Doctor of Philosophy, The Ohio State University, 2006, Medical Science

In this dissertation, statistical mechanics, graph theory, and machine learning methods have been used to study the topology, modularity, organization and robustness of the proteome network of Saccharomyces cerevisiae. The protein-protein interaction dataset is obtained by combining high confidence interactions, and is validated from multiple perspectives. Statistical mechanics is then used to analyze the connectivity distribution, graph spectrum, shortest path distance and clustering coefficients of the network, which indicates that the network is both scale-free and modular. Microarray gene expression profiles are used to compute the weight for each interaction and the network is represented as a weighted graph. An edge betweenness-based algorithm is developed and applied on the graph, and a set of functional modules is identified. The functional modules are then validated rigorously against gene annotation, growth phenotype and protein complexes. It is found that genes in the same functional module exhibit similar deletion phenotype, and that known protein complexes are largely contained in the functional modules. Studies on the relationship between the gene expression profiles of hubs and their interacting proteins indicate that subpopulations of hubs exist in the yeast proteome network, which are classified as core, local and global hubs. By examining these hub populations from the perspectives of protein complexes, interaction overlap, clustering coefficients, module connectivity, and visualization, it is found that global hubs form the backbone of module-module interaction, while core hubs are organizers within functional modules. In addition, it is found that each hub type preferentially interacts with hubs from the same population, which suggests an ordered architecture for the network. Studies on gene expression changes suggest that global hubs are the major and early responders in cellular response. Next, network breakdown simulation and graph spectrum ar (open full item for complete abstract)

Committee: Bo Yuan (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 1995, Electrical Engineering

A new formation of a neural network called a Density Based Random Vector Functional Link Network (DBRVFLN) is introduced to solve high dimensional real-world problems. It is a hybrid technique which uses the combination of a priori knowledge of the problem and randomness to prepare unknown factors. Simple but powerful feature extraction methods for handwritten digit recognition and script recognition are introduced. Handwritten digit recognition systems using neural networks are introduced. For the script recognition task, a global approach which uses whole sets of features of the image and an analytical approach from nonsegmented sequence of features via letters to a word using neural networks are designed and explored. The recognition systems are based on conventional preprocessing methodologies, novel feature extraction and reduction algorithms and quadratic approaches of neural networks such as Radial Basis Function Neural Network(RBNN) and DBRVFLN. The recognition systems are tested using unconstrained real-world databases. In the handwritten digit recognition task, the performance of the recognition system using DBRVFLN is better than that of RBNN if there is enough priori knowledge. To attain 1% substitution error rates, the current recognition system needs to tolerate rejection rat es of about 11%∼12%. In the global approach of script recognition task, the ability to construct a filter for one word is tested. While keeping a very low substitution error rate of 0.74%, the recognition system which uses DBRVFLN rejects 11.48% and has better performance with 2.78% in the rejection ratio than the system using RBNN even if they have the same number of enhancement nodes. The experimental results show that the random vector enhancements for the unknown factor in DBRVFLN act very nicely as decision enhancers and that they do improve the classification performances in comparison with RBNN, in the same recognition system

Committee: Yoh-Han Pao (Advisor) Subjects:

