dataesr/scientific-paragraphs-categorization huggingface dataset:
Cage mate handling responses (USVs and hand following) during a single test were used to investigate contagion. When fitted for both cohorts together, the fixed effects part of the model comprised treatment, cohort and a treatment X cohort interaction effects. The random effects part of the model reflected the fact that cages were nested within pairs of cages and they were all crossed with the two runs in which different cohorts were tested. Due to the difference in cage mate responses between cohorts, responses were fitted independently, with treatment as the only fixed effect. Predicted means and associated standard errors of the mean (SEM) reported were back transformed to the original scale produced by the REML output. As weight data met the assumptions of normality, two ANOVAs were used to investigate a potential difference in start and final weight in all rats between cohorts, with cohort fitted as a factor.
Start weight was taken on the first day of handling (rats aged 39 days old). 50kHz USV production during tickling was recorded on first and fifth day of consecutive handling and following a two-day break from handling, data were also collected from the sixth and tenth day. Cohort 1 data points are filled grey circles and cohort 2 data points are in clear filled squares. Data were analysed using Spearman's Rank Correlation in Minitab 17. The line was fitted by Prism 8 (GraphPad) software using Rs = 0. 43 as the slope.
Study areas and data collection To investigate the influence of predation risk and human disturbance on roe deer diel activity patterns, we analysed activity data obtained within the EURODEER project (http://eurodeer. org). Data were collected for a period spanning from 2003 to 2015 and included 431 adult roe deer of more than one year old (254 females and 177 males) from 12 contrasted populations located in 9 geographical regions across Europe (Fig. 1 ). At each study site, roe deer were caught during winter, sexed, aged and equipped with GPS collars (Lotek 3300, Lotek Small WildCell, Vectronic GPS Plus or e-obs) carrying activity or acceleration sensors. Collars were programmed to record an activity measurement every 5 min and a GPS fix every 4 to 6 hours (depending on the study areas and the year of monitoring).
Activity data The activity sensors on the GPS collars measured the overall level of activity by recording forward/backward and sideway motions (Vectronic, e-obs) or up/down and sideway motions (Lotek) on two axes, X and Y. Because activity measured on the third Z-axis was only available for 4 populations (28% of all individuals), we discarded data on this axis prior to analyses. Lotek 3300 collars measured activity as the count of contacts along the X-and Y-axes. For each 5-minute interval, the sensors provided the mean value of all activity measurements, indexing the average level of activity associated with the corresponding date and time interval, ranging from 0 to 255 for each axis. Lotek Small WildCell and Vectronic collars measured activity based on the true acceleration in the X-and Y-axes by indexing the difference in acceleration between two consecutive measurements and averaging these values within 5-minute intervals ranging from 0 to 255 for each axis.
Diel activity patterns across seasons and lynx densities We classified activity data into active vs inactive behaviour based on the frequency distribution of activity measurements  (Gervasi, Brunberg, & Swenson 2006)  for each collar type independently (for more details, see Appendix S1 in the Supporting Information file). This method uses a specific threshold to discriminate active and inactive behaviour which is, therefore, insensitive to variation in absolute values of activity among individuals. Using the above mentioned approach, we described deer diel activity patterns for each study site and quantified the degree of overlap between the non-hunting and hunting seasons, based on the observations where a given individual was active. To account for changes in the sun's position across seasons, instead of using clock time, we first scaled the time of day to sunrise and sunset.
Acknowledgements This manuscript was conceived within the  EURODEER collaborative project  (paper # 11  of the EURODEER series; http://www. euromammals. org). The authors are grateful to all members for their support for the initiative. The EURODEER spatial database is hosted by  Fondazione Edmund Mach. We also thank three anonymous referees and the associate editor for their constructive comments which contributed to markedly improve the manuscript. This project was supported by the  Marie-Claire Cronstedts  foundation to NCB and PK, by the " Move-It" ANR  grant  ANR-16-CE02-0010-02  to AJMH and NM and by the  Institute for Zoo and Wildlife Research (IZW) , the  Mammal Research Institute -Polish Academy of Sciences , the  Polish Ministry of Science and Higher Education  (grant no N  N304172536 ) to LS (data from Białowieża, Poland).
