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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.
Formation of Hubbard-like bands as a fingerprint of strong electron-electron interactions in FeSe
(2017)
We use angle-resolved photo-emission spectroscopy (ARPES) to explore the electronic structure of single crystals of FeSe over a wide range of binding energies and study the effects of strong electron-electron correlations. We provide evidence for the existence of "Hubbard-like bands" at high binding energies consisting of incoherent many-body excitations originating from Fe 3d states in addition to the renormalized quasiparticle bands near the Fermi level. Many high energy features of the observed ARPES data can be accounted for when incorporating effects of strong local Coulomb interactions in calculations of the spectral function via dynamical mean-field theory, including the formation of a Hubbard-like band. This shows that over the energy scale of several eV, local correlations arising from the on-site Coulomb repulsion and Hund's coupling are essential for a proper understanding of the electronic structure of FeSe and other related iron based superconductors.
The amount of proton stopping in central Pb+Pb collisions from 20–160 A GeV as well as hyperon and antihyperon rapidity distributions are calculated within the UrQMD model in comparison to experimental data at 40, 80, and 160 A GeV taken recently from the NA49 collaboration. Furthermore, the amount of baryon stopping at 160A GeV for Pb+Pb collisions is studied as a function of centrality in comparison to the NA49 data. We find that the strange baryon yield is reasonably described for central collisions, however, the rapidity distributions are somewhat more narrow than the data. Moreover, the experimental antihyperon rapidity distributions at 40, 80, and 160 A GeV are underestimated by up to factors of 3—depending on the annihilation cross section employed—which might be addressed to missing multimeson fusion channels in the UrQMD model. Pacs-Nr.: 25.75.2q, 24.10.Jv, 24.10.Lx
We derive the collision term in the Boltzmann equation using the equation of motion for the Wigner function of massive spin-1/2 particles. To next-to-lowest order in h, it contains a nonlocal contribution, which is responsible for the conversion of orbital into spin angular momentum. In a proper choice of pseudogauge, the antisymmetric part of the energy-momentum tensor arises solely from this nonlocal contribution. We show that the collision term vanishes in global equilibrium and that the spin potential is, then, equal to the thermal vorticity. In the nonrelativistic limit, the equations of motion for the energy-momentum and spin tensors reduce to the well-known form for hydrodynamics for micropolar fluids.
P-type ATPases are membrane proteins acting as ion pumps that drive an active transport of cations across the membrane against a concentration gradient. The required energy for the ion transport is provided by binding and hydrolysis of ATP. A reaction mechanism of ion transport and energy transduction is assumed to be common for all P-type ATPases and generally described by the Post-Albers cycle. Transient currents and charge translocation of P-type ATPases were extensively investigated by electrical measurements that apply voltage jumps to initiate the reaction cycle. In this study, we simulate an applied voltage across the membrane by an electric field and perform electrostatic calculations in order to verify the experimentally-driven hypothesis that the energy transduction mechanism is regulated by specific structural elements. Side chain conformational and ionization changes induced by the electric field are evaluated for each transmembrane helix and the selectivity in response is qualitatively analyzed for the Ca2+-ATPase as well as for structural models of the Na+/K+-ATPase. Helix M5 responds with more conformer changes as compared to the other transmembrane helices what is even more emphasized when the stalk region is included. Thus our simulations support experimental results and indicate a crucial role for the highly conserved transmembrane helix M5 in the energy transduction mechanism of P-type ATPases.
The neutron capture cross section of the s-process branch nucleus 63Ni affects the abundances of other nuclei in its region, especially 63Cu and 64Zn. In order to determine the energy-dependent neutron capture cross section in the astrophysical energy region, an experiment at the Los Alamos National Laboratory has been performed using the calorimetric 4πBaF2 array DANCE. The (n,γ) cross section of 63Ni has been determined relative to the well-known 197Au standard with uncertainties below 15%. Various 63Ni resonances have been identified based on the Q value. Furthermore, the s-process sensitivity of the new values was analyzed with the new network calculation tool NETZ.
