dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses were conducted using GraphPad Prism® software (version 8; GraphPad software, La Jolla, CA, USA) and R software, version 3. 6. 1 (R Foundation for Statistical Computing, Vienna, Austria). A p-value <0. 05 was considered statistically significant. Positivity was established according to manufacturers' instructions. Sensitivity and specificity data were those described in the instruction for utilization sheet from each manufacturer. Specificity given by manufacturers was obtained from pre pandemic samples. Abbreviations: Ab: antibodies, Ig: immunoglobulin, ELISA: enzyme-linked immunosorbent assay, CLIA: chemiluminescence immunoassay, ELFA: enzyme-linked fluorescent assay, RBD: receptor binding domain, CI: confidence interval, AU: arbitrary unit, UI: unit international.
ACKNOWLEDGMENTS This study was supported by Hospices Civils de Lyon and Fondation des Hospices Civils de Lyon. The respective suppliers kindly provided all the serological kits used. We thank the staff of the occupational health and medicine department of the Hospices Civils de Lyon who contributed to the sample collection. We thank all Clinical Research Associates, for their excellent work. We thank Karima Brahima and all the members of the clinical research department for their reactivity (DRS, Hospices Civils de Lyon). Human biological samples and associated data were obtained from NeuroBioTec (CRB Hospices Civils de Lyon, Lyon France, Biobank BB-0033-00046). We thank all the technicians from the virology laboratory of the Hospices Civils de Lyon who performed the assays on the automated platforms.
The emerging picture of protein nature reveals its intrinsic metastability. According to this idea, although a protein is kinetically trapped in a local free energy minimum that defines its native state, those kinetic barriers can be overcome by a complex mixture of the protein's intrinsic properties and environmental conditions, promoting access to more stable states such as the amyloid fibril. Proteins that are strongly driven towards aggregation in the form of these fibrils are called amyloidogenic. In this work we study the evolutionary rates of 81 human proteins for which an in vivo amyloid state is supported by experiment-based evidence. We found that these proteins evolve faster when compared with a large dataset of ~16,000 reference proteins from the human proteome. However, their evolutionary rates were indistinguishable from those of secreted proteins that are already known to evolve fast.
2013 ). To explore this idea, we assembled two large datasets of paired orthologous proteins between human and mouse (Homo sapiens -Mus musculus, with 16,589 proteins) and between human and cattle (Homo sapiens -Bos taurus, with 15,974 proteins). We also collected 11,789 common orthologous proteins in seven species related by a single phylogenetic tree (Homo sapiens; its close relatives Pan troglodytes, Macaca fascicularis and Rhinopithecus bieti; and three other mammals, Mus musculus, Bos taurus, and Sus scrofa). All these orthologs were extracted from the OMA database  (Altenhoff et al. 2018 , see Supplementary Information for further details). Using the Amypro database of validated amyloid precursor proteins  (Varadi et al. 2018 ) and manual curation from recent bibliography (see Supplementary Information), we identified 81 human proteins with experimental evidence of aggregation into an amyloid form.
These were defined as the "amyloids" dataset, in opposition with the rest of the proteins without evidence of amyloid fibril formation in vivo, which we took as the "reference" dataset. Evolutionary rates, expressed as the number of non-synonymous substitution rates per non-synonymous site (dN), were estimated for pairs of homologous proteins using the program YN00 from the package PAML  (Yang 1997) , additional information in Supplementary files). In the case of the seven species dataset, the program CODEML was used to estimate dN, using the same reference tree. Using the dataset of human -mouse orthologs, we found a higher dN for the amyloid proteins (Wilcoxon rank-sum test p-value < 0. 01) in comparison with the reference dataset (Figure  1 ) (All results shown here are for the human -mouse dataset, which are representative of the results obtained for the others; see Supplementary Figure  1  for details).
Unexpectedly for such fast-evolving proteins, amyloids also show higher expression levels compared with the reference proteins (Wilcoxon rank-sum test p-value < 0. 05), as well as higher abundance (Wilcoxon rank-sum test p-value < 0. 001, see Figure  1 ). These findings do not agree with the well-established observation that highly expressed proteins evolve slowly  (Drummond et al. 2005; Drummond et al. 2006; Park and Choi 2009; Zhang and Yang 2015). As extracellular location  (Feyertag et al. 2017 ) and the presence of disulphide bonds  (Feyertag and Alvarez-Ponce 2017)  have been found to characterize fast-evolving proteins, we split the dataset according to these variables. We annotated proteins as having disulphide bonds following UniProt annotations, and we used the database MetazSecKB  (Meinken et al.
