dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Descriptive statistics were first calculated for maternal and child characteristics at 2 and 5 years. Chi square tests were used to examine gender differences for categorical and t-tests for continuous variables. Finite mixture models, separate for boys and girls, were used to identify the clusters of children  [9, 31]. This latent class analysis method enables the management of mixed types of data (i. e. categorical and continuous) without preliminary data conversion by modeling the distribution of the observed variables with a mixture of parametric distributions and an assumption of their independence within clusters. Clusters are then defined as the subset of children drawn by the same mixture component. The method provides not only a partition but also the posterior class membership probabilities for each individual (based on model parameters and the individual's observed values).
Simultaneously, the finite mixture model permits the selection of variables for clustering  [32, 33]. Using Marbac and Sedki's approach  [33] , implemented in the R package VarSellCM  [32] , we can perform full model selection, that is, detect the relevant variables for clustering and selection of the number of clusters, according to the Bayesian information criterion (BIC)  [34]. By assuming that only a subset of the variables explains the partition, the selection of variables facilitates interpretation of the results and increases the accuracy of the estimators. In a clustering context, a variable is said to be irrelevant for clustering when its distribution is the same in each of the mixture components. The relevance of a given variable for the clustering is thus measured by its discriminating power (see  [32]  equation~3), which makes it possible to sort the variables according to their influence on the construction of the clusters.
Furthermore, although PA and sedentary behavior take place in multiple contexts, we measured only a selection of children's possible activities throughout the day. This prevents us from having a complete profile of their activity-related behavior. This may be particularly problematic for girls who tend to spend large amounts of time in non-TV sedentary activities that are seldom measured  [44]. Combining activity data collected via accelerometry (thus, objective measures of time spent in activities at different levels of intensity) and via time use diaries (to capture valuable contextual information) would be useful for confirming and building upon the findings presented here. It is, however, difficult to collect these types of data in large epidemiological studies. Another limitation is related to attrition in the EDEN cohort, which, as generally observed in this type of epidemiological study, is proportionately higher for children of low SEP families.
Acknowledgements We are extremely grateful to all the families who took part in this study; the midwives and psychologists who recruited and followed them; the whole EDEN team, including research scientists, engineers, technicians, and managers; and especially  Josiane Sahuquillo  and  Edith Lesieux  for their commitment and their role in the success of the study. We are particularly grateful to MA Charles who designed and initiated the EDEN study and to  B Heude  who has taken up its leadership and assumes the coordination of the whole study. We also acknowledge the commitment of the members of the EDEN Mother-Child Cohort Study Group:  I. Annesi-Maesano ,  J. Y. Bernard ,  J. Botton ,  M. A. Charles ,  P. Dargent-Molina ,  B. de Lauzon-Guillain ,  P. Ducimetière ,  M. de Agostini ,  B. Foliguet ,  A.
Acknowledgments The authors warmly thank medical students involved in data collection:  Laurence Clastres ,  Mathilde Lehmann ,  Aline Pellegrini ,  Marine Sisouvan ,  Ilana Slotine ,  Diem Soubou  and  Abigaëlle Vergnet , the following physicians and nurses who actively contributed to the screening:  Claire Aguilar ,  Caroline Charlier ,  Gérard Chéron ,  Claudine Duvivier ,  Muriel Fortier ,  Remy Gauzit ,  Béatrice Grandordy ,  Marc Lecuit ,  Léonie Meyer ,  Gabrielle Paluszek ,  Perrine Parize ,  Guillemette Thin  and  Christine Vinter.
Results: The first results were obtained on 7 subjects that were given pea protein (N = 3) or milk casein (N = 4). The mean ileal flow for all the volunteers was 1. 83 ± 0. 70 mL/30 min (mean ± SD), and was quite stable over time. The total N exogenous flow was 15. 50 ± 5. 83 mmol for pea protein and 13. 56 ± 3. 48 mmol for casein. The true ileal N digestibility was 95. 40 ± 1. 73% for pea protein and 95. 42 ± 1. 17% for casein. Ileal IAA digestibility is in progress. Conclusions: The intermediate results show no difference between pea protein and casein in terms of ileal digestibility, suggesting that pea protein could be a promising plant-based alternative. AA digestibilities and data from the remaining 9 volunteers to be tested will complete the study. Funding Sources: Roquette.
