dataesr/scientific-paragraphs-categorization huggingface dataset:
Coding and statistical analyses The behavior of the horses was recorded with a camera during the video projection and the choice test and analyzed later using BORIS software (v. 6. 0. 6; Friard and Gamba 2016). -Video projection Four types of measures were analyzed: 1. Facial expression. Facial expressions were measured using the variables described in  Lansade et al. 2018 : 1) the time spent with both ears oriented backward or forward; 2) the time spent with the neck high, medium or low; and 3) the time spent with the whites of the eyes visible (as we did not observe any half-closed eyes, we did not include the time spent with the eyes wide open in the analyses since it would have been the exact opposite of the time with the whites of the eyes visible). We only included in the analysis the variables that were expressed by the animals and visible enough on the video recordings to be analyzed without ambiguity (see Table  1  for the complete list).
-Choice test During the choice test, we observed whether the horse touched each experimenter and how many times each experimenter was touched. Indeed, physical contacts with the muzzle has been described as an attention seeking behavior in horses  (Malavasi and Huber 2016; Ringhofer and Yamamoto 2016; Trösch et al. 2019b ). The touches were usually brief, but if those were longer than three seconds, it was taken into account by counting a new occurrence every three seconds (examples of this procedure:  Lansade et al. 2018; Trösch et al. 2019b ). Data were analyzed by a coder who was blind to whether the video watched by the horse was positive or negative. Moreover, facial expressions and time facing the videos were analyzed by two coders since these components can be relatively ambiguous to record.
The arithmetic means of the values coded by the two coders were used for the statistical analyses. Interobserver reliabilities were calculated: ears oriented forward (ICC= 0. 96, lower bound= 0. 94), ears oriented backward (ICC= 0. 93, lower bound= 0. 90); low neck (ICC= 0. 93, lower bound= 0. 90); medium neck (ICC= 0. 98, lower bound= 0. 98); high neck (ICC= 0. 99, lower bound= 0. 98); whites of the eyes visible (ICC= 1, lower bound= 1); time spent paying attention to the video (ICC= 0. 95, lower bound= 0. 92). The values obtained are all considered to represent excellent reliability  (Koo and Li, 2016). Statistics were performed with Xlstat software (Addinsoft, Paris, France) for the analysis of facial expression and with R 3. 0. 2 (R Core Team 2013) for all the other analyses. The significance threshold was fixed at 0.
Biomechanical testing Static tensile tests of these two techniques were performed on the stifles using a traction system (AGS-X Shimadzu, Japan) with a pre-test of 20 mm/min traction until the load reached 10 N, thus straightening the system. The first static test consisted in a 1-mm/min traction until failure. Failure occurs if the displacement exceeds 15 mm. For the series of tests, the sampling rate for data acquisition was set at 10 Hz. A total of 14 experimental set-ups were randomly considered: 7 under the first surgical implantation technique and 7 under the second.
Data acquisition and processing During tests, acquisitions of the data were carried out using the TrapeziumX software (Shimadzu, Japan). For each implantation technique, two measures were taken i. The strength recorded at 3 mm (S 3mm ) of displacement of the cross traverse. ii. The maximum strength (M s ) and the displacement of the associated traverse stroke. iii. The mode of failure. The data were then processed with Microsoft Excel. Statistical analyses were performed using nonparametric Mann-Whitney tests.
Statistical analysis Due to lack of normality, preference for the flavored milk was analyzed with nonparametric tests. Inter-group comparisons were analyzed using two-tailed Mann-Whitney tests, and intra-group comparisons were analyzed using Wilcoxon tests. The preference was also compared to chance (0. 5) using Wilcoxon tests. Statistical analyses were performed using the statistical package Statistica 10 and the level of statistical significance was set at p≤0. 05. All behavioral data are represented as median and interquartile ranges. (N° CE19 -2015-01). All efforts were made to minimize the number of animals (15 animals per group).
Acknowledgements We particularly thank (1) the staff of the  UEPAO of INRAE Centre Nouzilly  (https://doi. org/10. 15454/1. 5573896321728955E12 )for animal breeding and daily care; (2) Edith Pajot (INRAE), director of NBO research unit for her constant support; (3)  Angélique Favreau-Peigné  ( INRAE ) for fruitful discussions on animal preferences; (4)  Isabelle Pellerin  ( UPMC, Paris VI ) for her work on the GC-MS/MS analyses; (5)  Melody Morisse  for experimental assistance.
