dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments-We thank  Luc Bousset  for assistance in TEM, and the  Proteomic-Gif SICaPS platform  ( I2BC ), supported by  IBiSA ,  Ile de France Region ,  Plan Cancer ,  CNRS  and  Paris-Sud University , for allowing us to use their MS equipment and in particular  Laïla Sago  and  David Cornu  for their assistance. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) ,  Institut National de la Santé et de la Recherche Médicale (Inserm) , and a  Convention Industrielle de Formation par la Recherche (CIFRE)  from the  Association Nationale de la recherché et de la technologie (ANRT). This work was supported by grants from the  Fondation pour la Recherche Medicale  (contract  DEQ. 20160334896  and  ALZ201912009776 ) and the  EU Joint Programme on Neurodegenerative Disease Research and Agence National de la Recherche  (contracts  PROTEST-70 ,  ANR-17-JPND-0005-01  and  Trans-PathND ,  ANR-17-JPND-0002-02 ).
, Davis, CA, USA), 1:100. After rinsing in PBS, the slides were incubated with secondary antibodies diluted to 1:100 in PBS for one hour at room temperature: donkey anti-mouse, donkey anti rabbit and goat anti-human, conjugated with Alexa 350, Alexa 488, Alexa 549 (Invitrogen, Eugene, OR, USA), Cy3 or Dylight 649 (Jackson ImmunoResearch Laboratories, West Grove, PA, USA). Slides were counterstained with DAPI, when needed, and mounted with Vectashield (Vector, Burlingame, CA, USA). Observations were made on an Olympus BX61 microscope equipped with a motorized plate in the Z axis. Images were captured with an Olympus DP61 camera and processed with Adobe Photoshop CS software. The length of the SC was measured using ImageJ software. For the squashes, several optical sections were recorded for each cell.
Results Males of M. mattheyi have a meiotic karyotype of 2n = 36, with 17 autosomal bivalents plus the X and Y sex chromosomes, all of them being acrocentric (Figure  1 ). The X is the largest chromosome, representing 9. 63% (±0. 97) of the total SC length in pachytene spermatocytes (n = 11 spermatocytes), while the Y chromosome is small and represents 4. 35% (±0. 70) of the total SC. Observations were made on an Olympus BX61 microscope equipped with a motorized plate in the Z axis. Images were captured with an Olympus DP61 camera and processed with Adobe Photoshop CS software. The length of the SC was measured using ImageJ software. For the squashes, several optical sections were recorded for each cell. The stack files were processed with ImageJ to make three-dimensional reconstructions as previously described  [43, 45].
Funding: This research was funded by Grant  CGL2014-53106-P  from  Ministerio de Economía y Competitividad (Spain) ,  French National Research Agency (ANR  grant  SEXREV 18-CE02-0018-01 ),  Del Duca Foundation  from the  Institut de France  ("subvention scientifique"). A. G. -F. was supported by a predoctoral fellowship from the  Ministerio de Economía y Competitividad (Spain)  and the  European Social Fund (European Commission ). M. R. was supported by a postgraduate grant from  Departamento de Biología ,  Universidad Autónoma de Madrid (Spain).
5 s per point, over a 2θ range from 20 ∘ to 80 ∘. The Xray diffraction patterns of the ThMn 12 -type structure was refined by Rietveld analysis  [69, 70]  using the Fullprof software  [71, 72]. The peak-shape function was selected as Thompson-Cox-Hastings pseudo--Voigt type  [73, 74]. To measure the quality of refinement, the "goodness-of-fit" indicators χ 2 and R B (Bragg factor) from the program output are set as follows: R B ¼ 100 P K I K ðoÞ À I K ðcÞ P K I K ðoÞ and χ 2 ¼ P i w i y i ðoÞ À y i ðcÞj 2 N À P where I K ðoÞ is the observed Bragg intensity and I K ðcÞ is the calculated one. y i ðoÞ is the intensity observed at the i th step in the step scanned powder diffraction pattern, y i ðcÞ is that calculated, and w i is the weight of the observation. N is the total number of points used in the refinement and P the number of refined parameters.
