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.

Identifying novel molecular mechanisms of ghrelin receptor signalling underlying neural control of food intake: interaction with stress and impulsivity

The gut-hormone, ghrelin, activates the centrally expressed growth hormone secretagogue 1a (GHS-R1a) receptor, or ghrelin receptor. The ghrelin receptor is a G-protein coupled receptor (GPCR) expressed in several brain regions, including the arcuate nucleus (Arc), lateral hypothalamus (LH), ventral tegmental area (VTA), nucleus accumbens (NAcc) and amygdala. Activation of the GHS-R1a mediates a multitude of biological activities, including release of growth hormone and food intake. The ghrelin signalling system also plays a key role in the hedonic aspects of food intake and activates the dopaminergic mesolimbic circuit involved in reward signalling. Recently, ghrelin has been shown to be involved in mediating a stress response and to mediate stress-induced food reward behaviour via its interaction with the HPA-axis at the level of the anterior pituitary. Here, we focus on the role of the GHS-R1a receptor in reward behaviour, including the motivation to eat, its anxiogenic effects, and its role in impulsive behaviour. We investigate the functional selectivity and pharmacology of GHS-R1a receptor ligands as well as crosstalk of the GHS-R1a receptor with the serotonin 2C (5-HT2C) receptor, which represent another major target in the regulation of eating behaviour, stress-sensitivity and impulse control disorders. We demonstrate, to our knowledge for the first time, the direct impact of GHS-R1a signalling on impulsive responding in a 2-choice serial reaction time task (2CSRTT) and show a role for the 5-HT2C receptor in modulating amphetamine-associated impulsive action. Finally, we investigate differential gene expression patterns in the mesocorticolimbic pathway, specifically in the NAcc and PFC, between innate low- and high-impulsive rats. Together, these findings are poised to have important implications in the development of novel treatment strategies to combat eating disorders, including obesity and binge eating disorders as well as impulse control disorders, including, substance abuse and addiction, attention deficit hyperactivity disorder (ADHD) and mood disorders.

Artificial neural network application in short-term prediction in an oscillating water column

Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.