dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this study, we used data from RNA-seq data of Creole kid goats abomasal mucosa samples at four time point after infection with H. contortus of two genotype, resistant and susceptible, to identify putative gene-related genomic variants. To our knowledge, this is the rst study to identify genomic variants from RNA-seq data in goats infected with a GIN. Other studies identi ed SNPs in goats using RNA-seq analysis for climate adaptation traits  [17]  and ber quality  [13]. One bene t to variant calling from RNA-seq is the focus on genes/transcripts that are expressed in the target tissue. However, there are some concerns about the information provided by such data. It was previously shown that some false positive SNPs identi ed in cDNA arise from alignment of a read to the wrong gene which represents a problem in gene families with highly conserved domains when using short sequence reads  [16, 22].
Funding: This research was funded by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection  and by the  French Government  under the " Investissements d'avenir" (Investments for the Future ) programme managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ) and the  Région Provence-Alpes-Côte d'Azur, European ERDF  funding (European regional development fund) and  PRIMMI ((Plateformes de Recherche et d'Innovation Mutualisées Méditerranée Infection ).
Fungal ball (FB) rhinosinusitis (RS) is the main type of non-invasive fungal RS. Despite positive direct examination (DE) of biopsies, culture remains negative in more than 60% of cases. The aim of the study was to evaluate the performance/efficacy of targeted metagenomics (TM) to analyze microbiota and mycobiota in FB and find microbial associations. Forty-five sinus biopsies from patients who underwent surgery for chronic RS were included. After DE and culture, DNA was extracted, then fungal ITS1-ITS2 and bacterial V3-V4 16S rDNA loci were sequenced (MiSeq TM Illumina). Operational taxonomic units (OTUs) were defined via QIIME and assigned to SILVA (16S) and UNITE (ITS) databases. Statistical analyses were performed using SHAMAN. Thirty-eight patients had FB and seven had non-fungal rhinosinusitis (NFRS).
All patients' files were reviewed by an ENT surgeon and a mycologist and were classified according to the criteria defined prior to sample processing for the metagenomic study. Patient information regarding age, clinical background, sinusitis localization and use of antifungal/antibiotic were collected and analyzed. This study complies with the ethical and legal requirements of French law (15 April 2019) and the Declaration of Helsinki. The database was officially registered with the French Data Protection Authority (Commission Nationale Informatique et Liberté) (no. 2221215). Written or verbal informed consent from all participants was waived because isolates were collected through routine clinical work and patients' identifiable information had already been anonymized prior to analysis.
Library Preparation and Sequencing Amplicon libraries targeting the V3-V4 16S hyper-variable regions were prepared for bacteria using primers 341 and 785 without degenerated nucleotides  [18]. For the ITS1 or ITS2 regions of fungi, amplicon libraries were prepared using primer pairs ITS1F/ITS2 and ITS3/ITS4, respectively  [19]. Amplicon PCR, PCR products, purification and sequencing were prepared as previously described by Angebault et al. [20]. The libraries were sequenced using the MiSeq Reagent Kit V2 500-cycle kit (2 × 250) (Illumina) on an Illumina MiSeq platform (Illumina, Evry, France). Raw data are available on the NCBI website (Bioproject PRJNA 701550). One negative control per extraction batch, consisting of DNA-free water, was submitted to the same extraction protocol. Negative controls were pooled for amplification and sequencing.
Taxonomic Assignment, Diversity After trimming the barcode and adapter sequences from the reads, paired-ends reads were merged, then filtered, according to Bokulich et al. [21] , within QIIME2  [22]. Sequences were denoised with Deblur  [23]  with a length of 250 pb for 16S and 200 pb for ITS. Amplicon sequence variants (ASVs) (that will be further called operational taxonomic units, OTUs) were assigned to SILVA (v132)  [24]  for bacterial reads (16S) and UNITE (v8. 0, released 18 November 2018)  [25]  for fungal reads (ITS1 and ITS2) us-ing vsearch  [26]  with a minimal query coverage set to 70%. Databases Mycobank  [27] , FungiBank (http://fungibank. pasteur. fr, accessed on 7 July 2021), NCBI RefSeq, Greengenes  [28] , RDP  [29]  and EzBioCloud  [30]  (http://www. ezbiocloud. net, accessed on 7 July 2021) were used to manually assign and verify OTUs.