PhD, University of Cincinnati, 2020, Arts and Sciences: Physics

The presented work in this thesis aims to apply functional MRI (fMRI) to advance understanding of how sleep duration impacts the way the brain responds in key networks for attention, memory, and reward processing in adolescents. Functional MRI is a noninvasive way to measure regional neuronal activity using changes in the blood oxygenation over time. This is different from standard MRI done clinically, which is primarily meant to provide the structural anatomic information. Functional MRI is often acquired to measure response to stimulation, but it can also be used to assess spontaneous activity that can be associated with regional connectivity. The first two chapters provide background of MRI and fMRI. Chapter 1 outlines the basic physics of MRI starting from proton spin and magnetization in an external magnetic field, then moving to radiofrequency excitation and the concept of relaxation time. The Bloch equation and MR signal formation are further described in this chapter. Chapter 2 describes the fundamental techniques of functional brain imaging. The basic principle to understand the human brain in action is called blood-oxygenation-level-dependent (BOLD) contrast. Further, the echo planer imaging (EPI) sequence used to generate fMRI is described. Finally, types of experimental fMRI designs are outlined in this chapter, as well as the corresponding statistical analysis. The subsequent three chapters detail the human subject fMRI research projects with which I have been involved in the course of my PhD studies. Chapter 3 describes a study to investigate the neuronal activation and performance changes in working memory and attention induced by mild chronic sleep restriction (SR) in adolescents. This study utilized a working-memory task with varying levels of difficulty. We observed degeneration of task performance as the level of difficulty increased overall, but without a detectable effect of sleep duration. However, fMRI showed that SR result (open full item for complete abstract)

Committee: Mark DiFrancesco Ph.D. (Committee Chair); Rostislav Serota Ph.D. (Committee Chair); F Paul Esposito Ph.D. (Committee Member); Gregory Lee Ph.D. (Committee Member); Jean Tkach Ph.D. (Committee Member) Subjects: Neurology

Doctor of Philosophy, Case Western Reserve University, 2020, EECS - Computer and Information Sciences

Complex diseases, like cancer or Type II Diabetes, result from the interplay between multiple genetic factors at different cellular levels as well as environmental factors. Deciphering the etiology of complex diseases mandates characterizing the function of many underlying factors and the relationships between these factors. The availability of a plethora of omic data at a genomic scale and the availability of disease-associated data from a broad range of populations present a valuable resource. When such wealth of data is utilized by integrative and efficient computational methods and robust statistical frameworks, it could help in elucidating the etiology of complex diseases and the realization of precision medicine. Due to the complexity of biological systems; the intricacy of inter-genomic interactions, the obscuring effect of many confounding factors, the high dimensionality and the high degree of noise in the data, effective use of omic data for accurate disease risk prediction faces important challenges. This problem is especially clear when dealing with complex diseases like cancer. In this thesis, we utilize multiple types of omic data as well as population-specific data and develop integrative computational methods to characterize the interplay between various factors that underlie complex diseases. We perform computational analyses at multiple levels, capture functional commonalities of disease-associated variants across different populations and model the interplay between disease-associated genes at the cellular level. We model and spot distortion in omic data by discovering new features that mitigate its negative impact on the predictive ability of biomarkers, hence improving the accuracy of disease risk prediction.

Committee: Mehmet Koyuturk (Committee Chair); Vinay Varadan (Committee Member); Erman Ayday (Committee Member); Ming-Chun Huang (Committee Member); An Wang (Committee Member) Subjects: Bioinformatics; Computer Science

PhD, University of Cincinnati, 2018, Arts and Sciences: Physics

With the advances in MRI, it has become possible to noninvasively observe function and structure of the developing brain in vivo. Functional magnetic resonance imaging (fMRI) of the brain is a non-invasive way to assess brain function using MRI signal changes associated with neuronal activity. The most widely used method is based on BOLD (Blood Oxygenation Level Dependent) signal changes caused by hemodynamic and metabolic neuronal responses. Functional connectivity has been defined as inter-regional temporal correlations among spontaneous BOLD fluctuations in different regions of the brain during a task as well as when the brain is idle. By identifying brain regions that exhibit highly correlated BOLD signal fluctuations, we can infer that the regions are functionally connected and co-activation during a particular task or at rest (fcMRI) suggests that these regions work together as part of a functional brain network. This method is now being used widely to study brain networks but has seen limited use in studies of the developing brain, particularly in infants. Functionally connected brain regions can be specified as components of integrated networks that enable specific sensory or cognitive brain functions. These brain networks demonstrate the basic connectivity pattern between brain regions, which can be represented mathematically using graph-theoretical approaches. Graph theory provides a convenient quantitative and visual format to sketch the topological organization of brain connectivity representing complex brain networks. Graph theory analysis also naturally provides quantitative descriptors of both global and regional topological properties of brain graphs. While this approach is now widely used with functional MRI data as a means of studying the topology of functional brain networks, it has not been applied to study the development of brain networks from birth, nor in the premature infant brain. The main goal of this dissertation is to use novel functiona (open full item for complete abstract)

