Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities.

The oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are crucial for the conversion of clean energy. Recently, dual-metal-site catalysts (DMSCs) have gained much attention due to their high atom utilization, stronger stability, and better catalytic performance. An advanced method that combines density functional theory (DFT) and machine learning (ML) has been employed in this study to investigate the adsorption free energies of adsorbates on hundreds of potential catalysts, with the aim of screening for catalysts that are highly active for the ORR and OER. The result of this study is that 30 DMSCs with ORR activity superior to Pt, 10 DMSCs with OER activity superior to RuO2, and 4 bifunctional catalysts for the OER and ORR are identified. This work provides guidance for the rational selection of metals on DMSCs to prepare catalysts with a high electrocatalytic performance for renewable energy applications.

Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications.

The recycling of discarded polyethylene terephthalate (PET) plastics produced metal-organic frameworks can effectively minimize environmental pollution and promote sustainable economic development. In this study, we developed a method using NaOH in alcohol and ether solvent environments to degrade PET plastics for synthesizing terephthalic acid. The method achieved a 97.5% degradation rate of PET plastics under a reaction temperature of 80 °C for 60 min. We used terephthalic acid as a ligand from the degradation products to successfully synthesize two types of monometallic and bimetallic CoZn-MOF materials. We investigated the impact of different metal centers and solvents on the electrochemical performance of the MOF materials. The result showed that the MOF-DMF/H2O material maintained a specific capacity of 1485.5 mAh g-1 after 100 cycles at a current density of 500 mA g-1, demonstrating excellent rate capability and cycling stability. In addition, our finding showed that the performance difference might be attributed to the synergistic effect of bimetallic Co2+ and Zn2+ in MOF-DMF/H2O, rapid lithium-ion diffusion and electron transfer rates, and the absence of coordinating solvents. Additionally, the non-in situ X-ray powder diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analysis results showed that lithium storage in the MOF-DMF/H2O electrode mainly depended on the aromatic C6 ring and carboxylate portions of the organic ligands in different charge and discharge states. Lithium ions can be reversibly inserted/removed into/from the electrode material. The physical adsorption on the MOF surface through electrostatic interactions enhanced both capacity and cycling stability. This research provides valuable insight for mitigating solid waste pollution, promoting sustainable economic development, and advancing the extensive applications of MOF materials in lithium-ion batteries.

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature.

The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.

BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction.

Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN.

Localized and Whole-Room Effects of Portable Air Filtration Units on Aerosol Particle Deposition and Concentration in a Classroom Environment

In indoor environments with limited ventilation, recirculating portable air filtration (PAF) units may reduce COVID-19 infection risk via not only the direct aerosol route (i.e., inhalation) but also via an indirect aerosol route (i.e., contact with the surface where particles deposited). We systematically investigated the impact of PAF units in a mock classroom, as a supplement to background ventilation, on localized and whole-room surface deposition and particle concentration. Fluorescently tagged particles with a volumetric mean diameter near two micrometers were continuously introduced into the classroom environment via a breathing simulator with a prescibed inhalation-exhalation waveform. Deposition velocities were inferred on >50 horizontal and vertical surfaces throughout the classroom, while aerosol concentrations were spatially monitored via optical particle spectrometry. Results revealed a particle decay rate consistent with expectations based upon the reported clean air delivery rates of the PAF units. Additionally, the PAF units reduced peak concentrations by a factor of around 2.5 compared to the highest concentrations observed and led to a statistically significant reduction in deposition velocities for horizontal surfaces >2.5 m from the aerosol source. Our results not only confirm PAF units can reduce particle concentrations but also demonstrate that they may lead to reduced particle deposition throughout an indoor environment when properly positioned. Practical ImplicationsO_LIPortable air filtration units should be prioritized in classrooms as part of a multi-layed strategy to mitigate potentially infectious particle transmission by direct aerosol transmission via inhalation and indirect aerosol transmission via particle deposition to surfaces and later contact with said surfaces. C_LIO_LIWhen placing portable air filtration unit(s) within a classrom space, one should consider the airflow field within the classroom, the characteristic operational mode (heating vs. cooling) of the heating, ventilation, and air conditioning system, the predominantly occupied areas of the classroom, and interference with the regular teaching and learning activities. C_LI

Behavioral activation for dementia caregivers: scheduling pleasant events and enhancing communications.

