dataesr/scientific-paragraphs-categorization huggingface dataset:
Open access identifying number ('participant ID'). During their first session, on providing consent to participate, the participant will be asked to provide general demographic data (eg, month and year of birth, sex, number of years of education, spoken languages, history of dementia, etc); responses will be associated with the appropriate participant ID. If consent is provided by an alternate decisionmaker, the participant's data will be collected from the designated proxy. This information will be used to control for confounding variables when conducting analyses of the collected data and will only be reported anonymously or in aggregate.
Speech data The speech of the different participants will be recorded as audio files for different cognitive tests. Depending on the test, the recorded speech will be either free speech or direct answers to questions, or verbal or visual stimuli. We will directly record the speech of the patients. From the audio files, we will use automatic speech recognition to obtain textual transcripts of the recordings. A subset of the data will be manually transcribed to compare results between automated and manual transcriptions. We will use either the internal microphone of the device (PC or tablet) or an external microphone for better recording quality. The recorded data will be automatically stored on the secured server.
Video data Towards our general goal to detect and remotely monitor cognitive decline in the context of dementia in a partially automated way, we intend to support the proposed speech analysis by a complementary computer-vision-based analysis. Such analysis exploits advanced methods related to automated face analysis, tracking, detection and recognition, as well as human behaviour analysis. First, we plan to record two-dimensional video data from all participants for the computer vision-based analysis. Then, we intend to analyse these data with a focus on finding facial/gestural behaviours and facial/gestural activities that are representative for cognitive performance and neuropsychiatric symptoms of dementia in different situations, such as free speech interviews, and in some cognitive tests (eg, patients are encouraged to describe the content of a series of images).
Stress measure data We would like to explore the use of additional objective markers of stress levels within this study. For this, for only a subgroup of participants (during the mobile home unit phase), we will extract through the Empatica E4 device physiological data during both administration methods: ► Electro-dermal activity: measures sympathetic nervous system activity manifested through the skin, by measuring the constantly fluctuating changes in certain electrical properties of the skin. ► Heart rate variability: derived from measuring blood volume pulse. ► Peripheral skin temperature: measured using an integrated infrared thermopile. ► Three-axis accelerometer: captures motion-based activity, which identifies intensity and frequency of movements that could be a seizure. We aim to compare the different time point measures with each other to assess the stress levels of the participants during the face-to-face and the remote assessment administration.
The enrolled subjects will have authorised access to the web-platform with their email and a generated temporary password valid only for the time of their participation in the study. The password will be entered by the person accompanying the subject (a clinician, part of the clinical study) to connect the subject to the platform. For the subjects who have no email, a unique identifier (temporary email) will be provided. The data collected during the study will be stored in a secured database. The database will be hosted by the Institute Claude Pompidou (ICP) servers. These servers are owned and part of the University Hospital of Nice (CHU NICE). These servers are secure and follow all the required security and healthcare data hosting regulations. The security of the servers is managed by the IT personnel of CHU NICE who has to sign a confidentiality agreement and only has access to anonymised clinical data.
An IT technician from the ICP will maintain the anonymised database. The technician will have all the rights on the database: ► Create/delete/edit the list of clinicians who can access the web-platform. ► Create/delete/edit the list of subjects participating in the clinical study. Regarding the mobile van, the technician is only there before the consultation to ensure the technical equipment is working correctly, then he will leave the van. The authorised clinicians will have the right to access the web-interface, which will allow them to perform the study. The collected data will automatically be stored in the database. Access to the stored data is strictly reserved to the clinicians with a secured account. The type of database is MySQL database. The security of the database is ensured by Advanced Encryption Standard techniques.
The following rules will be applied: ► The database will only be accessible from trusted hosts. ► No use of data from input without filtering. ► All types of data will be protected. ► The administrator of the database will not be a user (the IT technician) of the web platform. Security of transmission between clinicians and participants during study sessions: After the clinician and the participant are connected to the platform, video streams are circulating in both directions. Video streams are protected by integrating the following rules: ► The sessions of the clinical study will be scheduled by the involved clinicians and will not be publicly known. ► We will add a signalling protocol that will provide encryption of signalling traffic. ► The connection between the clinician and the patient will be a P2P connection, the media contents (audio and video channels) will be transmitted between peers directly in full duplex.
