dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample strategy and sample size calculation From a population of 1,868 PWS in Côte d'Or recorded in the French medico-administrative database PMSI (Programme de Médicalisation des Système d'Information) of the three hospitals of Cote d'Or, PWS were selected using a random stratified method according to age, sex and hospital, with a probability proportional to the total number of persons in each stratum. Assuming an average standard deviation of 8  [14]  and a precision of 1. 5, 112 subjects were required. Assuming that 20% of subjects could not be reached and a response rate of 50%, 300 subjects were required. Eligible patients were informed by letter and contacted by phone within 2 weeks for out-patients and by the medical staff for in-patients. Patients were included if they were over 18 years of age with a diagnosis of schizophrenia (Diagnosis: F20-F29 PMSI) according to the International Classification of Diseases 10 th Revision: ICD-10  [16]  and were psychically stable.
Data analysis Categorical variables were presented as percentages and continuous variables as the means with standard deviations (SD). Associations between the DMFT index or its components (dependent variables) and socio-demographic, lifestyle, clinical, treatment and oral health assessments were first analysed using analysis of variance and Pearson's correlation tests. Dental caries is an irreversible condition. Assessment of dental status by DMFT index to evaluate the state of oral health based on the presence of decayed teeth (D) which is expressed in a need for care and missing teeth (M) which justifies prosthetic rehabilitating. The presence of filled teeth (F) reflected the state of care. Tooth decay is directly dependent on dental hygiene (OHI-S). Poor oral health can may also affect the quality of life, OHrQOL and oral health is inseparable from general health  [24].
Study sample A study population of 302 persons was selected from the administrative databases. Of the randomized subjects, 22% (n=67) could not be reached (14 had died, 52 had moved but were still registered at their previous address, one did not have schizophrenia). Among those contacted (n=235), 113 refused to participate in the study, and 13 were unable to cooperate (Figure  1 ). The acceptance rate was thus 46. 4% (n=109 subjects). Table  1  shows the sociodemographic, lifestyle, clinical, treatment and oral health assessments of the sample.
Strengths and limitations To our knowledge, this is the first study to consider in-and out-patients with schizophrenia in France. However, regardless the sampling procedure used to obtain samples, cohorts are ultimately composed of volunteers who may differ from the target population and our results in Côte d'Or region cannot be generalized to all of France. This also highlighted the difficulties to include PWS in research protocols. Secondly, some limitations in the use of the PMSI database to identify PWS can be pointed out. Diagnostic codes F20-F29 of PMSI data bases are not accurate enough. Indeed they take into account heterogeneous schizophrenia symptoms and can induce an under-estimation of the schizophrenia population. Conversely, considering that schizophrenic symptoms might not be subsequently confirmed (transient schizophrenic disorder) could induce a risk of over-estimation.
In order to be more exhaustive, we should take in account chronic disease status, drugs and hospitalisations conditions (mean duration, number of hospitalizations. ) The REDSIAM (Réseau données Sniiram) network suggested different combinations of selected criteria in order to identify adult schizophrenia in the SNIIRAM (Système national d'information inter-regimes de l'assurance maladie) data base  [42]. Our study could introduce a national survey with the REDSIAM network. Thirdly, in this study, indicators for oral function (deficiencies in chewing, swallowing, speaking) were not considered even though they are important oral problems. Alcohol and drug consummation habits were not assessed either and could be explored in further studies.
Acknowledgements The authors thank the staff and patients of all the hospitals ( La Chartreuse Psychiatric Centre, University Hospital of Dijon and Semur en Auxois Hospitals)  who participated in the survey. We thank  Nicolas Abello ,  Sandrine Lombion  and  Valérie Bertaud  for their assistance in writing the manuscript and  Philip Bastable  for his help with English language correction. We are grateful to the  Regional Health Agency of Burgundy (Agence Régionale de Santé de Bourgogne)  and the  University Hospital of Dijon  for the financial support given to the study. We are awaiting the results of a cluster randomized controlled trial to assess the effectiveness of a therapeutic educational programme in oral health for PWS (www. ClinicalTrials. govunder number  NCT02167724 ) [43] funded by the  French Ministry of Health.
