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Description
Cancer is the second leading cause of death in the United States and novel methods of treating advanced malignancies are of high importance. Of these deaths, prostate cancer and breast cancer are the second most fatal carcinomas in men and women respectively, while pancreatic cancer is the fourth most fatal

Cancer is the second leading cause of death in the United States and novel methods of treating advanced malignancies are of high importance. Of these deaths, prostate cancer and breast cancer are the second most fatal carcinomas in men and women respectively, while pancreatic cancer is the fourth most fatal in both men and women. Developing new drugs for the treatment of cancer is both a slow and expensive process. It is estimated that it takes an average of 15 years and an expense of $800 million to bring a single new drug to the market. However, it is also estimated that nearly 40% of that cost could be avoided by finding alternative uses for drugs that have already been approved by the Food and Drug Administration (FDA). The research presented in this document describes the testing, identification, and mechanistic evaluation of novel methods for treating many human carcinomas using drugs previously approved by the FDA. A tissue culture plate-based screening of FDA approved drugs will identify compounds that can be used in combination with the protein TRAIL to induce apoptosis selectively in cancer cells. Identified leads will next be optimized using high-throughput microfluidic devices to determine the most effective treatment conditions. Finally, a rigorous mechanistic analysis will be conducted to understand how the FDA-approved drug mitoxantrone, sensitizes cancer cells to TRAIL-mediated apoptosis.
ContributorsTaylor, David (Author) / Rege, Kaushal (Thesis advisor) / Jayaraman, Arul (Committee member) / Nielsen, David (Committee member) / Kodibagkar, Vikram (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving

Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving reliable composite systems is a strong capability of assessing and inspecting physical damage of critical structural components. Installation of a robust Structural Health Monitoring (SHM) system would be very valuable in detecting the onset of composite failure. A number of major issues still require serious attention in connection with the research and development aspects of sensor-integrated reliable SHM systems for composite structures. In particular, the sensitivity of currently available sensor systems does not allow detection of micro level damage; this limits the capability of data driven SHM systems. As a fundamental layer in SHM, modeling can provide in-depth information on material and structural behavior for sensing and detection, as well as data for learning algorithms. This dissertation focusses on the development of a multiscale analysis framework, which is used to detect various forms of damage in complex composite structures. A generalized method of cells based micromechanics analysis, as implemented in NASA's MAC/GMC code, is used for the micro-level analysis. First, a baseline study of MAC/GMC is performed to determine the governing failure theories that best capture the damage progression. The deficiencies associated with various layups and loading conditions are addressed. In most micromechanics analysis, a representative unit cell (RUC) with a common fiber packing arrangement is used. The effect of variation in this arrangement within the RUC has been studied and results indicate this variation influences the macro-scale effective material properties and failure stresses. The developed model has been used to simulate impact damage in a composite beam and an airfoil structure. The model data was verified through active interrogation using piezoelectric sensors. The multiscale model was further extended to develop a coupled damage and wave attenuation model, which was used to study different damage states such as fiber-matrix debonding in composite structures with surface bonded piezoelectric sensors.
ContributorsMoncada, Albert (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Alzheimer's disease (AD) is the most common type of dementia, affecting one in nine people age 65 and older. One of the most important neuropathological characteristics of Alzheimer's disease is the aggregation and deposition of the protein beta-amyloid. Beta-amyloid is produced by proteolytic processing of the Amyloid Precursor Protein (APP).

