dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population and data collection Participants were recruited at the National Cancer Centre Hospital in the Republic of Korea between March 2011 and June 2014. Case samples of individuals who had been histologically confirmed as early GC patients within the preceding 3 months at the hospital's Centre for Gastric Cancer were collected. Early GC was defined as GC confined to the mucosa or submucosa, regardless of the lymph node metastasis status. Control samples were collected from individuals selected via health-screening examinations conducted by the Centre for Cancer Prevention and Detection at the same hospital. In total, 1,308 initial biopsy samples were collected (427 case and 881 control samples). After endoscopy and examination of the stomach, five further biopsy samples of the gastric mucosa were taken from each subject according to the Sydney system, and a biopsy sample in the greater curvature, at least 3 cm away from each tumour, was used for the methylation analysis (Figs.
Technical confirmation was achieved using 25 subjects who were included in both the discovery and validation sets. Demographic, lifestyle and medical history data were collected from the participants via a self-administered questionnaire. The H. pylori infection status was determined by a rapid urease test, a serological test and histological evaluation. For the rapid urease test, one biopsy specimen was taken from the greater curvature of the corpus. For the histological evaluation, four biopsy specimens were collected from the lesser curvature of the corpus and antrum. The H. pylori status was determined via Wright-Giemsa.
Cancer Genetics and Epigenetics 6] [27]  The HM450 array data (IDAT files) were imported and processed using R/Bioconductor (https:// bioconductor. org/). We excluded cross-reactive probes and probes for sites of known single-nucleotide polymorphisms (SNPs) with a minor allele frequency greater than 5%,  28  leaving a total of 452,162 probes for the analysis. The data were further normalized using the FunNorm function in the Bioconductor minfi package. 29, 30 The DNA methylation level b-values were logarithmically transformed to M-values to obtain data with a more normal distribution. Surrogate variable analysis (SVA) was performed on the methylome data to correct for potential batch effects and, especially, cell-type effects. 31 To validate the findings obtained via methylome profiling, we used a pyrosequencing system (PSQ 96MA, Biotage, Uppsala, Sweden), as described previously.
32 e used a linear regression model after adjusting for covariates (smoking, alcohol consumption and folate consumption, as well as H. pylori seropositivity for the casecontrol comparison model) to identify differentially methylated positions (DMPs) using the Bioconductor limma package. 33 We used the Bioconductor DMRcate package with the recommended proximity-based criteria to identify differentially methylated regions (DMRs). 34 Statistically significant DMPs and DMRs were defined as those with a false discovery rate (FDR)-adjusted p values <0. 05. Identified DMRs were additionally filtered using cutoff values of maximum delta-beta <5% and mean delta-beta <2%, with less than three CpGs constituting a DMR. The Bioconductor co-MET package was used to visualize the co-methylation patterns of several of the top DMRs.
Odds ratio (or) calculation and receiver operating characteristic curve construction OR calculation and receiver operating characteristic (ROC) curve construction were performed using SAS 9. 3 (SAS Institute, Cary, NC) and the PredictABEL R package, respectively. 36 We calculated ORs and their 95% confidence intervals (CIs) after cutting off the methylation levels of each gene by tertile based on the control population, and we also calculated the combined score of the three biomarker genes for GC risk. The two hypermethylated genes were given relative risk scores of 0, 1 and 2 with increasing tertiles of methylation levels, and the hypomethylated genes were given the opposite (decreasing) risk scores.
pylori status, and H. pylori infection appears to be strongly associated with hypermethylation across the methylome, especially in regulatory gene regions. Pathway analyses using the genes located closest to the DMPs (delta-beta 20%) revealed enrichment in the neuroactive ligand-receptor interaction, cancer, axon guidance and Rap1 signalling pathways (Supporting Information Fig. S2 ). In addition, pathway analysis with DMP genes was performed after excluding the genes that are not expressed in the stomach (https://www. proteinatlas. org/) (Supporting Information Table  S2 ). To further test whether the identified DMPs were enriched in TFBSs, we checked overlaps between the HP1 DMPs and the ENCODE datasets. The DMPs were significantly enriched in TFBSs compared with random probes or all probes in the 450 K array (Supporting Information Fig.