On the other hand, the transition elements are scattered over the three inequivalent sites which are 8i, 8j, and 8f. The iron atoms fully occupy the 8j and 8f sites while the 8i site is occupied by a blend of iron and chromium atoms. In Fig. 1 , the Gd atoms which occupy the site 2a (0, 0, 0) are represented by large orange spheres. Furthermore, the three different Wyckoff sites 8i (x, 0, 0), 8j (x; 0; 1  2 ) and 8f ( 1 4 ; 1 4 ; 1 4 ), populated by Fe1/Cr, Fe2 and Fe3 atoms, are illustrated by medium spheres in green, pink and yellow respectively. The X-ray diffraction patterns of GdFe 12Àx Cr x (x ¼ 2; 2:5 and 3) compounds are plotted in Fig. 2. The data were refined by the Rietveld refinement technique  [70]  through the use of the Fullprof program  [71, 72]. The Rietveld fitting demonstrate a good harmony between the observed and calculated profiles for all the sample compounds.
Transcriptomic Microarray Data Production, Pre-preprocessing and Analysis Blood samples for RNA extraction were collected from each animal at T0 and T1 using Tempus Blood RNA tubes (Thermo Fisher). Because blood interacts with every organ and tissue in the body and has crucial roles in immune response, inflammation and physiological homeostasis  (Mohr and Liew, 2007) , blood-based transcriptome was carried-out as a means for exploring the response to endurance. Total RNAs were then isolated using the Preserved Blood RNA Purification Kit I (Norgen Biotek Corp. , Ontario, Canada), according to the manufacturer's instructions. RNA purity and concentration were determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher) and RNA integrity was assessed using a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, United States).
All the 40 RNA samples were processed. Transcriptome profiling was performed using an Agilent 4 × 44K horse custom microarray (Agilent Technologies, AMADID 044466). All of the steps were performed by the @BRIDGe facility (INRAE Jouy-en-Josas, France, http://abridge. inra. fr/), as described previously  (Mach et al. , 2016; 2017b). The horse array was annotated as described by  Mach et al. (2016) ,  Mach et al. (2017b). In a limited number of cases, a manual annotation step was also included. Probe intensities were background-corrected using the "normexp" function, log 2 scaled and quantile normalized using the limma package (version 3. 1. 42. 2)  (Smyth, 2004)  in the R environment (version 3. 6. 1). Quantile normalization seeks to reduce technical variability and impose identical empirical distributions between samples to facilitate comparisons  (Smyth, 2004).
Only the probes which presented, on at least two arrays, intensity values at least 10% higher than the 95% percentile of all the negative control probes were kept. Subsequently, controls were discarded and the probes corresponding to genes were summarized. The obtained expression matrix "E1" was processed with the arrayQualityMetrics R package (version 3. 42. 0) (Kauffmann et al. , 2009)  for quality assessment. No outliers were detected. The resulting pre-processed normalized expression values were thus approximately normally distributed and suited for differential analysis via conventional linear models. The differential analysis was performed using the limma R package. A linear model was fitted for each gene, setting the time, the sex, the distance and whether the animal was eliminated from the race as fixed effects, and comparing T1 to T0.
The individual was included as a random effect using the "duplicateCorrelation" function  (Smyth et al. , 2005). The p-values were Bonferroni corrected setting a threshold of 0. 05. The expression matrix "E1" was then used to perform a scaled principal component analysis (PCA) with FactoMineR R package (version 2. 4)  (Lê et al. , 2008). To confirm the results of the DE analysis, the weighted gene co-expression network analysis (WGCNA) method was also run on the "E1" matrix using the WGCNA R package (version 1. 69)  (Langfelder and Horvath, 2008). The parameters for the analysis were set as follows: "corFnc" bicor, "type" signed hybrid, "beta" 10, "deepSplit" 4, "minClusterSize" 30, and "cutHeight" 0. 1. The eigengenes corresponding to each identified module were correlated individually to all the 1 H NMR and biochemical assay metabolites, i.
e. , a set of 56 different molecules (see next paragraphs). A module was then considered positively or negatively associated to this set of molecules if the Pearson r correlation values were ≥ |0. 65| for at least 5 molecules and if all the corresponding p-values were ≤ 1e-05. The positively and the negatively correlated modules defined in this way were merged to obtain a single gene list, which was subsequently compared to the differentially expressed genes (DEGs) list using a Venn diagram. Afterward, a literature-based meta-analytical enrichment test was carried out to assess whether the DEG list and the gene list obtained using WGCNA were enriched in genes related to mitochondria. To this aim, first a consensus list of genes related to these organelles was created (Supplementary Information and Supplementary Table  S2 ).
