dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants 190 volunteers French workers using WittyFit software participated in the study, and 129 completed the test-retest. Results VAS demand and VAS control correlated with the two Karasek domains from the JCQ, respectively, at 0. 59 and 0. 57 (p<0. 001). Test-retest reliability highlighted concordance coefficients higher than 0. 70. Sensitivity was higher than 70% for each VAS. External validity was acceptable. For both demand and control, VAS cut-offs were 75/100. Compared with other workers, senior executives and individuals with master's degrees had higher levels of job control but did not differ in job demand using the VAS and JCQ. Conclusions VAS demand and VAS control are valid, quick, easy to use, and reliable tools for the assessment of job demand and job control. They can be used in daily clinical practice for primary prevention and diagnosis.
Participants We implemented a cross-sectional observational study. Self-reported questionnaires were proposed to voluntary French workers using WittyFit software (https://wittyfit. com/). Users of WittyFit were disseminated through several national French companies, mostly from the service sector (tertiary sector). WittyFit is software that aims to improve well-being at work with an epidemiological design, and a collaborative partnership with researchers (public-private partnership between WittyFit and the University Hospital of Clermont-Ferrand). 30 Workers using WittyFit were proposed to answer validated selfquestionnaires on behavioural data for personalised evaluation. Workers can answer WittyFit questionnaires using a dedicated application, available for computers or smartphones, at any time.
The concept of WittyFit is to provide health profiling with an individualised feedback based on evidence-based medicine. It aims to support behavioural change over time, and to assess the relationships between changes in knowledge, practices, and health outcomes. Exclusion criteria of participants were the inability to read, understand or answer on-line questionnaires. Answering Wittyfit questionnaires implied consent. The study was conducted on workers using WittyFit software between 1 June 2016 and 30 June 2016. All data were anonymous. The name of the employee was never revealed. The database was implemented from a human resource generated number, automatically converted in another number into the WittyFit database. 30 Data provided by employers (eg, occupation, department, sick leave) were automatically associated with the human resource-generated number.
Secondary outcomes We retrieved sociodemographic data such as age, gender, education level and marital status. We also collected characteristics of work: occupation, number of hours at work per week, and seniority within the company. Sleep quantity was assessed by the number of hours of sleep per night. 31 Sleep quality, well-being, stress at work and stress at home were evaluated using VAS ranging from very low (0) to very high (100). 24 28 We evaluated depression (D) and anxiety (A) with the use of the Hospital and Anxiety Depression Scale (HAD). 32 The HAD is a 14-item selfreported measure with two subscales of seven items, one assessing anxiety (HAD-A) and the other assessing depression (HAD-D), rated from zero to three. As suggested by the authors, the HAD-A score is calculated by adding the scores for questions 1, 3, 5, 7, 9, 11 and 13; and the HAD-D score with questions 2, 4, 6, 8, 10, 12 and 14.
Statistics Sample size was determined according to COSMIN (COnsensus-based Standards for the selection of health status Measurement INstruments) recommendations  32 33  : (1) 'Rules-of-thumb vary from four to ten subjects per variable, with a minimum number of 100 subjects to ensure stability of the variance-covariance matrix' and (2) 'Often 0. 70 is recommended as a minimum standard for reliability. We gave a positive rating for reliability when the ICC (Intraclass correlation coefficients) or weighted Kappa was at least 0. 70 in a sample size of at least 50 patients. ' All analyses were performed using Stata software (V. 13, StataCorp) for a two-sided type I error of α=5%. Participant's characteristics were expressed as means±SD or median (IQR) for continuous data (assumption of normality assessed using the Shapiro-Wilk test) and as numbers (percentages) for categorical parameters.
However, comprehensive data regarding the potential role of LUS in predicting the risk of IDH are lacking. In two different randomized clinical trials, an LUS-guided strategy for dry weight assessment reported conflicting results: the first showed a marginal (almost significant) decrease in the percentage of patients experiencing one or more IDH episodes (34. 3% vs. 55. 6%, p = 0. 072), while the other showed a 26% higher relative risk of intradialytic cramps (HR 1. 26)  [23, 26]. This prospective cohort study aimed to evaluate the potential role of LUS, in particular that of B-line quantification, in predicting IDH episodes and detecting patients at a greater risk of developing IDH because of their fluid status or an overestimation of their dry weight. We also evaluated the relationship between IDH episodes and different parameters to help clarify the most important predictive factors of IDH.