Giant viruses of amoebae are distinct from classical viruses by the giant size of their virions and genomes. Pandoraviruses are the record holders in size of genomes and number of predicted genes. Three strains, P. salinus, P. dulcis, and P. inopinatum, have been described to date. We isolated three new ones, namely P. massiliensis, P. braziliensis, and P. pampulha, from environmental samples collected in Brazil. We describe here their genomes, the transcriptome and proteome of P. massiliensis, and the pangenome of the group encompassing the six pandoravirus isolates. Genome sequencing was performed with an Illumina MiSeq instrument. Genome annotation was performed using GeneMarkS and Prodigal softwares and comparative genomic analyses. The core genome and pangenome were determined using notably ProteinOrtho and CD-HIT programs.
The mate-pair library was prepared with 1. 5 µg of genomic DNA. Genomic DNA was simultaneously fragmented and tagged with a mate-pair junction adapter. The library profile and the concentration were visualized on a high-sensitivity bioanalyzer labchip (Agilent Technologies Inc. , Santa Clara, CA, United States). In each construction, libraries were normalized at 2 nM and pooled, denaturated, and diluted to reach a concentration of 15 pM, before being loaded onto the reagent cartridge, then onto the instrument along with the flow cell. Automated cluster generation and sequencing run were performed in a single 39-h run generating 2 × 151-bp long reads. The quality of the genomic data was analyzed by FastQC 1.
, 2015). An internal protein sequence database was used that was built primarily with two types of amino acid sequences: (i) sequences obtained by translating P. massiliensis open reading frames (ORFs); (ii) sequences obtained by translating the whole genome into the six reading frames then fragmenting the six translation products into 250 amino acid-long sequences with a sliding step of 30 amino acids. Contiguous sequences positive for peptide detection were fused and re-analyzed.
Genome Annotation Gene predictions were performed using GeneMarkS and Prodigal softwares, and results were merged  (Besemer and Borodovsky, 2005; Hyatt et al. , 2010). ORFs shorter than 50 amino acids were discarded. Predicted proteins were annotated by comparative genomics by using BLASTp searches against the NCBI GenBank non-redundant protein sequence database (nr), with an e-value threshold of 1e-3. ORFans were defined as ORFs without homolog in the nr database considering as thresholds an e-value of 1e-3 and a coverage of the query sequences by alignments of 30%. Functional annotation was refined by using DeltaBLAST searches  (Boratyn et al. , 2012). Best reciprocal hits were detected by the Proteinortho program with an amino acid identity percentage and a coverage thresholds of 30 and 70%, respectively  (Lechner et al.
Transcriptomic Analysis for Pandoravirus massiliensis Reads generated from the RNA extracts were mapped on the assembled genome by using the bowtie2 software with default parameters  (Langmead et al. , 2009; Langmead, 2010; Langmead and Salzberg, 2012). Mapping results were analyzed using the HTseq-count software, with the union mode  (Anders et al. , 2015). Only "aligned" results were taken into account. Predicted ORFs were considered as transcribed if at least 10 reads were aligned.
Search for Transposable Elements Miniature inverted repeat transposable elements (MITE) previously identified in the P. salinus genome were searched for by using the BLASTn program with an evalue threshold of 1e-3  (Sun et al. , 2015). MITE are DNA transposons whose size ranges between 100 and 600 bp and that require transposition enzymes from other, autonomous transposable elements.
Phylogenetic Analyses and Hierarchical Clustering Phylogeny reconstruction was performed based on the DNAdependent RNA polymerase subunit 1. Amino acid sequences were aligned using Muscle  (Edgar, 2004). The phylogenetic tree was built using FastTree with default parameters  (Price et al. , 2010). Hierarchical clustering was performed with the Mev program  (Chu et al. , 2008)  based on the presence/absence patterns of pandoravirus genes that are homologous to clusters of orthologous groups of proteins previously delineated for nucleocytoplasmic large DNA viruses and giant viruses of amoebae (NCVOGs)  (Yutin et al. , 2013).
The P. massiliensis genome (EMBL Accession no. OFAI01000000) was assembled in two scaffolds of 1,593,057 and 2,489 bp, and was predicted to encode 1,414 proteins (Table  1 ). Mean size (±SD) of these proteins is 299 ± 228 amino acids. Median size is 218 amino acids. A total of 25% of these predicted proteins are smaller than 136 amino acids, and 25% are larger than 397 amino acids, among which 15 proteins are larger than 1,000 amino acids. Among these 1,414 proteins, 786 (56%) have a homolog in the NCBI GenBank nr database (using a BLASTp e-value threshold of 1e-3), and 628 (44%) are ORFans (ORFs with no significant homolog in the NCBI nr database). Among ORFs that have a homolog in nr, 744 (95%) have genes from previously described pandoraviruses as best BLASTp hits. Two genes encode for Pro-tRNA and Cys-tRNA.
