Objective:To investigate the effects of chronic unpredictable mild stress(CUMS)on perineuronal nets(PNNs)andγ-aminobutyric acid(GABA)-ergic neurons in the medial prefrontal cortex(mPFC)of adult rats.Methods:28 rats w...Objective:To investigate the effects of chronic unpredictable mild stress(CUMS)on perineuronal nets(PNNs)andγ-aminobutyric acid(GABA)-ergic neurons in the medial prefrontal cortex(mPFC)of adult rats.Methods:28 rats were randomly divided into two groups.The model group adopted CUMS to establish a depression model,and the control group did not give any treatment.The density of PNNs and the percentage of PNNs positive(PNNs^(+))and parvalbumin positive(PV^(+))neurons in total PV^(+)neurons in the mPFC were detected by immunofluorescence.The protein expression of main components of PNNs,Aggrecan and Brevican,and GABA main synthase glutamic acid decarboxylase 67(GAD67)in the mPFC were detected by Western blot.Results:The density of PNNs and the percentage of PNNs^(+)and PV^(+)neurons in total PV^(+)neurons in the mPFC in the model group were decreased compared with the control group(P<0.05);The expression levels of PNNs component proteins Aggrecan and Brevican,and GABA main synthase GAD67 were also decreased in the model group(P<0.01).Conclusion:The levels of PNNs and GABA synthase GAD67 in the mPFC of rats were decreased after chronic unpredictable mild stress.展开更多
The perineuronal nets(PNNs)are a kind of extracellular matrix structure which mainly surround parvalbumin-positive(PV^(+))GABAergic interneurons.The aim of this study is to investigate the role of PNNs in the medial p...The perineuronal nets(PNNs)are a kind of extracellular matrix structure which mainly surround parvalbumin-positive(PV^(+))GABAergic interneurons.The aim of this study is to investigate the role of PNNs in the medial prefrontal cortex(mPFC)in the antidepressant effect of electroacupuncture treatment.Adult rats underwent chronic unpredictable mild stress(CUMS)for 28 days,and electroacupuncture treatment of Baihui and Yintang acupoints was administrated from day 8 to day 28.Then we detected the depressive-and anxiety like behavior of rats,the density of PNNs,the percentage of PV^(+)neurons that surround PNNs,the expression levels of PNNs constituting proteins in the mPFC.展开更多
One of the recent advancements in the electrical power systems is the smart-grid technology.For the effective functioning of the smart grid,the process like monitoring and controlling have to be given importance.In th...One of the recent advancements in the electrical power systems is the smart-grid technology.For the effective functioning of the smart grid,the process like monitoring and controlling have to be given importance.In this paper,the Wireless Sensor Network(WSN)is utilized for tracking the power in smart grid applications.The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data.The data is transmitted quickly by using the Probabilistic Neural Network(PNN),which aids in identifying the shortest path of the nodes.While transmitting the data from the smart grid to the(Internet of Things)IoT web page,it is secured by introducing the secret keys between the neighbouring nodes through the process of key-management.In this method,the combination of Lagrange’s theorem and the Location Based Key(LBK)management is used for better security performance.This approach deli-vers optimal performance in terms of security,throughput,packet loss and delay,which are comparatively better than the existing methods.展开更多
Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images ...Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.展开更多
The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life.The skin cancer is considered as one of the dangerous diseases among other types of cancer since it c...The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life.The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment.Therefore,an effective image processing approach is employed in this present study for the accurate detection of skin cancer.Initially,the dermoscopy images of skin lesions are retrieved and processed by eliminating the noises with the assistance of Gaborfilter.Then,the pre-processed dermoscopy image is segmented into multiple regions by implementing cascaded Fuzzy C-Means(FCM)algorithm,which involves in improving the reliability of cancer detection.The A Gabor Response Co-occurrence Matrix(GRCM)is used to extract melanoma parameters in an effi-cient manner.A hybrid Particle Swarm Optimization(PSO)-Whale Optimization is then utilized for efficiently optimizing the extracted features.Finally,the fea-tures are significantly classified with the assistance of Probabilistic Neural Net-work(PNN)classifier for classifying the stages of skin lesion in an optimal manner.The whole work is stimulated in MATLAB and the attained outcomes have proved that the introduced approach delivers optimal results with maximal accuracy of 97.83%.展开更多
The applications of wireless sensor network(WSN)exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field,the emergence of WSN has created marvelous chang...The applications of wireless sensor network(WSN)exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field,the emergence of WSN has created marvelous changes in monitoring the health conditions of the patients and so it is attracted by doctors and physicians.WSN assists in providing health care services without any delay and so it plays predominant role in saving the life of human.The data of different persons,time,places and networks have been linked with certain devices,which are collectively known as Internet of Things(IOT);it is regarded as the essential requirement of people in recent days.In the health care monitoring system,IOT plays a magnificent role,which has produced the real time monitoring of patient’s condition.However the medical data transmission is accomplished quickly with high security by the routing and key management.When the data from the digital record system(cloud)is accessed by the patients or doctors,the medical data is transferred quickly through WSN by performing routing.The Probabilistic Neural Network(PNN)is utilized,which authenticates the shortest path to reach the destination and its performance is identified by comparing it with the Dynamic Source Routing(DSR)protocol and Energy aware and Stable Routing(ESR)protocol.While performing routing,the secured transmission is achieved by key management,for which the Diffie Hellman key exchange is utilized,which performs encryption and decryption to secure the medical data.This enables the quick and secured transmission of data from source to destination with improved throughput and delivery ratio.展开更多
