dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Data and Analyses Statistical analyses were conducted using R version 4. 0. 0 software (The R Project for Statistical Computing; http://www. rproject. org, accessed on 20 October 2022). Generalized Linear Mixed Models (GLMM) were calculated. All models included patient identification as a random intercept to account for the lack of independence of observations (host sample size effects). Measurements were used as a response variable, and host sample size effect and geographical location were used as independent variables. The 'glmer' function of the lme4 package was used. The relevance of the terms was evaluated with the second-order Akaike Information Criterion (AICc) to account for small sample sizes  [36, 37]. When the inclusion of each term did not reduce AICc values by 2 or more units (∆AICc < 2), it was dropped from the model.
CVA and Mahalanobis distances were performed using both the CLIC package and the available online XYOM software (https://xyom. io/ accessed on 14 February 2023) and tested by non-parametric permutation tests with 1000 permutations each. Values were considered statistically significant when p < 0. 05  [23, [46] [47] [48]. Nevertheless, to avoid matrix singularities, ratios were discarded because of their overlap with other measurements, and only the 17 above-mentioned non-redundant measurements were used.
ITS rDNA Sequence Analysis The rDNA sequences demonstrate that the S. haematobium profile was "pure" in all the eggs analyzed. No hybrid profiles were detected in the chromatograms. Among the 162 sequences obtained of 452 bp, including the partial 5. 8S rRNA (138 bp) and the complete second Internal Transcribed Spacer (ITS-2) (314 bp), two pure S. haematobium haplotypes were obtained (Sh-1 and Sh-2). After a comparative analysis with other S. haematobium sequences from other countries (Africa and Europe), S. bovis, and S. haematobium x S. bovis, available in GenBank (Table  2 ), the alignment carried out allowed us to observe that Sh-1 showed sequence identity (100%) with S. haematobium (consensus sequence of GenBank Acc. Nos. GU257398, JQ397400-JQ397414, FJ588861, MT580953, MT884914 and MT158873) and Sh-2 was 100% identical to that previously described in the Ivory Coast (MG554667) and in Corsica, France (MW130296).
Variable positions in the 5. 8S and ITS-2 sequence alignment of Sh-1 and Sh-2 haplotypes of S. haematobium obtained in this study and other S. haematobium, S. bovis and S. haematobium x S. bovis sequences from GenBank. The numbers (to be read vertically) refer to variable positions obtained in the alignment made with MEGA X;. = identical; -= not sequenced. Heterozygotic positions are represented with the corresponding symbol of the IUPAC code for an incomplete nucleic acid specification.
ATAT--- S. haematobium x S. bovis Benin Y RYRY--- 1Tanzania (GenBank Acc. No. GU257398); Senegal, Mali, Gambia, Madagascar, Mauritius, South Africa, Zambia, Malawi, Tanzania, Sudan, Egypt, Cameroon, Nigeria, Guinea Bissau, and Liberia (JQ397400-JQ397414); Senegal (FJ588861 and MT580953); Zimbabwe (MT884914); and Benin (MT158873).
37 pg/mL ± 3. 43). Figure  2. Circulating levels of testosterone in plasma samples from horses castrated at birth and at 18 months. Plasma testosterone levels were measured for 33 months after birth in the two experimental groups using ELISA (S0 to S12: n = 11, S14 to S17: n = 8, and S28 to S33: n = 5). Data are presented as mean ± SEM. A variation analysis in testosterone levels compared to P0 within the same group was conducted using a Kruskal-Wallis test, followed by a Dunn's comparison (a ≠ b; c ≠ d). A Mann-Whitney test was performed to compare the testosterone levels at the same time between the two groups (* p ≤ 0. 05;; *** p ≤ 0. 001). In group 2 (the horses castrated at birth), the circulating testosterone level was consistently low but remained detectable, registering as early as 1 month (12.
