dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank the participants, their families and all investigators involved in the VERTIS CV study; the list of investigators can be found with the primary publication [11]. Medical writing and/or editorial assistance was provided by  M. Hammad  and  I. Norton , both of  Scion (London, UK ). This assistance was funded by  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , ( Kenilworth, NJ, USA ) and  Pfizer Inc. (New York, NY, USA). Authors' relationships and activities DZIC has received consulting fees or speaking honorarium, or both, from  Bristol-Myers Squibb ,  Novo Nordisk ,  Mitsubishis-Tanabe ,  MAZE ,  Janssen ,  Bayer ,  Boehringer Ingelheim ,  Eli Lilly ,  AstraZeneca ,  Merck & Co. , Inc. , Prometic and  Sanofi , and has received operating funds from  Janssen ,  Boehringer Ingelheim-Eli Lilly ,  Sanofi ,  AstraZeneca ,  Novo Nordisk  and  Merck & Co.
05 with Shapiro-Wilk test) (GraphPad Prism 8. 0; GraphPad Software, San Diego, CA, USA). The viral loads of Alpha/Delta and unvaccinated/vaccinated infected patients were compared according to symptoms and sampling time after symptom onset. Proportions between patient groups were compared using Fisher's exact test. We used regression model to identify variables independently associated with the SARS-CoV-2 viral load (Stata version 14 ® , StataCorp LP, College Station, TX, USA). Statistical significance was set at p < 0. 05. We tested the interactions between all the variables included in the final model.
Comparing the viral loads of several populations is a delicate process because many factors can influence it: the quality of the NP swab, the assay used, the delay between the sampling time and infection, the patient's symptoms, sex and age  [19]. Our data were obtained from a single-center study, in which all specimens were collected during the same period, using the same device and were processed with the same assays. We also performed a multivariate analysis with reference to symptom onset, age and sex. Nevertheless, the study has several limitations. First, symptoms and vaccination data were self-reported and the time between vaccination and infection has not been collected. Second, the number of patients infected with Alpha variant was limited. Third, although higher viral loads have been associated with greater infectiousness  [4] , we did not test for infectious virus.
Fourth, we collected no longitudinal data. Overall, our results demonstrate that both the SARS-CoV-2 variant and the host vaccination status influence the NP SARS-CoV-2 viral load in COVID-19 patients. It suggests that the Delta variant could be more infectious than the Alpha one and that vaccination might effectively reduce the spread of the virus even with the Delta variant. Institutional Review Board Statement: These analyses were part of the national SARS-CoV-2 surveillance. French law (CSP Art. L1121-1. 1) does not require institutional review board approval for anonymous non-interventional studies. Informed Consent Statement: Data were analyzed using an anonymized database. Such a protocol does not require written informed consent according to French Public Health law (CSP Art L 1121-1.
Statistical Analysis Continuous parametric data are presented as means ± standard deviation (SD), and categorical variables as numbers and percentages. To identify factors related to depressive symptoms in parents, a logistic regression was used. Factors selected from the univariate analyses (p < 0. 20) were entered into the multivariate model and selected using a backward selection method using the maximum likelihood ratio test. An adjusted odds-ratio (OR) and its 95% confidence intervals were reported. The same method was used to identify the risk factors of a worsening of the child's behavior. Statistical analyses were performed with SAS software version 9. 1 (SAS Institute, Cary, NC, USA).
Socio-Demographic and Contextual Data Families who responded for more than one child at a time (n = 5), or patients who responded for themselves (n = 2) were excluded from the analysis to allow comparability of parents' responses. A total of 533 families were included in the final analysis. Most respondents were female (95 % CI: 93. 5; 97. 2) and had male children (86. 7 %) with a mean age of 10. 5 years (95% CI: 7. 6; 13. 4). Most parents were well informed about the health situation and containment rules 89. 9% and had a trusted person who could come to their aid if needed (61. 3%). Twentyfour percentage of the population had a relative who had been affected by the coronavirus. All sociodemographic and contextual data are summarized in Table  1.
