Cytosine Methylation Affects the Mutability of Neighboring Nucleotides in Germline and Soma

Methylated cytosines deaminate at higher rates than unmethylated cytosines, and the lesions they produce are repaired less efficiently. As a result, methylated cytosines are mutational hotspots. Here, combining rare polymorphism and base-resolution methylation data in humans, Arabidopsis thaliana, and rice (Oryza sativa), we present evidence that methylation state affects mutation dynamics not only at the focal cytosine but also at neighboring nucleotides. In humans, contrary to prior suggestions, we find that nucleotides in the close vicinity (±3 bp) of methylated cytosines mutate less frequently. Reduced mutability around methylated CpGs is also observed in cancer genomes, considering single nucleotide variants alongside tissue-of-origin-matched methylation data. In contrast, methylation is associated with increased neighborhood mutation risk in A. thaliana and rice. The difference in neighborhood mutation risk is less pronounced further away from the focal CpG and modulated by regional GC content. Our results are consistent with a model where altered risk at neighboring bases is linked to lesion formation at the focal CpG and subsequent long-patch repair. Our findings indicate that cytosine methylation has a broader mutational footprint than is commonly assumed.

Hypercube-Based Crowding Differential Evolution with Neighborhood Mutation for Multimodal Optimization

In reality, multiple optimal solutions are often necessary to provide alternative options in different occasions. Thus, multimodal optimization is important as well as challenging to find multiple optimal solutions of a given objective function simultaneously. For solving multimodal optimization problems, various differential evolution (DE) algorithms with niching and neighborhood strategies have been developed. In this article, a hypercube-based crowding DE with neighborhood mutation is proposed for such problems as well. It is characterized by the use of hypercube-based neighborhoods instead of Euclidean-distance-based neighborhoods or other simpler neighborhoods. Moreover, a self-adaptive method is additionally adopted to control the radius vector of a hypercube so as to guarantee the neighborhood size always in a reasonable range. In this way, the algorithm will perform a more accurate search in the sub-regions with dense individuals, but perform a random search in the sub-regions with only sparse individuals. Experiments are conducted in comparison with an outstanding DE with neighborhood mutation, namely NCDE. The results show that the proposed algorithm is promising and computationally inexpensive.

Parallel Niche Genetic Algorithm for UAV Fleet Stealth Coverage 3D Corridors Real-Time Planning

This paper presents a parallel improved niche genetic algorithm (PINGA) for 3D stealth coverage corridors real-time planning of unmanned aerial vehicles (UAVs) operating in a threat rich environment. 3D corridor was suggested to meet the diversity kinematics constraints of UAVs. Niche genetic algorithm (NGA) was improved by merging neighborhood mutation operator and hill climbing algorithm, and performed in parallel. Additionally, the crowding strategy based on high value targets was used to generate coverage trajectories in the area of interest (AOI). Preliminary results in virtual environments show that the approach for UAVs high quality flight corridors planning is real-time and effective.