Thesis (Ph. D.)--Auburn University. / Abstract. Vita. Includes bibliographical references (p. 100-117).

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 116-117). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 116-117). Also available on the Internet.

Objectives: This pilot study examined how Methylphenidate (MPH0.5mg/kg) affects appetite sensations, food reinforcement, energy intake (EI), macronutrient consumption, and weight-loss in youth and adults living with obesity, without ADHD. Methods: This study employed a randomized, double-blind, placebo-controlled design. Eleven participants aged 28±6.9 yrs. (4 M, 7 F) were randomized to receive either MPH (n=5) or placebo group (n=6) for 60 days. Participants’ appetite sensations (Visual Analogue Scale), relative-reinforcing value of food (computer task), EI and macronutrient consumption (ad libitum buffet), and anthropometric measurements (DEXA) were measured at baseline and 60 days. Results: Repeated measures ANOVA revealed group x time interactions for appetite sensations [desire to eat (p=0.01), hunger (p=0.002), and prospective food consumption (p=0.006)]; with greater reductions in MPH group compared to placebo. For the sense of fullness, there was an interaction between group and time (p=0.01), with a greater increase for MPH compared to placebo. Body weight significantly decreased in both groups (p=0.01), with a moderate to large effect size favouring the MPH group (-2.66 kg vs. – 1.16 kg, Cohen’s d =0.76). Changes between MPH and placebo did not differ significantly on EI, macronutrient consumption, or food reinforcement. Conclusions: Our data indicate for the first time that MPH suppresses appetite in individuals with obesity resulting in a moderate–sized effect on weight loss in the short-term. These findings warrant a larger trial to more definitively examine the effect that MPH has on weight loss and maintenance of weight loss, thereby evaluating its potential as a novel pharmacological agent in the management of obesity.

Examining the effect of repeating stimuli on brain activity is important for theories of perception, learning and memory. Functional magnetic resonance imaging (fMRI) is a non-invasive way to examine repetition-related effects in the human brain. However the Blood-Oxygenation Level-Dependent (BOLD) signal measured by fMRI is far removed from the electrical activity recorded from single cells in animal studies of repetition effects. Despite that, there have been many claims about the neural mechanisms associated with fMRI repetition effects. However, none of these claims has adequately considered the temporal and spatial resolution limitations of fMRI. In this thesis, I tackle these limitations by combining simulations and modelling in order to infer repetition-related changes at the neural level. I start by considering temporal limitations in terms of the various types of general linear model (GLM) that have used to deconvolve single-trial BOLD estimates. Through simulations, I demonstrate that different GLMs are best depending on the relative size of trial-variance versus scan-variance, and the coherence of those variabilities across voxels. To address the spatial limitations, I identify six univariate and multivariate properties of repetition effects measured by event-related fMRI in regions of interest (ROI), including how repetition affects the ability to classify two classes of stimuli. To link these properties to underlying neural mechanisms, I create twelve models, inspired by single-cell studies. Using a grid search across model parameters, I find that only one model (“local scaling”) can account for all six fMRI properties simultaneously. I then validate this result on an independent dataset that involves a different stimulus set, protocol and ROI. Finally, I investigate classification of initial versus repeated presentations, regardless of the stimulus class. This work provides a better understanding of the neural correlates of stimulus repetition effects, as well as illustrating the importance of formal modelling.

In response to the global concerns towards oil scarcity and climate change, the automotive industry is currently focusing on improving fuel economy and reducing exhaust emissions. Modern downsized gasoline engines that come with a package that includes a boosting system, variable valve train and direct fuel injection system is effective for fuel economy improvement and emission reduction. However, the knocking issue becomes severe at high load operations as a result of the high intake boosting pressure. In regard to the part load conditions, the gas exchange process requires extra work to draw in air into the cylinder due to a lower amount of pressure in the intake manifold caused by the restriction of the throttle plate. The Miller cycle is regarded as a potential strategy of knock control for downsized gasoline engines. Extensive works have sought to examine the performance improvement via the Miller cycle, yet only limited research has been conducted on the manner in which it can influence knock suppression. The focus of this thesis is to investigate early and late intake valve closing timings in terms of how they affect the compression process, the ability to suppress engine knock and meet the power output required at high loads for spark-ignited gasoline engines. Apart from that, this research also demonstrates the Miller cycle potential by utilising fully variable valve timing in controlling the load at the part load condition without using a throttle. The early intake valve closing with different valve lifts was tested in order to investigate the impact during the gas exchange process, particularly the pumping losses and the potential to improve fuel economy. This study includes both experimental and simulation studies. A Lotus single-cylinder research engine referred to as SCORE was mainly used for the experimental component of the study. The simulation work was conducted using a one-dimensional spark ignition engine model built in the Ricardo WAVE software for naturally-aspirated and downsized engines. The engine model values are validated against the experimental values from the Lotus SCORE and Lotus SABRE engines. The combustion model with a reduced kinetics mechanism was validated using a Rover K-series engine. A broad matrix of the engine operations has been investigated combining a variety of engine speeds and engine loads. The Miller cycle effects on knock suppression in a downsized engine environment have been investigated in three parts, namely the Miller cycle at different speed-load, knock suppression with extreme Miller cycle, and knock analysis with combustion kinetics. Through the works, the Miller cycle has demonstrated its capability to suppress engine knocking in a more efficient manner as opposed to the standard engine operation. This is contributed by the fact that early and late intake valve closings could affect the end gas condition at the end of the compression stroke, thus making it possible to suppress the engine knocking. The experimental study for controlling load without using throttle under the naturally-aspirated condition found that the Miller cycle with an early intake calve closing strategy able to improve fuel consumption by reducing pumping losses. The downsized engine condition, which has been evaluated via modelling work, also showed an improved performance trend using the unthrottled Miller cycle strategy. The open cycle and close cycle efficiencies have improved through the Miller cycle implementation. The contribution of this work is made in order to establish the comparison of the Miller cycle strategy in suppressing knocking between the early intake valve closing and late intake valve closing under a boosted environment. For the part load condition of the downsized engine, the research contributes to the existing body of knowledge by comparing the throttle-less Miller cycle and the standard throttled operation as a load control strategy.