• Subjects who were employed in this clinical research institute. • Subjects who were considered inappropriate according to the judgment of the investigator. ). Subjects who met the inclusion and exclusion criteria completed the study according to the protocol. The information and characteristics of the subjects were investigated using questionnaires. Measurement of wrinkles. The Primos-CR (Canfield Scientific, Parsippany-Troy Hills, New Jersey) was used to evaluate the three-dimensional surface of the skin. Data files captured using the Primos-CR were analyzed using the Primos software. Measurement of skin sagging. F-RAY (BEYOUNG, Korea) images of facial contour curves of the selected cheek area were taken before and immediately after the use of the product. The angle of the contour line was analyzed for the captured contour image using an analysis program, Image Pro 1 10 (Media Cybernetics, USA).
The image captured using the Primos-CR was analyzed using the Primos software. Measurement of skin gloss (radiance). Mark-Vu (Mark-Vu 1 ; PSI PLUS Co. , Ltd. , Suwon, Korea), a skin diagnostic imaging system, was used to analyze the skin gloss. The analysis was performed in the designated area on the polarized image using the Image-Pro 1 10 (Media Cybernetics, USA) program. Measurement of hydration in the stratum corneum. Dermal hydration was measured dielectrically with an open-ended coaxial probe that was layered in structure (Moisture Meter-D; Delphin Technologies Ltd. ). Statistical analysis. Statistical analysis was performed using the SPSS 1 software program (IBM, USA). The normality was verified using the Shapiro-Wilk test and by examining the kurtosis and skewness. Statistical analysis of variables for parametric values was performed using the paired t-test and RM-ANOVA.
There is no single, straightforward definition of what counts as a critically ill cirrhotic patient. Some studies have focused on patients with high MELD scores  [11À13]. The MELD score takes into account bilirubin, INR and creatinine. It therefore captures the severity of liver failure (bilirubin and INR) and takes into account kidney failure, which is the most frequent extra-hepatic organ failure in cirrhotic patients  [1]. The MELD score therefore does not reflect other organ failures that affect critically ill cirrhotic patients, in particular cerebral, circulatory and respiratory failure. The ACLF classification, which was introduced in 2013, aims precisely at describing both hepatic and extra hepatic organ failures  [1]. While registry studies derived from national or international databases give access to large numbers of patients, they often lack granularity to assess precisely the level of organ failure of transplant candidates and recipients.
Study population This study is a cohort analysis using the French national CRIS-TAL database. It includes all consecutive patients aged 18 years and above who underwent single liver transplantation between January 1, 2008 and May 31, 2018. A total of 11,114 patients were transplanted during the study period. The exclusion criteria were as follows: (i) retransplantation (n = 989), (ii) patients transplanted with national priority status in an emergency setting (n = 898), (iii) patients transplanted with a graft coming from a living donor, or donation after circulatory death (n = 272) and (iv) patients transplanted without cirrhosis (n = 1,643). In addition, 4 patients were excluded from the study because there was no post-transplant follow up data and 147 patients were excluded because the variable concerning the pre-transplantation hospitalization status was missing.
At least one of the four elements that are taken into account to compute the MELD score (bilirubin level, INR, creatinine level and whether the patient is under dialysis or no) was missing on the day of transplantation for 596 patients. When the missing data were available within the 7 days prior to transplantation, they were used to compute the pre-transplantation MELD score (n = 63). Patients for whom these data were not available were excluded from the study (n = 533). A total of 7,312 patients from 18 transplantation centers were finally included in the cohort.
Data collection and variables recorded The national database CRISTAL, which is run by the Agence de la Biom edecine, prospectively collects data on all organ transplantat candidates, recipients, and donors in France together with candidate and recipient outcomes. The study was conducted in accordance with French legislation. According to French legislation, studies based on the national CRISTAL registry constitute part of the assessment of transplant outcomes and do not require ethics committee approval. Each center is responsible for entering data into the registry. Demographic, clinical, and laboratory data are collected at the time of listing, during pretransplant fol-lowÀup and at the time of transplant. Post-transplant data are also collected prospectively. Queries were performed to extract and subsequently analyze the following data: recipient gender, age, indication for LT, preÀLT hospitalization status, MELD score at the time of LT, donor gender, age and body mass index (BMI).
