dataesr/scientific-paragraphs-categorization huggingface dataset:
Adverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis. Unfortunately, this method faces a computational time problem. The objective of this paper is to describe the implementation of the WCE statistical model using Graphics Processing Unit (GPU) programming as a tool to obtain the spectrum of adverse drug reactions from medico-administrative databases. The process is divided into three steps: pre-processing of care pathways using the Python library Panda, calculation of temporal co-variables using the Python library "KeOps", estimation of the model parameters using the Python library "PyTorch" -standard in deep learning.
Programming the WCE method by distributing the heaviest portions (notably spline calculation) on the GPU makes it possible to accelerate the time required for this method by 1000 times using a computer graphics card and up to 10,000 times with a GPU server. This implementation makes it possible to use WCE on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology.
Introduction Adverse drug reaction is a major public health issue. A recent study by Makary  [1] , in 2016, places drug-related iatrogeny as the 3rd leading cause of death in the United States. Currently, post-marketing surveillance of drugs is carried out by a pharmacovigilance system, which is based on spontaneous reports from healthcare professionals, pharmaceutical companies and patients. However, this system suffers from underreporting, with less than 10% of serious adverse reactions being reported  [2] , and is subject to bias due to selective reporting. Most of the reported cases are considered as suspected adverse reactions. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals  [3]. However, these databases have mainly been used to confirm or refute potential signals reported  [4].
It would be very appropriate to use these databases not to confirm a suspected adverse reaction but to generate adverse reaction signals. The challenge in developing a real-life pharmacovigilance signal detection method presents two difficulties: (a) The choice of a powerful method that can detect a signal among a very large number of drugs and therefore face the problem of multiple tests and therefore the generation of potential false positives, but also face problems of computer resources. (b) Taking into account the temporal heterogeneity of the link between drug intake and adverse effects, which may appear in the short or medium term depending on the pathophysiological mechanism causing the adverse effect. Over the last decade, several international initiatives have worked on the development of pharmacoepidemiology tools: Mini-Sentinel  [5] , Observational Medical Outcomes Partnership  [6] , Pharmaco-epidemiological Research on Outcomes of Therapeutics by a European Consortium  [7] , Exploring and Understanding Adverse Drug Reactions  [8] , and the Asian Pharmacoepidemiology Network  [9].
The methods compared in these working groups are methods using pharmacovigilance feedback databases (not exhaustive) or so-called "single-candidate" methods, i. e. methods that seek to prove or disprove a hypothesis. These methods do not address our signal detection problem. We have recently adapted a pharmaco-epidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model  [10] , which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis  [11] , taking into account the temporal heterogeneity described below. Unfortunately, this method faces a computational time problem. Since 2007, Nvidia has been developing CUDA (originally an acronym for Compute Unified Device Architecture), which is a technology that uses a graphics processing unit (GPU) to perform general purpose computations in place of the processor (CPU), thus speeding up calculations.
WCE implementation on GPU The process is divided into three steps: x Pre-processing of care pathways using the Python library "Panda" -standard for processing tabular data (https://pandas. pydata. org/). Our raw data describes the set of prescriptions associated with a patient population. We extract a list of "times of interest" which correspond to the first intake of selected drugs, used as proxies for adverse side effects. x Calculation of temporal co-variables using the Python library "KeOps" (https://www. kernel-operations. io). For each time of interest, the prior exposure to the study drug ("suspect") is quantified using correlations with temporal filters -in the example of the original WCE model, these are B-spline functions. The computation of these "temporal co-variables" is efficiently performed using the KeOps library's "block" reduction operations, which distribute the patients over the thousands of cores of our graphics cards.
x Estimation of the model parameters using the Python library "PyTorch"standard in deep learning (https://pytorch. org/). Given the temporal co-variates computed in the previous step, estimating the maximum likelihood for the Cox/WCE model corresponds to minimizing a convex function with about ten free parameters. We find the solution to this problem using a quasi-Newton method (L-BFGS solver with "strong Wolfe" search), which converges in about ten iterations. We quantify the uncertainty of our estimator using a bootstrap method (N=1,000 to N=10,000 replicates), which is very efficiently implemented with PyTorch using a batch dimension. We can express the run time of our algorithm as a big O(Its * Drugs * Events * Survivors), where Its is the number of iterations in the convex optimizer for the Cox-PH problem (at most 100), Drugs is the number of drugs of interest, Events is the number of "Death" events -in practice, the prescription of a precise drug that we use as a proxy for the onset of an adverse effect and Survivors is the typical number of patients that do not develop the adverse effect.
