dataesr/scientific-paragraphs-categorization huggingface dataset:
We require one electron or one muon in the final state, along with several jets. All decay modes of the T and B quarks produce t quarks and/or W bosons, which are the dominant sources of leptons. In the high mass region that we consider, the decay products can have a large Lorentz boost and result in highly collinear final state particles. This search makes use of techniques to identify b quark jets and reconstruct hadronic decays of massive particles that are highly Lorentz-boosted in the reference frame of the TT system. The data are analyzed in two channels that are optimized for sensitivity to either boosted W or Higgs bosons, referred to as the "boosted W" and "boosted H" channels. The boosted W channel is most sensitive to scenarios where the T quark has a large branching fraction for bW decays (such as the electroweak singlet benchmark) while the boosted H channel has the highest sensitivity to scenarios with a large branching fraction to tH (such as the electroweak doublet benchmark).
Serum bank With human research ethics approval from the American Samoa Institutional Review Board, RRV serology was performed on 200 serum bank samples collected for a leptospirosis study in 2010. 13, 14 The community-based cross-sectional study included adults (aged 18 years and over) from all five inhabited islands of American Samoa. The study was designed to include a representative sample of the adult population in American Samoa, and consisted of random sampling on the main island of Tutuila (where >95% of the population reside) and the adjacent island of Aunu'u, and convenience sampling on the very small and remote Manu'a islands. Questionnaires were used to collect data on demographics, occupation, recreational activities, and household characteristics.
Independent variables associated with the outcome by a likelihood ratio test p-value of <0. 1 were subjected to a stepwise backward elimination process (p < 0. 05) to select the final variables for the multivariable model. Statistically significant odds ratios (OR) are indicated in the Table. Stata version 11. 1 software (StataCorp, College Station, TX, USA) was used for the statistical analyses; p-values of <0. 05 were considered statistically significant.
The information provided by the ELISA and neutralization tests differs, and the discrepancies between the results may be explained by the fact that, although detectable by ELISA, most of the anti-RRV IgG only had limited neutralizing capacity. This hypothesis is in accordance with the data presented in Figure  1     showing that the higher the specific absorbance by ELISA, the higher the percentage of RRV-positive sera by neutralization. This study included only samples from adults, so it is not possible to know whether RRV has circulated recently in American Samoa. However, the detection of imported cases from Fiji in 2009 and RRV seropositive persons living in French Polynesia since 2006 suggests that RRV may be circulating undetected in several PICTs, possibly misdiagnosed as dengue virus (DENV), CHIKV, or Zika virus (ZIKV) infections.
Sown functional diversity The four fl ower mixtures (the fi fth one being the control) were chosen based on their FD values using Rao's quadratic index  (Botta-Dukát, 2005). To create the mixtures, 20 fl ower species were chosen and described based on seven of their functional traits that were retrieved from  Lambinon et al. (2008)  and the TRY database  (Kattge et al. , 2011)  (Table  S1 ). The seven traits were chosen based on their potential effect on fl ower visitors. First, visual cues are known to be decisive for insects when selecting a plant  (Campbell et al. , 2010). Hence, the following traits were considered: (i) fl ower colour, (ii) UV refl ectance of the peripheral part of the fl ower, and (iii) whether the UV refl ectance of the internal fl ower part differed from that of the external part (also called "UV pattern").
Statistical analyses First, the linear relation between the sown and realized FD in each plot was tested for both years by using Pearson's correlation (P < 0. 05). Second, the effects of the sown mixtures on insect abundance (response variables: all predators pooled, and lacewings, ladybeetles, hoverfl ies individually) and total predator species richness (species of the three predator families pooled) were assessed by fi tting generalised linear mixed effect models (R function "glmer", package "lme4";  Bates et al. , 2014)  with Poisson error distribution (log-link function). Mixtures (C, VL, L, H, VH), years  (2014, 2015)  and their interaction were included as fi xed factors, and the plots included as random effects, as measures were repeated on seven consecutive occasions in the same plots each year.
