dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations The main strength of this study was the use of data from a population-based registry, making it possible to have a large representative sample of births with congenital anomalies over a 20-year period. The overall prevalence of anomalies and prevalence of specific anomalies in our registry were comparable to those of French and European registries, reflecting its good quality and completeness. This study also has limitations. The Paris Registry of Congenital Malformations records congenital anomalies diagnosed from the prenatal period to discharge home from the hospital. However, some congenital anomalies are detected after discharge, which leads to an underestimation of the proportion of congenital anomalies detected postnatally. For instance, a population-based study in France estimated that approximately one-quarter of congenital heart defects, not associated with chromosomal anomalies, are detected after 7 days of life.
Data collection and analysis Data analyzed in this study were collected using Meltwater (social media monitoring software). Online searches using this software identified tweets containing the key hashtags #COVID19 and #Africa. We searched 3 months of data, starting 1 week before the first lockdown in Africa, to examine online responses during the first COVID-19 lockdown period. We identified 103,655 tweets using the two hashtags in the 3 months between 14 March and 14 June 2020. These dates were chosen as we decided to start the search 1 week before the first lockdown so reactions to these being enacted and the announcements of the lockdowns could be analyzed. After removing duplicates, retweets (when someone shares an original tweet), and non-English language results, tweets were analyzed for themes using a grounded theory approach in NVivo, allowing the ideas and concepts to "emerge" from the data.
The first author read each tweet and noted key themes shared by the tweets, then the data was uploaded into NVivo, and codes were made of the key themes; each tweet was manually assigned to a code. Through this process, six key themes emerged. The threshold for a theme to be included was that it contained at least 10% of the tweets. Any theme that contained less than 10% of the tweets was not considered representative enough to be included. In the instance when a tweet was retweeted several times, only the first tweet and any other tweets that appeared to have relevant comments were downloaded. A total of 5,421 tweets mentioned lockdown, and this was reduced to 2,962 when retweets were taken out. These 2,962 tweets were uploaded into NVivo, and emerging narratives were identified based on the data.
Background: In patients with renal cell carcinoma (RCC) enrolled in the phase III KEYNOTE-564 trial (NCT03142334), disease-free survival (DFS) following nephrectomy was prolonged with use of adjuvant pembrolizumab therapy versus placebo. Patient-reported outcomes (PROs) provide an important measure of health-related quality of life (HRQoL) and can complement efficacy and safety results.
Outcomes The primary efficacy end point in KEYNOTE-564 (DFS) and safety and tolerability data (adverse events [AEs]) have previously been reported. 13, 14  The prespecified HRQoL secondary end points were leastsquares mean (LSM) change from baseline to week 52 in symptom scores as assessed by the EORTC QLQ-C30 global health status/quality of life (GHS/QoL) and physical functioning scale scores  17  and Functional Assessment of Cancer Therapy Kidney Cancer Symptom Index-Disease-Related Symptoms (FKSI-DRS). 18  Exploratory end points included mean change from baseline in EORTC QLQ-C30 functioning and symptom scales and the EQ VAS. 19  Other exploratory end points were the proportion of patients with improved, stable, or deteriorated symptom scores and the empirical mean change from baseline for the EORTC QLQ-C30 and the FKSI-DRS.
Statistical Analysis The PRO analysis population included all randomly assigned patients who received ≥ 1 dose of study treatment and completed ≥ 1 PRO assessment. The primary PRO analysis time point for change from baseline in HRQoL measures was defined as week 52. A constrained longitudinal data analysis model was applied to assess the mean change from baseline up to week 52 in the EORTC QLQ-C30 GHS/QoL and physical functioning subscale and the FKSI-DRS. Proportions of deterioration, stability, and improvement were calculated when there was a defined clinically meaningful change in score from baseline at any time during the study. The PRO completion rate at each time point was calculated as the proportion of patients who completed the PROs at the assessment time point among those in the PRO analysis population.
