A sequential Monte Carlo approach to the sequential design for discriminating between rival continuous data models

Here we present a sequential Monte Carlo approach to Bayesian sequential design for the incorporation of model uncertainty. The methodology is demonstrated through the development and implementation of two model discrimination utilities; mutual information and total separation, but it can also be applied more generally if one has different experimental aims. A sequential Monte Carlo algorithm is run for each rival model (in parallel), and provides a convenient estimate of the marginal likelihood (of each model) given the data, which can be used for model comparison and in the evaluation of utility functions. A major benefit of this approach is that it requires very little problem specific tuning and is also computationally efficient when compared to full Markov chain Monte Carlo approaches. This research is motivated by applications in drug development and chemical engineering.

Bayesian experimental design for models with intractable likelihoods

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables.

Driver sleepiness self-regulation : physiological and subjective evidence

Introduction: Sleepiness contributes to a substantial proportion of fatal and severe road crashes. Efforts to reduce the incidence of sleep-related crashes have largely focussed on driver education to promote self-regulation of driving behaviour. However, effective self-regulation requires accurate self-perception of sleepiness. The aim of this study was to assess capacity to accurately identify sleepiness, and self-regulate driving cessation, during a validated driving simulator task. Methods: Participants comprised 26 young adult drivers (20-28 years) who had open licenses. No other exclusion criteria where used. Participants were partially sleep deprived (05:00 wake up) and completed a laboratory-based hazard perception driving simulation, counterbalanced to either at mid-morning or mid-afternoon. Established physiological measures (i.e., EEG, EOG) and subjective measures (Karolinska Sleepiness Scale), previously found sensitive to changes in sleepiness levels, were utilised. Participants were instructed to ‘drive’ on the simulator until they believed that sleepiness had impaired their ability to drive safely. They were then offered a nap opportunity. Results: The mean duration of the drive before cessation was 36.1 minutes (±17.7 minutes). Subjective sleepiness increased significantly from the beginning (KSS=6.6±0.7) to the end (KSS=8.2±0.5) of the driving period. No significant differences were found for EEG spectral power measures of sleepiness (i.e., theta or alpha spectral power) from the start of the driving task to the point of cessation of driving. During the nap opportunity, 88% of the participants (23/26) were able to reach sleep onset with an average latency of 9.9 minutes (±7.5 minutes). The average nap duration was 15.1 minutes (±8.1 minutes). Sleep architecture during the nap was predominately comprised of Stages I and II (combined 92%). Discussion: Participants reported high levels of sleepiness during daytime driving after very moderate sleep restriction. They were able to report increasing sleepiness during the test period despite no observed change in standard physiological indices of sleepiness. This increased subjective sleepiness had behavioural validity as the participants had high ‘napability’ at the point of driving cessation, with most achieving some degree of subsequent sleep. This study suggests that the nature of a safety instruction (i.e. how to view sleepiness) can be a determinant of driver behaviour.

Multi-cursor multi-user mobile interaction with a large shared display

When using a mobile device to control a cursor on a large shared display, the interaction must be carefully planned to match the environment and purpose of the systems use. We describe a ‘democratic jukebox’ system that revealed five recommendations that should be considered when designing this type of interaction relating to providing feedback to the user; how to represent users in a multi-cursor based system; where people tend to look and their expectation of how to move their cursor; the orientation of screens and the social context; and, the use of simulated users to give the real users a sense that they are engaging with a greater audience.

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.