Incidence of resistance to benzimidazole fungicides in a field population of Venturia inaequalis /

The allele-specific polymerase chain reaction (PCR) was used to screen for the presence of benomyl resistance, and to characterize their levels and frequencies in field populations of Venturia inaequalis during two seasons. Three hundred isolates of V. inaequalis were collected each season from infected leaves of MalusX domestica. Borkh c.v. Mcintosh. The trees used were sprayed in the year prior to collection with five applications of benomyl, its homologue Azindoyle, or water. Monoconidial isolates of V. inaequalis were grown on 2% potato dextrose agar (PDA) for four weeks. Each isolate was taken from a single lesion from a single leaf. Total genomic DNA was extracted from the four week old colonies of V. inaequalis, prepared and used as a template in PCR reactions. PCR reactions were achieved by utilizing allele-specific primers. Each primer was designed to amplify fragments from a specific allele. Primer Vin was specific for mutations conferring the ben^^"^ phenotype. It was expected to amplify a 171 bp. DNA fragment from the ben^"^ alleles only. Primers BenHR and BenMR were specific for mutations conferring the ben"" and ben'^'' phenotypes, respectively. They were expected to amplify 172 bp. and 165 bp. DNA fragments from the ben"" and ben"^" alleles, respectively. Of the 953 isolates tested, 414 (69.9%) were benomyl sensitive (ben^) and 179 (30.1%) were benomyl resistant. All the benomyl resistant alleles were ben^"", since neither the ben"" nor the ben"" alleles were detected. Frequencies of benomyl resistance were 23%, 24%, and 23% for the 1997 collections, and were 46%, 26% and 38% for the 1998 collections for benomyl, Azindoyle and water treatments, respectively. Growth assay was performed to evaluate the applicability of using PCR in monitoring benomyl resistance in fungal field populations. Tests were performed on 14 isolates representing the two phenotypes (ben^ and ben^"'' alleles) characterized by PCR. Results of those tests were in agreement with PCR results. Enzyme digestion was also used to evaluate the accuracy and reliability of PCR products. The mutation associated with the ben^"'' phenotype creates a unique site for the endonuclease enzyme Bsh^236^ allowing the use of enzyme digestion. Isolates characterized by PCR as ben^'^'^ alleles had this restriction site for the SsA7l2361 enzyme. The most time consuming aspect of this study was growing fungal isolates on culture media for DNA extraction. In addition, the risk of contamination or losing the fungus during growth processes was relatively high. A technique for extracting DNA directly from lesions on leaves has been used (Luck and Gillings 1 995). In order to apply this technique in experiments designed to monitor fungicide resistance, a lesion has to be homogeneous for fungicide sensitivity. For this purpose, PCR protocol was used to determine lesion homogeneity. One hundred monoconidial isolates of V. inaequalis from 10 lesions (10-conidia/ lesion) were tested for their phenotypes with respect to benomyl sensitivity. Conidia of six lesions were homogeneous, while conidia of the remaining lesions were mixtures of ben^ and ben^ phenotypes. Neither the ben" nor the ben' phenotype was detected.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.