dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of molecular event neighborhood (in terms of connected nodes) in colon carcinoma Over the last two decades, several key molecular events in CRC development have been identified. For example, mutation/deletion of TP53 (a cell cycle controlling gene) has been observed in more than 50% of human cancers  (Knudson, 2001) , and high mutation rates and increased methylation levels have been detected for RASSF1A, KRAS, BRAF, and MGMT genes in CRC  (Grady and Markowitz, 2002; Suehiro et al. , 2008; Dworkin et al. , 2009). Abnormal modifications of the APC gene have been associated with very early CRC stages  (Suehiro et al. , 2008)  and aberrant alterations of MLH1 and MCC genes found in hereditary and sporadic forms of CRC, respectively  (Fukuyama et al. , 2008). In Listing 3, we are interested in querying the EpiGeNet database for the highest interdependency between molecular events in carcinoma, based on conditional relationship data from StatEpigen.
The objective is to explore potential hub events in CRC initiation. Results from this query (Table  2 ) indicate that MLH1 hypermethylation, JCVT gene expression, and CDKN2A:P16 hypermethylation are the three most frequently observed EPIGENET-AN E-G EVENTS GRAPH DATABASE interdependent events (based on the genetic-epigenetic phenomena in carcinoma, curated from the literature, and stored in the StatEpigen database). Listing 3. Cypher query to identify molecular event neighborhood (in terms of connected nodes) in colon carcinoma: 4. 3. The most plausible paths (with highest incidence) connecting specific key molecular events (e. g. , KRAS mutation and APC hypermethylation) in e. g. , adenoma We are interested in querying for occurrence of the most plausible paths (in terms of the connected graph of conditional relationships between molecular events implicated in CRC) that connect KRAS mutation and APC hypermethylation events [known to be important in CRC development  (Grady and Markowitz, 2002; Suehiro et al.
CONCLUSIONS AND FUTURE WORK In comparison with relational databases, the graph database approach facilitates integration of heterogeneous and highly connected biodata and offers a natural representation of relationships among various concepts specific to biological systems  (Have and Jensen, 2013). In addition, inspection of the diversity of the biological context, including traversal exploration in networks, identification of key elements (hubs) EPIGENET-AN E-G EVENTS GRAPH DATABASE within systems, and creation of modules to integrate concepts with high degree of similarity (e. g. , based on common features, functions, and associations), can benefit from the use of graph-based algorithms. Consequently, results from this type of analysis can help generation of new hypotheses (linking diverse and differently structured concepts) that would be more difficult to formulate without the use of graph-based approaches.
In this article, we have described EpiGeNet, a graph database that integrates data on geneticepigenetic interdependencies observed at different pathological levels in CRC development. First, we used the Cypher language to query differences between polyps and adenoma phenotypes with respect to molecular modifications. Results indicate a set of 43 genetic-epigenetic conditional relationships, with 26 such relationships having probability of occurrence = T > 0. 60. These events can be explored further to facilitate interpretation and identification of the mechanisms that differentiate between healthy phenotypes and those specific to CRC initiation. In Listings 3 and 4, details were queried on highly connected conditional molecular changes in carcinoma and on the most plausible paths connecting two major events.
Probabilistic computational models, reliant on available StatEpigen data (StatEpigen url, n. d. ), were developed to investigate the most plausible pathways for cancer progression, based on genetic-epigenetic modifications at different stages of colorectal tumor. Variants of these models have also been used previously for exploration of DNA methylation dynamics during CRC initiation and progression [e. g. , Roznova ˘t x and Ruskin (2013) and  Barat and Ruskin (2015) ]. The results from the EpiGeNet model adaptation can be incorporated into future computational and mathematical models of CRC development, with the added benefits of the combined probability basis for different event types (including those classified as self-aware or consecutive for same node) and graph algorithms for investigation of the gene connections and events involvement.
We have presented also a complementary graph database framework for integration of multiple heterogeneous biological types  (Lysenko et al. , 2016). This framework, which integrates data from major public resources [including DisGeNet  (Bauer-Mehren et al. , 2011) , DrugBank  (Knox et al. , 2011) , UniProt  (Consortium, 2015) ] and creates associations between different concepts (e. g. , drugs-proteins-diseases), can be complemented by the EpiGeNet database for exploration in the context of human colonic disease. While EpiGeNet has been developed initially using data available from the publicly accessible StatEpigen database  (Barat and Ruskin, 2010) , which indicates conditionality for epigenetic-genetic events in CRC, the future aim is integration of data on causality in molecular signals in CRC (i.
