, we measured joint angles manually from videos and to reliably measure joint angles, we decided on these definitions based on extensive piloting. For all joints, we chose to report the extension angle.
Statistics Analysis Data distribution was assessed using a ShapiroeWilk test, P <. 05, and found to be normally distributed. Joint angles, the fore and hind maximal protraction and retraction, the AUEC, and the stride parameters are dependent data, which were analyzed using a split-plot repeated general linear model. In this model, horses were added as random factors and conditions of shoeing as fixed factors. A Sidak's correction was also applied to identify main differences. Significant level was set at P ¼. 05 and all statistical analysis was performed using SPSS (version 20, IBM, Armonk, USA).
4. 0-3  [35] ). Species fecundity estimates were log-transformed to obtain a linear relationship between variables. Our sampling procedure involved a limited amount of faecal material which may have biased our prevalence and abundance estimates. Therefore, our observations were compared against a dataset  [36]  of previously published equine strongyle species prevalence (n = 29 papers) and relative abundances (n = 14 papers) provided as Additional file 2: Table  S2. These studies spanned all continents and involved various methodologies (necropsy, deworming), thus limiting confounding factors linked to climatic conditions or sampling methods. For each paper, species relative abundance and prevalence were computed from the data if not already provided. These individual estimates were subsequently averaged across studies for comparison with the observed values from our 48 horses.
To test for these interactions, species counts were regressed upon other species intensity in a pairwise manner after accounting for environmental variables (horse sex and sampling site) and by means of a Poisson regression. For this analysis, the 13 species with more than 200 individuals across horses were considered, as failure to do so results in strong positive associations for the minor species present. Because of the high number of statistical tests (n = 105), a Bonferroni correction was applied and a P-value below 0. 05/105 = 5 × 10 -4 was considered significant. Significant pairwise regression estimates (P < 5 × 10 -4 ) were used to generate a species network, visualized with the ggnet package  [44]. The network uses a force-directed algorithm from the matrix of interactions. Nodes act as repelling objects that are organized to minimize forces in the network.
Host sex effect on species relative abundance and prevalence The impact of host sex on strongyle abundance (worm counts) and prevalence was investigated for the 11 core strongyle species, i. e. most abundant and most prevalent. Cyathostomum catinatum showed the highest prevalence (47 horses infected) and was not included in the prevalence data analysis as not enough variability was present to assess factors of variation. To account for the data structure (Additional file 1: Table  S1 ), these effects were investigated using only the data from premises where horses of each gender were present (premises 2, 3, 4 and 6; n = 27 horses). Worm counts and species prevalence were modelled using a negative binomial and logistic regression, respectively. Samples were recovered in 2011 and 2012, hence introducing a putative confounding annual variation.
83, P < 0. 0001 and r (21) = 0. 90, P < 0. 0001 for prevalence and relative abundance estimates, respectively). This independent dataset also supported the outlying contribution of C. catinatum, C. nassatus and C. longibursatus, that infected 67. 3, 68. 9 and 72. 1% of horses and accounted for 17. 4, 18. 2 and 16. 8% of total worm burden across studies. Pearson's correlations between published estimates of worm fecundity and measured species abundance and prevalence were estimated to determine whether fecundity could underpin better dispersal abilities. These correlations suggested that fecundity was negatively correlated with species prevalence (Pearson's r (14) = -0. 71, P = 0. 004; Fig. 2 ) and that the same trend held for species relative abundance (Pearson's r (14) = -0. 48, P = 0. 08; Additional file 4: Figure  S1 ).
Discussion This study focused on the structure of equine strongyle communities under eastern Europe climatic conditions. The data suggested that reported species fecundity, approximated by the number of eggs in utero, was negatively correlated with species prevalence. This relationship was found both in our dataset and across previously published prevalence reports. In addition, horse sex had a statistically significant effect on strongyle community structure and diversity. Numerous reports on equine strongyle community structure have already evidenced the bimodal pattern Fig. 5  Interactions network based on relative abundances for recovered strongylid species. A network, drawn using a force-directed algorithm from the matrix of interactions estimated by pairwise Poisson regression on species counts after accounting for environmental effects.