dataesr/scientific-paragraphs-categorization huggingface dataset:
We have used both, correlation-consistent double-ζ polarized (cc-pVDZ), and triple-ζ polarized (cc-pVTZ) basis sets for all of our calculations. Comparison of these two sets of results provides a rough estimation of the effect of the basis set incompleteness. A systematic study of the convergence with respect to the basis set size was presented in Sec. V E for two small systems, He and H 2. The basic conclusions obtained from these two systems are: i) The convergence of the GW results is rather slow; ii) Fortunately the convergence of SCGW B and QSGW is very similar and differences between IPs calculated with these two schemes are converged within 0. 05 eV already for cc-pVTZ basis sets; iii) The convergence of CCSD(T) is somewhat faster than that of GW , which should be taken into account when analyzing the data presented here.
For the positive ions, unrestricted Hartree-Fock was used to produce the starting point for the CC calculations. 65 Our calculations compare well with the literature. We checked our CCSD(T)/cc-pVTZ calculations against the data from NIST database CCCBDB. 66 Ionization potential of atoms is the same as provided by NIST. Unfortunately, there are only adiabatic IPs available from NIST for the small molecules we consider. However, we compared the total energies of neutral molecules with the corresponding NIST values and found a good agreement within a few meV. ] [69] [70]  In the GW calculations, the IPs were obtained from the position of the first peak below Fermi level in the DOS of each molecule. The frequency grid resolution ∆ω used with the QSGW approach was 0. 05 eV for cc-pVDZ and 0. 1 eV for cc-pVTZ basis sets.
In the case of SCGW , a linear extrapolation to the limit of infinite frequency resolution was applied as discussed in subsection V B. Therefore, ∆ω = 0. 05 and 0. 025 eV were used in the calculations with cc-pVDZ basis set, and ∆ω = 0. 1 and 0. 05 eV for those using a cc-pVTZ basis set. The convergence with the number of dominant products, used here to express the products of basis functions, was monitored comparing the energies of the HOMO of the different molecules calculated at the Hartree-Fock level with our code and with NWChem. Our code uses the basis of dominant products to compute Hartree and exchange contributions to the energy and Hamiltonian. We found maximal differences of at most 6 meV (for nitrogen containing molecules), while the mean absolute error (MAE) of the HF-HOMO position is only 1.
6    49, 50  The mean absolute error (MAE) is calculated with respect to the CCSD(T) results for each basis separately. Experimental data are taken from the NIST CCCBDB database. 66 The numbers marked with asterisks * are experimental adiabatic IPs. -0. C O C 2 H 2 H C N C O N 2 L i 2 L i H L i F H F F 2 H 2 O ∆ IP (eV) cc-pVTZ G 0 W 0 -HF SCGW QSGW B CCSD(T) FIG. 6. Differences between vertical IPs calculated at the G0W0-HF, SCGW and QSGW levels, and those obtained from coupled-cluster calculations. Panels (a) and (b) show calculations performed using cc-pVDZ and cc-pVTZ basis sets respctively. The data for the IPs can be found in Table  V. The results for the IPs of all the studied systems are presented in Table  V. Before analyzing the GW results, it will be instructive to make some comments about our CC reference calculations.
As shown in detail below, the cc-pVTZ basis seems to be sufficient for this purpose. This is indicated by the fact that the qualitative and quantitative deviations of the different GW IPs with respect to the CCSD(T) results, and among them, are rather similar with the two basis sets used in this study (cc-pVDZ and cc-pVTZ). In any case, Table V provides a consistent comparison, using the same basis sets and the same numerical implementation, between different schemes to include correlation. Comparing our CCSD(T)/cc-pVTZ results with the experimental data in Table  V  we can find some significant deviations. The larger deviation (0. 96 eV) takes place for H 2. This is probably related to the lack of corrections due to the finite mass of nuclei and the structural relaxations in the final state in our calculations.
22 and 0. 27 eV when the larger cc-pVTZ basis is used. It is interesting to note, following our discussion Sec. V E, that the MAE of QSGW B IPs with respect to the CCSD(T) data is slightly larger than that of SCGW. If the observed differences were solely determined by the faster convergence of CCSD(T) results with respect to the basis set size, we would expect the opposite behavior. Therefore, we can speculate that, for the set of sixteen molecules considered here, it is likely that SCGW will provide better IPs (in average) than those given by QSGW B. However, coming back to Table V, we can say that using cc-pVTZ basis sets on average QSGW and SCGW perform very similarly. The maximal discrepancies are somewhat larger for SCGW : 0. 76 eV for the Be atom using the cc-pVTZ basis, to be compared with the 0.
