dataesr/scientific-paragraphs-categorization huggingface dataset:
Data coding As only few time lapse pictures included rabbits (225/11,909) and because we were interested in a night/ day effect on rabbit presence, we coded rabbit presence at the day/night scale rather than at the hour scale. For each date and each period (day or night), the presence/absence variable was coded "1" if at least one rabbit was seen, "0" otherwise. Most pictures (211/225) displayed only one rabbit. To code behavioral data acquired using the "motion detector" setting, we first excluded all the pictures displaying other animals than rabbits, rabbits outside the patch or young rabbits (whose individual decisions may differ from adults' , e. g. ,  [2, 39] , and who were too rare to be analyzed separately). We then randomly selected pictures taken with at least a 5 mn interval. This led to a total of 1289 pictures.
We used spaMM package  [41]  in R 3. 1. 2 [42] , to account for both spatial (using GPS coordinates of each patch) and temporal (using Julian date) autocorrelation in model residuals. We further included patch identity and date as random terms to account for non-independence between observations performed in the same place and at the same date. We selected the final model by fitting the complete model including the interaction and removing each term successively. The significance of each term was determined by assessing the change in deviance (i. e. , Likelihood Ratio Test) against a Chi squared distribution, with the appropriate degrees of freedom. We used the same approach to test whether cats were more frequently observed at night and in closed or open patches, including cat identity in the models to control for nonindependence between observations of the same individuals, and for the analyses previously described (i.
Acknowledgements Particular thanks to  Hervé Fauvre  for meteorological data and to  Joël White ,  Julien Cote ,  Jean-Baptiste Ferdy  and an anonymous referee for very constructive comments on a previous draft. Computations performed on EDB cluster, using a software developed by the  Rocks(r) Cluster Group (San Diego Supercomputer Center, University of California, San Diego  and its contributors), hosted by EDB. We thank  P. Solbès  and  K. Cilleros  for support.
Funding This work was supported by the  French Polar Institute  (IPEV, Program Number  279 ) and the  Zone Atelier  " Recherches sur l'Environnement Antarctique et Subantarctique ". EDB is part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ).
staining myofibrillar actomyosin ATPase Myofibrillar actomyosin ATPase staining. Two different procedures were used for staining myofibrillar actomyosin ATPase following the methods described by Brooke and Kaiser  7, 8  and by Guth and Samaha. 9, 10 istological Morphometry. Muscle fiber size and the absolute number or percent composition of different muscle fiber types were determined in transverse cryosections of the SM muscle. Quantitative evaluations were performed using images of stained cryosections with Scion Image software for Windows version Beta 4. 0. 2 (2000 Scion Corporation). Tissue type distribution was determined using Adobe Photoshop software (Adobe Systems Incorporated, San Jose, CA).
Acknowledgments BR and SZ thanks for support  A&C M-C Foundation for Translational Myology, Padova, Italy. Funding DC is funded by  AFM  ( 2017-20603 ),  ANR  ( 2013-J13R191 ), EFEM 2016, IBPS (2014),  NIH  ( 2013-1R01CA108857-01subcontractor ) and  UPMC Emergence  ( 2011-EME1115 ).
Study design and sample size estimation Considering the resource constraints, eight villages namely: Al Aqaieb, Al Helailat, Al Ghilayil, Hail Al Hedap, Da'an Al Hamra, Shnoot, Al Qasha'e and Al Sarah, were selected randomly based on the data on animal population obtained from the  Ministry of Agriculture and Fisheries (2014). A proportional allocation method was used to determine the sample size from each randomly selected village based on the goat population numbers in each village (Table  1 ). A total sample size of 324 animals (N = 324) was determined in which the individual animals were randomly selected from both sexes at different reproductive age. For calculating prevalence, a confidence limit of 95% was set, with the proportion of attributes (p) = 0. 7 (based on the pilot study results).
Data analysis The data obtained from the serological tests were entered into a Microsoft Excel spreadsheet. The total prevalence of brucellosis for individual tests was calculated by dividing the number of RBT, iELISA or CFT positive animals by the total number of animals that were tested. The true seroprevalence was estimated by dividing the number of animals tested positive in two or all three tests by the total number of animals tested. The individual level prevalence was estimated by dividing the number of seropositive animals by the total number of animals that were tested. The herd level prevalence was calculated by dividing the number of herds with at least one reactor in RBT, iELISA and/or CFT by the number of all herds tested. A Chi-square (x 2 ) test was done to compare the prevalence of brucellosis between the villages.
