dataesr/scientific-paragraphs-categorization huggingface dataset:
acknowledge the Bordeaux Imaging Center (Bordeaux, France) and the France BioImaging infrastructure supported by the French National Research Agency (ANR-10-INSB-04, "Investments for the future"). CD is supported by a PhD fellowship from both SIRIC BRIO and Région Nouvelle-Aquitaine. JV is supported by PhD scholarships from the French Ministry of Research (MENESR). This research was funded by La Fondation pour la Recherche Médicale to VM (équipe FRM 2018, grant number DEQ20180839586), La Ligue contre le Cancer (comité des Charentes et comité de la Gironde) to CB and l'AFEF (Association Française de l'Etude du Foie) to CB. The authors declare no competing financial interests. Author contributions Study design: CB, VM. Generation of experimental data: CD, JV, AB, IM, MP, NDS, CB. Analysis and interpretation of data: CD, JV, AB, AAR, JWD, PB, DP, MV, BB.
For samples containing gluconolactone (GL), the metabolites (including glucose, gluconate and gluconolactone) were quantified immediately after filtration (0. 2 µm, polyethersulfone membrane) of 500 µL of culture. The filtrate was kept on ice and rapidly analysed by NMR as described above (32 scans). Extracellular fluxes (i. e. glucose uptake, GNT accumulation and growth rates) were determined from the time course concentrations of biomass, substrates, and products using PhysioFit v2. 0. 4 39 (https://github. com/MetaSys-LISBP/PhysioFit).
Acknowledgements This study was funded by the  Agence Nationale de la Recherche , grant number  ANR-19-CE44-0005  (PerioMet). The authors thank  MetaToul (Metabolomics & Fluxomics Facilities, Toulouse, France , www. metatoul. fr) and its staff for technical support and access to the NMR facility. MetaToul is part of the  French National Infrastructure  for Metabolomics and Fluxomics (www. metabohub. fr), funded by the  ANR  ( MetaboHUB-ANR-11-INBS-0010 ).
Given the ancient S. cerevisiae -S. paradoxus split (estimated time of divergence 3. 27 x 10 8 generations), it seems unlikely that ILS polymorphisms would persist at such timescale given the short generation time and limited effective population size 17. Recent studies described S. cerevisiae and S. paradoxus isolates collected worldwide and generated both shortread datasets  14, 15, 18, 19, [21] [22] [23] [24] [25] [26] [27]  and high-quality whole-genome assemblies 28 , paving the way for a deep investigation of shared alleles across these two species. Here, we exploit the thousands of S. cerevisiae genomes and the dense map of diagnostic markers between the two species to detect S. paradoxus alleles at high resolution. We identify and describe the major hybridisation events and report that most introgressed blocks derive from recent clade-specific hybridisations, while a handful of ancient introgressions are shared across distinct clades.
Identification of S. paradoxus alleles and introgressions through marker patterns in S. cerevisiae We used a modified version of MuLoYDH 51 to screen 1,673 S. cerevisiae strains to identify those enriched in S. paradoxus alleles using as input the dataset of species-specific markers stored as BED files with either S. c. c. or CBS432 coordinates. The FASTA genomes were indexed with samtools faidx and bwa index (default options). The mapping of the short reads, against both S. c. c. and CBS432, was performed by piping BWA (v. 0. 7. 16a options -M -t) and samtools view (v. 1. 7-12 options -bS -F 1284). Not primary aligned, unmapped and duplicated reads were filtered out. samtools sort and index (default options) sorted the bam files and produced the indexes (bai). We collected the statistics about the short read mapping (samtools flagstat) and the coverage (samtools depth option -a piped with awk).
Acknowledgments We thank  Melania D'Angiolo ,  Eugenio Mancera ,  Nikolaos Vakirlis ,  Gilles Fischer ,  Etienne Danchin  and  Pedro Beltrao  for discussions and critical reading of the manuscript. We also thank  Vassiliki Koufopanou  for sharing the strain OS162 and  Ana Pontes  and  Jose Paulo Sampaio  for providing information on the origin of the Alpechin and Brazilian sequenced strains. This work was supported by  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01 ,  ANR-15-IDEX-01 ,  ANR-18-CE12-0004 ,  ANR-20-CE12-0020 ,  ANR-22-CE12-0015 ),  Fondation pour la Recherche Médicale  ( EQU202003010413 ),  UCA AAP  Start-up Deep tech,  CEFIPRA. NT was partially supported by the  PhD fellowship program Region PACA.