Exclusion criteria were as follows: (1) A pre-dialysis systolic BP < 90 mmHg. (2) A history of pneumonia in the previous weeks, co-existent lung fibrosis or interstitial lung disease, which are diseases that appear as multiple B-lines during an LUS regardless of the fluid status. In particular, patients affected by COVID-19 were excluded for 6 months after infection due to the potential persistence of B-lines  [27]. (3) Insufficient clinical information and no, or lacking, laboratory findings. Demographic data and medical history were extracted from the participants' clinical records. Laboratory parameters and any other dialysis data, such as the UF rate and dialysis prescription, were collected. Data were immediately recorded on individual case report forms. The pre-dialysis systolic BP was measured in a seated position after 5 min of rest, and either every 60 min or in the case of the onset of symptoms.
Data on left ventricular hypertrophy (LVH) were derived from our institution's periodic echocardiography, which is performed every year. Dry weight was assessed according to clinical criteria (blood pressure, presence of peripheral edema or pulmonary crackles, previous IDH episodes and cramps) which represent the gold standard ("trial and error") approach. The nephrologist in charge of the dialysis service performed a physical examination for the fluid status assessment. The prescribed UF volume was determined based on the patient's medical history, physical examination and the difference between the pre-dialysis weight and dry weight. The nephrologists who performed LUS were blinded to the health status of the subjects, their interdialytic weight gain, laboratory values and physical examination.
Univariate receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve (AUC) with a 95% confidence interval (CI). The maximum value of Youden's index was applied to ROC curves as a criterion to select the optimum cut-off point both for sensitivity and specificity. A p-value < 0. 05 was considered statistically significant. Statistical analysis was performed using the SPSS 22. 0 software package (IBM, Armonk, NY, USA).
PATIENT AND METHODS Patient. Clinical features were collected through a standardized form. Written informed consent was obtained from the patient's parents for genetic tests, data sharing, and publication of the patient's photographs according to French law and the principles of the Declaration of Helsinki. Molecular studies. Genomic DNA was extracted from peripheral blood leukocytes of the patient and her parents. Nextgeneration sequencing (NGS) was performed using a custom sequence capture (SeqCap EZ Choice system; Roche Nimble-Gen) of the exons and flanking intronic sequences of the main SAID-causing genes (i. e. , ADA2, AP1S3, CARD14, IL1RN, IL36RN, LACC1, LPIN2, LYN, MEFV, MVK, NCSTN, NLRC4, NLRP1, NLRP12, NLRP3, NOD2, FAM105B, PLCG2, POMP, PSMA3, PSMB4, PSMB8, PSMB9, PSTPIP1, RBCK1, RELA, SH3BP2, SLC29A3, STING1, TNFAIP3, TNFRSF1A, TNFRSF11A, WDR1).
NF-κB luciferase reporter assay. HEK293T cells were cotransfected in 12-well plates with the following plasmids: 200 ng of pcDNA3 empty vector or each of the LYN expression plasmids, 100 ng of pGL4. 32[luc2P/NF-κB-RE/Hygro] vector (Promega), and 10 ng of Renilla luciferase control reporter vector (Promega) for normalization. Cells were lysed 24 hours posttransfection and subjected to dual luciferase reporter assays (Promega) using a Tristar LB 941 Multimode Microplate Reader (Berthold Technologies). The firefly luciferase activity was normalized against the Renilla signal. Relative firefly/Renilla signals are reported. Statistical analyses. Differences were analyzed using the Student's unpaired 2-tailed t-test and were plotted with the Graph-Pad Prism 9 software. A P value less than 0. 05 was considered statistically significant.
5 SD at the age of 3 years, -2 SD at the age of 4 years). Identification of a de novo LYN variation. Study of genomic DNA from the patient's peripheral blood cells through an NGS-based approach (SAID gene panel) allowed identification of the c. 1522T>C, p. (Tyr508His) missense variation in the LYN gene (herein referred to as Y508H) (Figures  1D  and E ). Sequencing of parental DNA samples associated with the intrafamilial segregation study of 6 independent microsatellite markers revealed that the Y508H variation occurred de novo. This variation, which is absent from the genome aggregation database (gnomAD), is predicted to be pathogenic according to a Combined Annotation Dependent Depletion (CADD) phred score of 31. Tyr508 is a residue conserved across species as well as among all 7 other members of the Src tyrosine kinase family (Figure  1F ), suggesting that amino acid substitutions at this position could be detrimental to Lyn function.