A total of 74 ORFs have a significant BLASTp hit with a NCVOG. A total of 310 ORFs were found to be paralogous genes. Finally, 425 ORFs (30% of the gene content) belong to the strict core genome delineated for the six pandoraviruses. For the P. pampulha genome, a total of 864,982 reads were obtained, with a length ranging from 35 to 251 nucleotides; the average quality score per read was 37 for the forward and the reverse strands, respectively. The P. pampulha genome (EMBL Accession no. OFAJ01000000) was assembled in a single scaffold of 1,676,092 bp, and predicted to encode 2,368 proteins and two tRNA, a Pro-tRNA, and a Trp-tRNA (Table  1 ). Mean size of these proteins is 237 ± 219 amino acids. Among these ORFs, 58% have no homolog in the nr database. Among the 989 ORFs that have a homolog in nr, 974 (98%) have genes from previously described pandoraviruses as best BLASTp hits.
Three genes encode a Leu-tRNA, a Pro-tRNA, and a Pyl-tRNA. ORFans represent 67% of the ORF set. Among the 892 ORFs that have a homolog in nr, 872 (98%) have genes from previously described pandoraviruses as best BLASTp hits. Moreover, 72 ORFs are homologous to a NCVOG. We detected that 437 ORFs are paralogs. Finally, 428 ORFs (16%) were found to be shared with the five other pandoraviruses. All these three genomes were found to be linear double-stranded DNA, as described previously for P. salinus, P. dulcis, and P. inopinatum. Thus, here, PCR amplification performed with the attempt to test the circularity of the genome failed. BLASTp hits were found in nr for hundreds of additional short ORFs predicted in the genomes of P. pampulha and P. braziliensis, but e-values were >1e-3, and only short fragments from these sequences were usually involved in alignments obtained with these hits.
massiliensis and P. braziliensis (Figure  7 ). In addition, mean amino acid identities between orthologous proteins of P. salinus and P. massiliensis or P. braziliensis were similar (mean values, 50. 0 and 51. 1%, respectively), and lower than the mean amino acid identity between orthologous proteins of P. salinus and P. pampulha (61. 0%) (Figure  8 ). Taken together, on the basis of phylogenetic analysis, the presence/absence patterns of clusters of orthologous groups of proteins of Megavirales members, and amino acid identity of orthologous proteins, two major groups can be delineated for these six pandoravirus isolates. The first group is comprised by P. massiliensis and braziliensis, and the second group is comprised by P. salinus, P. dulcis, P. pampulha, and P. inopinatum. Comparison of genome architecture and co-linearity showed a general tendency among the different pandoravirus genomes for a greater co-linearity around the first third of the genome alignement by the MAUVE software, displaying large blocks with a high level of nucleotide identity (Figure  9 ).
Compared to the 352 clusters of genes described for the pandoravirus core genome, mimiviruses core genome comprises 267 clusters of genes based on 21 described genomes with a size ranging between 1,017 and 1,259 Mbp  (Assis et al. , 2017)  and the marseillevirus core genome comprises 202 clusters of genes based on 8 described genomes with a size ranging between 0. 347 and 0. 386 Mbp  (Dornas et al. , 2016). Strikingly, a significant number of pandoravirus predicted ORFs have no homolog in the international databases and no predicted functions. This proportion of ORFans remains greater than for other giant viruses of amoebae  (Colson et al. , 2017b). The P. massiliensis transcriptomic and proteomic analyses showed that at least a small proportion of these ORFan genes are transcribed and encode for proteins.
FUNDING This work was supported by the  French Government  under the " Investissements d'avenir" (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research)  (reference: Méditerranée Infection  10-IAHU-03 ), and by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
Cells were harvested for RNA preparation and cell cycle assay when indicated. For cell cycle assay, one million of cells were resuspended with 3 mM EDTA-PBS and fixed with cold absolute ethanol overnight. Then, cells were incubated with Hoechst (5 µg/mL) for 30 min at 37 °C (protected from light) followed by cell cycle profile measurement in a MACSQuant VYB (Miltenyi Biotec). Results were analyzed with Flow Logic software (Miltenyi Biotec). Drugs were added when indicated at the following concentrations: 0. 6 or 0. 2 µM 4HT (4-hydroxi-tamoxifen) (Sigma-Aldrich) for MEFs or Kp27MER respectively, 30 µg/mL of cycloheximide (Sigma-Aldrich), 10 µM of roscovitine (Sigma-Aldrich), 50 µM of Zn 2 SO 4 and 10 µM purvalanol A (Sigma-Aldrich). Transfection of MEFs was performed using PEI reagent (Polysciences, Inc).
