Master of Sciences, Case Western Reserve University, 2017, EECS - Computer Engineering

The Internet of Things as a concept is ever-evolving. Its construct revolves around the formation of Internet-connected "Things." This thesis introduces an adaptable, expandable and modular IoT communication framework. It facilitates platform-agnostic client compatibility and inherently adopts several data types and connection schemes. To demonstrate its modularity the framework integrates several modules on the nodes level, including End-User management, Hot-Plug peripherals, Autonomous Event Handling and End-to-End Message Encryption. A distributed messaging scheme along with network traffic analysis connecting to a single server (XMPP) have been conducted to validate the framework's interoperability and expandability. Results show that adding more nodes and increasing messaging frequency on nodes generates network traffic exponentially. Implementing XMPP allows for server-to-server communication on the application protocol layer, thus reducing the domain-centric network overhead. This framework opens the opportunity for future integration of modules, their features, and the analysis of IoT data in the broader scope.

Committee: Chris Papachristou Dr. (Committee Chair); Ming-Chun Huang Dr. (Committee Member); Michael Rabinovich Dr. (Committee Member) Subjects: Computer Engineering; Information Systems; Information Technology; Systems Design

Master of Science, The Ohio State University, 2022, Mechanical Engineering

It is not granted that the advantageous architecture of hybrid electric vehicles will result in improved economy and functionality. This is due to the complex nature of the tradeoff between fuel consumption and battery consumption in the vehicle and how it is controlled. Thus, as hybrid electric vehicles become more ubiquitous, it is necessary to conceive quicker and cheaper ways to prototype their controls. One feasible alternative to the immensely expensive prototypes produced by OEMs is to use an existing conventional vehicle platform as a host for a prototype. This method is explored in this paper and involves the installment of electric motors, a high voltage system, and, if desired, an engine swap. The systems' on-board serial communications structure must be commandeered in order to prototype hybrid supervisory controllers which interact with both the stock and added components. To achieve this a single programmable node equipped with a serial gateway can be inserted into the stock serial system. This tool can then be utilized to enable the torque splitting necessary between the two halves of the powertrain. During the development of this method, it was noted that the programmable node and its serial gateway had the power to enable many secondary features such as shift timing algorithms, P0 series charging, start/stop manipulation, and implementation of an ACC controller.

Committee: Giorgio Rizzoni (Committee Member); Shawn Midlam-Mohler (Advisor) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2020, Anatomy

The sinoatrial node (SAN) is the primary pacemaker of the human heart, which is responsible for initiating and conducting normal cardiac rhythm. When pacemaker cells of the SAN are unable to initiate or conduct heart rhythm, abnormalities such as bradycardia, tachycardia, and/or sinus arrest can occur. These abnormalities constitute just part of a much larger and complex pathological phenomenon known as sinus node dysfunction (SND). In the United States, SND is projected to increase among the elderly with an increasing aging population. However, the only current available treatment for SND is permanent pacemaker (PPM) implantation. PPM implantation is not without its own limitations. By itself, PPM implantation is a costly invasive procedure, which involves a long waiting period between its indication and implantation, not to mention the possibility of adverse events such as infections, and even death. Hence, the work described herein represents an effort to uncover the molecular characteristics of SND induced by heart failure (HF) to aid in the discovery and development of less invasive treatment options for a growing population of SND patients. HF is known to alter important microRNAs (miRs) and mRNAs across the human heart. However, the expression profiles of these miRs and mRNAs in the human HF SAN has not been studied. Our goal was to identify important genes and their modifying miRs in the SAN from HF human hearts, which could be used to target and treat SND. We first compared the mRNA and miR expression profiles between HF and non-heart failing (non-HF) human hearts by next generation sequencing (NGS) in human SAN samples. By NGS, we saw that out of >15,000 mRNAs and >2500 human microRNAs examined, HF significantly altered 831 mRNAs and 44 miRs in the human SAN. After this, we proceeded to use the ingenuity pathway analysis (IPA) software to predict miRs that target mRNAs involved in either SAN pacemaking or conduction. To further confirm the interactio (open full item for complete abstract)

Committee: Melissa Quinn PhD (Advisor); Vadim Fedorov PhD (Committee Member); James Cray PhD (Committee Chair) Subjects: Anatomy and Physiology

