Investigation of a neuronal nitric oxide synthase gene (NOS1) polymorphism in a multiple sclerosis population

Multiple Sclerosis (MS) is a chronic neurological disease characterized by demyelination associated with infiltrating white blood cells in the central nervous system (CNS). Nitric oxide synthases (NOS) are a family of enzymes that control the production of nitric oxide. It is possible that neuronal NOS could be involved in MS pathophysiology and hence the nNOS gene is a potential candidate for involvement in disease susceptibility. The aim of this study was to determine whether allelic variation at the nNOS gene locus is associated with MS in an Australian cohort. DNA samples obtained from a Caucasian Australian population affected with MS and an unaffected control population, matched for gender, age and ethnicity, were genotyped for a microsatellite polymorphism in the promoter region of the nNOS gene. Allele frequencies were compared using chi-squared based statistical analyses with significance tested by Monte Carlo simulation. Allelic analysis of MS cases and controls produced a chi-squared value of 5.63 with simulated P = 0.96 (OR(max) = 1.41, 95% CI: 0.926-2.15). Similarly, a Mann-Whitney U analysis gave a non-significant P-value of 0.377 for allele distribution. No differences in allele frequencies were observed for gender or clinical course subtype (P > 0.05). Statistical analysis indicated that there is no association of this nNOS variant and MS and hence the gene does not appear to play a genetically significant role in disease susceptibility.

The estrogen receptor 1 G594A polymorphism is associated with migraine susceptibility in two independent case/control groups

Migraine is a painful and debilitating disorder with a significant genetic component. Steroid hormones, in particular estrogen, have long been considered to play a role in migraine, as variations in hormone levels are associated with migraine onset in many sufferers of the disorder. Steroid hormones mediate their activity via hormone receptors, which have a wide tissue distribution. Estrogen receptors have been localized to the brain in regions considered to be involved in migraine pathogenesis. Hence it is possible that genetic variation in the estrogen receptor gene may play a role in migraine susceptibility. This study thus examined the estrogen receptor 1 (ESRα) gene for a potential role in migraine pathogenesis and susceptibility. A population-based cohort of 224 migraine sufferers and 224 matched controls were genotyped for the G594A polymorphism located in exon 8 of the ESR1 gene. Statistical analysis indicated a significant difference between migraineurs and non-migraineurs in both the allele frequencies (P=0.003) and genotype distributions (P=0.008) in this sample. An independent follow-up study was then undertaken using this marker in an additional population-based cohort of 260 migraine sufferers and 260 matched controls. This resulted in a significant association between the two groups with regard to allele frequencies (P=8×10−6) and genotype distributions (P=4×10−5). Our findings support the hypothesis that genetic variation in hormone receptors, in particular the ESR1 gene, may play a role in migraine.

Dopamine receptor genes and migraine with and without aura : an association study

OBJECTIVE: To investigate the role of the dopamine receptor genes, DRD1, DRD3, and DRD5 in the pathogenesis of migraine. BACKGROUND: Migraine is a chronic debilitating disorder affecting approximately 12% of the white population. The disease shows strong familial aggregation and presumably has a genetic basis, but at present, the type and number of genes involved is unclear. The study of candidate genes can prove useful in the identification of genes involved in complex diseases such as migraine, especially if the contribution of the gene to phenotypic expression is minor. Genes coding for proteins involved in dopamine metabolism have been implicated in a number of neurologic conditions and may play a contributory role in migraine. Hence, genes that code for enzymes and receptors modulating dopaminergic activity are good candidates for investigation of the molecular genetic basis of migraine. METHODS: We tested 275 migraineurs and 275 age- and sex-matched individuals free of migraine. Genotypic results were determined by restriction endonuclease digestion of polymerase chain reaction products to detect DRD1 and DRD3 alleles and by Genescan analysis after polymerase chain reaction using fluorescently labelled oligonucleotide primers for the DRD5 marker. RESULTS: Results of chi-square statistical analyses indicated that the allele distribution for migraine cases compared to controls was not significantly different for any of the three tested gene markers (chi2 = 0.1, P =.74 for DRD1; chi2 = 1.8, P =.18 for DRD3; and chi2 = 20.3, P =.08 for DRD5). CONCLUSIONS: These findings offer no evidence for allelic association between the tested dopamine receptor gene polymorphisms and the more prevalent forms of migraine and, therefore, do not support a role for these genes in the pathogenesis of the disorder.

