dataesr/scientific-paragraphs-categorization huggingface dataset:
JMP was supported by  ProyExcel_00049  financed by  Proyectos I+D+i of Junta de Andalucía 2021. LGL was supported by  Junta de Extremadura  ( IB20089 ,  Post-Doc  grant). JM was supported by a  Juan de la Cierva -Formación  contract ( FJCI-2017-34109 ) from the  Ministry of Science, Innovation and Universities , and he is currently supported by a postdoctoral researcher contract for scientific excellence under the  Plan Propio de I+D+i  of the  Universidad de Castilla -La Mancha (UCLM) , co-funded by the  European Social Fund Plus (ESF+). LZG was supported by funds from the  Hungary's National Research, Development and Innovation Office  ( K135841 ,  RRF-2. 3. 1-21-2022-00006 ). We also acknowledge the comments of four anonymous reviewers who improved the final version of this manuscript.
To further test if the Orion dosage determines the ability of epidermal cells to sense PS-exposing dendrites, we increased the Orion dosage by adding an orion duplication to the wildtype. The orion duplication strongly enhanced the debris level and dendrite loss in a CDC50 KO background (Figures 6D, 6I, and 6J), which by itself only causes weak PS exposure (I-L) Quantification of debris coverage ratio (I) and dendrite length (L). n = number of neurons: for TMEM16F OE + CDC50 KO, control and orion 1 /Y (same dataset as in Figure  1G ), orion 1 /+ (n = 18, 9 animals); for (I), Kruskal-Wallis (One-way ANOVA on ranks) and Dunn's test, p-values adjusted with the Benjamini-Hochberg method; for (L), one-way ANOVA and Tukey's test. For Nmnat KO, control and orion 1 /Y (same dataset as in Figure  1K ), orion 1 /+ (n = 15, 9 animals); Kruskal-Wallis (One-way ANOVA on ranks) and Dunn's test, p-values adjusted with the Benjamini-Hochberg method.
ACKNOWLEDGMENTS We thank  Larry Zipursky ,  Loren Looger ,  Xinhua Lin ,  BACPAC Resources Center, Drosophila Genomics Resource Center (DGRC) , the  National Cancer Institute (NCI) , and Addgene for plasmids;  Marc Freeman  and  Bloomington Stock Center  for fly stocks;  Cornell BRC Imaging facility  for access to microscopes (funded by  NIH  grant  S10OD018516 );  Cornell CSCU  for advice on statistics;  Mike Goldberg  for critical reading and suggestions on the manuscript. This work was supported by  NIH  grants ( R01NS099125  and  R21OD023824 ) awarded to C. H. , and by  ARC  grant ( PJA 20151203422 ) and  FRM grant  ( DEQ20160334870 ) awarded to J. -M. D.
Image processing and video analysis Videos acquired with the 10X objective were processed using ZEN 3. 3 software. They were first stitched together, and certain plans and time points were further chosen to follow the inflammatory response and host-parasite interactions over time. Videos acquired with the 20X objective were analyzed using the "Spots" tool on Imaris software. A track was generated for each cell and trajectories of joining cells were removed manually, as previously described  (Abdul Hamid et al. , 2020). Three parameters of EGFP + cells displacement properties were extracted for each time point: the average speed, the straightness and the displacement length.
) to visualize epidermis and a polyclonal antiserum obtained from an E. cuniculi infected rabbit (2 µg/mL) coupled with an anti-IgG-AlexaFluor 647 labeled secondary antibody (2 µg/mL). The anti-E. cuniculi polyclonal antibodies identify all parasitic development stages (data not shown). All samples were counterstained using 100 µg/L DAPI and mounted with ProLong TM Diamond Antifade Mountant (Thermo Fisher Scientific). Images were acquired on ZEISS LSM 800 confocal microscope (Carl Zeiss Microscopy, Germany) and observed with 20X and 40X objectives (CLIC platform, Faculty of Pharmacy, UCA). Three different lasers were used to observe the CW fluorescent signal of E. cuniculi spores, along with the DAPI signal of both cell and parasite nuclei (ex: 405; em: 410-546), the EGFP signal of phagocytes (ex: 488; em: 410-546), the AlexaFluor546 labeled anti-Keratin antibody specific signal of epidermis layer (ex: 561; em: 555-620) and the signal of the AlexaFluor647 coupled with the anti-E.
