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Mechanism of the electroneutral sodium/proton antiporter PaNhaP from transition-path shooting
(2019)
Na+/H+ antiporters exchange sodium ions and protons on opposite sides of lipid membranes. The electroneutral Na+/H+ antiporter NhaP from archaea Pyrococcus abyssi (PaNhaP) is a functional homolog of the human Na+/H+ exchanger NHE1, which is an important drug target. Here we resolve the Na+ and H+ transport cycle of PaNhaP by transition-path sampling. The resulting molecular dynamics trajectories of repeated ion transport events proceed without bias force, and overcome the enormous time-scale gap between seconds-scale ion exchange and microseconds simulations. The simulations reveal a hydrophobic gate to the extracellular side that opens and closes in response to the transporter domain motion. Weakening the gate by mutagenesis makes the transporter faster, suggesting that the gate balances competing demands of fidelity and efficiency. Transition-path sampling and a committor-based reaction coordinate optimization identify the essential motions and interactions that realize conformational alternation between the two access states in transporter function.
The hot and dense QCD matter produced in nuclear collisions at ultrarelativistic energy is characterized by very intense electromagnetic fields which attain their maximal strength in the early pre-equilibrium stage and interplay with the strong vorticity induced in the plasma by the large angular momentum of the colliding system. A promising observable keeping trace of these phenomena is the directed flow of light hadrons and heavy mesons produced in symmetric and asymmetric heavy-ion collisions as well as in proton-induced reactions. In particular, the splitting of the directed flow between particles with the same mass but opposite electric charge as a function of rapidity and transverse momentum gives access to the electromagnetic response of medium in all collision stages and in the different colliding systems. The highest influence of the electromagnetic fields is envisaged in the pre-equilibrium stage of the collision and therefore a significant imprint is left on the early-produced heavy quarks. The aim of this review is to discuss the current developments towards the understanding of the generation and relaxation time of the electromagnetic fields embedded in both large and small systems and their impact on the charge-odd directed flow of light and heavy particles, highlighting the experimental results and the different theoretical approaches. Since it is possible to perform realistic simulations of high-energy collisions that incorporate also the generated electromagnetic fields and vorticity, the study of the directed flow can provide unique insight into the early nonequilibrium phase and the ensuing QGP formation and transport properties.
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.
Neutron capture on 241Am plays an important role in the nuclear energy production and also provides valuable information for the improvement of nuclear models and the statistical interpretation of the nuclear properties. A new experiment to measure the 241Am(n, γ) cross section in the thermal region and the first few resonances below 10 eV has been carried out at EAR2 of the n_TOF facility at CERN. Three neutron-insensitive C6D6 detectors have been used to measure the neutron-capture gamma cascade as a function of the neutron time of flight, and then deduce the neutron capture yield. Preliminary results will be presented and compared with previously obtained results at the same facility in EAR1. In EAR1 the gamma-ray background at thermal energies was about 90% of the signal while in EAR2 is up to a 25 factor much more favorable signal to noise ratio. We also extended the low energy limit down to subthermal energies. This measurement will allow a comparison with neutron capture measurements conducted at reactors and using a different experimental technique.
Upon antibiotic stress Gram-negative pathogens deploy resistance-nodulation-cell division-type tripartite efflux pumps. These include a H+/drug antiporter module that recognizes structurally diverse substances, including antibiotics. Here, we show the 3.5 Å structure of subunit AdeB from the Acinetobacter baumannii AdeABC efflux pump solved by single-particle cryo-electron microscopy. The AdeB trimer adopts mainly a resting state with all protomers in a conformation devoid of transport channels or antibiotic binding sites. However, 10% of the protomers adopt a state where three transport channels lead to the closed substrate (deep) binding pocket. A comparison between drug binding of AdeB and Escherichia coli AcrB is made via activity analysis of 20 AdeB variants, selected on basis of side chain interactions with antibiotics observed in the AcrB periplasmic domain X-ray co-structures with fusidic acid (2.3 Å), doxycycline (2.1 Å) and levofloxacin (2.7 Å). AdeABC, compared to AcrAB-TolC, confers higher resistance to E. coli towards polyaromatic compounds and lower resistance towards antibiotic compounds.
We discuss in some detail the physics content of the new model, QGSJET-III-01, focusing on major problems related to the treatment of semihard processes in the very high energy limit. A special attention has been payed to the main improvement, compared to the QGSJET-II model, which is related to a phenomenological treatment of leading power corrections corresponding to final parton rescattering off soft gluons. In particular, this allowed us to use a twice smaller separation scale between the soft and hard parton physics, compared to the previous model version, QGSJET-II-04. Preliminary results obtained with the new model are also presented.
Steep rise of parton densities in the limit of small parton momentum fraction x poses a challenge for describing the observed energy-dependence of the total and inelastic proton-proton cross sections σtot/inelpp : considering a realistic parton spatial distribution, one obtains a too-strong increase of σtot/inelpp in the limit of very high energies. We discuss various mechanisms which allow one to tame such a rise, paying special attention to the role of parton-parton correlations. In addition, we investigate a potential impact on model predictions for σtotpp, related to dynamical higher twist corrections to parton-production process.
