dataesr/scientific-paragraphs-categorization huggingface dataset:
000253) and Rac1 regulation (p = 0. 000376) pathways for the F-SARC gene set (329 loci) followed by the SODD/TNFR1 signaling pathway (p = 0. 000869). The KEGG human database enrichment highlighted three closely related intracellular pathways: autophagy (p = 0. 0000917), mitophagy (p = 0. 000125), and the interleukin-17 (IL-17) signaling pathway (p = 0. 000921) (Figure  2a ). A similar evaluation of the CONTROL gene set suggested a highly significant overrepresentation of different intracellular processes, such as extracellular matrix (ECM) receptor interaction (p = 0. 000000000976-BIOPLANET and p = 0. 00000000616-KEGG), Beta-1 integrin cell surface interactions (p = 0. 000000437-BIOPLANET), ABC transporters (p = 0. 0000307-BIOPLANET), and various other transmembrane pathways (Figure  2b ).
No significant overlap was found between the F-SARC and the CONTROL gene series. Our previous data on a subset of five sarcoidosis families suggested that genetic defects in Rac1, mTOR, and autophagy-related pathways are relevant factors in familial predisposition to sarcoidosis  [4]. To evaluate a putative link between a significant number of control gene sets with Rac1 and mTOR, we artificially introduced the two Rac1 and mTOR gene symbols into the ENRICH. R software and analyzed the data with two databases of gene sets, BIOPLANET-2019 and KEGG-2021 (Figure  3 ). We did not observe any shift towards the Rac1 and/or mTOR pathway, and the top molecular pathways highlighted by the software were ECM receptor interaction (p = 0. 000000000983-BIOPLANET and p=0. 00000000616 -KEGG 2021).
Gene The inclusion of Rac1 and mTOR in the F-SARC gene set emphasizes the clustering to PIK3C1/AKT-and Rac1-related pathways (p = 0. 0000213 and p = 0. 0000347, respectively, in BIOPLANET) and to autophagy/mitophagy (p = 0. 00001392 and 0. 0001302, respectively in KEGG 2021-BIOPLANET) and Rac1 activity regulation (p = 0. 0000347-KEGG 2021) (data not shown). Despite the difference in the two gene set sizes (329-F-SARC and 5341-CONTROL), we estimate that our previous data linking the predisposition to inherited sarcoidosis to Rac1-and mTOR-related molecular hubs was confirmed in the 13 families analyzed in the present study.
Limitations Our study has several limitations: (1) it is only a descriptive work based on WES genetic screening without any functional studies; (2) the selection of gene variants was performed in the usual manner in WES analysis, i. e. , by progressively decreasing the MAF value of variants and using a genotype subtraction protocol with external and, when available, some intrafamilial healthy individuals, a strategy that can be criticized at different levels; (3) the in silico functional evaluation of gene variants remains a complex procedure and needs to cross various databases with putative contradictory results; and (4) mutation databases and protein network enrichment software use published and hopefully confirmed results, and may therefore be incomplete regarding genes' functions, reflecting the fact that their interactions are poorly understood.
Only few studies on rodent-borne diseases considered the potential impact of landscape configuration on infectious disease incidence (e. g. ,  [32, 33, 40, [67] [68] [69] [70] ). Moreover, despite the growing database of genome sequences and the increasing number of studies on genetic variability of orthohantaviruses, very few have evaluated the influence of the landscape factors and host genetic structure on the viral genetic diversity (e. g. ,  [49] ). Overall, the knowledge on how orthohantaviruses and host populations experience and genetically respond to the same landscape phenomena is rarely described in the literature  [71, 72] ; therefore, extended microevolutionary studies of host-parasite models are needed. In our previous study, we analyzed the influence of landscape features on dynamics and dispersal of PUUV-infected rodent populations in Northern France  [73].
Landscape Characteristics of the Study Sites Since bank voles are essentially tied to their forested habitat, each study site was characterized by several landscape indices according to few already standardized criteria  [74]. The aforementioned criteria were the proportion of forested habitat (Forest), the forest contiguity index (Contig), the forest shape index (Shape), the edge density, the number of patches of forest habitats, and the distance to the next patch. Forest coverage and fragmentation were calculated using ArcGIS ® software (Esri. ArcGIS ® , Redlands, CA, USA) on the basis of the CORINE Land Cover 2012 database. We used a circular buffer of 2 km diameter, which suited both our sampling scheme  [73]  and what is known about dispersal distances for the bank vole  [75].
