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Event-by-event fluctuations of the mean transverse momentum of charged particles produced in pp collisions at s√ = 0.9, 2.76 and 7 TeV, and Pb-Pb collisions at sNN−−−−√ = 2.76 TeV are studied as a function of the charged-particle multiplicity using the ALICE detector at the LHC. Dynamical fluctuations indicative of correlated particle emission are observed in all systems. The results in pp collisions show little dependence on collision energy. The Monte Carlo event generators PYTHIA and PHOJET are in qualitative agreement with the data. Peripheral Pb-Pb data exhibit a similar multiplicity dependence as that observed in pp. In central Pb-Pb, the results deviate from this trend, featuring a significant reduction of the fluctuation strength. The results in Pb--Pb are in qualitative agreement with previous measurements in Au-Au at lower collision energies and with expectations from models that incorporate collective phenomena.
We report on the production of inclusive Υ(1S) and Υ(2S) in p-Pb collisions at sNN−−−√=5.02 TeV at the LHC. The measurement is performed with the ALICE detector at backward (−4.46<ycms<−2.96) and forward (2.03<ycms<3.53) rapidity down to zero transverse momentum. The production cross sections of the Υ(1S) and Υ(2S) are presented, as well as the nuclear modification factor and the ratio of the forward to backward yields of Υ(1S). A suppression of the inclusive Υ(1S) yield in p-Pb collisions with respect to the yield from pp collisions scaled by the number of binary nucleon-nucleon collisions is observed at forward rapidity but not at backward rapidity. The results are compared to theoretical model calculations including nuclear shadowing or partonic energy loss effects.
We report on the production of inclusive Υ(1S) and Υ(2S) in p-Pb collisions at sNN−−−√=5.02 TeV at the LHC. The measurement is performed with the ALICE detector at backward (−4.46<ycms<−2.96) and forward (2.03<ycms<3.53) rapidity down to zero transverse momentum. The production cross sections of the Υ(1S) and Υ(2S) are presented, as well as the nuclear modification factor and the ratio of the forward to backward yields of Υ(1S). A suppression of the inclusive Υ(1S) yield in p-Pb collisions with respect to the yield from pp collisions scaled by the number of binary nucleon-nucleon collisions is observed at forward rapidity but not at backward rapidity. The results are compared to theoretical model calculations including nuclear shadowing or partonic energy loss effects.
The pT-differential production cross section of electrons from semileptonic decays of heavy-flavor hadrons has been measured at mid-rapidity in proton-proton collisions at s√=2.76 TeV in the transverse momentum range 0.5 < pT < 12 GeV/c with the ALICE detector at the LHC. The analysis was performed using minimum bias events and events triggered by the electromagnetic calorimeter. Predictions from perturbative QCD calculations agree with the data within the theoretical and experimental uncertainties.
The differential charged jet cross sections, jet fragmentation distributions, and jet shapes are measured in minimum bias proton-proton collisions at centre-of-mass energy s√=7 TeV using the ALICE detector at the LHC. Jets are reconstructed from charged particle momenta in the mid-rapidity region using the sequential recombination kT and anti-kT as well as the SISCone jet finding algorithms with several resolution parameters in the range R=0.2 to 0.6. Differential jet production cross sections measured with the three jet finders are in agreement in the transverse momentum (pT) interval 20<pjet,chT<100 GeV/c. They are also consistent with prior measurements carried out at the LHC by the ATLAS collaboration. The jet charged particle multiplicity rises monotonically with increasing jet pT, in qualitative agreement with prior observations at lower energies. The transverse profiles of leading jets are investigated using radial momentum density distributions as well as distributions of the average radius containing 80% (⟨R80⟩) of the reconstructed jet pT. The fragmentation of leading jets with R=0.4 using scaled pT spectra of the jet constituents is studied. The measurements are compared to model calculations from event generators (PYTHIA, PHOJET, HERWIG). The measured radial density distributions and ⟨R80⟩ distributions are well described by the PYTHIA model (tune Perugia-2011). The fragmentation distributions are better described by HERWIG.
Background: Berotralstat (BCX7353) is an oral, once-daily inhibitor of plasma kallikrein in development for the prophylaxis of hereditary angioedema (HAE) attacks.
Objective: Our aim was to determine the efficacy, safety, and tolerability of berotralstat in patients with HAE over a 24-week treatment period (the phase 3 APeX-2 trial).
Methods: APeX-2 was a double-blind, parallel-group study that randomized patients at 40 sites in 11 countries 1:1:1 to receive once-daily berotralstat in a dose of 110 mg or 150 mg or placebo (Clinicaltrials.gov identifier NCT03485911). Patients aged 12 years or older with HAE due to C1 inhibitor deficiency and at least 2 investigator-confirmed HAE attacks in the first 56 days of a prospective run-in period were eligible. The primary efficacy end point was the rate of investigator-confirmed HAE attacks during the 24-week treatment period.
Results: A total of 121 patients were randomized; 120 of them received at least 1 dose of the study drug (n = 41, 40, and 39 in the 110-mg dose of berotralstat, 150-mg of dose berotralstat, and placebo groups, respectively). Berotralstat demonstrated a significant reduction in attack rate at both 110 mg (1.65 attacks per month; P = .024) and 150 mg (1.31 attacks per month; P < .001) relative to placebo (2.35 attacks per month). The most frequent treatment-emergent adverse events that occurred more with berotralstat than with placebo were abdominal pain, vomiting, diarrhea, and back pain. No drug-related serious treatment-emergent adverse events occurred.
Conclusion: Both the 110-mg and 150-mg doses of berotralstat reduced HAE attack rates compared with placebo and were safe and generally well tolerated. The most favorable benefit-to-risk profile was observed at a dose of 150 mg per day.
In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols
(2016)
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copy-right work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce, or limit each other.