Accumulating evidence shows that the severity and rapidity of onset of diabetic retinopathy are influenced by genetic factors. Expression of the nitric oxide synthases is altered in the retinal vasculature in the early stages of diabetic retinopathy. We analyzed the allele distribution of a polymorphic pentanucleotide repeat within the 5' upstream promoter region of the NOS2A gene in samples of diabetic patients. In diabetic patients from Northern Ireland, the 14-repeat allele of the NOS2A marker was significantly associated with the absence of diabetic retinopathy. Carriers of this repeat had 0.21-fold the relative risk of developing diabetic retinopathy than noncarriers of this allele. They also had significantly fewer renal and cardiovascular complications. The ability of differing numbers of (CCTTT)(n) pentanucleotide repeats to induce transcription of the NOS2A gene was analyzed using a luciferase reporter gene assay in transfected colonic carcinoma cells. Interleukin 1beta (IL-1beta) induction was most effective in constructs carrying the 14-repeat allele. When cells were incubated in 25 mM glucose to mimic the diabetic state, IL-1beta induction was inhibited in all cases, but to a significantly lesser extent with the 14-repeat allele. These unique properties of the 14-repeat allele may confer selective advantages in diabetic individuals, which may delay or prevent microvascular complications of diabetes.

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As is typical in evolutionary algorithms, fitness evaluation in GP takes the majority of the computational effort. In this paper we demonstrate the use of the Graphics Processing Unit (GPU) to accelerate the evaluation of individuals. We show that for both binary and floating point based data types, it is possible to get speed increases of several hundred times over a typical CPU implementation. This allows for evaluation of many thousands of fitness cases, and hence should enable more ambitious solutions to be evolved using GP.

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Intrinsic evolution is often limited to using standard electronic

Self-modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Genetic Programming founded on Cartesian Genetic Programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to p.

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