This pre-selection bias remains a crucial limit of the literature on this topic. The second major limit of this study has to do with the fact that it relies on a registry cohort. Some explicative variables are therefore potentially absent from the database (such as pre-LT arterial lactate levels for example). This study lacks granularity to assess key findings related to respiratory failure in the context of decompensated cirrhosis: the indication of intubation (e. g. respiratory failure, encephalopathy, or both) and PaO 2 /FiO 2 levels, which are both pivotal and pragmatic factors that are used routinely to grasp the degree of respiratory failure at the bedside of intubated patients  [5, 14].
Acknowledgements We would like to thank the following clinical centers and surgical and medical transplant unit supervisors who participated in the CRISTAL national registry from which the data of this study were derived: Hôpital  Henri Mondor ,  Cr eteil  ( Daniel Azoulay ,  Christophe Duvoux );  Hôpital Piti e Salpêtri ere, Paris  ( Olivier Scatton ,  Filomena Conti );  Hôpital Paul Brousse, Villejuif  ( Daniel Cherqui ,  Didier Samuel );  Hôpital Beaujon ,  Clichy  ( François Durand ,  Olivier Soubrane );  Hôpital de la Croix-Rousse, Lyon (Jean-Yves Mabrut ,  J erôme Dumortier );  Hôpital Grenoble Alpes ,  La Tronche  ( Vincent Leroy ,  Mircea Chirica );  Hôpital Estaing ,  Clermont-Ferrand  ( Denis Pezet ,  Armand Abergel );  Hôpital de l'Archet, Nice (Antonio Iannelli, Rodolphe Anty); Hôpital Saint-Eloi, Montpellier (Francis Navarro, Georges Pageaux); Hôpital Huriez, Lille (Francois-Ren e Pruvot, S ebastien Dharancy); Hôpital de Hautepierre, Strasbourg (Philippe Bachellier, Lawrence Serfaty) ;  Hôpital Jean Minjoz, Besançon (Bruno Heyd ,  Vincent Di Martino );  Hôpital Rangueil ,  Toulouse  ( Nassim Kamar ,  Fabrice Muscari );  Hôpital Haut L evêque, Pessac (Laurence Chiche, Victor de L edinghen); Hôpital Pontchaillou, Rennes (Karim Boudjema, Pauline Houssel Debry) ;  Hôpital Trousseau Chambray, Chambray les Tours  ( Ephrem Salam  e;  Christine Silvain ).
2a ). In order to investigate whether the DNA methylome functions as a molecular sensor to UV exposure and underlies the difference in survival between UV-mutant versus non UV-mutant cutaneous melanoma patients, DNA methylome-wide analysis based on Infinium HumanMethylation450 (450 K) array was performed in BCH samples and compared with that in the TCGA cohort (the quality-control analysis and selection of the appropriate statistical model, including adjustment for potential confounders, are described in the Methods section and Supplementary Data 2-5). In BCH melanomas, of the 2620 differentially methylated regions (DMRs), 1541 (58. 8%) were hypermethylated and 1,079 (41. 2%) were hypomethylated (Fig. 2b ; Supplementary Data 2). A similar proportion of hypermethylated (62. 8%; 378 out of 602) and hypomethylated (37.
05) (Supplementary Data 9,  10). A number of other cancers and diseases were significantly enriched as well (Supplementary Data 9, 10). This was complemented by KEGG pathway analysis, revealing 28 and 30 significant pathways (p < 0. 05) in BCH and TCGA, respectively. Among them, a large proportion (10 pathways) were identical between BCH and TCGA, 8 and 6 of which remained significant after adjustment for the number of CpGs per gene and FDR, respectively (Fig. 3c , Supplementary Data 11-14). These pathways constituted of differentially methylated genes implicated in immune system regulation: hematopoietic cell lineage, allograft rejection, graft-versus-host disease, intestinal immune network for IgA production, antigen-processing presentation, inflammatory bowel disease, and relatedly, autoimmune diseases, such as type 1 diabetes mellitus, autoimmune thyroid disease, systemic lupus erythematosus and rheumatoid arthritis (Fig.
