dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016)  with α = 0. 01, applied on the MAD-standardized residuals of a linear model correcting for genotype, block and genotype-block interaction effects (Table  S5 ). The model was fitted using the function lm from the stats R package (R Core Team, 2021). Locule number was treated as an ordinal trait. When the degrees of freedom were insufficient due to a lower number of measurements, the correction for interaction effect was not applied. When the block information was not available for a particular trial, only the correction for genotype effect was applied. Simple descriptive statistics were computed for accession means of each trait after outlier removal, with the stat. desc function from the pastecs R package (v1. 3. 21; Grosjean et al. , 2018). Between-trial Pearson correlations were computed for each trait on the accession means per environment, to detect less correlated trials.
The Box-Cox family of power transformations was then applied to the most correlated trials together to optimize normality of residuals for each trait, after fitting a model including all the main effects for genotype, trial and block (and trial-block interaction when the number of measurements was sufficient) (Table  S5 ). Prior to Box-Cox transformation, traits containing negative values were transformed into only-positive ones adding an appropriate constant. The less correlated trials were transformed separately. This procedure was performed using the functions powerTransform and bcPower from the R package car (v3. 0. 13; Fox & Weisberg, 2019). Adjusted means per accession to be used as phenotypic input for GWAS were estimated using a BLUP model with the 'lme4' R package  (Bates et al. , 2015).
Genome-wide association study GWAS were conducted on both single-environment and multienvironment BLUPs, using the R package "Genomic Association and Prediction Integrated Tool" (GAPIT Version 3;  Wang & Zhang, 2021). The mean dosage value per marker across accessions was used for imputing the remaining missing values (5% of the genotypic matrix). Three GWAS models were used: MLM  (Yu et al. , 2006) , MLMM  (Segura et al. , 2012) , and BLINK  (Huang et al. , 2019). The first four PCs of a PCA of marker genotypes were added to all the GWAS models as covariate variables, to reduce the number of false positives due to population stratification. In addition, MLM and MLMM included a random term to correct for relatedness according to a kinship matrix based on the VanRaden formula, calculated by GAPIT3.
To control familywise error rate, a significance threshold of 5. 31 was calculated using the Bonferroni procedure (i. e. , dividing the desired significance of 0. 05 by the number of tested markers 10 195). Circular Manhattan plots were generated using the R package CMplot (v4. 2; Yin et al. , 2021). QTLs and candidate genes QTL intervals were calculated in order to group together highly linked QTNs (i. e. , the SNP and Indel markers significantly associated to a trait) identified using the three different GWAS models (MLM, MLMM, and BLINK) on up to six environments or across all environments. Intervals were defined as the first and last QTN position AE the extent of specific chromosomal LD decay. Each QTL name was defined as the associated trait code, followed by the chromosome number and the QTL's order among all QTLs for this trait along the chromosome.
Outlook We constructed the G2P-SOL core collection, a unique and valuable resource for the pepper community. Its great genotypic and phenotypic variability, representative of the worldwide diversity, will accelerate the comprehensive understanding of the genetic basis of multiple traits. The core collection will be available on request, along with the genotypic and phenotypic data collected as part of the G2P-SOL project. As research resources, we also provided comprehensive up-to-date summaries of previously detected QTLs for agronomic traits in pepper and previously published GWAS in C. annuum. We conducted multi-environment GWAS on a great variety of plant and fruit traits, and GWAS for biotic stress resistance to multiple pathogens, heat stress tolerance and metabolic compounds is underway.
We identified 97 new or already known genes potentially underlying 53 of the 119 highestconfidence QTLs. Further validation of specific genes will be needed to confirm their incidence on the phenotype. Nevertheless, these results demonstrate the usefulness and reliability of our core collection, phenotypic dataset and methodology. Traditionally, candidate genes identified for traits with simple inheritance can be used as diagnostic markers or as part of trait-specific markerassisted selection. In addition, the genomic and phenotypic matrices provided by this study should improve multivariate genomic prediction at the whole-genome level, and should speed up the identification of candidate accessions that are phenotypically closest to a predefined genetic ideotype, adapted to a targeted environment or to several environments  (Yang et al.