Methods Six BCS patients of European ancestry were retrospectively identified from the MS clinic database of the CRCHUM (Centre de Recherche de l'Université de Montréal). Patients were included if 1) they were ≥18 years old and 2) if MRI disclosed at least one alternating band (2 hyperintense band separated by 1 hypointense band). All patients provided written informed consent. The study was approved by local ethics committee (19. 302). Clinical data were recorded from the patients and their files. At baseline (define as BCS presentation), the following data were recorded: age, gender, past medical history, age at BCS onset, delay from onset to MRI and CSF results. Two authors (XA, LLG) retrospectively reviewed all the MRI. We specifically focused on the identification of additional BCS and MS lesions and on the results of spectroscopy and perfusion MRI (when available).
Plasma samples from patients with colorectal cancer were provided by the hospital centers of Clermont-Ferrand and Limoges in France, and of Salamanca in Spain, as well as from the "BCB colon" cohort from Montpellier in France (ICM Biobank (Biobank number BB-0033-00059)) and from the KPLEX I and KPLEX II (ClinicalTrials. gov Identifier: NCT02784639) studies previously conducted by the team. Plasma samples from individuals with breast cancer were obtained from the "IDEA Sein" study (ClinicalTrials. gov Identifier: NCT03255486) provided by the ICM Biobank (Biobank number BB-0033-00059) of Montpellier. Hepatocellular carcinoma plasma samples were provided by Eric Assenat and Marie Dupuy from Montpellier, and three pancreatic adenocarcinomas, one lymphoma, and one ovarian cancer samples were obtained from the hospital center of Salamanca in Spain.
Hemolysis did not affect cfDNA, but was an indicator of mishandling or improper storage of sample tubes which had led to blood cell deterioration. DNA Quantification by Q-PCR and Copy Number Calculation: Q-PCR amplifications were carried out at least in triplicate, in a 25 µL reaction volume on a CFX96 touch Real-Time PCR detection system (Bio-Rad) instrument, using the CFX manager software. Each PCR reaction mixture was composed of 12. 5 µL of SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, Marnes-la-Coquette, France), 2. 5 µL of each amplification primer (3 pmol µL -1 ), 2. 5 µL of Nuclease free water (Qiagen), and 5 µL of DNA extract. Thermal cycling was consisted of three repeated steps: a 3 min Hot-start polymerase activation-denaturation step at 95 °C, followed by 40 repeated cycles at 90 °C for 10 s, then at 60 °C for 30 s.
Statistical Analysis: Murine, cell culture supernatant, and exploratory cohort samples: Data were expressed as mean ± SD. The nonparametric Wilcoxon-Mann-Whitney test was used for the comparison of medians for murine, cell culture supernatant, and exploratory cohort data. ROC curve was presented and the AUC was calculated. A probability of less than 0. 05 was considered to be statistically significant; *p ≤ 0. 05, **p ≤ 0. 01, ***p ≤ 0. 001, ****p ≤ 0. 0001. Human plasma samples: The analysis was conducted using R 3. 6. 0 and the stats package. Colorectal cancer cohort: Patients with missing values on parameters used for tree regression were removed. This concerned 131 patients (129 CRC patients and 2 healthy patients). A two-sample nonparametric one-sided Mann-Whitney test with normal approximation (n > 50) was adopted for continuous variables comparisons among the populations studied, rather than a two-sample proportion test with continuity correction for binary variables comparison (Table  S2 , Supporting Information).
The difference between each step estimation and the targeted value were then calculated, with the 2. 5th and 97. 5th distance percentiles being obtained and added to the targeted punctual value. Second, the same method of 2000 resampling was applied to 155 CRC and 155 healthy patients among the previously established age categories. Third, the same method was applied to patients from the breast cancer cohort, with 2000 resampling of 236 patients. Machine learning: The cancer prediction algorithm was conducted by applying recursive partitioning for regression trees  [45]  using the rpart package. First, a global view of all the measured parameters was generated, in order to appreciate the tendency of each one, by ascending hierarchical clustering. The resulting heatmap was built using the ComplexHeatmap package (Figure  2c ), clustering patients (by columns), and parameters (by rows), based on the Euclidian distance and using the ward.
Acknowledgements The authors thank  Philippe Blache ,  Corinne Prevostel ,  Cormac Mc Carthy , and  Sandrine Bonizec. The authors also thank  Laura Muinelo Romay  ( Santiago de Compostela ),  Phil Crosbie , and  Caroline Dive  ( University Hospital of South Manchester ) for helpful discussions on pre-analytical conditions and patient cohorts. The authors thank  Dr. Joe Bryant  from the  Institute of Human Virology, Baltimore, USA. This work was funded by the  "SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553" and MSDAvenir through the MSD-Mitest grant.
