dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Data were collected anonymously and retrospectively from January to May of 2012, based on patient medical files using a structured questionnaire developed for the purposes of this study. Patient details were recorded, including factors considered to be predictors of non-recovery after CHIK according to the literature: age (>45 year), female gender, previous history of an osteoarthritic event, and severe acute CHIK  [12, 13, 18, 22, 29, 34]. Details on CHIK-RMSD included the date of the first visit to the rheumatologist, the clinical and biological histories, imaging features, and treatments since the acute CHIK infection. We recorded the following clinical data: i) joint involvement: small joints (metacarpophalangeal, proximal and distal interphalangeal, metatarsophalangeal, thumb interphalangeal joints and wrists), large joints (shoulders, elbows, hips, knees and ankles), vertebral, sacroiliac, temporomandibular and sternocostoclavicular areas; ii) joint count: polyarticular if more than 4 (oligoarticular if 4 or less); iii) articular inflammatory signs or symptoms defining arthritis: synovitis, warmth and/or redness over the joint ("hot" joint), prolonged morning stiffness (> 30 minutes), inflammatory pain (which improved with exercise, or worsened after rest or during the night), or arthritis that was clinically distinguished from oedema without join effusion; iv) periarticular involvement: enthesitis (inflammation of the tendon or ligament insertion into bone: Achilles tendonitis, plantar fasciitis), tenosynovitis, periostitis (tibial and ischial tuberosities inflammation), tendinopathy, bursitis, myalgia and neural tunnel syndrome.
Data analysis Patients without previously defined arthritis were classified as pCHIK musculoskeletal disorders (pCHIK-MSD) that were either loco-regional or diffuse (if > 4 painful areas). For patients with arthritis, we separated crystalline and non-crystalline rheumatic disorders. Crystalline arthritis was defined as a convincing clinical presentation associated with hyperuricemia that improved after gout treatment. Patients presenting non-crystalline polyarthritis were classified as chronic inflammatory rheumatism (CIR). CIRs were categorized into 3 groups (Table  1 )  [37, 38]. Patients who had not previously displayed rheumatic symptoms and developed CIR immediately after CHIK were classified as de novo pCHIK-CIR. We measured the median period from acute CHIK to the first rheumatology consultation for the pCHIK-CIR and pCHIK-MSD patient groups.
Acknowledgments The authors wish to thank  Drs. Laetitia Huiart  and  Cyril Ferdynus ,  Research and Development in epidemiology, University Hospital Felix Guyon, La Réunion ;  Dr. Pierre Yves CHOUC , rheumatologist, Martigues, France;  Dr. Karen Sokol ,  Sébastien Sicard  and  Sophie Domingues.
Acknowledgements. This manuscript has been greatly improved thanks to the careful reviews and insightful comments by the anonymous reviewers that the authors gratefully acknowledge. The financial support for this work has been partly provided by the joint project The  Virtual Institute DEad SEa Research Venue (DESERVE) of the Helmholtz Centers KIT ,  GFZ  and  UFZ  and their partners from  Germany, Israel, Jordan and Palestine , the  United States-Israel Binational Science Foundation (BSF)  under research grant no. 2008436 , the  United States National Science Foundation (NSF)  under grants  AGS-1036858  and  AGS-1401220 , research grant 4500568707 for 2011-2013 by the  Israeli Water Authority of the Ministry of Infrastructures , the  European Union   Sixth Framework programme project Climate Change and Impact Research: Mediterranean Environment  (CIRCE, http://www.
Attenuation and scatter correction 11. Data analysis of regional perfusion imaging 12. Data analysis of left ventricular function 13. Data analysis of hybrid imaging 14. Reports and image display. All sections have been updated and a lot of details were revisited. The present paper highlights the major changes presented in the updated 2015 guidelines and focuses on the selective coronary vasodilator regadenoson, recent developments on calculated radiation exposure [i. e. , new International Commission on Radiological Protection (ICRP) models, dedicated cardiac SPECT cameras and hybrid systems], recent developments in instrumentation (i. e. , dedicated cardiac SPECT and cardiac hybrid imaging systems) and coronary CT-contrast agents. All 14 sections are described in more detail in the fully revised version available at the EANM home page  [1].