DATA AVAILABILITY STATEMENT The raw phenotypic and VCF genotypic datasets generated for this study are accessible at https://doi. org/10. 57745/ S7JVEM. Seeds from the G2P-SOL core collection will be available upon reasonable request at INRAE GAFL CRB-Lég, considering the sharing of costs for their production and their shipping (e-mail veronique. lefebvre@inrae. fr).
Participants A total of 111 participants from Hungary took part in the study. The inclusion criteria were the absence of any neurological and psychiatric disorder, having an active employment, and being at least 18 years old. Ten participants were excluded because of self-reported current psychiatric disorder. Thus, a total of 101 participants' data were analyzed (M age = 41. 10 years; SD = 9. 29, 39. 22% males). Notably, this sample size is either comparable to or larger than those reported in most similar studies within the field  20, 22, 27, 29. We divided them into two groups: a high risk for work addiction group (HWA, n = 39; M age = 37. 51 years, SD = 7. 81 years; 30. 8% males) and a low risk for work addiction group (LWA, n = 62; M age = 42. 8 years, SD = 9. 96 years; 56% male), based on their scores on the Work Addiction Risk Test (WART)  42.
In addition, for some tasks, data from additional participants had to be excluded due to assessment issues (e. g. , failure of saving data, misunderstanding the instructions of the tasks, lack of motivation) and we also excluded those who scored outside two standard deviations (see Supplementary Materials S1. 1). The database can be found with and without excluding outliers on OSF (see Data availability statement). Regarding their educational level, the majority (85. 29%) of the participants had a college or university diploma, five participant (4. 9%) had a doctoral degree, nine of them (8. 2%) had a high school diploma, and one participant (0. 09%) had a vocational training without a high school diploma. Around three quarters (73. 55%).
Procedure We recruited the participants from the database of a previous study examining the relationship of work addiction and personality factors  64. In the previous study, we utilized a snowball sampling method to gather participants for an online questionnaire survey, where they completed several questionnaires, including one assessing work addiction. At the end of the online questionnaire, participants willingly provided their contact information, indicating their interest in potentially taking part in future research. While work addiction generally displays stability  [65] [66] [67]  ), individual cases may occasionally undergo spontaneous recovery or other changes over time. Therefore, though we invited individuals to participate based on their score of the Work Addiction Risk Test  42, 58  of this previous study, we based the final group assignments on the participants' actual WART scores from this study to accommodate potential fluctuations in work addiction levels.
Statistical analysis The IBM SPSS Statistics (Version 28) and JASP (Version 0. 17. 2) were used for statistical analyses  68, 69  and RStudio (Version 2023. 03. 0) was to create the plots  70. Given the sensitivity of cognitive functions to sociodemographic characteristics, we initially compared the HWA and LWA groups in terms of gender, age, education, and place of residence using independent sample t-test and Mann-Whitney tests depending on normality. Then, we compared performance on the neuropsychological tests and the questionnaire scores between the LWA and HWA groups. In the absence of a difference, we would employ one-way analysis of variance (ANOVA), and in the case of any significant difference, one-way analyses of covariance (ANCOVA) would be applied, with 'group' (LWA vs. HWA) as a between-subjects factor and the covariate.
These analyses were carried out in RStudio (Version 2023. 03. 0)  70  using the WRS2 package  74. Additionally, we conducted correlational analyses between the WART scores and the results of the cognitive tasks. Normality was tested applying the Shapiro-Wilk test. When the data did not meet the assumptions of normality, Spearman's rank correlation was employed. Conversely, when the data was normally distributed, Pearson's correlation coefficient was utilized. Finally, to examine the relationships between WART total scores and our explanatory variables, we used a hierarchical multiple linear regression analysis with two blocks of predictors. In the first block, we used the following independent variables to predict WART scores: BIS total score, FFOCI total score, and age. Subsequently, in the second block, we included the following variables as additional predictors: CSPAN, DSPAN, 1-back, 2-back and Go/No-Go discriminability scores, and WCST perseverative errors.
To investigate the relationships between WART total scores and our explanatory variables, we conducted a hierarchical multiple linear regression analysis with two blocks of variables. In the first step, we predicted WART scores from the self-report questionnaires, such as the BIS total score and the FFOCI total score, and age. In the second step, we included the neuropsychological tasks as predictors: CSPAN, DSPAN, 1-back, 2-back, Go/No-Go discriminability scores, and WCST perseverative errors. Correlations between these measures can be found in the Supplementary Materials, Table  S2 , while the results of the regression model are presented in Table  3. In the regression analysis, we did not analyze the data of those who were excluded from even one task. Thus, a total of 83 individuals' data were analyzed (see Supplementary Materials S1.