Committee: Scott Holland Ph.D. (Committee Chair); L. C. R. Wijewardhana Ph.D. (Committee Chair); Howard Jackson Ph.D. (Committee Member); Stephanie Merhar (Committee Member); Jean Tkach Ph.D. (Committee Member); Jason Woods Ph.D. (Committee Member) Subjects: Radiology

PhD, University of Cincinnati, 2011, Arts and Sciences: Mathematical Sciences

Narrative comprehension is a fundamental cognitive skill that involves the coordination of different functional brain regions. To investigate the network structure among the brain regions supporting this cognitive function, a Spectral Bayesian Network with Bayesian model averaging is developed based on the spectral density estimation of the functional Magnetic Resonance Imaging (fMRI) time series recorded from multiple brain regions. In this approach, the neural interactions and temporal dependence among different brain regions are measured by spectral density matrices after a Fourier transform of the fMRI signals to the frequency domain. A Bayesian model averaging method is then applied to build the network structure from a set of candidate networks. Using this model, brain networks of three distinct age groups are constructed to assess the dynamic change of network connectivity with respect to age. Networks of multivariate time series are also simulated from vector autoregressive models to compare the performances of the SBN with existing methods in learning network structure from time series data. In addition to the network modeling of the functional interactions among brain regions, the quantification of the functional connectivity between two brain regions is also very important for understanding how the functions of the human brain develop. Using spectral coherence and partial spectral coherence, the overall and direct functional connectivity strengths among the language-related neural circuits are computed based on fMRI time series data collected in 313 children ranging in age from 5 to 18 years in a story comprehension experiment. The age or gender effects on both the pair wise direct link and connection strength are studied to access children's development of brain functions for story comprehension. In addition, the connectivity differences between the left and right hemispheres, and the connections in both hemispheres that are directly related to the child (open full item for complete abstract)

Committee: Siva Sivaganesan PhD (Committee Chair); James Deddens PhD (Committee Member); Scott Holland PhD (Committee Member); Paul Horn PhD (Committee Member); Xiaodong Lin PhD (Committee Member); Seongho Song PhD (Committee Member) Subjects: Statistics

PhD, University of Cincinnati, 2008, Engineering : Biomedical Engineering

Identifying causal genes underlying susceptibility to human disease is a problem of primary importance in post-genomic era and current biomedical research. Recently, there has been a paradigm shift of such gene-discovery efforts from rare, monogenic conditions to common "oligogenic" or "multifactorial" conditions such as asthma, diabetes, cancers and neurological disorders. These conditions are referred as multifactorial because, susceptibility to these diseases is attributed to the combinatorial effects of genetic variation at a number of different genes and their interaction with relevant environmental exposures. The expectation is that identification and characterization of the causal genes implicated in the inherited component of disease susceptibility will lead to substantial advances in our understanding of disease. These advances in turn can lead to improvements in diagnostic accuracy, prognostic precision, the range and targeting of available therapeutic options and ultimately realize the promise of personalized or "tailor-made" medicine. The objective of my thesis therefore is to design, develop, and validate computational approaches for identification and prioritization of these causal genes. The first approach tests the hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships. We use a p-value-based meta-analysis method to prioritize the candidate genes based on functional annotation. For the very first time, we use and demonstrate, the utility of mouse phenotype annotations in human disease gene prioritization. Since this approach is limited to only genes with functional annotation, and because many human genes are yet to be functionally classified, we have developed another approach that is independent of gene functional annotations. We implemented a set of new algorithms to prioritize genes based on protein-protein interaction networks. Large scale cross-validation were performed fo (open full item for complete abstract)

