This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictabi...This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictability of water quality thus plays a crucial role in managing our ecosystems to make informed decisions and,hence,proper environmental management.This study addresses these challenges by proposing an effective machine learning methodology applied to the“Water Quality”public dataset.The methodology has modeled the dataset suitable for providing prediction classification analysis with high values of the evaluating parameters such as accuracy,sensitivity,and specificity.The proposed methodology is based on two novel approaches:(a)the SMOTE method to deal with unbalanced data and(b)the skillfully involved classical machine learning models.This paper uses Random Forests,Decision Trees,XGBoost,and Support Vector Machines because they can handle large datasets,train models for handling skewed datasets,and provide high accuracy in water quality classification.A key contribution of this work is the use of custom sampling strategies within the SMOTE approach,which significantly enhanced performance metrics and improved class imbalance handling.The results demonstrate significant improvements in predictive performance,achieving the highest reported metrics:accuracy(98.92%vs.96.06%),sensitivity(98.3%vs.71.26%),and F1 score(98.37%vs.79.74%)using the XGBoost model.These improvements underscore the effectiveness of our custom SMOTE sampling strategies in addressing class imbalance.The findings contribute to environmental management by enabling ecology specialists to develop more accurate strategies for monitoring,assessing,and managing drinking water quality,ensuring better ecosystem and public health outcomes.展开更多
Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing imbalance.Addressing the challenge of achieving SDG 2 in the face o...Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing imbalance.Addressing the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland changes.Accurately quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG 2.In this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of development.Our results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/yr.However,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same period.In the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing countries.Developing countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in 2022.Consequently,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.展开更多
This study investigates the distribution and imbalances of research funding in the field of Environmental Chemistry,utilizing application and funding data fromthe National Natural Science Foundation of China(NSFC)over...This study investigates the distribution and imbalances of research funding in the field of Environmental Chemistry,utilizing application and funding data fromthe National Natural Science Foundation of China(NSFC)over the past decade.The findings reveal significant regional disparities,with Eastern regions receiving over 70%of the national funding,while the Northeast accounts for only 4%to 6.5%.Additionally,the analysis shows notable differences in funding allocation among various research institutions,with a substantial portion of funds concentrated in a few leading institutions,leading to inequities across different types and levels of organizations.The impact of applicant gender on funding disparities is relatively minor;although female applicants have a slightly lower funding rate,the concentration of funds is marginally higher among females.Furthermore,the study highlights that key projects and talent-oriented initiatives,due to their significant funding concentration,exacerbate the existing imbalances.Overall,this research provides valuable insights for optimizing funding policies and advocates for a more equitable distribution of resources in Environmental Chemistry research,addressing the identified disparities.展开更多
This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the traini...This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.展开更多
BACKGROUND:Electrolyte imbalance is common following traumatic brain injury(TBI)and can significantly impact patient outcomes.We aimed to explore the occurrence,patterns,and consequences of electrolyte imbalance in ad...BACKGROUND:Electrolyte imbalance is common following traumatic brain injury(TBI)and can significantly impact patient outcomes.We aimed to explore the occurrence,patterns,and consequences of electrolyte imbalance in adult patients with TBI.METHODS:A retrospective study was conducted from 2016 to 2021 at a level 1 trauma center among hospitalized TBI patients.On admission,the levels of serum electrolytes,including sodium,potassium,calcium,magnesium,and phosphate,were analyzed.Demographics,injury characteristics,and interventions were assessed.The