Physical Sciences and Mathematics Commons^™

Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin

Dissertations

Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …

Molecular Mechanisms Of Amyloid-Like Fibril Formation, Sharareh Jalali

Dissertations

Proteins play a critical role in living systems by performing most of the functions inside cells. The latter is determined by the protein's three-dimensional structure when it is folded in its native state. However, under pathological conditions, proteins can misfold and aggregate, accounting for the formation of highly ordered insoluble assemblies known as amyloid fibrils. These assemblies are associated with diseases like Parkinson's and Alzheimer's. Strong evidence suggests that three mechanisms are critical for forming amyloid fibrils. These mechanisms are the nucleation of amyloid fibrils in solution (primary nucleation) as well as on the surface of existing fibrils (secondary nucleation) …

Exploring Topological Phonons In Different Length Scales: Microtubules And Acoustic Metamaterials, Ssu-Ying Chen

Dissertations

The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales.

Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the …

Quantifying Balance: Computational And Learning Frameworks For The Characterization Of Balance In Bipedal Systems, Kubra Akbas

Dissertations

In clinical practice and general healthcare settings, the lack of reliable and objective balance and stability assessment metrics hinders the tracking of patient performance progression during rehabilitation; the assessment of bipedal balance plays a crucial role in understanding stability and falls in humans and other bipeds, while providing clinicians important information regarding rehabilitation outcomes. Bipedal balance has often been examined through kinematic or kinetic quantities, such as the Zero Moment Point and Center of Pressure; however, analyzing balance specifically through the body's Center of Mass (COM) state offers a holistic and easily comprehensible view of balance and stability.

Building upon …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Continuum Modeling Of Active Nematics Via Data-Driven Equation Discovery, Connor Robertson

Dissertations

Data-driven modeling seeks to extract a parsimonious model for a physical system directly from measurement data. One of the most interpretable of these methods is Sparse Identification of Nonlinear Dynamics (SINDy), which selects a relatively sparse linear combination of model terms from a large set of (possibly nonlinear) candidates via optimization. This technique has shown promise for synthetic data generated by numerical simulations but the application of the techniques to real data is less developed. This dissertation applies SINDy to video data from a bio-inspired system of mictrotubule-motor protein assemblies, an example of nonequilibrium dynamics that has posed a significant …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud

Dissertations

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. …

Interactions Of Amyloid Peptides With Lipid Membranes, Yanxing Yang

Dissertations

The aggregation of amyloid proteins into fibrils is a hallmark of several diseases including Alzheimer’s (AD), Parkinson’s, and Type II diabetes. This aggregation process involves the formation of small size oligomers preceding the formation of insoluble fibrils. Recent studies have shown that these oligomers are more likely to be responsible for cell toxicity than fibrils. A possible mechanism of toxicity involves the interaction of oligomers with the cell membrane compromising its integrity. In particular, oligomers may form pore-like structures in the cell membrane affecting its permeability or they may induce lipid loss via a detergent-like effect. This dissertation aims to …

One-Stage Blind Source Separation Via A Sparse Autoencoder Framework, Jason Anthony Dabin

Dissertations

Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder …

Methods For Extending Biomedical Reference Ontologies And Interface Terminologies For Ehrr Text Annotation, Vipina Kuttichi Keloth

Dissertations

Biomedical ontologies and terminologies are a cornerstone in various electronic health record systems (EHRs) for encoding information related to diseases, diagnoses, treatments, etc. Ontologies in general represent entities (concepts) and events along with all interdependent properties and relationships in an efficient way to facilitate easy access, retrieval and sharing. With the landscape of medicine rapidly changing, biomedical ontologies and terminologies need to rapidly evolve to support interoperability, medical coding, record keeping, and healthcare activities in general, and to facilitate interdisciplinary research. Extending ontologies by identifying new and missing concepts plays a vital role in the maintenance of ontologies to keep …

