Theoretical Investigation of Electrocatalytic Reduction of Nitrates to Ammonia on Highly Efficient and Selective g-C2N Monolayer-Supported Single Transition-Metal Atoms.

Electrocatalytic reduction of nitrate (NO3RR) to synthesize ammonia (NH3) 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-C2N) (TM/g-C2N) for the reduction of nitrates to NH3. Based on the screening procedure, Zr/g-C2N and Hf/g-C2N are predicted as potential electrocatalysts for the NO3RR with limiting potential (UL) values of -0.28 and -0.27 V, respectively. The generation of byproducts such as dioxide (NO2), nitric oxide (NO), and nitrogen (N2) is hindered on Zr/g-C2N and Hf/g-C2N due to the high energy cost. The NO3RR activity of TM/g-C2N is closely related to the adsorption free energy of NO3-. The study not only proposes a competent electrocatalyst for enhancing NO3RR in ammonia synthesis but also provides a comprehensive understanding of the NO3RR mechanism.

Camera-Based Short Physical Performance Battery and Timed Up and Go Assessment for Older Adults With Cancer.

This paper presents an automatic camera-based device to monitor and evaluate the gait speed, standing balance, and 5 times sit-stand (5TSS) tests of the Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG) test. The proposed design measures and calculates the parameters of the SPPB tests automatically. The SPPB data can be used for physical performance assessment of older patients under cancer treatment. This stand-alone device has a Raspberry Pi (RPi) computer, three cameras, and two DC motors. The left and right cameras are used for gait speed tests. The center camera is used for standing balance, 5TSS, and TUG tests and for angle positioning of the camera platform toward the subject using DC motors by turning the camera left/right and tilting it up/down. The key algorithm for operating the proposed system is developed using Channel and Spatial Reliability Tracking in the cv2 module in Python. Graphical User Interfaces (GUIs) in the RPi are developed to run tests and adjust cameras, controlled remotely via smartphone and its Wi-Fi hotspot. We have tested the implemented camera setup prototype and extracted all SPPB and TUG parameters by conducting several experiments on a human subject population of 8 volunteers (male and female, light and dark complexions) in 69 test runs. The measured data and calculated outputs of the system consist of tests of gait speed (0.041 to 1.92 m/s with average accuracy of >95%), and standing balance, 5TSS, TUG, all with average time accuracy of >97%.

A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces.

Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.

Structural and dynamic mechanisms of GABAA receptor modulators with opposing activities.

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 ß/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.

A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI.

Decoding neural responses from multimodal information sources, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has the transformative potential to advance hybrid brain-computer interfaces (hBCIs). However, existing modest performance improvement of hBCIs might be attributed to the lack of computational frameworks that exploit complementary synergistic properties in multimodal features. This study proposes a multimodal data fusion framework to represent and decode synergistic multimodal motor imagery (MI) neural responses. We hypothesize that exploiting EEG nonlinear dynamics adds a new informative dimension to the commonly combined EEG-fNIRS features and will ultimately increase the synergy between EEG and fNIRS features toward an enhanced hBCI. The EEG nonlinear dynamics were quantified by extracting graph-based recurrence quantification analysis (RQA) features to complement the commonly used spectral features for an enhanced multimodal configuration when combined with fNIRS. The high-dimensional multimodal features were further given to a feature selection algorithm relying on the least absolute shrinkage and selection operator (LASSO) for fused feature selection. Linear support vector machine (SVM) was then used to evaluate the framework. The mean hybrid classification performance improved by up to 15% and 4% compared to the unimodal EEG and fNIRS, respectively. The proposed graph-based framework substantially increased the contribution of EEG features for hBCI classification from 28.16% up to 52.9% when introduced the nonlinear dynamics and improved the performance by approximately 2%. These findings suggest that graph-based nonlinear dynamics can increase the synergy between EEG and fNIRS features for an enhanced MI response representation that is not dominated by a single modality.

A Graph-Based Feature Extraction Algorithm Towards a Robust Data Fusion Framework for Brain-Computer Interfaces.

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.

Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs.

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.

A Bayesian Dynamical Approach for Human Action Recognition.

We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the motion data and their associated action labels. We parameterize these temporal processes with regard to a switching deep autoregressive prior to accommodate both multimodal and higher-order nonlinear inter-dependencies. This results in a dynamical deep generative latent model that parses meaningful intrinsic states in skeletal dynamics and enables action recognition. These sequences of states provide visual and quantitative interpretations about motion primitives that gave rise to each action class, which have not been explored previously. In contrast to previous works, which often overlook temporal dynamics, our method explicitly model temporal transitions and is generative. Our experiments on two large-scale 3D skeletal datasets substantiate the superior performance of our model in comparison with the state-of-the-art methods. Specifically, our method achieved 6.3% higher action classification accuracy (by incorporating a dynamical generative framework), and 3.5% better predictive error (by employing a nonlinear second-order dynamical transition model) when compared with the best-performing competitors.

