The group-IV monochalcogenides have attracted interest due to their potentials of ferroelectric and multiferroic properties. Recently, centrosymmetric γ-phase GeSe in the double-layer honeycomb lattice was theoretically predicted, but synthesized γ-phase...

We investigated effects of Neuregulin 1 (NRG1) on the expression of nicotinic acetylcholine receptor (nAChR) in major pelvic ganglion (MPG) from adult rat. MPG neurons were found to express transcripts for type I and III NRG1s as well as α and β-type epidermal growth factor (EGF)-like domains. Of the four ErbB receptor isoforms, ErbB1, ErbB2, and ErbB3 were expressed in MPG neurons. Treating MPG with NRG1β significantly increased the transcript and protein level of the nAChR α3 and β4 subunits. Consistent with these molecular data, nicotinic currents (I(ACh) ) were significantly up-regulated in NRG1β-treated sympathetic and parasympathetic MPG neurons. In contrast, the type III NRG1 and the α form of the NRG1 failed to alter the I(ACh) . Inhibition of the ErbB2 tyrosine kinase completely abolished the effects of NRG1β on the I(ACh) . Stimulation of the ErbB receptors by NRG1β activated the phosphatidylinositol-3-kinase (PI3K) and mitogen-activated protein kinase (MAPK). Immunoblot analysis revealed that PI3K-mediated activation of Akt preceded Erk1/2 activation in NRG1β-treated MPG neurons. Furthermore, specific PI3K inhibitors abrogated the phosphorylation of Erk1/2, while inhibition of MEK did not prevent the phosphorylation of Akt. Taken together, these findings suggest that NRG1 up-regulates nAChR expression via the ErbB2/ErbB3-PI3K-MAPK signaling cascade and may be involved in maintaining the ACh-mediated synaptic transmission in adult autonomic ganglia.

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BackgroundWith the invention of fitness trackers, it has been possible to continuously monitor a user’s biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user’s “activeness”, and investigates the feasibility in modeling and predicting the long-term activeness of the user.MethodsThe dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures–as well as a deep neural network and a simple regression model–were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user’s activeness falls below a certain threshold.ResultsA preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user’s activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user’s future activeness with precision, for example, a trained RNN model could predict–with the precision of 84%–when the user would be less active within the next hour given the latest 15 min of his activeness data.ConclusionsThis paper defines and investigates the notion of a user’s “activeness”, and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.

Black phosphorus (BP) has received much attention owing to its fascinating properties, such as a high carrier mobility and tunable band gap. However, these advantages have been overshadowed by the fast degradation of BP under ambient conditions. To overcome this obstacle, the exact degradation mechanisms need to be unveiled. Herein, we analyzed two sequential degradation processes and the layer‐dependent degradation rates of BP in the dark by scanning Kelvin probe microscopy (SKPM) measurements and theoretical modeling. The layer‐dependent degradation was successfully interpreted by considering the oxidation model based on the Marcus–Gerischer theory (MGT). In the dark, the electron transfer rate from BP to oxygen molecules depends on the number of layers as these systems have different carrier concentrations. This work not only provides a deeper understanding of the degradation mechanism itself but also suggest new strategies for the design of stable BP‐based electronics.

The application of two-dimensional materials has been expanded by introducing the twisted bilayer (TBL) system. However, the landscape of the interlayer interaction in hetero-TBLs has not yet been fully understood, while that in homo-TBLs has been extensively studied, with the dependence on the twist angle between the constituent layers. Here, we present detailed analyses on the interlayer interaction that depends on the twist angle in WSe 2 /MoSe 2 hetero-TBL via Raman and photoluminescence studies combined with first-principles calculation. We observe interlayer vibrational modes, moiréphonons, and the interlayer excitonic states that evolve with the twist angle and identify different regimes with distinct characteristics of such features. Moreover, the interlayer excitons that appear strong in the hetero-TBLs with twist angles near 0°or 60°have different energies and photoluminescence excitation spectra for the two cases, which results from different electronic structures and carrier relaxation dynamics. These results would enable a better understanding of the interlayer interaction in hetero-TBLs.