Reconstituting The Cyanobacterial Circadian Clock In Vitro, Pyong Hwa Kim

Dissertations

Cyanobacteria are photosynthetic organisms that are known to be responsible for oxygenating Earth’s early atmosphere. Having evolved to ensure optimal survival in the periodic light/dark cycle on this planet, their genetic codes are packed with various tools, including a sophisticated biological timekeeping system. Among the cyanobacteria is Synechococcus elongatus PCC 7942, the simplest clock-harboring organism with a powerful genetic tool that enabled the identification of its intricate timekeeping mechanism. The three central oscillator proteins—KaiA, KaiB, and KaiC—drive the 24 h cyclic gene expression rhythm of cyanobacteria, and the "ticking" of the oscillator can be reconstituted inside a test tube just …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …

Mechanisms Of Oscillations And Polyglot Entrainment In Neuronal And Circadian Models, Emel Khan

Dissertations

Entrainment is a type of synchronization in which the period of an endogenous oscillator matches the period of an external forcing signal and a stable phase relationship is maintained between them. Entrainment patterns are described in terms of the number of input oscillations (N) that are phase-locked to a number of output oscillations (M), referred to as N:M patterns. Arnold tongue diagrams are used to depict the regions of N:M entrainment patterns in the input period-amplitude parameter space. Although the entrainment of self-sustained oscillators by periodic forcing are well investigated is a well-studied problem, entrainment of damped oscillators has been …

Molecular Mechanism Of Cyanobacteria Circadian Clock Oscillator And Effect Of Co Factors On Its Oscillation, Manpreet Kaur

Dissertations

The circadian rhythms arise as an adaptation to the environmental 24-hour day and night cycle due to Earth's rotation. These rhythms prepare organisms to align their internal biological activities and day to day behavior or events with the environmental change of the 24-hour day and night cycle. Circadian rhythms are found widely in all living kingdoms of life on Earth. Cyanobacteria are photosynthetic prokaryotes which first used to study these circadian rhythms. Among cyanobacterial species, Synechococcus elongatus PCC 7942 (henceforth, S. Elongatus) is the simplest organism with a durable and sturdy circadian clock and is study as a model organism. …

Enrichment Of Ontologies Using Machine Learning And Summarization, Hao Liu

Dissertations

Biomedical ontologies are structured knowledge systems in biomedicine. They play a major role in enabling precise communications in support of healthcare applications, e.g., Electronic Healthcare Records (EHR) systems. Biomedical ontologies are used in many different contexts to facilitate information and knowledge management. The most widely used clinical ontology is the SNOMED CT. Placing a new concept into its proper position in an ontology is a fundamental task in its lifecycle of curation and enrichment.

A large biomedical ontology, which typically consists of many tens of thousands of concepts and relationships, can be viewed as a complex network with concepts as …

Mathematical Models And Tools To Understand Coupled Circadian Oscillations And Limit Cycling Systems, Guangyuan Liao

Dissertations

The circadian rhythm refers to an internal body process that regulates many body processes including the sleep-wake cycle, digestion and hormone release. The ability of a circadian system to entrain to the 24-hour light-dark cycle is one of the most important properties. There are several scenarios in which circadian oscillators do not directly receive light-dark forcing. Instead they are part of hierarchical systems in which, as \peripheral" oscillators, they are periodically forced by other \central" circadian oscillators that do directly receive light input. Such dynamics are modeled as hierarchical coupled limit cycle systems. Those models usually have a large population, …

Efficient Approximations For Stationary Single-Channel Calcium Nanodomains, Yinbo Chen

Dissertations

Mathematical and computational modeling plays an important role in the study of local Ca2+ signals underlying many fundamental physiological processes such as synaptic neurotransmitter release and myocyte contraction. Closed-form approximations describing steady-state distribution of Ca2+ in the vicinity of an open Ca2+ channel have proved particularly useful for the qualitative modeling of local Ca2+ signals. This dissertation presents several simple and efficient approximants for the equilibrium Ca2+ concentration near a point source in the presence of a mobile Ca2+ buffer, which achieve great accuracy over a wide range of model parameters. Such approximations provide an efficient method for estimating Ca2+ …