Results As previously reported, 994 patients were randomly assigned to treatment in KEYNOTE-564 between June 30, 2017, and September 20, 2019 (Fig. 1 ). 13  The database cutoff date for this analysis was June 14, 2021. The PRO analysis included 484 patients in the pembrolizumab group and 493 patients in the placebo group for the EORTC QLQ-C30 and EQ VAS, and 483 and 493 patients, respectively, for the FKSI-DRS. Completion and compliance rates for all PRO measures were > 90% at baseline (Supplementary Tables  S2-S4 ). At baseline, 438 patients (90. 5%) in the pembrolizumab group and 450 patients (91. 3%) in the placebo group completed the EORTC QLQ-C30. At week 52, completion rates were 62. 8% (compliance, 85. 4%) in the pembrolizumab group and 65. 9% (compliance, 84. 6%) in the placebo group for the EORTC QLQ-C30.
The MSD data-sharing website (available at https://engagezone. msd. com/ds_documentation. php) outlines the process and requirements for submitting a data request. Applications will be promptly assessed for completeness and policy compliance. Feasible requests will be reviewed by a committee of MSD subject matter experts to assess the scientific validity of the request and the qualifications of the requestors. In line with data privacy legislation, submitters of approved requests must enter into a standard data-sharing agreement with MSD before data access is granted. Data will be made available for request after product approval in the US and EU or after product development is discontinued. There are circumstances that may prevent MSD from sharing requested data, including country or region-specific regulations.
Acknowledgments This study was funded by  Merck Sharp & Dohme LLC , a subsidiary of  Merck & Co. , Inc. , Rahway, NJ, USA. The authors thank the patients and their families and all investigators and site personnel. Medical writing and/or editorial assistance was provided by  Robert Steger, PhD , and  Matthew Grzywacz, PhD , of  ApotheCom  ( Yardley, PA, USA ). This assistance was funded by  Merck Sharp & Dohme LLC , a subsidiary of  Merck & Co. , Inc. , Rahway, NJ, USA. Funding Funding for this research was provided by  Merck Sharp & Dohme LLC , a subsidiary of  Merck & Co. , Inc. , Rahway, NJ, USA.
Study Population The initial model population comprised all patients with cancer, although the subpopulation that would incur the relevant treatment costs in the analysis was those receiving ITDD. This approach was taken to differentiate incident cancer cases from prevalent cancer cases, which can be obtained from large, readily available data sets containing information about all patients with cancer but not specifically for patients with cancer with an ITDD. Consequently, the distribution of incident cases and prevalent cases in the entire cancer population can be applied to patients with ITDD. For this study and for simplicity of design, we assumed that the ratio of incident-to-prevalent patients in the entire population with cancer would be the same in the population with ITDD. The distribution of patients with cancer into incident and prevalent cases was obtained from an assessment of the Hospital Episode Statistics (HES) data base by Duarte et al.
40 The possibility of pump refills at the patient's home may improve access to ITDD, particularly in a population with cancer pain who often present with advanced ill health due to cancer and other comorbidities, limiting their ability to travel to bigger centers for implantation/pump refills and subsequent care. Recent systematic reviews with meta-analysis have shown that ITDD is an effective and safe intervention for the management of cancer pain, with statistically and clinically significant reductions in pain intensity observed. 41, 42 Improvements in quality of life were observed in the included studies that evaluated this outcome. 4] [45] [46]  A meta-analysis focused solely on patients who received ziconotide with morphine was not possible because the studies only provided aggregate data and results were not available for those specific patients alone.
Data collection and detection of anti-SARS-CoV-2 antibodies To estimate the SARS-COV-2 prevalence and associated factors to the infection, data was collected from a sample of 137 participants of the PrEP1519-Salvador site aged 16-21 years (as the cohort ages). A socio-behavioral questionnaire with questions about gender identity, access to health services, sexual practices, drug and alcohol use, and history of violence was applied at the PrEP1519 cohort baseline and quarterly visits. Furthermore, between June and October 2020 participants were also invited to answer socio-behavioral with questions concerning their experiences during the pandemic. The questionnaire could be accessed through a link sent via an instant messaging application and included questions on sociodemographics, behaviors during the pandemic, knowledge about SARS-CoV-2 transmission and prevention measures, and perceptions of the pandemic (COBra questionnaire).