This means that the total run time of our method is at most quadratic in the number of patients (Events and Survivors are both smaller than the total number of patients) and linear in the number of drugs of interest (in practice, 10 to 500). Our GPU implementation does not cut this theoretical time complexity since we still perform the exact same computations, without any approximation. However, they dramatically "improve the constant" by three orders of magnitude. Modern GPUs are made up of thousands of cores. In practice, the number of patients in our datasets range from 100k to 100M -with an understanding that we always extract subsets that correspond to prescription subgroups. As a consequence, the number of operations required to train our model ranges from about 100 * 100 * 100k * 100k = 10^14 to at most 100 * 100 * 100M * 100M = 10^20 for a hypothetical ubiquitous drug on a very large population.
The data driven WCE enabled the identification of eight ATC classes associated with the prescription of HCQ. The most relevant drugs were hydrocortisone, alendronic acid cholecalciferol, valsartan and chlormadinone. The experimental dataset composed of five tables: patient table with 2010 rows, 3 columns (id_patient, age, sex), size 775KB ; hydroxychloroquine prescription: 50,425 rows, 3 columns (id_patient, date, ATC_classes), size 1. 4MB ; all drug prescriptions: 1,175. 507 rows, 3 columns (id_patient, date, ATC_classes), size 36. 2MB ; all medical procedures: 190,712 rows, 3 columns (id_patient, date, Code_procedures), size 4. 8MB ; all medical diagnoses: 48,470 rows, 3 columns (id_patient, date, Code_CIM10), size 1. 5MB.
Discussion This paper proposed an implementation of the WCE method using GPU programming allowing to considerably accelerate it. This makes it possible to use this method on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology. Drug consumption data is remarkably compact and fit without any problem in the main "RAM" memory of our GPUs, which have ~24Gb of usable space. As a consequence, disk latency is much less of a problem than for e. g. medical imaging studies. Going forward, we intend to scale up to full nation-wide studies (~70M patients for the full French population, at most ~500M for the US or the European Union), with full files that may weigh up to 100Gb.
Methods This is a multicentre retrospective study, conducted in accordance with the principles of the Declaration of Helsinki. All cases of patients with HUV, as de ned by Schwartz criteria, and a biopsy-proven kidney involvement, identi ed through a survey sent to physicians from the French Study Group on Vasculitis network, were reviewed and included. Some of these patients were included in the national cohort by Jachiet et al. The following data was collected for each patient: demographic characteristics, systemic symptoms, skin lesions, adenopathy, rheumatic, ocular, eyes nose throat, lung, gastrointestinal, neurologic, cardiac involvement, and detailed renal parameters. AKI was de ned according to the RIFLE criteria as R (risk), I (injury) and F (failure)  (7). Serum levels of C-reactive protein, complement fractions C3 and C4, anti-C1q antibodies, antinuclear antibodies (ANA), antiphospholipid antibodies, rheumatoid factor, cryoglobulinemia, anti-neutrophil cytoplasmic antibodies (ANCA) and protein electrophoresis were collected.
Flow cytometry Cells were labeled with PE-or APC-conjugated anti-PD-L1, anti-CD160 (clone BY55), anti-HVEM or anti-HLA-A/B/C antibodies or their respective isotype-matched control antibodies. When indicated, cells were fixed and permeabilized prior to labeling according to the Cytofix/Cytoperm protocol (BD Biosciences). For detection of sCD160 binding, cells were pre-incubated for 1 h at RT with a His-tagged sCD160 fusion protein (sCD160-His), melanoma cell culture supernatants or culture medium (negative control). Ligation of sCD160 to the cells was further detected using an APC-coupled anti-His mAb or anti-CD160 H3 mAb plus APC-conjugated secondary antibodies. Where indicated, an anti-HLA-A/B/C mAb (clone W6/32) was added to the cells before incubation with sCD160-His. After acquisition on a Cytoflex cytometer (Beckman Coulter), data analysis was performed using FlowJo software (Tree Star Inc.