OGTTs (c) were performed following an overnight (16 h) fast, before VSG (Pre Op) and 91 days after VSG (n = 10) or sham operation (n = 9) (c-e). IPGTTs (g) were performed 12 days after caecal microbiota transfer (CMT) from VSG-treated GK rats (n = 5) or sham-operated GK rats (controls; n = 3-4) (g-i). Data are mean ± SEM. The Kruskal-Wallis test was used to analyse glucose tolerance data. The non-parametric Mann-Whitney U test was used to analyse the other variables. *p < 0. 05, **p < 0. 01, ***p < 0. 001 vs sham-operated GK rats (a-e) or GK rats inoculated with microbiota from sham-operated GK rats (g-i). CumG, cumulative glucose levels during the glucose tolerance test; ΔG, cumulative glucose levels above baseline VSG and dietary restriction are equally efficient to reduce hyperglycaemia in GK rats To evaluate the performance of VSG in improving glucose homeostasis in the GK strain, we carried out a pair-feeding experiment in GK rats 30 days after the sham operation.
We used these data to assess sequencing data consistency in the caecum and colon within individual animals and subsequently caecum-colon, caecum-caecum and colon-colon between animals of the same group. Mean and SD of the 100 most common 16S rDNA sequences illustrate the relatively large variability of data from the most frequent motifs in caecum and colon (ESM Figs 4a, b and 5a, b). Data from caecum and colon from the same animal were generally more similar (up to 99%) than any combination from different animals (ESM Figs  4c  and 5c ). For samples with paired caecum-colon microbiome sequencing datasets, comparisons of the 10 most prevalent rDNA motifs across all samples illustrate that within-individual similarity is higher than betweenindividual similarity (ESM Fig. 6 ). We carried out exact match searches for a 55mer sequence beginning with the sequence of the V13A reverse primer and selected 232 rDNA motifs present at an occurrence of >0.
005% in at least 50% of individuals, regardless of the sample's intestinal origin (ESM Table  2 ). Despite the large The number of biological replicates for each treatment group and intestinal site is given in parentheses The total amount of sequencing data obtained (mean length, in Gb) is given. Similar numbers of total and unique rDNA motifs were found in the four groups The proportion of highly frequent (>5%), moderately frequent (0. 5-5%) and rare (<0. 5%) motifs was calculated. The proportion of the gut microbiome that these categories account for is given in parentheses numbers of unique rDNA motifs identified in the dataset, the 52 most prevalent rDNA motifs accounted for 80% of the total data. Given the depth of metagenome sequencing and extensive within-individual similarities in rDNA motifs in caecum and colon, caecal sequence data were used in analyses of 16S rDNA motif enrichment.
Two motifs were present only in the gut microbiome of sham (V13A9832) or VSG (V13A7019) rats. The most frequent rDNA motif (V13A7759), accounting for up to 54. 5% of the microbiome of individual samples, was significantly over-represented in gastrectomised GK rats (22. 71%) than in controls (9. 07%, p = 0. 046) (Table  2 , Fig. 4b ). Sequence homology searches in microbial genome databases identified only 9 rDNA motifs showing maximum sequence similarities (100%) with the DNA sequence of a single (V13A7759, V13A9569, V13A12536, V13A6709, V13A399, V13A234, V13A622) or several (V13A1861, V13A12857) bacteria (Table  2 ). The most predominant motif in GK rats (V13A7759) is homologous to scaffold assemblies of P. copri (Table  2 ). Its significant enrichment in gastrectomised GK rats and after inoculation of microbiota from gastrectomised GK rats was confirmed by quantitative PCR of caecum DNA (Fig.
copri permissive Fig. 6  Effects of intestinal P. copri enrichment on plasma bile acid concentrations in the GK rat. Mass spectrometry methods were used to determine the plasma concentration of bile acids in GK rats following VSG (n = 8) or sham operation (n = 5) (a-d), in GK rats following caecal microbiota transfer (CMT) from VSG-treated GK rats (n = 5) or sham-operated GK rats (n = 4) (eh), in GK rats treated with a combination of kanamycin and vancomycin antibiotics (n = 5) or in the absence of antibiotics in controls (n = 4) (i-l), or in GK rats inoculated with P. copri (n = 6) or heat-inactivated P. copri (n = 3) (m-p). Non-parametric Mann-Whitney U tests were used for statistical analysis. Data are mean ± SEM. *p < 0. 05, **p < 0. 01, † p = 0. 05, ‡ p = 0. 06, § p = 0. 07 vs the relevant control.
