dataesr/scientific-paragraphs-categorization huggingface dataset:
Minimum peptide length was set to seven amino acids, and the minimum number of peptides was set to one. Andromeda results were validated by the target-decoy approach using a reverse database at a both a peptide and protein FDR of 1%. The iBAQ metric, which corresponds to the sum of all the peptide intensities divided by the number of observable peptides of a protein  (Schwanhäusser et al. , 2011) , was used to estimate absolute protein abundance. A threshold of iBAQ >6 × 10 8 was applied as it corresponds to an abrupt stall of the slope of the curve of iBAQ values.
Statistical Analysis For mice experiments, where two groups were compared, Student's t-test was used. A p-value < 0. 05 was considered to be significant (ns = non-significant, * P < 0. 05; * * P < 0. 01; * * * P < 0. 001; * * * * P < 0. 0001). In ELISA experiments, for comparison of the mean values of multiple groups, data that appeared to be statistically significant were compared by analysis of variance and non-parametric analysis using GraphPad Prism 6. 0 software (GraphPad Software, La Jolla, CA, USA). A p-value < 0. 05 was considered to be significant. (ns = non-significant, * P < 0. 05; * * P < 0. 01; * * * P < 0. 001; * * * * P < 0. 0001). The technical values of the test in terms of sensitivity and specificity were calculated on the basis of ROC curves.
The resulting PCR products were sequenced by both sides at Macrogen (Korea), sequences were then aligned by MEGA-X and compared with those available at GenBank by BLAST. New sequences were submitted to GenBank under the accession numbers MN527294-MN 527304. A Bayesian analysis of the 354-bp fragment obtained from the LipL32 gene of the species included in this study was set. Sequences from previously published Leptospira species were added. The K2 + G was selected as the best-fit model for our sequences and Leptospira santarosai (AY461927) was set as an outgroup. The Ngen was set to 30 million and the first 25% trees were discarded. The confidence intervals (CI) for prevalences were obtained using the Clopper-Pearson exact method (95%). For the comparison of the prevalences obtained in this study Chi square tests were applied setting the P value in 0.
borgpetersenii haplotype never recorded before. This situation may be due to the isolation that this area experienced during the barbarian invasions, when the villages close to the sea were abandoned and the Corsican population hid in the mountains. It is recorded that during those times other diseases, such as malaria and plague, were present in the island  [18]. The presence of rats probably infected with Leptospira species and the isolation could be the reason under the finding of the new haplotype, and the constant attacks that the island suffered may have brought rodents from different areas of the world. The presence of new haplotypes of Leptospira species due to isolation is also present in the Canary Islands  [8] , an Atlantic archipelago where the L. interrogans haplotype found in rodents (HQ231747) (Fig  2 ) also showed differences when compared to all the sequences uploaded to the GenBank data base.
This work was funded by post-doctoral fellowship (AFA)  CE/03/2015  " Collectivite Territoriale de Corse-Direction de L'Enseignement Superieur et de la Recherche ";  ProID2017010092 "Proyectos I + D de la Consejeria de Economı ´a, Industria ,  Comercio y Conocimiento de la Comunidad Autonoma de Canarias "; FEDER 2014-2020;  RD16/0027/0001   Spanish Ministry of Health, Consumer Affairs and Social Welfare of Spain. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Field data collection As part of a long-term capture-mark-recapture programme initiated in 1996, 6 days of capture occur each year, between the beginning of January and the beginning of March. We used large-scale drives with 30 to 100 beaters and up to 4 km of long nets. Upon capture, deer were transferred to a wooden retention box providing darkness and ventilation until the marking procedure. Roe deer were tranquilized just after capture by intramuscular injection of acepromazine (Calmivet, Vetoquinol, France; targeted dose of 0. 075 mg/kg). Marking procedures lasted ∼15 min and were performed by trained handlers. Each individual was weighed (to the nearest 10 g), and sex and age were recorded. Individuals were divided into two age classes (juveniles for 8-10 month-old individuals or adults for >18 month-old individuals) based on tooth eruption patterns  (Hewison et al.
