dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling and estimation of gene flow To go beyond simple data description and infer gene flow and admixture, a battery of statistical tools was used on the 700k-dataset  (Guarino-Vignon et al. , 2022). First, f3-admixture statistics were performed using qp3pop from the ADMIXTOOLS package  (Patterson et al. , 2012). These are used to test whether a target population can be modelled as the result of admixture between two test populations, and can be written as f3(source1, source2, target). D-statistics were calculated using the qpDstat program of the ADMIX-TOOLS package  (Patterson et al. , 2012)  to estimate the genetic contribution of a population (X), through admixture, to a population (Y) by comparison with a second population (Z)  (Green et al. , 2010). If the statistic D (Outgroup, X; Y, Z)>0, then X and Z are genetically closer than X and Y (and vice versa).
To assess whether the D-statistics deviates significantly from zero, p-values calculated from the Z-score were corrected for repeated testing using the Benjamini and Hochberg method  (Benjamini and Hochberg, 1995). qpAdm analyses with ADMIXTOOLS  (Patterson et al. , 2012)  were conducted to find the best admixture model for Central Asian populations and for the Iron Age Turkmenistan individual, and to estimate the contribution of each source population. Finally, DATES software  (Chintalapati et al. , 2022)  was used to estimate the date of the admixture events evidenced by qpAdm, by calculating the number of generations.
Continuity since the Iron Age and admixture giving rise to Indo-Iranian language populations To formally prove the assumptions made above, D-statistics were calculated with the form D (Outgroup, X; Turk-menistan_IA, Yaghnobis/Tajiks) with Mbuti as the outgroup, and all the ancient populations in our dataset as X. Because of the substantial disparity between the Ksirov-Kushan individuals, only the Iron Age Turkmen individual from Takhirbai 3, Turkmenistan_IA, was used as a representative of the Iron Age in South Central Asia. If the D-stat is positive for an ancient population, then it means that gene flow occurred between that ancient population and the Indo-Iranian group, with regard to the Iron Age individual. For the Yaghnobis, no positive D-stats were obtained, showing strong genetic continuity between the Iron Age and the Yaghnobis.
Origin of the Turkmen population With the PCA, the Turkmen population is placed in the Indo-Iranian cluster (figure  5A ) and with the ADMIX-TURE analysis it presents a profile significantly closer to the TABs (urban Tajiks) than to the other Turkic-Mongolian language populations, with, like the TABs, a small proportion of an East Asian ancestry (figure  5B ). Indeed,   (modifié d'après Guarino-Vignon et al. , 2022)  Guarino-Vignon all Turko-Mongol populations in Central Asia except Turkmens show a significantly large amount of the Baikal component (mean value: 50%) (t-test, p-value<2. 10 -1  ), while the Baikal component among Turkmens (mean value: 22%) is closer to the proportion among Tajiks (mean value: 15%). Furthermore, the identification of an "affinity profile" with all ancient populations in the database through the f3-outgroup (figure  5C-D ) shows that the Turkmen profile correlates much better with the Tajik profile (figure  5C ) than with the Kazakh profile (figure  5D ), which is the typical Turko-Mongol profile from Central Asia.
Finally, qpAdm show that the Turkmen can be modelled as resulting from admixture between Tajiks, up to about 90%, and a population with both BHG and East Asian ancestry up to 10%. This admixture is dated by DATES at about 687±100 years ago. All these results show that the Turkmen are probably an Indo-Iranian population that recently changed its language and cultural practice with no significant population replacement. This population is a notable example of a population changing their language and cultural practices without any substantial change in its genetic ancestry. This pattern does not correspond to the dominant mechanism in Central Asia, which features strong demic diffusion associated with the arrival of Turkic-Mongolian languages, but fits into the global pattern of Turkic language diffusion  (Yunusbayev et al.
Among the 18 Central Asian populations, 4 are Indo-Iranian speakers (TJE), TJA and TAB are Tajiks, and TJY are Yaghnobis. The 300k-dataset includes 242,406 SNPs for 5,129 individuals altogether. -The second, called the 700k-dataset, was assembled with shotgun-sequenced individuals from only three Central Asian populations: 3 Yaghnobis (TJY), 19 Tajiks (TJE) and 24 Turkmen (TUR), forming a dataset of 716,743 SNPs and 4,648 individuals (Guarino-Vignon.
