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Description
Specificity and affinity towards a given ligand/epitope limit target-specific delivery. Companies can spend between $500 million to $2 billion attempting to discover a new drug or therapy; a significant portion of this expense funds high-throughput screening to find the most successful target-specific compound available. A more recent addition to discovering highly specific targets is the application of phage display utilizing single chain variable fragment antibodies (scFv). The aim of this research was to employ phage display to identify pathologies related to traumatic brain injury (TBI), particularly astrogliosis. A unique biopanning method against viable astrocyte cultures activated with TGF-β achieved this aim. Four scFv clones of interest showed varying relative affinities toward astrocytes. One of those four showed the ability to identify reactive astroctyes over basal astrocytes through max signal readings, while another showed a statistical significance in max signal reading toward basal astrocytes. Future studies will include further affinity characterization assays. This work contributes to the development of targeting therapeutics and diagnostics for TBI.
ContributorsMarsh, William (Author) / Stabenfeldt, Sarah (Thesis advisor) / Caplan, Michael (Committee member) / Sierks, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
The ribosome is a ribozyme and central to the biosynthesis of proteins in all organisms. It has a strong bias against non-alpha-L-amino acids, such as alpha-D-amino acids and beta-amino acids. Additionally, the ribosome is only able to incorporate one amino acid in response to one codon. It has been demonstrated that reengineering of the peptidyltransferase center (PTC) of the ribosome enabled the incorporation of both alpha-D-amino acids and beta-amino acids into full length protein. Described in Chapter 2 are five modified ribosomes having modifications in the peptidyltrasnferase center in the 23S rRNA. These modified ribosomes successfully incorporated five different beta-amino acids (2.1 - 2.5) into E. coli dihydrofolate reductase (DHFR). The second project (Chapter 3) focused on the study of the modified ribosomes facilitating the incorporation of the dipeptide glycylphenylalanine (3.25) and fluorescent dipeptidomimetic 3.26 into DHFR. These ribosomes also had modifications in the peptidyltransferase center in the 23S rRNA of the 50S ribosomal subunit. The modified DHFRs having beta-amino acids 2.3 and 2.5, dipeptide glycylphenylalanine (3.25) and dipeptidomimetic 3.26 were successfully characterized by the MALDI-MS analysis of the peptide fragments produced by "in-gel" trypsin digestion of the modified proteins. The fluorescent spectra of the dipeptidomimetic 3.26 and modified DHFR having fluorescent dipeptidomimetic 3.26 were also measured. The type I and II DNA topoisomerases have been firmly established as effective molecular targets for many antitumor drugs. A "classical" topoisomerase I or II poison acts by misaligning the free hydroxyl group of the sugar moiety of DNA and preventing the reverse transesterfication reaction to religate DNA. There have been only two classes of compounds, saintopin and topopyrones, reported as dual topoisomerase I and II poisons. Chapter 4 describes the synthesis and biological evaluation of topopyrones. Compound 4.10, employed at 20 µM, was as efficient as 0.5 uM camptothecin, a potent topoisomerase I poison, in stabilizing the covalent binary complex (~30%). When compared with a known topoisomerase II poison, etoposide (at 0.5 uM), topopyorone 4.10 produced similar levels of stabilized DNA-enzyme binary complex (~34%) at 5 uM concentration.
ContributorsMaini, Rumit (Author) / Hecht, Sidney M. (Thesis advisor) / Gould, Ian (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2013
Description
Our research focuses on finding answers through decentralized search, for complex, imprecise queries (such as "Which is the best hair salon nearby?") in situations where there is a spatiotemporal constraint (say answer needs to be found within 15 minutes) associated with the query. In general, human networks are good in answering imprecise queries. We try to use the social network of a person to answer his query. Our research aims at designing a framework that exploits the user's social network in order to maximize the answers for a given query. Exploiting an user's social network has several challenges. The major challenge is that the user's immediate social circle may not possess the answer for the given query, and hence the framework designed needs to carry out the query diffusion process across the network. The next challenge involves in finding the right set of seeds to pass the query to in the user's social circle. One other challenge is to incentivize people in the social network to respond to the query and thereby maximize the quality and quantity of replies. Our proposed framework is a mobile application where an individual can either respond to the query or forward it to his friends. We simulated the query diffusion process in three types of graphs: Small World, Random and Preferential Attachment. Given a type of network and a particular query, we carried out the query diffusion by selecting seeds based on attributes of the seed. The main attributes are Topic relevance, Replying or Forwarding probability and Time to Respond. We found that there is a considerable increase in the number of replies attained, even without saturating the user's network, if we adopt an optimal seed selection process. We found the output of the optimal algorithm to be satisfactory as the number of replies received at the interrogator's end was close to three times the number of neighbors an interrogator has. We addressed the challenge of incentivizing people to respond by associating a particular amount of points for each query asked, and awarding the same to people involved in answering the query. Thus, we aim to design a mobile application based on our proposed framework so that it helps in maximizing the replies for the interrogator's query by diffusing the query across his/her social network.