Data analysis A descriptive analysis of the study population characteristics was carried out. Bivariate analysis to estimate the seroprevalence of anti-SARS-CoV-2 IgG or IgM antibody by study variables was conducted using the Chi-squared test or Fisher's exact test. A logistic regression model was performed to estimate adjusted odds ratios for the multivariable analysis. Posteriorly, based on this model, adjusted prevalence ratios (aPR) and respective 95% confidence interval (95%CI) of the association between study variables and SARS-CoV-2 infection were estimated using marginal standardization. The delta method is applied to obtain standard errors  [19, 20]. The variables with p-value ≤ 0. 20 in the bivariate analysis were selected to start modeling and only those with p-value < 0. 05 remained in the final model, using a backwards procedure.
Acknowledgments The authors would like to express their gratitude to the adolescents MSM and TGW for participating in this study; to their parents and guardians for signing consent forms when necessary, to the local team that carried out the fieldwork at a hard time in human history. To the institutions that contributed to the development of the study, notably in Salvador, the  Bahia State and Salvador City Health Departments , the  Diversity Center of the Bahia State Justice and Human Rights Department. To the institutions that allowed the study for their technical, scientific, and financial support such as the  Department of Chronic Diseases and Sexually Transmitted Infections of the Brazilian Ministry of Health , the  UNITAID  (grant number  2017-15-FIOTECPrEP ), and the  Agence Nationale de Recherche sur le SIDA et les Hépatites Virales (ANRS) , grant number  ANRS COV 31 COBra.
Participants and Procedure 34 healthy volunteers gave written informed consent to participate in this study approved by the Ethics Committee of the University of Liege, Belgium (reference B70720156274). They were paid for their participation. All procedures were performed following relevant guidelines and regulations. One participant was excluded due to abnormal structural MRI scan, and one participant due to a corrupted data file in one of the diffusion imaging acquisitions. The final sample consisted of 32 right-handed (Laterality Quotient Score > 70  (Oldfield, 1971 ) participants (17 males; mean age, 23. 5 years; range, 19-31 years). Self-reported questionnaires assessed sleep habits (PSQI  (Buysse et al. , 1989) ) and circadian topology (MEQ  (Horne & Ostberg, 1976 )) for the past month before inclusion.
Data Acquisition Brain MRI data were acquired on a whole-body 3T scanner (Magnetom Prisma, Siemens Medical Solutions, Erlangen, Germany). Diffusion MRI images were acquired with a 64-channel head coil, using a multislice 2D single-shot spin-echo echo-planar imaging with monopolar gradients. Parallel imaging with the iPAT technique was used with a reduction factor of 2, one excitation, and 2mm interleaved axial slices with no gap at an isotropic resolution of 2x2 mm on a 96x96 matrix. The echo time (TE) and repetition time (TR) were 69ms and 7400ms. There were 70 slices (b-values: 650, 1000 and 2000 sec/mm2 with 15, 30 and 60 directions respectively; 13 additional interleaved brain volumes were also acquired with no diffusion weighting, and two extra b=0 volumes were acquired with opposite phase encoding for susceptibility induced distortion correction).
MRI data processing In this section we detail the steps towards the estimation of the diffusion MRI (dMRI) derived metrics as well as towards the determination of correspondence across subjects and across timepoints. Microstructural changes associated to fast learning episodes have been previously reported in subcortical as well as in neocortical areas  (Brodt et al. , 2018; Keller & Just, 2016; Sagi et al. , 2012) , hence our analysis included both. We derived two streams of analysis optimized for each of them. Microstructural changes in the cortex were estimated via a surface-based analysis by projecting the dMRI metrics on the cortical surface. Microstructural changes in the grey matter of the hippocampus, caudate nucleus and putamen were assessed via a region of interest (ROI)-based analysis.
Pre-processing Correspondence across subjects, for both the surfaced-based and the ROI-based analysis, was determined via the anatomical (T1w) images via FreeSurfer software (https://surfer. nmr. mgh. harvard. edu/, v7. 1. 1). To improve the anatomical image analysis robustness, pre-processing steps inspired by the minimal pre-processing pipeline for the Human Connectome Project  (Glasser et al. , 2013)  were applied prior to the FreeSurfer analysis. First, anatomical images were reoriented to match the orientation of the standard MNI152 template images. Next, they were cropped to a smaller FOV removing the neck and the lower part of the head, and finally, they were aligned to match the MNI152 template via the anterior to posterior commissure ("ACPC") alignment procedure  (Glasser et al. , 2013).
