Association between polymorphisms in the progesterone receptor gene and endometriosis

The progesterone receptor (PR) is a candidate gene for the development of endometriosis, a complex disease with strong hormonal features, common in women of reproductive age. We typed the 306 base pair Alu insertion (AluIns) polymorphism in intron G of PR in 101 individuals, estimated linkage disequilibrium (LD) between five single-nucleotide polymorphisms (SNPs) across the PR locus in 980 Australian triads (endometriosis case and two parents) and used transmission disequilibrium testing (TDT) for association with endometriosis. The five SNPs showed strong pairwise LD, and the AluIns was highly correlated with proximal SNPs rs1042839 (Δ2 = 0.877, D9 = 1.00, P < 0.0001) and rs500760 (Δ2 = 0.438, D9 = 0.942, P < 0.0001). TDT showed weak evidence of allelic association between endometriosis and rs500760 (P = 0.027) but not in the expected direction. We identified a common susceptibility haplotype GGGCA across the five SNPs (P = 0.0167) in the whole sample, but likelihood ratio testing of haplotype transmission and non-transmission of the AluIns and flanking SNPs showed no significant pattern. Further, analysis of our results pooled with those from two previous studies suggested that neither the T2 allele of the AluIns nor the T1/T2 genotype was associated with endometriosis.

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Large scale participatory acoustic sensor data analysis : tools and reputation models to enhance effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

Using participatory action research in a community-based initiative addressing complex mental health needs

Objective: This paper describes the first phase of a larger project that utilizes participatory action research to examine complex mental health needs across an extensive group of stakeholders in the community. Method: Within an objective qualitative analysis of focus group discussions the social ecological model is utilized to explore how integrative activities can be informed, planned and implemented across multiple elements and levels of a system. Seventy-one primary care workers, managers, policy-makers, consumers and carers from across the southern metropolitan and Gippsland regions of Victoria, Australia took part in seven focus groups. All groups responded to an identical set of focusing questions. Results: Participants produced an explanatory model describing the service system, as it relates to people with complex needs, across the levels of social ecological analysis. Qualitative themes analysis identified four priority areas to be addressed in order to improve the system's capacity for working with complexity. These included: (i) system fragmentation; (ii) integrative case management practices; (iii) community attitudes; and (iv) money and resources. Conclusions: The emergent themes provide clues as to how complexity is constructed and interpreted across the system of involved agencies and interest groups. The implications these findings have for the development and evaluation of this community capacity-building project were examined from the perspective of constructing interventions that address both top-down and bottom-up processes.