Search Strategy Three databases (PubMed, CAB Abstracts, and Scopus) were interrogated using the search terms defined below. The final complete data search in all 3 databases was performed on September 21, 2018. An additional search was performed after the full-text screening and before data analysis on July 15, 2019. This additional search was performed only in PubMed because Scopus and CAB Abstracts do not allow selection for specific publication dates, only per year. The research questions include 4 key aspects: BVDV, risk factors, introduction, and delayed detection. The BVDV search terms included the following: BVD, BVDV, bovine viral diarrh(o)ea, bovine viral diarrh(o) ea, and bovine viral diarrh(o)ea virus. Risk factor search terms included the following: risk factor, purchase, import, trade, market, grazing, nose-to-nose contact, direct contact, over the fence contact, density, contact structure, herd, herd size, seasonal calving, calving pattern, housing system, management, biosecurity, vaccination, artificial insemination, embryo transfer, PI, persistent infection, and persistently infected.
Data Collection Data were extracted from all selected studies using an Excel (Microsoft Corp. , Redmond, WA) form that was prepared in advance. Data were extracted by one researcher (AvR) and checked by the other researcher (MM). A pilot test of the Excel form was conducted by these 2 researchers working together on 3 selected papers to increase uniformity in extracting the data. For each selected study, detailed data were extracted regarding study type, location, size of the study population, diagnostic tests used, risk factors studied in univariable and multivariable analysis, the effect size (odds ratio, OR; relative risk), confidence intervals, and the statistical analysis that was performed.
Publication bias could not be properly assessed due to the low number of studies included in our meta-analyses (n < 10;  Higgins et al. , 2019). Funnel plots were checked for asymmetry, with some indication of publication bias, but these plots are not reported as it was not possible to determine whether this was by chance or real asymmetry due to the low number of studies. Meta-analyses were performed using R statistical software (R Core Team, 2019) and the metafor package  (Viechtbauer, 2010).
, 2007; Xu et al. , 2007 ; Figure  4A ). Additional functions for RUN domains have been described, for example for the RUN domain present in NESCA, which blocks TRAF6-mediated polyubiquitination of the NF-kappa-B essential modulator and consequently induces NF-kB activation. This is just one of the ways in which RUN proteins can act in signal transduction and the coordination of membrane traffic with actin dynamics upon external stimulation  (Yoshida et al. , 2011). As well as promoting endosomal fusion through their binding to Rab or Rap GTPases  (Callebaut et al. , 2001;  FIGURE 2 | Evolution of RUN and FYVE domain or rufy genes among living organisms. Diagram illustrating the evolution of the rufy genes. Species representative of various taxonomic groups are listed, data were extracted from the Differential Expression Atlas Genes database (EMBL-EBI).
Gray numbers below alignment means the amino acids position after alignment. Black numbers surrounding the alignments represent the start (left) and end (right) positions of the domains in the peptide sequence of each protein. Alignment were realized with Seaviewer analyzer software  (Gouy et al. , 2010). Accession numbers for protein are following: human Rpip8 (NP_001138297. 1), mouse Rpip8 (NP_058039. 1), human Eea1 (NP_003557. 3), mouse Eea1 (NP_001001932. 1), human RUFY1 (NP_079434. 3), mouse RUFY1 (NP_766145. 1), human RUFY2 (NP_060457. 4), mouse RUFY2 (NP_081701. 2), human RUFY3 (NP_055776. 1), mouse RUFY3 (NP_081806. 1) human RUFY3XL (NP_001032519. 1), mouse RUFY3XL (NP_001276703. 1), human RUFY4 (NP_940885. 2), mouse RUFY4 (NP_001164112. 1), human FYCO1 (NP_078789. 2), mouse FYCO1 (NP_001103723.
These occur in addition to the prevention of CASP3-dependent apoptosis in dorsal root ganglions. RUFY3 appears therefore to be key for nervous system development, remodeling and function, explaining the embryonic lethality displayed upon rufy3 genetic inactivation in mouse  (Hertz et al. , 2019). With the current advance in genomics and single cell RNA sequencing, specific gene expression patterns can be revised and more accurately defined. Analysis of several genomic databases (BioGPS, NCBI, Human Atlas Protein, ImmGen, Ensembl) reveal that, in addition to neurons, RUFY3 expression can be detected in other tissues and cell types. Moreover, the rufy3 gene appears to have many transcriptional variants, leading to the expression of different protein isoforms. Two of these isoforms display a C-terminal region extended by 150 amino acids, compared to the previously identified neuronal isoform of RUFY3.
