dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene Ontology Gene ontology was characterized using http://geneontology. org/  74, 75. The identifiers GO:0007610 and GO:0007399 correspond to "Behavior" and "Nervous system development" terms respectively. Genes of Homo sapiens, Mus musculus and Rattus norvegicus associated with both GO terms were upload. Evolutionarily-conserved oocyte genes associated with behavior and neurodevelopment in vertebrates and linked to a diversity of human diseases Maternal transcriptome datasets of zebrafish (Danio rerio) and mouse (Mus musculus) were downloaded from NCBI SRA (respectively SRX3542388 and ERX1787686). The minimum threshold of TPM was fixed at 0. 5. Lists of genes belonging to "Behavior" and "Nervous system development" biological processes were downloaded (04/2020) from Gene Ontology/Amigo database for 3 mammalian species: rat (Rattus norvegicus), human (Homo sapiens) and mouse (Mus musculus).
These species were selected because they were the most described for these gene ontology (GO). All orthologous genes were obtained from BioMart on ENSEMBL platform  73. GO lists and maternal transcriptomes were merged using Rstudio (R version 3. 1. 5). The curated gene-disease associations lists were downloaded from the DisGeNET website and contains genedisease associations from UNIPROT, CGI, ClinGen, Genomics England, CTD, PsyGeNET and Orphanet. It was merged in R software (v4. 1. 0) with others data frame by "human gene name"  33.
Acknowledgements The project has received funding from  INRAE PHASE division ,  Région Bretagne  and  Agence Nationale de la Recherche  ( ANR   CAVEMOM  # ANR-21-CE13-0017 ). The authors thank  Pierre-Lo Sudan ,  Guillaume Gourmelen  and the  INRAE LPGP ISC  staff for animal care and behavioral setup development. MGX acknowledges financial support from  France Génomique National infrastructure , funded as part of the " Investissement d'Avenir" program  managed by  Agence Nationale pour la Recherche  under grant #  ANR-10-INBS-09. ANR-10-INBS-09.
Data availability All transcriptomics datasets generated as part of the study are publicly available. Bulk RNA-seq and single nuclei RNA-seq dataset are available on the NCBI SRA portal under accession numbers PRJNA929688 (bulk RNA-seq; s18-29; WT, MCH+, MCH-and mutant), PRJNA930338 (bulk RNAseq; s2; WT and mutant) and PRJNA1076130 (snRNA-seq; s18; MCH+ and MCH-).
Bar plots display the enrichment of DEG with identified human ortholog belonging the KEGG signaling pathways database. Grey bars display DEG (i. e. with an adjusted P-value <0. 05 in at least one cell population). Orange bars display neural DEG (i. e. with an adjusted P-value <0. 05 in at least one neural population). Red bars display exclusive neural DEG (i. e. with an adjusted P-value <0. 05 in at least one neural population and not DE in any nonneural population). Numbers correspond to the number of DEG in each DEG group for each pathway. The dashed vertical line separates the significant from the non-significant enrichments. Human orthologs of medaka genes were obtained using biomaRt (see methods). g Dysregulated transcription factors. Bar plot display the proportion of DEG with identified human ortholog for each transcription factor family.
(https://www. genomicus. bio. ens. psl. eu/genomicus-fish-03. 01/cgi-bin/search. pl). The complete list of human-medaka orthologs sharing the same Euteleostomi gene ancestor can be downloaded via an ftp server (ftp://ftp. biologie. ens. fr/pub/dyogen/genomicus- fish/03. 01/ancGenes. tgz). Genes associated with human diseases were subsequently obtained using the DisGeNET database (https://www. disgenet. org/search). This database integrates information on human gene-disease associations from various repositories including Mendelian, complex and environmental disease 33. Our gene datasets were compared to a random selection in the Japanese medaka genome. The process of random selection in the Japanese medaka genome involves averaging ten random draws from a pool of referenced coding genes (https://www.
