dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge the technical assistance of  Monica Resch ,  Lucia Martin ,  Estefania Prieto-Garcia  and  Monica Carabias-Carrasco , from the  University of Salamanca. Staff members ( Amandine Rocher ,  Hanna Kulyk ,  Nina Lager-Lachaud ,  Lara Gales ,  Floriant Bellvert ) of  MetaToul (Toulouse, France ) are gratefully acknowledged for technical support and access to mass spectrometry facilities. This work was funded by the  European Regional Development Fund ,  Agencia Estatal de Investigación  ( PID2019-105699RB-I00  and  PID2022-138813OB-I00 , to JPB),  HORIZON-MSCA-2021-DN-01  ( ETERNITY ,  101072759  to JPB and AA),  Fundación La Caixa  ( HR23-00793  to JPB),  Instituto de Salud Carlos III  ( CB16/10/00282  to JPB;  PI18/00285 ;  RD16/0019/0018  to AA),  Junta de Castilla y León  ( CS/151P20  and  Escalera de Excelencia   CLU-2017-03  to JPB and AA).
PC is supported by grants from  Methusaleum funding (Flemish government)  and the  Fund for Scientific Research-Flanders  ( FWO-Vlaanderen G0E4419N ). MetaToul is part of the national infrastructure MetaboHUB, and is funded by the  French National Research Agency (ANR)  with grant number  MetaboHUB-ANR-11-INBS-0010.
The peaks associated to genes overexpressed in non-muscular somite cell populations are enriched in T-box motifs, consistently with the expression in our dataset of various TFs of this family such as Eomes/Tbr1/Tbx21, Tbx15/18/22, and Brachyury2 (Fig. 3b ) and with previously reported expression of these genes in forming somites  [49] [50] [51]. The muscular somite population peaks are enriched in motifs shared with the non-muscular somite, but are also enriched in motifs for Myogenic Regulatory Factors (MRF) such as Mrf4 (Myf6), which is also highly expressed in this cell type (Fig. 3b ), in line with both the expression of the various amphioxus MRFs described by in situ hybridization  52  , and the role of their orthologues in vertebrate myogenesis  53. The strongest TF-motif association concerns the previously reported notochordal marker Foxaa (Foxa2) (Fig.
10. 0 R library 104. Cell cluster ordering was fixed according to annotated cell types; and gene order was determined using the highest FC value per cluster. Genes were selected based on minimum differential expression per metacell/cell type, with a maximum number of markers per clusters selected in each case (the actual thresholds used in each heatmap are specified in the corresponding figure legends). Finally, we selected cells belonging to the endoderm, neural and somitic metacells (Supplementary Table  4 ), and reclustered them using the same metacell-based approach as described for the whole dataset (except that in this case we allowed for smaller metacells, with 10 cells; (Supplementary Table  4b-d ). The two-dimensional arrangement of the resulting metacells was curated based on the expression of cell type-specific known markers of various cell subtypes (Supplementary Fig.
Analysis of neurula regulatory regions We used the ATAC-seq data from the 21 hpf embryo to build a catalogue of neurula regulatory regions. For comparison, we also used previously published  15  ATAC-seq libraries of 15 hpf and 36 hpf embryos (the closest developmental timepoints available in that study; NCBI SRA accession numbers SRR6245277 to SRR6245279), as well as H3K4me3 ChIP-seq libraries from these same timepoints (SRA accession numbers SRR6245317 to SRR6245320). The ATAC-seq libraries corresponding to the 15, 21 and 36 hpf embryos were mapped separately to the B. lanceolatum genome using bwa 0. 7. 17 (mem algorithm 106 ). The resulting BAM files were (i) filtered using alignmentSieve (from the deeptools 3. 5. 1 package 107 ) to exclude weak alignments MAPQ > 30), (ii) corrected to shift the left and right ends of reads, to account for ATAC mapping biases (+4/-5 bp in the positive and negative strands, using the --ATACshift flag in alignmentSieve), and (iii) filtered to only include nucleosome-free alignments (--maxFragmentLength 120 with alignmentSieve).
