Molecular Mechanism of Yangshen Maidong Decoction in the Treatment of Chronic Heart Failure based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations.

Chronic heart failure (CHF) is a complex multifactorial clinical syndrome leading to abnormal cardiac structure and function. The severe form of this ailment is characterized by high disability, high mortality, and morbidity. Worldwide, 2-17% of patients die at first admission, of which 17-45% die within 1 year of admission and >50% within 5 years. Yangshen Maidong Decoction (YSMDD) is frequently used to treat the deficiency and pain of the heart. The specific mechanism of action of YSMDD in treating CHF, however, remains unclear. Therefore, a network pharmacology-based strategy combined with molecular docking and molecular dynamics simulations was employed to investigate the potential molecular mechanism of YSMDD against CHF. The effective components and their targets of YSMDD and related targets of CHF were predicted and screened based on the public database. The network pharmacology was used to explore the potential targets and possible pathways that involved in YSMDD treated CHF. Molecular docking and molecular dynamics simulations were performed to elucidate the binding affinity between the YSMDD and CHF targets. Screen results, 10 main active ingredients, and 6 key targets were acquired through network pharmacology analysis. Pathway enrichment analysis showed that intersectional targets associated pathways were enriched in the Prostate cancer pathway, Hepatitis B pathway, and C-type lectin receptor signaling pathways. Molecular docking and molecular dynamics simulations analysis suggested 5 critical active ingredients have high binding affinity to the 5 key targets. This research shows the multiple active components and molecular mechanisms of YSMDD in the treatment of CHF and offers resources and suggestions for future studies.

Novel C4P2 monolayers: forming Z-scheme heterojunction and Janus structure for high-efficiency metal-free photocatalytic water splitting.

Metal-free two-dimensional (2D) semiconductors have garnered significant attention in the realm of photocatalytic water splitting, primarily owing to their inherent clean, stable, and efficient photoresponsive properties. Motivated by it, we have proposed two types of stable C4P2 monolayers with indirect band gaps, mediocre carrier mobility and excellent optical absorption in visible-light and ultraviolet regions. Although the too-low work function of monolayer α-C4P2 and the too-high work function of monolayer ß-C4P2 make them only suitable for single-side redox reaction in photocatalytic water splitting, the creation of an α-C4P2/ß-C4P2 Z-scheme heterojunction, combined with the Janus monolayer γ-C4P2 that integrates features of both α and ß structures, effectively addresses this limitation, fulfilling the prerequisites for comprehensive photocatalytic water splitting. Furthermore, the calculations indicate that the α-C4P2/ß-C4P2 Z-scheme heterojunction and Janus monolayer γ-C4P2 not only demonstrate improved carrier mobility and optical absorption but also feature internal electric fields that effectively enhance driving energy and photo-induced charge separation. Notably, Janus monolayer γ-C4P2 achieves a high electron mobility of ∼105 cm2 V-1 s-1 and an impressive solar-to-hydrogen conversion efficiency of 25.62%.

Optical feedback noise-immune cavity-enhanced optical heterodyne molecular spectrometry for sub-Doppler-broadened detection of C2H2: publisher's note.

This publisher's note contains a correction to Opt. Lett.49, 202 (2024)10.1364/OL.507004.

Optical feedback noise-immune cavity-enhanced optical heterodyne molecular spectrometry for sub-Doppler-broadened detection of C2H2.

A novel, to the best of our knowledge, noise-immune cavity-enhanced optical heterodyne molecular spectrometry (NICE-OHMS) has been developed, utilizing optical feedback for laser-to-cavity locking with a common distributed-feedback diode laser. The system incorporates active control of the feedback phase and feedforward control of the laser current, allowing for consecutive laser frequency detuning by scanning a piezoelectric transducer (PZT) attached to the cavity. To enhance the fidelity of the spectroscopic signal, wavelength-modulated (wm) NICE-OHMS is implemented. Benefiting from the optical feedback, a modulation frequency of 15 kHz is achieved, surpassing the frequencies typically used in traditional NICE-OHMS setups. Then, the sub-Doppler-broadened wm-NICE-OHMS signal of acetylene at 1.53 µm is observed. A seven-fold improvement in signal to noise ratio has been demonstrated compared to NICE-OHMS alone and a limit of detection of 6.1 × 10-10cm-1 is achieved.