For every model, data over-dispersion was tested and found to occur for the summed predator, ladybeetle and hoverfl y abundance. For these variables, generalised linear models with negative binomial error distribution were fi tted (R function "glm. nb", package "MASS", Venables & Ripley, 2002), as suggested by Ver  Hoef & Boveng (2007). The effects of fi xed factors in every model were tested using likelihood-ratio tests (P < 0. 05). When their interaction was signifi cant, analyses were performed for each year separately. Third, the effect of the realized FD on insect abundance (response variables: all predators pooled, and lacewings, ladybeetles, hoverfl ies individually) and total predator species richness (numbers of species of the three predator families pooled) were assessed for each year separately, as each year each plot potentially had a different value of FD, by fi tting generalised linear mixed effect models.
FD values were included as fi xed factors and plots were included as random factors. Data over-dispersion was tested and found to occur for the summed predator, ladybeetle and hoverfl y abundance in 2014. Thus, generalised linear models with negative binomial error distribution were fi tted instead. The effects of realized FD in every model were tested using likelihood-ratio tests (P < 0. 05). All analyses were done using R program (R Core  Team, 2013).
ACKNOWLEDGMENTS. The authors thank the  Experimental Farm of Gembloux Agro-Bio Tech  ( University of Liège ) for providing technical support,  F. Van De Meutter  (The  Research Institute for Nature and Forest -INBO, Belgium ) for verifying hoverfl y identifi cations, the TRY initiative on plant traits (http://www. try-db. org)for providing the data on fl ower traits, and the two anonymous reviewers whose accurate comments and suggestions improved the manuscript. The  Belgian National Fund for Scientifi c Research (FNRS)  awarded  FRIAs  ( Fonds pour la Recherche en Industrie et Agronomie) PhD scholarship  to  T. Lopes. S. Hatt ,  R. Uyttenbroeck , and this study in general was funded by  CARE AgricultureIsLife (University of Liège ) and the  Chinese Opening Fund of State Key Laboratory for Biology of Plant Diseases and Insect Pests  ( SKLOF201601 ).
The stated changes allow to assume that ozone has a favorable effect on the regeneration of mucous membrane due to the improvement of tissue microcirculation, tissue oxygenation, activation of oxygen depending enzymatic processes and reduction of inflammation. To our mind, the stated results are extremely important for the solvation of the problem of adaptation of patients to removable dentures, as their negative effect, first of all, is reflected on the mucous membrane of the denture-bearing area, which is initially not intended for functioning in such conditions. The data of laser Doppler flowmetry, which were used to evaluate the parameters of tissue microcirculation in the mucous membrane of the denture-bearing area, showed an expressed positive effect of the ozone therapy. These data are explained by such ozone effect as biochemical activation of the functions of erythrocytes, increase of call membrane flexibility, improvement of tissue oxygenation (including the endothelium of blood vessels), activation of nitrogen oxide synthesis, which leads to the vascular distention.
Because this gene expression change was observed in both HFD and ob/ob mice, it was clear that the change is not the result of a specific genetic background. More likely, these data suggest that WISP1 expression in adipose tissue could play a critical Lysates were subjected to anti-Flag or anti-PPARγ immunoprecipitation (IP) and followed by immunoblotting (IB) with anti-PPARγ or anti-Flag antibodies respectively. Protein expressions were determined by direct immunoblotting (Input). Two independent experiments were performed. (d) MEF cells lysates were subjected to anti-WISP1 or anti-PPARγ immunoprecipitation (IP) and followed by immunoblotting (IB) with anti-PPARγ or anti-WISP1 antibodies, respectively. Immunoprecipitations with IgG were used as controls. Protein expressions were determined by direct immunoblotting (Input).
Bands were revealed with an enhanced chemiluminescence detection system (Millipore) and visualized on Chemidoc systems (Biorad)  45. Protein expression was quantified by densitometric analysis of the immunoblots using Image Lab software developed by Bio-Rad. For quantification of WISP1 in the conditioned cell culture supernatant, sandwich ELISA were performed using isoform-specific capture and detection antibodies (R&D Systems) according to the manufacturers' instructions. Immunoprecipitation and ubiquitination assay. Cells extracts were prepared using RIPA buffer supplemented with protease inhibitor cocktail (Merck Millipore) and 1 mM PMSF, cleared by centrifugation, and incubated with PPARγ or Flag antibodies overnight, followed by adsorption to Sepharose-coupled protein G for 1 hour. Immune complexes were washed five times with TNMG buffer (20 mM Tris; HCl pH 8, 150 mM NaCl, 5 mM MgCl 2 , 0,5% NP40, 10% Glycerol), separated by SDS-PAGE, and analyzed by immunoblotting.