Policy analysis of disaster health management in China

Humankind has been dealing with all kinds of disasters since the dawn of time. The risk and impact of disasters producing mass casualties worldwide is increasing, due partly to global warming as well as to increased population growth, increased density and the aging population. China, as a country with a large population, vast territory, and complex climatic and geographical conditions, has been plagued by all kinds of disasters. Disaster health management has traditionally been a relatively arcane discipline within public health. However, SARS, Avian Influenza, and earthquakes and floods, along with the need to be better prepared for the Olympic Games in China has brought disasters, their management and their potential for large scale health consequences on populations to the attention of the public, the government and the international community alike. As a result significant improvements were made to the disaster management policy framework, as well as changes to systems and structures to incorporate an improved disaster management focus. This involved the upgrade of the Centres for Disease Control and Prevention (CDC) throughout China to monitor and better control the health consequences particularly of infectious disease outbreaks. However, as can be seen in the Southern China Snow Storm and Wenchuan Earthquake in 2008, there remains a lack of integrated disaster management and efficient medical rescue, which has been costly in terms of economics and health for China. In the context of a very large and complex country, there is a need to better understand whether these changes have resulted in effective management of the health impacts of such incidents. To date, the health consequences of disasters, particularly in China, have not been a major focus of study. The main aim of this study is to analyse and evaluate disaster health management policy in China and in particular, its ability to effectively manage the health consequences of disasters. Flood has been selected for this study as it is a common and significant disaster type in China and throughout the world. This information will then be used to guide conceptual understanding of the health consequences of floods. A secondary aim of the study is to compare disaster health management in China and Australia as these countries differ in their length of experience in having a formalised policy response. The final aim of the study is to determine the extent to which Walt and Gilson’s (1994) model of policy explains how disaster management policy in China was developed and implemented after SARS in 2003 to the present day. This study has utilised a case study methodology. A document analysis and literature search of Chinese and English sources was undertaken to analyse and produce a chronology of disaster health management policy in China. Additionally, three detailed case studies of flood health management in China were undertaken along with three case studies in Australia in order to examine the policy response and any health consequences stemming from the floods. A total of 30 key international disaster health management experts were surveyed to identify fundamental elements and principles of a successful policy framework for disaster health management. Key policy ingredients were identified from the literature, the case-studies and the survey of experts. Walt and Gilson (1994)’s policy model that focuses on the actors, content, context and process of policy was found to be a useful model for analysing disaster health management policy development and implementation in China. This thesis is divided into four parts. Part 1 is a brief overview of the issues and context to set the scene. Part 2 examines the conceptual and operational context including the international literature, government documents and the operational environment for disaster health management in China. Part 3 examines primary sources of information to inform the analysis. This involves two key studies: • A comparative analysis of the management of floods in China and Australia • A survey of international experts in the field of disaster management so as to inform the evaluation of the policy framework in existence in China and the criteria upon which the expression of that policy could be evaluated Part 4 describes the key outcomes of this research which include: • A conceptual framework for describing the health consequences of floods • A conceptual framework for disaster health management • An evaluation of the disaster health management policy and its implementation in China. The research outcomes clearly identified that the most significant improvements are to be derived from improvements in the generic management of disasters, rather than the health aspects alone. Thus, the key findings and recommendations tend to focus on generic issues. The key findings of this research include the following: • The health consequences of floods may be described in terms of time as ‘immediate’, ‘medium term’ and ‘long term’ and also in relation to causation as ‘direct’ and ‘indirect’ consequences of the flood. These two aspects form a matrix which in turn guides management responses. • Disaster health management in China requires a more comprehensive response throughout the cycle of prevention, preparedness, response and recovery but it also requires a more concentrated effort on policy implementation to ensure the translation of the policy framework into effective incident management. • The policy framework in China is largely of international standard with a sound legislative base. In addition the development of the Centres for Disease Control and Prevention has provided the basis for a systematic approach to health consequence management. However, the key weaknesses in the current system include: o The lack of a key central structure to provide the infrastructure with vital support for policy development, implementation and evaluation. o The lack of well-prepared local response teams similar to local government based volunteer groups in Australia. • The system lacks structures to coordinate government action at the local level. The result of this is a poorly coordinated local response and lack of clarity regarding the point at which escalation of the response to higher levels of government is advisable. These result in higher levels of risk and negative health impacts. The key recommendations arising from this study are: 1. Disaster health management policy in China should be enhanced by incorporating disaster management considerations into policy development, and by requiring a disaster management risk analysis and disaster management impact statement for development proposals. 2. China should transform existing organizations to establish a central organisation similar to the Federal Emergency Management Agency (FEMA) in the USA or the Emergency Management Australia (EMA) in Australia. This organization would be responsible for leading nationwide preparedness through planning, standards development, education and incident evaluation and to provide operational support to the national and local government bodies in the event of a major incident. 3. China should review national and local plans to reflect consistency in planning, and to emphasize the advantages of the integrated planning process. 4. Enhance community resilience through community education and the development of a local volunteer organization. China should develop a national strategy which sets direction and standards in regard to education and training, and requires system testing through exercises. Other initiatives may include the development of a local volunteer capability with appropriate training to assist professional response agencies such as police and fire services in a major incident. An existing organisation such as the Communist Party may be an appropriate structure to provide this response in a cost effective manner. 5. Continue development of professional emergency services, particularly ambulance, to ensure an effective infrastructure is in place to support the emergency response in disasters. 6. Funding for disaster health management should be enhanced, not only from government, but also from other sources such as donations and insurance. It is necessary to provide a more transparent mechanism to ensure the funding is disseminated according to the needs of the people affected. 7. Emphasis should be placed on prevention and preparedness, especially on effective disaster warnings. 8. China should develop local disaster health management infrastructure utilising existing resources wherever possible. Strategies for enhancing local infrastructure could include the identification of local resources (including military resources) which could be made available to support disaster responses. It should develop operational procedures to access those resources. Implementation of these recommendations should better position China to reduce the significant health consequences experienced each year from major incidents such as floods and to provide an increased level of confidence to the community about the country’s capacity to manage such events.