Saccharomyces collections Genome sequences analysed in this study were previously published. The majority of the isolates belong to the 1011 S. cerevisiae collection  14. We included additional datasets to widen the sample size of several clades and specifically added strains from the following sources: wild and industrials Chinese 15 , wild Brazilian 18 , olive oil  19  , wild North American 27 , beer  22, 48  , Brazilian Cachaça 21 , flor  26  , clinical  25  and industrial  23, 24. We checked the fastqs files quality with FastQC and assembled a collection of 1676 strains that covers all the currently known S. cerevisiae diversity in terms of both ecology and geography (Supplementary Table  2 ). The Alpechin strains from Greece and Italy have been recently sequenced by us and not included in the global analysis of this paper, but they present an introgression pattern consistent with the previously described ones.
Definition of diagnostic markers dataset The six assemblies of S. c. c. , CBS432, N44, YPS138, UFRJ50816 and UWOPS91-917. 1 were masked in correspondence of both repetitive elements (LTR, Core X, Y', Ty elements) and rapidly evolving sequences such as telomeric regions, introns, pseudogenes and noncoding exons, replacing the sequences with 'N' values. based on genome annotations. We discarded ambiguously annotated, broken or double annotated sequences. For each ortholog we performed multisequence alignment with MUSCLE (v3. 8. 31) and we generated an UPGMA gene tree (MUSCLE with -maketree default options). We used the Robinson-Foulds metric (RF. dist function in R package phangorn v. 2. 10. 0) to measure the distance of gene trees from the species tree. Most gene trees (4,569/5191; 88%) retraced the species tree structure and are informative for detecting potentially introgressed genes.
Identification of introgressions from S. paradoxus using statistical tests We developed a pipeline that, taking as input either short read datasets or de novo assemblies, performs Patterson's D 8 , f  53, 54  and df  55  statistics. These statistics rely on the comparison of alleles across 4 populations (P) whose phylogenetic relationship can be described as (((P1,P2),P3),O) where O stands for outgroup. In our study, P1 and P2 represent two different populations of S. cerevisiae, P3 a population of S. paradoxus while S. jurei is used as an outgroup in P4. For each S. cerevisiae strain selected, we generated an artificial whole genome assembly by replacing the specific isolated alternative alleles on the scaffold of S. c. c. To do this, we mapped the selected S. cerevisiae short-reads against S.
This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, using the PubMed ® database. The search terms ''ototoxicity'', ''hearing loss'' and ''drugs'' were combined. We included studies published between September 2013 and June 2023, and focused on medicines and drugs used in hospitals. The review highlighted a number of articles reporting the main drug classes potentially involved: namely, immunosuppressants, antimalarials, vaccines, antibiotics, antineoplastic agents, diuretics, nonsteroidal anti-inflammatory drugs and analgesics. The presumed ototoxic mechanisms were described, together with the therapeutic and preventive options developed over the last ten years.
Antimalarials A recent systematic review of 22 studies including data from 21 countries found little evidence from prospective human studies that malaria treatment results in a high incidence of permanent ototoxic hearing loss  [17]. Studies included focused on quinine or derivatives, artemisinin combination therapies, and multiple drug combinations. Further, in a recent retrospective study of 32 newborns exposed to doses of antimalarial drugs (including 27 exposed to chloroquine),  Patatt et al. (2022)  identified no cases of hearing loss in newborns of mothers who had used antimalarial drugs  [18]. Newborns were screened using transient otoacoustic evoked emissions and automated collection of auditory brainstem evoked potentials. However, a study carried out in Nigeria in 156 patients showed that chloroquine remains one of the drugs most frequently implicated in ototoxicity in Africa, alongside chloramphenicol and gentamicin  [19].
Antibiotics Aminoglycosides are generally used for serious bacterial and mycobacterial infections, including gentamicin for meningitis and sepsis, tobramycin or amikacin for respiratory infections in people with cystic fibrosis  [28] , and kanamycin for tuberculosis. Adverse effects are dose-dependent  [29]. Sagwa et al. (2017)  used the 2014 World Health Organization Global Database of Individual Safety Reports (VigiBase ® ) to investigate the association between the use of streptomycin, amikacin, kanamycin and capreomycin in the treatment of tuberculosis and pharmacovigilance reports of ototoxicity  [30]. Reporting of deafness was disproportionately associated with amikacin use, followed by kanamycin use. A systematic review by Dillard (2021) reported the prevalence of post-treatment hearing loss in patients with drug-resistant tuberculosis  [31].