ACKNOWLEDGMENTS Authors would like to acknowledge access to the Neo4j 2. 3. 1 framework and the StatEpigen database. They would also like to thank the team of the  CNRS/IN2P3 Computing Centre ,  Mr. J. Bussery ,  Mr. B. Guillon ,  Dr. G. Marchetti , and  Dr. G. Rahal  for their support in deploying and accessing the Neo4j graph database.
FUNDING This work has been supported by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. IMI 115446  (eTRIKS), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies. A. L. and C. J. R. acknowledge support from the  BBSRC  through their strategic funding of  Rothamsted Research.
Study design and patients The design of the RESPONSE trial (ClinicalTrials. gov identifier, NCT01243944) has been reported in detail elsewhere  [4]. Briefly, patients with PV and spleen volume ≥450 cm 3 who had not undergone prior JAK inhibitor therapy and were resistant to or intolerant of hydroxyurea were randomized 1:1 to ruxolitinib (initial dosage, 10 mg twice daily) or BAT; dose modification was permitted. BAT options included hydroxyurea (at a dose that did not cause unacceptable side effects), interferon (IFN) or pegylated (PEG) IFN, pipobroman, anagrelide, immunomodulators (e. g. , lenalidomide, thalidomide), phlebotomy, or no medication. Phosphorus-32, busulfan, and chlorambucil were prohibited. In cases of lack of response or toxicity requiring drug discontinuation, a change of BAT was permitted.
The JAK2 p. V617F mutation was detected by real-time PCR or high-resolution melting analysis. Deep sequencing based on the Ion Torrent™ Personal Genome Machine™ (PGM) System (Thermo Fisher Scientific Inc. , Waltham, MA, USA) was used to analyze mutations in 22 genes associated with the JAK/STAT pathway (JAK1, JAK2 [other than JAK2 p. V617F], JAK3, EZH2, ASXL1, TET2, IDH1, IDH2, CBL, SRSF2, DNMT3A, NFE2, SOCS1, SOCS2, SOCS3, SH2B3, STAT1, STAT3, STAT5A, STAT5B, SF3B1, and U2AF1). Ion Torrent PGM data were aligned against the human genome (version 19) using NextGENe® software 2. 3. 1 (SoftGenetics, LLC, State College, PA, USA). Mutations were confirmed using conventional sequencing methods. Mutations in CALR were analyzed using high-resolution capillary electrophoresis and confirmed with Sanger sequencing.
Patients and baseline characteristics Baseline JAK2 p. V617F status data were available for 215 of 222 patients initially randomized in the RESPONSE trial. Of 215 patients with baseline JAK2 p. V617F allele assessments, 104 of 107 patients initially randomized to ruxolitinib and 107 of 108 patients randomized to BAT were JAK2 p. V617F-positive (Table  1 ). Ninety-seven patients in the BAT arm crossed over to ruxolitinib and were evaluable for JAK2 p. V617F allele burden. Median (range) time to crossover in these patients was 34. 0 weeks (31. 0-53. 1). The mean (SD) treatment duration was 25 months (10) and 8 months (2) for patients originally randomized to ruxolitinib and BAT, respectively. Of the patients who received IFN treatment before randomization (ruxolitinib arm, n = 18; BAT arm, n = 16), the majority (n = 11 and n = 15, respectively) received non-PEG IFN only.
Previous studies of PEG IFN-α2a found that many patients with PV who achieved a molecular response with PEG IFN-α2a also experienced reductions in JAK2 p. V617F allele burden after 6 months of treatment  [8] [9] [10]. In the current analysis, patients experienced molecular responses irrespective of when ruxolitinib was initiated during the disease course or whether there had been previous therapy with IFN. Although the use of phase 3, randomized data for allele burden evaluations in patients with PV is novel, it is important to underscore that the strength of evidence for this post hoc analysis is limited. Furthermore, blood samples collected to evaluate allele burden were not intended for evaluations of molecular response or correlations with clinical outcomes. Collectively, the data from this exploratory analysis suggest that ruxolitinib treatment (in randomized and crossover treatment arms) for up to 4 years provided progressive reductions in JAK2 p.
