Myopia is a common ocular condition worldwide and the mechanism of myopia is still not clear. A number of animal models of myopia and refractive error development have been proposed. The fact that form deprivation myopia could be induced in tilapia fish, as shown previously in my research, suggests the possibility that tilapia could be a new animal model for myopia research. In the first part of this thesis the tilapia model was perfected and then, based on this model, the effect of systemic hormones (thyroid hormones) associated with eye and body development was investigated during refractive error development. Lastly, the physiological and morphological changes on the retina were further studied with optical coherence tomography (OCT). In these experiments, significant amounts of myopia, and hyperopia were induced within two weeks using goggles with lens inserts as in other higher vertebrate animal models, e.g. chicks. The results from form deprivation treatment also show that the sensitivity of tilapia eyes may be an age related effect during the emmetropization process. The larger the fish, the less hyperopic the fish eye, though the small eye artefact may be a factor. The susceptibility of the refractive development of the eye to the visual environment may be also linked to plasma hormone levels. It was found that induced refractive errors could be shifted in the hyperopic direction with high levels of thyroid hormones. Also, after 2 weeks of treatment with negative or positive lens/goggles, the tilapia retina becomes thinner or thicker, respectively. When the goggles are removed, the thickness of the retina changes within hours and gradually returns to normal. However, the circadian retinomotor movement is a complicating factor since it affects the retinal thickness measurement with OCT at some time points. In conclusion, tilapia represent a good lower vertebrate model for myopia research, suggesting a universal mechanism of myopia development, which may involve systemic hormones and immediate, short term retinal responses.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 83-93). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 83-93). Also available on the Internet.

Thèse (M.Ed.) -- Université du Québec à Chicoutimi, 2006. / Bibliogr.: f. [96]-101. Document électronique également accessible en format PDF. CaQCU

Gene expression regulatory networks are molecular networks which describe interactions among gene products in terms of biochemical reactions. This helps us understand the molecular mechanisms underlying important biological processes as well as cell functioning as a whole. For instance, the phenomenon of bacterial competence, whereby a bacterium enters a transiently differentiated state, incorporating DNA fragments from its environment into its genome, has been studied with the help of such gene regulatory circuits (Suel et al., 2006; Maamar and Dubnau, 2005). As a result, a genetic circuit has been taken into account in order to describe the transition from a vegetative state to a transient state of competence and vice versa. In this work, we are going to study a genetic circuit presented by Suel et al. (2007) to describe this dynamical behaviour. The authors introduce model reduction techniques to study the behaviour of stochastic chemical system of X species by means of an adiabatic two dimensional model. While the adiabatic model helps us understand about the dynamics near the steady state, it gives an incorrect description of the time-scales of the competent state. For this reason, it is necessary to build up a model which better describes the system realistically. In the thesis, I propose an approximate two-dimensional model of the full high-dimensional system and from that, the dynamics of the system can be simulated more accurately compared to that of Suel et al. (2007). I then show how to put the noise back into the approximate model to be able come up with a stochastic model which can mathematically describe the dynamical behaviour of the original high dimensional system. I also found out that the evolution of the system is not well approximated by a Langevin process. This leads to a gap between the real behavior which is described by Gillespie's stochastic simulation and the Langevin approximation. To overcome this, I have fixed the stochastic Langevin model by incorporating empirically tunable noise into the model so as to obtain a similar behaviour as observed in the original system. I also introduce the chemical Fokker-Planck equation aimed to estimate the probability density function of species concentrations which are involved in the biochemical system.

