Matching Items (969)
Description
Alzheimer's Disease (AD) is a progressive neurodegenerative disease accounting for 50-80% of dementia cases in the country. This disease is characterized by the deposition of extracellular plaques occurring in regions of the brain important for cognitive function. A primary component of these plaques is the amyloid-beta protein. While a natively unfolded protein, amyloid-beta can misfold and aggregate generating a variety of different species including numerous different soluble oligomeric species some of which are precursors to the neurofibrillary plaques. Various of the soluble amyloid-beta oligomeric species have been shown to be toxic to cells and their presence may correlate with progression of AD. Current treatment options target the dementia symptoms, but there is no effective cure or alternative to delay the progression of the disease once it occurs. Amyloid-beta aggregates show up many years before symptoms develop, so detection of various amyloid-beta aggregate species has great promise as an early biomarker for AD. Therefore reagents that can selectively identify key early oligomeric amyloid-beta species have value both as potential diagnostics for early detection of AD and as well as therapeutics that selectively target only the toxic amyloid-beta aggregate species. Earlier work in the lab includes development of several different single chain antibody fragments (scFvs) against different oligomeric amyloid-beta species. This includes isolation of C6 scFv against human AD brain derived oligomeric amyloid-beta (Kasturirangan et al., 2013). This thesis furthers research in this direction by improving the yields and investigating the specificity of modified C6 scFv as a diagnostic for AD. It is motivated by experiments reporting low yields of the C6 scFv. We also used the C6T scFv to characterize the variation in concentration of this particular oligomeric amyloid-beta species with age in a triple transgenic AD mouse model. We also show that C6T can be used to differentiate between post-mortem human AD, Parkinson's disease (PD) and healthy human brain samples. These results indicate that C6T has potential value as a diagnostic tool for early detection of AD.
ContributorsVenkataraman, Lalitha (Author) / Sierks, Michael (Thesis advisor) / Rege, Kaushal (Committee member) / Pauken, Christine (Committee member) / Arizona State University (Publisher)
Created2013
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 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
Description
Liquid-liquid interfaces serve as ideal 2-D templates on which solid particles can self-assemble into various structures. These self-assembly processes are important in fabrication of micron-sized devices and emulsion formulation. At oil/water interfaces, these structures can range from close-packed aggregates to ordered lattices. By incorporating an ionic liquid (IL) at the interface, new self-assembly phenomena emerge. ILs are ionic compounds that are liquid at room temperature (essentially molten salts at ambient conditions) that have remarkable properties such as negligible volatility and high chemical stability and can be optimized for nearly any application. The nature of IL-fluid interfaces has not yet been studied in depth. Consequently, the corresponding self-assembly phenomena have not yet been explored. We demonstrate how the unique molecular nature of ILs allows for new self-assembly phenomena to take place at their interfaces. These phenomena include droplet bridging (the self-assembly of both particles and emulsion droplets), spontaneous particle transport through the liquid-liquid interface, and various gelation behaviors. In droplet bridging, self-assembled monolayers of particles effectively "glue" emulsion droplets to one another, allowing the droplets to self-assembly into large networks. With particle transport, it is experimentally demonstrated the ILs overcome the strong adhesive nature of the liquid-liquid interface and extract solid particles from the bulk phase without the aid of external forces. These phenomena are quantified and corresponding mechanisms are proposed. The experimental investigations are supported by molecular dynamics (MD) simulations, which allow for a molecular view of the self-assembly process. In particular, we show that particle self-assembly depends primarily on the surface chemistry of the particles and the non-IL fluid at the interface. Free energy calculations show that the attractive forces between nanoparticles and the liquid-liquid interface are unusually long-ranged, due to capillary waves. Furthermore, IL cations can exhibit molecular ordering at the IL-oil interface, resulting in a slight residual charge at this interface. We also explore the transient IL-IL interface, revealing molecular interactions responsible for the unusually slow mixing dynamics between two ILs. This dissertation, therefore, contributes to both experimental and theoretical understanding of particle self-assembly at IL based interfaces.