Systematics of the Ustilago-Sporisorium-Macalpinomyces complex of smut fungi

Smut fungi are important pathogens of grasses, including the cultivated crops maize, sorghum and sugarcane. Typically, smut fungi infect the inflorescence of their host plants. Three genera of smut fungi (Ustilago, Sporisorium and Macalpinomyces) form a complex with overlapping morphological characters, making species placement problematic. For example, the newly described Macalpinomyces mackinlayi possesses a combination of morphological characters such that it cannot be unambiguously accommodated in any of the three genera. Previous attempts to define Ustilago, Sporisorium and Macalpinomyces using morphology and molecular phylogenetics have highlighted the polyphyletic nature of the genera, but have failed to produce a satisfactory taxonomic resolution. A detailed systematic study of 137 smut species in the Ustilago-Sporisorium- Macalpinomyces complex was completed in the current work. Morphological and DNA sequence data from five loci were assessed with maximum likelihood and Bayesian inference to reconstruct a phylogeny of the complex. The phylogenetic hypotheses generated were used to identify morphological synapomorphies, some of which had previously been dismissed as a useful way to delimit the complex. These synapomorphic characters are the basis for a revised taxonomic classification of the Ustilago-Sporisorium-Macalpinomyces complex, which takes into account their morphological diversity and coevolution with their grass hosts. The new classification is based on a redescription of the type genus Sporisorium, and the establishment of four genera, described from newly recognised monophyletic groups, to accommodate species expelled from Sporisorium. Over 150 taxonomic combinations have been proposed as an outcome of this investigation, which makes a rigorous and objective contribution to the fungal systematics of these important plant pathogens.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

The relationship between parents’ self-efficacy beliefs, outcome expectations, child behaviour, and management of atopic dermatitis