primary outcome was the in-hospital mortality.Multivariate logistic regression analysis was performed to identify independent predictors of mortality in TBI patients.RESULTS:A total of 922 TBI patients were included in the analysis,of whom 902(98%)had electrolyte imbalance.The mean age of patients with electrolyte imbalance was 32.0±15.0 years.Most patients were males(94%).The most common electrolyte abnormalities were hypocalcemia,hypophosphatemia,and hypokalemia.The overall in-hospital mortality rate was 22%in the entire cohort.In multivariate logistic analysis,the predictors of mortality included age(odds ratio[OR]=1.029,95%confidence intervals[CI]:1.013-1.046,P<0.001),low GCS(OR=0.883,95%CI:0.816-0.956,P=0.002),high Injury Severity Score(ISS)scale(OR=1.051,95%CI:1.026-1.078,P<0.001),hypernatremia(OR=2.175,95%CI:1.196-3.955,P=0.011),hyperkalemia(OR=4.862,95%CI:1.222-19.347;P=0.025),low serum bicarbonate levels(OR=0.926,95%CI:0.868-0.988,P=0.020),high serum lactate levels(OR=1.128,95%CI:1.022-1.244,P=0.017),high glucose levels(OR=1.072,95%CI:1.014-1.133,P=0.015),a longer activated partial thromboplastin time(OR=1.054,95%CI:1.024-1.084,P<0.001)and higer international normalized ratio(INR)(OR=3.825,95%CI:1.592-9.188,P=0.003).CONCLUSION:Electrolyte imbalance is common in TBI patients,with the significant prevalence of hypocalcemia,hypophosphatemia,and hypokalemia.However,hypernatremia and hyperkalemia were associated with the risk of mortality,emphasizing the need for further research to comprehend electrolyte dynamics in TBI patients.展开更多
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
Although various anti-inflammatory medications,such as ephedrine,are employed to manage cough-variant asthma,their underlying mechanisms are yet to be fully understood.Recent studies suggest that exosomes derived from...Although various anti-inflammatory medications,such as ephedrine,are employed to manage cough-variant asthma,their underlying mechanisms are yet to be fully understood.Recent studies suggest that exosomes derived from airway epithelial cells(AECs)contain components like messenger RNAs(mRNAs),micro-RNAs(miRNAs),and long noncoding RNA(lncRNA),which play roles in the occurrence and progression of airway inflammation.This study investigates the influence of AEC-derived exosomes on the efficacy of ephedrine in treating cough-variant asthma.We established a mouse model of asthma and measured airway resist-ance and serum inflammatory cell levels.Real-time polymerase chain reaction(RT-qPCR),Western blotting,and enzyme-linked im-munosorbent assay(ELISA)analyses were used to assess gene and protein expression levels.Exosomes were isolated and character-ized.RNA immunoprecipitation(RIP)and RNA pull-down assays were conducted to examine the interaction between hnRNPA2B1 and lnc-TRPM2-AS1.In the ovalbumin(OVA)-challenged mouse model,ephedrine treatment reduced inflammatory responses,air-way resistance,and Th1/Th2 cell imbalance.Exosomes from OVA-treated AECs showed elevated levels of lnc-TRPM2-AS1,which were diminished following ephedrine treatment.The exosomal lnc-TRPM2-AS1 mediated the Th1/Th2 imbalance in CD4^(+)T cells,with its packaging into exosomes being facilitated by hnRNPA2B1.This study unveils a novel mechanism by which ephedrine ameli-orates OVA-induced CD4^(+)T cell imbalance by suppressing AEC-derived exosomal lnc-TRPM2-AS1.These findings could provide a theoretical framework for using ephedrine in asthma treatment.展开更多
Rheumatoid arthritis(RA)is a chronic autoimmune disorder marked by persistent synovial inflammation and joint degradation,posing challenges in the development of effective treatments.Nuciferine,an alkaloid found in lo...Rheumatoid arthritis(RA)is a chronic autoimmune disorder marked by persistent synovial inflammation and joint degradation,posing challenges in the development of effective treatments.Nuciferine,an alkaloid found in lotus leaf,has shown promising anti-inflammatory and anti-tumor effects,yet its efficacy in RA treatment remains unexplored.This study investigated the antiproliferative effects of nuciferine on the MH7A cell line,a human RA-derived fibroblast-like synoviocyte,revealing its ability to inhibit cell proliferation,promote apoptosis,induce apoptosis,and cause G1/S phase arrest.Additionally,nuciferine significantly reduced the migration and invasion capabilities of MH7A cells.The therapeutic potential of nuciferine was further evaluated in a collagen-induced arthritis(CIA)rat model,where it markedly alleviated joint swelling,synovial hyperplasia,cartilage injury,and inflammatory infiltration.Nuciferine also improved collagen-induced bone erosion,decreased pro-inflammatory cytokines and serum immunoglobulins(IgG,IgG1,IgG2a),and restored the balance between T helper(Th)17 and regulatory T cells in the spleen of CIA rats.These results indicate that nuciferine may offer therapeutic advantages for RA by decreasing the proliferation and invasiveness of FLS cells and correcting the Th17/Treg cell imbalance in CIA rats.展开更多
