dataesr/scientific-paragraphs-categorization huggingface dataset:
To identify additional lung cancer susceptibility loci we conducted a meta-analysis of four lung cancer GWAS in populations of European ancestry, the MD Anderson Cancer Center (MDACC) GWAS; the Institute of Cancer Research (ICR) GWAS; the National Cancer Institute (NCI) GWAS and the International Agency for Research on Cancer (IARC) GWAS (Online Methods) that were genotyped using either Illumina HumanHap 317, 317+240S, 370Duo, 550, 610 or 1M arrays (Supplementary Table  1 ). After filtering the studies provided genotypes on 11,348 cases and 15,861 controls (Supplementary Table  1 ). Prior to undertaking meta-analysis of the GWAS, we searched for potential errors and biases in the datasets. Quantile-quantile (Q-Q) plots of genomewide association test statistics showed minimal inflation rendering substantial cryptic population substructure or differential genotype calling between cases and controls unlikely (λ=1.
Genome-wide association studies The meta-analysis was based on data from four previously reported lung cancer GWAS of European populations: the MD Anderson Cancer Center lung cancer study (MDACC-GWAS)  3  ; the UK lung cancer GWAS from the Institute for Cancer Research (ICR-GWAS)  6  ; the NCI lung cancer GWAS (NCI-GWAS)  13  and the IARC lung cancer GWAS (IARC-GWAS)  2. In each of the studies, SNP genotyping had been performed using Illumina HumanHap 317, 317+240S, 370, 550, 610 or 1M arrays (Supplementary Table  1 ). Tumour histology was abstracted from Certified Tumor Registrars and coded using WHO ICD-O-2 and ICD-O-3. Quality assurance was done by re-abstracting 10% of all cancer diagnoses per year. After initial data control, the NCI-GWAS included 5,739 cases and 5,848 controls; however, an additional 26 cases and 112 controls were excluded due to changes in case status and further quality control filtering.
NCI-GWAS- ICR-GWAS-This comprised 1,952 cases (1,166 male; mean age at diagnosis 57 years, SD 6) with pathologically confirmed lung cancer ascertained through the Genetic Lung Cancer Predisposition Study (GELCAPS) conducted between March 1999 and July 2004  48. All cases were British residents and self-reported to be of European Ancestry. To ensure that data and samples were collected from bona fide lung cancer cases and avoid issues of bias from survivorship only incident cases with histologically or cytologically (only if not AD) confirmed primary disease were ascertained. Tumours from patients were classified according to ICD-O3; Specifically, SQ: 8070/3, 8071/3, 8072/3, 8074/3; AD: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3, 8560/3, 8251/3, 8490/3, 8570/3, 8574/3; with tumours with overlapping histologies classified as mixed.
Patient DNA was derived from EDTA-venous blood samples using conventional methodologies. Genotype frequencies were compared with publicly accessible data generated by the UK Wellcome Trust Case-Control Consortium 2 (WTCCC2) study  49  of individuals from the 1958 British Birth Cohort (58BC) and blood service typed using Illumina Human1. 2M-Duo Custom_v1 Array BeadChips. MDACC-GWAS-Cases and controls were ascertained from a case-control study at the U. T. M. D. Anderson Cancer Center conducted between 1997 and 2007  3. Cases were newly diagnosed, patients with histologically-confirmed lung cancer presenting at M. D. Anderson Cancer and who had not previously received treatment other than surgery. Clinical and pathological data were abstracted from patient medical records and lung cancer histology was coded according to the major histological groups.
Specifically, as per ICD-O-2 these groups were, SQ: 8070/3, AD: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3, 8251/3 and 8490/3. Only patients with predominantly or wholly AD or SQ cancers were included; those with mixed histology or unspecified lung cancers, were excluded from the study. Controls were healthy individuals seen for routine care at Kelsey-Seybold Clinics, in the Houston Metropolitan area. Controls were frequency matched to cases according to smoking behaviour, age in 5-year categories, ethnicity, and sex. Former smoking controls were further frequency matched to former smoking cases according to the number of years since smoking cessation (in 5-year categories). After applying quality control data were available on 1,150 cases and 1,134 controls.