Data processing The collected data will be processed to generate pseudoanonymous metadata, which will be used by the different technical and clinical partners of the project. These technical partners (who are not the clinicians involved in passing the tests and authorised to access the participant data) will never have access to the raw data: identity of the patient, personnel information, videos and audio or any other information which allows identifying the subject. The only processed data will be: scores of tests, videos and audio files. The processing will be done on the servers of the Institut Claude Pompidou, a processing which will generate anonymous metadata which will be transmitted by the IT technician to the partners with no information about the identity of the participants. Concretely, an IT technician (or engineer) working for the Institut Claude Pompidou, and authorised to access all patient's data as part of his duties (creating patients records, correcting information about patient information, etc) will be the only non-clinical person who can access the identifying data of the participants (speech and video recordings).
Speech analysis The audio and transcribed text data will be processed to obtain acoustic, lexico-syntactic and semantic features. First, acoustic features will be extracted from the audio samples. The audio data will be passed through frequency domain transformations to obtain standard acoustic measures used in language processing, such as Mel frequency cepstral coefficients based on the cepstral representation of the acoustic signal which detects periodicity in its spectrum, jitter and shimmer measures based on irregularities in signal periodicity, recurrence period density entropy and pitch period entropy based on measures of aperiodicity and noise in the signal, measures based on the frequency domain harmonics, signal to noise ratios and filled and unfilled pause features, among others.
Video analysis For the video analysis, we will first annotate the data in an initial step to find similar video sequences (eg, such as depicted patients talking, as well as such showing smiles or neutral states). From these annotated sequences, features that target behavioural analysis  23 24  will be extracted. Specifically, we will use algorithms for face detection and then extract dense trajectories from the video sequence and represent these with facial spatio-temporal features, which incorporate motion of sampled points in the video sequence. Using the extracted features, emotions and engagement of the patients will be computed. Combining the features from video and speech analysis, we will perform an analysis to correlate the emotions expressed by a patient and their engagement during the cognitive tests with the cognitive disorders they could be affected with.
Acknowledgements This research is funded by the  EIT Health Activity 19249, DeepSPA , with great support from  Modis Belgium ,  Janssen Clinical Innovation , the Association IA, ADMR  04 , and the  Centre Hospitalier  and the city of Digne-les-bains, France. Special thanks to  Valérie DEPRAD President ,  Francis Kuhn  and  Emmanuelle Martin Vice-President  of  ADMR 04. Contributors Conceptualisation: AK, RZ, RG, KL, IHGBR, PR; methodology: RZ, AK, IHGBR; software: RG, MDT, FB; validation: RZ, AK, PL, VB; formal analysis: NL, HL, RZ; investigation: RZ, AK, PL, VB; resources: PR, PL, VB; data curation: RG, FB, MDT; writing-original draft preparation: AK, RZ, RG; visualisation: RZ, RG; supervision: PR, FB; project administration: AK, FB; funding acquisition: FB, PR. All authors have read and agreed to the published version of the manuscript.
Bioinformatics analyses Bioinformatics analyses were performed in the Linuxbased operating system Ubuntu 20. 04 LTS with the R software environment version 4. 0. 2. To assemble a dataset of immunoproteasome-related genes, genes were identified from the review of Ferrington and Gregorson (2012) via text mining with the Pubtator central algorithm  [15]. Functional enrichment of targeted genesets was analyzed using the online Toppgene application  [16]. The transparent bioinformatics code for all analyses performed in R software is provided at the address: https:// github. com/ cdest erke/ covid 19if.
Transcriptome analyses of COVID-19 lung samples We obtained transcriptomes from COVID-19 lung biopsies from dataset GSE150316 in the Gene Expression Omnibus database (https:// www. medrx iv. org/ conte nt/ 10. 1101/ 2020. 07. 30. 20165 241v1)  [17]. A comparison of the transcriptomes between lung samples presenting high and low levels of SARS-CoV-2 RNA was performed with Gene Set Enrichment Analysis (GSEA) software version 4. 0. 3 [18]  using version 7. 2 of the MSigDB database  [19]. A network of genes implicated in the functional enrichment in antigen presentation was created with Cytoscape standalone software version 3. 6. 0 [20]. Principal component analyses were performed with the R-package FactoMineR  [21]. Correlated expression plots were created using the R-package corrgram. The genomic relationships among activated immunoproteasome components were visualized by loading version HG38 of the human genome in the Integrative Genomics Viewer version 2.
