Browsing by Subject "Bioinformatics"
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Item A Computational Approach To Rna Sequencing For Secondary Structure Prediction(2016) Bhattarai, Anup; Stojkovic, Vojislav; Computer Science and Bioinformatics Program; Master of ScienceRNA is not only a messenger of genetic codes from DNA to protein but also an active molecule in various biological functions. An RNA sequence and the structure formed determines the functions. Prediction of secondary structure of RNA is useful in determining its functions such as regulation of gene expressions, sensing of ligand, enzymatic features, translational control in mRNA, and replication in single-stranded RNA viruses. Also, RNA structures will provide insights into evolution, biology, and design of therapeutics. In this thesis research, I used hydrogen bond maximizing algorithm to predict RNA secondary structure, hydrogen bond maximizing program designed by Rex A. Dyer to create a Nussinov matrix, and a recursive or iterative algorithm to decode the Nussinov matrix, which gives RNA secondary structure. The Perl and Python programming languages have been used to solve the same problem in a recursive and iterative way. The comparison of execution time between an iterative and a recursive algorithm was done. The comparison between the programming languages Perl and Python gave an insight into speed, readability, and simplicity of these two programming languages, and comparison between recursive and iterative algorithms showed which one was faster in practice.Item A Connection Of Quantum Computation And Dna Computation Using The Bloch Sphere(2011) Inkoom, Patrick K.; Stojkovic, Vojislav; Lupton, William; Computer Science and Bioinformatics Program; Master of ScienceQuantum and DNA computing are distributing and parallel types of computing. They are useful for solving problems which require high complexity computations and (or) massive data set computations such as searching, sorting, merging, pattern recognition, image processing, encryption, etc. Quantum and DNA algorithms cannot be efficiently simulated on classical computers because classical computers cannot efficiently deal with the parallelism. The quantum circuit model is adequate to describe quantum algorithms whereas DNA circuit model is adequate to describe DNA algorithms. This thesis establishes the relationship between a quantum qubit and a DNA string using the Bloch sphere. The Bloch sphere is a convenient graphical representation of a qubit in a 3-dimensional space. The model used in this thesis presents one-to-one mapping between qubits, DNA strings, and points on the Bloch sphere. The model is implemented at the real computer - von Neumann machine, therefore, there are restrictions related to the precision of qubits value, the length of DNA strings, and the precisions of coordinates values. The Bloch sphere and the relations between qubits, DNA strings and points are implemented as "Quantum and DNA Computation Simulation Programming System". Quantum and DNA Computation Simulation Programming System is implemented in the Java programming language. This system is a modified and upgraded interactive Quantum Computation applet developed at the Johns Hopkins Center for Educational Resources. The modifications include connecting (a) DNA strings and the Bloch sphere and (b) DNA strings and qubits. There are many researches in Quantum Computing and DNA Computing as independent study fields, but this is the first research which tries to connect Quantum and DNA Computing together using the Bloch sphere.Item A Decision Support System (Dss) For Classification And Retrieval Of Leukemia From Microscopic Blood Cell Images(2017) Alabdulrahman, May Salem; Rahman, Mahmudur; Computer Science and Bioinformatics Program; Master of ScienceLeukemia accounts for almost a third (30%) of all childhood cancers. Out of the four main types, the Acute Lymphocytic Leukemia (ALL) is the most fatal if left untreated due to its rapid spread into the bloodstream and other vital organs. The detection of ALL in its early stages reduces the mortality considerably. Visual microscopic examination of peripheral blood slides still used as the standard leukemia diagnosis technique. Although, it suffers from problems such as subjective interpretations, operator tiredness and efficiency, the morphological analysis can be easily automated and performed directly in blood microscopic images. The development of a fully automated screening system may provide hematologists with significant aid in the effort to detect and classify leukemia cells more effectively and efficiently. To date, only a few research efforts have focused on finding the likelihood of malignancy based on applying some feature extraction and classification schemes. The objective of this research is to provide hematologist with a screening system for ALL detection and recognition that will be able to respond to image based visual queries by displaying relevant microscopic blood images of past cases, along with the associated pathological diagnosis (classification). This work is focusing on the development of such a system based on image segmentation, feature extraction, image classification, similarity