增强型绿色荧光蛋白(enhanced green fluorescent protein,EGFP)是一种优化的突变型GFP,DFL是从甘菊中分离出的LFY基因的同源序列。为了研究DFL基因的功能和表达模式,研究利用小片段克隆法将linker序列插入到EGFP基因5′端启始密码子前...增强型绿色荧光蛋白(enhanced green fluorescent protein,EGFP)是一种优化的突变型GFP,DFL是从甘菊中分离出的LFY基因的同源序列。为了研究DFL基因的功能和表达模式,研究利用小片段克隆法将linker序列插入到EGFP基因5′端启始密码子前面,在pBI121载体的CaMV35S启动子的3′端后面插入一段多克隆位点,成功地构建了pBI-DFL-EGFP表达载体。通过设计特异引物,利用PCR技术扩增得了到拟南芥LFY基因的启动子序列,用粘性末端PCR技术将pBI-DFL-EGFP表达载体中CaMV35S启动子替换成LFY基因启动子,构建成了pLFY-DFL-EGFP表达载体。用含有pBI-DFL-EGFP和pLFY-DFL-EGFP质粒的农杆菌侵染洋葱表皮细胞,在荧光显微镜下分别用蓝光激发,均观测到了荧光。这一结果表明,融合蛋白DFL∷EGFP表达载体构建成功,同时还证明了通过PCR技术克隆到的LFY启动子序列具有启动子功能。展开更多
There are many genes which control the flowering development. FLORICAULA/LEAFY gene is one of controlling flower meristem identity of plant. We isolated the homologue gene (DFL) of FLORICAULA/LEAFY from D. lavandulifo...There are many genes which control the flowering development. FLORICAULA/LEAFY gene is one of controlling flower meristem identity of plant. We isolated the homologue gene (DFL) of FLORICAULA/LEAFY from D. lavandulifolium. The genomic sequence of DFL include three extron and two intron. The number and position of introns are conserved with other most FLORICAULA/LEAFY homologue. This result is helpful for better understanding the mechanisms of plant flowering and may have important effect in the forestry and angriculture.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
文摘增强型绿色荧光蛋白(enhanced green fluorescent protein,EGFP)是一种优化的突变型GFP,DFL是从甘菊中分离出的LFY基因的同源序列。为了研究DFL基因的功能和表达模式,研究利用小片段克隆法将linker序列插入到EGFP基因5′端启始密码子前面,在pBI121载体的CaMV35S启动子的3′端后面插入一段多克隆位点,成功地构建了pBI-DFL-EGFP表达载体。通过设计特异引物,利用PCR技术扩增得了到拟南芥LFY基因的启动子序列,用粘性末端PCR技术将pBI-DFL-EGFP表达载体中CaMV35S启动子替换成LFY基因启动子,构建成了pLFY-DFL-EGFP表达载体。用含有pBI-DFL-EGFP和pLFY-DFL-EGFP质粒的农杆菌侵染洋葱表皮细胞,在荧光显微镜下分别用蓝光激发,均观测到了荧光。这一结果表明,融合蛋白DFL∷EGFP表达载体构建成功,同时还证明了通过PCR技术克隆到的LFY启动子序列具有启动子功能。
文摘There are many genes which control the flowering development. FLORICAULA/LEAFY gene is one of controlling flower meristem identity of plant. We isolated the homologue gene (DFL) of FLORICAULA/LEAFY from D. lavandulifolium. The genomic sequence of DFL include three extron and two intron. The number and position of introns are conserved with other most FLORICAULA/LEAFY homologue. This result is helpful for better understanding the mechanisms of plant flowering and may have important effect in the forestry and angriculture.
基金This work is supported by the National Hi-Tech Research and Development 863 Program of China （No 2002AA881030）, the Nature Science Foundation of Jiangsu Province （No. BK2005027, No. BK2002040） and the 211 Foundation of Soochow University,
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