Facile preparation and performance study of antibacterial regenerated cellulose carbamate fiber based on N-halamine.

With the outbreak of coronavirus disease (COVID-19) which has incalculable disasters and economic losses, people have given increasing attention to the health and safety of textile and fiber materials. In this study, an eco-friendly, facile, and cost-effective wet-spinning cellulose carbamate fiber technology was developed, and N-halamine regenerated cellulose fiber (RCC-Cl) with rechargeable and rapid bactericidal properties were prepared by the Lewis acid-assisted chlorination method. The chemical properties of the fibers were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, thermogravimetric analysis, and energy-dispersive X-ray spectroscopy. The mechanical and surface topography of the treated fiber was investigated by tensile testing and scanning electron microscopy. The results showed that the mechanical properties of RCC-Cl fibers can reach a breaking strength of 12.1 cN/tex and a breaking elongation of 41.4% with the optimized spinning process. Furthermore, RCC-Cl showed excellent antimicrobial activities, which can inactivate Escherichia coli and Staphylococcus aureus at a concentration of 107 CFU/mL within 1 min. This work provided a novel approach to produce regenerated cellulose fibers with antibacterial properties, showing great potential in the field of functional textiles.

Shared structural mechanisms of general anaesthetics and benzodiazepines.

Most general anaesthetics and classical benzodiazepine drugs act through positive modulation of γ-aminobutyric acid type A (GABAA) receptors to dampen neuronal activity in the brain1-5. 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 GABAA 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 GABAA 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 GABAA 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.

Structure of a human synaptic GABAA receptor.

Fast inhibitory neurotransmission in the brain is principally mediated by the neurotransmitter GABA (γ-aminobutyric acid) and its synaptic target, the type A GABA receptor (GABAA receptor). Dysfunction of this receptor results in neurological disorders and mental illnesses including epilepsy, anxiety and insomnia. The GABAA receptor is also a prolific target for therapeutic, illicit and recreational drugs, including benzodiazepines, barbiturates, anaesthetics and ethanol. Here we present high-resolution cryo-electron microscopy structures of the human α1ß2γ2 GABAA receptor, the predominant isoform in the adult brain, in complex with GABA and the benzodiazepine site antagonist flumazenil, the first-line clinical treatment for benzodiazepine overdose. The receptor architecture reveals unique heteromeric interactions for this important class of inhibitory neurotransmitter receptor. This work provides a template for understanding receptor modulation by GABA and benzodiazepines, and will assist rational approaches to therapeutic targeting of this receptor for neurological disorders and mental illness.

Analysis of an N-terminal deletion in subunit a of the Escherichia coli ATP synthase.

Subunit a is a membrane-bound stator subunit of the ATP synthase and is essential for proton translocation. The N-terminus of subunit a in E. coli is localized to the periplasm, and contains a sequence motif that is conserved among some bacteria. Previous work has identified mutations in this region that impair enzyme activity. Here, an internal deletion was constructed in subunit a in which residues 6-20 were replaced by a single lysine residue, and this mutant was unable to grow on succinate minimal medium. Membrane vesicles prepared from this mutant lacked ATP synthesis and ATP-driven proton translocation, even though immunoblots showed a significant level of subunit a. Similar results were obtained after purification and reconstitution of the mutant ATP synthase into liposomes. The location of subunit a with respect to its neighboring subunits b and c was probed by introducing cysteine substitutions that were known to promote cross-linking: a_L207C + c_I55C, a_L121C + b_N4C, and a_T107C + b_V18C. The last pair was unable to form cross-links in the background of the deletion mutant. The results indicate that loss of the N-terminal region of subunit a does not generally disrupt its structure, but does alter interactions with subunit b.

Probing the proton channels in subunit N of Complex I from Escherichia coli through intra-subunit cross-linking.

Respiratory Complex I appears to have 4 sites for proton translocation, which are coupled to the oxidation of NADH and reduction of coenzyme Q. The proton pathways are thought to be made of offset half-channels that connect to the membrane surfaces, and are connected by a horizontal path through the center of the membrane. In this study of the enzyme from Escherichia coli, subunit N, containing one of the sites, was targeted. Pairs of cysteine residues were introduced into neighboring α-helices along the proposed proton pathways. In an effort to constrain conformational changes that might occur during proton translocation, we attempted to form disulfide bonds or methanethiosulfonate bridges between two engineered cysteine residues. Cysteine modification was inferred by the inability of PEG-maleimide to shift the electrophoretic mobility of subunit N, which will occur upon reaction with free sulfhydryl groups. After the cross-linking treatment, NADH oxidase and NADH-driven proton translocation were measured. Ten different pairs of cysteine residues showed evidence of cross-linking. The most significant loss of enzyme activity was seen for residues near the essential Lys 395. This residue is positioned between the proposed proton half-channel to the periplasm and the horizontal connection through subunit N, and is also near the essential Glu 144 of subunit M. The results suggest important conformational changes in this region for the delivery of protons to the periplasm, or for coupling the actions of subunit N to subunit M.

