Phenylalanine Ammonia Lyase GmPAL1.1 Promotes Seed Vigor under High-Temperature and -Humidity Stress and Enhances Seed Germination under Salt and Drought Stress in Transgenic Arabidopsis.

Seed vigor is an important agronomic attribute, essentially associated with crop yield. High-temperature and humidity (HTH) stress directly affects seed development of plants, resulting in the decrease of seed vigor. Therefore, it is particularly important to discover HTH-tolerant genes related to seed vigor. Phenylalanine ammonia lyase (PAL, EC 4.3.1.24) is the first rate-limiting enzyme in the phenylpropanoid biosynthesis pathway and a key enzyme involved in plant growth and development and environmental adaptation. However, the biological function of PAL in seed vigor remains unknown. Here, GmPAL1.1 was cloned from soybean, and its protein was located in the cytoplasm and cell membrane. GmPAL1.1 was significantly induced by HTH stress in developing seeds. The overexpression of GmPAL1.1 in Arabidopsis (OE) accumulated lower level of ROS in the developing seeds and in the leaves than the WT at the physiological maturity stage under HTH stress, and the activities of SOD, POD, and CAT and flavonoid contents were significantly increased, while MDA production was markedly reduced in the leaves of the OE lines than in those of the WT. The germination rate and viability of mature seeds of the OE lines harvested after HTH stress were higher than those of the WT. Compared to the control, the overexpression of GmPAL1.1 in Arabidopsis enhanced the tolerance to salt and drought stresses during germination. Our results suggested the overexpression of GmPAL1.1 in Arabidopsis promoted seed vigor at the physiological maturation period under HTH stress and increased the seeds' tolerance to salt and drought during germination.

Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies.

This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples.