dataesr/scientific-paragraphs-categorization huggingface dataset:
All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Fondation pour la Recherche Médicale (CorPopImm to MW), the French Government's Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases" (Investissement d'Avenir grant n • ANR-10-LABX-62-IBEID), and INCEPTION programs (Investissement d'Avenir grant ANR-16-CONV-0005). SFK's laboratory is funded by Strasbourg University Hospitals (SeroCoV-HUS; PRI 7782), the Agence Nationale de la Recherche (ANR-18-CE17-0028), Laboratoire d'Excellence TRANSPLANTEX (ANR-11-LABX-0070_TRANSPLANTEX) and Institut National de la Santé et de la Recherche Médicale (UMR_S 1109). The NH-COVAIR Study was funded by a grant from the Meath Foundation, Tallaght University Hospital. A. H.
Positive eFluor dead cells were excluded from the analysis. At least 30,000 events were acquired for the phenotyping. Compensations were performed using UltraComp eBeads (Thermofisher Scientific) stained positively with each of the antibodies used in the experiment. Baseline photomultiplier tube voltages of the instrument were set using unstained cells, showing the background fluorescence of the experimental samples. Data were analyzed using FlowJo v10 analysis software package.
Statistical Analysis Data were depicted in figures as the mean along with standard deviation. Level of significance for comparison between conditions was determined by nonparametric Kruskal-Wallis comparison test followed by post-hoc Fisher's Least Significant Difference (LSD) test. All statistical analyses were performed with R software using the lmtest package from the CRAN project. This method uses the Piepho (  2004 ) algorithm (as implemented in the multcompView package) to generate a compact letter display of all pairwise comparisons of estimated marginal means  (21). Statistics indicated with lower-case letters allow to compare, for each marker or cytokine, all conditions of stimulation within live (upper panel) and eventually heat-killed bacteria (lower panel). Conditions that share the same lower-case letters are not significantly different (p > 0.
ACKNOWLEDGMENTS The authors are thankful to  Jean-Marc Cavaillon  and  Orhan Rasid  for sharing initial DCs differentiation protocols, cytometry reagents and panels, and training of MM. We are thankful to  Milena Hasan ,  Sophie Novault  and  Pierre-Henri Commere  from the  Cytometry  and  Biomarkers UTechS  at  Institut Pasteur  for initial recommendation on cytometry techniques. We also thank  Marie-Ameĺie Goujart  from the  Hematology laboratory at the MediPole of New Caledonia  for access to the FACSCanto II cytometer. We thank  Delphine Bonhomme  for the schematic and discussions about cytometry. We thank  Ivo Boneca  and  Cyrille Goarant  for discussions, constant support, and critical reading of the manuscript.
All of the pseudocode for our algorithms appears at the end of this paper. Acknowledgements: The authors would like to thank the Max Planck Institute for Mathematics, which hosted the two of them for a joint visit from February 1 -April 15, 2020. They are especially grateful to have had access to the parallel computers at the MPIM. While completing this research, H. H. was partially supported by the European Research Council under Programme H2020-EU. 1. 1. , ERC Grant ID: 648329 (codename GRANT), and by his Humboldt professorship. L. T. was partially supported by the Max Planck Institute for Mathematics for her sabbatical during the 2019 -2020 academic year. This work began while she was employed by Oberlin College. She is grateful to Oberlin for supporting her during the early stages of this paper.
Implementation details We wrote our program in C++ (though mainly simply in C). We used gmp (the GNU MP multiple precision library) for a few operations, but relied mainly on 64-bit and 128-bit arithmetic. Some key procedures were parallelized by means of OpenMP pragmas. Basics on better sieving. Let us first go over two well-known optimization techniques. The first one is useful for sieving in general; the second one is specific to the use of sieves to compute µ(n). (1) When we sieve (function SegPrimes, SegMu or SegFactor), it is useful to first compute how our sieve affects a segment of length M = 2 3 • 3 2 • 5 • 7 • 11, say. (For instance, if we are sieving for primes, we compute which elements of Z/M Z lie in (Z/M Z) *. ) We can then copy that segment onto our longer segment repeatedly, and then start sieving by primes and prime powers not dividing M.
