dataesr/scientific-paragraphs-categorization huggingface dataset:
Data elements and completeness A detailed database is aimed to facilitate the investigation of various current and future topics. Data elements are listed in detail in online supplemental appendix 2. Over time, new data elements may become necessary for new individual projects. Should this occur, investigators will Open access quickly supplement the missing variables. Patient age, sex, admission NIHSS score, recanalisation status before and after thrombectomy and 3 months outcome measured by mRS are obligatory data items and must be present for all patients (otherwise a patient is not eligible to be included to the registry). In general, missing data for any variable or patient must not exceed 10%.
Data sources All paper-based or electronic patient files including laboratory values and imaging data will be used to capture the EVA-TRISP registry data points. Based on these different sources alongside repeated clinical evaluations undertaken by the dedicated EVA-TRISP collaborator, registry data points that initially remain missing during the early stroke treatment process will in the majority of cases be possible to reconstruct and thereafter reported to the local registry (eg, NIHSS scores). In most cases, the local EVA-TRISP investigators form the local stroke team and will be actively seeing the patients already in the emergency room and/or at their own stroke units and can therefore guarantee completeness of data in most cases.
Quality control Quality control is another crucial step in multicentre large-sized registries since missing data are a frequent problem impairing the reliability and generalisability of registry-borne data. The EVA-TRISP registry is different in this sense because data are not yet directly collected to a central registry, but each centre collects their own data to their own institutional registry according to a standard harmonised database item list and SOP. Thus, clear variable definitions are easily available for the user when data are entered. It is the responsibility of the local EVA-TRISP collaborator (usually a senior stroke physician) to check and account for the validity and completeness of all data points introduced to the local registry. Therefore, missing data are expected to be very low.
Furthermore, all centres have agreed to include all consecutive patients attempted with an EVT, and all centres will undertake frequent spot checks against internal hospital administrative systems covering EVT procedures as to not leave any patient out of the registry. Therefore, our registry data will likely include all EVTs performed within a region and population, practically equalling to a populationbased study, although being hospital based, because EVT is usually available only at stroke centres serving a predefined region and the inhabitant population. Most of the required data come from routine procedures that are standardly collected and recorded in stroke patient care pathways as part of the clinical routine, and therefore, these data points are almost always retrievable. In the subsequent quality control process, the data files from the individual participating centres are merged into a single file for further maintenance analyses.
Pseudonymised individual centre data are sent to the centre leading the specific project using encrypted transfer protocols. The subsequent data management of the merged database will implement checks for missing data along with checks for range, consistency and illogical data (eg, NIHSS score cannot be minus or over 42 points and can only be full points and not decimals; patient age at stroke onset can be only in digits and is expected to be from 16 years and very rarely over 100 years). Also other procedures regarding quality check will be implemented at milestone points, such as comparing the performance of data reporting among centres.
The unlimited time span requires careful evaluation by ethics committees. If the consortium decides to end the registry, each centre's data will be adequately returned to the owners, and the registry data will be deleted achieving an absolutely non-retrievable condition according to technical SOPs of the registry-holding centre. Thereafter, each individual centre will be free to decide how to proceed with their own datasets. Similarly, if a centre decides to resign from the registry, their data will be adequately returned and will be deleted from the main database file after confirmation that the data are safely received by the local principal investigator. While all patient-related data including clinical, laboratory and imaging data are completely anonymised, each centre will keep a key file within their local electronic hospital system with patient identifiers matching to the patient code in the registry (eg, if necessary to go back to patient files).
Data ownership, access, use and publications Each centre is self-financing in data collection and is the indisputable owner of their own data. If and when a mutual single databank is constructed, each individual researcher with the necessary formal training and permissions will be able to insert data directly to the central database on the internet and have access to the researcher's own centre's data without limitations. Individual scientists working on a properly agreed on single project and doing data analyses will be granted proper access to all data. In case that an individual centre refrains from participating in a particular analysis, their data will not be included in that analysis. Any publication that is produced from the registry data will include authors from each contributing centre in accordance to number of patients delivered as well as active involvement in analyses and writing work.