Cadmium(Cd)and excess molybdenum(Mo)pose serious threats to animal health.Our previous study has determined that Cd and/or Mo exposure can cause ovarian damage of ducks,while the specific mechanism is still obscure.To...Cadmium(Cd)and excess molybdenum(Mo)pose serious threats to animal health.Our previous study has determined that Cd and/or Mo exposure can cause ovarian damage of ducks,while the specific mechanism is still obscure.To further investigate the toxic mechanism of Cd and Mo co-exposure in the ovary,forty 8-day-old female ducks were randomly allocated into four groups for 16 weeks,and the doses of Cd and Mo in basic diet per kg were as follows:control group,Mo group(100 mg Mo),Cd group(4 mg Cd),and Mo+Cd group(100 mg Mo+4 mg Cd).Cadmium sulfate 8/3-hydrate(CdSO_(4)·8/3H_(2)O)and hexaammonium molybdate((NH_(4))_(6)Mo_(7)O_(24)·4H_(2)O)were the origins of Cd and Mo,respectively.At the 16th week of the experiment,all ovary tissues were collected for the detection of related indexes.The data indicated that Mo and/or Cd induced trace element disorders and Th1/Th2 balance to divert toward Th1 in the ovary,which activated endoplasmic reticulum(ER)stress and then provoked necroptosis through triggering RIPK1/RIPK3/MLKL signaling pathway,and eventually caused ovarian pathological injuries and necroptosis characteristics.The alterations of above indicators were most apparent in the joint group.Above all,this research illustrates that Mo and/or Cd exposure can initiate necroptosis through Th1/Th2 imbalance-modulated ER stress in duck ovaries,and Mo and Cd combined exposure aggravates ovarian injuries.This research explores the molecular mechanism of necroptosis caused by Mo and/or Cd,which reveals that ER stress attenuation may be a therapeutic target to alleviate necroptosis.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
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
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
文摘This study demonstrates the complexity and importance of water quality as a measure of the health and sustainability of ecosystems that directly influence biodiversity,human health,and the world economy.The predictability of water quality thus plays a crucial role in managing our ecosystems to make informed decisions and,hence,proper environmental management.This study addresses these challenges by proposing an effective machine learning methodology applied to the“Water Quality”public dataset.The methodology has modeled the dataset suitable for providing prediction classification analysis with high values of the evaluating parameters such as accuracy,sensitivity,and specificity.The proposed methodology is based on two novel approaches:(a)the SMOTE method to deal with unbalanced data and(b)the skillfully involved classical machine learning models.This paper uses Random Forests,Decision Trees,XGBoost,and Support Vector Machines because they can handle large datasets,train models for handling skewed datasets,and provide high accuracy in water quality classification.A key contribution of this work is the use of custom sampling strategies within the SMOTE approach,which significantly enhanced performance metrics and improved class imbalance handling.The results demonstrate significant improvements in predictive performance,achieving the highest reported metrics:accuracy(98.92%vs.96.06%),sensitivity(98.3%vs.71.26%),and F1 score(98.37%vs.79.74%)using the XGBoost model.These improvements underscore the effectiveness of our custom SMOTE sampling strategies in addressing class imbalance.The findings contribute to environmental management by enabling ecology specialists to develop more accurate strategies for monitoring,assessing,and managing drinking water quality,ensuring better ecosystem and public health outcomes.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3907403)the National Natural Science Foundation of China(Grant No.42201499)the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022ORP03).
文摘Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing imbalance.Addressing the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland changes.Accurately quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG 2.In this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of development.Our results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/yr.However,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same period.In the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing countries.Developing countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in 2022.Consequently,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.
基金supported by the Major Project of Philosophy and Social Science Research of Jiangsu(No.2019SJZDA073).