After 12-18 hours, the same volume of media was added to reach the final volume of the corresponding plate and 48 hours after transduction lentivirus-containing media was replaced by fresh media and puromycin selection when indicated. Annexin V binding assay. Cells were seeded in a 60% confluence and 12 h later treated with 5 µM purvalanol A for 24 h. Cells were harvested, washed twice with PBS-3 mM EDTA, resuspended in 100 µL of Binding Buffer (10 mM Hepes/NaOH, 140 mM NaCl and 2. 5 mM CaCl 2 , pH 7. 4) and 2 µL FITC Annexin V (BD Bioscience) and incubated for 30 min at 4 °C. Cells were washed twice with PBS and resuspended in 250 µL of PBS. Annexin V binding was assayed in MACSQuant VYB (Miltenyi Biotec) and the results were analyzed with Flow Logic software (Miltenyi Biotec). mRNA expression analysis by qPCR.
For cDNA conversion, the iScript cDNA Synthesis Kit from Bio-Rad was used following manufacturer's protocol for 1 µg of RNA as template. SYBR Select Master Mix from Applied Biosystems was used to amplify cDNA in a CFX Connect Real-Time PCR Detection System from Bio-Rad. qPCRs were analyzed with the CFX Manager software. The mRNA expression of genes of interest was normalized to β-actin mRNA expression. Murine primer sequences used (forward and reverse primers in 5′→3′ direction) Cdk1: CGGCGAGTTCTTCACAGAG and AACCGGAGTGGAGTAACGAG; Cdk4:GGCCTGTGTCTATGGTCTGG and TTCAGCCACGGGTTCATATC; Ccna2:GCCAGCTGAGCTTAAAGAAAC and AACGTTCACTGGCTTGTCTTC; Ccnb1: GACGTAGA CGCAGATGATGG and GCCAGTCAATGAGGATAGCTC; Ccne2: GCATTCTGACCTGGAACCAC and GGAAGCAATGAACAATGAGG; Skp2: AATCTGCACCCAGACGTGAC and TGGAGCACTCGGACAGAATC; bactin: AGACTTCGAGCAGGAGATGG and AGTTTCATGGATGCCACAGG.
Protein quantification was carried out using the Qubit Protein Assay Kit in a Qubit 2. 0 Fluorometer. Samples were resolved by SDS-PAGE and transferred to a nitrocellulose membrane. Blocking was carried out at room temperature using 4% BSA in TBS-T for 1 hour. Primary antibodies were used diluted in TBS-T 1%BSA at a final concentration of 1:1000 unless indicated. IRDye800 or lRDye680 (LiCor Biosciences) secondary antibodies or Trueblot anti-rabbit or anti-mouse IgG DyLight 800 secondary antibodies (Rockland) were used. Signals were recorded with and Odyssey Infrared Imaging Scanner (LiCor Biosciences). Quantification and densitometry analysis were carried out using the ImageJ software. Primary antibodies: cdc2 p34-(17) (Santa Cruz Biotechnology, sc-54, monoclonal antibody used for immunoprecipitations and western blots); cdc2 p34 C19 polyclonal (sc-954, used for western blots); cdk2 M2 (sc-163); cdk4 C22 (sc-260); cyclin A2 H432 (sc-239); cyclin B1 GNS1 (sc-245, used 1:500 in western blots); cyclin E H111 (sc-248, used 1:500); His-probe H15 (sc-813); myc N262 (sc-764, used 1:2000 5% BSA); p27 C19 (sc-528); p27 C19 (sc-528-G); skp2 p45 H435 (sc-7164); actin I19 (sc-1616); phospho-Cdk substrate motif (Cell Signaling, 9477), Phospho-Thr187 p27 (Invitrogen 71-7700, used 1:1000 in western blots); phospho-retinoblastoma (Ser780) Cell Signaling #9307, used 1:500 in western blots).