Identification of a novel missense variation in TNFAIP3 To identify the molecular basis of the patients' disease, targeted next generation sequencing (NGS) for autoinflammatory diseases was conducted on a DNA sample from proband II. 2. It revealed a nonpreviously reported heterozygous variation c. 707T>C, p. (Leu236Pro) in the TNFAIP3 gene. Sanger sequencing confirmed this result in the proband and identified this variation in the related affected individuals (I. 1 and II. 1) (Figure  1A ). The affected leucine residue at position 236 (Leu236) is located in the OTU domain of A20 (Figure  1B ) and is invariant across species (Figure  1C ) supporting a deleterious effect of the substitution of this amino acid. The c. 707T>C, p. (Leu236Pro) variant has not been described in sequence-variant databases, such as the genome aggregation database (gnomAD, https://gnomad.
Decreased stability of the A20_Leu236Pro protein With the aim of determining the molecular basis of reduced A20 protein expression in the patients and further assessing the impact of the p. (Leu236Pro) missense variation on the protein, we performed in silico stability analyses of the available human crystal structure of the OTU domain of A20 in its active form (Protein Data Bank: 3ZJD)  (Kulathu et al. , 2013)  using the bioinformatics tool PremPS  (Yuting Chen et al. , 2020). The single variation Leu236Pro of A20 is destabilizing, as indicated by the positive value of 1. 820 kcal mol -1 for the change of unfolding Gibbs free energy ΔΔG. Similar results were obtained using two other prediction tools: Site Directed Mutator (ΔΔG= 1. 860 kcal mol -1 ) and MAESTRO (Multi AgEnt STability pRedictiOn) (ΔΔG= 1.
Among the OTU domain missense variations, the pathogenic significance is demonstrated for three: p. (Leu236Pro) (reported in this study), p. (Glu192Lys)  (Kadowaki et al. , 2021)  and p. (Cys243Tyr)  (Shigemura et al. , 2016) , which all fulfill the American College of Medical Genetics and Genomics (ACMG) criteria  (Richards et al. , 2015). The remaining two missense variations located in the OTU domain, p. (Asn102Ser) and p. (Ile310Thr), cannot be classified as pathogenic. In fact, the p. (Asn102Ser) variation has an allele frequency in the general population gnomAD database of 3482/281824, which is incompatible with the rare occurrence of HA20; we therefore classified this variation as not pathogenic despite the potential localization of Asn102 residue within a pocket in the active site of A20 that is important for its deubiquitinase activity  (Komander and Barford, 2008; Lin et al.
Sanger sequencing -TNFAIP3 was amplified by PCR and sequenced using the Big-Dye Terminator sequencing kit (Applied Biosystems, Foster City, California) on an ABI3730XL automated capillary DNA sequencer (Applied Biosystems). Sequences were analyzed against the reference sequence (NM_ 006290) using the SeqScape software (Applied Biosystems). Plasma and PBMC isolation -Whole blood samples from patients and healthy individuals (provided by the Etablissement Français du Sang) were collected in EDTA-tubes. Plasma was isolated by blood centrifugation at 950g for 10 minutes and kept at -80°C for cytokine measurements. For PBMC (Peripheral Blood Mononuclear Cell) isolation, blood samples were diluted with an equal volume of PBS-EDTA (1 mM) and PBMCs were collected by centrifugation (800g, 15 minutes) of the diluted samples on Pancoll gradient tubes (PANbiotech, Aidenbach).
, 2021). The primary antibodies used were: polyclonal rabbit A20 (Cell Signaling 5630 -RRID: AB_10698880), anti-GFP-HRP (Cell Signaling 2037 -RRID: AB_1281301), anti-GAPDH-HRP (Cell Signaling 51332 -RRID: AB_2799390) and anti-α-Tubulin-HRP (Cell Signaling 9099 -RRID: AB_10695471). The secondary antibodies used were Anti-Rabbit IgG-HRP, Sigma Aldrich A0545 -RRID: AB_257896. Proteins were detected with Amersham ECL Select Western Blotting Detection Reagent (GE healthcare, Chicago, Illinois) and BioRad ChemiDoc Imaging Systems was used for detection. The ImageJ software was used for signal quantification. NF-κB luciferase assay -HEK293T cells were transiently transfected with the indicated quantities of TNFAIP3 WT and mutant plasmids, 100 ng of pGL4. 32[luc2P/NF-κB-RE/Hygro] vector and 10 ng of Renilla Luciferase Control Reporter Vector (pGL4-hRluc), purchased from Promega.
