dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison in Computation Time In a second experiment, we studied the computation time depending on the number of processes. To do so, we fixed a depth limit for each mesh, and ran our software by increasing the number of processes from 1 to 32. Using the same depth limit for each run implies doing exactly the same subdivision whatever the number of processes, and thus all the final 3-dmaps, and the corresponding 3-maps, are equal. For 1 process, we use the non distributed version of combinatorial maps provided in CGAL  [27]. The depth limits were respectively equal to 5, 4, 5, 5, 5 and 4 for the different meshes Armadillo, Blade, Bunny, Cow, Elephant and Iphigenia. We chose these limits in order to respect the memory limit of 3 gigabytes per process allowed by the IN2P3 computing center. We can see in Figure  11  the overall computation time of our subdivision method, depending on the number of processes, for each mesh.
Some of the popular non-commercial tools include myCBR, jColibri, CAT-CBR, CASPAIN, CBR Shell, FreeCBR, and eXiTCBR. Based on a detailed comparative study performed on these different software tools,  19, 21, 22  myCBR and jColibri were initially selected, and jColibri2 (Java framework)  23  was finally adopted in this project to design a CB-DSS for breast cancer management. In the following section, we present the overall framework of the proposed CB-DSS for DESIREE, describe the related functional blocks, and explain the details of the proposed CB-DSS, which include the clinical data integration using case-base knowledge model, the similarity analysis, and the case retrieval algorithm using semantic similarity measure and the patient case representation. Section 3 presents a case study to show the complete operation of the proposed CB-DSS.
Among the four steps, as retrieval serves as the basis for the subsequent steps, implantation of an accurate case retrieval algorithm becomes crucial. In the present work, with jColibri software tool, the retrieval algorithm is defined using "local" and "global" similarity functions. Local similarity functions measure the distance between the simple attributes, whereas the global similarity function applies the results from local similarity measures to compare the compound attributes. In the proposed CB-DSS, a patient case is represented as compound attribute, composed of several simple attributes, including physiological and clinical attributes, such as age, gender, BIRADS, histological type, HER2 receptor, tumour size, etc. Thus, local similarity functions are first applied to compute the distance between simple attributes in the query case against the ones characterizing patient cases in the case base.
To accommodate different data structure, including linear, tree structure, case retrieval nets etc. , different CBR case base interfaces, including lineal case base, cached lineal case base, and ID indexed lineal case base are used. In the present work, patient cases, which are currently simulated data using information from clinical partners as reference are stored using MySQL in a linear structure using lineal case base and the case base connector is implemented to access and retrieve cases from the case base. Hibernate library is internally used to execute the case base connector. To access data from the BCKM for semantic similarity measure computation, ontology connector is used. Internally, OntoBridge library is used to connect to the ontology. The query patient case is stored outside the CB-DSS, within the DESIMS component.
The exchange of patient data between the DESIMS and the CB-DSS is done through the HAPI FHIR server. Using the standard FHIR resources, including Patient, Observation, Condition, BodySite, and Specimen, the FHIR server retrieves and stores the query case from the DESIMS as a patient bundle. The patient bundle is then parsed and decoded by the CB-DSS for further analysis. The attributes present in the patient case are divided into two case components, namely description component (patient description) and solution component (decision made by the BU). Similarity analysis is performed on the description variables to retrieve similar patient case(s) with description and solution variables. For example, in a patient case, the description component would include variables such as patient's age, TNM staging, BIRADS, tumour size etc.
Background: Epidemiological and experimental evidence support a protective effect of dietary polyphenols on chronic diseases, but high quality longitudinal data are needed, including details on categories of polyphenols. Our objective was to investigate the prospective association between total and individual classes and subclasses of dietary polyphenols and the risk of major cardiovascular disease in the NutriNet-Santé cohort. Methods: A total of 84,158 participants, who completed at least three 24 h dietary records, were included between May 2009 and June 2017. Individual polyphenols intakes were obtained by matching food consumption data from the 24 h dietary records with the Phenol-Explorer polyphenol composition database. Multivariable Cox proportional hazards models were used to characterize the associations between dietary polyphenols and the incidence of cardiovascular diseases, comparing tertile T3 vs.