Obtaining a precise answer to this question would require longitudinal or experimental research. Precisely because of this another important aspect that warrants further investigation is the phenomenon of burnout, which is commonly associated with work addiction  36. Consequently, future research endeavors should consider including assessments of burnout and controlling for its influence during data analysis. These considerations highlight the need for future research to encompass a more diverse work addiction population, account for comorbidities, address sample size limitations, and explore the impact of burnout on cognitive functioning. In the present study, our aim was to investigate executive functions and related personality traits in work addiction. Our results suggest a selective impairment of executive functions in work addiction.
Data availability All data are available on the following links: https:// osf. io/ fj9zp/? view_ only= 5f400 c12eb 8146d f84cc 43590 eb015 31; https:// github. com/ vekteo/ Card_ sorti ng_ jsPsy ch; https:// github. com/ vekteo/ Nback_ jsPsy ch; https:// github. com/ vekteo/ GoNoGo_ jsPsy ch.
Results Gal-3 signature is associated with disease severity, lung, and cardiac impairment within molecular clusters of patients with SSc Three functional molecular clusters were identified within a cohort of patients with SSc enrolled in the PRECISESADS cross-sectional study (NCT02890121). The global characteristics of all patients and healthy volunteers (HV) are summarized in Table  1. To perform the clustering of the 249 samples obtained from patients with SSc, whole-blood transcriptomic data were analyzed using a selection flowchart described previously  28  (Supplementary Figs. 1  and 2 ). The analysis allowed to cluster patients into three subgroups (Fig. 1a ). Cluster 1 (C1) contained 86 patients (34. 5%), cluster 2 (C2) 120 patients (48. 2%), and cluster 3 (C3) 43 patients (17. 3%).
C2 was enriched in sine scleroderma SSc (ssSSc) patients and showed a relatively 'healthy-like' transcriptional profile (Supplementary Fig. 3e ), consistent with the fact that these patients are characterized by milder disease manifestation and lack of skin involvement, despite the presence of lung or cardiac malfunction  9. To investigate a potential association between the Gal-3 network and SSc, we first established a list of known Gal-3 partners through bioinformatic queries within the Ingenuity Pathway Analysis (IPA) and GPS-Prot databases and established a non-redundant list of 307 interactants (Supplementary Data 1, column A) of which 248 could be retrieved from whole-blood RNA sequencing (RNAseq) data from the PRECISESADS SSc cohort, after exclusion of 59 genes absent from the dataset (Supplementary Data 1, column C).
All results are depicted as boxes and whiskers plots defined by minimal, maximal, and median values in each dataset, except in c, for which means ± sem values are shown. Statistical analyses in e and h were performed on log-transformed data using a one-way ANOVA for comparison between the HOCl and control group, and with a one-way ANOVA for comparison between all tested items and their HOCl control group, using Dunnett's adjustment. A similar analysis was performed in d, without log transformation. Statistical analyses in b, f, and g were performed on log-transformed data using a mixed ANCOVA model for comparison between the HOCl and control group, and using a mixed ANCOVA model for comparison between all tested items and their HOCl control group, using Dunnett's adjustment. Statistical analyses in c were performed using a repeated measures ANOVA for comparison between the HOCl and control group, and with a one-way ANOVA for comparison between all tested items and their HOCl control group, using Dunnett's adjustment.
TD139 showed positive anti-fibrotic and anti-inflammatory benefits after intratracheal administration in a mouse model of IPF  19, 20  , and reduced disease-associated plasma biomarkers after inhaled formulation in a phase 1/2a clinical study in IPF patients  21. Belapectin, a galactoarabino-rhamnogalacturonan polysaccharide polymer with low micromolar binding affinity to both Gal-3 and Gal-1  40  did not meet its primary and secondary endpoints in a phase 2b study in patients with NASH, cirrhosis, and portal hypertension, and was repurposed in a subgroup of patients for the prevention of esophageal varices in NASH cirrhosis  41  (ClinicalTrials. gov NCT04365868). A large number of other approaches have focused on targeting Gal-3 with carbohydrate or noncarbohydrate-based compounds, most having been evaluated in preclinical studies and just a few of them reaching clinical stages in various indications  [42] [43] [44] [45].