Molecular Characterization and Phylogenetic Analysis We performed DNA extraction and amplified fragments of the internal transcribed region (ITS), the D1-D2 region of the large subunit (LSU) ribosomal DNA, and the actin gene (11) (Appendix, https://wwwnc. cdc. gov/EID/article/26/9/20-0276-App1. pdf). We conducted a preliminary similarity searching using BLASTn (https://blast. ncbi. nlm. nih. gov) against curated fungal reference databases. We conducted multiple sequence alignments and single-gene phylogenies in MEGA7  (12). In addition to the clinical isolates and the type strain of N. obscura, we incorporated the corresponding sequences of N. draconii, N. chlamydospora, N. guarroi, N. vriesii, and N. arthrosporioides that are published in GenBank (Appendix). N. hominis was not included because of the lack of LSU and actin sequences in the public databases.
Molecular Characterization and Phylogenetic Analyses Similarity comparisons in public databases showed that all isolates belong to the genus Nannizziopsis and had percentage identity ranges of 96. 0%-99. 8% (475 bp length) for LSU, 88. 0%-99. 0% (>700 bp length) for ITS, and 85. 0%-98. 7% (>500 bp length) for actin genes. N. guarroi (GenBank accession no. MH874904) had the highest number of hits for LSU, whereas N. vriesii (accession no. HF547893) had the highest number of hits for the actin gene. For ITS, the highest-scoring hits corresponded to a Nannizziopsiaceae strain (GenBank accession no. MF688808; 99%), followed by Nannizziopsis spp. (GenBank accession no. KY771169; 98. 7%). Multiple alignments for ITS2, LSU, and actin regions consisted of 283, 476, and 572 positions, of which 33 (11.
6%), 31 (6. 5%), and 128 (22. 4%) were variable, respectively. The topologies observed on individual gene trees were very similar to those observed on a combined tree. The combined LSUactin-ITS2 dataset of 1,331 positions had 192 (14. 4%) of variable nucleotides. The multilocus phylogenetic analysis revealed 2 main well-supported clades: 1 grouping all the clinical isolates, including the type strain of N. obscura and the named N. obscura species complex clade, and an additional clade assembling the 3 reptile Nannizziopsis species isolated from Iguana iguana (N. guarroi) and from bearded dragons, Pogona vitticeps (N. draconii, N. chlamydospora). N. vriesii and N. arthrosporioides were separated from the rest of the isolates and from each another (Figure  2 ).
Patients could carry latent forms of the fungus and have onset of an opportunistic infection when their immunity fails, as described for other fungi, such as Cryptococcus neoformans  (17)  and Histoplasma spp. or other endemic mycoses  (18). These 9 new cases and the previously reported cases  (1, (3) (4) (5) (6) (7)  highlight the difficulties in identifying the N. obscura species complex. These fungal infections are likely underdiagnosed because of features shared with more common species, such as Trichophyton spp. or Trichosporon spp. Matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry should accelerate the process of identifying organisms and updating public databases, such as the online mass spectrometry platform (https://msi. happy-dev. fr), which now includes a N.
Acknowledgments The authors thank all the clinicians who cared for the patients, especially  Aude Barnier  in the case of patient 6. We thank  Mohammad Akhoundi  for his ITS2 sequencing;  Cécile Gautier  for technical assistance regarding culture of strains, MIC determinations, and PCR tests; and  Eurofins Genomics France  for strain sequencing. We are also deeply thankful to  Françoise Dromer , who commented on the manuscript. The National Reference Center for Invasive Mycoses and Antifungals is supported by the  French Center for Diseases Control  ( Santé Publique France ) and  Institut Pasteur in Paris. The funders had no role in the study design, data collection, or analysis or interpretation of data.
were morphologically and molecularly identical. Sequences marked with asterisks (*) refer to strains published by Nourrisson et al. (6). Scale bar indicates nucleotide substitutions per character. Figure 2. Maximum-likelihood tree obtained from combined large subunit ribosomal DNA, actin, and internal transcribed spacer 2 sequence data obtained from genomic analysis of Nannizziopsis obscura isolates from 9 patients from West Africa, France, 2004-2020, and reference sequences. Neighbor-joining bootstrap values or maximum-likelihood values are indicated on the branches. Support branch values <70% are not shown. Culture collection numbers appear next to sequences retrieved from GenBank, and type strains are indicated by a "T" after the species name. Patients from whom clinical isolates analyzed in this study were obtained are shown as P1-P9.
