Modelling the information seeking user by the decision they make

The article focuses on how the information seeker makes decisions about relevance. It will employ a novel decision theory based on quantum probabilities. This direction derives from mounting research within the field of cognitive science showing that decision theory based on quantum probabilities is superior to modelling human judgements than standard probability models [2, 1]. By quantum probabilities, we mean decision event space is modelled as vector space rather than the usual Boolean algebra of sets. In this way,incompatible perspectives around a decision can be modelled leading to an interference term which modifies the law of total probability. The interference term is crucial in modifying the probability judgements made by current probabilistic systems so they align better with human judgement. The goal of this article is thus to model the information seeker user as a decision maker. For this purpose, signal detection models will be sketched which are in principle applicable in a wide variety of information seeking scenarios.

Using natural user interfaces for collaborative process modelling in virtual environments

Modelling business processes for analysis or redesign usually requires the collaboration of many stakeholders. These stakeholders may be spread across locations or even companies, making co-located collaboration costly and difficult to organize. Modern process modelling technologies support remote collaboration but lack support for visual cues used in co-located collaboration. Previously we presented a prototype 3D virtual world process modelling tool that supports a number of visual cues to facilitate remote collaborative process model creation and validation. However, the added complexity of having to navigate a virtual environment and using an avatar for communication made the tool difficult to use for novice users. We now present an evolved version of the technology that addresses these issues by providing natural user interfaces for non-verbal communication, navigation and model manipulation.

Mechanical testing and modelling of a bone-implant construct

Finite Element modelling of bone fracture fixation systems allows computational investigation of the deformation response of the bone to load. Once validated, these models can be easily adapted to explore changes in design or configuration of a fixator. The deformation of the tissue within the fracture gap determines its healing and is often summarised as the stiffness of the construct. FE models capable of reproducing this behaviour would provide valuable insight into the healing potential of different fixation systems. Current model validation techniques lack depth in 6D load and deformation measurements. Other aspects of the FE model creation such as the definition of interfaces between components have also not been explored. This project investigated the mechanical testing and FE modelling of a bone– plate construct for the determination of stiffness. In depth 6D measurement and analysis of the generated forces, moments and movements showed large out of plane behaviours which had not previously been characterised. Stiffness calculated from the interfragmentary movement was found to be an unsuitable summary parameter as the error propagation is too large. Current FE modelling techniques were applied in compression and torsion mimicking the experimental setup. Compressive stiffness was well replicated, though torsional stiffness was not. The out of plane behaviours prevalent in the experimental work were not replicated in the model. The interfaces between the components were investigated experimentally and through modification to the FE model. Incorporation of the interface modelling techniques into the full construct models had no effect in compression but did act to reduce torsional stiffness bringing it closer to that of the experiment. The interface definitions had no effect on out of plane behaviours, which were still not replicated. Neither current nor novel FE modelling techniques were able to replicate the out of plane behaviours evident in the experimental work. New techniques for modelling loads and boundary conditions need to be developed to mimic the effects of the entire experimental system.

Sampling stored-product insect pests : a comparison of four statistical sampling models for probability of pest detection

Background: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species. Results: Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,1 the Poisson model,1 the double logarithmic model2 and the compound model3 - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages. Conclusions: This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.

A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Operational modal analysis (OMA) is prevalent in modal identifi cation of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the infl uence of such randomness to modal identifi cation. This research has attempted modifi ed kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identifi cation. The modal identifi cation results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfi l the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verifi ed effective in modal analysis.

Development and use of risk adjusted statistical process control tools for the monitoring and improvement of clinical outcomes in interventional cardiology

This thesis explored the development of statistical methods to support the monitoring and improvement in quality of treatment delivered to patients undergoing coronary angioplasty procedures. To achieve this goal, a suite of outcome measures was identified to characterise performance of the service, statistical tools were developed to monitor the various indicators and measures to strengthen governance processes were implemented and validated. Although this work focused on pursuit of these aims in the context of a an angioplasty service located at a single clinical site, development of the tools and techniques was undertaken mindful of the potential application to other clinical specialties and a wider, potentially national, scope.

Using natural user interfaces for collaborative process modelling in virtual environments

A demo video showing the BPMVM prototype using several natural user interfaces, such as multi-touch input, full-body tracking and virtual reality.

Modelling risk at low exposure railway level crossings : supporting an argument for low-cost level crossing warning devices with lower levels of safety integrity

This paper describes a risk model for estimating the likelihood of collisions at low-exposure railway level crossings, demonstrating the effect that differences in safety integrity can have on the likelihood of a collision. The model facilitates the comparison of safety benefits between level crossings with passive controls (stop or give-way signs) and level crossings that have been hypothetically upgraded with conventional or low-cost warning devices. The scenario presented illustrates how treatment of a cross-section of level crossings with low cost devices can provide a greater safety benefit compared to treatment with conventional warning devices for the same budget.

