dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection of studies Studies obtained from the literature search were screened by title and abstract by ALT and VND, using covidence. org, a browserbased software for literature review. Full texts were examined if the articles were considered relevant. A preliminary manuscript for an ongoing unpublished study was included in the review  (Sifna et al. , unpublished). Studies were included if they were original articles with information on MDR/RR-TB treatment outcomes as defined by WHO or adaptations thereof. Studies using treatment outcome measures, such as mortality or culture conversion only were excluded. Other exclusion criteria included studies solely including patients who are still on treatment, relapse among patients with MDR/RR-TB without reconfirmation of MDR/RR-TB, patients with only pre-extensively drug-resistant (XDR)/XDR-TB, small sample size (n < 25), case reports/series, reviews, and studies using first-line treatment for MDR-TB.
Eligibility assessment Studies using the recommended outcomes of MDR-TB treatment or adaptations thereof were included for analysis. The adapted outcome definitions were assessed before inclusion to ensure crosscomparability. Studies with overlapping cohorts were identified to the best of our ability and the study (i) containing the most cases in the same cohort, (ii) with most elaborate outcome classification, (iii) and/or with the most recent update to end of treatment outcomes was included and prioritized in that order. Multiple authors were contacted to preclude overlapping data, and results stratified by country were provided, allowing exclusion of overlapping data from the multiple-country study  ( Schwoebel et al. , 2020 ).
Data extraction Data were extracted into a preformed Excel spreadsheet (Microsoft, Redmond, WA, USA). Information collected included data on first author, year of publication, study design, period, country, population size, age, percentage of males/females, percentage of HIV-positive individuals, drug-resistance patterns, treatment regimens (Appendix 4), length of treatment, information on previous treatment, use of directly observed therapy (DOT), stratified treatment outcomes if available, and definitions of treatment outcome. The distribution of treatment outcomes in the individual studies was calculated before stratification by country and random-effects analysis. If a study had multiple cohorts with different treatment regimes, and the results were available, the results were stratified based on the regime.
Statistical analyses Statistical analyses were performed using the meta package (version 5. 2-0) in R (version 4. 1. 3). Because we anticipated considerable between-study heterogeneity, a random-effects model was used to pool the proportion of successful outcomes, using logit transformation and inverse variance method. We used Knapp-Hartung adjustments to calculate the confidence interval (CI) around the pooled effect. Clopper-Pearson CIs were calculated for individual studies. A pooled success rate was also calculated for the Central and West African subgroups. A pooled unsuccessful outcome was also calculated. A sensitivity analysis with removal of outliers was conducted, where studies were considered outliers if their CI did not overlap with the CI of the pooled effect. Moreover, an analysis of studies using only WHO treatment outcomes and adapted treatment outcome definitions was performed.
Results In total, 867 records were identified through databases, of which 84 full-text articles were examined for relevance ( Figure  1  ). Two additional articles were obtained from other sources  ( Kamara et al. , 2022 , Sifna et al. , unpublished). Of the remaining articles, 14 were included for quantitative analysis  ( Akinsola et al. , 2018 , Aznar et al. , 2019 , Bakare et al. , 2021 , Bulabula et al. , 2019 , Ciza et al. , 2020 , Hassane-Harouna et al. , 2020 , Ige and Oladokun, 2018 , Kamara et al. , 2022 , Kuaban et al. , 2015 , Ngabonziza et al. , 2020 , Piubello et al. , 2020 , Schwoebel et al. , 2020 , Sifna et al. , unpublished, Souleymane et al. , 2021 ). Several articles contained overlapping data and were excluded. The 14 studies included for analysis represented 14 of 26 countries in the two regions ( Figure  2  ).
, 2009)  is an interactive tool proposed by the National Cancer Institute to estimate an individual's risk of developing colorectal cancer over a specific period (National Institutes of Health (NIH)ccrisktool. cancer. gov). CCRAT includes the clinical variables age, race, sigmoidoscopy/colonoscopy and polyps history, vigorous activity, servings of vegetables, relatives with a colorectal cancer, non-steroidal anti-inflammatory drugs and estrogen use, cigarettes smoked, and body mass index. The relative risks and attributable risks used to compute the absolute risk were derived from an American populationbased case-control data (Utah, Minnesota and Northern California), while the estimation of the marginal age-specific cancer hazard rates were obtained from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) Program.