Data on paternity adequate to estimate the opportunity for sexual selection are lacking in chamois  (Corlatti et al. , 2015) , but the limited sexual size dimorphism  (Rughetti & Festa-Bianchet, 2011b ) and absence of sex differences in adult survival  (Loison, Festa-bianchet, Gaillard, Jorgenson, & Jullien, 1999)  suggest a lower opportunity for sexual selection than in highly polygynous ungulates such as red deer (Cervus elaphus) and bighorn sheep  (Vanpé et al. , 2008). After describing horn growth in both sexes, we examined sex-specific relationships between horn growth during the first 5 years of life and later survival. To do this, we considered three survival metrics: the probability of reaching 9 years of age (metric for medium-term survival), the probability of reaching 15 years of age (conditional on having survived to age 9; metric for long-term survival), and longevity.
| Statistical analyses We first ran linear mixed models using the "nlme" library (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2013) in R v. 3. 5. 1 (http:// www. r-proje ct. org) to explore the variation in increment length as a function of age (factor), sex, and their interaction. Individual identity was fitted as a random effect to account for nonindependence of increments from the same individual. We used all individuals in the larger data set for which those data were available (1,760 measures for 253 males and 208 females). Because it is difficult to distinguish horn growth in the first and second year of life in chamois, we combined the first two increments (L1-L2, see  Corlatti et al. , 2017; Rughetti & Festa-Bianchet, 2011a  for a similar procedure). To test whether absolute late horn growth was associated with early horn growth, we fitted a linear model for each sex with log-transformed sum of increments 3, 4, and 5 (L3-L5) as a function of log-transformed L1-L2.
Field data 158 sites were sampled in French rivers from 2009 to 2011 (Fig. 1 ). Sites were sampled using electrofishing by the the Agence Française de la Biodiversité (AFB), the Fédérations Départementales de Pêche et de Protection des Milieux Aquatiques (FDPPMA) or directly by our own team. Sampling was always carried out during summer to avoid seasonal effects on parasite prevalence, and the sampling effort was kept as constant as possible. At each site, the number of dace sampled was recorded, their individual body length (mm) was measured, and their individual infection status (parasitized by T. polycolpus or unparasitized) was determined. Data on infection status was used to calculate parasite prevalence at each site (number of parasitized dace divided by the total number of dace sampled).
Environmental data We focused on thirteen abiotic and biotic variables that may explain the spatial distribution of T. polycolpus. We focused on biotic variables measured at the individual host level (but averaged at the site level, see hereafter), and both abiotic and biotic variables measured at the site level (see Table  1  for a detailed list). At the individual level, we considered host body length (mm), expected heterozygosity (He) computed from a set of microsatellite data (Mazé-Guilmo 2016) and identity of the main host species of T. polycolpus on which we recorded infections (i. e. either L. burdigalensis or L. leuciscus, Table  1 ). Each of these variables was averaged at the site level before being included in the SDM. At the site level, we first extracted biotic variables related to the fish community composition using the AFB database  (Poulet et al.
2011 , Blanchet et al. 2014 ). This database contains fish occurrence data from yearly fish sampling in many sites evenly distributed across France. For each sampling site we drew a list of fish species occurrence over the period 2009-2011, from which we summed the total number of fish species ('fish species richness') and the total number of host species for T. polycolpus ('number of host species', Table  1 ) defined according to  Lootvoet et al. (2013). We then extracted abiotic variables belonging to four main categories namely climatic variables, hydrographic variables, landscape variables and habitat fragmentation variables (Table  1 ). Water temperature was not available across all sampling sites and was thus modeled based on air temperature variables. More specifically, we predicted the mean annual water temperature based on a combination of the mean annual air temperature, and the mean monthly air temperatures for January and July extracted for each site from the SAFRAN database (LeMoine 2002) using a random forest model (see  Chevalier et al.