基金National Natural Science Foundation of China(No.81803857)Autonomous Subject of Beijing University of Chinese Medicine(No.2018-JYBZZXJSJJ002)。
文摘Objective:To investigate the effects of chronic unpredictable mild stress(CUMS)on perineuronal nets(PNNs)andγ-aminobutyric acid(GABA)-ergic neurons in the medial prefrontal cortex(mPFC)of adult rats.Methods:28 rats were randomly divided into two groups.The model group adopted CUMS to establish a depression model,and the control group did not give any treatment.The density of PNNs and the percentage of PNNs positive(PNNs^(+))and parvalbumin positive(PV^(+))neurons in total PV^(+)neurons in the mPFC were detected by immunofluorescence.The protein expression of main components of PNNs,Aggrecan and Brevican,and GABA main synthase glutamic acid decarboxylase 67(GAD67)in the mPFC were detected by Western blot.Results:The density of PNNs and the percentage of PNNs^(+)and PV^(+)neurons in total PV^(+)neurons in the mPFC in the model group were decreased compared with the control group(P<0.05);The expression levels of PNNs component proteins Aggrecan and Brevican,and GABA main synthase GAD67 were also decreased in the model group(P<0.01).Conclusion:The levels of PNNs and GABA synthase GAD67 in the mPFC of rats were decreased after chronic unpredictable mild stress.
文摘The perineuronal nets(PNNs)are a kind of extracellular matrix structure which mainly surround parvalbumin-positive(PV^(+))GABAergic interneurons.The aim of this study is to investigate the role of PNNs in the medial prefrontal cortex(mPFC)in the antidepressant effect of electroacupuncture treatment.Adult rats underwent chronic unpredictable mild stress(CUMS)for 28 days,and electroacupuncture treatment of Baihui and Yintang acupoints was administrated from day 8 to day 28.Then we detected the depressive-and anxiety like behavior of rats,the density of PNNs,the percentage of PV^(+)neurons that surround PNNs,the expression levels of PNNs constituting proteins in the mPFC.
文摘One of the recent advancements in the electrical power systems is the smart-grid technology.For the effective functioning of the smart grid,the process like monitoring and controlling have to be given importance.In this paper,the Wireless Sensor Network(WSN)is utilized for tracking the power in smart grid applications.The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data.The data is transmitted quickly by using the Probabilistic Neural Network(PNN),which aids in identifying the shortest path of the nodes.While transmitting the data from the smart grid to the(Internet of Things)IoT web page,it is secured by introducing the secret keys between the neighbouring nodes through the process of key-management.In this method,the combination of Lagrange’s theorem and the Location Based Key(LBK)management is used for better security performance.This approach deli-vers optimal performance in terms of security,throughput,packet loss and delay,which are comparatively better than the existing methods.
文摘Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.
文摘The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life.The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment.Therefore,an effective image processing approach is employed in this present study for the accurate detection of skin cancer.Initially,the dermoscopy images of skin lesions are retrieved and processed by eliminating the noises with the assistance of Gaborfilter.Then,the pre-processed dermoscopy image is segmented into multiple regions by implementing cascaded Fuzzy C-Means(FCM)algorithm,which involves in improving the reliability of cancer detection.The A Gabor Response Co-occurrence Matrix(GRCM)is used to extract melanoma parameters in an effi-cient manner.A hybrid Particle Swarm Optimization(PSO)-Whale Optimization is then utilized for efficiently optimizing the extracted features.Finally,the fea-tures are significantly classified with the assistance of Probabilistic Neural Net-work(PNN)classifier for classifying the stages of skin lesion in an optimal manner.The whole work is stimulated in MATLAB and the attained outcomes have proved that the introduced approach delivers optimal results with maximal accuracy of 97.83%.
文摘The applications of wireless sensor network(WSN)exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field,the emergence of WSN has created marvelous changes in monitoring the health conditions of the patients and so it is attracted by doctors and physicians.WSN assists in providing health care services without any delay and so it plays predominant role in saving the life of human.The data of different persons,time,places and networks have been linked with certain devices,which are collectively known as Internet of Things(IOT);it is regarded as the essential requirement of people in recent days.In the health care monitoring system,IOT plays a magnificent role,which has produced the real time monitoring of patient’s condition.However the medical data transmission is accomplished quickly with high security by the routing and key management.When the data from the digital record system(cloud)is accessed by the patients or doctors,the medical data is transferred quickly through WSN by performing routing.The Probabilistic Neural Network(PNN)is utilized,which authenticates the shortest path to reach the destination and its performance is identified by comparing it with the Dynamic Source Routing(DSR)protocol and Energy aware and Stable Routing(ESR)protocol.While performing routing,the secured transmission is achieved by key management,for which the Diffie Hellman key exchange is utilized,which performs encryption and decryption to secure the medical data.This enables the quick and secured transmission of data from source to destination with improved throughput and delivery ratio.
