Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).

Timing the Emergence of Resistance to Anti-HIV Drugs with Large Genetic Barriers

New antiretroviral drugs that offer large genetic barriers to resistance, such as the recently approved inhibitors of HIV-1 protease, tipranavir and darunavir, present promising weapons to avert the failure of current therapies for HIV infection. Optimal treatment strategies with the new drugs, however, are yet to be established. A key limitation is the poor understanding of the process by which HIV surmounts large genetic barriers to resistance. Extant models of HIV dynamics are predicated on the predominance of deterministic forces underlying the emergence of resistant genomes. In contrast, stochastic forces may dominate, especially when the genetic barrier is large, and delay the emergence of resistant genomes. We develop a mathematical model of HIV dynamics under the influence of an antiretroviral drug to predict the waiting time for the emergence of genomes that carry the requisite mutations to overcome the genetic barrier of the drug. We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments. Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately. Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.

Whole exome sequencing in an Indian family links Coats plus syndrome and dextrocardia with a homozygous novel CTC1 and a rare HES7 variation

Background: Coats plus syndrome is an autosomal recessive, pleiotropic, multisystem disorder characterized by retinal telangiectasia and exudates, intracranial calcification with leukoencephalopathy and brain cysts, osteopenia with predisposition to fractures, bone marrow suppression, gastrointestinal bleeding and portal hypertension. It is caused by compound heterozygous mutations in the CTC1 gene. Case presentation: We encountered a case of an eight-year old boy from an Indian family with manifestations of Coats plus syndrome along with an unusual occurrence of dextrocardia and situs inversus. Targeted resequencing of the CTC1 gene as well as whole exome sequencing (WES) were conducted in this family to identify the causal variations. The identified candidate variations were screened in ethnicity matched healthy controls. The effect of CTC1 variation on telomere length was assessed using Southern blot. A novel homozygous missense mutation c.1451A > C (p.H484P) in exon 9 of the CTC1 gene and a rare 3'UTR known dbSNP variation (c.*556 T > C) in HES7 were identified as the plausible candidates associated with this complex phenotype of Coats plus and dextrocardia. This CTC1 variation was absent in the controls and we also observed a reduced telomere length in the affected individual's DNA, suggesting its likely pathogenic nature. The reported p.H484P mutation is located in the N-terminal 700 amino acid regionthat is important for the binding of CTC1 to ssDNA through its two OB domains. WES data also showed a rare homozygous missense variation in the TEK gene in the affected individual. Both HES7 and TEK are targets of the Notch signaling pathway. Conclusions: This is the first report of a genetically confirmed case of Coats plus syndrome from India. By means of WES, the genetic variations in this family with unique and rare complex phenotype could be traced effectively. We speculate the important role of Notch signaling in this complex phenotypic presentation of Coats plus syndrome and dextrocardia. The present finding will be useful for genetic diagnosis and carrier detection in the family and for other patients with similar disease manifestations.

Characterization of human symptomatic rotavirus isolates MP409 and MP480 having ‘long’ RNA electropherotype and subgroup I specificity, highly related to the P6[1],G8 type bovine rotavirus A5, from Mysore, India

In an epidemiological study of symptomatic human rotaviruses in Mysore, India during 1993 and 1994, isolates MP409 and MP480 were isolated from two children suffering from severe, acute dehydrating diarrhea. Both isolates exhibited 'long' RNA pattern and subgroup I specificity suggesting the likelihood of their animal origin. Both isolates did not react with monoclonal antibodies (MAbs) specific for serotypes G1 to G6 as well as CIO. To determine the genetic origin of these isolates, complete nucleotide sequences of genes encoding the outer capsid proteins VP4 and VP7, nonstructural proteins NSP1 and NSP3 and viral enterotoxin protein NSP4 from MP409 and partial sequences of genes from MP480 were determined. Comparison of the 5' and 3' terminal sequences of 250 nucleotides revealed complete identity of the gene sequences in both strains suggesting that MP409 and MP480 are two different isolates of a single strain. Comparison of the nucleotide and deduced amino acid sequences of VP4, VP7, NSP1 and NSP3 of MP409 with published sequences of strains belonging to different serotypes revealed that both outer capsid proteins VP4 and VP7 and NSP1 are highly related to the respective proteins from the P6[1], G8 type bovine rotavirus A5 isolated from a calf with diarrhoea in Thailand and that the NSP3 is highly homologous to that of bovine rotaviruses. The NSP 1 protein showed greatest sequence identity with NSP4s belonging to the KUN genetic group to which NSP4s from human G2 type strains and bovine rotaviruses belong. MP409 and MP480 likely signify interspecies transmission of P6[1], G8 type strains from cattle to humans and represent the first P6[1] type rotaviruses isolated in humans. These and our previous studies on the asymptomatic neonatal strain I321 are of evolutionary and epidemiological significance in the context of close association of majority of the Indian population with cattle.

A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm

The K-means algorithm for clustering is very much dependent on the initial seed values. We use a genetic algorithm to find a near-optimal partitioning of the given data set by selecting proper initial seed values in the K-means algorithm. Results obtained are very encouraging and in most of the cases, on data sets having well separated clusters, the proposed scheme reached a global minimum.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.