Motility properties of recruited innate immune cells are modified in presence of Encephalitozoon cuniculi spores into the mouse ear pinna Using Imaris software, the motility properties (average speed, straightness, displacement length) of recruited cells were analyzed from the previously acquired time-lapse videos. A previously set up analysis protocol was used, and cell trajectories were analyzed after 2 h (early time-point) and 72 h (later time-point) (Abdul Hamid et al. , 2020; Figure  5 ). The motility of all cells at the injection areas were analyzed in response to E. cuniculi spores or to PBS (control mice). In infected mice, both cells that interacted with parasites or cells that did not were included. The presence of parasites modified the 3 motility parameters of recruited cells, as compared to the control condition (Figure  5 ).
These multiple treatments altered the morphology and the infectivity of the spores, as a low number of infectious foci were counted in vitro on MDCK cells compared to HFF cells infected by the inoculum (data not shown). In vivo, the first imaging experiments performed showed heterogeneity in the intensity of the inflammatory responses (EGFP + fluorescent signal) measured in the ear tissue of LysM-EGFP infected mice 24 h pi, probably in relation to the presence of cellular debris still present in the inoculum (data not shown). In order to overcome these drawbacks, the cell lineage used to produce the spores in vitro was changed and HFF cells were used. Numerous subcultures were necessary as HFF cells produce a lower number of spores compared to MDCK cells, but they didn't detach from the cell layers once infected.
These data correlated with the macroscopic observations of inflammatory responses on infected ear tissues, including vasodilation, edema and punctiform redness observed until 7 days pi. The CW + parasite signal was quantified in parallel to the EGFP + phagocytes signal. It decreased drastically after 24 h, which correlates with the massive recruitment of phagocytes at this time-point and the combined phagocytosis and lysis of spores. The parasite signal further slowly decreased but was still present at day 7 pi. These first observations, associated with the ability of the parasites to cycle observed at day 7 pi, are in favor of parasite maintenance in the skin. The mice ear pinna model then enabled us to follow in situ and over time the dynamics of the inflammatory responses to E. cuniculi spores at the cellular level.
, 2018; Brdícková et al. , 2020; Sak et al. , 2021). These in vivo observations therefore reinforce the role of phagocytes, namely Mo, as Trojan horses for the dissemination of Microsporidia to other organs, in our newly set up model  (Dalboni et al. , 2021). The mouse ear pinna model further enabled us to obtain reproducible quantitative data on motility parameters (speed, displacement, straightness) of recruited innate immune cells at the injection site. Using the Imaris software, stringent algorithm settings were applied, as previously described  (Abdul Hamid et al. , 2020 , 2021). While analyzing high numbers of cells (Table  1 ), heterogeneity in results was observed and was the reflection of the complexity of innate immune responses that are set up at early time-points, depending on molecular signals (cytokines, chemokines), cell-parasite interactions or not at the injection site, contact time between cell and parasites, distance between cells and parasites at the injection site and phenotypes of recruited inflammatory cells.
Acknowledgments We thank  Ivo Boneca  ( Bacteria-Cell Interactions Unit, Pasteur Institute, Paris, France ) for the LysM-EGFP transgenic mice line,  Caroline Vachias  [ Clermont Imagerie Confocal: CLIC (GReD, Clermont Auvergne University )] for her help with setting up confocal microscopy acquisition parameters and subsequent image analysis and processing,  Lorraine Novais-Gameiro  and  Christelle Blavignac  ( Centre d'Imagerie Cellulaire Santé: CICS, Clermont Auvergne University ) for the realization of tissue cryosections and SEM acquisitions,  Louisa Bordji  for the realization of immunolabellings,  Ivan Wawrzyniak  and  Juliette Gilbert  for their help on cellular culture, and  Abdelkrim Alloui  for ensuring mice care and housing. We also thank  Elisabeth Billard ,  Céline Nourrisson ,  Philippe Poirier  ( Microbe, Intestine, Inflammation and Host Susceptibility: M2ISH, Clermont Auvergne University ) and Florian  Reginald Akossi  ( Hosts-Parasites Relationships, LMGE, Clermont Auvergne University ) for helpful discussions.
Although statistically significant, such results were generated via the joint analysis of two SNPs from which only one, rs10191329, associated significantly with MS severity in both the exploratory and the replications cohorts. To be fully accurate, such an analysis should have been performed on this SNP alone. Finally, based on a survey of previous GAWS datasets, the authors report that "among other traits, rs10191329A has been inversely associated with intelligence (Supplementary Table  15 )". However, as reported by Harroud et al. 1  in their Supplementary Table  15 , rs10191329 was also found to associate with four immune traits, namely "lymphocyte counts", "white blood cell count", "monocyte count" and "eosinophils count". Indeed, the trait "intelligence" is the only CNS-related trait which associates with rs10191329, what is more, in only one study 2 reaching both a relatively poor p-value (1.
