dataesr/scientific-paragraphs-categorization huggingface dataset:
All subjects were genotyped using the Illumina HumanCore Exome chip set. Principal Components Analysis and k-means were then used to characterize the ancestry of the participants. A random sample of subjects from the main "European" cluster was genotyped with the Omni5 chip set. These data were then used to impute missing genotypes from the remaining subjects genotyped only using the HumanCore Exome array. From the 9836 patients, a total of 8703 cases including 3230 patients with human epidermal growth factor receptor 2-positive breast cancer were analyzed. Despite having 80% power to detect an odds ratio of 1. 23 in this population, no variant achieved genome-wide significance for association with the occurrence of human epidermal growth factor receptor 2-positive breast cancer vs. any other subtype of breast tumour.
METHODS PHARE was a randomized phase 3 clinical trial comparing 6-month and 12month adjuvant trastuzumab exposures (NCT00381901) and included a subset of 1430 HER2-positive breast cancer cases with germline DNA available for GWAS analyses. 8 SIGNAL was a prospective cohort specifically designed for GWAS analyses of early breast cancer patients, enrolled at the time of their adjuvant chemotherapy from June 2006 to December 2013 (RECF1098, www. e-cancer. fr). As both studies were carried out simultaneously in the same base population, they can be combined as a large observational clinical cohort. Clinical and pathological data were prospectively provided directly from the patients' medical teams using standardized forms, and centralized at Fig. 2  Manhattan plot of associations between SNPs and HER2 status.
Subject recruiting, data and blood collection Eligibility criteria for both SIGNAL and PHARE included the following: female patients over 18 years of age (range 21. 8-90. 9, median 53. 7 years), with histologically confirmed invasive breast cancer. Additionally, patients in PHARE needed to have pathologically confirmed HER2-postive breast cancer, and adequate (>50%) left ventricular ejection fraction to continue after 2 months of trastuzumab treatment. Patients must have received (neo) adjuvant chemotherapy and/or breast-axillary surgery before recruitment, and signed informed consent. HER2 status was determined as part of the patient's standard care, independent of our observational Fig. 3  Quantile-Quantile plot of p-values from the GWAS of HER2 status. Analyses from 8703 patients, 3230 of whom are HER2-positive, are represented.
SNPs with >5% missing data, a Hardy-Weinberg p-value < 0. 001, or a minor allele frequency <0. 1%, or that were present in duplicate or triplicate were excluded from further analysis. Principal Components Analysis, where the first two vectors were used to define sample populations, and k-means were then used to characterize the ancestry of the participants. These analyses were conducted using the EIGENSTRAT program of the smartpca. perl package, followed by k-means clustering using the kmeans function of the NbClust package in R. Data on HapMap subjects samples (release 28) were included in order to provide scale and points of reference for European, African, and Asian clusters. Polymorphisms that overlap both our data and HapMap were used for PCA. Our original analysis plan and budget allowed for genotyping 2000 subjects using the Omni5 chip set, composed of over 4,000,000 variants.
We therefore chose to genotype a random sample (using a random number generator in R) of subjects from the main "European" cluster with the Omni5 chip set for imputation. To reduce the potential for residual population stratification, only samples from the main cluster of European individuals were included in the present analysis. These data were then used to impute missing genotypes from the remaining subjects genotyped only using on the HumanCore Exome array. Omni5 data were filtered based on HWE, and SNP and sample completion rates, as for the HumanCore Exome data. Furthermore, in order to perform imputation, only SNPs with data available for all individuals were retained. Both the HumanCore Exome and Omni5 data were prephased using MaCH1. The Omni5 data were used as the reference, and HumanCore Exome data were imputed to this reference using Minimac3.
Imputed SNPs with a Hardy-Weinberg p-value below 0. 001, no map position, present multiple times on the chip, from the Y chromosome, were monomorphic, were poorly imputed (Q < 30%), and with a minor allele frequency <1% were further filtered from analyses. Association testing was carried out through logistic regression using probABEL, comparing the additive model of genotype distribution between clinically confirmed HER2-positive and HER2-negative breast cancer patients. Models were adjusted for the first two principal components from the PCA, and age at diagnosis. Genome-wide significance levels were estimated using the effective number of tests based on linkage disequilibrium between all markers used in our population through the SimpleM program in R. 14  The number of effective markers is estimated at 345,906, corresponding to a Bonferroni-corrected p-value threshold of 1.
These results demonstrate the distinctiveness of the study's variables. The Statistical Package for Social Sciences was used (SPSS 20. 0 for Windows, Inc. , Chicago, IL, USA). Descriptive statistics were used to analyze demographic variables. Correlation analysis was run to test the correlations between all independent, dependent and mediator and moderator variables. Baron and Kenny's  [34]  path analysis procedure was used for testing mediating variables. The following steps were applied. In the first and second step, the independent variable (psychological contract fulfillment) should be significantly correlated with the dependent variable (mission statement motivation) and mediator (affective organizational commitment). In the third step, the mediator (affective commitment) must be correlated with the dependent variable (mission statement motivation).
ACKNOWLEDGEMENTS The authors declare no competing interests. VHH and MB acknowledge the graduate program " Posgrado en Ciencias Biológicas " of  Universidad Nacional Autónoma de México and Laboratorio de Nacional de Ciencias de la Sostenibilidad. This work was supported by the scholarship support provided by  CONACyT  to VHH. RAB is grateful for a sabbatical grant from  DGAPA  and to  Prof. Lee Eiden  from  NIMH  for his hospitality during the development of this work. CV was financially supported by project  CONACyT 180380. NN acknowledges the  University of Edinburgh Chancellor's Fellowship  and the  Royal Society University Research Fellowship. JRRA is grateful to  CONACyT and FORDECyT  Grant No. 265667  for partial financial support during the development of this work. The authors also thank  Carlos Galván-Ampudia  for valuable comments to the manuscript.
Stress and emotional disturbances Stress may enhance airway inflammation by modulating immune cell function through neural and hormonal pathways. Psychological triggers have been consistently associated with exacerbations and emergency treatments. 85 In Puerto Rican children, caregiver depression, perceived stress, and child's negative life events were associated with high trigger scores. 86 nother survey concluded that anxiety may exacerbate the effects of disrupted sleep on childhood asthma control. 87  Cochrane Database Systematic Review only found two intervention studies, and it concluded that family therapy may be a useful adjunct to medication for asthmatic children. 88.
The model created for the temperature For the temperature series, it is necessary to make a model that will be rely on known representations taken from data calculated by the French model of national circulation CNRM-CM5, explained above. The temperature variations are considered on each hour and the components of this series are deterministic and stochastic. The temperature on a certain time t (calculated in hours) is analyzed as following: (1) where: T mean represents the mean temperature of the time series and T sin (t), T day (t) and T stoch (t) are functions that represent the temperature variations of an year, of every day and the stochastic part of the model.
After using the Matlab software for the model, 30 random climates have been created, following a period of 15 years in order to see the deterioration of the piece of wood in this period of time. Also, the model creates an evolution of the material's behavior with an exact moment when the deterioration begins. This program of deterioration can be applied on any type of wood and it can provide us the necessity of increasing the wood section in certain regions where specific humidity and temperature have a great influence. The conclusions of this study are important because they show the evolution of the behavior of a wooden piece that has not been treated and is directly influenced by the climate conditions: temperature and relative humidity. However, models have to be verified with real data and with practical experiments on different types of wood, submitted to different climate conditions in order to establish a pattern of wood degradation.
Patients Samples were collected in two consecutive cohorts of French newly diagnosed high-grade osteosarcoma patients included between January 2005 and December 2011 in OS2005 study aiming to collect samples for biological research in patients treated with standard chemotherapy before the opening of OS2006 trial (NCT00470223), a national study including a randomised trial evaluating zoledronate and collection of samples for biological research. Written informed consent was obtained from patients and/or their parents/guardians. Patients included in OS2005 study were aged below 25 and received preoperative chemotherapy based on high-dose methotrexate (HDMTX) plus etoposide-ifosfamide  [15]. In OS2006 trial, patients under 18 years received the same HDMTX-based chemotherapy as in OS2005 study; patients over 25 years old received doxorubicin, ifosfamide and platinum  [16] ; and patients between 18 and 25 received either HDMTX-based chemotherapy or the adult regimen, as decided by each participating centre at the beginning of the study.
Acknowledgements We would like to thank: -all patients and parents who accepted to participate in this study; - Nadir Cheurfa ,  Florian Roquet ,  Saïd Maalem ,  Guillaume Danton ,  Pascale Jan ,  Naïma Bonnet ,  Karine Buffard  and  Marta Jimenez  for data management, data analysis and study coordination assistance; -all investigators who participated in the trial:  Jacques-Olivier Bay ,  Claire Berger ,  Jean-Pierre Bergerat ,  François Bertucci ,  Emmanuelle Bompas ,  Binh Bui Nguyen ,  Liana-Stefania Carausu ,  Loïc Chaigneau ,  Olivier Collard ,  Gérard Couillault ,  Didier Cupissol ,  Corinne Delcambre ,  Anne Deville ,  Catherine Devolder ,  Florence Duffaud ,  Virginie Gandemer ,  Stéphanie Gorde Grosjean ,  Cécile Guillemet ,  Nicolas Isambert ,  Justyna Kanold ,  Pierre Kerbrat ,  Véronique Laithier ,  Odile Lejars ,  Axel Le Cesne ,  Claude Linassier ,  Patrick Lutz Frédéric Millot ,  Odile Minckes ,  Isabelle Pellier ,  Nicolas Penel ,  Christophe Piguet ,  Dominique Plantaz ,  Isabelle Ray-Coquard ,  Maria Rios ,  Hervé Rubie ,  Laure Saumet ,  Claudine Schmitt ,  Antoine Thyss ,  Jean-Pierre Vannier ,  Cécile Verité-Goulard ; - David Young  for his editorial assistance; - Association RMHE  for its support in the publishing process.