Patient population Patients with systemic sclerosis (SSc) and healthy volunteers (HV) were obtained from the European multi-center cross-sectional study of the PRECISESADS IMI consortium which involved patients from seven systemic autoimmune diseases. The PRECISESADS cohort participants did not receive any compensation. SSc patients were aged between 50 and 68 years (median 60 years) and comprised 212 women and 37 men representing 85% and 15% of patients, respectively. Healthy volunteers (HV) were aged between 47 and 59 years (median 53 years) and comprised 307 women and 58 men, representing 84% and 16% of HV, respectively. Sex rather than gender was considered for women and men, based on self-reporting. All patients and HV gave written informed consent for the study that was approved by local ethical committees of the 19  The cross-sectional study (registered as NCT02890121 in Clin-icalTrials.
Their characteristics are reported in Table  1. Molecular subgroup discovery and identification of a Gal-3 fingerprint associated with SSc clusters Patient subgroup discovery was based on the pre-processed RNAseq data and clustering methodologies previously used for primary Sjögren syndrome patients from the PRECISESADS cohort  28. Except when indicated, data analyses were carried out using either an assortment of R system software (http://www. R-project. org, V4. 0. 1) packages including those of Bioconductor v3. 17, or original R code. R packages are indicated when appropriate. In order to analyze RNAseq data, first, bcl2fastq2 Conversion Software v2. 20 was used to demultiplex sequencing data and convert BCL files. Quality control was obtained with FastQC tools v0. 11. 18 and adapters were removed with Cutadapt v1.
18. Transcriptome alignment was done with STAR v2. 5. 2b on GEN-CODE v19 annotation (hg19) and read counts were obtained with RSEM v1. 2. 31. For normalizations and batch correction, read counts were normalized by the variance stabilizing transformation vst function from the DESeq2 v1. 30. 0R package. To reduce the effect of the RIN, a correction was applied using the ComBat function from the sva v3. Our objective was to obtain a robust classification scheme ensuring the identification of highly homogeneous SSc subgroups. The strategy used iterates unsupervised and supervised steps and is therefore designated as a "semi-supervised" approach. The training set comprised 263 SSc samples and the test set comprised 65 samples. To determine the number of clusters of patients (unsupervised step), a consensus clustering between three methods was performed: (i) Agglomerative Hierarchical Clustering (hclust function from stats v4.
0. 2R package) with Pearson correlation as a similarity measure and Ward's linkage method, (ii) k-means clustering (k-means function from stats R package) with four groups and (iii) Gaussian mixture clustering (mclust function from mclust v5. 4. 6R package). The number of clusters was determined as the best consensus between three unsupervised clusterings (https://github. com/psBiostat/GAL3_PAPER. git)  62. From 328 patients, 249 (219 in the training and 30 in the discovery test) were considered as consensus in the clustering (Supplementary Fig. 2 ). The top discriminating genes were defined with randomForest function from randomForest v4. 6-14R package. Heatmaps were obtained with the ComplexHeatmap v2. 6. 2R package. Enrichment analysis was performed using transcriptional module repertoire and generated fingerprint representations with BloodGen3Module v0.
99. 36. A non-redundant list of Gal-3 interacting proteins was compiled by bioinformatics by querying the GPS-prot database  63  , allowing the referencing of 210 Gal-3 interactants, and the Ingenuity Pathway Analysis database  64  , allowing the referencing of 276 Gal-3 interactants. Merging the 2 databases finally led to the identification of 307 proteins, listed in Supplementary Data 1 (column A), among which 59 were found absent in datasets of the PRECISESADS SSc cohort (Supplementary Data 1, column B). Among the 248 remaining genes, we searched for those that were significantly associated with PRE-CISESADS SSc clusters by applying the randomForest method (ran-domForest function from randomForest R package  65  ). To obtain a robust selection, we generated 200 bootstrap replicates. Only genes frequently chosen by randomForest over bootstrap samples were selected, which improved the stability of the results.
