dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Design This was a retrospective analysis that included data from 1432 patients in a prospective multicenter study. The primary outcome was 30-day mortality. We also used the 3-month outcome to ensure consistency of the primary outcome and allay concerns of censoring bias  [27]. We derived two groups of models: baseline and final models. Baseline models were derived using admission variables only, whereas the final model group incorporated clinical events such as catecholamine therapy, renal replacement therapy, noninvasive ventilation, invasive ventilation, prone position, and tracheostomy, in addition to the baseline variables. We evaluated both model groups using stratified 3-fold cross-validation to mitigate the variability of a single derivation-validation random split. Furthermore, we derived baseline and final models on an EU patient cohort and externally validated them on a non-EU cohort that included Asian, African, and US patients.
Clinical Data Sources and Study Population Patient data were obtained from 151 ICUs across 26 independent countries, including European ICUs, and from ICUs in Asia, Africa, and the United States as part of the multinational COVIP trial (NCT04321265). This study was conducted in line with the European Union General Data Privacy Regulation directive. As in previous successful studies  [6, 26, 28] , national coordinators recruited the ICUs, coordinated national and local ethical permissions, and supervised patient recruitment at the national level. In the COVIP studies, ethical approval was obligatory for study participation. The electronic case report form (eCRF) and database were hosted on a secure server in Aarhus University, Denmark. Data from 1432 elderly (aged 70 years and above) COVID-19-positive patients admitted to a participating ICU between February 4 and May 26, 2020, were recorded.
Study Data Demographic data included age, gender, weight, height, and BMI. Furthermore, information on admission characteristics prior to ICU hospitalization, duration of hospital stay, day of symptom onset, and comorbidities were available. Preexisting comorbidities were recorded in the eCRF: diabetes, ischemic heart disease, renal insufficiency, arterial hypertension, pulmonary comorbidity, and chronic heart failure. During the ICU stay, data on bacterial coinfection were noted, in addition to Sequential Organ Failure Assessment (SOFA) subscores (respiratory, cardiovascular, hepatic, coagulation, renal, and neurological systems). Laboratory values included partial oxygen pressure and the fraction of inspired oxygen (FiO2), and their ratio. Six clinical events of interest (catecholamine therapy, renal replacement therapy, noninvasive and invasive ventilation, prone position, and tracheostomy) were recorded along with the time the event occurred.
In their analysis, ensemble-based models were superior to other algorithms (including LR and XGB). Specific laboratory values and oxygen saturation were the most important factors for ICU admission, whereas impaired kidney function and differential blood count best predicted mortality  [20]. However, this previous study primarily used data from patients, of all ages, presenting to the emergency room. Domínguez-Olmedo et al  [19]  used data from 1823 patients with confirmed COVID-19 and established an XGB model. Their model found lactate dehydrogenase activity, C-reactive protein level, neutrophil count, and urea level to be the most important variables, reaching an AUC of 0. 93 (95% CI 0. 89-0. 98) for sensitivity and 0. 91 (95% CI 0. 86-0. 96) for specificity. Pan et al  [22]  used data from 123 patients with COVID-19 admitted to an ICU to construct an XGB model, and identified eight factors (albumin level, creatinine, eosinophil percentage, lactate dehydrogenase, lymphocyte percentage, neutrophil percentage, prothrombin time, and total bilirubin) that were predictive for ICU mortality.
Limitations This study has some methodological limitations in common with the other COVIP studies  [11, 26, [39] [40] [41] [42]. COVIP did not contain a control group of younger COVID-19 patients for comparison or a comparable age cohort of patients who were not or could not be admitted to the ICU. In addition, the COVIP database does not include information on pre-ICU care and triage decisions. These treatment limitations might also affect the care of older ICU patients  [43]. Furthermore, COVIP recruited patients in 26 countries, and thus the participating countries varied widely in their care structure, resulting in considerable heterogeneity in treatments given.