Investigation of the 1758G>C and 2880A>G variants within the NCOA3 gene in a breast cancer affected Australian population

NCOA3 is a known low to moderate-risk breast cancer susceptibility gene, amplified in 5–10% and over expressed in about 60% of breast tumours. Additionally, this over expression is associated with Tamoxifen resistance and poor prognosis. Previously, two variants of NCOA3, 1758G > C and 2880A > G have been associated with breast cancer in two independent populations. Here we assessed the influence of the two NCOA3 variants on breast cancer risk by genotyping an Australian case–control study population. 172 cases and 178 controls were successfully genotyped for the 1758G > C variant and 186 cases and 182 controls were successfully genotyped for the 2880A > G variant using high-resolution melt analysis (HRM). The genotypes of the 1758G > C variant were validated by sequencing. χ2 tests were performed to determine if significant differences exist in the genotype and allele frequencies between the cases and controls. χ2 analysis returned no statistically significant difference (p > 0.05) for genotype frequencies between cases and controls for 1758G > C (χ2 = 0.97, p = 0.6158) or 2880A > G (χ2 = 2.09, p = 0.3516). Similarly, no statistical difference was observed for allele frequencies for 1758G > C (χ2 = 0.07, p = 0.7867) or 2880A > G (χ2 = 0.04, p = 0.8365). Haplotype analysis of the two SNPs also showed no difference between the cases and the controls (p = 0.9585). Our findings in an Australian Caucasian population composed of breast cancer sufferers and an age matched control population did not support the findings of previous studies demonstrating that these markers play a significant role in breast cancer susceptibility. Here, no significant difference was detected between breast cancer patients and healthy matched controls by either the genotype or allele frequencies for the investigated variants (all p ≥ 0.05). While an association of the two variants and breast cancer was not detected in our case–control study population, exploring these variants in a larger population of the same kind may obtain results in concordance with previous studies. Given the importance of NCOA3 and its involvement in biological processes involved in breast cancer and the possible implications variants of the gene could have on the response to Tamoxifen therapy, NCOA3 remains a candidate for further investigations.

Investigation of two Wnt signalling pathway single nucleotide polymorphisms in a breast cancer-affected Australian population

In the mammary gland, Wnt signals are strongly implicated in initial development of the mammary rudiments and in the ductal branching and alveolar morphogenesis that occurs during pregnancy. Previously, we identified two Wnt signaling pathway-implicated genes, PPP3CA and MARK4, as having a role in more aggressive and potentially metastatic breast tumors. In this study, we examined two SNPs within PPP3CA and MARK4 in an Australian case-control study population for a potential role in human breast cancers. 182 cases and 180 controls were successfully genotyped for the PPP3CA SNP (rs2850328) and 182 cases and 177 controls were successfully genotyped for the MARK4 SNP (rs2395) using High Resolution Melt (HRM) analysis. Genotypes of randomly selected samples for both SNPs were validated by dye terminator sequencing. Chi-square tests were performed to determine any significant differences in the genotype and allele frequencies between the cases and controls. Chi-square analysis showed no statistically significant difference (p > .05) for genotype frequencies between cases and controls for rs2850328 (χ2 = 1.2, p = .5476) or rs2395 (χ2 = .3, p = .8608). Similarly, no statistical difference was observed for allele frequencies for rs2850328 (χ2 = .68, p = .4108) or rs2395 (χ2 = .02, p = .893). Even though an association of the polymorphisms rs2850328 and rs2395 and breast cancer was not detected in our case-control study population, other variants within the PPP3CA and MARK4 genes may still be associated with breast cancer, as both genes are implicated with processes involved in the disease as well as their mutual partaking in the Wnt signaling pathway.

