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We analyze the phase structure of the nonlinear mean-field meson theory of baryonic matter (nucleons plus delta resonances). Depending on the choice of the coupling constants, we find three physically distinct phase transitions in this theory: a nucleonic liquid-gas transition in the low temperature, Tc<20 MeV, low density, ρ≃0.5ρ0, regime, a high-temperature (T≃150 MeV) finite density transition from a gas of massive hadrons to a nearly massless baryon, antibaryon plasma, and, third, a strong phase transition from the nucleonic fluid to a resonance-dominated ‘‘delta-matter’’ isomer at ρ>2ρ0 and Tc<50 MeV. All three phase transitions are of first order. It is shown that the occurrence of these different phase transitions depends critically on the coupling constants. Since the production of pions also depends strongly on the coupling constants, it is seen that the equation of state cannot be derived unambiguously from pion data.
We investigated the excitation of surface plasmon polaritons on gold films with the metallized probe tip of a scattering-type scanning near-field optical microscope (s-SNOM). The emission of the polaritons from the tip, illuminated by near-infrared laser radiation, was found to be anisotropic and not circularly symmetric as expected on the basis of literature data. We furthermore identified an additional excitation channel via light that was reflected off the tip and excited the plasmon polaritons at the edge of the metal film. Our results, while obtained for a non-rotationally-symmetric type of probe tip and thus specific for this situation, indicate that when an s-SNOM is employed for the investigation of plasmonic structures, the unintentional excitation of surface waves and anisotropic surface wave propagation must be considered in order to correctly interpret the signatures of plasmon polariton generation and propagation.
Low-level-laser therapy (LLLT) is an effective complementary treatment, especially for anti-inflammation and wound healing in which dermis or mucus mast cells (MCs) are involved. In periphery, MCs crosstalk with neurons via purinergic signals and participate in various physiological and pathophysiological processes. Whether extracellular ATP, an important purine in purinergic signaling, of MCs and neurons could be modulated by irradiation remains unknown. In this study, effects of red-laser irradiation on extracellular ATP content of MCs and dorsal root ganglia (DRG) neurons were investigated and underlying mechanisms were explored in vitro. Our results show that irradiation led to elevation of extracellular ATP level in the human mast cell line HMC-1 in a dose-dependent manner, which was accompanied by elevation of intracellular ATP content, an indicator for ATP synthesis, together with [Ca2+]i elevation, a trigger signal for exocytotic ATP release. In contrast to MCs, irradiation attenuated the extracellular ATP content of neurons, which could be abolished by ARL 67156, a nonspecific ecto-ATPases inhibitor. Our results suggest that irradiation potentiates extracellular ATP of MCs by promoting ATP synthesis and release and attenuates extracellular ATP of neurons by upregulating ecto-ATPase activity. The opposite responses of these two cell types indicate complex mechanisms underlying LLLT.
In Chinese medicine acupuncture points are treated by physical stimuli to counteract various diseases. These stimuli include mechanical stress as applied during the needle manipulation or tuina, high temperatures as applied during moxibustion, and red laser light applied during laser acupuncture. This study aimed to investigate cellular responses to stimuli that might occur in the tissue of acupuncture points. Since they have a characteristically high density of mast cells that degranulate in response to acupuncture, we asked whether these processes lead to ATP release. We tested in in vitro experiments on mast cells of the human mast-cell line HMC-1 the effects of the physical stimuli; mechanical stress was applied by superfusion of the cells with hypotonic solution, heat was applied by incubation of the cells at 52°C, and red laser light of 657 nm was used for irradiation. We demonstrate that all the stimuli induce ATP release from model human mast HMC-1 cells, and this release is associated with an intracellular free Ca2+ rise. We hypothesize that ATP released from mast cells supplements the already known release of ATP from keratinocytes and, by acting on P2X receptors, it may serve as initial mediator of acupuncture-induced analgesia.
In this proceeding, the deep Convolutional Neural Networks(CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin processes. To overcome the intrinsic randomness existed in a stochastic process, we treat the final spectra as image-type inputs which preserve sufficient spatiotemporal correlations. As a practical example, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of chiral phase transition can be characterized in a 1+1-dimensional Langevin equation for σ field. The well-trained CNNs accurately classify the first-order phase transition and crossover from σ field configurations with fluctuations, in which the noise does not impair the performance of the recognition. In reconstructing the dynamics, we demonstrate it is robust to extract the damping coefficients η from the intricate field configurations.
The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.
We present a new type of flow analysis, based on a particle-pair correlation function, in which there is no need for an event-by-event determination of the reaction plane. Consequently, the need to correct for dispersion in an estimated reaction plane does not arise. Our method also offers the option to avoid any influence from particle misidentification. Using this method, streamer chamber data for collisions of Ar+KCl and Ar+BaI2 at 1.2 GeV/nucleon are compared with predictions of a nuclear transport model.
A deep convolutional neural network (CNN) is developed to study symmetry energy (Esym(ρ)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff Esym(ρ) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different Esym(ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of Esym(ρ)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm.
Complex I couples the free energy released from quinone (Q) reduction to pump protons across the biological membrane in the respiratory chains of mitochondria and many bacteria. The Q reduction site is separated by a large distance from the proton-pumping membrane domain. To address the molecular mechanism of this long-range proton-electron coupling, we perform here full atomistic molecular dynamics simulations, free energy calculations, and continuum electrostatics calculations on complex I from Thermus thermophilus. We show that the dynamics of Q is redox-state-dependent, and that quinol, QH2, moves out of its reduction site and into a site in the Q tunnel that is occupied by a Q analog in a crystal structure of Yarrowia lipolytica. We also identify a second Q-binding site near the opening of the Q tunnel in the membrane domain, where the Q headgroup forms strong interactions with a cluster of aromatic and charged residues, while the Q tail resides in the lipid membrane. We estimate the effective diffusion coefficient of Q in the tunnel, and in turn the characteristic time for Q to reach the active site and for QH2 to escape to the membrane. Our simulations show that Q moves along the Q tunnel in a redox-state-dependent manner, with distinct binding sites formed by conserved residue clusters. The motion of Q to these binding sites is proposed to be coupled to the proton-pumping machinery in complex I.
We analyzed a eukaryotically encoded rubredoxin from the cryptomonad Guillardia theta and identified additional domains at the N- and C-termini in comparison to known prokaryotic paralogous molecules. The cryptophytic N-terminal extension was shown to be a transit peptide for intracellular targeting of the protein to the plastid, whereas a C-terminal domain represents a membrane anchor. Rubredoxin was identified in all tested phototrophic eukaryotes. Presumably facilitated by its C-terminal extension, nucleomorph-encoded rubredoxin (nmRub) is associated with the thylakoid membrane. Association with photosystem II (PSII) was demonstrated by co-localization of nmRub and PSII membrane particles and PSII core complexes and confirmed by comparative electron paramagnetic resonance measurements. The midpoint potential of nmRub was determined as +125 mV, which is the highest redox potential of all known rubredoxins. Therefore, nmRub provides a striking example of the ability of the protein environment to tune the redox potentials of metal sites, allowing for evolutionary adaption in specific electron transport systems, as for example that coupled to the PSII pathway.