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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
To further the efforts producing energy from more renewable sources, microbial electrochemical cells (MXCs) can utilize anode respiring bacteria (ARB) to couple the oxidation of an organic substrate to the delivery of electrons to the anode. Although ARB such as Geobacter and Shewanella have been well-studied in terms of their

To further the efforts producing energy from more renewable sources, microbial electrochemical cells (MXCs) can utilize anode respiring bacteria (ARB) to couple the oxidation of an organic substrate to the delivery of electrons to the anode. Although ARB such as Geobacter and Shewanella have been well-studied in terms of their microbiology and electrochemistry, much is still unknown about the mechanism of electron transfer to the anode. To this end, this thesis seeks to elucidate the complexities of electron transfer existing in Geobacter sulfurreducens biofilms by employing Electrochemical Impedance Spectroscopy (EIS) as the tool of choice. Experiments measuring EIS resistances as a function of growth were used to uncover the potential gradients that emerge in biofilms as they grow and become thicker. While a better understanding of this model ARB is sought, electrochemical characterization of a halophile, Geoalkalibacter subterraneus (Glk. subterraneus), revealed that this organism can function as an ARB and produce seemingly high current densities while consuming different organic substrates, including acetate, butyrate, and glycerol. The importance of identifying and studying novel ARB for broader MXC applications was stressed in this thesis as a potential avenue for tackling some of human energy problems.
ContributorsAjulo, Oluyomi (Author) / Torres, Cesar (Thesis advisor) / Nielsen, David (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Popat, Sudeep (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The accurate and fast determination of carbon dioxide (CO2) levels is critical for many health and environmental applications. For example, the analysis of CO2 levels in exhaled breath allows for the evaluation of systemic metabolism, perfusion, and ventilation, and provides the doctors and patients with a non-invasive and simple method

The accurate and fast determination of carbon dioxide (CO2) levels is critical for many health and environmental applications. For example, the analysis of CO2 levels in exhaled breath allows for the evaluation of systemic metabolism, perfusion, and ventilation, and provides the doctors and patients with a non-invasive and simple method to predict the presence and severity of asthma, and Chronic Obstructive Pulmonary Disease (COPD). Similarly, the monitoring of CO2 levels in the atmosphere allows for assessment of indoor air quality (IAQ) as the indoor CO2 levels have been proved to be associated with increased prevalence of certain mucous membrane and respiratory sick building syndrome (SBS) symptoms. A pocket-sized CO2 analyzer has been developed for real-time analysis of breath CO2 and environmental CO2. This CO2 analyzer is designed to comprise two key components including a fluidic system for efficient gas sample delivery and a colorimetric detection unit integrated into the fluidic system. The CO2 levels in the gas samples are determined by a disposable colorimetric sensor chip. The sensor chip is a novel composite based sensor that has been optimized to provide fast and reversible response to CO2 over a wide concentration range, covering the needs of both environmental and health applications. The sensor is immune to the presence of various interfering gases in ambient or expired air. The performance of the sensor in real-time breath-by-breath analysis has also been validated by a commercial CO2 detector. Furthermore, a 3D model was created to simulate fluid dynamics of breath and chemical reactions for CO2 assessment to achieve overall understanding of the breath CO2 detection process and further optimization of the device.
ContributorsZhao, Di (Author) / Forzani, Erica S (Thesis advisor) / Lin, Jerry Ys (Committee member) / Torres, Cesar (Committee member) / Tsow, Tsing (Committee member) / Xian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2014
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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
Metabolic engineering is an extremely useful tool enabling the biosynthetic production of commodity chemicals (typically derived from petroleum) from renewable resources. In this work, a pathway for the biosynthesis of styrene (a plastics monomer) has been engineered in Escherichia coli from glucose by utilizing the pathway for the naturally occurring