, 2019; Hodgkinson et al. , 2014; Hussin et al. , 2015). Imputation and quality control are described in the Supplementary File 1. The availability of genotyping information and clinical variables led us to consider two subcohorts for evaluating the predictive models (cf. "Predictive scores" section). The validation of the CCRAT model was done using individuals with a computable CCRAT absolute risk (hereinafter referred as "CCRAT cohort"). The validation of the PRS models was done using individuals from the CCRAT cohort with genotyping information (hereinafter referred as "PRS cohort") (Fig. 1. For identifying cases with a colorectal cancer (invasive or in situ), we used the MED-ECHO administrative health database with the Tonelli et al. algorithm  (Tonelli et al. , 2016) : individuals with at least two claims in two years or one hospitalization related to a colorectal cancer.
The incidence date was the date of first hospital discharge or first claim with the appropriate International Classification of Diseases (ICD)  (ICD-9: 153, 154, 2303 (ICD-9: 153, 154, , 2304 ; ICD-10: C18, C19, C20, C21, D010, D011). Data were available from January 1st, 1998 to March 31st, 2016. Dates of death were also retrieved from the RAMQ. The outcome was the time before occurrence of a colorectal cancer from the enrollment in the cohort. Patients without colorectal cancer were censored at five years, at death or on March 31st, 2016 (administrative censoring).
Original and tailored colorectal cancer risk Assessment Tool models We considered two different approaches to estimate the absolute risk: (i) the original CCRAT model as implemented by the National Cancer Institute and (ii) a tailored CCRAT model that uses the Canadian marginal age-specific hazard rates and the distribution of the risk score. To compute the original CCRAT model's absolute risks at five years, at the inclusion date, we extracted the CCRAT's variables from the CARTaGENE questionnaire (Table  A. 1 of the Supplementary File 1) and used the NIH SAS macro, version 3. 0 updated in January 30th, 2019 (dceg. cancer. gov/tools/risk-assessment/ccratsasmacro) with the R language. For the tailored CCRAT model, we used the Individualized Coherent Absolute Risk Estimator (iCARE) package  (Choudhury et al.
More information about SNPs included can be found in Supplementary File 3. To compute the absolute risk, we also used the iCARE package. The distribution of the risk scores were obtained by the sampling at random of 10% of the individuals from the PRS cohort, for each gender, with small probability weights for the cancer cases. We reported the results using the 90% remaining individuals (hereinafter referred as "PRS validation cohort", Fig. 1. For estimating the absolute risk of colorectal cancer with a combination of both clinical (CCRAT) and genetic data (PRS), we used the same procedure as described in the sub-section "PRS models". In practice, the combination was simply the sum of the PRS and the CCRAT relative risks (hereinafter referred as "combined models"). The same methodology was used to compute the tailored CCRAT model in the PRS validation cohort.
Results Overall, 24,682 individuals were included in the CCRAT cohort for the validation of the original and tailored CCRAT models. Genotyped data was available for 6,747 individuals (Fig. 1. The median age at enrollment was 53. 2 years [Q1-Q3 47. 3-60. 1] for participants of the CCRAT cohort and 53. 8 years [48. 3-61. 0] for the PRS cohort, with 80 (0. 32%) and 24 (0. 36%) individuals having a colorectal cancer during the five years of follow-up in the CCRAT cohort and the PRS cohort, respectively. The median time of follow-up in each cohort was of 3. 1 [2. 5-5] and 5 years [3. 1-5], respectively. The Table  A. 2 in Supplementary File 1 compare the baseline characteristics between the CCRAT and PRS cohorts. The most notable differences are reported here. Compared to the CCRAT cohort, the PRS cohort had more individuals from the first enrollment phase (69.
g. ,  (Shin et al. , 2014; Ma et al. , 2010). A main strength is that it relies on a large population-based cohort representative of the Quebec urban population of middle-aged and older adults. Moreover, the linkage with administrative health databases improves the outcome quality and accuracy. Nevertheless, there are some limitations linked to the data collection. As we only included participants with European ancestries, results may be obviously less accurate for other populations. The information regarding family history of colorectal cancer was only available for participants enrolled during the first phase, while the colonoscopy/sigmoidoscopy history was only available for participants enrolled during the second phase. However, it is worth noting that the CCRAT model can handle missing data for these two variables.