Ethics, informed consent and privacy Each centre has received necessary official approval from their respective local authorities and/or ethical committees according to their national and local rules (online supplemental appendix 3). These permits include transfer of data between EVA-TRISP centres. Necessity of individual informed consent is dependent on national rules and will be collected if necessary. Data are shared with respect to the EU law 2016/679 about General Data Protection Regulation. In the long run, the aim is to have a permanent database residing at a member site. Establishment and maintenance of the permanent database at one centre will be initiated only after a separate ethics approval.
An early example has been the challenge of the usefulness of the upper age limit of 80 years for IVT based on comprehensive, observational studies. Eventually, the third International Stroke Trial proved that indeed patients aged 80 years and older benefit from IVT too. 35 sing the TRISP registry, we previously examined the safety of IVT in a number of patient subgroups where RCT-based data did not exist. 0] [51]  These registry-based novel data contributed to the numbers of patients treated safely and successfully with IVT globally. Ongoing and planned research projects within the EVA-TRISP registry collaboration that may fill important knowledge gaps are investigations on: (I) stroke due to cervical artery dissection, (2) stroke with low baseline NIHSS, (3) stroke specifically in the anterior cerebral artery (ACA) territory, (4) stroke patients with preexisting dependency, (5) significance of cerebral collaterals, (6) significance of tandem occlusions and (7) stroke patients with active cancer.
Imaging findings have also been used for patient selection in some highly successful IVT and EVT RCTs. 4 5 54-56  Detailed imaging information is even more crucial when EVT is considered. Therefore, the establishing of an imaging repository parallel with the EVA-TRISP registry received a widespread support from members. To date, the choice of imaging modalities, parameters and thresholds varies widely across medical centres. No standardised imaging protocols currently exist, other than joint statements from professional societies. 57 A large, multicentre neuroimaging registry with state-of-the-art re-evaluation of images combined with detailed clinical data of IVT/EVT-treated stroke patients would be helpful for validating between modalities, defining thresholds, enhancing automated assessments and creating standards in neuroimaging for AIS.
Strengths and limitations of the EVA-TRISP registry Strengths of EVA-TRISP registry include: (1) the high completeness level of data with few missing data, (2) large sample sizes that reduce the risk of bias and allows adjustments for confounders, (3) the systematic and standardised data ascertainment that increases the homogeneity of the study population, (4) the intrinsic motivation of the study personnel that leads to a high rate of completeness of ascertained data sets, contributing to a high-quality registry and (5) the dynamic nature of the EVA-TRISP database due to the commitment of the centres to adapt the local database and add variables retrospectively and prospectively. In addition, (6) a large number of variables is gathered including those with unknown prognostic importance. This allows addressing novel yet unidentified research questions.
(3) All EVA-TRISP centres are experienced in stroke treatment. This expertise implies-as a downside-a limited generalisability of findings to all stroke providers with less expertise and less advanced settings. (4) The majority of our included patients are Caucasians and from high-income countries. Thus, we cannot compare ethnical differences, nor can we compare health systems with various funding levels. (5) Currently, there is no 'core lab' to validate haemorrhagic complications and 3-month mRS ratings. As valid for other major registries like SITS and GWTG, local interpretation of outcome data may differ between sites. Since EVA-TRISP centres are mostly high-volume centres with long-standing experience in maintaining IVT databases, this bias is likely to be smaller than in most of the other registries.
SUMMARY The EVA-TRISP collaboration is an open platform dedicated to conduct joint research projects in AIS patients treated with IVT and/or EVT. The aims of the EVA-TRISP collaboration are to increase knowledge on the safety and efficacy of IVT and EVT, study outcomes after IVT and EVT, to evaluate processes of acute stroke care as well as to document and improve acute stroke care quality. Our previous achievements prove that this collaboration has the potential to provide versatile observational information on treatment of AIS patients during daily clinical practice. Prospective and standardised documentation of individual patient data according to consensus definitions is a major requirement to maintain the quality of the EVA-TRISP registry. Publishing this methodology paper improves the transparency of the registry and collected data.