2015)  to define subsets of membrane-related, intra-and extra-cellular proteins. We found that amyloidogenic proteins evolve as fast as secreted proteins (with or without disulphide bonds) (Wilcoxon rank-sum test P > 0. 01). Also, we did not observe any influence of disulphide bonds in the evolutionary rates of amyloid datasets (Figure  2 ). Secreted proteins are synthesized into the endoplasmic reticulum. In the secretion process, they interact with chaperones and folding assistant enzymes to promote the right folding process and recognize misfolded proteins  (Braakman and Hebert 2013). These quality controls have been pointed out as responsible for the lack of correlation between expression level (or protein abundance) and evolutionary rates, likely due to a relaxed pressure in translation errors  (Feyertag et al.
2017). The same control mechanisms could be responsible for the fast evolutionary rates of secreted proteins (Figure  2 ) due to the buffering effects of slightly destabilizing substitutions imposed by chaperones on their clients  (Tokuriki and Tawfik 2009; Williams and Fares 2010; Alvarez-Ponce et al. 2019 ). As about 70% of the amyloidogenic proteins in our dataset are secreted proteins, we assumed that the same mechanisms could also explain their fast evolutionary rates. It is well documented that similar quality control mechanisms are applied to cytoplasmic amyloids; for example, chaperone systems are activated to avoid amyloid fibril toxicity, avoiding and, in some cases, reverting the fibril formation  (Yerbury et al. 2007; Wilson et al. 2008; Månsson et al. 2014; Landreh et al. 2015; Wentink et al.
2019; Hervás and Oroz 2020). Accordingly, we did not find a statistically significant difference between the evolutionary rates of secreted and cytoplasmic amyloidogenic proteins (Wilcoxon rank-sum test, p-value = 0. 8061, see Supplementary Figure  2 ). Why do amyloidogenic proteins aggregate while secreted proteins don't, considering that both are under a strong fold quality control? As protein stability has been used to characterize aggregation and misfolding  (Knowles et al. 2014) , we mapped both sets of proteins in the ProTherm database  (Kumar et al. 2006). Using Gibbs unfolding free energies (ΔG 0 ) and melting temperatures (T m ) as a measure of protein stability  (Becher et al. 2018)  we did not find any significant difference between amyloidogenic and secreted proteins (see Supplementary Figure  3 ).
Also, as derived from their APR occurrence (estimated from TANGO predictions)  (Fernandez-Escamilla et al. 2004 ), we found counterintuitive results because secreted proteins show the same APR content than the amyloid dataset (see Supplementary Figure  3 ). Although Gibbs unfolding free energies and melting temperatures have been extensively used to estimate protein stability and the tendency to unfold and aggregate, measuring such tendencies is more complex due to the metastable nature of proteins  (Baldwin et al. 2011). Metastability is a condition characterizing a protein state that is kinetically stable but has a thermodynamic tendency to change. The native state of proteins is described by a set of structures that are kinetically trapped in a local free energy minimum under physiological conditions.
2022 ). Taking expression level, disorder (or protein flexibility), protein conformational diversity and protein abundance as proxies for the aggregation propensity, we found that those parameters are significatively higher for the amyloid dataset than for the secreted proteins (Figure  3 ). As in previous works  (Zea et al. 2013) , we measured the conformational diversity of a protein using the normalized maximum RMSD (max RMSD100,  Carugo and Pongor 2001)  calculated between all pairs of its known structural conformers obtained from the CoDNaS database  (Monzon et al. 2016). When partial correlations were estimated to control the effect on dN over different confounding parameters in the amyloid dataset (n = 25, see Supplementary Table  1 ), using log transformed values we found significant correlations of dN with max RMSD100 (Pearson's ρ = 0.