matching, and finally performance evaluation in a public image dataset (ALL-IDB) of peripheral blood samples of normal individuals and leukemic patients. The experimental results demonstrate the effectiveness of our system and show the potential of real clinical integration with 96.62 % accuracy level.Item A Lattice Path Algorithm for RNA Secondary Structure Prediction: an Application to a Yellow Fever Virus Molecule(2018) Kutbi, Ayat; Nkwanta, Asamoah; Computer Science and Bioinformatics Program; Master of ScienceBioinformatics algorithms and tools have been established within explicit contexts for experimental modeling and data analysis. In this thesis, a lattice path algorithm based on RNA combinatorics is proposed for RNA secondary structure prediction. The developed lattice path algorithm graphically represents a certain subset of lattice path, a type of combinatorical objects enumerated by the RNA numbers, that are related to RNA secondary sequences which are composed of the RNA alphabet {A,U,G,C}. The algorithm supports visualizing all possible predicted foldings of an RNA sequence. The algorithm is applied to an RNA subsequence of a conserved structure of the 3' untranslated region of the yellow fever virus. The thermodynamic algorithm, RNAfold, is utilized to obtain minimum free energy and base pair probabilities of the predicted folds. Constraints are applied to enforce particular RNA secondary structure foldings. By applying the lattice path algorithm and RNAfold, the results show additional possible folds of the target RNA sequence. Results illustrate new predicted secondary sequences for the conserved structure of the 3' untranslated region of the yellow fever virus. The lattice path algorithm correctly predicted the secondary structure of an RNA subsequence derived from the peptidyl transferase center region of a ribosomal RNA. Subject to further validation, the algorithm could be applied to the prediction of mucleic acids aptamers for different medical applications. With a precise computational approach and a rigorous model of bioinformatics algorithms, RNA secondary structure prediction can be achieved with this approach, which can be valuable for understanding RNA structure and function.Item A Molecular Modeling Approach Towards The Discovery Of Inhibitors For Rift Valley Fever Virus Nucleocapsid Protein(2013) Bonyi, Enock; Wachira, James; Computer Science and Bioinformatics Program; Master of ScienceRift Valley Fever Virus (RVFV) is a zoonotic pathogen belonging to the genusPhlebovirus and family Bunyaviridae within the negative-strand RNA genome viruses superfamily. It causes severe illnesses in animals and humans yet there are no FDA approved drugs or human vaccines. RVFV is spread by mosquitoes or contact with infected animal carcasses and is endemic to Africa and Peninsula regions of the Middle East, but there is concern that it could spread to other regions of the world given that the mosquito vectors are more widely distributed than the pathogen itself. It is cross-listed by the US Department of Agriculture (USDA) and Department of Human and Health Services (DHHS) as a select agent, which are organisms or toxins with the potential of being used for bioterrorism and for which there are no available countermeasures. With crystal structures of RVFV nucleocapsid (N) protein becoming recently available, it was hypothesized that identification of its mode of interaction with RNA could lead to selection of compounds with the ability to inhibit genome packaging. The main goal of this thesis research was to study through computational techniques the interaction of N with the RNA genome with an aim of identifying putative inhibitors by screening drug-like molecular libraries. The coordinates for the crystal structures of N were obtained from Protein Data Bank (PDB) and used in dockings and virtual screening. Using sequence alignment and molecular modeling, invariant residues in phleboviral N proteins were mapped to the model with a view of refining the modes of interactions. The RVFV N protein was analyzed for potential drug/ligand binding cavities with Pocket-Finder while molecules derived from the NCI Diversity Set III, Drug Bank and PubChem libraries were screened with AutoDock Vina using the PyRx virtual screening tool. Docking Server and Discovery studio were used to determine protein-ligand interactions. These studies successively developed a theoretical model describing the N-protein interactions with RNA. Further, we identified six compounds as potential inhibitors which subject to experimental verification, could serve as a starting point for the development of drugs that target the genome packaging step in RVFV and related RNA viruses.Item An Integrated Platform of Analytical Chemical Techniques for Profiling of Therapeutic Glycoproteins(2016-01-01) Gray, Andrea Renay; LaCourse, William R; Chemistry & Biochemistry; ChemistryGlycans are extensively dispersed in biological systems in "free state" and in attached forms such as glycoproteins, glycolipids, and proteoglycans. Glycans are commonly investigated as critical species in therapeutic glycoprotein drug development. Therapeutic glycoprotein drugs have high specificities granting precise action and long