Metabolic engineering is an extremely useful tool enabling the biosynthetic production of commodity chemicals (typically derived from petroleum) from renewable resources. In this work, a pathway for the biosynthesis of styrene (a plastics monomer) has been engineered in Escherichia coli from glucose by utilizing the pathway for the naturally occurring amino acid phenylalanine, the precursor to styrene. Styrene production was accomplished using an E. coli phenylalanine overproducer, E. coli NST74, and over-expression of PAL2 from Arabidopsis thaliana and FDC1 from Saccharomyces cerevisiae. The styrene pathway was then extended by just one enzyme to either (S)-styrene oxide (StyAB from Pseudomonas putida S12) or (R)-1,2-phenylethanediol (NahAaAbAcAd from Pseudomonas sp. NCIB 9816-4) which are both used in pharmaceutical production. Overall, these pathways suffered from limitations due to product toxicity as well as limited precursor availability. In an effort to overcome the toxicity threshold, the styrene pathway was transferred to a yeast host with a higher toxicity limit. First, Saccharomyces cerevisiae BY4741 was engineered to overproduce phenylalanine. Next, PAL2 (the only enzyme needed to complete the styrene pathway) was then expressed in the BY4741 phenylalanine overproducer. Further strain improvements included the deletion of the phenylpyruvate decarboxylase (ARO10) and expression of a feedback-resistant choristmate mutase (ARO4K229L). These works have successfully demonstrated the possibility of utilizing microorganisms as cellular factories for the production styrene, (S)-styrene oxide, and (R)-1,2-phenylethanediol.
ContributorsMcKenna, Rebekah (Author) / Nielsen, David R (Thesis advisor) / Torres, Cesar (Committee member) / Caplan, Michael (Committee member) / Jarboe, Laura (Committee member) / Haynes, Karmella (Committee member) / Arizona State University (Publisher)
Created2014
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Description
A new photocatalytic material was synthesized to investigate its performance for the photoreduction of carbon dioxide (CO2) in the presence of water vapor (H2O) to valuable products such as carbon monoxide (CO) and methane (CH4). The performance was studied using a gas chromatograph (GC) with a flame ionization detector (FID)

A new photocatalytic material was synthesized to investigate its performance for the photoreduction of carbon dioxide (CO2) in the presence of water vapor (H2O) to valuable products such as carbon monoxide (CO) and methane (CH4). The performance was studied using a gas chromatograph (GC) with a flame ionization detector (FID) and a thermal conductivity detector (TCD). The new photocatalytic material was an ionic liquid functionalized reduced graphite oxide (IL-RGO (high conductive surface))-TiO2 (photocatalyst) nanocomposite. Brunauer-Emmett-Teller (BET), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and UV-vis absorption spectroscopy techniques were employed to characterize the new catalyst. In the series of experiments performed, the nanocomposite material was confined in a UV-quartz batch reactor, exposed to CO2 and H2O and illuminated by UV light. The primary product formed was CO with a maximum production ranging from 0.18-1.02 µmol(gcatalyst-hour)-1 for TiO2 and 0.41-1.41 µmol(gcatalyst-hour)-1 for IL-RGO-TiO2. A trace amount of CH4 was also formed with its maximum ranging from 0.009-0.01 µmol(gcatalyst-hour)-1 for TiO2 and 0.01-0.04 µmol(gcatalyst-hour)-1 for IL-RGO-TiO2. A series of background experiments were conducted and results showed that; (a) the use of a ionic liquid functionalized reduced graphite oxide -TiO2 produced more products as compared to commercial TiO2, (b) the addition of methanol as a hole scavenger boosted the production of CO but not CH4, (c) a higher and lower reduction time of IL-RGO as compared to the usual 24 hours of reduction presented basically the same production of CO and CH4, (d) the positive effect of having an ionic liquid was demonstrated by the double production of CO obtained for IL-RGO-TiO2 as compared to RGO-TiO2 and (e) a change in the amount of IL-RGO in the IL-RGO-TiO2 represented a small difference in the CO production but not in the CH4 production. This work ultimately demonstrated the huge potential of the utility of a UV-responsive ionic liquid functionalized reduced graphite oxide-TiO2 nano-composite for the reduction of CO2 in the presence of H2O for the production of fuels.
ContributorsCastañeda Flores, Alejandro (Author) / Andino, Jean M (Thesis advisor) / Forzani, Erica (Committee member) / Torres, Cesar (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
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