Single-cell analysis of lung samples from healthy donors and patients with mild and severe COVID-19 In order to validate, at the single-cell level, the disruption observed in immune molecules in the transcriptome analysis of lung cells from COVID-19 patients, single-cell transcriptome (10 × Genomics) data from bronchoalveolar lavage fluid samples were downloaded in H5 format from the dataset GSE145926  [23]. From this dataset, we created a merged matrix by aggregating a total of 90696 transcriptomes. This included six healthy donor samples-GSM4475048, GSM4475049, GSM4475050, GSM4475051, GSM4475052, GSM4475053-comprising 39900 transcriptomes; three samples from patients with mild COVID-19-GSM4339769, GSM4339770, GSM4339772-comprising 9710 transcriptomes; and three samples from patients with severe COVID-19-GSM4339771, GSM4339773, GSM4339774-comprising 41086 transcriptomes.
MHC class I immunopeptidome of SARS-CoV-2 virus Protein sequences corresponding to the totality of the proteome of the Wuhan-Hu-1 isolate of SARS-CoV-2 were downloaded from the NCBI nucleotide database at the following address: https:// www. ncbi. nlm. nih. gov/ nucco re/ 17981 74254. Data on the prevalence of HLA alleles in 20 ethnicities from the American bone marrow registry were collected from the HLA Allele Frequency net database; USA NMDP bone marrow registry database available at the address: http:// www. allel efreq uenci es. net/  [24]. The distributions of HLA alleles among world regions were predicted by unsupervised analysis and validated by Random Forest estimation of error of bagging  [25]. Immunopeptidome binding affinity was predicted for each MHC class I binding pocket with the algorithm NetMHCpan class I version 4.
1  [26]. Predicted relationships between peptides and HLA alleles were divided into three subgroups: weak, with binding affinities estimated between 500 and 5000 nM; regular, with binding affinities estimated between 50 and 500 nM; and strong, with binding affinities estimated under 50 nM. A heatmap was created to represent the relative binding activities of MHC class I alleles using the R-package pheatmap, with Euclidean distances and the Ward. D2 method. Cleveland plots of HLA prevalence in different ethnic groups and barplots of peptide binding were created with the R-library ggpubr.
Activation of MHC class I antigen presentation in the lungs of COVID-19 patients is associated with the amount of SARS-CoV-2 RNA in the tissue A certain proportion of COVID-19 patients admitted to hospitals develop acute respiratory distress syndrome  [27]. We thus decided to investigate the lung transcriptomes of patients with COVID-19 using geneset enrichment analysis (GSEA) to identify differences between two groups of patients who presented different levels of SARS-CoV-2 virus in their tissues, as detected by in situ hybridization (ISH) of viral RNA. A cohort of 14 patients was screened using 45 different microarray experiments in lung samples and their SARS-COV2 levels were also assessed. So, a two-group stratification of GSE150316 transcriptome cohort was defined according to their level of SARS-COV2 in lung (threshold 5 percent).
Activation of genomic cluster associated with the immunoproteasome in the lungs of COVID-19 patients correlates with the amount of SARS-CoV-2 RNA in the tissue During MHC antigen presentation, catalytic subunits of the immunoproteasome are tightly regulated in different cell types, leading to the production of distinct repertoires of presented peptides  [28]. To assess potential changes in the expression of such genes in COVID-19 patients, we first assembled a geneset of immunoproteasome-associated genes. Using the PubTator Central text-mining algorithm  [15] , which explores the full text of articles in Pubmed Central, we analyzed all gene citations in Ferrington and Gregerson's  [28]  review of immunoproteasome literature (PMC4405001). The resulting immunoproteasome-related gene set was then used to conduct a GSEA analysis between the two groups of COVID-19 patients (GSE150316) with low and high levels of virus in the lung.
