dataesr/scientific-paragraphs-categorization huggingface dataset:
33-0. 33-0. 33 for the triplets of trait categories. No more than three categories of a given trait belonged to a given trait syndrome over the three biological compartments. All the paragons were projected as supplementary individuals on the factorial space defined by the first three axes of the fuzzy correspondence analysis (FCA) applied to the trait dataset. When several paragons were defined for a given trait syndrome, the location of an "average paragon" was also calculated as the centroid of the group of paragons based on their respective coordinates on the FCA factorial space, considering a number of successive FCA axes sufficient for taking into account at least 50% of the total variance in the dataset of the corresponding biological compartment. Finally, we identified the taxa (among diatoms, macroinvertebrates, and fishes, respectively) best representing each trait syndrome, by selecting the taxa that were closest to the location of the average paragon of each trait syndrome in the FCA factorial space, based on the Euclidean distance.
To be representative of the trait syndromes, the selection of taxa was made using two successive criteria-(i) exhibiting the lowest distance to the average paragon of this trait syndrome (across all the trait syndromes), and (ii) being among the ten taxa with the shortest distance to this average paragon. All statistical analyses were done using R software version 4. 0. 3 [57]. The correlation coefficients (Spearman's rho) between each pair of trait category × MoA were calculated by using the cor. test() function. The heatmaps were drawn using the function heatmap2() from the package gplots  [58]. The hierarchical cluster analyses allowed us to identify which trait syndromes were performed by using the hclust() function, with ward. D2 as the algorithm. Generalized additive models were done with the gam() function, and the models were compared using the anova.
Paragons as Model Taxa Best Representing Trait Syndromes Paragons were projected into a trait-based space explaining at least 50% of the total variance in the FCA, described by the first nine (diatoms), eight (macroinvertebrates), and eleven (fishes) axes defined by the FCA applied to each trait dataset. Then, using the Euclidean distance (ED) between each pair of "average paragon × taxon", we identified taxa with trait combinations most similar to that of the paragon of a given trait syndrome. This procedure was repeated for the three biological compartments. First, the average paragons of diatoms were widely dispersed in the FCA factorial space (Figure  5 ). TS1 and TS5 exhibited the paragons farthest from each other (ED = 4. 59), in coherence with the results of the clustering analysis (Figure  2 ).
The trait syndromes of diatoms were mainly dominated by the trait categories linked to ecological preferences, for example, trophy, pH, or oxygen levels. Such an observation may easily be explained by the smaller number of biological traits in the diatom trait dataset compared to the macroinvertebrate and fish trait datasets, which included both a larger number of traits and many more numerous life-history and morphological traits. Trait syndromes positively related to high toxicity levels included mostly characteristics of diatoms found in nutrient and/or organic matter-contaminated media  [63] , with biological traits enabling the cells to cope with the lack of light or even to move in the biofilm matrix. In contrast, trait syndromes negatively related to high toxicity levels were rather characteristic of media less polluted, with various traits related to colonies, morphology, and guilds.
Trait syndromes positively linked to high micropollutant exposure levels were indicative of taxa found in slow-flowing waters, able to live in a large range of temperature, current velocity, and oxygen level, and feeding on detritus and plants, potentially r-strategists (e. g. , exhibiting a high number of oocytes). The diversity of trait categories found in all the fish trait syndromes was quite high compared to that of diatoms and macroinvertebrates, but it was actually indicative of the high number and diversity of traits taken into account in the fish dataset. Winemiller et al. [69]  did observe that due to a high number of traits linked to body shape in their dataset, such traits frequently drove trait-based species ordination. In our dataset, such traits were also quite frequent (32% of the trait categories), but it seems that they did not significantly drive the composition of fish trait syndromes.
g. , CMEM and MULTI for diatoms, BAAPR for macroinvertebrates). This pattern may arise if high exposure levels to micropollutants with such MoAs were observed in a very limited number of sampling events or sites in the dataset. Nevertheless, this pattern may also contribute to the identification of key trait syndromes. For example, TS2 was a key trait syndrome for macroinvertebrates (see Tables  S5  and S7 ), as it was the only one exhibiting a GAM always better than all the GAM based on each of the trait categories belonging to this syndrome (i. e. , the first pattern), whereas all the other trait syndromes did not exhibit GAM better than their own trait categories (i. e. , this third pattern). Overall, all these results have highlighted the importance of including diverse and complementary biological and ecological information when evaluating the environmental risk induced by cocktails of pollutants.
