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Zhang X, Deng Y, Xiang J, et al. Drug Des Devel Ther. 2020;14:3393–3405. The authors have advised that the Acknowledgments statement before the Disclosure section was missed on page 3404 of the published paper. The Acknowledgments statement should read as follows: AcknowledgmentsThis work was supported by Youth Program of National Natural Science Foundation of China (21708050), Hunan Provincial Health Committee’s General Subsidy Project (20200100), General Program of National Natural Science Foundation of China (8187032317) and In-hospital Youth Fund of Xiangya Hospital of Central South University (2015Q03). Read the original article &nbsp

Xinxu Zhang, Yuanyuan Deng, Juan Xiang, Huixia Liu, Jiani Zhang, Jie Liao, Ke Chen, Bo Liu, Jun Liu, Ying Pu Department of Geriatric Endocrinology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, People’s Republic of ChinaCorrespondence: Ying PuDepartment of Geriatric Endocrinology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, Hunan 410008, People’s Republic of ChinaTel +86 137 8727 3373Email puying@mail.csu.edu.cnBackground: Previous studies have shown that curcumin derivatives can improve the fatty degeneration of liver tissue that occurs in nonalcoholic fatty liver disease (NAFLD). However, the specific mechanism for that improvement remains unclear. We examined whether the curcumin derivative galangin could reduce the fatty degeneration of liver tissue in mice with NAFLD by inducing autophagy, from the perspective of both prevention and treatment.Methods: C57BL/6J mice were randomly assigned to a prevention group (given galangin and a HFD simultaneously) or a treatment group (given galangin after being fed an HFD). The prevention group was treated with galangin (100 mg/kg/d) or an equal volume of normal saline (NS) while being fed an HFD. Some mice were treated with an autophagy inhibitor (3-methyladenine, 3-MA; 30 mg/kg/biwk, i.p.) while being fed an HFD and galangin. HepG2 cells were cultured in DMEM medium containing both free fatty acids and galangin.Results: Galangin was found to reduce the fatty degeneration of liver tissue induced by eating an HFD at both the prevention and treatment levels, and that effect might be related to an enhancement of hepatocyte autophagy. Inhibition of autophagy by 3-MA blocked the protective effect of galangin on hepatic steatosis. At the cellular level, galangin reduced lipid accumulation and enhanced the level of hepatocyte autophagy.Conclusion: In vitro and in vivo studies showed that galangin cannot only improve pre-existing hepatic steatosis

Semiconductors are widely used as counter electrodes in quantum dot-sensitized solar cells. However, many counter electrode materials have poor conductivity and require tedious post-treatment procedures. Here, our groups develop a highly transparent MS2@N-doped C film materials (M = Ni, Fe) derived from layer-by-layer self-assembly of a M-TCPP film as a counter electrode in bifacial CdS/CdSe quantum dot-sensitized solar cells. Devices based on the MS2@N-doped C films exhibited higher respective front- and reverse-side power conversion efficiencies (i.e., 4.57% and 3.98% for the NiS2@N-doped C film and 3.18% and 2.63% for the FeS2@N-doped C film) than those of Pt-based devices (2.39% and 1.74%). We attribute the outstanding catalytic activity and excellent stability of the MS2@N-doped C film materials to the homogeneous sulfides within the transparent nitrogen-doped C film, as confirmed by electrochemical analyses, including cycle voltammetry, impedance spectroscopy and Tafel-polarization measurements., QC 20191213

The goal of the research is to adopt artificial intelligence techniques for regression test case selection in an industrial setting. Currently, the selection of test cases is made manually by test engineers referring to software change documentation. A machine learning-based solution which selects test cases with high potential in uncovering software defects is proposed and results were analysed. Past historical results of test cases and various metadata, such as the ID, name and priority of test cases, were used in data training. At the pre-processing stage, data were analysed, cleaned and normalised. Then, a two-part balancing method, comprising of outliers removal and resampling with an algorithm, was applied to the imbalanced data before it can be fitted to various machine learning models. The model that best fits the system requirement, which recommends at most 50% of the total test cases with no false negative predictions and as few false positive predictions as possible, is selected to be implemented as an executable application. The finalised model, based on random forest, recommends 1,626 test cases (7.35% of total test cases) for execution with no false negative and 882 false positive predictions (3.98% of total test cases) out of 22,137 test cases. This fulfils the two objectives of this research, which is to construct a recommendation system capable of reducing at least 50% of all test cases, and to ensure that there are no false negative and minimal false positive predictions. The current test system is a black-box system, meaning the software’s functionalities are tested without accessing its internal code structure. The model’s performance can be improved with a white-box test system, where the software source code can be accessed, and information such as which specific code segments are causing test case failure is available.