Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications

Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.

Wound healing angiogenesis : the clinical implications of a simple mathematical model

Nonhealing wounds are a major burden for health care systems worldwide. In addition, a patient who suffers from this type of wound usually has a reduced quality of life. While the wound healing process is undoubtedly complex, in this paper we develop a deterministic mathematical model, formulated as a system of partial differential equations, that focusses on an important aspect of successful healing: oxygen supply to the wound bed by a combination of diffusion from the surrounding unwounded tissue and delivery from newly formed blood vessels. While the model equations can be solved numerically, the emphasis here is on the use of asymptotic methods to establish conditions under which new blood vessel growth can be initiated and wound-bed angiogenesis can progress. These conditions are given in terms of key model parameters including the rate of oxygen supply and its rate of consumption in the wound. We use our model to discuss the clinical use of treatments such as hyperbaric oxygen therapy, wound bed debridement, and revascularisation therapy that have the potential to initiate healing in chronic, stalled wounds.

An application of the Health Belief Model to understanding use of child restraint practices

Aims Multi-method study including two parts: Study One: three sets of observations in two regional areas of Queensland Study Two: two sets of parent intercept interviews conducted in Toowoomba, Queensland. The aim of Study Two is to determine parents’ views, opinions and knowledge of child restraint practices and the Queensland legislative amendment.

Reducing aggression on our roads : testing a comprehensive model of aggressive driving

Background: Trauma resulting from traffic crashes poses a significant problem in highly motorised countries. Over a million people worldwide are killed annually and 50 million are critically injured as a result of traffic collisions. In Australia, road crashes cost an average of $17 billion annually in personal loss of income and quality of life, organisational losses in productivity and workplace quality, and health care costs. Driver aggression has been identified as a key factor contributing to crashes, and many motorists report experiencing mild forms of aggression (e.g., rude gestures, horn honking). However despite this concern, driver aggression has received relatively little attention in empirical research, and existing research has been hampered by a number of methodological and conceptual shortcomings. Specifically, there has been substantial disagreement regarding what constitutes aggressive driving and a failure to examine both the situational factors and the emotional and cognitive processes underlying driver aggression. To enhance current understanding of aggressive driving, a model of driver aggression that highlights the cognitive and emotional processes at play in aggressive driving incidents is proposed. Aims: The research aims to improve current understanding of the complex nature of driver aggression by testing and refining a model of aggressive driving that incorporates the person-related and situational factors and the cognitive and emotional appraisal processes fundamental to driver aggression. In doing so, the research will assist to provide a clear definition of what constitutes aggressive driving, assist to identify on-road incidents that trigger driver aggression, and identify the emotional and cognitive appraisal processes that underlie driver aggression. Methods: The research involves three studies. Firstly, to contextualise the model and explore the cognitive and emotional aspects of driver aggression, a diary-based study using self-reports of aggressive driving events will be conducted with a general population of drivers. This data will be supplemented by in-depth follow-up interviews with a sub-sample of participants. Secondly, to test generalisability of the model, a large sample of drivers will be asked to respond to video-based scenarios depicting driving contexts derived from incidents identified in Study 1 as inciting aggression. Finally, to further operationalise and test the model an advanced driving simulator will be used with sample of drivers. These drivers will be exposed to various driving scenarios that would be expected to trigger negative emotional responses. Results: Work on the project has commenced and progress on the first study will be reported.

Virtual worlds as a model-view approach to the communication of business processes models

The improvement and optimization of business processes is one of the top priorities in an organization. Although process analysis methods are mature today, business analysts and stakeholders are still hampered by communication issues. That is, analysts cannot effectively obtain accurate business requirements from stakeholders, and stakeholders are often confused about analytic results offered by analysts. We argue that using a virtual world to model a business process can benefit communication activities. We believe that virtual worlds can be used as an efficient model-view approach, increasing the cognition of business requirements and analytic results, as well as the possibility of business plan validation. A healthcare case study is provided as an approach instance, illustrating how intuitive such an approach can be. As an exploration paper, we believe that this promising research can encourage people to investigate more research topics in the interdisciplinary area of information system, visualization and multi-user virtual worlds.