01, (iv) enabling multiple summit detection (--call-summits flag), and (v) disabling the modelling of peak extension for ChIP-seq libraries (--nomodel flag). We then assigned the MACS2-predicted regulatory elements to their proximal genes, based on their distance to each gene's transcription start site (TSS). Specifically, we selected wellsupported MACS2 regulatory elements (q-value < 1×10 -6 ), standardized their lengths to 250 bp (125 bp to each side of the predicted peak summit), and assigned each peak to nearby genes based on distance to their TSS (excluding genes further away than 20 kbp, and genes located beyond a more proximal gene). Peaks overlapping the promoter region of a particular gene (defined based on TSS coordinates +/-50/200 bp or coincidence with H3K4me3 ChIPseq peaks for the 15 and 36 hpf datasets) were not assigned to any other gene.
pl utility in homer 4. 11 111 Specifically, we set a constant peak size of 250 bp and attempted to identify motifs for each cell type, using k-mers of length 8, 10, 12, and 14; and tolerating up to four mismatches in the global optimization step. In order to build a final motif collection for amphioxus, we concatenated the cell type-specific de novo motifs with known TF binding motifs from the CIS-BP database (as available the 3rd of March, 2023)  112. Specifically, we used 3,547 experimentally determined motifs (with SELEX or PBMs), corresponding to vertebrate or tunicate species (Homo sapiens, Mus musculus, Xenopus tropicalis, Xenopus laevis, Danio rerio, Tetraodon nigroviridis, Meleagris gallopavo, Gallus gallus, Anolis carolinensis, Takifugu rubripes, Ciona intestinalis, and Oikopleura dioica).
Cross-species cell type comparison We used SAMap 1. 0. 2 [ref] to evaluate the similarity between B. lanceolatum cell types and the previously published developmental single-cell transcriptomes of Danio rerio  41  (reference gene set in original study: GRCz10 v1), Xenopus tropicalis  40  (reference gene set in original study: Xenbase version 9. 0), and Ciona intestinalis  39  (reference gene set in original study: KH2012 from the Ghost Database (http://ghost. zool. kyoto-u. ac. jp/download_kh. html). For each query species, we used the UMI tables corresponding to the timepoints closest to the B. lanceolatum 21hpf developmental stage (12 in total): 14 hpf, 18 hpf and 24 hpf for D. rerio (GEO accession: GSE112294); S14, S16, S18, S20 and S22 for X. tropicalis (GSE113074); and the initial, early, middle and late tailbud stages for C.
intestinalis (GSE131155). For C. intestinalis, we used the cell type annotations used in the original paper. For the two vertebrates, we used the consensus cell annotations employed by Tarashansky et al. 114. To run SAMAp, we first created a database of pairwise alignments with blastp 2. 5. 0 (comparing B. lanceolatum peptides to each query species separately; in the case of Danio rerio we used blastx/tblastn instead of blastp as the original gene set  41  was only available as un-translated transcripts). Second, we used the cell-level UMI counts of each gene to calculate the SAMap mapping scores for each pair of cell types (between B. lanceolatum and each of the 12 query developmental datasets in other species), using all cells within each cluster for score calculation. Finally, we identified shared marker genes between cell types of B.
lanceolatum TFs with reference human gene names. For genes used to assign metacells to known amphioxus embryonic territories and named in the manuscript, we either used the previously published amphioxus gene names when they exist, or a name based on fine orthology analysis. Amino acid sequences from B. lanceolatum were used to search Genbank for putative homologues by blasp. Sequences were aligned using ClustalX 126. Alignments were manually corrected in SeaView 127. Maximum Likelihood phylogenetic trees were reconstructed using IQ-TREE v2. 1 123 with default parameters (fast bootstraping and automatic best model search). Genes with no clear orthology signal were named based on the presence of known protein domains.
In situ hybridization DIG labeled probes were synthesized from fragments cloned into pBKS, or from PCR amplified DNA fragments purchased at IDT, using the appropriate RNA polymerase (T7, T3 or SP6) and the DIG-labeling Mix (Roche). Embryos at 21 hpf (19°C) were fixed in paraformaldehyde (PFA) 4% in MOPS buffer, dehydrated in 70% ethanol and kept at -20°C. In situ hybridization was undertaken as previously described in  26. The accession numbers/sequences used for probe synthesis are given in Supplementary Table  5.
Cell types are colour-coded by developmental layer (endoderm, mesoderm/muscle, neuroectoderm, ectoderm, and other), and, in the case of the multi-stage chordate datasets, by developmental time-point (colour intensity). b, Graph representation of transcription factors (TFs, circular nodes) shared (i. e. connected by an edge) between amphioxus and homologous cell types in Ciona, Danio and Xenopus (square nodes). Specific TFs are considered to be shared between two cell types if they are significantly overexpressed in both. TFs in bold are shared between all species considered. For amphioxus, we required fold-change > 1. 25, and BH-adjusted p-value < 0. 05. For the matched cell types from other species, we required significant overexpression in at least one of the developmental timepoints considered.