Les méthodes in silico présentent aussi leurs limites. Elles relèvent de l'interprétation de bases de données ou du criblage de molécules à potentiel thérapeutique. Elles apportent un gain de temps et de moyen importants. Néanmoins, la complexité des interactions moléculaires qui existent entre une substance testée et son environnement dans l'organisme entier reste encore difficile à modéliser. Il est donc indispensable de combiner différentes approches expérimentales afin de générer des connaissances qui soient valides en recherche fondamentale et appliquée, ce qui nécessite l'utilisation de modèles intégrés. L'usage des animaux dans la recherche biomédicale, qu'elle soit humaine ou vétérinaire, fait l'objet de débats au sein de la société et alimente de nombreuses réflexions dans le monde de la recherche.
Map construction The topographic base of the map was derived from the ASTER GDEM V2 of 30 × 30 m spatial resolution. The shaded relief was prepared using 3D Analyst tool of ArcGIS in four layers with azimuths of 20, 60, 150, and 330°and constant Sun angle of 30°. The course of rivers was redrafted from the Landsat 8 imagery, together with roads and summits which were traced from the freely available Soviet Military Topographic Maps and modified according to the real field GPS tracks. Geological boundaries were interpreted from the Landsat 8 scenes p137-138r28-29. These satellite images, free of cloud cover for the year 2015, were downloaded from the USGS web site and processed by image-enhancement techniques using Grass GIS and Python. The principal component analysis and factor loading analysis with emphasis on geological information showed that the band combination (6, 7, 4) had the highest contrast that could be further enhanced by decorrelation stretch.
g. Janoušek et al. , 2014; Jiang et al. , 2017; Kröner et al. , 2010). The names of the geological units and lithotectonic zones generally respect the traditional terminology used by  Markova (1975)  and by the reports of the geological maps at a scale 1:200,000  (Rauzer et al. , 1987; Togtokh, Banzragch, Gunbileg, Amgalanbayar, & Altangerel, 2014; Ufliand, Filippova, Chekhovich, Durante, & Savin, 1966). The geological units were depicted according to the lithostratigraphy and tectonic affiliation to the pre-Mesozoic units, Mesozoic and Cenozoic cover. Plutonic rocks and upper Palaeozoic volcaniclastic complexes are described separately. The lithostratigraphic columns were constructed according to the observed field relationships and the published lithostratigraphic data. The chronological assignment of the geological events was carried out according to the published data cited in the text and completed by unpublished radiometric data of the Czech Geological Survey summarized in the Appendix 1.
Acknowledgments We are thankful to  M. Batsaikhan  for logistic support of expeditions,  T. Ganchuluun  and  A. Shagdarsuren  for technical help in the field and Mongolian drivers for secure driving on hardly accessible roads. The discussions with O. Tumurtogoo over the first version of maps contributed to the understanding of geological relationships. The authors wish to thank  H. Apps ,  O. Pamukçu  and  U. Bold  for a critical review of the manuscript.
Demographic History of Three European A. lyrata ssp. petraea Populations Confirms a Scenario of Range Expansion We analyzed whole-genome sequence data for 46 Arabidopsis lyrata individuals, of which, 22 were collected in a range edge population in Norway (Spiterstulen, SP), and 17 and seven individuals from two core populations in Germany (Plech, PL) and Austria (a scattered sample, AUS; supplementary fig. S1a , Supplementary Material online), respectively. A principal component analysis (PCA) confirmed that our sample was partitioned in three geographically and genetically distinct populations. The first principal component (PC) explained Adaptation and load in A. lyrata. doi:10. 1093/molbev/msaa322 MBE 24. 95% of the variance, separating the Northern site from the two Central European sites (PL and AUS).
Materials and Methods Plant Material, Sequencing, and Data Preparation Genomic sequences of A. l. ssp. petraea populations of 22 individuals originating from Spiterstulen in Norway (SP; 61. 41 N, 8. 25E), 17 individuals originating from Plech in Germany (PL; 49. 65 N, 11. 45E), and a scattered sample of seven individuals from Austria (AUS; 47. 54 N, 15. 58E; 47. 55 N, 15. 59E; 47. 58 N, 16. 9E) were used in the analysis (supplementary fig. S1a , Supplementary Material online). Details on the sequencing methodology are given in supplementary information, Supplementary Material online.