The reliability, accuracy and minimal detectable difference of a multi-segment kinematic model of the foot-shoe complex

Kinematic models are commonly used to quantify foot and ankle kinematics, yet no marker sets or models have been proven reliable or accurate when wearing shoes. Further, the minimal detectable difference of a developed model is often not reported. We present a kinematic model that is reliable, accurate and sensitive to describe the kinematics of the foot–shoe complex and lower leg during walking gait. In order to achieve this, a new marker set was established, consisting of 25 markers applied on the shoe and skin surface, which informed a four segment kinematic model of the foot–shoe complex and lower leg. Three independent experiments were conducted to determine the reliability, accuracy and minimal detectable difference of the marker set and model. Inter-rater reliability of marker placement on the shoe was proven to be good to excellent (ICC = 0.75–0.98) indicating that markers could be applied reliably between raters. Intra-rater reliability was better for the experienced rater (ICC = 0.68–0.99) than the inexperienced rater (ICC = 0.38–0.97). The accuracy of marker placement along each axis was <6.7 mm for all markers studied. Minimal detectable difference (MDD90) thresholds were defined for each joint; tibiocalcaneal joint – MDD90 = 2.17–9.36°, tarsometatarsal joint – MDD90 = 1.03–9.29° and the metatarsophalangeal joint – MDD90 = 1.75–9.12°. These thresholds proposed are specific for the description of shod motion, and can be used in future research designed at comparing between different footwear.

The role of insulin and IGF2 signalling on metabolic pathways in prostate cancer progression