High doses of salicylates cause reversible bilateral SNHL due to damage to hair cells, reduced vascular supply to the cochlea and inhibition of cyclooxygenase  [9]. Ototoxic drug damage may be a more important factor in hearing loss in older adults. The prevalence of ototoxic drug use for comorbid chronic diseases and chronic pain is high, and vulnerability to ototoxic drug effects is increased due to impaired renal function  [61]. Joo et al. (2018)  used datasets from two population-based US epidemiological studies to question the impact of frequently prescribed drugs (mainly NSAIDs and acetaminophen) on hearing loss in elderly patients  [61]. More than ninety per cent of the participants were taking a drug considered to be ototoxic. Possible ototoxic mechanisms included the fact that highdose salicylates and NSAIDs can reduce cochlear blood flow and damage OHCs, while acetaminophen can cause oxidative stress, leading to degeneration and damage to hair cells (its antidote is N-acetyl-cysteine).
Other drugs Lee et al. (2021) used a pharmacovigilance algorithm based on electronic medical records in a South-Korean national cohort, with the diagnosis code of bilateral hearing loss  [11]. Patients prescribed cimetidine, hydroxyzine and sucralfate developed bilateral hearing loss more frequently than patients not prescribed these drugs. External validation was performed using the U. S. Food and Drug Administration Adverse Event Reporting System database (2012-2018). Despite the fact that in some cases bilateral SNHL may have developed for causes unrelated to ototoxicity, the authors confirm that hydroxyzine and sucralfate may have a potential ototoxic effect, although the mechanisms involved remain unknown. Using the French pharmacovigilance database reporting adverse drug reactions between 2007 and 2017,  Favrelière et al.
These drugs may alter elec-tromagnetic conduction in hair cells and afferent nerves by interacting with ␥-aminobutyric acid receptors. Na+ channel blockers, such as carbamazepine, phenytoin and oxcarbazepine, could also reduce the firing frequency of neurons in the peripheral and central auditory pathways  [63]. Foch et al. (2018)  assessed the association between in utero drug exposure and the occurrence of SNHL in 2-yearold children  [64]. A case-control study was carried out using the French database for assessing drug risk during pregnancy. Cases were defined as children with an abnormal hearing test recorded on the 24th-month certificate. After adjustment for confounding factors, SNHL was found to be associated with exposure to valproic acid and low-dose acetylsalicylic acid during pregnancy.
For example, intratympanic injections of Nacetylcysteine have been shown to be effective in preventing cisplatin-induced ototoxicity in humans  [66]. The optimal dosage and efficacy of N-acetylcysteine injections via this route in patients with head and neck cancer is currently the subject of a Phase 2 trial (clinicalTrials: NCT04291209). The second intratympanic drug to be studied in randomized trials is dexamethasone. This drug may be effective in limiting cisplatin-induced ROS production and associated inflammation. Freyer et al. (2019)  reviewed trials comparing intratympanic dexamethasone with no treatment in adult patients, with heterogeneous results  [66]. Studies of intratympanic dexamethasone are limited in the same way as those of intratympanic N-acetylcysteine by sample size, statistical approach and study design.
We previously investigated the role of vitamin A and vitamin D in the etiology of Congenital Anomalies of the Kidney and Urinary Tract (CAKUT)  [14]. We explored the overlap between vitamin 2 target genes and gene sets related to CAKUT. We observed significant enrichment of vitamin A target genes in CAKUT-related gene sets. Here, we propose ODAMNet ("Overlap, Diffusion, Active Module, Network"), a Python package that allows performing systematic analyses with i) multiple chemicals, ii) multiple rare diseases, and iii) multiple integrative bioinformatics approaches to explore the possible relationships between chemicals and rare diseases. In ODAMNet, targets of the chemicals are retrieved from the Comparative Toxicogenomics Database (CTD)  [15] , and rare disease pathways are retrieved from WikiPathways  [16].
Software architecture ODAMNet is a Python package for the investigation of chemical -rare disease relationships. It takes a list of chemicals as input and automatically retrieves the genes that are targeted by these chemicals from the Comparative Toxicogenomics Database (CTD) (Fig  1 ). Rare disease pathways are retrieved automatically from WikiPathways and networks are downloaded automatically from NDEx  [17]. The user can also provide their own target genes, pathways of interest and biological networks. Then, ODAMNet can perform three different approaches. The first approach is an overlap analysis between the target genes and the rare disease pathways. The second approach is active module identification (AMI) using DOMINO  [18] , and then performs an overlap analysis between the identified active modules and rare disease pathways.