Statistical analysis One-way analysis of variance (ANOVA) was performed on the measured or calculated treatment means using SPSS statistical software (v. 22, IBM Corp. ). Prior to ANOVA, the data was tested for normality and homogeneity of variance; based on these tests, no transformations were required. A generalized linear model procedure was used in which the fixed effect was 'treatment'. The error term was treatment × block × vine for all the data analyzed. Differences between treatment means and control means were determined at the 5% significance level using Tukey's HSD test.
■ EXPERIMENTAL SECTION The nanodisks were fabricated by electron beam lithography on a 15 nm thick Si 3 N 4 substrate. The disk height was fixed at 30 nm, and the diameter was varied between 150 and 500 nm. We performed EELS and CL experiments on a VG STEM machine at 100 kV acceleration voltage. All the data shown hereafter have been synthesized from EELS and CL spectral images taken on the same disks but at different times. For numerical simulations, we used the MNPBEM toolbox,  23  which relies on the boundary element method. Details are given in the Methods section.
The firstand secondorder edge modes (dipole and quadrupole) are well resolved in the EELS data (green spectrum, D and Q) but are outside the CL detection range. Mode B is the RBM (a film mode of first order), and mode C is a mixed edge/film mode, also referred to as (0, 1) and (1, 1) modes, respectively. 5 For both data sets, mode patterns and spectral signatures are thus mostly identical, in accordance with previous work on EELS and CL. 16 mportantly, however, the RBM CL signal is quite strong (B, blue spectrum in Figure  2b ), which can readily be assigned to retardation-induced light coupling, given the disk diameter of 500 nm. Aiming at a quantization of the light-coupling sizedependence, we now trace the signal intensity of the RBM as a function of the disk diameters for EELS and CL. EELS and CL spectra for all nanodisk diameters 150-500 nm are shown in Figure  3.
Experimental data, again from three different disk regions, i. e. , disk center (blue), along the circumference at R/2 (red), and the edge region (green), are displayed in Figure  3a. For a detailed description of the spectrum normalization for the EELS and CL data see the Supporting Information ("Normalization" section). On one hand, the peak positions of the RBM shift in a similar manner for both EELS and CL, as indicated by the blue arrows. On the other hand, the different RBM peak intensities in EELS and CL are evident from the spectra. In particular, the CL RBM peak intensity strongly increases with increasing disk size, while EELS shows an almost constant RBM intensity for all disk diameters. We note that all plasmon eigenmodes discussed in this work are below 3 eV, and therefore damping due to interband transitions is not to be expected.
This removes the contribution of the neighboring surface plasmon SP 1 (compare red and blue EEL spectra in Figure  3a ). Thereby, we extract an EELS signal for each disk diameter that originates from the RBM only. The experimental CL-to-EELS ratio values are renormalized to the simulated value of the 400 nm diameter disk, using the same normalization factor for all experimental ratio values. With the normalization, the experimental and the simulated data agree very well. We note that an alternative way to analyze the data is via the center-to-edge signal ratio, leading basically to the same results (Supporting Information, "Centerto-edge ratio", Figure  S2 ). In any case, the CL-to-EELS ratio increases with disk diameter, showing a concurrent increase in scattering strength and radiation damping of the RBM.
The RBM emits mostly into the substrate, with a maximum at a polar angle θ of 135°and a weaker emission into the upper direction with an emission maximum at θ = 45°. The RBM emission into the upper direction is strongly reduced for smaller disks, virtually disappearing for a disk diameter of 100 nm (see Figure  S3  in the Supporting Information), in accordance with the small CL intensity measured for small disks. At θ = 0°, i. e. , in the direction perpendicular to the surface, there is no emission even for larger disk diameters, as expected from the RBM charge symmetry. It is thus evident that an RBM cannot be excited by a plane wave under perpendicular incidence for all disk sizes, while it can be excited by a plane wave with tilted incidence for large disks. In general, these results emphasize the reciprocity of data from CL and optical spectroscopy.