Atopic dermatitis (AD) is a chronic inflammatory skin condition, characterized by intense pruritis, with a complex aetiology comprising multiple genetic and environmental factors. It is a common chronic health problem among children, and along with other allergic conditions, is increasing in prevalence within Australia and in many countries worldwide. Successful management of childhood AD poses a significant and ongoing challenge to parents of affected children. Episodic and unpredictable, AD can have profound effects on children’s physical and psychosocial wellbeing and quality of life, and that of their caregivers and families. Where concurrent child behavioural problems and parenting difficulties exist, parents may have particular difficulty achieving adequate and consistent performance of the routine management tasks that promote the child’s health and wellbeing. Despite frequent reports of behaviour problems in children with AD, past research has neglected the importance of child behaviour to parenting confidence and competence with treatment. Parents of children with AD are also at risk of experiencing depression, anxiety, parenting stress, and parenting difficulties. Although these factors have been associated with difficulty in managing other childhood chronic health conditions, the nature of these relationships in the context of child AD management has not been reported. This study therefore examined relationships between child, parent, and family variables, and parents’ management of child AD and difficult child behaviour, using social cognitive and self-efficacy theory as a guiding framework. The study was conducted in three phases. It employed a quantitative, cross-sectional study design, accessing a community sample of 120 parents of children with AD, and a sample of 64 child-parent dyads recruited from a metropolitan paediatric tertiary referral centre. In Phase One, instruments designed to measure parents’ self-reported performance of AD management tasks (Parents’ Eczema Management Scale – PEMS) and parents’ outcome expectations of task performance (Parents’ Outcome Expectations of Eczema Management Scale – POEEMS) were adapted from the Parental Self-Efficacy with Eczema Care Index (PASECI). In Phase Two, these instruments were used to examine relationships between child, parent, and family variables, and parents’ self-efficacy, outcome expectations, and self-reported performance of AD management tasks. Relationships between child, parent, and family variables, parents’ self-efficacy for managing problem behaviours, and reported parenting practices, were also examined. This latter focus was explored further in Phase Three, in which relationships between observed child and parent behaviour, and parent-reported self-efficacy for managing both child AD and problem behaviours, were explored. Phase One demonstrated the reliability of both PEMS and POEEMS, and confirmed that PASECI was reliable and valid with modification as detailed. Factor analyses revealed two-factor structures for PEMS and PASECI alike, with both scales containing factors related to performing routine management tasks, and managing the child’s symptoms and behaviour. Factor analysis was also applied to POEEMS resulting in a three-factor structure. Factors relating to independent management of AD by the parent, involving healthcare professionals in management, and involving the child in management of AD were found. Parents’ self-efficacy and outcome expectations had a significant influence on self-reported task performance. In Phase Two, relationships emerged between parents’ self-efficacy and self-reported performance of AD management tasks, and AD severity, child behaviour difficulties, parent depression and stress, conflict over parenting issues, and parents’ relationship satisfaction. Using multiple linear regressions, significant proportions of variation in parents’ self-efficacy and self-reported performance of AD management tasks were explained by child behaviour difficulties and parents’ formal education, and self-efficacy emerged as a likely mediator for the relationships between both child behaviour and parents’ education, and performance of AD management tasks. Relationships were also found between parents’ self-efficacy for managing difficult child behaviour and use of dysfunctional parenting strategies, and child behaviour difficulties, parents’ depression and stress, conflict over parenting issues, and relationship satisfaction. While significant proportions of variation in self-efficacy for managing child behaviour were explained by both child behaviour and family income, family income was the only variable to explain a significant proportion of variation in parent-reported use of dysfunctional parenting strategies. Greater use of dysfunctional parenting strategies (both lax and authoritarian parenting) was associated with more severe AD. Parents reporting lower self-efficacy for managing AD also reported lower self-efficacy for managing difficult child behaviour; likewise, less successful self-reported performance of AD management tasks was associated with greater use of dysfunctional parenting strategies. When child and parent behaviour was directly observed in Phase Three, more aversive child behaviour was associated with lower self-efficacy, less positive outcome expectations, and poorer self-reported performance of AD management tasks by parents. Importantly, there were strong positive relationships between these variables (self-efficacy, outcome expectations, and self-reported task performance) and parents’ observed competence when providing treatment to their child. Less competent performance was also associated with greater parent-reported child behaviour difficulties, parent depression and stress, parenting conflict, and relationship dissatisfaction. Overall, this study revealed the importance of child behaviour to parents’ confidence and practices in the contexts of child AD and child behaviour management. Parents of children with concurrent AD and behavioural problems are at particular risk of having low self-efficacy for managing their child’s AD and difficult behaviour. Children with more severe AD are also at higher risk of behaviour problems, and thus represent a high-risk group of children whose parents may struggle to manage the disease successfully. As one of the first studies to examine the role and correlates of parents’ self-efficacy in child AD management, this study identified a number of potentially modifiable factors that can be targeted to enhance parents’ self-efficacy, and improve parent management of child AD. In particular, interventions should focus on child behaviour and parenting issues to support parents caring for children with AD and improve child health outcomes. In future, findings from this research will assist healthcare teams to identify parents most in need of support and intervention, and inform the development and testing of targeted multidisciplinary strategies to support parents caring for children with AD.

Quantitative approaches to content analysis : identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Cellulosic ethanol from sugarcane bagasse in Australia : exploring industry feasibility through systems analysis, techno-economic assessment and pilot plant development

