Pairwise similarity of TopSig document signatures

This paper analyses the pairwise distances of signatures produced by the TopSig retrieval model on two document collections. The distribution of the distances are compared to purely random signatures. It explains why TopSig is only competitive with state of the art retrieval models at early precision. Only the local neighbourhood of the signatures is interpretable. We suggest this is a common property of vector space models.

Radiotherapy Monte Carlo simulation using cloud computing technology

Cloud computing allows for vast computational resources to be leveraged quickly and easily in bursts as and when required. Here we describe a technique that allows for Monte Carlo radiotherapy dose calculations to be performed using GEANT4 and executed in the cloud, with relative simulation cost and completion time evaluated as a function of machine count. As expected, simulation completion time decreases as 1=n for n parallel machines, and relative simulation cost is found to be optimal where n is a factor of the total simulation time in hours. Using the technique, we demonstrate the potential usefulness of cloud computing as a solution for rapid Monte Carlo simulation for radiotherapy dose calculation without the need for dedicated local computer hardware as a proof of principal. Funding source Cancer Australia (Department of Health and Ageing) Research Grant 614217

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

NOS1AP is associated with increased severity of PTSD and depression in untreated combat veterans

Background Post traumatic stress disorder (PTSD) and depressive disorder are over represented in combat veterans. Veterans with both disorders have an increased risk of suicide. The nitric oxide synthase 1 adaptor protein (NOS1AP) gene, which modulates stress-evoked N-methyl-D-aspartate (NMDA) activity, was investigated in combat veterans. Methods A comprehensive genetic analysis of NOS1AP and its association with PTSD was investigated in Vietnam combat veterans with PTSD (n=121) and a group of healthy control individuals (n=237). PTSD patients were assessed for symptom severity and level of depression using the Mississippi Scale for Combat-Related PTSD and the Beck Depression Inventory-II (BDI). Results The G allele of NOS1AP SNP rs386231 was significantly associated with PTSD (p = 0.002). Analysis of variance revealed significant differences in BDI-II and Mississippi scores between genotypes for rs386231 with the GG genotype associated with increased severity of depression (p = 0.002 F = 6.839) and higher Mississippi Scale for Combat-Related PTSD scores (p = 0.033). Haplotype analysis revealed that the C/G haplotype (rs451275/rs386231) was significantly associated with PTSD (p = 0.001). Limitations The sample sizes in our study were not sufficient to detect SNP associations with very small effects. In addition the study was limited by its cross sectional design. Conclusions This is the first study reporting that a variant of the NOS1AP gene is associated with PTSD. Our data also suggest that a genetic variant in NOS1AP may increase the susceptibility to severe depression in patients with PTSD and increased risk for suicide.

KPNA3 variation is associated with schizophrenia, major depression, opiate dependence and alcohol dependence

KPNA3 is a gene that has been linked to schizophrenia susceptibility. In this study we investigated the possible association between KPNA3 variation and schizophrenia. To investigate a wider role of KPNA3 across psychiatric disorders we also analysed major depression, PTSD, nicotine dependent, alcohol dependent and opiate dependent cohorts. Using a haplotype block-based gene-tagging approach we genotyped six KPNA3 single nucleotide polymorphisms (SNPs) in 157 schizophrenia patients, 121 post-traumatic stress disorder patients, 120 opiate dependent patients, 231 alcohol dependent patients, 147 nicotine dependent patients and 266 major depression patients. One SNP rs2273816 was found to be significantly associated with schizophrenia, opiate dependence and alcohol dependence at the genotype and allele level. Major depression was also associated with rs2273816 but only at the allele level. Our study suggests that KPNA3 may contribute to the genetic susceptibility to schizophrenia as well as other psychiatric disorders.

Managing older workers during a period of tight labour supply

This article reports on a recent survey of employer attitudes and policies towards older workers in Australia at a time of sustained economic growth and ongoing concerns about labour shortages. Findings from a survey of 590 employers with more than 50 employees in the State of Queensland point to an unusually strong orientation towards the recruitment of older workers among respondents, although the retraining of older workers is not prioritised by the majority. The issue of workforce ageing is viewed as being of medium-term importance by the majority of respondents, although for a substantial number the issue is of immediate concern. Both sector and organisation size are predictive of the application of a broad range of policies targeting older workers, with public-sector and larger organisations more likely to be active. Concerns about workforce ageing and labour supply are predictive of employer behaviours regarding older workers, suggesting that sustained policy making may be emerging in response to population ageing over and above more immediate concerns about labour shortages and that this broad thrust of organisational policy making may be immune to the point in the economic cycle. This study found no evidence that the flexible firm will not countenance an ageing workforce.

A novel DRD2 single-nucleotide polymorphism associated with schizophrenia predicts age of onset : HapMap tag-single-nucleotide polymorphism analysis

Background: Dopamine D2 receptor (DRD2) is thought to be critical in regulating the dopaminergic pathway in the brain which is known to be important in the aetiology of schizophrenia. It is therefore not surprising that most antipsychotic medication acts on the Dopamine D2 receptor. DRD2 is widely expressed in brain, levels are reduced in brains of schizophrenia patients and DRD2 polymorphisms have been associated with reduced brain expression. We have previously identified a genetic variant in DRD2, rs6277 to be strongly implicated in schizophrenia susceptibility. Methods: To identity new associations in the DRD2 gene with disease status and clinical severity, we genotyped seven single nucleotide polymorphisms (SNPs) in DRD2 using a multiplex mass spectrometry method. SNPs were chosen using a haplotype block-based gene-tagging approach so the entire DRD2 gene was represented. Results: One polymorphism rs2734839 was found to be significantly associated with schizophrenia as well as late onset age. Individuals carrying the genetic variation were more than twice as likely to have schizophrenia compared to controls. Conclusions: Our results suggest that DRD2 genetic variation is a good indicator for schizophrenia risk and may also be used as a predictor age of onset.