Conclusions and Perspectives In summary, our study has demonstrated that large-scale empirical data allow for highlighting strong relationships among many micropollutant types or modes of action and diverse combinations of trait-based attributes (=trait syndromes) from freshwater benthic diatom, benthic macroinvertebrate, and fish communities. Trait syndromes exhibited both consistent and specific responses to combinations of main micropollutants and pesticides' modes of action across the three biological compartments over a large-scale set of various environmental situations. Individual trait categories contributed to varying extents to those responses. Further evaluating which trait(s) contributed most to the observed responses will allow for inferring more precisely potential ecological mechanisms at play.
Main functions targeted by pesticides when referring to the R4P database. When the mode of action has not been identified (i. e. , for the other micropollutant types), only the name of the pollutant type is given. For each, the total number of compounds (or compound families) involved and their codes for downstream analyses are provided. In the right column, a star (*) indicates the types of functions (or micropollutants) that were not kept in the following analyses (see the end of Section 2. 2 for more details about the selection step).
Method The current study used two different datasets. The first dataset consisted of viewer data. It was extracted from a database of a previous study (de  Bérail et al. , 2019). Data were collected online using LimeSurvey, and participants were recruited from different Facebook groups, especially student groups. Student Facebook groups bring together students from the same city or university. The second dataset was new and it consisted of YouTuber video characteristics obtained through video coding. The previous study (de  Bérail et al. , 2019)  included participants from different countries. In order to simplify the video coding, by French coders, only data from participants recruited in French student Facebook groups have been extracted for the current study. Complete data are available for 600 viewers who named 302 different favorite YouTubers.
To keep the number of coded videos within a reasonable limit, only data from the respondents who named a YouTuber cited by at least one other participant as a favorite YouTuber were retained. Data from a total of 370 viewers who named 72 different YouTubers were retained for data analysis. Table 1 presents all the viewers' variables. The names of the 72 different YouTubers are provided in the Appendix (see Table  A1 ). YouTuber video characteristics were measured through video coding. Table  A2  in the Appendix presents all the YouTuber video characteristics. For each of the 72 YouTubers cited, five videos were coded. The five last videos of each YouTuber uploaded before February 1st 2018 were selected, as these videos were the last videos uploaded before the time of the study. The last five uploaded videos were selected as they were assumed to be the most representative of the nature of the YouTuber's current content at the time of the study.
The number of videos coded per YouTuber was limited to five in order to keep the video coding process feasible. Video coding was thus undertaken for 360 videos (approximately 90 hours of video content). A codebook was specifically designed for this study to measure YouTuber video characteristics. In order to test intercoder reliability, a sample of at least 50 units, or 10% of the full sample, was considered as appropriate  (Lombard et al. , 2002). Two independent coders then coded 50 randomly selected videos. Intercoder reliability was tested using Krippendorff's α for ordinal variables and ranged from 0. 82 to 0. 91, which is acceptable  (Lombard et al. , 2002). Krippendorff's α for all the coded variables are presented in the Appendix (Table  A2 ). In order to merge the two datasets, each participant was assigned a score for each YouTuber video characteristic that corresponded to the score obtained through video coding of their favorite YouTubers' videos.