Committee: Bruce Aronow (Committee Chair); Anil Jegga (Committee Co-Chair); Marepalli Rao (Committee Member) Subjects: Bioinformatics

Doctor of Philosophy in Engineering, University of Toledo, 2011, Chemical Engineering

Polymer nanocomposites (PC) have gained considerable attention recently because of the wide range of properties they can provide. However, design of a high loading PC with uniform dispersion and good interfacial interaction between the nanofibers and polymer has been a challenging issue. In this work, techniques to develop and characterize a novel high loading functional nanofiber network (FNN) with enhanced mechanical strength, conductivity and improved gas transport properties are discussed. These FNN materials consist of polymer matrix, nanofiber network and bound functional groups to nanofiber surface. The nanofibers form a highly connected network or mesh, polymer matrix provides mechanical stability and processiblity to the resulting composite. Functional groups which are covalently bound to the surface of the nanofiber provide compatibility between nanofibers and polymer matrix and also desirable properties such as in the case of gas separation they react reversibly with the target species. Processing methods to in corporate functionalized carbon nanofiber (CNF) poly(dimethylsiloxane) (PDMS) were investigated. Possible applications for FNN were studied and a comprehensive characterization of the composite was performed in order to understand the impact of incorporation of CNF and different surface functionality on the physical and electrical properties on the PDMS matrix. Also, the CNF were functionalized with the following classes of groups to investigate the effect of surface chemistry of interfacial region on the transport properties: 1.No affinity group: this includes pristine CNF (CNF- P), oxidized CNF (CNF-OX) and PDMS (OH) functionalized CNF (CNF- PDMS (OH)). Inclusion of these fibers results in increase in stiffness of matrix and reduction in free volume. This is expected to translate to a decrease in permeability of all gases studied. 2.Small molecule with affinity groups: This includes ionic liquid functionalized CNF, PDMS (NH2) functionalized (open full item for complete abstract)

Committee: Maria Coleman PhD (Committee Chair); Saleh Jabarin PhD (Committee Member); Isabel Escobar PhD (Committee Member); Jamie Hestekin PhD (Committee Member); Yakov Lapitsky PhD (Committee Member) Subjects: Chemical Engineering; Chemistry; Materials Science; Nanotechnology

Doctor of Philosophy, Case Western Reserve University, 1992, Biomedical Engineering

A neural network control system has been designed for the purpose of controlling cyclic movements in Functional Neuromuscular Stimulation (FNS) systems. The design of the control system directly addresses three problems faced in the implementation of FNS control systems: customizing the control system parameters for a particular individual, adapting these parameters to account for changes in the musculoskeletal system, and resisting mechanical disturbances. The control system is implemented by a two-stage neural network that utilizes adaptive feedforward and feedback control techniques. The first stage of the neural network, the Pattern Generator, generates a cyclic pattern of activity. The design of this stage is based upon neural models of vertebrate motor control systems. The signals from the Pattern Generator are adaptively filtered by the second stage, the Pattern Shaper. A learning algorithm that accounts for system dynamics and input time delays was developed for use in adapting the Pattern Shaper filtering properties. Computer simulated models of electrically stimulated muscles acting on one- and two-segment skeletal systems were used to assess the potential utility of the neural network control system in FNS control. Results of the evaluation demonstrated that the control system can automatically c ustomize stimulation parameters, adapt them on-line, and resist mechanical disturbances. The control system was also demonstrated to be capable of controlling movements of multi-joint systems and of utilizing biarticular muscle effectively. The success of the control system in this evaluation indicates that it may provide significant improvements to existing FNS control system technology and suggests that the technique should be investigated further. These studies also indicate that this strategy may be appropriate for other applications in the control of dynamic, nonlinear systems with input time delays. The use of biologically motivated neural networks in the P (open full item for complete abstract)

Committee: Howard Chizeck (Advisor) Subjects: Engineering, Biomedical