Plasmids and transfection The plasmid pBlueScript SK(+) carrying the DNA of the full-length genome of gt3 Kernow C-1 p6 strain (Gen-Bank accession number JQ679013, kindly provided by S. U Emerson) was used  [20]. The ORF3-null mutant (HEV-p6-ΔORF3) and the 5R/5A mutant (HEV-p6-5R/5A) of HEV- p6 were generated as described in  [15]  and  [10] , respectively. The plasmid pBlueScript SK(+)/HEV-p6 expressing a V5-tagged ORF1 (V1 insertion) has been described previously  [21]. The plasmid pBlueScript SK(+) carrying the DNA of the p6-V1-Puro replicon was generated from the pBlueScript SK(+)/p6-V1 plasmid  [21]  in which the ORF3/ORF2 region was replaced by a fragment encoding the resistance gene to puromycin as well as the last 321 amino acid residues of ORF2. Capped genomic HEV RNAs were prepared with the mMESSAGE mMACHINE kit (Ambion) and delivered to PLC3 or Huh-7.
Modeling of the N-terminus of ORF2 We used an inhouse ColabFold implementation of Alpha-Fold2  [23]  to generate models of the N-terminus of ORF2. Briefly, we used the ORF2 sequence or only the sequence encompassing up to residue 311 (comprising the shell (S) domain) or up to residue 446 (with the middle (M) domain added), with or without the 13 N-terminal residues (i. e. fulllength or OR2i), to query the UniRef30 database (March or June 2021 release) with either HHBlits of MMseqs2  [24]. The resulting sequence alignments typically comprised 180-500 hits with 25-400 sequences aligned to any residue of the query. These alignments were used as inputs to Alpha-Fold2, and 5 models were generated for each. Resulting models were aligned to the S domain of PDB 3iyo. Molecules A, B and C, the three distinct positions in the T = 3 icosahedral HEV capsid, were tried.
We also designed a P3 peptide for obtaining antibodies recognizing the different forms of ORF2 (Fig. 1a ). To verify the specificity between strains/ genotypes, sequence alignments were carried out for each peptide (data not shown). Following mice immunization and hybridoma screening, one clone from P1 immunization (P1H1), two clones from P2 immunization (P2H1 and P2H2) and one clone from P3 immunization (P3H2) were selected and further characterized. Specificity of generated antibodies was first analyzed in western blotting (WB) experiments with supernatants (SN) and lysates of PLC3 cells electroporated with the gt3 p6 strain (PLC3/HEV cells) and mock electroporated PLC3 cells (Fig. 1b ). SN of PLC3/HEV cells contains quasienveloped infectious HEV particles (ORF2i) but also large amounts of ORF2g/c proteins, whereas cells express the intracellular ORF2i form that assembles to form intracellular particles  [7, 19].
Acknowledgements We thank  Suzanne U. Emerson  ( NIH, USA ),  Jérôme Gouttenoire  ( University of Lausanne ) and  Ralf Bartenschlager  ( University of Heidelberg ) for providing us with reagents. We thank  Valentin de Masson d'Autume  for his technical contribution. We thank  Raphaël Guérois  for sharing a standalone ColabFold implementation of AlphaFold2 and the  I2BC integrative bioinformatics core facility BIOI2  for assistance with the high-performance computing infrastructure.
Funding This work has been funded by  Inserm Transfert  ( MAT-PI-17006 ),  Pasteur Institute of Lille (DiagHepE) ,  Région Hauts-de-France (DiagHepE) ,  Agence Nationale de Recherche sur le SIDA, les hépatites virales et les Maladies Infectieuses Emergentes (ANRS-MIE)  and the  University of Tours. Cyrine Bentaleb ,  Kévin Hervouet  and  Maliki Ankavay  were supported by a fellowship from the  ANRS-MIE. Charline Camuzet  and  Martin Ferrié  were supported by the  Pasteur Institute of Lille and Région Hauts-de-France. Charline Camuzet  was also supported by  Inserm Transfert.
