Matching Items (214)
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Description
A major goal of synthetic biology is to recapitulate emergent properties of life. Despite a significant body of work, a longstanding question that remains to be answered is how such a complex system arose? In this dissertation, synthetic nucleic acid molecules with alternative sugar-phosphate backbones were investigated as potential ancestors

A major goal of synthetic biology is to recapitulate emergent properties of life. Despite a significant body of work, a longstanding question that remains to be answered is how such a complex system arose? In this dissertation, synthetic nucleic acid molecules with alternative sugar-phosphate backbones were investigated as potential ancestors of DNA and RNA. Threose nucleic acid (TNA) is capable of forming stable helical structures with complementary strands of itself and RNA. This provides a plausible mechanism for genetic information transfer between TNA and RNA. Therefore TNA has been proposed as a potential RNA progenitor. Using molecular evolution, functional sequences were isolated from a pool of random TNA molecules. This implicates a possible chemical framework capable of crosstalk between TNA and RNA. Further, this shows that heredity and evolution are not limited to the natural genetic system based on ribofuranosyl nucleic acids. Another alternative genetic system, glycerol nucleic acid (GNA) undergoes intrasystem pairing with superior thermalstability compared to that of DNA. Inspired by this property, I demonstrated a minimal nanostructure composed of both left- and right-handed mirro image GNA. This work suggested that GNA could be useful as promising orthogonal material in structural DNA nanotechnology.
ContributorsZhang, Su (Author) / Chaut, John C (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It

Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Human islet amyloid polypeptide (hIAPP), also known as amylin, is a 37-residue intrinsically disordered hormone involved in glucose regulation and gastric emptying. The aggregation of hIAPP into amyloid fibrils is believed to play a causal role in type 2 diabetes. To date, not much is known about the monomeric state

Human islet amyloid polypeptide (hIAPP), also known as amylin, is a 37-residue intrinsically disordered hormone involved in glucose regulation and gastric emptying. The aggregation of hIAPP into amyloid fibrils is believed to play a causal role in type 2 diabetes. To date, not much is known about the monomeric state of hIAPP or how it undergoes an irreversible transformation from disordered peptide to insoluble aggregate. IAPP contains a highly conserved disulfide bond that restricts hIAPP(1-8) into a short ring-like structure: N_loop. Removal or chemical reduction of N_loop not only prevents cell response upon binding to the CGRP receptor, but also alters the mass per length distribution of hIAPP fibers and the kinetics of fibril formation. The mechanism by which N_loop affects hIAPP aggregation is not yet understood, but is important for rationalizing kinetics and developing potential inhibitors. By measuring end-to-end contact formation rates, Vaiana et al. showed that N_loop induces collapsed states in IAPP monomers, implying attractive interactions between N_loop and other regions of the disordered polypeptide chain . We show that in addition to being involved in intra-protein interactions, the N_loop is involved in inter-protein interactions, which lead to the formation of extremely long and stable β-turn fibers. These non-amyloid fibers are present in the 10 μM concentration range, under the same solution conditions in which hIAPP forms amyloid fibers. We discuss the effect of peptide cyclization on both intra- and inter-protein interactions, and its possible implications for aggregation. Our findings indicate a potential role of N_loop-N_loop interactions in hIAPP aggregation, which has not previously been explored. Though our findings suggest that N_loop plays an important role in the pathway of amyloid formation, other naturally occurring IAPP variants that contain this structural feature are incapable of forming amyloids. For example, hIAPP readily forms amyloid fibrils in vitro, whereas the rat variant (rIAPP), differing by six amino acids, does not. In addition to being highly soluble, rIAPP is an effective inhibitor of hIAPP fibril formation . Both of these properties have been attributed to rIAPP's three proline residues: A25P, S28P and S29P. Single proline mutants of hIAPP have also been shown to kinetically inhibit hIAPP fibril formation. Because of their intrinsic dihedral angle preferences, prolines are expected to affect conformational ensembles of intrinsically disordered proteins. The specific effect of proline substitutions on IAPP structure and dynamics has not yet been explored, as the detection of such properties is experimentally challenging due to the low molecular weight, fast reconfiguration times, and very low solubility of IAPP peptides. High-resolution techniques able to measure tertiary contact formations are needed to address this issue. We employ a nanosecond laser spectroscopy technique to measure end-to-end contact formation rates in IAPP mutants. We explore the proline substitutions in IAPP and quantify their effects in terms of intrinsic chain stiffness. We find that the three proline mutations found in rIAPP increase chain stiffness. Interestingly, we also find that residue R18 plays an important role in rIAPP's unique chain stiffness and, together with the proline residues, is a determinant for its non-amyloidogenic properties. We discuss the implications of our findings on the role of prolines in IDPs.
ContributorsCope, Stephanie M (Author) / Vaiana, Sara M (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Ros, Robert (Committee member) / Lindsay, Stuart M (Committee member) / Ozkan, Sefika B (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups

Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Cyanovirin-N (CV-N) is a naturally occurring lectin originally isolated from the cyanobacteria Nostoc ellipsosporum. This 11 kDa lectin is 101 amino acids long with two binding sites, one at each end of the protein. CV-N specifically binds to terminal Manα1-2Manα motifs on the branched, high mannose Man9 and Man8 glycosylations

Cyanovirin-N (CV-N) is a naturally occurring lectin originally isolated from the cyanobacteria Nostoc ellipsosporum. This 11 kDa lectin is 101 amino acids long with two binding sites, one at each end of the protein. CV-N specifically binds to terminal Manα1-2Manα motifs on the branched, high mannose Man9 and Man8 glycosylations found on enveloped viruses including Ebola, Influenza, and HIV. wt-CVN has micromolar binding to soluble Manα1-2Manα and also inhibits HIV entry at low nanomolar concentrations. CV-N's high affinity and specificity for Manα1-2Manα makes it an excellent lectin to study for its glycan-specific properties. The long-term aim of this project is to make a variety of mutant CV-Ns to specifically bind other glycan targets. Such a set of lectins may be used as screening reagents to identify biomarkers and other glycan motifs of interest. As proof of concept, a T7 phage display library was constructed using P51G-m4-CVN genes mutated at positions 41, 44, 52, 53, 56, 74, and 76 in binding Domain B. Five CV-N mutants were selected from the library and expressed in BL21(DE3) E. coli. Two of the mutants, SSDGLQQ-P51Gm4-CVN and AAGRLSK-P51Gm4-CVN, were sufficiently stable for characterization and were examined by CD, Tm, ELISA, and glycan array. Both proteins have CD minima at approximately 213 nm, indicating largely β-sheet structure, and have Tm values greater than 40°C. ELISA against gp120 and RNase B demonstrate both proteins' ability to bind high mannose glycans. To more specifically determine the binding specificity of each protein, AAGRLSK-P51Gm4-CVN, SSDGLQQ-P51Gm4-CVN, wt-CVN, and P51G-m4-CVN were sent to the Consortium for Functional Glycomics (CFG) for glycan array analysis. AAGRLSK-P51Gm4-CVN, wt-CVN, and P51G-m4-CVN, have identical specificities for high mannose glycans containing terminal Manα1-2Manα. SSDGLQQ-P51Gm4-CVN binds to terminal GlcNAcα1-4Gal motifs and a subgroup of high mannose glycans bound by P51G-m4-CVN. SSDGLQQ-wt-CVN was produced to restore anti-HIV activity and has a high nanomolar EC50 value compared to wt-CVN's low nanomolar activity. Overall, these experiments show that CV-N Domain B can be mutated and retain specificity identical to wt-CVN or acquire new glycan specificities. This first generation information can be used to produce glycan-specific lectins for a variety of applications.
ContributorsRuben, Melissa (Author) / Ghirlanda, Giovanna (Thesis advisor) / Allen, James (Committee member) / Wachter, Rebekka (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The main objective of this study is to develop an innovative system in the form of a sandwich panel type composite with textile reinforced skins and aerated concrete core. Existing theoretical concepts along with extensive experimental investigations were utilized to characterize the behavior of cement based systems in the presence