Loss of Complex I activity in the Escherichia coli enzyme results from truncating the C-terminus of subunit K, but not from cross-linking it to subunits N or L.

Complex I is a multi-subunit enzyme of the respiratory chain with seven core subunits in its membrane arm (A, H, J, K, L, M, and N). In the enzyme from Escherichia coli the C-terminal ten amino acids of subunit K lie along the lateral helix of subunit L, and contribute to a junction of subunits K, L and N on the cytoplasmic surface. Using double cysteine mutagenesis, the cross-linking of subunit K (R99C) to either subunit L (K581C) or subunit N (T292C) was attempted. A partial yield of cross-linked product had no effect on the activity of the enzyme, or on proton translocation, suggesting that the C-terminus of subunit K has no dynamic role in function. To further elucidate the role of subunit K genetic deletions were constructed at the C-terminus. Upon the serial deletion of the last 4 residues of the C-terminus of subunit K, various results were obtained. Deletion of one amino acid had little effect on the activity of Complex I, but deletions of 2 or more amino acids led to total loss of enzyme activity and diminished levels of subunits L, M, and N in preparations of membrane vesicles. Together these results suggest that while the C-terminus of subunit K has no dynamic role in energy transduction by Complex I, it is vital for the correct assembly of the enzyme.

Constraining the Lateral Helix of Respiratory Complex I by Cross-linking Does Not Impair Enzyme Activity or Proton Translocation.

Complex I (NADH:ubiquinone oxidoreductase) is a multisubunit, membrane-bound enzyme of the respiratory chain. The energy from NADH oxidation in the peripheral region of the enzyme is used to drive proton translocation across the membrane. One of the integral membrane subunits, nuoL in Escherichia coli, has an unusual lateral helix of ∼75 residues that lies parallel to the membrane surface and has been proposed to play a mechanical role as a piston during proton translocation (Efremov, R. G., Baradaran, R., and Sazanov, L. A. (2010) Nature 465, 441-445). To test this hypothesis we have introduced 11 pairs of cysteine residues into Complex I; in each pair one is in the lateral helix, and the other is in a nearby region of subunit N, M, or L. The double mutants were treated with Cu(2+) ions or with bi-functional methanethiosulfonate reagents to catalyze cross-link formation in membrane vesicles. The yields of cross-linked products were typically 50-90%, as judged by immunoblotting, but in no case did the activity of Complex I decrease by >10-20%, as indicated by deamino-NADH oxidase activity or rates of proton translocation. In contrast, several pairs of cysteine residues introduced at other interfaces of N:M and M:L subunits led to significant loss of activity, in particular, in the region of residue Glu-144 of subunit M. The results do not support the hypothesis that the lateral helix of subunit L functions like a piston, but rather, they suggest that conformational changes might be transmitted more directly through the functional residues of the proton translocation apparatus.

Mutagenesis of the L, M, and N subunits of Complex I from Escherichia coli indicates a common role in function.

BACKGROUND: The membrane arm of Complex I (NADH:ubiquinone oxidoreductase) contains three large, and closely related subunits, which are called L, M, and N in E. coli. These subunits are homologous to components of multi-subunit Na(+)/H(+) antiporters, and so are implicated in proton translocation. METHODOLOGY/PRINCIPAL FINDINGS: Nineteen site-specific mutations were constructed at two corresponding positions in each of the three subunits. Two positions were selected in each subunit: L_K169, M_K173, N_K158 and L_Q236, M_H241, N_H224. Membrane vesicles were prepared from all of the resulting mutant strains, and were assayed for deamino-NADH oxidase activity, proton translocation, ferricyanide reductase activity, and sensitivity to capsaicin. Corresponding mutations in the three subunits were found to have very similar effects on all activities measured. In addition, the effect of adding exogenous decylubiquinone on these activities was tested. 50 µM decylubiquinone stimulated both deamino-NADH oxidase activity and proton translocation by wild type membrane vesicles, but was inhibitory towards the same activities by membrane vesicles bearing the lysine substitution at the L236/M241/N224 positions. CONCLUSIONS/SIGNIFICANCE: The results show a close correlation with reduced activity among the corresponding mutations, and provide evidence that the L, M, and N subunits have a common role in Complex I.