Automated sequencing was performed using the ABI PRISM™ Dye Terminator sequencing kit (Thermo Fisher Scientific, Waltham, MA, USA). To ensure that any possible contamination did not perturb our results, the PUUV RNA extraction-amplification process was done twice, in November 2012 and in April 2013, allowing us to contrast the amplified genome sequences. The newly recovered S, M, and L genome segment sequences (1299, 453, and 412 nt in length, respectively) for 33 PUUV strains circulating in the Ardennes were deposited in GenBank under accession numbers MN241149-MN241247.
Landscape Analyses We investigated the effects of landscape structure on the genetic diversities using statistical logistic regressions performed with the RStudio platform v1. 2. 1335  [84] , applying the packages MuMIn v1. 43. 6  [85]  and nlme v3. 1-140  [86]. Model selection was performed using Akaike's information criterion (AIC)  [87, 88]. The model with the lowest AIC value was viewed as the most parsimonious, i. e. , the model explaining the majority of variance with the fewest parameters  [88]. The significance of each explanatory variable was tested using Wald tests based on z-values. Beforehand, we calculated the correlations between each variable of the landscape and the PUUV and bank vole datasets, in order to detect highly correlated variables which may have hampered GLM analyses because of multicollinearity  [89].
2%; M = 0-13. 9%; L = 0-17. 3%) (see Table  2 ) compared to other PUUV datasets published earlier at similar geographical scales  [47, 49, 52]. Within sampling sites, the diversity of the analyzed genomes was much lower, ranging from 0% to 6. 8%. In two of the sampling localities (Sauville and Briquenay), PUUV was not detected. Nevertheless, the genetic diversity of strains from the Woiries locality was higher than from the others (Table  2 ). Along these lines, the chromatogram of the L genome segment of the Woiries_58 strain showed double peaks at certain nucleotide sites. These double peaks were distributed not randomly along the sequence but appeared exclusively at nucleotide positions that genetically discriminate strains from the different studied localities (Figure  S1 ). Overall, there was a high diversity of virus sequences with 30 different types for the S segment, 14 for the M segment, 12 for the L segment, and 31 when all three segments were taken into account (concatenated segments).
Effect of Landscape Features and Bank Vole Population Dynamics on PUUV Prevalence and Diversity We independently calculated a correlation matrix for landscape, bank vole, and PUUV variables to detect the values that were highly correlated, aiming to preserve only the weakly or not correlated variables of our datasets. Accordingly, we conserved the following variables: the proportion of forested habitat (i. e. , Forest), the forest contiguity (i. e. , Contig), and the forest shape (i. e. , Shape) variables of the landscape dataset (Table  1 ), the allelic richness (A) variable of the bank vole's dataset (Table  S2 ), and the nucleotide diversity (i. e. , NtDiv) and amino-acid diversity (i. e. , AaDiv) variables of the PUUV dataset (Table  2 ). Bank vole diversity was positively correlated with landscape features; the best model based on AIC values only included the Forest variable (AIC: 17.
Conclusions Since the start of PUUV studies, it was evident that host abundance is a modulator of the microparasite prevalence. However, there are not many reports where bank vole diversity or landscape features were considered to predict the virus prevalence and/or emergence. We studied the genetic diversity and distribution of PUUV strains in its specific host population, incorporating the environmental features affecting the host-virus tandem into the analysis. Our data strongly support the hypothesis that the bank voles display an elevated gene flow and large population sizes over broad forested areas. Nevertheless, bank vole populations showed signs of genetic drift and genetic isolation in the studied locations with low coverage and contiguity of forest habitats. Moreover, bank vole immigration from large continuous populations into small isolated ones could be an essential process for the occurrence and transmission of PUUV in fragmented landscapes and, consecutively, the expansion or the extinction of the virus.
