[ Objective] The study is to generate pharmaceutical protein via plant transgenic technique. [Methed] Using the cotyledons with petiole as transformation receptor, the fusion gene of rapeseed oil-body gene and bFGF wa...[ Objective] The study is to generate pharmaceutical protein via plant transgenic technique. [Methed] Using the cotyledons with petiole as transformation receptor, the fusion gene of rapeseed oil-body gene and bFGF was introduced into the rapeseed （ Brassica campestris L. ） by Agrobacterium tumefaciens-mediated transformation; meanwhile regeneration conditions of rapeseed were also optimized, and the regenerated resistant plantlets were detected by PCR and Southern blot. [ Result] This fusion gene had been integrated into rapeseed genome successfully, and the optimized conditions of transformation and regeneration were as follows： explants pre-culture for 2 d, co-culture for 3 d, bacteria solution OD600 for 0.3 and infection time for 5 min. [ Conclusion] The results laid a solid foundation for extraction, isolation and purification of protein in transgenic plant seeds.展开更多
Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for...Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for bridge expansion joints based on long-term monitoring data was developed. The effects of environmental factors on the expansion joint displacement were analyzed. Multiple linear regression models were obtained to describe the correlation between displacements and the dominant environmental factors. The damage alarming index was defined based on the multiple regression models. At last, the X-bar control chart was utilized to detect the abnormal change of the displacements. Analysis results reveal that temperature and traffic condition are the dominant environmental factors to influence the displacement. When the confidence level of X-bar control chart is set to be 0.003, the false-positive indications of damage can be avoided. The damage sensitivity analysis shows that the proper X-bar control chart can detect 0.1 cm damage-induced change of the expansion joint displacement. It is reasonably believed that the proposed technique is robust against false-positive indication of damage and suitable to alarm the possible future damage of the expansion joints.展开更多
Industrial control systems （ICSs） are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the progr...Industrial control systems （ICSs） are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the programmable logic controller （PLC） controls the actuators directly. A PLC executing a malicious program can cause significant property loss or even casualties. The number of attacks targeted at PLCs has increased noticeably over the last few years, exposing the vulnerability of the PLC and the importance of PLC protection. Unfortunately, PLCs cannot be protected by traditional intrusion detection systems or antivirus software. Thus, an effective method for PLC protection is yet to be designed. Motivated by these concerns, we propose a non-invasive power- based anomaly detection scheme for PLCs. The basic idea is to detect malicious software execution in a PLC through analyzing its power consumption, which is measured by inserting a shunt resistor in series with the CPU in a PLC while it is executing instructions. To analyze the power measurements, we extract a discriminative feature set from the power trace, and then train a long short-term memory （LSTM） neural network with the features of normal samples to predict the next time step of a normal sample. Finally, an abnormal sample is identified through comparing the predicted sample and the actual sample. The advantages of our method are that it requires no software modification on the original system and is able to detect unknown attacks effectively. The method is evaluated on a lab testbed, and for a trojan attack whose difference from the normal program is around 0.63%, the detection accuracy reaches 99.83%.展开更多
基金Supported by Bioreactor Important Special Item of 863-Program inthe "Eleventh Five-Year" Plan (No. 2007AA100503)Science and Technology Development Key Plan of Jilin Province( No.20070922)+1 种基金Cultivation Fund of Scientific and Technical Innovation Project Major Program of Higher Education Institutions ( No.70S018)Science and Technology Plan of Changchun City (No.06GG150)~~
文摘[ Objective] The study is to generate pharmaceutical protein via plant transgenic technique. [Methed] Using the cotyledons with petiole as transformation receptor, the fusion gene of rapeseed oil-body gene and bFGF was introduced into the rapeseed （ Brassica campestris L. ） by Agrobacterium tumefaciens-mediated transformation; meanwhile regeneration conditions of rapeseed were also optimized, and the regenerated resistant plantlets were detected by PCR and Southern blot. [ Result] This fusion gene had been integrated into rapeseed genome successfully, and the optimized conditions of transformation and regeneration were as follows： explants pre-culture for 2 d, co-culture for 3 d, bacteria solution OD600 for 0.3 and infection time for 5 min. [ Conclusion] The results laid a solid foundation for extraction, isolation and purification of protein in transgenic plant seeds.
基金Project(2009BAG15B03) supported by the National Science and Technology Ministry of ChinaProjects(51178100, 51078080) supported by the National Natural Science Foundation of China+1 种基金Project(BK2011141) supported by the Natural Science Foundation of Jiangsu Province, ChinaProject(12KB02) supported by the Open Fund of the Key Laboratory for Safety Control of Bridge Engineering(Changsha University of Science and Technology), Ministry of Education, China
文摘Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for bridge expansion joints based on long-term monitoring data was developed. The effects of environmental factors on the expansion joint displacement were analyzed. Multiple linear regression models were obtained to describe the correlation between displacements and the dominant environmental factors. The damage alarming index was defined based on the multiple regression models. At last, the X-bar control chart was utilized to detect the abnormal change of the displacements. Analysis results reveal that temperature and traffic condition are the dominant environmental factors to influence the displacement. When the confidence level of X-bar control chart is set to be 0.003, the false-positive indications of damage can be avoided. The damage sensitivity analysis shows that the proper X-bar control chart can detect 0.1 cm damage-induced change of the expansion joint displacement. It is reasonably believed that the proposed technique is robust against false-positive indication of damage and suitable to alarm the possible future damage of the expansion joints.
基金Project supported by the National Basic Research Program(973)of China(No.2015AA050202)
文摘Industrial control systems （ICSs） are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the programmable logic controller （PLC） controls the actuators directly. A PLC executing a malicious program can cause significant property loss or even casualties. The number of attacks targeted at PLCs has increased noticeably over the last few years, exposing the vulnerability of the PLC and the importance of PLC protection. Unfortunately, PLCs cannot be protected by traditional intrusion detection systems or antivirus software. Thus, an effective method for PLC protection is yet to be designed. Motivated by these concerns, we propose a non-invasive power- based anomaly detection scheme for PLCs. The basic idea is to detect malicious software execution in a PLC through analyzing its power consumption, which is measured by inserting a shunt resistor in series with the CPU in a PLC while it is executing instructions. To analyze the power measurements, we extract a discriminative feature set from the power trace, and then train a long short-term memory （LSTM） neural network with the features of normal samples to predict the next time step of a normal sample. Finally, an abnormal sample is identified through comparing the predicted sample and the actual sample. The advantages of our method are that it requires no software modification on the original system and is able to detect unknown attacks effectively. The method is evaluated on a lab testbed, and for a trojan attack whose difference from the normal program is around 0.63%, the detection accuracy reaches 99.83%.
