Flower color is an important characteristic of ornamental plants and is determined by various chemical components, including anthocyanin. In the present study, combined metabolomics and transcriptomics analysis was used to explore color variations in the chrysanthemums of three cultivars, of which the color of JIN is yellow, FEN is pink, and ZSH is red. A total of 29 different metabolites, including nine anthocyanins, were identified in common in the three cultivars. Compared with the light-colored cultivars, all of the nine anthocyanin contents were found to be up-regulated in the dark-colored ones. The different contents of pelargonidin, cyanidin, and their derivates were found to be the main reason for color variations. Transcriptomic analysis showed that the color difference was closely related to anthocyanin biosynthesis. The expression level of anthocyanin structural genes, including DFR, ANS, 3GT, 3MaT1, and 3MaT2, was in accordance with the flower color depth. This finding suggests that anthocyanins may be a key factor in color variations among the studied cultivars. On this basis, two special metabolites were selected as biomarkers to assist in chrysanthemum breeding for color selection.

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Abstract-Simulation based decision making tools, such as simulation cloning, "what-if" analysis, and etc., has being a beneficial way to analyzing of multiple alternative scenarios, however, there is no guarantee that a simulator could obtain a feasible scenario meeting the flood mitigation requirements, let alone an optimal one. Motivated by J. R. Marden and his colleague's work "cooperative control and potential games", a novel technique, the alternative scenario selection game, was proposed in this paper to solve the flood mitigation optimization problem, in which the alternative scenario selection problem was modeled as a potential game with appropriately defined players' utilities. A SAP (Spatial Adaptive Play) based learning algorithm for the potential game with suboptimal Nash equilibria was introduced to help all players to converge to a consensus after finite iteration steps. Case study and performance evaluation shows that the proposed technique is feasible and stable, within a few iteration steps, all players could quickly reach the goal of the expected flood peak point.