Fine-tuned local coordination environment of Pt-N in nanocarbons for efficient propane dehydrogenation.

Based on the disturbance of electronic density, nitrogen-doped nanocarbons show promising properties to anchor metal clusters. However, precisely regulating the coordination mode between N species and the active site remains challenging. Herein, we rationally designed three N types (graphitic N, pyridinic N and pyrrolic N) in nanocarbons to anchor Pt clusters for the benchmark propane dehydrogenation. The specific activity of the pyridinic-N-doped catalyst was 147.54 molC3H6 molPt-1 h-1 at 550 °C, which was 1.3 times higher than those of graphitic- and pyrrolic-N-doped catalysts. Unlike the regular tetrahedron Pt cluster in the graphitic-N catalyst or the distorted three-layered Pt cluster in the pyrrolic-N catalyst, the Pt cluster in the pyridinic-N catalyst was an inverted tetrahedron, which increased the contact degree without geometric repulsion towards C-H bond scission. The geometric parameters of detached H and C atoms in the methylene group for the pyridinic N catalyst was decreased to strengthen the C-H bond scission. After CH3CHCH3* adsorption, the Bader charge of the Pt active site also became highly positive, which tailored the d-band center closer to the Fermi level and provided more vacant orbitals for C-H bond breakage. Therefore, pyridinic N in nanocarbons is promising to anchor small-sized Pt for alkane dehydrogenation in terms of geometric and electronic effects.

Metal-free Janus α- and ß-SiCP4: designing stable and efficient two-dimensional semiconductors for water splitting.

Two-dimensional (2D) semiconductors exhibit exceptional potential in the field of photocatalytic water splitting due to their unique structural characteristics and photoelectric properties. In this study, based on first-principles density functional theory, we theoretically proposed two SiCP4 Janus 2D semiconductors with high stability, namely monolayer α- and ß-SiCP4. By performing the calculation of HSE06 functionals, the band structures of monolayer α- and ß-SiCP4 have been estimated, and the results show that both α- and ß-SiCP4 are direct-band-gap semiconductors with band gaps of 1.64 eV and 1.91 eV, respectively. Meanwhile, the band edge levels of monolayer α- and ß-SiCP4 meet the band structure requirements of photocatalysts in water splitting. Notably, because of the internal build-in electric fields and tiny band gaps, monolayer α- and ß-SiCP4 exhibit separated photogenerated electron-hole pairs and high solar-to-hydrogen (STH) efficiency, reaching up to 33.68% and 23.72%, respectively. Additionally, we also investigate the impact of uniaxial strain on electronic, optical and photocatalytic properties of monolayer α- and ß-SiCP4 considering pH values ranging from 0 to 14. Our results demonstrate that the maximum STH efficiency for α-SiCP4 is achieved under X-direction strain (η) of 2%, Y-direction strain (η) of 8%, and pH values between 2 and 4. Conversely, ß-SiCP4 exhibits the highest STH efficiency under X-direction strain (η) of 8%, Y-direction strain (η) of 6%, and pH values between 2 and 4.

Self-calibrated NICE-OHMS based on an asymmetric signal: theoretical analysis and experimental validation.

