A5100524666- Protein Structure and Dynamics
- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- Smart Grid Security and Resilience
- Literary Theory and Cultural Hermeneutics
- Industrial Technology and Control Systems
- Microbial Natural Products and Biosynthesis
- Target Tracking and Data Fusion in Sensor Networks
- Radiopharmaceutical Chemistry and Applications
- Diet, Metabolism, and Disease
- Non-Invasive Vital Sign Monitoring
- Diabetes Management and Education
- Machine Learning in Materials Science
- Soft Robotics and Applications
- Smart Grid Energy Management
- Monoclonal and Polyclonal Antibodies Research
- Quantum Dots Synthesis And Properties
- Magnetic and Electromagnetic Effects
- Electrostatic Discharge in Electronics
- Inertial Sensor and Navigation
- Antibiotic Resistance in Bacteria
- Contemporary Literature and Criticism
- Vehicle emissions and performance
- Global Maternal and Child Health
- Gold and Silver Nanoparticles Synthesis and Applications
Shenzhen Academy of Robotics
2024
Tianjin University
2024
Shanghai Jiao Tong University
2021-2023
Zhejiang University
2023
State Key Laboratory of Industrial Control Technology
2022
Renji Hospital
2021
Zhejiang A & F University
2016
Qilu Hospital of Shandong University
2015
Hubei University
2013
Northeastern University
2007
The decarbonization of energy systems has posed unprecedented challenges in system complexity and operational uncertainty that render it imperative to exploit cutting-edge artificial intelligence (AI) technologies realize real-time, autonomous power operation control. In particular, deep reinforcement learning (DRL)-based approaches are extensively studied implemented several trials worldwide. Nevertheless, the vulnerability DRL brings new security threats have not been well identified...
Binding affinity prediction of three-dimensional (3D) protein-ligand complexes is critical for drug repositioning and virtual screening. Existing approaches usually transform a 3D complex to two-dimensional (2D) graph, then use graph neural networks (GNNs) predict its binding affinity. However, the node edge features 2D are extracted based on invariant local coordinate systems complex. As result, these can not fully learn global information complex, such as physical symmetry topological...
With the increasing penetration of inverter-based renewable energy resources, deep reinforcement learning (DRL) has been proposed as one most promising solutions to realize real-time and autonomous control for future carbon-neutral power systems. In particular, DRL-based frequency approaches have extensively investigated overcome limitations model-based approaches, such computational cost scalability large-scale Nevertheless, real-world implementation DRLbased methods is facing following...
Objective: Nesfatin-1, originates from the precursor protein nucleobindin 2 (NUCB2) and plays an important role in development of metabolic syndrome (MetS), including obesity hypertension. This study aimed to determine whether 1012C>G polymorphism NUCB2 gene is correlated with MetS Chinese Han population. Materials Methods: The was detected a population 326 patients 165 healthy subjects. Results: showed lower CG GG genotypes, as well G allele frequencies, compared Unconditional logistic...
With the increase in drug resistance rates of pathogens isolated from complicated intra-abdominal infections (cIAIs), ceftazidime/avibactam (CAZ-AVI) is increasingly used clinically. However, given high cost and fact that not yet covered by health insurance payment, this study evaluated cost-effectiveness CAZ-AVI plus metronidazole versus meropenem as a first-line empiric treatment for cIAIs perspective Chinese healthcare system.A decision analytic model with one-year time horizon was...
Predicting the docking between proteins and ligands is a crucial challenging task for drug discovery. However, traditional methods mainly rely on scoring functions, deep learning-based approaches usually neglect 3D spatial information of ligands, as well graph-level features which limits their performance. To address these limitations, we propose an equivariant transformer neural network protein-ligand pose prediction. Our approach involves fusion ligand by feature processing, followed...
We stimulated the exposed toad heart by a low frequency and high energy magnetic. By analyze data of this experiment, it shows that pulsating weak would make change after Weak heartbeat strengthened, single peak curve become two peaks with atria wave ventricle magnetic stimulation. But cycling rhythmic pulsatile doesn't change.