1,4-Dioxane Biodegradation In Propanotrophs: Molecular Foundations And Implications For Environmental Remediation, Li Fei

Dissertations

1,4-Dioxane (dioxane) has emerged with an escalating concern given its human carcinogenicity and widespread occurrence in groundwater. Bioremediation is promising as an effective and cost-efficient treatment alternative for in situ or ex situ cleanup of dioxane and co-existing pollutants in the field. Soluble di-iron monooxygenases (SDIMOs) are reputed for their essential roles in initiating the cleavage of dioxane and other pollutants. In this doctoral dissertation, molecular foundations for SDIMOs-mediated dioxane biodegradation are untangled to promote the development and implication of site-specific bioremediation and natural attenuation strategies. This dissertation focused on propanotrophic bacteria given their pivotal roles in dioxane metabolism and …

Comparison Of Longitudinal Changes In Resting State Functional Magnetic Resonance Imaging Between Alzheimer’S And Healthy Controls, Berk Can Yilmaz

Theses

Resting State Functional Magnetic Resonance Imaging (rs-fMRI) is a technique that is widely used for analyzing brain function using different approaches and methods. This study involves rs-fMRI analysis of Blood Oxygenation Level Dependent (BOLD) signals acquired from Alzheimer’s disease (AD) Patients and Healthy Controls (HC). Each subject in the study had both functional and anatomical images with at least one rs-fMRI scan with their Anatomical (T1) scans. Previous rs-fMRI studies have demonstrated that AD shows differences in Amplitude of Low Frequency (<0.1 Hz) Fluctuations (ALFF), and Regional Homogeneity (ReHo) measures according to HCs.

The aim of the study is to investigate individual and group level differences using ReHo and mALFF related …

Modeling Single Microtubules As A Colloidal System To Measure The Harmonic Interactions Between Tubulin Dimers In Bovine Brain Derived Versus Cancer Cell Derived Microtubules, Arooj Aslam

Dissertations

The local properties of tubulin dimers dictate the properties of the larger microtubule assembly. In order to elucidate this connection, tubulin-tubulin interactions are be modeled as harmonic interactions to map the stiffness matrix along the length of the microtubule. The strength of the interactions are measured by imaging and tracking the movement of segments along the microtubule over time, and then performing a fourier transform to extract the natural vibrational frequencies. Using this method the first ever reported experimental phonon spectrum of the microtubule is reported. This method can also be applied to other biological materials, and opens new doors …

Nanocarbon Modification Of Membranes For Enhanced Water Desalination And Water Treatment, Worawit Intrchom

Dissertations

Water scarcity is foreseen to be one of the great global issues in the coming decades. The challenges are not only in providing water supply to cope with the growing public demand, but recovering clean water to natural resources. Clean water supply, from brackish and seawater is attractive. Membrane distillation (MD) is an emerging thermal membrane-based process that has been used for desalination and other pollutant separations from water. MD can be operated at low temperature, so low-grade energy sources are a good alternative heat source for MD. High salt rejection and low membrane fouling also make MD interesting for …

Data Assimilation For Conductance-Based Neuronal Models, Matthew Moye

Dissertations

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the …

Amyloid Proteins And Fibrils Stability, Farbod Mahmoudinobar

Dissertations

Compared to globular proteins that have a stable native structure, intrinsically disordered peptides (IDP) sample an ensemble of structures without folding into a native conformation.One example of IDP is the amyloid-beta(Abeta) protein which is the main constituent of senile plaques in the brain of Alzheimer's patients.Understanding the process by which IDPs undergo structural changes to form oligomers that eventually aggregate into senile plaques/amyloid fibrils may significantly advance the development of novel therapeutic methods to treat neurodegenerative diseases, for which there is no cure to date. This dissertation has two main objectives. The first one is to investigate and identify structural …