Acknowledgements This work benefited from access to the  Observatoire Océanologique de Banyuls-sur-Mer , an  EMBRC-France  and  EMBRC-ERIC  site. Embryo imaging experiments were undertaken using the material of the BIOPIC platform. The laboratory of H. E. and S. B. was supported by the  CNRS , and by the  "Agence Nationale de la Recheche"  under the grants  ANR-19-CE13-0011  and  ANR-21-CE13-0034. Research in A. S-P. group was supported by the  European Research Council  ( ERC-StG 851647 ) and the  Spanish Ministry of Science and Innovation  ( PID2021-124757NB-I00 ). X. G-B. is supported by the  European Union 's  H2020 research and innovation program  under  Marie Sklodowska-Curie grant  agreement  101031767. A. E. is supported by  FPI PhD fellowships  from the  Spanish Ministry of Science and Innovation.
J. J. T. was supported by the  Spanish Ministerio de Economía y Competitividad  (grant  PID2019-103921GB-I00 ). M. I. laboratory research has been funded by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( ERCCoG-LS2-101002275  to MI), by the  Spanish Ministry of Economy and Competitiveness  ( PID2020-115040GB-I00  to MI) and by the ' Centro de Excelencia Severo Ochoa 2013 -2017 '( SEV-2012-0208 ).
Humans exhibit sex differences in the prevalence of many neurodevelopmental and neurodegenerative conditions. To better understand the translatability of a critical nonhuman primate model, the rhesus macaque, we generated one of the largest multibrain region bulk transcriptional datasets for this species and characterized sex-biased gene expression patterns. We demonstrate that these patterns are similar to those in humans and are associated with overlapping regulatory mechanisms, biological processes, and genes implicated in sex-biased human disorders, including autism. We also show that sex-biased genes exhibit greater genetic variance for expression and more tissue-specific expression patterns, which may facilitate the rapid evolution of sex-biased genes. Our findings provide insights into the biological mechanisms underlying sex-biased disease and validate the rhesus macaque model for the study of these conditions.
Filling in these gaps regarding the critically important rhesus macaque model could greatly aid in the development of potential therapies for sex-biased brain disorders in humans. To address this, we generated one of the largest nonhuman primate brain transcriptional datasets (n=527 samples), and quantified sex differences in gene expression across 15 brain regions (Figure  1 ; Supplementary Table  1 ) from 36 free-ranging adults (20 females, 16 males; identified using chromosomal and phenotypic sex; Supplementary Figure  1 ; Supplementary Table  2 ). This substantial sample size allowed us to characterize, for the first time, patterns of sex-biased gene expression across the rhesus macaque brain, to link these patterns to human sex differences in the brain and disease, and to illuminate some of the evolutionary mechanisms underlying these patterns.
For example, although our analyses include relatively more samples for both the ASD and normative sex datasets, these data represent different developmental periods (the ASD expression dataset analyzed here includes children and adults  (53) ; the macaque and human GTEx datasets include adults only). This may impact results since ASD and sex both represent "developmentally moving targets"  (60). Finally, more recent analyses (incorporating data from more individuals) report that ASD-upregulated microglial/immune gene modules are male-biased prenatally but are then female-biased during certain postnatal periods  (60). This is consistent with prenatal male-biased and postnatal female-biased expression of a microglia-enriched neurodevelopmental gene module in the human brain (ME3)  (44) , in addition to reversal of sex differences in microglial colonization and activation prior to adolescence in rats  (43).
Disease gene set enrichment analysis (GSEA) Gene set enrichment analyses for disease ontology (DO) terms were performed using human risk genes downloaded from the DISEASES resource, which integrates the results of text mining and manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases  (108). (Macaca mulatta) Ensembl IDs were linked to human diseases from this database using oneto-one human orthologues (and their associated proteins) from the R package bioMart. Diseases with at least 10 associated genes were retained for further analysis. Genes were ranked according to their mean standardized betas (i. e. , mashr beta divided by posterior SD, averaged across regions). For each disease in the final dataset (N=1257), we used two-sample Kolmogorov-Smirnov (KS) tests to compare the cumulative distribution functions of the mean standardized betas of genes associated with the disease to those associated with every other disease in the data set.