Study Overview and Procedures This cohort study used data from Healthy Passages, a longitudinal study of diverse public school students from a stratified random sample of schools from Birmingham, Alabama; Houston, Texas; and Los Angeles County, California. 28, 29  Data collection began in 2010 when students were in the 5th grade (mean [SD] age, 11. 1 [0. 5] years) (wave 1) and continued 2 years later (wave 2, when most were in 7th grade) and 5 years later (wave 3, 10th grade). Permission to be contacted was provided for 6663 children; 5147 (77%) participated. Data were collected from children and parents via audio computer-assisted self-administered interviews in English or Spanish. Study methods and procedures are detailed elsewhere. 28 All participants completed written procedures to obtain appropriate consent to participate.
For the current study, wave 3 data were analyzed crosssectionally and completed in June 2021. The inclusion criteria were respondents who completed the sexual orientation items. The analytic sample and denominator for this study was 4256 youths (  640 LGB, 3616 non-LGB). These youth completed interviews at wave 1 and wave 3 and answered key items used in this analysis. Study activities were reviewed and approved by the institutional review.
Statistical Analysis Cross-sectional descriptive statistics characterized the overall sample of youth by sexual minority status (LGB vs non-LGB). Bivariate analyses assessed whether the distribution of key demographic variables differed by LGB status. Separate logistic regression models were fit regressing each sociodemographic variable on LGB status, accounting for complex survey design (SAS proc surveyweights, cluster, strata). Odds ratios (OR) and 95% CIs were estimated for bivariate models. Variables statistically significant at the P <. 05 level in bivariate models and conceptualized as confounders were retained in multivariable regression models. Separate multivariable logistic regression models were fit regressing each outcome on sexual minority status (LGB vs non-LGB youth), and controlling for student wave 1 age in years, gender, race and ethnicity, and household education, income, and marital status.
A logistic regression model restricted to LGB youth examined whether comfort level talking to a clinician about sexual orientation was associated with any physician or health care clinician knowing youths' sexual minority status. Adjusted ORs (aORs) and 95% CIs for multivariable logistic regression models are presented. All statistical analyses were adjusted for the complex sampling and survey design (weight, cluster, strata). Covariate-adjusted proportions were obtained using a recycled predictions approach. Complete case analyses were conducted because missing data percentages were low (<10%) and did not significantly differ for LGB vs non-LGB youth. Per the STROBE guidelines,  34  a sensitivity analysis was conducted using multiple imputation procedures to handle missing data values and found similar results to the complete case analyses in terms of direction, magnitude, and significance.
The demographic and geographic diversity of our sample is a strength, but results may not be generalizable to youth outside of the contexts from which participants were drawn. Furthermore, the study, conducted in English and Spanish, may underrepresent youth who prefer other languages. Parent-reported sociodemographic data occurred in wave 1, so it may have changed by wave 3 when youth outcomes were assessed. Additionally, disclosure of sexual orientation to parents may play a role in youth's access to and utilization of care and should be considered in future studies. This study did not include information about gender identity, so we were not able to comment on the needs of transgender youth.
Statistical Analysis Data analyses were performed with IBM SPSS Statistics, version 25 (IBM Corporation, Armonk, NY, USA) and MedCalc Statistical Software version 19. 2 (MedCalc Software BVBA, Ostend, Belgium). Categorical variables were expressed in frequency and percentage. The Chi-squared test was used to compare categorical variables between groups. Continuous variables were expressed as mean ± standard deviation (SD) or interquartile ranges, Student's t-test or Mann-Whitney U test were applied to identify different groups in continuous variables. Given the sexual dimorphism in body composition, multivariate logistic regression analysis and receiver-operating characteristic (ROC) curve analyses were presented strati ed by sex. The binary logistic regression analysis was used to estimate the odds ratio (OR) (with the corresponding 95% con dent interval--95% CI) of T2DM with the Obesity indices and TyG-related parameters the diagnostic value for T2DM was de ned by areas under the curve (AUC) in ROC curve analyses.
Intriguingly, we found that WHtR was more sensitive (70. 80 %) than WC (50. 22 %) in men, despite WHtR had the lowest AUC (0. 571 for men). As a consequence, WHtR may have a better predictive ability than WC, and can be used for T2DM screening. According to previous reports, WHtR ≥ 0. 5 might be the best cut-point value for diagnosing T2DM  [30]. A study by Yang et al  [31]. reported the WHtR cutoffs for the T2DM detection were 0. 512 and 0. 514 for men and women among 996 older adults in China. In our study, the cut-off point of WHtR was 0. 49 and 0. 52 for men and women, which suggested that the cutoff value in elderly Chinese men with normal-weight had decreased. However, the results of the WC and WHtR cut-off values need to be interpreted with caution, as the sample data for our study was collected only from Shenzhen --a large southern city in China.