In addition to the large proportion of overlap in biological pathways between BCH and TCGA described above, there was a significant overlap in DNA methylation alterations at the gene and CpG levels between BCH and TCGA (Fig. 4a ). Out of the 458 CpGs from 169 genes significantly overlapping (p = 2. 3e-109 and p = 3. 71e-29, respectively) between BCH and TCGA cohorts (Supplementary Data 18, Fig. 4a ), 6 CpGs (HOXC9, KCNQ1DN and MGMT genes) were hypermethylated and 30 CpGs (TAPBP, ERICH3, FINL2, ZNF732, SLC6A18, MFSD13A, SLFN12L, and IFNLR1 genes) were hypomethylated in both BCH and TCGA, considering CpGs with absolute effect sizes ≥10% and with no significant enrichment in SNPs (Supplementary Fig. 1b, c ). We complemented the cohort-specific analyses with a DMR meta-analysis across the BCH and TCGA datasets (Fig.
4b ). As the results demonstrate, there are 45,915 CpGs significantly differentially methylated across the two datasets between UVmutant and non UV-mutant cutaneous melanomas (FDR < 0. 05), of which a high proportion of CpGs (equal to 24,711 CpGs or equivalent to 53. 8%) have the same direction of effect between  BCH and TCGA (Supplementary Data 19, Fig. 4b ). 121 metaanalysis CpGs (FDR < 0. 05) overlapped with the 458 CpGs that are common between the BCH and TCGA cohort-specific analyses. As expected, the meta-analysis yields a larger number of significant hits (due to higher statistical power) than the cohort-specific analyses. However, the former is more prone to false positivity especially given some clinicopathological and ethnic dissimilarities (Table  1 ) and methodological differences between the two cohorts in inferring UV signature status (WGS versus WES, respectively).
For this reason, (1) we additionally report the more stringent Bonferroni threshold, which yielded similar results as FDR (Supplementary Data 20, Fig. 4b ), and (2) we present the meta-analysis results as a complementary method that reinforces the robustness of the findings across the different cohorts and analysis approaches, while prioritizing the more conservative cohort-specific analysis which yields signals that are common between BCH and TCGA and which, though less profuse, are less prone to error. Thus, we further investigated whether the 36 CpGs in common between BCH and TCGA could be used to predict the survival of patients with cutaneous melanoma. Among them, cg06230948-TAPBP, cg18930100-TAPBP, cg19495013-FIGNL2, and cg26835312-IFNLR1 were significantly associated with survival in BCH after adjustment for multiple testing (FDR < 0.
This was complemented by testing whether the DNA methylome could also underlie differences between pathologically and molecularly different subtypes of melanomas with interaction by UV mutation status, namely between BRAF, RAS, NF1 and TN molecular groups and between melanoma types predominantly associated with UV exposure (cutaneous melanoma) and those not UVassociated (acral melanoma). To date, there is only one study that described the methylome landscape of acral melanomas  20  , and our work additionally highlights genes and biological pathways that are epigenetically deregulated in this uncommon melanoma type in comparison with cutaneous melanoma analysed in the same cohort encompassing patients of European and Latin-American descents. The only available melanoma dataset with methylome and genome data for replication of our BCH findings was from TCGA.
The number of detectable signals was higher in the BCH relative to the TCGA cohort, and this is probably not due to statistical power differences as both datasets had similar sample sizes. This could be rather due in the BCH dataset to (1) the better quality of samples and/or their processing using our in-house optimized automated workflow to generate DNA methylome data, coupled to a priori designed sample distribution on the array that minimizes confounding with batch effects based on statistical semi-randomization, and (2) more accurate technology, using WGS rather than WES, to assess UV exposure. Even though there were some clinicopathological and ethnic dissimilarities (Table  1 ) and methodological differences between the two cohorts in predicting UV signature status (WGS versus WES, respectively), we observed consistent findings in both at the CpG, gene and biological pathway levels.
3c ). Taken together, TAPBP and MHC-I machinery genes, dysregulated by DNA methylation mechanisms as observed in our study, represent promising targets for epigenetic therapy and for predicting clinical response to immunotherapy. TAPBP methylation significantly predicted patient prognosis in both BCH and TCGA. Even though the sample size of expression data was the same as that of methylation data, the association between TAPBP expression and survival was not significant. This suggests that the difference is not merely due to statistical power but could indeed reflect biological basis. DNA methylation does not act solely through affecting gene transcription but is known to also associate with chromosomal instability, the induction of splice variants, alterations in enhancer regions, changes in microRNA binding regions and expression control regions, and mutations  [26] [27] [28].