Navigation is at the heart of today’s mobile robotic systems. The use of landmarks for the guidance of autonomous systems is an essential part of the process. Shape, size, and colour are some of the properties of the landmarks. In this work biological and other methods have been developed for navigating a simulated mobile robot back to its home. Optical flow which is not a property of the landmarks but a property of the camera motion, has been used for building topological maps which are used to localise a robot. The advantage of this approach is that there is no need for storing or retrieving images. Any computation is performed on the basis of vectors. Metric maps are generated using a parsimonious localisation and mapping algorithm using a laser range finder. The purpose of this research is to effectively navigate a robot to its home position using computationally efficient biological and other techniques for navigation. Biology is seen as an alternative solution to the problems robots’ encounter which include algorithmic complexity, performance, and power consumption among others. Biological inspiration provides simple, yet effective methods for the solutions of such problems. The careful examination of such methods has twofold gain. The study of the principles of biological organisms entails making better autonomous systems that will, in turn help us perceive and understand better the underlying mechanisms of biological organisms. It is therefore essential to have an understanding of how biological and robotic systems work. This work discusses the techniques and strategies found both in animals and robots. It then goes on to describe the implementation of a series of simulation algorithms inspired by biology and other fields that assist in the navigation process, and in particular, homing. Their results are discussed and analysed. The novelty of this research lies in its methods for robot homing that make use of optical flow information to recognise a location as well as methods based on a laser range finder to avoid obstacles, perform path planning, localise a robot, and map the environment.

Biological evolution contains a general trend of increasing complexity of the most complex organisms. But artificial evolution experiments based on the mechanisms described in the current theory generally fail to reproduce this trend; instead, they commonly show systematic trends of complexity minimisation. In this dissertation we seek evolutionary mechanisms that can explain these apparently conflicting observations. To achieve this we use a reverse engineering approach by building computational simulations of evolution. One highlighted problem is that even if complexity is beneficial, evolutionary simulations struggle with apparent roadblocks that prevent them from scaling to complexity. Another is that even without roadblocks, it is not clear what drives evolution to become more complex at all. With respect to the former, a key roadblock is how to evolve ‘irreducibly complex’ or ‘nondecomposable’ functions. Evidence from biological evolution suggests a common way to achieve this is by combining existing functions – termed ‘tinkering’ or ‘building block evolution’. But in simulation this approach generally fails to scale across multiple levels of organisation in a recursive manner. We provide a model that identifies the problem hindering recursive evolution as increasing ‘burden’ in the form of ‘internal selection’ as joined functions become more complex. We show how having an ontological development process that occurs by local growth, as present in most complex biological organisms, resolves this problem, enabling evolution to occur recursively. Meanwhile, to understand what drives complexity in evolution we provide a model showing that under certain conditions a well-studied concept from the computational study of algorithms – complexity lower bounds – applies in evolution. The model shows how the ‘difference’ between the conditions required by an organism’s replicator and its external environment results in a minimum complexity floor that varies as the external environment changes. We find that selection in such a system produces a system-wide, overall trend of increasing complexity of the most complex organisms (as environments are colonised), coupled with local trends of complexity minimisation in individual environments (as evolution seeks to minimise its cost of resources) –thereby resolving the tension between biological observations and theoretical outcomes. Our simulations and analytic results demonstrate (a) how evolution can, when complexity is beneficial, scale to complexity over multiple organisational levels, and (b) the conditions in which complexity is beneficial in evolution. These models describe a set of phenotypic, ontogenetic and environmental conditions that are generally present in biological evolution, in which evolution consistently generates an overall trend of increasing complexity of the most complex organisms.

The investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with standard laboratory techniques, and even when this is possible it is generally time consuming, complex and expensive. Recent work has shown how a simplified behavioural approach to modelling neurons can allow “virtual” experiments to be carried out that map the behaviour of a simulated structure onto a hypothetical biological one, with correlation of behaviour rather than underlying connectivity. The problems with such approaches are numerous. The first is the difficulty of simulating realistic aggregates efficiently, the second is making sense of the results and finally, it would be helpful to have an implementation that could be synthesised to hardware for acceleration. This work presents a VHDL implementation of a neuron model which is verified through simulations of the Caenorhabditis Elegans (C.Elegans) locomotory system. The C.Elegans system is synthesized into hardware showing a massive improvement in simulation time because the hardware design runs in real-time, meaning a 19 second simulation takes 19 seconds vs. the 1.5 hours taken by the CAD simulation. Logic cells using the VHDL neuron model are produced and verified through simulation to demonstrate the deterministic side of modelling neuronal circuits. The C.Elegans design is then analysed using these neuron logic cells to produce a simple logic version of C.Elegans which produces the same outputs given the same inputs as the Neuron C.Elegans model. Finally the hardware neuron concept was extended to the case of a general purpose programmable neuron array designed to have 100 neurons and 200 synapses The configuration of the neurons and synapses is written to the device over a simple SPI bus, with a second SPI bus used to simultaneously write enable data and read the current states of the neuron outputs. The design was demonstrated to work correctly using the reference C.Elegans design.

A generic model of the interaction between sexual and asexual morphs in an ecological context was developed from the lotka-Volterra model of the population dynamics of two competing species (Doncaster, Pound & Cox, 2000). This revealed the threshold difference between the resource niches of sexual and asexual populations that allows coexistence between the two morphs. Coexistence depended on the respective carrying capacities of the sexual and asexual competitors, which we showed to be contingent upon the intrinsic growth capacity of the sexual population. The analytical model was extended to study (a) competition between a sexual population and numerous asexual clones; (b) competition between two metapopulation. A special formulation of Slatkin’s (1974) three-dimensional model of two competing metapopulations was developed with Lotka-Volterra dynamics, which yielded novel predictions for the behaviour of local and regional communities of species. Coexistence between sexual and asexual morphs maybe unsustainable due to the accumulation of clonal diversity over time, and indeed coexistence is rarely observed in nature. To study the dynamics of this accumulation, a stochastic model was developed for competition between asexual clones and a genetically diverse sexual population that exploits a range of resource niches. A model of the accumulation of deleterious mutations over time was incorporated into this framework. Monte-Carlo simulations demonstrated that a sexual population may, over time, exclude asexual clones whose fitness deteriorates due to the accumulation of deleterious mutations. These results suggest that whilst coexistence between sexual and asexual morphs is possible over ecological timescales the long-term outcome of an asexual invasion is determined by: 1) The relationship between genetic variation and niche breadth in the sexual population. 2) The rate as which the relative fitness of the asexual population declines with mutation accumulation. Reciprocal scenarios of the invasion of an asexual population by sexual mutants and sexual colonists are also considered.

Gait and face biometrics have a unique advantage in that they can be used when images are acquired at a distance and signals are at too low a resolution to be perceived by other biometrics. Given such situations, some traits can be difficult to extract automatically but can still be perceived semantically using human vision. It is contended that such semantic annotations are usable as soft biometric signatures, useful for identification tasks. Feature subset selection techniques are employed to compare the distinguishing ability of individual semantically described physical traits. Their identification ability is also explored, both in isolation and in the improvement of the recognition rates of some associated gait biometric signatures using fusion techniques. This is the first approach to explore semantic descriptions of physiological human traits as used alone or to complement primary biometric techniques to facilitate recognition and analysis of surveillance video. Potential traits to be described are explored and justified against their psychological and practical merits. A novel dataset of semantic annotations is gathered describing subjects in two existing biometric datasets. Two applications of these semantic features and their associated biometric signatures are explored using the data gathered. We also draw on our experiments as a whole to highlight those traits thought to be most useful in assisting biometric recognition overall. Effective analysis of surveillance data by humans relies on semantic retrieval of the data which has been enriched by semantic annotations. A manual annotation process is time-consuming and prone to error due to various factors. We explore the semantic content-based retrieval of surveillance captured subjects. Working under the premise that similarity of the chosen biometric signature implies similarity of certain semantic traits, a set of semantic retrieval experiments are performed using well established Latent Semantic Analysis techniques.