The bitwise operations required to decode the factorization of n are very fast, particularly if one is willing to go beyond the C standard; we used instructions available in gcc ( builtin clzl, builtin ctzl). Implementing the algorithm in integer arithmetic. Manipulating rationals is time consuming in practice, even if we use a specialized library. (Part of the reason is the frequent need to reduce fractions a/b by taking the gcd of a and b. ) It is thus best to implement the algorithm -in particular, procedure SumByLin and its subroutines -using only integer arithmetic. Doing so also makes it easier to verify that the integers used all fit in a certain range (|n| < 2 127 , say), and of course also helps them fit in that range, in that we can simplify fractions before we code: (a/bc)/(d/bf ) (say) becomes af /bd, represented by the pair of integers (af, bd).
, 75 with v = x 2/5 /3. We have drawn a line of slope 3/5, with constant coefficient chosen by least squares to fit the points with 68 ≤ n ≤ 75. We also ran our code for x = 2 n , 68 ≤ n ≤ 75, on a 128-core machine based on two AMD EPYC 7702 (2GHz) processors. The results were of course the same as on the first computer, but running time scaled more poorly, particularly when passing from 2 73 to 2 74. (For whatever reason, the program gave up on n = 2 75 on the second computer. ) The percentage of total time taken by the case of a large non-free variable was also much larger than on the first computer, and went up from 2 73 to 2 74. The reason for the difference in running times in the two computers presumably lies in the differences between their respective memory architectures. The dominance (in the second computer) of the case of a large non-free variable, whose usage of sieves is the most memory-intensive part of the program, supports this diagnosis.
13 16-18 The method of informing the subjects to be included in the study was based on different modalities according to the regions (this was specifically requested by the French Public Health authorities at the time of the beginning of the study). The subjects were informed of the study by individual letters based on age selection for the Aquitaine region, by individual letters based on occupational sectors for the Rhône-Alpes, by televised or written press for Lower and Upper Normandy. A total of 122 181 subjects were approached to enrol in the cohort without selection concerning the exposure and 16 885 responded and sent a completed exposure questionnaire. The ARDCo population included 14 218 recognisable subjects drawn from databases compiled by the 'Caisse Primaire d'Assurance maladie' national health insurance organisation.
Available information included characteristics of civil status, social and occupational data (complete occupational calendar) and information concerning smoking status for a wide fraction of subjects. Among these 14 218 subjects, 13 481 were men (94. 8%) and 737 were women (5. 2%). Average age at inclusion was 60 years or less for 3332 subjects (23. 4%), between 60 and 75 years for 10 490 subjects (73. 8%) and 75 years or more for 396 subjects (2. 8%). Subjects for whom a CT scan was sent to regional coordinating centres constituted the Asbestos Post EXposure Survey (APEXS) population. Among these subjects, 4804 (37. 7%) benefited from at least one chest CT scan in form of a CD-Rom (the films were not retained but only the CDs) and constitute the 'CT-Scan population' of the present study. The 15 subjects having presented with cancer of the head and neck prior to inclusion have been excluded from analysis.
Data collection Asbestos exposure As previously described elsewhere,  16  information on the occupational exposure of individuals included in the cohort was available thanks to evaluation of individual asbestos exposure by industrial hygienists using data from a standardised questionnaire, describing all job positions occupied during the subject's occupational career, hence enabling us to calculate a cumulative exposure index (CEI). Asbestos exposure was assessed according to occupation and industrial activities. The level of exposure was classified into four classes defined for each job occupied by each subject, comprising a four-level scale: low level (passive exposure), corresponding to a numerical value of '0. 01 equivalent fibres/ mL'; low-intermediate, corresponding to a numerical value of '0.
Data collection for incident cancer cases and mortality from cancer A follow-up study was conducted in subjects who had enrolled in the ARDCo and APEXS programmes. Figures for new head and neck cancers were recovered annually, from the date of enrolment to 1 July 2019, from the National Health insurance, which collects these data for medical cost coverage purposes. A follow-up study of mortality was also organised in the study population. The vital status of each subject in the cohort was collected from the National directory for identification of physical persons up to 1 July 2019. For deceased subjects, both underlying and contributing causes of death according to death certificates available up to 31 December 2015 were then obtained from the INSERM CEPI DC.