Reads with ≥98. 7 and ≥94. 5% homology and 0. 0 e-value were considered for identification at species and genus level, respectively  [31]. Data analysis was further performed by SHAMAN (http://shaman. pasteur. fr, accessed on 7 July 2021)  [32]. Rarefaction curves were computed to evaluate the quality of the taxonomic diversity assessment. Diversity indexes (Shannon, Simpson and Inverse Simpson) were calculated to compare the homogeneity of the samples in terms of microbiota and mycobiota composition.
Statistical Analysis Read count normalization was performed using DESeq2 normalization method  [33]. The generalized linear model (GLM) implemented in the DESeq2 R package was applied to detect differences in abundance of taxa between fungal balls and controls  [34]. The latter was computed, including the patient, type of sinusitis (FB versus control) and the amplification target as main effects. Resulting p-values were adjusted according to the Benjamini and Hochberg procedure  [35]. A relative abundance cut-off > 1% of total reads and >5% per sample was used for graphical display of the results. Clinical characteristics of the patients and microbiological data were reported as percentage, mean and standard deviation (SD) if variable followed a Gaussian distribution or median and interquartiles (Q1-Q3) otherwise.
Mean α-diversity indices, Shannon, Simpson and inverse Simpson were 2. 08 (±0. 47), 0. 41 (±0. 13), 0. 23 (±0. 07) and 1. 60 (±0. 40) for ITS1 and 1. 58 (±0. 36), 0. 21 (±0. 12), 0. 11 (±0. 06) and 1. 29 (±0. 24) for ITS2. They showed a narrower diversity with ITS2 despite a higher number of OTUs (Figure  S1 ). At the phylum level, the diversity profiles generated with primers targeting ITS1 showed an increased proportion of Basidiomycetes over Ascomycetes compare to ITS2 (p < 0. 001) (Figure  1 ). Considering these findings, further analysis was performed on the ITS2 dataset. Mean α-diversity indices, Shannon, Simpson and inverse Simpson were 2. 08 (±0. 47), 0. 41 (±0. 13), 0. 23 (±0. 07) and 1. 60 (±0. 40) for ITS1 and 1. 58 (±0. 36), 0. 21 (±0. 12), 0. 11 (±0. 06) and 1. 29 (±0. 24) for ITS2.
The possibility or necessity to validate metagenomics data using an independent technique such as quantitative PCR (qPCR) is a matter of discussion in progress among the community, similarly to what is performed in transcriptomic analyses  [46]. In that case, expression of the gene of interest and control genes are measured against defined standards, usually housekeeping genes' expression, known to be consistently expressed in all cells and despite varying conditions  [46]. In targeted metagenomics, qPCR could be used to quantify the fungal and bacterial biomass of species of interest (i. e. , Aspergillus and Haemophilus in the present study) in order to obtain more comparable results among samples. However, because fungal species differ markedly in number of rRNA operon copies per genome, the performance of qPCR is limited  [36].
Mesowear and Microwear Methods The horses' diet in each site was studied using dental mesowear and microwear analysis. These two techniques provide information about the diet on two different timescales: mesowear indicates the average diet during the individual's life or its last months or years  [82, 83] , and microwear reveals the diet of the last few days to months of an animal's life  [84] [85] [86] [87] [88]. Combined together, these two methods allow us to understand the evolution of the animal's diet in relation to seasonal variations over a period of several months. All the meso-and microwear data were collected by the author except for those from Payre, studied by F. Rivals  [14, 89].