ContributorsFrost, Denzil (Author) / Dai, Lenore L (Thesis advisor) / Torres, César I (Committee member) / Nielsen, David R (Committee member) / Squires, Kyle D (Committee member) / Rege, Kaushal (Committee member) / Arizona State University (Publisher)
Created2013
Description
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid growth of power systems and the concomitant technological advancements, the goal of achieving smart grids is no longer a vision but a foreseeable reality. Hence, the existing grids are undergoing infrastructural modifications to achieve the diverse characteristics of a smart grid. While there are many subjects associated with the operation of smart grids, this dissertation addresses two important aspects of smart grids: increased penetration of renewable resources, and increased reliance on sensor systems to improve reliability and performance of critical power system components. Present renewable portfolio standards are changing both structural and performance characteristics of power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. The present study investigates the impact of increased penetration of PV systems on steady state performance as well as transient stability of a large power system which is a portion of the Western U.S. interconnection. Utility scale and residential rooftop PVs are added to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance. With increased penetration of the renewable energy resources, and with the current loading scenario, more transmission system components such as transformers and circuit breakers are subject to increased stress and overloading. This research work explores the feasibility of increasing system reliability by applying condition monitoring systems to selected circuit breakers and transformers. A very important feature of smart grid technology is that this philosophy decreases maintenance costs by deploying condition monitoring systems that inform the operator of impending failures; or the approach can ameliorate problematic conditions. A method to identify the most critical transformers and circuit breakers with the aid of contingency ranking methods is presented in this study. The work reported in this dissertation parallels an industry sponsored study in which a considerable level of industry input and industry reported concerns are reflected.
ContributorsEftekharnejad, Sara (Author) / Heydt, Gerald (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Si, Jennie (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012
Description
The combined heat and power (CHP)-based distributed generation (DG) or dis-tributed energy resources (DERs) are mature options available in the present energy mar-ket, considered to be an effective solution to promote energy efficiency. In the urban en-vironment, the electricity, water and natural gas distribution networks are becoming in-creasingly interconnected with the growing penetration of the CHP-based DG. Subse-quently, this emerging interdependence leads to new topics meriting serious consideration: how much of the CHP-based DG can be accommodated and where to locate these DERs, and given preexisting constraints, how to quantify the mutual impacts on operation performances between these urban energy distribution networks and the CHP-based DG. The early research work was conducted to investigate the feasibility and design methods for one residential microgrid system based on existing electricity, water and gas infrastructures of a residential community, mainly focusing on the economic planning. However, this proposed design method cannot determine the optimal DG sizing and sit-ing for a larger test bed with the given information of energy infrastructures. In this con-text, a more systematic as well as generalized approach should be developed to solve these problems. In the later study, the model architecture that integrates urban electricity, water and gas distribution networks, and the CHP-based DG system was developed. The pro-posed approach addressed the challenge of identifying the optimal sizing and siting of the CHP-based DG on these urban energy networks and the mutual impacts on operation per-formances were also quantified. For this study, the overall objective is to maximize the electrical output and recovered thermal output of the CHP-based DG units. The electrici-ty, gas, and water system models were developed individually and coupled by the devel-oped CHP-based DG system model. The resultant integrated system model is used to constrain the DG's electrical output and recovered thermal output, which are affected by multiple factors and thus analyzed in different case studies. The results indicate that the designed typical gas system is capable of supplying sufficient natural gas for the DG normal operation, while the present water system cannot support the complete recovery of the exhaust heat from the DG units.
ContributorsZhang, Xianjun (Author) / Karady, George G. (Thesis advisor) / Ariaratnam, Samuel T. (Committee member) / Holbert, Keith E. (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation considers an integrated approach to system design and controller design based on analyzing limits of system performance. Historically, plant design methodologies have not incorporated control relevant considerations. Such an approach could result in a system that might not meet its specifications (or one that requires a complex control architecture to do so). System and controller designers often go through several iterations in order to converge to an acceptable plant and controller design. The focus of this dissertation is on the design and control an air-breathing hypersonic vehicle using such an integrated system-control design framework. The goal is to reduce the number of system-control design iterations (by explicitly incorporate control considerations in the system design process), as well as to influence the guidance/trajectory specifications for the system. Due to the high computational costs associated with obtaining a dynamic model for each plant configuration considered, approximations to the system dynamics are used in the control design process. By formulating the control design problem using bilinear and polynomial matrix inequalities, several common control and system design constraints can be simultaneously incorporated into a vehicle design optimization. Several design problems are examined to illustrate the effectiveness of this approach (and to compare the computational burden of this methodology against more traditional approaches).