half-lives allowing infrequent dosing compared to low-molecular-mass chemical drugs. Strong evidence linking bioactivity and efficacy to glycosylation necessitate understanding, measuring, and controlling glycosylation in glycoprotein-based drugs. The oligosaccharide content of glycoprotein products, in addition to the thorough characterization of biosimilars, has become increasingly important. Glycans are involved in a wide range of biological and physiological processes including regulatory and recognition functions, cellular immunity, growth, and development. The functions of glycans are often dependent on the structure and types of oligosaccharides attached to the proteins. The structures of glycans are highly diverse, complex, and heterogeneous due to post-translational modifications and physiological conditions, making it remarkably challenging to comprehensively characterize glycan profiles and determine their structures. High performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) and liquid chromatography with mass spectrometry (LC-MS) methods are compared based on their sensitivity to glycan detection and efficiency to separate a simple glycan mixture. Liquid chromatography with high-resolution mass spectrometry (LC-HRMS) and multi-enzyme digestions are used to develop a list of analytical controls needed for the primary sequence characterization of biosimilar therapeutics compared to their original innovator compounds. Analytical figures of merit determined for a standard glycan using LC-MS revealed detection limits in the sub parts per million range. Chromatographic figures of merit affirmed higher column efficiency with HPAEC-PAD. Multi-enzyme analysis dramatically increased sequence coverage for three protein models, two of which are highly used classes of therapeutic biologics. A critical list of analytical controls was developed for primary sequence characterization of biosimilar therapeutics highlighting consistency as an absolute necessity in the characterization process.Item Bioinformatic analysis of proteins associated with human familial hemiplegic migraine.(2011-05-18) Lewis, Jada LaShawn; Sakk, Eric; Master of ScienceItem Biological Sequence Analysis Using Hadoop/Mapreduce As A Distributed Computing Model(2012) Paudel, Roshan; Stojkovic, Vojislav; Computer Science and Bioinformatics Program; Master of ScienceMost Biological (DNA, RNA or Protein) sequence analyzing algorithms are complex and require extensive execution time and memory. Serial Biological Sequence Processing Algorithms do not use the computing power of present computers very efficiently. Today, researchers and scientists have developed and tested many programming models for parallelizing and optimizing algorithms to decrease execution time and memory used. MapReduce is a programming model based on functional programming, where users implement interface of two functions - map and reduce. In general, map is a kind of application of functions and reduce is he aggregations of the results of those applications. MapReduce Programming Model is patented by Google. In this research, Hadoop implementation of MapReduce was used. Hadoop and Hadoop Distributed File System are open source models of MapReduce and Google File System. Hadoop framework automatically transforms map and reduce applications into map and reduce tasks. All known biological sequences and their functional annotations are stored in biological databases. A newly determined biological sequence should be compared with each and every known corresponding biological sequence to detect potential structural or evolutionary relationships. From a computational point of view, a major challenge is to align the query biological sequence to a very large collection of biological sequences and sort them according to the score of their alignment with the input biological sequence. The solution has to be fast and scalable. The main goals of this thesis research are: * To build a fully-distributed Ubuntu Hadoop cluster of four nodes. * To configure and test Hadoop cluster in the LittleFe cluster computer. * To seek, determine and measure the efficiency of program in terms of used time and memory. The main achievements/results of this thesis research are: * Transformation of the LittleFe BCCD operating system cluster computer into the Ubuntu operating system cluster computer. * Two Hadoop examples- the RandomTextWriter.java and SecondarySort.java were modified into the Hadoop MRGenerateDNA.java program to generate big file of random DNA sequences and the Hadoop MRSortDNA.java program to sort DNA sequences in an order respectively. * Proved that Hadoop is an efficient programming model to develop new parallel algorithms for biological sequence processing based on Map Reduce Programming model.Item Comparison of dChip and Affymetrix microarray analysis: NRF-1 and PGC1 gene expression in type II diabetes.