The main objective of this study is to develop an innovative system in the form of a sandwich panel type composite with textile reinforced skins and aerated concrete core. Existing theoretical concepts along with extensive experimental investigations were utilized to characterize the behavior of cement based systems in the presence of individual fibers and textile yarns. Part of this thesis is based on a material model developed here in Arizona State University to simulate experimental flexural response and back calculate tensile response. This concept is based on a constitutive law consisting of a tri-linear tension model with residual strength and a bilinear elastic perfectly plastic compression stress strain model. This parametric model was used to characterize Textile Reinforced Concrete (TRC) with aramid, carbon, alkali resistant glass, polypropylene TRC and hybrid systems of aramid and polypropylene. The same material model was also used to characterize long term durability issues with glass fiber reinforced concrete (GFRC). Historical data associated with effect of temperature dependency in aging of GFRC composites were used. An experimental study was conducted to understand the behavior of aerated concrete systems under high stain rate impact loading. Test setup was modeled on a free fall drop of an instrumented hammer using three point bending configuration. Two types of aerated concrete: autoclaved aerated concrete (AAC) and polymeric fiber-reinforced aerated concrete (FRAC) were tested and compared in terms of their impact behavior. The effect of impact energy on the mechanical properties was investigated for various drop heights and different specimen sizes. Both materials showed similar flexural load carrying capacity under impact, however, flexural toughness of fiber-reinforced aerated concrete was proved to be several degrees higher in magnitude than that provided by plain autoclaved aerated concrete. Effect of specimen size and drop height on the impact response of AAC and FRAC was studied and discussed. Results obtained were compared to the performance of sandwich beams with AR glass textile skins with aerated concrete core under similar impact conditions. After this extensive study it was concluded that this type of sandwich composite could be effectively used in low cost sustainable infrastructure projects.
ContributorsDey, Vikram (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2012
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Description
A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first

A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing.
ContributorsYang, Lei (Author) / Zhang, Junshan (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Xue, Guoliang (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Woven fabric composite materials are widely used in the construction of aircraft engine fan containment systems, mostly due to their high strength to weight ratios and ease of implementation. The development of a predictive model for fan blade containment would provide great benefit to engine manufactures in shortened development cycle

Woven fabric composite materials are widely used in the construction of aircraft engine fan containment systems, mostly due to their high strength to weight ratios and ease of implementation. The development of a predictive model for fan blade containment would provide great benefit to engine manufactures in shortened development cycle time, less risk in certification and fewer dollars lost to redesign/recertification cycles. A mechanistic user-defined material model subroutine has been developed at Arizona State University (ASU) that captures the behavioral response of these fabrics, namely Kevlar® 49, under ballistic loading. Previously developed finite element models used to validate the consistency of this material model neglected the effects of the physical constraints imposed on the test setup during ballistic testing performed at NASA Glenn Research Center (NASA GRC). Part of this research was to explore the effects of these boundary conditions on the results of the numerical simulations. These effects were found to be negligible in most instances. Other material models for woven fabrics are available in the LS-DYNA finite element code. One of these models, MAT234: MAT_VISCOELASTIC_LOOSE_FABRIC (Ivanov & Tabiei, 2004) was studied and implemented in the finite element simulations of ballistic testing associated with the FAA ASU research. The results from these models are compared to results obtained from the ASU UMAT as part of this research. The results indicate an underestimation in the energy absorption characteristics of the Kevlar 49 fabric containment systems. More investigation needs to be performed in the implementation of MAT234 for Kevlar 49 fabric. Static penetrator testing of Kevlar® 49 fabric was performed at ASU in conjunction with this research. These experiments are designed to mimic the type of loading experienced during fan blade out events. The resulting experimental strains were measured using a non-contact optical strain measurement system (ARAMIS).
ContributorsFein, Jonathan (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Dwindling energy resources and associated environmental costs have resulted in a serious need to design and construct energy efficient buildings. One of the strategies to develop energy efficient structural materials is through the incorporation of phase change materials (PCM) in the host matrix. This research work presents details of a