Variants in the human potassium channel gene (KCNN3) are associated with migraine in a high risk genetic isolate

The calcium-activated potassium ion channel gene (KCNN3) is located in the vicinity of the familial hemiplegic migraine type 2 locus on chromosome 1q21.3. This gene is expressed in the central nervous system and plays a role in neural excitability. Previous association studies have provided some, although not conclusive, evidence for involvement of this gene in migraine susceptibility. To elucidate KCNN3 involvement in migraine, we performed gene-wide SNP genotyping in a high-risk genetic isolate from Norfolk Island, a population descended from a small number of eighteenth century Isle of Man ‘Bounty Mutineer’ and Tahitian founders. Phenotype information was available for 377 individuals who are related through the single, well-defined Norfolk pedigree (96 were affected: 64 MA, 32 MO). A total of 85 SNPs spanning the KCNN3 gene were genotyped in a sub-sample of 285 related individuals (76 affected), all core members of the extensive Norfolk Island ‘Bounty Mutineer’ genealogy. All genotyping was performed using the Illumina BeadArray platform. The analysis was performed using the statistical program SOLAR v4.0.6 assuming an additive model of allelic effect adjusted for the effects of age and sex. Haplotype analysis was undertaken using the program HAPLOVIEW v4.0. A total of four intronic SNPs in the KCNN3 gene displayed significant association (P < 0.05) with migraine. Two SNPs, rs73532286 and rs6426929, separated by approximately 0.1 kb, displayed complete LD (r 2 = 1.00, D′ = 1.00, D′ 95% CI = 0.96–1.00). In all cases, the minor allele led to a decrease in migraine risk (beta coefficient = 0.286–0.315), suggesting that common gene variants confer an increased risk of migraine in the Norfolk pedigree. This effect may be explained by founder effect in this genetic isolate. This study provides evidence for association of variants in the KCNN3 ion channel gene with migraine susceptibility in the Norfolk genetic isolate with the rarer allelic variants conferring a possible protective role. This the first comprehensive analysis of this potential candidate gene in migraine and also the first study that has utilised the unique Norfolk Island large pedigree isolate to implicate a specific migraine gene. Studies of additional variants in KCNN3 in the Norfolk pedigree are now required (e.g. polyglutamine variants) and further analyses in other population data sets are required to clarify the association of the KCNN3 gene and migraine risk in the general outbred population.

Investigation of the [−/A]8 and C1236T genetic variations within the human toll-like receptor 3 gene for association with multiple sclerosis

Multiple sclerosis (MS) is a serious cause of neurological disability among young adults. The clinical course remains difficult to predict, and the pathogenesis of the disease is still modestly understood. Autoimmunity is thought to be a key aspect of the disease, with autoreactive T cells thought to mediate central nervous system (CNS) inflammation to some extent. Toll-like receptors are known to mediate cellular recognition of pathogens by way of patterns of molecular presentation. Toll-like receptor 3 is coded by the gene TLR3 and is recognized as an important factor in virus recognition and is known to be involved in the expression of neuroprotective mediators. We set out to investigate two variations within the TLR3 gene, an 8 bp insertion-deletion \[-/A](8) and a single base-pair variation C1236T, in subjects with MS and matched healthy controls to determine whether significant differences exist in these markers in an Australian population. We used capillary gel electrophoresis and TaqMan genotyping assay techniques to resolve genotypes for each marker, respectively. Our work found no significant difference between frequencies for TLR3 \[-/A](8) by genotype (chi(2)=1.03, p=0.60) or allele (chi(2)=1.09, p=0.30). Similarly, we found no evidence for the association of TLR3 C1236T by genotype (chi(2)=0.35, p=0.84) or allele frequency (chi(2)=0.31, p=0.58). This work reveals no evidence to suggest that these markers are associated with MS in the tested population. Although the role of TLR3 and the wider toll-like receptor family remain significant in neurological and CNS inflammatory disorders, our current work does not support a role for the two tested variants in this gene with regard to MS susceptibility.

Improving the statistical preparation for measuring soil N2O flux by closed chamber

Nitrous oxide emissions from soil are known to be spatially and temporally volatile. Reliable estimation of emissions over a given time and space depends on measuring with sufficient intensity but deciding on the number of measuring stations and the frequency of observation can be vexing. The question of low frequency manual observations providing comparable results to high frequency automated sampling also arises. Data collected from a replicated field experiment was intensively studied with the intention to give some statistically robust guidance on these issues. The experiment had nitrous oxide soil to air flux monitored within 10 m by 2.5 m plots by automated closed chambers under a 3 h average sampling interval and by manual static chambers under a three day average sampling interval over sixty days. Observed trends in flux over time by the static chambers were mostly within the auto chamber bounds of experimental error. Cumulated nitrous oxide emissions as measured by each system were also within error bounds. Under the temporal response pattern in this experiment, no significant loss of information was observed after culling the data to simulate results under various low frequency scenarios. Within the confines of this experiment observations from the manual chambers were not spatially correlated above distances of 1 m. Statistical power was therefore found to improve due to increased replicates per treatment or chambers per replicate. Careful after action review of experimental data can deliver savings for future work.

Econometric Modelling with Time Series : Specification, Estimation and Testing

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010

Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross–Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross–Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross–Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process.