As an ultra-sensitive detection technique, the noise-immune cavity enhanced optical heterodyne molecular spectroscopy (NICE-OHMS) technique has great potential for assessment of the concentration of trace gases. To determine gas concentrations at the ppt or lower level with high accuracy, it is desirable that the technique exhibits self-calibration (or calibration-free) capabilities. Although being sensitive, NICE-OHMS has so far not demonstrated any such ability. To remedy this, this paper provides a self-calibrated realization of NICE-OHMS that is based on a switching of the feedback target of the DeVoe-Brewer (DVB) locking procedure from the modulation frequency of the frequency modulation spectroscopy (FMS) to the cavity length, which creates an asymmetrical signal whose form and size can be used to unambiguously assess the gas concentration. A comprehensive theoretical model for self-calibrated NICE-OHMS is established by analyzing the shift of cavity modes caused by intracavity absorption, demonstrating that gas absorption information can be encoded in both the laser frequency and the NICE-OHMS signal. To experimentally verify the methodology, we measure a series of dispersion signals under different levels of absorbance using a built experimental setup. An instrument factor and the partial pressure are obtained by fitting the measured signal through theoretical expressions. Our results demonstrate that fitted values are more accurate for higher partial pressures than for lower. To improve on the accuracy at low partial pressures, it is shown that the instrument factor obtained by fitting the signal at large partial pressures (in this case, above 7.8 µTorr) can be set to a fixed value for all fits. By this, the partial pressures can be assessed with a relative error below 0.65%. This technique has the potential to enable calibration-free ultra-sensitive gas detection.

Structure-engineering the stability, electronic, optical and photocatalytic properties of hexagonal C2P2 monolayers.

Structure engineering presents unique opportunities in materials science field, including material design and modification. Herein, we applied structure engineering to double-sublayer hexagonal C2P2 monolayers so as to form two novel non-Janus structures and two new Janus structures. Based on first-principles calculations, the stability, electronic, optical, and photocatalytic properties of the C2P2 monolayers, including the two discovered structures and four new C2P2 monolayers, have been investigated. The results showed that these C2P2 monolayers are highly stable in energetics, dynamics, and thermodynamics. We also found that counterrotating 60° between the top and bottom sublayers could make the C2P2 monolayers become more stable. The calculations of the project band structures indicated that the new C2P2 monolayers were semiconductors with indirect band gaps ranging from 1.02 eV to 2.62 eV. Meanwhile, it was also suggested that the distributions of VBM and CBM in the two Janus C2P2 monolayers were out-of-plane due to their internal electric fields. Moreover, the carrier mobility of the C2P2 monolayers was anisotropic between an armchair and zigzag direction and quite high (reaching 103 cm2 V-1 s-1) in the zigzag direction. Additionally, all the C2P2 monolayers had large exciton binding energies (∼1.0 eV) and considerable absorption in the visible-light region. Furthermore, except for the CP-3 monolayer, all the C2P2 monolayers, including CP-1, CP-2, CP-4, CP-5, and CP-6, have great potential for metal-free visible-light photocatalytic water splitting. Our calculations reveal that structure engineering is particularly applicable to multi-sublayer two-dimensional materials for discovering new members and tuning their properties.

Two-dimensional H- and F-BX (X = O, S, Se, and Te) photocatalysts with ultrawide bandgap and enhanced photocatalytic performance for water splitting.

We theoretically propose a type of monolayer structure, H- or F-BX (X = As, Sb; Y = P, As), produced by surface hydrogenation or fluorination, with high stability, large band structures and high light absorption for photocatalytic water splitting. Based on first-principles calculations with the HSE06 functional, the electronic properties and optical properties were explored to reveal their potential performance in semiconductor devices. Additionally, owing to the Janus structure and high electronegativity of the monolayers, our calculations showed that surface fluorination can easily create an internal electric field compared with surface hydrogenation, which results in different trends of increasing bandgaps in monolayer H- and F-BX. We also found that the monolayers H- and F-BX have suitable band edges and high solar to hydrogen (STH) efficiency, enabling them to be photocatalysts for water splitting. Our work not only proposes eight monolayer semiconductors for expanding the number of two-dimensional semiconductors, but also provides a guide for how to regulate semiconductors for application in photocatalytic water splitting by using surface hydrogenation and fluorination.