These steps were implemented via the FMRIB Software Library (FSL, v6. 0)  (Jenkinson et al. , 2012; Smith et al. , 2004; Woolrich et al. , 2009). A preliminary analysis showed that the anatomical image marked bias field affected the FreeSurfer analysis outcome quality, hence all images underwent an extra bias field correction step via the N4ITK algorithm  (Tustison et al. , 2010). Finally, in order to assist the final registrations to MNI space and to assist FreeSurfer with its brain mask creation process, a rough brain mask was generated using the method described in  Glasser et al (2013). For dMRI, the pre-processing steps included susceptibility induced distortion correction, eddy current induced distortion correction and head motion correction. Susceptibility induced distortions were estimated from b=0 volumes with opposed phase encoding via FSL's topup  (Andersson et al.
Model fitting The NODDI model was fitted to all available diffusion data (b = 0, 650, 1000, 2000 s/mm2) via a modified version of the NODDI-toolbox (http://mig. cs. ucl. ac. uk/index. php?n=Tutorial. NODDImatlab) implemented in Matlab (The Math Works, Inc. MATLAB. Version 2019b). For a robust parameter estimation  (Jelescu et al. , 2016; Zhang et al. , 2012) , the intra-neurite bulk diffusivity was fixed to the value of 2. 28 µm2/ms, which is supported by previous studies using a non-conventional, generalized version of dMRI acquisitions  (Guerreri et al. , 2020). DTI was fitted to the data with b-values up to 1000 s/mm2 via FSL's DTIFIT tool.
Inter-subject group correspondence FreeSurfer software was used to establish the correspondence between subjects by segmenting subcortical areas  (Fischl et al. , 2002 (Fischl et al. , , 2004) )  as well as registering each subject cortical surface to a template surface  (Dale et al. , 1999; Fischl & Dale, 2000). The pre-processed anatomical images underwent FreeSurfer routine. A visual quality assessment was performed at the end of each of the three main sections of the routine. Ad hoc interventions were carried out when necessary to ensure good quality of the segmentations and of the surface placement. The hippocampus is known to have an important role in spatial navigation  (Epstein et al. , 2017). In particular, different sub-structures are believed to have different functions  (Harland et al.
Sleep Data Sleep duration and quality were estimated by means of self-reports of the nights preceding Day 1 and Day 4. Mean sleep duration was not significantly different between RS and SD groups on the night before Day 1 (RS = 439 ± 63 min, SD = 453 ± 41 min; p > 0. 2) and the night before Day 4 (RS = 437 ± 55 min, SD = 458 ± 44 min; p > 0. 2). Within each group, duration of each night did not differ significantly (RS, p > 0. 8; SD, p > 0. 7). Likewise, a similar analysis conducted on subjective sleep quality assessed by means of a 6-point scale failed to evidence between-group differences. Subjective sleep quality was equivalent between groups for each night (night before Day 1: RS = 3. 7 ± 1. 2, SD = 3. 5±1. 1; p>0. 5; night before Day4: RS = 3. 6±0. 7, SD = 3. 7±0. 9; p>0. 9). Finally, within-group comparisons did not reveal any significant difference in sleep quality between both nights (RS p > 0.
Acknowledgments This study was supported by the  Fonds de la Recherche Scientifique  (F. R. S. -F. N. R. S. ) and the  Fonds Wetenschappelijk Onderzoek -Vlaanderen  (F. W. O. ) under the  Excellence of Science (EOS)  Project ( MEMODYN , No. 30446199 ). W. S. is supported by F. N. R. S. Aspirant Research Fellowship. M. G. received postdoctoral salary support from the  EOS  grant. At the time of the study, T. V. was funded by an  Université Libre de Bruxelles (ULB)   Individual Fellowship. M. D. is supported by the  Fonds pour la Recherche dans l'Industrie et l'Agriculture (FRIA)  Fellowship. C. S. is FNRS Research Associate.
