dataesr/scientific-paragraphs-categorization huggingface dataset:
A Galaxy webtool for custom H 2 O 2 transducer mining The BioSensor workflow enables the prediction of metabolic reactions connecting any query metabolite to H 2 O 2. When given the International Chemical Identifier (InChI) of the molecule to detect, the workflow returns an interactive web page showing, if they are known, the potential pathways connecting the target to the chosen detectable molecule and provides additional information, such as MetaNetX  15  reaction IDs or EC numbers, to facilitate the identification of potential enzymatic transducers (Supplementary Figure  S2 ). One noticeable feature provided over the previously released biosensor prediction tool, SensiPath  16  , is the integration of potential promiscuous activity of the predicted enzymatic transducers. As a pilot study, we used the Galaxy Biosensor workflow to identify molecules of interest that could be converted to H 2 O 2 with 1 or 2 enzymatic steps, focusing on disease-associated metabolites according to the HMDB database  17  (Figure  1B ).
This operation is performed until the number of steps is reached or no new products are found. RetroPath2. 0 is available at myExperiment. org (https://www. myexperiment. org/workflows/4987. html), as a conda package on anaconda. org (https://anaconda. org/conda-forge/retropath2_wrapper), as well as a Galaxy node on the Galaxy ToolShed (https://toolshed. g2. bx. psu. edu/view/tduigou/retropath2/9c8ac9980bd6). RetroPath2. 0 release r20220104 was used. rp2biosensor is an open-source Python software that extracts from the retrosynthetic network generated by RetroPath2. 0 the subnetwork of interest, linking the biosensor to the compound to be detected, and produce an interactive web page showing the transducing reactions. Briefly, rp2biosensor parses the retrosynthesis network outputted by RetroPath2.
0, completes the predicted reactions by putting back co-substrates and co-products omitted during the retrosynthesis using the rxn_rebuild (https://github. com/brsynth/rxn_rebuild) Python package, enumerates the shortest path linking the compound of interest, i. e. the biosensor, to the compound to be detected, e. g. lactate, and finally outputs the resulting subnetwork as an interactive web page to let the user browse the results. rp2biosensor source code is available on GitHub (https://github. com/brsynth/rp2biosensor) , is released as a conda package on anaconda. org (https://anaconda. org/conda-forge/rp2biosensor), and as a Galaxy node on the Galaxy ToolShed (https://toolshed. g2. bx. psu. edu/view/tduigou/rp2biosensor/b0efd4b2ffba). rp2biosensor version 3. 0. 0 was used.
For the efficiency assessment of the developed tool, the HMDB database version 5. 0 was used to explore detectable compounds from H 2 O 2. Only compounds fulfilling the following criteria have been kept: compounds should be associated with at least one disease, have a valid InChI structure, contain at least one carbon, and have a molecular weight of at most 1 kDa. RetroPath2. 0 was set for a 2-step exploration using both forward and retro reaction rules with diameters ranging from 2 to 12. If both 1-and 2-step pathways exist for a given compound, only shortest paths are reported by rp2biosensor.
Reporter signal measurements To measure GFP fluorescence, 8 hour kinetics were performed at 37°C with either a Cytation 3 a Synergy HTX plate reader (Biotek Instruments) using excitation/emission settings of 485 nm and 528 nm, respectively. Collected data were normalized by FITC Mean Equivalent Fluorescence (MEF) through conversion factors that were established for each plate reader using fluorescein standards with the same plates and machine settings as the ones used in the experiments, as per Jung et al. 33  & Batista et al. 25. For CPRG reporter measurement, OD574 was measured. Data were normalized by subtracting a blank sample containing everything but reporter DNA. For the luciferase reporter, after the 37°C incubation step, 20 µL of the final cell-free reactions mix were added to a white 96-well plate.
Metrics of the Sarcosine, Choline and Lactate sensor were calculated on the data presented on figure3D, 3E, & 3F. The max fold change is the ratio of fluorescence between the induced (OFF) and induced state (ON), the leakage is the fluorescence measured in the uninduced state, the swing is the difference of fluorescence between the uninduced and induced states. EC50 is the half-maximal effective concentration. MEF (Mean Equivalent Fluorescence) quantifies the fluorescence measured by the plate reader as equal to the one generated by a certain amount of FITC.