PHD, Kent State University, 2019, College of Arts and Sciences / Department of Mathematical Sciences

The matrix exponential has been identified as a useful tool for the analysis of undirected networks, with sound theoretical justifications for its ability to model important aspects of a given network. Its use for directed networks, however, is less developed and has been less successful so far. In this dissertation we discuss some methods to identify important nodes in a directed network using the matrix exponential, taking into account that the notion of importance changes whether we consider the influence of a given node along the edge directions (downstream influence) or how it is influenced by directed paths that point to it (upstream influence). In addition, we introduce a family of importance measures based on counting walks that are allowed to reverse their direction a limited number of times, thus capturing relationships arising from influencing the same nodes, or being influenced by the same nodes, without sacrificing information about edge direction. These measures provide information about branch points. This dissertation is also concerned with the identification of important edges in a network, in both their roles as transmitters and receivers of information. We propose a method based on computing the matrix exponential of a matrix associated with a line graph of the given network. Both undirected and directed networks are considered. Edges may be given positive weights. Computed examples illustrate the performance of the proposed method. In addition to the identification of important nodes and edges in unweighted and edge-weighted networks, we study the importance of nodes in node-weighted graphs. To the best of our knowledge, adjacency matrices for node-weighted graphs have not received much attention. This dissertation describes how the line graph associated with a node-weighted graph can be used to construct an edge-weighted graph, that can be analyzed with available methods. Both undirected and directed graphs with positive node weights are con (open full item for complete abstract)

Committee: Lothar Reichel (Advisor); Omar De la Cruz Cabrera (Advisor) Subjects: Mathematics

PhD, University of Cincinnati, 2019, Medicine: Biostatistics (Environmental Health)

Network analysis has been playing an important role in bioinformatics analysis of transcriptomic data. By identifying differentially expressed genes that are “connected” with other differentially expressed genes, or genes that are not differentially expressed but “connected” with differentially expressed genes, it is meant to associate genes and phenotypes that simple differential expression analysis cannot do. Despite its popularity, the false positive discoveries in network analysis of transcriptomic data has not been systematically reviewed to date. In this study, we define false positive rate for each gene as its probability of being implicated in the null datasets, and seek to identify potential factors that could contribute to the inflated false positive rate for network analysis. By evaluating representative subnetwork detection, network propagation, and heterogeneous network analysis methods for transcriptomic data, we first demonstrated the inter-gene correlation, network node degree, and heterogeneous node association as the potential factors. We then proposed and evaluated two methods to reduce the bias caused by node degree and inter-gene correlation respectively and demonstrated their utility. The node degree and the inter-gene correlation are intrinsic factors of the biologic network and the transcriptomic data. Our solution to reduce the false positive rate caused by these factors are generalizable to other network analysis methods and transcriptomic data.

Committee: Mario Medvedovic Ph.D. (Committee Chair); Anil Jegga D.V.M. (Committee Member); Liang Niu Ph.D. (Committee Member); Marepalli Rao Ph.D. (Committee Member) Subjects: Bioinformatics

Master of Science in Electrical Engineering, University of Toledo, 2013, College of Engineering

Processing large graph datasets represents an increasingly important area in computing research and applications. The size of many graph datasets has increased well beyond the processing capacity of a single computing node, thereby necessitating distributed approaches. As these datasets are processed over a distributed system of nodes, this leads to an inter-node communication cost problem (also known as inter-partition communication), negatively affecting the system performance. This research proposes new graph partitioning algorithms to minimize the inter-node communication by achieving a sufficiently balanced partition. Initially, an intuitive graph partitioning algorithm using Random Selection method coupled with Breadth First Search is developed for reducing inter-node communication by achieving a sufficiently balanced partition. Second, another graph partitioning algorithm is developed using Particle Swarm Optimization with Breadth First Search to reduce inter-node communication further. Simulation results demonstrate that the inter-node communication using PSO with BFS gives better results (reduction of approximately 6% to 10% more) compared to the RS method with BFS. However, both the algorithms minimize the inter-node communication efficiently in order to improve the performance of a distributed system.

