5 different forests of Pinus massoniana, Schima superba, Liquidambar formosana, P. massoniana × S. superba, P. massoniana × L. formosana as the research object were set up to study the Cr, Cu and Zn content ...5 different forests of Pinus massoniana, Schima superba, Liquidambar formosana, P. massoniana × S. superba, P. massoniana × L. formosana as the research object were set up to study the Cr, Cu and Zn content of degraded red soil region in subtropics. The soil heavy metal pollution degree was evaluated by national environmental quality standard (II class). The results showed that three soil metals of P. massoniana × S. superba were the highest, and the soil metals enrichment ability was strong. The order of single factor pollution index of metal elements was Cu (1.38) > Cr (0.81) > Zn (0.42), and moderately pollution, pollution warning and no pollution, respectively. There was no significant correlation between three soil heavy metals and soil total carbon (TC), total nitrogen (TN) and total phosphorus (TP). These results suggested that the accumulation of heavy metal elements was not derived from the parent material of soil. There was a significant positive correlation between the three metal elements which indicated that the sources of the three elements were similar. The structural equation model showed that the direct and indirect effects among the influencing factors ultimately affected the activity of heavy metals by cascade effects.展开更多
The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with...The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.展开更多
文摘5 different forests of Pinus massoniana, Schima superba, Liquidambar formosana, P. massoniana × S. superba, P. massoniana × L. formosana as the research object were set up to study the Cr, Cu and Zn content of degraded red soil region in subtropics. The soil heavy metal pollution degree was evaluated by national environmental quality standard (II class). The results showed that three soil metals of P. massoniana × S. superba were the highest, and the soil metals enrichment ability was strong. The order of single factor pollution index of metal elements was Cu (1.38) > Cr (0.81) > Zn (0.42), and moderately pollution, pollution warning and no pollution, respectively. There was no significant correlation between three soil heavy metals and soil total carbon (TC), total nitrogen (TN) and total phosphorus (TP). These results suggested that the accumulation of heavy metal elements was not derived from the parent material of soil. There was a significant positive correlation between the three metal elements which indicated that the sources of the three elements were similar. The structural equation model showed that the direct and indirect effects among the influencing factors ultimately affected the activity of heavy metals by cascade effects.
基金Project supported by the National Key Research and Development Program of China(Grant No.2023YFF1204402)the National Natural Science Foundation of China(Grant Nos.12074079 and 12374208)+1 种基金the Natural Science Foundation of Shanghai(Grant No.22ZR1406800)the China Postdoctoral Science Foundation(Grant No.2022M720815).
文摘The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.