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Comparative Metabolite Profiling and Hormone Analysis of Perennial and Annual Rice
Xiuqin Zhao,Ting Zhang,Liyu Huang,Huimin Wu,Fengyi Hu,Fan Zhang,Linghua Zhu,Binying Fu 한국식물학회 2012 Journal of Plant Biology Vol.55 No.1
Perenniality is one of the important topics in rice breeding which is generally accompanied by complex physiobiochemical processes. To understand the metabolic characteristics of perennial rice, in the present study, gas chromatography–mass spectrometry and enzyme-linked immunosorbent assays were used to profile the distribution patterns of 33 primary metabolites and hormones [indole-3-acetic acid (IAA) and zeatin riboside (ZR)] of annual (RD23) and perennial (Oryza longistaminata and the line AA with RD23 genetic background) rice genotypes. Results showed that both metabolites and hormones have distinct genotype and organ distribution patterns, and considerable variations were observed between the metabolites in stem bases of perennial and annual rice. Most of the metabolites,including sugars, organic acids, and amino acids, significantly accumulated in the stem bases of perennial rice by decreasing the level in roots and leaves. Fifteen metabolites consistently accumulated significantly in the stem bases of both perennial genotypes. Additionally, the organ-level IAA content and IAA/ZR ratio in the two perennials were considerably higher than those in RD23. The present study indicated that the significant accumulation of the metabolites at stem base and the higher IAA/ZR ratio are involved
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Han Hu,Jian Yan,Liyue Zhu,Hong-Sheng Xi 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.6
This paper presents a novel Markov switching state space control model for dynamically switching resource configuration scheme to achieve power conservation for multimedia server cluster systems. This model exploits the hierarchical dynamic structure of network system and its construction is flexible and scalable. Using this analytical model, the problem of power conservation is posed as a constrained stochastic optimization problem with the goal of minimizing the average power consump-tion subject to the constraint on the average blocking ratio. Applying Lagrange approach and online estimation of the performance gradient, a policy iteration algorithm is proposed to search the optimal policy online. This algorithm does not depend on any prior knowledge of system parameters, and con-verges to the optimal solution. Simulation results demonstrate the convergence of the proposed algo-rithm and effectiveness to different access workloads.
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