Participants In total, 51 healthy adults participated in the study (30 females, median age = 23 ± 2. 5 y). This includes three different cohorts (Supplementary Table  1 ) of participants who participated in a pilot Experiment 1 (n = 8), in Experiment 2 with behavioral testing only (n = 16), and in Experiment 2 with fMRI (n = 27). All participants gave informed consent prior to participating and all data were recorded anonymously. Participants were screened for exclusion criteria: left-handedness, age below 18 or above 40, any history of neurologic or psychiatric illness, regular use of drugs or medication, and contraindications to MRI scanning. Participants were informed that they would receive a fixed amount for their participation (25€ for behavioral studies, 75€ for MRI sessions). The study was approved by the Ethics Committee of the Pitié-Salpêtrière Hospital (Paris, France).
Tasks Before performing the tasks, participants were given written instructions, which were also repeated orally step by step. Tasks presentation and behavioral recordings were programmed with MATLAB using the psychophysics Toolbox (www. psychtoolbox. org). Rating tasks. Participants were instructed to report the subjective value of a set of rewards, the subjective cost of a set of efforts, and the subjective cost of a set of punishments, should they (hypothetically) experience these items. For each item, they were asked to indicate the quantity of the item that had the same subjective value (or subjective cost) than earning (or loosing) 1€ and 5€, successively. Items and their units (e. g. number, grams, meters, etc. ) were written in the center of the screen, and participants had to indicate the equivalent quantity with a keyboard.
To illustrate how the predicted delay of task completion was determined for each model, we simulated the expected net value of an option combining a subjective gain of 100 a. u. and a subjective cost of 85 a. u with a temporal discounting rate of 0. 05 for reward and 0. 2 for effort. We also performed 10 6 simulations, varying all free parameters with a deadline set to 30 days, and estimated the average procrastination duration in the space of discount rate parameters, marginalizing over reward values and effort costs. The simulations spanned the following ranges for the different parameters and variables: Ke = 0-0. 5, Kr = 0-0. 5, R = 40-60 a. u. , E = 20-40 a. u. , θ = 0. 5. We then informed these models with participant's data (expected gratification, form-filling cost ratings, and temporal discounting rates for reward and effort) to model the observed procrastination duration.
Bayesian model estimation and selection The different models were inverted using a variational Bayes approach under the Laplace approximation  72, 73  , implemented in the VBA toolbox (available at https://mbb-team. github. io/VBA-toolbox). This algorithm not only inverts nonlinear models with an efficient and robust parameter estimation, but also estimates the model evidence, which represents a trade-off between accuracy (goodness of fit) and complexity (degrees of freedom). The following non informative priors were used for parameters estimation: μ = 0, σ = 1 for Kr and Ke; μ = 1, σ = 1 for θ. The model log-evidence was then used as a criterion to select which model best accounted for temporal discounting and recurrent procrastination.
Participation was completely voluntary and participants were assured confidentiality of all the collected information. The enrolled mothers completed questionnaires with RAs at three timepoints: face-to-face immediately after childbirth (T1) and via telephone at 3 months (T2) and 6 months (T3) postpartum. They received a gift voucher upon completion of each study questionnaire. Data collection was completed between February 2018 and July 2019.
Data collection At T1, the RAs collected sociodemographic information of the participants and their spouses in addition to obstetric data including breastfeeding practices, complications during pregnancy, labor, postpartum care, living arrangements, social support (from husband, parents, and in-laws), and childcare assistance (from a nanny and or house helper). Infant data [birth weight (g), length (cm), and Apgar score (at 1 and 5 min)] were also reported from the hospital medical records. At T2 and T3, women were contacted by the same RAs to collect information on the maternal and infant health including anthropometrics [maternal body mass index (BMI) = kg/m 2 -calculated and evaluated according to the World Health Organization classification and infant weight (g) and length (cm)], breastfeeding practices, living arrangements, work status, marital status, social support, and childcare assistance.
At the three follow-up points, the participants were asked to fill out the Edinburgh Postnatal Depression Scale (EPDS). To account for language differences among the participants, the RAs had to be fluent in Arabic and English and questionnaires were pilot tested in both languages prior to use. Variables were classified into two categories: fixed and time-dependent. Fixed variables are those where the effects do not change or change at a constant rate over time and include background demographics and all the obstetric data that were collected immediately after childbirth. Variables that are predicted to change over time were considered time-dependent and included maternal work status, marital status, BMI, breastfeeding in the past 7 days, perception of infant health, paternal support in childcare, and current living arrangement.