The phenotypic data are summarized in Table  1. Two of the 3 patients were born prematurely, and no birth term information is available for the third patient  (9). All 3 patients presented with severe inflammatory disease with neonatal onset, characterized by recurrent fever, skin manifestations (such as purpuric skin rash, urticarial lesions, and atopic dermatitis), and arthralgia and/or arthritis, and disease was associated with elevated biologic inflammation markers (high CRP levels and hyperleukocytosis). Other manifestations were observed in 2 of the patients: ocular manifestations (2 of 3 patients), hepatosplenomegaly (2 of 2 patients with available data), abdominal pain (2 of 2 patients with available data), and testicular pain (2 of 2 male patients). Anemia and severe thrombocytopenia requiring splenectomy, as well as circulating autoantibodies and liver damage (increased liver enzymes and, at biopsy a periportal lymphocytic infiltrate, vanishing bile duct disease, and periportal bridging fibrosis) were reported only in the patient carrying the Y508* variation.
Funded by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Agence Nationale de la Recherche  ( ANR-17-CE17-0021-01 ), and  ImmunAID , which received funding from the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement  779295 ). Dr. El Khouri 's work was supported by the  EMERGENCE-PhenomAID  grant from  Sorbonne Université.
Myf5-derived lineage contributes to connective tissue cells in the absence of neural crest Given that the number of cells examined in the EOM and pharyngeal arch mesodermal clusters from the E10. 5 dataset was lower than for anterior somites, we decided to validate the relevance of Myf5derived non-myogenic cells in these cranial regions directly in vivo. We thus examined the EOM, larynx and upper back muscles in the early fetus at E14. 5 using a Myf5-lineage reporter mouse (Myf5 Cre/+ ; R26 TdTomato/+ ) combined with a contemporary reporter for Pdgfra+ non-myogenic cells (Pdgfra H2BGFP/+ ) (Figure  3 ). Notably, we observed tdTom/H2BGFP double-positive cells in regions of EOM, laryngeal and upper back muscles that were partially or fully deprived of neural crest  (Adachi et al. , 2020; Comai et al.
Myf5-derived cells can maintain a molecular crosstalk following bifurcation in cell fate To investigate in more detail potential paracrine cell-cell communication between myogenic and nonmyogenic cells, we examined their signalling complementarity together with their anatomical proximity. We first generated an E11. 5 Myf5-lineage traced (Myf5 Cre/+ ;R26 mTmG/+ ) sc-RNAseq dataset (Figure  S1A , E-F, Figure  S3A, C ) and focused on the EOM, which was clearly distinguished as an independent cluster based on the co-expression of several markers including Pitx2 and Alx4  (Bothe and Dietrich, 2006)  (Figure  4A ). Here, RNA velocity revealed strong myogenic/non-myogenic bi-directional cell-fate transitions (Figure  4B ). In agreement with the E10. 5 mesodermal sc-RNAseq dataset, EOM progenitors presented a strong dichotomy in Pdgfa and Pdfgra expression between myogenic and non-myogenic cells, respectively.
To confirm if the potential crosstalk between these clusters is maintained at later stages of EOM development, we interrogated the anatomical proximity of these cells by performing single molecule fluorescent in situ hybridization (RNAscope) for Pdgfa and Pdgfra on E14. 5 lineagetraced Myf5 Cre/+ ;R26 mTmG/+ fetuses (Figure  4C-D' ). In accordance with the scRNAseq analysis, we observed a complementary pattern of expression of Pdgfa (membrane GFP+) and Pdgfra (membrane GFP-) transcripts (Figure  4C-D' ). Gene set enrichment analysis of EOM myogenic and non-myogenic driver genes revealed that transmembrane receptor protein kinase and SMAD activity were shared terms between the 2 clusters, indicating that specific complementary signalling networks could be actively maintained between these two populations (Figure  S4A ).