Lysates were subjected to immunoblotting with anti-PPARγ and anti-Flag antibodies. Immunoblot signals were quantified by densitometry, and normalized with β-actin. Data were expressed as means ± SEM; *p < 0. 05; **p < 0. 01; ***p < 0. 005. Data are representative of 3 independent experiments (c) MEF cells were transfected with 2 µg of Flag-WISP1expression vector in combination with 2 µg of PPARγ expression vector for 24 hours. Lysates were subjected to anti-Flag or anti-PPARγ immunoprecipitation (IP) and followed by immunoblotting (IB) with anti-PPARγ or anti-Flag antibodies respectively. Protein expressions were determined by direct immunoblotting (Input). Two independent experiments were performed. (d) MEF cells lysates were subjected to anti-WISP1 or anti-PPARγ immunoprecipitation (IP) and followed by immunoblotting (IB) with anti-PPARγ or anti-WISP1 antibodies, respectively.
Acknowledgements We thank  Dr L. Fajas  and  Dr N. Hennuyer  for providing various vectors. We like to acknowledge  Marie Garcia  and  Dr Cécile Martinerie  from  INSERM U938  for animal studies and adipose tissue fractionation samples. This study was supported by  INSERM ,  CNRS ,  Sorbonne Universités UPMC Univ Paris 06  and the  Groupement d'Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC)  Grant  N°R14211DD. M. Z was supported by a doctoral research fellowship from  Cancéropôle Ile-de-France  ( J14U280 ).
Study population The design of the SPIRIT Women study was reported elsewhere. [12, 13]  The trial was conceived as a substudy of the main SPIRIT Women study that was registered in 2007 (NCT 00496938). Subsequent changes in the original protocol were made and the randomized study was registered a separate one in 2010 (NCT01182428). Complete study protocol is available among Supporting Information files (S1 Study Protocol). 455 female patients admitted for PCI at 25 centers (22 in Europe and 3 in South America), between September 2008 and December 2009, were randomly assigned (2:1) to receive DP-EES (Xience V and Xience Prime, Abbott Vascular, Santa Clara, CA, USA) or DP-SES (Cypher Select, Cordis, Miami Lakes, Florida, USA). The trial was funded by Abbott Vascular. The study complied with the declaration of Helsinki and was approved by the institutional review board at each participating center (S1 Table ).
This study was funded by  Abbott Cardiovascular Systems, Inc. , a subsidiary of  Abbott Vascular, from Abbott, Biotronik, Boston Scientific, Biosensors, Medtronic, Edwards , and  St Jude. J. Mauri  has received research grants from  Abbott Vascular. Peter Ju  ¨ni has received research grants to the institution from  Astra Zeneca ,  Biotronik ,  Biosensors International ,  Eli Lilly  and  The Medicines Company , and serves as unpaid member of the steering group of trials funded by  Astra Zeneca ,  Biotronik, Biosensors, St. Jude Medical  and  The Medicines Company.
Acknowledgments: We are indebted to the  Parc Natural Régional de Camargue (PNRC)  for the coordination of this study, and to the  Region Provence Alpe Côte d'Azur , the  Departmental Council of Bouches-du-Rhône , and the local authorities of  Arles-Crau-Camargue-Montagnette  and  Metropole Aix-Marseille-Provence  for funding the scientific monitoring and use of Techno Bam traps. We are grateful to  Loïc Willm  for producing the map of the study site and to all volunteers who assisted in the collection of samples and kindly lent their calf to science, in particular  Catherine Lavallée-Chouinard ,  Erika Audry , and  Céline Hanzen.
Subjects came from 68 studies collaborating in the Breast Cancer Association Consortium (BCAC) and Discovery, Biology and Risk of Inherited Variants in Breast Cancer Consortium (DRIVE) (Supplementary Table  1 ). Using the 1000 Genomes Project (Phase 3) reference panel, we imputed genotypes for ˜21M variants. After filtering on minor allele frequency (MAF)>0. 5% and imputation quality score>0. 3 (see Online Methods), we assessed the association between breast cancer risk and 11. 8M SNPs adjusting for country and ancestry-informative principal components. We combined these results with results from the iCOGS project (46,785 cases and 42,892 controls)2 and 11 other breast cancer GWAS (14,910 cases, 17,588 controls), using a fixed-effect metaanalysis. Of 102 loci previously associated with breast cancer in Europeans, 49 showed evidence for association with overall breast cancer in the OncoArray dataset at P<5x10 -8 and 94 at P<0.