This gene list was then annotated in a descriptive way. First, gene symbols were converted into the corresponding KEGG Orthology (KO) codes using the "db2db" tool (https://biodbnet-abcc. ncifcrf. gov/ db/db2db. php) from the bioDBnet suite and using the then upto-date underlying databases. Then, the retrieved KO codes were processed with the "Reconstruct Pathway" tool (https://www. genome. jp/kegg/tool/map_pathway. html) from the KEGG Mapper suite. Eventually, the obtained KEGG pathways underwent some manual editing step to make them easier to interpret, namely 1) only firstand third-level hierarchies including at least 15 genes were kept for visualization; 2) the "Human diseases" first-level hierarchy, with all its child taxonomic terms, was removed; 3) the "Metabolic pathways" third-level hierarchy was discarded as it was redundant with respect to the other third-level hierarchies of the "Metabolism" term.
Then, the same gene list was intersected with the genes found expressed on the microarray (i. e. , the "E1" matrix). The subset thus obtained was separately intersected with the DEG list and the WGCNA gene modules. A Fisher's exact test and hypergeometric test were then used to evaluate the overrepresentation of genes related to mitochondrial functions in each list. The intersection between the genes related to mitochondria found on the microarray and the DEGs was referred to as "mtrelated genes". It was functionally annotated using ClueGO (version 2. 5. 7) (Bindea et al. , 2009)  by carrying out a rightsided test. Significance was set at a Benjamini-Hochberg adjusted p-value of 0. 05 and the k-score was fixed at 0. 4. Only the "KEGG January 30, 2019" ontology was selected.
Plasma samples were subsequently thawed at room temperature. Using 5 mm NMR tubes, 600 µL of plasma were added to 100 µL deuterium oxide for field locking. The 1 H NMR spectra were acquired at 500 MHz with an AVANCE III (Bruker, Billerica, MA, United States) equipped with a 5 mm reversed QXI Z-gradient high-resolution probe. Water signal was suppressed with a pre-saturation pulse (3. 42 × 10 -5 W) during a 3s-relaxation delay at the water resonance frequency. The spectrum was divided into 0. 001 ppm regions (bins) over which the signals were integrated to obtain intensities. The high-and low-field ends were removed, leaving only the data between 9. 5 and 0. 0 ppm. The region between 4. 5 and 5. 0 ppm, which corresponded to the signal of residual water, was also removed. Chemical signals were first normalized according to a relevant internal standard, the socalled C1-α-glucose doublet, with signal at 5.
23 ppm. Next, the bins (which exhibit skewed distributions across samples) were normalized according to the spectra using the probabilistic quotient method to correct for unwanted technical biases, and were subsequently centered and scaled to unit variance  (Dieterle et al. , 2006). Further details on sample preparation, data acquisition, data quality control, spectroscopic data preprocessing, and data pre-processing including bin alignment, normalization, scaling and centering are broadly discussed elsewhere (Le  Moyec et al. , 2014). As specified in  Plancade et al. (2019) , metabolite identification was then performed by using information acquired from other available biochemical databases, namely HMD (http://www. hmdb. ca/), KEGG (https://www. genome. jp/kegg/), METLIN (http://metlin. scripps.
edu/), ChEBI (http://www. ebi. ac. uk/ Databases/), and LIPID MAPS (http://www. lipidmaps. org/) and the literature (Le  Moyec et al. , 2014; Le Moyec et al. , 2019; Mach et al. , 2017b; Jang et al. , 2017). Each peak was assigned to a metabolite when chemical shifts of peaks in the samples were the same as in the publicly available reference databases or literature (with a shift tolerance level of ±0. 005 ppm), in order to counteract the effects of measurements and pre-processing variability introduced by factors such as pH and solvents. A manual curation for identified compounds was carried out by an expert in horse metabolomics (Le  Moyec et al. , 2014). Eventually, the relative abundance of each metabolite was calculated as the area under the peak  (Zheng et al. , 2011). A total of 50 metabolites was identified, which belonged to the following broad categories: amino acids, including aromatic and branchedchain amino acids, energy metabolism-related metabolites, saccharides, and organic osmolytes (Supplementary Table  S3 ).