029, Fig. 2C ), TAP1 (p-value = 0. 0033, Fig. 2C ), TAP2 (p-value = 0. 0053, Fig. 2C ) and CALR (p-value = 0. 021, Fig. 2C ). When we examined the expression of immunoproteasome-related genes in the transcriptome dataset, we observed a positive correlation between the expression of PSMB8 and TAP1, CALR and CANX, TAP1 and TAP2, and TAP1 and PSMB10 (Fig. 2D ). Co-regulated genes of the immunoproteasome were found to map together to the 6p21. 32 genomic cluster of the human genome (HG38), specifically, inside the HLA class II region, from 32. 820 to 32. 860 kb (Fig. 2E ). These results suggest that in the lungs of COVID-19 patients, immunoproteasome components located in a 40-kb span of the 6p21. 32 genomic region are activated in a manner that may be dependent on the viral load of SARS-CoV-2.
Single-cell transcriptomes of bronchoalveolar cells reveal immunoproteasome activation in M1 macrophage cells during mild cases of COVID-19 Next, patterns of immunoproteasome activation were investigated in a dataset of single-cell transcriptomes obtained from bronchoalveolar fluid lavages from six healthy donors, three patients with mild COVID-19, and three patients with severe COVID-19  [23]. These data were merged together for the purpose of UMAP dimensionality reduction (Fig. 3A ) using the R-package Seurat  [29] ; after canonical correlation and filtration, the merged transcriptome analysis comprised 90,696 cells, with a representative proportion of each cell subgroup. A dotplot of the relative expression of macrophage-related markers appears in Fig. 3B. In these bronchoalveolar samples, we found that the M1 macrophage marker CD68, was expressed with a higher level in a cell population smaller in size for mild COVID-19 patients as compared to severe COVID-19 patients.
Single-cell heterogeneity in PSMB8 expression shapes a cell trajectory in alveolar M1 macrophages during mild COVID-19 Alveolar macrophages play a critical role in regulating immune responses  [30]  and maintaining homeostasis in the lung  [31, 32]. Macrophages have been categorized as being "polarized" towards either the M1 (CD68+) proinflammatory phenotype, which mediates resistance to pathogens, or the M2 (CD163+) anti-inflammatory phenotype, which promotes tissue remodeling  [33]. Using the same dataset of single-cell transcriptomes of bronchoalveolar cells in patients with mild and severe COVID-19  [23] , we decided to examine the relationship between CD68+ macrophages and immunoproteasome components in more detail. With the R-package monocle.
First, we investigated the prevalence of the 69 most common HLA class I alleles (types A, B, and C only) worldwide  [36]. This was accomplished through use of the bone marrow registry dataset from the USA, which documents the HLA status of 2,790,874 donors, representing 20 ethnicities with worldwide distribution (Additional file 1: Table  S6 )  [24]. In total, 69 alleles were found to be prevalent in the 20 ethnic groups examined. An unsupervised principal component analysis based on the prevalence of these 69 alleles clustered the 20 ethnicities into three main groups, representing three major regions of the world: Africa, Asia, and Europe/Americas (Additional file 1:   S3E ). Other HLA alleles, instead, demonstrated more of a worldwide distribution (Additional file 1: Figure  S3B ). This pattern of clustering into three world regions based on allele prevalence was further validated by a Random Forest learning algorithm (100 trees, total error rate 0.
For this analysis, we collected 38 protein sequences encoded by the entire genome of SARS-CoV-2 Wuhan-Hu-1 from the NCBI database (Additional file 1: Table  S7 )  [37]. An analysis of these 38 protein sequences and the 69 tested alleles predicted 102,524 possible peptide binding events (Additional file 1: Table  S8 ). As described by the literature associated with the NetMHCpan algorithm  [26]  and applied in the study of  Barquera et al. [38] , it is possible to distinguish three subgroups of MHC class I molecules based on their binding affinities: strong peptide binders, with a binding affinity between 0 and 50 nM; regular peptide binders, with a binding affinity between 50 and 500 nM; and weak peptide binders, with a binding affinity between 500 and 5000 nM. Peptide counts for the three binding subgroups and the 69 investigated HLA alleles are provided in Additional file 1: Table  S9 ; based on their proportions, we were able to identify seven clusters of alleles (Fig.