Quality control of GWAS datasets Standard quality control was performed on all scans excluding individuals with low call rate (<90%) and extremely high or low heterozygosity (i. e. P<1. 0×10 -4 ), as well as all individuals evaluated to be of non-European ancestry (using the HapMap version 2 CEU, JPT/CHB and YRI populations as a reference; Supplementary Table  1 ). For apparent firstdegree relative pairs, we removed the control from a case-control pair; otherwise, we excluded the individual with the lower call rate.
Replication series To validate promising associations from meta-analysis were made use of in silico data and imputed genotypes from Harvard and deCODE GWAS datasets together with data from direct genotyping Heidelberg-EPIC, ICR, IARC and Toronto replication series. Harvard For the Harvard Lung Cancer Susceptibility Study, details of participant recruitment have been described previously  50. Replication was based on data derived from 1,000 cases and 1,000 controls genotyped using Illumina Humanhap610-Quad arrays. Cases were patients aged >18 years, with newly diagnosed, histologically confirmed primary NSCLC. Controls were healthy non-blood-related family members and friends of patients with cancer or with cardiothoracic conditions undergoing surgery. The histological classification of lung tumors was performed by two staff pulmonary pathologists at the Massachusetts General Hospital according to ICD-O-3; Specifically, AD: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3 8560/3; LCC: 8012/3, 8031/3; SQ: 8070/3, 8071/3, 8072/3, 8074/3; and other NSCLC: 8010/3, 8020/3, 8021/3, 8032/3, 8230/3.
cancerregistry. is). The sources of data in the ICaR are all pathology and hematology laboratories, all hospital departments and health care facilities in the country. ICaR registration is based on the ICD system and included information on histology (Systemized Nomenclature of Medicine, SNOMED). The ICaR registration also uses the ICD-O system which takes histology diagnosis into account. Over 94% of diagnoses in the ICaR have histological confirmation. According to the ICaR, Briefly, according to the ICaR a total of 4,252 lung cancer patients were diagnosed from January 1, 1955, to December 31, 2010. Recruitment of both prevalent and incident cases was initiated in 1998, the recruitment is ongoing and DNA samples from lung cancer cases are subjected to whole-genome genotyping as they are collected.
The Icelandic sample set is drawn from the Icelandic population, a small homogeneous founder population with almost no detectable population substructure. Thus there was no need adjust for such substructure in the association analysis. In addition, the comprehensive Icelandic genealogy database allowed us to exclude individuals not of Icelandic origin from the analysis. SNP genotypes were phased using the method of long range phasing  51  ; for the HumanHap series of chips, 304,937 SNPs were used for long range phasing, whereas for the Omni series of chips 564,196 SNPs were used. An initial imputation step was carried out on each chip series separately to create a single harmonized, long-range phased genotype dataset consisting of 707,525 SNPs for 95,085 Icelandic individuals. Two sets of genotypes were imputed into this dataset with methods previously described 52 : (i) genotypes for about 38 million variants using the 1000 genomes phase I integrated variant set (v3) as training set, and (ii) genotypes for about 34 million variants identified in 2,230 whole genome sequenced Icelanders.
Lung cancer cases were recruited at the hospitals in the network of University of Toronto. Controls were randomly selected from individuals registered in the family medicine clinics databases, frequency matched with cases on age and sex. All subjects were interviewed and information on lifestyle risk factors, occupational history, medical and family history collected using a standard questionnaire. Tumours were centrally reviewed by the reference pathologist (a member of the IASLC committee) and a second pathologist in the University Health Network. If reviews conflicted, consensus was arrived at following discussion. Coding of histology was based on 2001 WHO/IASLC. After applying standardized quality control procedures and restricting to participants with self-reported European ancestry, data and samples were available on 1,084 cases and 966 controls.
Statistical and bioinformatic analysis Data were imputed for all scans for over 10 million SNPs using data from the 1000 Genomes Project (Phase 1 integrated release 3, March 2012) as reference, using IMPUTE2 v2. 1. 1  53  , MaCH 58 v1. 0 or minimac (version 2012. 10. 3) 59 software (Supplementary Table  1 ). Genotypes were aligned to the positive strand in both imputation and genotyping. Imputation was conducted separately for each scan in which prior to imputation each GWAS dataset was pruned to a common set of SNPs between cases and controls. As previously advocated we set thresholds for imputation quality to retain both potential common and rare variants for validation  13, 60. Specifically, poorly imputed SNPs defined by an RSQR<0. 30 with MaCH or an information measure Is<0. 40 with IMPUTE2 were excluded from the analyses.