Importantly, these previously uncharacterized longer isoforms (RUFY3XL) possess the same RUN domain and a putative FYVE domain in their C-terminus (Figure  1A ), indicating that RUFY3 is a legitimate member of the RUFY family. In contrast to classic FYVE zinc fingers, genomic databases reveal this putative FYVE domain appears to lack the tandem histidine residue cluster that defines affinity for PtdIns(3)P (Figure  3B ). Interestingly, the SH3 binding site embedded in the RUFY1 and RUFY2 FYVE domains is also present in RUFY3XL, suggesting a potential signal transduction activity for this uncharacterized isoform. The translation of rufy3xl mRNA into a functional protein and its capacity to bind PtdIns(3)P remain to be demonstrated. If true, a role for RUFY3 in the coordination of endosome dynamics or organelle transport could be hypothesized.
RUFY4 RUFY4 is a 64 kDa that is atypical among the RUFY family members, since it bears several non-conserved residues in its RUN domain and it lacks the tandem histidine cluster and the SH3 binding domain normally present in the FYVE domain (Figures  1A, 3A, B ). RUFY4 was shown to interact with PtdIns(3)P enriched membranes  (Terawaki et al. , 2015 (Terawaki et al. , , 2016)). Interestingly, EMBL-EBI and SMART genomic databases show that rufy4 is present only in mammals, suggesting that rufy4 is the most recently evolved gene in the RUFY family (Figure  2 ). RUFY4 levels are extremely low in most cells and tissues with the exception of lungs and lymphoid organs. RUFY4 was found to be strongly induced in vitro in dendritic cells differentiated from bone marrow progenitors in presence of GM-CSF and IL-4.
FYCO1 FYCO1 is a 150 kDa protein bearing a RUN and a FYVE domains. In several databases, fyco1 was misidentified as rufy3, although these two genes are present on completely distinct chromosomes, in human chromosome 3 and 4, respectively. At the sequence level, although it is larger, FYCO1 appears to be a RUFY4 ortholog gene (Figures  3A, B ), suggesting that FYCO1 belongs to the RUFY family. We therefore propose that it could be annotated as RUFY5 to fit the family nomenclature. Separating its N-terminal RUN domain from the FYVE zinc finger, FYCO1 has several CC domains, as well as a LC3/ATG8 Interacting Region (LIR) and a Golgi Dynamic (GOLD) domain in its C-terminus (Figure  1A ). FYCO1 preferentially interacts with MAP1LC3A/B of the Atg8-familly proteins through its LIR  (Olsvik et al.
FUNDING The PP laboratory is "Equipe de la Fondation de la Recherche Médicale" (FRM) sponsored by the grant DEQ20180339212. The project was supported by grants from l'Agence Nationale de la Recherche (ANR) «DCBIOL Labex ANR-11-LABEX-0043», «INFORM Labex ANR-11-LABEX-0054» funded by the "Investissements d'Avenir" French government program. The research was also supported by the Ilídio Pinho foundation and FCT -Fundaçȃo para a Ciência e a Tecnologia -and Programa Operacional Competitividade e Internacionalizaçȃo -Compete2020 (FEDER) -references POCI-01-0145-FEDER-016768 and POCI-01-0145-FEDER-030882.
Gray numbers below alignment means the amino acids position after alignment. Black numbers surrounding the alignments represent the start (left) and end (right) positions of the domains in the peptide sequence of each protein. Alignment were realized with Seaviewer analyzer software (Gouy et al. , 2010). Accession numbers for protein are following: human Rpip8 (NP_001138297. 1), mouse Rpip8 (NP_058039. 1), human Eea1 (NP_003557. 3), mouse Eea1 (NP_001001932. 1), human RUFY1 (NP_079434. 3), mouse RUFY1 (NP_766145. 1), human RUFY2 (NP_060457. 4), mouse RUFY2 (NP_081701. 2), human RUFY3 (NP_055776. 1), mouse RUFY3 (NP_081806. 1) human RUFY3XL (NP_001032519. 1), mouse RUFY3XL (NP_001276703. 1), human RUFY4 (NP_940885. 2), mouse RUFY4 (NP_001164112. 1), human FYCO1 (NP_078789. 2), mouse FYCO1 (NP_001103723.
Strengths and limitations of this study ► This study represents the first multicentre study describing outcomes in patients suffering from both sleep apnoea and recurrent vasovagal syncope. ► A large range of variables describing sympatoexcitation, sleep apnoea and quality-of-life will be monitored over a minimum follow-up period of 12 months. ► Potential changes in syncope rates associated with sleep apnoea syndrome treatment will be quantified for the first time. ► As with all observational studies, care must be taken when inferring causality. ► The resulting dataset will provide a first evidence base required for the design of subsequent confirmatory studies. Open access positions and during sleep (often then associated with awakening accompanied by panic or a need to micturate/ defecate).