We tested two alternative hypotheses, namely that the cumulative distribution function for the target set was either less than or greater than that of the background set. P-values were adjusted using the Benjamini-Hochberg method, and tests with adjusted p-values less than 0. 05 were considered significant. These analyses were also run within each region (using ranked standardized betas), and on the mean standardized betas from our analysis of the human GTEx data (described above). We also tested whether the sex-biased genes identified here tend to exhibit altered expression levels in human disease. Whole cortex ASD-upregulated and -downregulated genes were collected from  (53). (Macaca mulatta) Ensembl IDs were linked to human diseases from this database using one-to-one human orthologues (and their associated proteins) from the R package bioMart.
Comparisons of loss-of-function (LOF) mutation tolerance We used LoFtools  (110)  to assign LOF metrics to each gene. This database is based on the ratio of loss-offunction to synonymous mutations, with lower LoFtool percentiles representing more intolerance to functional variation. Homo sapiens gene names from the LoFtools list were converted to (Macaca mulatta) Ensembl gene IDs using the bioMart R package, resulting in a set of 7786 orthologous genes. Per gene, we modeled LOF intolerance percentiles as a function of the absolute value of the difference between male and female mean residual expression values (see above) across all samples and regions. We tested for a significant association between these variables as above (see Tissue specificity).
Acknowledgements We thank those who make our research possible, particularly the  Caribbean Primate Research Center  and staff from both the  Cayo Santiago Field Station and Sabana Seca Field Station. We also thank  Madeleine Rosenstein  and for assisting in data collection and  Dr. Armin Raznahan  for providing valuable feedback. The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health  (commonfund. nih. gov/GTEx). Additional funds were provided by the  NCI, NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. Donors were enrolled at  Biospecimen Source Sites  funded by  NCI\Leidos Biomedical Research, Inc. subcontracts to the  National Disease Research Interchange  ( 10XS170 ),  Roswell Park Cancer Institute  ( 10XS171 ), and  Science Care, Inc.
The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/gapthrough dbGaP accession number phs000424. v8. p2. Funding: Support for this research was provided by the  National Institutes of Health  ( NIMH R01MH118203 ,  NIMH   U01MH121260 ,  NIMH   R01MH096875 ,  NIA R01AG060931 ,  NIA R00AG051764 ) and the  National Science Foundation  ( BCS 1800558 ,  BCS 1752393 ). The Caribbean Primate Research Center is supported by the  National Institutes of Health  ( NCRR/ORIP P40OD012217 ). ARD's past and current support include the  New York University MacCracken Fellowship , a  Graduate Research Fellowship  from the  National Science Foundation  ( DGE1342536 ), and a  Postdoctoral Intramural Research Award  from the  National Institute of Mental Health.
0. 36. Trimmed reads were mapped to the Homo sapiens GRCh38 reference genome available on ENSEMBL using the STAR aligner v. 2. 5. 2b. Unique gene hit counts were calculated by using feature Counts from the Subread package v. 1. 5. 2, with only unique reads that fell within exon regions being counted. Fastq files are accessible through the GEO portal  [29]  under the GSE219159 accession number. Differential gene expression analysis was performed using DESeq2 suite  [30]. Following the removal of genes with no detected expression, weakly expressed genes were filtered upon a threshold value of expression calculated by using the HTSfilter package  [31]. Genes were then declared as differentially regulated when their absolute fold change (FC) was > 1. 5 with a BH adjusted p-value < 0. 05. Functional annotations were made by interrogating MSigDB hallmarks  [32]  through the R cluster Profiler package  [33]  using enricher function for over-representation analysis (ORA) and the integrated GSEA function for global gene set enrichment analysis  [34].
Transcription factors binding motifs analysis Enrichment for transcription factors binding motifs was assessed within a -10/+10 kb window centered on the TSS of genes regulated either by E2, 4-OHT or both ligands. To do so, we used the RcisTarget R package  [34]  to interrogate the presence of TF motifs or the presence of an actual TF binding site (TFBS) as determined from Encode knowledge in these windows (hg38 genome annotations). We retained associations having a Normalized Enrichment Score (NES) ≥ 3. Resulting motifs-to-genes and TFBS-to-genes-associations were then merged and sorted in order to generate a Transcription Factor-togenes association file which was then transformed in incidence matrices using the Sort_Incidence_matrix function of the RcisTarget package and subsequently sorted in edges and nodes files compatible with visualization under Cytoscape v3.