ACKNOWLEDGEMENTS We would like to thank all individuals who participated in this study. Additionally we are grateful to patients' clinicians and allied health care professions. We thank  Zhuo Chen  and  Kevin Boyle  for sample handling and data management of the Toronto study; additionally,  Laura Admas  and  Li Rita Zhang  for the field recruitment. We thank  Li Su ,  Yang Zhao ,  Geoffrey Liu ,  John Wain ,  Rebecca Heist  and  Kofi Asomaning. We thank  G. Thomas  and  Synergy Lyon Cancer  ( Lyon France ) for High Performance Computing support and  Dr J. Olivier  and  A. Chabrier  for IARC's PGM Ion torrent sequencing optimisation and Taqman genotyping, respectively. We thank  David Goldgar  for sharing information from CIMBA on sequence variation in BRCA2 from familial breast cancer analysis.
1. 05/2. 1. 00/03. 0101 ), NIH  ( R01-CA111703  and  UO1-CA63673 )  Fred Hutchinson Cancer Research Center, US National Cancer Institute (NCI)  ( R01 CA092039 ),  FP7  grant ( REGPOT 245536 ), the  Estonian Government  ( SF0180142s08 ),  RDF in the frame of Centre of Excellence in Genomics and Estoinian Research Infrastructure 's Roadmap and by  University of Tartu  ( SP1GVARENG ) and an  IARC Postdoctoral Fellowship  (MNT). NCI:  Intramural Research Program  of  NIH ,  NCI, U. S. Public Health Service contracts NCI  ( N01-CN-45165 ,  N01-RC-45035 ,  N01-RC-37004 ,  NO1-CN-25514 ,  NO1-CN-25515 ,  NO1-CN-25512 ,  NO1-CN-25513 ,  NO1-CN-25516 ,  NO1-CN-25511 ,  NO1-CN-25524 ,  NO1-CN-25518 ,  NO1-CN-75022 ,  NO1-CN-25476 ,  NO1-CN-25404 ), The  American Cancer Society , The  NIH Genes , Environment and Health Initiative partly by  HG-06-033-NCI-01  and  RO1HL091172-01 , genotyping at the  Johns Hopkins University Center for Inherited Disease Research  ( U01HG004438 ,  NIH HHSN268200782096C ) and study coordination at the  GENEVA Coordination Center  ( U01 HG004446 ).
Characteristics of patients and healthy subjects Sixteen patients referred to the University Hospital Zurich, Switzerland, for coronary angiography with the diagnosis of ACS between 08/2010 and 11/2011 were enrolled as part of a larger cohort study (SPUM-ACS, clinical trial number NCT01000701). All patients aged 18 years and older presenting within 72 h after pain onset with the main diagnosis of ST-elevation myocardial infarction (STEMI) or non-ST elevation myocardial infarction (NSTEMI) were included in the study. Patients had symptoms compatible with angina pectoris (chest pain, dyspnoea) andat least one of the following inclusion criteria: (i) persistent ST-segment elevation or depression, T inversion or dynamic ECG changes, new LBBB; (ii) evidence of positive Troponin based on high sensitive TnT measurement (≥14 ng/L) with rise and/or fall in serial TnT levels.
Individuals were included following a detailed medical history, physical exam with vital signs, and blood draw. Exclusion criteria comprised autoimmune disease or neoplasm, inability to comprehend study, treatment with corticosteroids or other immunomodulatory therapy, or vaccination in the three preceding months. All individuals were older than 18 years of age with birth control in place for at least 2 months prior to inclusion in women of childbearing age in the absence of pregnancy or breast feeding. Informed consent was obtained from all individuals with the approval by the Kantonale Ethik-Kommission Zurich, Switzerland (EK-1688), for patients with ACS and the Comitede Protection des Personnes CPP Sud-Est III, France (Numero EudraCT : OL039/Susa), for healthy subjects.
Each Vx-J1, J2, J3, J4, Jn product was separated as a function of its size. Constel'ID w software (ImmunID Technologies) was used for further analytical studies, including generation of three-dimensional repertoire illustration. Numeration and diversity of T cells is presented together through NDL w scoring which enables to determine a correlation between lymphocyte counts and T cell diversity. A decreased percentage (i. e. a decreased TCR diversity) defines a state of divpenia (www. divpenia. com). Normal values for TCR b-chain combinatorial diversity were defined based on a cohort of 16 healthy volunteers extracted from a larger study (SuSa Study, AFSSAPS clinical trial number 2010-A00428-31, ImmunID; age range, 42. 8 -74. 9 years; 6 females and 10 males).