For the global differential gene expression analysis between HV and each patient subgroup, we applied a linear model (lmFit function from limma v3. 46. 0R package) on vst transformation gene expression dataset. The resulting p-values were adjusted for multiple hypothesis testing and filtered to retain differentially expressed genes with false discovery rate (FDR) adjusted p-values ≤ 0. 05 and an absolute foldchange (|FC|) ≥ 1. 3. Ingenuity Pathway Analysis (IPA) was applied to determine the most significantly dysregulated canonical pathways with Benjamini-Hochberg FDR adjusted p-values ≤ 0. 05 and |FC| ≥ 1. 3.
5 mg/kg. TD139 was dissolved in 100% DMSO and diluted in endotoxin-free PBS to obtain a final concentration of 2% DMSO and 0. 25 mg/mL of TD139. Body weights were measured at day minus 1 and every two or three days until the termination day. The evolution of skin thickening was monitored by using an external caliper, every 7 days until euthanasia. Longitudinal data were analyzed using a repeated measures ANOVA for comparison between HOCl and control group (model induction) and with a one-way ANOVA for comparison between all tested items and their HOCl control group, using Dunnett's adjustment. The baseline (day minus one) was added as covariable. Statistical analyses were performed using SAS software, v9. 4. Blood samples were collected from the venous sinus by retro-orbital puncture at day minus one, day 8, and 14 and processed to plasma by using lithium-heparin tubes for PK analyses and cytokine measurements.
The gating strategy was as follows (Supplementary Fig. 14 ): Immune cells = CD45+ cells; B cells = CD19+ cells; NK cells = CD335+ cells; CD4+T cells = CD3+CD4+ cells; CD8+T cells = CD3+CD8+ cells. Among CD19-CD3-CD335-cells: alveolar macrophages = autofluorescence high CD11b -/low and other myeloid cells (monocytes, dendritic cells, neutrophils, and eosinophils) = autofluorescence low/int CD11b +/high. Statistical analyses for immune cells in BAL were determined with SAS software v9. 4 using a one-way ANOVA for comparison between the HOCl-treated and control group (model induction) and with a one-way ANOVA for comparison between all tested items and their HOCl control group, using Dunnett's adjustment on log-transformed data.
Nuclei were stained with Hoechst 33342 dye (62249, ThermoFisher Scientific). Digital images were processed with the Zeiss LSM Browser. The number of F4/80-expressing cells was quantified using QuPath software (v. 0. 4. 1) as a percentage of the number of cells. Data were expressed as the fold-change compared to the control group. Statistical analyses were performed using a one-way ANOVA for comparison between the HOCl-treated and control group (model induction) and with a one-way ANOVA for comparison between all tested items and their HOCl control group, using Dunnett's adjustment. All statistical analyses were performed using SAS software v9. 4.
PK and ADA measurements Plasma samples were assayed for mAb concentrations using a generic method implemented on a Gyrolab xPlore automated immunoassay system  75. Briefly, in-house biotinylated mouse anti-human IgG CH2 capture antibody (ThermoFisher, MA5-16929) was immobilized at 100 µg/mL on the surface of streptavidin-coated beads in Gyrolab Bioaffy CD200 (P0004180, Gyros Protein Technologies), and diluted plasma samples were loaded. Then, in-house 647-Alexafluor conjugated mouse anti-human IgG antibody (BD bioscience 555784) was used as a detection reagent at 10 nM. Sample concentrations were calculated using the Gyrolab software (Gyros Protein Technologies). Anti-drug antibodies (ADA) were measured with a generic method implemented on a Gyrolab xPlore automated immunoassay system. Mouse plasmas were coincubated with excess concentration (10 µg/ mL) of D11 or E07 mAbs directly in the mixing chamber of the Gyrolab mixing CD96 (P0020455, Gyros Protein Technologies).
Then, the ADA-mAb complexes were captured by biotinylated Goat anti-human IgG (Southern Biotech 2014-08) coated at 100 µg/mL on the streptavidin beads within the compact disc. Finally, 10 nM of AlexaFluor 647 goat anti-mouse IgG (H + L) cross-adsorbed secondary antibody (Thermo-Fisher A-21235) was used as a detection agent. Values were expressed as fluorescence units with a significant cut-off threshold in each experimental run determined as the mean of 3 standard deviations calculated from the mean of signals obtained from 15 control untreated mice. Statistical analyses were performed using SAS software v9. 4.