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No description available.

This thesis is concerned with developing and implementing a method for modelling and projecting cancer incidence data. The burden of cancer is an increasing problem for society and therefore, the ability to analyse and predict trends in large scale populations is vital. These predictions based on incidence and mortality data collected by cancer registries, can be used for estimation of current and future rates, which is helpful for public health planning. A large body of work already exists on the use of various modelling strategies, methods and fitting techniques. A multilevel method of preparing the data is proposed, fitted to historical data using regression modelling, to predict future rates of incidence for a given population. The proposed model starts with a model for the total incidence of the population, with each successive level stratifying the data into progressively more specific groupings, based on age. Each grouping is partitioned into subgroups, and each subgroup is expressed as a proportion of the parent group. Models are fitted to each of the proportional age-groups, and a combination of these models produces a model that predicts incidence for a specific age. A simple, efficient implementation of the modelling procedure is described, including key algorithms and measures of performance. The method is applied to data from populations that have very different melanoma incidence (the USA and Australia). The proportional structure reveals that the proportional age trends present in both populations are remarkably similar, indicating that there are links between causative factors in both populations. The method is applied fully to data from a variety of populations, and compared with results from existing models. The method is shown to be able to produce results that are reliable and stable, and are generally significantly more accurate than those of other models.

Functionally, a vertically split fovea should confer an advantage to the processor. Visual stumuli arriving to each eye would be vertically split and the two parts sent to different hemispheres, obeying the crossed nature of the visual pathways. I test the prediction of a functional advantage for the separate lateralisation of text processing from the two eyes. I explore this hypothesis by means of psycholinguistic experimentation and cognitive modelling. I employed a haploscope to show foveated text to the two eyes separately, controlling for location and presentation duration, and guaranteeing that each eye could not see the other eye's stimuli. I carried out a series of experiments, based on this novel paradigm, to explore the effects of a vertically split fovea on correctness of word perception. The experiments showed: (i) words presented exclusively to the contralateral hemifoveas are more correctly reported than words presented exclusively to the ipsilateral hemifoveas; (ii) the same full word shown to both eyes and available for fusion led to better perception; (iii) word endings with fewer type-count neighbours were more accurately reported, as were beginnings with larger type-count neighbours; (iv) uncrossed-eye stumuli were better perceived than crossed-eye stimuli; (v) principled roles in a model of isolated word recognition for lexical and sublexical neighbourhood statistics, syllabicity, hemispheric fine- and coarse-coding differences, sex of the reader, handedness, left and right eye, and visual pathways. Finally, I propose a connectionist model of visual word recognition that incorporates these findings and is a basis for further exploration.

The systems of interest in this study are the spread of epidemics and invasions from a small propagule introduced into an arena that was initially devoid of the given species or stage of illness. In reaction-diffusion models, populations are continuous. Populations at low densities have the same growth functions as populations at high densities. In nature, such low densities would signify extinction of a population or of a disease. This property can be removed from reaction-diffusion models by small changes in the formulation so that small populations become extinct. This can be achieved by the use of a threshold density or an Allee effect, so there is negative growth at low densities. Both these alterations were made to the Fisher model, a predator-prey model and a two stage and a three stage epidemic model. A semi-numerical method, termed the Shooting method, was developed to predict the shapes and velocities of these wave fronts. This was found to correctly predict the velocity, the peak density of the invading stage or species and the width of the wave front. It was found that in oscillatory cases of the multi species models, a high threshold can remove the wave train or wake which would normally follow the wave front, so the wave becomes a soliton. The next step is to investigate probable causes of persistence behind the initial wavefront. To do this, discrete time and space versions of the models were formulated so that experiments investigating persistence can be carried out in a two dimensional arena with less computational effort. The formulations were chosen so that at reasonable time and space steps the discrete models show no behaviour different to that of the reaction diffusion model, and so that the Shooting method could also be used to make predictions about these wavefronts. Three mechanisms of persistence are investigated; environmental heterogeneity, long range dispersal and self organised patterns.

Computer models, or simulators, are widely used in a range of scientific fields to aid understanding of the processes involved and make predictions. Such simulators are often computationally demanding and are thus not amenable to statistical analysis. Emulators provide a statistical approximation, or surrogate, for the simulators accounting for the additional approximation uncertainty. This thesis develops a novel sequential screening method to reduce the set of simulator variables considered during emulation. This screening method is shown to require fewer simulator evaluations than existing approaches. Utilising the lower dimensional active variable set simplifies subsequent emulation analysis. For random output, or stochastic, simulators the output dispersion, and thus variance, is typically a function of the inputs. This work extends the emulator framework to account for such heteroscedasticity by constructing two new heteroscedastic Gaussian process representations and proposes an experimental design technique to optimally learn the model parameters. The design criterion is an extension of Fisher information to heteroscedastic variance models. Replicated observations are efficiently handled in both the design and model inference stages. Through a series of simulation experiments on both synthetic and real world simulators, the emulators inferred on optimal designs with replicated observations are shown to outperform equivalent models inferred on space-filling replicate-free designs in terms of both model parameter uncertainty and predictive variance.