Dwindling energy resources and associated environmental costs have resulted in a serious need to design and construct energy efficient buildings. One of the strategies to develop energy efficient structural materials is through the incorporation of phase change materials (PCM) in the host matrix. This research work presents details of a finite element-based framework that is used to study the thermal performance of structural precast concrete wall elements with and without a layer of phase change material. The simulation platform developed can be implemented for a wide variety of input parameters. In this study, two different locations in the continental United States, representing different ambient temperature conditions (corresponding to hot, cold and typical days of the year) are studied. Two different types of concrete - normal weight and lightweight, different PCM types, gypsum wallboard's with varying PCM percentages and different PCM layer thicknesses are also considered with an aim of understanding the energy flow across the wall member. Effect of changing PCM location and prolonged thermal loading are also studied. The temperature of the inside face of the wall and energy flow through the inside face of the wall, which determines the indoor HVAC energy consumption are used as the defining parameters. An ad-hoc optimization scheme is also implemented where the PCM thickness is fixed but its location and properties are varied. Numerical results show that energy savings are possible with small changes in baseline values, facilitating appropriate material design for desired characteristics.
ContributorsHembade, Lavannya Babanrao (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Alkali-activated aluminosilicates, commonly known as "geopolymers", are being increasingly studied as a potential replacement for Portland cement. These binders use an alkaline activator, typically alkali silicates, alkali hydroxides or a combination of both along with a silica-and-alumina rich material, such as fly ash or slag, to form a final product

Alkali-activated aluminosilicates, commonly known as "geopolymers", are being increasingly studied as a potential replacement for Portland cement. These binders use an alkaline activator, typically alkali silicates, alkali hydroxides or a combination of both along with a silica-and-alumina rich material, such as fly ash or slag, to form a final product with properties comparable to or better than those of ordinary Portland cement. The kinetics of alkali activation is highly dependent on the chemical composition of the binder material and the activator concentration. The influence of binder composition (slag, fly ash or both), different levels of alkalinity, expressed using the ratios of Na2O-to-binders (n) and activator SiO2-to-Na2O ratios (Ms), on the early age behavior in sodium silicate solution (waterglass) activated fly ash-slag blended systems is discussed in this thesis. Optimal binder composition and the n values are selected based on the setting times. Higher activator alkalinity (n value) is required when the amount of slag in the fly ash-slag blended mixtures is reduced. Isothermal calorimetry is performed to evaluate the early age hydration process and to understand the reaction kinetics of the alkali activated systems. The differences in the calorimetric signatures between waterglass activated slag and fly ash-slag blends facilitate an understanding of the impact of the binder composition on the reaction rates. Kinetic modeling is used to quantify the differences in reaction kinetics using the Exponential as well as the Knudsen method. The influence of temperature on the reaction kinetics of activated slag and fly ash-slag blends based on the hydration parameters are discussed. Very high compressive strengths can be obtained both at early ages as well as later ages (more than 70 MPa) with waterglass activated slag mortars. Compressive strength decreases with the increase in the fly ash content. A qualitative evidence of leaching is presented through the electrical conductivity changes in the saturating solution. The impact of leaching and the strength loss is found to be generally higher for the mixtures made using a higher activator Ms and a higher n value. Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is used to obtain information about the reaction products.
ContributorsChithiraputhiran, Sundara Raman (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniyam D (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012