superfamily  [13] [14] [15]. Since FMN and FAD are essential and versatile cofactors in a plethora of vital redox processes  [16] [17] [18] [19] [20]  , and also participate in signal transduction during apoptosis  21  , embryonic development  22  , chromatin remodeling  23  , nucleotide synthesis  24  , t RNA methylation  25  , protein folding  26  , and light dependent processes in plants as well as in sensory transduction in microorganisms  [27] [28] [29] [30] [31]  , their biosynthesis is worth to be understood. Structures of prokaryotic FADSs are available only for Thermotoga maritima (TmFADS, PDB ID: 1MRZ,  11, 32  ), Corynebacterium ammoniagenes (CaFADS, PDB ID: 2X0K  10  ) and the human pathogen Streptococcus pneumoniae (SpnFADS, PDB ID: 3OP1). Except for the exhaustive biochemical and biophysical characterization of CaFADS  1, 5, 33  , the rest of family members have been poorly studied  4, 34, 35.
Methods Cloning, Overexpression and Purification of SpnFADS. The DNA sequence encoding SpnFADS (accession number ADM84672, SPAP_1083), containing the NdeI and XhoI restriction sites and codons optimized for its expression in E. coli, was synthetically produced by Gen-Script. This DNA sequence was cloned into a modified pET-15b vector (Novagen) that contains an N-terminal His 6 -Tag sequence followed by a PreScission Protease site upstream of the NdeI and XhoI cloning sites. The final construct was fully sequenced at Sistemas Genómicos (www. sistemasgenomicos. com) and transformed into the E. coli strain Bl21 Star TM (DE3) (Invitrogen). Transformed cells were grown at 37 °C in 2xTY medium (1. 6% (w/v) tryptone, 1% (w/v) yeast extract, and 0. 5% (w/v) NaCl) with 100 μg/mL of ampicillin until cells reached an OD 600 of 0.
For samples containing ligands, the mixtures were pre-incubated at 4 °C for 10 min under mild stirring to favor complex formation prior to immobilization  47. ADP, ATP and PP i were added at 250 µM, while FAD, FMN and RF were used at 50 µM. SpnFADS solutions (∼0. 5 μM) were immobilized on small fresh exfoliated muscovite mica pieces (Electron Microscopy Sciences) for 10 min at room temperature. The sample and holder were introduced in a liquid cell previously cleaned with 20% isopropanol and Millipore ultrapure water. AFM measurements were conducted in 20 mM PIPES, pH 6. 0, freshly prepared 2 mM dithiothreitol (DTT, Sigma-Aldrich) and 0. 8 mM MgCl 2 at room temperature. Raw images were processed using the WSxM freeware  50. Percentages of different features were calculated taking several sample areas with several scan sizes, zooming with the WSxM software functions without losing relevant information and discarding artifacts  51.
A considerable amount of features were collected for the statistical population study. Total protein percentages were also calculated considering the number of molecules involved in each type of association. Errors were calculated from the dispersion of results in the analysis of different AFM images corresponding to different areas of the sample. Structural prediction of enzyme-ligand interactions. Models for ligand binding at the RFK and FMNAT sites of SpnFADS were produced comparing its structure (chain B of the crystal structure PDB ID: 3OP1) with the previously reported CaFADS crystal structures (for the RFK module) or models (FMNAT module) in complex with their oxidized substrates  10, 36. The PyMol 0. 99 52 software was used for complex modelling, structural analysis and figures production.
Acknowledgements This work has been supported by the  Spanish Ministry of Economy and Competitiveness (MINEICO)  [ BIO2013-42978-P  and  BIO2016-75183-P  to M. M. ;  CTQ2013-44367-C2-2-P  and  BFU2016-75633-P  to R. H. -G. ], and the  Government of Aragón-FEDER  [ B18  and  B89 ]. A. L. , A. V-C. and R. H-G. thank  ARAID  for financial support. E. L. received a fellowship from  Bruker , and M. S. and C. M. from  DGA. The authors thank also  J. L Díez  and  I. Echániz  for technical support.
Patients and material The cohort contained peripheral blood samples of 35 patients with SS at B2 stage according to the ISCL/EORTC criteria. According to the French Public Health and Bioethical Law, the present study was considered by our research direction lawyer as a non-interventional study without the need for ethics committee approval (Article L1121-1 and Article R1121-3). Moreover, informed consent was given to all patients with written consent for the use of the clinical data and research on their biological material. All data were collected anonymously. In the 35 patients with SS, peripheral blood mononuclear cells (PBMC) were isolated using Pancoll (PAN-Biotech, Aidenbach, Germany) according manufacturer's recommendations at SS diagnosis stage prior to treatment and for 28 patients during follow-up with a total of 63 analyzed blood samples.