67 eV deviation for F 2 in the case of QSGW. The G 0 W 0 -HF is on average only slightly worse than SCGW and quite comparable to QSGW B, with a MAE of 0. 28 (0. 22) eV and a maximal error of 0. 86 (0. 77) eV for the N 2 (CO) molecule using the cc-pVTZ (cc-pVDZ) basis. We can now compare our results with previously published data for the IPs of small molecules computed with self-consistent GW schemes. For this purpose we will use the results obtained with the more complete cc-pVTZ basis. 24] [25] [26] 43  We are only aware of three very recent studies using the QSGW method for small molecules: one dealing with small sodium clusters up to five atoms  73  , one studying small conjugated molecules  27  and one for first row atoms. 18 e start with the SCGW results. Stan et al. 22, 23  performed all-electron SCGW calculations using large bases of Slater orbitals.
Their IPs are systematically larger than those reported here, although the differences are relatively small, lower than 0. 19 eV for all the molecules except for LiF, for which the difference grows up to 0. 46 eV. The MAE over the 12 molecules is only 0. 14 eV for SCGW and 0. 15 eV for G 0 W 0 -HF calculations. Therefore, the overall agreement between our SCGW /cc-pVTZ results and those of Caruso et al. is rather good. Now we compare our QSGW with the very scarce data available in the literature. Ke has recently studied the IPs and electron affinities of a number of conjugated molecules using the QSGW "mode A" method. 27 Ke uses a cc-pVTZ basis, similar to that utilized here, and reports 11. 31 eV and 11. 44 eV for the IP of C 2 H 2 calculated at the level of QSGW A and G 0 W 0 -HF, respectively.
This is in excellent agreement with our corresponding results of 11. 43 eV and 11. 54 eV and indicates that, at least for this molecule and the cc-pVTZ basis set, the calculated IP is rather stable against the use either QSGW A or B schemes. Bruneval 18 reported 24. 46 (24. 72), 9. 11 (9. 16) and 21. 62 (21. 79) eV, respectively, for the IPs of He, Be and Ne calculated using the QSGW A (G 0 W 0 -HF) approach and a very complete cc-pV5Z basis (of Cartesian kind). These values are in good agreement with our results although they are always somewhat larger. This is due to the use of a smaller cc-pVTZ basis set in our case, as clearly demonstrated by the excellent agreement between data calculated using the MOLGW program  18  and our code when the same basis set are used (Table  IV ). Furthermore, focusing on the results published by Bruneval in Ref.
18, comparing our G 0 W 0 -HF with those reported in Figure  1  of that paper, we find that the results reported there for the cc-pVTZ basis are almost identical to those presented here. This again indicates a very welcome consistency between both sets of calculations. Finally, we can compare our GW vertical IPs with the experimental data in Table  V. This comparison should be taken with some caution: as commented above, the comparison might be affected by other factors different from the ability of the GW schemes to capture electron correlations. In any case, it is interesting to obtain a quantitative measure of the deviation. The MAE with respect to the experimental data are similar for the SCGW and QSGW B results obtained using the cc-pVTZ basis, 0. 26 and 0. 35 eV, respectively. It increases to 0.
cardiovascular disease and stroke, suggesting the nonstroke content of the Immunochip may provide additional information when considering the stroke phenotype. 5, 6 We report here the use of the Immunochip as the initial phase of a targeted GWAS, followed by metaanalysis with full GWAS data from WTCCC2 and an international collaboration of ischemic stroke GWAS data (METASTROKE). This is followed by in silico replication (i. e. , ascertainment from previous data without the need for de novo genotyping) with data from the IN-TERSTROKE and VISP studies. METHODS Study design and participating studies. The discovery sample consisted of 6 cohorts of patients of European ancestry with ischemic stroke. Participating centers were based in Belgium, Germany, the Netherlands (the PROMISe Study), Poland, Sweden, and the UK (2 cohorts, one from London [Imperial College; the BRAINS study] and one from Glasgow).
