dataesr/scientific-paragraphs-categorization huggingface dataset:
Jurkat coculture assay. All Jurkat cells were maintained in RPMI plus 10% fetal bovine serum plus 1% penicillin/streptomycin. CTPS1-KO Jurkat cells and CTPS1-KO Jurkat cells infected with a pLenti 7. 3 lentivirus vector either empty or containing the CTPS1 or mutant CTPS1Δ18 sequences along with a GFP reporter gene were supplemented with 200 μM of cytidine. After an initial expansion, part of the CTPS1 or Δ18CTPS1-complemented cells were cytidine starved, leading to the loss of uninfected cells (GFP -). The number of GFP + cells was assessed with FlowJo software using Super Enhanced Dmax. This quantification was used to adjust the number of cells needed to obtain a mix of 10% of GFP-expressing cells with 90% of WT Jurkat or CTPS1-KO Jurkat cells. The cells were then seeded at a density of 150 × 10 3 cells/ mL with or without 200 μM of cytidine.
Representative dot plot FACS analysis of naive and memory CD4 + cells (left) and CD8 + cells (right) are also depicted. (C) Frequencies of senescent cells (CD8 + CD57 + ) gated on CD3 + cells are shown in the dot plot graph (left). Representative dot plot FACS analysis of senescent CD8 + cell populations are depicted at right. Data were obtained from FACS analysis of cell-specific staining. (D) Frequencies of naive (CD19 + CD27 -), memory (CD19 + CD27 + ) (left), circulating marginal zone (MZ) (CD19 + CD27 + IgM + IgD + ) and switched B cells (CD19 + CD27 + IgM -IgD -) (right) are shown in the dot plot graph. Representative dot plots FACS analysis of naive/memory (left) and marginal zone/switched (right) B cell populations are depicted. Data were obtained from FACS analysis after cell-specific staining.
Acknowledgments We acknowledge the patients, their families, and the healthy donors for cooperation and blood gifts. We also thank the members of the  Latour lab  for discussions. SL is a senior scientist at the  Centre National de la Recherche Scientifique (CNRS, France ). This work was supported by grants from  Ligue Contre le Cancer-Equipe Labellisée (France)  (to SL),  Inserm (France) ,  ANR-18-CE15-0025-01  (to SL) and  ANR-10-IA-HU-01  ( Imagine Institute ), the  European Research Council   ERC-2009-AdG_20090506 n°FP7-249816  (to AF) and Proof of Concept ERC-2015-PoC_Master/ERC-2015-PoC_680465_SAFEIMMUNOSUPPRESS (to AF, SL). NM is a fellowship recipient of the  Association National de la Recherche Technologique  under agreement with the industrial partner  Step Pharma. ACB is supported by  Imagine Institute.
Acknowledgments Funding: This research was supported by the  Swiss National Science Foundation  and  EPFL  (to M. D. P. ) and the  European Union 's  Horizon 2020 Research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 665667  (to C. C. ). A. R. acknowledges the support from  Swiss National Science Foundation  (grant number  BSCGI0_157802 ). J. -F. L. thanks the  H2020 program  of the  European Union  (project  Euro-Sequences ,  H2020-MSCA-ITN-2014 , grant agreement  n°642083 ) and the  CNRS  for financial support. The PhD position of N. F. K. was supported by the  ITN Euro-Sequences. The authors also thank  L. Charles  and  C. Chendo  ( Aix Marseille Université ) for the electrospray ionization-HRMS analysis of the polymers and  M. J. Marcaida  for support with aerolysin production.
Rationale: The urinary proteome reflects molecular drivers of disease. Objectives: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.
Measurements Anthropometric, clinical, and routine biochemical data were recorded on ICU admission. Variables of interest included the indication for admission to the ICU, the Charlson Comorbidity Index (CCI)  [13, 14] , the Sequential Organ Failure Assessment (SOFA)  [15]  score, blood pressure, serum creatinine and cystatin C, and blood glucose. Information also collected on admission, included treatment with mechanical ventilation, extracorporeal membrane oxygenation, or renal replacement therapy. The CCI is a method of categorizing comorbidities of patients based on the International Classification of Diseases (ICD) diagnosis codes found in administrative data. It included 19 categories (Additional file 1: Table  S1 ). Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient.