Flow cytometry analyzes and sorting In peripheral blood monuclear cells, TCR Vβ was determined using flow cytometry with the IOTest Beta Mark TCR Repertoire Kit (Beckman Coulter, Marseille, France) designed to identify 24 distinct TCR Vβ families according to manufacturer's recommendations. Data acquisition was performed using a BD FACSCANTO II flow cytometer and analyzed using BD FACSDIVA™ software (BD Biosciences, le Pont de Claix, France). Tumor and non-tumor populations were sorted based on TCR Vβ22, CD4 and CD8 expression using BD FACSAr-iaTM II cell sorter (BD Biosciences).
Targeted deep sequencing Genomic mutation of TP53 was studied by Next Generation Sequencing (NGS) using the Ion Ampliseq TP53 panel (ThermoFisher Scientific, Life Technologies, Les Ullis France). The design covers 100% of the coding sequence of TP53 including mutations at canonical splice sites and generates 24 amplicons on a length of 1 280 bases. Libraries were amplified by emulsion PCR and enriched using automatic system IonChef (ThermoFisher Scientific). Ion sphere particles were then sequenced with the Ion torrent personal genome machine (ThermoFisher Scientific) on 316v2 or 318v2 Chips (ThermoFisher Scientific) with high mean coverage > 3000X. Torrent Suite™ version 5. 0 software (ThermoFisher Scientific) was used to perform data analysis. Reads were mapped to the human hg19 reference genome.
Data processing, alignment and mutation calling were performed using the Torrent Suite™. The Variant Caller detected point mutations with a variant frequency !2% for Single Nucleotide Variation (SNV) and !5% for short insertion/deletion (INDEL). VCF files generated by Variant Caller were annotated by ANNOVAR  [30]. In the 10 patients with TP53 mutation, BAM of wild-type samples for TP53 were also checked using Alamut Software (Interactive Biosoftware, Rouen, France).
Statistical analysis Data were analyzed according to the number of dependent variables and to the nature of dependent and independent variables. In order to test the normality of our data the Shapiro-Wilk test was used. For non-parametric statistical analysis the Wilcoxon-Mann Whitney or the Fisher's exact test were used. This test is suitable for sample number below 30. To remove outlier values, Dixon's test was applied. Survival was evaluated by Cox's proportional hazards analysis. The significance level was taken at P 0. 05. The statistical tests were performed using the MedCalc software (Ostend, Belgium).
In the control group, median age was 74 and the gender ratio was 3. Among the 10 TP53 mutations, two of them were present within the domain encoding for tetramerization property, and seven in the DNA-binding domain. Prediction analysis of Polyphen-2 (Polymorphism Phenotyping v2, http://genetics. bwh. harvard. edu/pph2/) showed a score greater than 0,96 for the 3 missense mutations. The three variants with INDELs were found absent in the COSMIC v77 database. The 2 in-frame deletions of 5 and 8 Amino Acids involved the DNA-binding domain and the frameshift mutation resulted in a stop codon at the beginning of this domain. TP53 mutations can be reliably detected in sequential blood specimens and in the skin of patients with SS.
P53 expression We analyzed p53 and mdm-2 expression in tumoral cells in 17 skin samples of patients with SS by visual comparison with PD1 staining of SS cells to minimize counting of non-tumoral cells (data not shown). Indeed, p53 protein level was found increased by abnormal stabilization of the mutated form and sometimes associated with mdm-2 protein overexpression in CTCL cases  [41, 42]. The cutoff used for IHC positivity was an immunoreactivity of more than 30% of tumoral cells but we also evaluated staining intensity as reported for individual cases (Fig  2 ). In patients with TP53 mutation, p53 expression was observed in 89% of cases (8/9). Five of these cases presented a double TP53 alteration (one mutated allele and one deleted allele) but were stained for p53 in 30-80% of tumour cells.