Acknowledgements First, I would like to express my gratitude to the  Société d'Anthropologie de Paris , which did me the honour of awarding me its 2021 PhD prize. I would also like to thank my thesis directors:  E. Heyer  and  C. Bon  for their wonderful mentoring. I would like to thank  Ludovic Orlando ,  Laure Ségurel  and  Paul Verdu  for their fruitful comments about this study, as well as  Tatyana Hegay , who made sampling in Central Asia possible.
Statistical analysis Data are expressed as means ± standard error mean (S. E. M) for n different experiments and analyzed by Graphpad Prism (Version 7 for Windows, Graphpad Software, San Diego, CA, USA). Statistical variance between different groups was determined by applying Two-way Anova test for vascular reactivity studies and One-way Anova test for quantitative confocal microscopy results followed by Bonferroni's Multiple Comparison post hoc test. Group differences were considered statistically significant at P < 0. 05. To study the endothelium-dependent relaxation, rings were contracted with phenylephrine (PE, 1 µM) before the addition of increasing concentrations of acetylcholine (ACh). (C-D) The function of the vascular smooth muscle was assessed in rings with endothelium contracted with PE (1 µM) before the construction of a concentration-relaxation curve to either sodium nitroprusside (SNP, a NO donor) or levcromakalim (Lev, an ATP-sensitive potassium channels opener) in the presence of L-NA and TRAM-34 plus UCL-1684 and indomethacin to prevent the contribution of NO, EDH and vasoactive prostanoids, respectively.
Transgene constructs in the GM plant Molecular characterisation of oilseed rape MON 94100 was performed by next-generation sequencing (NGS) and junction sequence analysis (JSA) in order to determine insert copy number and to confirm the absence of plasmid backbone and T-DNA II sequences, and NGS sequencing on PCR amplified fragments to determine size and organisation of the inserted sequences. The approach used is acceptable in terms of coverage and sensitivity. Overall, the quality of the sequencing methodology and data sets was assessed by the EFSA GMO Panel and is in compliance to the requirements listed in the EFSA Technical Note (2018). NGS/JSA of the whole genome indicated that oilseed rape MON 94100 contains a single insert, consisting of a single copy of the T-DNA I in the same configuration as in the PV-BNHT508701 transformation vector.
Glycosylation detection analysis demonstrated that none of the DMO proteins were glycosylated. Amino acid sequence analysis of the plant-derived DMO proteins by mass spectrometry (MS) methods showed that the DMO proteins matched the deduced sequence as defined by the dmo gene, for DMO and DMO + 27 present in oilseed rape MON 94100. These sequence analysis data were consistent with the previously analysed MON 87708-derived DMO protein variants. In addition, the MS data showed that the DMO N-terminal methionine was removed. Such modifications are common in eukaryotic proteins (e. g. Polevoda and Sherman, 2000). N-terminal sequence analysis of the DMO purified from oilseed rape MON 94100 confirmed that, as expected, MON 94100 produced two variants of the DMO protein, DMO + 27 and DMO. Functional equivalence was demonstrated in comparison to the soybean MON 87708 by a biochemical in vitro activity assay which showed that oilseed rape and soybean-derived DMO proteins had comparable activity for the intended herbicide.
Dietary exposure assessment to new constituents In line with Regulation (EU) No 503/2013, the applicant provided dietary exposure estimates to DMO protein newly expressed in MON 94100 oilseed rape. Dietary exposure was estimated based on protein expression levels reported in this application for MON 94100 oilseed rape treated with the intended herbicide dicamba, the current available consumption data and feed practices, the foods and feeds currently available on the market and the described processing conditions. For the purpose of estimating dietary exposure, the levels of DMO protein in MON 94100 oilseed rape seeds were derived from replicated field trials (four replicates from five locations, n = 20) in 2018 in the United States and Canada. Table  1 in Section   3. 3. 4 shows the protein expression levels used to estimate both human and animal dietary exposure.
Human dietary exposure Currently, oil is almost the only food commodity derived from oilseed rape regularly consumed by the European population, although other food commodities derived from oilseed rape have been approved as novel food in recent years, e. g. protein isolates (EFSA NDA Panel, 2013) and oilseed rape powder  (EFSA NDA Panel, 2020). Different preparations of protein isolates from oilseed rape meal are found under commercial names. Currently, both protein isolates and oilseed rape powder seem to be absent in the European market as these products were not found in Mintel's Global New Products Database. 19 In addition, no consumption data for protein isolates and meat imitates from oilseed rape have been reported in the EFSA Comprehensive European Food Consumption Database (EFSA consumption database).
