Identification and analysis of venom gland-specific genes from the coastal taipan (Oxyuranus scutellatus) and related species

Australian terrestrial elapid snakes contain amongst the most potently toxic venoms known. However, despite the well-documented clinical effects of snake bite, little research has focussed on individual venom components at the molecular level. To further characterise the components of Australian elapid venoms, a complementary (cDNA) microarray was produced from the venom gland of the coastal taipan (Oxyuranus scutellatus) and subsequently screened for venom gland-specific transcripts. A number of putative toxin genes were identified, including neurotoxins, phospholipases, a pseudechetoxin-like gene, a venom natriuretic peptide and a nerve growth factor together with other genes involved in cellular maintenance. Venom gland-specific components also included a calglandulin-like protein implicated in the secretion of toxins from the gland into the venom. These toxin transcripts were subsequently identified in seven other related snake species, producing a detailed comparative analysis at the cDNA and protein levels. This study represents the most detailed description to date of the cloning and characterisation of different genes associated with envenomation from Australian snakes.

Time complexity and convergence analysis of domain theoretic Picard method

We present an implementation of the domain-theoretic Picard method for solving initial value problems (IVPs) introduced by Edalat and Pattinson [1]. Compared to Edalat and Pattinson's implementation, our algorithm uses a more efficient arithmetic based on an arbitrary precision floating-point library. Despite the additional overestimations due to floating-point rounding, we obtain a similar bound on the convergence rate of the produced approximations. Moreover, our convergence analysis is detailed enough to allow a static optimisation in the growth of the precision used in successive Picard iterations. Such optimisation greatly improves the efficiency of the solving process. Although a similar optimisation could be performed dynamically without our analysis, a static one gives us a significant advantage: we are able to predict the time it will take the solver to obtain an approximation of a certain (arbitrarily high) quality.

Distributed processing, reconfigurable processes and active network

The fast spread of the Internet and the increasing demands of the service are leading to radical changes in the structure and management of underlying telecommunications systems. Active networks (ANs) offer the ability to program the network on a per-router, per-user, or even per-packet basis, thus promise greater flexibility than current networks. To make this new network paradigm of active network being widely accepted, a lot of issues need to be solved. Management of the active network is one of the challenges. This thesis investigates an adaptive management solution based on genetic algorithm (GA). The solution uses a distributed GA inspired by bacterium on the active nodes within an active network, to provide adaptive management for the network, especially the service provision problems associated with future network. The thesis also reviews the concepts, theories and technologies associated with the management solution. By exploring the implementation of these active nodes in hardware, this thesis demonstrates the possibility of implementing a GA based adaptive management in the real network that being used today. The concurrent programming language, Handel-C, is used for the description of the design system and a re-configurable computer platform based on a FPGA process element is used for the hardware implementation. The experiment results demonstrate both the availability of the hardware implementation and the efficiency of the proposed management solution.