Home range determination and landscape variables Home ranges were calculated using GPS data after removing the first 10 days following capture and release, since capture and handling are known to induce a transient modification of space use  (Morellet et al. , 2009). For each individual, we calculated a home range based on the 15 following days using the 90% fixed kernel method excluding locations during excursions  (Worton 1989; Börger et al. , 2006)  with the 'adehabi-tatHR' R package  (Calenge 2006). We hypothesized that the home range used during this particular period would be representative of the home range used just prior to capture, since roe deer exhibit high spatial fidelity  (Hewison et al. , 1998). Based on aerial photographs of the study site (from the IGN's BD Ortho, http://professionnels.
ign. fr/bdortho-50cm), we manually digitized homogeneous habitat polygons (in ArcView GIS 3. 3, Esri, Redlands, CA, USA). Each polygon was then assigned to a habitat type (e. g. woodland, hedgerow, meadow or crop; the full list is available in Supplementary Data 1) from field observations each summer. For each home range, we calculated the proportion of surface area composed of refuge habitats (woodland, hedgerows and scrubland). Here, we considered that the proportion of available refuge habitat within the home range (comprised between 0. 07 and 0. 99, with an average value of 0. 1980). We also calculated the probability that an individual used refuge habitat during the day (variable used in Set 1 hereafter) and at night (variable used in Set 2 hereafter), as the ratio of the number of GPS locations within a refuge habitat divided by the total number of GPS locations during daytime (at 12:00) and night-time (at 00:00) for that individual (see  Bonnot et al.
Statistical analyses Individual repeatability of FCMs was estimated using 261 observations of 221 individuals and mixed models using the restricted maximum likelihood (REML) method, with the 'rptR' package  (Stoeffel et al. , 2017)  for Gaussian distributions. Individuals sampled only once were included in these analyses as this procedure may improve power to estimate among-individual variance, so avoiding biased results  (Martin et al. , 2011). To analyze variation in FCM levels, we performed linear mixed-effects models (LMMs) on 125 observations of 116 individuals. Fewer animals were used for these analyses compared to the repeatability analyses because not all animals were equipped with GPS collars. One individual with an extremely low FCM value (<100 ng/g) was removed, because we suspected some problems with sample quality and/or possible contamination  (Lexen et al.
, 2008). Two individuals with particularly high FCM values (>4500 ng/g) were also identified in our dataset. Consequently, we present results including these two values in the main text and excluding them in Supplementary Data, which did not change the main biological relationships (see Supplementary File 2). FCM level was analyzed as the response variable in four sets of models, each testing a specific hypothesis: Sets 1 and 2: to test the relationship between proximity to anthropogenic structures and stress level and to evaluate the extent to which use of refuge habitat during daytime (Set 1) and night-time (Set 2) could influence this relationship, we included mean distance to the nearest anthropogenic structure during daytime (or night-time), probability of using refuge habitat during daytime (or night-time) and their twoway interaction.
Acknowledgements We thank all the  CEFS  staff and all the field volunteers for the organization and their assistance during the roe deer captures. We thank the local hunting associations and the  Fédération Départementale des Chasseurs de la Haute Garonne. We also thank  Edith Klobetz-Rassam  for EIA analysis, as well as  Delphine Ducros ,  Laura Gervais ,  Erwan Quéméré  and  Sergine Ponsard  for constructive discussions and comments that helped to improve this manuscript. We also thank the two anonymous referees and the associate editor for their insightful comments on the manuscript.
Funding This project was funded by  INRA, VetAgro Sup  and ONCFS; was supported by the ' Mov-It' ANR grant  ( ANR-16-CE02-0010-02 ) to A. J. M. H. and N. M. ; and was also performed in the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon , within the  programme 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ).
Observations Daily observations were conducted from February to May, when most foals were over 6 months of age. Special attention was paid to various variables commonly used to assess the quality of the mother-offspring relationship in horses. These include mare-foal distances, choice of the dam as preferred neighbor, frequency and quality of mare-foal interactions including suckling activities  [37, 65, 66]. All observation sessions took place during the daytime period between 10:00am to 5:00pm, with 3 to 6 observation days per week and per study group (depending on the weather conditions, e. g. , snowstorms, temperature, etc. ). Observation periods changed every day following a rotation schedule. Observations were tape-recorded and transcribed later. Three observers were involved in data collection.