The Phenol-Explorer database includes food composition data on 502 different polyphenols from 452 different foods  [16]. This database has been used in a few longitudinal studies to assess the association between polyphenols intakes and CVDs or mortality risk  [17] [18] [19] [20] [21]. However, currently available results are somewhat inconsistent with respect to the role of specific classes and subclasses of polyphenols in CVDs. Therefore, the aim of the present prospective study was to assess the association between total polyphenol intakes, as well as classes and subclasses of polyphenols, and the risk of incident CVDs, coronary heart diseases (CHD), and cerebrovascular diseases (CD) in the French NutriNet-Santé cohort.
Study Population Participants were volunteers from the NutriNet-Santé study, a prospective observational cohort study launched in May 2009 to evaluate the determinants of eating behaviours, and the relationships between nutrition and chronic disease risk. The NutriNet-Santé study's objectives and methods have been extensively described in detail elsewhere  [22]. Electronic informed consent was obtained from all participants. All procedures were approved by the International Research Board of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the French National Information and Citizen Freedom Commission "CNIL" (no. 908450 and 909216). NutriNet-Santé is registered at clinicaltrials. gov as NCT03335644.
Cases Ascertainment Participants self-declared health events through the yearly health status questionnaire, through a specific check-up questionnaire for health events or at any time through a dedicated interface on the study website. Following this declaration, participants were invited to send their medical records. Then, medical data were reviewed for the validation of major health events. Vital status and causes of death were identified via the national death registry (CepiDC Inserm). The present study focused on all first incident stroke, myocardial infarctions, acute coronary syndromes, and angioplasty diagnosed between the inclusion and June 2017.
Dietary Data and Estimation of Dietary Polyphenols Intakes At baseline, and every sixth-months, participants were invited to complete three non-consecutive validated  [27] [28] [29]  web-based 24-h dietary records, randomly distributed between week and weekend days to account for intra-individual variability. Participants reported all foods and beverages consumed at each meal or any other eating occasion. Portion sizes were assessed using photographs directly included in the computerised interface, from a validated picture booklet  [30] , if the exact quantity consumed in grams or volume was unknown by the participant. Intakes of nutrients and foods were estimated using a published food composition database  [31]. We excluded energy under-reporting participants using the method proposed by Black  [32].
The Phenol-Explorer database was used to estimate intake of total polyphenols, and the main classes and subclasses of polyphenols (www. phenol-explorer. eu) [16]. The total polyphenol content for a given food was computed by summing all individual polyphenols estimated as determined by chromatography, except for proanthocyanidins, for which content data obtained by normal phase HPLC were used  [33] , and for total intake of polyphenols quantified by the Folin Ciocalteu assay  [16] , or by summing all individual polyphenols. The effect of food cooking and processing was also accounted for by applying polyphenol-specific retention factors from Phenol-Explorer  [34]  to foods when needed to improve the reliability of the intakes estimation. This was done by creating a table containing polyphenol contents for each food item, and then multiplying the average retention factor of each cooked item with the quantity of each polyphenol (polyphenol classes/subclasses or individual polyphenols).
We controlled for age (time-scale); sex; number of dietary records; BMI (Body Mass Index) (kg/m 2 ); physical activity (missing/low/moderate/low), computed following IPAQ (International Physical Activity Questionnaire) recommendations  [26] ; smoking status (non/former/current smokers); educational level (<high-school degree/≥high-school degree); family history of CVD (yes/no); energy intake without alcohol (kcal/day); alcohol intake (g/day); and season of completion of baseline 24-h dietary records (spring/summer or fall/winter). In sensitivity analyses, we also tested the additional adjustment for baseline hypertension, type 2 diabetes, dyslipidaemia, medical treatment for these conditions, folates, and fibers. Non-linear associations between intakes of either total polyphenol or each subclass of polyphenol and cardiovascular disease risk were tested using restricted cubic spline (RCS) functions using the SAS ® macro written by Desquilbet and Mariotti  [35].
Flavanols have only been shown to be inversely associated with the risk of CVD in a single study  [40] , since this category has often been grouped with other flavonoids  [21, [41] [42] [43] [44] [45]. To our knowledge, the only study that showed a link between the risk of CVD and proanthocyanidins has focused on myocardial infarction mortality, using a US polyphenol composition table  [44]. In the present study, we have also observed inverse associations for stilbenes, hydroxybenzoic acids, and total intake of polyphenols with the risk of CVD, although RCS procedure revealed a non-linear shape. Few other studies have been able to study these relationships in observational cohorts, since composition data for these classes of polyphenols have only been available recently in the Phenol-Explorer database  [46].
