A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a...A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.展开更多
A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adh...A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.展开更多
The study of machine learning has revealed that it can unleash new applications in a variety of disciplines.Many limitations limit their expressiveness,and researchers are working to overcome them to fully exploit the...The study of machine learning has revealed that it can unleash new applications in a variety of disciplines.Many limitations limit their expressiveness,and researchers are working to overcome them to fully exploit the power of data-driven machine learning(ML)and deep learning(DL)techniques.The data imbalance presents major hurdles for classification and prediction problems in machine learning,restricting data analytics and acquiring relevant insights in practically all real-world research domains.In visual learning,network information security,failure prediction,digital marketing,healthcare,and a variety of other domains,raw data suffers from a biased data distribution of one class over the other.This article aims to present a taxonomy of the approaches for handling imbalanced data problems and their comparative study on the classification metrics and their application areas.We have explored very recent trends of techniques employed for solutions to class imbalance problems in datasets and have also discussed their limitations.This article has also identified open challenges for further research in the direction of class data imbalance.展开更多
Hypoxic-ischemic injury is a common pathological dysfunction in clinical settings.Mitochondria are sensitive organelles that are readily damaged following ischemia and hypoxia.Dynamin-related protein 1(Drp1)regulates ...Hypoxic-ischemic injury is a common pathological dysfunction in clinical settings.Mitochondria are sensitive organelles that are readily damaged following ischemia and hypoxia.Dynamin-related protein 1(Drp1)regulates mitochondrial quality and cellular functions via its oligomeric changes and multiple modifications,which plays a role in mediating the induction of multiple organ damage during hypoxic-ischemic injury.However,there is active controversy and gaps in knowledge regarding the modification,protein interaction,and functions of Drp1,which both hinder and promote development of Drp1 as a novel therapeutic target.Here,we summarize recent findings on the oligomeric changes,modification types,and protein interactions of Drp1 in various hypoxic-ischemic diseases,as well as the Drp1-mediated regulation of mitochondrial quality and cell functions following ischemia and hypoxia.Additionally,potential clinical translation prospects for targeting Drp1 are discussed.This review provides new ideas and targets for proactive interventions on multiple organ damage induced by various hypoxic-ischemic diseases.展开更多
Increasing evidence indicates that mitochonarial lission imbalance plays an important role in derayed neuronal cell death. Our previous study round that photo biomodulation improved the motor function of rats with spi...Increasing evidence indicates that mitochonarial lission imbalance plays an important role in derayed neuronal cell death. Our previous study round that photo biomodulation improved the motor function of rats with spinal cord injury.However,the precise mechanism remains unclear.To investigate the effect of photo biomodulation on mitochondrial fission imbalance after spinal cord injury,in this study,we treated rat models of spinal co rd injury with 60-minute photo biomodulation(810 nm,150 mW) every day for 14 consecutive days.Transmission electron microscopy results confirmed the swollen and fragmented alte rations of mitochondrial morphology in neurons in acute(1 day) and subacute(7 and 14 days) phases.Photo biomodulation alleviated mitochondrial fission imbalance in spinal cord tissue in the subacute phase,reduced neuronal cell death,and improved rat posterior limb motor function in a time-dependent manner.These findings suggest that photobiomodulation targets neuronal mitochondria,alleviates mitochondrial fission imbalance-induced neuronal apoptosis,and thereby promotes the motor function recovery of rats with spinal cord injury.展开更多
Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.展开更多
Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Com...Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.展开更多
Denervation-induced skeletal muscle atrophy can potentially cause the decline in the quality of life of patients and an increased risk of mortality.Complex pathophysiological mechanisms with dynamic alterations have b...Denervation-induced skeletal muscle atrophy can potentially cause the decline in the quality of life of patients and an increased risk of mortality.Complex pathophysiological mechanisms with dynamic alterations have been documented in skeletal muscle atrophy resulting from innervation loss.Hence,an in-depth comprehension of the key mechanisms and molecules governing skeletal muscle atrophy at varying stages,along with targeted treatment and protection,becomes essential for effective atrophy management.Our preliminary research categorizes the skeletal muscle atrophy process into four stages using microarray analysis.This review extensively discusses the pathways and molecules potentially implicated in regulating the four stages of denervation and muscle atrophy.Notably,drugs targeting the reactivare oxygen species stage and the inflammation stage assume critical roles.Timely intervention during the initial atrophy stages can expedite protection against skeletal muscle atrophy.Additionally,pharmaceutical intervention in the ubiquitin-proteasome pathway associated with atrophy and autophagy lysosomes can effectively slow down skeletal muscle atrophy.Key molecules within this stage encompass MuRF1,MAFbx,LC3II,p62/SQSTM1,etc.This review also compiles a profile of drugs with protective effects against skeletal muscle atrophy at distinct postdenervation stages,thereby augmenting the evidence base for denervation-induced skeletal muscle atrophy treatment.展开更多
BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influ...BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influence and respond to a variety of behavioral and mental conditions.AIM To explore the correlation between cognitive impairment and gut microbiota imbalance in patients with schizophrenia.METHODS A total of 498 untreated patients with schizophrenia admitted to our hospital from July 2020 to July 2022 were selected as the case group,while 498 healthy volunteers who underwent physical examinations at our hospital during the same period were selected as a control group.Fluorescence in situ hybridization was employed to determine the total number of bacteria in the feces of the two groups.The cognitive function test package was used to assess the score of cognitive function in each dimension.Then,the relationship between gut microbiota and cognitive function was analyzed.RESULTS There were statistically significant differences in the relative abundance of gut microbiota at both phylum and class levels between the case group and the control group.In addition,the scores of cognitive function,such as attention/alertness and learning ability,were significantly lower in the case group than in the control group(all P<0.05).The cognitive function was positively correlated with Actinomycetota,Bacteroidota,Euryarchaeota,Fusobacteria,Pseudomonadota,and Saccharibacteria,while negatively correlated with Bacillota,Tenericutes,and Verrucomicrobia at the phylum level.While at the class level,the cognitive function was positively correlated with Class Actinobacteria,Bacteroidia,Betaproteobacteria,Proteobacteria,Blastomycetes,and Gammaproteobacteria,while negatively correlated with Bacilli,Clostridia,Coriobacteriia,and Verrucomicrobiae.CONCLUSION There is a relationship between the metabolic results of gut microbiota and cognitive function in patients with schizophrenia.When imbalances occur in the gut microbiota of patients,it leads to more severe cognitive impairment.展开更多
文摘A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.
基金the High-Performance Computing Platform of Beijing University of Chemical Technology(BUCT)for supporting this papersupported by the Fundamental Research Funds for the Central Universities(JD2319)+2 种基金the CNOOC Technical Cooperation Project(ZX2022ZCTYF7612)the National Natural Science Foundation of China(51775029,52004014)the Chinese Universities Scientific Fund(XK2020-04)。
文摘A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.
文摘The study of machine learning has revealed that it can unleash new applications in a variety of disciplines.Many limitations limit their expressiveness,and researchers are working to overcome them to fully exploit the power of data-driven machine learning(ML)and deep learning(DL)techniques.The data imbalance presents major hurdles for classification and prediction problems in machine learning,restricting data analytics and acquiring relevant insights in practically all real-world research domains.In visual learning,network information security,failure prediction,digital marketing,healthcare,and a variety of other domains,raw data suffers from a biased data distribution of one class over the other.This article aims to present a taxonomy of the approaches for handling imbalanced data problems and their comparative study on the classification metrics and their application areas.We have explored very recent trends of techniques employed for solutions to class imbalance problems in datasets and have also discussed their limitations.This article has also identified open challenges for further research in the direction of class data imbalance.
基金This work was supported by the National Natural Science Foundation of China(82272252,82270378)the Senior Medical Talents Program of Chongqing for Young and Middle-agedthe Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University.
文摘Hypoxic-ischemic injury is a common pathological dysfunction in clinical settings.Mitochondria are sensitive organelles that are readily damaged following ischemia and hypoxia.Dynamin-related protein 1(Drp1)regulates mitochondrial quality and cellular functions via its oligomeric changes and multiple modifications,which plays a role in mediating the induction of multiple organ damage during hypoxic-ischemic injury.However,there is active controversy and gaps in knowledge regarding the modification,protein interaction,and functions of Drp1,which both hinder and promote development of Drp1 as a novel therapeutic target.Here,we summarize recent findings on the oligomeric changes,modification types,and protein interactions of Drp1 in various hypoxic-ischemic diseases,as well as the Drp1-mediated regulation of mitochondrial quality and cell functions following ischemia and hypoxia.Additionally,potential clinical translation prospects for targeting Drp1 are discussed.This review provides new ideas and targets for proactive interventions on multiple organ damage induced by various hypoxic-ischemic diseases.