Committee: Robert Green (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); William Acosta (Committee Member); Mansoor Alam (Committee Member) Subjects: Computer Engineering; Computer Science

PhD, University of Cincinnati, 2023, Medicine: Molecular Genetics, Biochemistry, & Microbiology

Congenital heart defects (CHDs) are the most common type of congenital birth defect and can be caused by mutations in genes that are involved in the specification or maintenance of cardiomyocyte identity. There are multiple subpopulations of cardiomyocytes within the heart: those that populate the atrium, ventricle, atrioventricular canal (AVC), and pacemaker/sinoatrial node (SAN), each possessing distinct properties that allow the heart to function properly. It has been shown that cardiomyocytes possess a certain amount of plasticity that allow them to change identity based on the expression of different transcription factors. However, our understanding of this plasticity, particularly with regard to the atrium, remains incomplete. The Nr2f (Coup-tf) family of transcription factors are known conserved regulators of atrial differentiation and maintenance. Mutations in NR2F2 are associated with CHDs in humans and work in mice has shown that Nr2f2 is required to maintain atrial identity at the expense of ventricular identity. Previous work from the Waxman lab has shown that zebrafish Nr2f1a, the functional equivalent of mammalian Nr2f2 with respect to heart development, is required for differentiation of atrial cardiomyocytes at the venous pole and restriction of the AVC. Yet the mechanisms by which Nr2f proteins function within the atrium are still not completely understood. In this work, we investigated the mechanisms by which Nr2f transcription factors function within the atrium to maintain atrial identity. Using nr2f1a mutant zebrafish, we found that loss of Nr2f1a leads to a progressive acquisition of ventricular identity within the atrium, consistent with previous mouse data; however, we found that this occurs specifically within the cardiomyocytes of the expanded AVC of nr2f1a mutants. At the venous pole of the atrium, we found a progressive expansion of SAN identity. We show that Nr2f1a represses pacemaker identity in part by maintaining atrial (open full item for complete abstract)

Committee: Joshua Waxman Ph.D. (Committee Chair); Brian Gebelein Ph.D. (Committee Member); Katherine Yutzey Ph.D. (Committee Member); David Wieczorek Ph.D. (Committee Member); Rhett Kovall Ph.D. (Committee Member) Subjects: Developmental Biology

Master of Sciences, Case Western Reserve University, 2023, EECS - Computer and Information Sciences

Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network propagation), which provide limited ability to take into account negatively-labeled nodes (i.e., nodes that are known to be not associated with the label of interest). Specialized algorithms to incorporate negatively labeled samples generally focus on learning or readjusting the edge weights to drive walks away from negatively-labeled nodes and toward positively-labeled nodes. This approach has several disadvantages, as it increases the number of parameters to be learned, and does not necessarily avoid regions of the network that are rich in negatively-labeled nodes. We reformulate random walk with restarts and network propagation to enable “variable restarts”, that is the increased likelihood of restarting at a positively-labeled node when a negatively-labeled node is encountered. Based on this reformulation, we develop CusTaRd, an algorithm that effectively combines variable restart probabilities and edge re-weighting to avoid negatively-labeled nodes. To assess the performance of CusTaRd, we perform comprehensive experiments on four network datasets commonly used in benchmarking label propagation and node classification algorithms. Our results show that CusTaRd consistently outperforms competing algorithms that learn/readjust edge weights, when negatives are available in the close neighborhood of positives.

Committee: Mehmet Koyutürk (Advisor); An Wang (Committee Member); Yinghui Wu (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 2021, Electrical and Computer Engineering

Hardware Trust and Assurance (HT&A) is the study of securing hardware from faults and security threats in much the same way as software security is for software. However, because hardware is, by definition, built using physical components, it is often cost prohibitive to patch out threats and bugs after said hardware is implemented and deployed as such fixes require the physical replacement or modification of said hardware. This paper explores the scope of secondary circuit effects metrics as a use for HT&A, like Switching Rate Activity (SRA) which is explored in this paper. This paper does so by going over the previous use cases of SRA then showing how SRA can be used in a statistical manner to confirm the existence of and locate a fault in a design. The goal of this is to work toward more statisical analysis of secondary circuit effects, like power or netlists. Using Xcelium, Genus, Global Foundries 22nm Fully-Depleted Silicon on Insulator node, a prime number based testbench, and the Carry Multiplexed Adder, it was shown that a statistically measurable difference exists in the SRAs of a design when a fault is introduced. With a p-score of 1.03*10-14 for the means hypothesis test and a p-score of 0.407 for the variance test, the chosen faults did in fact change the behavior of the design when viewed from an SRA lens. In addition, this paper shows that, given the measurable SRA statistical difference of faults and the fact that faults will not propagate their effects all throughout a design when viewed from an SRA perspective, a path exists to find and locate faults from just the SRA effects alone.