Mitochondria Imaging And Targeted Cancer Treatment, Tinghan Zhao

Dissertations

Mitochondria are essential organelles as the site of respiration in eukaryotic cells and are involved in many crucial functions in cell life. Dysfunction of mitochondrial metabolism and irregular morphology have been frequently found in human cancers. The capability of imaging mitochondria as well as regulating their microenvironment is important both scientifically and clinically. Mitochondria penetrating peptides (MPPs), certain peptides that are composed of cationic and hydrophobic amino acids, are good candidates for mitochondria targeting. Herein, a novel MPP, D-argine-phenylalanine-D-argine-phenylalanine-D-argine-phenylalanine-NH₂ (rFrFrF), is conjugated with a rhodamine-based fluorescent chromophore (TAMRA). The TAMRA-rFrFrF probe exhibits advantageous properties for long-term mitochondria tracking of …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Engineering Of Escherichia Coli 2-Oxoglutarate Dehydrogenase Complex With Mechanistic And Synthetic Goals, Joydeep Chakraborty

Dissertations

The Escherichia coli 2-oxoglutarate dehydrogenase complex (OGDHc) compromises multiple copies of three enzymes - 2-oxoglutarate dehydrogenase (E1o), dihydrolipoyl succinyltransferase (E2o), and dihydrolipoyl dehydrogenase (E3). OGDHc is found in the Krebs cycle and catalyzes the formation of the all-important succinyl-Coenzyme A (succinyl-CoA). OGDHc was engineered to understand the catalytic mechanism and optimized for chemical synthetic goals.

Succinyl-CoA formation takes place within the catalytic domain of E2o via a transesterification reaction. The succinyl group from the thiol ester of S8-succinyldihydrolipoyl-E2o is transferred to the thiol group of CoA. Mechanistic studies were designed to investigate enzymatic transthioesterification. His375 and Asp374 was shown to …

Model-Based Deep Autoencoders For Characterizing Discrete Data With Application To Genomic Data Analysis, Tian Tian

Dissertations

Deep learning techniques have achieved tremendous successes in a wide range of real applications in recent years. For dimension reduction, deep neural networks (DNNs) provide a natural choice to parameterize a non-linear transforming function that maps the original high dimensional data to a lower dimensional latent space. Autoencoder is a kind of DNNs used to learn efficient feature representation in an unsupervised manner. Deep autoencoder has been widely explored and applied to analysis of continuous data, while it is understudied for characterizing discrete data. This dissertation focuses on developing model-based deep autoencoders for modeling discrete data. A motivating example of …

Fouling And Aging In Membrane Filtration : Hybrid Afm-Based Characterization, Modelling And Reactive Membrane Design, Wanyi Fu

Dissertations

Membrane filtration has been extensively used in water and wastewater treatment, desalination, dairy making, and biomass/water separation. However, membrane fouling, aging and insufficient removal efficiency for dissolved organic matters remain major challenges for wider industrial applications. In order to tackle these challenges, this doctoral dissertation investigates mechanisms of membrane fouling and development of antifouling membrane filtration technologies. Specifically, four major research areas are explored: (i) nanoscale physicochemical characterization of the chemically modified polymeric membranes; (ii) quantitative modelling between membrane properties and membrane fouling and defouling kinetics; (iii) development of quantitative structure-activity relationships for membranes that undergo thermal and chemical aging …

Methods To Improve The Remediation Of Polycyclic Aromatic Hydrocarbons (Pahs) In Aerobic And Anaerobic Environments, Brian Wartell

Dissertations

Oil spills occur regularly in terrestrial environments and crude oil can contain many compounds that are highly resistant to degradation. Among these compounds are high levels of polycyclic aromatic hydrocarbons (PAHs) which are not only toxic but can also be carcinogenic and/or mutagenic. The first chapter of this dissertation includes an extensive review chapter on the variables affecting the anaerobic degradation of hydrocarbons, with a particular focus on PAHs. Electron acceptors, electron donors, temperature, salinity, pH all play key roles in determining the possibility effective of effective degradation occurring. Thus, by addressing solutions, such as biostimulation, improving environmental variables for …