Radiological Analysis 2. 2. 1. Diameter Measurement Diameter measurements were performed using Endosize software (Therenva version 3. 1. 25, Rennes, France) in the perpendicular axis of the centerline (CL) at six levels for the true lumen (TL), false lumen (FL), and the total aortic diameter of the thoracic aorta (Figure  1 ). have stated that using the diameter measurements is not sensitive and adequate to evaluate the behavior of an aortic dissection due to the long extent of the aorta and its pathology  [14]  and, although time consuming, that aortic volume measurement was the most relevant and consistent method to reliably assess an aortic remodeling during a follow-up  [15] [16] [17] [18] [19]. The volumetric measurement has been largely studied in abdominal aneurysm repair  [20] , but there are few studies on volume analysis with the thoracic aorta  [15]  and in an aortic dissection or other thoracic aortic diseases, the role of volume measurement remains unknown.
Diameter Measurement Diameter measurements were performed using Endosize software (Therenva version 3. 1. 25, Rennes, France) in the perpendicular axis of the centerline (CL) at six levels for the true lumen (TL), false lumen (FL), and the total aortic diameter of the thoracic aorta (Figure  1 ). To assess the intra-and interobserver variability of the aortic diameter measurements, a random sample of 50 CT scans was analyzed a second time by the same observer and by a second radiologist.
Volume Calculation Custom MATLAB application (Figure  2 ). We developed a custom application through the MATLAB software (named TAV (thoracic aorta volume)) to evaluate the volumes of the thoracic aorta. The method is based on the addition of the elementary volume (EV). It is assumed that the area of a surface (Si) that is identified on an axial CT scan image does not vary over a certain height hi. This hypothesis is valid if the EVi remains straight, i. e. , its tortuosity (Ti) remains low. Ti is the ratio between the EV curvilinear length (li) and its straightline length (hi). The height is optimal when the tortuosity is close to 1. A maximum tortuosity threshold is therefore chosen to validate the hypothesis of a constant area over hi. The elementary volume Vi is then equal to Si x hi. The total volume is the sum of all of the elementary volumes over the segment of interest.
Volume Calculation Validation: Semiautomated Method Coupled with the Standard Method To validate the custom application of the TAV, the results were compared with those results obtained when using a reference method made by another observer  [21] [22] [23]. The volume analysis was performed with semiautomated segmentation, which determines the boundaries around the voxels with a similar intensity (TL and contrast aortic lumen). For the FL, a manual selection was carried out at each slice. To assess the intra-and interobserver variability of the aortic volume measurements, a random sample of 50 CT scans was analyzed a second time by the same observer and by a second radiologist. We developed a custom application through the MATLAB software (named TAV (thoracic aorta volume)) to evaluate the volumes of the thoracic aorta.
Statistical Analysis All analyses were performed using the Statistical Package for Social Sciences software, version 20 (SPSS, IBM Corporation, New York, NY, USA). The medians and interquartile ranges (Q1-Q3) are used to describe the continuous variables; the categorical variables are presented as numbers and frequencies. The risk factors for an aneurysmal evolution and the distal reinterventions were assessed with a univariate analysis. The normality of the continuous variables was assessed by the Kolmogorov-Smirnov test. In cases of an abnormal distribution, Mann-Whitney tests were performed. In cases of a normal distribution, T-tests were performed. The categorical variables were compared by the χ 2 or Fisher's exact tests. To identify the independent predictors for long-term dissection-related events, a binary logistic regression was conducted (descending method).
Study Population Between January 2010 and December 2016, 309 patients were treated in our center for a TAAD. Among them, the rate of stroke was 11. 6% (36/309), the rate of a tamponade at admission time was 14. 2% (44/309) and the in-hospital mortality was 17. 7% (55/309). Thirty-two patients did not have a RAD, and 48 patients were lost to follow-up. Among the 174 survivors with a RAD, 38 patients died during follow-up (<3 years of follow-up) from non-aortic causes and without reoperation in the distal aorta and were excluded, 81 patients were excluded due to the absence of at least the 3 years follow-up or due to the absence of at least one of the three CT scan follow-ups during the first year. Finally, 54 patients were included. The demographic data of the patients are presented in Table  1.