Alzheimer's disease (AD) is the most common type of dementia, affecting one in nine people age 65 and older. One of the most important neuropathological characteristics of Alzheimer's disease is the aggregation and deposition of the protein beta-amyloid. Beta-amyloid is produced by proteolytic processing of the Amyloid Precursor Protein (APP). Production of beta-amyloid from APP is increased when cells are subject to stress since both APP and beta-secretase are upregulated by stress. An increased beta-amyloid level promotes aggregation of beta-amyloid into toxic species which cause an increase in reactive oxygen species (ROS) and a decrease in cell viability. Therefore reducing beta-amyloid generation is a promising method to control cell damage following stress. The goal of this thesis was to test the effect of inhibiting beta-amyloid production inside stressed AD cell model. Hydrogen peroxide was used as stressing agent. Two treatments were used to inhibit beta-amyloid production, including iBSec1, an scFv designed to block beta-secretase site of APP, and DIA10D, a bispecific tandem scFv engineered to cleave alpha-secretase site of APP and block beta-secretase site of APP. iBSec1 treatment was added extracellularly while DIA10D was stably expressed inside cell using PSECTAG vector. Increase in reactive oxygen species and decrease in cell viability were observed after addition of hydrogen peroxide to AD cell model. The increase in stress induced toxicity caused by addition of hydrogen peroxide was dramatically decreased by simultaneously treating the cells with iBSec1 or DIA10D to block the increase in beta-amyloid levels resulting from the upregulation of APP and beta-secretase.
ContributorsSuryadi, Vicky (Author) / Sierks, Michael (Thesis advisor) / Nielsen, David (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Life Cycle Assessment (LCA) is used in the chemical process sector to compare the environmental merits of different product or process alternatives. One of the tasks that involves much time and cost in LCA studies is the specification of the exact materials and processes modeled which has limited its widespread

Life Cycle Assessment (LCA) is used in the chemical process sector to compare the environmental merits of different product or process alternatives. One of the tasks that involves much time and cost in LCA studies is the specification of the exact materials and processes modeled which has limited its widespread application. To overcome this, researchers have recently created probabilistic underspecification as an LCA streamlining method, which uses a structured data classification system to enable an LCA modeler to specify materials and processes in a less precise manner. This study presents a statistical procedure to understand when streamlined LCA methods can be used, and what their impact on overall model uncertainty is. Petrochemicals and polymer product systems were chosen to examine the impacts of underspecification and mis-specification applied to LCA modeling. Ecoinvent database, extracted using GaBi software, was used for data pertaining to generic crude oil refining and polymer manufacturing modules. By assessing the variation in LCA results arising out of streamlined materials classification, the developed statistics estimate the amount of overall error incurred by underspecifying and mis-specifying material impact data in streamlined LCA. To test the impact of underspecification and mis-specification at the level of a product footprint, case studies of HDPE containers and aerosol air fresheners were conducted. Results indicate that the variation in LCA results decreases as the specificity of materials increases. For the product systems examined, results show that most of the variability in impact assessment is due to the differences in the regions from which the environmental impact datasets were collected; the lower levels of categorization of materials have relatively smaller influence on the variance. Analyses further signify that only certain environmental impact categories viz. global warming potential, freshwater eutrophication, freshwater ecotoxicity, human toxicity and terrestrial ecotoxicity are affected by geographic variations. Outcomes for the case studies point out that the error in the estimation of global warming potential increases as the specificity of a component of the product decreases. Fossil depletion impact estimates remain relatively robust to underspecification. Further, the results of LCA are much more sensitive to underspecification of materials and processes than mis-specification.
ContributorsMurali, Ashwin Krishna (Author) / Dooley, Kevin (Thesis advisor) / Dai, Lenore (Thesis advisor) / Nielsen, David (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Damage detection in heterogeneous material systems is a complex problem and requires an in-depth understanding of the material characteristics and response under varying load and environmental conditions. A significant amount of research has been conducted in this field to enhance the fidelity of damage assessment methodologies, using a wide range

Damage detection in heterogeneous material systems is a complex problem and requires an in-depth understanding of the material characteristics and response under varying load and environmental conditions. A significant amount of research has been conducted in this field to enhance the fidelity of damage assessment methodologies, using a wide range of sensors and detection techniques, for both metallic materials and composites. However, detecting damage at the microscale is not possible with commercially available sensors. A probable way to approach this problem is through accurate and efficient multiscale modeling techniques, which are capable of tracking damage initiation at the microscale and propagation across the length scales. The output from these models will provide an improved understanding of damage initiation; the knowledge can be used in conjunction with information from physical sensors to improve the size of detectable damage. In this research, effort has been dedicated to develop multiscale modeling approaches and associated damage criteria for the estimation of damage evolution across the relevant length scales. Important issues such as length and time scales, anisotropy and variability in material properties at the microscale, and response under mechanical and thermal loading are addressed. Two different material systems have been studied: metallic material and a novel stress-sensitive epoxy polymer.