20 However, following an overly conservative approach, the applicant provided estimates of the dietary exposure to DMO protein making use of the available consumption data on meat imitates and protein supplements in the EFSA consumption database assuming they are derived from MON 94100 oilseed rape. Human dietary exposure was estimated across different European countries on different population groups: young population (infants, toddlers, 'other children'), adolescents, adult population (adults, elderly and very elderly) and special populations (pregnant and lactating women). Mean protein expression values on fresh weight basis are considered as the most adequate to estimate human dietary exposure (both acute and chronic) when working with raw primary commodities that are commonly consumed as processed blended commodities (EFSA, 2019a).
Different recipes and factors were considered to estimate the quantity of seeds in the consumed commodities before assigning newly expressed protein levels to the relevant commodities. 21 No losses in the newly expressed proteins during processing were considered. The highest chronic and acute dietary exposure to DMO protein (high consumers) was estimated as 9. 7 lg/kg body weight (bw) per day and 14. 9 lg/kg bw per day, respectively, in both cases in the age class 'Adults' via the consumption of 'Protein and protein components for sports people'. Additional dietary exposure to DMO protein might occur via the consumption of pollen supplements under the assumption that these supplements contain pollen from MON 94100 oilseed rape. Consumption data on pollen supplements are available for a few consumers across eight different European countries.
Quoiqu'il en soit, les dynamiques inverses des deux termes « biocontrôle » (en augmentation) et « lutte biologique » (en diminution) sont très nettes dans les écrits scientifiques en français. Nous soulignons ici le fait que l'utilisation des principales bases de données internationales (comme Web of Science ou Scopus) n'est pas adaptée pour comparer, en français, l'évolution des articles scientifiques sur la lutte biologique et sur le biocontrôle. En effet, la langue d'interrogation de ces bases est l'anglais et l'interprétation d'une requête avec, par exemple, un accent circonflexe (comme dans le terme « biocontrôle ») n'est pas possible. Nous ne les avons donc pas utilisées pour cette analyse quantitative.
Remerciements. Nous remercions Jean-Noël Aubertot (INRAE), Nicolas Borowiec (INRAE), Hélène Delatte (CIRAD), Alain Ratnadass (CIRAD) et Pierre Silvie (IRD) pour leur relecture de l'article et leurs remarques constructives. Nous remercions également les deux évaluateurs anonymes pour leurs enrichissants commentaires et leurs pertinentes corrections.
1a ). The core group of genes present in all individuals consisted of <7. 2% of the overall microbial gene pool (n = 1,805,693; Fig. 1b ). Yet, these core genes showed highly conserved abundance rank structure across individuals, representing 29. 7 to 38. 4% of the overall microbiome abundance. Only 22. 5% (n = 922,362) of the recently published horse gut microbiome gene catalog  30  overlapped with our gene catalog, indicating that the functional potential of gut microbiomes in horses is vast and currently under-sampled. Taxonomic annotation of the microbial gene catalog was implemented with Kaiju 31 using a greedy mode and the NCBI nr_euk reference database. While 39% of the gene sequences were unclassified, this approach annotated 61% of the gene sequences (Fig. 1c ) and revealed a diverse community of 95 phyla encompassing 1110 families and 4179 unique genera (Supplementary Note, Supplementary Data 3).
1d ). The most observed protozoa were Stentor, Stylonychia, Paramecium, Ichthyophthirius, and Tetrahymena. The taxonomic analysis also revealed that the core group of genes was defined by Prevotella (5. 10%) and Bacteroides (2. 98%), both dominant in the gut of marathon and triathlon athletes, respectively  33  , along with Ruminococcus (3. 17% of genes), Treponema (3. 11%), Clostridium (2. 70%), Butyrivibrio (2. 42%), and Fibrobacter (1. 37%; Fig. 1e ; Supplementary Data 4). All of these fully agree with the core microbiota of horses inferred from 16S rRNA-based sequencing  24, [34] [35] [36]. To gain functional insights, we annotated genes via KEGG orthologous groups (KOs) and carbohydrate-active enzymes (CAZymes) using the non-supervised orthologous groups (EggNOG) database. Results revealed a total of 12,060 KOs and 137 unique CAZymes, which encompassed 44% (n = 11,132,404) and 3.
