18-Fluorodeoxyglucose positron emission tomography/computed tomography in the management of aggressive non-Hodgkin's B-cell lymphoma

18-Fluorodeoxyglucose (FDG-PET/CT) is an established imaging modality that has been proven to be of benefit in the management of aggressive B-cell non-Hodgkin's lymphoma, such as diffuse large B-cell lymphoma and advanced stage follicular lymphoma. The combination of anatomic and functional imaging afforded by FDG-PET/CT has led to superior sensitivity and specificity in the primary staging, restaging, and assessment of response to treatment of hematological malignancies when compared to FDG-PET and CT alone. The use of FDG-PET/CT for post treatment surveillance imaging remains controversial, and further study is needed to ascertain whether this modality is cost effective and appropriate for use in this setting.

Interactions between nematodes, potato and soil-borne micro-organisms and their effect on the management of potato cyst nematodes

Potato cyst nematodes (PCN) cause significant damage to the potato crop worldwide and growers experience economic losses related to yield loss and the cost of control measures. Experiments were set up to further elucidate the complex tritrophic PCNpotato-soil bacteria relationship. Bacterial strains isolated from the sugar beet rhizosphere were shown to be hatch active towards Globodera pallida and to be capable of successfully colonising the sugar beet rhizosphere when applied exogenously. A trap-crop system, based on these isolates, was proposed. Ridge and bulk soil taken from a commercial potato field were incubated with sterile potato root leachate (sPRL) and subsequent in vitro hatching assays showed that PCN hatch was influenced by microorganisms present in the ridge, but not in the bulk soil. Community level physiological profiling (CLPP) of ridge and bulk soil, using BIOLOG EcoplatesTM, demonstrated differences in bacterial functional diversity between the two soil types. An investigation of the inter-species competition between G. pallida and G. rostochiensis showed that G. pallida performed significantly better, in terms of multiplication rate, in competition with G. rostochiensis compared to its multiplication rate in single-species populations. Effectively removing the early hatch of G. rostochiensis in pot trials led to the removal of this competitive advantage of G. pallida suggesting that this advantage was due, at least in part, to morphological changes to the root caused by the early hatching of G. rostochiensis.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.