Overlap of the TRIP12 Genome-Wide DNA Methylation Profile with Other Neurodevelopmental Disorders Conditions on EpiSign™ To perform analysis of differentially methylated regions (DMRs) and functional annotation, we again compared the TRIP12 cohort to age-, sex-and array-matched controls from the EpiSign™ Knowledge Database (EKD). Only controls without a known episignature or controls unaffected by a neurodevelopmental phenotype were used in this analysis. We generated a list of differentially methylated probes (DMPs) for the TRIP12 cohort and compared it to the DMP lists for 56 other episignature cohorts on the EpiSign™ v3 clinical classifier previously described by Levy et al. [25]  (Figure  6 ). A list of EpiSign™ disorders and their abbreviations are listed in Supplementary Table  S3. There were 4813 DMPs for TRIP12, and the range across all cohorts was 279 to 151,848 DMPs  [25].
However, two individuals (Cases 21 and 22), whom we believed to have (likely) pathogenic variants, clustered with controls, indicating the absence of the TRIP12 episignature. Case 21 with the c. 2482C>G p. (Pro828Ala) variant had obesity and mild intellectual disability, features which are not unique for TRIP12 variants. Overall, in silico tools predicted this missense variant to be tolerated (SIFT  [27] : Tolerated, score 0. 33; PolyPhen-2 [28]: Probably Damaging, score 0. 998; Align GVGD  [29] : class C0; and Grantham  [30] : score 27). Case 22 with the c. 5800 C>A p. (Pro1934Thr) variant was born prematurely and had abnormal facial shape, delayed speech, language, and gross motor development. These characteristics are in line with the expected syndrome phenotype of a TRIP12 variant. Intellectual disability can, by definition, not reliably be diagnosed before 3 years of age.
e. , carrying mutations for a particular functional gene domain or mutations in different genes that involve the same functional active protein complex. It should be noted that in our workflow, updates on variant classification databases and screening of inconclusive/unresolved patient signatures within newly discovered signatures are standard operating procedures within the signature discovery workflow of our laboratory  [11, 21]. We showed the strong effect of genetic variation within TRIP12 on DNA methylation, resulting in predominant genomic hypomethylation. Our results demonstrated that all of the differentially methylated regions identified in individuals with a TRIP12 (likely) pathogenic variant were hypomethylated. Therefore, we can hypothesize that functional proteins within this pathway might lead directly to hypomethylation of specific loci.
Sample Processing DNA was obtained from whole blood using standard techniques. Bisulphite conversion was performed according to Illumina protocol (Illumina, San Diego, CA, USA). DNA methylation analysis was carried out using 500 ng of bisulfite-converted DNA as the input for the Illumina Infinium MethylationEPIC BeadChip arrays (San Diego, CA, USA) according to manufacturer's protocols. In order to minimize batch effect, samples were randomly divided over separate batches. Analysis and discovery of episignatures were carried out based on our laboratory's previously published protocols  [22, 23, 35]. In brief, intensity data files (IDATs) containing methylated and unmethylated signal intensities were analyzed in R (version 4. 1. 1). The methylation data normalization was performed using the Illumina normalization method with background correction using the minfi package (version 1.
40. 0) (accessed on 2 July 2022)  [36]. From the 866,836 total probes available, the following probes were eliminated; probes with detection p value > 0. 1, probes located on chromosomes X and Y (n = 19,120 and n = 561, respectively), probes containing single-nucleotide polymorphisms (SNPs) at or near the CpG interrogation site or single-nucleotide extension sites (n = 31,647) and probes that cross-react with other genomic regions (n = 72,487). Samples containing failed probes of more than 5% (p-value > 0. 1, calculated by the minfi package) were removed. Principal component analysis (PCA) was performed to examine batch structure and identify case or control outliers. Matched controls were randomly selected from the EKD  [22]  and matched by age, sex and array type using the MatchIt package (version 4.