Dental Mesowear Mesowear analysis is a method based on the observation of wear patterns on ungulate molar cusps that indicate the diet of an individual animal  [83, 90]. The sharpness and the morphology of cusp apices are correlated with relative attritive and abrasive dental wear. Thus, a diet with low abrasion (and high attrition) shows very sharp and high relief molar buccal cusps and, on the contrary, a diet with high abrasion (due especially to the consumption of siliceous grass  [91] ) will result in rounded, blunted and low relief cusps. We selected only upper intact adult teeth. As for the bones, taphonomic processes constrained data collection since the slightest alteration (e. g. , breaks or polish) in cusp integrity led to its exclusion from selection. Observation of the cusp shape on horse teeth is easier on upper ones, which decreases the chance of potential mistakes.
We used the Mann-Whitney test when we were comparing only two samples. In both cases, since the sample size is quite small, we used the exact p-value. The Spearman correlation test was used to evaluate the liaison intensity between the diet categories defined by enamel wear studies and the size of the different horse populations. All the statistics were carried out using the Xlstat v. 2014. 5. 03 software with a risk threshold of 5%.
Based on the metapodials, and following the recommendations of  [18] , we estimated the body mass for each cohort (Figure  3 ). The average body mass within late Middle Pleistocene and early Late Pleistocene horse populations in southern France varied from 468 kg at Pech de l'Azé II l. 6-4 to 571 kg at Coudoulous II l. 9. They therefore fall within the 'large' (450-550 kg) and 'very large' (>550 kg) weight classes defined by the authors of  [18]. The variability of the weights of these populations, of the order of 100 kg, is therefore quite low. The Spearman correlation test was used to evaluate the liaison intensity between the diet categories defined by enamel wear studies and the size of the different horse populations. All the statistics were carried out using the Xlstat v. 2014. 5. 03 software with a risk threshold of 5%.
In Payre l. F, the metacarpal is long, slender at the diaphysis, but more developed at the extremities. Finally, the third metacarpals of Coudoulous II l. 9 and the Rameaux-Amont, located at the bottom right of the graph, are both very long and very robust compared to the other populations. Figure  6  presents a comparison of the same type of data for the third metatarsals (summarised in Table  4 ). For this PCA, the data used were: maximum bone length (M. 1), width and thickness of the diaphysis (M. 3 and 4) and distal end (M. 11 and 14), and width of the proximal end (M. 5). In order to keep only well-represented data on axes 1 and 2, we had to exclude the distal sagittal crest (M. 12) and proximal end (M. 6) thickness and the distal supra-articular width (M. 10). Thus, the PCA represents 70.
Data Availability Statement: The data presented in this study are available in this paper. Acknowledgments: I am particularly grateful to Jean-Philip Brugal who directed my PhD, from where this study is extracted. I would also like to thank LAMPEA, ED 355, and Aix-Marseille University for the help and funding they provided during this work. I also would like thank F. Rivals for sharing the teeth wear data from Payre. Finally, I would like to thanks the five reviewers and the academic editors, Juan Rofes, Janine Ochoa and Emmanuelle Stoetzel who help to greatly improve this manuscript.
Introduction Leptospirosis is a worldwide zoonotic disease caused by spirochetes of the genus Leptospira. The genus Leptospira comprises 35 species that are divided into more than 300 serovars  [1]. Among these species, ten are pathogenic and can infect and cause disease in humans and animals (Leptospira interrogans, Leptospira borgpetersenii, Leptospira santarosai, Leptospira noguchii, Leptospira weillii, Leptospira kirschneri, Leptospira alexanderi, Leptospira mayottensis, Leptospira alstonii, and Leptospira kmetyi), and five are known as intermediates and may cause various mild clinical manifestations of leptospirosis  [2]. Leptospirosis is an important but neglected disease. A study based on a systematic review of published morbidity and mortality studies and databases estimated, using modeling, that there were annually 1.
Statistical analysis The prevalence was calculated as the number of animals with positive serum devised by the total number of animals studied in each group. The prevalence rates were compared between species and location using Chi-square when the number of observations was sufficient, or the Fisher exact test when it was not. Odds ratios (OR), 95% confidence interval (CI) and p values were calculated separately for each variable. p ≤ 0. 05 was considered significant. All statistical tests were carried out using the Epi Info Software (7. 1. 3. 0 version, CDC Atlanta, USA).