Acknowledgements We thank members of the  Bonnet and Faulon labs  for critical feedback and fruitful discussions. We also thank the patients for participating in this study, as well as personnel of the  Beausoleil Clinic  for help in collecting the samples. PS, PV, JLF, JS, and JB acknowledge funding support by the  ANR   SynBioDiag grant  ( ANR-18-CE33-0015 ). JLF acknowledges funding support by the  ANR grants iCFree (BiopNSE  grant number  13001533 ) and Aladin (EQUIPEX+ grant number  15000882 ). JB acknowledges support from the  ERC starting "COMPUCELL"  (grant number  657579 ),  INSERM  and the  Bettencourt Schuller foundation. The  Centre de Biologie Structurale  acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)  ( ANR-10-INSB-05-01 ). MK acknowledges funding support from  INRAe's MICA department, Université Paris-Saclay, Ile de France (IdF) region's DIM-RFSI , and  ANR DREAMY  ( ANR-21-CE48-003 ).
For those proteins that passed the quality control, data were log2 transformed to reach normal distribution. Afterwards, the plate batch effect was corrected by subtracting for each individual and each protein the difference between the overall protein average minus the plate-specific protein average. Finally, values below LOQ1 and above LOQ2 were imputed using a truncated normal distribution implemented in the truncdist R package  (Nadarajah and Kotz, 2006)  (see details on the QC in the Supplementary material (Appendix A, section S2)) and a descriptive table of the proteins evaluated in the study in Supplementary Excel (Appendix B) file (Table  B 1.
Outdoor air pollution exposures and plasmatic proteins analyses We assessed the association between childhood outdoor exposure to air pollution and protein levels using the omicRexposome R package (Bioconductor -omicRexposome,). Each air pollutant exposure (at different locations, windows, and pollutants) was related to the plasmatic levels of each protein through linear regressions adjusted for covariates using the omicRexposome based on limma R package  (Ritchie et al. , 2015). Models were adjusted for a common set of confounders identified a priori based on literature: child's sex, cohort, self-reported maternal education, self-reported ancestry child's age and mean outdoors temperature of each participant at residential or school addresses. The effect size was expressed as log2 fold change (log2FC) in protein levels per IQR change of the exposure.
Considering all the above, this study might provide more evidence to promote and implement new strategies and public policies to reduce exposure to air pollution. sociation of Diabetic Patients (AFD)), French Agency for Environmental Health Safety (now ANSES), Mutuelle Générale de l'Education Nationale a complementary health insurance (MGEN), French national agency for food security, French-speaking association for the study of diabetes and metabolism (ALFEDIAM). INMA was supported by grants from the Instituto de Salud Carlos III, CIBERESP, and the Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the Lithuanian Agency for Science  Innovation and Technology (6-04-2014_31V-66)   Health, and Child Health: 2012-15). MC received funding from Instituto Carlos III (Ministry of Economy and Competitiveness) (CD12/00563 and MS16/00128).
The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. The Rhea project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV. 2007. 1. 2. 2. 2. Project No 211250 Escape, EU FP7-2008-ENV-1. 2. 1. 4 Envirogenomarkers, EU FP7-HEALTH-2009-single stage CHICOS, EU FP7 ENV. 2008. 1. 2. 1. 6. Proposal No 226285 ENRIECO, EU-FP7-HEALTH-2012 Proposal No 308333 HELIX), and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011-2014; "Rhea Plus": Primary Prevention Program of Environmental Risk Factors for Reproductive.
Acknowledgments ISGlobal acknowledges support from the  Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX 2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Programme ). The  CRG/ UPF  Proteomics Unit also acknowledges support from "Secretaria.