To determine the neutron flux in activation experiments, a commonly used monitor is zirconium and in particular the stable isotopes 94,96Zr. 96Zr is very sensitive to epithermal neutrons. Despite its widespread application, most gamma intensities of the radioactive neutron capture product, 97Zr, yield large uncertainties. With the help of a new γ spectroscopy setup and GEANT simulations, we succeeded in determining a new set of γ-ray intensities with significantly reduced uncertainties.
The two-nucleon potential is assumed to be a quadratic function of momentum: ν = ν1 (r) + pν2(r)p. The BETHE-GOLDSTONE equation (l = 0) has been solved for two different choices of ν. An analytical, approximate solution is obtained.
An elementary derivation of the optical potential for high energies is given. For the determination of the optical potential only the knowledge of the scattering amplitude for free nucleons and of the autocorrelation function for density fluctuations is necessary. The numerical calculation of the real- and imaginary part of the optical potential was performed using the Tabakin potential.
We present the application of an evolutionary genetic algorithm for the in situ optimization of nanostructures that are prepared by focused electron-beam-induced deposition (FEBID). It allows us to tune the properties of the deposits towards the highest conductivity by using the time gradient of the measured in situ rate of change of conductance as the fitness parameter for the algorithm. The effectiveness of the procedure is presented for the precursor W(CO)6 as well as for post-treatment of Pt–C deposits, which were obtained by the dissociation of MeCpPt(Me)3. For W(CO)6-based structures an increase of conductivity by one order of magnitude can be achieved, whereas the effect for MeCpPt(Me)3 is largely suppressed. The presented technique can be applied to all beam-induced deposition processes and has great potential for a further optimization or tuning of parameters for nanostructures that are prepared by FEBID or related techniques.
A small electrostatic storage ring is the central machine of the Frankfurt Ion Storage Experiments (FIRE) which will be built at the new Stern-Gerlach Center of Frankfurt University. As a true multiuser, multipurpose facility with ion energies up to 50 keV, it will allow new methods to analyze complex many-particle systems from atoms to very large biomolecules. With envisaged storage times of some seconds and beam emittances in the order of a few mm mrad, measurements with up to 6 orders of magnitude better resolutions as compared to single-pass experiments become possible. In comparison to earlier designs, the ring lattice was modified in many details: Problems in earlier designs were related to, e.g., the detection of light particles and highly charged ions with different charge states. Therefore, the deflectors were redesigned completely, allowing a more flexible positioning of the diagnostics. Here, after an introduction to the concept of electrostatic machines, an overview of the planned FIRE is given and the ring lattice and elements are described in detail.
For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set. This process of decorrelation can split into an initial exponential decrease and a subsequent diffusive process on the chaotic attractor causing the final loss of predictability. Both processes can be either of the same or of very different time scales. In the latter case the two trajectories linger within a finite but small distance (with respect to the overall extent of the attractor) for exceedingly long times and remain partially predictable. Standard tests for chaos widely use inter-orbital correlations as an indicator. However, testing partially predictable chaos yields mostly ambiguous results, as this type of chaos is characterized by attractors of fractally broadened braids. For a resolution we introduce a novel 0-1 indicator for chaos based on the cross-distance scaling of pairs of initially close trajectories. This test robustly discriminates chaos, including partially predictable chaos, from laminar flow. Additionally using the finite time cross-correlation of pairs of initially close trajectories, we are able to identify laminar flow as well as strong and partially predictable chaos in a 0-1 manner solely from the properties of pairs of trajectories.
The aim of this paper is to understand resonance production (and more generally particle production) for different collision systems, namely proton-proton (pp), proton-nucleus (pA), and nucleus-nucleus (AA) scattering at the LHC. We will investigate in particular particle yields and ratios versus multiplicity, using the same multiplicity definition for the three different systems, in order to analyse in a compact way the evolution of particle production with the system size and the origin of a very different system size dependence of the different particles.