It identifies proteins with a high tendency to aggregate and was successfully used to mark pathological aggregation  (Ciryam et al. 2013). Proteins are said to be supersaturated when their concentrations exceed their solubility levels, increasing the propensity to aggregate. We found that σ f , but not σ u , is higher in the amyloid dataset when compared with secreted proteins. We also observed a high correlation between dN and σ f (Spearman's ρ=0. 639, p-value < 0. 01, n= 17, power = 0. 81). Although the number of amyloidogenic proteins is reduced due to the availability of σ f , when we added σ f to the previous model we found that the explained variance on dN was ~38% (Adjusted R 2 = 0. 388, p-value = 0. 012, see Supplementary Table  3 ). Applying the same model to secreted proteins gave a non-significant linear model (Adjusted R 2 = 0.
008, p-value = 0. 198). To summarize, we have explored the evolutionary rates of a population of human proteins (n=81) with experimentally based evidence of aggregation as amyloid fibrils. We found that amyloidogenic proteins evolve faster than the reference dataset, in spite of being highly expressed and abundant. Although we did not observe significant differences in the evolutionary rates of secreted and amyloidogenic proteins (Figure  2 ), we found substantial differences in other features. Firstly, conformational diversity is higher in amyloidogenic proteins, also evidenced by the higher presence of disordered or highly flexible regions (Figure  3 ). This increased conformational diversity could raise the chances of exposing APRs in slightly unfolded conformers, driving the protein towards fibril formation.
and GP are researchers and GIB, JM, JMD and CEGD are PhD fellows from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). DJZ is funded by PRAIRIE (N°L S: 233070). This work was supported by Universidad Nacional de Quilmes (PUNQ 1004/11 and PP 2019), Agencia Nacional de Promoción Científica y Tecnológica (PICT-2014-3430) and the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement N°7 78247.
To assess the prognostic performance of the adipose tissue biomarkers (CATi/VATi/SATi) for the prediction of outcome (ICU admission or death), ROC curves were generated for each subgroup. We further determined the optimal threshold values for maximization of the sensitivity and speci city according to Youden's index. P-values < 0. 05 were considered statistically signi cant. All statistical analysis were performed using JMP Pro version 16 (SAS Institute Inc. , Cary, NC, USA, 1989-2022) and graphs were created with Graphpad Graphics software (San Diego, California USA).
Acknowledgements We wish to thank for their precious help the other members of the COVID-19 APHP. SU Group:  Romain Tilmont ,  Romain Chenu ,  Louise Meyfroit ,  Nicoletta Pasi ,  Schahrazed Larbi-Messaoud ,  Suzanne Laroche ,  Cécile Ciangura ,  Marc Popelier ,  Sophie Jacqueminet ,  Marine Halbron ,  Agnès Hartemann.
Methods: We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions.
However, how all these dimensions of heterogeneity inter-relate with each other is completely unknown. Perhaps subgroups with distinct CSF profiles, atrophy patterns and clinical phenotypes are present. Multimodal subtyping studies are urgently needed to address this question, but such studies are lacking so far  [11]. The goal of the current study was to parse the heterogeneity within probable DLB by using a multimodal subtyping method applied on the combination of CSF biomarkers, structural MRI, and clinical and demographic measures. We gathered data from a large multi-center cohort of patients with probable DLB (N = 107). Firstly, we identified subgroups of patients with factorial analysis and multimodal clustering. Secondly, we characterized the resulting subgroups across key CSF, MRI, clinical, and demographic measures.
Participants Participants were selected from the European DLB consortium (E-DLB)  [12]. The E-DLB consortium archives data from 40 centers across Europe, including patients with probable DLB, Parkinson's disease with dementia, or AD. For the current study, we included patients with probable DLB from the E-DLB centers that had MRI and CSF biomarkers available. Six centers satisfied these criteria, including the Alzheimer Center Amsterdam, Amsterdam UMC (Amsterdam, the Netherlands, n = 38); Day Hospital of Geriatrics, Memory Resource and Research Centre (Strasbourg, France, n = 38); Karolinska Institutet (Stockholm, Sweden, n = 17); University of Brescia (Brescia, Italy, n = 6); Ace Alzheimer Center Barcelona (Barcelona, Spain, n = 5); and Stavanger University Hospital (Stavanger, Norway, n = 3).
One example of this is the high frequency of medial temporal atrophy in cluster 1, and the high frequency of posterior brain atrophy in clusters 2 and 3. Previous studies showed that medial temporal atrophy in DLB is associated with amyloid-β pathology  [7, [42] [43] [44] , while posterior brain atrophy is associated with the combined effect of amyloid-β and tau-related pathologies  [42] , matching the pattern of tau accumulation in the posterior cortex in positron emission tomography studies in DLB  [26, 47, 48]. In addition, our data suggest a possible association between higher tau pathology and shorter disease duration. Cluster 3, the subgroup with the highest tau pathology, was among the clusters with the shortest disease duration. Previous studies showed that tau pathology is associated with a worse prognosis in DLB patients  [49].
