Orexin recruits non-selective cationic conductance and endocannabinoid to dynamically modulate firing of caudal pontine reticular nuclear neurones.

The neuropeptide orexin is involved in motor circuit function. However, its modulation on neuronal activities of motor structures, integrating orexin's diverse downstream molecular cascades, remains elusive. By combining whole-cell patch-clamp recordings and neuropharmacological methods, we revealed that both non-selective cationic conductance (NSCC) and endocannabinoids (eCBs) are recruited by orexin signalling on reticulospinal neurones in the caudal pontine reticular nucleus (PnC). The orexin-NSCC cascade provides a depolarizing force that proportionally enhances the firing-responsive gain of these neurones. Meanwhile, the orexin-eCB cascade selectively attenuates excitatory synaptic strength in these neurones by activating presynaptic cannabinoid receptor type 1. This cascade restrains the firing response of the PnC reticulospinal neurones to excitatory inputs. Intriguingly, non-linear or linear interactions between orexin postsynaptic excitation and presynaptic inhibition can influence the firing responses of PnC reticulospinal neurones in different directions. When presynaptic inhibition is in the lead, non-linear interactions can prominently downregulate or even gate the firing response. Conversely, linear interactions occur to promote the firing response, and these linear interactions can be considered a proportional reduction in the contribution of depolarization to firing by presynaptic inhibition. Through the dynamic employment of these interactions, adaptive modulation may be achieved by orexin to restrain or even gate the firing output of the PnC to weak/irrelevant input signals and facilitate those to salient signals. KEY POINTS: This study investigated the effects of orexin on the firing activity of PnC reticulospinal neurones, a key element of central motor control. We found that orexin recruited both the non-selective cationic conductances (NSCCs) and endocannabinoid (eCB)-cannabinoid receptor type 1 (CB1R) system to pontine reticular nucleus (PnC) reticulospinal neurones. The orexin-NSCC cascade exerts a postsynaptic excitation that enhances the firing response, whereas the orexin-eCB-CB1R cascade selectively attenuates excitatory synaptic strength that restrains the firing response. The postsynaptic and presynaptic actions of orexins occur in an overlapping time window and interact to dynamically modulate firings in PnC reticulospinal neurones. Non-linear interactions occur when presynaptic inhibition of orexin is in the lead, and these interactions can prominently downregulate or even gate firing responses in PnC reticulospinal neurones. Linear interactions occur when postsynaptic excitation of orexin is in the lead, and these interactions can promote the firing response. These linear interactions can be considered a proportional reduction of the contribution of depolarization to firing by presynaptic inhibition.

Highly Active NiO-Ni(OH)2 -Cr2 O3 /Ni Hydrogen Evolution Electrocatalyst through Synergistic Reaction Kinetics.

High activity catalysts for hydrogen evolution reaction (HER) play a key role in converting renewable electricity to storable hydrogen fuel. Great effort has been devoted to the search for noble metal free catalysts to make electrolysis viable for practical applications. Here, a non-precious metal oxide/metal catalyst with high intrinsic activity comparable to Pt/C was reported. The electrocatalyst consisting of NiO, Ni(OH)2 , Cr2 O3 , and Ni metal exhibits a low overpotential of 27, 103, and 153 mV at current densities of 10, 100, and 200 mA cm-2 , respectively, in a 1.0 m NaOH electrolyte. The activity is much higher than that of NiOx /Ni or Cr2 O3 alone, showing the synergistic effect of NiOx /Ni and Cr2 O3 on catalyzing HER. Density functional theory calculations shows that NiO and Cr2 O3 on Ni surface lower the disassociation energy barrier for breaking H-OH bond, while Ni(OH)2 and Cr2 O3 create preferred sites on Ni surface with near-zero H* adsorption free energy to promote H* to gaseous H2 evolution. These synergistic effects of multiple-oxides/metal composition enhance the disassociation of H-OH and the evolution of H* to gaseous H2 , thus achieving high activity and demonstrating a promising composition design for noble metal free catalyst.

Sputter-Deposited High Entropy Alloy Thin Film Electrocatalyst for Enhanced Oxygen Evolution Reaction Performance.

Thin film catalysts, giving a different morphology, provide a significant advantage over catalyst particles for the gas evolution reaction. Taking the advantages of sputter deposition, a high entropy alloy (HEA) thin film electrocatalyst is hereby reported for the oxygen evolution reaction (OER). The catalyst characteristics are investigated not only in its as-deposited state, but also during and after the OER. For comparison, unary, binary, ternary, and quaternary thin film catalysts are prepared and characterized. The surface electronic structure modification due to the addition of a metal is studied experimentally and theoretically using density functional theory calculation. It is demonstrated that sputtered FeNiMoCrAl HEA thin film exhibits OER performance superior to all the reported HEA catalysts with robust electrocatalytic activity having a low overpotential of 220 mV at 10 mA cm-2 , and excellent electrochemical stability at different constant current densities of 10 and 100 mA cm-2 for 50 h. Furthermore, the microstructure transformation is investigated during the OER, which is important for the understanding of the OER mechanism provided by HEA electrocatalyst. Such a finding will contribute to future catalyst design.

Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques.

Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients' conditions and early detection of clinical high-risks. Detecting such symptoms require a trained clinician's expertise, which is prohibitive due to cost and the high patient-to-clinician ratio. In this paper, we propose a machine learning method using Transformer-based model to help automate the assessment of the severity of the thought disorder of schizophrenia. The proposed model uses both textual and acoustic speech between occupational therapists or psychiatric nurses and schizophrenia patients to predict the level of their thought disorder. Experimental results show that the proposed model has the ability to closely predict the results of assessments for Schizophrenia patients base on the extracted semantic, syntactic and acoustic features. Thus, we believe our model can be a helpful tool to doctors when they are assessing schizophrenia patients.