Therefore, we examined the dynamic induction of tyrosine kinase ligands and receptors in the EOM. As observed in the scRNAseq dataset of anterior somites (Figure  2 ) and by RNAscope on tissue sections at the EOM level (Figure  4C-D' ), Pdgfra expression was actively induced in non-myogenic Myf5-derived cells while Pdgfa expression was found in myogenic cells (Figure  4E-F ). Notably, two additional tyrosine kinase receptors, namely Bmpr1b and Ephb1, were found to be among the top 100 driver genes of the myogenic EOM compartment, indicating that myogenic commitment is associated with upregulation of these receptors in the EOM (Figure  4F, Table1 ). Strikingly, two of their respective ligands Bmp4 and Efnb1, were found to be specifically expressed in non-myogenic cells (Figure  4E ). These results favor a model where complementary paracrine signalling networks operates between myogenic and non-myogenic Myf5-derived cells (Figure  4G ), while their cellular juxtaposition is maintained through fetal stages.
To identify the regulatory factors underlying this bipotency, we used SCENIC, a regulatory network inference algorithm  (Aibar et al. , 2017). This tool allows regrouping of sets of correlated genes into regulons (i. e. a transcription factor and its targets) based on binding motifs and co-expression. Use of this pipeline significantly reduced the number of variables from thousands of genes to a few hundred regulatory modules, while preserving the general aspect of the data, particularly the bipartite distribution of myogenic and non-myogenic cells (Figure  5A ). The top regulons of this analysis revealed active transcription factors underlying myogenic and non-myogenic cell fates in the EOM at E11. 5. Notably, Myf5, Pitx1, Mef2a and Six1, transcription factors known to be implicated in myogenic development  (Buckingham and Rigby, 2014) , appeared among the top regulons in myogenic cells whereas Fli1, Ebf1, Ets1, Foxc1, Meis1 and Six2, genes known for their involvement in adipogenic, vascular, mesenchymal and tendon development  (Jimenez et al.
Myf5-derived contribution to connective tissues is sustained through muscle initiation Although we identified Myf5-derived non-myogenic cells in various regions of the embryo, it was not clear if this population was self-sustaining, or continuously generated throughout development. To address this issue, we performed 2 more scRNAseq experiments at E12. 5 and E14. 5, using contemporary Myf5 labelling (Myf5 GFP-P/+ ; Figures  6, S1B, S3A, ). In accordance with the earlier datasets, cells that appeared to belong to muscle anlagen of EOM, somites and caudal arches progressed towards a non-myogenic state (Figure  6A-C' ). To assess the identity of these cells, we performed a gene set enrichment network analysis combining the differentially expressed genes of nonmyogenic clusters of all stages. We found that all stages contributed equally to "GO Molecular Function" and "Reactome pathways" terms in spite of their relatively diverse gene expression signatures (Figure.
We performed this operation for each dataset independently and displayed them as individual networks (Figure  7A-D ). Finally, we overlapped the list of these "driver regulators" to identify the common transcription factors guiding the non-myogenic cell fate decision (Figure  7E ). Notably, Foxp2, Hmga2, Meis1, Meox2 and Tcf7l2 were identified in all 4 datasets as key driver regulators, and thus are likely to play significant role in the non-myogenic transition (Figure  7E , Table  2 ). Interestingly, Tcfs and Lef1 were among these top common regulators and they form a complex effector for the canonical Wnt pathway. Previous work showed that during cranial myogenesis, neural crest cells release inhibitors of the Wnt pathway to promote myogenesis  (Tzahor et al. , 2003). It is thus tempting to speculate that in the absence of neural crest, mesoderm-derived bipotent progenitors can give rise to connective tissue by maintaining canonical Wnt activity.
To assess this hypothesis, we examined the expression of Axin2, a common readout for Wnt/b-cat activity  (Babb et al. , 2017; Moosdijk et al. , 2020). Interestingly, Axin2 levels were elevated in the non-myogenic portion of all the different datasets (Figure  7F-I ). Additionally, Dkk2, which has been described as an activator of Wnt/b-cat pathway in the neural crest  (Devotta et al. , 2018) , was also found to be elevated, indicative of a putative positive-feedback loop mechanism supporting the maintenance of this population. Brown adipocytes, neurons, pericytes and rib cartilage have been reported to express Myf5 in ancestral cells  (Daubas et al. , 2000; Haldar et al. , 2008; Sebo et al. , 2018; Stuelsatz et al. , 2014). Interestingly, when Myf5 expression is disrupted, cells can acquire non-myogenic fates and contribute to connective tissue (this study), cartilage, and dermis  (Tajbakhsh et al.