(2011-05-18) Ng'ang'a, Catherine W.; Sakk, Eric; Master of ScienceItem Determining Biological Significance Of Putative Exons Through The Application Of Annotation-Agnostic Methods To Measure Potential Hallmarks Of Functionality(2022-05) Hoch, Bianca; Darby, Miranda Dr.; Laufer, Craig Dr.; Jenkins, Conor; Hood College Biology; Biomedical and EnvironmentalThe aim of this study is to infer the potential for biological function of putative exons which arise from repetitive element (REL) exonization events in human brain tissues. The data pipeline in this project utilizes existing annotation-agnostic methods and public RNA-seq data consortiums to determine the exact boundaries and levels of expression of these putative exons which may be unrepresented in current gene annotations. Biological function of putative exons is assessed by examining splicing events which could give rise to novel transcripts that contain these putative exons. These splicing events are assessed in regard to overall expression, prevalence across samples, and tissue specificity. Documentation and source code is available at: https://github.com/biancabifx/PutativeExonFunctionPipeline.Item Developing a Bioinformatics Pipeline to Assess the Potential Functional Impact of Novel Protein Isoforms(2020-04-20) Klein, Alyssa; Darby, Miranda; Hood College Biology; Craig Laufer; Georgette Jones; Hood College Bioinformatics ProgramWhile we know the sequence of the nucleotides that make up the DNA of the human genome, the process of annotating those nucleotides according to the transcripts that originate from them remains incomplete. Novel transcripts continue to be identified, and so methods must be devised to characterize these novel transcripts and prioritize them for future study by assessing potential hallmarks of function. One of the potential hallmarks of function is presence of an open reading frame with potential to produce a protein isoform that is not yet annotated. Based on the sequence of the novel protein isoform, an initial assessment of the potential functional impact of expression of the novel protein can be made: 1) Identification of the loss and/or gain of protein domains in the novel isoform versus the current annotated form of the protein. 2) Analysis of proteins that have lost and/or gained functional domains by generating 3D models of the annotated proteins and novel protein isoforms to facilitate potential understanding of functional impact. 3) Differential expression analysis of the putative exons at the RNA level to investigate disease-specificity of expression and potential changes in expression of the novel isoform in disease.Item Developing a Library Collection in Bioinformatics: Support for an Evolving Profession(IGI Global, 2013) Martin, VictoriaThis chapter provides guidelines for developing a university library collection for bioinformatics programs. The chapter discusses current research and scholarly communication trends in bioinformatics and their impact on information needs and information seeking behavior of bioinformaticians and, consequently, on collection development. It also discusses the criteria for making collection development decisions that are largely influenced by the interdisciplinary nature of the field. The types of information resources most frequently used by bioinformaticians are described, specific resources are suggested, and creative options aimed at finding ways for a bioinformatics library collection to expand in the digital era are explored. The author draws on literature in bioinformatics and the library and information sciences as well as on her ten years of experience providing bioinformatics user services at George Mason University. The chapter is geared towards practicing librarians who are charged with developing a collection for bioinformatics academic programs as well as future librarians taking courses on collection development and academic librarianship.Item Distributed Search Of Biological Databases Using Hadoop/Mapreduce(2015) Fashola, Babatunde Olaide; Stojkovic, Vojislav; Computer Science and Bioinformatics Program; Master of ScienceThe main goals of this thesis research were to: 1. Make a computational platform/environment for thesis research. 2. Develop a MapReduce search algorithm that employs the scalability of a Hadoop cluster and the MapReduce functionalities to make the search of a biological database faster. 3. Implement the MapReduce search algorithm using the Java programming language, and running the consequent Java application in a Hadoop multi-node cluster in the cloud. 4. Compare execution times of - The MapReduce search program - The serial search programs – Boyer-Moore Algorithm and Knuth-Morris-Pratt Algorithm 13 GB of downloadable GenBank data was processed over the Hadoop framework installed on a 12-node cluster comprised of the Amazon EC2 t2.micro instance types. The execution time of the distributed search program is 46% faster than the execution times of the serial programs. Hence, the present search algorithms used for accessing the biological databases can incorporate the MapReduce programming model to improve their performancesItem Formalization Of Transcription And Translation Processes By Turing Machines(2015) Drabo, Hassane Kalifa; Stojkovic, Vojislav; Sakk, Eric; Computer Science and Bioinformatics Program; Master of ScienceThe human nuclear genome consists of a set of 23 pairs of chromosomes which is made of long DNA molecules that contain information bytes called genes. The human genome contains about 21,000 genes. In the cell, each of the genes codes