The methylation patterns of the IGF2 and IGF2R genes in bovine spermatozoa are not affected by flow-cytometric sex sorting

The objectives of this study were to investigate the effect of sexing by flow cytometry on the methylation patterns of the IGF2 and IGF2R genes. Frozen-thawed, unsorted, and sex-sorted sperm samples from four Nellore bulls were used. Each ejaculate was separated into three fractions: non-sexed (NS), sexed for X-sperm (SX), and sexed for Y-sperm (SY). Sperm were isolated from the extender, cryoprotectant, and other cell types by centrifugation on a 40:70% Percoll gradient, and sperm pellets were used for genomic DNA isolation. DNA was used for analyses of the methylation patterns by bisulfite sequencing. Methylation status of the IGF2 and IGF2R genes were evaluated by sequencing 195 and 147 individual clones, respectively. No global differences in DNA methylation were found between NS, SX, and SY groups for the IGF2 (P=0.09) or IGF2R genes (P=0.38). Very specific methylation patterns were observed in the 25th and 26th CpG sites in the IGF2R gene. representing higher methylation in NS than in the SX and SY groups compared with the other CpG sites. Further, individual variation in methylation patterns was found among bulls. In conclusion, the sex-sorting procedure by flow cytometry did not affect the overall DNA methylation patterns of the IGF2 and IGF2R genes, although individual variation in their methylation patterns among bulls was observed. Mol. Reprod. Dev. 79:7784, 2012. (C) 2011 Wiley Periodicals, Inc.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.