38% (n = 665,235) of the gene catalog, respectively. Most KOs had essential microbial gut functions, including metabolism (transport and metabolism of amino acids, carbohydrates, nucleotides, and lipids), genetic information processing, and signaling (Supplementary Data 5). We also identified potential functions related to the biosynthesis of secondary metabolites, quorum sensing, and prokaryotic defense system, which help maintain the microbiome's structure and the host's health (Fig. 2a ). Given the abundance and variety of carbohydrates in the horse diet, the functionalities of glycosidic bond processing enzymes were investigated using the CAZy database. The great majority of these CAZYmes pertained to glycosyl-transferase (GT, 26. 8%, n = 228,878 genes) and glycoside hydrolase (GH, 64.
2a-h ). Among these, 121 MAGs were estimated to be near complete; MAGs in this subset had minimal contamination (≤ 5%) and high completeness (> 95%). According to the Genome Taxonomy Database Toolkit (GTDB-Tk)  43  , this MAG repertoire was assigned to 361 bacteria and 11 archaea, involving bacteria from the Bacteroidetes and Firmicutes phyla, followed by Spirochaetes, Euryarchaeota, Verrucomicrobia, Fibrobacteres, and Cyanobacteria phylum (Fig. 2d ). However, only 83 and 41 MAGs were classified at the genus and species levels, respectively (Fig. 2e ). The MAGs pertaining to the Cyanobacteria, Proteobacteria, and Verrucomicrobia phyla displayed variable abundances between hosts (Fig. 2f ). This trend was bolstered at the lower taxonomic level, except for MAGs assigned to Fibrobacter spp. (Supplementary Fig.
2h ). Most MAGs encoded enzymes that degrade polysaccharides (Supplementary Note, Supplementary Data 8, Supplementary Fig. 3 ). Notably, most MAGs (n = 369) were new for horses  44, 45  , increasing the mappability of metagenomes and expanding our understanding of the horse microbiomes. The dominant basal gut phylotypes defined two taxonomical and functional microbial communities. We first profiled microbial taxa in each metagenomic baseline sample at the taxonomic (NCBI nr_euk reference database) and functional (EggNOG database) levels. Then, we retained the most dominant microbial phylotypes. These phylotypes harbored 1,146 unique genera (accounting for 95% of the classified sequences) with abundance and prevalence in the top 25% and 50% quantiles, respectively (Supplementary Data 9). They were represented mainly by prokaryotes (91%), a handful of eukaryotes (7.
It showed enrichment of CAZymes that might act in a concerted manner to cleave plant polysaccharides into fermentable monosaccharides (GH6, GH39, GH54, GH82, GH84, GH99) or host glycosaminoglycans (PL8, PL12, GH18, GH20, GH29, GH88, and GH95; DESeq2, adjusted p < 0. 05; Supplementary Data 13, Supplementary Fig. 4d ). Cluster 1 also had more CAZymes for host glycans degradation (14. 15%) than members of cluster 2 (9. 63%). In agreement, the latter presented an enrichment of KOs related to glycan biosynthesis and metabolism (K09953, K18770, K12309, K12551, K01137, K03276, K12985, K14459). Cluster 1 also exhibited large metabolic increases (n = 520 KOs) in amino acids metabolism (K12256, K15226, K03852, K08688, K18049, K21062), energy production, e. g. , genes within lipid metabolism (K10781, K16795, K01795, K01049, K12309), propanoate metabolism (K11264, K01659 K00382 K01720), fructose and mannose (K19633) and pyruvate (K02594), as well as methane production (K00202, K00204, K00583, K05884; DESeq2, adjusted p < 0.
05, two-sided Wilcoxon rank-sum test), and none of them were the major driving forces of the microbiome composition (PerMANOVA; R 2 = 0. 102 ± 0. 089 and pvalues ranging from 0. 055 to 0. 82; Supplementary Fig. 7a-d ). Likewise, KOs (PerMANOVA; R 2 = 0. 034 ± 0. 010 and p-values ranging from 0. 940 to 0. 969) and CAZymes profiles (PerMA-NOVA; R 2 = 0. 030 ± 0. 016 and p-values ranging from 0. 250 to 0. 971) were also independent of nutrient intakes. Gut community composition was neither linked to horse sex (PerMANOVA; R 2 = 0. 2486, p = 0. 266), or age of the athletes (PerMANOVA; R 2 = 0. 0952, p = 0. 409; Supplementary Fig. 7e-g ). Moreover, clusters 1 and 2 did not overlap with the horse kinship (Supplementary Fig. 8 ). After confirming that these confounding factors did not define the gut microbiome phylotypes, we next tested if any hostcentered omic or phenomics dataset, including transcriptomics, metabolomics, blood biochemical assay profiles, acylcarnitines, and cardiovascular fitness parameters, best captured the distributions of metagenomic samples (Supplementary Note, Supplementary Data 15-18, respectively).
