Intraspecific evolution of Rhizoctonia solani AG-1 IA associated with soybean and rice in Brazil based on polymorphisms at the ITS-5.8S rDNA operon

Rhizoctonia solani AG-1 IA causes leaf blight on soybean and rice. Despite the fact that R. solani AG-1 IA is a major pathogen affecting soybean and rice in Brazil and elsewhere in the world, little information is available on its genetic diversity and evolution. This study was an attempt to reveal the origin, and the patterns of movement and amplification of epidemiologically significant genotypes of R. solani AG-1 IA from soybean and rice in Brazil. For inferring intraspecific evolution of R. solani AG-1 IA sampled from soybean and rice, networks of ITS-5.8S rDNA sequencing haplotypes were built using the statistical parsimony algorithm from Clement et al. (2000) Molecular Ecology 9: 1657-1660. Higher haplotype diversity (Nei M 1987, Molecular Evolutionary Genetics Columbia University Press, New york: 512p.) was observed for the Brazilian soybean sample of R. solani AG-1 IA (0.827) in comparison with the rest of the world sample (0.431). Within the south-central American clade (3-2), four haplotypes of R. solani AG-1 IA from Mato Grosso, one from Tocantins, one from Maranhao, and one from Cuba occupied the tips of the network, indicating recent origin. The putative ancestral haplotypes had probably originated either from Mato Grosso or Maranhao States. While 16 distinct haplotypes were found in a sample of 32 soybean isolates of the pathogen, the entire rice sample (n=20) was represented by a single haplotype (haplotype 5), with a worldwide distribution. The results from nested-cladistic analysis indicated restricted gene flow with isolation by distance (or restricted dispersal by distance in nonsexual species) for the south-central American clade (3-2), mainly composed by soybean haplotypes.

Global optimization using a genetic algorithm with hierarchically structured population

This paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems. The population has individuals distributed in several overlapping clusters, each one with a leader and a variable number of support individuals. The hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree. Computational tests evaluate different population structures, population sizes and crossover operators for better algorithm performance. A set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature, namely, two genetic algorithms, a simulated annealing, a differential evolution and a particle swarm optimization. The results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons: the number of function evaluations and rate of success. The method also has a superior performance if the number of problems solved is taken into account. (C) 2013 Elsevier B.V. All rights reserved.