The urinary biomarkers included albuminuria and NGAL  [21]. The urinary proteome is well characterized, and reference standards are available  [22]. Urine proteome analysis was performed on urine samples collected on admission and bio-banked until assayed. Detailed information on urine sample preparation, proteome analysis by capillary electrophoresis coupled to mass spectrometry, data processing, and sequencing of the urinary peptides allowing identification of parent proteins is available in previous publications  [9]  and the methods section of the online-only supplement.
Statistics SAS, version 9. 4, maintenance level 5 (SAS Institute Inc. , Cary, NC) was used for database management and statistical analysis. Departure of distributions from normality was evaluated by the Shapiro-Wilk test. The biomarker distributions with the exception of ACM128 were transformed by sorting measurements from the smallest to the highest and then applying the inverse cumulative normal function  [23]. Means, medians, and proportions were compared using the large-sample z-test or ANOVA, the Wilcoxon rank-sum test, and Fisher's exact test, respectively. We computed 95% confidence interval of rates as R AE 1:96 Â ffiffiffiffiffiffiffiffiffiffiffiffiffi ðR=TÞ p , where R and T are the rate and the denominator used to calculate the rate.
Construction and initial validation of the classifier For discovery of the new classifier associated with allcause mortality following ICU discharge, 30% of the cohort (299 survivors and 70 nonsurvivors) were randomly selected from all enrolled patients (n = 1243). For validation, the remainder of the cohort was analyzed (699 survivors and 175 nonsurvivors). In the discovery phase, the association of mortality with urinary peptides was assessed by Wilcoxon testing targeting a significance level of 0. 05. The peptides that remained associated with the 1-year mortality were combined into a single multidimensional classifier, using support vector machine modeling, as implemented in the MosaCluster software, version 1. 7. 0, and as described in detail in the methods section of the online supplement.
Proteasix and pathway analysis Proteases responsible for the generation of the urinary peptide fragments were predicted in silico, using Proteasix  [25, 26]. Proteolytic enzymes mapped to at least 10 cleavage sites in the peptides associated with post-ICU mortality were analyzed. The protein-protein interactome was constructed, using the STRING database (STRING, version 11. 0; https://string-db. org). Proteins corresponding to sequenced urinary peptides and the in silico predicted proteolytic enzymes were included for the interactome analysis. The pathway enrichment was evaluated against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database with the false discovery rate set at P < 0. 01. Changes in urinary collagen fragments might reflect alterations in protease activity, but may also be the result of a change in collagen turnover in tissues, such as for instance as a consequence of increased cross-linking.
Discovery and validation The characteristics of surviving and nonsurviving patients in the discovery and validation datasets were broadly similar (see Additional file 1: Tables  S2  and S3  and Figure  S1 ). The analysis of the discovery dataset (number of deaths/survivors, 70/299) enabled identification of 128 peptides, which were significantly associated with the 1-year post-ICU mortality. Of 63 peptides with sequence information available, 28 (44. 4%) were collagen fragments (Additional file 1: Table  S5 ). The 128 peptides were combined in a multidimensional classifier, termed ACM128 (Additional file 1: Figure  S1 ). With the C and With the exception of urinary albumin (P = 0. 69), all circulating (BNP, hsTnT, hsTnI, ADM, sSD2, and NGAL) and urinary biomarkers (NGAL and ACM128) had higher levels in nonsurvivors than survivors (Table  2 ).
First, we collected all-cause and not cause-specific mortality. However, all-cause mortality has the advantage that this outcome does not require any adjudication as causespecific mortality does. Second, our study combines data from patients admitted to the ICU, because of surgical, medical, or mixed indications. While this may be considered as a weakness, it also facilitates the generalizability of our results. Finally, although the ACM128 outperformed established risk factors and widely used risk scores including Charlson Comorbidity Index and SOFA score as well as circulating and urinary biomarkers, cost-effectiveness analyses still have to be done as a lead on to further studies paving the way for the clinical application of ACM128.
Acknowledgements The authors gratefully acknowledge the clerical assistance of  Vera De Leebeeck  and  Renilde Wolfs  at the  Studies Coordinating Centre in Leuven, Belgium. The French and European  Outcome Registry in Intensive Care Unit Study  was supported by  The Programme Hospitalier de la Recherche Clinique  ( AON 10-216 ) and by the  Societé Française d' Anesthésie-Réanimation. The  European Union  ( HEALTH-F7-305507 HOMAGE ) and the  European Research Council (Advanced Researcher  grant  2011-294713-EPLORE  and  Proof-of-Concept  grant  713601-uPROPHET ) and the  European Research Area Net for Cardiovascular Diseases  ( JTC2017-046-PROACT ) supported research at the  Studies Coordinating Centre, Leuven, Belgium. The author acknowledges funding received from the  European Society of Cardiology  in form of an  ESC Research Grant.