Using an immunization strategy, we and others identified antigen-specific Treg as a critical component of atheroprotection in mice. 11 -13 However, more data on the origin, recruitment, and kinetics of Treg during myocardial infarction are needed before such an approach can be evaluated in a clinical trial. The recently completed phase II GLACIER study (NCT01258907) reported no change in inflammatory activity in an index arterial vessel after 12 weeks of treatment with a monoclonal antibody targeting oxidized forms of LDL compared with controls, as measured by FDG-PET/CT imaging ([18F ]-2-deoxyglucose positron emissiontomography/computed tomography but showed a good safety profile. Pending full publication of the data, the choice of primary endpoint, treatment duration, and patients appear pivotal for future trials to address efficacy of immunotherapy in atherosclerotic disease.
An additional gate generator was used to veto additional triggers for 3 µs after an accepted trigger to allow the T3B DAQ to record the full time window for each event and to provide sufficient time for all oscilloscopes to return to the state of waiting for triggers. Since the trigger rate was typically at the level of a few 100 Hz this did not result in a significant dead time of the data acquisition. For both setups, the trigger scintillator coincidence signal was also recorded on one channel of the T3B data acquisition, to identify potential double particle events and to reject calibration triggers of the W-AHCAL DAQ. Particle identification was possible via two threshold Cherenkov detectors upstream of the calorimeter prototypes, with their thresholds set to distinguish between pions and kaons and between kaons and protons.
Data Set and Event Reconstruction The data samples considered in this paper are taken from run periods in September 2011 together with the W-AHCAL, and in October 2011 with the SDHCAL. In both test beam programs data were collected at various energies and with both positive and negative polarity. Due to the small active area of the T3B experiment, large event samples are needed for a precise measurement of the late shower components, so we concentrate here on the analysis of data taken with positive mixed hadron beams at 60 GeV, which is by far the largest data sample available at one energy for both configurations. For the runs with the W-AHCAL and the SDHCAL the same beam settings were used, resulting in an identical muon contamination of the hadron beams of approximately 5%. Since no tail catcher that could be used as a muon veto was available for the SDHCAL data run, and a full synchronisation of the data streams of the W-AHCAL and T3B was not performed, muons events could not be rejected.
They are present in both the tungsten absorber and the steel absorber data set. In addition to the hadron runs, large samples of muon data with an energy of 180 GeV were collected for calibration purposes together with the SDHCAL. The data are stored as raw waveforms by the T3B DAQ. These waveforms are further processed by a dedicated calibration and reconstruction software, which identifies the time of each firing microcell of the photon sensor and provides a time distribution of identified photon equivalents (p. e. ) with sub-nanosecond resolution. From the identified single photon signals, detector hits are reconstructed by requiring a minimum of 8 p. e. within a time window of 9. 6 ns. This time window is chosen since it approximately corresponds to the recovery time of the photon sensor, thus making the occurrence of afterpulses within this time window very unlikely.
Results In this section, the analysis results of a reconstructed and calibrated subset of the test beam data acquired with the T3B experiment are presented. The analyzed data comprise hadron shower events (from impinging π + and protons, with a fractional contribution of 60% and 40%) at 60 GeV as well as muon data at 180 GeV beam energy. Since muons do not induce hadronic cascades, their energy depositions occur promptly. At the same time, the muon data are also subject to all detector effects. Thus, the timing of the hadron data sets is investigated relative to the standard signal of muons. Unless stated otherwise, the analysis includes all T3B first hits in a time window of -20 ns to 200 ns around the hardware trigger time (t = 0). Each hit is characterized by its time, which is determined with a precision of approximately 1 ns  [6] , by its energy in units of MIP and by its distance from the beam axis, given by the position of the cell it occurs in.
Comparison of Data to Monte Carlo Simulations To determine the accuracy of shower timing in GEANT4, the T3B data are compared to simulations based on different hadronic physics models, as introduced in Section 5. Figure  7  shows the time distribution of the first hits in steel and tungsten absorbers compared to the three physics lists. While QBBC and QGSP_BERT_HP reproduce the distribution well for both absorbers, QGSP_BERT shows some discrepancy with the data. In steel, the shower activity in the intermediate time period from 10 ns to 60 ns is slightly underestimated, while in tungsten the late component > 50 ns is overestimated by up to a factor of four. Figure  8  shows the energy dependence of the mean time of first hit, which is well reproduced by all hadronic models considered here in steel, but deviates substantially for QGSP_BERT in tungsten.