3. 4) [37]  at a ratio of 1:5. Methylation levels for each probe (beta values) were converted to M-values by logit transformation and linear regression applied to identify differentially methylated probes using the limma package (v3. 50. 0)  [38]. Estimated blood cell proportions were incorporated into the model matrix as confounding variables  [39]. The blood cell types used as covariates are CD4+ and CD8+ cells, natural killer cells, monocytes, granulocytes and B-cells as described in the minfi package. p-values were moderated using the eBayes function in the limma package.
1. 1) and multidimensional scaling (MDS) by scaling of the pair-wise Euclidean distances between samples. To assess the robustness of the episignatures, multiple rounds of leave-25%-out cross validation were performed. In each round, 25% of TRIP12 samples were used as testing samples and the remaining samples were used for probe selection. The corresponding unsupervised clustering plots were visualized. The e1071 R package (version 1. 7-9) was used to train a support vector machine (SVM) classifier and construct a multi-class prediction model as previously described  [21].
Functional Annotation and Comparison between EpiSign™ Cohort Functional annotation and EpiSign™ cohort comparisons were performed according to our previously published methods  [25]. In short, to assess the percentage of differentially methylated probes (DMPs) shared between the TRIP12 episignature and the 56 other neurodevelopmental conditions on the EpiSign™ clinical classifier, heatmaps and circos plots were produced. Heatmaps were plotted using the R package pheatmap (version 1. 0. 12) and circos plots using the R package circlize (version 0. 4. 15) [40]. To determine the genomic location of the DMPs, probes were annotated in relation to CpG islands (CGIs) and genes using the R package annotatr (version 1. 20. 0) [41]  with AnnotationHub (version 3. 2. 2) and annotations hg19_cpgs, hg19_basicgenes, hg19_genes_intergenic, and hg19_genes_intronexonboundaries.
Differentially Methylated Regions (DMRs) Differentially methylated regions (DMRs) were detected using the DMRcate package in R (v 2. 8. 3)  [26] , and regions containing at least 5 significantly different CpGs within 1kb, with a minimum mean methylation difference of 5% and a Fisher's multiple comparison p-value < 0. 01, were considered significant. DMRs were annotated using the UCSC Genome Browser Data Integrator with GENCODE V3lift37 comprehensive annotations and further characterized using UCSC Genome Browser tools (accessed on 3 March 2022) (https:// genome. ucsc. edu).
Assessment Participants were invited for face-to-face follow-up meetings at baseline, and 6 months (limited data as this assessment was introduced later in the course of the study), 12 months and 24 months after baseline. Information regarding transition to psychosis were followed up for 2 years using available clinical records, in case face-to-face meetings were not possible. Tobacco smoking was assessed with the Composite International Diagnostic Interview (CIDI)  (19). The CIDI defines smokers as people who smoked daily during at least 1 month over the past 12 months. In addition, participants were asked how many cigarettes they smoked per day in the time frame they smoked the most during the past months. Studies have confirmed good test-retest and interrater reliability of the CIDI as well as good agreement of CIDI diagnosis with routine clinical diagnosis and applied checklists  (20).
Statistical analysis Latent class mixed model analysis (LCMM) was used to empirically identify and visualize clusters of participants with similar trajectories of tobacco smoking over time within one sample. For reporting of study design and analyses we followed state-of-the-art guidelines (GRoLTS checklist) (31). Missing values at baseline were replaced applying multiple imputation procedure to be able to include participants with at least one assessment. With maximum likelihood (ML) estimation LCMM then makes use of all available data, regardless of intermittent missing data and/or later dropout. Subject and time were used to infer latent class trajectories of cigarettes smoked per day. The actual individual time of measurement (days since baseline) was used to account for possible deviation around the planned assessment date.
The overall cumulative risk of psychosis onset for individuals with different trajectories was plotted with the Kaplan-Meier cumulative event function and 95% confidence intervals (CI)  (33). LCMM was conducted using the lcmm R package  (34) , cox proportional hazard regression analyses were analyzed using survival R package (35) and survminer R package  (33)  to plot Kaplan-Meier functions with R version 3. 6. 2. All other analyses were performed using SPSS version 26.