Artificial selection for male winners in the Siamese fighting fish Betta splendens correlates with high female aggression

In Southeast Asia, males of the Siamese fighting fish Betta splendens have been selected across centuries for paired-staged fights. During the selection process, matched for size males fight in a small tank until the contest is resolved. Breeders discard losing batches and reproduce winner batches with the aim of increasing fight performance. We assessed the results of this long-term selection process by comparing under standard laboratory conditions male and female aggressive behaviour of one strain selected for staged fights ("fighters") and one strain of wild-types. The aggressive response of adult fish was tested against their mirror image or a size-matched conspecific. Fighter males were more aggressive than wild-type males for all measured behaviours. Differences were not only quantitative but the pattern of fight display was also divergent. Fighter males had an overall higher swimming activity, performing frequent fast strikes in the direction of the intruder and displaying from a distance. Wild-type males were less active and exhibited aggressive displays mostly in close proximity to the stimuli. Females of the fighter strain, which are not used for fights, were also more aggressive than wild-type females. Aggressive behaviours were correlated across male and female fighter siblings, suggesting common genetic and physiological mechanisms to male and female aggression in this species. The study further shows that results were largely independent of the stimulus type, with the mirror test inducing similar and less variable responses than the live conspecific presentation. These results suggest that selection for male winners co-selected for high-frequency and metabolic demanding aggressive display in males and also enhanced female aggression, opening a wide range of testable hypothesis about the ultimate and proximate mechanisms of male and female aggression in B. splendens.

Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection

For studying cancer and genetic diseases, the issue of identifying high correlation genes from high-dimensional data is an important problem. It is a great challenge to select relevant biomarkers from gene expression data that contains some important correlation structures, and some of the genes can be divided into different groups with a common biological function, chromosomal location or regulation. In this paper, we propose a penalized accelerated failure time model CHR-DE using a non-convex regularization (local search) with differential evolution (global search) in a wrapper-embedded memetic framework. The complex harmonic regularization (CHR) can approximate to the combination ℓp(12≤p<1) and ℓ_q (1 ≤ q < 2) for selecting biomarkers in group. And differential evolution (DE) is utilized to globally optimize the CHR’s hyperparameters, which make CHR-DE achieve strong capability of selecting groups of genes in high-dimensional biological data. We also developed an efficient path seeking algorithm to optimize this penalized model. The proposed method is evaluated on synthetic and three gene expression datasets: breast cancer, hepatocellular carcinoma and colorectal cancer. The experimental results demonstrate that CHR-DE is a more effective tool for feature selection and learning prediction.