Stilbenes and hydroxybenzoic acids were inversely associated with CVD risk, mostly in one study, a cohort of participants over 55 years of age of the PREDIMED study  [17, 18]. Regarding the total intake of polyphenols, only the PREDIMED study evaluated the total intake of polyphenols and the prospective risk of CVD, and showed, concordantly with our study, an inverse association between total polyphenol intake, assessed as the sum of individual polyphenols via the Phenol-Explorer database, and the risk of CVD and all-cause mortality  [17, 18]. Intriguingly, in contrast to the PREDIMED study, which used the sum of individual polyphenols as an indicator, we only found a significant result when estimating total polyphenol intakes on the basis of the Folin-Ciocalteu method. This spectrophotometric method is the most widely used experimental method to quantify total polyphenols, but is less specific since it includes antioxidant compounds other than polyphenols and better reflects the total antioxidant activity in foods  [47].
A diet rich in fruits and vegetables and, to a lesser extent, tea (the main contributor to flavonols intake), and probably/controversially a diet containing red wine (the main contributor of stilbenes) could prevent CD-which is in line with findings concerning a protective role of the Mediterranean diet  [65]. Strengths of our study include its prospective design and large sample size, detailed data on risk factors and confounders related to CVDs risk, the precise evaluation of polyphenol intakes using validated 24-h dietary records coupled with the most complete and detailed polyphenol database, Phenol-Explorer database, as well as the validation of cardiovascular events by a medical committee. Generalization of our findings to populations other than French adults may be limited due to the mode of consumption and the different diets of the countries.
Acknowledgments: The authors thank all the volunteers of the NutriNet-Santé cohort. We extend special thanks to  Younes Esseddik ,  Thi Duong Van ,  Frédéric Coffinieres ,  Mac Rakotondrazafy ,  Régis Gatibelza  and  Paul Flanzy  (computer scientists); and  Nathalie Arnault ,  Véronique Gourlet ,  Fabien Szabo ,  Julien Allegre  and  Laurent Bourhis  (data-manager/biostatisticians) for their technical contribution to the  NutriNet-Santé study. The authors have no conflict of interest to disclose.
Funding: This research was funded by PhD grants from the  Région Ile-de-France  (public funding: CORDDIM and DIM Astrea),  Université Paris 13 , the  French National Research Agency (Agence Nationale de la Recherche)  in the context of the 2013  Programme de Recherche Systèmes Alimentaires Durables  ( ANR-13-ALID-0001 ). The  NutriNet-Santé study  was supported by the following public institutions:  Ministère de la Santé, Institut de Veille Sanitaire (InVS) ,  Institut National de la Prévention et de l'Education pour la Santé (INPES) ,  Région Ile-de-France (CORDDIM) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Université Paris 13. The funders had no role in the design, implementation, analysis, or writing of this article.
Coincident CFP1 and Pol II binding at accessible, expressed TSSs We next examined more closely the relationship between CFP1, Pol II and expression level using public RNA-seq data sets. We limited our analysis to accessible TSSs (defined by overlap with a 1x-normalised, input-subtracted ATAC-seq signal > 10); this accessibility makes these strong candidate TSSs for the observed expression (Fig. 3a, b ). Accessible TSSs of expressed genes in both cell types were occupied with CFP1, regardless of the presence of a CGI (Fig. 3c-f  ). A few accessible TSSs appeared non-expressed in the available public erythroid data set (GSE74246) while displaying some CFP1 binding in our independent data set (bottom of Fig. 3c, d ). We considered it most likely that these are small artefacts due to variability between RNA-seq data sets.
ChIP-Seq analysis Reads were aligned using the hisat2 aligner version 2. 0. 3 with the --no-spliced-alignment option, but otherwise default parameters. Reads were aligned to a splicing-unaware index of the human GRCh38 genome. ChIP peaks were identified using the ENCODE tool Phantompeakqualtools  [67] , an R script that called peaks using the SPP library  [68]   thresholded by FDR < 0. 01, except for H3K27me3 in EBV cells, in which case peaks were thresholded by FDR < 0. 1. Narrow peaks were used for analysis of all ChIPs except the H3K27me3 mark, in which case region peaks were analysed. As a confirmation in specific cases, ChIP peaks were secondarily identified using the callpeak function of MACS2, version 2. 1. 1. 20,160,309, with narrow peaks called using a q-value threshold at 0. 01, and broad peaks with the --broad-cutoff 0.