For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.

Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
ContributorsZhang, Jinjun (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2014
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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

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.
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
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Description
The production of nanomaterials has been increasing and so are their applications in various products, while the environmental impacts and human impacts of these nanomaterials are still in the process of being explored. In this thesis, a process for

producing nano-titanium dioxide (nano-TiO2) is studied and a case-study has been

The production of nanomaterials has been increasing and so are their applications in various products, while the environmental impacts and human impacts of these nanomaterials are still in the process of being explored. In this thesis, a process for

producing nano-titanium dioxide (nano-TiO2) is studied and a case-study has been conducted on comparative Life Cycle Assessment (LCA) of the application of these nano-TiO2 particles in the sunscreen lotion as a UV-blocker with the conventional organic chemical sunscreen lotion using GaBi software. Nano-TiO2 particles were identified in the sunscreen lotion using Transmission Electron Microscope suggesting the use of these particles in the lotion.

The LCA modeling includes the comparison of the environmental impacts of producing nano-TiO2 particles with that of conventional organic chemical UV-blockers (octocrylene and avobenzone). It also compares the environmental life cycle impacts of the two sunscreen lotions studied. TRACI 2.1 was used for the assessment of the impacts which were then normalized and weighted for the ranking of the impact categories.

Results indicate that nano-TiO2 had higher impacts on the environment than the conventional organic chemical UV-blockers (octocrylene and avobenzone). For the two sunscreen lotions studied, nano-TiO2 sunscreen variant had lower environmental life cycle impacts than its counterpart because of the other chemicals used in the formulation. In the organic chemical sunscreen variant the major impacts came from production of glycerine, ethanol, and avobenzone but in the nano-TiO2 sunscreen variant the major impacts came from the production of nano-TiO2 particles.

Analysis further signifies the trade-offs between few environmental impact categories, for example, the human toxicity impacts were more in the nano-TiO2 sunscreen variant, but the other environmental impact categories viz. fossil fuel depletion, global warming potential, eutrophication were less compared to the organic chemical sunscreen variant.
ContributorsThakur, Ankita (Author) / Dooley, Kevin (Thesis advisor) / Dai, Lenore (Committee member) / Lind, Mary Laura (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Alzheimer's disease (AD) is the most common form of dementia leading to cognitive dysfunction and memory loss as well as emotional and behavioral disorders. It is the 6th leading cause of death in United States, and the only one among top 10 death causes that cannot be prevented, cured or

Alzheimer's disease (AD) is the most common form of dementia leading to cognitive dysfunction and memory loss as well as emotional and behavioral disorders. It is the 6th leading cause of death in United States, and the only one among top 10 death causes that cannot be prevented, cured or slowed. An estimated 5.4 million Americans live with AD, and this number is expected to triple by year 2050 as the baby boomers age. The cost of care for AD in the US is about $200 billion each year. Unfortunately, in addition to the lack of an effective treatment or AD, there is also a lack of an effective diagnosis, particularly an early diagnosis which would enable treatment to begin before significant neuronal damage has occurred.

Increasing evidence implicates soluble oligomeric forms of beta-amyloid and tau in the onset and progression of AD. While many studies have focused on beta-amyloid, soluble oligomeric tau species may also play an important role in AD pathogenesis. Antibodies that selectively identify and target specific oligomeric tau variants would be valuable tools for both diagnostic and therapeutic applications and also to study the etiology of AD and other neurodegenerative diseases.