Study design and patients This multicentre, national cohort study analysed data from the French RMD COVID-19 cohort, which has been previously described  [6, 17]. Briefly, the French RMD COVID-19 cohort included patients aged 18 years or older with confirmed inflammatory RMD and a confirmed diagnosis of COVID-19 with nasal swabs/serology or highly suspected disease with compatible symptoms (any three or more of the following signs or symptoms: fever, general weakness or fatigue, headache, anosmia, ageusia, cough, chills, diarrhoea, myalgia, shortness of breath, high-risk exposure and close contact, chest CT findings suggestive of COVID-19 pneumonia). The study was done in compliance with the research methodology MR-004 (research not involving humans connected to studies and evaluations in the field of health); it was approved by Lille University Hospital (Lille, France) and was declared to the Data Protection Authority for France (Commission Nationale de l'Informatique et des Libertés) (reference DEC20-107).
Data collection All cases of patients with inflammatory rheumatic and musculoskeletal diseases and highly suspected or confirmed COVID-19 were reported retrospectively. Individual data regarding diagnosis of or specific ongoing treatments for inflammatory rheumatic and musculoskeletal diseases were captured from physicians via a single national data entry portal. Data collected from patients medical records have been previously described in detail  [6, 17]. The data cut-off date was June 15, 2021. Before dataset lock, the final database was monitored to collect missing data, validate the evolution of COVID-19, remove duplicate or erroneous reports, and check data consistency.
The imputation procedure was performed under the missing-at-random assumption, with the predictive mean-matching method for continuous variables and logistic regression (binary, ordinal, or multinomial) models for categorical variables. All statistical tests were performed at the two-tailed α level of 0. 05 using SAS software, 9. 4 release (SAS Institute, Cary, NC, USA). 1 ). Of the 73 RMD patients treated with anti-IL6 therapy, 6 were treated with sarilumab and 67 with tocilizumab. Of the anti-IL6 patients, 30 were treated with intravenous anti-IL6 therapy, 41 were treated with the subcutaneous form, and 2 were unknown. Patients who received anti-IL6 therapy were more likely to be female, and showed higher prevalence rates of cardiovascular diseases, BMI (kg/m 2 ) ≥40, hypertension and corticosteroid use than those who did not receive anti-IL6 therapy.
This article presents a collection of data on approximately 150 copper-based alloys. The data compilation is based on articles published since 1993 and consists of about 1830 records. Each record contains a unique set of descriptors, such as composition and processing route, and targets, including properties such as hardness, yield strength, ultimate tensile strength, and electrical conductivity. The dataset includes information on the composition in mass percent of 20 alloying elements, and hundreds of temperature-time thermal treatments and thermomechanical conditions. The database is continually updated and hosted on an open data repository. Some of the data are presented graphically in the article to aid interpretation. This study intends to promote the identification of more sustainable alternatives to Cu-Be alloys, which is particularly relevant in developing non-toxic and environmentallyfriendly alloys.
Data records The complete database on copper-based alloys is available as a csv file via a public online repository at Figshare  5. The database contains 1831 records from about 50 articles. An i dividual record is defined as having a unique set of descriptors (composition, processing route), targets (properties), and reference combinations. The data provided in the database are intended to reflect the information available in the literature best. For example, despite the importance of the microstructure on properties, this feature is missing or not properly described from most of the articles, so it is not provided in the database. However, thermal and thermomechanical treatments applied to control the microstructure, which in-fine affect the properties, are recorded, including the temporal dependency of the property targets.
The database records consist of the following fields that are not equally populated for every records due to the lack of literature data:   • Descriptors: • Alloy composition: alphabetized nominal alloy composition, in weight percent. • Processing method: the conditions under which the alloy was processed after synthesis. They include the temperatures, T ss (K) and T ag (K), and times t ss (h) and t ag (h) of the solid solutionning and precipitation aging thermal treatments, respectively. Cold reduction is filed in when cold rolling was applied prior or after thermal treatment. The secondary thermo-mechanical treatment is indicated by "Y" (yes) or "N" (no) to denote whether additional treatments were applied after aging. These treatments can involve multiple cold rolling and aging processes, among others.