Data accessibility Data used in this study are available from the Dryad Digital Repository  (Bonnot, Couriot et al. 2019)  at https://doi. org/10. 5061/dryad. 1zcrjdfnm. Raw data are also available through the EURODEER platform. Anyone is welcome to join the EURODEER project and obtain an access to the database after contacting the persons in charge (see https://eurodeer. org/contacts/).
Salivary analytes that showed non-normal distribution were inversely transformed before the analysis one-way ANOVA of repeated measures followed by Tukey's multiple comparison test to assess whether or not the different salivary analytes had changed at different times after the stress stimuli. Additionally, a post hoc analysis by a stand-alone power program for statistical testing (G-Power)  [36]  was employed using the means and standard deviations of the salivary analytes to guarantee that the significance level (α = 5%, p < 0. 05) and the power required (1β ≥ 80%) were correctly obtained with the number of horses evaluated. Spearman's or Pearson's r were calculated between the salivary analytes at the stress time (T3) and the behaviour parameters depending on whether the data were non-normally or normally distributed, in order to know whether there was correlation between them.
An r value of less than 0. 30 was considered as a negligible correlation, following the Rule of Thumb  [37]. A p-value less than 0. 05 was considered as being statistically significant. Data analyses were performed using spread-sheet software (Excel 2000; Microsoft Corporation, Redmond, WA, USA) and Graph Pad Software Inc (GraphPad Prism, version 6. 0c for Mac; Graph Pad Software Inc. , San Diego, CA, USA).
Study design Data from patients with suspected schistosomiasis examined at Toulouse and Rennes University Hospitals between January 2016 and March 2018 were collected. Only patients with at least a Schistosoma PCR and a serology analysis (including Western Blot), alone or combined with excreta examination, and without any previous anti-Schistosoma treatment were retained in the study. The diagnostic procedures were part of the standard diagnostic work-up for schistosomiasis investigation as described below. The design of this study aimed to evaluate the performances of Schistosoma detection by PCR in two contexts: i) in stool, urine and biopsy samples in comparison to conventional microscopy diagnosis, and ii) in the serum for the diagnosis of active infection and monitoring of treatment efficacy.
Statistical analysis Data were analyzed using GraphPad Prism software. Data were expressed as median with interquartile range [25 th ;75 th percentile]. Nonparametric Mann-Whitney and Kruskal-Wallis tests were performed for distribution comparison. For proportion comparisons, the chi-square test or Fisher's exact test was used, as appropriate. Kappa coefficient was used to measure the agreement between each test. Correlation between blood eosinophil count and PCR Ct values was assessed with the Spearman rank correlation test. Differences were considered statistically significant if the p-value was < 0. 05. DNA burden during post-treatment follow-up was calculated as the ratio of (45-Ct value after treatment)/(45-Ct value before treatment) � 100.
Participants Four brown long-coat adult female guinea pigs (Cavia porcellus) (GPs) were used. They had been purchased at an early age (around 8-weeks old) from a pet shop and raised in a family setting until they were involved in this study. Each animal was handled positively on a daily basis (at least 30 min per day, every day) and lived alone in its cage. This training was maintained during the study. Only healthy and non-aggressive animals were selected. To avoid potential stress or weariness, each GP was used for a maximum of three sessions per day. We analysed the data only for GPs for which we had a full 15-min video-recording (i. e. , some of the recordings were stopped by the child playing with the camera) representative of the whole session. Thus, 44 sessions involving three GPs were included in this study from the initial pool of 90 sessions  [45].
Data Collection and Analyses The data analyses were based on the 15-min video recordings. Here, we focused on the behaviours in the cage area. The observation of behaviour and the recording of events were performed by two raters blind to the child's diagnosis. Both raters had previous experience in coding human-animal interactions. The coding procedure was divided into two steps. First, there was a training part when two raters coded half of the videos together. Then, the other half of the data was coded by only one rater.
The behaviours were grouped in three general categories: GP positive behaviours toward a child (i. e. , approaching and exploring a child), GP negative behaviours toward a child (i. e. , avoiding, retreating and jumping) and GP maintenance behaviours (i. e. , feeding, self-grooming, body shaking and resting/sleeping)  [56]. The data concerning the GP time budgets and the frequencies of the GP behaviours during the onset and the end of physical interactions were compared between the two groups (ASD and TD).
