dataesr/scientific-paragraphs-categorization huggingface dataset:
The green crosses correspond to the asymmetry measured by  Korzennik (2005)  in the intensity power spectrum. described in  Korzennik (2005). Indeed, the asymmetry profile B(ν) resulting from this spectral analysis, while in agreement with the values inferred by  Barban et al. (2004) , shows less dispersion. As such, it allows for a more robust comparison of our results with observations. The dataset used contains 4 periods of 72 days each. In  Korzennik (2005) , the asymmetries are already directly given in terms of a parameter α which is easily related to the parameter B, through B = α/2. We only considered angular degrees between l = 0 and l = 20, and we averaged the values of B over bins of width 30 µHz.
Acknowledgements. The authors wish to thank the anonymous referee for his/her useful comments, which helped considerably improve this manuscript. J. P, K. B and R. S acknowledge financial support from the  'Programme National de Physique Stellaire' (PNPS)  of  CNRS/INSU and from the 'Axe Fédérateur Etoiles' of Paris Observatory. H. G. L. acknowledges financial support by the  Sonderforschungsbereich SFB 881  " The Milky Way System " (subprojects  A4 ) of the  German Research Foundation (DFG).
Cohorts and Datasets The Hopital Universitaire de Lille (HUL) cohort (ABOS, ClinicialTrials. gov: NCT 01129297) was recruited amongst obese patients visiting the Obesity Surgery Department at the Centre Hospitalier Universitaire de Lille. All patients fulfilled criteria for, and were willing to undergo weight-loss surgery. More details on the constitution and characterization of this prospective cohort are in the Supplemental Data section. Liver needle biopsies were obtained at the time of surgery from 910 patients undergoing bariatric surgery  (32). Anthropometric, histological and metabolic characteristics are indicated in Table  1. RNA extraction, purification, labeling and hybridization procedures for microarray analysis have been reported  (32). Transcriptome analysis was performed using Affymetrix Human Transcriptome Array (HTA) 2.
0 and. CEL files were normalized in a single run of the apt-probeset-summarize command (gc-scale-rma analysis using meta probesets and full quantile normalization, APT program 2. 10. 0, ThermoFisher). Expression data are available at NCBI GEO:GSE130991. The Universitair Ziekenhuis Antwerpen (UZA) cohort was recruited amongst overweight patients visiting the Obesity Clinic at UZA suspected to have NAFLD based on imaging and blood biochemistry assays [see  (33)  for details]. Liver biopsies were from 178 obese patients of which 79 further underwent gastric bypass surgery. Anthropometric, histological and metabolic characteristics, RNA extraction, purification, labeling and hybridization procedures have been previously reported  (12). Transcriptome analysis was performed using Affymetrix Human Gene (HuGene) 2.
0ST and. CEL files were normalized as above. Expression data are available at NCBI GEO:GSE83452. Ahrens and collaborators described the Universitätsklinikum Dresden (UKD) cohort which includes lean (control, n=18) and morbidly obese patients (BMI>42kg/m², n=45) either classified as healthy (no steatosis, n=18), NAFL (steatosis only, n=12) or NASH (steatosis and inflammation, n=15) with low fibrosis stage (F≤1, mild)  (11). Transcriptome analysis was performed by these authors using Affymetrix HuGene 1. 1ST arrays and. CEL files were normalized as above. Gene expression data are available at NCBI GEO:GSE48452. Moylan and collaborators described the Duke University (DU) cohort composed of 72 overweight or obese patients (29kg/m 2 <BMI<46 kg/m 2 ) who were stratified according to the fibrosis stage [(F≤1, moderate, n=40) and (F≥3, severe, n=32)] as recommended in  (34).
Differential gene expression analysis The source of variation in gene expression was investigated using multivariate ANOVA on normalized log2-transformed gene expression signals. Computation of F-ratio (variation explained by the test variable/unexplained variation) for each factor considered in the differential model clearly confirmed sex as the factor explaining the highest expression variance to the dataset (Figure  3 ). Accordingly, DEGs were first identified, as usually performed in signature discovery studies, by a single Limma run comparing either NoNASH to NASH gene profiles irrespective of sex, or considering only female or male patients (thereafter referred to as "All", "Women" or "Men" respectively). A variable number of genes was found significantly DE when comparing NASH to NoNASH patients in the men  (3, 083) , women (297) and all patients (3,466) strata (FDR< 10%).