Committee: Steven Bibyk (Advisor); Ayman Fayed (Committee Member) Subjects: Electrical Engineering

MS, University of Cincinnati, 2021, Engineering and Applied Science: Computer Science

Graph Embedding alternatively known as Representation learning on Graphs has gained a lot of significance in many machine learning applications such as Classification, Prediction, and recommender systems. Many recent methods have developed ways to learn the features and structure of a graph in low dimensional space, however, most of them only focus on the topological information. The extensive node or edge content information available in structured tabular data is only used by data science algorithms separately. In our work, we aim to combine the information available in the form of rich textual or other demographic node attribute information to add context to the graph entities and their interactions. We demonstrate that this refinement of the node embeddings to use the information available in multiple contexts enhance the feature learning in graphs to perform better in Visualization as well as Model building in an unsupervised, generalized way. Specifically, our framework 1. Aims to Combine the node embeddings obtained from the flexible Random walk technique that learns low-dimensional features from network data as well as the node attribute data. 2. Provides a novel and generic approach to refine the Node embeddings by using the Multi-Dimensional Scaling Technique. 3. Compares the visualizations on the refined embeddings in 2D space generated by UMAP and traditional Multi-Dimensional scaling. 4. Provides a way to overcome the transductive nature of Multi Dimension scaling techniques by predicting the refined embeddings for the Unseen/new data using the Triangulation method. We have conducted experiments on 3 real-world datasets and evaluated the efficacy of the final embeddings in low dimensional data separability as well as in multi-label classification. We achieved a maximum classification model accuracy improvement of 441.3% or a minimum of 2.9% when we use combined embeddings overall. We applied our generic framework (open full item for complete abstract)

Committee: Raj Bhatnagar Ph.D. (Committee Chair); Yizong Cheng Ph.D. (Committee Member); Nan Niu Ph.D. (Committee Member) Subjects: Computer Science

MARCH, University of Cincinnati, 2021, Design, Architecture, Art and Planning: Architecture

Gentrification, one of the most controversial topics in the United States over the last half a century, is often seen as a catalyst to increasing property value in the surrounding context. However, the resulting displacement it causes has resulted in negative housing repercussions across the nation. Located on a riverine border with Canada, Detroit, Michigan is a city that has been marred by crime, poverty, and urban blight since the 1970s and gentrification is something the citizens have fought against.1 Especially with their need for affordable housing being one of the most prevalent in the country, with over one-third of the population living below the poverty rate.2 By reviewing the existing and upcoming affordable housing and mixed-use projects that have been constructed and proposed in Detroit neighborhoods over the last few decades, I aim to propose an architectural intervention that attempts to address what a myriad of projects have failed to address and provide an alternative community to the city; moreover, a hub. Most notably, creating job opportunities, areas for indoor and outdoor recreation, and a model for surrounding neighborhoods to follow... this proposed intervention will aim to provide more than a temporary solution. A community model with tailored housing units to different family sizes and structures, various amenities, and commercial aspects integrated within will be designed and proposed for a real site within the metropolis. Weaving into a network of innovative nodes of happening spaces across the city that have begun sprouting up over the last 5-10 years of Detroit rebirth, this hub will tie into this infrastructure, while also providing something new for the city. Lastly, this mixed-use project will aim to provide a blueprint for not only for this particular borough of Detroit, but also for the other poverty-stricken neighborhoods in the city, and potentially across the United States.