Data retrieval by queries ODAMNet can retrieve the datasets required for an analysis, which are chemical target genes, rare disease pathways and biological networks, by querying the relevant databases. For the retrieval of genes targeted by a list of chemicals, the required input is a list of MeSH identifiers (https://meshb. nlm. nih. gov/) that correspond to those chemicals. Chemical target genes which have been reported for human are automatically retrieved from CTD (http://ctdbase. org/tools/batchQuery. go) using HTTP requests. The user can select the type of chemical -target gene associations. By default, directAssociation parameter is set to "True" and returns target genes solely for the input chemicals. Alternatively, setting this parameter to "False" will retrieve target genes not only for the input chemicals but also for any of its descendant chemicals.
Data input by the user While the automatic retrieval of chemical targets and pathways from relevant databases is the default workflow, ODAMNet also allows the user to input custom target genes and gene sets such as annotation terms or pathways. In the case of using such custom gene sets, the user is expected to provide the background gene sets (as GMT files) that will be used in the statistical tests. It is possible to use gene sets from different databases (e. g. WikiPathways, Reactome  [21] , Gene Ontology (GO)  [22, 23]  in the same analysis; in this case, a background gene set for each database should also be provided. As stated in the previous section, in random walk with restart, ODAMNet does not download and use networks automatically. However ODAMNet provides assistive functions to download networks from NDEx.
This network is the fusion of three datasets (Lit-BM, Hi-Union  [26]  and APID  [27] ). It is composed of 15,390 nodes and 131,087 edges. The molecular complexes network (UUID: 419ae651-cf05-11ed-a79c-005056ae23aa) was also downloaded from NDEx. This network is the fusion of two molecular complex databases (CORUM  [28]  and HuMap  [29] ). It is composed of 8,497 nodes and 62,073 edges. The last network downloaded from NDEx is the Reactome pathways network (UUID: b13e9620-cefd-11ed-a79c-005056ae23aa). This network was built based on data derived from Reactome proteinprotein interaction data  [21]. It is composed of 4,598 nodes and 19,292 edges. All results are available on https://github. com/MOohTus/ODAMNet/tree/v1. 1. 0. Queries were made on 7 September 2022.
g. all the rare disease pathways from WikiPathways. Conversely, when investigating a specific disease, a set of chemicals and disease associated pathways can be determined and tests can be performed to assess their relationships. Furthermore, when the relationship between a chemical and a disease is already known, ODAMNet enables a focused analysis on the shared genes. The automatic retrieval of chemical target genes, rare disease pathways and biological networks from relevant databases is the default workflow for ODAMNet. This ensures the usage of up-to-date data and enhances ease-of-use. ODAMNet also accepts direct custom input of target genes, gene sets and biological networks. This has multiple advantages. First of all, it allows reproducibility; target genes and rare disease pathways from specific versions of CTD and WikiPathways can be stored and used in ODAMNet.
Treatment of these iMGL with a very low level of recombinant hyperphosphorylated tau (pTau, WT, 1N4R (21); 50 nM; Supplemental Figure  3 ) for 6 hours significantly upregulated Gal3 protein and transcript levels (Figure  1 , effects of pTau (Supplemental Figure  4, J  and K ). As a control, treatment with TD139 alone for 6 hours caused 205 upregulated genes and 132 downregulated genes (Supplemental Figure  4L ). Nevertheless, these genes did not exhibit significant enrichment in specific biological processes as observed in the gene ontology (GO) pathway analysis using DAVID Bioinformatics Resources (https://david. ncifcrf. gov/). Principal component analysis of the overall samples is shown in Supplemental Figure  4M. To assess the expression profiles of Gal3-ER genes, we selected 8 genes based on RNA-Seq analysis of iMGLs treated with pTau for 6 hours (Figure  2C ).
To investigate the transcriptomic profile of Gal3-positive microglia by single-cell RNA-Seq (scRNA-Seq), hippocampal tissues of WT and Tau22 mice (12. 5 months old, n = 8 in each group) were harvested and subjected to mechanical dissociation. After myelin removal, the cell suspensions were stained with an anti-CD11b antibodya myeloid cell markerand isolated by FACS. The CD11b-sorted cells were subjected to scRNA-Seq by using the 10× Genomics platform (Figure  4D ). We conducted quality control (Supplemental Figure  11,  A-D ), principal component analysis, and dimensionality reduction using uniform manifold approximation and projection (UMAP) (Supplemental Figure  11, E  and F ). The isolated cells, mostly microglia, were assigned to each cluster based on the expression of established markers from the PanglaoDB database  (34).