9d ). This analysis only supported the presence of Mogibacterium and the yet undefined members of Rhodospirillaceae, Enterobacteriaceae, Planococcaceae, and Sphingobacteriaceae families in more fit individuals but not the other 252 poorly-described bacteria members. This likely reflects an under-representation of these taxa in existing 16 S rRNA reference databases. Conversely, the sPLS-DA corroborated the enrichment of Clostridiales, Fig. 3  Basal dominant gut phylotypes composition is associated with cardiovascular fitness. a Dominant phylome and its prevalence at different detection thresholds (relative abundance). The percentage of shared items and the proportion of shared samples are represented on the yand x-axis. b NMDS ordination analysis (Bray-Curtis distance) of dominant phylotype composition.
Points are colored by phylum. f Taxonomic distribution of the dominant phylotypes grouped by phyla in each individual (n = 11). Individuals are split by cluster: cluster 1 (n = 3) and cluster 2 (n = 8). g NMDS ordination plot shows the covariates contributing significantly to the variation of dominant phylotypes determined by the envfit() function. The arrows for each variable indicate the direction of the effect and are scaled by the unconditioned r 2 value. Dots represent samples (n = 11), which are colored according to the type of community: cluster 1 (red color, n = 3) and cluster 2 (blue color, n = 8). h Effect sizes of the main variables affecting the NMDS ordination. The length of the horizontal bars shows the amount of variance (r 2 ) explained by each covariate in the model. Covariates are colored according to the type of dataset: athletic performance are in green and mitochondrial-related genes in blue.
To further characterize the microbiome-host crosstalk and identify molecular differences between the two types of cardiovascular outcomes in elite horses, we integrated multi-omic datasets from the host and associated gut microorganisms through a multivariate matrix factorization approach (DIABLO; see "Methods"). To achieve this integrated perspective coined as holo-omics  49  , we combined host-centered omic and phenomics data with the fecal shotgun metagenomics, SCFAs composition, and the concentrations of bacteria, anaerobic fungi, and protozoa. First, we observed strong covariation between the dominant phylotypes and the genetic functionalities derived from KOs (r 2 = 0. 99) and CAZymes (r 2 = 0. 98; Fig. 4a ). This apparent correlation supports the added value of microbiome functions for status prediction rather than composition alone, as noted in human athletes  21, 50.
Subject metadata, including morphometric characteristics and daily macronutrient diet intake records, is depicted in Supplementary Data 1. Daily nutrient intake calculations are described elsewhere  24. Performance measurement. The endurance race was split into ~30-40 km phases. At the end of each phase, veterinarians checked horses (referred to as a vet gate). The heart recovery time was the primary criterion evaluated at the vet gate as it is shown to be an excellent complement to a physical assessment of an individual. The heart rate was measured at each vet gate by the riders and a veterinarian using a heart rate meter and a stethoscope. Any horse deemed unfit to continue (due to a It should be noted that the time interval between arrival at the vet gate and the time needed to decrease the heart rate below 64 bpm was counted as part of the overall riding time.
Transcriptomic microarray data production, pre-preprocessing, and analysis. According to the manufacturer's instructions, total RNAs were isolated using the Preserved Blood RNA Purification Kit I (Norgen Biotek Corp. , Ontario, Canada). 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, USA). All the 22 RNA samples were processed. The transcriptome microarray data production, pre-processing, and analysis are depicted in Mach et al. 25. Transcriptome profiling was performed using an Agilent 4X44K horse custom microarray (Agilent Technologies, AMADID 044466). All of the steps are detailed here  73, 74. We refer to our previous work for more details on the pre-processing, normalization, and application of linear models  25.
Given our interest in understanding the role played by mitochondria during exercise, the set of 801 differentially expressed mitochondrial genes reported by our team  25  was selected for the downstream steps of analysis (Supplementary Data 15). Proton magnetic resonance ( 1 H NMR) metabolite analysis in plasma. As described elsewhere  24, 70  , the plasma metabolic phenotype of endurance horses was obtained from 1 H NMR spectra at 600 MHz. The 1 H NMR spectra were acquired at 500 MHz with an AVANCE III (Bruker, Wissembourg, France) equipped with a 5 mm reversed QXI Z-gradient high-resolution probe. Further details on sample preparation, data acquisition, quality control, spectroscopic data pre-processing, and data pre-processing, including bin alignment, normalization, scaling, and centering, are broadly discussed elsewhere  75.