Receptor interacting protein kinase 1 (RIPK1) is an enzyme acting downstream of tumor necrosis factor alpha to control cell survival and death. RIPK1 expression has been reported to cause drug resistance in cancer cells; but so far, no published studies have investigated the role of RIPK1 in vitamin D action. In the present study, we investigated whether RIPK1 played any role in 1,25-dihydroxyvitamin D3 (1,25D3)-induced growth suppression. In our studies, RIPK1 decreased the transcriptional activity of vitamin D receptor (VDR) in luciferase reporter assays independently of its kinase activity, suggesting a negative role of RIPK1 in 1,25D3 action. RIPK1 also formed a complex with VDR and deletion analyses mapped the RIPK1 binding region to the C-terminal ligand-binding domain of VDR. Subcellular fractionation analyses indicated that RIPK1 increased VDR retention in the cytoplasm, which may account for the inhibition of VDR transcriptional activity. Consistent with the reporter analyses, 1,25D3-induced growth suppression was more pronounced in RIPK1-null mouse embryonic fibroblasts (MEF) and RIPK1 knockdown ovarian cancer cells than control cells. We have also shown that VDR was involved in RIPK1-mediated cell death pathway in a cell line specific manner. In vivo study showed that VDR deletion delayed the necroptotic response to tumor necrosis factor alpha in mice. Western blot analyses of platinum sensitive and resistant cell lines showed a correlation between RIPK1 expression and drug resistance, suggesting a possible role of RIPK1 in drug resistance. In conclusion, this study is the first to define RIPK1 as a VDR repressor, projecting RIPK1 depletion as a potential strategy to increase the potency of 1,25D3 and its analogs for cancer intervention.

Alpha matting is one of the key techniques in image processing and is used to extract accurate foreground from a still image or video sequences. Chroma keying is a special case of alpha matting with a solid background color. Color spill is one of the difficulties in chroma keying, and it has not been effectively solved by current methods. Sometimes, an image contains both reflected regions and transparent regions. When the foreground in such images is chroma keyed, reflection on the foreground is often falsely treated as transparency and causes unreal foreground extraction and composition. This problem is called color spill. Color spill suppression aims to extract the opaque foreground with the correct transparency descriptor (i.e. alpha value) and remove the reflected background color on it. When the background color presented on the foreground is simultaneously caused by reflection and transparency, color spill suppression becomes extremely challenging. It is because that the reflection removal and the actual transparency estimation is a dilemma. Our proposed method for color spill suppression is to separate reflected regions from transparent regions, and process reflected regions as foreground while keeping transparency unchanged at the same time. In this thesis, we propose a novel method for color spill suppression for chroma keying. The quality of the estimated alpha matte could be significantly improved. In our approach, we suppress color spill by using the polarization and the optical flow algorithm based on disparity estimation. Specifically, we make the assumption that reflection changes more than transparency when the scene is captured by a binocular camera with a polaroid filter. Based on this assumption, we took stereo images with polarization filter, registered stereo images by optical flow and conducted the variance analysis on histograms of input images to separate transparency and reflection. Our experiments show that the opaque foreground with background color spill can be reliably extracted while the real transparency can be kept.

Studies exploring the neural correlates of the emotion regulation strategy called cognitive change (reappraisal) have been thoroughly discussed and synthesized. This is not the case for another emotion regulation strategy called response modulation. The aim of this thesis was to conduct a systematic review on the neural correlates of one specific emotion regulation technique in response modulation, called expressive suppression. Expressive suppression is the inhibition of emotion expressive behavior. Using a systematic search, screening, and selection process, out of the initial 557 articles eight studies were included for data extraction and discussion. The studies exposed participants to negative emotion-inducing stimuli and instructed participants to either suppress their emotional behavior or to watch the stimuli without regulating the emotion. All studies used functional magnetic resonance imaging (fMRI). Expressive suppression yielded increased activation in frontal and parietal regions, especially in the right dorsolateral prefrontal cortex and bilateral inferior parietal cortex, compared to a non-regulation condition. Results of amygdala activation were inconsistent with different studies showing an increase, decrease, or no difference in activation during expressive suppression versus the non-regulation contrast condition. The thesis ends with a discussion of methodological issues and future directions.

Although tremor is the most common movement disorder, there exist few effective tremor-suppressing devices, in part because the characteristics of tremor throughout the upper limb are unknown. To clarify, optimally suppressing tremor requires a knowledge of the mechanical origin, propagation, and distribution of tremor throughout the upper limb. Here we present the first systematic investigation of how tremor propagates between the shoulder, elbow, forearm, and wrist. We simulated tremor propagation using a linear, time-invariant, lumped-parameter musculoskeletal model relating joint torques and the resulting joint displacements. The model focused on the seven main degrees of freedom (DOF) from the shoulder to the wrist and included coupled joint inertia, damping, and stiffness. We deliberately implemented a simple model to focus first on the most basic effects. Simulating tremorogenic joint torque as a sinusoidal input, we used the model to establish fundamental principles describing how input parameters (torque location and frequency) and joint impedance (inertia, damping, and stiffness) affect tremor propagation. We expect that the methods and principles presented here will serve as the groundwork for future refining studies to understand the origin, propagation, and distribution of tremor throughout the upper limb in order to enable the future development of optimal tremor-suppressing devices.