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Description
Since Darwin popularized the evolution theory in 1895, it has been completed and studied through the years. Starting in 1990s, evolution at molecular level has been used to discover functional molecules while studying the origin of functional molecules in nature by mimicing the natural selection process in laboratory. Along this line, my Ph.D. dissertation focuses on the in vitro selection of two important biomolecules, deoxynucleotide acid (DNA) and protein with binding properties. Chapter two focuses on in vitro selection of DNA. Aptamers are single-stranded nucleic acids that generated from a random pool and fold into stable three-dimensional structures with ligand binding sites that are complementary in shape and charge to a desired target. While aptamers have been selected to bind a wide range of targets, it is generally thought that these molecules are incapable of discriminating strongly alkaline proteins due to the attractive forces that govern oppositely charged polymers. By employing negative selection step to eliminate aptamers that bind with off-target through charge unselectively, an aptamer that binds with histone H4 protein with high specificity (>100 fold)was generated. Chapter four focuses on another functional molecule: protein. It is long believed that complex molecules with different function originated from simple progenitor proteins, but very little is known about this process. By employing a previously selected protein that binds and catalyzes ATP, which is the first and only protein that was evolved completely from random pool and has a unique α/β-fold protein scaffold, I fused random library to the C-terminus of this protein and evolved a multi-domain protein with decent properties. Also, in chapter 3, a unique bivalent molecule was generated by conjugating peptides that bind different sites on the protein with nucleic acids. By using the ligand interactions by nucleotide conjugates technique, off-the shelf peptide was transferred into high affinity protein capture reagents that mimic the recognition properties of natural antibodies. The designer synthetic antibody amplifies the binding affinity of the individual peptides by ∼1000-fold to bind Grb2 with a Kd of 2 nM, and functions with high selectivity in conventional pull-down assays from HeLa cell lysates.
ContributorsJiang, Bing (Author) / Chaput, John C (Thesis advisor) / Chen, Julian (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2013
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 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
Description
The principle of Darwinian evolution has been applied in the laboratory to nucleic acid molecules since 1990, and led to the emergence of in vitro evolution technique. The methodology of in vitro evolution surveys a large number of different molecules simultaneously for a pre-defined chemical property, and enrich for molecules with the particular property. DNA and RNA sequences with versatile functions have been identified by in vitro selection experiments, but many basic questions remain to be answered about how these molecules achieve their functions. This dissertation first focuses on addressing a fundamental question regarding the molecular recognition properties of in vitro selected DNA sequences, namely whether negatively charged DNA sequences can be evolved to bind alkaline proteins with high specificity. We showed that DNA binders could be made, through carefully designed stringent in vitro selection, to discriminate different alkaline proteins. The focus of this dissertation is then shifted to in vitro evolution of an artificial genetic polymer called threose nucleic acid (TNA). TNA has been considered a potential RNA progenitor during early evolution of life on Earth. However, further experimental evidence to support TNA as a primordial genetic material is lacking. In this dissertation we demonstrated the capacity of TNA to form stable tertiary structure with specific ligand binding property, which suggests a possible role of TNA as a pre-RNA genetic polymer. Additionally, we discussed the challenges in in vitro evolution for TNA enzymes and developed the necessary methodology for future TNA enzyme evolution.
ContributorsYu, Hanyang (Author) / Chaput, John C (Thesis advisor) / Chen, Julian (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2013
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 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
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
One of the main challenges in planetary robotics is to traverse the shortest path through a set of waypoints. The shortest distance between any two waypoints is a direct linear traversal. Often times, there are physical restrictions that prevent a rover form traversing straight to a waypoint. Thus, knowledge of the terrain is needed prior to traversal. The Digital Terrain Model (DTM) provides information about the terrain along with waypoints for the rover to traverse. However, traversing a set of waypoints linearly is burdensome, as the rovers would constantly need to modify their orientation as they successively approach waypoints. Although there are various solutions to this problem, this research paper proposes the smooth traversability of the rover using splines as a quick and easy implementation to traverse a set of waypoints. In addition, a rover was used to compare the smoothness of the linear traversal along with the spline interpolations. The data collected illustrated that spline traversals had a less rate of change in the velocity over time, indicating that the rover performed smoother than with linear paths.
ContributorsKamasamudram, Anurag (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The ability to design high performance buildings has acquired great importance in recent years due to numerous federal, societal and environmental initiatives. However, this endeavor is much more demanding in terms of designer expertise and time. It requires a whole new level of synergy between automated performance prediction with the human capabilities to perceive, evaluate and ultimately select a suitable solution. While performance prediction can be highly automated through the use of computers, performance evaluation cannot, unless it is with respect to a single criterion. The need to address multi-criteria requirements makes it more valuable for a designer to know the "latitude" or "degrees of freedom" he has in changing certain design variables while achieving preset criteria such as energy performance, life cycle cost, environmental impacts etc. This requirement can be met by a decision support framework based on near-optimal "satisficing" as opposed to purely optimal decision making techniques. Currently, such a comprehensive design framework is lacking, which is the basis for undertaking this research. The primary objective of this research is to facilitate a complementary relationship between designers and computers for Multi-Criterion Decision Making (MCDM) during high performance building design. It is based on the application of Monte Carlo approaches to create a database of solutions using deterministic whole building energy simulations, along with data mining methods to rank variable importance and reduce the multi-dimensionality of the problem. A novel interactive visualization approach is then proposed which uses regression based models to create dynamic interplays of how varying these important variables affect the multiple criteria, while providing a visual range or band of variation of the different design parameters. The MCDM process has been incorporated into an alternative methodology for high performance building design referred to as Visual Analytics based Decision Support Methodology [VADSM]. VADSM is envisioned to be most useful during the conceptual and early design performance modeling stages by providing a set of potential solutions that can be analyzed further for final design selection. The proposed methodology can be used for new building design synthesis as well as evaluation of retrofits and operational deficiencies in existing buildings.
ContributorsDutta, Ranojoy (Author) / Reddy, T Agami (Thesis advisor) / Runger, George C. (Committee member) / Addison, Marlin S. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Microelectronic industry is continuously moving in a trend requiring smaller and smaller devices and reduced form factors with time, resulting in new challenges. Reduction in device and interconnect solder bump sizes has led to increased current density in these small solders. Higher level of electromigration occurring due to increased current density is of great concern affecting the reliability of the entire microelectronics systems. This paper reviews electromigration in Pb- free solders, focusing specifically on Sn0.7wt.% Cu solder joints. Effect of texture, grain orientation, and grain-boundary misorientation angle on electromigration and intermetallic compound (IMC) formation is studied through EBSD analysis performed on actual C4 bumps.
ContributorsLara, Leticia (Author) / Tasooji, Amaneh (Thesis advisor) / Lee, Kyuoh (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2013