dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and participants The ESMO-CoCARE is an observational prospective study, based on a longitudinal multicentre survey of patients with cancer with any solid or haematological malignancy who were diagnosed with COVID-19. The data reside in the ESMO-CoCARE registry, developed and maintained as an electronic REDCap (Research Electronic Data Capture) database housed at Centre Hospitalier Universitaire Vaudois (CHUV) in Lausanne, Switzerland. Active data collection is planned until the end of the pandemic as declared by the World Health Organization (WHO), or the end of epidemic situation in each region, with subsequent follow-up as needed. Data on clinical features, course of the disease, management and outcomes are collected for both cancer and COVID-19 disease. The aim of the study is primarily descriptive of the characteristics of COVID-19 in patients with cancer, exploring associations with both cancer and COVID-19 outcomes.
Data reported here were extracted from medical records of consecutive patients diagnosed with COVID-19 from 1 January 2020 up to 18 May 2021. COVID-19 diagnosis included both laboratory-confirmed COVID-19 cases (irrespective of symptoms and clinical presentation) and cases with only clinical diagnosis of COVID-19, based on signs such as fever >38 C, cough, diarrhoea, otitis, dysgeusia, anosmia, myalgia, arthralgia, conjunctivitis and rhinorrhoea, lymphocyte count <1. 0 Â 10 9 /l, and/or chest radiographic or lung computed tomography imaging suggestive of SARS-CoV-2-19 pneumonia.
The variables initially included were gender, age, ethnicity, Eastern Cooperative Oncology Group (ECOG) performance status (PS), smoking status, BMI (<25 kg/m 2 versus !25 kg/m 2 ), comorbidities, cancer type/stage/status at COVID-19 diagnosis, COVID-19 symptoms (symptomatic/ asymptomatic) and the following inflammation-based biomarkers measured prior to COVID-19 diagnosis: neutrophilto-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), OnCovid Inflammatory Score (OIS). The backward elimination method with removal criterion P >0. 10 was utilized to obtain the factors with significant effects. Multicollinearity and proportionality assumption based on the Schoenfeld residuals were checked. Data were analysed using SAS version 9. 4 (SAS Institute, Cary, NC) and R version 4. 0. 0 (R Foundation, Vienna, Austria) software.
Cohort description and cancer disease characteristics From January 2020 to May 2021, a total of 1626 eligible patients with COVID-19 diagnosis and a history of active malignancy or in remission were registered in the CoCARE database and comprised the analysis cohort. COVID-19 was diagnosed most often in March and April 2020 (16. 6% and 16. 1%, respectively), followed by December 2020 and January 2021 (12. 2% and 11. 8%, respectively; Supplementary Figure  S1 , available at https://doi. org/10. 1016/j. esmoop. 2022. 100499. ). Registration of patients was performed at 37 participating centres in 20 countries, from 6 June 2020 to 18 May 2021. United Kingdom (32%) and Spain (24%) contributed the highest proportion of patients, with 31% from other European countries; 7% and 6% were registered from African and Asian/Oceanian countries, respectively (Supplementary Table  S1  and Supplementary Figure  S2 , available at https:// doi.
Based on these, additional inflammatory-based biomarkers were calculated, according to the OnCovid dataset. 18 Two OnCovid inflammatory markers involved C-reactive protein, and thus were not analysed here. NLR, PLR and OIS, measured prior to COVID-19 for ESMO-CoCARE patients, are summarized in Supplementary Table  S9 , available at https://doi. org/10. 1016/j. esmoop. 2022. 100499.
We observed some differences in type and quality of data collected over time, in line with the increasing clinical experience and knowledge in managing patients with COVID-19. Finally, the quality of data depended on each centre, without the implementation of a centralized audit system. Despite these limitations, a unique electronic case report form and the multicentre, multicountry nature of the study with >1500 cases included empower a robust statistical analysis partly mitigating the selection bias. In conclusion, in our study, male sex, older age, smokers, non-Asian ethnicity, poor ECOG PS, lower BMI, presence of comorbidities, symptomatic COVID-19, higher NLR, lower OIS, haematological malignancies, more advanced disease stage and progressive cancer status were identified as risk factors for COVID-19 adverse outcomes in patients with cancer.