基金supported by the National Natural Science Foundation of China,Nos.81070996 (to ZW) and 815 72151 (to XYH)Shaanxi Provincial Key R&D Program,Nos.2020ZDLSF02-05 (to ZW),2021ZDLSF02-10 (to XYH)。
文摘Increasing evidence indicates that mitochonarial lission imbalance plays an important role in derayed neuronal cell death. Our previous study round that photo biomodulation improved the motor function of rats with spinal cord injury.However,the precise mechanism remains unclear.To investigate the effect of photo biomodulation on mitochondrial fission imbalance after spinal cord injury,in this study,we treated rat models of spinal co rd injury with 60-minute photo biomodulation(810 nm,150 mW) every day for 14 consecutive days.Transmission electron microscopy results confirmed the swollen and fragmented alte rations of mitochondrial morphology in neurons in acute(1 day) and subacute(7 and 14 days) phases.Photo biomodulation alleviated mitochondrial fission imbalance in spinal cord tissue in the subacute phase,reduced neuronal cell death,and improved rat posterior limb motor function in a time-dependent manner.These findings suggest that photobiomodulation targets neuronal mitochondria,alleviates mitochondrial fission imbalance-induced neuronal apoptosis,and thereby promotes the motor function recovery of rats with spinal cord injury.
基金The authors would like to extend their gratitude to Universiti Teknologi PETRONAS (Malaysia)for funding this research through grant number (015LA0-037).
文摘Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
文摘Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.
基金supported by the National Natural Science Foundation of China(Grant No.32200940)Science and Technology Bureau of Nantong(Grant Nos.JC2020101,JC2021085)Municipal Health Commission of Nantong(Grant No.MA2020019).
文摘Denervation-induced skeletal muscle atrophy can potentially cause the decline in the quality of life of patients and an increased risk of mortality.Complex pathophysiological mechanisms with dynamic alterations have been documented in skeletal muscle atrophy resulting from innervation loss.Hence,an in-depth comprehension of the key mechanisms and molecules governing skeletal muscle atrophy at varying stages,along with targeted treatment and protection,becomes essential for effective atrophy management.Our preliminary research categorizes the skeletal muscle atrophy process into four stages using microarray analysis.This review extensively discusses the pathways and molecules potentially implicated in regulating the four stages of denervation and muscle atrophy.Notably,drugs targeting the reactivare oxygen species stage and the inflammation stage assume critical roles.Timely intervention during the initial atrophy stages can expedite protection against skeletal muscle atrophy.Additionally,pharmaceutical intervention in the ubiquitin-proteasome pathway associated with atrophy and autophagy lysosomes can effectively slow down skeletal muscle atrophy.Key molecules within this stage encompass MuRF1,MAFbx,LC3II,p62/SQSTM1,etc.This review also compiles a profile of drugs with protective effects against skeletal muscle atrophy at distinct postdenervation stages,thereby augmenting the evidence base for denervation-induced skeletal muscle atrophy treatment.
文摘BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influence and respond to a variety of behavioral and mental conditions.AIM To explore the correlation between cognitive impairment and gut microbiota imbalance in patients with schizophrenia.METHODS A total of 498 untreated patients with schizophrenia admitted to our hospital from July 2020 to July 2022 were selected as the case group,while 498 healthy volunteers who underwent physical examinations at our hospital during the same period were selected as a control group.Fluorescence in situ hybridization was employed to determine the total number of bacteria in the feces of the two groups.The cognitive function test package was used to assess the score of cognitive function in each dimension.Then,the relationship between gut microbiota and cognitive function was analyzed.RESULTS There were statistically significant differences in the relative abundance of gut microbiota at both phylum and class levels between the case group and the control group.In addition,the scores of cognitive function,such as attention/alertness and learning ability,were significantly lower in the case group than in the control group(all P<0.05).The cognitive function was positively correlated with Actinomycetota,Bacteroidota,Euryarchaeota,Fusobacteria,Pseudomonadota,and Saccharibacteria,while negatively correlated with Bacillota,Tenericutes,and Verrucomicrobia at the phylum level.While at the class level,the cognitive function was positively correlated with Class Actinobacteria,Bacteroidia,Betaproteobacteria,Proteobacteria,Blastomycetes,and Gammaproteobacteria,while negatively correlated with Bacilli,Clostridia,Coriobacteriia,and Verrucomicrobiae.CONCLUSION There is a relationship between the metabolic results of gut microbiota and cognitive function in patients with schizophrenia.When imbalances occur in the gut microbiota of patients,it leads to more severe cognitive impairment.
