The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.

Primary characterization of genes involved in the colony development of the insect pathogenic fungus Metarhizium anisopliae /

Strain improvement of the insect pathogenic fungus Metarhizium anisopUae is necessary to increase its virulence towards agricultural pests and thus improve its commercial efficacy. Nevertheless, the release of genetically modified conidia in crop fields may negatively affect the ecosystem. Controlling conidiation is a potential means of limiting the release of engineered strains since conidia are the infective propagules and the means of dispersal. The purpose of this study was to research the colony development of M. anisopUae to identify potential targets for genetic manipulation to control conidiation. Following Agrobacterium tumefaciem insertional mutagenesis, phenotypic mutants were characterized using Y-shaped adaptor dependent extension PCR. Four of 1 8 colony development recombinants had T-DNA flanking sequences with high homology to genes encoding known signaling pathway proteins that regulate pathogenesis and/or asexual development in filamentous fungi. Conidial density counts and insect bioassays suggested that a Serine/Threonine protein kinase COTl homolog is not essential for conidiation or virulence. Furthermore, a choline kinase homolog is important for conidiation, but not virulence. Finally, the regulator of G protein signaling CAG8 and a NADPH oxidase NoxA homolog are necessary for conidiation and virulence. These genes are candidates for further investigation into the regulatory pathways controlling conidiation to yield insight into promising gene targets for biocontrol strain improvement.

Characterisation and optimisation of the flavour of health-promoting, plantderived bitterants in functional beverages.

Flavour is a combination of taste, odour, and chemesthetic sensations. Close associations exist between these sensory modalities, and thus, the overall flavour of a food or beverage product can change when the intensity of one or more of these sensations is altered. Strategies to modify flavour are often utilized by the food industry, and are central to the engineering of new and reformulated products. For functional food and beverages, flavour modification is particularly important, as fortifying agents can elicit high levels of less than desirable sensations, such as bitterness and astringency. The application of various flavour modifying strategies can decrease the perceived intensity of these sensations, and in tum, improve the sensory profile of the product. This collection of studies describes the sensory characteristics of experimental functional beverages fortified with trans-resveratrol, (+)-catechin, and/or caffeine, and examines the impact of novel flavour modifying strategies on the perceived flavour of these beverages. In the first study, results demonstrate that the flavour profile of Cabemet Sauvignon wines fortified with 20 mglL and 200 mg/L of trans-resveratrol is not perceived as different compared to control wine (0 mglL). However, Riesling wine fortified with 200 mg/L is perceived as significantly higher in bitterness compared to 20 mglL and control. For some functional food formulations, alternative strategies for flavour modification are needed. Traditional methods, such as the addition of sucrose and sodium chloride, may decrease the perceived 'healthiness' of a product, and thus, may be sub-optimal. In a second study, high and low concentrations of five different bitter inhibiting compounds - 'bitter blockers' - (B-cyclodextrin, homoeridictyol sodium salt, carboxymethylcellulose - low viscosity, zinc sulfate, magnesium sulfate) were tested for their efficacy towards decreasing the bitterness of high and low concentrations of caffeine and (+)catechin - two health-relevant, plant-derived bitterants. B-cyclodextrin and homoeridictyol sodium salt were the most effective blockers at decreasing (+ )-catechin and caffeine, respectively. In addition to bitter blockers, additional flavour modifying strategies, either alone or in combination - may also be successful in functional food formulations. Both sucrose and rebaudioside A - a plant-derived sweetener - were effective at decreasing the bitterness of (+)catechin. When added to (+)-catechin along with B-cyc1odextrin, both sweeteners provided the most effective decrease in bitterness compared to binary, ternary, or quaternary mixtures of (+)catechin together with bitter blockers, sweeteners, andlor odourants. The perceived intensity of sensations elicited by sweeteners and odourants was not affected by the addition of bitter blockers, and thus, their impact within these complex matrices is minimal. In addition, withinmodal (taste-taste) compared to cross-modal (taste-odour) sensory interactions were more effective at decreasing the bitterness of (+ )-catechin. Overall, results from these studies demonstrate that certain novel, alternative flavour modifying approaches may be successful towards lowering the bitterness and astringency elicited by (+ )-catechin and caffeine in aqueous solutions.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

Dominion of Canada and Colony of Newfoundland Gazetteer and Classified Business Directory (1899)

Business directory for Canada and Newfoundland for the year 1899

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

A Woodruff Genealogy: Twelve Generations from The Colony of Connecticut in New England and The Province of Upper Canada 1636-1959

A Woodruff family genealogy in ten chapters. The chapters are titled: The Five New England Generations, Ezekiel's Seven Children, The Upper Canada Descendants, In Retrospect, Niagara (Newark), St. Davids, The War of 1812-1814, Some More Recent Portraits, Sources and References, Appendix. The first page of the book is inscribed "Best Wishes to Margaret and Percy from Norris", Oct. 5, 1959.

Using PageRank Algorithm in Analyzing Dictionary Graphs and PageRank in Dynamic Graphs

In this thesis we are going to analyze the dictionary graphs and some other kinds of graphs using the PagerRank algorithm. We calculated the correlation between the degree and PageRank of all nodes for a graph obtained from Merriam-Webster dictionary, a French dictionary and WordNet hypernym and synonym dictionaries. Our conclusion was that PageRank can be a good tool to compare the quality of dictionaries. We studied some artificial social and random graphs. We found that when we omitted some random nodes from each of the graphs, we have not noticed any significant changes in the ranking of the nodes according to their PageRank. We also discovered that some social graphs selected for our study were less resistant to the changes of PageRank.