ACKNOWLEDGEMENTS The  ESMO Co-CARE Registry Steering Committee  thanks the following for their contribution in the development, monitoring, implementation, support and coordination of the project:  Klizia Marinoni  and  Delanie Young  ( ESMO Scientific and Medical Division) ,  Isabelle Scherer  (ESMO Legal Officer),  Vanessa Pavinato  ( ESMO Communication Head )  Keith McGregor  ( ESMO CEO ),  Jean-Yves Douillard  (former  ESMO CMO) ;  Institut Curie  in its role of Controller with special mention to the Legal Office Staff  Veronique Gillon ,  Jeremie AstraZeneca ,  BMS ,  Boehringer-Ingelheim ,  Codiak Biosciences ,  Inzen Therapeutics ,  Merck-Serono ,  MSD ,  Pfizer , Replimune. OM reports personal fees from  Bristol-Myers Squibb ,  MSD ,  Novartis ,  Roche ,  Amgen ,  NeraCare GmbH , outside the submitted work.
Hoffmann-La Roche/Genentech, Illumina, Medscape ,  Merck Sharp and Dohme ,  Novartis ,  PER ,  Pfizer ,  Prime, RTP, Sanofi, Takeda ; receipt of grants/research supports from being a (sub)investigator in trials (institutional financial support for clinical trials) sponsored by  Amgen ,  AstraZeneca ,  Biodesix ,  Boehringer Ingelheim, Bristol-Myers Squibb, Clovis, F. Hoffmann-La Roche / Genentech ,  GSK ,  Illumina ,  Lilly ,  Merck Sharp and Dohme ,  Merck Serono, Mirati ,  Novartis , and  Pfizer ,  Phosplatin Therapeutics. ER reports investigator-initiated trial (funds paid to the institution) supported by  Astra-Zeneca , BMS; serves on the consultancy/advisory board for  Astra-Zeneca ,  Merck ,  Roche ,  Pierre Fabre. All other authors have declared no conflicts of interest.
Colorectal cancer (CRC) is a growing public health challenge, featuring a multifactorial etiology and complex host-environment interactions. Recently, increasing evidence has pointed to the role of the gut microbiota in CRC development and progression. To explore the role of gut microbes in CRC, we retrieved metagenomic data from 156 stools from the European Nucleotide Archive database and mapped them against the VFDB database for virulence factors (VFs). GO annotations of VFs and KEGG pathways were then performed to predict the microbial functions and define functional pathways enriched in the tumor-associated microbiota. Interestingly, 306 VFs were detected in the metagenomic data. We revealed the enrichment of adenomas with VFs involved in cell adhesion, whereas in the early stages of CRC they were enriched in both adhesins and isochorismatase.
One of the many crucial analyses that we can cite is profiling the taxonomic composition of microbial communities and mapping it against a database such as the Virulence Factor Database (VFDB), which is a reference database for virulence factors produced by bacterial pathogens  [14]. Indeed, pathogenic bacteria can produce virulence factors (VFs) that could affect different signaling pathways and induce colorectal carcinogenesis  [15]. VFs could be categorized into several classes based on their functions: adherence and colonization factors, invasion factors, capsules and other surface components, and endotoxins  [16]. It has been previously shown that the virulence factor of enterotoxigenic Bacteroides fragilis (ETBF) and Fusobacterium adhesin A (FadA) can bind to intestinal epithelial cell receptors and cleave the extracellular domain of E-cadherin (anchoring junction protein), causing a loss of cell-cell contact and leading to the activation of the NF-kB pathway  [17, 18].
Published Fecal Metagenomes We retrieved gut metagenomic data of the project PRJEB6070 from the European nucleotide archive. This project involves 156 fecal samples from the French population, including 53 from CRC patients (15 from stage I, 7 from stage II, 10 from stage III, and 21 from stage IV), 42 samples from adenoma patients (15 from large adenoma and 27 from small adenoma), 61 samples from randomly selected controls, and 43 samples from the German population (38 fecal samples from CRC patients and 5 samples from healthy subjects). These 199 samples were sequenced using two different techniques: whole-genome shotgun and 16S rRNA gene sequencing. We only analyzed the whole metagenomic data from 156 French samples  [22]. (Supplementary Data S1). Datasets of published fecal metagenomes are available from the European Nucleotide Archive (ENA) database (https://www.
Detection of Virulence Factors (VFs) In order to detect the virulence factors of bacterial pathogens, we mapped the reads against the virulence factor database (VFDB)  [14]. The mapping was realized using tools from PATRIC website (https://patricbrc. org/app/MetagenomicReadMapping) accessed from 9 December 2021 to 6 January 2022  [23, 24].
