Notes

TGF-β1-509C/T polymorphism along with the risk of ESCC in the China Han population.
Increasing evidence has shown that brain functions are seriously influenced by the heterogeneous structure of a brain network, but little attention has been paid to the aspect of signal propagation. We here study how a signal is propagated from a source node to other nodes on an empirical brain network by a model of bistable oscillators. We find that the unique structure of the brain network favors signal propagation in contrast to other heterogeneous networks and homogeneous random networks. Surprisingly, we find an effect of remote propagation where a signal is not successfully propagated to the neighbors of the source node but to its neighbors' neighbors. To reveal its underlying mechanism, we simplify the heterogeneous brain network into a heterogeneous chain model and find that the accumulation of weak signals from multiple channels makes a strong input signal to the next node, resulting in remote propagation. Furthermore, a theoretical analysis is presented to explain these findings.We investigate the State-Controlled Cellular Neural Network framework of Murali-Lakshmanan-Chua circuit system subjected to two logical signals. By exploiting the attractors generated by this circuit in different regions of phase space, we show that the nonlinear circuit is capable of producing all the logic gates, namely, or, and, nor, nand, Ex-or, and Ex-nor gates, available in digital systems. Further, the circuit system emulates three-input gates and Set-Reset flip-flop logic as well. Moreover, all these logical elements and flip-flop are found to be tolerant to noise. These phenomena are also experimentally demonstrated. Thus, our investigation to realize all logic gates and memory latch in a nonlinear circuit system paves the way to replace or complement the existing technology with a limited number of hardware.The dynamics of four coupled microcells with the oscillatory Belousov-Zhabotinsky (BZ) reaction in them is analyzed with the aid of partial differential equations. Identical BZ microcells are coupled in a circle via identical narrow channels containing all the components of the BZ reaction, which is in the stationary excitable state in the channels. Spikes in the BZ microcells generate unidirectional chemical waves in the channels. A thin filter is put in between the end of the channel and the cell. To make coupling between neighboring cells of the inhibitory type, hydrophobic filters are used, which let only Br2 molecules, the inhibitor of the BZ reaction, go through the filter. To simulate excitatory coupling, we use a hypothetical filter that let only HBrO2 molecules, the activator of the BZ reaction, go through it. click here New dynamic modes found in the described system are compared with the "old" dynamic modes found earlier in the analogous system of the "single point" BZ oscillators coupled in a circle by pulses with time delay. The "new" and "old" dynamic modes found for inhibitory coupling match well, the only difference being much broader regions of multi-rhythmicity in the "new" dynamic modes. For the excitatory type of coupling, in addition to four symmetrical modes of the "old" type, many new asymmetrical modes coexisting with the symmetrical ones have been found. Asymmetrical modes are characterized by the spikes occurring any time within some finite time intervals.Bone morphogenetic proteins (BMPs) are an important family of growth factors playing a role in a large number of physiological and pathological processes, including bone homeostasis, tissue regeneration, and cancers. In vivo, BMPs bind successively to both BMP receptors (BMPRs) of type I and type II, and a promiscuity has been reported. In this study, we used biolayer interferometry to perform parallel real-time biosensing and to deduce the kinetic parameters (ka, kd) and the equilibrium constant (KD) for a large range of BMP/BMPR combinations in similar experimental conditions. We selected four members of the BMP family (BMP-2, 4, 7, 9) known for their physiological relevance and studied their interactions with five type-I BMP receptors (ALK1, 2, 3, 5, 6) and three type-II BMP receptors (BMPR-II, ACTR-IIA, ACTR-IIB). We reveal that BMP-2 and BMP-4 behave differently, especially regarding their kinetic interactions and affinities with the type-II BMPR. We found that BMP-7 has a higher affinity for the type-II BMPR receptor ACTR-IIA and a tenfold lower affinity with the type-I receptors. While BMP-9 has a high and similar affinity for all type-II receptors, it can interact with ALK5 and ALK2, in addition to ALK1. Interestingly, we also found that all BMPs can interact with ALK5. The interaction between BMPs and both type-I and type-II receptors in a ternary complex did not reveal further cooperativity. Our work provides a synthetic view of the interactions of these BMPs with their receptors and paves the way for future studies on their cell-type and receptor specific signaling pathways.Single-molecule localization microscopy allows practitioners to locate and track labeled molecules in biological systems. When extracting diffusion coefficients from the resulting trajectories, it is common practice to perform a linear fit on mean-squared-displacement curves. However, this strategy is suboptimal and prone to errors. Recently, it was shown that the increments between the observed positions provide a good estimate for the diffusion coefficient, and their statistics are well-suited for likelihood-based analysis methods. Here, we revisit the problem of extracting diffusion coefficients from single-particle tracking experiments subject to static noise and dynamic motion blur using the