Multivariate analysis was based on TSFE and CEI as continuous variables. Linearity hypothesis of CEI has been checked using the package mfp (multivariable fractional polynomials). HRs for CEI were presented for an increase of 10 f. years/mL. For the complementary study in the CT-Scan sample, the role of pleural plaques was also analysed. Statistical analysis was carried out using R Studio. All statistical tests were two-sided and statistical significance was defined as p<0. 05.
Workplace Mortality study In men, a total of 37 cases of death from head and neck cancer (excluding 8 from laryngeal cancers) were registered in the follow-up study. Multivariate analyses, including smoking status as a confounding factor and TSFE, showed a close to significant dose-effect response between CEI and mortality from head and neck cancer (HR 1. 03 (95% CI 1. 00 to 1. 05)) for an increase of 10 f. years/mL (data not shown) and a significant relationship, even after exclusion of cases of death from laryngeal cancer (HR 1. 03 (95% CI 1. 01 to 1. 06)) for an increase of 10 f. years/mL (table  5 ). We also conducted an analysis of the quantitatively significant group of pharyngeal cancers and observed a significant doseresponse relationship between CEI of exposure to asbestos, in the incidence study (HR 1.
Cytokines quantification in the co-cultures experiments Supernatants were retrieved 48 hours after co-culture initiation. Cytokine quantification was performed using the Luminex Technology (Luminex Corp). Invitrogen ProcartaPlex (ThermoFisher) were used and samples were processed according to the manufacturer instructions. Supernatants were processed on a MAGPIX instrument (Luminex Corp) and analyzed with the MAGPIX software.
Quantification and statistical analysis Microscopy images quantification Images were quantified using the Icy software. A cut-off value was selected detecting the most specific staining. The same cut-off value for each staining was conserved across the distinct conditions. Areas of fluorescence were then extracted (Supplementary Figure  2A ). The total volume of DAPI staining was considered as an estimation for the organoid volume. The volume of caspase 3 staining and the number of infiltrated T cells for each organoid were reported to the organoid volume to obtain an epithelial cell apoptosis and a T infiltrated cells rate for each structure. For each patient, the median value of all the data of organoid volume, cell apoptosis rate and T cells infiltrating the organoid for each condition was plotted.
Live imaging quantification For live imaging acquisitions, all CD45+ cells were detected, counted (HK means cell detector plugin) and tracked for the 10 hours on all the z stacks (Spot tracking and Track manager plugin). The cell tracking was realized only for cells in contact with the organoid. The average speed and total displacement were calculated by Icy software (Motion profiler plugin of Track manager). The instant speed of each cell for each time point were also recovered (Instant speed plugin of Track manager) and analyzed with Rstudio software to calculate the arrest coefficient. This coefficient was defined as the percentage of time during which the cell was arrested (3 successive arrest time lapse) on all its trajectory.
Statistical analysis Descriptive statistics were calculated for all quantitative variables as medians with their respective interquartile ranges (IQRs). For phenotypic, microscopic and cytokine quantification data, variables data was also expressed as medians. Differences between conditions were evaluated with Mann-Whitney test for unpaired comparisons and Wilcoxon matched-pairs signed rank test when appropriate i. e when comparing experiments with patients in different conditions or timepoints. Spearman's rank correlation test was used when necessary to evaluate the correlation between variables. All statistical analyses were 2-tailed and a P value of <0. 05 was considered as statistically significant. To perform these analyses, R software version 3. 2. 2 (R Foundation for Statistical Computing, Vienna, Austria) and GraphPad Prism (GraphPad Software, San Diego, CA) were used.
Study approval The ELYP cohort was approved by the French Ethic Committee -Hopital Saint-Louis (CPP 2016-01-05 RBM) and declared to ClinicalTrials. gov (NCT02693340). The REMIND study was approved by Agence Francaise de SećuritéSanitaire et des Produits de Santé(AFSSAPS) (IDRCB: 2009-A00205-52) and the French Ethic Committee -Hopital Saint-Louis (CPP 2009/17) and declared to ClinicalTrials. gov (NCT03458195). The IMCO cohort was approved by the French Ethic Committee -Hopital Saint-Louis (CPP 2016/45) and declared to ClinicalTrials. gov (NCT03015038). All patients provided an informed written consent form. relationships that could be constructed as a potential conflict of interest.