• Targets: • Hardness (HV): experimentally reported room temperature Vickers hardness. • Yield strength (MPa): measured room temperature yield strength. • Ultimate tensile strength (MPa): measured room temperature ultimate tensile strength. • Electrical conductivity (%IACS): stands for International Annealed Copper Standard, which is the percentage of room temperature electrical conductivity a material has relative to copper. Fig. 6  The material property space of Cu-based alloys is examined in terms of hardness (HV) versus electrical conductivity (%IACS), with a specific focus on the region occupied by Cu-Be alloys. The color of the symbols indicates the influence of cold rolling, with red symbols representing the presence of secondary thermomechanical treatments. • Reference: to ensure the data quality is upheld, a digital object identifier (DOI) link is provided for each record, which links to the original source.
technical Validation The data presented in this article were carefully collected, processed, and thoroughly verified for accuracy by a team of experienced materials scientists and material engineers who possess a deep understanding of metallurgy, copper alloys, and their properties. The verification process involved a two-step validation procedure. Firstly, during the data extraction phase, we implemented a double-check system. Each data point was independently extracted by two members of our team. Discrepancies between the two extractions were discussed and resolved by referring back to the original source. This process ensured that the data extraction was accurate and consistent. Secondly, we performed a statistical analysis on the collected data. This allowed us to identify any outliers or data points that deviated significantly from the expected values based on the existing literature and theoretical predictions.
Usage Notes This dataset is intended to guide researcher efforts for future Cu-based alloy design and development. It can be helpful as training data for data mining and machine learning applications (e. g. scikit-learn  52  ) and produce compelling visualizations in conjunction with standard Python data processing packages (e. g. Matplotlib  53  ) as illustrated in Figs. 5 6 7 , where the relationship between hardness and electrical conductivity is displayed. Individual records are shown by symbols with color maps associated with different features. Figure  5  evidences the distinct regions of the property space occupied by different Cu-based alloys classes. The color saturation, corresponding to the density of the data points, highlights the prevalence of low alloyed Cu, Cu-Ni-Si, Cu-Be, and Cu-Ti alloys in specific ranges of (Electrical conductivity, Hardness), namely (80, 120), (30-40, 200-300), (20, 350), and (15, 325) respectively.
Study design PROSPECT-AD collaborates with already ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by adding the collection of speech data to existing protocols or as follow-up assessments over the telephone. Participants at preclinical stages are mainly recruited from existing parent cohorts across Europe to form an international 'probability-spectrum' population covering the entire continuum of anticipated probability for Alzheimer 's dementia development. This characterization of cognitive, biomarker and risk factor (genetic and environmental) status of each research participants over time combined with audio recordings of speech samples will provide the necessary well-phenotyped population for developing predictive longitudinal models for AD covering the entire disease course and concurrently create a pool of highly characterized individuals for the validation analysis.
Data collection Speech data will be collected at each assessment time point. All data will be collected via the Mili platform over the phone. There will be no paper source documentation for this study. In the platform, the fully automated system uses software ("Mili" phone) to connects with participants who receive an ordinary phone call to complete cognitive assessments. The software guides the interaction through a predefined protocol. The protocol includes short cognitive tests (e. g. , episodic memory word list test). The platform software used for the study is developed by ki:elements and its implementation follows high security standards and common regulatory requirements so that they are ready to use even for clinical trials (Computer System Validation, GCP guidelines, FAIR principles, GDPR requirements).