Biochemical Assay Data Production Blood samples for biochemical assays were collected at T0 and T1 using 10 ml BD Vacutainer EDTA tubes (Becton Dickinson, Franklin Lakes, NJ, United States). As detailed in  Plancade et al. (2019) , after clotting the tubes were centrifuged and the harvested serum was stored at 4 °C until analysis (no more than 48 later, in all cases). Sera were assayed for total bilirubin, conjugated bilirubin, total protein, creatinine, creatine kinase, β-hydroxybutyrate, aspartate transaminase (ASAT), γ-glutamyltransferase and serum amyloid A levels on a RX Imola analyzer (Randox, Crumlin, United Kingdom). The biochemical values obtained are reported in the Supplementary Table  S4.
Fecal Measurements, 16S Data Production and Analysis Fresh fecal samples were obtained while monitoring the horses before the race (no more than 24 h before starting the race, in all cases). One fecal sample from each animal was collected off the ground immediately after defecation as described by  Mach et al. (2017a)  and  Plancade et al. (2019) , and three aliquots (200 mg) were prepared. Since most of the horses experienced dehydration after the race, the gastrointestinal emptying was significantly delayed and therefore it was not possible to recover the feces after the race. Aliquots for pH determination were kept at room temperature, while aliquots for SCFA analysis and DNA extraction were snap-frozen. Since most of the horses experienced dehydration after the race, the gastrointestinal emptying was significantly delayed and consequently we were not able to recover the feces after the race.
e. , the "G2" matrix); and 6) the concentration of fecal microorganisms at T0. As a first integration approach, a global non-metric multidimensional scaling (NMDS) ordination was used to extract and summarize the variation in mt-related genes (the "response variable") using the "metaMDS" function in vegan R package. To determine the number of dimensions for each NMDS, stress values were calculated. Stress values are a measure of how much the distances in the reduced ordination space depart from the distances in the original p-dimensional space. High stress values indicate a greater possibility that the structuring of observations in the ordination space is entirely unrelated to that of the original full-dimensional space. The other five data sets (the "explanatory variables") were then fitted to the ordination plots using the "envfit" function in the vegan R package  (Clarke and Ainsworth, 1993)  with 10,000 permutations.
To determine which set of covariates provided the most parsimonious model, automatic stepwise model selection for constrained ordination methods was used as implemented by the "ordistep" function of the vegan R package. To test for robustness, a forward automatic model selection on a distance based RDA (db-RDA) was then performed using the "ordiR2step" function of the vegan package in R  (Oksanen et al. , 2013). This provided an estimation of the linear cumulative effect size of all the identified non-redundant covariates and of their independent fraction in the best model. In the case of this latter function, the mt-related genes matrix was modified using the Hellinger transformation prior to the analysis. These two RDA functions use different criteria for variable selection. The "ordistep" funciton uses the Akaike's information criterion (AIC) and p-value < 0.
05 obtained from Monte Carlo permutation tests, while "ordiR2step" uses the adjusted coefficient of determination (r 2 adj ). In both cases, the procedure begins by comparing a null model containing no variables and a test model containing one variable, where every possible covariate is considered. As a third integrative approach, the N-integration algorithm DIABLO of the mixOmics R package was used. It is to be noted that, in the case of the N-integration algorithm DIABLO, the variables of all the data sets were also centered and scaled to unit variance prior to integration. In this case, the relationships existing among all six data sets were studied by adding a further categorical variable, i. e. , the performance of horses. Horses that had a poor performance or that had been eliminated (n 8) were compared to horses that had completed the race (n 12, Supplementary Table  S1 ).
DIABLO seeks to estimate latent components by modeling and maximizing the correlation between pairs of pre-specified datasets to unravel similar functional relationships between them   (Singh et al. , 2019). A full weighted design was considered and, to predict the number of latent components and the number of discriminants, the "block. splsda" function was used. In both cases, the model was first fine-tuned using the leave-one-out cross-validation by splitting the data into training and testing. Then, classification error rates were calculated using balanced error rates (BERs) between the predicted latent variables with the centroid of the class labels (i. e. , eliminated vs non eliminated horses) using the "max. dist" function. BERs account for differences in the number of samples between different categories.