Committee: Joss Kiely Ph.D. (Committee Chair); Vincent Sansalone M.Arch. (Committee Member) Subjects: Architecture

Master of Computer Science, Miami University, 2021, Computer Science and Software Engineering

Causal maps use directed network structures to represent causality between concepts in a system, and are vital for conceptual modeling- a core activity in the field of Modeling & Simulation (M&S). Simulation models are generated from collections of maps, introducing scalability challenges as modelers are unable to effectively process large collections manually, or when maps contain many concepts. Despite this, there is a paucity of research on reducing human interventions across the various steps in causal mapping. In this thesis, we develop Network Science tools to overcome these challenges and present a framework for processing maps automatically. First, we demonstrate how the accepted practice of manually transforming evidence into maps introduces significant bias and that indirect elicitation must be fully documented. To further reduce the risk of bias from modelers, we present and evaluate a method to combine maps using semantic and causal information. We then develop a systematic, data-driven approach to extract a useful model from combined maps, in part by characterizing whether recently proposed metrics on identifying central concepts are feasible in large maps. Our approach is validated through studies on suicide modeling and can subsequently be used to process causal maps in many other research areas.

Committee: Philippe Giabbanelli PhD (Advisor); Karen Davis PhD (Committee Member); Vaskar Raychoudhury PhD (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Environmental Engineering

Many benefits can be realized by real-time modeling and controlling of water distribution systems. However, such improvements can only be achieved if the driving conditions of the hydraulic model are continuously updated. Water demands are arguably the most challenging of those inputs due to complex spatial and temporal variations present over short times. The overall objective of this dissertation is to contribute to the spatial estimation and forecasting of water demands with probabilistic algorithms. Only after the water demands and associated uncertainty are accurately estimated, further demand prediction and uncertainty propagation can be properly estimated to support the real-time management of WDS. The spatial modeling of water demands, in the current dissertation, was improved by finding the set of demand clusters that best approximate the actual spatial distribution of water demands. The application results demonstrated that the hydraulic likelihood was the best and only necessary metric needed to identify water demand clusters. Additionally, the analysis of the tradeoff between hydraulic likelihood and the overall mean of the maximum demand pattern differences could be used to identify the best number of clusters. A high quality cluster solution was achieved with a relatively small number of additional sensors while utilizing a realistic amount of computational power. The current dissertation also studied the improvement of water demand estimation by evaluating several alternative priors under both normal operating and failure scenarios. Overall, the application results demonstrated the benefits of priors, such as the Seasonal Uncertain Autoregressive (SUAR) model, that incorporate a longer past-history of information to maintain accurate demand estimation performance under both normal operating conditions and failure modes. Additionally, two forecast methods, k nearest neighbors (knn) and a seasonal autoregressive model (SAR), were compared in terms of (open full item for complete abstract)

Committee: Patrick Ray Ph.D. (Committee Chair); Dominic Boccelli Ph.D. (Committee Member); Xi Chen Ph.D. (Committee Member); Drew McAvoy Ph.D. (Committee Member) Subjects: Environmental Engineering

MDES, University of Cincinnati, 2020, Design, Architecture, Art and Planning: Design

A proliferation of introductory visual programming language raises the question of how to introduce VPLs to more creators and how to improve the usability and learnability of the VPL platform. This paper compares two different teaching methods and visual programming paradigm software to observe the influence of different factors on the use of visual programming software by adult learners in the designer group. A more constructive teaching style using gamification between participants and a more behaviorist teaching style using small lecture and interaction were exposed to participants in different instruction group. Different visual programming platforms were also tested in each group. User experience scores based on performance score and self-reported scores were collected during and after participants operating on the visual programming software. The independent-sample t-tests were used to answer the research question that: is there a mean difference in scores for operating and self-rating between different instruction groups and different visual programming platform. The test result shows that there is a mean difference in the efficiency (performance score) between the behaviorist instruction approach and the constructivism instruction approach for using visual programming software. The performing scores in the constructive teaching group are statistically significantly higher than the performing scores in the behaviorist teaching group. In addition, designers who exposed to the imperative visual programming software also perform better than those exposed to declarative visual programming software. The study of constructive education in teaching visual programming language worth further exploration, in fact, under the trend of digital learning, constructive learning mechanism and the auxiliary of visual programming, a combination of both to learn programming, especially for programming beginners' introductory courses, has a positive effect.

