643 research outputs found

    Measurement of charm production at central rapidity in proton-proton collisions at s=2.76\sqrt{s} = 2.76 TeV

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    The pTp_{\rm T}-differential production cross sections of the prompt (B feed-down subtracted) charmed mesons D0^0, D+^+, and D+^{*+} in the rapidity range y<0.5|y|<0.5, and for transverse momentum 1<pT<121< p_{\rm T} <12 GeV/cc, were measured in proton-proton collisions at s=2.76\sqrt{s} = 2.76 TeV with the ALICE detector at the Large Hadron Collider. The analysis exploited the hadronic decays D0^0 \rightarrow Kπ\pi, D+^+ \rightarrow Kππ\pi\pi, D+^{*+} \rightarrow D0π^0\pi, and their charge conjugates, and was performed on a Lint=1.1L_{\rm int} = 1.1 nb1^{-1} event sample collected in 2011 with a minimum-bias trigger. The total charm production cross section at s=2.76\sqrt{s} = 2.76 TeV and at 7 TeV was evaluated by extrapolating to the full phase space the pTp_{\rm T}-differential production cross sections at s=2.76\sqrt{s} = 2.76 TeV and our previous measurements at s=7\sqrt{s} = 7 TeV. The results were compared to existing measurements and to perturbative-QCD calculations. The fraction of cdbar D mesons produced in a vector state was also determined.Comment: 20 pages, 5 captioned figures, 4 tables, authors from page 15, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/307

    Differential body composition effects of protease inhibitors recommended for initial treatment of HIV infection: A randomized clinical trial

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    This article has been accepted for publication in Clinical Infectious Diseases ©2014 The Authors .Published by Oxford University Press on Clinical Infectious Disease 60.5. DOI: 10.1093/cid/ciu898Background. It is unclear whether metabolic or body composition effects may differ between protease inhibitor-based regimens recommended for initial treatment of HIV infection. Methods. ATADAR is a phase IV, open-label, multicenter randomized clinical trial. Stable antiretroviral-naive HIV-infected adults were randomly assigned to atazanavir/ritonavir 300/100 mg or darunavir/ritonavir 800/100 mg in combination with tenofovir/emtricitabine daily. Pre-defined end-points were treatment or virological failure, drug discontinuation due to adverse effects, and laboratory and body composition changes at 96 weeks. Results. At 96 weeks, 56 (62%) atazanavir/ritonavir and 62 (71%) darunavir/ritonavir patients remained free of treatment failure (estimated difference 8.2%; 95%CI -0.6 to 21.6); and 71 (79%) atazanavir/ritonavir and 75 (85%) darunavir/ritonavir patients remained free of virological failure (estimated difference 6.3%; 95%CI -0.5 to 17.6). Seven vs. five patients discontinued atazanavir/ritonavir or darunavir/ritonavir due to adverse effects. Total and HDL cholesterol similarly increased in both arms, but triglycerides increased more in atazanavir/ritonavir arm. At 96 weeks, body fat (estimated difference 2862.2 gr; 95%CI 726.7 to 4997.7; P=0.0090), limb fat (estimated difference 1403.3 gr; 95%CI 388.4 to 2418.2; P=0.0071), and subcutaneous abdominal adipose tissue (estimated difference 28.4 cm2; 95%CI 1.9 to 55.0; P=0.0362) increased more in atazanavir/ritonavir than in darunavir/ritonavir arm. Body fat changes in atazanavir/ritonavir arm were associated with higher insulin resistance. Conclusions. We found no major differences between atazanavir/ritonavir and darunavir/ritonavir in efficacy, clinically-relevant side effects, or plasma cholesterol fractions. However, atazanavir/ritonavir led to higher triglycerides and total and subcutaneous fat than darunavir/ritonavir and fat gains with atazanavir/ritonavir were associated with insulin resistanceThis is an Investigator Sponsored Research study. It was supported in part by research grants from Bristol‐Myers Squibb and Janssen‐Cilag; Instituto de Salud Carlos III (PI12/01217) and Red Temática Cooperativa de Investigación en SIDA G03/173 (RIS‐EST11), Ministerio de Ciencia e Innovación, Spain. (Registration number: NCT01274780; registry name: ATADAR; EUDRACT; 2010‐021002‐38)

    Autonomous on-board data processing and instrument calibration software for the Polarimetric and Helioseismic Imager on-board the Solar Orbiter mission

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    This is an open access article. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.A frequent problem arising for deep space missions is the discrepancy between the amount of data desired to be transmitted to the ground and the available telemetry bandwidth. A part of these data consists of scientific observations, being complemented by calibration data to help remove instrumental effects. We present our solution for this discrepancy, implemented for the Polarimetric and Helioseismic Imager on-board the Solar Orbiter mission, the first solar spectropolarimeter in deep space. We implemented an on-board data reduction system that processes calibration data, applies them to the raw science observables, and derives science-ready physical parameters. This process reduces the raw data for a single measurement from 24 images to five, thus reducing the amount of downlinked data, and in addition, renders the transmission of the calibration data unnecessary. Both these on-board actions are completed autonomously. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.This work was carried out in the framework of the International Max Planck Research School for Solar System Science at the Max Planck Institute for Solar System Research. Solar Orbiter is a mission led by the European Space Agency with contribution from the National Aeronautics and Space Administration (NASA). The Polarimetric and Helioseismic Imager instrument is supported by the German Aerospace Center (DLR) under grant Nos. 50 OT 1201 and 50 OT 1901. The Spanish contribution has been partly funded by the Spanish Research Agency under projects under grant Nos. ESP2016-77548-C5 and RTI2018-096886-B-C5, partially including European FEDER funds. IAA-CSIC members acknowledge and funds from the Spanish Ministry of Science and Innovation “Centro de Excelencia Severo Ochoa” Program under grant No. SEV-2017-0709. The solar data used in the tests are the courtesy of NASA/SDO HMI science team. Parts of the work shown in this paper have been introduced at the SPIE Astronomical Telescopes + Instrumentation conference.42 EditorialPeer reviewe

    Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

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    Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

    Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

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    Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

    Comparison of seven prognostic tools to identify low-risk pulmonary embolism in patients aged <50 years

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    Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

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    To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping

    Spectropolarimetric investigation of magnetohydrodynamic wave modes in the photosphere: First results from PHI on board Solar Orbiter

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    This is an Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Context. In November 2021, Solar Orbiter started its nominal mission phase. The remote-sensing instruments on board the spacecraft acquired scientific data during three observing windows surrounding the perihelion of the first orbit of this phase. Aims. The aim of the analysis is the detection of magnetohydrodynamic (MHD) wave modes in an active region by exploiting the capabilities of spectropolarimetric measurements. Mthods. The High Resolution Telescope (HRT) of the Polarimetric and Helioseismic Imager (SO/PHI) on board the Solar Orbiter acquired a high-cadence data set of an active region. This is studied in the paper. B-ω and phase-difference analyses are applied on line-of-sight velocity and circular polarization maps and other averaged quantities. Results. We find that several MHD modes at different frequencies are excited in all analysed structures. The leading sunspot shows a linear dependence of the phase lag on the angle between the magnetic field and the line of sight of the observer in its penumbra. The magnetic pore exhibits global resonances at several frequencies, which are also excited by different wave modes. Conclusions. The SO/PHI measurements clearly confirm the presence of magnetic and velocity oscillations that are compatible with one or more MHD wave modes in pores and a sunspot. Improvements in modelling are still necessary to interpret the relation between the fluctuations of different diagnostics. © The Authors 2023.Solar Orbiter is a space mission of international collaboration between ESA and NASA, operated by ESA. We are grateful to the ESA SOC and MOC teams for their support. The German contribution to SO/PHI is funded by the BMWi through DLR and by MPG central funds. The Spanish contribution is funded by AEI/MCIN/10.13039/501100011033/ (RTI2018-096886-C5, PID2021-125325OB-C5, PCI2022-135009-2) and ERDF “A way of making Europe”; “Center of Excellence Severo Ochoa” awards to IAA-CSIC (SEV-2017-0709, CEX2021-001131-S); and a Ramón y Cajal fellowship awarded to DOS. The French contribution is funded by CNES. The authors wish to acknowledge scientific discussions with the Waves in the Lower Solar Atmosphere (WaLSA; https://WaLSA.team) team, which has been supported by the Research Council of Norway (project no. 262622), The Royal Society (award no. Hooke18b/SCTM), and the International Space Science Institute (ISSI Team 502).Peer reviewe

    Wavefront error of PHI/HRT on Solar Orbiter at various heliocentric distances

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    This is an Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Aims. We use wavefront sensing to characterise the image quality of the High Resolution Telescope (HRT) of the Polarimetric and Helioseismic Imager (SO/PHI) data products during the second remote sensing window of the Solar Orbiter (SO) nominal mission phase. Our ultimate aims are to reconstruct the HRT data by deconvolving with the HRT point spread function (PSF) and to correct for the effects of optical aberrations on the data. Methods. We use a pair of focused–defocused images to compute the wavefront error and derive the PSF of HRT by means of a phase diversity (PD) analysis. Results. The wavefront error of HRT depends on the orbital distance of SO to the Sun. At distances > 0.5 au, the wavefront error is small, and stems dominantly from the inherent optical properties of HRT. At distances < 0.5 au, the thermo-optical effect of the Heat Rejection Entrance Window (HREW) becomes noticeable. We develop an interpolation scheme for the wavefront error that depends on the thermal variation of the HREW with the distance of SO to the Sun. We also introduce a new level of image reconstruction, termed ‘aberration correction’, which is designed to reduce the noise caused by image deconvolution while removing the aberrations caused by the HREW. Conclusions. The computed PSF via phase diversity significantly reduces the degradation caused by the HREW in the near-perihelion HRT data. In addition, the aberration correction increases the noise by a factor of only 1.45 compared to the factor of 3 increase that results from the usual PD reconstructions. © The Authors 2023.Solar Orbiter is a space mission of international collaboration between ESA and NASA, operated by ESA. We are grateful to the ESA SOC and MOC teams for their support. The German contribution to SO/PHI is funded by the BMWi through DLR and by MPG central funds. The Spanish contribution is funded by FEDER/AEI/MCIU (RTI2018-096886-C5), a “Center of Excellence Severo Ochoa” award to IAA-CSIC (SEV-2017-0709), and a Ramón y Cajal fellowship awarded to DOS. The French contribution is funded by CNES.With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001131-S).Peer reviewe
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