Analysis of Population Structure Genetic diversity and differentiation along the chromosomes were calculated with PopGenome package  (Pfeifer et al. 2014)  in the R environment version 3. 4. 4 (R Core Team 2018). Specifically, we calculated pairwise nucleotide fixation index (F ST ), nucleotide diversity between (d xy ), and within population (p) in 10-kb nonoverlapping windows for each chromosome with functions F_ST. stats, diversity. stats. between and diversity. stats. within, respectively  (Hudson et al. 1992; Wakeley 1996). In order to avoid biased F ST estimates  (Cruickshank and Hahn 2014) , the windows that had F ST above 0. 8, d xy , and p below 0. 001 in at least one population comparison, were removed from the analysis. Tajima's D was calculated with the function neutrality. stats of PopGenome.
The LD for the field-collected SP and PL individuals was calculated along the genome with the default functions of PopLDdecay  (Zhang et al. 2019 ) and the values were plotted in R. PCA of the genomic data was conducted with adegenet package  (Jombart 2008 ) using a data set including only every 300th SNP to reduce computational load. This reduced data set of 233,075 SNPs was also used to calculate SNP-based F IS for each population with Hierfstat (Goudet 2005) package function basic. stats (Alexander et al. 2009; Goudet and Jombart 2015). The F IS value of each gene was estimated based on the median F IS value of its SNPs, for SP and PL. For the admixture analysis  (Alexander et al. 2009 ) bed files were generated with PLINK  (Purcell et al. 2007 ), which were then analyzed for clusters K ¼ 1 till K ¼ 5, with 10 iterations of cross-validation each.
Each model was repeated 50 times and ones with the highest likelihoods used for model selection were based on AIC scores. Confidence intervals were estimated by fitting the supported model to 100 nonparametric bootstrap SFS. We used these models to define effective populations sizes (N e ), divergence times (T), and migration rates (m). To evaluate how the estimated demography influences measures of positive selection, we used the N e , T, and m parameters in combination with recombination rate estimates derived from an A. lyrata linkage map (H€ am€ al€ a et al. 2017) to generate 10,000 10-kb fragments with ms  (Hudson 2002). These data were then used to define neutral expectations for analysis of positive selection. Additionally, we used the program smcþþ  (Terhorst et al. 2017)  to infer the population size and split time in the PL and SP populations.
Estimating the Distribution of Fitness Effects of Fixed and Segregating Variants For analyzing the strength of selection, vcf files were refiltered for each population separately, as described in the section "Data Preparation. " This was done to retain the largest possible number of informative positions in each of the two populations. Sites with data for more than 80% of the individuals were randomly down sampled so that each position had the same number of alleles. Because the SP and PL Takou et al. doi:10. 1093/molbev/msaa322 MBE populations differed in the number of individuals sampled, individuals in the SP population were further randomly down-sampled at each position to give the same number of alleles in both populations. The folded site frequency spectrum was determined for each population.
2016). Using the demographic model inferred from the data (supplementary fig. S2a  and b , Supplementary Material online), we simulated over 4 Â 10 9 independent mutations, assuming best fit DFE estimated for h ¼ 0. 5 (Point Estimate or Gamma distributed) and h ¼ 0 (Gamma distribution). We monitored the mean perindividual load in each generation by computing the weighted sum of s of all segregating and fixed alleles. We performed the simulations under three demographic model: the demography of PL (as in supplementary fig. S2a , Supplementary Material online), the demography of SP (as in supplementary fig. S2b , Supplementary Material online), and a third demographic scenario, which was identical to the scenario in SP, with the exception that the bottleneck was extended for 300,000 years, before the population returned to its ancestral size.
Comparative Analysis of Growth Rate and Biomass Accumulation in a Common Garden Experiment We propagated clonally 10 genotypes from SP and 10 from PL to study growth in a common garden setting. The experiment was initiated in September 2017 and ended August 2018 and took place at the garden of the University of Cologne. Throughout the growing season (March to August), we scored monthly diameter size, in millimeters, as a proxy for vegetative growth. At the end of the growing season, we harvested the above ground material to estimate the dry to fresh weight ratio of the plants as a proxy for the plants' biomass. The phenotypic data are provided in supplementary table S11, Supplementary Material online. Differences between the two populations were tested in R with linear mixed models using the library lme4  (Bates et al.