Investigation of glutathione S-transferase zeta and the development of sporadic breast cancer

Background Certain genes from the glutathione S-transferase superfamily have been associated with several cancer types. It was the objective of this study to determine whether alleles of the glutathione S-transferase zeta 1 (GSTZ1) gene are associated with the development of sporadic breast cancer. Methods DNA samples obtained from a Caucasian population affected by breast cancer and a control population, matched for age and ethnicity, were genotyped for a polymorphism of the GSTZ1 gene. After PCR, alleles were identified by restriction enzyme digestion and results analysed by chi-square and CLUMP analysis. Results Chi-squared analysis gave a χ2 value of 4.77 (three degrees of freedom) with P = 0.19, and CLUMP analysis gave a T1 value of 9.02 with P = 0.45 for genotype frequencies and a T1 value of 4.77 with P = 0.19 for allele frequencies. Conclusion Statistical analysis indicates that there is no association of the GSTZ1 variant and hence the gene does not appear to play a significant role in the development of sporadic breast cancer.

Principles of information accountability : an eHealth perspective

Information accountability is seen as a mode of usage control on the Web. Due to its many dimensions, information accountability has been expressed in various ways by computer scientists to address security and privacy in recent times. Information accountability is focused on how users participate in a system and the underlying policies that govern the participation. Healthcare is a domain in which the principles of information accountability can be utilised well. Modern health information systems are Internet based and the discipline is called eHealth. In this paper, we identify and discuss the goals of accountability systems and present the principles of information accountability. We characterise those principles in eHealth and discuss them contextually. We identify the current impediments to eHealth in terms of information privacy issues of eHealth consumers together with information usage requirements of healthcare providers and show how information accountability can be used in a healthcare context to address these needs. The challenges of implementing information accountability in eHealth are also discussed in terms of our efforts thus far.

Scanning the genome for essential hypertension loci

1. Essential hypertension occurs in people with an underlying genetic predisposition who subject themselves to adverse environmental influences. The number of genes involved is unknown, as is the extent to which each contributes to final blood pressure and the severity of the disease. 2. In the past, studies of potential candidate genes have been performed by association (case-control) analysis of unrelated individuals or linkage (pedigree or sibpair) analysis of families. These studies have resulted in several positive findings but, as one may expect, also an enormous number of negative results. 3. In order to uncover the major genetic loci for essential hypertension, it is proposed that scanning the genome systematically in 100- 200 affected sibships should prove successful. 4. This involves genotyping sets of hypertensive sibships to determine their complement of several hundred microsatellite polymorphisms. Those that are highly informative, by having a high heterozygosity, are most suitable. Also, the markers need to be spaced sufficiently evenly across the genome so as to ensure adequate coverage. 5. Tests are performed to determine increased segregation of alleles of each marker with hypertension. The analytical tools involve specialized statistical programs that can detect such differences. Non- parametric multipoint analysis is an appropriate approach. 6. In this way, loci for essential hypertension are beginning to emerge.

Expression of glucocorticoid and progesterone nuclear receptor genes in archival breast cancer tissue.

BACKGROUND: Previous studies in our laboratory have shown associations of specific nuclear receptor gene variants with sporadic breast cancer. In order to investigate these findings further, we conducted the present study to determine whether expression levels of the progesterone and glucocorticoid nuclear receptor genes vary in different breast cancer grades. METHODS: RNA was extracted from paraffin-embedded archival breast tumour tissue and converted into cDNA. Sample cDNA underwent PCR using labelled primers to enable quantitation of mRNA expression. Expression data were normalized against the 18S ribosomal gene multiplex and analyzed using analysis of variance. RESULTS: Analysis of variance indicated a variable level of expression of both genes with regard to breast cancer grade (P = 0.00033 for glucocorticoid receptor and P = 0.023 for progesterone receptor). CONCLUSION: Statistical analysis indicated that expression of the progesterone nuclear receptor is elevated in late grade breast cancer tissue.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Distributed low-power image processing in wireless sensor networks for intelligent video surveillance applications

