Multi-objective improvement of software using co-evolution and smart seeding

Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner

Solubilization of Proteins in 2DE: An Outline

Protein solubilization for two-dimensional electrophoresis (2DE) has to break molecular interactions to separate the biological contents of the material of interest into isolated and intact polypeptides. This must be carried out in conditions compatible with the first dimension of 2DE, namely isoelectric focusing. In addition, the extraction process must enable easy removal of any nonprotein component interfering with the isoelectric focusing. The constraints brought in this process by the peculiar features of isoelectric focusing are discussed, as well as their consequences in terms of possible solutions and limits for the solubilization process

Polyphasic analysis of Acidovorax citrulli strains from northeastern Brazil

ABSTRACT Bacterial fruit blotch (BFB) of cucurbit plants is caused by Acidovorax citrulli and represents a serious concern to melon (Cucumis melo L.) growers worldwide, including those in Brazil. Thirty-four A. citrulli strains from different melon production areas of northeastern Brazil were characterized for their virulence on melon fruits and their substrate utilization and molecular profiles. Based on the analysis of BFB severity on melon fruits, the A. citrulli strains were divided into three groups, classified as mildly, moderately or highly virulent. Although host-related groups were not observed, the watermelon and ‘melão-pepino’ strains exhibited only low or moderate virulence on melon fruit. Substrate utilization profiles revealed that 94 % of the 95 tested compounds were used by A. citrulli strains as a carbon source. Overall, based on substrate utilization, low variability was observed with no relationship to host of origin. The formation of one group of A. citrulli strains based on Repetitive Sequence-based PCR (rep-PCR) analysis confirmed the low variability observed in the substrate utilization analyses. Bayesian inference based on the analysis of 23S rDNA partial sequence data resulted in one well-supported clade and clustered the strains with the A. citrulli-type species with high posterior probability support. Based on the markers used, the Brazilian A. citrulli strains belong to a single group, which corresponds to the previously described Group I for this bacterium in the United States

Device Nimbus: An Intelligent Middleware for Smarter Services for Health and Fitness

The world is experiencing an unprecedented explosion in the number of smart devices and mobile apps that are available to users. In the health and fitness domains, many of the devices and technologies in the market place are restricted to proprietary platforms, typically working in isolation with fixed hardware settings. Consequently, an important challenge is to investigate techniques for combining and analyzing data encapsulated by these ubiquitous technologies. In this paper, we introduce an intelligent middleware—Device Nimbus—to meet this challenge. The middleware supports the integration of distributed and heterogeneous mobile sensor data, enabling both context and predictive analysis. We describe a minimum viable product of Device Nimbus and report the results of preliminary tests spanning multiple data sources focused on fitness apps, illustrating the efficacy of the middleware