Hardware and software requirements Dedicated workstations capable of two-dimensional and three-dimensional displays of CCTA and SPECT data are a basic requirement for hybrid data analysis. Aside from projection of standard views of SPECT data, the combination of hardware and software has to offer capabilities for volume rendering of stress and rest SPECT data sets (uncorrected and attenuation corrected), common reconstruction and reformatting techniques for CCTA data [including transaxial stacks, multiplanar (curved) reformations, maximum intensity projections and volume-rendering]  [64] , and automatic or manual image co-registration and fusion. If non-contrast enhanced CT data is acquired for AC of SPECT, the software should offer computation of coronary artery calcium scores.
True hybrid cardiac imaging and data analysis using volume rendered, co-registered, and fused SPECT and CCTA data sets should be preferred over sole side-by-side analysis because of accurate segmental assignment to coronary artery territories and documented incremental value  [68] [69] [70]. Fused images of myocardial perfusion SPECT and CCTA can be obtained using data sets acquired on a hybrid scanner (SPECT/CT) or via co-registration of images obtained from separate stand-alone scanners. Software-based automated registration is accurate and fast  [70]. However, automated coregistration of data from cardiac imaging is more challenging than that from other body areas. Errors may arise from cardiac motion, breathing motion, and cardiac contraction  [71]. Additionally, different anatomic features are depicted by the two modalities rendering automatic object recognition and image registration difficult, particularly if large perfusion defects are present in SPECT images.
Data analysis The combined diagnostic information from myocardial perfusion SPECT imaging and CCTA is complementary: the negative predictive value of CCTA to exclude CAD is close to perfect (Fig. 3 )  [49, 72, 73]. However, specificity and positive predictive value of CCTA for identification of haemodynamically significant luminal narrowing have been documented to be less robust. CCTA is associated with a general overestimation of the severity of coronary artery stenosis and difficulties to differentiate between slight differences in stenosis severity. SPECT offers complementary diagnostic information as to the haemodynamic significance of coronary artery lesions detected by CCTA. Also, uninterpretable segments on CCTA due to strong calcifications or artefacts can be well studied with SPECT.
By contrast, CCTA adds to the diagnostic value of SPECT through documentation of multivessel disease (with possible balanced ischaemia not detectable by semiquantitative SPECT analysis) or diagnosis of subclinical atherosclerosis. Interpretation of CCTA studies is beyond the scope of this document and is covered in detail in the respective guidelines of the Society of Cardiovascular Computed Tomography  [64]. In brief, CCTA should be first reviewed in transaxial images, complemented by multiplanar reformations to better visualize suspected lesions. Diagnostic conclusions are not based on three-dimensional, volume-rendered CCTA representations. By contrast, in hybrid imaging, a panoramic threedimensional view is offered by integrating volume-rendered CT data with the perfusion information from SPECT.
As a consequence, the widest possible range of source and test body configurations needs to be explored when testing the different aspects of EEP, and this is one of the aims of STE-QUEST, which will test for EEP violation in the gravitational fields of the Sun and the Moon. Furthermore, although not discussed further here, the STE-QUEST data can also be analyzed to search for violation of EEP in other source fields, e. g. that of galactic dark matter as in Ref. [118]. Such future searches will be part of the legacy of STE-QUEST.
Performing such an experiment in space provides the benefits of variable boost orientations and longer free-fall times, but there is no fundamental change in the existing analysis. The results of that analysis along with the WEP sensitivity goals of STE-QUEST imply that sensitivities ranging from the 10 -11 to 10 -7 levels per measurement cycle will be possible for up to 8 combinations of SME coefficients. After incorporating data from the large number of orbits throughout the mission, constraints ranging from 10 -14 to 10 -10 levels are expected. These sensitivities would provide improvements of up to 5 orders of magnitude over existing constraints. Another way in which the STE-QUEST mission as currently proposed could attain sensitivity to Lorentz violation is through red-shift tests. Coefficients for Lorentz violation, which couple to gravitational fields in the SME lead to modified space-time curvature  [13, 92]  as well as additional modifications to clock frequencies, and specific predictions for red-shift experiments have been made  [14, 92].