for a specific protein and is assigned a specific function. Transcription and translation are the processes used by cells to produce a string of amino acids that is a foundation of protein. Transcription: Transcription occurs in the nucleus where the cell copies the gene sequence into messenger RNA (mRNA). This is the first step of gene expression where a complementary DNA sequence is produced. Translation: The ribosome which consists of RNA and proteins reads the mRNA sequence and translates it into the amino acid sequence of the protein. The ribosome reads three nucleotides at a time. Each three-nucleotide codon specifies a particular amino acid. The nucleotide triplets are "stop" codons (UAA, UAG, and UGA) that signal the ribosome that the protein is complete. The goals and accomplishments in this research are: • Design Turing machines to simulate the famous transcription and translation problem • Formally describe the transcription and translation problem using Turing machines terminology (states, rules) • Implement unrestricted grammar (a type 0 grammar) that defines/specifies the language of transcription and translation processes. • Compute the complexity of the transcription and translation problem. The final solution is the composition of over forty Visual Turing machines. This research is very important because this is one of the first successful attempts to describe biochemical processes in a formal way and move biology from an experimental science into a computational science.Item Identification Of Molecular Phenotypes Of Urothelial Carcinoma Of The Bladder(2017) Olaku, Oluwole; Rahman, Mahmudur; Computer Science and Bioinformatics Program; Master of ScienceThe prevalence of bladder cancer is one of the common causes of high mortality in the world. Advances in the identification of genomic alterations that lead to urothelial carcinoma of the bladder (BCa), and recent approval of immune check point inhibitors have provided durable systemic treatment options for patients with advanced or metastatic bladder cancer. In view of positive clinical responses to cabozantinib an immunotherapy drug in a recent phase II clinical trial at the National Cancer Institute, a broad panel of putative cabozantinib targeted receptor tyrosine kinases (RTKs) was examined to identify new therapeutic targets. Alterations in The Cancer Genome Atlas (TCGA) BCa datasets revealed that patients with muscle invasive disease had at least one RTK gene amplified and/or mRNA upregulated. Furthermore, many TCGA patient samples displayed both RTK and cognate ligand overexpression, specifically GAS6/AXL, MST1/MST1R and CSF1/CSF1R pathways, creating the potential for autocrine RTK signaling to drive BCa oncogenesis in these patients. Recent studies on other bladder cancer provisional datasets have identified groups of receptor tyrosine kinases, DNA repair genes (DNADR) and transcriptional activators in the epithelial mesenchymal transition (EMT) pathway. The goal of this research is to identify the cohorts that have these genetic alterations in these groups (RTK, EMT, DNADR) and observe the impact on overall and disease-free survival on those with altered and unaltered genes. The results highlighted that alterations to EMT transcriptional activators had the most significant impact on overall and disease-free survival of the bladder cancer patients, potentially highlighting the pathway as a potential biomarker for drug targeted therapyItem A more appropriate Protein Classification using Data Mining(2010-11-30) Rahman, Muhammad Mahbubur; Alam, Arif Ul; Mamun, Abdullah Al; Mursalin, Tamnun E.Research in bioinformatics is a complex phenomenon as it overlaps two knowledge domains, namely, biological and computer sciences. This paper has tried to introduce an efficient data mining approach for classifying proteins into some useful groups by representing them in hierarchy tree structure. There are several techniques used to classify proteins but most of them had few drawbacks on their grouping. Among them the most efficient grouping technique is used by PSIMAP. Even though PSIMAP (Protein Structural Interactome Map) technique was successful to incorporate most of the protein but it fails to classify the scale free property proteins. Our technique overcomes this drawback and successfully maps all the protein in different groups, including the scale free property proteins failed to group by PSIMAP. Our approach selects the six major attributes of protein: a) Structure comparison b) Sequence Comparison c) Connectivity d) Cluster Index e) Interactivity f) Taxonomic to group the protein from the databank by generating a hierarchal tree structure. The proposed approach calculates the degree (probability) of similarity of each protein newly entered in the system against of existing proteins in the system by using probability theorem on each six properties of proteins.Item Parallel Sorting Of Biological Sequences Using The Intel� Concurrent Collections(2012) Nembhard, Fitzroy; Lupton, William; Stojkovic, Vojislav; Computer Science and Bioinformatics Program; Master of SciencePerforming analyses of and computations with biological sequence data, such as deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), require