Personal Data The following data about participants will be collected: Participants will be provided with information regarding the study in the participant information sheet and asked to explicitly consent to the storage of this data in these databases. The phone calls to the participants will be made through the ki:elements software. The data is recorded and then transferred to a safe server designated for the study center (EPAD -Edinburgh server, DESCRIBE/ DELCODE -DZNE Rostock server and BioFINDER Primary Care -ki:elements server hosted by Google  Germany GmbH in Frankfurt). Ki:elements saves the collected electronic data in an encoded, passwordprotected database. The webhosting institute will ensure adequate physical and environmental safety including storage in rooms with strictly limited physical access.
Data Information Flow The project will collect speech data from participants as part of the study protocol using the ki:elements software. This raw data will be stored locally on the study site server and transferred to Google Germany GmbH server (Frankfurt, Germany) for analysis by ki:elements. The speech data will be converted to numerical features at which point the data is considered pseudonymised. The analyzed speech data is owned by the specific study site and used with licensing agreements by ki:elements and the Alzheimer Drug Discovery Foundation. Data from previous studies will be transferred to ki:elements to be combined with speech data and used for algorithm development work. Participants who were previously enrolled in the specific cohort have consented to data sharing for future studies/will be asked to provide consent as part of this study for their previously acquired data to be used for this study.
Proposed Data analyses To achieve the primary objective of the study, three different types of variables will be computed and extracted from speech recordings: 1. Classical neuropsychological outcome variables, i. e. total score and subscores (immediate and delayed recall) of verbal memory task, number of correctly named words in fluency tasks, repetitions. 2. Novel or qualitative outcome variables will be derived from speech recordings of the cognitive tests. These are automatically processed using the proprietary speech analysis pipeline from ki:elements that involves automatic speech recognition to transcribe and to extract semantic and linguistic information. For the verbal fluency task this includes variables such as semantic clusters which are determined using predefined semantic subcategories, mean cluster size is calculated as the sum of cluster sizes divided by the number of clusters, the number of switching clusters is defined as the total number of switches between clusters, and temporal clusters, represent temporal alignments of produced words.
This will lead to a dataset where each participant has multiple repeated measurements of the same task. Results from multiple time points will be compared using repeated measures ANOVA and repeated measures correlation. For the explorative objective, audio features (paralinguistic -or low-level speech descriptors as described above) will automatically be extracted from the audio signal and textual features (linguistic) from transcripts of the free speech task and compared to classical standard measures of neuropsychiatric symptoms. For this, we will perform correlation analysis and train regression models to evaluate the predictive diagnostic power of the extracted markers. Participants' attitude towards the automated phonebased assessment will be gathered through questionnaires and interviews in order to evaluate its feasibility.
Participants Physicians were invited via email from the available distribution listings extracted from Chiesi Farmaceutici S. p. A, and its affiliates or from external providers proprietary databases for each country involved. Extended scouting was performed to collect a contact database covering all the target countries. The physicians contacted had to read and acknowledge a legal release message to be able to access the questionnaire. The participant inclusion criteria applied to interventional cardiologists and non-invasive hospital-based cardiologists who managed patients with NSTE-ACS and agreed to participate in the research. The following countries were included: Austria, Belgium, the Czech Republic, Denmark, Finland, France, Germany, Greece, Italy, Norway, Slovenia, Spain, Sweden, Switzerland, the Netherlands, Turkey and the United Kingdom.
Participation on a Data Safety Monitoring or Advisory Board from DANBLCOK trial and TARGET CTCA trial. Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid: Deputy Editor: EHJ Quality of Care and Clinical Outcomes, NICE Indicator Advisory Committee, Chair ESC Quality Indicator Committee. Stock options in Car-dioMatics. Receipt of equipment, materials, drugs, medical writing, gifts or other services from Kosmos device. Kurt Huber has received lecture and consulting fees from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Chiesi, Daiichi Sankyo, Novartis, and Sanofi. Giuseppe Musumeci has received payment as an individual for honorarium from Amgen, AstraZeneca, Chiesi, Edwards and Novartis.