Acknowledgments The support of the study in France by a grant from  Fondation Assistance Publique-Hôpitaux de Paris Pour la Recherche  is greatly appreciated. In Norway, the study was supported by a grant from  Health Region West. In addition, EOSCsecretariat. euprovided support and has received funding from the  European Union 's  Horizon Programme call   H2020-INFRAEOSC-05-2018-2019 , grant agreement number  831644. This work was supported by the  Forschungskommission of the Medical Faculty of Heinrich-Heine-University Düsseldorf  (grant  2018-32  to GW and grant  2020-21  to RB for a Clinician Scientist Track). The complete list of COVIP collaborators is provided in Multimedia Appendix 10.
Acknowledgements A. V. T. acknowledges funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant agreement no. 785219  and no. 881603. A. V. T. acknowledges support from  Swedish Research Council  Grant (no. 2017-04173 ), acknowledge the  Vibrational Spectroscopy Platform of Umeå University  and  A. Shchukarev  for support with XPS test. L. A. , S. B. , and K. O. K. acknowledge help of  J. Rothe ,  K. Dardenne ,  A. Beck ,  T. Prussman , and  T. Vitova  at the CAT-ACT beamline of KARA during the HERFD-XANES experiment at the  U M 4 edge. The authors would like to thank  S. Weiss  from the  HZDR safety group  for his help in handling radioactive samples for the HERFD-XANES experiment. L. A, S. B. , and K. O. K. acknowledge the support from the  European Commission Council  under  ERC  [Grant no.
759696 ]. A. S. K. , A. Y. R. , A. L. T, P. V. Z. , and S. N. K. acknowledge support by the  Russian Ministry of Science and Education  under Grant no. 075-15-2021-1353. The work was also supported by  Romanian Ministry of Research Innovation and Digitalization (M. E. ) ,  Romania , under ECSEL-JU POC 2014-2020 projects:  OCEAN12  Contract no. 9/1. 1. 3H/20. 01. 2020Code MySMIS 129948; PIn3S Contract no. 11/1. 1. 3H/03. 04. 2020Code MySMIS 135127 ". Experimental studies were partially performed on the equipment acquired with the funding of  M. V. Lomonosov Moscow State University Program of Development.
Single-cell RNA sequencing and data processing Single cell processing procedure is defined by the MGX platform (Montpellier GenomiX). Single cell suspension was obtained by a Chromium Single-Cell Controller (10X Genomics). Library preparation was performed with Single Cell 3' Reagent kits V3. 1 (10X Genomics) using 10x Next GEM Technology barcode and validated by DNA quantification with Fragment Analyzer (kit High Sensitivity NGS) and qPCR (ROCHE LightCycler 480). Libraries were sequenced with an illumina NovaSeq 6000 (Illumina) and SBS (Sequence By Synthesis) techniques using NovaSeq Reagent kits (100 cycles). Output results and matrix generation were processed with 10X Genomics Cell ranger v3. 1. 0 software (http://10xgenomics. com). All available mitochondrial gene sequences were recovered from the NCBI database  (34, 35)  and gene names corresponding to these sequences (Supplementary Table  2 ) were retrieved using blastn (36) on the Biomphalaria glabrata genome annotation version 1.
Cell clustering and genes expression Filtered matrix sorted by Cell Ranger (10X Genomics Cell Ranger 3. 1. 0) was used and analyzed on R (Version 4. 1. 0 (2021-05-18)) using Seurat package [Version 4. 0. 4 (38) ]. Hemocytes with more than 50 unique expressed genes were kept and genes expressed in 3 or more cells were retained for the generation of the gene-cell data matrix and downstream analysis. Low quality cells were filtered out excluding cells with less than 750 or more than 3100 unique expressed genes and cells with a proportion of mitochondrial transcript higher than 5% were excluded from the analysis. Log-normalized method given by the Seurat package was used to normalize the data. Thereafter, 2. 000 highly variable genes were obtained using FindVariableFeatures function, these genes were scaled and used to perform Principal Component Analysis (PCA).