When raising the FC threshold of reliable DEGs up to 1. 5 (Supp. Table  3 ), this overlap increased in proportion (13 overlapping transcripts; 87% of Gwomen and 15% of Gmen), while 74 and 2 genes remained men-and women-specific respectively (Supp. Figure  5 ). Men-specific genes with highest absolute log2FC values were Solute Carrier Family 22 member 10, SLC22A10) (σFCm = 0:42), Chitinase-3-like protein 1, CHI3L1 (σFCm = 3. 36) and Hermansky-Pudlak syndrome 5 protein, HPS5 (σFCm =2. 24) and the 2 womenspecific genes were HYDIN1(σFCw = 0. 52) and HYDIN2 (σFCw = 0. 39). While suggesting a clear dichotomy between male and female patients, this latter analysis did not provide an overview of altered liver functions in NASH. A gene ontology (GO) term enrichment was thus performed on reliable Gwomen, Gmen and Gall using the Biological Processes (BP) database (Table  3 ).
We compared the predictive performances of our signatures to those of randomlygenerated signatures or single gene predictors. RF-based signatures were consistently more efficient at classifying NASH vs NoNASH patients from independent cohorts. A single gene predictor could perform better than a signature for a given dataset, but not as efficiently on other cohorts. For example, FAT1, a gene regulating cell-cell contact, which was highly predictive of NASH in male patients from the HUL cohort, turned out to be inefficient in the UKD cohort (Supp. Figure  12 ). Thus signatures are required to extrapolate classification performances to other cohorts by reducing prediction variability of single gene predictors. Among the tested 3 signatures elaborated from Gwomen, Gmen and Gall, Swomen was the more robust with a limited size (n=20).
Biological term enrichment. Top 10 enriched gene ontology terms for reliable DEGs in NASH vs NoNASH men, women and all patients sub-cohorts. P-values and Benjamini-Hochberg FDR were computed using DAVID and the Biological Process Direct GO terms database, enrichments were ranked following p-values. Enrichments with corresponding FDR < 10%, 1% and 0. 1% are tagged with *, ** and *** respectively. Vandel et al. , HEP-20-0090 R1.
Financial support This work was supported by grants from  Agence Nationale pour la Recherche  ( ANR-16-RHUS-0006-PreciNASH  and  ANR-10-LBEX-46 ), the  European Union  ( FP6 Hepadip   FP6-018734  and  FP7 Resolve ,  FP7-305707 ),  Fondation de France  (Grant  2014 00047965 ),  Fondation pour la Recherche Médicale  (Equipe labellisée,  DEQ20150331724 ). BS is a recipient of an  Advanced ERC Grant  ( 694717 ).
2001; Pollock et al. 2002; Cadigan & Brattey 2003 , 2006 ). However, ancillary information is often available  (McGarvey & Feenstra 2002). Catch and fishing-effort data are collected for most exploited stocks. Classical stock assessment models (e. g. Virtual Population Analysis or Statistical Catch-at-Age Analysis) typically provide estimates of total and fishing mortality, and thereby total abundance. Such information can be used as input in the analysis of tagging data of harvested fish populations  (McGarvey & Feenstra 2002). The common sole (Solea solea, L. ) is a flatfish substantively harvested in the Western English Channel to the North Sea (ICES 2017). Today, stock assessment is conducted separately for three ICES stock divisions (VIIe, VIId, IV; ICES 2017). The Eastern English Channel (EEC, ICES division VIId) stock, managed as one homogeneous population, has been overexploited over the last ten years (ICES 2017).
Dispersal of the subadult components has been described: the larvae drift passively over short distances to local coastal nurseries  (Rochette 2011) , where the juveniles remain prior to migration offshore at maturity  (Riou et al. 2001) , where the fish reproduce and enter the fishery. Observed contrast in key life history traits (density-at-age and length-at-age data) have suggested potential spatial structuring within the adult component of the EEC common sole stock within three discrete spatial areas  (Du Pontavice et al. 2018; Randon et al. 2018). However, the movements of adult sole between these areas remain largely unknown. Here we have drawn on an extensive mark-recapture database collected over 49 years to analyze fish movements and assess connectivity of the adult common sole population within the EEC and adjacent management areas.