When indicated, the immunocomplexes were incubated with 10 µM purvalanol A (or DMSO as control) in the presence of ATP for 30 min at 30 °C. Afterwards, the His-p27 was added to each reaction, mixed, incubated for 30 min at 30 °C and analyzed as described. Normal IgG and kinase buffer supplemented with ATP and substrate were used as negative controls for the kinase reaction. The relative kinase activity was determined by densitometric quantification of the western blot using the ImageJ software and represented as the fraction of pT187-p27 signal relative to total p27. Gel-filtration chromatography of total protein extracts. 10 7 cells were lysed as described in 200-300 µL non-denaturing chromatography lysis buffer (50 mM Hepes-NaOH pH 7. 2, 150 mM NaCl, 0. 05% Triton X-100, 1 mM EDTA, 2. 5 mM EGTA, 1 mM DTT, 10% glycerol and 1 mM PMSF (protease inhibitor, Calbiochem) and phosphatase (Sigma-Aldrich) inhibitors added immediately before use), incubated on ice for 30 min and protein extracts clarified at 14000 rpm for 20 min at 4 °C and transferred to a new tube.
Acknowledgements The work was supported by grant  SAF2017-88026-R  from  MINECO ,  Spanish Government , to JL and MDD (partially funded by  FEDER program  from  European Union ). L. G. G. was recipient of  FPI fellowship  from  Spanish Government. We are grateful  Sandra Zunzunegui  for technical assistance and  John Sedivy  and  M. Dolores Delgado  for helpful discussions.
Data validation and statistical analysis Survey data used for this analysis were double entered and validated using EpiData Entry software (version 3. 1, EpiData Association, Odense, Denmark). A descriptive analysis was performed wherein proportions were used to summarize categorical variables related to the ownership and use of bed nets. The ownership of bed nets at household level was assessed using two indicators, (a) % of households with at least one ITN/ LLIN and (b) % of households with ITN/LLIN per two household members  [1]. Indicators for assessing bed net utilization included the total number and proportion of household members who slept under an ITN/LLIN during the previous night. Information on the physical condition of bed nets was also gathered including material type, presence, or absence of holes and the hole size.
Discussion This is the first community-based study assessing access and utilization of bed nets among different types of migrant population in the Regional Artemisinin Resistance Initiative (RAI) project areas in Myanmar. The present study showed nearly 95% of households had access to bed net of any type, whereas only around half of all households had ITN/LLINs. About 35. 3% of households actually slept under an ITN/ LLIN last night. Thus, the study reports poor access to ITN/LLIN though utilization is high among those who have access to bed nets. The major strengths of the study were that data were obtained from a large survey among migrant population in RAI areas, the response rate was high, the interviewers were well trained and supervised, and the subject is an identified national operational research priority.
Data quality was ensured through double data entry and validation using EpiData entry software. We also adhered to STROBE guidelines for the reporting of observational studies  [12]. The study had some limitations as well. The study did not explore the reasons as to why people do not use bed nets despite possessing them. Further qualitative studies are required to understand the reasons for the same. The study findings have a number of policy and practice implications. Firstly, only half of the households had access to ITN/LLINs, which is the recommended type of bed net. This is much lower than the desired target of 80%. Migrant population is one of the most vulnerable with high risk for malaria due to their socio-cultural practices and occupation. This suggests the need for urgent attention and community  distribution of ITN/LLINs to address the shortage of ITN/ LLIN coverage in this vulnerable group.
High throughput immune repertoire sequencing is promising to lead to new statistical diagnostic tools for medicine and biology. Successful implementations of these methods require a correct characterization, analysis and interpretation of these datasets. We present IGoR -a new comprehensive tool that takes B or T-cell receptors sequence reads and quantitatively characterizes the statistics of receptor generation from both cDNA and gDNA. It probabilistically annotates sequences and its modular structure can investigate models of increasing biological complexity for different organisms. For B-cells IGoR returns the hypermutation statistics, which we use to reveal co-localization of hypermutations along the sequence. We demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization.
Probabilistic assignment of recombination scenarios V(D)J recombination selects two or three segments (V and J for TCR α and BCR lights chains; V, D, and J for TCR β and BCR heavy chains) from a library of germline genes, and assembles them while deleting base pairs and inserting other non-templated ones at the junctions (Fig. 1a ). B cell receptors can further diversify through somatic hypermutations during affinity maturation. The recombination process is degenerate, as the same sequence can be generated in many different ways  [11]. IGoR starts by listing the possible recombination and hypermutation scenarios leading to an observed sequence in the dataset. It then assigns probability weights reflecting the likelihood of these scenarios. As the example in Fig. 1a  shows, explored scenarios can be very different yet have comparable contributions to the sequence likelihood.