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Knock-down of OsLOX by RNA interference leads to improved seed viability in rice
Suyang Bai,Niqing He,Lu Zhou,Beibei Shen,Wei Wu,Xi Liu,Ling Jiang,Jianmin Wan 한국식물학회 2015 Journal of Plant Biology Vol.58 No.5
Previous work found that lipoxygenases were key enzymes in lipid peroxidation, which causes grain deterioration during storage. In order to obtain better seed viability in rice, 10 marker-free knock-down lines were obtained in the progeny of endogenous OsLOX knock-down mutations caused by the RNAi technology. After artificial accelerated aging, there were four types of knock-down lines with higher seed viability than wild type (receptor parent). OsLOX3 knock-down line NPF1 was of special interest. In a series of experiments, including Southern blots, analysis of OsLOX3 expression, and enzymatic activity, NPF1 had better seed viability than wild-type. We also investigated the main agronomic characters of both knock-down lines and non-transgenetic wild type families. Knock-down lines were identified with generally excellent agronomic characteristics similar to the wild-type.
A Novel Information Fusion Model for Assessment of Malware Threat
Chao Dai,Jianmin Pang,Xiaochuan Zhang,Guanghui Liang,Hong Bai 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.5
It is not only important for security analysts to judge some binary code is malicious or not, but also to understand the malware “what to do” and “what’s the impact it posed on our information system”. In this paper, we proposed a novel information fusion model to quantitate the threat of malware. The model consists of three levels: the decision making level information fusion, the attribute level information fusion and the behavior level information fusion. These three levels portray special characteristics of malware threat distributed in the assessment model. Combined with the static analysis technology and real-time monitor technology, we implemented a framework of malware threat assessment. The experiment demonstrates that our information fusion model for malware threat assessment is effective to quantitate the threat of malware in accuracy and differentiation degree. In the end, we discussed several issues that could improve the performance of the model.