All cohorts provided geographically and ancestry-matched controls. For the purposes of meta-analysis, the UK cohorts were treated as a single center in line with previous analyses undertaken in WTCCC2. 1  Analysis plan. The analysis plan for this study was to perform a single meta-analysis of available data as follows: (1) association analysis of imputed Immunochip data; (2) meta-analysis with HAPMAP2imputed WTCCC2 data and METASTROKE consortium data for which summary statistics were available; and (3): in silico look-up of significant SNPs from meta-analysis in the INTERSTROKE cohort  7  and the VISP cohort. 8 The populations used in both WTCCC2 and METASTROKE have been previously reported. 1, 3 The WTCCC2 data have also been contributed to METASTROKE. Therefore, for this analysis the WTCCC2 data were removed from METASTROKE to prevent duplication of individuals, as was the BRAINS dataset, which overlapped with BRAINS cases contributing to the Immunochip discovery cohort.
org. Data genotyping, imputation, and statistical analysis. The Immunochip consortium developed as an immune-related targeted GWAS array comprising ;200,000 SNPs. 4   this design of the Immunochip, WTCCC2 disease areas, including ischemic stroke, were able to suggest ;3,000 novel SNPs for incorporation into the array. The Immunochip therefore contains a subset of stroke-specific SNPs from an early analysis of WTCCC2 ischemic stroke data. However, for this study we used the entire Immunochip content. The 6 discovery phase cohorts used the commercially available Immunochip array (Illumina, San Diego, CA). Genotyping for the PROMISe study (the Netherlands) was performed independently in Utrecht, the Netherlands. Genotyping for the remaining 5 case cohorts was performed at the Sanger Centre, Hinxton, Cambridge, UK.
Swedish controls were provided and genotyped by the Swedish SLE network, Uppsala, Sweden. Belgian control samples were provided through the efforts of the International Multiple Sclerosis Genetics Consortium. Analysis and quality control (QC) of the PROMISe study (the Netherlands) was performed in Utrecht and for all other cohorts was performed at St George's, University of London, UK. The Immunochip datasets were each imputed separately to the 1,000 Genomes Phase 1 integrated variant set (March 2012) using IMPUTE v2. 2. 2. 11 Standard parameters were used with the exception of the number of haplotypes (k), which was increased to 100 to maximize accuracy. In total, between 123,920 and 135,006 SNPs were directly genotyped for the Belgian, German, Polish, Swedish, and British cohorts. After imputation and QC filtering on IMPUTE-info scores with filtering threshold ,0.
300 (l 1000 5 1. 066), showing little evidence for inflation in the stroke subset or the Immunochip overall. QQ plots are shown in figure e-1. Association analysis of clean imputed datasets was performed for each Immunochip cohort individually using the frequentist test under an additive model as implemented in SNPTEST v2,  13  including sex and 10 principal components as covariates. Imputed genotype probabilities were taken into account using a missing data likelihood score test or an expectation-maximization method for SNPs with low MAF or high uncertainty. Association analyses were performed on all ischemic stroke cases and for the defined subtypes of large artery stroke, small vessel disease, and cardioembolic stroke. Meta-analyses were performed using an inverse-variance weighted fixed-effects model as implemented in METAL.
14 SNPs were taken into consideration only if they were present in at least 50% of datasets, were genotyped or imputed in all phases, and if the p value for Cochrane Q test for heterogeneity exceeded 1 3 10 23. For targeted replication with data from INTERSTROKE and VISP, summary statistics were provided for 2 SNPs (rs17696736 and rs10744777) and meta-analyzed as above. Conditional analysis. Conditional analysis of the Chr12 locus was performed by including genotype dosage of each of the 10 genome-wide significant SNPs as a covariate in the logistic regression model independently, as well as by inclusion of all 10 SNPs together. This was performed on Immunochip and WTCCC2 data only since individual level genotypes were not available for METASTROKE. Risk factor defined analysis. A case-only risk factor defined analysis was performed by classifying presence (case) or absence (control) of defined risk factors in the Immunochip and WTCCC2 cohorts (individual genotype level data were not available for the METASTROKE, INTERSTROKE, or VISP cohorts).
The 5 cardiovascular risk factors hypertension, diabetes, hypercholesterolemia, coronary artery disease/ischemic heart disease, and smoking status were then assessed independently across the Chr12 locus. RESULTS Meta-analysis of Immunochip data. Analysis of the 6 Immunochip cohorts comprising 3,420 cases and 6,821 controls resulted in identification of 3 SNPs spanning 2 independent loci on chromosomes 10q26 and 19q13 exceeding a genome-wide significance threshold of 5 3 10 28 for all ischemic stroke, and a further 9 SNPs spanning 5 loci in large artery ischemic stroke (top SNPs in each of the 7 loci shown in table 2, block 1, full findings in table e-2). No SNP exceeded a genome-wide threshold in cardioembolic stroke or small vessel disease when examining Immunochip cohorts alone. Meta-analysis with WTCCC2 and METASTROKE data confirmed previously published associations between the HDAC9 locus on chromosome 7p21 and large artery stroke, and between PITX2 and ZFHX3 loci at 4q25 and 16q22 and cardioembolic stroke.
