The role of the IL-1 type 1 receptor in the life, death and differentiation of adult hippocampal neural precursor cells

Neurogenesis occurs in two distinct regions of the adult brain; the subgranular zone (SGZ) of the dentate gyrus (DG) in the hippocampus, and the subventricular zone (SVZ) lining the lateral ventricles. It is now well-known that adult hippocampal neurogenesis can be modulated by a number of intrinsic and extrinsic factors e.g. local signalling molecules, exercise, environmental enrichment and learning. Moreover, levels of adult hippocampal neurogenesis decrease with age, at least in rodents, and alterations in hippocampal neurogenesis have been reported in animal models and human studies of neuropsychiatric and neurodegenerative conditions. Neuroinflammation is a common pathological feature of these conditions and is also a potent modulator of adult hippocampal neurogenesis. Recently, the orphan nuclear receptor TLX has been identified as an important regulator of adult hippocampal neurogenesis as its expression is necessary to maintain the neural precursor cell (NPC) pool in the adult DG. Likewise, exposure of animals to voluntary exercise has been consistently demonstrated to promote adult hippocampal neurogenesis. Lentivirus (LV)- mediated gene transfer is a useful tool to elucidate gene function and to explore potential therapeutic candidates across an array of conditions as it facilitates sustained gene expression in both dividing and post-mitotic cell populations. Both intrinsic and extrinsic factors are important regulators of adult hippocampal neurogenesis. Examining how these factors are affected by an inflammatory stimulus, and the subsequent effects on adult hippocampal neurogenesis provides important information for the development of novel treatment strategies for neuropsychiatric and neurodegenerative conditions in which adult hippocampal neurogenesis is impaired. The aims of the series of experiments presented in this thesis were to examine the effect of the pro-inflammatory cytokine interleukin-1β (IL-1β) on adult hippocampal NPCs both in vitro and in vivo. In vitro, we have shown that IL-1β reduces proliferation of adult hippocampal NPCs in a dose and time-dependent manner. In addition, we have demonstrated that TLX expression is reduced by IL-1β. Blockade of IL-1β signalling prevented both the IL-1β-induced reduction in cell proliferation and TLX expression. In vivo, we examined the effect of short term and long term exposure to LV-IL-1β in sedentary mice and in mice exposed to voluntary running. We demonstrated that impaired hippocampal neurogenesis is only evident after long term exposure to IL-1β. In mice exposed to voluntary running, hippocampal neurogenesis is significantly increased following short-term but not long-term exposure to running. Moreover, short-term running effectively prevents any IL-1β-induced effects on hippocampal neurogenesis; however, no such effects are seen following long-term exposure to running.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

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.