05. Five additional loci previously shown to be associated with breast cancer in Asian women also showed evidence in the European ancestry OncoArray dataset (P<0. 01; Supplementary Tables 2-4)3-5. We also assessed the association with breast cancer in Asians including 7,799 cases and 6,480 controls from the OncoArray project and 6,269 cases and 6,624 controls from iCOGS. Of the 94 loci previously identified in Europeans that were polymorphic in Asians, 50 showed evidence of association (P<0. 05). For the remaining 44, none showed a significant difference in the estimated odds ratio (OR) for overall breast cancer between Europeans and Asians (P>0. 01; Supplementary Table  5 ). The correlation in effect sizes for all known loci between Europeans and Asians was 0. 83, suggesting that the majority of known susceptibility loci are shared between these populations.
Online Methods Details of the studies and genotype calling and quality control (QC) for the iCOGS and eleven other GWAS are described elsewhere2,20. Seventy-eight studies participated in the breast cancer component of the OncoArray, of which 67 studies contributed European ancestry data and 12 contributed Asian ancestry data (one study, NBCS, was excluded as there were no controls from Norway) (Supplementary Table  1 ). The majority of studies were population-based case-control studies, or case-control studies nested within populationbased cohorts, but a subset of studies oversampled cases with a family history of the disease. All studies provided core data on disease status and age at diagnosis/observation, and the majority provided additional data on clinico-pathological factors and lifestyle factors, which have been curated and incorporated into the BCAC database (version 6).
001, poor Illumina intensity and clustering metrics, or deviation from the expected frequency as observed in the 1000 Genomes Project. This resulted in manual adjustment of the cluster file for 3,964 variants, and the exclusion of 16,526 variants. The final cluster file was then applied to the full dataset. We excluded probable duplicates and close relatives within each study, and probable duplicates across studies. We excluded samples with a call rate <95% or samples with extreme heterozygosity (4. 89 SD from the mean for the ethnicity). Ancestry was computed using a principal component analysis, applied to the full OncoArray dataset, using 2318 informative markers on a subset of ~47,000 samples. The analysis presented here was restricted to women of European ancestry, defined as individuals with an estimated proportion of European ancestry >0.
8, and women of East Asian ancestry (estimated proportion of Asian ancestry >0. 4), with reference to the HapMap (v2) populations, based on the first two principal components. After quality control exclusions and removing overlaps with the previous iCOGS and GWAS genotyping used in the analysis, the final dataset comprised data from 61,282 cases and 45,494 of European ancestry 7,799 cases and 6,480 controls of Asian ancestry. We excluded SNPs with a call rate <95% in any consortium, SNPs not in Hardy-Weinberg equilibrium (P<10 -7 in controls or P <10 -12 in cases) and SNPs with concordance <98% among 5,280 duplicate sample pairs. For the imputation, we additionally excluded SNPs with a MAF<1% and a call rate <98% in any consortium, SNPs that could not be linked to the 1000 Genomes Project reference or differed significantly in frequency from the 1000 Genomes Project dataset (using the criterion where p 0 and p 1 are the MAFs in the 1000 Genomes Project and OncoArray European datasets, respectively).
Genotype Imputation All samples were imputed using the October 2014 (version 3) release of the 1000 Genomes Project dataset as the reference panel and number of sampled haplotypes per individual (Nhap)=800. The iCOGS, OncoArray and nine of the GWAS datasets were imputed using a two-stage imputation approach, using SHAPEIT2 for phasing and IMPUTEv2 for imputation21,22. The imputation was performed in 5Mb non-overlapping intervals. The subjects were split into subsets of ~10,000 samples; where possible subjects from the same study were included in the same subset. The BPC3 and EBCG studies were imputed separately using MACH and Minimac23,24. 99. 6% of SNPs with frequency >1% were imputable with r 2 >0. 3 in the OncoArray dataset and 99. 1% in the iCOGS dataset. We generated estimated genotypes for all SNPs that were polymorphic (MAF>0.