Funding The study received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 ) (grant agreement no  308333 ) ( HELIX  project), the  H2020-EU. 3. 1. 2. - Preventing Disease Programme  (grant agreement no  874583 ) ( ATHLETE  project) and from the  European Union 's  Horizon 2020 research and innovation programme  (grant Agreement number:  733206 ) (Early Life stressors and  Lifecycle Health (LIFECYCLE) ). BiB received funding from the  Welcome Trust  ( WT101597MA ), from the  UK Medical Research Council (MRC)  and  Economic and Social Science Research Council (ESRC)  ( MR/N024397/ 1 ), and the  National Institute for Health Research (NIHR) Applied Research Collaboration for Yorkshire and Humber. The EDEN study was supported by  Foundation  for medical research (FRM),  National Agency for Research (ANR) ,  National Institute for Research in Public health (IRESP: TGIR cohorte santé 2008 program ),  French Ministry of Health (DGS) ,  French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A) , and  Human Nutrition National Research Programs, Paris-Sud University, Nestlé ,  French National Institute for Population Health Surveillance (InVS) ,  French National Institute for Health Education (INPES) , the  European Union   FP7  programmes (FP7/ 2007-2013, HELIX, ESCAPE, ENRIECO, Medall projects),  Diabetes National Research Program  (through a collaboration with the French As-.
| Context This paper reports one part of a larger longitudinal mixed methods study that follows nursing students from their first to the fourth year of study to understand how their self-esteem changes over time and the relationship between self-esteem and clinical competence development. Four out of the 16 vocational colleges offering a Bachelor's degree in nursing in French-speaking Belgium were invited to participate in the study, which used purposive sampling based on geographical, network, size and institutional criteria yielding maximum diversity  (Dancot et al. , 2021). A cohort of 815 first-and second-year students was assembled in October 2017 using accidental sampling (attending class) and followed annually with quantitative and qualitative data collection. Quantitative data included sociodemographic information, self-esteem assessment and academic performance.
Bioinformatics Housekeeping gene selection. We refined the 20housekeeping gene list defined in the NanoString IO360 panel using three independent datasets grouping TNBC patients retrieved from xenaBrowser  28  [The Cancer Genome Atlas (TCGA) BRCA primary tumors with 123 patients, Chin et al. 2006   29  : 41 patients and Hess et al. 2006  30  : 27 patients]. We computed gene expression variability as the inter-quantile (5%-95%) ratio in each dataset and selected 6 of the 20 genes showing ratios under 1. 5 in all datasets: ERCC3, POLR2A, SF3A1, TBC1D10B, TMUB2, UBB. NanoString data processing. Raw RCC files of 115 patients were loaded into R environment and normalized using Nano-StringNorm package. 31 We normalized the expression values with options CodeCount ¼'geo. mean', Background ¼'mean.
2sd', SampleContent ¼'housekeeping. geo. mean', round. values ¼ TRUE, take. log ¼ TRUE. Unsupervised analysis. We first computed gene expression pairwise Spearman's correlations and selected gene pairs with a score of !0. 7. We then selected modules of correlated genes having at least 10 members using the edge. betweenness. community function from igraph R package. 32 he 573 remaining genes and 115 patients were clustered according to gene expression using PCA and HCPC functions from FactoMineR R package  33  with options ncp ¼ 10 for PCA and default parameters and we extracted four gene clusters and five patient clusters.
Gene set functional enrichment Gene clusters. We evaluated functional gene cluster (from unsupervised clustering and the extended signature) enrichment using enricher function from ClusterProfiler R package  34  and Reactome, WikiPathways and Hallmarks from the MSigDB  35  and gene sets provided by NanoString. The universe was set as the genes present in the NanoString setup. The most significant term per unsupervised gene cluster was reported. We reported gene sets showing an adjusted P value under 0. 05 for the extended signature. Patient comparison. We first computed single-sample gene set enrichment analysis (ssGSEA) scores using GSVA R package  36  with norm. ssgsea option set to FALSE and gene sets provided by NanoString (biological functions and immune cell population signatures). We also computed immune cell population infiltration using Microenvironment Cell Populations (MCP) counter.
