Zymomonas mobilis is one of the most promising microorganisms for bioethanol production. However, its practical use on industrial scale is impeded by its high sensitivity to acetate, which is present in high concentration in pretreated biomass. This research develops an adaptive mutation method for generating acetate-tolerant strains for bioethanol production. The goal is to obtain Zymomonas mobilis strain capable of growing and producing ethanol in the presence of acetate at a concentration typical of a pretreated biomass (2-3%). The interplay between the ability of fermentative production of ethanol and acetate tolerance will be investigated through careful fermentation studies. The potential cross-tolerance to other inhibitors, commonly present in pretreated biomass will be evaluated. A preliminary study on the mechanism of acetate tolerance at the cell membrane level will be conducted. The strain developed through this research will be useful in bioethanol production from biomass. The insights into tolerance mechanisms gained through this study will allow a more rational approach to further engineer a better producing strain.

In recent years, the field of evolutionary dynamic optimisation has seen significant increase in scientific developments and contributions. This is as a result of its relevance in solving academic and real-world problems. Several techniques such as hyper-mutation, hyper-learning, hyper-selection, change detection and many more have been developed specifically for solving dynamic optimisation problems. However, the complex structure of algorithms employing these techniques make them unsuitable for real-world, real-time dynamic optimisation problem using embedded systems with limited memory. The work presented in this thesis focuses on a compact approach as an alternative to population based optimisation algorithm, suitable for solving real-time dynamic optimisation problems. Specifically, a novel compact dynamic optimisation algorithm suitable for embedded systems with limited memory is presented. Three novel dynamic approaches that augment and enhance the evolving properties of the compact genetic algorithm in dynamic environments are introduced. These are 1.) change detection scheme that measures the degree of dynamic change 2.) mutation schemes whereby the mutation rates is directly linked to the detected degree of change and 3.) change trend scheme the monitors change pattern exhibited by the system. The novel compact dynamic optimization algorithm outlined was applied to two differing dynamic optimization problems. This work evaluates the algorithm in the context of tuning a controller for a physical target system in a dynamic environment and solving a dynamic optimization problem using an artificial dynamic environment generator. The novel compact dynamic optimisation algorithm was compared to some existing dynamic optimisation techniques. Through a series of experiments, it was shown that maintaining diversity at a population level is more efficient than diversity at an individual level. Among the five variants of the novel compact dynamic optimization algorithm, the third variant showed the best performance in terms of response to dynamic changes and solution quality. Furthermore, it was demonstrated that information transfer based on dynamic change patterns can effectively minimize the exploration/exploitation dilemma in a dynamic environment.

Currently, human norovirus is the leading cause of acute gastroenteritis and accounts for most cases of foodborne illnesses in the United States each year. Due to its tissue culture inefficiency, studies of human norovirus have been crippled for more than forty years.Tulane virus (TV) stands out as a suitable surrogate of human norovirus given its high amino acid identity with human norovirus and its well-established cell culture system. It was first isolated from rhesus macaques (Macaca mulatta) in 2008 and identified as a novel Calicivirusrepresenting a new genus, Recovirus genus (Farkas et al., 2008). However, there are still unanswered questions about its infectious cycle and the essential factors for its infection. In this study, we have obtained a TV variant (the 9-6-17 strain) that has lost the binding ability to the B-type histo-blood group antigen (HBGA), which was proposed to be the receptor of both TV and human norovirus. In the first chapter, we outline how the sequence analysis,structural biology studies, and mutagenesis studies of the 9-6-17 TV strain have shed light on the interaction with its host cell receptor. To investigate the key residues for HBGA binding, we established the full-length infectious clone of the 9-6-17 TV strain. We present a highly selective transformation of serine 367, located in the predicted HBGA binding site, into a lysine residu e. Our results advance the understanding of genetic changes in TV required for adaptation to cell culture environments. Cryo-EM is an awarding winning technique that has been the greatest scientific breakthrough in recent years. It was awarded the Nobel Prize in Chemistry in 2017. Despite the technological advances of the direct electron detector and image processing software, several major roadblocks remain for high-resolution structure determination with cryo-EM. In the later chapters, we explored the most efficient way of using VPP to enhance image contrast, how to tackle the airwater interface problem by encapsulating target protein, how to reach a higher resolution by refining high order parameters, and the helical indexing problem in real space. These technical advances would benefit the whole cryo-EM community by providing convenient tools or insights for future directions.