2018  for more details). Hydrographic variables were extracted from the 'Réseau Hydrographique Théorique' database (RHT,  Pella et al. 2012 ) at each site: altitude (m), river slope (%), riverine distance of each site from its river source (km), area of the upstream watershed (km 2 ), Strahler index (i. e. the hierarchical level in the hydrographic network of each river), river width (m), river depth (m), average size of bed sediment (mm), minimum average monthly river flow (m 3 s -1 ) and average natural river flow (m 3 s -1 ). Landscape variables were extracted from the Corine land cover 2012 database (<www. statistiques. developpement-durable. gouv. fr/corine-land-cover-0>) as percentages of main land cover types within a 10 km radius around each site using the first level of land cover description (agricultural land, artificialized lands, forest and semi-natural lands and wetlands grouped with bodies of water).
A1 , Table  A1 ). For the hydrographic variables, we conserved the first two axes (58% and 15% of the variance respectively, Supplementary material Appendix 1 Fig. A1a , Table  A1 ) that we named 'Hydrology' and 'Topology' respectively. For landscape variables we conserved the first two axes (50% and 28% of the variance respectively, Supplementary material Appendix 1 Fig. A1b , Table  A1 ) that we arbitrarily named 'Landscape 1' and 'Landscape 2'. Finally, we extracted fragmentation variables from the 'Référentiel des Obstacles à l'Ecoulement' database (ROE, <www. onema. fr/REFERENTIEL-DES-OBSTACLES-A-L>) to quantify habitat fragmentation by weirs (number of obstacles being < 3 m height) or dams (number of obstacles being > 3 m height) within a 10 km radius around each sampling site. We input whether a weir was present directly upstream, downstream or both upstream and downstream for each sampling site (within a 2 km radius) to generate a more direct estimate of fragmentation at the sampling site level (categorical variable: upstream, downstream, both upstream and downstream or no weir at all).
Statistical analyses We developed a SDM to explain T. polycolpus prevalence at the site level by using a model accounting for spatial dependence among sites as we detected a strong signal of spatial autocorrelation for T. polycolpus prevalence (Moran test, p-value < 0. 001). We used the R package 'spaMM'  (Rousset and Ferdy 2014)  and its function 'corrHLfit' to specify geographic coordinates of each sampling site into continuous random terms. We first built a full model fitted on the dataset covering the current range of T. polycolpus (i. e. parasitized areas, Fig. 1 , n = 128 sites) to test the significance of each variable on parasite prevalence. It included all explicative variables (Table  1 ) as well as the quadratic term for body length because preliminary visual inspections suggested a non-linear relationship between prevalence and body length.
A binomial error terms distribution with a logit link function was fitted to the prevalence data. Then, likelihood ratio tests based on PQL/L fits were performed by contrasting the full model to a model excluding the explicative variable of interest, so as to generate a χ 2 statistic and its associated p-value for each explicative variable. Secondly, we parametrized the model on a restricted dataset including 75% of the sampling sites covering the current range of T. polycolpus (i. e. parasitized areas, Fig. 1 ). The parameterized model allowed predicting T. polycolpus prevalence both on the remaining 25% of the dataset covering the current range of T. polycolpus and in sites out of the range limit of T. polycolpus (Lineage III-unparasitized, Fig. 1 ). This procedure was reiterated 100 times to account for the uncertainty arising from the selection of the calibration dataset.
Data filtering and catalogue construction We relied on the  'Stacks' pipeline (ver. 2. 3. 4)  to both filter the raw dataset and identify SNPs genotyped at the individual level  (Catchen et al. 2013). First, we removed reads with unidentified bases and/or with low quality scores (below a phred33 quality score of 20) using 'process_radtags'. PCR duplicates were also removed using 'clone_filter'. To build our catalogue of loci, alleles and ultimately SNPs from the filtered reads, we ran the 'denovo_map. pl' using as parameters value: m = 3, M = 2, n = 4 (i. e. set of parameters maximizing the number of SNPs having high likelihood according to preliminary tests). Finally, 'populations' was used to select loci with a minimum allele frequency above 3%, with a likelihood over -20, with a minimum coverage depth greater than 3, occurring in the three populations and in at least 40% of the individuals for each population.