Acknowledging BC molecular and histological heterogeneity, all cohorts were stratified according to histological subtype-invasive carcinoma of no special type (NST) or invasive lobular carcinoma (ILC), as well as the ER and HER2 status (Table  1 , Supplementary Fig. 1 ). Of note, some differences in the tumor characteristics of patients were observed across the cohorts, most probably related to the respective inclusion criteria (Supplementary Data 2). In subsequent analyses, BMI was considered either as a continuous variable or as a categorical variable of three categories: lean, overweight, and obese. There was no evidence of a difference in the distribution of BMI found between the METABRIC and ICGC cohorts, while the proportion of obese patients was lower in ELBC and MIND-ACT (Table  1 ).
It was observed in all cohorts that BMI was positively correlated with age and menopausal status, as previously shown  28. Overweight and obese patients were more likely to be diagnosed with larger tumors and at a more advanced stage in all cohorts (Supplementary Data 3). In MINDACT, the prevalence of NST and hormone receptor-positive (HR+) disease was also higher in obese patients compared to lean and overweight patients (Supplementary Data 3). No statistically evident associations between BMI and other standard clinicopathological characteristics were observed (Supplementary Data 3).
Association of BMI with genome instability and mutational signatures To investigate the differences in genome instability, as well as mutational signatures according to BMI, we retrieved relevant data from Nik-Zainal et al. where these genomic features were profiled using whole genome sequencing data of tumors from the ICGC cohort  22. We first evaluated the association of BMI with genomic instability using the total counts of somatic small mutations, including substitutions and insertions/deletions (indels), and genomic rearrangements as surrogates. In patients with NST ER+/HER2-, the total numbers of somatic substitutions and indels did not appear to differ between BMI categories (Fig. 3a , b, Supplementary Data 11). On the other hand, the count of somatic rearrangements was higher in tumors from overweight compared to lean patients (Fig.
Obesity-associated changes in bulk transcriptomic profile of breast cancer Having identified genomic features associated with BMI, we proceeded to dissect the expression profile of breast tumors to unravel more insights into how their phenotypes might vary according to patients' BMI. We investigated potential differences in gene expression profile in breast tumors according to BMI categories in MINDACT, the largest cohort with bulk profiling gene expression data available. We identified several differentially expressed genes (DEGs) in tumors from obese versus lean patients with NST ER+/HER2 (Fig. 4a ). We then examined the expression levels in different BMI categories for a set of selected genes with known functional roles in the BC-obesity axis  1, 2, 14  (Supplementary Figs. 10 11 12 ). Notable differences in the expression of leptin (LEP) and IL-6 (IL6) were observed between tumor bulk from obese and lean patients in the NST ER+/HER2-subgroup (Supplementary Fig.
Obesity-associated changes in cancer cell-specific transcriptomic profile We explored the recently published BC-derived single cell BioKey dataset from Bassez et. al. (Table  1 , Supplementary Figs. 1, 22), focusing on patients with NST ER+/HER2-(Figs. 5a-h, p, 6a-d ) and NST ER -/HER2-(Figs. 5i-p, 6e-h ). We first investigated cancer cell-specific transcriptomic profile and identified more DEGs with more pronounced differences according to BMI than in the bulk profiling (Fig. 5a , i, Supplementary Data 12). Among 17 genes consistently overexpressed in cancer cells from obese versus lean patients in both subgroups, while some of these genes have been reported as markers of proliferation and progression, e. g. ,  CD24  37, 38  , claudins (CLDN3, CLDN4)  39  , several other genes were thought to be associated with favorable tumor characteristics, e.
Obesity-associated changes in non-malignant cell type-specific transcriptomic profile and the TME Inspection of cell type-specific differential expression in nonmalignant cells using the BioKey data revealed an elevated inflammation in the obesity context. In NST ER+/HER2-tumors, this inflammation showed signs of multi-directionality, owing to simultaneous differential enrichment of contradictory pathways in various cellular compartments, e. g. , (I) overexpression of antigen-presenting genes in B cells and mast cells, (II) downregulation of interferon (IFN) response genes in T cells; (III) overexpression of pro-inflammatory and wound healing-like pathway genes in fibroblasts; and (IV) overexpression of pro-inflammatory genes as well as anti-inflammatory genes in endothelial cells (Fig. 5b-h , p, Supplementary Figs.