Call number: LD2668 .T4 1979 S62 / Master of Science

BACKGROUND:Airway hyperresponsiveness (AHR), a primary characteristic of asthma, involves increased airway smooth muscle contractility in response to certain exposures. We sought to determine whether common genetic variants were associated with AHR severity.METHODS:A genome-wide association study (GWAS) of AHR, quantified as the natural log of the dosage of methacholine causing a 20% drop in FEV1, was performed with 994 non-Hispanic white asthmatic subjects from three drug clinical trials: CAMP, CARE, and ACRN. Genotyping was performed on Affymetrix 6.0 arrays, and imputed data based on HapMap Phase 2, was used to measure the association of SNPs with AHR using a linear regression model. Replication of primary findings was attempted in 650 white subjects from DAG, and 3,354 white subjects from LHS. Evidence that the top SNPs were eQTL of their respective genes was sought using expression data available for 419 white CAMP subjects.RESULTS:The top primary GWAS associations were in rs848788 (P-value 7.2E-07) and rs6731443 (P-value 2.5E-06), located within the ITGB5 and AGFG1 genes, respectively. The AGFG1 result replicated at a nominally significant level in one independent population (LHS P-value 0.012), and the SNP had a nominally significant unadjusted P-value (0.0067) for being an eQTL of AGFG1.CONCLUSIONS:Based on current knowledge of ITGB5 and AGFG1, our results suggest that variants within these genes may be involved in modulating AHR. Future functional studies are required to confirm that our associations represent true biologically significant findings.

This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures.

Context-aware systems use context information to decide what adaptation actions to perform in response to changes in their environment. Depending on applications, context information includes physical context (e.g. temperature and location), user context (e.g. user preferences and user activity), and ICT context (e.g. device capabilities and battery power). Sensors are the main mean of capturing context. Unfortunately, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and the highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This thesis conducts a novel framework for QoC in context-aware systems, which is called MCFQoC (Multilayered-Context Framework for Quality of Context). The main innovative features of our framework, MCFQoC, include: (1) a new definition that generalizes the notion of QoC to encompass sensed context as well as user profiled context; (2) a novel multilayer context model, that distinguishes between three context abstractions: context situation, context object, and context element in descending order. A context element represents a single value and many context elements can be compound into a context object. Many context objects in turn form a context situation; (3) a novel model of QoC parameters which extends the existing parameters with new quality parameter and explicitly distributes the quality parameters across the three layers of context abstraction; (4) a novel algorithm, RCCAR (Resolving Context Conflicts Using Association Rules), which has been developed to resolve conflicts in context data using the Association Rules (AR) technique; (5) a novel mechanism to define QoC policy by assigning weights to QoC parameters using a multi-criteria decision-making technique called Analytical Hierarchy Process (AHP); (6) and finally, a novel quality control algorithm called IPQP (Integrating Prediction with Quality of context Parameters for Context Quality Control) for handling context conflicts, context missing values, and context erroneous values. IPQP is extension of RCCAR. Our framework, MCFQoC, has been implemented in MatLab and evaluated using a case study of a flood forecast system. Results show that the framework is expressive and modular, thanks to the multilayer context model and also to the notion QoC policy which enables us to assign weights for QoC’s parameters depending on quality requirements of each specific application. This flexibility makes it easy to apply our approach to a wider type of context-aware applications. As a part of MCFQoC framework, IPQP algorithm has been successfully tested and evaluated for QoC control using a variety of scenarios. The algorithm RCCAR has been tested and evaluated either individually and as a part of MCFQoC framework with a significant performance concerning resolving context conflicts. In addition, RCCAR has achieved a good success comparing to traditional prediction methods such as moving average (MA), weighted moving average, exponential smoothing, doubled exponential smoothing, and autoregressive moving average (ARMA).