Quantifying the Impact of Imaging Through Body Walls on Shear Wave Elasticity Measurements.

In the context of ultrasonic hepatic shear wave elasticity imaging (SWEI), measurement success has been determined to increase when using elevated acoustic output pressures. As SWEI sequences consist of two distinct operations (pushing and tracking), acquisition failures could be attributed to (i) insufficient acoustic radiation force generation resulting in inadequate shear wave amplitude and/or (ii) distorted ultrasonic tissue motion tracking. In the study described here, an opposing window experimental setup that isolated body wall effects separately between the push and track SWEI operations was implemented. A commonly employed commercial track configuration was used, harmonic multiple-track-location SWEI. The effects of imaging through body walls on the pushing and tracking operations of SWEI as a function of mechanical index (MI), spanning 5 different push beam MIs and 10 track beam MIs, were independently assessed using porcine body walls. Shear wave speed yield was found to increase with both increasing push and track MI. Although not consistent across all samples, measurements in a subset of body walls were found to be signal limited during tracking and to increase yield by up to 35% when increasing electronic signal-to-noise ratio by increasing harmonic track transmit pressure.

Two dimensional twin T-graphene: monolayer for visible-light photocatalytic water splitting and bulk for anode material of magnesium batteries.

Two-dimensional (2D) carbon allotropes with all-sp2-hybridization have demonstrated great potential in nano-photoelectric devices, but the application of semiconductor photocatalysts for water splitting and anodes in magnesium batteries remains unoptimistic. Motivated by this, we theoretically study a highly stable all-sp2-hybridized 2D carbon allotrope twin T-graphene (TTG) via first-principles simulations. And through the calculations of the HSE06 functional, we find that TTG has a wide bandgap (2.70 eV) and suitable band edge positions satisfying the criteria of photocatalysts for overall water splitting. Additionally, TTG exhibits excellent photocatalytic properties for overall water splitting reflecting a high STH efficiency (12.34%), strong absorption coefficient in the visible-light region and the carrier mobility being high for electrons but low for holes. By investigating the strain effects, we get that, with biaxial compressive strain, the ability of overall photocatalytic water splitting can be effectively improved including STH up to ∼30%. Moreover, the bulk TTG also exhibits great potential as an anode material of magnesium batteries with a theoretical capacity of 556 mA h g-1, average voltage of 0.74 V and diffusion energy barrier of ∼0.96 eV. Our results would broaden the application of all-sp2-hybridized 2D carbon allotropes in the semiconductor photocatalytic and magnesium batteries field.

Improving Network Representation Learning via Dynamic Random Walk, Self-Attention and Vertex Attributes-Driven Laplacian Space Optimization.

Network data analysis is a crucial method for mining complicated object interactions. In recent years, random walk and neural-language-model-based network representation learning (NRL) approaches have been widely used for network data analysis. However, these NRL approaches suffer from the following deficiencies: firstly, because the random walk procedure is based on symmetric node similarity and fixed probability distribution, the sampled vertices' sequences may lose local community structure information; secondly, because the feature extraction capacity of the shallow neural language model is limited, they can only extract the local structural features of networks; and thirdly, these approaches require specially designed mechanisms for different downstream tasks to integrate vertex attributes of various types. We conducted an in-depth investigation to address the aforementioned issues and propose a novel general NRL framework called dynamic structure and vertex attribute fusion network embedding, which firstly defines an asymmetric similarity and h-hop dynamic random walk strategy to guide the random walk process to preserve the network's local community structure in walked vertex sequences. Next, we train a self-attention-based sequence prediction model on the walked vertex sequences to simultaneously learn the vertices' local and global structural features. Finally, we introduce an attributes-driven Laplacian space optimization to converge the process of structural feature extraction and attribute feature extraction. The proposed approach is exhaustively evaluated by means of node visualization and classification on multiple benchmark datasets, and achieves superior results compared to baseline approaches.

