Biofilms are highly organized bacterial structures that are attached to a surface. They are ubiquitous in nature and may be detrimental, causing numerous types of illnesses in living organisms. Biofilms in the human oral cavity are the main cause of dental caries and periodontal diseases and can act as a source for pathogenic organisms to spread within the body and cause various types of systemic diseases. Streptococcus mutans is the primary etiological agent of dental caries, the single most chronic childhood disease. In many cases, quorum sensing (QS) is required for initial formation and subsequent development of biofilms and the signaling molecule autoinducer 2 (AI- 2) has been well studied as an inter-species QS signaling molecule. However, recent reports also suggest that AI-2-mediated signaling is important for intra-species biofilm formation in both Gram-negative and positive bacteria. Therefore, there is significant interest in understanding the role of different QS signals such as AI-2 in oral biofilm formation. Microfluidic devices provide biomimetic environments and offer a simple method for executing multiple stimuli experiments simultaneously, thus, can be an extremely powerful tool in the study of QS in biofilms. In this study, we report conditions that support the development of S. mutans biofilms in microchannel microfluidic devices, and the effects of extracellular addition of chemically synthesized (S)-4,5-dihydroxy-2,3-pentanedione (DPD; precursor of AI-2) on mono-species S. mutans luxS (AI-2 deficient strain) biofilm formation using a gradient generating microfluidic device. S. mutans wild type (WT) and luxS biofilms were developed in nutrient rich medium (25% brain heart infusion medium, BHI + 1% sucrose) for up to 48 h. Maximum biofilm formation with both strains was observed after 24 h, with distinct structure and organization. No changes in S. mutans luxS biofilm growth or structure were observed upon exposure to different concentrations of AI-2 in a gradient generating device (0 to 5 M). These results were also validated by using a standard 96-well plate assay and by verifying the uptake of AI-2 by S. mutans luxS. Our data suggest that extracellular addition of AI-2 does not complement the luxS deletion in S. mutans with respect to biofilm formation.

Autoinducer 2 (AI-2) molecules produced by bacterial cells are thought to be involved in controlling a variety of bacterial cellular processes by coordinated gene and protein expression. Previous work in our laboratory has shown that ground beef contains compounds that can interfere with AI-2-mediated bioluminescence expression in Vibrio. harveyi. The underlying hypothesis of this work was that AI-2 molecules affect the protein expression in Escherichia coli O157:H7 and AI-2 inhibitory molecules negate the influence of AI-2 molecules. The main objectives of this study were to identify, characterize, and isolate the factors responsible for inhibition of AI-2 molecules from ground beef extracts, elucidate the role of LuxS/AI-2 cell signaling system in E. coli O157:H7 protein expression, and determine if inhibitory factors present in ground beef extract can negate the influence of AI-2 molecules on the protein expression. Using a solvent extraction procedure and gas chromatography analysis, AI-2 inhibitory factors present in ground beef extracts were identified as both medium and long chain fatty acids. When identified fatty acids were tested at different concentrations for AI-2 inhibition, AI-2 inhibition ranging from 25% to 90% was observed. Both ground beef extracts and mixture of selected fatty acids also resulted in 2- to 4-fold reduced AI-2 influenced biofilm formation by E. coli K12 cells. Identification of LuxS/AI-2-mediated protein expression in E. coli O157:H7 was conducted using two dimensional gel electrophoresis. Protein expression analysis showed that the LuxS/AI-2 system modulates the expression of proteins involved in different cellular processes such as carbohydrate and amino acid metabolism, stress response, and formation of flagella and motility. When AI-2 inhibitory factors were added along with AI-2 molecules, the expression patterns of three AI-2-influenced proteins (GlmS, SpeE, and NikA) were changed suggesting that AI-2 inhibitors can negate the influence of AI-2 molecules on protein expression of selected proteins. Collectively, these results highlight that proteins associated with different cellular processes in E. coli O157:H7 can be modulated depending on whether cells are in contact with AI-2 molecules in the presence or absence of AI-2 inhibitory factors.

