番茄褐色皱纹果病毒(tomato brown rugose fruit virus,ToBRFV)严重威胁番茄等茄科园艺作物的生产安全。本研究根据其外壳蛋白(coat protein,CP)基因及其同属病毒的差异序列,设计了特异性重组酶介导等温核酸扩增技术(recombinase-aided ...番茄褐色皱纹果病毒(tomato brown rugose fruit virus,ToBRFV)严重威胁番茄等茄科园艺作物的生产安全。本研究根据其外壳蛋白(coat protein,CP)基因及其同属病毒的差异序列,设计了特异性重组酶介导等温核酸扩增技术(recombinase-aided amplification,RAA)引物,并基于CRISPR/Cas12a的设计原则,设计了靶向RT-RAA扩增产物的CRISPR RNA(crRNA)。通过优化获得了检测信号最强的反应体系,其中报告基因FQ终浓度为600nmol/L、Cas12a和crRNA终浓度分别为200nmol/L和1000nmol/L,最终总反应时间仅为30 min。该方法可特异性检测ToBRFV,对携带ToBRFV的番茄样品RNA检测灵敏度为RT-PCR和RT-qPCR的10000和100倍,检测限为3.46fg/μL。阳性样品验证结果显示,本研究建立的RT-RAA-CRISPR/Cas12a检测技术可以在不同来源的辣椒、番茄侵染的植物叶片、果实及种子中检测到ToBRFV,表明该技术可用于番茄褐色皱纹果病毒的快速、灵敏的可视化检测。展开更多
Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and ...Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.展开更多
文摘番茄褐色皱纹果病毒(tomato brown rugose fruit virus,ToBRFV)严重威胁番茄等茄科园艺作物的生产安全。本研究根据其外壳蛋白(coat protein,CP)基因及其同属病毒的差异序列,设计了特异性重组酶介导等温核酸扩增技术(recombinase-aided amplification,RAA)引物,并基于CRISPR/Cas12a的设计原则,设计了靶向RT-RAA扩增产物的CRISPR RNA(crRNA)。通过优化获得了检测信号最强的反应体系,其中报告基因FQ终浓度为600nmol/L、Cas12a和crRNA终浓度分别为200nmol/L和1000nmol/L,最终总反应时间仅为30 min。该方法可特异性检测ToBRFV,对携带ToBRFV的番茄样品RNA检测灵敏度为RT-PCR和RT-qPCR的10000和100倍,检测限为3.46fg/μL。阳性样品验证结果显示,本研究建立的RT-RAA-CRISPR/Cas12a检测技术可以在不同来源的辣椒、番茄侵染的植物叶片、果实及种子中检测到ToBRFV,表明该技术可用于番茄褐色皱纹果病毒的快速、灵敏的可视化检测。
基金the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,for funding this work under the Research Groups Funding Program Grant Code Number(NU/RG/SERC/12/43).
文摘Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.