Acknowledgments We thank all members of the  U1160 laboratory  for their valuable help. We thank all the investigators and staff from the  REMIND study group  for the collection and data management of patients included in the study. We are grateful to the  Technological Core Facility (Plateforme Technologique de l'IRSL) of the Institut de Recherche Saint-Louis and Institut Imagine  for their continuous support in microscopy techniques. We also thank the  Cytomorpholab at the Hopital Saint-Louis, Unitéde Theŕapie Cellulaire  and especially  Manuel Thery  and  Benoıt Viannay  for their precious help including access to their microscopy platform and expertise. We finally thank  Dr Dominique Cazals-Hatem ,  Nathalie Colnot  from the Beaujon hospital pathology department that processed the samples paraffin blocks and performed the histological analysis of margins.
Standard Protocol Approvals and Subject Characterization This study was carried out in accordance with the protocol validated by the Ethics Committee EST IV from Strasbourg in France. Trial registration at clinicaltrials. gov is NCT04546698. All written informed consent forms followed the ethical guidelines that the 1964 Declaration of Helsinki had set. MS patients were diagnosed according to the 2018 revised McDonald criteria  [13]  and were recruited from the neurology unit of the "Centre Hospitalier Régional d'Orléans". Healthy subjects were recruited from the "Établissement Français du Sang" (Orléans, France). Starting with blood samples, the study design is summarized in Figure  1. Samples from MS patients with acute relapse were obtained during an inflammatory phase before any treatment to limit relapse.
Statistical Analysis All graphic data are presented as mean ± SEM. Statistical analyses were performed with R software using, according to data distribution, either one factor within subject ANOVA with Tukey contrasts for post hoc comparisons or the Kruskal-Wallis test and pairwise Wilcoxon test with Holm-Sidak p-value adjustment. Statistical differences were considered significant when p < 0. 05, with asterisks denoting the degree of significance (* p < 0. 05; ** p < 0. 01; *** p < 0. 001). The graphical representations were performed with GraphPad Prism 9. 0 software.
Acknowledgments: We are grateful to all the members of the neurology department of the  Regional Hospital Center of Orleans  and the PRIMMO platform for their help in including patients, and also to the patients for donating their samples. Thanks to  Chloé Robin  from the  Center for Molecular Biophysics  ( CNRS UPR4301 ,  Orleans ) for the access to the culture room. Thanks also to MotivHealth for recruiting  Colleen Guillard , and  SFR FED 4226  for its financial support for the publication.
, 2017). This study was instrumental in showing that adult endothelial cells could undergo EHT if they were exposed to the right conditions. In the past five years, the tremendous progress in single-cell RNA sequencing (sc-RNA-seq) has allowed the generation of several cell atlases such as the Tabula Muris  (Tabula Muris Consortium, 2018). We used this atlas in comparison to embryonic AGM datasets to make the first direct comparison between the transcriptome of adult and embryonic endothelial cells. We ranked the potential of adult endothelial cells to undergo the EHT based on their gene expression patterns and gene regulatory networks. Our work provides an important resource for the rational design of a reprogramming strategy for the generation of HSCs ex vivo. Additionally, it provides an example of how existing datasets can be explored to generate novel knowledge and contribute to the advancement of regenerative medicine.
sc-RNA-seq analysis showed a partial similarity between embryonic and adult endothelial cells To find how similar endothelial cells from the embryo and adult tissues were to each other, we compared the single cells from the AGM region between E9. 5 and E11 coming from three different datasets called Embryo_dataset_1  (Hou et al. , 2020) , Embryo_dataset_2  (Vink et al. , 2020)  and Embryo_dataset_3  (Zhu et al. , 2020)  with the ones from Tabula Muris (Tabula Muris Consortium, 2018) (Supplementary Figure  1 ). This atlas is composed of twenty tissues but only twelve were found to contain endothelial cells after clustering analysis (Figure  1a ). We confirmed that in each of the datasets there was a valid population of endothelial cells, well-separated from the other clusters. In the three AGM datasets, a large population of cells, expressing endothelial marker genes was identified.
