Species and Organ Diversity in the Effects of Hydrogen Peroxide on Superoxide Dismutase Activity In Vitro

Superoxlde dlsmutase （SOD） is ubiquitous in aerobic organisms and constitutes the first link In the enzyme scavenging system of reactive oxygen species. In the present study, species and organ diversity of SOD activity In a solution and In an in-gel assay system, as well as the effects of hydrogen peroxide （H202） on SOD activity, were Investigated. In a solution assay system, SOD activity of jackfruIt root, shoot, leaves, axes, and cotyledons, of maize embryos and endosperms, of mung bean leaves and seeds, of sacred lotus axes and cotyledons, and of rice and wheat leaves was Increased by 1-15 mmol/L H2O2. However, SOD activity In rice root and seeds, maize roots and leaves, mung bean roots and shoots, and wheat seeds was decreased by 1-15 mmol/L H2O2. The SOD activity of wheat root and soybean roots, leaves, axes, and cotyledons was Increased by 1-4 mmol/L H2O2, but was decreased by concentrations of H2O2 〉4 mmol/L. The SOD activity of soybean shoots was not affected by 1-15 mmol/L H2O2. The SOD activity In crude mltochondrla of jackfruIt, maize, and upas seeds, as well as In purified mitochondria of jackfruIt, was also Increased by 1-15 mmol/L H2O2. In the In-gel assay system, the SOD In jackfruIt cotyledons was comprised of Mn-SOD, Cu/Zn-SOD, and Fe-SOD, the crude mltochondria of jackfruit seeds and maizes embryo was comprised of Mn-SOD and Cu/ Zn-SOD, and the crude mltochondria of maize seeds was comprised of Mn-SOD only. In the present study, H2O2 markedly Inhibited Cu/Zn-SOD and Fe-SOD activity.

一种基于多样性信息和收敛度的多目标粒子群优化算法

为了提高多目标粒子群算法优化解的多样性和收敛性,提出了一种基于多样性信息和收敛度的多目标粒子群优化算法(Multiobjective Particle Swarm Optimization based on the Diversity Information and Convergence Degree,dicd MOPSO).首先,利用非支配解多样性信息评估知识库中最优解的分布状态,设计出一种全局最优解选择机制,平衡了种群的进化过程,提高了非支配解的多样性和收敛性;其次,基于种群多样性信息设计出一种飞行参数调整机制,增强了粒子的全局探索能力和局部开发能力,获得了多样性和收敛性较好的种群.最后,将dicd MOPSO应用于标准测试函数测试,实验结果表明,dicd MOPSO与其他多目标算法相比不仅获得了多样性较高的可行解,而且能够较快的收敛到Pareto前沿.

基于速度约束和全局多样性的MOPSO算法

针对多目标优化给传统多目标粒子群算法(Multi-objective Particle Swarm Optimization,MOPSO)带来的收敛速度慢、解分布不均导致算法多样性差的问题,提出一种基于速度约束(Speed-constraints)和全局多样性(Global Diversity)的MOPSO算法。利用粒子速度更新过程中引入新的速度引导方向并融入收缩控制因子来增强粒子的全局搜索能力和局部开发能力;通过精英库中非支配解的多样性信息评估最优解的分布状态,设计全局最优解选择机制自适应选择最优解,提高算法的多样性。实验结果表明,与其它现有算法相比较,上述算法反向代距离和超体积均有明显改善,获得的可行性解更均匀地分布到Pareto前沿。