Recombinant human tau (rhTau) in monomeric, dimeric, trimeric and fibrillar forms were synthesized and purified to perform LDH assay on human neuroblastoma cells, so that trimeric but not monomeric or dimeric rhTau was identified as extracellularly neurotoxic to neuronal cells. A novel biopanning protocol was designed based on phage display technique and atomic force microscopy (AFM), and used to isolate single chain antibody variable domain fragments (scFvs) that selectively recognize the toxic tau oligomers. These scFvs selectively bind tau variants in brain tissue of human AD patients and AD-related tau transgenic rodent models and have potential value as early diagnostic biomarkers for AD and as potential therapeutics to selectively target toxic tau aggregates.
ContributorsTian, Huilai (Author) / Sierks, Michael R (Thesis advisor) / Dai, Lenore (Committee member) / Tillery, Stephen H (Committee member) / Nielsen, David R (Committee member) / Stabenfeldt, Sarah (Committee member) / Arizona State University (Publisher)
Created2014
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Description
A method of determining nanoparticle temperature through fluorescence intensity levels is described. Intracellular processes are often tracked through the use of fluorescence tagging, and ideal temperatures for many of these processes are unknown. Through the use of fluorescence-based thermometry, cellular processes such as intracellular enzyme movement can be studied and

A method of determining nanoparticle temperature through fluorescence intensity levels is described. Intracellular processes are often tracked through the use of fluorescence tagging, and ideal temperatures for many of these processes are unknown. Through the use of fluorescence-based thermometry, cellular processes such as intracellular enzyme movement can be studied and their respective temperatures established simultaneously. Polystyrene and silica nanoparticles are synthesized with a variety of temperature-sensitive dyes such as BODIPY, rose Bengal, Rhodamine dyes 6G, 700, and 800, and Nile Blue A and Nile Red. Photographs are taken with a QImaging QM1 Questar EXi Retiga camera while particles are heated from 25 to 70 C and excited at 532 nm with a Coherent DPSS-532 laser. Photographs are converted to intensity images in MATLAB and analyzed for fluorescence intensity, and plots are generated in MATLAB to describe each dye's intensity vs temperature. Regression curves are created to describe change in fluorescence intensity over temperature. Dyes are compared as nanoparticle core material is varied. Large particles are also created to match the camera's optical resolution capabilities, and it is established that intensity values increase proportionally with nanoparticle size. Nile Red yielded the closest-fit model, with R2 values greater than 0.99 for a second-order polynomial fit. By contrast, Rhodamine 6G only yielded an R2 value of 0.88 for a third-order polynomial fit, making it the least reliable dye for temperature measurements using the polynomial model. Of particular interest in this work is Nile Blue A, whose fluorescence-temperature curve yielded a much different shape from the other dyes. It is recommended that future work describe a broader range of dyes and nanoparticle sizes, and use multiple excitation wavelengths to better quantify each dye's quantum efficiency. Further research into the effects of nanoparticle size on fluorescence intensity levels should be considered as the particles used here greatly exceed 2 ìm. In addition, Nile Blue A should be further investigated as to why its fluorescence-temperature curve did not take on a characteristic shape for a temperature-sensitive dye in these experiments.
ContributorsTomforde, Christine (Author) / Phelan, Patrick (Thesis advisor) / Dai, Lenore (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal

Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal processing, pattern recognition, data mining, high fidelity probabilistic progressive damage models, physics based damage models, and regression analysis. Due to the wide application of carbon fiber reinforced composites and their multiscale failure mechanisms, it is necessary to emphasize the research of SHM on composite structures. This research develops a comprehensive framework for the damage detection, localization, quantification, and prediction of the remaining useful life of complex composite structures. To interrogate a composite structure, guided wave propagation is applied to thin structures such as beams and plates. Piezoelectric transducers are selected because of their versatility, which allows them to be used as sensors and actuators. Feature extraction from guided wave signals is critical to demonstrate the presence of damage and estimate the damage locations. Advanced signal processing techniques are employed to extract robust features and information. To provide a better estimate of the damage for accurate life estimation, probabilistic regression analysis is used to obtain a prediction model for the prognosis of complex structures subject to fatigue loading. Special efforts have been applied to the extension of SHM techniques on aerospace and spacecraft structures, such as UAV composite wings and deployable composite boom structures. Necessary modifications of the developed SHM techniques were conducted to meet the unique requirements of the aerospace structures. The developed SHM algorithms are able to accurately detect and quantify impact damages as well as matrix cracking introduced.
ContributorsLiu, Yingtao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Rajadas, John (Committee member) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012