JackStrawPlot and ElbowPlot functions were used to determine the top principal component (15:25) that is most representative of the data set for each reduction. The dataset clustering was conducted by the FindClusters function according to identified PCs. Visualization in two dimensions of the clustering was made through the use of non-linear dimensional reduction, UMAP (Uniform Manifold Approximation and Projection for dimension reduction) and tSNE (t-distributed Stochastic Neighbor Embedding) projection. We used Seurat (FindAllMarkers option using wilcoxon rank sum test for each cluster) to determine differentially expressed genes and gene markers (Supplementary Table  2 ) for each cluster (Log2FC>0. 25 and min. pct > 0. 25) and created a heatmap to represent the top n marker genes defining each cluster.
Cell trajectory analysis The cell lineage analysis was performed with bioinformatic tools Monocle3  (40)  and slingshot  (41)  on the data processed by the Seurat package mentioned above after conversion to SingleCellExperiment object. Some transcriptomic clusters considered as being the most distant and different with respect to the marker genes that define them were removed from the analysis to perform this cell lineage. To identify temporally expressed genes we used a general additive model (GAM) based on bioinformatic workshop  (42). R script used for all the bioinformatic analysis done with Seurat and Monocle3 packages were available on Zenodo (accession #: 10. 5281/zenodo. 6951346).
Identification of 7 distinct clusters in B. glabrata hemocytes by scRNA-seq To examine hemocyte heterogeneity by scRNA-seq, isolated hemocytes from a pool of 50 naive snails were subjected to droplet scRNAseq. Approximately 3000 cells were captured using the 10X Genomics Chromium microfluidic technique and submitted to RNA sequencing. After an initial process by CellRanger (10x Genomics Cell Ranger 3. 1. 0), the sequencing data were aligned to the B. glabrata reference genome (90. 1% reads mapped to the genome). We obtain 2624 estimated cells with a median gene per cell corresponding to 1885 genes among the 19 820 total genes detected and a sequencing saturation at 66%. To retain high quality single cell RNA-seq data, we removed low-quality cells with gene number less than 750 and mitochondrial genes less than 5%.
Seurat R package (Version 4. 0. 4) was used for data processing. Following such quality control, 2193 high-quality cells were used for further analysis. For each cell, an average number of 2470 genes expressed were obtained and 7 transcriptomic clusters were identified (Figure  1F ). Regarding read coverage by clusters, cells in clusters 0, 2, 4 and 5 have a higher number of reads compared to cells in clusters 3, 1 and 6 (Supplementary Figure  1 ). These last 3 clusters may represent cells with low transcriptional activity or small cells like blast-like cells. Most of the hemocytes gathered within a main cluster (cluster 0) which represented 73. 8% of the total cells. The other clusters (cluster 1 to 6) represented from 7. 7% (cluster 1) to 1. 9% of the cells respectively (Figure  1F ). We were able to define transcriptomic profiles and specific marker genes for each cluster (Figures  1G, H ).
Gene expression signatures and candidate markers for each hemocyte cluster Annotation and understanding of Biomphalaria glabrata genome/transcriptome/proteome are still very sparse. Amongst the 31 985 defined genes for about 44 406 transcripts, less than the half possess at least a protein domain information, making every enrichment or functional analysis conditioned by annotation bias. Also, it exists a redundancy in B. glabrata gene annotation, meaning that same gene annotation or transcripts can have different gene name/position and different transcripts. Knowing such limits but in order to create a transcriptomic identity card for the different clusters, we were interested in the function of the marker genes detected with Seurat (Supplementary Table  2 ). When possible (sufficient number of genes), we performed a Gene Ontology enrichment analyses with the blast2GO tool (Supplementary Table  3 ), taking into account only the marker genes present in at least 80% of the hemocytes from the cluster considered.
Acknowledgments Thanks to cytometry facilities of the  MRI platform (Montpellier ressource imagerie ) as well as the EDyP service platform for their help in the development and implementation of cell sorting and label-free proteomics. Thanks to the technical staff of the  IHPE laboratory ,  Jean-Francois Allienne  for his expertise in molecular biology,  Anne Rognon  and  Damien Pouzol  for the maintenance of mollusc strains and their expertise on animal experimentation.