Study procedures and data collection Individuals who met the eligibility criteria and consented to the study were interviewed using a pretested structured questionnaire. A doorto-door approach was used to locate participants. To avoid double counting, each individual was asked if they had lled a similar questionnaire prior to recruitment for the study. Literate persons were allowed to ll the questionnaire by themselves after receiving instructions on how to ll the questionnaire. A face-to-face interview was done for illiterate people. Data collected were sociodemographic information, information on the use of recreational drugs and quality of life assessment of participants. Questionnaire was available in English. The following sociodemographic variables were collected; gender, age, marital status (single, married or widowed), residence (internally displaced, not internally displaced), employment status (employed, housewife, student, unemployed/retired), employment type (physical, non-physical, or combined), level of education (no education, primary, secondary and tertiary education) and average monthly income (< FCFA50.
Data management and data analysis Data collected were entered into Microsoft excel 2016, were cleaned, exported and was analyzed using Statistical Package for Social Sciences (SPSS v20). Continuous variables were summarized using mean and standard deviation. Categorical variables were summarized using counts and percentages. Sociodemographic variables were compared between recreational drug users and non-recreational drug users using Pearson's chi-square or Fisher's exact test. Mean quality of life scores across various domains were compared using independent t-test or one-way ANOVA. We used multivariate linear regression models to determine factors independently associated with WHOQOL-BREF scores. Statistical signi cance was set at p < 0. 05.
Cell size and quantitation of expression of markers such as p63α (p63α bright cells) was performed by using the BZ-X analyzer software (version 1. 3. 0. 3) with the macro hybrid cell-count function. By specifying the mask area, the software tracked information about multiple parameters such as the intensity of fluorescence signals in different channels, cell counts, and target area measurements  65, 66. For the cell size counting, phase contrast images of single LSCs placed in a hematocytometer were used. Conditioned media and LSC treatment. Conditioned media (CM) was obtained from 1 × 10 4 /cm 2 Wnt6-transduced NIH-3T3 cultured in DMEM supplemented with 10% BCS (Invitrogen) and 1% penicillin/ streptomycin (Invitrogen) for 4 days. Following collection, the CM underwent centrifugation at 1000×g for 10 min and was concentrated by using 30-kDa Amicon Ultra Centrifugal Filters (Sigma-Aldrich).
Images were taken with a Keyence BZ-X710 inverted microscope (Osaka, Japan), and characteristics such as fluorescence intensity, number of cells, and size were evaluated with the BZ-X analyzer software (version 1. 3. 0. 3) as described above. mRNA in situ hybridization using RNAscope assays. The RNAscope V2 Fluorescent Assay (ACD Biosystems, Newark, CA) was performed according to the ACD Biosystems protocol for fresh-frozen tissue. Corneal sections from 3 donors were hybridized with the Wnt6 mRNA probe. Assays were performed in 2 independent experiments for each donor. To confirm the mRNA integrity of the tissues, positive control probes targeting human housekeeping genes Polr2a, PPIB, and HPRT were visualized (Supplemental Fig. 1 ). The probes were amplified according to the manufacturer's instructions and labeled with the fluorophore Opal 520 nm (Akoya Biosciences, FP1487001KT, 1:250), Opal 570 nm (Akoya Biosciences, FP1488001KT, 1:1500), and Opal 690 nm (Akoya Biosciences, SKU FP1497001KT, 1:1500).
The BM secretome of patient-xenografts provides a reservoir of potential direct mediators of AML pathogenesis Our transcriptomic data unravel molecular insights into the response of the BM niche to the presence of AML, but how much of this response derives from a direct crosstalk is unclear. To gain insight into changes in the BM milieu upon human AML engraftment, we performed a proteomic analysis of the BM secretome in AML xenografts (Figure  5A ). Although we observed a certain degree of heterogeneity among AML samples, possibly linked to genetic diversity in our cohort (Figure  5B ; Table  1 ), we identified a large common secretome signature within the AML cohort (Tables  S4  and S5 ). We focused our attention on the top commonly deregulated proteins and processes and validated their translational impact by comparing our dataset to recently published proteomics data retrieved from healthy donors and AML patients, as well as to a large cohort of patientderived transcriptomic dataset  (Bagger et al.