For most of these coefficients, these are unprecedented levels of sensitivity. There are also dependences on additional SME coefficients, which are not captured by the Schmidt model. These include dependences on neutron coefficients and dependences that exist because of the relatively rapid movement of the nucleons inside an atomic nucleus  [7]. The extremely sensitive data provided by STE-QUEST would allow extracting these additional dependences. In conclusion of this section, STE-QUEST offers the possibility to explore a large parameter space of the SME and to thereby constrain, or uncover, violations of Lorentz and CPT symmetry. In particular, coefficients in the proton and electron sector will be constrained from the clock measurements, while the 85 Rb-87 Rb atom interferometer will provide new constraints in the gravitational sector, with expected improvements of up to five orders of magnitude on present limits.
Preparation of genomic overlapping amplicons Three overlapping amplicons were produced from the RNA extract of each clinical sample. RT-PCR assays were performed using the Superscript III One-Step RT-PCR Platinum TaqHifi kit (Life Technologies). The mixture (final volume: 50 μL) contained 25 μL of Reaction Mix, 2 μL of nucleic acid extract, 100 nM of each primer, 1 μL of Enzyme Mix and 20 μL of Nuclease-Free Water. Primers were designed from the full-length sequence of the virus when available (TBEV, CHIKV) or from the alignment of sequences from related viruses retrieved from GenBank (E-30) (Tables A and B in S1 File). The amplicons obtained were as follows (positions according to full-length genomic sequence). TBEV_1: 1-4970, TBEV_2: 3866-8026, TBEV_3: 7851-11100; E-30_1: 6-2287, E-30_2: 2166-5404, E-30_3: 5232-7441; CHIKV_1: 1-3635, CHIKV_2: 3486-7112, CHIKV_3: 6998-11296.
Amplified DNA was analysed using the Ion PGM Sequencer according to the manufacturer's instructions. The read sequences obtained were trimmed, first using quality score, then by removing the primers used to amplify the complete genome by RT-PCR and finally at the 5' and 3' termini by systematically removing 6 nt. Only reads with a length greater than 29 nt were used and mapped for comparison to thesequences of the corresponding viruses: CHIKV H20235/STMARTIN/2013 strain (available through the European Virus Archive website under reference no. 1540), the TBEV Oshima 5-10 strain (GenBank accession number AB062063) or the E-30 1-MRS2013 strain (GenBank accession number KF920598). Mutation frequencies (proportion of viral genomes with the mutation) for each position were calculated simply as the number of reads with a mutation compared to the reference divided by the total number of reads at that site.
All reads are available in NCBI Sequence Read Archive; BioProject PRJNA291127; accession number SRP062989. For viruses (CHIKV and TBEV) for which the sequence of the original virus contain in the sample was known, the integrity of the sequenced genomes was considered as verified when the nucleotides similarity was !99. 9%. For viruses (E-30) for which the sequence of the original virus contain in the sample was unknown, the integrity of the sequenced genomes was verified by aligning with the closest complete genomes available in the database.
This work was supported by the project Predemics, http://predemics. biomedtrain. eu/cms/default. aspx( European Union Seventh Framework Programme  under grant no. 278433 ); the  European Virus Archive goes Global , http://global. europeanvirus-archive. com/ (European Union's  Horizon 2020 research and innovation programme  under grant agreement no. 653316 ); and the  Agence Nationale de la Recherche , http://www. agence-nationale-recherche. fr/(grant  ANR-14-CE14-0001   RNA Vacci-Code ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
While the HMMR association study in BRCA1/2 mutation carriers drew on a partial dataset from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), the depicted mechanistic model highlighted additional gene candidates for breast cancer risk; i. e. , AURKA, TPX2, and TUBG1  [4]. In a previous CIMBA study, no evidence of association was found between functional variation in AURKA and breast cancer risk among BRCA1/2 mutation carriers  [5]. However, these results were based on a more limited CIMBA dataset (4,935 BRCA1 and 2,241 BRCA2 mutation carriers) and did not comprehensively assess variation in the AURKA Competing Interests: PP is an author of this study and is a member of the PLOS ONE Editorial Board; this does not alter the authors' adherence to PLOS ONE Editorial policies and criteria.
In order to maximize the number of ovarian cancer cases, breast cancer was not considered to be a censoring event in this analysis, and mutation carriers who developed ovarian cancer after breast cancer diagnosis were considered affected in the ovarian cancer analysis. To adjust for the non-random sampling of mutation carriers with respect to their disease status, data were analyzed by modeling the retrospective likelihood of the observed genotypes conditional on the disease phenotypes  [18]. The associations were assessed using the 1-degree of freedom score test statistic based on this retrospective likelihood. To allow for the non-independence among related individuals, the correlation between the genotypes was taken into account using a kinship-adjusted version of the score test statistic  [16].
