Statistical considerations in the kinetic analysis of PET-FDG brain tumour studies

Dynamic positron emission tomography (PET) imaging can be used to track the distribution of injected radio-labelled molecules over time in vivo. This is a powerful technique, which provides researchers and clinicians the opportunity to study the status of healthy and pathological tissue by examining how it processes substances of interest. Widely used tracers include 18F-uorodeoxyglucose, an analog of glucose, which is used as the radiotracer in over ninety percent of PET scans. This radiotracer provides a way of quantifying the distribution of glucose utilisation in vivo. The interpretation of PET time-course data is complicated because the measured signal is a combination of vascular delivery and tissue retention effects. If the arterial time-course is known, the tissue time-course can typically be expressed in terms of a linear convolution between the arterial time-course and the tissue residue function. As the residue represents the amount of tracer remaining in the tissue, this can be thought of as a survival function; these functions been examined in great detail by the statistics community. Kinetic analysis of PET data is concerned with estimation of the residue and associated functionals such as ow, ux and volume of distribution. This thesis presents a Markov chain formulation of blood tissue exchange and explores how this relates to established compartmental forms. A nonparametric approach to the estimation of the residue is examined and the improvement in this model relative to compartmental model is evaluated using simulations and cross-validation techniques. The reference distribution of the test statistics, generated in comparing the models, is also studied. We explore these models further with simulated studies and an FDG-PET dataset from subjects with gliomas, which has previously been analysed with compartmental modelling. We also consider the performance of a recently proposed mixture modelling technique in this study.

From field to fermentation: characterisation and application of non-dairy cultures in dairy foods

This thesis investigates the phenotypic and genotypic diversity of non-dairy L. lactis strains and their application to dairy fermentations. A bank of non-dairy lactococci were isolated from grass, vegetables and the bovine rumen. Subsequent analysis of these L. lactis strains revealed seven strains to possess cremoris genotypes which did not correlate with their observed phenotypes. Multi-locus sequence typing (MLST) and average nucleotide identity (ANI) highlighted the genetic diversity of lactis and cremoris subspecies. The application of these non-dairy lactococci to cheese production was also assessed. In milk, non-dairy strains formed diverse volatile profiles and selected strains were used as adjuncts in a mini Gouda-type cheese system. Sensory analysis showed non-dairy strains to be strongly associated with the development of off-flavours and bitterness. However, microfluidisation appeared to reduce bitterness. A novel bacteriophage, ɸL47, was isolated using the grass isolate L. lactis ssp. cremoris DPC6860 as a host. The phage, a member of the Siphoviridae, possessed a long tail fiber, previously unseen in dairy lactococcal phages. Genome sequencing revealed ɸL47 to be the largest sequenced lactococcal phage to date and owing to the high % similarity with ɸ949, a second member of the 949 group. Finally, to identify and characterise specific genes which may be important in niche adaptation and for applications to dairy fermentations, comparative genome sequence analysis was performed on L. lactis from corn (DPC6853), the bovine rumen (DPC6853) and grass (DPC6860). This study highlights the contribution of niche specialisation to the intra-species diversity of L. lactis and the adaptation of this organism to different environments. In summary this thesis describes the genetic diversity of L. lactis strains from outside the dairy environment and their potential application in dairy fermentations.