The definition of sICH is in accordance with the definition used in the European Cooperative Acute Stroke Study II ('an intracranial hemorrhage was defined as symptomatic if the patient had clinical deterioration causing an increase in the National Institute of Health Stroke Scale Box 1 Universal standards and requirements for the databases and centres contributing to the EVA-TRISP registry* EVA-TRISP centres are comprehensive stroke centres with highvolume EVT applications -typically university hospitals or closely affiliated to university hospitals. ► Treatment of acute ischaemic stroke patients with EVT according to guidelines valid at the relevant time or documentation of deviation therefrom. *EVA-TRISP welcomes participation and project proposals of further centres fulfilling the commitment and the outlined requirements.
► Prospective registry of consecutive patients with systematic check-up of missing cases. ► Comprehensive collection of baseline characteristics according to consensus definitions stated in this and the previous methodology papers. Prospective assessment of haemorrhagic complications (symp-tomatic intracerebral haemorrhage according to the European Cooperative Acute Stroke Study (ECASS) ECASS II criteria) and functional outcome at 3 and 12 months (according to the modified Rankin Scale; either telephone interview, postal questionnaire or follow-up visit). ► Approval of institutional review board to maintain the respective endovascular treatment (EVT) database and to obtain 3-month and 12-month follow-up data. copyright. BMJ Open: first published as 10. 1136/bmjopen-2020-042211 on 9 August 2021.
On April 4 th , France reached an unprecedented milestone: 6,838 patients were hospitalized in intensive care unit (ICU), a record "in French medical history"  (1). Among all specific populations that may have a particularly poor COVID-19 outcome, patients with schizophrenia (SCZ) should be given particular attention. Since the Second World War, when SCZ patients died massively in asylums from malnutrition (the so-called "extermination douce" (gentle extermination)(2)), the somatic care of patients with SCZ has lagged behind that of other mentally healthy patients. We have recently found, in a national database study, that patients with schizophrenia received less high-intensity care than those without severe mental disorder  (3) , and it is unknown whether COVID-19 patients with SCZ benefit from the same care as non-SCZ patients.
Data sources and collection Data were obtained from the APHM/AMU clinical data warehouse. This warehouse contains all the clinical data available on all inpatient visits to one of the APHM/AMU facilities. No data were manually abstracted from the electronic medical record or charts. The data obtained included patients' sociodemographic data, clinical data (i. e. , smoking status, overweight and obesity, symptoms, Charlson comorbidity index score  (10)  and main comorbidities), triage vital signs, initial laboratory test results, initial COVID-19 treatment, ICU admission, Simplified Acute Physiology Score II (SAPS II), length of ICU stay and ICU management (i. e. , mechanical ventilation, renal replacement therapy), palliative care, and outcomes (i. e. , length of hospital stay and in-hospital mortality).
Clinical data, ICU admission and treatments, palliative care and outcomes were based on the 10th revision of the International Statistical Classification of Diseases (ICD10) and procedural codes were based on the classification commune des actes médicaux (CCAM) associated the French from the Programme de Medicalisation des Systèmes d'Information (PMSI) -French medico-administrative database based on diagnosis related-groups (DRGs). The Charlson comorbidity index predicts 10-year survival in patients with multiple comorbidities and was used as a measure of total comorbidity burden  (10). The protocol recommended by a medical team from Marseilles for COVID-19 treatment was a combination of hydroxychloroquine (200 mg of oral hydroxychloroquine, three times daily for ten days) and azithromycin (500 mg on day 1 followed by 250 mg daily for the next four days)  (11)  thus explaining the important amount of this prescription in our study.
This result is also in favor of a risk increase in institutionalized SCZ patients and against an increased risk of COVID-19 infection in the global SCZ population. However, these results should be interpreted with caution, as we only identified 15 SCZ patients, and these results should be replicated in future wider databases. Our results support a strategy of systematic detection in institutionalized SCZ patients. This has already been done in a homeless shelter in Boston where 36% of the residents tested positive  (20).