The p values presented were based on the adjusted score test. To estimate the HRs, the effect of each SNP was modeled as either a per-allele or genotype on the log-scale by maximizing the retrospective likelihood. The evidence of heterogeneity in the associations between countries/study-centers was also evaluated. Associations with breast and ovarian cancer risks were assessed simultaneously within a competing risk analysis framework  [11, 18]. The significant FDR-adjusted associations (for rs299290 in HMMR and for rs2426618 in AURKA/CSTF1) were subsequently explored using imputed genotypes based on data from the 1,000 Genomes project (March 2012 version  [19] ). The IMPUTE2 software  [20]  was used for imputation of non-genotyped SNPs. Associations of each marker with cancer risk were assessed using a similar score test to that used for the genotyped SNPs, but based on the posterior genotype probabilities at each imputed marker for each individual.
Expression Analysis The association between gene expression and survival after breast cancer diagnosis was assessed using the NKI-295 dataset of sporadic primary breast tumors  [22, 23]  and a standard Cox proportional hazards model. All possible pairwise gene interactions including HMMR or AURKA microarray probes (2 and 1 probes, respectively) were evaluated using this model. The quantitative analysis of HMMR expression isoforms was carried out using mRNA extracted from lymphoblastoid cell lines of nine rs299290-TT and six rs299290-CC BRCA2 mutation carriers, and the following TaqMan (Applied Biosystems) probes in real-time PCR assays: hs01063269 for total HMMR expression; hs0106328 for the inclusion of exon 4; and hs00234864 for the inclusion of exon 11.
Genome Analyses Data for formaldehyde-assisted isolation of regulatory elements (FAIREs) that marked transcriptionally active regions in normal human mammary epithelial cells (HMECs) were downloaded from the Gene Expression Omnibus (GEO) reference GSE46074  [24]. Sequence reads were trimmed for the adaptor, masked for low-complexity and low-quality sequences/reads and subsequently aligned to the genome version hg19 using TopHat  [25]  with default parameters. Peaks were called using HOMER  [26] , applying a triangle-based distribution, a median length of 150 base pairs, and an α value of 0. 01 (99. 0% CI). Replicates were analyzed individually and uniquely merged using BEDTools  [27]. Chromatin immunoprecipitation data of ERα were downloaded from the GEO reference GSE32222 and analyzed with MACS (version 2.
0. 9; macs2diff function)  [28]. Significance was defined as a false discovery rate < 1%, using default values for all other parameters. Differentially bound genomic regions were assigned to the closest ENSEMBL (version 62) annotated gene using the R-Bioconductor package ChIPpeakAnno  [29]. Histone modification and chromatin segmentation data in HMECs were obtained from the UCSC Genome Browser (hg19) and correspond to the GEO references GSE29611 and GSE38163, respectively, deposited by the ENCODE project  [30].
Evolutionary analysis of BRCA1 and RHAMM The full-length nucleotide and protein sequences from 20 (for BRCA1/BRCA1) and 26 (for HMMR/RHAMM) mammalian species, which included human and naked mole rat, were downloaded from the OrthoMaM 2. 0 database  [31]  (S2 Table ). For evolutionary analysis, a multiple sequence alignment (MSA) of the corresponding amino acid sequences was generated using the algorithm implemented in PRANK v. 140110  [32]. To prevent the inclusion of incorrectly aligned positions, all MSA positions with low statistical support (posterior probabilities < 0. 99) in the PRANK alignment were excluded. Next, the high-quality protein MSAs were used as guide in the alignment of the corresponding coding sequences (CDS MSAs). The level of functional constraints acting on the coding regions of both genes was analyzed using the maximum likelihood method implemented in the codeml program of PAML v.