文摘This study investigates the distribution and imbalances of research funding in the field of Environmental Chemistry,utilizing application and funding data fromthe National Natural Science Foundation of China(NSFC)over the past decade.The findings reveal significant regional disparities,with Eastern regions receiving over 70%of the national funding,while the Northeast accounts for only 4%to 6.5%.Additionally,the analysis shows notable differences in funding allocation among various research institutions,with a substantial portion of funds concentrated in a few leading institutions,leading to inequities across different types and levels of organizations.The impact of applicant gender on funding disparities is relatively minor;although female applicants have a slightly lower funding rate,the concentration of funds is marginally higher among females.Furthermore,the study highlights that key projects and talent-oriented initiatives,due to their significant funding concentration,exacerbate the existing imbalances.Overall,this research provides valuable insights for optimizing funding policies and advocates for a more equitable distribution of resources in Environmental Chemistry research,addressing the identified disparities.
基金supported by the Chinese Academy of Science"Light of West China"Program(2022-XBQNXZ-015)the National Natural Science Foundation of China(11903071)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China and administered by the Chinese Academy of Sciences。
文摘This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.
文摘BACKGROUND:Electrolyte imbalance is common following traumatic brain injury(TBI)and can significantly impact patient outcomes.We aimed to explore the occurrence,patterns,and consequences of electrolyte imbalance in adult patients with TBI.METHODS:A retrospective study was conducted from 2016 to 2021 at a level 1 trauma center among hospitalized TBI patients.On admission,the levels of serum electrolytes,including sodium,potassium,calcium,magnesium,and phosphate,were analyzed.Demographics,injury characteristics,and interventions were assessed.The primary outcome was the in-hospital mortality.Multivariate logistic regression analysis was performed to identify independent predictors of mortality in TBI patients.RESULTS:A total of 922 TBI patients were included in the analysis,of whom 902(98%)had electrolyte imbalance.The mean age of patients with electrolyte imbalance was 32.0±15.0 years.Most patients were males(94%).The most common electrolyte abnormalities were hypocalcemia,hypophosphatemia,and hypokalemia.The overall in-hospital mortality rate was 22%in the entire cohort.In multivariate logistic analysis,the predictors of mortality included age(odds ratio[OR]=1.029,95%confidence intervals[CI]:1.013-1.046,P<0.001),low GCS(OR=0.883,95%CI:0.816-0.956,P=0.002),high Injury Severity Score(ISS)scale(OR=1.051,95%CI:1.026-1.078,P<0.001),hypernatremia(OR=2.175,95%CI:1.196-3.955,P=0.011),hyperkalemia(OR=4.862,95%CI:1.222-19.347;P=0.025),low serum bicarbonate levels(OR=0.926,95%CI:0.868-0.988,P=0.020),high serum lactate levels(OR=1.128,95%CI:1.022-1.244,P=0.017),high glucose levels(OR=1.072,95%CI:1.014-1.133,P=0.015),a longer activated partial thromboplastin time(OR=1.054,95%CI:1.024-1.084,P<0.001)and higer international normalized ratio(INR)(OR=3.825,95%CI:1.592-9.188,P=0.003).CONCLUSION:Electrolyte imbalance is common in TBI patients,with the significant prevalence of hypocalcemia,hypophosphatemia,and hypokalemia.However,hypernatremia and hyperkalemia were associated with the risk of mortality,emphasizing the need for further research to comprehend electrolyte dynamics in TBI patients.
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supported by The Scientific Research Project of Traditional Chinese Medicine in Hunan Province(No.A2024027)The National Inheritance Studio of Distinguished Veteran TCM Experts(Letter of National Traditional Chinese Medicine Education[2022]No.75)Natural Science Foundation of Hunan Province(Nos.2021JJ40422,2023JJ60260).