493 ng/mL. These findings indicate an enhancement of bone matrix catabolism during foal growth, regardless of whether castration was performed early or late. These results may also suggest an increase in the turnover of the bone ECM (an imbalance between osteoformation/osteolysis) during foal growth. Circulating levels of biomarkers of bone metabolism in blood samples from horses castrated at birth and at 18 months. Plasma N-MID levels (A) as well as serum CTX-I levels (B) were measured for 33 months after birth in the two experimental groups using ELISA (S0 to S12: n = 11, S14 to S17: n = 8, and S28 to S33: n = 5). Data are presented as mean ± SEM. A variation analysis in the biomarker levels compared to P0 within the same group was performed using a Kruskal-Wallis test followed by a Dunn's comparison (a = b; c = d).
Statistical Analyses All data were reported as mean ± SEM. Statistical analyses were conducted using GraphPad Prism 8. 0. 1 software (San Diego, CA, USA). A Kruskal-Wallis test, followed by a Dunn's multiple comparisons test, was performed to compare biomarker concentration levels with P0 within each experimental group (castrated at 18 months and at birth). Additionally, a Mann-Whitney test was used for statistical analysis to compare samples at the same time between the two groups. Differences were considered significant at p ≤ 0. 05.
Acknowledgments: We would like to thank  M. Melin  ( Novotec, Bron, France ) for her assistance in preparing the osteochondral sections and  C. Ballandone  ( BIOTARGEN, UNICAEN ) for her assistance with the ELISA. We also extend our gratitude to  Carré International  at the  University of Caen Normandie (France)  for their editorial support with this article.
20 Figure 6. 206 Figure 6. Circulating levels of biomarkers of bone metabolism in blood samples from horses castrated at birth and at 18 months. Plasma N-MID levels (A) as well as serum CTX-I levels (B) were measured for 33 months after birth in the two experimental groups using ELISA (S0 to S12: n = 11, S14 to S17: n = 8, and S28 to S33: n = 5). Data are presented as mean ± SEM. A variation analysis in the biomarker levels compared to P0 within the same group was performed using a Kruskal-Wallis test followed by a Dunn's comparison (a ≠ b; c ≠ d). A Mann-Whitney test was conducted to compare the biomarker levels at the same time between the two groups, and no differences were observed.
Sequence analysis and taxon assignation The sequence reads were first analyzed with the OBITOOLS package  (Boyer et al. , 2016). Forward and reverse reads were assembled with the alignpairedend function, and we kept only sequences with a good score of alignment (rnorm>0. 8). Sequences were attributed to their samples with the ngsfilter function with default parameters. Subsequently, assigned sequences were analyzed with the dada2 package  (Callahan et al. , 2016)  following the pipeline available in www. nemabiome. ca. The dada2 pipeline returns Amplicon Sequence Variant (ASV) which are sequence variants differing by as little as one nucleotide  (Callahan et al. , 2017). Following  Beaumelle et al. , (2021) , the gastrointestinal nematodes were identified with three different methods of assignation: BLASTn  (Altschul et al.
, 1990)  against the NCBI database (November 2022), and AssignTaxonomy  (Callahan et al. , 2016; Wang et al. , 2007)  and IDTaxa  (Murali et al. , 2018)  against the nematode ITS2 rDNA database 1. 1. 0  (Workentine et al. , 2020)  or the nematode β-tubulin isotype 1 DNA reference sequence supplied in  Avramenko et al. , (2019). We chose to attribute a confidence level to taxonomic identification at species level: high or moderate confidence if the three or two methods of assignation, respectively, were congruent. We also adjusted the sequence filtering based on an adapted procedure of  Calderón-Sanou et al. , (2020). We kept only ASVs present in at least 2 replicates of the same samples and removed ASVs that were not assigned to genus level for the ITS2 and species level for β-tubulin isotype 1.
Identification of non-synonymous mutations in codons 167, 198 and 200 For each nematode species, all β-tubulin isotype 1 ASVs were aligned to one of the β-tubulin isotype 1 consensus sequences of the reference database  (Avramenko et al. , 2019)  using the AlignSeqs function of the DECIPHER package  (Wright, 2016). We examined each β-tubulin isotype 1 ASV at codon 167,198 and 200 positions to record whether the codon is associated with a non-synonymous mutation. However, we ignored the other polymorphism sites of the Exon.