No specific enriched processes were identified for the upregulated DEGs in Tau22/Lgals3 -/- versus Tau22/Lgals3 +/+ mice. These findings are consistent with what were observed in human iMGLs, confirming that Gal3 plays a principal role in governing the microglia-mediated immune response in tauopathy. We next categorized these DEGs by their enriched cell type based on the Tabula Muris Consortium database (39) (Supplemental Figure  21D ), and, as predicted, we found that the largest population of DEGs identified between Tau22/Lgals3 -/-and Tau22/Lgals3 +/+ mice was enriched in microglia (21. 3%; Supple-cells (Figure  8 ). When encountering pathological tau, microglia become active and release Gal3 into the extracellular space either directly or via EVs (Figure  3O ). Under the tested conditions, we found that Gal3 directly facilitated the aggregation of pTau into β-pleated-sheet structures (Figure  3L ).
Primers used for the quantification of genes of interest are listed in Supplemental Table  8. scRNA-Seq of microglia. Adult microglial isolation was performed according to the protocol detailed elsewhere  (39). For single-cell gene expression library construction, FACS-sorted cells were immediately captured in droplets that were emulsified with gel beads by using the 10× Genomics Chromium Next GEM Single Cell 3' GEM, Library & Gel Bead Kit v3. 2 reagent (10× Genomics) according to the manufacturer's protocols. These libraries were sequenced using a NovaSeq 6000 (Illumina). The details of the bioinformatics analysis of microglia are provided in the Supplemental Methods. Statistics. Data in histograms and dot plots are expressed as the mean ± SEM, while violin plots show the medians with the 25th and 75th percentiles.
Statistical comparisons were performed with GraphPad Prism 9 software using the 2-tailed unpaired Student's t test (for 2 comparison groups with normal distribution criteria) or 1-or 2-way ANO-VA with Tukey's multiple comparison test (for groups across variables, comparing all groups to each other). For all tests, a P value < 0. 05 was considered significant. All experiments were conducted in at least triplicate unless otherwise indicated. No statistical methods were applied to predetermine the sample size. Study approval. Human brain samples were obtained from the UCD Alzheimer's Disease Center. All animal studies were conducted following protocols approved by the Institutional Animal Care and Utilization Committee (IACUC, Academia Sinica, Taiwan). AG10129. We thank Academia Sinica Core Facility and Innovative Instrument Project (AS-CFII108-113) for cell sorting service.
Acknowledgments This research was supported by  Academia Sinica and Ministry of Science and Technology, Taiwan  [ AS-GC-110-MDO5 ,  AS-GC-110-05 ,  MOST 107-2320-B-001 -013 -MY3 ]. The " Alzheimer & Tauopathie" laboratory  is supported by programs d' investissements d'avenir LabEx (excellence laboratory) DIS-TALZ  ( Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease ). ML, HTH, and MHK were supported by grants from the  National Institutes of Health  ( R41AG057274  and  R01AG062435 ). LWJ was supported by  UCD Alzheimer's Disease Research Center -US NIH  grant  P30.
Data availability. All data presented in this study are either included in this article and its Supplemental Information or are available upon reasonable request to the corresponding author. Uncropped Western blots can be found in the Supplemental Material. Supplemental data and Supporting Data Values are provided with this paper. RNA data SRA files have been deposited in the NCBI's Sequence Read Archive (https:// www. ncbi. nlm. nih. gov/sra; PRJNA1017614 and PRJNA1018595).
MS/MS spectra of fragmented precursors were acquired at a resolution of 15,000 and assigned to peptide sequences by the MASCOT Server 2. 5. 1 search engine (Matrix Science) using the P. cinnabarinus annotated proteome, with fixed and variable modifications (Datasets 3; 4). Proteins were validated when at least two different peptides (p-value <0. 01) were detected, resulting in a protein identification false discovery rate below 1%. Protein abundance was estimated using the Normalized Spectral Abundance Factor (NSAF)  (33). The Met content of the proteins was calculated using the entire sequence. The Met content corrected by the NSAF was calculated using the following formula:.