Identification of Brucella melitensis from bacterial isolates Although 37 samples showed strong seropositivity in serological tests, only three (8. 1%) bacterial isolates were obtained from the culture. Two isolates were obtained from milk sample cultures and one isolate was obtained from a blood sample culture. Using serum agglutination test, the bacterial isolates were initially identified as Brucella melitensis. Gram staining of the bacterial isolates showed a typical morphology for Gram-negative coccobacilli bacteria (data not shown). Moreover, the three isolates gave 99% identification probability for Brucella melitensis when entered into Vitek 2 system (Gram-negative card).
Acknowledgements This work was supported by grant  HIS   MAJESTY's Sultan Qaboos Bin Said  for strategic research, project no. SR/ANVS/01/14. We thank  Dr. Ehsan Mekki ,  Mrs. Umaima Al Hanai  and  Mr. Sunil Rajamony  for excellent technical assistance. A special thanks goes to the  Ministry of Agriculture & fisheries and Al Jabal Al Akhdar community  for facilitating the sample collection and cooperating with the team during the questionnaire.
This first functional expression and pharmacological characterization of the C. elegans L-AChR provided a strong basis to investigate its parasitic nematode counterparts. Studies of parasitic nematode L-AChRs highlighted some striking differences with the C. elegans L-AChR  [11, 12]. Noticeably, whereas the LEV-8 subunit is a component of the C. elegans L-AChR, searches in the genomic databanks from Brugia malayi (Clade III) and Trichinella spiralis (Clade I) failed to identify a lev-8 ortholog in these parasites  [29]. The absence of lev-8 orthologs was also reported in parasitic nematode species phylogenetically closely related to C. elegans (Clade V) for which levamisole is successfully used as an anthelmintic (i. e. Haemonchus contortus, Teladorsagia circumcincta, Trichostrongylus colubriformis) and has known spastic potency  [30, 31].
Heterogeneous distribution of acr-8 and lev-8 genes in the phylum Nematoda Using the C. elegans ACR-8 amino-acid sequence as a query, tBLASTn searches were performed against 68 nematode genomic databases available in WormBase-ParaSite (version 8; http://parasite. wormbase. org/) (S1 Table ). This revealed that acr-8 homologs are present in all the available genomes from the nematode species representing the Clades III, IV and V (20, 17 and 24 species, respectively). Strikingly, when a similar search was performed using the closely related C. elegans LEV-8 amino-acid sequence as a query, lev-8 homologs could only be identified in a subset of 21 species out of 68, including 17 Clade V species and the five Ascaridae species from Clade III. Noticeably, no lev-8 homolog could be found in the 17 genomes of Clade IV species studied.
In addition to the available complete coding acr-8 sequences from the Clade V species C. elegans, H. contortus and O. dentatum (JF416644, EU006785 and JX429921, respectively), in the present study full-length acr-8 cDNAs were obtained from other distantly related parasitic nematode species such as the vertebrate parasites Ascaris suum (Clade III, pig worm) and Dirofilaria immitis (Clade III, dog heart worm) and the plant parasite Meloidogyne incognita (Clade IV, root-knot nematode). Based on their respective orthologous relationship with Cel-ACR-8 (Fig  1   [37]  and submitted to GenBank under accession numbers: KY654347, KY654349, and KY654350, respectively. All sequences shared typical features of an AChR subunit including a predicted signal peptide, a "Cys-loop", four transmembrane domains and a YxxCC motif defining them as alpha subunits (S2 Fig).
Cloning of complete coding cDNA sequences of acr-8 from A. suum, C. elegans, D. immitis and M. incognita Complete coding sequences corresponding to Asu-acr-8, Cel-acr-8, Dim-acr-8 and Min-acr-8, were obtained using first-strand cDNA as PCR template. Amplifications were performed with the Phusion High fidelity Polymerase (New England Biolabs) with a forward primer including the first ATG codon in combination with a reverse primer including the first stop codon. Amplicons were inserted in the transcription vector pTB207  [27]  using the In-Fusion HD cloning kit (Clontech). Primer sequences are reported in S2 Table. The novel complete coding sequences of Asu-acr-8, Dim-acr-8 and Min-acr-8 were deposited to GenBank under the accession numbers: KY654347, KY654349 and KY654350, respectively.