Statistical analyses on measures of nemabiomes To measure the differences of nemabiome among the two host species (sheep and ibex) and the 4 study sites (Aiguillette de Lauzet, Montagne de l'Oule, Champsaur and Belledonne), we considered two measures of diversity, i. e. , the alpha diversity and the beta diversity. The alpha diversity was measured with the Shannon index that considers richness and evenness of communities and the beta diversity was measured with the weighted UniFrac index estimated using the R phyloseq package  (McMurdie and Holmes, 2013). The weighted UniFrac distance is a phylogenetic distance between the set of ASV from each nemabiome weighted by the transformed abundance of each ASV  (Lozupone and Knight, 2005). The phylogenetic distances were computed from a phylogenetic tree which was conducted using a maximum likelihood tree with the GTR+G+I model according to the ModelTest function  (Posada and Crandall, 1998; Schliep, 2011).
The exact count of ITS2 reads were transformed with the Hellinger transformation (e. g. , square root on relative frequencies) to account for the high number of zero in community table and to decrease the influence of rare ASVs in statistical analyses  (Legendre and Legendre, 2012). We tested the effects of host species and site, including their interaction, on alpha diversity with linear models, and on beta diversity using perMANOVA (adonis2, vegan R package  (Oksanen et al. , 2020) ). All possible models including the null model were computed. For perMANOVA models, we used a custom function to compute Akaike's information criterion corrected for small sample size (AICc) based on residual sums of squares  (Dyson, 2018). In a model selection approach, for both alpha and beta diversity, all possible models were ranked using the AICc and we selected the model with the lowest AICc value.
Acknowledgements The authors warmly thank all the professionals from the  Office Français de la Biodiversité  and all the trainees for data collection, and  J. S. Gilleard ,  E. Redman  from the  University of Calgary  and  C. Lionnet  from the  Laboratoire d'Ecologie Alpine (LECA ) and other members of the lab to help us developing the deep sequencing analyses for nematodes. The research benefited from the support of  AnaBM (USMB)  and  AEEM (UGA)  laboratory facilities.
Funding This project was founded by the  Office Français de la Biodiversité , the  Laboratoire d'Ecologie Alpine (LECA)  and  VetAgro Sup -Pôle d'Expertise Vétérinaire et Agronomique des Animaux Sauvages (EVAAS , France; http://evaas. vetagro sup. fr/;  DGAL-VetAgro Sup -INRAE  funding). G. Bourgoin  was supported by the  AgreenSkills+ fellowship program  ( European Union program ;  MarieCurie FP7 COFUND People Programme ; grant agreement  n_609398 ).
Creme Global was mandated with the management of the data collection from survey participants and with presenting it in a fully anonymised format ensuring complete data privacy (unpublished communication with Cosmetics Europe). Each participating company was required to submit a standardised set of information on each of its formulations in the market during the 12-month period. The survey had a high level of response and participation from not only Cosmetics Europe membership, but also from the wider EU Cosmetics Industry. The final dataset includes data on over 47,000 formulations. The datasets obtained for each ingredient from the 2016 Preservative Use Survey was used to derive the concentration distribution of each preservative in each product type (see Table  2  for the example with propyl paraben; and Supplementary Data 3 for the survey method).
The Creme Care™ model incorporates a large dataset of habits and practice data from over 40,000 subjects from across six countries in Europe (Spain, France, Germany, Italy, Poland and Great Britain) and the United States (around 30,000 from the European countries and 10,000 for the United States). The data is provided by the Kantar World Panel Usage Toiletries and Cosmetics Database (http://www. kantarwo rldpanel. com/global). Habits and practice (H&P) data are in the form of panellists' diaries; in the models used in this work, data cover the period from the 4th quarter of 2014 to the 3rd quarter of 2015 inclusive. European subjects of age 18 and above were considered (27157 subjects). Every individual in the habits and practice database logged product use by the hour for 7 consecutive days.
Usage events logged in the diary describe the day and time of a usage event, the product type used and the site(s) of application. If multiple products were used at the same time, these are listed in separate records. This provides detailed daily product co-use information. Habits and practices data supply the frequency of use and application site components in the modelling of exposure for each product of interest. Usage data are collated on a total of 70 products overall among personal care, cosmetics, air care and household products. For the purposes of the three preservative ingredients covered in this paper, a subset of products was considered as included in Table  3. The Kantar panel data did not include eye shadow, eye liner or mascara. The habits and practices data for these three products were acquired from a study by Biesterbos  (Biesterbos et al.