Analysis of the potential causative variant The rs299290 variant represents a missense amino-acid change in HMMR exon 11 that is predicted to be benign/neutral/tolerated by several algorithms: Valine 369 to Alanine in accession number NP_001136028. 1; MutationAssessor score = 0; Polyphen score = 0. 005; and SIFT score = 0. 73. Subsequent examination of the splicing of exon 11 and of exon 4, the latter of which is known to be differentially spliced in different conditions and cell types  [36] , did not reveal alterations or differences between mRNA samples with different rs299290 genotypes (S1 Fig. ). Nonetheless, rs299290 is located *14 kb from the HMMR promoter region that is active in mammary epithelial cells, as detected by the analysis of data from genome occupancy profiling  [24]  (Fig.
g. , gene co-expression) to a degree that is significantly higher than expected by chance  [8]. Thus, we assessed the GxGs suggested above for their equivalence with interactions between gene expression profiles across breast tumors, considering breast cancer survival as the outcome. There are no large tumor series from BRCA1/2 mutation carriers that allow gene expression interactions in these mutation backgrounds to be assessed; therefore our analysis was restricted to sporadic cases and aimed to explore complex relationships between HMMR and AURKA or TUBG1. Using the NKI-295 dataset  [22] , HMMR expression was found to be significantly associated with overall survival: two microarray probes gave identical results: HR = 1. 78 and p = 0. 001. AURKA expression was also found to be associated with overall survival (single probe HR = 2.
Trust. RE and EB are supported by Cancer Research United Kingdom Grant C5047/ A8385. FCCC: The authors acknowledge support from The University of Kansas Cancer Center (P30 CA168524) and the Kansas Bioscience Authority Eminent Scholar Program. AKG was funded by 5U01CA113916, R01CA140323, and by the Chancellors Distinguished Chair in Biomedical Sciences Professorship. GC-HBOC: The German Consortium of Hereditary Breast and Ovarian Cancer (GC-HBOC) is supported by the German Cancer Aid (grant no 109076, RKS) and by the Center for Molecular Medicine Cologne (CMMC). GEMO: The study was supported by the Ligue National Contre le Cancer; the Association "Le cancer du sein, parlonsen!" Award; and the Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program.
G-FAST: TVM is a postdoctoral researcher funded by the Fund for Scientific Research Flanders (FWO). GOG: This study was supported by National Cancer Institute grants to the Gynecologic Oncology Group (GOG) Administrative Office and Tissue Bank (CA 27469), the GOG Statistical and Data Center (CA 37517), and GOG's Cancer Prevention and Control Committee (CA 101165). MHG, PLM and Dr. Savage were supported by funding from the Intramural Research Program, NCI. HCSC: Was supported by a grant RD12/00369/0006 and 12/00539 from ISCIII (Spain), partially supported by European Regional Development FEDER funds. HEBCS: The HEBCS was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (132473), the Finnish Cancer Society and the Sigrid Juselius Foundation.
HEBON: The HEBON study is supported by the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI2007-3756, the Netherlands Organization of Scientific Research grant NWO 91109024, the Pink Ribbon grant 110005 and the BBMRI grant NWO 184. 021. 007/CP46. HUNBOCS: Hungarian Breast and Ovarian Cancer Study was supported by Hungarian Research Grant KTIA-OTKA CK-80745 and the Norwegian EEA Financial Mechanism HU0115/NA/2008-3/ÖP-9. ICO: The ICO-IDIBELL study was supported by grants from the Spanish Ministry of Health ISCIII FIS (PI10/ 01422, PI12/01528, and PI13/00285) and RTICC (RD12/0036/0008); the Ramón Areces (XV), Eugenio Rodríguez Pascual (2012), and Roses Contra el Cancer (2012) Foundations; the Spanish Association Against Cancer (AECC 2010); and the AGAUR Generalitat de Catalunya (2009-SGR290 and 2009-SGR293).
UCHICAGO: UCHICAGO is supported by NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032 and by the Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women's Cancer Research Alliance and the Breast Cancer research Foundation. OIO is an ACS Clinical Research Professor. UCSF: UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UPENN: National Institutes of Health (NIH) (R01-CA102776 and R01-CA083855; Breast Cancer Research Foundation; Rooney Family Foundation; Susan G. Komen Foundation for the cure, Basser Research Center for BRCA. WCP: The Women's Cancer Program (WCP) at the Samuel Oschin Comprehensive Cancer Institute is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN).
Acknowledgments This work is dedicated to the memory of  Olga M. Sinilnikova , who inspired the CIMBA group and led studies on BRCA1 modifiers. We wish to thank all study participants and clinicians for their valuable contributions to the iCOGS study. We also wish to thank the individual contributors from the following groups: BCFR, EMBRACE, GEMO, HEBON, kConFab, and  SWE-BRCA  (listed in S5 Table ). Additional acknowledgments are also detailed in S5 Table.