These are defined in the online supplementary material. Regional analysis of the Chr12 locus was performed on Immunochip and WTCCC2 data (a lack of individual level genotypes prevented this analysis from being conducted in METASTROKE data). Table  4  shows the lack of association, when classifying cases and controls by the presence of risk factors, for rs10744777. Therefore we found no evidence that the association with stroke was mediated via a single conventional cardiovascular risk factor. 12q24. 12 and intracerebral hemorrhage. We also performed in silico replication of the 12q24. 12 locus in ICH. Examining 1,088 cases of ICH and 1,102 controls revealed no association with rs10744777 (OR 5 1. 03, 95% confidence interval 0. 90-1. 17, p 5 0. 695) (figure  1B ). DISCUSSION Adopting a GWAS approach with direct genotyping and imputation with the Immunochip array, followed by meta-analysis with existing GWAS data in ischemic stroke, we identified a novel risk locus for ischemic stroke on chromosome 12q24.
Event Triggering Events are recorded by dilepton (electron or muon) triggers using different trigger configurations to obtain the data in 2010 and 2011. The 2010 data were selected by a low-threshold di-muon trigger with a transverse momentum trigger threshold of 4 GeV. The muons are required to have opposite charge, originate from the same event vertex, and satisfy m > 0. 5 GeV. The muon trigger efficiency is determined from a large sample of J/ψ → µµ events and is measured differentially in the transverse momentum p µ T and pseudorapidity of the muon, η µ. Due to significant charge dependence at low p µ T and high pseudorapidity, separate trigger efficiencies are produced for positive and negative muons. From these results, the efficiency of the di-muon trigger conditions are obtained differentially in m.
The 2011 muon data were collected with a di-muon trigger with a transverse momentum threshold of 10 GeV. The efficiency was determined using a tag-and-probe method on a Z → µµ sample recorded using a single-muon trigger with an 18 GeV transverse momentum threshold. The di-electron trigger uses calorimetric information to identify two narrow electromagnetic energy depositions. Electron identification algorithms use further calorimetric information on the shower shape and fast track reconstruction to find electron candidates with a minimum required transverse energy of 12 GeV. The efficiency as a function of transverse energy and pseudorapidity of the electron is determined using a Z → ee sample recorded using a single-electron trigger with a 20 GeV E T threshold, following ref. [19].
426 simulation of heavy-flavour (b b, cc) jet production. The Monte Carlo (MC) generators are interfaced to Tauola 2. 4  [30]  and Photos 3. 0  [31]  to describe τ decays and the effects of QED final-state radiation respectively. Multiple pp collisions within a single bunch crossing, referred to as pile-up interactions, are accounted for by overlaying simulated minimum-bias events produced in Pythia tuned to ATLAS data  [32, 33]. The generated particle four-momenta are passed through the ATLAS detector simulation  [34] , which is based on Geant4  [35]. The simulated events are reconstructed and selected using the same software chain as for data. The MC samples are adjusted using factors derived from data to reflect mismodellings of the lepton momentum scale and resolution, trigger efficiency, lepton reconstruction efficiency, and isolation efficiencies  [19, [36] [37] [38].
Systematic uncertainties The systematic uncertainties on the measured cross sections are estimated by repeating the measurement after varying each source of uncertainty in the MC samples. The multijet background uncertainties are determined from the comparison of different estimation methods using data and MC simulation. In the nominal electron channel, the detector resolution, energy scale, and reconstruction efficiency uncertainties are propagated to the measured cross sections by varying the MC simulation used to determine the response matrix R. The data and MC statistical uncertainties are propagated by sets of pseudo-experiments where the measured spectrum and the response matrix elements are varied by their statistical uncertainties. The contribution to the uncertainty on the unfolded spectrum due to the data and MC sample size, referred to as the statistical uncertainty on the unfolding, is 1.