We also kept only one SNP per locus to limit linkage disequilibrium between SNPs. When applied to the whole database (i. e. 95 individuals because one individual was discarded due to its poor sequencing quality), this parameter set resulted in a catalog of 14 255 SNPs. A final filtering excluded SNPs with more than 30% of missing values from further analyzes using the library 'poppr' in R  (Kamvar et al. 2014). This resulted in a final dataset of 2543 SNPs genotyped from 95 individuals.
Genome wide association study We used a genome wide association study (GWAS) approach to test whether prevalence at the individual level was significantly related to particular SNPs. We relied on GLMs with a quasibinomial error term linking parasite prevalence at the individual level (parasitized or unparasitized, response variable) to the genotype (coded as a categorical factor: homozygote for allele a or b and heterozygote ab) of each SNP. One model per SNP marker (i. e. 2543) was run in which we controlled for population structure using the k -1 discriminant functions described previously. We also included the host body length and He as continuous co-variables with their respective quadratic terms. Type I ANOVAs were performed to extract the p-value associated to each SNP marker. p-values were then corrected for multiple comparisons using a false discovery rate approach (FDR) and using the 'fdrtool' R package  (Strimmer 2008).
Acknowledgements -We would like to thank all the people that contributed to this study, notably people who helped collecting data on the field such as people from our own team during that period ( V. Dubut ,  I. Paz-Vinas ,  C. Veyssière ) but also people from The  Agence National pour la Biodiversité (AFB)  and the  Fédérations Départementales de Pêche et de Protection des Milieux Aquatiques (FDPPMA). We are also grateful to people who gave advice and feedback on this work such as  M. Bernard ,  M. Chevalier ,  J-B. Ferdy ,  L. Fourtune ,  E. Goetz ,  C. Mathieu  and  J. Prunier. Funding -This work was funded by the  Agence Nationale de la Recherche  (project  INCLIMPAR , grant no. ANR-11-JSV7-0010 ) and the  Institut Universitaire de France. EMB and EMG were supported by a PhD grants from the  French Ministry for Education and Sciences.
Animals and Management Holstein heifer and bull calves (n = 24; 5 heifers and 19 bulls) were enrolled at the University of Florida Dairy Unit (Hague, FL; a dairy research facility housing Holstein cattle from birth to lactation) during the fall, from September to December 2018. Our distribution of heifers and bulls was restricted by a concurrent trial at this facility that was enrolling heifers, such that we consecutively enrolled all available calves. Calves were managed according to the standard operating procedures for this facility with all study procedures reviewed and approved by the University of Florida Institutional Animal Care and Use Committee  (IACUC Study #201609416). Because no previous data were available describing our primary response variable (shelter use) with which to perform a power calculation, sample size was estimated based on previous experiments describing changes in social behavior associated with disease in calves under similar housing management as the present study  (Hixson et al.
Data Collection Each pen was recorded by a digital video camera (Axis M2026-LE Network Camera; Axis Communications, Lund, Sweden) mounted in the center of the outside wall of the pen, approximately 3 m from the ground. Behavior was recorded continuously from video using Behavioral Observation Research Interactive Software (BORIS;  Friard and Gamba, 2016). Each calf was individually identified based on coat markings. We recorded behavior for 3 consecutive days, beginning immediately following the disbudding procedure or handling (1200 h). We defined shelter use as half or more of the calf's body located within the shelter. Preliminary observation revealed frequent social use of the shelter, and so we recorded social use with identity of companion noted. Transitions into and out of the shelter were recorded as events, with entrances specified as either a solitary entrance (focal calf entering the shelter when it was not occupied) or social entrance (focal calf entering the shelter when at least one other calf was already inside, regardless of whether shared use was initiated by the focal calf), and exits from the shelter specified as either a solitary exit (focal calf leaving the shelter when no other calf was located inside) or social exit (focal calf leaving the shelter when it was occupied by another calf).