Exploring the photocatalytic properties and carrier dynamics of 2D Janus XMMX' (X = S, Se; M = Ga, In; and X' = Te) materials.

Recently, two-dimensional (2D) Janus structures have been extensively explored because of their robust electron mobility and unique photocatalytic properties. In spite of the increasing interest, the origin of high photocatalytic activities and the behaviors of photoinduced carriers in this kind of materials have not been well understood. Herein, we present a step-by-step protocol based on the first-principles calculations combined with the ab initio non-adiabatic molecular dynamics (NAMD) simulations to unveil the origin of high photocatalytic activity of highly stable typical 2D Janus XMMX' structures (X = S, Se; M = Ga, In; and X' = Te). Their band structures, optical properties, exciton binding energies, carrier effective masses, solar-to-hydrogen efficiency, hot carrier relaxation and recombination times, etc. have been calculated. We find that the difference between X and X' atoms on the two surfaces of the XMMX' monolayer not only builds an out-of-plane electric field, which significantly affects the charge distributions on the valence band maxima (VBM) and the conduction band minima (CBM) and subsequently decreases the exciton binding energy, but also transforms the indirect band structures of XM into the direct ones with well suitable energy gaps for visible-light absorption as well as endows the XMMX' structures with unequal electron and hole mobility, rapid hot carrier relaxation and slow electron-hole recombination processes on a timescale of tens of nanoseconds. The current work suggests that Janus XMMX' monolayers are good photocatalytic materials for overall water splitting and provides a guide to regulate the materials' properties for efficient energy harvesting and optoelectronic applications.

Revealing the promotion of carbonyl groups on vacancy stabilized Pt4/nanocarbons for propane dehydrogenation.

Nanocarbons are promising supports for Pt clusters applied in propane dehydrogenation (PDH), owing to their large surface areas and tunable chemical properties. The vacancies and oxygen-containing groups (OCGs) in nanocarbons can enhance catalytic performance by tailoring the coordination environment of Pt clusters. Herein, 46 nanocarbons with coexisting vacancies and OCGs were designed to support Pt clusters, of which the influences on PDH were revealed by density functional theory calculations. Nanocarbons with divacancies (V2) and CO edge groups were screened out as the most appropriate support for Pt clusters in PDH. Due to the V2, tetrahedral Pt clusters were distorted into three-layered configurations, contributing to enhanced binding strength and a favorable reactive pathway starting from the methylene group in propane. This changed the rate-determining step to the first C-H bond scission with a low energy barrier. The introduction of CO edge groups coexisting with V2 further improved the stabilization of Pt clusters, resulting from the increased electron transfer from Pt atoms to C atoms. The abilities to break C-H bonds and inhibit C-C bond cracking were also enhanced as compared to the nanocarbons with only V2. Therefore, this work provides references on the regulation of vacancies and OCGs in carbon-based catalysts.

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory.

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.

CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition.

Electroencephalography (EEG) is recorded by electrodes from different areas of the brain and is commonly used to measure neuronal activity. EEG-based methods have been widely used for emotion recognition recently. However, most current methods for EEG-based emotion recognition do not fully exploit the relationship of EEG channels, which affects the precision of emotion recognition. To address the issue, in this paper, we propose a novel method for EEG-based emotion recognition called CR-GCN: Channel-Relationships-based Graph Convolutional Network. Specifically, topological structure of EEG channels is distance-based and tends to capture local relationships, and brain functional connectivity tends to capture global relationships among EEG channels. Therefore, in this paper, we construct EEG channel relationships using an adjacency matrix in graph convolutional network where the adjacency matrix captures both local and global relationships among different EEG channels. Extensive experiments demonstrate that CR-GCN method significantly outperforms the state-of-the-art methods. In subject-dependent experiments, the average classification accuracies of 94.69% and 93.95% are achieved for valence and arousal. In subject-independent experiments, the average classification accuracies of 94.78% and 93.46% are obtained for valence and arousal.