Anthropometric data The subjects' bodyweight and height are essential pieces of information for the exposure algorithm. These are used for the calculation of exposure per unit bodyweight and per unit surface area of skin. The Kantar survey that provides the habits and practices data does not include a record of subjects' bodyweight and height. As there was no available dataset on body measurements (bodyweight and skin surface area) for the European population that is both comprehensive and paired, a data-based simulation method was used to assign measurements to European subjects on the basis of gender and age. The method models height and weight as a bivariate distribution, and is based on national mean height and weight data listed in Table  5. Variance and covariance parameters describe how variables vary, singly and together.
This was the largest survey of its kind by the industry and provided the most comprehensive use survey, complying with competition law, and generated a large collaborative data set curated and quality controlled by an 'honest broker' (Creme Global). To observe the historical trend of product use prior to year 2016, data from the commercially available Mintel Global New products Database (GNPD; (http://portal. mintel. com/)) were used (see Table  6  for propyl paraben). Mintel perform analysis of market trend data where multiple sources of data from 86 of the world's major economies are derived using local shoppers who obtain accurate information from the retail field. There are 32 European countries' data in the database and it provides excellent insights into products on the market annually; data on 500,000 new consumer products are added each year and trends can be observed over time.
A representative final occurrence figure within a product type was derived. The database is divided into categories such as Face/Neck Care, Body Care, Eye Care, and Lip Care. Using the site's search function the total number of products in each cosmetic and personal care product category can be obtained. By determining the number of stock-keeping units (SKUs) within a product category that contain the ingredient, relative to the total number of cosmetic SKUs, the occurrence can be derived, which in this model is assumed to be the likelihood that a product contains the ingredient. The method has some uncertainty because it assumes equal market share of different products on the market. In addition, it doesn't take into account brand loyalty, where consumers tend to have preferences to purchasing particular brands of products.
In a recent paper by Tonero-Velez (2020), purchasing data were used as a proxy for product use data. Market basket analysis methodology can be used to indicate the potential for concomitant product use, or data can be used from the cosmetic product category distribution in EPA's Chemicals and Products Database (CPDat)  (Dionisio et al. , 2018). Therefore, it is important that all modelling is transparent in terms of the data and sources used, and in this study first-hand proprietary data from the cosmetics industry from a large collaborative survey, together with a 10-year view from Mintel, provides the most reliable source of data to use in exposure assessment. Incorporation of ingredient occurrence data, including non-use data, into exposure modelling brings refinement by discounting exposures where the ingredient is not present in the product of interest in the aggregated exposure calculation.
It is assumed data for these products that the Dutch population is amongst EU citizens. representative of female European consumers. These product types are not major contributors to overall exposure, and are of low influence in the aggregate exposure calculation. Mintel GNPD database +/- High Quality commercial data contains cosmetics that are from 32 European countries, a good representation of values rounded up products sold in Europe. conservatively and is regarded as the best data of its type over a ten year period, with a very good representation of the products sold in Europe which gives a low potential to cause overestimation or underestimation. The data on propyl paraben +/- Sufficiently representative use in the cosmetic and PCP data for the European industry (both consumer direct from the concentration and manufacturing industry.
These differences emerge by sexual maturity, during puberty and adolescence  17  ; thus, women and men are sexually dimorphic  18. A study conducted by O'Toole and collaborators in 1998  19  reported that the faces of women and men in photographs were correctly categorized 95% of the time in their database. In addition, the voices of adult women and men differ. In average the fundamental frequency is 120 Hz for women's voices and 200 Hz for men's voices  20. Women retain higher frequencies than men  21. This disparity is due to sexual dimorphism in the vocal tract anatomy of adults, as men have a larger larynx  22. After adolescence, men have also more resonant voices due to their longer vocal tract  23. As well as individual behaviour, the vocal expression of masculinity or femininity can be controlled even before puberty, before the anatomical difference in the vocal apparatus emerges  24.