Limitations This study had several limitations. First, the study population only included patients within the Marseilles metropolitan area, which may limit the generalizability of these results. Second, the sample size for the SCZ patients was limited (N=15). However, this first description of real-life data in patients with SCZ could be shared with other institutions and countries to achieve a larger sample and to provide a more complete picture of SCZ and COVID-19  (21). Third, the data were collected from the electronic health record database. This precluded the level of detail that would have been possible with a manual medical record review. Additional limitations of our study include missing data for some variables and potential for inaccuracies in the electronic health records.
If Body Mass Index (BMI) is 18. 5 to <25: normal weight; If BMI is 25. 0 to <30: overweight; If BMI is 30. 0 or higher: obesity. Symptoms and comorbidities based on the 10th revision of the International Statistical Classification of Diseases from the Programme de Medicalisation des Systèmes d'Information (PMSI) -French medico-administrative database based on diagnosis related-groups (DRG). p-value in bold: statistical significance.
Acknowledgment. David Braunstein ,  Myriam Dubuc ,  Alexandra Murcia ,  Gilbert Pironti ,  Vincent Pradel ,  Anne Remacle ,  Fanny Romain ,  Catherine Seyler ,  Françoise Volot  and all the other members of the  Department of Medical Information ,  Bruno Lacarelle  and  Amin Ben Lassoued (Biology) ,  Martine Charbit  (Pharmacy),  Alain Lavit ,  Christophe Rozier  (DSN), and all the health care professionals for their great work during the COVID-19 pandemic. Funding. This work was funded by  Assistance Publique -Hôpitaux Marseille (APHM) -Aix-Marseille University (AMU)  and the  European Health   Data & Evidence Network  from the  European Union 's  Horizon 2020 research and innovation program.
* If Body Mass Index (BMI) is 18. 5 to <25: normal weight; If BMI is 25. 0 to <30: overweight; If BMI is 30. 0 or higher: obesity. Symptoms and comorbidities based on the 10th revision of the International Statistical Classification of Diseases from the Programme de Medicalisation des Systèmes d'Information (PMSI) -French medico-administrative database based on diagnosis related-groups (DRG). p-value in bold: statistical significance.
Flow cytometry sorting of Venusþ cells Cells were harvested with TrypLE Select at day 13 of differentiation, quenched with 3 volumes of M3Y, centrifuged 4 min at 200 g, resuspended at 5 M/mL in M3Y, and sorted using a FACSAria Fusion cell sorter (BD) in M3Y at þ4 C. Venusþ cells were collected and either used immediately or cryoconserved in Cryostor10 (Stem Cell Technologies) at À80 C. 2. 8. RNA-seq libraries and data processing Cells differentiated to day 13 in 24-well plates (N ¼ 4, from 2 independent differentiations) were sorted according to Venus expression, and RNAs were prepared with the RNeasy Micro kit (Qiagen). Libraries were prepared using SMART-SeqX v4 UltraX Low Input RNA Kit for Sequencing (Takara Bio Europe) and the Nextera XT DNA Library Preparation Kit (Illumina, San Diego, USA), purified with SPRIselect beads (Beckman-Coulter, Villepinte, France), and sequenced on an Illumina HiSeq 4000 (single-end 50 bp reads).
Reads were mapped onto the hg38 human genome using STAR version 2. 7. 5a  [22]. Quantification of gene expression was performed using HTSeq version 0. 6. 1  [23]  and gene annotations from Ensembl release 98. Normalization of read counts and differential expression analysis between Venus-negative and Venus-positive samples were performed using the method implemented in the DESeq2 Bioconductor library version 1. 16. 1  [24]. Differential expression analyses for comparing Venus-positive and Venus-negative samples as well as the NEUROG3 À/À hESC line differentiated to day 13 and its wild-type counterpart  [25]  were performed using a negative binomial GLM fit and Wald significance test implemented in the Bioconductor package DESeq2 version 1. 16. 1  [26]. The variables considered for the GLM model were condition (for the Venus-labeled cells) and batch and condition (in the NEUROG3 À/À comparison), in which batch corresponds to two differentiations (2 controls and 2 mutants per differentiation).