Committee: Renee Seward M.Des. (Committee Chair); Chia Han Ph.D. (Committee Member) Subjects: Educational Software

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science and Engineering

Graphs arise naturally in many complex systems where they are used to represent entities and relationships between them. The analysis of graph-based models has wide applications like evaluating the significance of interactions between individual entities, identifying important subcomponents, discovering hidden interactions, and making complex inferences about the functions of the underlying systems. Many of these applications require meaningful representation of nodes, and several graph-embedding algorithms have recently been developed to embed nodes in meaningful vector spaces. However, it is not clear how the performance of these algorithms depends on the structural features of graphs, which can vary a lot across real world domains. It would thus be useful to identify the main features that influence the performance of embedding approaches, and to develop a method that can determine the most suitable method for any given graph. The research described in this dissertation applies a systematic approach to comparing various graph-embedding methods on several types of graphs, relates their performance to the structural features of the graphs, and develops a system to select the best embedding method based on graph features. By evaluating the node embedding algorithms for link prediction on several synthetic graph models and real-world network datasets, this study demonstrates the fact that the structural properties of a graph have a significant effect on how well any given node embedding algorithm performs on it. For a particular graph, the performance of a node embedding algorithm can be predicted based on the structural properties, and this relationship holds across a wide range of network types and real-world networks. The results in this dissertation lead to several insights about which algorithms work for various types of graphs.

Committee: Ali Minai Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Yizong Cheng Ph.D. (Committee Member); Jaroslaw Meller Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2019, Biomedical Sciences PhD

Type 2 diabetes is a metabolic disease affecting millions of people around the world. Cognitive and mood impairments are among its many debilitating complications, but disease mechanism(s) remain elusive. Here, we present a series of behavioral tasks that demonstrate impairment of cognitive flexibility in db/db mice, a commonly used type 2 diabetes model. Using immunohistochemistry, we demonstrate disruption of axon initial segments (AIS) and nodes of Ranvier, excitable axonal domains regulating neuronal output, in brain regions associated with cognitive and mood impairments. Finally, we present results of exercise treatment that ameliorates AIS disruption in these animals. Establishing cognitive flexibility deficits in db/db mice that parallel disease complications in patients with type 2 diabetes allows future research to test novel treatment strategies, while discovering disruption of excitable axonal domains fills the missing gap in our understanding of disease pathophysiology.

Committee: Keiichiro Susuki M.D., Ph.D. (Advisor); Mark M. Rich M.D., Ph.D. (Committee Member); Kathrin L. Engisch Ph.D. (Committee Member); Khalid M. Elased Pharm.D., Ph.D. (Committee Member); Lynn K. Hartzler Ph.D. (Committee Member) Subjects: Behavioral Sciences; Biomedical Research; Neurosciences

Master of Science, University of Akron, 2019, Mathematics

The balanced minimum evolution (BME) polytope is a structure representative of a problem in biology, in particular in the study of phylogenetic trees. In this scope, the polytope is used to answer the question of how a set of species are related to one another. In this paper we explore generalized instances of the BME polytope for networks. For one of these generalized BME polytopes we focus on the discovery of new facets and their corresponding equations, while for the other we give the facets of the polytope and discuss the relationship that they have to another well known polytope outside of the field of biology. Furthermore, we also provide the dimension reducing equalities that were discovered which hold for every BME polytope and then prove their existence.

Committee: Stefan Forcey PhD (Advisor); Malena Espanol PhD (Committee Member); James Cossey PhD (Committee Member) Subjects: Mathematics

Doctor of Philosophy, Northeast Ohio Medical University, 2017, Integrated Pharmaceutical Medicine