CAttSleepNet: Automatic End-to-End Sleep Staging Using Attention-Based Deep Neural Networks on Single-Channel EEG.

Accurate sleep staging results can be used to measure sleep quality, providing a reliable basis for the prevention and diagnosis of sleep-related diseases. The key to sleep staging is the feature representation of EEG signals. Existing approaches rarely consider local features in feature extraction, and fail to distinguish the importance of critical and non-critical local features. We propose an innovative model for automatic sleep staging with single-channel EEG, named CAttSleepNet. We add an attention module to the convolutional neural network (CNN) that can learn the weights of local sequences of EEG signals by exploiting intra-epoch contextual information. Then, a two-layer bidirectional-Long Short-Term Memory (Bi-LSTM) is used to encode the global correlations of successive epochs. Therefore, the feature representations of EEG signals are enhanced by both local and global context correlation. Experimental results achieved on two real-world sleep datasets indicate that the CAttSleepNet model outperforms existing models. Moreover, ablation experiments demonstrate the validity of our proposed attention module.

Deep Link-Prediction Based on the Local Structure of Bipartite Networks.

Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global structure that cannot analyze the role of the local structure in link prediction. To tackle this problem, this paper proposes a deep link-prediction (DLP) method by leveraging the local structure of bipartite networks. The method first extracts the local structure between target nodes and observes structural information between nodes from a local perspective. Then, representation learning of the local structure is performed on the basis of the graph neural network to extract latent features between target nodes. Lastly, a deep-link prediction model is trained on the basis of latent features between target nodes to achieve link prediction. Experimental results on five datasets showed that DLP achieved significant improvement over existing state-of-the-art link prediction methods. In addition, this paper analyzes the relationship between local structure and link prediction, confirming the effectiveness of a local structure in link prediction.

Tailoring the catalytic performance of single platinum anchored on graphene by vacancy engineering for propane dehydrogenation: a theoretical study.

Propane dehydrogenation (PDH) is an effective approach to produce propylene. Downsizing the Pt species to single atom catalysts (SACs) has become a hotspot, owing to the maximum utilization and excellent catalytic behavior. However, the agglomeration of SACs is the decisive limitation for high temperature PDH. Herein, single Pt atoms were anchored on graphene with different types of vacancies, and their catalytic performances on PDH were explored based on density functional theory (DFT). As the vacancy size increased, the catalytic activity decreased. It was because the combined site of the detached H atom in propane would transfer from the Pt atom to the C atom around vacancies, thus increasing the migration distance and lowering the activity. However, with the increase of vacancy size, the selectivity to propylene was improved, owing to the enhanced repulsion between C atoms in graphene and propylene. Therefore, instead of stabilizing the single atom, vacancies in carbon materials can also tailor the catalytic performance by geometric disturbance. This fundamental work opens up the possibility for purposeful SAC design in PDH.

Template Guiding for the Encapsulation of Uniformly Subnanometric Platinum Clusters in Beta-Zeolites Enabling High Catalytic Activity and Stability.

Subnanometric metal clusters have attracted extensive attention because of their unique properties as heterogeneous catalysts. However, it is challenging to obtain uniformly distributed metal clusters under synthesis and reaction conditions. Herein, we report a template-guidance protocol to synthesize subnanometric metal clusters uniformly encapsulated in beta-zeolite, with the metal ions anchored to the internal channels of the zeolite template via electrostatic interactions. Pt metal clusters with a narrow size range of 0.89 to 1.22 nm have been obtained on the intersectional sites of beta-zeolite (Pt@beta) with a broad range of Si/Al molar ratios (15-200). The uniformly distributed Pt clusters in Pt@H-beta are subject to strong electron withdrawal by the zeolite, which promotes transfer of active hydrogen, providing excellent activity and stability in hydrodeoxygenation reactions. A general strategy is thus proposed for the encapsulation of subnanometric metal clusters in zeolites with high thermal stability.