Avian influenza virus (AIV) is a type A virus of the family Orthomyxoviridae and its outbreaks not only cause economic losses in poultry, but also are worldwide threats to human health. The phenotypic changes in host cells induced by pathogens are always accompanied by remarkable changes in gene expression. Therefore understanding the gene expression profile of infected cells at the global level is important to get insights into interactions between hosts and viruses. Different genomic approaches have been utilized in the current study to investigate the host-AIV interactions in chickens. The Ser to Asn mutation on position 631 in the chicken Mx1 protein was reported to result in a positive antiviral function in vitro. With AIV infection, the Mx1 mRNA expression levels in heterozygous birds were significantly up-regulated. Additional mutations on the chicken Mx1 coding region were identified by sequencing. The results showed that most identified mutations were co-segregated with S631N mutation except one insertion in the position of 1544bp in the heterozygous birds. We speculate this insertion might be related to the up-regulation of mRNA expression of heterozygous birds with AIV infection. The miRNAs play critical roles in biological processes and are important effectors in host-pathogen interactions. The miRNA deep sequencing was used to profile miRNAs in AIV infected or non-infected chickens. Differentially expressed miRNAs identified have expanded our knowledge in the functions of these potential immune related chicken miRNAs regulating host response to AIV infection. Both microarray and transcriptome analysis by RNA-Seq were used in the current study to investigate the global gene expression of host response to AIV infection. Through the comprehensive analysis, a list of strong candidate miRNAs such as miR-32 and their host target genes including Mx1 were identified for further elucidating the regulatory mechanism of host-AIV interaction. In summary, we have identified many important candidate host genes and miRNAs which play important roles in the modulation of host response to AIV infection using genomic approaches. Further investigation of underline regulatory mechanisms of these genes, miRNAs or related pathways, followed by functional analysis, could lay solid foundation for understanding cellular and molecular mechanisms of the host-AIV interactions, thereby, pave a way for the development of novel protective strategies against AIV infection in chickens.

Context. Video games are a big entertainment industry. Many video games let players play against or together. Some video games also make it possible for players to play against or together with computer controlled players, called bots. Artificial Intelligence (AI) is used to create bots. Objectives. This thesis aims to implement cooperative behaviors between two bots and determine if the behaviors lead to an increase in win ratio. This means that the bots should be able to cooperate in certain situations, such as when they are attacked or when they are attacking. Methods. The bots win ratio will be tested with a series of quantitative experiments where in each experiment two teaming bots with cooperative behavior will play against two teaming bots without any cooperative behavior. The data will be analyzed with a t-test to determine if the data are statistical significant. Results and Conclusions. The results show that cooperative behavior can increase performance of two teaming Real Time Strategy bots against a non-cooperative team with two bots. However, the performance could either be increased or decreased depending on the situation. In three cases there were an increase in performance and in one the performance was decreased. In three cases there was no difference in performance. This suggests that more research is needed for these cases.

This thesis investigates cooperative and intelligent control of autonomous multi-robot systems in a dynamic, unstructured and unknown environment and makes significant original contributions with regard to self-deterministic learning for robot cooperation, evolutionary optimization of robotic actions, improvement of system robustness, vision-based object tracking, and real-time performance. A distributed multi-robot architecture is developed which will facilitate operation of a cooperative multi-robot system in a dynamic and unknown environment in a self-improving, robust, and real-time manner. It is a fully distributed and hierarchical architecture with three levels. By combining several popular AI, soft computing, and control techniques such as learning, planning, reactive paradigm, optimization, and hybrid control, the developed architecture is expected to facilitate effective autonomous operation of cooperative multi-robot systems in a dynamically changing, unknown, and unstructured environment. A machine learning technique is incorporated into the developed multi-robot system for self-deterministic and self-improving cooperation and coping with uncertainties in the environment. A modified Q-learning algorithm termed Sequential Q-learning with Kalman Filtering (SQKF) is developed in the thesis, which can provide fast multi-robot learning. By arranging the robots to learn according to a predefined sequence, modeling the effect of the actions of other robots in the work environment as Gaussian white noise and estimating this noise online with a Kalman filter, the SQKF algorithm seeks to solve several key problems in multi-robot learning. As a part of low-level sensing and control in the proposed multi-robot architecture, a fast computer vision algorithm for color-blob tracking is developed to track multiple moving objects in the environment. By removing the brightness and saturation information in an image and filtering unrelated information based on statistical features and domain knowledge, the algorithm solves the problems of uneven illumination in the environment and improves real-time performance.

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This thesis investigates cooperative and intelligent control of autonomous multi-robot systems in a dynamic, unstructured and unknown environment and makes significant original contributions with regard to self-deterministic learning for robot cooperation, evolutionary optimization of robotic actions, improvement of system robustness, vision-based object tracking, and real-time performance. A distributed multi-robot architecture is developed which will facilitate operation of a cooperative multi-robot system in a dynamic and unknown environment in a self-improving, robust, and real-time manner. It is a fully distributed and hierarchical architecture with three levels. By combining several popular AI, soft computing, and control techniques such as learning, planning, reactive paradigm, optimization, and hybrid control, the developed architecture is expected to facilitate effective autonomous operation of cooperative multi-robot systems in a dynamically changing, unknown, and unstructured environment. A machine learning technique is incorporated into the developed multi-robot system for self-deterministic and self-improving cooperation and coping with uncertainties in the environment. A modified Q-learning algorithm termed Sequential Q-learning with Kalman Filtering (SQKF) is developed in the thesis, which can provide fast multi-robot learning. By arranging the robots to learn according to a predefined sequence, modeling the effect of the actions of other robots in the work environment as Gaussian white noise and estimating this noise online with a Kalman filter, the SQKF algorithm seeks to solve several key problems in multi-robot learning. As a part of low-level sensing and control in the proposed multi-robot architecture, a fast computer vision algorithm for color-blob tracking is developed to track multiple moving objects in the environment. By removing the brightness and saturation information in an image and filtering unrelated information based on statistical features and domain knowledge, the algorithm solves the problems of uneven illumination in the environment and improves real-time performance.