Analysis As data were not distributed normally, non-parametric statistical tests were used  [75]. The statistical tests were performed using Statistica®13 (Statsoft, Tulsa, USA). All means are given ± SE (standard error). Two main types of analyses were performed: (1) we first investigated the age of foals at weaning and the potential factors of variation (for all study groups); (2) we retrospectively compared the behavior of both partners during the two weeks prior to weaning to that scored in the two weeks following weaning (only for study groups 1 and 2). Weaning age was defined as the age at which the foal was last observed performing nutritive suckling. We also determined the duration of the dry period (i. e. , the time from the last suckling of the current foal until the birth of the next foal) by recording the date of birth of the next foal.
Acknowledgments: The authors warmly thank both  Sveinn Ragnarsson  and  Eysteinn Steingrimsson  at the  Equine department at H ōlar University , and  Sigrídur Björnsdottir at Kalfastadir  for their hospitality and to give us access to the horses. The authors are also very grateful to Jeanette DEZART who verified and improved the English language of the manuscript.
Statistical analyses Comment citer ce document : Lefaucheur, L. ,  Lebret, B. (2020). The rearing system modulates biochemical and histological differences in loin and ham muscles between basque and large white pigs. Animal, sous presse (sous presse), sous presse. , DOI : 10. 1017/S175173112000066X 9 Statistical Analysis System (SAS) software (version 9. 4, 2013, SAS Institute, Cary, NC, USA) was used to analyze the data (details in Supplementary Material S1). Data for the ADFI and feed efficiency of LWA, LWC, BA and BC pigs, for which the pen was considered the statistical unit, were analyzed using ANOVA (GLM procedure), considering the treatment (4 levels) and replicate (2 levels) as fixed effects. Contrasts between breeds and between the A and C systems for a given breed were determined  (Lebret et al.
, 2014). For all other traits, the animal was considered the statistical unit. First, data were analyzed using ANOVA (GLM procedure), considering the treatment (LWC, LWA, BC, BA and BE) and replicate (R1 and R2) as fixed effects to calculate residues. The normality of residues was checked (Shapiro-Wilk test, P  0. 05). When necessary, data were log-or square-root transformed to obtain a normal distribution of residues, which were checked using the same test. Subsequently, contrasts between breeds were determined from the ANOVA using only the LWC, LWA, BC and BA pigs, to balance the production systems between breeds. Within each breed, contrasts between rearing systems were determined to evaluate effects of the A vs. C system for the LW breed, and the A vs. C and E vs. C systems for the B breed.
Candida auris population structure. We next examined the global genomic data set for evidence of population substructure and recent admixture. Principal component analysis (PCA) identified four well-separated populations corresponding to clades I, II, III, and IV, and the tight clustering of isolates within each clade suggests there is no recent admixture in any isolates (Fig. 3a ). To assess genetic diversity at the clade level, we compared population genetic statistics, including nucleotide diversity (), Tajima's D (TD), fixation index (F ST ) and pairwise nucleotide diversity (D XY ) (Fig. 3b to e ). Overall, clades I and III showed the lowest genetic diversity ( ϭ 1. 51eϪ5 and ϭ 1. 42eϪ5, respectively); clade IV exhibited nearly three times these levels ( ϭ 4. 23eϪ5), and clade II presented the highest genetic diversity ( ϭ 1.
0. 5 (www. repeatmasker. org/). Tandem repeats were determined using Tandem Repeats Finder (https://tandem. bu. edu/trf/trf. download. html) using the basic search option. Variant identification. We used FastQC v0. 11. 5 (https://www. bioinformatics. babraham. ac. uk/ projects/fastqc/) and PRINSEQ v0. 20. 3 (44)  to assess read quality and perform filtering for low-quality sequences using "-trim_left 15 -trim_qual_left 20 -trim_qual_right 20 -min_len 100 -min_qual_mean 25 -derep 14. " Paired-end reads were aligned to the C. auris assembly strain B8441 (GenBank accession number PEKT00000000. 2) (14) using BWA mem v0. 7. 12  (45). Variants were then identified using GATK v3. 7 (46) using the haploid mode and GATK tools RealignerTargetCreator, IndelRealigner, HaplotypeCaller for both single nucleotide polymorphisms (SNPs) and indels, CombineGVCFs, GenotypeGVCFs, Gath-erVCFs, SelectVariants, and Variant Filtration.