BACKGROUND: Dementia caregiving is often associated with increase in depressive symptoms and strained relationships. This study tested whether telephone-delivered psychoeducation combined with an enhanced behavioral activation (BA) module had a better effect on the well-being of Alzheimer's caregivers than psychoeducation alone. The focus is on enhancing the competent use of coping skills via BA. The program is delivered by telephone to increase accessibility and sustainability for caregivers. Senior citizens are trained as paraprofessionals to deliver the BA module to increase the potential for sustainability of the program. METHODS AND SUBJECTS: The study compared two telephone interventions using a 4-month longitudinal randomized controlled trial. For the first 4 weeks, all participants received the same psychoeducation program via telephone. Then for the following 4 months, eight biweekly telephone follow-up calls were carried out. For these eight follow-up calls, participants were randomized into either one of the two following groups with different conditions. For the psychoeducation with BA (PsyED-BA) group, participants received eight biweekly sessions of BA practice focused on pleasant event scheduling and improving communications. For the psychoeducation only (PsyED only) group, participants received eight biweekly sessions of general discussion of psychoeducation and related information. A total of 62 family caregivers of persons living with dementia were recruited and 59 (29 in the PsyED-BA group and 30 in the PsyED only group) completed the whole study. RESULTS: As compared to the group with psychoeducation and discussion, the group with enhanced BA had decreased levels of depressive symptoms. The study had a low attrition rate. CONCLUSION: Results suggested that competence-based training could be effectively administered through the telephone with the help of senior citizens trained and engaged as paraprofessionals. Results contribute to the present literature by offering some framework for developing effective, accessible, sustainable, and less costly interventions.

Inhibition of DNA-dependent protein kinase catalytic subunit by small molecule inhibitor NU7026 sensitizes human leukemic K562 cells to benzene metabolite-induced apoptosis / 华中科技大学学报（医学）（英德文版）

Benzene is an established leukotoxin and leukemogen in humans. We have previously reported that exposure of workers to benzene and to benzene metabolite hydroquinone in cultured cells induced DNA-dependent protein kinase catalytic subunit (DNA-PKcs) to mediate the cellular response to DNA double strand break (DSB) caused by DNA-damaging metabolites. In this study, we used a new, small molecule, a selective inhibitor of DNA-PKcs, 2-(morpholin-4-yl)-benzo[h]chomen-4-one (NU7026), as a probe to analyze the molecular events and pathways in hydroquinone-induced DNA DSB repair and apoptosis. Inhibition of DNA-PKcs by NU7026 markedly potentiated the apoptotic and growth inhibitory effects of hydroquinone in proerythroid leukemic K562 cells in a dose-dependent manner. Treatment with NU7026 did not alter the production of reactive oxygen species and oxidative stress by hydroquinone but repressed the protein level of DNA-PKcs and blocked the induction of the kinase mRNA and protein expression by hydroquinone. Moreover, hydroquinone increased the phosphorylation of Akt to activate Akt, whereas co-treatment with NU7026 prevented the activation of Akt by hydroquinone. Lastly, hydroquinone and NU7026 exhibited synergistic effects on promoting apoptosis by increasing the protein levels of pro-apoptotic proteins Bax and caspase-3 but decreasing the protein expression of anti-apoptotic protein Bcl-2. Taken together, the findings reveal a central role of DNA-PKcs in hydroquinone-induced hematotoxicity in which it coordinates DNA DSB repair, cell cycle progression, and apoptosis to regulate the response to hydroquinone-induced DNA damage.

Inhibition of DNA-dependent protein kinase catalytic subunit by small molecule inhibitor NU7026 sensitizes human leukemic K562 cells to benzene metabolite-induced apoptosis / 华中科技大学学报（医学）（英德文版）

Benzene is an established leukotoxin and leukemogen in humans. We have previously reported that exposure of workers to benzene and to benzene metabolite hydroquinone in cultured cells induced DNA-dependent protein kinase catalytic subunit (DNA-PKcs) to mediate the cellular response to DNA double strand break (DSB) caused by DNA-damaging metabolites. In this study, we used a new, small molecule, a selective inhibitor of DNA-PKcs, 2-(morpholin-4-yl)-benzo[h]chomen-4-one (NU7026), as a probe to analyze the molecular events and pathways in hydroquinone-induced DNA DSB repair and apoptosis. Inhibition of DNA-PKcs by NU7026 markedly potentiated the apoptotic and growth inhibitory effects of hydroquinone in proerythroid leukemic K562 cells in a dose-dependent manner. Treatment with NU7026 did not alter the production of reactive oxygen species and oxidative stress by hydroquinone but repressed the protein level of DNA-PKcs and blocked the induction of the kinase mRNA and protein expression by hydroquinone. Moreover, hydroquinone increased the phosphorylation of Akt to activate Akt, whereas co-treatment with NU7026 prevented the activation of Akt by hydroquinone. Lastly, hydroquinone and NU7026 exhibited synergistic effects on promoting apoptosis by increasing the protein levels of pro-apoptotic proteins Bax and caspase-3 but decreasing the protein expression of anti-apoptotic protein Bcl-2. Taken together, the findings reveal a central role of DNA-PKcs in hydroquinone-induced hematotoxicity in which it coordinates DNA DSB repair, cell cycle progression, and apoptosis to regulate the response to hydroquinone-induced DNA damage.

Quantitative analysis and classification of tongue inspection based on the visible reflection spectrum / 中西医结合学报

In this study, optical data on color of tongue tips were collected by using a visible reflection spectrum system. Quantitative method of tongue inspection and classification of tongue states including pale, pale red, red and crimson manifestations were investigated.