Prostate cancer (CaP) is the most commonly diagnosed cancer in males in Australia, North America, and Europe. If found early and locally confined, CaP can be treated with radical prostatectomy or radiation therapy; however, 25-40% patients will relapse and go on to advanced disease. The most common therapy in these cases is androgen deprivation therapy (ADT), which suppresses androgen production from the testis. Lack of the testicular androgen supply causes cells of the prostate to undergo apoptosis. However, in some cases the regression initially seen with ADT eventually gives way to a growth of a population of cancerous cells that no longer require testicular androgens. This phenotype is essentially fatal and is termed castrate resistant prostate cancer (CRPC). In addition to eventual regression, there are many undesirable side effects which accompany ADT, including development of a metabolic syndrome, which is defined by the U.S. National Library of Medicine as “a combination of medical disorders that increase the risk of developing cardiovascular disease and diabetes.” This project will focus on the effect of ADT induced hyperinsulinemia, as mimicked by treating androgen receptor positive CaP cells with insulin in a serum (hormone) deprived environment. While this side effect is not widely explored, in this thesis it is demonstrated for the first time that insulin upregulates pathways important to CaP progression. Our group has previously shown that during CaP progression, the enzymes necessary for de novo steroidogenesis are upregulated in the LNCaP xenograft model, total steroid levels are increased in tumours compared to pre castrate levels, and de novo steroidogenesis from radio-labelled acetate has been demonstrated. Because of the CaP dependence on AR for survival, we and other groups believe that CaP cells carry out de novo steroidogenesis to survive in androgen deprived conditions. Because (a) men on ADT often develop metabolic syndrome, and (b) men with lifestyle-induced obesity and hyperinsulinemia have worse prognosis and faster disease progression, and because (c) insulin causes steroidogenesis in other cell lines, the hypothesis that insulin may contribute to CaP progression through upregulation of steroidogenesis was explored. Insulin upregulates steroidogenesis enzymes at the mRNA level in three AR positive cell lines, as well as upregulating these enzymes at the protein level in two cell lines. It has also been demonstrated that insulin increases mitochondrial (functional) levels of steroid acute regulatory protein (StAR). Furthermore, insulin causes increased levels of total steroids in and induction of de novo steroid synthesis by insulin has been demonstrated at levels induced sufficient to activate AR. The effect of insulin analogs on CaP steroidogenesis in LNCaP and VCaP cells has also been investigated because epidemiological studies suggest that some of the analogs developed may have more cancer stimulatory effects than normal insulin. In this project, despite the signalling differences between glargine, X10, and insulin, these analogs did not appear to induce steroidogenesis any more potently that normal insulin. The effect of insulin of MCF7breast cancer cells was also investigated with results suggesting that breast cancer cells may be capable of de novo steroidogenesis, and that increase in estradiol production may be exacerbated by insulin. Insulin has also been long known to stimulate lipogenesis in the liver and adipocytes, and has been demonstrated to increase lipogenesis in breast cancer cells; therefore, investigation of the effect of insulin on lipogenesis, which is a hallmark of aggressive cancers, was investigated. In CaP progression sterol regulatory element binding protein (SREBP) is dysregulated and upregulates fatty acid synthase (FASN), acetyl CoA-carboxylase, and other lipogenesis genes. SREBP is important for steroidogenesis and in this project has been shown to be upregulated by insulin in CaP cells. Fatty acid synthesis provides building blocks of membrane growth, provides substrates for acid oxidation, the main energy source for CaP cells, provides building blocks for anti-apoptotic and proinflammatory molecules, and provides molecules that stimulate steroidogenesis. In this project it has been shown that insulin upregulates FASN and ACC, which synthesize fatty acids, as well as upregulating hormone sensitive lipase (HSL), diazepam-binding inhibitor (DBI), and long-chain acyl-CoA synthetase 3 (ACSL3), which contribute to lipid activation of steroidogenesis. Insulin also upregulates total lipid levels and de novo lipogenesis, which can be suppressed by inhibition of the insulin receptor (INSR). The fatty acids synthesized after insulin treatment are those that have been associated with CaP; furthermore, microarray data suggests insulin may upregulate fatty acid biosynthesis, metabolism and arachidonic acid metabolism pathways, which have been implicated in CaP growth and survival. Pharmacological agents used to treat patients with hyperinsulinemia/ hyperlipidemia have gained much interest in regards to CaP risk and treatment; however, the scientific rationale behind these clinical applications has not been examined. This thesis explores whether the use of metformin or simvastatin would decrease either lipogenesis or steroidogenesis or both in CaP cells. Simvastatin is a 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitor, which blocks synthesis of cholesterol, the building block of steroids/ androgens. It has also been postulated to down regulate SREBP in other metabolic disorders. It has been shown in this thesis, in LNCaP cells, that simvastatin inhibited and decreased insulin induced steroidogenesis and lipogenesis, respectively, but increased these pathways in the absence of insulin. Conversely, metformin, which activates AMP-activated protein kinase (AMPK) to shut down lipogenesis, cholesterol synthesis, and protein synthesis, highly suppresses both steroidogenesis and lipogenesis in the presence and absence of insulin. Lastly, because it has been demonstrated to increase steroidogenesis in other cell lines, and because the elucidation of any factors affecting steroidogenesis is important to understanding CaP, the effect of IGF2 on steroidogenesis in CaP cells was investigated. In patient samples, as men progress to CRPC, IGF2 mRNA and the protein levels of the receptors it may signal through are upregulated. It has also been demonstrated that IGF2 upregulates steroidogenic enzymes at both the mRNA and protein levels in LNCaP cells, increases intracellular and secreted steroid/androgen levels in LNCaPs to levels sufficient to stimulate the AR, and upregulated de novo steroidogenesis in LNCaPs and VCaPs. As well, inhibition of INSR and insulin-like growth factor 1 receptor (IGF1R), which IGF2 signals through, suggests that induction of steroidogenesis may be occurring predominantly through IGF1R. In summary, this project has illuminated for the first time that insulin is likely to play a large role in cancer progression, through upregulation of the steroidogenesis and lipogenesis pathways at the mRNA and protein levels, and production levels, and demonstrates a novel role for IGF-II in CaP progression through stimulation of steroidogenesis. It has also been demonstrated that metformin and simvastatin drugs may be useful in suppressing the insulin induction of these pathways. This project affirms the pathways by which ADT- induced metabolic syndrome may exacerbate CaP progression and strongly suggests that the monitoring and modulation of the metabolic state of CaP patients could have a strong impact on their therapeutic outcomes.

The development of a multi-segment kinematic model of footwear

In gait analysis, both shoe mounted and skin mounted markers have been used to quantify the movement of the foot inside the shoe. However, these models have not been demonstrated as reliable or accurate in shod conditions. The purpose of this study was to develop an accurate and reliable marker set to describe foot-shoe complex kinematics during stance phase.