a lot of processing time and memory using sequential algorithms. Today, programmers and scientists have developed and tested a few models for parallelizing and optimizing algorithms to improve results in bioinformatics. However, some of these approaches have not made efficient use of multi-core systems or computers with many processors. The Intel® Concurrent Collections is a software tool and library for transforming serial programs into semantically equivalent parallel programs. The Intel® Concurrent Collections approach is a new and unique technique for designing parallel programs. It overcomes the over-constraint nature of serial languages by providing a conclusive programming concept and allows for programs to be run efficiently on multi-core systems and computers with many processors. The main goals of this research are: to design a serial C/C++ program for sorting biological sequences based on the Divide and Conquer methodology, to transform the serial C/C++ program into a semantically equivalent parallel C/C++ program using the Intel® Concurrent Collections, to compare and analyze execution times of the serial and parallel programs and to make appropriate conclusions on the suitability of the Divide and Conquer methodology for parallelization, to provide suggestions on the suitability of the Intel® Concurrent Collections technology for parallelization of serial algorithms, and to show the importance of parallelization of bioinformatics algorithms. The main results/achievements of this thesis research are: successful parallel sorting of biological sequences using the merge sort Divide and Conquer algorithm, successfully conducted experiments on the Intel® Many-core Testing Lab, which runs with the RedHat Enterprise Linux operating system and is comprised of 32 processors and 265GB RAM, proof that the Intel® Concurrent Collections programming model can, by parallelization, improve efficiency and speed of algorithms involved in bioinformatics and computational biology, and a conclusion that there are some limitations in the prerelease version of the platform.Item Perl Implementation Of A Contact-Waiting Time Metric For Rna Folding(2011) Nguewou, Helene N.; Nkwanta, Asamoah; Computer Science and Bioinformatics Program; Master of ScienceThe aim of this thesis is to create a user-friendly prediction tool for RNA folding kinetics by using the Perl programming language. RNA folding rates can be determined experimentally for RNA sequences of various lengths. However, predicting the folding rates of long sequences can be very difficult, due to the high number of independent computations required. To avoid this problem, different types of folding metrics have been developed for indirectly predicting RNA folding rates. One such metric is the contact-waiting time (CWT) metric. The CWT metric is a measure that correlates well with the natural logarithm of the folding rates of RNA sequences. This metric takes into account the specific energetic contributions of the type of base pairs that are formed and the entropic cost associated with the formation of isolated base pairs. The implementation of the algorithm used to compute the CWT metric is converted from MATLAB to Perl and applied to HIV RNA sequences. The purpose of creating the Perl implementation of the CWT metric is to make the measure more widely available and easy to use as a bioinformatics tool. An executable version of the CWT algorithm is provided as a supplementary file to the thesis.Item The Application Of Information Retrieval Techniques To The Mining Of Bioinformatics Data(2011) Odebode, Iyanuoluwa Emmanuel; Sakk, Eric; Computer Science and Bioinformatics Program; Master of ScienceThis thesis explores the application of information retrieval and text mining techniques to the mining of bioinformatics data. Information retrieval can be defined as a set of processes that involves querying a collection of objects in order to extract relevant information from the data. The goal of this work is to invoke a mathematical structure on bioinformatics database objects that facilitate the use of vector space techniques encountered in text mining and information retrieval systems. The approach presented is quite general and applicable to various categories of bioinformatics data such as text, sequence, or structural objects. The main contribution of this thesis is to demonstrate how vector space techniques typically encountered in the field of text information retrieval can be applied to bioinformatics data. Much of the work in this thesis is devoted to the numerical encoding of bioinformatics sequence data such that relevant biological and chemical characteristics are preserved; hence, the Blocks Database is applied as the template for testing the applied techniques. It is established that the vector space technique is consistent with pattern classification methodologies commonly applied within the bioinformatics literature, also numerous subspace decomposition techniques are presented and applied to classify patterns.Item The non-invariance of pattern recognition measures using neural networks as applied to the protein secondary structure prediction problem.(2011-05-18) Alexander, Ayanna M.; Sakk, Eric; Master of Science