Translocation Assay COS-1 cells were seeded on 48-well plates (20,000 cells/well) and, 24 h after seeding, were transfected (Lipofectamine™ 3000) with 100 ng/well pEGFP-hCAR+Ala construct. The cells were treated with CITCO (10 µM), diazepam, nordazepam, temazepam and oxazepam (10 µM and 30 µM), or control (DMSO 0. 1%) for 24 h. For microscopy, living cells were stained with Hoechst 33342 (0. 2 µM, 5 min at 37 • C) (Sigma-Aldrich). Confocal microscopy was performed with a Nicon Ti ECLIPSE microscope and Nikon A1 plus camera (Nikon, Tokyo, Japan) using 405 and 488 nm lasers. Microphotographs were taken using the NIS Elements AR 4. 20 software (Laboratory Imaging, Czech Republic). Four microphotographs of every treatment were taken and cells with green fluorescence protein (GFP) cytosolic or nuclear localization was counted in more than 150 cells in each picture.
Receptor and Ligands Preparation The crystal structure of the hCAR model was retrieved from the RCSB Protein Data Bank (www. rcsb. org) (PDB code: 1XVP)  [22]. The model was limited to chains D (containing the ligand binding domain) and H (nuclear receptor coactivator). The binding domain was checked for possible errors and clashes and then prepared by standard procedure employing AutoDock Tools 1. 5. 6  [23]. Water molecules were deleted, polar hydrogens were added, Kollman charges were assigned, and the prepared model was converted to a PDBQT format. Structures of ligand molecules were drawn in PerkinElmer Chem3D (version 19. 0. 1. 28, Waltham, MA, USA), energetically minimized utilizing the inbuilt MM2 force filed and exported as PDB files. Preparation of ligands was done by AutoDock Tools 1.
Figures Figures representing the docking results were generated in Chimera 1. 13  [25]. Only residues in proximity to ligands are displayed. All other residues and all non-polar hydrogens are omitted for clarity. The 2D ligand-protein interaction diagram was generated in LigPlot+  [26]. Numbering in figures and in text is fully in accordance with the numbering used in the 1XVP model. Nevertheless, if this sequence is compared with the canonical sequence from Uniprot. org databank (Q14994-1), residues 232-235 are missing and, therefore, numbering differs by 4 from this point.
Time-Resolved Fluorescence Energy Transfer (TR-FRET CAR) Coactivator Binding Assay The LanthaScreen ® TR-FRET CAR Coactivator Binding Assay (Thermo Fisher Scientific, Waltham, MA, USA) was used with some modifications to the manufacturers' protocol reported in our previous paper  [27]. In order to allow better interaction of benzodiazepines with the CAR LBD, incubation time was extended to 3 h. For fluorescence measurement, the Synergy Biotek plate reader was used with appropriate filters (BioTek Instruments Inc. , Winooski, VT, USA). Results were calculated as an interaction of the CAR-LBD with the coactivator fragment relative to the control (DMSO 0. 1%). Data are presented as the means and SD of a technical quadruplicate (n = 4).
Statistics Data are presented as the mean ± standard deviation (SD). A one-way analysis of variance (ANOVA) with a Dunnett's post hoc test or Bonferroni test were applied. GraphPad Prism 8. 4. 3 Software (GraphPad Software, Inc. , San Diego, CA, USA) was used to perform statistical analysis. A p-value of < 0. 05 was considered to be statistically significant.
Diazepam Interacts with CAR-LBD Protein in TR-FRET Assay in the Absence of a Cellular Background Subsequently, we studied the behavior of these compounds in a TR-FRET CAR coactivator assay with a recombinant hCAR LBD protein. This assay allows detection of the molecule-to-CAR LBD protein interaction when the cellular background and signaling is avoided. Thus, indirect CAR activation via dephosphorylation is not possible in the protein assay (Figure  3 ). In the TR-FRET CAR coactivator assay, diazepam has shown statistically significant activity from 20 µM. Other benzodiazepines had no statistically significant activity to activate CAR-LBD in the assay up to 50 µM (Figure  3 ). Phenobarbital did not activate the CAR LBD in the assay. Our TR-FRET assay data correspond with results from the luciferase gene reporter assays (Figure  2 ) and confirm direct CAR-LBD activation with diazepam, but not with temazepam, oxazepam, or nordazepam.