Statistical analysis Collected data were analyzed using the Statistical Package for the Social Sciences software version 26. 0 (IBM Corp, New York, USA). Descriptive statistics (frequency count and percentages) were used to summarize sample characteristics. A chi-squared test was conducted to compare dichotomous sociodemographic, obstetric, and social support variables based on EPDS scores, immediately after childbirth. To account for the longitudinal nature of our data we used a generalized estimating equations (GEE) analysis to explore significant risk factors associated with postpartum depression symptomatology across time. An exchangeable correlation structure and ordinal logistic link function were specified. With the aim to examine significant risk factors of postpartum depression over the 6-month postpartum period, we first conducted a cumulative GEE analysis (Model 1; T1-T3) where all variables assessed at the three time-points were included.
It included women from different nationalities and ethnic groups which provides a clear representation of mothers residing in the country. The study design is another strength to underscore. A longitudinal cohort study where data were collected prospectively, allows to identify of changes over time and provides insight into cause-and-effect relationships. Although the used Edinburgh Postnatal Depression Scale is a self-report scale, it is widely established to be a valid, reliable, and culturally appropriate screening tool to detect probable postpartum depression. Finally, numerous potential covariates from prior literature were accounted for in the present study.
Acknowledgments This work was partly carried out with the support of the  Karlsruhe Nano Micro Facility (KNMFi , www. knmf. kit. edu), a  Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT , www. kit. edu). This work was funded by the  Deutsche Forschungsgemeinschaft (DFG)  under Grant No. 416700351  and the  Free State of Thuringia  in the framework of Forschergruppe 2017FGR0074. The authors thank  Rafael Celestre  for measuring the surface roughness of the glassy carbon lenses.
VZV-related clinical manifestations were retrospectively retrieved through medical charts. According to local procedures, antiviral prophylaxis by oral valacyclovir 500 mg bid was initiated at allo-HSCT and continued for at least 12 months. The study has been approved by a regional review board (Comité de Protection des Personnes Sud-Est V, Grenoble, France, number 69HCL17_0769) and is registered in ClinicalTrials. gov (NCT03659773). Concomitantly, blood samples from healthy individuals (HI), recruited among adult donors to the Lyon (France) blood bank (Etablissement Francais du Sang, EFS), were obtained to serve as controls. According to the EFS-standardized procedures for blood donation and to provisions of the articles R. 1243-49 and following ones of the French public health code, a written non-opposition to the use of donated blood for research purposes was obtained from HI.
Concentrations of stimulants, as well as length of incubation, had been determined by preliminary assays (data not shown). At each time point, IFN-g was measured in PBMC supernatants using the ELLA nanofluidic system (Bio-Techne, Minneapolis, MI, USA) according to the manufacturers' instructions. All values below the lowest limit of quantification (LLOQ = 0. 17 pg/ml) provided by the manufacturer were reduced to a value equal to LLOQ/√2 (= 0. 12 pg/ml). Patients were classified into "VZV-responders" and "VZV-nonresponders" according to the concentration of IFN-g in supernatants from VZV-stimulated PBMC, with a positivity threshold arbitrarily fixed at twice (4. 83 pg/ml) the highest secretion obtained in the control condition (2. 415 pg/ml). Pellets collected from paired wells were analyzed for T-cell proliferation using the Click-It ™ EdU AF488 flow kit (C10420; Life Technologies, Carlsbad, CA, USA) to measure incorporation of 5-ethynyl-2′-deoxyuridine (EdU) according to the previously published protocol  (26).
Briefly, the percentage of EdU + proliferating cells (among CD3 + cells) was obtained by flow cytometry analyses performed on a Navios flow cytometer (Beckman Coulter, Brea, CA, USA). For each experiment, a minimum of 2. 5 × 10 3 CD3 + cells were recorded. Data were analyzed using Kaluza software (version 1. 2, Beckman Coulter). Values equal to "zero" proliferation were set to 0. 01% to be plottable on a logarithmic scale.