Sequence analysis Database searches were performed with the tBLASTn or BLASTP algorithms  [62]. Deduced protein sequences were aligned using the MUSCLE software  [63]. Amino-acids homologies and identities were defined using the EMBOSS Needle program available at EMBL-EBI (http:// www. ebi. ac. uk/Tools/psa/emboss_needle/). Signal peptide predictions were performed using the SignalP 4. 1 server  [64]  and membrane-spanning regions were predicted using the SMART server  [65]. Phylogenetic analyses were performed on coding sequences predicted from genomic data available in databases or cloned cDNA sequences when available. Sequences were aligned as codons using the MAFFT plugin (v1. 3. 3) of Geneious (v7. 1. 2, Biomatters Ltd)  [66]. Regions corresponding to the signal peptide and the intracellular loop between TM3 and TM4 that could not be aligned unambiguously were removed.
Expression of GFP driven by Hco-acr-8 promoter A 2,9 region upstream the initiation codon of Hco-acr-8 was PCR amplified on genomic DNA prepared from adult males of H. contortus using the NucleoSpin Tissue kit (Macherey-Nagel, Duren, Germany) following manufacturer recommendations. Primers were designed upon scaffold 5709 sequence available in H. contortus PRJEB506 Wormbase parasite database. Primer sequences are provided in S2 Table. The amplicon was subcloned upstream of the GFP coding sequence in the pPD95. 75 (Addgene) expression vector and micro-injected into C. elegans N2 gonad (50 ng μl -1 ). Expression of GFP was monitored on fixed wormed from the F1 progeny by fluorescence microscopy.
C. elegans thrashing assays Experiments were performed on age synchronized worms by picking L4 stage a day before the assay. The next day, one-day old adult hermaphrodite C. elegans were picked into 900 μL of M9 buffer containing 0. 1% BSA in a 12 well plate. The worms were left to settle for 10 min. The number of thrashes was counted for 1 min. Then 100 μL of either M9 (control), Lev or Pyr solution was added to the wells to give a final concentration of 10, 25, 50, 100 or 150μM Lev or Pyr. The number of thrashes was counted after 10, 20, 30 and 40 min (and 60 min for some concentrations) of exposure to Lev. The data were plotted as a mean ± standard error of the mean.
Ascaris suum: ACR-8 KY654347; Caenorhabditis elegans: ACR-8 NP_509745; ACR-12 NP_510262; ACR-14 NP_495716; ACR-16 NP_505207; LEV-1 NP_001255705; LEV-8 NP_509932; UNC-29 NP_492399; UNC-38 NP_491472; UNC-63 NP_491533. Haemonchus contortus: ACR-8 EU006785; UNC-38 GU060984; UNC-63 GU060985; RIC3. 1 HQ116823; UNC-74 HQ116821; UNC-50 HQ116822; GAPDH HM145749; Oesophagostomum dentatum: ACR-8 JX429921; Teladorsagia circumcincta: ACR-8 HQ215517; Trichostrongylus colubriformis: ACR-8 HQ215518; Meloidogyne incognita: ACR-8 KY654350. Dirofilaria immitis: ACR-8 KY654349. S1 Table. Identification of Cel-acr-8and Cel lev-8 homologs in nematode genomic data available in Wormbase-parasite databank. Deduced amino-acid sequences from Cel-ACR-8 (Genbank accession number: JF416644. 1) and Cel-LEV-8 (Genbank accession number: NM_077531.
4) were used to perform a tBlastn search against a set of genomic databanks from nematodes available in WormBase Parasite (https://parasite. wormbase. org/). Nematode Clades as determined by Blaxter et al. [68]. When available, full length cDNA sequences are indicated in bold: GB refers to Genbank accession number whereas WB refers to Worm base accession number. Life style abbreviations: VP: vertebrate parasite; IP: Insect parasite; FL: free-living. (DOCX) S2 Table. Sequences of primers and ds-siRNA. (DOCX) S1 Video. H. contortus L2 larvae pharyngeal pumping in culture medium. (MPG) S2 Video. H. contortus L2 larvae in culture medium without siRNA (control). (MPG) S3 Video. H. contortus L2 larvae exposed to 1μM siRNA targeting Hco-unc-38 after 72h. (MPG) S4 Video. H. contortus L2 larvae exposed to 1μM siRNA targeting Hco-unc-63 after 72h.