24 hours later, cells were treated with 10 ng/mL TNFα for 5 hours then lysed with Passive Lysis Buffer (Promega). Firefly luciferase activity was measured in duplicate according to the manufacturer's instructions and normalized against the Renilla signal. Crystal structure and amino acid interaction analysis -The PremPS (Predicting the Effects of Mutations on Protein Stability) tool, a computational method that predicts protein stability changes induced by missense variations available as a web server (https://lilab. jysw. suda. edu. cn/research/PremPS/)  (Yuting Chen et al. , 2020)  was used to predict protein stability and to analyze amino acid interactions of the Protein Data Bank-available crystal structure of A20 OTU domain in reduced, active state at 1. 87 Å resolution (PDB accession number 3ZJD)  (Kulathu et al.
, 2013). The amino acid interactions were shown as provided by the PremPS online tool. Three-dimensional structure was visualized and amino acids were positioned using the PyMol Software. Identification of TNFAIP3 missense variations reported in the literature -Literature review of TNFAIP3 variations was performed in PubMed using the terms ''HA20'', "A20" or "TNFAIP3" and ''variation'' or "mutation". Uncropped Western blot images of GFP-A20 and α-tubulin protein expression (n=3).
ACKNOWLEDGMENTS We thank the patients and their family as well as the control individuals for their cooperation. The research reported in this manuscript was funded by:  Institut National de la Santé et de la Recherche Médicale (INSERM) ;  Agence Nationale de la Recherche  ( ANR-17-CE17-0021-01 );  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  779295 ; and  Sorbonne Université EMERGENCE-PhenomAID. The authors declare that they have no conflict of interest.
Quantification of total HBV DNA, including rcDNA delivered by incoming virions, cccDNA and replicative intermediates generated by pgRNA reverse transcription, showed a basal level as a result of the input virions entering the cells that remained stable until 48 hpi, followed by an increase indicating the initiation of pgRNA reverse transcription and active viral replication (Figure  1E  and H ). Taken together, these data show that PF-rcDNA to cccDNA conversion occurs early after infection and precedes a phase in which it occurs concomitantly with viral transcription (from 24 hpi onward) of established cccDNA pool and total DNA accumulation (from 48 hpi onward) with similar kinetics in HepG2 hNTCP cells and primary human hepatocytes.
Discussion The HBV cccDNA must adopt a chromatin structure similar to that of the host cell chromosomes for its transcription  6, 38, 39  in the infected hepatocyte nuclei. Coupling Southern blot and ChIP-qPCR analysis, we found that histone loading occurred simultaneously with repair of the partially double-stranded cccDNA precursor (PF-rcDNA). Although our data were generated from a population of cccDNA containing infected hepatocytes, we can assume that the breaks into PF-rcDNA strands, evoking a target for the DNA repair machinery, represent a trigger for HIRA recruitment enabling subsequent histone deposition coupled to DNA repair, in agreement with the role of HIRA in priming damaged chromatin early after damage recognition. 40 revious work conducted in cell-free systems have reproduced steps leading from PF-rcDNA to supercoiled cccDNA without the need for histone chaperones.
Acknowledgments The authors thank  Dr D. Ray-Gallet  for expert advice and for the pEYFP-N1-HIRA and pEYFP- N1-HIRA W799A D800A  constructs, and  Maud Michelet ,  Jennifer Molle ,  Anaëlle Dubois , and  Océane Floriot  for their help in the isolation of primary human hepatocytes, as well as  Professor Michel Rivoire 's surgical staff for providing liver resections.
Funding This work was supported by the  Agence Nationale de Recherche (ANR)  program " Investissement d' avenir " Laboratoires d'Excellence (LabEx) DEVweCAN  ( Cancer Development and Targeted Therapies)  grant  ANR-10-LABX-61 , and by the  Agence Nationale de Recherches sur le Sida  et les hépatites virales |Maladies.