PhD / The aim of this thesis was to investigate the possibility of integrating frozen-thawed boar semen into reproductive technologies and into commercial production of pigs in Australia. This was to be achieved by establishing a semen freezing and AI regime that was of a standard acceptable to industry, and integrating the resultant frozen-thawed sperm into other reproductive technologies, such as flow cytometric sperm sorting and IVF. Initially, a protocol for freezing and thawing boar semen was established, based on the method described by Westendorf et al. (1975) and attempts were made to modify this protocol to improve the post-thaw sperm quality, as determined by in vitro assessment of motility, acrosome integrity and longevity. First, the egg yolk used in the freezing extenders was investigated, and the chicken yolk was replaced with either duck or quail yolk. It was shown that there was no benefit in substituting yolk from duck or quail for the chicken yolk traditionally used in freezing extender. Second, the effect of seminal plasma addition to the freezing extender, or seminal plasma addition to resuspension medium post-thaw was tested. Incorporating whole seminal plasma into the freezing extender at levels above 50% was found to be detrimental to post-thaw sperm quality. Reducing levels to 20% of the final volume improved acrosome integrity, but adversely affected motility of sperm. However, adding 20% seminal plasma to the resuspension medium used after thawing of boar semen had no significant influence on sperm quality compared with resuspension in medium without seminal plasma. The antioxidant catalase, and the iron chelator desferal added to the freezing extender, did not improve post-thaw sperm quality, nor was any benefit seen with addition of these substrates to the resuspension medium post-thaw. However, the bioactive phospholipid PAF and its regulating enzyme PAF:AH appeared to enhance post-thaw motility and acrosome integrity of sperm, respectively, when added to the semen pre-freezing. Unfortunately, due to the restrictions imposed on rPAF:AH as a research drug, it was not possible to test the in vivo effects at this time. After the in vitro experiments were completed, the in vivo fertility of frozen-thawed sperm was tested using the optimal freezing protocol and a novel technology, enabling non-surgical deep intrauterine insemination of sows. The aim was to establish the lowest possible dose of frozen-thawed sperm that could be used, without compromising fertility. Successful pregnancies were achieved with doses as low as 62.5 x 106 frozen-thawed sperm but the farrowing rates were too low to be practicable on a commercial scale. This is the first report of litters born after insemination of such a low dose of frozen-thawed sperm and using the novel DIU insemination technique. However, it was concluded that a double dose of 250 x 106 frozen-thawed sperm was the minimum dose required for maintaining acceptable fertility. Reduction in sperm numbers to such an extent made it possible to consider non-surgical insemination of sex-sorted, frozen-thawed semen. Previously, pregnancies had been achieved only after surgical insemination of sex-sorted boar sperm, or with DIU insemination of unfrozen sperm, immediately after sex-sorting. The low numbers of sex-sorted sperm available restricted the inseminate dose used here to 50 x106 motile sperm. A litter of 5 piglets was born after a low-dose, DIU insemination of sex-sorted, frozen-thawed sperm. This is the first report of piglets born after insemination with sex-sorted frozen-thawed sperm and non-surgical insemination. The low farrowing rate achieved in this experiment prompted the investigation of integrating sex-sorted, frozen-thawed boar sperm into IVF. Morulae were produced after IVF with sex-sorted, frozen-thawed sperm and successfully transferred using non-surgical techniques. This is the first report of pregnancy achieved with non-surgical transfer of embryos produced after IVF and IVC of IVM oocytes with sex-sorted, frozen-thawed boar sperm. Unfortunately, the pregnancy did not hold, and the embryos were lost prior to Day 32, but PCR of non-transferred embryos confirmed successful pre-selection of sex. Overall, this thesis demonstrated that it is still not economically feasible to incorporate frozen-thawed boar semen into the commercial production of pigs although it has considerable application in breeding programmes. However, the development of novel techniques enabling reduction in sperm dose, and for non-surgical transfer of embryos into recipient sows and incorporation of frozen-thawed semen into these technologies means that progress is being made with the integration of reproductive technologies and frozen-thawed semen into the pig industry.