Funding This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ). DD and BG were supported by the  ANR AEROSNAIL  ( ANR-19-CE11-0016-01 ) from the  French National Research Agency (ANR). BG was supported by the  BQR  from the  University of Perpignan. All authors contributed to the article and approved the submitted version.
Identification of actionable targets and therapy matching Another key challenge includes which specific alterations should be classified as 'actionable'. To support classification, the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP) jointly published a four-tiered system classification system for the interpretation and reporting of sequence variants in cancer. 26 The European Society for Medical Oncology (ESMO) also recommends the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT) variant classification guidelines, with subtle differences from the AMP/ASCO/CAP Guidelines. 64 oreover, for clinical decision making, a variety of tools and platforms have recently been developed. Evergrowing knowledge bases such as the Cancer Genome Interpreter Cancer Biomarkers Database (CGI), Clinical Interpretation of Variants in Cancer (CIViC), Jackson Laboratory Clinical Knowledgebase (JAX-CKB), Molec-ularMatch (MMatch), OncoKB, and the Precision Medicine Knowledgebase (PMKB) facilitate interpretation.
Materials and Methods The development of the French version of myfood24 can be divided into three main steps: 1. Developing the food composition database; 2. Adapting portion sizes to the French dietary habits; 3. Evaluating the French version. This methodology aligns with the tool's other versions. In particular, the development of the food database was needed first considering the already existing structure of myfood24, which is an open-ended and online dietary assessment method. More details on the different steps can be found below and are illustrated in the supplementary materials.
Development of the French Food Composition Database The first step of the database's development was to identify usable French food composition data. These were the French national food composition database, CIQUAL 2017  [5] , and part of J. -P. Blanc's book of food back-of-pack nutrition information  [6]. No data from myfood24 s other language versions have been used, as only the generic items would have been relevant, and these were already in the CIQUAL tables. The second step was to remove all items that were not likely to be consumed due to their nature or processing. For example, to avoid confusing the user, "raw beetroot" has been deleted as it is not commonly consumed in France. The third step was to remove all non-specific combined foods present in the CIQUAL tables (n = 50) when their components were quantifiable or more precise options were available.
Selected items from J. -P. Blanc's book of food items' back-of-pack nutritional data  [6]  were copied manually from other dietary recall software an item at a time. Due to the time-consuming nature of this work, these items were selected before being input; as such, they do not represent the entire branded database. Food selected for addition from J. -P. Blanc's book were chosen to increase the diversity within some of the CIQUAL subgroups and make the database more representative of the French food and drink market.
Food Portion Estimation Each food item has been assigned a portion description to help the participant estimate their intake. Portion suggestions will be prompted on-screen, with pictures, if available, and the participant will choose the portion size picture closest to what they ate or write the exact weight (in grams) of the item. Data were also available on usual portion size, as consumed, from the Food Observatory (Observatoire de l'Alimentation (OQALI)) website  [8]. OQALI monitors the food offered on the French market and collects portion size information on labels when indicated by the manufacturers. These are available by food group. OQALI did not provide information for all groups, so when information was not available, portion sizes were collected on French supermarkets' websites and Open Food Facts France  [9].
Respondents' Characteristics Thirty-four respondents out of 428 subjects invited were recruited to evaluate the photos (cooperation rate = 9%). In total, 82% of them were women (n = 28) (see Table  2 ). The 2017 official French food composition tables were made up of 2807 food and drink items. Nine of CIQUAL's subgroups (salad-based dishes, other meat products, dairy products and desserts, jams, ice creams, sorbets, frozen desserts, cooking aids, and food for particular nutritional uses) that were deemed not to adequately reflect the French market were enhanced with 225 selected items for J. -P. Blanc's book's database, for a total of 3032 items. After further steps detailed in Figure  2 , the final database included 2981 food and drink items, 28 accompaniment groups, and 211 portion groups.