Interestingly, the clotting cascade and platelet activation pathways are also downregulated (Tfpi, Dtk, KLKB1, Prekallikrein, a2-Antiplasmin, Coagulation Factor X; Figure  5D ; Table  S4 ). Integration of niche transcriptome and BM secretome predicts molecular nodes involved in niche alteration in AML To predict direct interactions responsible of the BM niche and AML crosstalk, we integrated the secretome and transcriptome data. Ligand-receptor analysis allowed the identification of upregulated and downregulated nodes of interaction between the BM milieu and niche components (Figure  S6 ). The interactome showing ligand-receptor pairs involved in specific pathways is summarized in Figure  6A  and can be explored at https:// giovannidiana. github. io/integratedomics/. Overall, we noticed a peculiar dual distribution of signaling pathways between the osteo-endothelial niche and the mesenchymal components (Figure  6A ).
Flow cytometry analysis and cell sorting Flow cytometry analysis was performed using a Fortessa flow cytometer (BD Biosciences, Oxford, UK). Dead cells and debris were excluded from the analysis using 4,6, diamidino-2-phenylindole (DAPI) or SYTOX Green (ThermoFisher) staining. Cell sorting was performed using a FACS Aria SORP (BD Biosciences, Oxford, UK). Data were analyzed by FACSDiva (BD) or FlowJo (version 9. 9) software. To sort BM niche cells, human and murine hematopoietic cells were excluded using an EasySep Human CD45 Positive Selection kit and a murine CD45 positive selection kit (Stem Cell Technologies, Vancouver, Canada) according to the manufacturer's instructions, with a purity of 85 to 99%. Remaining cells were stained with hCD45 (2B11), mCD45 (30-F11), Ter119 (Ter-119) and mCD31 (390), all from eBioscience, and specific stromal cells were sorted in RNAeasy RTL buffer (RNeasy Mini kit, QIAGEN) based on the expression of CD31 or specific fluorescent reporters (GFP for Nestin and Osx; DSRED for Ng2; CFP for Col2.
3; Cherry for labeled niche). Flow cytometry plots displayed in this manuscript represent one set of data points from at least 3 replicates. Sample preparation for RNA-sequencing RNA was extracted using RNeasy Mini kit (QIAGEN) following manufacturer instructions. The quality and concentration of total RNA were determined on Agilent 2100 Bioanalyzer using the Eukaryote Total RNA Pico Assay. Most of the total RNA has average RIN number 5-8, with a concentration at least 4 pg/ml. Some of the samples were concentrated on a speedvac without heat to obtain a final volume of 5 ml.
Assay details are provided in  Gold et al. (2010). (Karolchik et al. , 2004). The ''-forwardprob'' parameter was set to ''0'' and all other parameters were kept as default. Normalization was performed with DESeq2 package (median of ratios method) (version 1. 22. 2; Love et al. , 2014) with subsequent regularization using log 10 transformation. Differential expression analysis was then performed with the DESeq2 package under the R programming environment (version 3. 2. 3) (R Development Core Team, 2015). Genes with FDR < 0. 1 were judged to be differentially expressed. Genes from each given pairwise comparison were ranked using the Wald statistic. To analyze the transcriptome of stromal components in human xenografts, dimensionality of gene expression from AML, CB and ctrl samples was reduced with the t-distributed stochastic neighbor embedding (tSNE) technique for 2D visualization using the plot_tsne function from the bioplotr package on R.
A comparison analysis was then performed between AML and ctrl samples in order to obtain differentially expressed genes. This analysis was performed using the contrast function within DESeq2 package. Linear correlation coefficients were calculated for control (ctrl) samples and a sample distance heatmap was then obtained using the heatmap package under R environment. Dimensionality reduction analysis was performed using gene expression data from ctrl samples. Principal component analysis (PCA) was made by using the expression matrix of normalized and regularized genes using the prcomp function on R. The results were visualized on a 3D map using the plot3d function from the rgl package on R. Multiple comparison of stromal populations in control samples was performed within the DESeq2 computing environment.