Data Collection The dataset collates four mark-recapture experiments carried out by (i) the Center for Environment Fisheries and Aquaculture Science (CEFAS, UK) from  1955 to 1985 and 2002 to 2007 (Burt & Millner 2008)   2 ). For each release experiment, Petersen discs were used  (Burt & Millner 2008). Discs are uniquely numbered and are securely attached to the dorsal flank of the fish. Adults and subadults were released close to the capture position as soon as was possible after capture. Recaptured marked fish were recovered by fishermen, who provided dates and spatial coordinates of the recaptures.
Core modeling structure The final dataset used in the modeling approach consists of 32739 released and 4036 recovered fish with the following associated information (individual based): dates and areas of release and recovery and release experiment. The dataset was analyzed through a multi-event mark-recapture modeling approach  (Pradel et al. 2005)   3. Observation process. The observation process (that ultimately defines the likelihood) links observation to the true biological state of each individual at time. Only fish caught by the fishery can potentially be observed. Alive fish or fish dead from natural causes are unobservable. Once a marked fish is caught by the fishery, it can eventually be declared. The declaration of a marked fish caught is modeled through a Bernoulli distribution with declaration rate (see details hereafter).
Model fit and selection All models were built and fitted using the E-SURGE program (version 1. 9. 0, Choquet and Nogue 2010). Details about the implementation of the model in E-SURGE are given in Appendix A. Parameters (movement and declaration rates) were estimated in the maximum likelihood framework (MLE). The data consist in the sequence of observation events for all individuals, starting with (the area of release, ) at the tagging   = 1,. ,5 event, and ending with (the area of recovery, ) when the fish is recaptured and   = 1,. ,5 declared (with a series of 0 between the tagging and the recapture event as the fish can only be recaptured once) or ending with a series of 0 for fish that are never recaptured. The likelihood writes down as the product of Bernoulli distributions (declaration process) over all time steps and all fish (considered independent), marginalized over the probability distribution of all possible hidden states defined from the product of Multinomial distributions for the movement and mortality processes.
RESULTS The GOF test performed on the full dataset (from 1970 to 2018) revealed a lack of fit ( 2 , and ) when over-dispersion is not accounted for. An = 57. 476 P = 0. 000 df = 24 overdispersion coefficient ( ) of 2. 39 was applied to all models to account for  overdispersion ( superior to one,  Burnham and Anderson 2003, Choquet et al. 2009). Overall, the full model (model 1, Table  3 ) best explains the tagging-recovery data including seasonal movements, specific declaration rate for each release experiment, and fishing mortality varying across space and time. The data also support the hypotheses of different detection rates per release experiments (QAIC model 1 < QAIC model 3, experiment. Declaration rate estimates presented in Figure  6  for model 1 were similar among models 1, 2 and 4-7 (Supplementary Files; Table  S1 ).
Results are also robust to the delineation of the five areas. The analysis performed on a dataset without fish released and recovered near a border provided results (Supplementary Files; Figure  S3 ) comparable to those obtained with no buffering (Supplementary Files; Tables  S2, S3  and S4). Finally, the conclusion that very low connectivity exists between areas is robust to the time series of data considered. No strong differences exist between movement probability estimates obtained with the whole dataset  between the FrE and NS areas is an exception. Between these areas, higher movements are estimated when considering historical release experiments only. Between foraging and overwintering seasons, all fish from NS move to FrE (Table  S3 ), whereas from spawning to foraging and overwintering to spawning seasons all fish from the FrE move to NS (Supplementary Files; Table  S2 ).
However, these estimated probabilities of movements can be explained by the small amount of fish released and recovered in the FrE area: 82 over the 1970-1998 period in comparison with the 1307 fish released and recovered in the NS in the most recent period. Movement probabilities inferred from recent tagging data collected from 2002 to 2018 are similar to the ones estimated with the entire dataset (Supplementary Files; Tables  S2, S3  and S4 ), with only small movements among areas. There are however small differences in estimates of movement probabilities between the recent tagging data and the full-time series, which mostly concern the three areas of the EEC. Between the foraging and overwintering seasons, higher than average movement probability are estimated from the UK to the FrE areas, as well as from the FrW to the FrE areas (Supplementary Files; Table  S2 ).