1%) in either European or Asian samples (˜21M SNPs). For the current analysis, however, we restricted to SNPs with MAF>0. 5% in the European OncoArray dataset (11. 8M SNPs). One-step imputation (without pre-phasing) was performed, on the iCOGS and OncoArray datasets, as a quality control step for those associated loci where the imputation quality score was <0. 9. Imputation quality for the lead variants, as assessed by the IMPUTE2 quality score in the OncoArray dataset, was >0. 80 for all but one locus (Supplementary Table  28 ) rs72749841, quality score=0. 65).
Principal Components Analysis To adjust for potential (intra-continental) population stratification in the OncoArray dataset, principal components analysis was performed using data from 33,661 uncorrelated SNPs (which included 2,318 SNPs specifically selected on informativeness for determining continental ancestry) with a MAF of at least 0. 05 and maximum correlation of 0. 1 in the OncoArray dataset, using purpose-written software (http://ccge. medschl. cam. ac. uk/software/ pccalc). For the main analyses, we used the first ten principal components, as additional components did not further reduce inflation in the test statistics. We used nine principal components for the iCOGS and up to ten principal components for the other GWAS, where this was found to reduce inflation.
As an additional sensitivity analysis, we computed the effect sizes for the 65 novel loci adjusting for studythe effect sizes were essentially identical to those presented. Estimates were derived using ProbAbel for the BPC3 and EBCG studies25, SNPTEST for the remaining GWAS and purpose written software for the iCOGS and OncoArray datasets. OR estimates and standard errors were combined in a fixed effects inverse variance meta-analysis using METAL26, adjusting the GWAS (but not iCOGS or OncoArray) results for genomic control as described previously2. For the GWAS, results were included in the analysis for all SNPs with MAF>0. 01 and imputation r 2 >0. 3. For iCOGS and OncoArray we included all SNPs with r 2 >=0. 3 and MAF>0. 005 (11. 8M SNPs in total). We viewed the primary tests of association as those based on all the meta-analysis over all stages, as this has been shown to be powerful than tests based on a test-replication approach27.
Eight sets of variants were associated with breast cancer at P<5x10 -8 but were close to previous susceptibility regions, and these became non-significant after adjustment for the previously identified lead variant. Two SNPs on 22q13. 2, rs141447235 and rs73161324, were both associated with overall breast cancer but, despite lying >500kb apart, were strongly correlated with each other (r 2 =0. 50) and hence were considered as a single novel signal. For SNPs showing evidence of association, we additionally computed genotype-specific ORs for the iCOGS and OncoArray dataset, and per-allele ORs for ER-negative and ERpositive disease. Departures from a log-additive model were evaluated using a one degree of freedom likelihood ratio test, comparing the log-additive model (genotypes parametrised as the number of rare alleles carried) with the general model estimating ORs for each genotype.
The genotype-specific risks for all variants were consistent with a log-additive model (P>0. 01; Supplementary Table  29 ). Tests for differences in the OR by ER-status were derived using case-only analyses, in which estimates were derived by logistic regression separately in the iCOGS and OncoArray datasets, adjusted as before, and then combined in a fixed-effects meta-analysis. These analyses were performed in R28. We assessed heterogeneity in the OR estimates among studies within each of the OncoArray, iCOGS and GWAS components, and between the (combined) estimates for the three components, using both the I 2 statistic and the P-value for Cochran's Q statistic (Supplementary Table  28 ). There was no evidence of heterogeneity among studies in the ORs for any of the loci in the OncoArray, but three loci showed some evidence of heterogeneity in the ORs among the GWAS, iCOGS and OncoArray datasets.
To determine whether there were multiple independent signals in a given region, we performed multiple logistic regression analysis using SNPs within 500kb of each lead SNP, adjusting for the lead SNP. We used the genotypes derived by one-step imputation, performed the analyses separately in the iCOGS and Oncoarray datasets and combined the results (adjusted effect sizes and standard errors) using a fixed effects meta-analysis. For one of the two loci for which there was an additional signal significant at P<5x10 -8 , the lead SNP from the one-step imputation differed from the lead SNP in the overall analysis, but was strongly correlated with it (Supplementary Table  9 ).