The cross-section contribution from photon induced processes, ∆ PI , is estimated using the MRST2004QED PDF set  [58]  in which photon radiation from the quark lines is included in the parton evolution equations. The cross-section predictions are calculated using the NLO and NNLO MSTW2008 sets as appropriate. The full cross-section predictions including all corrections are shown in tables 8 and 9 for nominal and extended analysis respectively. The corrections and associated uncertainties are also listed in tables 10 and 11 for both fiducial measurements. The ∆ PI corrections contribute 2-3% of the theoretical predictions. The comparisons between the measured cross sections and the theoretical predictions are shown in figure  6. The Fewz NLO predictions provide a poor description of the data at low m which simultaneously overestimates and underestimates the nominal and extended measurements respectively.
for (a) the nominal analysis and (b) the extended analysis as a function of the invariant mass m (solid points) compared to NLO predictions from Fewz, NLO+LLPS predictions from Powheg and NNLO predictions from Fewz (all including higher-order electroweak and photon induced corrections). The predictions are calculated using MSTW2008 PDF sets with the appropriate order of perturbative QCD. The uncertainty bands include the PDF and α s variations at 68% CL, scale variations between 0. 5 and 2 times the nominal scales, and the uncertainty in the PI correction. The ratios of all three theoretical predictions (solid lines) to the data are shown in the lower panels. The data (solid points) are displayed at unity with the statistical (inner) and total (outer) measurement uncertainties. To quantify the level of agreement between the measured cross sections and the predictions, the value of the χ 2 function is calculated taking into account the correlated experimental systematic uncertainties as well as the theoretical uncertainties arising from the PDFs and scale variations.
The χ 2 function is defined as in ref. [16]  and the results are shown in table  12. The values of the χ 2 function obtained with MSTW2008 PDFs are good when compared to Powheg or Fewz at NNLO; however the Fewz NLO prediction yields very large values. Thus, the measured cross sections are significantly more compatible with the NNLO prediction than with the NLO prediction. To investigate to what extent the disagreement with a pure NLO calculation is dependent on the PDF used, a QCD analysis of the data is performed. In this analysis the χ 2 function is evaluated after fitting the PDFs to deep inelastic scattering data from HERA  [16]  and the new measurements presented here. The QCD fit is performed using MCFM  [59]  and ApplGrid  [60]  in the HERAFitter framework  [16, [61] [62] [63] [64] [65] [66] [67]  at NLO and at NNLO using additional NNLO K-factors obtained from Fewz.
1. 3 Table  9. Theory predictions for NLO+LLPS and for fixed-order calculations at NLO and NNLO including higher-order electroweak corrections, for the extended analysis of the differential cross section dσ dm as a function of the invariant mass m. The scale uncertainty is defined as the envelope of variations for 0. 5 ≤ µ R , µ F ≤ 2 for Powheg. For Fewz the scale uncertainty is defined by the variation 0. Table  13. The χ 2 values from NLO and NNLO QCD fits made using the ATLAS nominal and extended Drell-Yan cross-section measurements and the HERA-I dataset accounting for the experimental correlated systematic uncertainties.
A polyclonal antibody against b-actin (diluted 1:3000; Sigma Aldrich) served as the internal control. Densitometric quantification of the immunoblot bands was performed using Bio-Rad Quantity One software. Enzyme-linked immunosorbent assay (ELISA). TGF-b protein levels were evaluated in lung samples and serum from patients with iPAH and controls using an ELISA (R&D systems, Lille, France). ENG protein concentrations were determined in the same serum samples and in supernatants from PECs isolated from patients with iPAH and controls using ELISA kits, according to the manufacturer's instructions (R&D Systems, Lille). Effect of TGF-b on Smad phosphorylation. PECs (300 000 cells per well, 6-well plates coated with 0. 1% gelatin) were plated in 10% FCS/MCB131 medium for 24 hours then starved for 24 hours in serum-free medium.
The cDNA was diluted 1:20 for use in a quantitative real-time-polymerase chain reaction (QRT-PCR). The primers were designed using Primer Express Software (Applied Biosystems, Courtaboeuf, France) for human TGF-b, ALK1, ALK5, and ENG and for both human and mouse preproendothelin-1 (preproET-1), platelet-derived growth factor (PDGF)-A and -B, fibroblast growth factor (FGF)-2, epidermal growth factor (EGF), interleukin (IL)-6, and monocyte chemoattractant protein (MCP)-1. Specific QRT-PCR primer sequences for all genes were listed in Table  S1. To avoid inappropriate amplification of residual genomic DNA, intron-spanning primers were selected and internal control 18S rRNA primers were used. For each sample, the amplification reaction was performed in duplicate using Syber Green mix and specific primers.