DISCUSSION In the present study, we have analyzed for the first time a substantive database of markrecapture data, with 32739 subadult and adult common sole being released over a period of 49 years. Our results suggest minimal movements of sole among areas in the EEC, and even lower immigration from adjacent areas, but low to moderate emigration to the NS and to the WC, mainly between the overwinter and spawning seasons. Our results provide evidence of low connectivity among common sole subpopulations within the EEC, and no inputs from the adjacent spatial management areas. This evidence of local segregation is consistent with recent studies using patterns in key life history traits  (Du Pontavice et al. 2018; Randon et al. 2018)  for the common sole in the EEC.
Reliability of the estimates of movement Commercially exploited fish populations are often widely monitored, which offers the opportunity to use survey-generated estimates of population vital rates, expert knowledge and/or stock assessment  (McGarvey & Feenstra 2002). In our study, we used estimates of fishing and natural mortality from published stock assessments (ICES 2017) as input fixed a priori in our state-space capture-mark-recapture modeling approach. Models incorporating all data available on the survival process (e. g. yearly and spatially varying fishing mortality) best explain the data. Seasonal movement probabilities estimated in our study revealed low movements, with small variations when considering different periods within the time series. However, the historical release experiments ( and ) were not designed We recognize, however, some weaknesses in our approach, some of which open up exciting opportunities for future research.
Our model is structured on a seasonal time step of 4-months that reflect biological seasons of the life cycle of the sole in the Eastern Channel. Although this is justified from a biological point of view, this choice excludes data from fish that were released and recovered within the same seasonal time step. The percentage of fish removed because of the chosen time step remains low (7% of released fish). For those removed fish, 94% were recovered in their zone of release, in accordance with the results obtained on a 4-monthly basis. In all models, mortality rates (fishing and natural) were considered known and estimated parameters are the movement and declaration probabilities. Estimating movement, mortality and detectability is possible in certain configuration of mark-recapture models  (Royle et al.
This paper is an important step toward the construction of an integrated model to simultaneously estimate fishing mortality and dispersion by integrating tagging data within the stock assessment model. Tagging data, however, can have a complex structure, which when coupled with a poorly balanced sampling design, can provide further challenge to obtaining robust statistical inferences from tagging-integrated models  (Maunder & Punt 2013). Consequently, we attempted here to develop a first analysis of the tagging data before integrating them in a more complex structure. Our approach also considers natural mortality to be constant in space and in time. In stock assessment evaluations, natural mortality is usually fixed and the fishing mortality is estimated relative to the fixed value of natural mortality (ICES 2017).
GOODNESS-OF-FIT TEST Goodness-of-fit (GOF) tests are an important component of capture-recapture modeling: they are used to assess the accuracy of a model in capturing the variance of the data  (Pradel et al. 2005). The GOF test is divided into two components the 3G test, which compares the capture histories of newly-tagged and previously-tagged individuals released at the same time, and the M component testing a trap-dependence effect by comparing future capture histories between individuals released on the current occasion versus individuals released on a previous occasion, for all individuals that are seen again. In our case study, the 3G test does not exist, because our datasets only includes dead recoveries  (Gauthier & Lebreton 2008). The GOF test then relies on the M component only.
GOF is performed on a reduced model that considers parameters to be state and timedependent with only six states 'dead' or 'alive' in each area  (Pradel et al. 2003; Duriez et al. 2009; Fernández-Chacón et al. 2016). GOF cannot handle multiple unobservable states and models with more than 10 different events. We then pooled together subareas of the EEC and summarized observations in seven types of events (not encountered = 0, encountered alive in WC = 1, recovered dead in WC = 4, encountered alive in EEC = 2, recovered dead in ECC = 5, encountered alive in NS = 3, recovered dead in NS = 6). The GOF test was conducted prior to model selection using the U-CARE software (version 2. 2,  Choquet et al. 2009). The GOF test performed on our data was significant relvealing a lack-of-fit ( , and ) to the general  2 = 57.