Acknowledgments This work is part of the  MATULILA  project funding to  Anne Pham-Ledard  supported by the  University Hospital of Bordeaux , the  Ligue contre le Cancer Comitede Gironde  grant to  Sandrine Poglio  and the  Association pour la Recherche Contre le Cancer  grant to  Edith Chevret. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
Currently, treatment choice in mRCC is based on risk grouping of patients by clinical parameters such as the patientʼs performance status, and serum biochemical measurements, and histological features of the tumor  [10]. A comparison study into several clinical risk grouping models, including that of the International Metastastic Renal Cell Carcinoma Database Consortium  [11]  and the Memorial Sloan Kettering Cancer Center (MSKCC)  [12] , showed modest discriminatory values for survival (area under the receiver operating curve 0. 66) and indicated that addition of tumor-specific or patient-specific biomarkers is likely required for the improvement of the accuracy of these models  [13]. Advances in high-throughput technologies have paved the way to personalized medicine using biomarkers. For mRCC, potential prognostic molecular biomarkers such as PBRM1, BAP1, and KDM5C tumor mutations  [14] ; IL8  [15] , VEGF, and PlGF levels  [16] ; ABCB1 and VEGFR-3 germline polymorphisms  [17] ; and miRNA levels have been identified  [18, 19].
Collection of clinical data Clinical information from all patients was collected by medical file review and entered in web-based case record forms (CRFs) (Supplementary material and Table  1 ). All data were managed, exchanged cross-border, and used according to the data protection laws in Europe. Data included demographic information, baseline clinical characteristics, treatment lines, drug toxicities (Common Terminology Criteria for Adverse Events [CTCAE version 4. 0] Z 3), tumor response (i. e. , Complete remission, partial remission, stable disease, or progressive disease), and death. Tumor response was defined according to RECIST version 1. 1 and based on patient evaluation by local caregivers as given in the radiology report or medical record (no review of imaging). Clinical data were extracted from the web-based CRFs and checked for missing and inconsistent data.
Platform and pharmacokinetic/pharmacodynamic analyses EuroTARGET encompasses multiplatform omics profiling and pharmacokinetic/pharmacodynamic (PK/PD) analyses. Genome-wide germline DNA variation data were measured using Illumina HumanOmniExpress BeadChips. Tumor material was profiled using next-generation wholeexome sequencing (Illumina; tumor DNA and matched germline DNA), RNA sequencing (Illumina), PamChip kinase assays, and Illumina Infinium HumanMethyla-tion450 BeadChips. PK/PD models were developed using NONMEM 7. 3 software.
Availability of data All clinical and platform data generated in EuroTAR-GET will be made freely available in an anonymized way for the research community as of March 2018. The data can be accessed through the European Genome-phenome Archive (EGA) which is the controlled access repository under the European Bioinformatics Institute (EMBL-EBI). Interested parties will be able to find the EuroTARGET project under the Studies section.
Discussion EuroTARGET is a European collaborative project, which aims to discover and validate biomarkers to personalize treatment of patients with mRCC. Here, we describe the EuroTARGET patient population, a large, extensively phenotyped cohort of 920 patients with mRCC with most patients receiving sunitinib as first-line TKI. For most patients, genome-wide germline DNA variation data are available, making this the largest cohort for prognostic  a Please note that in "Median PFS time," progression events after stopping TKI treatment are considered as events, even when time between stop of treatment and event was long. In "median PFS time while being on TKI," patients are censored at the moment they stop TKI treatment meaning that events that occur shortly after TKI treatment stop are not taken into account.
5%) 66 (9. 3%) 2 (4. 9%) 10 (6. 0%) Unknown 40 (4. 3%) 27 (3. 8%) 5 (12. 2%) 8 (4. 8%) Material available DNA 824 (89. 6%) 650 (91. 2%) 28 (68. 3%) 146 (88. 0%) RCC tissue 142 (15. 4%) 113 (15. 8%) 15 (36. 6%) 14 (8. 4%) Normal kidney tissue 95 (10. 3%) 75 (10. 5%) 8 (19. 5%) 12 (7. 2%) TMA 247 (26. 8%) 193 (27. 1%) 16 (39. 0%) 38 (22. 9%) TMA ¼ tissue microarray; IMDC ¼ International Metastastic Renal Cell Carcinoma Database Consortium; WHO ¼ World Health Organization.
The chosen datasets were resting-EEG recordings separated by 2 weeks from patients with mild cognitive impairment (MCI), subjective cognitive complaints (SCC), temporal lobe epilepsy (TLE), and healthy controls (HC). Thereby, in the present paper we extend our previous findings on variability between measures and between neurological subgroups  (Höller et al. , 2017 ) by examining the listed potential moderators of reliability. A better understanding of these factors could pave the way for a methodological paradigm shift in brain network research.