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Most general anaesthetics and classical benzodiazepine drugs act through positive modulation of [gamma]-aminobutyric acid type A (GABA.sub.A) receptors to dampen neuronal activity in the brain.sup.1-5. However, direct structural information on the mechanisms of general anaesthetics at their physiological receptor sites is lacking. Here we present cryo-electron microscopy structures of GABA.sub.A receptors bound to intravenous anaesthetics, benzodiazepines and inhibitory modulators. These structures were solved in a lipidic environment and are complemented by electrophysiology and molecular dynamics simulations. Structures of GABA.sub.A receptors in complex with the anaesthetics phenobarbital, etomidate and propofol reveal both distinct and common transmembrane binding sites, which are shared in part by the benzodiazepine drug diazepam. Structures in which GABA.sub.A receptors are bound by benzodiazepine-site ligands identify an additional membrane binding site for diazepam and suggest an allosteric mechanism for anaesthetic reversal by flumazenil. This study provides a foundation for understanding how pharmacologically diverse and clinically essential drugs act through overlapping and distinct mechanisms to potentiate inhibitory signalling in the brain. Cryo-electron microscopy structures of GABA.sub.A receptors bound to intravenous anaesthetics and benzodiazepines reveal both common and distinct transmembrane binding sites, and show that the mechanisms of action of anaesthetics partially overlap with those of benzodiazepines., Author(s): Jeong Joo Kim [sup.1] , Anant Gharpure [sup.1] , Jinfeng Teng [sup.1] , Yuxuan Zhuang [sup.2] , Rebecca J. Howard [sup.2] , Shaotong Zhu [sup.1] , Colleen M. Noviello [...]

The search for stable and efficient single-atom catalysts (SACs) for the electrocatalytic nitrogen reduction reaction (eNRR) has garnered significant theoretical interest. In this study, we evaluate the eNRR performance of eighteen two-dimensional 1T-MoSe2-supported transition metal single-atom catalysts (TM@1T-MoSe2, TM=V, Cr, Mn, Fe, Co, Ni, Nb, Mo, Tc, Ru, Rh, Pd, Ta, W, Re, Os, Ir, and Pt) using density functional theory calculations. We assess the stability of each TM@1T-MoSe2, as well as the limiting potential of eNRR and the ammonia selectivity on each stable TM@1T-MoSe2. Our results show that W@1T-MoSe2 is the most promising single-atom catalyst for eNRR, with a limiting potential of -0.23 V via the distal pathway starting from three co-adsorbed nitrogen molecules. Furthermore, the multi-adsorption of N2 on W@1T-MoSe2 effectively suppress the hydrogen evolution reaction, thus enhancing the selectivity of the eNRR. This research provides a promising avenue for the development of a new class of 1T-MoSe2-based single-atom catalysts for electrocatalytic ammonia synthesis. [ABSTRACT FROM AUTHOR]

γ-Aminobutyric acid type A (GABAA) receptors are pentameric ligand-gated ion channels abundant in the central nervous system and are prolific drug targets for treating anxiety, sleep disorders and epilepsy. Diverse small molecules exert a spectrum of effects on γ-aminobutyric acid type A (GABAA) receptors by acting at the classical benzodiazepine site. They can potentiate the response to GABA, attenuate channel activity, or counteract modulation by other ligands. Structural mechanisms underlying the actions of these drugs are not fully understood. Here we present two high-resolution structures of GABAA receptors in complex with zolpidem, a positive allosteric modulator and heavily prescribed hypnotic, and DMCM, a negative allosteric modulator with convulsant and anxiogenic properties. These two drugs share the extracellular benzodiazepine site at the α/γ subunit interface and two transmembrane sites at β/α interfaces. Structural analyses reveal a basis for the subtype selectivity of zolpidem that underlies its clinical success. Molecular dynamics simulations provide insight into how DMCM switches from a negative to a positive modulator as a function of binding site occupancy. Together, these findings expand our understanding of how GABAA receptor allosteric modulators acting through a common site can have diverging activities. GABAA receptors are important targets for anxiety, sedation and anesthesia. Here, the authors present structures bound by zolpidem (Ambien), the most prescribed hypnotic in the US, and DMCM, a negative modulator, providing insights into receptor modulation. [ABSTRACT FROM AUTHOR]

Electrocatalytic reduction of nitrate (NO 3 RR) to synthesize ammonia (NH 3 ) can effectively degrade nitrate while producing a valuable product. By utilizing density functional theory calculations, we investigate the potential catalytic performance of a range of single transition-metal (TM) atoms supported on nitrogenated holey doped graphene (g-C 2 N) (TM/g-C 2 N) for the reduction of nitrates to NH 3 . Based on the screening procedure, Zr/g-C 2 N and Hf/g-C 2 N are predicted as potential electrocatalysts for the NO 3 RR with limiting potential ( U L ) values of -0.28 and -0.27 V, respectively. The generation of byproducts such as dioxide (NO 2 ), nitric oxide (NO), and nitrogen (N 2 ) is hindered on Zr/g-C 2 N and Hf/g-C 2 N due to the high energy cost. The NO 3 RR activity of TM/g-C 2 N is closely related to the adsorption free energy of NO 3 - . The study not only proposes a competent electrocatalyst for enhancing NO 3 RR in ammonia synthesis but also provides a comprehensive understanding of the NO 3 RR mechanism.

Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI., Method: EEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier., Results: Nonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features., Significance: These findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.

Objective: The topological information hidden in the EEG spectral dynamics is often ignored in the majority of the existing brain-computer interface (BCI) systems. Moreover, a systematic multimodal fusion of EEG with other informative brain signals such as functional near-infrared spectroscopy (fNIRS) towards enhancing the performance of the BCI systems is not fully investigated. In this study, we present a robust EEG-fNIRS data fusion framework utilizing a series of graph-based EEG features to investigate their performance on a motor imaginary (MI) classification task., Method: We first extract the amplitude and phase sequences of users' multi-channel EEG signals based on the complex Morlet wavelet time-frequency maps, and then convert them into an undirected graph to extract EEG topological features. The graph-based features from EEG are then selected by a thresholding method and fused with the temporal features from fNIRS signals after each being selected by the least absolute shrinkage and selection operator (LASSO) algorithm. The fused features were then classified as MI task vs. baseline by a linear support vector machine (SVM) classifier., Results: The time-frequency graphs of EEG signals improved the MI classification accuracy by ∼5% compared to the graphs built on the band-pass filtered temporal EEG signals. Our proposed graph-based method also showed comparable performance to the classical EEG features based on power spectral density (PSD), however with a much smaller standard deviation, showing its robustness for potential use in a practical BCI system. Our fusion analysis revealed a considerable improvement of ∼17% as opposed to the highest average accuracy of EEG only and ∼3% compared with the highest fNIRS only accuracy demonstrating an enhanced performance when modality fusion is used relative to single modal outcomes., Significance: Our findings indicate the potential use of the proposed data fusion framework utilizing the graph-based features in the hybrid BCI systems by making the motor imaginary inference more accurate and more robust.