movement patterns at the scale of the English Channel, and relatively little tagging effort was expended in the two areas along the French coast (FrW and FrE). This deficit of data in the oldest release experiments can explain the difference in movement parameters estimated for the 1970 to 1998 time series with regards to the full time series or to the recent release experiment. Only the recent release experiments ( and ) sampled all three EEC areas more evenly. Jointly CEFAS 2002 IFREMER 2016 analyzing all four tagging experiments allowed for a more balanced dataset, and reduced singularities and potential bias incorporated by the spatial locations of fish releases between release experiments.
goodness-of-fit (GOF) test was conducted prior to model selection to check if our release and recapture data met the assumption of a general model (the Arnason-Model selection was used to assess which model formulation in Table3was the best supported by the data. Model selection was based on the Akaike's information criterion corrected for overdispersion (QAIC). The model with the lowest score of QAIC is considered as the best model. Only models with the same dataset (same length of time series of data) were compared together.
Model definition. Models 1 to model 7 are fitted to the full time series. Model 1 is the most complex model. Model selection is performed on the full dataset (1970 to 2018) from model 1 to less complex alternatives in the dispersal, fishing mortality inputs and detection rates. QAIC represents the difference in QAIC in comparison to model 1. Models 8 and 9 have the same structure as model 1, but are fitted to truncated time series and hence are not considered in the model selection process. Map of the model geographical definition with the five areas potentially structuring common sole stocks (ICES. 2017; IFREMER 2019): Western Channel (WC, ICES division VIIe), English coast of the Eastern English Channel (UK, ICES division VIId), Southern French coast (FrW, ICES division VIId), Northern French coast of the Eastern English Channel (FrE, ICES division VIId) and the southern part of the North Sea (NS, ICES division IVc).
goodness-of-fit (GOF) test was conducted prior to model selection to check if our release and recapture data met the assumption of a general model (the Arnason-Model selection was used to assess which model formulation in Table3was the best supported by the data. Model selection was based on the Akaike's information criterion corrected for overdispersion (QAIC). The model with the lowest score of QAIC is considered as the best model. Only models with the same dataset (same length of time series of data) were compared together. Schwarz Model, Pradel et al. 2003). We performed a GOF test using the U-CARE software (version 2. 2, Choquet et al. 2009). GOF tests a 'trap-dependence' effect by comparing future recapture histories between individuals released on the current occasion versus individuals released on a previous occasion, for all individuals that are seen again (for more details on GOF test, see Appendix A).
org/10. 1172/ JCI141455DS1). The only gene in common in both individuals was YIPF5, with patient I being homozygous for a missense, p. (Ala181Val), and patient II harboring a homozygous in-frame deletion variant, p. (Lys106del). Neither variant was listed in the gnomAD database (>120,000 individuals  [ref. 20] , accessed May 18, 2020), and both affect residues that are highly conserved across species (up to Saccharomyces cerevisiae). The p. (Ala181Val) amino acid change was predicted to be likely to affect protein function by 2 of 4 tools used to assess the effect of missense variants, Sorting Intolerant from Tolerant (SIFT)  (21)  and MutationTaster  (22)  (Supplemental Table  3 ). Testing for the mutations in parental samples confirmed that the unaffected parents were heterozygous for the mutations.
No difference was detected in the number of TUNEL + cells in the grafts (data not shown). β Cells from patients' induced pluripotent stem cells harboring the p. (Ile98Ser) mutation are sensitive to ER stress-induced apoptosis. To further assess the impact of one of the YIPF5 missense mutations in a directly patient-relevant model, we generated patients' induced pluripotent stem cells (iPSCs) and differentiated them into pancreatic endocrine cells. PBMCs were obtained from patients IIIa and IIIb, who are both homozygous for the p. (Ile98Ser) mutation, and reprogrammed into iPSCs using Sendai virus. The 4 iPSC HeLa cells. Among the residues investigated were p. Gly97 and p. Ile98, which are mutated in families III and V. Substitutions at these positions were not found to result in whorl formation in the study by Dykstra et al.