principle of maximum likelihood. Taking advantage of an efficient real-space formulation, we extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate with the help of the expectation-maximization algorithm. This formulation naturally leads to a probabilistic assignment of trajectories to subpopulations. We employ the theory to analyze experimental tracking data that cannot be explained with a single diffusion coefficient. We test how well a dataset conforms to the assumptions of a diffusion model and determine the optimal number of subpopulations with the help of a quality factor of known analytical distribution. To facilitate use by practitioners, we provide a fast open-source implementation of the theory for the efficient analysis of multiple trajectories in arbitrary dimensions simultaneously.Accurately estimating the nucleation rate is crucial in studying ice nucleation and ice-promoting and anti-freeze strategies. In classical nucleation theory, estimates of the ice nucleation rate are very sensitive to thermodynamic parameters, such as the chemical potential difference between water and ice Δμ and the ice-water interfacial free energy γ. However, even today, there are still many contradictions and approximations when estimating these thermodynamic parameters, introducing a large uncertainty in any estimate of the ice nucleation rate. Starting from basic concepts for a general solid-liquid crystallization system, we expand the Gibbs-Thomson equation to second order and derive second-order analytical formulas for Δμ, γ, and the nucleation barrier ΔG*, which are used in molecular dynamics simulations. These formulas describe well the temperature dependence of these thermodynamic parameters. This may be a new method of estimating Δμ, γ, and ΔG*.Nitrogen and water are very abundant in nature; however, the way they chemically react at extreme pressure-temperature conditions is unknown. Below 6 GPa, they have been reported to form clathrate compounds. Here, we present Raman spectroscopy and x-ray diffraction studies in the H2O-N2 system at high pressures up to 140 GPa. We find that clathrates, which form locally in our diamond cell experiments above 0.3 GPa, transform into a fine grained state above 6 GPa, while there is no sign of formation of mixed compounds. We point out size effects in fine grained crystallites, which result in peculiar Raman spectra in the molecular regime, but x-ray diffraction shows no additional phase or deviation from the bulk behavior of familiar solid phases. Moreover, we find no sign of ice doping by nitrogen, even in the regimes of stability of nonmolecular nitrogen.Symmetry-adapted perturbation theory (SAPT) has become an invaluable tool for studying the fundamental nature of non-covalent interactions by directly computing the electrostatics, exchange (steric) repulsion, induction (polarization), and London dispersion contributions to the interaction energy using quantum mechanics. Further application of SAPT is primarily limited by its computational expense, where even its most affordable variant (SAPT0) scales as the fifth power of system size [O(N5)] due to the dispersion terms. The algorithmic scaling of SAPT0 is reduced from O(N5)→O(N4) by replacing these terms with the empirical D3 dispersion correction of Grimme and co-workers, forming a method that may be termed SAPT0-D3. Here, we optimize the damping parameters for the -D3 terms in SAPT0-D3 using a much larger training set than has previously been considered, namely, 8299 interaction energies computed at the complete-basis-set limit of coupled cluster through perturbative triples [CCSD(T)/CBS]. Perhaps surprisingly, with only three fitted parameters, SAPT0-D3 improves on the accuracy of SAPT0, reducing mean absolute errors from 0.61 to 0.49 kcal mol-1 over the full set of complexes. Additionally, SAPT0-D3 exhibits a nearly 2.5× speedup over conventional SAPT0 for systems with ∼300 atoms and is applied here to systems with up to 459 atoms. Finally, we have also implemented a functional group partitioning of the approach (F-SAPT0-D3) and applied it to determine important contacts in the binding of salbutamol to G-protein coupled β1-adrenergic receptor in both active and inactive forms. SAPT0-D3 capabilities have been added to the open-source Psi4 software.Over the past two decades, coherent multidimensional spectroscopies have been implemented across the terahertz, infrared, visible, and ultraviolet regions of the electromagnetic spectrum. A combination of coherent excitation of several resonances with few-cycle pulses, and spectral decongestion along multiple spectral dimensions, has enabled new insights into wide ranging molecular scale phenomena, such as energy and charge delocalization in natural and artificial light-harvesting systems, hydrogen bonding dynamics in monolayers, and strong light-matter couplings in Fabry-Pérot cavities. However, measurements on ensembles have implied signal averaging over relevant details, such as morphological and energetic inhomogeneity, which are not rephased by the Fourier transform. Recent extension of these spectroscopies to provide diffraction-limited spatial resolution, while maintaining temporal and spectral information, has been exciting and has paved a way to address several challenging questions by going beyond ensemble averaging. The aim of this Perspective is to discuss the technological developments that have eventually enabled spatially resolved multidimensional electronic spectroscopies and highlight some of the very recent findings already made possible by introducing spatial resolution in a powerful spectroscopic tool.
Here's my website: https://www.selleckchem.com/pharmacological_epigenetics.html