1 option. Peak colocalisation analysis was carried out using the ChIPpeakAnno package in R, with maximum gap of 1 kb. General genome arithmetic, including peak intersection with TSSs, was carried out using bedtools  [69]. ChIP heatmap plots were generated using the bamCompare, computeMatrix and plotHeatmap functions of deepTools version 2. 5. 4  [70]. Read depths were compared between ChIP-seq and input data sets using bamCompare with flags --scaleFactors-Method readCount --ratio subtract --binSize 50.
Identification of a set of putative enhancers Putative genomic enhancers were identified by a stepwise procedure using Assay for Transposase-Accessible Chromatin (ATAC), Pol II and H3K4me1 signals. First, normalised genome-wide signals for each ChIP were constructed by normalising each pileup to 1x genome coverage, and subtracting similarly normalised input data using the deepTools bamCompare program as described above. Genomic regions > 2 kb from gene TSSs exhibiting ATAC-seq signal value > 10 that also exhibited a Pol II signal value > 5 were accepted as putative enhancers and confirmed by plotting heatmaps of H3K4me1 and H3K27ac around these loci.
RNA-seq analysis and identification of housekeeping and cell type-specific genes RNA-seq data from NCBI GEO (accession GSE74246, three erythroblast samples, and GSE88627, four GM12878 EBV-lymphoblast samples) were aligned to a special purpose, splice-aware version of the GRCh38 human genome downloaded from the hisat2 web page (https ://ccb. jhu. edu/softw are/hisat 2/index. shtml ). The hisat2 aligner, version 2. 0. 5, was used with default parameters. Analysis of expression was performed in R: read counts for each locus were obtained in unpaired mode using the featureCounts function from the Rsubread package, and reads per kilobase of transcript per million mapped reads (RPKM) values were calculated using the rpkm function from the edgeR package. Housekeeping genes and candidate tissue-specific genes were identified from processed RNA-seq expression data (16 tissues) warehoused by the EBI Illumina body map resource (NCBI GEO accession GSE30611).
Analysis of TrxG subunit occupancy in a high-confidence subset of peak regions To analyse which subunits of TrxG-related methyltransferase complexes colocalised, we compared six ChIP data sets in erythroid cells: CFP1 and SET1A, subunits of SET1A/B complexes; HCF1, a subunit of SET1A/B and MLL1/2 complexes; Menin, a subunit of MLL1/2 complexes; UTX, a subunit of MLL3/4 complexes; and RBBP5, a core subunit of all the complexes. A high-confidence set of peaks in each data set was defined as its most highly enriched tenth percentile, as detected by SPP. Bedtools was used to merge these regions into a non-redundant set of high-confidence peak regions. Subsequently peaks of all strengths detected by SPP were analysed for the presence or absence in these high-confidence regions.
ERY, top; and EBV, bottom. c Venn diagrams showing CFP1 peaks within 1-kb of TSSs are strongly associated with H3K4me3 histone mark and poorly associated with H3K27me3 repressive histone mark. Cell types are ERY (upper) and EBV (lower). Public data sets: * NCBI GEO GSE36985, ** NCBI GEO GSE50893. d Mutual exclusivity of CFP1 and the H3K27me3 mark in ERY (upper) and EBV (lower) cell types van de Lagemaat et al. Epigenetics & Chromatin (2018) 11:59 Lagemaat et al. Epigenetics & Chromatin (2018) 11:59.
Acknowledgements We are very grateful to  Zhanyun Tang  and  Bob Roeder  for the CFP1 antibody. We would like to thank our colleagues  Alan Archibald ,  Philipp Voigt  and  Duncan Sproul  for critically reading the manuscript. We also thank  Jim Hughes  for curating data sets obtained in Oxford. High-throughput sequencing was provided by the  Oxford Genomics Centre  (http://www. well. ox. ac. uk/ogc/ home/) and  Edinburgh Genomics  (http://genom ics. ed. ac. uk).