The M. porcinum CSURP1564 genome was incorporated into in silico DNA-DNA hybridization (DDH) (23) using GGDC version 2. 0 (24), with reference genomes selected on the basis of the 16S rRNA gene sequence. This yielded 99. 7% sequence similarity with M. porcinum IP141460001 (GenBank accession number MVIG00000000), 49. 5% with M. boenickei CIP 107829 (FUWC00000000), 34. 5% with M. conceptionense D16 (CTEF00000000), 34. 4% with M. farcinogenes.
(CWKH00000000), 31. 40% with M. fortuitum CT6 (NCBI reference sequence number NZ_CP011269), and 20. 5% with M. avium 104 (NCBI reference sequence NC_008595). This result confirmed that M. porcinum strain CSURP1564 differs from M. conceptionense in the M. fortuitum complex. This is the first report of MinION technology applied to the genome sequencing of a nontuberculous Mycobacterium species  (25). In our experience, MinION technology has been helpful in determining genome backbones. Reporting on the M. porcinum CSURP1564 genome sequence will help to establish DNA-based methods for its detection and identification in environmental, animal, and clinical specimens to complement the rpoB gene that we previously developed  (2, 26). Accession number(s). The draft genome sequence of M. porcinum CSURP1564 has been deposited at the European Bioinformatics Institute (EBI), European Nucleotide Archive (ENA), under the accession number OLMG00000000 (OLMG01000001 to OLMG01000005).
Statistical analysis Statistical analysis of the data (feed intake, body weight, FCR, liver fat, liver weight, intestinal A. galli worm burden, A. galli eggs in hens excreta, and serum and yolk antibodies) were performed using JMP statistical software v. 8 (SAS Institute Inc, Cary, NC). The pen was considered the experimental unit. For the parameters measured once, data were subjected to a one-way ANOVA to test the effect of treatments. For parameters measured over time, data were subjected to a two-way ANOVA with repeated measures using JMP statistical software v. 8, and differences among group means were analyzed by applying Tukey's HSD test at a probability level of 0. 05. Fixed effects used were treatment, age, and their interation. To compare the group differences in parameters between infected and non-infected groups, a t test was used.
Ascaridia galli egg count and worm count data were not normally distributed and thus were cube root transformed before analysis. Antibody level before infection (baseline) was fitted as a covariate when analyzing antibody levels PI. Pearson correlation coefficients and associated significance were generated using JMP software to determine the relationship among excreta egg count at the pen level, performance parameters (feed intake, FCR, egg production, and egg mass), and serum IgY. FACS: Flow cytometric data were analyzed using IDEAS application software version 6. 0 (Amnis, Seattle, WA). Populations of intact single cells were gated by analyzing forward scatter side scatter plots and confirmed by fluorescent micrograph analysis of cells from imaging flow cytometric data. Within these single cell populations, cells that were positive for CD4 + or CD8 + or double positive compared to isotype controls were gated and quantified.
Because of the use of anthelmintics in the control group, data from the infected birds alone were used to investigate this correlation. No effect of parasite infection on body weight or eviscerated body weight was observed at 40 wk of age. Liver weight was significantly higher in hens of the medium infected group (45. 3 ± 0. 87g) compared hens to control, low, and high (41. 7 ± 0. 68g, 42. 4 ± 0. 93g, and 43. 95 ± 0. 84g, P = 0. 02, respectively). However, liver weight relative to body weight was similar in hens of all groups. Similarly, there was no difference in liver fat content of control, low, medium, or high infected hens. Intestinal intraepithelial CD4 + and CD8 + lymphocyte populations were monitored during the A. galli infection using flow cytometry (Table  2 ). No significant differences among hens of any treatment groups were observed.
ACKNOWLEDGMENTS This research was conducted within the Poultry CRC, established and supported under the  Australian Government's Cooperative Research Centers Program  and is a part of Poultry  CRC  sub-project 1. 5. 9. The authors would like to thank  Amy Bell ,  CSIRO  for technical assistance with ELISA tests. All students and staff of  Animal House at University of New England, Australia , are acknowledged for their helping hands during this project.