Towards Bayesian experimental design for nonlinear models that require a large number of sampling times

The use of Bayesian methodologies for solving optimal experimental design problems has increased. Many of these methods have been found to be computationally intensive for design problems that require a large number of design points. A simulation-based approach that can be used to solve optimal design problems in which one is interested in finding a large number of (near) optimal design points for a small number of design variables is presented. The approach involves the use of lower dimensional parameterisations that consist of a few design variables, which generate multiple design points. Using this approach, one simply has to search over a few design variables, rather than searching over a large number of optimal design points, thus providing substantial computational savings. The methodologies are demonstrated on four applications, including the selection of sampling times for pharmacokinetic and heat transfer studies, and involve nonlinear models. Several Bayesian design criteria are also compared and contrasted, as well as several different lower dimensional parameterisation schemes for generating the many design points.

Lignocellulosics as a renewable feedstock for chemical industry : chemical hydrolysis and pretreatment processes

Lignocellulosic materials including agricultural, municipal and forestry residues, and dedicated bioenergy crops offer significant potential as a renewable feedstock for the production of fuels and chemicals. These products can be chemically or functionally equivalent to existing products that are produced from fossil-based feedstocks. To unlock the potential of lignocellulosic materials, it is necessary to pretreat or fractionate the biomass to make it amenable to downstream processing. This chapter explores current and developing technologies for the pretreatment and fractionation of lignocellulosic biomass for the production of chemicals and fuels.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Chris Howlett - "Flashbacks"

This is a review of Brisbane artist Christopher Howlett's 2009 exhibitions at Metro Arts and the Brisbane Town Hall. The review discusses the artist's use of 'modding' and other digital hacking strategies to explore the ethical dimensions of topics including Michael Jackson and the war in Iraq.

Abundance of Natural Riparian Forests and Tree Plantations in the Amudarya Delta of Uzbekistan and their impact on emissions of soil-borne greenhouse gases

Through a forest inventory in parts of the Amudarya river delta, Central Asia, we assessed the impact of ongoing forest degradation on the emissions of greenhouse gases (GHG) from soils. Interpretation of aerial photographs from 2001, combined with data on forest inventory in 1990 and field survey in 2003 provided comprehensive information about the extent and changes of the natural tugai riparian forests and tree plantations in the delta. The findings show an average annual deforestation rate of almost 1.3% and an even higher rate of land use change from tugai forests to land with only sparse tree cover. These annual rates of deforestation and forest degradation are higher than the global annual forest loss. By 2003, the tugai forest area had drastically decreased to about 60% compared to an inventory in 1990. Significant differences in soil GHG emissions between forest and agricultural land use underscore the impact of the ongoing land use change on the emission of soil-borne GHGs. The conversion of tugai forests into irrigated croplands will release 2.5 t CO2 equivalents per hectare per year due to elevated emissions of N2O and CH4. This demonstrates that the ongoing transformation of tugai forests into agricultural land-use systems did not only lead to a loss of biodiversity and of a unique ecosystem, but substantially impacts the biosphere-atmosphere exchange of GHG and soil C and N turnover processes.

Successful outcomes in vocational education and training courses : how pedagogy and expectations influence achievement

This paper reports on a four year Australian Research Council funded Linkage Project titled Skilling Indigenous Queensland, conducted in regional areas of Queensland, Australia from 2009 to 2013. The project sought to investigate vocational education, training (VET) and teaching, Indigenous learners’ needs, employer cultural and expectations and community culture and expectations to identify best practice in numeracy teaching for Indigenous VET learners. Specifically it focused on ways to enhance the teaching and learning of courses and the associated mathematics in such courses to benefit learners and increase their future opportunities of employment. To date thirty-nine teachers/trainers/teacher aides and two hundred and thirty-one students consented to participate in the project. Nine VET courses were nominated to be the focus on the study. This paper focuses on questionnaire and interview responses from four trainers, two teacher aides and six students. In recent years a considerable amount of funding has been allocated to increasing Indigenous Peoples’ participation in education and employment. This increased funding is predicated on the assumption that it will make a difference and contribute to closing the education gap between Indigenous and non-Indigenous Australians (Council of Australia Governments, 2009). The central tenet is that access to education for Indigenous People will create substantial social and economic benefits for regional and remote Indigenous People. The project’s aim is to address some of the issues associated with the gap. To achieve the aims, the project adopted a mixed methods design aimed at benefitting research participants and included: participatory collaborative action research (Kemmis & McTaggart, 1988) and, community research (Smith, 1999). Participatory collaborative action research refers to a is a “collective, self-reflective enquiry undertaken by participants in social situations in order to improve the rationality and justice of their own social and educational practices” (Kemmis et al., 1988, p. 5). Community research is described as an approach that “conveys a much more intimate, human and self-defined space” (p. 127). Community research relies on and validates the community’s own definitions. As the project is informed by the social at a community level, it is described as “community action research or emancipatory research” (Smith, 1999, p. 127). It seeks to demonstrate benefit to the community, making positive differences in the lives of Indigenous People and communities. The data collection techniques included survey questionnaires, video recording of teaching and learning processes, teacher reflective video analysis of teaching, observations, semi-structured interviews and student numeracy testing. As a result of these processes, the findings indicate that VET course teachers work hard to adopt contextualising strategies to their teaching, however this process is not always straight forward because of the perceptions of how mathematics has been taught and learned historically. Further teachers, trainers and students have high expectations of one another with the view to successful outcomes from the courses.