Funding: This work was supported by the  InoMed ERDF  project " Pre-application research into innovative medicines and medical technologies  " (OPVaV  CZ. 02. 1. 01/0. 0/0. 0/18_069/0010046 )funded by the  Ministry of Education, Youth and Sports of the Czech Republic  and by the  European Union  (to P. P. M. D. and J. D. ), by the  Czech Scientific Agency GACR   19-14497S  grant (to P. P. S. M. and  K. Ch. ), and student project SVV  260 549  to (J. S. ), the  Czech Academy of Sciences RVO  project  68378050  (CAS)(to K. Ch. ), project  M2018126   Czech Centre for Phenogenomics  provided by  Ministry of Education, Youth and Sports of the Czech Republic (MEYS)  and  CZ. 1. 05/2. 1. 00/19. 0395Higher quality and capacity for transgenic models by  MEYS  and  ERDF  (to K. C. ). Authors declare no conflict of interest.
Test of the U3 allele expression and stability Total RNA was extracted from recombinant JH84 cells grown for 24 h in YPD liquid medium, using the procedure of  (7). For each assays, 20 g of total RNA were fractionated on a denaturing 6% polyacrylamide gel. Northern-blot analyses were performed as previously described  (19) , using a 5 -end labeled oligonucleotide (Supplementary Table  S1 ), complementary to the 3 -end of the yeast U3 snoRNA as the probe, and the 5 -end labeled U6 oligonucleotide (Supplementary Table  S1 ), complementary to U6 snRNA, for standardization of the data. The radioactivity of the hybridization bands were quantified with a phosphorimager (GE Healthcare) using the Image Quant software. The ratio between the radioactivity measured for the U3 snoRNA hybridization signals and the total radioactivity measured in U3 snoRNA and U6 snRNA hybridization signals was estimated for each recombinant strain.
To probe the absorption and MCD around the Ni M 2,3 resonances, we used five probing XUV wavelengths with energies 64. 6 eV, 65. 7 eV, 66. 3 eV, 66. 7 eV and 68 eV. For each energy, we recorded CCD images by accumulation of 150 to 250 shots with and without IR pump for both applied magnetic field directions and both XUV helicities. For symmetry reasons, reversing the XUV probe beam helicity is equivalent to a reversal of the applied magnetic field direction. This allows us to average the measured curves, resulting in two transmitted intensities T ± = I ± I 0. We grouped the data such as to obtain a data point every 30 fs, the error bars presented in all our figures reflect this grouping and averaging procedure. Typical transmitted intensity time traces are plotted in Figure  2  (a) for an XUV energy of 66.
Funding: This work was funded within the  EU-H2020 Research and Innovation Programme , No. 654360  NFFA-Europe (BR). The authors are grateful for the financial support received from the  CNRS-MOMENTUM , the  SNSF  project (No. 200021-160186 ), the  UMAMI   ANR-15-CE24-0009 , and the  CNRS-PICS  programs.
Study population This study included a total of 2,891 subjects: 1,039 unrelated individuals who were diagnosed with iRBD (based on the International Classification of Sleep Disorders criteria, version 2 or 3) and 1,852 controls, all of European ancestry. The iRBD patients and controls were recruited through 18 centers in Canada and Europe, including centers from France, Germany, Austria, UK, Czech Republic, Italy, and Belgium. Diagnosis was performed with video polysomnography, the gold standard for iRBD diagnosis, by sleep specialists and neurologists. At recruitment, none of the patients had an overt neurodegenerative disease (PD, DLB, MSA etc. ), which defined them as iRBD. In the current study, we had follow-up data available for 540 iRBD patients, 190 of which (35%) had converted since recruitment to PD, DLB or MSA or other overt neurodegenerative disease.
The controls were not examined by polysomnography, but since only 1% of them is expected to develop iRBD, it is unlikely that this can affect the results. The European ethnicity of the participants was ascertained using GWAS data available in our lab, by performing principle component analysis and clustering with the European cohort available in HapMap v3. Details on age and on sex of the participants have been previously described,  15  and data available from Dryad (Table e-1): https://doi. org/10. 5061/dryad. vt4b8gtqd. Differences in age and sex were taken into account as needed in the statistical analysis. All patients and controls were of European ancestry (principal component analysis [PCA] of GWAS data was used to confirm ancestry, as compared to data from HapMap v. 3 and hg19/GRCh37).