We thus computed signature scores by applying this method to each patient and signature gene sets [the eight-gene and extended signature, the Immunological Constant of Rejection (ICR) from Hendrickx et al. 38  and TIS  27  ]. A higher score represents a higher activity of the analyzed genes. Cohorts were then separated into two subgroups according to the optimal cut-off defined with surv_cutpoint function from survminer R package. Validation datasets. We selected 442 patients with the filter 'PAM50-basal' from KMplot breast cancer cohort  39  and used online tools to compute the relapse-free survival (RFS) probabilities. We used the median value of our eight-gene and extended signature and separated the cohort according to the optimal score. We tested our signature on the METABRIC dataset in which we selected 251 ER, PR, HER2-negative status and basal PAM50 patients.
Statistical analysis Statistical analysis was carried out using the 9. 4 SAS software version (SAS Institute, Inc. , Cary, NC). The qualitative data were described by their numbers and percentages, and the quantitative data by their medians and their ranges (min-max). The quantitative data were analyzed as continuous variables. Distant-relapse-free interval (DRFI) was defined as the time interval between the surgery date and the date of distant recurrence or death from breast cancer, whichever occurred first. Follow-up times were censored at last contact if no distant relapse had occurred or if patients had died without distant relapse. The follow-up was calculated using the inverse KaplaneMeier method, where deaths are censored. The KaplaneMeier method was used to estimate DRFI, and the results were compared between groups with logrank tests.
Sample and setting ERMIES was a multicentre randomized two-arm controlled trial that tested the efficacy of a long-term (two years) structured group self-management educational intervention in improving blood glucose in nonrecent, insufficiently controlled T2DM patients compared to a 3-month initial education course only. The detailed protocol of the trial (NCT01425866 ClinicalTrials. gov) is described elsewhere  [25]. The main eligibility criteria included age ≥ 18 years, type 2 diabetes treated for more than one year, HbA1c ≥ 7. 5% for ≥ 3 months, without any severe currently evolving complication (ischaemic or proliferative retinopathy, severe chronic renal insufficiency (clearance < 15 ml/min), coronary heart disease, foot lesions), and absence of any major physical or cognitive handicap.
After written consent was obtained, participants were randomized to either the intervention or control arm of the study (allocation ratio 1:1). Computing randomization was stratified on 2 factors: centres (five strata) and antidiabetic treatment (two strata: insulin-treated or not). The calculated necessary number of subjects to include in the study was 99 per arm. It was increased to 120 for taking into account an estimated data deficiency of 20% at two years (drop out, refusals, deaths), making a total number to include 240 subjects  [25]. The trial design included an initial group education course conducted by trained educators blinded to the subsequent group allocation within the 12 weeks following inclusion. At the end of these 12 weeks, patients allocated to the intervention group were invited to receive ongoing structured education within group sessions (3-10 patients) for 90 to 120 min each at 16, 32, 48, 64, 80, and 96 weeks.
Measures Attrition and retention rates were measured at each visit during the study. Whenever possible, reasons for withdrawing from the study were gathered directly. The adverse events motivating attrition were analysed individually from the original data and completed through self-administered questionnaires. Clinical features and anthropometric indicators (body mass index, waist circumference) were assessed by physicians or nurses. Data collected at baseline were grouped into five categories: sociodemographic, clinical, health care access and pathway, lifestyle, and psychometric scales. Adherence to treatment was evaluated using the Compliance Evaluation Test (CET) as described by Girerd et al. [26]. Health practices (level of physical activity and food consumption) were assessed by questionnaires used in Reunion Island for a number of descriptive or intervention studies: RECONSAL  [27] , REDIA-prev1  [27] , and REDIA-prev2  [28].
Analyses The completeness and accuracy of the data were double checked during data recording on the Ennov Clinical software from the paper questionnaire and then by consistency tests programmed by the data manager (Ennov Clinical V. 7. 5, Ennov Group, Paris, France). The initial dataset for regression analysis comprised 27 baseline variables (i. e. , measured at the trial's entry) including 19 variables with at least one missing observation but never more than 11. To minimize selection bias due to incomplete data at baseline, we used a multiple imputation strategy under missing-at-random assumption. As the multivariate description of the variables highlighted an arbitrary missing-value pattern (data not shown), we selected the multivariate imputation using chained equations to impute baseline missing data using the mi impute command from Stata software (version 13.