Acknowledgments The authors express their gratitude to the Military Hunting Society of Canjuers, in particular to  Martial Grioche , as well as to the hunters of the Carpiagne camp, particularly to  Maurice Vergos  and  Dominique Orsetti. We would also like to thank  Audrey Chapron  and  Marine Le Guyader  for their technical assistance. This study was supported by the  French Military Health Service  and the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program "  Investissements d'avenir  ", reference  ANR-10-IAHU-03 , the  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI.
Selection of Species for DART Testing. The rat is generally appropriate for DART testing and is the most often used rodent species for reasons of practicality, general knowledge of pharmacology in this species, the extensive toxicology data usually available for interpretation of nonclinical observations and the large amount of historical background data. The mouse is also often used as the rodent species for many of the same reasons. For assessment of EFD only, a second mammalian nonrodent species is typically evaluated, although there are exceptions. The rabbit has proven to be useful in identifying human teratogens that have not been detected in rodents and is routinely used as the non-rodent species based on the extensive historical background data, availability of animals, and practicality.
g. metabolite profile, plasma protein binding), and class related tolerance or safety precedents  (Butler et al. , 2017). More recently, justification for species selection were published from a cross-industry database, along with comprehensive insight into the decisions made within two major pharmaceutical companies  (Prior et al. , 2020b). For species selection it is important to understand which studies/ research are required, how this is documented, who is involved and how decisions are made. Appropriate species selection is expected to facilitate better translatability of nonclinical data to human. In addition, such information may assist in situations where a switch of species is required during development, which is often a challenging task. In general, the regulatory guidance for species selection is high-level and not detailed (Tables  1a  and 1b ) i.
Other guidelines are available within specific regions or for specific products such as vaccines, gene and cell therapies, advanced therapy medicinal products, etc. Checks for other or updated guidelines and policies is advised. See the ICH website (ICH website, 2020) and EMEA, 2017 (EMEA, 2017) for further details. 4. The studies/data ("factors") that are considered and/or used to assist in species selection 5. Compare and contrast the approaches taken with NCES and biologics A questionnaire devised to explore the key aspects of species selection as highlighted above, was sent to several biopharmaceutical/ pharmaceutical (industry) companies and CROs to obtain data and the results were examined and discussed. Furthermore, we have provided some brief case examples of species selection approaches and the process/documentation employed.
The companies were mainly based in the EU, but also included the USA, Canada, and Japan. This distribution was considered sufficient to provide a reasonable dataset and adequate insight of current practice since it included examples of big, mid-size and small (bio)pharmaceutical companies along with major CROs from various jurisdictions. The questionnaire focussed on species selection for general and reproductive toxicology, as a major component of nonclinical safety studies, but there was no separation of questions or responses between these broad categories. Carcinogenicity and other nonclinical studies (such as in vivo safety pharmacology and genotoxicity) were not included within the remit, since species selection is either clearly specified within regulatory guidelines or generally follows the species selected for general toxicology studies.
The following aspects were addressed, split into three main sections: 1) Process and documentation: General information on the functions/ disciplines involved in the toxicology species selection process and the final decision maker(s); whether companies had a formal process to guide or outline the steps/data to consider for toxicology species selection, how this was documented and how the decision was recorded; questions relating to regulatory interactions to determine if information to justify the toxicology species used was included within regulatory submissions, and if the choice of toxicology species had been questioned/rejected or evaluation of additional species requested by regulatory authorities. 2) Species: The rodent and non-rodent species and strains/breeds that were typically considered (defined as 'thought about using information in literature and/or previous knowledge within project') or assessed (defined as 'tested this species for suitability'), and those which were considered on a case-by-case basis.
