Investigation of the genetic susceptibility to osteoporosis in the Irish population

Osteoporosis is a complex skeletal disorder characterized by compromised bone strength. Variation in bone mineral density (BMD) is a contributing factor. The aim of this research as to select informative single nucleotide polymorphisms (SNPs) in potential candidate genes from loci suggestively linked to BMD variation for fine mapping. The gene regulated by oestrogen in breast cancer 1 (GREB1), located at 2p25.1, was selected. GREB1 transcription is initiated early in the oestrogen receptor alpha regulated pathway. There was significant association between GREB1_03 and BMD variation at the lumbar spine and femoral neck (FN) in the discovery cohort. Significant association was observed between GREB1_04 and FN BMD in the replication cohort. The development and differentiation enhancing factor 2, the integrin cytoplasmic domain associated protein 1 and A-disintegrin and metalloprotease 17 were selected due to their respective roles in cell mobility and adhesion. There was no linkage or association observed between the Chr2 cluster SNPs and BMD. Two factors in bone remodelling are the attraction of bone cell precursors and endocrine regulation of the process, primarily through the action of parathyroid hormone (PTH). The C-C chemokine receptor type 3 (CCR3) encodes a CC chemokine receptor expressed in osteoclast precursors. The PTH receptor type 1 (PTHR1) encodes a G-protein coupled receptor for PTH. Association was observed between CCR3 haplotypes and BMD variation at the FN. There was no linkage or association observed between PTHR1 SNPs and BMD variation. Population genetic studies with complex phenotypes endeavour to elucidate the traits genetic architecture. This study presents evidence of association between GREB1 and BMD variation and as such, introduces GREB1 as a novel gene target for osteoporosis genetics studies. It affirms that common genomic variants in PTHR1 are not associated with BMD variation in Caucasians and supports the evidence that CCR3 may be contributing to BMD variation

Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.