Retinal vascular function and cardiovascular risk factors

The current platform of conventional cardiovascular risk assessments tends to forsake the importance of endothelial function - a key biological mechanism by which cardiovascular risk factors exert their propensity for adverse vascular events. Moreover, the presence and severity of endothelial dysfunction in ‘low-risk’ individuals suggests considerable variability in pre-clinical risk that could potentially be detected well before the onset of disease. The aim of the present thesis was to investigate the presence and impact of retinal vascular dysfunction, as a barometer of endothelial function, in otherwise healthy individuals with one or more cardiovascular risk factors, but low to moderate cardiovascular risk. Systemic circulatory influences on retinal vascular function were also evaluated. The principle sections and findings of this work are: 1. Ageing effect on retinal vascular function • In low-risk individuals, there are age differences in retinal vascular function throughout the entire functional response curve for arteries and veins. Gender differences mainly affect the dilatory phase and are only present in young individuals. 2. Retinal vascular function in healthy individuals with a family history of cardiovascular disease • In low-risk individuals with a family history of cardiovascular disease, impairments in microvascular function at the retinal level correlate with established plasma markers for cardiovascular risk. 3. Ethnic differences in retinal vascular function • When compared to age-matched White Europeans, in low-risk middle-aged South Asians, there are impairments in retinal vascular function that correlate with established cardiovascular risk indicators. 4. Systemic circulatory influences on retinalµvascular function • Systemic antioxidant capacity (redox index) and plasma markers for cardiovascular risk (lipids) influence retinal microvascular function at both arterial and venous levels. 5. Retinal vascular function in individuals with obstructive sleep apnoea: a preliminarystudy • Patients with moderate to severe sleep apnoea exhibit attenuated retinal vascular function.

A cell-permeable tool for analysing APP intracellular domain function and manipulation of PIKfyve activity

The mechanisms for regulating PIKfyve complex activity are currently emerging. The PIKfyve complex, consisting of the phosphoinositide kinase PIKfyve (also known as FAB1), VAC14 and FIG4, is required for the production of phosphatidylinositol-3,5-bisphosphate (PI(3,5)P2). PIKfyve function is required for homeostasis of the endo/lysosomal system and is crucially implicated in neuronal function and integrity, as loss of function mutations in the PIKfyve complex lead to neurodegeneration in mouse models and human patients. Our recent work has shown that the intracellular domain of the Amyloid Precursor Protein (APP), a molecule central to the aetiology of Alzheimer's disease binds to VAC14 and enhances PIKfyve function. Here we utilise this recent advance to create an easy-to-use tool for increasing PIKfyve activity in cells. We fused APP's intracellular domain (AICD) to the HIV TAT domain, a cell permeable peptide allowing proteins to penetrate cells. The resultant TAT-AICD fusion protein is cell permeable and triggers an increase of PI(3,5)P2. Using the PI(3,5)P2 specific GFP-ML1Nx2 probe we show that cell-permeable AICD alters PI(3,5)P2 dynamics. TAT-AICD also provides partial protection from pharmacological inhibition of PIKfyve. All three lines of evidence show that the APP intracellular domain activates the PIKfyve complex in cells, a finding that is important for our understanding of the mechanism of neurodegeneration in Alzheimer's disease.

The relationship of systemic markers of renal function and vascular function with retinal blood vessel responses

Purpose: To test the hypothesis of a significant relationship between systemic markers of renal and vascular function (processes linked to cardiovascular disease and its development) and retinal microvascular function in diabetes and/or cardiovascular disease.Methods: Ocular microcirculatory function was measured in 116 patients with diabetes and/or cardiovascular disease using static and continuous retinal vessel responses to three cycles of flickering light. Endothelial function was evaluated by von Willebrand factor (vWf), endothelial microparticles and soluble E selectin, renal function by serum creatinine, creatinine clearance and estimated glomerular filtration rate (eGFR). HbA1c was used as a control index.Results: Central retinal vein equivalence and venous maximum dilation to flicker were linked to HbA1c (both p<0.05). Arterial reaction time was linked to serum creatinine (p=0.036) and eGFR (p=0.039), venous reaction time was linked to creatinine clearance (p=0.018). Creatinine clearance and eGFR were linked to arterial maximum dilatation (p<0.001 and p=0.003 respectively) and the dilatation amplitude (p=0.038 and p=0.048 respectively) responses in the third flicker cycle. Of venous responses to the first flicker cycle, HbA1c was linked to the maximum dilation response (p=0.004) and dilatation amplitude (p=0.017), vWf was linked to the maximum constriction response (p=0.016), and creatinine clearance to the baseline diameter fluctuation (p=0.029). In the second flicker cycle, dilatation amplitude was linked to serum creatinine (p=0.022). Conclusions: Several retinal blood vessel responses to flickering light are linked to glycaemia and renal function, but only one index is linked to endothelial function. Renal function must be considered when interpreting retinal vessel responses.

Estimation and prediction of average vehicle occupancies using traffic accident records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver’s age, and driver’s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Regulatory factors controlling muscle mass : competition between innate immune function and anabolic signals in regulation of atrogin-1 in Atlantic salmon

The research was supported by an industrial PhD studentship between University of Aberdeen and by BioMar Ltd., for Z. Heidari.

Early growth characteristics and the risk of reduced lung function and asthma : a meta-analysis of 25,000 children

Peer reviewed

Regulatory factors controlling muscle mass : competition between innate immune function and anabolic signals in regulation of atrogin-1 in Atlantic salmon

The research was supported by an industrial PhD studentship between University of Aberdeen and by BioMar Ltd., for Z. Heidari.

Early growth characteristics and the risk of reduced lung function and asthma : a meta-analysis of 25,000 children

Peer reviewed

Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.

A bidirectional relationship between executive function and health behavior : evidence, implications, and future directions

This work was supported by a grant from the UK Economic and Social Research Council (ES/L010437/1).

Control and prediction for blackouts caused by frequency collapse in smart grids

C.-W.W. is supported by a studentship funded by the College of Physical Sciences, University of Aberdeen. M.S.B. acknowledges EPSRC grant NO. EP/I032606/1.

Regulatory factors controlling muscle mass : competition between innate immune function and anabolic signals in regulation of atrogin-1 in Atlantic salmon

The research was supported by an industrial PhD studentship between University of Aberdeen and by BioMar Ltd., for Z. Heidari.