Androgen receptor (AR) signaling remains the key therapeutic target in the management of hormone-naïve-advanced prostate cancer (PCa) and castration-resistant PCa (CRPC). Recently, landmark molecular features have been reported for CRPC, including the expression of constitutively active AR variants that lack the ligand-binding domain. Besides their role in CRPC, AR variants lead to the expression of genes involved in tumor progression. However, little is known about the specificity of their mode of action compared with that of wild-type AR (AR-WT). We performed AR transcriptome analyses in an androgen-dependent PCa cell line as well as cross-analyses with publicly available RNA-seq datasets and established that transcriptional repression capacity that was marked for AR-WT was pathologically lost by AR variants.
Besides their role in therapeutic response, AR variants seem to play a key role in prostate cancer progression to a more aggressive stage. Indeed, AR-Q641X and AR-V7, but not AR-WT, lead to the upregulation of mesenchymal markers, in particular, N-Cadherin, Vimentin, Snail, and ZEB1  [25]. Moreover, contrary to constitutively active AR variants, AR-WT binding to AREs present in CDH2 intron 1 following dihydrotestosterone stimulation is not accompanied by an upregulation of Ncadherin  [26]. Altogether, these data suggest that AR-WT and constitutively active AR variants control differently the expression of a panel of genes at the transcriptional level. To go deeper in this hypothesis, we performed in the present study RNA-seq and proteomic analyses, as well as cross-analyses of experimental data with other publicly available PCa cell transcriptomic datasets to decipher a full landscape on distinctive transcriptional activities of AR-WT, AR-V7, and AR-Q641X in PCa cells.
Source and information of publicly available RNA-seq data Three GEO RNA-seq datasets, GSE125014  [30] , GSE148397  [31] , and GSE151429  [32] , were downloaded from the publicly available Gene Expression Omnibus (GEO) database (https://www. ncbi. nlm. nih. gov/ geo/). GSE125014 dataset refers to LNCaP cells treated with 10 nM DHT or 10 μM enzalutamide (MDV3100) for 4 and 24 h. GSE148397 dataset concerns VCaP cells treated with 1 nM R1881 alone or together with 500 nM (low) or 2 μM (high) darolutamide for 8 and 22 h. GSE151429 dataset refers to LNCaP cells expressing in a doxycycline manner ARv567es, a constitutively active AR variant. Transcriptomic changes were evaluated 24 h after culturing in a steroid-free medium containing 30 ngÁmL -1 doxycycline.
Data normalization and differential gene expression analysis on DESeq2 All raw expression data were normalized and implemented in the DESeq2 Bioconductor library (DESeq2 1. 30. 0) on R (version 4. 0. 3) with default settings  [33, 34]. Comparisons of interest were performed in order to obtain the (base 2) log of the fold changes (log2FC) and the corresponding adjusted P-values. Indeed, from our experimental dataset, differentially expressed genes were calculated either between DHTtreated control or AR-WT expressing cells and vehicletreated control, or between vehicle-treated AR-Q641X or AR-V7 expressing cells and vehicle-treated control as indicated. From GSE125014 dataset, differentially expressed genes were calculated between DHT-or enzalutamideand vehicle-treated LNCaP cells. From GSE148397 dataset, differentially expressed genes were calculated between R1881-or R1881 plus darolutamide-and vehicle-treated VCaP cells.
From GSE151429 dataset, differential gene expression was calculated between doxycycline-treated cells and vehicle-treated cells in the absence of androgen. To take into account the large dispersion observed with low read counts and to obtain more accurate log2FC estimates, shrinkage of the estimates (lfcShrink function) was applied using 'apeglm' (version 1. 12. 0) as type of shrinkage estimator  [35]. For all RNA-seq data, log2FC results are expressed as the mean ratio of the indicated number of observations for each condition. P-values were adjusted for multiple testing using the guideline of Benjamini and Hochberg  [36] , and differences were considered statistically significant when P-value < 0. 05. Differentially expressed genes were defined according to the following criteria: P-value < 0.