2. 11. Bioinformatics analyses 2. 11. 1. Data processing Image analysis and base calling were performed using RTA 2. 7. 3 and bcl2fastq 2. 17. 1. 14. Reads were trimmed using cutadapt v1. 9. 1 with option: -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA -m 5 -e 0. 1. Pairedend reads were mapped to Homo Sapiens genome (assembly hg38) using Bowtie2 (release 2. 3. 4. 3, parameter: -N 1 -X 1000). Reads overlapping with ENCODE hg38 blacklisted region V2 were removed using Bedtools. Reads were size selected to <120 bp and >150 bp, as it has been reported that small reads define more precisely TF binding sites, whereas larger reads (>150 bp) result from sites occupied by nucleosomes  [18, 19]. Bigwig tracks were generated using bamCoverage from deepTools for 120 bp and !150 bp fragments separately.
Tracks were normalized with RPKM method. The bin size was 20. 120 bp fragments were used for samples obtained with anti-NEUROG3 (VLSR28), HA (VLSR27) and the control Donkey anti-Sheep (DAsh, thereafter named CTRL, VLSR29) antibodies and !150 bp fragments for samples obtained with anti-H3K4me3 (VLSR32), anti-H3K27me3 (VLSR44), and the Rabbit anti-Mouse control (RAM, VLSR41) antibodies. Bigwig tracks (reads 120 bp long for NEUROG3, HA, and CTRL samples and !150 pb for histone marks) were displayed on the reference genome hg38 using the UCSC genome browser. For simplicity, only the DAsh CTRL is illustrated throughout the manuscript. Heatmaps and K-means clustering was done using seqMINER v1. 3. 3g [29]. To compare with previously published data obtained from human in vitro-derived pancreatic endocrine progenitors  [30] , multipotent progenitors  [31] , and adult islets  [32] , we converted coordinates of bed and bigwig files to hg19 coordinates using the UCSC Liftover and bigwigLiftOver tools (https:// github.
Peak calling Peak calling was performed with the Sparse Enrichment Analysis for CUT&RUN SEACRv1. 3 tool  [33]  (https://seacr. fredhutch. org), using the norm and stringent modes on the 120 bp size selected reads and VLSR29 (DAsh CTRL) as a control for VLSR27 (HA) and VLSR28 (NEUROG3) datasets. To identify overlapping genomic regions, peak coordinates were intersected using the BEDtools 2. 22. 0 command intersect interval files (http://use. galaxeast. fr).
Association of peaks with genomic features and genes Genomic annotation was first performed using the HOMER v3. 4  [34]  annotatePeaks. pl script with the default settings (promoters-transcription start site (TSS) from À1 kb to þ100 bp to the TSS and transcription termination sites (TTS) from À100 bp to þ1 kb of the TTS). GREAT 4. 0. 4 [35]  was used to assign NEUROG3/HA peaks to their nearest coding gene(s) using basal settings (each gene is assigned a basal regulatory domain of 5 kb upstream and 1 kb downstream of its TSS. The gene regulatory domain is extended in both directions to the nearest gene's basal domain, but no more than 1000 kb extension in one direction. Each peak is associated with all genes with whose regulatory domain it overlaps). The NEUROG3 peaks or the distal peaks defined by GREAT (>5 kb from TSS) were intersected with enhancers regions of hESC-derived endocrine progenitors (EN) lifted over to the hg38 genome ([30], GSE139816).
Motifs identification and analyses De novo motif discovery was performed using the HOMER v3. 4  [34]  findMotifsGenome. pl script with default settings (200-bp windows centered on peak summits, motif lengths set to 8, 10, and 12 bp, hypergeometric scoring). For enrichment of known motifs, the entire peak sequence was considered using the -size given option. For the 6 most significant de novo motifs identified, known best match motifs were associated if their Homer score was >0. 85. Known cooccurring motifs were manually curated to exclude redundant bHLH motifs. Co-occurrence of the de novoeidentified NEUROG3 motif and known RFX6 or FOXA2 motifs was done on the entire peak sequences using the HOMER script annotatePeaks. pl with -size given and -m <motifn. motif> options. 2. 11. 5. Functional annotations Gene functional annotation and clustering was carried out with DAVID v6.