Sites were filtered with Variant Filtration using "QD Ͻ 2. 0 FS Ͼ 60. 0 MQ Ͻ 40. 0. " Genotypes were filtered if the minimum genotype quality Ͻ 50, percent alternate allele Ͻ 0. 8, or depth Ͻ 10 (https://github. com/broadinstitute/broad-fungalgroup/blob/master/ scripts/SNPs/filterGatkGenotypes. py). Genomic variants were annotated, and the functional effect was predicted using SnpEff v4. 3T  (47). The annotated variant call format (VCF) file was used to determine the genotypes of known mutation sites in ERG11 and FKS1. To determine the mating-type locus (MTLa and MTL␣) in each isolate, the average read depth at the locus was computed from the aligned bam file and normalized by the total coverage depth. Phylogenetic and phylodynamic analyses. For phylogenetic analysis, sites with an unambiguous SNP in at least 10% of the isolates (n ϭ 222,619) were concatenated.
Data and resource availability. All Illumina sequence data generated by this project are available in the NCBI SRA under BioProject accession numbers PRJNA328792, PRJNA470683, PRJNA493622, and PRJNA595978. The phylogenetic tree has been deposited in Microreact (https://microreact. org/project/ Candidaauris). A set of isolates representing each of the five clades are available from the CDC and FDA Antimicrobial Resistance (AR) Isolate Bank (https://www. cdc. gov/drugresistance/resistance-bank/index. html).
ACKNOWLEDGMENTS We thank  Anuradha Chowdhary ,  Joveria Farooqi ,  Nelesh Govender ,  Mathew Fisher , and  Koichi Makimura  for their continued support and sharing isolates and  Mathew Fisher  and  Angela Early  for comments on the manuscript. We also thank  Jacques Meis  for coordinating isolate acquisition;  Javier Peman  and  Olga Rivero-Menendez  for support with the Spanish isolates;  Erika Santiago ,  Jovanna Borace , and  Angel Cedeño  from  Hospital Santo Tomas ; and  Soraya Salcedo ,  Adriana Marín ,  Carmen Varón ,  Nohora Villalobos ,  Jairo Perez ,  Julian Escobar  for participating in the collection of strains and data from Colombian institutions. We also thank  Daniel Park  and  Christopher Tomkins-Tinch  for cloud computing support for BEAST analysis. This project has been funded in part with federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under award  U19AI110818  to the  Broad Institute.
, 2016). Moreover, agricultural pollution, such as water pollution, is typically plagued by the nonpoint source issue, which makes it difficult to identify the polluters and thus to regulate efficiently. Using a large database and a life-cycle approach,  Poore and Nemecek (2018)  examine the environmental impacts of various food products on five dimensions: GHG emissions, the use of land, the use of water, eutrophication, and acidification. They unambiguously show that all animal products-not only beef-have the biggest impact on the environment. Moreover, given the heterogeneity of multiple impact pathways and outcomes, they recommend a comprehensive regulatory approach, and conclude that a "dietary change can deliver environmental benefits on a scale not achievable by producers". Finally, directly regulating consumption rather than production may avoid domestic animal products being penalized versus imports.
Bègue,  Loren Geille ,  Hervé Guyomard ,  Sara Meade ,  Luca Panzone  and  Louis-Georges Soler  for their useful comments. We acknowledge the research program PAV at  INRAE  for research funding as well as the  ANR  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ) and the  FDIR chair at TSE.
5%, w/v) diluted in 1-300 in PBS (pH 7. 2) for both nematode species. The kinetics of larval exsheathment between the different experimental treatments was monitored by microscopic observation (200×). The percentages of exsheathed larvae were identified at 20, 40, and 60 min intervals. Four replicates were run for each ET to examine the changes in the proportion of exsheathed larvae with time. Statistical Analyses of the Results. All statistical analyses were performed with SAS 9. 4 (SAS Institute, Cary NC). Responses of H. contortus and T. colubriformis were examined in separate, but identical, statistical models. The response variable was the difference in response from the control, which was calculated with the following formula (where trt refers to treatment):  This response accounted for variation between controls in separate trials.
