Objective To explore whether the protein Deglycase protein 1(DJ1)can ameliorate Alzheimer’s disease(AD)-like pathology in Amyloid Precursor Protein/Presenilin 1(APP/PS1)double transgenic mice and its possible mechani...Objective To explore whether the protein Deglycase protein 1(DJ1)can ameliorate Alzheimer’s disease(AD)-like pathology in Amyloid Precursor Protein/Presenilin 1(APP/PS1)double transgenic mice and its possible mechanism to provide a theoretical basis for exploring the pathogenesis of AD.Methods Adeno-associated viral vectors(AAV)of DJ1-overexpression or DJ1-knockdown were injected into the hippocampus of 7-month-old APP/PS1 mice to construct models of overexpression or knockdown.Mice were divided into the AD model control group(MC),AAV vector control group(NC),DJ1-overexpression group(DJ1+),and DJ1-knockdown group(DJ1-).After 21 days,the Morris water maze test,immunohistochemistry,immunofluorescence,and western blotting were used to evaluate the effects of DJ1 on mice.Results DJ1+overexpression decreased the latency and increased the number of platform traversals in the water maze test.DJ1-cells were cured and atrophied,and the intercellular structure was relaxed;the number of age spots and the expression of AD-related proteins were significantly increased.DJ1+increased the protein expression of Nuclear factor erythroid 2-related factor 2(NRF2),heme oxygenase-1(HO-1),light chain 3(LC3),phosphorylated AMPK(p-AMPK),and B cell lymphoma-2(BCL-2),as well as the antioxidant levels of total superoxide dismutase(T-SOD),total antioxidant capacity(T-AOC),and Glutathione peroxidase(GSH-PX),while decreasing the levels of Kelch-like hydrates-associated protein 1(Keap1),mammalian target of rapamycin(mTOR),p62/sequestosome1(p62/SQSTM1),Caspase3,and malondialdehyde(MDA).Conclusion DJ1-overexpression can ameliorate learning,memory,and AD-like pathology in APP/PS1 mice,which may be related to the activation of the NRF2/HO-1 and AMPK/mTOR pathways by DJ1.展开更多
Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms...Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 81872626 and 82003454]Chinese Nutrition Society-Bright Moon Seaweed Group Nutrition and Health Research Fund[grant number CNS-BMSG2020A63]Key R&D and promotion projects in Henan Province[grant number 212102310219 and 212102310110]。
文摘Objective To explore whether the protein Deglycase protein 1(DJ1)can ameliorate Alzheimer’s disease(AD)-like pathology in Amyloid Precursor Protein/Presenilin 1(APP/PS1)double transgenic mice and its possible mechanism to provide a theoretical basis for exploring the pathogenesis of AD.Methods Adeno-associated viral vectors(AAV)of DJ1-overexpression or DJ1-knockdown were injected into the hippocampus of 7-month-old APP/PS1 mice to construct models of overexpression or knockdown.Mice were divided into the AD model control group(MC),AAV vector control group(NC),DJ1-overexpression group(DJ1+),and DJ1-knockdown group(DJ1-).After 21 days,the Morris water maze test,immunohistochemistry,immunofluorescence,and western blotting were used to evaluate the effects of DJ1 on mice.Results DJ1+overexpression decreased the latency and increased the number of platform traversals in the water maze test.DJ1-cells were cured and atrophied,and the intercellular structure was relaxed;the number of age spots and the expression of AD-related proteins were significantly increased.DJ1+increased the protein expression of Nuclear factor erythroid 2-related factor 2(NRF2),heme oxygenase-1(HO-1),light chain 3(LC3),phosphorylated AMPK(p-AMPK),and B cell lymphoma-2(BCL-2),as well as the antioxidant levels of total superoxide dismutase(T-SOD),total antioxidant capacity(T-AOC),and Glutathione peroxidase(GSH-PX),while decreasing the levels of Kelch-like hydrates-associated protein 1(Keap1),mammalian target of rapamycin(mTOR),p62/sequestosome1(p62/SQSTM1),Caspase3,and malondialdehyde(MDA).Conclusion DJ1-overexpression can ameliorate learning,memory,and AD-like pathology in APP/PS1 mice,which may be related to the activation of the NRF2/HO-1 and AMPK/mTOR pathways by DJ1.
基金Canada Research Chair(950231363,XZ),Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants(RGPIN-20203530,LX)the Social Sciences and Humanities Research Council of Canada(SSHRC)Insight Development Grants(430-2018-00557,KX).
文摘Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.