5) and continue to do so in the fetus, indicating that these bipotent cells persist well after muscles are established. Here, we also identifed a core set of transcription factors specifically active in the non-myogenic cells across all datasets. We propose that these genes guide bipotent cells to a non-myogenic fate and thus confer mesenchymal properties to non-committed progenitors. Recent studies have identified anatomically distinct fibroblastic populations using single-cell transcriptomics, yet unique markers could not be identified  (Muhl et al. , 2020; Sacchetti et al. , 2016) , making characterisation of cell subtypes challenging. Tcf4/Tcf7l2 was identified as a master regulator of fibroblastic fate during muscleassociated connective tissue development although it is also expressed in myogenic progenitors at lower levels  (Kardon et al.
MATERIALS & METHODS scRNAseq data generation For E10. 5 to E12. 5 embryos, the cranial region above the forelimb was dissected in ice-cold 3% FBS in PBS and mechanically dissociated with forceps and pipetting. The same procedure was applied at E14. 5 but the dissection was refined to the pharyngeal and laryngeal regions. Tissues were then digested in TrypLE (ThermoFisher Cat #: 12604013) during 3 rounds of 5-min incubation (37°C, 1400 RPM), interspersed with gentle pipetting to further dissociate the tissue. Cells were resuspended in FBS 3%, filtered, and incubated with Calcein Blue (eBioscience, Cat #: 65-0855-39) and Propidium Iodide (ThermoFisher Cat #: P1304MP) to check for viability. Viable cells were sorted on BD FACSAria™ III and manually counted using a hemocytometer. RNA integrity was assessed with Agilent Bioanalyzer 2100 to validate the isolation protocol prior to scRNAseq (RIN>8 was considered acceptable).
RNA velocity and driver genes RNA velocity analyses were performed using scvelo  (Bergen et al. , 2020)  in python. This tool allows inferring velocity flow and driver genes using scRNAseq data, with major improvements from previous methods  (Manno et al. , 2018). First, unspliced and spliced transcript matrices were generated using velocyto  (Manno et al. , 2018)  command line function, which outputs unspliced, spliced, and ambiguous matrices as a single loom file. These files were combined with filtered Seurat objects to yield objects with unspliced and spliced matrices, as well as Seurat-generated annotations and cell-embeddings (UMAP, tSNE, PCA). These datasets were then processed following scvelo online guide and documentation. Velocity was calculated based on the dynamical model  (using scv.
Seurat preprocessing scRNAseq datasets were preprocessed using Seurat in R (https://satijalab. org/seurat/) (Butler et al. , 2018). Cells with more than 20% of mitochondrial gene fraction were discarded. The number of genes expressed averaged to 4000 in all 4 datasets. Dimension reduction and UMAP generation were performed following Seurat workflow. Doublets were inferred using DoubletFinder v3  (McGinnis et al. , 2019). Cell cycle genes, mitochondrial fraction, number of genes, number of UMI were regressed in all datasets following Seurat dedicated vignette. We noticed that cell cycle regression, although clarifying anatomical diversity, seemed to induce low and high UMI clustering (Figure  S1E-F ). For the E10. 5 and E11. 5 datasets, 2 replicates were generated from littermates and merged after confirming their similitude.
For subsequent datasets (E12. 5 and E14. 5), no replicates were used. Annotation and subsetting were also performed in Seurat. "Myogenic" and "Non-myogenic" annotations were based on Pdgfa and Pdgfra expression and myogenic genes Myf5, Myod, and Myog. Cells not expressing Pdgfa were annotated as "non-myogenic" unless they express myogenic genes. Cells expressing Pdgfa were annotated as "myogenic". We noticed that at later stages, Pdgfa expression decreases in Myog+ cells. Thus, driver genes of connective tissue at E12. 5 and E14. 5 were determined using cluster annotations obtained from Leiden-based clustering.
Gene regulatory network inference Gene regulatory networks were inferred using SCENIC (R implementation)  (Aibar et al. , 2017)  and pySCENIC (python implementation)  (Sande et al. , 2020). This algorithm allows regrouping of sets of correlated genes into regulons (i. e. a transcription factor and its targets) based on motif binding and co-(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted May 26, 2021. ; https://doi. org/10. 1101/2021. 05. 26. 445757 doi: bioRxiv preprint expression. UMAP and heatmap were generated using regulon AUC matrix (Area Under Curve) which refers to the activity level of each regulon in each cell.