An imbalance in mitochondrial dynamics induced by oxidative stress may lead to hepatocyte epithelial mesenchymal transition (EMT) and liver fibrosis. However, the underlying molecular mechanisms have not been fully elucidated. This study investigated the role of mitochondrial dynamics in hepatocyte EMT and liver fibrosis using an in vitro human (L-02 cells, hepatic cell line) and an in vivo mouse model of liver fibrosis. Findings showed that oxidative stress-induced mitochondrial DNA damage was associated with abnormal mitochondrial fission and hepatocyte EMT. The reactive oxygen species (ROS) scavengers apocynin and mito-tempo effectively attenuated carbon tetrachloride (CCl 4 )-induced abnormal mitochondrial fission and liver fibrosis. Restoring mitochondrial biogenesis attenuated hepatocyte EMT. Oxidative stress-induced abnormal hepatocyte mitochondrial fission events by a mechanism that involved the down regulation of PGC-1α. PGC-1α knockout mice challenged with CCl 4 had increased abnormal mitochondrial fission and more severe liver fibrosis than wild type mice. These results indicate that PGC-1α has a protective role in oxidative stress-induced-hepatocyte EMT and liver fibrosis.

The electrochemical oxidative cross‐dehydrogenative coupling of arylsulfinic acids with thiophenols was achieved via a radical process. A wide range of arylsulfinic acids and substituted thiophenols were found to be tolerated, providing unsymmetrical thiosulfonates in good to excellent yields. This electrochemical method can also be used for the reaction of arylsulfinic acids with disulfides or diselenides to obtain thiosulfonates or selenosulfonates.magnified image

No abstract

No abstract

Deep learning achieves remarkable performance on pattern recognition, but can be vulnerable to defects of some important properties such as robustness and security. This tutorial is based on a stream of research conducted since the summer of 2018 at a few UK universities, including the

No abstract

Background: A fibrotic liver may have an impaired regenerative capacity. Because liver transplantation is donor limited, understanding the regenerative ability of a fibrotic liver is important.Methods: A two-thirds partial hepatectomy (PH) was performed in C57Bl/6 mice with or without carbon tetrachloride (CCl 4 ) treatment. Liver regeneration in the fibrotic liver after PH was assessed by the intrahepatic expression of the cell cycle regulators p53, p21, cyclin D1, c-Fos and CDK2 using Western blot analysis. In addition, the expression of PGC-1α and the cell proliferationrelated proteins PCNA and phosphate histone H3 was determined by Western blot and immunohistochemical staining analyses. Histone epigenetic modification of the PGC-1α promoter was investigated through chromatin immunoprecipitation (ChIP) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays. The impact of PGC-1α on liver regeneration after PH was further evaluated in PGC-1α-knockout mice.Results: A decreased expression of PGC-1α and liver regeneration-related genes in the fibrotic liver was detected after a PH. Histone acetylation at the PGC-1α promoter led to increases in PGC-1α expression and the survival rate in the fibrotic group after a PH. PGC-1α-mediated liver regeneration was further demonstrated in PGC-1α f/f albcre +/0 mice. Conclusion:Targeting PGC-1α may represent a strategy to improve the treatment of PH in patients with liver fibrosis.

The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However, current methods for universal adversarial attacks are based on additive perturbation, which cause misclassification when the perturbation is directly added to the input images. In this paper, for the first time, we show that a universal adversarial attack can also be achieved via non-additive perturbation (e.g., spatial transformation). More importantly, to unify both additive and non-additive perturbations, we propose a novel unified yet flexible framework for universal adversarial attacks, called GUAP, which is able to initiate attacks by additive perturbation, non-additive perturbation, or the combination of both. Extensive experiments are conducted on CIFAR-10 and ImageNet datasets with six deep neural network models including GoogleLeNet, VGG16/19, ResNet101/152, and DenseNet121. The empirical experiments demonstrate that GUAP can obtain up to 90.9% and 99.24% successful attack rates on CIFAR-10 and ImageNet datasets, leading to over 15% and 19% improvements respectively than current state-of-the-art universal adversarial attacks. The code for reproducing the experiments in this paper is available at https://github.com/TrustAI/GUAP.