Still, more datasets will be needed to better reinforce the causality of the associations, for example, by using in larger sample sizes germline data as proxies for TAPBP methylation through Mendelian Randomization. In addition to the findings focused on TAPBP, we reported that the UV mutational signature is associated with a high load (thousands) of epigenetic alterations affecting the methylome landscape of cutaneous melanoma. This highlights that, even though the UV-mutant and non UV-mutant cutaneous melanomas are supposed to share the same pathological/cellular origin, they may need to be classified separately, at least based on their underlying epigenomic landscape, which has the potential to capture markers of both exposures and cell identity. Moreover, the non UV-mutant cutaneous melanoma, by resembling in its epigenome the acral melanoma, may have a poorer prognosis and require a different therapeutic approach than the UV-exposed cutaneous melanoma.
This is in line with our data showing that patient survival is worse for the non UV-mutant cutaneous and acral melanomas relative to the UV-mutant cutaneous melanomas. Our findings also corroborate those of another study showing that cutaneous melanoma patients harboring the UV mutation signature had higher disease-free and overall survival  11. The consistency between findings is notable especially that our dataset included a different ethnic group and a more accurate methodology (WGS rather than WES) to infer the UV signature. A recent study investigated the genetic changes related to UV in clinical melanoma samples and found 10 genes commonly mutated in UV-mutant relative to non UV-mutant cutaneous melanoma  11. Among them, PKHD1L1, LRP1B, ADGRV1 and DNAH10 were hypomethylated in UV-mutant compared with non UV-mutant cutaneous melanoma patients in BCH (Supplementary Fig.
Specifically, we leveraged publicly available data and complemented that with the generation of new datasets, with larger sample sizes, higher genomic coverage, more detailed phenotypic assessment, high-quality frozen tissue samples, and the inclusion of melanomas other than cutaneous and of ethnicities besides European-descent. In fact, less than 5% of genetic studies worldwide include participants with multiple ethnicity  32  , specifically in acral melanoma research  33  , and our work helps address this timely advocated need  32, 33  by contributing to genomics and epigenomics data from populations of non-European descent. By investigating epigenetic markers of UV exposure in human melanoma tissues from two distinct populations and overlaying the DNA methylome landscape onto the transcriptome and genome maps of UV-mutant cutaneous relative to non UV-mutant cutaneous and acral melanomas, this work contributes to (1) uncovering potentially powerful exposure and cancer epigenetic biomarkers that can be exploited in risk stratification; (2) enhancing tumor classification within and across melanoma types; (3) revealing molecular drivers in melanomagenesis that could be at the origins of this cancer, hence, suitable for targeted therapy; and (4) diminishing population disparities and knowledge inequalities in melanoma pathobiology.
The UV mutational signature identification was performed using the SomaticSignatures Bioconductor package  42. We used the Non-negative Matrix Factorization (NMF) algorithm  43  to determine the consensus signatures among the 71 patients. At the moment of the analyses, we used the 21 signatures 10 that were available and identified a consensus signature with more than 0. 8 cosine similarity. For both BCH and TCGA cohorts, we classified samples as harboring an UV mutation signature (Cosmic Signature 7) based on the recommended criteria in which C > T transitions at dipyrimidine sites accounted for more than 60% or CC > TT mutations more than 5% of the total mutation burden  9. Bisulfite conversion. The isolated DNA (500 ng) from BCH-cohort was bisulfitemodified using the EZ DNA Methylation Kit (Zymo Research catalog no #ZD5004) following the manufacturer's instructions for Illumina Infinium 450 K beadchip assay.
Modified DNA was stored at -20 °C when short intervals were required between bisulfite conversion and further processing, and at -80 °C for long-term storage. 450K DNA methylome-wide array and analysis. The 450K data of BCH were generated in-house, and those of TCGA were downloaded from the GDC Legacy Portal  9. For BCH, bisulfite converted DNA samples were profiled using 450K (Illumina catalog no WG-314-1003) and a well-established workflow optimized at IARC for high-throughput analyses through an automated robotic system (Freedom EVO 150 by Teca) that can process the chips with minimal human error. Chips are scanned using Illumina iScan to produce two-color raw data files (. idat format). Sample allocation to the arrays was based on a semi-randomization design that ensures minimum confounding by technical variation and minimizes the masking of biological covariates of interest by batch effects.