Peaks were detected by MACS2. Venn diagrams show that CFP1 peaks within 1-kb of TSSs are strongly associated with H3K4me3 histone mark and poorly associated with H3K27me3 repressive histone mark. Cell types are (A) ERY and (B) EBV. Public data sets: * NCBI GEO GSE36985, ** NCBI GEO GSE50893. Fig. S3: UCSC tracks showing CFP1 and other ChIP signals in gene loci in erythroblasts (ERY) and EBV-transformed B-lymphoblasts (EBV). Hg38 coordinates for multiple genes, CpG islands (CGI, green boxes), and putative regulatory regions (blue boxes) are shown. CFP1 signals are shown in dark reds, inputs in grey, histone H3 signals in blues and open chromatin marks in greens. All ChIP pileups are scaled to 1x coverage genome-wide and shown in a range 0-50, except CFP1 (Roeder) is shown with extended range and H3K27me3 graphs scaled by 2x.
When biot-AuNPs are added to the solution, Tb-sAv/biot-AuNP donor-acceptor assemblies are formed, leading to a mixture of free (excess) Tb-sAv donors and Tb-sAv/biot-AuNP assemblies. The overall PL decay can thus be considered to be the sum of the individual decays of these two species, each described by a Kohlrausch decay, with the subscripts D and DA referring to the free donor and the donor-acceptor assemblies, respectively (eq 3). τ τ = [- ̃] + [- ̃] β β I t A t A t ( ) exp ( / ) exp ( / ) D D DA DA D D A (3) For the values of τD and β D , we used the values obtained from the measurement of the pure Tb-sAv donor and keep these fixed throughout our analysis, leaving only A D , A DA , τ̃D A , and β DA to be determined by curve fitting. This was achieved by fitting the model eq 3 to the data using the "lmfit" package  34  in Python by minimizing the sum-over-squares of the residuals with the Levenberg-Marquardt algorithm.
This procedure yielded stable and reproducible measurement of the position of the resonant light-scattering maximum λ max RLS and mitigates problems due to noise on the measured spectrum. For the measurement of the PL decay curves of the Tb-AuNPs, an EI fluorescence plate reader (Edinburgh Instruments, U. K. ) was used. For the multichannel scaler, 4000 detection bins of 2 μs integration time were used. A nitrogen laser (LTB, Berlin, Germany) was used for excitation (337. 1 nm, 20 Hz, 600 flashes). A 494 ± 20 nm bandpass filter was used for analyzing the Tb PL. Multiexponential fits to the data were done with FAST software version 3. 1 (Edinburgh Instruments, U. K. ). Kohlrausch decay analysis was done using the Python programming language and the lmfit package. 34 All the assays were measured in black 96-well microtiter plates with an optimal working volume of 150 μL.
DNA was extracted, according to a modified cetyltrimethylammonium bromide (CTAB) protocol (  8 ), from a culture grown to its exponential phase in King B broth. Sequencing of the 16S rRNA gene showed that this strain shared 100% identity with more than 100 Pseudomonas aeruginosa strains of the GenBank 16S database. A library was created with the TruSeq Nano DNA library preparation kit (Illumina, USA). The shotgun sequencing was performed in one Illumina MiniSeq run at a 2 ϫ 151-bp paired-end read length, with a MiniSeq high-output kit, resulting in a 259ϫ genome coverage. Once quality control was carried out with FastQC version 0. 11. 5 (9), genome assembly was performed with SPAdes genome assembler 3. 10 (10), with settings fixed in "paired-end assembly, careful mode," yielding 131 contigs (Ն200 bp), which were then arranged with BioEdit version 7.
Data availability. This whole-genome shotgun (WGS) project was deposited at DDBJ/EMBL/GenBank under the accession number NFRZ00000000. The version described in this paper is the first version, NFRZ01000000. The 131 contigs have been deposited under the accession numbers NFRZ01000001 through NFRZ01000131. Raw sequencing data sets have been registered in the NCBI Sequence Read Archive database  (19)  under the accession number SRR5513015.
Acknowledgements We to thank staffs at the blood bank unit of  Phramongkutklao Hospital, Bangkok, Thailand  for handling of the buffy coat of healthy donors. We sincerely thank to  Dr Arther E. Brown  for English language proong. Ms Piamsiri Sawaisorn  and  Assoc. Prof. Dr Kulachart Jangpatarapongsa  were funded by the  Royal Golden Jubilee PhD Program  (Grant No. PHD 0156/ 2556 ) from the  Thailand Research Fund.