DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.

Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model.

Proteins derived from microorganisms that survive in the harshest environments on Earth have stable activity under extreme conditions, providing rich resources for industrial applications and enzyme engineering. Due to the time-consuming nature of experimental determinations, it is imperative to develop computational models for fast and accurate prediction of protein optimal conditions. Previous studies were limited by the scarcity of data and the neglect of protein structures.

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures.

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model.

A Prolonged Artificial Nighttime-light Dataset of China (1984-2020).

Nighttime light remote sensing has been an increasingly important proxy for human activities. Despite an urgent need for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. A Night-Time Light convolutional LSTM network is proposed and applied the network to produce a 1-km annual Prolonged Artificial Nighttime-light DAtaset of China (PANDA-China) from 1984 to 2020.

Atrial Fibrillation Detection with Single-Lead Electrocardiogram Based on Temporal Convolutional Network-ResNet.

Atrial fibrillation, one of the most common persistent cardiac arrhythmias globally, is known for its rapid and irregular atrial rhythms. This study integrates the temporal convolutional network (TCN) and residual network (ResNet) frameworks to effectively classify atrial fibrillation in single-lead ECGs, thereby enhancing the application of neural networks in this field. Our model demonstrated significant success in detecting atrial fibrillation, with experimental results showing an accuracy rate of 97% and an F1 score of 87%.

Capturing critical gem-diol intermediates and hydride transfer for anodic hydrogen production from 5-hydroxymethylfurfural.

The non-classical anodic H production from 5-hydroxymethylfurfural (HMF) is very appealing for energy-saving H production with value-added chemical conversion due to the low working potential (~0.1 V vs RHE). However, the reaction mechanism is still not clear due to the lack of direct evidence for the critical intermediates.

A new retinal OCT-angiography diabetic retinopathy dataset for segmentation and DR grading.

Purpose: Diabetic retinopathy (DR) is one of the most common diseases caused by diabetes and can lead to vision loss or even blindness. The wide-field optical coherence tomography (OCT) angiography is non-invasive imaging technology and convenient to diagnose DR.

Methods: A newly constructed Retinal OCT-Angiography Diabetic retinopathy (ROAD) dataset is utilized for segmentation and grading tasks.

Unraveling the capture mechanism of gaseous AsO over H-ZSM-5 zeolite from coal-fired flue gas: Experimental and theoretical insights.

Gaseous AsO discharged from coal-fired power plants results in severe detriments to the ecological environment. It is of great urgency to develop highly efficient AsO capture technology for reducing atmospheric arsenic contamination. Utilizing solid sorbents for gaseous AsO capture is a promising treatment for AsO capture.

Symmetry-Broken 2D Lead-Tin Mixed Chiral Perovskite for High Asymmetry Factor Circularly Polarized Light Detection.

Symmetry-broken-induced spin splitting plays a key role for selective circularly polarized light absorption and spin carrier transport. Asymmetrical chiral perovskite is rising as the most promising material for direct semiconductor-based circularly polarized light detection. However, the increase of asymmetry factor and extension of response region remain to be a challenge.

Immobilizing Ionic Liquids onto Functionalized Surfaces for Sensing Volatile Organic Compounds.

Herein, a highly sensitive volatile organic compound (VOC) gas sensor is demonstrated using immobilized ionic liquid (IL), 1-butyl-3-methylimidazolium hexafluorophosphate, onto surfaces functionalized by the quaternary ammonium group -NR, -COOH, and -NH, , N-IL, COOH-IL, and NH-IL, respectively. These functional groups ensure highly tunable interactions between the IL and surfaces, efficiently modulating the electrical resistance of the immobilized IL upon exposure to acetone and toluene. The immobilized IL to both acetone and toluene displays significant electronic resistance changes at a concentration of 150 ppm, falling in the order NH-IL > N-IL > COOH-IL for acetone while COOH-IL > NH-IL > N-IL for toluene.

Microstructural probing of phosphonium-based ionic liquids on a gold electrode using colloid probe AFM.

Atomic force microscopy (AFM) with a gold colloid probe modeled as the electrode surface is employed to directly capture the contact resonance frequency of two phosphonium-based ionic liquids (ILs) containing a common anion [BScB] and differently lengthened cations ([P] and [P]). The comparative interfacial studies are performed by creating IL films on the surface of gold, followed by measuring the wettability, thickness of the films, adhesion forces, surface morphology and AFM-probed contact resonance frequency. In addition, the cyclic voltammetry and impedance spectroscopy measurements of the neat ILs are measured on the surface of the gold electrode.

First-principles investigation on the electronic structures of CdSe S and simulation of CdTe solar cell with a CdSe S window layer by SCAPS.