Survey Results The survey results were assessed through the questionnaire that the participants had to answer after their third recall with the tool. Table  5  shows the System Usability Scale (SUS) scores for myfood24 France. The median SUS score was good (73, Q1-Q3: 66-84), which indicates that myfood24 France is usable; however, the mean value was lower (68) and corresponds to an acceptable system  [15, 16]. The median SUS score of adults (63, Q1-Q3: 46-76) over 30 years old (y. o. ) was 10 points lower than that for young adults (73, Q1-Q3: 63-78). The median SUS score for women (73% of the participants) was 73 and 60 for men. Twenty-four users did not encounter any difficulty loading and opening the application and two encountered difficulties between one and four times. Other technical issues were noted by three users when filling out the app: hitting the browser's back button is impossible (n = 1) or deletes data (n = 1), and some foods are impossible to find (n = 1) as they were not available and/or under a different name than the used search terms.
The majority of the participants agreed that the interface is uncluttered (n = 24) and intuitive (n = 23). The messages guiding the user through the completion process were judged useful and just enough by 81% of the users. Moreover, 11% found them too numerous and 8% not numerous enough. The search function of myfood24 was considered easy to use by 81% of the participants, but only 27% of them mentioned finding their food quickly. Participants who reported having difficulty finding the right foods stated two major causes, which were that "Some (or all) of (the) foods searched for were not available (brands)" (n = 17) and "Foods were not sorted in a relevant way" (n = 8). Most participants also stated that the database was lacking some items, that there should be more branded foods. They also reported that the high number of search results and their display in alphabetical order made finding the right item or ingredient a time-consuming task.
Three users highlighted the speed of the tool as a positive aspect, especially for reporting, which is obtained instantly after submitting the 24HDR. All participants noted that the photos did not always correspond to the food they were looking for and that it would be more relevant to have specific photos to correctly estimate the portions. The messages included for accompaniments and commonly forgotten foods were deemed relevant, but sometimes too long, especially in the case of food accompaniments. The concept of prompts was appreciated, but the proposed accompaniments were too numerous and therefore discouraged reading. The database was described by the participants as limited in relation to branded items and exotic foods, since they often had to choose a substitute. Many users found the recipe builder time consuming and preferred to search for a substitute food rather than create their own recipes.
Size of Database and Missing Data This database is based on the CIQUAL 2017, which may have limited the food choice in myfood24 France. The enhancement of some CIQUAL subgroups is not sufficient to represent the French food market's diversity. French cuisine is full of culture, heritage, traditions, and pride, and is constantly evolving. It is so complex and rich that the United Nations Educational, Scientific and Cultural Organization (UNESCO) "inscribed [the French gastronomic meal] in 2010 [. ] on the Representative List of the Intangible Cultural Heritage of Humanity"  [17]. As manufacturers launch new products regularly, a food database will always be missing data. The French version does not cover as many items as the UK and German versions, which contain information on numerous branded items (UK: over 88,000 branded items).
Alternatives to Portion Pictures A few alternatives to portion pictures exist. One is weighing everything that is consumed. However, such a time-consuming task may change the participant's eating behaviour  [20]  and interfere with the reporting quality. For instance, the complex approach to measurement is likely to impact on reported data detail as subjects are less motivated and may drop out. Household measures or descriptions could also be used (e. g. , a slice of bread). Estimates using household measures "can be improved with training but the effects may deteriorate with time"  [20]. Studies have shown that portion pictures are more useful than household measures to evaluate the amounts consumed  [20]. The participant could also take a picture and then image recognition would be performed either by a trained person or through artificial intelligence (AI).
Why Use an Online System Rather Than a Paper-Based One? Technology and, more specifically, online systems can ease dietary assessments, including for the participants. In 2020, France counted 92% internet users in the country (83% inhabitants connecting daily). On average, that year, the French spent 19 hours on the internet per week  [26]. Using an online system to assess diet now allows individuals to use systems they are familiar with and data are processed in real time  [3]. Moreover, in 2020, no less than 84% of people had a smartphone (against 61% owning a computer)  [26]. In 2019, 51% of French people preferred to use their smartphone as their first option to go on the internet, against 31% for computers  [27]. The myfood24 online system is therefore easy to access, especially through its mobile-optimised version.