ContributorsSridharan, Srikanth (Author) / Rodriguez, Armando A (Thesis advisor) / Mittelmann, Hans D (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S (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
Description
The basal ganglia are four sub-cortical nuclei associated with motor control and reward learning. They are part of numerous larger mostly segregated loops where the basal ganglia receive inputs from specific regions of cortex. Converging on these inputs are dopaminergic neurons that alter their firing based on received and/or predicted rewarding outcomes of a behavior. The basal ganglia's output feeds through the thalamus back to the areas of the cortex where the loop originated. Understanding the dynamic interactions between the various parts of these loops is critical to understanding the basal ganglia's role in motor control and reward based learning. This work developed several experimental techniques that can be applied to further study basal ganglia function. The first technique used micro-volume injections of low concentration muscimol to decrease the firing rates of recorded neurons in a limited area of cortex in rats. Afterwards, an artificial cerebrospinal fluid flush was injected to rapidly eliminate the muscimol's effects. This technique was able to contain the effects of muscimol to approximately a 1 mm radius volume and limited the duration of the drug effect to less than one hour. This technique could be used to temporarily perturb a small portion of the loops involving the basal ganglia and then observe how these effects propagate in other connected regions. The second part applied self-organizing maps (SOM) to find temporal patterns in neural firing rate that are independent of behavior. The distribution of detected patterns frequency on these maps can then be used to determine if changes in neural activity are occurring over time. The final technique focused on the role of the basal ganglia in reward learning. A new conditioning technique was created to increase the occurrence of selected patterns of neural activity without utilizing any external reward or behavior. A pattern of neural activity in the cortex of rats was selected using an SOM. The pattern was then reinforced by being paired with electrical stimulation of the medial forebrain bundle triggering dopamine release in the basal ganglia. Ultimately, this technique proved unsuccessful possibly due to poor selection of the patterns being reinforced.
ContributorsBaldwin, Nathan Aaron (Author) / Helms Tillery, Stephen I (Thesis advisor) / Castaneda, Edward (Committee member) / Buneo, Christopher A (Committee member) / Muthuswamy, Jitendran (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2014
Description
This study focuses on state estimation of nonlinear discrete time systems with constraints. Physical processes have inherent in them, constraints on inputs, outputs, states and disturbances. These constraints can provide additional information to the estimator in estimating states from the measured output. Recursive filters such as Kalman Filters or Extended Kalman Filters are commonly used in state estimation; however, they do not allow inclusion of constraints in their formulation. On the other hand, computational complexity of full information estimation (using all measurements) grows with iteration and becomes intractable. One way of formulating the recursive state estimation problem with constraints is the Moving Horizon Estimation (MHE) approximation. Estimates of states are calculated from the solution of a constrained optimization problem of fixed size. Detailed formulation of this strategy is studied and properties of this estimation algorithm are discussed in this work. The problem with the MHE formulation is solving an optimization problem in each iteration which is computationally intensive. State estimation with constraints can be formulated as Extended Kalman Filter (EKF) with a projection applied to estimates. The states are estimated from the measurements using standard Extended Kalman Filter (EKF) algorithm and the estimated states are projected on to a constrained set. Detailed formulation of this estimation strategy is studied and the properties associated with this algorithm are discussed. Both these state estimation strategies (MHE and EKF with projection) are tested with examples from the literature. The average estimation time and the sum of square estimation error are used to compare performance of these estimators. Results of the case studies are analyzed and trade-offs are discussed.
ContributorsJoshi, Rakesh (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2013