Data collection. Gaze responses. The horses' gaze direction towards the two different projection screens during playback events was analysed. Horses were considered to be looking at a video if they turned their head towards the projection screen. One camera was placed under each projection screen. Horses were considered to look at the video when their faces were directed towards the camera such that the observer could see their opposite eye. Thus, in this preferential looking paradigm, the gazes towards the congruent and incongruent videos for each trial were analysed. To avoid bias due to lateralization, horses were excluded from the analysis if they did not meet a criterion. Indeed, some horses looked almost exclusively at one projection screen (either placed on their left or on their right, independent of the congruence of the video presented).
Statistical analysis. The statistical analysis was carried out using RStudio 3. 3. 6 statistics software. A comparison was made between the mean preference indexes and the theoretical value corresponding to a random choice obtained by chance (mu = 0). The normality of the distribution of variables was assessed graphically using the function qqPlot in the package car. Data were analysed with a parametric test: a bilateral one sample Student's t test using the function t. test. The heart rate responses to the voices (man's or woman's voice) were analysed with a paired Student's t test. The significance threshold throughout this study was set at P < 0. 05.
Acknowledgements We are extremely grateful for the entire team of the experimental unit  UEPAO  for not only allowing to use the horses and facilities but also assisting us in taking care of the animals. We are sincerely thankful to all the volunteers who agreed to be recorded to create the stimuli used for this experiment. Finally, we warmly thank  Mathias Sanna Rogues  and  Tania Chauvin  for proofreading the manuscript and the  Nature Research Editing Service  for.
| Fungal sequence processing Sequencing data were processed using cutadapt v2. 3 and DADA2 v1. 10 as implemented in R v3. 5. 1. The forward and reverse 5 0 ITS2 primers, as well their 3 0 reverse complements, were trimmed from the sequencing data using cutadapt in paired-end mode with a maximum allowed error rate of 0. 1. From this point forward, sequencing data were processed the same as described above for the 16S rRNA sequences unless otherwise noted. Briefly, sequencing data quality was assessed and compiled into a single comprehensive report. Sequencing reads were filtered using a maximum expected error of 2 and minimum length cutoff of 50 bases. A parametric error model then was estimated using a form of unsupervised machine-learning. Sequencing reads were dereplicated. Exact amplicon SVs were inferred for each sample using the DADA2 sample inference algorithm and the estimated error model.
Samples were pooled together for sample inference. Full, denoised sequences were obtained by merging the inferred forward and reverse reads. An SV table was assembled from the denoised sequences. Chimeric sequences then were removed from the table. A taxonomy table was assembled by assigning taxonomy to each SV in the SV table using the General Fasta release files from the UNITE ITS database. 28 All species level assignment was accomplished using the DADA2::addSpecies function, with exact matching used to assign species when possible.
RNA was then purified using the Qiagen RNeasy mini kit (ref: 74104) using manufacturer's protocol and eluted in 42 μl of ultrapure water. Quality and concentration were assessed by spectrophotometry with the Agilent 2100 Bioanalyzer system and using the Agilent RNA 6000 nano kit (ref 5067-1511). Library construction and sequencing were performed at the Génome Quebec platform. The TruSeq stranded mRNA library construction kit (Illumina Inc. USA) was used following the manufacturer's protocol with 300 ng of total RNA per sample. Sequencing of the 24 samples was performed in 2x100 bp paired-end on a Illumina HiSeq 4000. Sequencing data are available at the NCBI-SRA under the BioProject accession number PRJNA491632.
This transcriptome assembly was conducted using the Galaxy instance of the IHPE laboratory and of the Ifremer  [49, 50]. After ensuring read quality using FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc), we mapped S. haematobium and S. bovis paired end reads respectively on their reference genomes using the fast and sensitive mapper HISAT2 (Galaxy version 2. 1. 0,  [51] ) with default parameters. Exon/intron structure was then recovered using StringTie (Galaxy version 2. 1. 4,  [52] ) and with genomes' annotations as extra input. The results from each sample were then merged in one single file per species (one for S. haematobium and one for S. bovis) using the tool StringTieMerge. From the resulting gtf files, spliced exons for each transcript were extracted from the two reference genomes using gffreads tool (Galaxy version 2.