The colors of the genes and TF nodes are representative of their fold-change in expression in the conditions indicated within the legends of each panel (4-OHT impact refers to the 4-OHT vs DMSO comparison). (D and H) Normalized counts in TeloHAEC-endothelial cells expressing hERα-WT (n = 6 replicates per group) treated for 6 hours with E2 (1nM) or 4-OHT (100 nM) alone or in combination with E2. Data presented are means ± SEM. Statistical relevance was measured by one-way ANOVA followed by Bonferroni post-test and is represented as follows: **** P<0. 0001 for the control vs. treated groups comparisons; and $ P<0. 05; $$ P<0. 01 $$$$ P<0. 0001 for the comparisons between the 4-OHT or E2 4-OHT vs. E2 conditions. (E) Western blot for SMAD1, NR2F2 and NR2F6 expression normalized to vinculin in TeloHAEC endothelial cells expressing hERα-WT treated for 6 hours with E2 (1 nM) or 4-OHT (100 nM) alone or in combination with E2 (n = 3 replicates per group).
Acknowledgments The authors thank  Sonia Negroni ,  Guy Carcassés  and all the staffs at the  ANEXPLO-GENOTOUL platform  ( US006 CREFFRE, Toulouse, France ) for skillful technical assistance in animal functional experimentation. We are also grateful to  Anne Salvayre-Negre  for providing the Telo-HAEC cells, the  GeT-TQ Genopole platform (INSERM UMR1297, Toulouse, France ) for technical contribution to qRT-PCR experiments ( Frederic Martins  and  Emeline Lhuillier ), the  TRI-Genotoul  (confocal microscopy) Platform  of Toulouse  ( Rémy Florès-Florès ).
Acknowledgment This work was supported by the  French national program   EC2CO-Biohefect/Ecodyn//Dril/MicrobiEen (Influence du parasitisme sur la distribution des flux d'énergie dans les réseaux trophiques ). The authors would like to thank all the people who contributed to the success of these experiments:  Tiphaine Boursier ,  Baptiste Carrere , and  Léo Bricout  for performing the preliminary experiments;  Pierre Fédérici ,  Jérôme Mathieu ,  Gérard Lacroix ,  Thomas Tully ,  Thibaud Monnin ,  Gabrielle Ringot ,  Julien Gasparini ,  Adrien Frantz ,  Clotilde Biard , and  Eric Edeline  for their loans of equipment and technical help; and  Claude Yéprémian  for providing the Scenedesmus sp. and  Lauren Boutier  the European minnows. The authors also thank  Victoria Grace  for reviewing the English language.
ATX-ɣ is brain-specific and differs from ATX-β by a 25 aa insertion, while its activity is similar to the β isoform (Fig. 1 )  [3]. The ATX structure has been widely studied with 51 experimental entries in the Protein Data Bank to date  [6]. It relies on (i) two flexible somatomedin B (SMB)-like domains, involved in protein-protein interactions, (ii) a conserved phosphodiesterase domain responsible for catalytic activity, and (iii) an inactive nuclease domain (Fig. 1 )  [7]. Structural insights into ATX show that the active site contains two zinc ions, allowing lysophosphatidylcholine binding and cleavage. A major point of substrate recognition by the ATX active site is the presence of a hydrophobic pocket, allowing the accommodation of both LPA and LPC  [7]. ATX is able to bind and process various LPCs into bioactive LPAs, in particular LPC 18:1 and LPC 16:0 species, which are abundant in the human body  [8].
They occupy both the tunnel and the active site, avoiding the LPC entrance. They are steroid-derived hybrids and only two inhibitors of this family are known, with I8K as the more active inhibitor with an IC 50 of 30 nM  [22]  (PDB: 7Z0N). As previously described, various ATX inhibitors have already been developed and some are now in clinical trials  [2, [28] [29] [30]. However, compounds with satisfactory pharmacokinetics parameters and low toxicity are limited. There is still no approved drug for ATX brain-related diseases. Moreover, ATX distribution is tissue-specific and most of the research has been focused on the α and β isoforms. For ATX-ɣ, plausibly related to many neurological diseases, a deeper understanding is needed to design and develop more specific inhibitors of this isoform and to fully understand its pathophysiological role.