Science Bulletin

Journal Information
ISSN / EISSN : 2095-9273 / 2095-9281
Published by: Elsevier BV (10.1016)
Total articles ≅ 3,127
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Qingling Zhu, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, Ming Gong, et al.
Published: 25 October 2021
The publisher has not yet granted permission to display this abstract.
Xingdong Li, , Qi Huang, Fanyu Zhao
Published: 22 October 2021
The publisher has not yet granted permission to display this abstract.
Feng Liu, Huayong Wu, Yuguo Zhao, Decheng Li, Jin-Ling Yang, Xiaodong Song, Zhou Shi, A-Xing Zhu,
Published: 22 October 2021
Abstract:
Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90 m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This analysis was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.
, Siyu Chen, Wei Wang, Sin Man Lam, Yang Xu, Shaohua Zhang, Huimin Pan, Jingjing Liang, Xiahe Huang, Yu Wang, et al.
Published: 21 October 2021
Abstract:
Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of pathologies, ranging from steatosis to nonalcoholic steatohepatitis (NASH). The factors promoting the progression of steatosis to NASH are still unclear. Recent studies suggest that mitochondrial lipid composition is critical in NASH development. Here, we showed that CDP-DAG synthase 2 (Cds2) was downregulated in genetic or diet-induced NAFLD mouse models. Liver-specific deficiency of Cds2 provoked hepatic steatosis, inflammation and fibrosis in five-week-old mice. CDS2 is enriched in mitochondria-associated membranes (MAMs), and hepatic Cds2 deficiency impaired mitochondrial function and decreased mitochondrial PE levels. Overexpression of phosphatidylserine decarboxylase (PISD) alleviated the NASH-like phenotype in Cds2f/f;AlbCre mice and abnormal mitochondrial morphology and function caused by CDS2 deficiency in hepatocytes. Additionally, dietary supplementation with an agonist of peroxisome proliferator-activated receptor alpha (PPARα) attenuated mitochondrial defects and ameliorated the NASH-like phenotype in Cds2f/f;AlbCre mice. Finally, Cds2 overexpression protected against high-fat diet-induced hepatic steatosis and obesity. Thus, Cds2 modulates mitochondrial function and NASH development.
Jie Chen, Zhonghui Shen, Qi Kang, Xiaoshi Qian, Shengtao Li, Pingkai Jiang,
Published: 20 October 2021
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Yu Wang, Zhuohao Zhang, Hanxu Chen, Han Zhang, Hui Zhang,
Published: 19 October 2021
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Lvlv Ji, Yingying Zhu, Xue Teng, Tao Wang, , Thomas J. Meyer,
Published: 19 October 2021
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