Copper deficiency suppresses and copper addition enhances the expression of vascular endothelial growth factor (VEGF), which is primarily controlled by hypoxia‐inducible factor 1 (HIF‐1) transcription factor. The regulatory subunit of HIF‐1α senses oxygen availability and is also affected by various transition metals. The purpose of this study was to determine whether or not copper is required for activation of HIF‐1. Copper depletion by copper chelator tetraethylenepentamine (TEPA) decreased the hypoxia‐induced transactivity of HIF‐1, assessed by hypoxia responsive element‐dependent luciferase reporter assay, but not HIF‐1α protein level in HepG2 cells. Further examination showed that one of the copper chaperonesfor Cu,Zn‐SOD (CCS) is also involved in the HIF‐1 activation. CCS depletion by siRNA attenuated HIF‐1 transactivity along with decreases in VEGF expression under both normoxic and hypoxic conditions. HIF‐1α could be co‐immunoprecipitated by anti‐CCS antibody in the nuclear extract of HepG2 cells under hypoxic conditions, implying a direct interaction between HIF‐1α and CCS. These results suggested that copper is required for HIF‐1α transcriptional activation and the copper chaperone CCS is critically involved. Supported by NIH grants HL59225 and HL63760 and Kentucky Science and Engineering Foundation Grant 008‐888.

OBJECTIVE-Glycogen synthase kinase (GSK)-3␤ plays an important role in cardiomyopathies. Cardiac-specific metallothionein-overexpressing transgenic (MT-TG) mice were highly resistant to diabetes-induced cardiomyopathy. Therefore, we investigated whether metallothionein cardiac protection against diabetes is mediated by inactivation of GSK-3␤.RESEARCH DESIGN AND METHODS-Diabetes was induced with streptozotocin in both MT-TG and wild-type mice. Changes of energy metabolism-related molecules, lipid accumulation, inflammation, nitrosative damage, and fibrotic remodeling were examined in the hearts of diabetic mice 2 weeks, 2 months, and 5 months after the onset of diabetes with Western blotting, RT-PCR, and immunohistochemical assays.RESULTS-Activation (dephosphorylation) of GSK-3␤ was evidenced in the hearts of wild-type diabetic mice but not MT-TG diabetic mice. Correspondingly, cardiac glycogen synthase phosphorylation, hexokinase II, PPAR␣, and PGC-1␣ expression, which mediate glucose and lipid metabolisms, were significantly changed along with cardiac lipid accumulation, inflammation (TNF-␣, plasminogen activator inhibitor 1 [PAI-1], and intracellular adhesion molecule 1 [ICAM-1]), nitrosative damage (3-nitrotyrosin accumulation), and fibrosis in the wild-type diabetic mice. The above pathological changes were completely prevented either by cardiac metallothionein in the MT-TG diabetic mice or by inhibition of GSK-3␤ activity in the wild-type diabetic mice with a GSK-3␤-specific inhibitor.CONCLUSIONS-These results suggest that activation of GSK-3␤ plays a critical role in diabetes-related changes in cardiac energy metabolism, inflammation, nitrosative damage, and remodeling. Metallothionein inactivation of GSK-3␤ plays a critical role in preventing diabetic cardiomyopathy.

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Defective glycolysis and angiogenesis in the heart of diabetic patients and in experimental diabetic animal models have been reported. The aim of this study was to determine whether overexpression of hypoxia-inducible factor (HIF)-1␣ protects from myocardial injury in diabetic mice by increasing myocardial glycolysis and angiogenesis. Cardiac-specific HIF-1␣-overexpressing transgenic and age-matched wild-type control mice were treated with streptozotocin to induce diabetes. Changes in glucose transporters, glycolytic enzymes, angiogenic factors and cardiac morphology were examined in the hearts by real-time RT-PCR, Western blotting, enzymatic assay, and histological assays. HIF-1␣ overexpression elevated hexokinase II (HK-II) protein level and total HK activity in nondiabetic heart and prevented the decreases in HK-II mRNA, protein, and total HK activity in diabetic heart. In addition, the reduction of glucose transporter I, but not glucose transporter 4, was restored in HIF transgenic mouse heart along with a recovery of myocardium ATP production. HIF-1␣ overexpression also normalized diabetes-reduced vascular endothelial growth factor concentration along with a sustained myocardial capillary density and an inhibition of cardiomyocyte hypertrophy and cardiac fibrosis. Therefore, elevation of HIF-1␣ provides a cardiac protection from diabetic-induced impairment in glucose metabolism and angiogenesis via up-regulation of HIF-1 target genes.

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website 1 .

Metallothionein (MT) protects against heavy metal-induced cellular damage and may participate in other fundamental physiological and pathological processes, such as antioxidation, proliferation, and cell survival. Previously, we have shown that elevation of MT by transgene or by induction with zinc protects the heart against diabetic cardiomyopathy by mechanisms such as antidiabetes-induced oxidative stress and inactivation of glycogen synthase kinase-3, which mediates glucose metabolism. We also reported that MT overexpression rescued the diabetic-induced reduction of hypoxia-inducible factor (HIF)-1α, which plays an important role in glucose utilization and angiogenesis. Here, we showed that overexpression of MT increased hexokinase (HK)-II expression under control conditions and attenuated diabetes-decreased HK-II expression. Glycolytic flux assay demonstrated that MT increased glycolysis output in high glucose-containing media-cultured H9c2 cells. The diabetes-induced reduction in cardiac capillaries was also attenuated by MT overexpression. Furthermore, MT induction significantly increased HIF-1 expression under both control and diabetic conditions. Moreover, in the present study, we demonstrated that MT-enhanced HIF-1α activity is likely through a mechanism of protein nuclear translocation. These results suggest that MT induces HIF-1α expression, leading to increased HK-II in the diabetic heart.

Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.