Functional Profiling of VFs, Pathway Analysis, and Association of VF's Functions with Cancer Progression Correlated VFs with cancer progression were mapped against the Uniprot-Gene Ontology (GO) database (https://www. uniprot. org/help/gene_ontology) accessed from 9 December 2021 to 6 January 2022, and the functional annotation pathway mapping of VFs was realized using KEGG pathway (https://www. kegg. jp/kegg/pathway. html) they were accessed from accessed from 20 December 2021 to 10 January 2022. A Venn diagram was used to display the most detected functions of VFs correlated with each CRC stage compared to healthy state (http://bioinformatics. psb. ugent. be/webtools/Venn/) accessed on 25 January 2022.
Published Fecal Metagenomes By mapping the reads against the Virulence Factor Database (VFDB), we detected the presence of 306 VFs (Supplementary Data S2). These factors are produced by pathogenic microbial species that belong to different phyla: Firmicutes (Clostridium perfringens, Enterococcus faecalis, Enterococcus faecium, Streptococcus agalactiae and Streptococcus pneumoniae), Actinobacteria (Corynebacterium diphtheriae), and Proteobacteria (Escherichia coli, Haemophilus influenzae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Salmonella enterica, Shigella dysenteriae, Shigella flexneri and Yersinia pestis).
Results The dataset consisted of 29,520 observations: 4920 for Alphabetical Decision Task (hereafter, ADT), 9840 for Lexical Decision Task (LDT), 9840 for Grammatical Decision Task (GDT), and 4920 for Non-Reading Task (NRT). Firstly, we provide descriptive statistics on RTs and error rates to give an overview of performance in each task. Condition means for RTs and error rates are shown in Table  1. The main analysis of the lexical decision task included all words that were tested, including morphologically complex words. Additional analyses limited to only morphologically simple words showed exactly the same pattern.
78) and LDT (r = 0. 72) than with GDT (r = 0. 57). This descriptive pattern was confirmed by the analysis of the statistical difference between correlations using the cocor R package  30. The difference between correlations involving adjacent levels was not statistically significant (r(ADT, LDT) = 0. 83 vs. r(LDT,GDT) = 0. 76, p = 0. 35), whereas the differences between correlations involving non-adjacent levels were significant (r(ADT, GDT) = 0. 56 vs. r(ADT, LDT) = 0. 83, p < 0. 001; r(ADT,GDT) = 0. 56 vs. r(LDT,GDT) = 0. 76, p < 0. 01). These results could be explained by the fact that the binary-decision process shared by all tasks has a higher impact on tasks where the cognitive demand is lowest as in ADT and LDT in which letter and word processing is highly automatized as compared to GDT.
Methods Participants. An online study consisting of three reading tasks (Alphabetical Decision, Lexical Decision, Grammatical Decision) and one non-reading task was programmed and hosted on a Labvanced server  34. Forty-eight participants (28 female, 20 male) were recruited via Prolific, an online platform dedicated to the recruitment of participants. Prior to the beginning of the experiment, participants were informed that data would be collected anonymously, and they provided informed consent before the experiment was initiated. The study was approved by the ethics committee of Comité de Protection des Personnes SUD-EST IV (No. 17/051). The experiment was performed in accordance with relevant guidelines and regulations and in accordance with the Declaration of Helsinki. The order of tasks was counterbalanced across participants.
Lexical decision task (LDT). One hundred and twenty English words were selected among those used in the grammatically correct sequences of the grammatical decision task (see next experiment section). These words were tagged as adjectives, nouns, or verbs. Due to these selection criteria, some high-frequency words used in the GDT (such as determiners, articles, prepositions) were not used in the LDT. According to the Subtlex-UK database  36  , words had a mean log-frequency of 2. 17 (SD = 0. 14) and a mean length of 6. 05 letters (SD = 1. 26 letters). Pseudowords were selected among those used in the English Lexicon Project 37 and were matched with words on the number of letters. Stimuli were presented in 14pt Lucida Sans Unicode font.
Grammatical decision task (GDT). Stimuli consisted of 240 English 4-word sequences, forming a grammatically correct structure such as "alcohol is a toxin. " The sequences were taken from the Google 4-gram English database  38  , where the term "gram" refers to a word. Stimulus selection was operated as follows. First, we chose the 4-gram for which all words figured in the Subtlex-UK database  39. Then, we excluded the 4-gram which contained adjectives, nouns, or verbs with less than 3 letters or more than 8 letters and whose frequency lay ± 1. 75 SD beyond the average word frequency. Moreover, we ensured that the mean word lemma log10 frequency by 4-gram fell within the [1; 1] standard interval. Finally, we kept the 4-gram with Standard Frequency Index (SFI)  40  values ranging between 1. 83 and 3.