We apply the phenomenological Reggeon field theory framework to investigate rapidity gap survival (RGS) probability for diffractive dijet production in proton–proton collisions. In particular, we study in some detail rapidity gap suppression due to elastic rescatterings of intermediate partons in the underlying parton cascades, described by enhanced (Pomeron–Pomeron interaction) diagrams. We demonstrate that such contributions play a subdominant role, compared to the usual, so-called “eikonal”, rapidity gap suppression due to elastic rescatterings of constituent partons of the colliding protons. On the other hand, the overall RGS factor proves to be sensitive to color fluctuations in the proton. Hence, experimental data on diffractive dijet production can be used to constrain the respective model approaches.
We study the kinetic and chemical equilibration in 'infinite' parton-hadron matter within the Parton-Hadron-String Dynamics transport approach, which is based on a dynamical quasiparticle model for partons matched to reproduce lattice-QCD results – including the partonic equation of state – in thermodynamic equilibrium. The 'infinite' matter is simulated within a cubic box with periodic boundary conditions initialized at different baryon density (or chemical potential) and energy density. The transition from initially pure partonic matter to hadronic degrees of freedom (or vice versa) occurs dynamically by interactions. Different thermody-namical distributions of the strongly-interacting quark-gluon plasma (sQGP) are addressed and discussed.
We investigate the implications of the r-modes instability on the composition of a compact star rotating at a sub-millisecond period. In particular, the only viable astrophysical scenario for such an object, wich might present inside the Low Mass X-ray Binary associated with the x-ray transient XTE J1739-285, is that it has a strangeness content. Since previous analysis indicate that hyperonic stars or stars containing a kaon condensate are unlikely because of the mass-shedding constraint, the only remaining possibility is that such an object is either a strange quark star or a hybrid quark-hadron star.
Hadron production and their suppression in Pb+Pb collisions at LHC at a center-of-mass energy of sNN=2.76 TeV are studied within a multiphase transport (AMPT) model whose initial conditions are obtained from the recently updated HIJING 2.0 model. The centrality dependence of charged hadron multiplicity dNch/dη at midrapidity was found quite sensitive to the largely uncertain gluon shadowing parameter sg that determines the nuclear modification of the gluon distribution. We find final-state parton scatterings reduce considerably hadron yield at midrapidity and enforces a smaller gluon shadowing to be consistent with dNch/dη data at LHC. With such a constrained parton shadowing, charged hadron and neutral pion production over a wide transverse momenta range are investigated in AMPT. Relative to nucleon–nucleon collisions, the particle yield in central heavy ion collisions is suppressed due to parton energy loss. While the calculated magnitude and pattern of suppression is found consistent with that measured in Au+Au collisions at sNN=0.2 TeV at RHIC, at the LHC energy the suppression is overpredicted which may imply the medium formed at LHC is less opaque than expected from simple RHIC extrapolations. Reduction of the QCD coupling constant αs by ∼30% in the higher temperature plasma formed at LHC as compared to that at RHIC was found to reproduce the measured suppression at LHC.
Presolar grain isotopic ratios as constraints to nuclear physics inputs for s-process calculations
(2023)
The isotopic abundances in presolar SiC grains of AGB origin provide important and precise constraints to those star nucleosynthesis models. By comparing the values of the s-element abundances resulting from calculations with the ones measured in these dust grains, it turns out that new measurements of weak-interaction rates in ionized plasmas, as well as of neutron-capture cross sections, are needed, especially in the region near the neutron magic numbers 50 and 82.
Presolar grains and their isotopic compositions provide valuable constraints to AGB star nucleosynthesis. However, there is a sample of O- and Al-rich dust, known as group 2 oxide grains, whose origin is difficult to address. On the one hand, the 17O/16O isotopic ratios shown by those grains are similar to the ones observed in low-mass red giant stars. On the other hand, their large 18O depletion and 26Al enrichment are challenging to account for. Two different classes of AGB stars have been proposed as progenitors of this kind of stellar dust: intermediate mass AGBs with hot bottom burning, or low mass AGBs where deep mixing is at play. Our models of low-mass AGB stars with a bottom-up deep mixing are shown to be likely progenitors of group 2 grains, reproducing together the 17O/16O, 18O/16O and 26Al/27Al values found in those grains and being less sensitive to nuclear physics inputs than our intermediate-mass models with hot bottom burning.
We study here hot nuclear matter in the quark meson coupling model which incorporates explicitly quark degrees of freedom, with quarks coupled to scalar and vector mesons. The equation of state of nuclear matter including the composite nature of the nucleons is calculated at finite temperatures. The calculations are done taking into account the medium-dependent bag constant. Nucleon properties at finite temperatures as calculated here are found to be appreciably different from the value at T=0.
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.
The proton-removal mechanism of the 12CB reaction induced on a carbon target via elementary nucleon-nucleon scattering is investigated in exclusive triple-coincidence measurements. The observed two-nucleon angular correlations are found to be consistent with quasi-free scattering of a projectile-like proton off a target-like nucleon. Exclusive cross sections for one-step pp and pn interactions are determined as [formula] and [formula], respectively. The extracted quasi-free component amounts up to 58(4)% of the total proton-removal cross section. The results are compared to total proton-removal cross sections obtained from the experiment and eikonal reaction theory.