Statistical Analyses The data were analysed using Minitab 15© and IBM SPSS Statistics 19©. As our data did not fit a normal distribution, we applied non-parametric statistical tests  [58]. The significance threshold was p = 0. 05. Mann-Whitney U tests were used in order to compare two independent samples (e. g. , the difference in behaviours between the two groups of children). Kruskall-Wallis tests and Mann-Whitney U-tests were used to compare the differences in behaviours among the three GPs used.
Results The data analysis included 4 h, 39 min and 7 s of video recordings, excluding non-visible elements (e. g. , dead angle, GP hidden by the child). Of the 44 children, only 4 ASD-children never approached the GP. Thus, all analyses were based on 18 ASD children and 22 TD children. We recorded the same frequencies of GP interaction sequences during the 15-min session for ASD children and for TD children (n ASD = 18, n TD = 22, X ASD = 11. 9 ± 3. 7, X TD = 12. 5 ± 4. 4, U = 399, p = 0. 899). On average, the TD children-GP interactions lasted significantly longer than the ASD children-GP interactions (n ASD = 18, n TD = 22, X TD = 32. 1 ± 0. 4 s, X ASD = 22. 6 ± 0. 6 s, U = 34. 4, p = 0. 017). No significant differences in frequencies or durations were found between the GPs (Kruskall-Wallis tests p > 0.
The main limitation of our study was our methodological choice to focus only on the onset and end of the interactions. At the same time, this was the strength of our study for the following reasons. The data collected at the onset and the end of animal-human interactions can have an impact on the course and quality of the interaction, respectively. Moreover, this choice allowed a more comprehensive collection of behaviours. During these two time periods, both the child and GP were clearly visible, while at other times for instance the GP was hidden by the child's body. This methodological issue should be solved for future studies. As often observed in similar studies, the sample size is one of the other main limitations. We had four GPs but retained only three for methodological reasons. As suggested by Gut and colleagues in a similar study on four GPs  [6] , the results have to be considered with caution given inter-individual variations in the animals' behaviour.
The patient underwent a first keratoplasty on day 14. Two days later, a small amount of mycelial growth was observed within the corneal fragment on the agar plate, leading to a strong suspicion of keratomycosis. Surprisingly, none of the various subcultures on Sabouraud-chloramphenicol agar plates yielded positive results, whereas subculture on brain-heart infusion medium (BHI) resulted in the rapid growth of a large mycelium at 35 C. All attempts to identify this mycelium on the basis of microscopic morphology or by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) failed. Indeed, despite the high quality of the protein spectrum, it could not be attributed to any of the microorganisms present in the Bruker Taxonomy (7815 entries), Bruker Filamentous Fungi (364 mass spectrum profiles (MSP)), US National Institute of Health (NIH) mold (365 profiles) or MSI (Mass spectrometry Identification) fungal (> 22 000 spectra) databases.
The corneal biopsy specimen and the mycelium extracted from BHI were, therefore, sent for panfungal PCR diagnosis on day 29. Biomolecular analyses were performed with panfungal primers (ITS1/ITS4 and NL1/NL4) and sequencing results obtained on day 37 unambiguously identified the species present as Pythium insidiosum (Genbank accession numbers MN148708 and MN148707).
Additional time was also lost to the various attempts to identify this fungus-like organism by MALDI-TOF MS, because there is currently no commercial reference spectrum for P. insidiosum in the various MSP databases commonly used for microbial identification  (Drissner and Freimoser, 2017) , although recent studies have concluded that MALDI-TOF MS is well suited for P. insidiosum identification  (Krajaejun et al. , 2018 and Mani et al. , 2019). Therefore, after confirmation by sequencing, we created a reference MSP on the Microflex mass spectrometer, according to Bruker's MSP creation protocol (v. 1. 1). These reference spectra was added to our local database but also to the MSI database platform (https://msi. happy-dev. fr/) to provide a free identification platform for MALDI-TOF identification and facilitate the diagnosis of Pythium infections in the future, both at our institute and at other healthcare (or veterinary) centers.