83, and we removed 4-gram ending with determiners, articles, prepositions, postpositions, and particles. Two hundred and forty 4-word sequences were retained for the study. One hundred and twenty 4-gram were used as a correct grammatical sequence. The 120 remaining 4-gram were used to form ungrammatical sequences by substituting one or several words with another valid English word of the same length. This led to 120 grammatical and 120 ungrammatical sequences which were randomly presented to participants. Stimuli were presented in 14 pt Lucida Sans Unicode font. Non-reading task (NRT). Forty black and white drawings were selected from the MultiPic database  41. MultiPic is a normative database of 750 pictures of concrete concepts dedicated for the investigation of language, visual, memory and/or attention processes.
Acknowledgements This work has been supported by the  center of excellence on Language, Communication and Brain (ILCB) , funded by the  French National Agency for Research (ANR ,  ANR-16-CONV-0002 ), the  Excellence Initiative of Aix-Marseille University A*MIDEX  ( ANR-11-IDEX-0001-02 ) and the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). Brice Brossette  was supported by a PhD scholarship from the  Ecole Normale Supérieure (ENS) of Lyon. Jonathan Grainger  was supported by the  European Research Council  ( ERC 742141 ).
A "thinkaloud" process was employed, where participants were asked to speak their thoughts aloud as they read all instructions and completed each item. 60 All interviews were conducted in the participant's native language. Semi-structured interview guides were developed based on the literature review and input from expert clinician advisors. Verbatim transcripts were qualitatively analysed using thematic analysis methods and ATLAS. ti software. 62 atient quotes were highlighted and grouped by theme/topic, and frequency counts were generated per item and instruction of the ADAQ©. 60, 63 Subgroup comparisons identified patterns in instrument interpretation. Three expert clinicians (RR [Geriatric Psychiatrist and Researcher in Mental Health and Cognition], NR [Professor of Respiratory Medicine in a University Tertiary Care Hospital], and KK [Primary Care Practitioner, Generalist, and Professor and International Researcher in Cardiometabolic Diseases]), with experience of treating patients with a range of different conditions and expertise in medication adherence provided advice and input at key time points.
Mesh construction The triangle grid used for the simulations was created using an interface developed at BGS IT&E. The main data sources employed for the mesh construction were coastline polygons from OpenStreetMap (last update of used data set: 2018-06-10 09:33), bathymetric information from EMODnet (2016 version) and HOMONIM digital terrain models (DTM). The DTM sources have gridded resolutions of ∼210 and ∼110 m respectively, with depths defined with respect to the mean sea level  (MSL). Although the coastline is generally located at high water levels with an exact definition that varies from country to country, we have chosen to impose a constant 2 m minimum depth value at the coastline to preserve the shoreline geometry and avoid unrealistic wave height gradients at the nearshore that could be triggered by the combination of large tidal sea level variations (wet and dry effect) with inadequate spatial resolution in very shallow areas close to the shore.
Satellite altimetry data Given the advantages of altimeters' spatial coverage, the general performance evaluation of the model results was done by comparing results with the ESA Sea State CCI V2 altimeter dataset. We used the "denoised"  (Schlembach et al. , 2020; Quilfen and Chapron, 2021)  significant wave height (SWH) at 1 Hz, to estimate the performance indicators in an along track statistical 130 analysis of the wave heights, and for time averaged values over the complete modelled domain. The adjusted denoised SWH has an along track spatial resolution equivalent to approximately 7 km. We use the following statistical parameters: The Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), Scatter Index (SI), Mean Bias (BIAS) and the Normalized Mean Bias (NMB): RMSE(X) = (X mod -X obs ) 2 N (1) NRMSE(X) = (X mod -X obs ) 2 X 2 obs (2) SI(X) = (X mod -X mod ) -(X obs -X obs ) 2 X 2 obs (3) BIAS(X) = 1 N (X mod -X obs ) (4) NMB(X) = (X mod -X obs ) X obs (5) where X obs are the observed quantities from in situ or satellite measurements, X mod are the modelled quantities (spectral values or integrated wave parameters), and N the total amount of analyzed data.