From the 112 variables from the ERMIES database, 17 variables were combined into 4 scores regarding health practices (dietary balance, physical activity separately at work and at home, checked as described above, and smoking (Y/N)), 64 variables were combined into the 4 psychometric scales (including 6 subscales) described in the previous paragraph, and 43 variables of adjustment were included in the MCA. The MCA performed separately for sociodemographic, clinical, health care access and pathway, lifestyle characteristics and psychometric scales allowed us to eliminate 16 variables potentially duplicating or not relevant, leaving 27 candidate variables to include in the regression models after the multiple imputation for missing data. The selection of the initial multivariate model was based on a bivariate analysis statistical significance level of 20%.
Backwards elimination was then used to select variables for the final model with a p value of 5%. The hazard ratio (HR) in the Cox regression model estimated the relative likelihood of attrition during the study. The proportional hazards assumption was checked by Schoenfeld tests of residuals. The statistical analyses for descriptive analysis, MCA, Kaplan-Meier were performed using SAS version 9. 4 software (SAS Inc. , Cary, NC, USA). Cox proportional hazards regression analysis and linear regression analysis were performed using STATA version  13.
Ethical consideration The ERMIES RCT was registered in the European RCT database in August 2011. It received approval from the ethics committee, "Comité de Protection des Personnes (CPP) Sud-ouest et Outre Mer III", and authorization from the French Health Products Safety Agency (Agence française de sécurité sanitaire des produits de santé, Afssaps). Data were computerized according to the reference methodology for clinical trials (MR 001) of the French commission on freedom of information (CNIL). The present study used the computerized anonymous data of the patients who signed an informed consent to participate in the ERMIES study.
Health literacy is the result of a balance between individual skills and relationships with professionals, services and the health system. Low personal and social resources, burdensome family and social situations may hinder engagement with self-management  [55]. The principal strength of the present study was the large baseline dataset with 140 variables (including sociodemographic, clinical, psychological, health care access, and lifestyle information) allowing us to draw a detailed image of the population included in the study and enabling an extensive analysis of potential factors associated with attrition. Our work also had several limitations. First, this study was conducted in a small number of patients (n = 100) due to difficulties in patient enrolment and RCT interruption as the calculated necessary number of subjects (n = 240) was not obtained within a reasonable period (5 years).
Acknowledgements The authors would like to acknowledge the support and help of the staff of the  Clinical Investigation Center  ( CIC 1410 INSERM ) and the  Methodological Support Unit of Reunion University Hospital ,  Sylvaine Porcherat  and  Corinne Mussard ,  Nadège Naty  for their contribution to data entry,  Karim Boussaïd  for data management,  Cyril Ferdynus  for statistical advices and  François Favier  for administrative support. The authors wish to thank all the staff from Hospital secondary centers and from the  RéuCARE diabetes management network , engaged in the follow-up, education support, and treatment of participants recruited as well as in the logistic of the ERMIES study. Authors are grateful too to the team of the DRCI of the  CHU de La Réunion , especially  Liliane Cotte ,  Fidéline Filleul ,  Vanessa Basque ,  Emilie Techer , for strong support of the ERMIES study.
We previously demonstrated that antibodies isolated from macaques were very close to human ones. Nevertheless, to be as safe as possible after administration to patient, particularly if several injections are required, we perform the germline-humanization of 2LF. This humanization step was realized firstly in the FR regions ("super-humanization"). The sequences of the human germline genes closest to the sequence of 2LF were identified with IMGT database. The amino acids diverging between the germline sequence and the VH and VL sequence of 2LF were identified and for each potential "mutation" an antibody variant was synthesized, produced and tested in surface plasmon resonance. As summarized in Table  1 , only one mutation induces a slight decrease in affinity and was not retained. A mutant that combined the 28 selected mutations was produced for further characterization.