Case selection Medical records of 26 horses admitted to Milton Equine Hospital between January 2007 and June 2015 with fractures of the medial and/or lateral condyle that propagated into the diaphysis of McIII and MtIII were reviewed. Data collected included history, age, breed, sex, physical examination, lameness grade, circumstances of fracture (racing or training), radiographic evaluation, anaesthesia and recovery records (quality, length, technique, sedation), surgical technique, post-operative management and rehabilitation, complications and outcome. Horses were included in the study if they had long (more than 30% of the total length of the bone) lateral or medial condylar fractures repaired under general anaesthesia using cortical screws placed in lag fashion.
Race record and outcome Thoroughbred and STB data were analysed separately, and individual preand post-surgical racing performances were compared. Racing data were obtained from two databases (equibase. com and trackit. standardbredcanada. ca) including return to racing, time between surgery and first race, starts, yearly starts, top three placings, earnings before and after surgery, lifetime starts, lifetime earnings, number of years in racing after surgery. Telephone interviews with the referring veterinarian, owner or trainer helped detail rehabilitation period and outcome if no return to racing were recorded. The follow-up occurred between 2 and 10 years after fracture repair. Surgery was considered successful if the horse raced.
Animals, handling and slaughtering The study was carried out as part of a research program approved by the Ethical Committee of INRA Auvergne-Rhône-Alpes (ARA) (National Institute for Agricultural Research, INRA-Herbipôle, Theix, France). Individual data from 6 experiments grouping a total of 480 young bulls were used in this study. The dataset included Charolais (n = 264), Salers (n = 93), Limousin (n = 74), and Blond d'Aquitaine (n = 49) breeds. The 4 breeds are the major suckled breeds slaughtered in France. For the Charolais breed dataset, the animals are from 5 consecutive years' experiments most of them from the experimental unit INRA-UE232 (Bourges). Irrespective of the experiment and production year, all the animals were raised and managed under similar experimental practices in the INRA research center.
Statistical analyses The individual data were analyzed using SAS statistical software (SAS 9. 4, SAS Institute INC, Cary, NC, USA) and  XLSTAT 2017. 19. 6 (AddinSoft, Paris, France). Before analysis, data exploration and database preparation were scrutinized for data entry errors and outliers using Smirnov-Grubb's outlier test at 5% level. After this, a general description analysis was performed by computing size, mean, standard deviation and minimum and maximum ranges of each variable (Table  1 ). For animal clustering into meat eating quality clusters, unsupervised learning methods (k-means, partitioning around medoids (PAM) and hierarchical cluster analysis (HCA)) were tested and those retained because they gave the best results were k-means algorithm and HCA. The former is an iterative clustering method used to automatically partition a dataset into k groups where the number of clusters k is assumed to be fixed a priori by minimizing relative distances and the latter is a cluster analysis method, which seeks to build a hierarchy and binary tree of clusters.
Before clustering, for each quality trait cluster (pHu, tenderness, juiciness and beef flavor), the missing data were considered as missing at random regarding the outcome variable. It was thus decided to exclude cattle with missing data to construct the meat eating quality clusters and not impute missing data values  (Donders et al. , 2006). Moreover, for each quality trait and before clustering, the data were first normalized for each confounded or present effect in the database by computing Z-scores. Z-scores represent the deviation of each trait observation relative to the mean of the corresponding animal irrespective of the breed, experiment and other confounded effects and were calculated using PROC STANDARD of SAS, which standardizes data to a mean of 0 and standard deviation of 1. The average silhouette width (S i ) criterion was used for the validation of the clustering results (i.
Acknowledgments This study is conducted under project number  S3-23000846  (given to M. G. ) funded by  Auvergne Rhône-Alpes Region  and  FEDER. The 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-UE1414 Herbipôle  for their help in animal management, handling, slaughtering, sampling and data collection. The authors would gratefully acknowledge the coordinators of the different INRA projects namely  Gilles Renand  ( UMR 1313  GABI, INRA),  Jean-François Hocquette ,  Didier Micol  and  Jacques Agabriel  ( UMR 1213 Herbivores, INRA ) for their help and data collection.