Renal failure and the neural control of the kidney

Renal failure (RF) is associated with an over activation of the sympathetic nervous system. The aim of this thesis was to investigate the hypothesis that as the kidney progresses into RF there is an inappropriate and sustained activation of renal afferent nerves which results in a dysregulation of basal RSNA and reflexly controlled RSNA by the high and low pressure baroreceptors. Baroreflex gain curves for both RSNA and HR were generated in control and RF rats. This study clearly showed a blunted high-pressure baroreflex in RF rats, an impairment which was almost completely corrected by bilateral renal denervation. The integrity of the low-pressure cardiopulmonary receptors to inhibit RSNA was investigated using acute saline volume. Again, a blunted reflex sympatho-inhibition of RSNA was observed, which was corrected by renal denervation. Finally a functional study to examine how the renal excretory response to volume expansion differed in RF was carried out. This study revealed an impairment of the low-pressure baroreflex control of the sympathetic outflow. The result of these studies suggest that cisplatin induced RF initiates a neural signal from within the kidney, which over rides the normal reflex regulation of RSNA by the high and low – pressure baroreceptors and that this impairment in function can be normalised by renal denervation. This raises further questions as to the mechanisms involved in the afferent over activation arising from the diseased kidneys.

Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.