ContributorsSwaminathan, Neelakantan (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the advent of social media (like Twitter, Facebook etc.,) people are easily sharing their opinions, sentiments and enforcing their ideologies on others like never before. Even people who are otherwise socially inactive would like to share their thoughts on current affairs by tweeting and sharing news feeds with their friends and acquaintances. In this thesis study, we chose Twitter as our main data platform to analyze shifts and movements of 27 political organizations in Indonesia. So far, we have collected over 30 million tweets and 150,000 news articles from RSS feeds of the corresponding organizations for our analysis. For Twitter data extraction, we developed a multi-threaded application which seamlessly extracts, cleans and stores millions of tweets matching our keywords from Twitter Streaming API. For keyword extraction, we used topics and perspectives which were extracted using n-grams techniques and later approved by our social scientists. After the data is extracted, we aggregate the tweet contents that belong to every user on a weekly basis. Finally, we applied linear and logistic regression using SLEP, an open source sparse learning package to compute weekly score for users and mapping them to one of the 27 organizations on a radical or counter radical scale. Since, we are mapping users to organizations on a weekly basis, we are able to track user's behavior and important new events that triggered shifts among users between organizations. This thesis study can further be extended to identify topics and organization specific influential users and new users from various social media platforms like Facebook, YouTube etc. can easily be mapped to existing organizations on a radical or counter-radical scale.
ContributorsPoornachandran, Sathishkumar (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2013
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
The biological and chemical diversity of protein structure and function can be greatly expanded by position-specific incorporation of non-natural amino acids bearing a variety of functional groups. Non-cognate amino acids can be incorporated into proteins at specific sites by using orthogonal aminoacyl-tRNA synthetase/tRNA pairs in conjunction with nonsense, rare, or 4-bp codons. There has been considerable progress in developing new types of amino acids, in identifying novel methods of tRNA aminoacylation, and in expanding the genetic code to direct their position. Chemical aminoacylation of tRNAs is accomplished by acylation and ligation of a dinucleotide (pdCpA) to the 3'-terminus of truncated tRNA. This strategy allows the incorporation of a wide range of natural and unnatural amino acids into pre-determined sites, thereby facilitating the study of structure-function relationships in proteins and allowing the investigation of their biological, biochemical and biophysical properties. Described in Chapter 1 is the current methodology for synthesizing aminoacylated suppressor tRNAs. Aminoacylated suppressor tRNACUAs are typically prepared by linking pre-aminoacylated dinucleotides (aminoacyl-pdCpAs) to 74 nucleotide (nt) truncated tRNAs (tRNA-COH) via a T4 RNA ligase mediated reaction. Alternatively, there is another route outlined in Chapter 1 that utilizes a different pre-aminoacylated dinucleotide, AppA. This dinucleotide has been shown to be a suitable substrate for T4 RNA ligase mediated coupling with abbreviated tRNA-COHs for production of 76 nt aminoacyl-tRNACUAs. The synthesized suppressor tRNAs have been shown to participate in protein synthesis in vitro, in an S30 (E. coli) coupled transcription-translation system in which there is a UAG codon in the mRNA at the position corresponding to Val10. Chapter 2 describes the synthesis of two non-proteinogenic amino acids, L-thiothreonine and L-allo-thiothreonine, and their incorporation into predetermined positions of a catalytically competent dihydrofolate reductase (DHFR) analogue lacking cysteine. Here, the elaborated proteins were site-specifically derivitized with a fluorophore at the thiothreonine residue. The synthesis and incorporation of phosphorotyrosine derivatives into DHFR is illustrated in Chapter 3. Three different phosphorylated tyrosine derivatives were prepared: bis-nitrobenzylphosphoro-L-tyrosine, nitrobenzylphosphoro-L-tyrosine, and phosphoro-L-tyrosine. Their ability to participate in a protein synthesis system was also evaluated.
ContributorsNangreave, Ryan Christopher (Author) / Hecht, Sidney M. (Thesis advisor) / Yan, Hao (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is commonly known that High Performance Computing (HPC) systems are most frequently used by multiple users for batch job, parallel computations. Less well known, however, are the numerous HPC systems servicing data so sensitive that administrators enforce either a) sequential job processing - only one job at a time on the entire system, or b) physical separation - devoting an entire HPC system to a single project until recommissioned. The driving forces behind this type of security are numerous but share the common origin of data so sensitive that measures above and beyond industry standard are used to ensure information security. This paper presents a network security solution that provides information security above and beyond industry standard, yet still enabling multi-user computations on the system. This paper's main contribution is a mechanism designed to enforce high level time division multiplexing of network access (Time Division Multiple Access, or TDMA) according to security groups. By dividing network access into time windows, interactions between applications over the network can be prevented in an easily verifiable way.
ContributorsFerguson, Joshua (Author) / Gupta, Sandeep Ks (Thesis advisor) / Varsamopoulos, Georgios (Committee member) / Ball, George (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.
ContributorsVerdicchio, Michael (Author) / Kim, Seungchan (Thesis advisor) / Baral, Chitta (Committee member) / Stolovitzky, Gustavo (Committee member) / Collofello, James (Committee member) / Arizona State University (Publisher)
Created2013