Oxidative damage to DNA may play an important role in both normal ageing and in neurodegenerative diseases. The deleterious consequences of excessive oxidations and the pathophysiological role of reactive oxygen species have been intensively studied in Alzheimer's disease. Although the role of oxidative stress in the aetiology of Alzheimer's disease is still not clear, the detection of an increased damage status in the cells of patients could have important therapeutic implications. The levels of oxidative damage in peripheral lymphocytes of 24 Alzheimer's disease patients and of 21 age-matched controls were determined by comet assay applied to freshly isolated blood samples with oxidative lesion-specific DNA repair endonucleases (endonuclease III for oxidized pyrimidines, formamidopyrimidine glycosylase for oxidized purines). It was demonstrated that Alzheimer's disease is associated with elevated levels of oxidized pyrimidines and purines (p<0.0001) as compared with age-matched control subjects. It was also demonstrated that the comet assay is useful as a biomarker of oxidative DNA damage when used with oxidative lesion-specific enzymes.

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ABSTRACT. The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.

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Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.