Data availability We obtained transcriptomes from COVID-19 lung biopsies from dataset GSE150316 in the Gene Expression Omnibus database (https:// www. medrx iv. org/ conte nt/ 10. 1101/ 2020. 07. 30. 20165 241v1). Single-cell transcriptomes from bronchoalveolar fluid lavage were obtained from dataset GSE145926  [23]. Mendeley Dataset associated with this manuscript: Supplemental table 8 database is provided as a Mendeley dataset: https:// doi. org/ 10. 17632/ rjmyz 3j52y. 2 Internet links: https:// data. mende ley. com/ datas ets/ rjmyz 3j52y/2.
Identification of EPHX1 variants in two unrelated patients To identify novel genetic causes responsible for lipoatrophic diabetes, WES was carried out on a parent-offspring trio. The index case, patient 1, is a 25-year-old woman originating from Mauritania. Her disease phenotype was not explained by variants in genes known to be involved in lipoatrophic diabetes, as assessed by the analysis of a panel of genes used in routine genetic diagnosis  (Jeru et al. , 2019). The analysis of exome data led to the identification of a heterozygous variant in exon 7 of EPHX1  (Retterer et al. , 2016) , which was subsequently confirmed by Sanger sequencing: c. 997A>C, p. (Thr333Pro) (NM_000120. 4) (Figure  1A, B ). This variant was found to be de novo after paternity confirmation by genotyping a set of polymorphic markers.
The presence of chromosomal abnormalities was also previously excluded in the two patients by karyotype and SNP chromosome microarrays. Several additional lines of evidence supported the causal role of the two variants in the disease phenotype. These variants were absent from databases reporting variants from the general population (gno-mAD, ExAC, dbSNP, and Exome Variant Server), as well as from ClinVar, a database that aggregates information about genomic variations and their relationship to human diseases. The variants identified herein affected amino acids strongly conserved throughout evolution, even in zebrafish and Xenopus tropicalis (Figure  1 -figure supplement 1). The two variants predicted changes in the polarity of the corresponding amino acids, as well as in the charge for p. Gly430Arg.
, 2000; Ulu et al. , 2016). The gnomAD database, which collects variants from the general population, reports several dozen predicted loss-of-function variants in EPHX1, including nonsense, frameshift, and canonical splice site variants. This suggests that the pathogenic effect of the missense variants identified in patients 1 and 2 is not only due to a loss-of-function, but may be associated with a dominant negative mechanism. A previous study on the 3D-structure of EPHX1 suggests that this enzyme, which is mostly membrane bound, might be dimeric  (Zou et al. , 2000). This would be in favor of a dominant negative effect, as well as the formation of higher-order complexes within the ER observed in overexpression studies. Although this aspect of EPHX1 structure has so far been poorly explored, it may have important consequences for EPHX1 function, for example, by pre-orienting the enzyme for binding of either substrates or functional partners.
Identification of the potentially pathogenic variants were determined using Sirius software (IntegraGen SA, France). For patient 2, exome sequencing was performed on the proband and both parents as previously described  (Retterer et al. , 2016). EPHX1 variants were confirmed by Sanger sequencing with the Big Dye Terminator v3. 1 sequencing kit (Thermo Fisher Scientific, MS, USA) after PCR amplification and analyzed on a 3500xL Dx device with the SeqScape v2. 7 software (Thermo Fisher Scientific, MS, USA). EPHX1 variants were described based on the longest isoform (NM_000120. 4) using Alamut 2. 11 (Sophia Genetics, Switzerland) and Human Genome Variation Society guidelines. Variants were classified according to the ACMG recommendations  (van Loo et al. , 2006). Protein sequences were aligned using the Clustal Omega software, and the degree of conservation was presented with help of the BoxShade software.
The coverslips were mounted in DAKO fluorescence mounting media (#S3023; Agilent Technologies, CA, USA). Images were acquired using a SP2-inverted confocal microscope (Leica Biosystems, Germany), equipped with an HCX PL APO CS 63X/1. 32 oil immersion objective, and analyzed using Leica Confocal Software and FIJI Software. For each experiment, all images were acquired with constant settings (acquisition time and correction of signal intensities).