The short-circuit current density ( ) of CdTe solar cells both in the short and long wavelength regions can be effectively enhanced by using CdS/CdSe as the composite window layer. CdS/CdSe composite layers would interdiffuse to form the CdSe S ternary layer during the high temperature deposition process of CdTe films. In this paper, the electronic properties of CdSe S (0 ≤ ≤ 1) ternary alloys are investigated by first-principles calculation based on the density functional theory (DFT) and the performance of CdS/CdSe/CdTe devices are modeled by SCAPS to reveal why CdS/CdSe complex layers have good effects.

Long non-coding RNA DARS-AS1 promotes tumor progression by directly suppressing PACT-mediated cellular stress.

Cancer cells evolve various mechanisms to overcome cellular stresses and maintain progression. Protein kinase R (PKR) and its protein activator (PACT) are the initial responders in monitoring diverse stress signals and lead to inhibition of cell proliferation and cell apoptosis in consequence. However, the regulation of PACT-PKR pathway in cancer cells remains largely unknown.

Optimize data-driven multi-agent simulation for COVID-19 transmission.

Background: Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned.

Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data.

The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas.

Unveiling the Origin of Alkali Metal (Na, K, Rb, and Cs) Promotion in CO Dissociation over MoC Catalysts.

Molybdenum carbide (Mo2C) is a promising and low-cost catalyst for the reverse water−gas shift (RWGS) reaction. Doping the Mo2C surface with alkali metals can improve the activity of CO2 conversion, but the effect of these metals on CO2 conversion to CO remains poorly understood. In this study, the energies of CO2 dissociation and CO desorption on the Mo2C surface in the presence of different alkali metals (Na, K, Rb, and Cs) are calculated using density functional theory (DFT).

Molecular-Level Insights into Unique Behavior of Water Molecules Confined in the Heterojunction between One- and Two-Dimensional Nanochannels.

With the increasing importance of nanoconfined water in various heterostructures, it is quite essential to clarify the influence of nanoconfinement on the unique properties of water molecules in the pivotal heterojunction. In this work, we reported a series of classical molecular dynamics (MD) simulations to explore nanoconfined water in the subnanometer-sized and nanometer-sized heterostructures by adjusting one-dimensional (1-D) carbon nanotubes with different diameters and two-dimensional (2-D) graphene sheets with different interlayer distances. Our simulation results demonstrated that water molecules in the 1-D/2-D heterojunction show an obvious structural rearrangement associated with the remarkable breaking and formation of hydrogen bonds (HBs), and such rearrangements in the subnanometer-sized systems are much more pronounced than those in the nanometer-sized ones.

Molecular interactions of ionic liquids with SiO surfaces determined from colloid probe atomic force microscopy.

Ionic liquids (ILs) interact strongly with many different types of solid surfaces in a wide range of applications, lubrication, energy storage and conversion, However, due to the nearly immeasurable large number of potential ILs available, identifying the appropriate ILs for specific solid interfaces with desirable properties is a challenge. Theoretical studies are highly useful for effective development of design and applications of these complex molecular systems. However, obtaining reliable force field models and interaction parameters is highly demanding.

Study on SO and Cl sensor application of 2D PbSe based on first principles calculations.

In this paper, we use 2D PbSe to design a gas sensor to monitor the presence of SO and Cl. We use first principles to verify the feasibility of this material, such as atomic structure, band gap, differential charge density and Bader charge. The results show that 2D PbSe can distinctly adsorb SO and Cl.

Molecular-Level Understanding of Surface Roughness Boosting Segregation Behavior at the ZIF-8/Ionic Liquid Interfaces.

Here, we perform a series of classical molecular dynamics simulations for two different [HEMIM][DCA] and [BMIM][BF] ionic liquids (ILs) on the ZIF-8 surface to explore the interfacial properties of metal-organic framework (MOFs)/IL composite materials at the molecular level. Our simulation results reveal that the interfacial structures of anions and cations on the ZIF-8 surface are dominated by the surface roughness due to the steric hindrance, which is extremely different from the driving mechanism based on solid-ion interactions of ILs on flat solid surfaces. At the ZIF-8/IL interfaces, the open sodalite (SOD) cages of the ZIF-8 surface can block most of the large-size cations outside and significantly boost the segregation behavior of anions and cations.

Highly-stable cobalt metal organic framework with sheet-like structure for ultra-efficient water oxidation at high current density.

The development of efficient and robust non-precious electrocatalysts for water oxidation at a mild condition is extremely desirable for industrial water splitting. Herein we developed a facile solvothermal strategy to synthesize cobalt metal organic frameworks (Co-MOFs) with sheet-like structure, which showed highly promising performance for electrocatalytic oxygen evolution. The best Co-MOF sample afforded an ultra-high oxygen evolution current density of 63.

In situ inorganic conductive network formation in high-voltage single-crystal Ni-rich cathodes.

High nickel content in LiNiCoMnO (NCM, x ≥ 0.8, x + y + z = 1) layered cathode material allows high specific energy density in lithium-ion batteries (LIBs). However, Ni-rich NCM cathodes suffer from performance degradation, mechanical and structural instability upon prolonged cell cycling.