Distributed Wireless Smart Camera (DWSC) network is a special type of Wireless Sensor Network (WSN) that processes captured images in a distributed manner. While image processing on DWSCs sees a great potential for growth, with its applications possessing a vast practical application domain such as security surveillance and health care, it suffers from tremendous constraints. In addition to the limitations of conventional WSNs, image processing on DWSCs requires more computational power, bandwidth and energy that presents significant challenges for large scale deployments. This dissertation has developed a number of algorithms that are highly scalable, portable, energy efficient and performance efficient, with considerations of practical constraints imposed by the hardware and the nature of WSN. More specifically, these algorithms tackle the problems of multi-object tracking and localisation in distributed wireless smart camera net- works and optimal camera configuration determination. Addressing the first problem of multi-object tracking and localisation requires solving a large array of sub-problems. The sub-problems that are discussed in this dissertation are calibration of internal parameters, multi-camera calibration for localisation and object handover for tracking. These topics have been covered extensively in computer vision literatures, however new algorithms must be invented to accommodate the various constraints introduced and required by the DWSC platform. A technique has been developed for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera internal parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera's optical centre and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. For object localisation, a novel approach has been developed for the calibration of a network of non-overlapping DWSCs in terms of their ground plane homographies, which can then be used for localising objects. In the proposed approach, a robot travels through the camera network while updating its position in a global coordinate frame, which it broadcasts to the cameras. The cameras use this, along with the image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to localised objects moving within the network. In addition, to deal with the problem of object handover between DWSCs of non-overlapping fields of view, a highly-scalable, distributed protocol has been designed. Cameras that follow the proposed protocol transmit object descriptions to a selected set of neighbours that are determined using a predictive forwarding strategy. The received descriptions are then matched at the subsequent camera on the object's path using a probability maximisation process with locally generated descriptions. The second problem of camera placement emerges naturally when these pervasive devices are put into real use. The locations, orientations, lens types etc. of the cameras must be chosen in a way that the utility of the network is maximised (e.g. maximum coverage) while user requirements are met. To deal with this, a statistical formulation of the problem of determining optimal camera configurations has been introduced and a Trans-Dimensional Simulated Annealing (TDSA) algorithm has been proposed to effectively solve the problem.

Young children's statistical reasoning : a tale of two contexts

This thesis explored the knowledge and reasoning of young children in solving novel statistical problems, and the influence of problem context and design on their solutions. It found that young children's statistical competencies are underestimated, and that problem design and context facilitated children's application of a wide range of knowledge and reasoning skills, none of which had been taught. A qualitative design-based research method, informed by the Models and Modeling perspective (Lesh & Doerr, 2003) underpinned the study. Data modelling activities incorporating picture story books were used to contextualise the problems. Children applied real-world understanding to problem solving, including attribute identification, categorisation and classification skills. Intuitive and metarepresentational knowledge together with inductive and probabilistic reasoning was used to make sense of data, and beginning awareness of statistical variation and informal inference was visible.

Simulated lifecycle costs of ultracapacitors in battery electric vehicles

The pulse power characteristics of ultracapacitors appear well suited to electric vehicle applications, where they may supply the peak power more efficiently than the battery, and can prevent excessive over sizing of the battery pack due to peak power demands. Operation of ultracapacitors in battery electric vehicles (BEVs) is examined for possible improvements in system efficiency, vehicle driving range, battery pack lifetime, and potential reductions in system lifecycle cost. The lifecycle operation of these ultracapacitors is simulated using a custom-built, dynamic simulation code constructed in Matlab. Despite apparent gains in system efficiency and driving range, the lifecycle cost benefits as simulated appear to be marginal, and are heavily influenced by the incremental cost of power components. However, additional factors are identified which, in reality, will drive ultracapacitors towards viability in electric vehicle applications.

Privacy oriented access control for electronic health records

Information privacy is a critical success/failure factor in information technology supported healthcare (eHealth). eHealth systems utilise electronic health records (EHR) as the main source of information, thus, implementing appropriate privacy preserving methods for EHRs is vital for the proliferation of eHealth. Whilst information privacy may be a fundamental requirement for eHealth consumers, healthcare professionals demand non-restricted access to patient information for improved healthcare delivery, thus, creating an environment where stakeholder requirements are contradictory. Therefore, there is a need to achieve an appropriate balance of requirements in order to build successful eHealth systems. Towards achieving this balance, a new genre of eHealth systems called Accountable-eHealth (AeH) systems has been proposed. In this paper, an access control model for EHRs is presented that can be utilised by AeH systems to create information usage policies that fulfil both stakeholders’ requirements. These policies are used to accomplish the aforementioned balance of requirements creating a satisfactory eHealth environment for all stakeholders. The access control model is validated using a Web based prototype as a proof of concept.

Endophenotype profiles of MTHFR and ACE gene polymorphisms in migraine susceptibility

Background Migraine is a debilitating neurological disorder affecting approximately 12% of the Caucasian population. There are two main sub-types of migraine, migraine without aura (MO) and migraine with aura (MA). Migraine exhibits varied phenotypic expression with sufferers experiencing a range of neurological and other symptoms. It is likely that multiple susceptibility genes play a role in this varied phenotypic expression, thus investigation of genotype-phenotype relationships may provide valuable insights into the role of susceptibility genes in this disorder. Methods This study investigated the links between migraine susceptibility genes, methylenetetrahydrofolate reductase (MTHFR) and angiotensin converting enzyme (ACE), and clinical manifestation through statistical analyses. Results The result showed that for the MTHFR genotypes, there was a statistically significant correlation with the TT homozygous genotype and visual disturbances, unilateral head pain and physical activity discomforts. It was also found that bilateral head pain was associated with the male gender. Conclusion From these study results, it is plausible to state that MTHFR genotypes affect the phenotypic expression of migraine disease manifestation.