The CL system 28 consists of a parabolic mirror that can be put into optical focus through a three-axis highprecision stage. Light reflected by the mirror is sent onto one end of an optical fiber bundle. The other end of the optical fiber is fitted to an optical spectrometer that is not fixed on the STEM column. All experiments on the same disks have been performed on the same day. As the acquisition parameters were very different, EELS and CL experiments have been made in sequences. All data presented in this work stem from spectral images. EELS spectra have been spectrally realigned and deconvoluted from the zero-loss peak, using a Richardson-Lucy deconvolution algorithm (see Figure  S5  in the Supporting Information). 29 CL spectra have been converted from nm to eV units by applying a λ 2 prefactor (see Supporting Information, "Normalization" section).
■ ACKNOWLEDGMENTS This work was supported by the FWF SFB NextLite (F4905-N23), ESTEEM2 (FP7 project, no. 312483), NAWI Graz, and the Graz Centre for Electron Microscopy (ZFE). We thank Markus Krug and Gernot Schaffernak for fruitful discussions on optical spectroscopy of metallic nanoparticles.
As far as we know this is the first time that an explicit parameter space is provided for the emergence of coastal eddies or meanders from an unstable buoyant current. This (T p , γ ) parameter space might be quite convenient for real coastal currents because approximated values for these two dimensionless parameters could be easily estimated with a minimal number of in situ mea-surements. For instance, hydrographic sections are sufficient to quantify the first baroclinic deformation radius R d , the maximum geostrophic velocity V max , and the vertical extent H jet of the surface current. Then, a standard bathymetric data set will provide a correct averaging of the bottom slope s in the cross-shore direction. If we consider the coastal Bransfield Current, which inspired the setup of the model used in this study and which has been extensively studied by  Savidge and Amft (2009) ,  Sangrá et al.
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. The trigger  [11]  consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. For B + → J/ψ K + and B 0 → J/ψ K * 0 decays, the data are collected by using the hardware muon trigger, which requires a single muon with large transverse momentum (from p T > 1. 4 GeV/c to p T > 1. 8 GeV/c) or a pair of muons with a large product of their transverse momenta (from √ p T,1 p T,2 > 1.
The value recently measured by LHCb  [23]  with an independent data set is used for the former and corresponds to A CP (B + → J/ψ K + ) = (0. 09 ± 0. 27 (stat) ± 0. 07 (syst)) × 10 -2. The measurement of   s → D - s π + signal mass regions with the results of the global fits overlaid. Left and right plots correspond to data recorded in proton-proton collisions at centre-of-mass energies of 7 and 8 TeV, respectively. For the B 0 s decay, the asymmetries are obtained by folding the decay-time distributions into one oscillation period, and the offset t 0 = 0. 2 ps corresponds to the selection requirement on the decay time. the kaon detection asymmetry is obtained from D-meson decays produced directly in proton-proton collisions, using the same technique reported in Ref. [19]. It consists of measuring raw asymmetries from the two decay modes, D + → K -π + π + and D + → K 0 S π + with K 0 S → π + π -, to obtain the K + π -detection asymmetry, A D (K + π -), in each (p T , y) bin of the B + mesons.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO  (The Netherlands);  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FASO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF  (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
The expected EWK WV contributions for a representative point in the aQGC parameter space (α 4 = 0. 1, α 5 = 0) and for the SM (α 4 = α 5 = 0) are shown for comparison. The quoted errors include all systematic uncertainties in the expected yields. The error in the total background is computed including correlations between the various background components. E miss T ) of the multijet background are obtained from the data in these control regions, after subtracting the MC predictions for the non-multijet backgrounds. The multijet event yield is estimated by first performing a fit to the E miss T distribution of the data that pass the final event selection, but with the E miss T > 30 GeV and p T (W lep ) > 150 GeV criteria removed. The final multijet yield estimate is then obtained by scaling this fit result by the efficiency for multijet events to pass the E miss T > 30 GeV and p T (W lep ) > 150 GeV requirements.
The expected aQGC confidence intervals are about 40% more stringent when including this category than when only using the resolved events. The observed 2D confidence region (solid black contour) for α 4 and α 5 , at 95% CL. The expected 2D confidence region (dotted black contour) is also shown, computed using the Asimov dataset  [73]. Results from this analysis (in black) are compared to observed and expected confidence regions from previous ATLAS analyses of W ± W ±  [17]  (in red) and WZ  [6]  (in cyan) VBS production.
Figure5: The observed 2D confidence region (solid black contour) for α 4 and α 5 , at 95% CL. The expected 2D confidence region (dotted black contour) is also shown, computed using the Asimov dataset [73]. Results from this analysis (in black) are compared to observed and expected confidence regions from previous ATLAS analyses of W ± W ± [17]  (in red) and WZ [6]  (in cyan) VBS production.
; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.