A subject-specific model of human buttocks and thighs in a seated posture

Finite element analyses of the human body in seated postures requires digital models capable of providing accurate and precise prediction of the tissue-level response of the body in the seated posture. To achieve such models, the human anatomy must be represented with high fidelity. This information can readily be defined using medical imaging techniques such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Current practices for constructing digital human models, based on the magnetic resonance (MR) images, in a lying down (supine) posture have reduced the error in the geometric representation of human anatomy relative to reconstructions based on data from cadaveric studies. Nonetheless, the significant differences between seated and supine postures in segment orientation, soft-tissue deformation and soft tissue strain create a need for data obtained in postures more similar to the application posture. In this study, we present a novel method for creating digital human models based on seated MR data. An adult-male volunteer was scanned in a simulated driving posture using a FONAR 0.6T upright MRI scanner with a T1 scanning protocol. To compensate for unavoidable image distortion near the edges of the study, images of the same anatomical structures were obtained in transverse and sagittal planes. Combinations of transverse and sagittal images were used to reconstruct the major anatomical features from the buttocks through the knees, including bone, muscle and fat tissue perimeters, using Solidworks® software. For each MR image, B-splines were created as contours for the anatomical structures of interest, and LOFT commands were used to interpolate between the generated Bsplines. The reconstruction of the pelvis, from MR data, was enhanced by the use of a template model generated in previous work CT images. A non-rigid registration algorithm was used to fit the pelvis template into the MR data. Additionally, MR image processing was conducted to both the left and the right sides of the model due to the intended asymmetric posture of the volunteer during the MR measurements. The presented subject-specific, three-dimensional model of the buttocks and thighs will add value to optimisation cycles in automotive seat development when used in simulating human interaction with automotive seats.

Towards an accurate biomechanical model of the ankle

When compared with similar joint arthroplasties, the prognosis of Total Ankle Replacement (TAR) is not satisfactory although it shows promising results post surgery. To date, most models do not provide the full anatomical functionality and biomechanical range of motion of the healthy ankle joint. This has sparked additional research and evaluation of clinical outcomes in order to enhance ankle prosthesis design. However, the limited biomechanical data that exist in literature are based upon two-dimensional, discrete and outdated techniques1 and may be inaccurate. Since accurate force estimations are crucial to prosthesis design, a paper based on a new biomechanical modeling approach, providing three dimensional forces acting on the ankle joint and the surrounding tissues was published recently, but the identified forces were suspected of being under-estimated, while muscles were . The present paper reports an attempt to improve the accuracy of the analysis by means of novel methods for kinematic processing of gait data, provided in release 4.1 of the AnyBody Modeling System (AnyBody Technology, Aalborg, Denmark) Results from the new method are shown and remaining issues are discussed.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Integration of opinion into customer analysis model

Nowadays, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes. A use case study is also presented in this paper to show the advantages of using OLAP and data cubes to analyze costumers’ opinions.

Statistical eye model for normal eyes

Purpose. To create a binocular statistical eye model based on previously measured ocular biometric data. Methods. Thirty-nine parameters were determined for a group of 127 healthy subjects (37 male, 90 female; 96.8% Caucasian) with an average age of 39.9 ± 12.2 years and spherical equivalent refraction of −0.98 ± 1.77 D. These parameters described the biometry of both eyes and the subjects' age. Missing parameters were complemented by data from a previously published study. After confirmation of the Gaussian shape of their distributions, these parameters were used to calculate their mean and covariance matrices. These matrices were then used to calculate a multivariate Gaussian distribution. From this, an amount of random biometric data could be generated, which were then randomly selected to create a realistic population of random eyes. Results. All parameters had Gaussian distributions, with the exception of the parameters that describe total refraction (i.e., three parameters per eye). After these non-Gaussian parameters were omitted from the model, the generated data were found to be statistically indistinguishable from the original data for the remaining 33 parameters (TOST [two one-sided t tests]; P < 0.01). Parameters derived from the generated data were also significantly indistinguishable from those calculated with the original data (P > 0.05). The only exception to this was the lens refractive index, for which the generated data had a significantly larger SD. Conclusions. A statistical eye model can describe the biometric variations found in a population and is a useful addition to the classic eye models.