Only interactions with r ≥ |0. 70| were visualized using CIRCOS. To visualize the high-confidence molecule coassociations determined by CIRCOS, only those with r ≥ |0. 70| and more than 15 connections were automatically visualized using the organic layout algorithm in Cytoscape (version 3. 8. 1) (http://cytoscape. org). Finally, we performed a pairwise integration, focusing only on mt-related genes and microbiota data, and using the regularized canonical correlation analysis (rCCA) method as implemented by the mixOmics R package (https://cran. r-project. org/web/packages/ mixOmics/). The penalization parameters were estimated using the "shrinkage" method and setting the "ncomp" parameter to two. The correlation matrix thus obtained was filtered by retaining only the genes and the genera for which at least one association value data point presented r ≥ |0.
Cohort Characteristics To elucidate how mitochondria and gut microbiota are linked during endurance exercise, we studied 20 healthy endurance horses selected from the cohort already described by  Plancade et al. (2019)  and Le  Moyec et al. (2019)  (Figure  1 ). All of the animals were of similar age and performance level (Supplementary Table  S1 ). They performed a long exercise during about 8 h at an average speed of 17. 1 ± 1. 67 km/h with some rest periods every 30-40 km. Whole transcriptome profiles, 1 H NMR metabolome profiles and biochemical assay data were obtained from blood samples collected at both T0 (pre-ride) and T1 (post-ride), while SCFAs measurements, 16S rRNA data and the concentration of bacteria, anaerobic fungi and ciliate protozoa were generated from fecal samples at T0 alone.
Blood Transcriptome Profiles and Mitochondrial Related Genes To gather information about the global structure of the blood transcriptome, a scaled PCA was carried out on the expressed genes (n 11,232). The first component accounted for 43% of the total variability and revealed a marked separation of the two time points (Figure  2A ). We then carried out a standard differential analysis between the two time points. After Bonferroni correction of the raw p-values, a total of 6,021 differentially expressed genes was obtained at an adjusted p < 0. 05, of which 2,658 were upregulated and 3,363 downregulated at T1 respect to T0 (Supplementary Table  S10 ; Figure  2B ). These results were then complemented using a WGCNA  (Langfelder and Horvath, 2008)  on the expressed genes. WGCNA identified three gene modules, corresponding to 7,914 genes, that were correlated to the 1 H NMR and biochemical assay metabolites (Supplementary Table  S11 ).
, 2016)  and the literature (Supplementary Information and Supplementary Table  S2 ). This consensus list was also descriptively annotated to gain global insight into the main biological functions represented within it. A total of 80 thirdlevel KEGG hierarchies were identified, with a strong representation of pathways related to carbohydrate and lipid metabolism (8 and 9 ontology terms, respectively). Eight pathways were associated with amino acid metabolism, while five were linked to the apoptosis process (Supplementary Figure  S2 ). This subset was crossed with each of the DEG and WGCNA module gene lists. In the case of the DEG list, the intersection with this subset yielded a total of 801 genes (Supplementary Table  S12  and Figure  2C ). Both the Fisher's exact test and the hypergeometric test showed strong levels of overrepresentation, with p-values of 1.
The intestinal microbial community found in the total set of 20 individuals as a whole was made up of a core of 23 genera, the core being defined as the genera shared by 99% of all sampling events with a minimum 0. 1% mean relative abundance. Overall, 61% of the core genera belonged to the Firmicutes phylum, mainly to the Lachnospiraceae and Ruminococcaceae families (Supplementary Figure  S4A ). A total of 100 unique genera was identified in the microbiota (Supplementary Table  S7 ). The majority of these genera (80%) fell among the 20 most abundant (Supplementary Figure  S4B ) and accounted for more than 75% of the sequences in the data (Supplementary Table  S7 ). To deeper understand how the gut microbiota functioned, SCFAs, pH measurements and the loads of anaerobic fungi, protozoa and total bacteria in feces were also investigated.
(C) Grouped bar chart showing the cumulative effect sizes of covariates on mt-related gene expression (coloured bars) compared to individual effect sizes assuming covariate independence (grey bars) using a stepwise model selection using distance-based redundancy analysis (dbRDA). Covariates are coloured according to the type of dataset: red for biochemical assay metabolites, and blue for bacteria. (D) Plot showing the covariates that contribute significantly to the variation of mt-related genes determined by stepwise model selection using redundancy analysis (RDA). The arrows for each variable show the direction of the effect and are scaled by the unconditioned r 2 value. Covariates are coloured according to the type of dataset: red for biochemical assay metabolites and dark blue for bacteria.