A5111519355- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- Astronomical Observations and Instrumentation
- Metabolomics and Mass Spectrometry Studies
- Advanced NMR Techniques and Applications
- Advanced MRI Techniques and Applications
- NMR spectroscopy and applications
- Molecular spectroscopy and chirality
- Inertial Sensor and Navigation
- Enzyme Structure and Function
- Probability and Risk Models
- Game Theory and Applications
- Diverse Aspects of Tourism Research
- Reinforcement Learning in Robotics
- Computational Drug Discovery Methods
- Scientific Computing and Data Management
- Image and Signal Denoising Methods
- Topic Modeling
- Nanoparticles: synthesis and applications
- Machine Learning in Materials Science
- Advanced Nanomaterials in Catalysis
- Refrigeration and Air Conditioning Technologies
- Immune Cell Function and Interaction
- Regional Economic and Spatial Analysis
- Metaheuristic Optimization Algorithms Research
Chinese Academy of Sciences
2015-2025
Wuhan Institute of Physics and Mathematics
2015-2025
Qingdao Agricultural University
2025
University of Chinese Academy of Sciences
2024-2025
Harbin Engineering University
2023
Wuhan National Laboratory for Optoelectronics
2021
Huazhong University of Science and Technology
2021
First Affiliated Hospital of Xiamen University
2020
Fujian Medical University
2020
Shandong University
2018
Aeromonas species are among the main pathogens causing rainbow trout infections. Silver nanoparticles (AgNPs) have a broad spectrum of antimicrobial properties and usually produced by various green-synthesis methods. However, application commercialized AgNPs has not fully been clarified. Thus, objective this study was to evaluate antibacterial activities (range sizes 10–12 nm) on two contrasting A. salmonicida strains (I-1 I-4), isolated from trout; mechanism, histopathological alterations...
B-factor is a measure of ray attenuation or scattering caused by atomic thermal motion during X-ray diffraction protein crystal structure. reflects the vibration atoms; hence, it most common experimental descriptor flexibility and has been extensively applied in studies dynamics, screening bioactive small molecules, engineering. The prediction profiles considerable significance for analyzing dynamic properties unknown proteins. Deep learning technology developed rapidly recent years widely...
Metabolomics plays a crucial role in understanding metabolic processes within biological systems. Using specific pulse sequences, NMR-based metabolomics detects small and macromolecular metabolites that are altered blood samples. Here we proposed method called spectral editing neural network, which can effectively edit separate the signals of macromolecules
Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schr\"{o}dingerNet equation (SE) by integrating various neural network (NN)-based wavefunction ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on Born-Oppenheimer approximation (BOA) and require a separate computationally expensive training for each nuclear configuration. In this work, we propose novel NN architecture, Schr\"{o}dingerNet, to...
In this note we consider the two-dimensional risk model introduced in Avram, Palmowski and Pistorius (2008) with constant interest rate. We derive integral-differential equations of Laplace transforms, asymptotic expressions for finite-time ruin probabilities respect to joint times T max ( u 1 , 2 ) min respectively.
The fast motions of proteins at the picosecond to nanosecond timescale, known as dynamics, are closely related protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding allosteric effects. most used NMR approach study dynamics is model free method, uses order parameter S2 describe amplitude internal motion local group. However, obtain through experiments quite complex lengthy. In this paper, we present a machine learning for predicting parameters based...
The fast motions of proteins at the picosecond to nanosecond timescale, known as dynamics, are closely related protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding allosteric effects. most used NMR approach study dynamics is model free method, uses order parameter S2 describe amplitude internal motion local group. However, obtain through experiments quite complex lengthy. In this paper, we present a machine learning for predicting parameters based...
Summary Hypertension has been suggested to be mediated by immunity and inflammation. As immune system genes, killer cell immunoglobulin‐like receptors ( KIR s) genes play an important role in the pathogenesis of autoimmune diseases. We conducted a community population‐based case–control study analyse associations between hypertension. were genotyped using sequence‐specific primer polymerase chain reaction 380 unrelated essential hypertensives 527 normotensives. The frequencies 2 DS 5 gene...
The component pursuit problem is introduced under blind environment when Gaussian noise present. An improved quantitative measure of non-Gaussianity, called moments, deduced correspondingly. After analyzing the useful property an objective function presented which can be suitable in noisy context. As a result, one-unit algorithm with bias removal for quasi-whitened data. Computer simulations illustrate better performance proposed approach.