Funding This research was funded by the  Academy of Finland  (grant numbers  251388  and  310549  to M. K. ). Authors J. -P. S. and H. H. received funding through a  Marie Curie Initial Training Network  from the  European Union ("LegumePlus" ,  PITN-GA-2011-289377 ). Analyses and characterization of ETs on the UHPLC-ESI-MS/MS system were made possible by a  Strategic Research Grant  of  University of Turku (Ecological Interactions ).
72 m was used. Data were collected at 59. 5 Hz and 1 Hz for the accelerometer data and GPS data, respectively. The sensors were powered with two 3. 7 V lithium batteries (2. 6 Ah). Data were stored on a secure digital card and downloaded after the experiment. The dataloggers were 98. 2 mm × 51. 60 mm× 36. 0 mm in size and weighed 250 g. The dataloggers were fixed on a collar and positioned on the right side of the neck. The collars were tightly adjusted and a 500 g counterweight was added to prevent them from turning around. The x-axis detected the up-down direction, the y-axis detected the backward-forward direction and the z-axis detected the left-right direction.
Data Collection Accelerometer and GPS Data Collars were mounted on the 26 chosen animals at 5:00 p. m. on 30 April 2018 and removed 5 days later at the same time, which corresponds to both the duration of the complete grazing rotation and the autonomy of the sensor battery. It should be noted that a period of habituation to the device by the cows was not necessary as the complete device represented approximately 0. 1% of the animal's weight  [16]. The data collected from 21:00 h to 05:00 h were not used in this study to focus on the daytime behavior exclusively.
Structural and Botanical Characteristics The characteristics were collected in the paddocks grazed during the experiment. The first paddock was a permanent grassland of 1. 6 ha (thereafter referred to PG). The second paddock was a temporary grassland of 2. 3 ha (thereafter referred to TG). The locations of the structural elements, slope and moist soil areas are provided in Figure  2. The pasture characteristics considered in each paddock are the following: (i) The structural characteristics in the paddock including trees, hedges and boundaries were identified and geolocated (longitude and latitude) using the data from the French's National The pasture characteristics considered in each paddock are the following: (i) The structural characteristics in the paddock including trees, hedges and boundaries were identified and geolocated (longitude and latitude) using the data from the French's National Geographic Institute  [19].
It should be mentioned that no moist soil area was found in the TG. (iv) The plant species were identified and recorded using a method based on the quadrat method  [20, 21]. Approximately 97 measures per hectare were carried out in each pasture, corresponding to 165 measures in the PG and 182 in the TG. For every measure, a rating ranging between 1 and 10 was attributed to the five most represented species in the area. The more a plant species was represented in the area, the closer its rating was to 10. The other plant species identified in the area were only noted without a rating. The bare ground was also considered in the rating. Seventy-six different plant species were identified in the PG and 41 in the TG. Each measure was also geolocated in the paddock based on the geolocation data obtained during grass height measurements with the GrassHopper plate meter (Section 2.
• Resting while standing: standing without movement or rumination. The steps carried out to develop this methodology are outlined in Appendix A (Figure  A1 ) and we refer to Riaboff et al. [14, 15]  for a detailed description. The successive steps applied in the present study are illustrated in Figure  3. First, the raw accelerometer sequences collected from the 26 dairy cows during the 5 days of grazing were split into segments (windows) of 10 s, with 90% of the data being in common with two consecutive windows (overlap). This pre-processing step was performed in Matlab R2018a. The eXtreme Gradient Boosting (XGB) model fitted by Riaboff et al. [14]  was then directly used to predict the behaviors using the xgboost package  [22]  in R 3. 6. 1  [23]. At the end of this step, the behavior of each cow throughout every successive window of 10 s was predicted for all 5 days of grazing.
Calculation of the Time-budget Expressed in Each Zone of the Pastures The aims of this step were to (1) collect the position of cows in pastures while they were expressing a behavior and (2) compute the overall cow location and time-budget for every behavior expressed in every zone in the pastures per cow and per day. The date and time associated to each record were collected for both the accelerometer and GPS data. The time synchronization between these two sensors was previously ensured. Each 10 s window of behavior was thus associated with the position (longitude and latitude) of the cow while she was expressing the behavior, based on the date and time of the accelerometer and GPS records. It should be noted that the GPS data were collected every second (1 Hz), therefore there were 10 recorded positions associated to each 10 s window.