Identification of S Alleles We genotyped individuals at the self-incompatibility locus (Slocus) with a genotyping pipeline  (Genete et al. 2019 ) using raw Illumina reads from each individual and a database of all available sequences of SRK (the self-incompatibility gene expressed in the pistil) from A. lyrata, A. halleri, and Capsella grandiflora (source: GenBank and unpublished sequences). Briefly, this pipeline uses Bowtie2 to align raw reads against each reference sequence from the database and produces summary statistics with Samtools (v1. 4) allowing to identify alleles at the S-locus (S-alleles). Coverage statistics allow to reliably identify homozygote versus heterozygote individuals at the S-locus. Genotype data was used to compute population genetic statistics using Fstat  (Goudet 1995) : number of alleles per sample, sample allelic richness (a standardized estimate of the number of alleles taking into account differences in sample sizes among populations, computed after the rarefaction method described in El Mousadik and Petit  [1996] ), gene diversity (expected heterozygosity under Hardy-Weinberg hypotheses), and F ST (unbiased estimate of the among population fixation index).
Acknowledgments We thank  M. Nothnagel ,  B. Wieters , and  G. Schmitz  for insightful discussions and comments on the results;  Diego Ortega del Vecchyo  for help with PReFerSim,  M. Nordborg  and  P. Novikova  for PL sequences;  B. Laenen  for help with the DFE data preparation;  V. Kovacova  for help with inference of derived and ancestral alleles;  K. Bell  for lab assistance; students and gardeners at the  University of Cologne  for assisting with the field experiment; and  Cologne Center for Genomics (Now West Germany Center)  for sequencing of SP samples. This project was funded by  ERC  projects No. 648617 Adaptoscope, No. 648321  (Novel) and  Horizon 2020 research and innovation program  (Grant No. ERC-StG 679056 HOTSPOT ), via a grant to L. Y. The authors also thank the  R egion Hauts-de-France , the  Ministere de l'Enseignement Sup erieur et de la Recherche (CPER Climibio) , the  Biocenter Oulu  and the  European Fund for Regional Economic Development  for their financial support.
Data Availability All sequence data are available in either NCBI Short Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra) or in the European Nucleotide Archive (ENA; https://www. ebi. ac. uk/ ena) with accession codes: SAMN06141173-SAMN06141198 (SRA; Mattila et al. 2017), SRP144592 (SRA; H€ am€ al€ a et al. 2018), PRJEB34247 (ENA; Marburger et al. 2019), and PRJEB33206 (ENA; whole-genome sequences generated for this project and the rest of PL sequences).
DNA methylation analysis Methylation profiles for every cell type were analyzed individually in R using the Minfi (23) and ChAMP package (24) following the pipeline according to Marabita el al (25). Briefly, type 1 and type 2 probes were normalized using quantile normalization and BMIQ. Sex of the samples was confirmed using the GetSex function from the Minfi package and the cell type identity was confirmed using the cell type deconvolution method from Minfi based on the Houseman algorithm (26). The following probes were filtered out: i) probes not passing the detection p-value cutoff of 0. 01, ii) probes with known SNPs, and iii) X and Y chromosome probes. Batch effects were identified using principal component analysis (PCA) and corrected using ComBat from the SVA package (27). The loading methylation profiles was performed in this manner for each cohort used in this study.
Differentially methylated positions (DMPs) were determined with linear modeling using the limma package (28) in a model that included age and sex as covariates. The influence of treatment has been investigated using both PCA and covariate regression and potential confounding effects of the treatment status in this cohort have been excluded. Differences were calculated between RRMS and healthy controls, RRMS and SPMS, SPMS and healthy controls. In addition, eBayes was used to find differences in at least one of the comparisons.
Functional annotations Genes associated with DMP probe IDs from the Illumina manifest were uploaded to Ingenuity Pathway Analysis (IPA) database (Qiagen) and core expression analysis was performed to identify affected canonical pathways and functional annotations. Immune tissues, including primary immune cells and cell lines, were used. Right-tailed Fisher's exact test was used to calculate a p-value determining the probability that each biological function assigned to that data set is due to chance alone. Canonical pathways/functional annotations were grouped into clusters by calculating the similarity of pathways/annotations using the relative risk (RR) of each pathway appearing with each pathway based on the genes enriched within the pathway. Only pathways representing a minimum of 5 differentially methylated genes were selected for functional exploration.