Of note, cells coexpressing endothelial and hematopoietic genes was found only in two out of three datasets  (Hou et al. , 2020 & Zhu et al. , 2020). We reanalyzed the data from Embryo_dataset1 (Supplementary Figures  2  & 3 ) while we used the clusters identified by Zhu et al. for the Embryo_dataset3 (see methods section). Only one endothelial cluster was found in the Embryo_dataset2  (Vink et al. , 2020). For the further integration with adult tissues, we picked the clusters related to the EHT. The datasets were combined together using the data integration function of Seurat  (Stuart et al. , 2019). We then performed clustering analysis and identified fourteen distinct clusters from a total of 9541 cells (Figure  1b ). We examined the relative composition of each cluster in relation to their origin (adult and embryonic).
, 2018). We therefore examined specifically how the genes coding these TFs were expressed in adult tissues in comparison to the embryonic ones. Gene expression from endothelial populations was taken for expression and coexpression analysis in each tissue. As expected, the frequency of the TF gene expression was close to one hundred percent in embryonic endothelial cells (Figure  3a ). That was especially striking for Embryo_dataset_1 and Embyo_dataset_2. Interestingly, the Embryo_dataset_3 showed lower frequency level compared to the other two datasets. This is likely connected to the 10X Genomics technology which cannot detect very effectively low expressed genes such as transcription factors  (Wang et al. , 2021). When we examined the endothelial cells from the adult tissues, we found that Erg, Fli1, Lmo2, Cbfb, Gata2, Ldb1 and Cbfa2t3 had moderate frequency of gene expression (above 25%) while Tal1, Lyl1 and Runx1 had low frequency (below 25%) (Figure  3a ).
We next computed co-expression values of the TF gene expression in endothelial cells in embryonic and adult datasets. Consistent with the high frequency of TF gene expression (Figure  3a ), embryonic endothelial cells from Embryo_dataset_1 and Embyo_dataset_2 revealed a high level of co-expression, more than 50% of the cells were expressing at least nine out of ten transcription factors at the same time (Figure  3b ). The Embryo_dataset_3 showed much lower level of TF co-expression in line with the lower frequency of TF expression observed previously (Figure  3a ). Among the adult tissues, the highest levels of co-expression were observed in aorta, brain, lung, pancreas and trachea with about 50% of endothelial cells co-expressing five to seven TFs. We questioned how the partial lack of gene expression of these transcription factors could affect their regulatory programs in endothelial cells.
Identification of putative target genes of the seed transcription factors in endothelial populations To perform gene regulatory network analysis, we used the scTarNet R package that was developed for our previous study specifically focused on the mouse embryo  (Bergiers et al. , 2018). This method is based on choosing transcription factors as seeds for GRN analysis. We chose specifically the transcription factors that were examined above. Hereafter, they will be mentioned as seed TFs. A network was generated for each tissue where relationship between seed TFs and target genes was determined (Figure  4a ). Of note, we only used the Embryo_dataset_1 along with the 12 adult tissues for this analysis. The Embryo_dataset_2 was not used because it did not capture the endothelial cells expressing blood genes.
The Embryo_dataset_3 was excluded because of the lack of sensitivity of the 10X genomics technology  (Wang at al. , 2021). To know in which cell clusters the seed TF -target gene interaction was happening, we specifically identified the cells in which the target genes were expressed. The results were highlighted in an UMAP plot. In Figure  4b , we show the results of this analysis for the brain. We did the same for each tissue and summarized the output in Figure  5. We found that seed TFs were positively associated with target genes, which were expressed in endothelial populations of almost every dataset, except diaphragm, fat and trachea. Aside from the Embryo_dataset_1, the highest grade of association between seed TFs and target genes were observed in brain, heart, kidney, liver and lung tissues.
Discussion In this study, we investigated the process of endothelial-to-hematopoietic transition in the context of the adult mouse. We relied on the analysis of a number of high-quality singlecell transcriptomics atlases to address this question. We first combined the datasets from the AGM, source of HSCs in the embryo, with the twelve tissues of the Tabula Muris where endothelial cells could be detected. Although we found that embryonic cells could cluster with a number of adult endothelial cells from different origins, endothelial cells with haemogenic characteristics could not be found in adult tissues. That reinforced the notion that the EHT is a transition occurring primarily at the embryonic stage. We went on to determine which adult endothelial cells in the body were the closest to haemogenic endothelium.