Recent developments in materials and material joining [specifically Aluminum and Pulse Gas Metal Arc Welding (GMAW-P) technology] have increased the scope and extent of their areas of application. However, stern market demand for the improved weld quality necessitates the need for automation of the welding processes. As a result, improvements in the process parameter feedback, sensing and control, are necessary to successfully develop the automated control technology for the welding processes. Hence, several aspects of the GMAW-P process have been investigated in this study in order to improve its control techniques. Welding was conducted on 6XXX aluminium, using 1.2 mm diameter 4047 aluminum electrode and argon shielding gas. An extensive collection of high speed camera pictures were taken over a wide range of pulse parameters and wire feed rates using a xenon shadowgraph setup to improve understanding of the physics of GMAW-P process. Current and voltage signals were recorded concurrently too. This investigation explores the effects of different process parameters namely pulsing parameters (Peak current (IP), Base Current (IB), Peak time (TP), Base Time (TB)) and wire feed rate on metal transfer phenomena in GMAW-P. Number of drops per pulse, arc length and droplet diameter were measured for aluminium electrodes by high speed videography. The pulsing parameters and wire feed rate were varied to investigate their effect on the metal transfer behaviour. Analysis showed that transition between the different metal transfer modes is strongly influenced by the electrode extension. Lower electrode extension reduced the number of droplets detached per pulse, while at higher electrode extension, spray mode is observed due to increased influence of the resistance heating. Analysis of the current and voltage signals were correlated with the high speed films. A simple derivative filter was used to detect the sudden changes in voltage difference associated with metal transfer during GMAW-P. The chosen feature for detection is the mean value of the weld current and voltage. A new algorithm for the real time monitoring and classification of different metal transfer modes in GMAW-P has been developed using voltage and current signals. The performance of the algorithm is assessed using experimental data. The results obtained from the algorithm show that it is possible to detect changes in metal transfer modes automatically and on-line. Arc stability in the GMAW-P has a close relationship with the regularity of metal transfer, which depends on several physical quantities (like voltage, current, materials, etc.) related to the growth and transfer of the metal droplet. Arc state in GMAW-P can be assessed quantitatively in terms of number of drops per pulse, droplet diameter and arc length. In order to assess the arc state in GMAW-P quantitatively, statistical and neural network models for number of drops/pulse, droplet diameter and arc length were developed using different waveform factors extracted from the current waveform of GMAW-P. To validate the models, estimated results were compared to the actual values of the number of drops per pulse, droplet diameter and arc length, observed during several welding conditions. Determination of stable one drop per pulse (ODPP) parametric zone containing all the combinations of peak current (IP), base current (IB), peak time (TP), and base time (TB) that results in stable operation of GMAW-P, is one of the biggest challenges in GMAW-P. A new parametric model to identify the stable ODPP condition in aluminium which also considers the influence of the background conditions and wire feed has been proposed. Finally, a synergic control algorithm for GMAW-P process has been proposed. Synergic algorithm proposed in this work uses the sensing and prediction techniques to analyse state of the arc and correct the pulsing parameters for achieving the stable ODPP. First arc state is estimated using the signal processing techniques and statistical methods to detect the occurrence of short circuit, unstable ODPP or multiple drops per pulse (MDPP) in GMAW-P system. If the arc state is not stable ODPP, then parametric model and genetic algorithm (GA) is used to assess the deviation of the existing pulsing parameters from the stable operation of GMAW-P process and automatically adjust pulsing parameters to achieve stable ODPP.

No description available.

Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving immunotherapy initiatives. However, progress in this domain is hindered by an incomplete characterisation of the regulatory mechanisms involved in cancer immunity. Addressing this challenge, this thesis is formulated around a fundamental line of inquiry: how do we quantitatively describe the immune system with respect to tumour heterogeneity? Describing the molecular interactions between cancer cells and the immune system is a fundamental goal of cancer immunology. The first part of this thesis describes a three-stage association study to address this challenge in pancreatic ductal adenocarcinoma (PDAC). Firstly, network-based approaches are used to characterise PDAC on the basis of transcription factor regulators of an oncogenic KRAS signature. Next, gene expression tools are used to resolve the leukocyte subset mixing proportions, stromal contamination, immune checkpoint expression and immune pathway dysregulation from the data. Finally, partial correlations are used to characterise immune features in terms of KRAS master regulator activity. The results are compared across two independent cohorts for consistency. Moving beyond associations, the second part of the dissertation introduces a causal modelling approach to infer directed interactions between signaling pathway activity and immune agency. This is achieved by anchoring the analysis on somatic genomic changes. In particular, copy number profiles, transcriptomic data, image data and a protein-protein interaction network are integrated using graphical modelling approaches to infer directed relationships. Generated models are compared between independent cohorts and orthogonal datasets to evaluate consistency. Finally, proposed mechanisms are cross-referenced against literature examples to test for legitimacy. In summary, this dissertation provides methodological contributions, at the levels of associative and causal inference, for inferring the contextual basis for tumour-specific immune agency.