文摘Although various anti-inflammatory medications,such as ephedrine,are employed to manage cough-variant asthma,their underlying mechanisms are yet to be fully understood.Recent studies suggest that exosomes derived from airway epithelial cells(AECs)contain components like messenger RNAs(mRNAs),micro-RNAs(miRNAs),and long noncoding RNA(lncRNA),which play roles in the occurrence and progression of airway inflammation.This study investigates the influence of AEC-derived exosomes on the efficacy of ephedrine in treating cough-variant asthma.We established a mouse model of asthma and measured airway resist-ance and serum inflammatory cell levels.Real-time polymerase chain reaction(RT-qPCR),Western blotting,and enzyme-linked im-munosorbent assay(ELISA)analyses were used to assess gene and protein expression levels.Exosomes were isolated and character-ized.RNA immunoprecipitation(RIP)and RNA pull-down assays were conducted to examine the interaction between hnRNPA2B1 and lnc-TRPM2-AS1.In the ovalbumin(OVA)-challenged mouse model,ephedrine treatment reduced inflammatory responses,air-way resistance,and Th1/Th2 cell imbalance.Exosomes from OVA-treated AECs showed elevated levels of lnc-TRPM2-AS1,which were diminished following ephedrine treatment.The exosomal lnc-TRPM2-AS1 mediated the Th1/Th2 imbalance in CD4^(+)T cells,with its packaging into exosomes being facilitated by hnRNPA2B1.This study unveils a novel mechanism by which ephedrine ameli-orates OVA-induced CD4^(+)T cell imbalance by suppressing AEC-derived exosomal lnc-TRPM2-AS1.These findings could provide a theoretical framework for using ephedrine in asthma treatment.
基金supported by the National Natural Science Foundation of China(No.82274329,82304991)the China Postdoctoral Science Foundation(No,2023M732336)Shanghai Science and Technology Committee Sailing Program Foundation(No.23YF1442500)。
文摘Rheumatoid arthritis(RA)is a chronic autoimmune disorder marked by persistent synovial inflammation and joint degradation,posing challenges in the development of effective treatments.Nuciferine,an alkaloid found in lotus leaf,has shown promising anti-inflammatory and anti-tumor effects,yet its efficacy in RA treatment remains unexplored.This study investigated the antiproliferative effects of nuciferine on the MH7A cell line,a human RA-derived fibroblast-like synoviocyte,revealing its ability to inhibit cell proliferation,promote apoptosis,induce apoptosis,and cause G1/S phase arrest.Additionally,nuciferine significantly reduced the migration and invasion capabilities of MH7A cells.The therapeutic potential of nuciferine was further evaluated in a collagen-induced arthritis(CIA)rat model,where it markedly alleviated joint swelling,synovial hyperplasia,cartilage injury,and inflammatory infiltration.Nuciferine also improved collagen-induced bone erosion,decreased pro-inflammatory cytokines and serum immunoglobulins(IgG,IgG1,IgG2a),and restored the balance between T helper(Th)17 and regulatory T cells in the spleen of CIA rats.These results indicate that nuciferine may offer therapeutic advantages for RA by decreasing the proliferation and invasiveness of FLS cells and correcting the Th17/Treg cell imbalance in CIA rats.
基金supported by the National Natural Science Foundation of China(No.31960722)。
文摘Cadmium(Cd)and excess molybdenum(Mo)pose serious threats to animal health.Our previous study has determined that Cd and/or Mo exposure can cause ovarian damage of ducks,while the specific mechanism is still obscure.To further investigate the toxic mechanism of Cd and Mo co-exposure in the ovary,forty 8-day-old female ducks were randomly allocated into four groups for 16 weeks,and the doses of Cd and Mo in basic diet per kg were as follows:control group,Mo group(100 mg Mo),Cd group(4 mg Cd),and Mo+Cd group(100 mg Mo+4 mg Cd).Cadmium sulfate 8/3-hydrate(CdSO_(4)·8/3H_(2)O)and hexaammonium molybdate((NH_(4))_(6)Mo_(7)O_(24)·4H_(2)O)were the origins of Cd and Mo,respectively.At the 16th week of the experiment,all ovary tissues were collected for the detection of related indexes.The data indicated that Mo and/or Cd induced trace element disorders and Th1/Th2 balance to divert toward Th1 in the ovary,which activated endoplasmic reticulum(ER)stress and then provoked necroptosis through triggering RIPK1/RIPK3/MLKL signaling pathway,and eventually caused ovarian pathological injuries and necroptosis characteristics.The alterations of above indicators were most apparent in the joint group.Above all,this research illustrates that Mo and/or Cd exposure can initiate necroptosis through Th1/Th2 imbalance-modulated ER stress in duck ovaries,and Mo and Cd combined exposure aggravates ovarian injuries.This research explores the molecular mechanism of necroptosis caused by Mo and/or Cd,which reveals that ER stress attenuation may be a therapeutic target to alleviate necroptosis.
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.
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.