The 25 genes were fully captured (coding sequence and 3'-and 5'-untranslated regions) as reported previously with molecular inversion probes (MIPs). 18  The protocol is available on https://github. com/gan-orlab/MIP_protocol. Table e-2 details the probes used in the current study for the MIPs capture, data available from Dryad (Table e-2). Following the capture, we performed next-generation sequencing with the Illumina HiSeq 2500\4000 platform at the Génome Québec Innovation Centre. Sequencing reads were aligned to the hg19 reference genome using the Burrows-Wheeler Aligner. 19  We then used the Genome Analysis Toolkit (GATK, v3. 8) for quality control and to call variants,  20  and ANNOVAR was used for variant annotation. 21  We extracted the variant frequencies for the detected variant from the Genome Aggregation Database (GnomAD).
01) we used logistic regression adjusting for sex and age, using PLINK v1. 9. Linkage disequilibrium between the variants we found and the respective GWAS top hits was examined using the reference cohort of non-Finnish European embedded in LDlink (https://ldlink. nci. nih. gov/). 25 We used the genotype-tissue expression database (GTExhttps://www. gtexportal. org) to examine the effects of common variants on gene expression. We further performed in silico structural analysis of BST1 to test whether the rare coding variants that were found to be associated with iRBD in our analysis could potentially affect the enzyme structure and activity. The atomic coordinates of human BST1 bound to ATP-γ-S were downloaded from the Protein Data Bank (ID 1isg). We evaluated the steric clashes caused by the variants we identified using the "mutagenesis" toolbox in PyMol v.
Association of common variants in the target genes with iRBD To test whether common variants in our target genes are associated with iRBD, we performed logistic regression (using PLINK v1. 9 software) adjusted for age and sex for common variants (MAF>0. 01) detected at coverage depth of >15X. A nominal association was observed in 12 variants across all genes (data available from Dryad, Table  e-5 ), but no association remained statistically significant after Bonferroni correction for multiple comparisons (set at p<0. 0005). Of the variants with nominal associations, one variant in the ITGA8 3' UTR (rs896435, OR=1. 15, 95% CI = 1. 01-1. 32, p=0. 04) is the top hit from the most recent PD GWAS,  3.
= age at onset; AAS = age at sampling; ACMSD = Aminocarboxymuconate semialdehyde decarboxylase; BST1 = Bone marrow stromal cell antigen 1; CADD = Combined Annotation Dependent Depletion; CCDC62 = Coiled-coil domain containing 62; CI = confidence interval; DOC = depth of coverage; F_A = frequency in affected; F_C = frequency in control; FGF20 = Fibroblast growth factor 20; GAK = Cyclin G associated kinase; GATK = Genome Analysis Toolkit; GnomAD = Genome Aggregation Database; GPNMB = Glycoprotein (transmembrane) nmb; GTEx = Genotype-tissue expression; GWAS = genome-wide association study; hg19 = human genome version 19; HIP1R = Huntingtin interacting protein 1 related; iRBD = isolated rapid-eye-movement sleep behavior disorder; ITGA8 = Integrin subunit alpha 8; LAMP3 = Lysosomal-associated membrane protein 3; LD = linkage disequilibrium; LOF = loss-offunction; MAF = minor allele frequency; MAPT = Microtubule-associated protein tau; MCCC1 = Methylcrotonoyl-CoA carboxylase 1 (α); MIPs = molecular inversion probes; NS = nonsynonymous; OR = odds ratio; PCA = principle component analysis; PM20D1 = Peptidase M20 domain containing 1; QC = quality control; QS = quality score; REM = rapid eye movement; RIT2 = Ras Like without CAAX 2; SCARB2 = Scavenger receptor class B, member 2; SETD1A = SET domain containing 1A, histone lysine methyltransferase ; SIPA1L2 = Signal induced proliferation associated 1 like 2; SKAT = sequence kernel association test; SKAT-O = optimized SKAT; SLC41A1 = Solute carrier family 41 (magnesium transporter), member 1; STK39 = Serine/threonine kinase 39; STX1B = Syntaxin 1B; SYT11 = Synaptotagmin 11; TMEM163 = Transmembrane protein 163; USP25 = Ubiquitin specific peptidase 25; UTR = untranslated region; vPSG = video polysomnography.