In this regard, the Open Targets Genetics (OTG) platform offers the unique opportunity to survey simultaneously as much as 60 eQTL datasets generated by distinct consortia and obtained across 92 tissues and cell types  5. The OTG platform is more than a catalog of previously published data as it performs retrospectively an integrated statistical fine-mapping across thousands of trait-associated loci including expression quantitative traits (eQTLs) and protein quantitative trait loci (pQTLs)  5. We thus surveyed the OTG database and extracted results linking the SNP rs10191329 with expression and/or protein quantitative traits. We observed that rs10191329 do not associate with any expression or protein quantitative trait previously described in the CNS, across 18 distinct CNS regions. In contrast, rs10191329 was reported to associate with eQTLs in: i) blood cells for N-Acetylglucosamine Kinase (NAGK), Zinc Finger Protein 638 (ZNF638), Methylmalonyl-CoA Epimerase (MCEE), C-Type Lectin Domain Family 4 Member F (CLEC4F) and M-Phase Phosphoprotein 10 (MPHOSPH10), ii) interferon-gamma (IFN-)-stimulated monocytes for NAGK)and iii) testis and induced pluripotent stems cells Dysferlin (DYSF)(Table  1 ).
Among the different target genes regulated by rs10191329, NAGK was by far the gene for which the largest size effect and most significant p-value were observed. Of note, such a regulatory effect was reported in both blood cells and monocytes stimulated with interferongamma (IFN-). Moreover, according to OTG, rs10191329 associated with only one protein quantitative trait: the plasma levels of NAGK (beta value = -0. 165, p-value = 2. 5E-7). There is thus compelling evidence indicating that the regulatory SNP rs10191329 targets NAGK in immune cells rather than ZN638 or DYSF in neural cells. In line with these observations, a survey of the single-cell RNA-seq database provided by the Human Protein Atlas shows that, among the 6 candidate genes selected above, NAGK exhibits the highest expression levels in immune cells, notably dendritic cells, monocytes and macrophages)(Supplementary Table  1 ).
Since NAGK mRNA levels are decreased in subjects bearing the rs10191329A allele, one may thus propose that rs10191329A is linked to MS severity via an alteration of the MDP/NOD2 tolerogenic pathway. Experimental investigations are needed to specifically test this hypothesis. To explore the relevance of additional pathways which may putatively link MS severity to NAGK, we performed an enrichment analysis on the currently known list of NAGK protein partners recorded in the protein-protein interaction database BioGrid 9 (Supplementary Table  2 ). Based on a survey of 5 libraries of pathways, namely Reactome 10 , BioPlanet 11 , KEGG  12  , Panther  13  and WikiPathways  14  , the most statistically significant enrichment was found for the pathway termed "glycogenesis and neoglucogenesis" (adjusted p-value: 0.
Data availability statement: data extracted from the OGT database can be accessed on the OTG website (https://genetics. opentargets. org/), data extracted from the Human Protein Atlas (HPA) website (https://www. proteinatlas. org/), data extracted from the BioGrid databes can accessed on the BioGrid website (https://thebiogrid. org/), enrichment analyses can be performed via the Enrichr website (https://maayanlab. cloud/Enrichr/).
Data collection and data management Participants were recruited between December 2020 and February 2021, with examinations and data collection taking place between March and August 2021. Prior to gynaecological examinations and sample collection, six study midwives were trained to perform colposcopy for diagnosing FGS. At recruitment, each participant was assigned a unique identifier to ensure anonymous identification and attribution to each image captured. To each sample, a unique corresponding sample identifier was assigned. Conformity and quality control were performed at laboratory level prior to storage. Background characteristics of the participant, such as socio-demographic information, clinical history, and personal habits were captured in case report form (CRF). All CRFs were checked for missing entries manually by study midwives and researchers following standard operating procedures.
Acknowledgements We would like to thank the study participants, without whom this work would not be possible, and the technical staff, including drivers, laboratory technicians, study midwives, data clerks and community workers. We thank the  Coalition for Operational Research on NTDs , which mainly funded the project, so as all the other donors ( DZIF ,  EKFS ,  Bayer Foundation ) who partially contributed to the success of this study. A special thanks to all the country authorities who allowed and supported the implementation of this study.