Extracellular markers were stained with the following antibodies: CD45 APC-Vio770 (clone REA737, Miltenyi Biotec) or CD45-BV421 (clone 30 F-11, BD Bioscience), CD4 PercP-Vio700 (clone REA604, Miltenyi Biotec), and CD8 PE-Vio770 (clone REA601, Miltenyi Biotec). After fixation and permeabilization using the Fixation/Permeabilization Solution Kit (BD Biosciences) according to the manufacturer's instructions, intracellular staining of IFN-γ and IL-9 was performed using an anti-mouse IFN-γ APC or PE antibody (clone AN. 18. 17. 24 or REA638, Miltenyi Biotec) and mouse IL-9 APC or BV421 antibody (clone RM9A4, BD Bioscience), respectively. Single-cell suspensions were analyzed by flow cytometry using BD LSR FORTESSA cytometer (BD Biosciences) equipped with BD FACSDiva software (BD Biosciences). Flow cytometry data were then analyzed using Flowlogic software (Miltenyi Biotec).
IFN-γ intracellular staining was performed using the Fixation/Permeabilization Solution Kit (BD Biosciences) and IFN-γ PE antibody (clone REA638, Miltenyi Biotec). Cells were analyzed using a BD LSRFORTESSA cytometer equipped with BD FACSDiva software and data were analyzed using FlowLogic software. Gating strategies are presented in online supplemental figure  S1B. Total CD8 cells were isolated from STING -/-mouse spleen and lymph nodes using anti-CD8 microbeads (Ly-2) (Miltenyi Biotec) according to the manufacturer's instructions.
18. 17. 24, Miltenyi Biotec) or anti-IL-17 BV605 (clone TC11-18H10, BD Bioscience), respectively. Cells were analyzed using a BD FACSCANTO cytometer or a BD LSRFORTESSA cytometer equipped with BD FACSDiva software, and data were analyzed using FlowLogic software. Gating strategies are presented in online supplemental figure  S1C-E.
Open access Gene expression in CD4 T cells isolated from STING V154M/WT mice and MC38 TILs was analyzed using the TaqMan Fast Advanced Master Mix (Applied Biosystems) and TaqMan Gene Expression Assay (Applied Biosystems) according to the manufacturer's instructions. A list of TaqMan assays is depicted in online supplemental table S1. As for gene expression in CD4 T cells isolated from MC38 TILs, equivalent of 6 ng of cDNA were preamplified with TaqMan PreAmp Master Mix (Applied Biosystems) combined with pooled TaqMan Gene Expression Assays in a final volume of 25 µL and according to the manufacturer's instructions. Expression of target genes was normalized to the expression of mouse Actb or human ACTB (relative expression (RE)). When indicated, the fold change (FC) in RE was calculated by normalizing data to control conditions (WT cells treated with control, for each time point).
Western blot Total protein extracts were obtained by lysing CD4 T cells into ice-cold RIPA Buffer (Pierce) containing Protease Inhibitor Cocktail (Sigma) and Phosphatase Inhibitor Cocktail 3 (Sigma) on ice for 10 min. Lysates were centrifuged at 13,000 g at 4°C for 10 min, and supernatants were retrieved for WB analyses. For subcellular fractionation, cytosolic (Cyt) and nuclear (Nuc) fractions were purified using the ProteoExtract Subcellular Proteome Extraction Kit (Merk) according to manufacturer's instructions. Protein lysates were separated on 4%-15% gradient precast gels (Bio-Rad) before transfer on polyvinylidene difluoride membranes using a MIXED MW program on the Trans-Blot Turbo Transfer System (Biorad). Membranes were blocked in tris-buffered saline (TBS) containing 0. 1% Tween 20 (TBS-T) and supplemented with 5% bovine serum albumin (BSA) or non-fat dry milk for 1 hour at room temperature and incubated overnight at 4°C with primary antibodies diluted in the appropriate blocking buffer.
cGAMP-stimulated mouse T H 1 and T H 9 cell RNAseq analyses Mouse naive CD4 T cells were isolated and treated with cGAMP or control and polarized in vitro into T H 1 or T H 9 cells as described previously for 16 and 48 hours. Total RNA was extracted with TriReagent according to the manufacturer's instructions. Libraries were prepared from 500 ng of total RNA using TruSeq Stranded Total RNA Library Prep kit (Illumina) after rRNA removal with Ribo-zero rRNA Removal Kit (Illumina) following the manufacturers' instructions. RNA sequencing was performed on NextSeq500 device (Illumina). The RNAseq libraries were sequenced with single-end 76 bp reads. Raw FASTQ files were trimmed for residual adapter Open access sequences and quality filtered through Trimmomatic software. 32 Reads were pseudo-aligned against mm10 genome through Kallisto software and differential expression analysis was performed with DESeq2 R package.