In Eq. (  12 ) it is essential that the universal gas constant is expressed in units consistent with those of the Henry's law constant (i. e. if K H is expressed in SI units of Pa m 3 mol -1 then R = 8. 314 Pa m 3 K -1 mol -1 ; if K H is expressed in units of atm L mol -1 then R = 0. 08206 L atm mol -1 ). Besides databases listing experimentally determined Henry's law constant values, a number of tools are available for predicting this parameter  (Dearden and Schuurmann, 2003; Katritzky et al. , 1998). The HenryWin tool gives users a prediction of K H in SI units, but also as the dimensionless K air = K air,u partition coefficient  (Dearden and Schuurmann, 2003). As already stated above, the Henry's law constant describes the airwater distribution ratio of neutral compounds in non-saturated and pure water, single solute systems.
Quasi steady-state dynamic dynamic model For 612 of the 1194 chemicals considered  (Armitage et al. , 2014) , we have used hepatic metabolic clearance estimates reported in the R Fig. 1. Comparative simulations of steadystate distribution in vitro with the previously published (9) (grey) and VIVD model (black) assuming neutrality; simulations represent primary human hepatocytes cultured in 10% FBS containing 24 and 1. 9 gL -1 of albumin and TAG, respectively treated with 1 nM (red dotted line) of each test compound individually. package httk  (Wambaugh et al. , 2018) (v1. 7). Those estimates were corrected for the fraction unbound in medium by Wambaugh  (Wambaugh et al. , 2018)  and colleagues, but not for the partitioning into cells (i. e. for the partition coefficient K cell,u ) so we divided the values given in httk by that parameter, correcting units as required.
Acknowledgments We would like to thank  Alltech  for the financial support of this study, and for the revisions of this manuscript. This work was also supported by the  Regional Council of Bourgogne -Franche Comté , the  "Fonds Européen de DEveloppement Régional (FEDER). We thank the entire technical staff who contributed actively to the care of horses, samplings and laboratory analyses at Agrosup Dijon. We are grateful to the  genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group  for providing help and storage resources thanks to Galaxy instance http://sigenaeworkbench. toulouse. inra. fr".
Whole-genome sequencing and assembly Bacterial genomic DNA was purified using MagNA Pure 96 Instrument (Roche). Next-generation sequencing was performed by the Mutualized Platform for Microbiology (P2M) at Institut Pasteur, using the Nextera XT DNA Library Preparation kit (Illumina), the NextSeq 500 sequencing systems (Illumina), and the CLC Genomics Workbench 9 software (Qiagen) for analysis. Draft genomes with 50x minimum coverage, a total size < 5. 3 Mb, and a minimum N50 of 10,000 nt were used for subsequent analysis. All raw reads generated and/ or contig sequences were submitted to NCBI under the project number PRJEB29877 and are available under genome accession numbers ERR3047203 to ERR3047514. We also downloaded 182 assembled genome sequences from the NCBI and PATRIC (www. patricbrc.
cgMLST scheme definition, phylogenetic analysis and comparaison with MLST schemes To determine a core gene set, 103 high-quality genome sequences of Leptospira covering the whole diversity of the Leptospira genus, i. e. , representative isolates from the three clusters (50% from the pathogens, 12% from the intermediates, and 38% from the saprophytes) were selected (S1 Table ); 50% of the genomes were downloaded from NCBI, the others were sequenced as described above. From this set we inferred the genus core genome using the CoreGeneBuilder pipeline  [40]  and L. interrogans serovar Copenhageni strain Fiocruz L1-130 (GCF_000007685) as a reference. The pipeline's first step relies on the eCAMBer software  [41] , which consists of a de novo annotation of the genomes (except the reference) using Prodigal  [42]  and the harmonization of the positions of the stop and start codons.
Indeed, the presence of paralogs inside a typing scheme can lead to ambiguities, as a candidate gene might be attributable to two different core gene loci. To detect those potential paralogs, we compared each allele of each locus against all the alleles of all the other loci using the software BLAT  [44]. If a single hit was found between two different loci (more than 70% protein identity between two alleles), we removed both. (ii) Second, we also removed genes that belong to one of the 3 existing Leptospira MLST schemes  [29, 45, 46]  and the ribosomal genes, so that they can be analyzed independently. (iii) Third, we also removed loci whose length varies too much among alleles, which is useful in reduceing ambiguities during the genotyping process. We aligned the protein sequences and removed those for which the alignment contained more than 10% of gaps (total number of gaps compared to the total number of characters).