Microarray analysis, data processing and bioinformatic analysis Transcript levels data was generated using Clariom ™ S HT, human (Thermo Fisher Scientific, Massachusetts, USA). The expression pattern of the transcripts was analyzed using the robust multi-array average (RMA) algorithm and to construct a principal component analysis (PCA), the volcano plot and the heat-map. Different transcript levels with a significant p-value < 0. 05 and fold changes of 2 or higher and of -2 or lower were retained. The gene ontology (GO) enrichment analysis, the pathways, and networks of differentially expressed transcripts between pre-and post-BT samples were analyzed using Ingenuity Pathway Analysis (IPA) software (IPA; Qiagen Inc. , http:// www. ingen uity. com). All our data are accessible at the gene expression Omnibus (GEO) repository (https:// www.
Statistical analysis Statistical analysis for qRT-PCR, western blots and immunohistochemistry were conducted using GraphPad Prism Version 9. 0 statistical software (GraphPad Software). Data are expressed as mean ± standard error. Comparisons between groups when data were normally or not normally distributed were made with the Student's t test or the Mann-Whitney U test. For correlation analysis Pearson or Spearman tests were used. Significance was accepted at a value of P < 0. 05.
DEGs were then functionally categorized using the IPA software. A large portion of DEGs was involved in anti-viral response, anti-microbial response and pathogen induced cytokine storm signaling pathway (Fig. 1C ). Further analysis revealed that the interleukin (IL)-6,8,12,13,17, toll-like receptor (TLR), interferon, p38 MAP kinase, wound healing, inducible nitric oxide synthase (iNOS), mammalian target of rapamycin (mTOR), production of nitric oxide and reactive oxygen species in macrophages, signal transducer and activator of transcription 3 (STAT3) and inflammasome signaling canonical pathways were modulated post-BT (Additional file 5: Fig. S2B  and Additional file 3: Table  S4 ). IPA software was used to build an interaction network. Strikingly, the interaction network showed that BECs pre-and post-BT signatures were highly enriched in genes associated to S100A alarmin family.
S100A family gene and protein validation To validate these microarray data, qRT-PCR was performed on an independent collection of BECs samples obtained from severe asthma patients pre-and post-BT treatment. We first documented baseline expression of S100A7, S100A8 and S100A9 genes in severe asthma and compared to BECs obtained from healthy controls. Figure  2A  shows that S100A7, S100A8 and S100A9 genes were highly expressed in BECs of severe asthmatics in comparison to those of healthy controls. This expression is on average fivefold for S100A7 (p = 0. 01), 4. 8-fold for S100A8 (p = 0. 008) and threefold for S100A9 (p = 0. 01) higher in BECs from severe asthmatics in comparison to healthy control. Post-BT data showed a significant decrease compared to pre-BT values in S100A7 (fourfold decrease, p = 0.
08 (See figure on next page. ) Fig. 1  Transcriptomic analysis of BECs of severe asthmatics pre-and post-BT measured using microarray. A Volcano plot showing the distribution of transcript expression P values and fold changes. All transcripts were tested using the transcriptome analysis console (TAC) software. B Heat map. The molecular signatures pre-BT and post-BT were visualized by hierarchical clustering based on the differential transcripts' expression. C Interaction network constructed using the ingenuity pathway analysis software. Nodes shaded in pink or red represent genes that are upregulated, and blue nodes are downregulated post-BT. A solid line represents a direct interaction and a dotted line an indirect interaction. D Several S100A family genes were interconnected in the same network in the post-BT signature.
Human liver samples Liver samples were obtained from patients from both genders with advanced fibrosis (F2-F4 Metavir score, n = 11) undergoing liver resection or liver transplantation at the digestive surgical department of Beaujon Hospital (Clichy, France). Patients' clinical data are presented in Table  1. Patients' age is between 33 and 73. All patients gave written consent to participate in the study. The study conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Paris North Hospitals, Paris Cité University, AP-HP (N°CER-2021-88). Fresh liver specimens were examined by a pathologist and samples were collected at distance from the tumor (when present) and surgical margins.
