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Single-pion production in proton-proton collisions at 1.25 GeV: measurements by HADES and a PWA
(2014)
We report on the single-pion production in proton-proton collisions at a kinetic energy of 1.25 GeV based on data measured with HADES. Exclusive channels npπ+ and ppπ0 were studied simultaneously. The parametrization of production cross sections of the one-pion final states by means of the resonance model has been obtained. Independently, the extraction of the leading partial waves in the data were analyzed within the framework of the partial wave analysis (PWA). Contributions for the production of ∆(1232) and N(1440) intermediate states have been deduced.
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.
Transverse momentum spectra of π±, K± and p(p¯) up to pT = 20 GeV/c at mid-rapidity in pp and Pb-Pb collisions at sNN−−−√= 2.76 TeV have been measured using the ALICE detector at the Large Hadron Collider. The proton-to-pion and the kaon-to-pion ratios both show a distinct peak at pT≈3GeV/c in central Pb-Pb collisions. Below the peak, pT < 3 GeV/c, both ratios are in good agreement with hydrodynamical calculations, suggesting that the peak itself is dominantly the result of radial flow rather than anomalous hadronization processes. For pT > 10 GeV/c particle ratios in pp and Pb-Pb collisions are in agreement and the nuclear modification factors for π±, K± and p(p¯) indicate that, within the systematic and statistical uncertainties, the suppression is the same. This suggests that the chemical composition of leading particles from jets in the medium is similar to that of vacuum jets.
Na(+)/H(+) exchangers are essential for regulation of intracellular proton and sodium concentrations in all living organisms. We examined and experimentally verified a kinetic model for Na(+)/H(+) exchangers, where a single binding site is alternatively occupied by Na(+) or one or two H(+) ions. The proposed transport mechanism inherently down-regulates Na(+)/H(+) exchangers at extreme pH, preventing excessive cytoplasmic acidification or alkalinization. As an experimental test system we present the first electrophysiological investigation of an electroneutral Na(+)/H(+) exchanger, NhaP1 from Methanocaldococcus jannaschii (MjNhaP1), a close homologue of the medically important eukaryotic NHE Na(+)/H(+) exchangers. The kinetic model describes the experimentally observed substrate dependences of MjNhaP1, and the transport mechanism explains alkaline down-regulation of MjNhaP1. Because this model also accounts for acidic down-regulation of the electrogenic NhaA Na(+)/H(+) exchanger from Escherichia coli (EcNhaA, shown in a previous publication) we conclude that it applies generally to all Na(+)/H(+) exchangers, electrogenic as well as electroneutral, and elegantly explains their pH regulation. Furthermore, the electrophysiological analysis allows insight into the electrostatic structure of the translocation complex in electroneutral and electrogenic Na(+)/H(+) exchangers.
In reply to internal or external danger stimuli, the body orchestrates an inflammatory response. The endogenous triggers of this process are the damage-associated molecular patterns (DAMPs). DAMPs represent a heterogeneous group of molecules that draw their origin either from inside the various compartments of the cell or from the extracellular space. Following interaction with pattern recognition receptors in cross-talk with various non-immune receptors, DAMPs determine the downstream signaling outcome of septic and aseptic inflammatory responses. In this review, the diverse nature, structural characteristics, and signaling pathways elicited by DAMPs will be critically evaluated.
While patients with chronic hepatitis C virus (HCV) infection are treated in order to prevent liver-related morbidity and mortality, we rely on sustained virological response (SVR) as a virological biomarker to evaluate treatment efficacy in both clinical practice as well as in drug development. However, conclusive evidence for the clinical benefit of antiviral therapy or validity of SVR as surrogate marker, as derived from trials randomizing patients to a treatment or control arm, is lacking. In fact, the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis (HALT-C) trial recently showed an increased mortality rate among interferon-treated patients compared to untreated controls. Consequently, the recommendation to treat patients with chronic HCV infection was challenged.
Here, we argue that the possible harmful effect of long-term low-dose pegylated interferon mono therapy, as was observed in the HALT-C trial cohort, cannot be extrapolated to potentially curative short-term treatment regimens. Furthermore, we discuss SVR as a surrogate biomarker, based on numerous studies which indicated an association between SVR and improvements in health-related quality of life, hepatic inflammation and fibrosis, and portal pressure as well as a reduced risk for hepatocellular carcinoma (HCC), liver failure and mortality.
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.