4 µs. The data show late components of hadronic showers, which are substantially more pronounced in tungsten than in steel. The late component is predominantly concentrated at lower hit energies, but also extends up to several MIP-equivalents in tungsten. The importance of the late energy depositions increases with increasing distance from the shower axis, due to the wide lateral spread of late activity driven by neutrons in contrast to the more concentrated evolution of the prompt electromagnetic subshowers and relativistic hadrons. The comparison of detailed detector simulations with the data shows that the time structure is generally quite well modelled in steel. In contrast, the tungsten data is only reproduced by models with a dedicated treatment of low-energy neutrons, such as the high-precision neutron package in QGSP_BERT_HP, or alternative implementations used in the QBBC model.
Acknowledgements We gratefully acknowledge help by the technical staff at several CALICE institutes for their help with the WAHCAL / T3B, and SDHCAL / T3B test beams. We also gratefully acknowledge the DESY and CERN managements for their support and hospitality, and their accelerator staff for the reliable and efficient beam operation. The authors would like to thank the  RIMST (Zelenograd) group  for their help and sensors manufacturing. This work was supported by the  European Commission  under the  FP7 Research Infrastructures project AIDA , grant agreement no. 262025 ; by the  Bundesministerium für Bildung und Forschung, Germany ; by the the  DFG cluster of excellence 'Origin and Structure of the Universe' of Germany ; by the  Helmholtz-Nachwuchsgruppen  grant  VH-NG-206 ; by the  BMBF , grant no.
ACKNOWLEDGMENTS This study was funded through a grant ( DE-FG02-09ER15526 ) from the  Physical Biosciences Program of the Office of Basic Energy Sciences, U. S. Department of Energy , to  C. J. S. A grant to E. S. , M. A. , and  C. J. S. from the  US-Israel Binational Agricultural Research and Development Fund  ( IS-4038-07 ) allowed the preparation of the CP OX constructs and transgenic lines. The TIRF microscopy facility was funded in part by the  Bindley Bioscience Center at Purdue University. We are grateful to  Hongbing Luo  for excellent care and maintenance of plant materials.
All these background shapes are determined from simulation. To reduce the number of free parameters in the fit to the K * ± K ∓ sample, the yields of the backgrounds from charmless hadronic B meson decays with missing particles are fixed relative to the yield for the B 0 → K * + π -cross-feed component according to expectation. The yield of the B + → D 0 h + , D 0 → K 0 S π + π -component is determined from the fit to data. The yield for the Λ 0 b → K * -p contribution is also a free parameter in the fit to K * ± K ∓ candidates, but is fixed to zero in the fit to K * ± π ∓ candidates. The combinatorial background is modelled with linear functions in both B and K * candidate mass distributions, with parameters freely varied in the fit to data except for the m 0 threshold in B candidate mass, which is fixed from fits to sideband data.
Sequence of the isogenic wild-type strain JJC40 was determined in parallel and reads from mutant and wild-type genomes were aligned using Illumina's package CASAVA-1. 7. 0. The point mutation was detected by Illumina's package CASAVA-1. 7. 0, and the 10 kb duplication using Illumina's software GenomeStudio. b-galactosidase assays. b-galactosidase assays for measures of SOS induction were performed as described  [46]. Since isolated JJC2067 colonies could not be propagated owing to the growth advantage of suppressor mutations, pAM-holD containing clones were grown overnight in MM lacking IPTG and diluted 50 fold in MM for the experiment. Cultures were checked for the loss of pAM-holD and for containing at most 1% suppressor mutations. The same procedure was used for JJC2068 as a control.
Acknowledgments We are grateful to  Dr. John Pearson  for helpful reading of the manuscript and to  Dr. Taekjip Ha  for helpful discussion. We thank  Dr. Michael Cox  and  Dr. J. Camberg  for the gift of antibodies against SSB and FtsZ respectively, and  Drs. Russell Maurer ,  Steve Sandler  and  James Keck  for strains and plasmids. We thank  Dilyana Dimitrova  for performing bgalactosidase assays.