5 mg/kg b. w, 26. 5, #361110, BioLegend), or Ac-6-FP, or their respective anti-IgG2a kappa isotype control (12. 5 mg/kg b. w, #400281, BioLegend) or vehicle (1% DMSO in ×1 PBS) and daily until the sacrifice by cervical dislocation at day 1, day 2 or day 4 following the last CCl 4 injection (Fig. 2d ). A second model of NASH-induced liver fibrosis consisted in 8 weeks choline-deficient L-amino acid defined High Fat Diet (CDAA-HFD) feeding (#A06071302i, Brogaarden, 60 kcal% fat, and 0. 1% methionine, irradiated). Fibrosis regression was studied at 1 and 8 days after switching to normal diet, with daily injection of Ac-6-FP or its vehicle (Fig. 2c ). Timelines of injections shown in Fig. 2a, c , d were created with BioRender® software.
Macrophage depletion by clodronate liposomes To deplete macrophages, C57BL/6 J mice with established fibrosis were given one i. v injection of liposome-encapsulated clodronate (Clodronate Liposomes, 0. 1 ml/10 g, B#C23J0518, Liposoma). Liposomeencapsulated PBS was used as control (B#P24J0518, Liposoma). Ac-6-FP was then administered daily until the sacrifice at day 1, day 2, or day 4 following the last CCl 4 injection. Timeline of injections shown in Fig. 3g  were created with BioRender® software.
Flow cytometric analysis Cell suspensions prepared from non-parenchymal liver were stained at 4 °C in FACS buffer (1× PBS containing 2%FBS and 2 mM EDTA). Surface staining was performed with the following antibodies ( Liver MAIT cells were identified in B6-MAIT CAST by multicolor flow cytometry from CD45 + CD19 -CD11b -TCRβ + as GFP+ CD4 -CD8 -T cells. Similar findings were obtained when MAIT cells were gated with APCconjugated mouse MR1 tetramers loaded with the active ligand 5-OP-RU (NIH, USA); APC-conjugated mouse MR1 tetramers loaded with the non-activating ligand Ac-6-FP were used as negative control "control tet" (Fig. S3 ). Data acquisition was performed using a BD Biosciences Fortessa X20 cytometer and analyzed using FlowJo software (Tree Star, 10. 7. 1 version).
RNA isolation and RNA-Seq analysis Following Ly6C hi and Ly6C lo macrophage sorting, cells were directly frozen at -80 °C and RNA extracted extemporaneously using RNeasy Micro kit (74004, QIAgen). RNAs were qualified and quantified with AGILENT Tapestation 2200 on High Sensitivity RNA chip. RNA library preparation was realized following manufacturer's recommendations (NEBNext Single Cell/Low Input RNA Library Prep from New england Biolabs). cDNA and final libraries were controlled with AGILENT Tapestation 2200 on High Sensitivity DNA chip. Final equimolar pooled library preparations were sequenced on Nextseq 500 ILLU-MINA with HighOutPut cartridge (2x400Millions of 75 bases reads). Corresponding to 2x20Millions of reads per sample after demultiplexing. RNA-Seq data analysis was performed by GenoSplice technology (www.
genosplice. com). Sequencing, data quality, reads repartition (e. g. , for potential ribosomal contamination), and inner distance estimation was performed using FastQC, Picard-Tools, Samtools, and RSeQC. Reads were mapped using STARv2. 4. 0 38 on the mm10 Mouse genome assembly. Gene expression regulation study was performed as already described  [39] [40] [41] [42]. Briefly, for each gene present in the Mouse FAST DB v2018_1 annotations, reads aligning on constitutive regions (that are not prone to alternative splicing) were counted. Based on these read counts, normalization was performed using DESeq2  43. Genes were considered as expressed if their FPKM value was greater than 95% of the background FPKM value based on intergenic regions defined from the Mouse FAST DB v2018_1 annotations. Only genes expressed in at least one of the two compared experimental conditions were further analyzed.
DESeq2 normalized counts were log2 transformed and each Ac-6-FP treated sample was normalized according to the mean between the corresponding VEH log2 normalized counts (i. e. , Ly6C hi Ac-6-FP and Ly6C lo Ac-6-FP samples were normalized to respective mean vehicle samples). A paired t test was performed between Ly6C hi Ac-6-FP/mean vehicle ratios and Ly6C lo Ac-6-FP/mean vehicle ratios. Results were considered statistically significant for uncorrected p values ≤0. 05 and fold-changes ≥1. 5. Enrichment analyses were performed using WebGestaltR  44  on KEGG pathways. The sequence datasets have been deposited in the Gene Expression Omnibus with the accession number GSE183906 (Supplementary data 1) [https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE183906].