Cloud computing : how does it differ from IT outsourcing and what are the implications for practice and research?

The purpose of this paper is to provide an evolutionary perspective of cloud computing (CC) by integrating two previously disparate literatures: CC and information technology outsourcing (ITO). We review the literature and develop a framework that highlights the demand for the CC service, benefits, risks, as well as risk mitigation strategies that are likely to influence the success of the service. CC success in organisations and as a technology overall is a function of (i) the outsourcing decision and supplier selection, (ii) contractual and relational governance, and (iii) industry standards and legal framework. Whereas CC clients have little control over standards and/or the legal framework, they are able to influence other factors to maximize the benefits while limiting the risks. This paper provides guidelines for (potential) cloud computing users with respect to the outsourcing decision, vendor selection, service-level-agreements, and other issues that need to be addressed when opting for CC services. We contribute to the literature by providing an evolutionary and holistic view of CC that draws on the extensive literature and theory of ITO. We conclude the paper with a number of research paths that future researchers can follow to advance the knowledge in this field.

Development and evaluation of a program to improve clinician and patient communication during a telehealth consultation : C.R.I.S.P. Telehealth

Introduction: The delivery of health care in the 21st century will look like no other in the past. The fast paced technological advances that are being made will need to transition from the information age into clinical practice. The phenomenon of e-Health is the over-arching form of information technology and telehealth is one arm of that phenomenon. The uptake of telehealth both in Australia and overseas, has changed the face of health service delivery to many rural and remote communities for the better, removing what is known as the tyranny of distance. Many studies have evaluated the satisfaction and cost-benefit analysis of telehealth across the organisational aspects as well as the various adaptations of clinical pathways and this is the predominant focus of most studies published to date. However, whilst comments have been made by many researchers about the need to improve and attend to the communication and relationship building aspects of telehealth no studies have examined this further. The aim of this study was to identify the patient and clinician experiences, concerns, behaviours and perceptions of the telehealth interaction and develop a training tool to assist these clinicians to improve their interaction skills. Methods: A mixed methods design combining quantitative (survey analysis and data coding) and qualitative (interview analysis) approaches was adopted. This study utilised four phases to firstly qualitatively explore the needs of clients (patients) and clinicians within a telehealth consultation then designed, developed, piloted and quantitatively and qualitatively evaluated the telehealth communication training program. Qualitative data was collected and analysed during Phase 1 of this study to describe and define the missing 'communication and rapport building' aspects within telehealth. This data was then utilised to develop a self-paced communication training program that enhanced clinicians existing skills, which comprised of Phase 2 of this study to develop the interactive program. Phase 3 included evaluating the training program with 26 clinicians and results were recorded pre and post training, whilst phase 4 was the pilot for future recommendations of this training program using a patient group within a Queensland Health setting at two rural hospitals. Results: Comparisons of pre and post training data on 1) Effective communication styles, 2) Involvement in communication training package, 3) satisfaction pre and post training, and 4) health outcomes pre and post training indicated that there were differences between pre and post training in relation to effective communication style, increased satisfaction and no difference in health outcomes between pre and post training for this patient group. The post training results revealed over half of the participants (N= 17, 65%) were more responsive to non-verbal cues and were better able to reflect and respond to looks of anxiousness and confusion from a 'patient' within a telehealth consultation. It was also found that during post training evaluations, clinicians had enhanced their therapeutic communication with greater detail to their own body postures, eye contact and presentation. There was greater time spent looking at the 'patient' with an increase of 35 second intervals of direct eye contact and less time spent looking down at paperwork which decreased by 20 seconds. Overall 73% of the clinicians were satisfied with the training program and 61% strongly agreed that they recognised areas of their communication that needed improving during a telehealth consultation. For the patient group there was significant difference post training in rapport with a mean score from 42 (SD = 28, n = 27) to 48 (SD = 5.9, n = 24). For communication comfort of the patient group there was a significant difference between the pre and post training scores t(10) = 27.9, p = .002, which meant that overall the patients felt less inhibited whilst talking to the clinicians and more understood. Conclusion: The aim of this study was to explore the characteristics of good patient-clinician communication and unmet training needs for telehealth consultations. The study developed a training program that was specific for telehealth consultations and not dependent on a 'trainer' to deliver the content. In light of the existing literature this is a first of its kind and a valuable contribution to the research on this topic. It was found that the training program was effective in improving the clinician's communication style and increased the satisfaction of patient's within an e-health environment. This study has identified some historical myths that telehealth cannot be part of empathic patient centred care due to its technology tag.