Bee sampling and identification Faunistic investigations have been carried out with the selected walking-line transects technique, according to  Turrisi et al. (2021). In the four sites investigated, the observations were carried out for three consecutive years, from February 2007 to February 2010, based on a monthly collection. The transects were 200 m long and 1 m wide and were visited four times per day at 9. 00, 12. 00, 15. 00 and 18. 00. During the field surveys, the specimens were counted, registered, and collected when necessary. Further data on the collecting protocol are available from  Turrisi et al. (2021). This study also considers the already well-known faunistic data of the site called 'Leucatia', obtained by V. Nobile over twenty years. This site was therefore considered as a 'control site', and the known data were used for comparison.
Statistical analysis PRIMER-E software was used for the statistical processing of faunistic data, starting from a matrix of presence/absence and abundance of species  (Clarke & Warwick, 2001). To describe the Table  1. Criteria for the definition of the environmental biopermeability scale of the investigated sites. composition, structure, and dynamics of the bee communities, the Shannon-Weaver index, the Pielou equipartition index, and the Simpson index were also calculated. In addition, the average value of each index was calculated for each site using an analysis of variance. A multivariate analysis was carried out to elucidate different ecological traits. Using Non-Metric Multidimensional (nMDS) methodology, were compared and graphed the populations of the different sites, based on the species composition and the specimen number.
Areas For the Aci Catena (AC) site, a specific analysis was carried out to highlight any changes in the structure of the bee community caused by an unexpected change in agricultural practices (mechanical weeding) in the second year of the survey. The principal component analysis (PCA) method was also used for creating a graph of the principal components, with the samples inserted as a function of the species/sample matrix. The species with the greatest variability were highlighted by plotting their vectors on the main component graph, and bubble plots were produced for each of them to evaluate the number of specimens in each sample. For the cartographic processing, a GIS system was used through the ArcView program to expand the observations to the investigated site and the surrounding area, through soil maps and the degree of urbanization; the latter was assessed through the photointerpretation of digital orthographic projections.
Acknowledgements We thank  Kimie Mase ,  Tomomi Iso ,  Sachiko Kakimoto  for technical assistance,  Akiko Wakui ,  Yoichiro Hashimoto  for TEM imaging and EDX. We also thank  Hiroshi Yoshida ,  Fumiaki Hamazato  for critical review and helpful suggestions on the manuscript. This work was supported by  Hitachi High-Tech Corporation. However, any opinions, findings, conclusions, or other recommendations expressed herein do not reflect the views of the funder.
ACKNOWLEDGMENTS This work was supported by  INSERM and the Ministère de l'Education Nationale de la Recherche et de la Technologie , by state funding from the  Agence Nationale de la Recherche (ANR)  under the " Investissements d'Avenir" program  ( ANR-10-IAHU-01 ), and by a public grant " RHU-C'IL-LICO " overseen by the  ANR  as part of the second " Investissements d'Avenir" program  ( ANR-17-RHUS-0002 ). We thank  Marie-Claire Gubler  for providing histologic pictures of kidney biopsies from patients with nephronophthisis, shown in Figure 1.
Methodology Electronic structure and geometric optimizations were carried out via first-principles density-functional theory (DFT) as implemented within the Quantum Espresso (QE) package https://doi. org/10. 26434/chemrxiv-2023-kx7wh-v3 ORCID: https://orcid. org/0000-0001-5337-4450 Content not peer-reviewed by ChemRxiv. License: CC BY-NC-ND 4. 0  [50] [51]. A vacuum of 20 Å was used to minimize the interactions due to replicas of the unit cells. A plane-wave basis set, a scalar-relativistic ultrasoft pseudopotentials and a gradient-corrected exchange-correlation (xc-)functional (the Perdew-Burke-Ernzerhof, PBE After obtaining the Wannier Hamiltonian, we used a home-made code based on non-equilibrium Green's functions to obtain transmission coefficients. Transmission was calculated using 44 k-points in the planar direction (Y).