Comparative skin histological and transcriptomic analysis of Rana kukunoris with two different skin colors.

This study compares the skin structures of Rana kukunoris with two different skin colors living in the same area of Haibei in the Northeastern Qinghai-Tibet Plateau. The skin thickness of the khaki R. kukunoris was significantly greater than that of the brown R. kukunoris (P < 0.01), and significantly more mucous and granular glands were present on the dorsal skin of the khaki frog (P < 0.05). Meanwhile, the melanocytes on the dorsal skin of the brown frog were significantly larger than those on the khaki one (P < 0.05). Morphological changes in the expansion and aggregation of melanocytes seemed to deepen the skin color of R. kukunoris. Moreover, transcriptome sequencing identified tyrosine metabolism, melanogenesis, and riboflavin metabolism as the main pathways involved in melanin formation and metabolism in brown R. kukunoris. TYR, MC1R was upregulated as the skin color of R. kukunoris was deepened and contributed to melanin production and metabolism. In contrast, the khaki frog had significantly more upregulated genes and metabolic pathways related to autoimmunity. The khaki frog appeared to defend against ultraviolet (UV) radiation-induced damage by secreting mucus and small molecular peptides, whereas the brown frog protected itself by distributing a large amount of melanin. Hence, the different skin colors of R. kukunoris might represent different adaptation strategies for survival in the intense UV radiation environment of the Qinghai-Tibet Plateau.

A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows.

A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following.