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Bacteria are the most abundant organisms on the planet. They live almost in all environments, including those that are most extreme. All land and water ecosystems depend heavily upon their activity. Bacteria play essential roles in cycling of nutrients such as carbon, nitrogen, and sulphur. Due to their short cell cycle, they must be able to swiftly adapt to the conditions of their habitat to survive. Microbial growth itself is an autocatalytic process. There are three distinct phases of the growth curve: lag, exponential (log), and stationary. Bacterial cells must change their gene expression between these phases in order to adapt to the new conditions. The first stage of gene expression is transcription. The key enzyme of this stage is RNA polymerase (RNAP) that transcribes DNA into RNA. RNAP is regulated by a number of accessory proteins and also small molecule effectors. Understanding how RNAP functions is essential for understanding how bacteria cope with changing environments. This Thesis presents studies of selected aspects of bacterial gene expression regulation at the level of transcription, using Bacillus subtilis as the model organism. The first part of this Thesis focuses on protein determinants of the ability of RNAP to be regulated by the concentration of the initiating nucleoside...

The Soil and Water Assessment Tool (SWAT) was used to quantify the potential impacts of clearcutting on hydrologic and water quality components, and assess potential forest biomass production and perform sensitivity analysis of crop parameters to predict forest biomass in the Upper Pearl River Watershed. Results based on clearcutting indicated that the hydrologic and water quality components increases with increase in percentage of forest area harvested. The most significant effect was observed from 55% and 75% harvesting scenarios. The results based on SWAT’s performance to simulate potential forest biomass production showed satisfactory performance and revealed that the watershed has the potential to produce approximately 49 tons/ha of annual forest biomass. The results also revealed that predicted forest biomass was sensitive to three out of the seven crop parameters: Fraction of maximum leaf area index, corresponding to 1stpoint on the optimal leaf area development curve, Radiation use efficiency and maximum LAI.

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Accurate prediction of tsunami waves requires complete boundary and initial condition data, coupled with the appropriate mathematical model. However, necessary data is often missing or inaccurate, and may not have sufficient resolution to capture the dynamics of such nonlinear waves accurately. In this thesis we demonstrate that variational data assimilation for the continuous shallow water equations (SWE) is a feasible approach for recovering both initial conditions and bathymetry data from sparse observations. Using a Sadourny finite-difference finite volume discretisation for our numerical implementation, we show that convergence to true initial conditions can be achieved for sparse observations arranged in multiple configurations, for both isotropic and anisotropic initial conditions, and with realistic bathymetry data in two dimensions. We demonstrate that for the 1-D SWE, convergence to exact bathymetry is improved by including a low-pass filter in the data assimilation algorithm designed to remove scale-scale noise, and with a larger number of observations. A necessary condition for a relative L2 error less than 10% in bathymetry reconstruction is that the amplitude of the initial conditions be less than 1% of the bathymetry height. We perform Second Order Adjoint Sensitivity Analysis and Global Sensitivity Analysis to comprehensively assess the sensitivity of the surface wave to errors in the bathymetry and perturbations in the observations. By demonstrating low sensitivity of the surface wave to the reconstruction error, we found that reconstructing the bathymetry with a relative error of about 10% is sufficiently accurate for surface wave modelling in most cases. These idealised results with simplified 2-D and 1-D geometry are intended to be a first step towards more physically realistic settings, and can be used in tsunami modelling to (i) maximise accuracy of tsunami prediction through sufficiently accurate reconstruction of the necessary data, (ii) attain a priori knowledge of how different bathymetry and initial conditions can affect the surface wave error, and (iii) provide insight on how these can be mitigated through optimal configuration of the observations. / Thesis / Candidate in Philosophy

Immune thrombocytopenia (ITP) is an autoimmune hematologic disorder characterized by a low platelet count due to increased platelet destruction or decreased production. In primary ITP, the patient can have a low platelet count (<100 billion cells/L) for clinically unknown reasons. ITP is a rare disease that affects approximately 3/100 000 adults each year and some patients may experience bleeding symptoms. Autoantibody-mediated autoimmunity plays a role in the destruction of platelets by targeting platelet glycoproteins (GPs). Autoantibodies against platelet membrane GPIIbIIIa and GPIbIX are observed in about 50% of patients through direct antigen-capture assays, and 18% in patients through indirect antigen-capture assays. It is possible that some antibodies may not be detectable due to affinity or titre, or there may be other factors involved in platelet destruction. Currently, there is no definitive diagnostic test available for ITP, as a result of low assay sensitivity and different mechanisms involved in disease pathogenesis. The objective of this study was to use a novel approach to increase autoantibody detection unique to ITP patients. Total IgG was purified from patient and control plasma samples. A streptavidin-based antigen-capture assay was optimized to test the effect of biotinylation on the detection of anti-GPIIbIIIa and anti-GPIbIX autoantibodies in primary ITP patients (n=14), secondary ITP patients (n=3), non-immune thrombocytopenic controls (n=2) and healthy controls (n=16). Streptavidin-coated biosensors were used in an optimized biolayer interferometry (BLI) assay to study autoantibodies binding to biotinylated GPIIbIIIa and GPIbIX. Detection of anti-GPIIbIIIa autoantibodies in the streptavidin antigen-capture assay had a sensitivity of 24% and anti-GPIbIX autoantibodies had a sensitivity of 25%. BLI showed binding of autoantibodies in approximately 5% of ITP samples for both GPIIbIIIa and GPIbIX. The samples that had detectable autoantibodies in the antigen-capture assay did not have detectable antibodies in the BLI assay. BLI was not able to confirm antibody detection found in enzyme immunoassays. / Thesis / Master of Science (MSc) / Platelets are blood cells involved in clotting at sites of injury. Immune thrombocytopenia (ITP) is a disease defined by a low platelet count that can lead to bleeding. ITP is a rare disease that affects 3 in 100 000 adults every year. ITP is thought to be caused by proteins known as antibodies that bind self-platelets and lead to their destruction. These antibodies are directly found on approximately 50% of patients’ platelets, and only 18% of patients have antibodies in circulation. It is possible in many patients, antibodies are present at a low concentration, or are too weak to be detected in antibody tests. In this study, a new technology known as biolayer interferometry was employed to find antibodies in a higher percentage of patients. Results showed only 6% of ITP patients had detectable antibodies in their circulation. This research will improve our understanding of antibodies in ITP.