Dataset demographics Fourteen completed questionnaires were received from 11 (bio) pharmaceutical companies and 3 CROs from within the UK and Europe (8), Japan (4) and USA or Canada (2). All of the companies provided data for NCEs (n = 14) but one company did not provide the detailed level of information requested for NCEs for the factors section, so results reported in Table  2  are based on n = 13. For biologics, one company did not work with these types of molecules/products and another company did not provide the detailed information for the factors' section, so results reported in Table  2  are based on n = 12. Additionally, for Table  2 , some companies provided a response of not applicable (N/A) instead of one of the three available categories, indicating the specific test/factor was not applicable for either NCEs or biologics.
e. trying to minimize animal use and maximize the likelihood of identifying responses that are similar to those expected in humans. At target assessment, non-rodent species selection is based on evidence from the literature and databases, including in-house databases allowing genome-based characterization of the different animal species for potential use. For small molecules, it is recommended to clone the target from animal species used (if not commercially available) for binding/ functional assays and to establish functional assays to compare on-target activity, if feasible. This information is needed for informed selection of the animal species to assure human relevant safety assessment, avoiding production of potentially irrelevant findings, and potency assessment for safety margin calculation for human starting dose.
The sex and the weight of stillborn piglets as well as the number of mummified piglets were recorded for reproductive data collection. All live-born piglets were then put inside a PVC box, under the IR lamp and inside the creep area of the farrowing crate, to provide an environment close to thermo-neutrality. At the end of farrowing, piglets were reweighed (BW0) and cross-fostered to obtain the experimental litters of 12 piglets each. Piglets with BW0 lower than 700 g were not used because they are often considered as a runt and frequently euthanized. The time span between birth and first suckling was 251 and 58 min in UN and 247 and 54 min, respectively for first born and last born piglet of the litter. The number of 12 piglets per sow was set to assure that in each litter all piglets had access to functional teats.
Statistical analyses Data were analyzed using IBM SPSS Statistics software  (version 22, 2013). For an overview of reproductive traits and original litter traits, descriptive statistics were performed. The within-litter-CV and average birth weight comparison between smaller and larger original litters was made using the average alive born piglets of all litters as the limit value. The GLM procedure with the one-way ANOVA was used considering litter size ≤ 13 born alive piglets (n = 28) or (≥ 14 born alive piglets (n = 24) as fixed effect and batch as a random effect. The experimental litters were compared with the same procedure but using treatment (UN or HET) as a fixed effect and batch as a random effect. After litters were created, there were 38. 8% cross-fostered piglets and 61. 2% resident piglets (P < 0.
(2011) and Le Dividich et al. (2017). This weak relation is probably related to the various decreasing patterns of colostrum IgG concentrations, not measured in this trial but reported in other studies  (Devillers et al. , 2004a; Charneca et al. , 2015)  and the various intake behaviors of piglets over time. Because IgG was only determined in a sub-set of litters and many piglet losses occurred before blood sampling, there is no sufficient data to compare IgG levels of surviving and dying piglets. High mortality has been correlated with low levels of serum IgG  (Devillers et al. , 2011). However, these mortalities may simply be associated with insufficient nutrition rather than disease. As an example, the study by  Devillers et al. (2011)  showed that piglets dying during the first three postnatal days had 44% less serum IgG concentrations at 2 days of age than survivors, but had consumed 2.
Acknowledgements The authors wish to thank the direction and workers of the Montalva Group (owner of the farm) for the authorization for experimentation and help during the fieldwork. The authors would also like to thank the help during experimental procedures of several animal science and veterinary medicine students from the  University of Évora , and by the technician  Luís Branco. The serum IgG analyses, performed by  Françoise Thomas  ( INRAE UMR Pegase)  is also acknowledged.
Financial support statement This work is funded by  FEDER Funds through the Operational Program for Competitiveness Factors-COMPETE , by  National Funds through FCT-Foundation for Science and Technology, Portugal , under the  Strategic  Project  PEst-C/AGR/UI0115/2011 , by  National Funds through FCT-Foundation for Science and Technology, Portugal , under the Project  UIDB/05183/2020  and by the  Department of Animal Science of the University of Évora ,  Portugal (Animal Science Master program ).