Overlap with disease-associated loci and meQTLs To determine if there is an overlap of the top-ranked shared DMPs from omicsNPC with MS-associated genetic variants, we used a set of 234 recently reported MS-associated SNPs (7). The Genomic Association Test (GAT) tool (  37 ) was applied to estimate the significance of the overlap between MS-associated SNPs and omicsNPC DMPs. The analysis was run for bins of 2 kb windows based on the average distance between SNPs and CpGs taken from GeMes (38). For a comparison, the overlap was tested for other diseases matched for the number of SNPs with MS as well as for common control SNPs which were matched in the CpG probe density for the bins run. Furthermore, omicsNPC probes were investigated for potential meQTLs using the Blueprint data (39). OmicsNPC probes were extracted from the full Blueprint dataset comprising monocytes and T cells.
They include APAF1, ASIC2, BAIAP2, CALB2, CDH23, CLDN14, CR1, CX3CR1, GAB1/2, GLI3, GNAO1, GRID2, GRIN1, GRM2, ITPR2/3, JAK2, MAPK10, NTN1/GN1, TGFBR1 and TUBB2A/6 (Table  S6 , GNAO1, JAK2, CALB2 and GLI3 are shown in Fig. 5D ). Indeed, in addition to immune-related processes and functions, the SP group displayed an enrichment of changes in genes implicated in neuronal functions, such as "Axonal Guidance Signaling", "CREB Signaling in Neurons", "eNOS Signaling" and "Synaptic Long Term Potentiation/Depression". To examine this association of SP changes in blood with neurodegenerative functions, we analyzed DNA methylation data from whole blood in an independent cohort (n = 275) (11). Gene Ontology analyses revealed shared (Fig.
Funding This study was supported by grants from the  Swedish Research Council (MJ, FP, JT) , the  Swedish Association for Persons with Neurological Disabilities (MJ) , the  Swedish Brain Foundation (MJ, JT) , the  Stockholm County Council -ALF  project (MJ, FP),  AstraZeneca -AstraZeneca-Science for Life Laboratory collaboration (MJ, FP, JT), StratNeuro (JT) ,  STATEGRA FP7  (JT), the  European Research Council  (FP/2007-2013) /  ERC  Grant Agreement n. 617393; CAUSALPATH (IT, VL) and  Karolinska Institute 's funds (MJ). L. Kular is supported by a fellowship from the  Margaretha af Ugglas Foundation.
Availability of data and materials The Illumina 450K array data from CD4 + T cells, CD8 + T cells, CD14 + monocytes and whole blood are available in the Gene Expression Omnibus (GEO) database under accession number GSEXX (deposition pending), GSEXX (deposition pending), GSE43976 and GSE106648, respectively. The Illumina 450K array data from CD19 + B cells will be made available from the corresponding author upon request.
INTRODUCTION Updating relational databases is a well understood subject. Tuples can be inserted into a table, deleted from a table, or values of some attributes of existing tuples can be modified. Update statements that insert, delete, and modify tuples are part of the SQL Standard. These statements are separate from SQL queries; once a database is updated and is in a consistent state, new queries can be asked about it. Our understanding of updates in the popular graph database model, however, is still very rudimentary. The complexity of the graph model that combines stored data with the graph topology allows for rather complex update operations that can be intertwined with graph queries. Here we present the story of update features in Neo4j  [21] , one of the most popular property graph databases 1.
The query language of Neo4j is called Cypher. It was inspired by the developments of SQL, XPath and SPARQL. After its invention at Neo4j in 2011, it has evolved to support a comprehensive feature set based on extensive graph pattern matching capabilities. Its implementations are not limited to the Neo4j product, and include other products such as SAP HANA Graph  [19] , RedisGraph  [20] , Agens Graph (over PostgreSQL)  [8]  and Memgraph  [4] , as well as open-source projects, such as Cypher for Apache Spark  [1]  and Cypher over Gremlin  [2]. Cypher is used in hundreds of production applications across many industry vertical domains, such as financial services, telecommunications, manufacturing and retail, logistics, government and healthcare. While the design of Cypher was influenced by SQL, the way in which the language operates is rather different from SQL.