These findings imply that even if the average wave heights might be well estimated without tidal forcing, the propagation and evolution of the wave fields will be different. This can be observed in the H s and T m01 time series at buoy 62059 (Fig. 10 ). Comparing with wave heights retrieved from altimeter data with 1 year simulations, we identified areas influenced by bottom friction dissipation by looking at changes in H s. We found that these changes can be observed at depths smaller than 50 m. In Using 5 available missions from the Sea State CCI V2 dataset we performed a validation of the modelled wave height as function of the distance to the coast, between years 2002 to 2018. We observed an overall increase of H s differences with our model for distances to the coast smaller than 10 km that can reach -8% (in average) at 5 km from the coast.
Data availability. The used coast line polygons, bathymetric data, bottom sediment type maps and buoy data have been take from the following web portals: -OpenStreetMap coast line polygons: https://osmdata. openstreetmap. de/data/coastlines. html -EMODnet terrain model: https://portal. emodnet-bathymetry. eu -HOMONIM bathymetric data: https://diffusion. shom. fr/pro/risquesbathymetrie/mnt-facade-atl-homonim. html -EMODnet bottom sediment: https://www. emodnet-geology. eu/data-products/seabed-substrates/ -Buoys with spectral data provided by CMEMS In Situ TAC: http://www. marineinsitu. eu/dashboard/ -Long period wave heights data from the Agency of Maritime and Coastal Services (Agentschap Maritieme Dienstverlening en Kust): https://meetnetvlaamsebanken. be/.
Bioinformatic processing of the sequences Amplicon data were analyzed using a combination of VSEARCH v2. 13. 3 and QIIME 1. 9. 1. softwares  (Caporaso et al. , 2010; Zgadzaj et al. , 2016). Raw sequences were split based on their unique barcodes and the primer sequences, and low-quality sequences were trimmed off using an in-house script. Next, the paired-end sequences were merged using the USEARCH software v11. 0. 667 and the resultant sequences were quality-filtered. Singletons were removed using VSEARCH. The sequence reads were then clustered into operational taxonomic units (OTUs) at 97% similarity level using the UPARSE pipeline. Chimeric sequences that were identified using the reference-based methods were removed from the data. Representative sequences were classified using the BLAST algorithm with SILVA v.
13. 2 and UNITE v8. 0 reference databases  (Quast et al. , 2013; Nilsson et al. , 2019). Mitochondrial and chloroplast DNA sequences, as well as the OTUs with a total relative abundance of <0. 00001 in all samples were discarded. The raw sequencing data have been submitted to the Sequence Read Archive under the accession number PRJNA820166 (16S and ITS2) and PRJNA782391 (ITS1).
Permutational multivariate ANOVA (PERMANOVA) statistical tests were performed using the R packages, vegan, with the adonis function having 999 or 1,000 permutations  (Xiong et al. , 2021b). Venn diagrams were drawn using the OECloud tools (https:// cloud. oebiotech. cn) to analyze the overlapping and unique OTUs between Lithocarpus and Pinus samples. Differential abundance between Lithocarpus and Pinus samples was assessed at phylum and class levels using the STAMP software and Welch's tests followed by Benjamini-Hochberg FDR corrections  (Parks et al. , 2014). This analysis was also performed at OTUs level using the Edge's generalized linear model (GLM) in the "edgeR" packages, with trimmed mean of M-values (TMM) normalization method and a threshold of significance set at p < 0. 05  (Robinson et al.
01  (Barberan et al. , 2014). The pairwise comparisons based on abundances were performed using the rcorr function in the "Hmisc" package and the value of p was adjusted using the Benjamini-Hochberg method  (Zhu et al. , 2019). Co-occurrence networks were obtained, with each node representing one OTU and each edge denoting a strong and significant connection. Network visualization and calculations of network topological properties (e. g. , degree and modularity) were performed using the interactive Gephi platform  (Xiong et al. , 2021b). Cladograms for taxonomy were drawn using the R package Metacoder  (Foster et al. , 2017). All the statistical analyses for data were performed using the R software v3. 6. 0.
Funding This work was supported by the  Beijing Advanced Innovation Center for Tree Breeding by Molecular Design and the Laboratory of Excellence ARBRE  (grant number  ANR-11-LABX-0002-01 ). We also acknowledge grants from the  China Postdoctoral Science Foundation  ( 2019M660508  to QZ), the  National Natural Science Foundation of China  (grant number  U1802231  to YD), and the  Second Tibetan Plateau Scientific Expedition and Research Program (STEP , No. 2019QZKK0503  to YD). Zeng et al. 10. 3389/fmicb. 2022. 916337Frontiers in Microbiology 11 frontiersin. org.