With the advent of the sixth-generationwireless technology,the importance of using artificial intelligence of things(AIoT)devices is increasing to enhance efficiency.As massive volumes of data are collected and stored...With the advent of the sixth-generationwireless technology,the importance of using artificial intelligence of things(AIoT)devices is increasing to enhance efficiency.As massive volumes of data are collected and stored in these AIoT environments,each device becomes a potential attack target,leading to increased security vulnerabilities.Therefore,intrusion detection studies have been conducted to detect malicious network traffic.However,existing studies have been biased toward conducting in-depth analyses of individual packets to improve accuracy or applying flow-based statistical information to ensure real-time performance.Effectively responding to complex andmultifaceted threats in large-scale AIoT environments is challenging.This study proposes a hybrid multivariate network traffic(HyMNeT)feature-based intrusion detection system that applies a hybrid meta-heuristic feature selection approach to create a secure and efficient AIoT environment.The HyMNeT system selects critical features by applying mutual information maximization(MIM)and the maximal information coefficient(MIC)based on statistical features of the network traffic flow and raw packet features.This system employs the reference vector-guided evolutionary algorithm to search for optimal thresholds that maximizeMIMscores whileminimizingMIC scores.An evaluation of the selected multivariate network traffic feature set using four machine learning models on the BoT-IoT and ToN-IoT datasets resulted in average accuracy,precision,recall,and F1-score values of 0.9844,0.9897,0.9844,and 0.9859,respectively.This work demonstrates that HyMNeT performs detection consistently and stably across all models.展开更多
The epigenomic landscape regulates gene expression and chromatin dynamics,with histone and RNA modifications playing crucial roles.Although studies have elucidated the interactions among chromatin modifications,DNA me...The epigenomic landscape regulates gene expression and chromatin dynamics,with histone and RNA modifications playing crucial roles.Although studies have elucidated the interactions among chromatin modifications,DNA methylation,and mRNA modifications,the relationships among RNA modifications and their collective influence on RNA metabolism remain poorly understood.Grasping these epigenetic mechanisms is essential for improving crop resilience and productivity.In this study,we explored the co-occurrence and functional interactions of three significant mRNA modifications in Arabidopsis(Arabidopsis thaliana)and rice(Oryza sativa):N^(4)-acetylcytidine(ac^(4)C),N^(6)-methyladenosine(m^(6)A),and 5-methylcytosine(m^(5)C).Our results indicate that these modifications frequently coexist in the same transcripts,exhibiting distinct spatial distributions across species.Notably,the m^(6)A modification enhances the ac^(4)C-mediated destabilization of RNA secondary structures,especially when modifications are clustered,thereby promoting RNA stability.In Arabidopsis,the ac^(4)C modification improved translational efficiency and the m^(6)A modification amplified this effect in a distance-dependent manner;by contrast,in rice the influence of m^(6)A is independent of distance.The m^(5)C modification has minimal impact on RNA structure or stability but modulates m^(6)A-associated transcript stability in a contextdependent manner.Our findings shed light on the dynamic regulatory code of combinatorial RNA modifications,highlighting species-specific mechanisms of post-transcriptional regulation.This research offers valuable insights into the intricate interplay of RNA modifications,with implications for advancing agricultural biotechnology through a deeper understanding of plant RNA functionality.展开更多
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(RS-2023-00267476)by the Ministry of Trade,Industry and Energy(MOTIE)and the Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program(No.P0028271).
文摘With the advent of the sixth-generationwireless technology,the importance of using artificial intelligence of things(AIoT)devices is increasing to enhance efficiency.As massive volumes of data are collected and stored in these AIoT environments,each device becomes a potential attack target,leading to increased security vulnerabilities.Therefore,intrusion detection studies have been conducted to detect malicious network traffic.However,existing studies have been biased toward conducting in-depth analyses of individual packets to improve accuracy or applying flow-based statistical information to ensure real-time performance.Effectively responding to complex andmultifaceted threats in large-scale AIoT environments is challenging.This study proposes a hybrid multivariate network traffic(HyMNeT)feature-based intrusion detection system that applies a hybrid meta-heuristic feature selection approach to create a secure and efficient AIoT environment.The HyMNeT system selects critical features by applying mutual information maximization(MIM)and the maximal information coefficient(MIC)based on statistical features of the network traffic flow and raw packet features.This system employs the reference vector-guided evolutionary algorithm to search for optimal thresholds that maximizeMIMscores whileminimizingMIC scores.An evaluation of the selected multivariate network traffic feature set using four machine learning models on the BoT-IoT and ToN-IoT datasets resulted in average accuracy,precision,recall,and F1-score values of 0.9844,0.9897,0.9844,and 0.9859,respectively.This work demonstrates that HyMNeT performs detection consistently and stably across all models.
基金support from the National Natural Science Foundation of China(32270623)the Natural Science Foundation of Hunan Province(2024JJ2016)+2 种基金Hunan Science and Technology Innovation Plan(2025ZYJ003)China Tobacco Hunan Industrial Co.,Ltd.Research Project(KY2023YC0015)support from the China Tobacco Genome Project(110202101037,JY-14).
文摘The epigenomic landscape regulates gene expression and chromatin dynamics,with histone and RNA modifications playing crucial roles.Although studies have elucidated the interactions among chromatin modifications,DNA methylation,and mRNA modifications,the relationships among RNA modifications and their collective influence on RNA metabolism remain poorly understood.Grasping these epigenetic mechanisms is essential for improving crop resilience and productivity.In this study,we explored the co-occurrence and functional interactions of three significant mRNA modifications in Arabidopsis(Arabidopsis thaliana)and rice(Oryza sativa):N^(4)-acetylcytidine(ac^(4)C),N^(6)-methyladenosine(m^(6)A),and 5-methylcytosine(m^(5)C).Our results indicate that these modifications frequently coexist in the same transcripts,exhibiting distinct spatial distributions across species.Notably,the m^(6)A modification enhances the ac^(4)C-mediated destabilization of RNA secondary structures,especially when modifications are clustered,thereby promoting RNA stability.In Arabidopsis,the ac^(4)C modification improved translational efficiency and the m^(6)A modification amplified this effect in a distance-dependent manner;by contrast,in rice the influence of m^(6)A is independent of distance.The m^(5)C modification has minimal impact on RNA structure or stability but modulates m^(6)A-associated transcript stability in a contextdependent manner.Our findings shed light on the dynamic regulatory code of combinatorial RNA modifications,highlighting species-specific mechanisms of post-transcriptional regulation.This research offers valuable insights into the intricate interplay of RNA modifications,with implications for advancing agricultural biotechnology through a deeper understanding of plant RNA functionality.