In order to fully examine the application of association analysis to medical data for the purpose of deriving medical diagnoses, we survey classical association analysis and approaches, the current challenges faced by medical association analysis and proposed solutions, and finally culminate this knowledge in a proposition for the application of medical association analysis to the identification of food intolerance. The field of classical association analysis has been well studied since its introduction in the seminal paper on market basket research in the 1990's. While the theory itself is relatively simple, the brute force approach is prohibitively expensive and thus, creative approaches utilizing various data structures and strategies must be explored for efficiency. Medical association analysis is a burgeoning field with various focuses, including diagnosis systems and gene analysis. There are a number of challenges faced in the field, primarily stemming from characteristics of analysis of complex, voluminous and high dimensional medical data. We examine the challenges faced in the pre-processing, analysis and post-processing phases, and corresponding solutions. Additionally, we survey proposed measures for ensuring the results of medical association analysis will hold up to medical diagnosis standards. Finally, we explore how medical association analysis can be utilized to identify food intolerances. The proposed analysis system is based upon a current method of diagnosis used by medical professionals, and seeks to eliminate manual analysis, while more efficiently and intelligently identifying interesting, and less obvious patterns between patients' food consumption and symptoms to propose a food intolerance diagnosis.

This thesis presents an in depth study of the expansion of settlement in Christchurch between 1850-62, the pre-Municipal Council years. It is confined spatially to the 'central city' area within the four Avenues. Four research problems are studied: (1) The laying out of Christchurch by the New Zealand Company surveyors for the Canterbury Association, in particular, the reasons for the use of a grid street pattern. It was found that many factors were involved in its use. These included the personal preferences of the surveyors and the Association committee members, the topographic nature of the site chosen and contemporary planning convention. (2) The initial selection of town sections by the colonists and the reasons behind their choices. The 'orders of choice' of the sections have been tabulated and mapped to show section preference and how the settlers perceived the economic value of particular areas of the town grid for their commercial prospects. Town section auctions, leases, sales and subdivisions as well as church land are investigated. (3) The sale of the Town Reserves surrounding the town section area is also studied; the reason for their early sale, the nature and characteristics of the sales including prices and purchasers and their occupations. It was found that the prices paid were significantly related to the spatial positions of the lots within the Reserve blocks and to the time of their sale, in response to contemporary land values. There was no significant relationship between prices and the occupations of the buyers. (4) Public works undertaken by the Association and the Provincial Government are studied and their relationship with immigration and population figures. The scale of public works carried out at particular times was found to relate both to available finance and to immigration, the source of labour. Various aspects of public works, for example, street and footpath formation and bridging and drainage work were found to be indicative of differential growth between periods of depression and prosperity.

This research recommends a new approach to brand strategy for Korean professional football teams, focusing on the relationships between team brand identity as the basic element of sports team branding, team brand loyalty as the most desirable goal, and identification between fans and teams as the mediator between identity and loyalty. Nowadays, professional football teams are no longer merely sporting organisations, but organisational brands with multi-million pound revenues. It is vital for football teams to build a relevant brand strategy based on the relationship with their fans. Existing research on sports branding suggests that fans who are deeply identified with a specific team tend to possess extremely high loyalty, holding a particular team as central to their identity. Therefore, managing the relationships between team brand identity, fan-team identification, and team brand loyalty can be the most powerful brand strategy for football teams, particularly for Korean football teams that do not retain strong fan bases and yet desire to gain consumers who identify with them. Through two empirical studies and case study analysis this research investigated a construct of team brand identity in the professional football context. Consumers’ associations with football teams were examined and 13 elements of a team brand identity scale were developed. It was revealed that team brand identity is composed of four identity dimensions which are experience, visual, non-product, and product. Case studies, with a further literature review of team brand identity, clarified and confirmed the first study findings. The final empirical study tested and confirmed the correlated and serial relationships, and provided the basis for the new theoretical model on which to build the brand strategy.

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This thesis analyzes the movement for Trinity River improvement and describes the methods used to promote the project.

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world database from a grocery outlet to explore the behavior of confidence and lift, two popular interest measures used for rule mining. The results show that confidence is systematically influenced by the frequency of the items in the left hand side of rules and that lift performs poorly to filter random noise in transaction data. Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules. The new measures show significant better performance than lift for applications where spurious rules are problematic. / Series: Research Report Series / Department of Statistics and Mathematics