To derive a phylogenetic tree based on cgMLST gene loci, the allelic sequences of each locus were extracted and aligned as protein sequences using MAFFT v7  [47]. The concatenation of all loci yielded to a supermatrix of characters. IQ-TREE v1. 5. 4  [48]  was used to infer a phylogenetic tree from this supermatrix of characters with an LG+G evolutionary model. Branch supports were assessed with both bootstrap (1,000 replicates) and aLRT-SH methods  [49]. All trees were drawn using the iTOL webserver  [50]. To evaluate classical MLST against the newly defined cgMLST scheme, all available Leptospira STs were downloaded from the Oxford University MLST database at https://pubmlst. org/ leptospira/  [51]  which comprises schemes 1, 2, and 3 developped by Boonsilp et al. [45] , Varni et al. [46]  and Ahmed et al.
Leptospira isolates and genomes A total of 327 Leptospira isolates were sequenced, covering the diversity of the Leptospira genus. A complementary set of 182 genome sequences of Leptospira strains, mostly reference strains from the Leptospira Genome project  [20] , was downloaded from GenBank and PATRIC (S1 Table ). The total set of 509 genomes contained representatives of most Leptospira species currently described. The clusters of pathogens, intermediates and saprophytes were represented by 402, 31, and 76 genomes, respectively. Geographically, the dataset was highly diverse: strains were isolated from different geographical areas (Africa: 19, East Asia: 17, Caribbean: 13, Central America: 7, Europe: 73, Indian Ocean: 123, Middle East: 4, North America: 24, Oceania: 11, Pacific Ocean: 14, South America: 101, Southeast Asia: 97).
The ecological sources of the strains were also diverse: 111 were from the environment, 226 were from humans, while the remaining isolates were from various animal hosts, such as rodents, cows, dogs, and pigs (S1 Table ). The strains corresponded to 42 species including 15 novel species isolated from the environment in Japan, Mayotte, France, Malaysia, Algeria, and New Caledonia  [54]. There were 26 serogroups and 73 serovars in the dataset (S1 Table ). The strains selected for this study are therefore highly diverse geographically, ecologically and taxonomically. The general features of the 509 genomes are reported in S1 Table and summarized in S1 Fig. Genomic assembly sizes ranged from 3,450,639 to 5,267,227 base pairs. Pathogens had a heterogeneous genome size, which was larger on average than the genome size of intermediates, which in turn had a larger genome than saprophytes (p < 0.
Genome-based phylogeny of the genus Leptospira To define the phylogenetic diversity of the dataset, 545 selected genes (see Methods, section cgMLST definition) were translated, aligned and then concatenated (S2 Table ). The resulting phylogenetic tree is shown in S2 Fig. ANI  analysis  [54]  revealed 42 species defined using the 95% ANI cutoff  [56, 57] , including 15 novel species for which a formal description was proposed elsewhere  [54]. The phylogenetic tree with representatives of each species (Fig  1 ) is consistent with previous data  [1]  showing two major clades, the "saprophytes" containing species isolated in the natural environment and not responsible for infections and "pathogens" containing all the species responsible for infections in both humans and animals, plus environmental species for which the virulence status is not clearly established.
This led us to re-identify some isolates. For example, Ocean, North America, Central America, Caribbean, South America, Europe, Africa, Middle East, Indian Ocean, Asia, Southeast Asia, Oceania. The size of the pie charts is proportional to the number of isolates (see key). The 'Others' category includes all pathogenic species not individualized in the key. The figure has been generated using R and the package "rworldmap", which is free under the GPL-2 and GPL-3 licenses (https://cran. r-project. org/web/packages/rworldmap/index. html). https://doi. org/10. 1371/journal. pntd. 0007374. g002 strain GWTS assigned to pathogen L. alstonii based on the 16S rRNA and secY genes  [63, 64]  did not cluster with the L. alstonii reference strain and formed a distinct branch in our phylogenetic tree (S2 Fig).