△^12 fatty acid desaturase gene has been targeted as a logical candidate controlling the high oleate trait in peanut seeds. By RT-PCR method, the full-length cDNAs of △^12 fatty acid desaturase gene were isolated fr...△^12 fatty acid desaturase gene has been targeted as a logical candidate controlling the high oleate trait in peanut seeds. By RT-PCR method, the full-length cDNAs of △^12 fatty acid desaturase gene were isolated from peanut （Arachis hypogaea L.） genotypes with normal and high ratio of oleic to linoleic acid, which were designated AhFAD2B and AhFAD2B＇, respectively. Sequence alignment of their coding regions revealed that an extra A was inserted at the position ＋442 bp of AhFAD2B＇ sequence of high oleic acid genotypes, which resulted in the shift of open reading frame and a truncated protein AhFAD2B＇, with the loss of one histidine box involved in metal ion complex required for the reduction of oxygen. Analysis of transcript level showed that the expression of △^12 fatty acid desaturase gene in high oleic acid genotype was slightly lower than that in normal genotype. The enzyme activity experiment of yeast （Saccharomyces cerevisiae） cell transformed with AhFAD2B or AhFAD2B＇ proved that only AhFAD2B gene product showed significant △^12 fatty acid desaturase activity, but AhFAD2B＇ gene product did not. These results suggested that the change of AhFAD2B＇ gene sequence resulted in lower activity or deactivation of △^12 fatty acid desaturase in high oleic acid genotype.展开更多
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a...Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.展开更多
文摘△^12 fatty acid desaturase gene has been targeted as a logical candidate controlling the high oleate trait in peanut seeds. By RT-PCR method, the full-length cDNAs of △^12 fatty acid desaturase gene were isolated from peanut （Arachis hypogaea L.） genotypes with normal and high ratio of oleic to linoleic acid, which were designated AhFAD2B and AhFAD2B＇, respectively. Sequence alignment of their coding regions revealed that an extra A was inserted at the position ＋442 bp of AhFAD2B＇ sequence of high oleic acid genotypes, which resulted in the shift of open reading frame and a truncated protein AhFAD2B＇, with the loss of one histidine box involved in metal ion complex required for the reduction of oxygen. Analysis of transcript level showed that the expression of △^12 fatty acid desaturase gene in high oleic acid genotype was slightly lower than that in normal genotype. The enzyme activity experiment of yeast （Saccharomyces cerevisiae） cell transformed with AhFAD2B or AhFAD2B＇ proved that only AhFAD2B gene product showed significant △^12 fatty acid desaturase activity, but AhFAD2B＇ gene product did not. These results suggested that the change of AhFAD2B＇ gene sequence resulted in lower activity or deactivation of △^12 fatty acid desaturase in high oleic acid genotype.
基金Supported by the State Key Development Program for Basic Research of China （No.2002CB312200） and the National Natural Science Foundation of China （No.60574019）.
文摘Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
