Networks as a novel tool for studying team ball sports as complex social systems

This paper describes and evaluates the novel utility of network methods for understanding human interpersonal interactions within social neurobiological systems such as sports teams. We show how collective system networks are supported by the sum of interpersonal interactions that emerge from the activity of system agents (such as players in a sports team). To test this idea we trialled the methodology in analyses of intra-team collective behaviours in the team sport of water polo. We observed that the number of interactions between team members resulted in varied intra-team coordination patterns of play, differentiating between successful and unsuccessful performance outcomes. Future research on small-world networks methodologies needs to formalize measures of node connections in analyses of collective behaviours in sports teams, to verify whether a high frequency of interactions is needed between players in order to achieve competitive performance outcomes.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

The benefits of not managing change and not communicating : towards a complex systems view of communication in evolving organisations

The relationship between change in organisations and communication about change in organisations can be analysed as a particular case of a general debate in social theory about the extent to which reality is socially constructed. Social constructivists emphasise the role of language in the construction of social realities, enacted through controlling the message agenda; material determinists assert that economic and social structural factors are more constitutive of reality as seen in strategies emphasising structural and resource interventions. Here we define a third view of language and materiality - one that leads to the potential for a reflexive, experimental approach to change based on the view that organisations are complex evolving systems.

Schooling and refugees : engaging with the complex trajectories of globalisation

This paper examines the complexities associated with educating a mobile and politically marginalised population, refugee students, in the state of Queensland, Australia. Historically, schools have been national institutions concerned with social reproduction and citizenship formation with a focus on spatially fixed populations. While education authorities in much of the developed world now acknowledge the need to prepare students for a more interconnected world of work and opportunity, they have largely failed to provide systemic support for one category of children on the move - refugees. We begin this paper with a discussion of forced migration and its links with ‘globalisation’. We then present our research findings about the educational challenges confronting individual refugee youth and schools in Queensland. This is followed with a summary of good practice in refugee education. The paper concludes with a discussion of how nation-states might play a more active role in facilitating transitions to citizenship for refugee youth.

Stochastic simulation of chemical reactions in spatially complex media

Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.

Domestication to crop improvement: Genetic resources for sorghum and saccharum (Andropogoneae)

Background Both sorghum (Sorghum bicolor) and sugarcane (Saccharum officinarum) are members of the Andropogoneae tribe in the Poaceae and are each other's closest relatives amongst cultivated plants. Both are relatively recent domesticates and comparatively little of the genetic potential of these taxa and their wild relatives has been captured by breeding programmes to date. This review assesses the genetic gains made by plant breeders since domestication and the progress in the characterization of genetic resources and their utilization in crop improvement for these two related species. Genetic Resources The genome of sorghum has recently been sequenced providing a great boost to our knowledge of the evolution of grass genomes and the wealth of diversity within S. bicolor taxa. Molecular analysis of the Sorghum genus has identified close relatives of S. bicolor with novel traits, endosperm structure and composition that may be used to expand the cultivated gene pool. Mutant populations (including TILLING populations) provide a useful addition to genetic resources for this species. Sugarcane is a complex polyploid with a large and variable number of copies of each gene. The wild relatives of sugarcane represent a reservoir of genetic diversity for use in sugarcane improvement. Techniques for quantitative molecular analysis of gene or allele copy number in this genetically complex crop have been developed. SNP discovery and mapping in sugarcane has been advanced by the development of high-throughput techniques for ecoTILLING in sugarcane. Genetic linkage maps of the sugarcane genome are being improved for use in breeding selection. The improvement of both sorghum and sugarcane will be accelerated by the incorporation of more diverse germplasm into the domesticated gene pools using molecular tools and the improved knowledge of these genomes.