Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Rege, Kaushal
Description
Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.
Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.
The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.
ContributorsDong, Yuwen (Author) / Rivera, Daniel E (Thesis advisor) / Dai, Lenore (Committee member) / Forzani, Erica (Committee member) / Rege, Kaushal (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
Description
The prevalence of antibiotic resistant bacterial pathogens has increased since the introduction of penicillin in the 1940s. Insufficient development of novel antibacterial agents is leaving us with a failing arsenal of therapies to combat these pathogenic organisms. We have identified a clay mineral mixture (designated CB) that exhibits in vitro antibacterial activity against a broad spectrum of bacterial pathogens, yet the antibacterial mechanism of action remains unknown. Antibacterial susceptibility testing of four different clay samples collected from the same source revealed that these natural clays had markedly different antibacterial activity. X-ray diffraction analyses of these minerals revealed minor mineralogical differences across the samples; however, ICP analyses demonstrated that the concentrations of many elements, Fe, Co, Cu, Ni, and Zn in particular, vary greatly across the four clay mixture leachates. Supplementation of a non-antibacterial leachate containing lower concentrations of Fe, Co, Ni, Cu, and Zn to final ion concentrations and a pH equivalent to that of the antibacterial leachate resulted in antibacterial activity against E. coli and MRSA, confirming the role of these ions in the in vitro antibacterial clay mixture leachates. The prevailing hypothesis is that metal ions participate in redox cycling and produce ROS, leading to oxidative damage to macromolecules and resulting in cellular death. However, E. coli cells showed no increase in DNA or protein oxidative lesions and a slight increase in lipid peroxidation following exposure to CB-L. Supplementation of CB-L with ROS scavengers eliminated oxidative damage in E. coli, but did not rescue the cells from killing, indicating that in vitro killing is due to direct metal toxicity and not to indirect oxidative damage. Finally, we ion-exchanged non-antibacterial clays with Fe, Co, Cu, and Zn and established antibacterial activity in these samples. Treatment of MRSA skin infections with both natural and ion-exchanged clays significantly decreased the bacterial load after 7 days of treatment. We conclude that 1) in vitro clay-mediated killing is due to toxicity associated directly with released metal ions and not to indirect oxidative damage and 2) that in vivo killing is due to the physical properties of the clays rather than metal ion toxicity.
ContributorsOtto, Caitin Carol (Author) / Haydel, Shelley (Thesis advisor) / Stout, Valerie (Committee member) / Roberson, Robby (Committee member) / Sandrin, Todd (Committee member) / Rege, Kaushal (Committee member) / Arizona State University (Publisher)
Created2014