Overcoming many of the constraints to early stage investment in biofuels production from sugarcane bagasse in Australia requires an understanding of the complex technical, economic and systemic challenges associated with the transition of established sugar industry structures from single product agri-businesses to new diversified multi-product biorefineries. While positive investment decisions in new infrastructure requires technically feasible solutions and the attainment of project economic investment thresholds, many other systemic factors will influence the investment decision. These factors include the interrelationships between feedstock availability and energy use, competing product alternatives, technology acceptance and perceptions of project uncertainty and risk. This thesis explores the feasibility of a new cellulosic ethanol industry in Australia based on the large sugarcane fibre (bagasse) resource available. The research explores industry feasibility from multiple angles including the challenges of integrating ethanol production into an established sugarcane processing system, scoping the economic drivers and key variables relating to bioethanol projects and considering the impact of emerging technologies in improving industry feasibility. The opportunities available from pilot scale technology demonstration are also addressed. Systems analysis techniques are used to explore the interrelationships between the existing sugarcane industry and the developing cellulosic biofuels industry. This analysis has resulted in the development of a conceptual framework for a bagassebased cellulosic ethanol industry in Australia and uses this framework to assess the uncertainty in key project factors and investment risk. The analysis showed that the fundamental issue affecting investment in a cellulosic ethanol industry from sugarcane in Australia is the uncertainty in the future price of ethanol and government support that reduces the risks associated with early stage investment is likely to be necessary to promote commercialisation of this novel technology. Comprehensive techno-economic models have been developed and used to assess the potential quantum of ethanol production from sugarcane in Australia, to assess the feasibility of a soda-based biorefinery at the Racecourse Sugar Mill in Mackay, Queensland and to assess the feasibility of reducing the cost of production of fermentable sugars from the in-planta expression of cellulases in sugarcane in Australia. These assessments show that ethanol from sugarcane in Australia has the potential to make a significant contribution to reducing Australia’s transportation fuel requirements from fossil fuels and that economically viable projects exist depending upon assumptions relating to product price, ethanol taxation arrangements and greenhouse gas emission reduction incentives. The conceptual design and development of a novel pilot scale cellulosic ethanol research and development facility is also reported in this thesis. The establishment of this facility enables the technical and economic feasibility of new technologies to be assessed in a multi-partner, collaborative environment. As a key outcome of this work, this study has delivered a facility that will enable novel cellulosic ethanol technologies to be assessed in a low investment risk environment, reducing the potential risks associated with early stage investment in commercial projects and hence promoting more rapid technology uptake. While the study has focussed on an exploration of the feasibility of a commercial cellulosic ethanol industry from sugarcane in Australia, many of the same key issues will be of relevance to other sugarcane industries throughout the world seeking diversification of revenue through the implementation of novel cellulosic ethanol technologies.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

External orientation and business model adaptation in young and nascent firms : preliminary analysis from the CAUSSE project

Discovering factors that help or impede business model change is an important quest, both for researchers and practitioners. In this study we present preliminary findings based on the CAUSEE survey of young and nascent firms in Australia. In particular, we seek to determine an association between business model adaptation and external orientation among young and nascent firms within the random sample and amongst an oversample of high potential firms. The concept of external orientation is made operational by asking respondents whether, and to what extent, they rely on certain sources of advice and information. We find that high potential firms are more likely to have made at least some change to their business model, that greater use of external sources of advice is generally significantly associated with business model adaptation, but also that there appear to be different patterns of behaviour between the random sample and the over sample.

Confirmatory factor analysis of the Behaviour of Young Novice Drivers Scale (BYNDS)

The greatly increased risk of being killed or injured in a car crash for the young novice driver has been recognised in the road safety and injury prevention literature for decades. Risky driving behaviour has consistently been found to contribute to traffic crashes. Researchers have devised a number of instruments to measure this risky driving behaviour. One tool developed specifically to measure the risky behaviour of young novice drivers is the Behaviour of Young Novice Drivers Scale (BYNDS) (Scott-Parker et al., 2010). The BYNDS consists of 44 items comprising five subscales for transient violations, fixed violations, misjudgement, risky driving exposure, and driving in response to their mood. The factor structure of the BYNDS has not been examined since its development in a matched sample of 476 novice drivers aged 17-25 years. Method: The current research attempted to refine the BYNDS and explore its relationship with the self-reported crash and offence involvement and driving intentions of 390 drivers aged 17-25 years (M = 18.23, SD = 1.58) in Queensland, Australia, during their first six months of independent driving with a Provisional (intermediate) driver’s licence. A confirmatory factor analysis was undertaken examining the fit of the originally proposed BYNDS measurement model. Results: The model was not a good fit to the data. A number of iterations removed items with low factor loadings, resulting in a 36-item revised BYNDS which was a good fit to the data. The revised BYNDS was highly internally consistent. Crashes were associated with fixed violations, risky driving exposure, and misjudgement; offences were moderately associated with risky driving exposure and transient violations; and road-rule compliance intentions were highly associated with transient violations. Conclusions: Applications of the BYNDS in other young novice driver populations will further explore the factor structure of both the original and revised BYNDS. The relationships between BYNDS subscales and self-reported risky behaviour and attitudes can also inform countermeasure development, such as targeting young novice driver non-compliance through enforcement and education initiatives.