With a global prevalence of 3.54% for a population aged 40-80 years and projections indicating worldwide affliction numbers increasing to 76 million people by 2020, glaucoma undoubtedly remains the leading cause of irreversible blindness worldwide (Tham et al., 2014). The research I have conducted over the past five years in the field of glaucoma has demonstrated that anterograde axonal transport loss occurs before retrograde transport deficits are seen and semifunctional RGC axons persist in the DBA/2J mouse model of glaucoma for much longer than previously thought (Dengler-Crish et al., 2014). While axons and synapses of the retinal projection are present after transport loss (Dengler-Crish et al., 2014; Crish et al., 2013), it is unclear from an ultrastructural perspective how morphologically intact and/or different these connections are. Furthermore, axonal elements such as nodes of Ranvier (NOR), and synaptic elements such as active zones, maintain strict morphometric and molecular composite standards. Apparent minor modifications to any of these elements can have major ramifications on normal function of the neural unit. However, very little is known about the relationship between axonal abnormalities and synaptic structure defects, in glaucoma. To clarify these relationships, my thesis work has examined the ultrastructure of RGC terminals in the neuropil of the superior colliculus, the morphometry and composition of NORs, as well as, axon size and cytoskeletal matrices in the optic nerve of a naturally occurring glaucomatous mouse model.

Committee: Samuel Crish PhD (Advisor); Denise Inman PhD (Committee Chair); Brett Schofield PhD (Committee Member); Hans Thewissen PhD (Committee Member); Christine Dengler-Crish PhD (Committee Member) Subjects: Aging; Biomedical Research; Neurosciences

Doctor of Philosophy, The Ohio State University, 2016, Pharmacy

Heart failure remains a leading cause of morbidity and mortality in the United States. Many of the deaths attributed to heart failure are sudden, presumably due to lethal arrhythmia. It is a combination of structural and electrical remodeling within the failing heart that promotes the abnormalities of normal rhythm that lead to arrhythmia. This remodeling can occur in the ventricle, resulting in tachyarrhythmia or the sinus node, where it can cause either brady- or tachyarrhythmias. Potassium currents mediate the repolarization phase of ventricular action potential, as well as the diastolic phase of the sinoatrial node action potential. One of the purposes of the research described in this dissertation is to understand, from the standpoint of cellular electrophysiology, how alterations of potassium currents play a role in heart-failure induced arrhythmia. The second purpose is to determine if management of serum potassium levels by pharmacists is an effective strategy in patients to minimize proarrhythmia risk in patients taking antiarrhythmic medications. We found that heart failure duration is very important in the progressive reduction of the repolarization reserve of K+ currents in the ventricle. Our results differ from other models, as we were able to identify IKr reduction in chronic heart failure compared to short-term duration heart failure. As a consequence of depleted repolarization reserve, chronic heart failure resulted in a high frequency of early afterdepolarizations (cellular arrhythmia). We also found increased ventricular tissue fibrosis in chronic heart failure, a hallmark of human end stage heart failure which is often absent in short-term pacing models. Our chronic heart failure model was also used to investigate the role of adenosine-induced sinus node dysfunction in heart failure. Failing sinoatrial node cells had slower intrinsic firing rates versus normal control cells. We were able to demonstrate an increase in the sensitivity of the ra (open full item for complete abstract)

Committee: Cynthia Carnes PharmD/PhD (Advisor); Sandor Gyorke PhD (Committee Member); Peter Mohler PhD (Committee Member); Kari Hoyt PhD (Committee Member) Subjects: Pharmacy Sciences

MS, University of Cincinnati, 2015, Engineering and Applied Science: Computer Science

Most web applications serve dynamic data by either deferring an initial page response until the data has been retrieved, or by returning the initial response immediately and loading additional content through AJAX. We investigate another option, which is to return the initial response immediately and send additional content through a WebSocket connection as the data becomes available. We intend to illustrate the performance of this proposition, as compared to popular conventions of both a server-only and an AJAX approach for achieving the same outcome. This dissertation both explains and demonstrates the implementation of the proposed model, and discusses an analysis of the findings. We use a Node.js web application built with the Cornerstone web framework to serve both the content being tested and the endpoints used for data requests. An analysis of the results shows that in situations when minimal data is retrieved after a timeout, the WebSocket method is marginally faster than the server and AJAX methods, and when retrieving populated files or database records it is marginally slower. The WebSocket method considerably outperforms the AJAX method when making multiple requests in series, and when making requests in parallel the WebSocket and server approaches both outperform AJAX by a tremendous amount.

Committee: Paul Talaga Ph.D. (Committee Chair); Fred Annexstein Ph.D. (Committee Member); John Franco Ph.D. (Committee Member) Subjects: Computer Science