Functional enrichment analysis To identify significantly enriched pathways in different experimental conditions, gene set enrichment analysis (GSEA) was performed on preranked datasets sorted by log2FC using the GSEA 4. 1. 0 desktop application  [37, 38]. A conservative scoring approach was defined by setting the scoring scheme parameter to classic (unweighted). Gene set used for analysis with GSEA was the Molecular Signature Database (MSigDB) hallmark collection (v7. 2). The GSEA Preranked tool provides for each gene set of the collection an enrichment score that reflects how often members of that gene set occur at the top or bottom of the ranked dataset. Then, the score was normalized for each gene set to account for the size of the set. Only results with a false discovery rate (FDR) q-value < 0.
25 were considered significant, as defined by the publishers of the GSEA tool, and presented ranked by their GSEA normalized enrichment score. To further analyze pathways and biological functions that could be specifically associated with AR-repressive activity, significantly downregulated genes in the three datasets were pooled. The resulting panel of AR-repressed genes was uploaded on the Gene Ontology (GO) Consortium's Web site (http:// geneontology. org/) for overrepresentation analysis among the three GO categories biological process (BP), molecular function (MF), and cellular component (CC)  [39, 40]. Results with a P-value < 0. 05 and a fold enrichment > 2 were selected as significant, and the first ten enriched terms of each category were presented according to their P-value. Furthermore, the MSigDB hallmark collection was used on the Enrichr online tool (https://maayanlab.
For the time-course proteomic approach, proteins were identified by database searching using Sequest-HT (Thermo Fisher Scientific) with Proteome Discoverer 2. 4 software (PD2. 4, Thermo Fisher Scientific) on Homo Sapiens database (SwissProt, reviewed, release 2020_04_06, 20286 entries). Oxidation and carbamidomethylation were set as variable and fixed modification, respectively. Peptides were filtered with an FDR at 1%, rank 1, and proteins were identified with a minimum of 2 unique peptides. The Label-Free Quantification was based on the XIC (Extracted Ion Chromatogram), where protein abundancies were calculated from the average of peptide abundancies using the TOP N (where N = 3, the 3 most intense peptides for each protein), and only the unique peptide was used for the quantification. Quantification values were exported in Perseus for statistical analysis  [44].
For AR interactome analysis, proteins were identified by database searching against human database using Maxquant 1. 6. 5. 0. Precursor and fragment mass tolerance before recalibration were set at 20 ppm and 0. 6 Da, respectively. Trypsin was set as enzyme, and up to two missed cleavages were allowed. Carbamidomethylation was set as fixed modification, oxidation, and N-term acetylation as variable modifications. Proteins were identified with a minimum of two unique peptides and were filtered with an FDR < 1. Normalization and quantitative values (iBAQ) were processed with Perseus 1. 6. 2. 0.
BioID interactome analysis After the normalization step of mass spectrometry data, differences in AR partners were then calculated between AR-Q641X and AR-WT or AR-V7 and AR-WT conditions. To identify partners that have been specifically lost in the presence of AR variants, differentially underrepresented proteins (P-value < 0. 05 and Log2FC < -1) were searched from intersected AR-Q641X or AR-V7 and AR-WT data. The list of interactors underrepresented in the presence of AR variants compared with AR-WT was then submitted to Gene Ontology analysis as described above  (2. 6). Identification of experimentally proved AR interactors was performed using the BioGRID database (https://thebiogrid. org)  [45].
We then used a publicly available RNA-seq GEO dataset, GSE125014, obtained in LNCaP cells expressing ARv456es in a doxycycline-inducible manner. A similar asymmetry of down-and upregulated genes was also observed (Fig. S2 ), reinforcing our idea that compared with AR-WT, transcriptional repression capacities of constitutively active AR variants are disturbed. In this light, the following gene panel, TMPRRSS2, KLK3, ITGA3, HDAC9, COL16A1, SMARCD3, and ITGB4 was selected to further validate by RT-qPCR this dual transcriptional regulation between DHTactivated AR-WT and constitutively active AR variants in LNCaP and in C4-2B cells. As expected, Table  1. Significant differentially expressed genes. LNCaP cells were transduced to express AR-WT, AR-Q641X, AR-V7, or the empty eGFP plasmid (control) and were exposed to DHT or vehicle (EtOH) as indicated.