OBJECTIVE: To identify the intervention mechanism of the effect of electroacupuncture on the expression of Ang/Tie-2 m RNA and protein in rats with acute cerebral infarction induced by middle cerebral artery occlusion...OBJECTIVE: To identify the intervention mechanism of the effect of electroacupuncture on the expression of Ang/Tie-2 m RNA and protein in rats with acute cerebral infarction induced by middle cerebral artery occlusion(MCAO).METHODS: Altogether 120 Wistar rats were subjected to MCAO by inserting a nylon filament, andthen divided into 3 groups: control group, injured group and electroacupuncture group. The injured and electroacupuncture groups were further divided into the following 7 subgroups according to the time after MCAO: 3, 6, 12, 24 h, 3, 7 and 12 d, with 8 rats in each subgroup. The electroacupuncture group was given electroacupuncture treatment at Shuigou(GV 26) instantly after operation. The rats were killed at different time points according to their groups, and then the expression levels of Ang/Tie-2 m RNA and protein were detected using Real-Time polymerase chain reaction and immunohistochemical staining.RESULTS: The m RNA and protein expression levels of Ang/Tie-2 in the electroacupuncture group were significant higher than that in the injured group.CONCLUSION: The results suggested that electroacupuncture could significantly regulate the expression of Ang/Tie-2 m RNA and protein in the rats with acute cerebral infarction induced by MCAO, and enhance angiogenesis after ischemic penumbra.展开更多
Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial ...Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compar展开更多
基金Supported by Tianjin Natural Science Foundation of Key Projects:Acupuncture Adjustment Th17/Treg Balance Against Nerve Inflammation Damage(No.16JCZDJC36200)And the National Natural Science Foundation of China:Acupuncture Regulation VD Brain Nerve Inflammation and Peripheral Immune Suppression of the Effect and Mechanism(No.81473766)+1 种基金Based on ACE/ACE2 axis and VEGF-Dll4/Notch Pathway to Study the Molecular Mechanism of Acupuncture Intervention in the Establishment of Collateral Circulation of Cerebral Infarction(No.81674056)Molecular Mechanism of Neurotoxicity Induced by Acupuncture in Rats with Cerebral Infarction based on Shh and Wnt Signaling Pathway(No.81473765)
文摘OBJECTIVE: To identify the intervention mechanism of the effect of electroacupuncture on the expression of Ang/Tie-2 m RNA and protein in rats with acute cerebral infarction induced by middle cerebral artery occlusion(MCAO).METHODS: Altogether 120 Wistar rats were subjected to MCAO by inserting a nylon filament, andthen divided into 3 groups: control group, injured group and electroacupuncture group. The injured and electroacupuncture groups were further divided into the following 7 subgroups according to the time after MCAO: 3, 6, 12, 24 h, 3, 7 and 12 d, with 8 rats in each subgroup. The electroacupuncture group was given electroacupuncture treatment at Shuigou(GV 26) instantly after operation. The rats were killed at different time points according to their groups, and then the expression levels of Ang/Tie-2 m RNA and protein were detected using Real-Time polymerase chain reaction and immunohistochemical staining.RESULTS: The m RNA and protein expression levels of Ang/Tie-2 in the electroacupuncture group were significant higher than that in the injured group.CONCLUSION: The results suggested that electroacupuncture could significantly regulate the expression of Ang/Tie-2 m RNA and protein in the rats with acute cerebral infarction induced by MCAO, and enhance angiogenesis after ischemic penumbra.
基金The completion of this research was made possible thanks to The Natural Sciences and Engineering Research Council of Canada(NSERC)and a start-up grant from Concordia University.
文摘Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compar
