Physical Sciences and Mathematics Commons^™

Reports Of Autosomal Recessive Disease And Consanguineous Mating Within The Human Population, Johnathon L. Schluter

Master's Theses

It is anecdotally evident when investigating published reports of autosomal recessive disease that a substantial number of cases are the result of related (consanguineous) mating. This research seeks to quantify the percent of manuscripts describing autosomal recessive diseases published between 2000 and 2020 in which consanguineous mating is indicated. We analyzed 602 peer-reviewed manuscripts to identify the percentage of cases presented in which consanguineous mating was indicated, the underlying genes (novel gene or new mutation) and geographical region. These papers were accessed through a specific set of parameters on the free access PubMed Central (PMC) database. A total of 552 …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

When Brain Meets Artificial Intelligence, Lu Zhang

Computer Science and Engineering Dissertations

When we review the history of development of artificial intelligence (AI), we will find that brain science plays a pivotal role in fostering breakthroughs in AI, such as artificial neural networks (ANNs). Today, AI has made remarkable strides, particularly with the emergence of large language models (LLMs), surpassing expectations and achieving human-level performance in certain tasks. Nonetheless, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI, promoting their mutual enhancement and collaborations. This involve establishing connections from brain science to AI (brain-inspired AI), and reversely, from AI to …

Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta

Theses and Dissertations--Computer Science

End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …

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 …

Exploring Soil Microbial Dynamics In Southern Appalachian Forests: A Systems Biology Approach To Prescribed Fire Impacts, Saad Abd Ar Rafie

Doctoral Dissertations

Prescribed fires in Southern Appalachian forests are vital in ecosystem management and wildfire risk mitigation. However, understanding the intricate dynamics between these fires, soil microbial communities, and overall ecosystem health remains challenging. This dissertation addresses this knowledge gap by exploring selected aspects of this complex relationship across three interconnected chapters.

The first chapter investigates the immediate effects of prescribed fires on soil microbial communities. It reveals subtle shifts in porewater chemistry and significant increases in microbial species richness. These findings offer valuable insights into the interplay between soil properties and microbial responses during the early stages following a prescribed fire. …

Coupling Chemical And Genomic Data Of Marine Sediment-Associated Bacteria For Metabolite Profiling, Stephanie P. Suarez

USF Tampa Graduate Theses and Dissertations

Marine sediment-associated bacteria house many new and exciting novel secondary metabolites. These metabolites can be tested for bioactivity against various types of cancer and fungal, bacterial, and viral infections. In this thesis, we investigated the combination of biosynthetic gene cluster information with mass spectra to perform a chemical profiling of sediment- associated bacteria. Furthermore, we utilized a scoring technique to provide an identification and confidence score to each annotated compound. The sediment was collected from east Arthur Harbor, Palmer Station, Antarctica, at depths of 20 ft and 60 ft. After plating on agar, 52 unique bacterial strains were isolated, with …

Tracing And Segmentation Of Molecular Patterns In 3-Dimensional Cryo-Et/Em Density Maps Through Algorithmic Image Processing And Deep Learning-Based Techniques, Salim Sazzed

Computer Science Theses & Dissertations

Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization of actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell’s interior with its surroundings. However, determining the precise organization of actin filaments is challenging due to the poor quality of cryo-electron tomography (cryo-ET) images, which suffer from low signal-to-noise (SNR) ratios and the presence of missing wedge, as well as diverse shape characteristics of actin filaments. To address these formidable challenges, the primary component of this dissertation focuses …

Growth Of Purple Sulfur Bacteria Allochromatium Vinosum On Solid Phase Metal Sulfides As Sulfur And Electron Sources, Hugo Alarcon

Open Access Theses & Dissertations

Purple sulfur bacteria (PSB) are photosynthetic microorganisms known for their vital roles in geochemical cycles, especially the sulfur cycle, within anoxic photic environments. PSB are also key contributors to the nitrogen, carbon, and oxygen cycles. This study focuses on the autotrophic growth of Allochromatium vinosum, a model strain of PSB, that utilize solid-phase metal sulfides (MS) as both sulfur and electron donors. Through characterizing the growth profiles of A. vinosum on pyrite (FeS2), nickel sulfide (NiS), and iron monosulfide (FeS) nanoparticles, respectively, and investigating the bacteria-MS interaction mechanisms, this work expands our current knowledge of the metabolic capabilities and flexibility …

Computational Analysis Of Antibody Binding Mechanisms To The Omicron Rbd Of Sars-Cov-2 Spike Protein: Identification Of Epitopes And Hotspots For Developing Effective Therapeutic Strategies, Mohammed Alshahrani

Computational and Data Sciences (PhD) Dissertations

The advent of the Omicron strain of SARS-CoV-2 has elicited apprehension regarding its potential influence on the effectiveness of current vaccines and antibody treatments. The present investigation involved the implementation of mutational scanning analyses to examine the impact of Omicron mutations on the binding affinity of four categories of antibodies that target the Omicron receptor binding domain (RBD) of the Spike protein. The study demonstrates that the Omicron variant harbors 23 unique mutations across the RBD regions I, II, III, and IV. Of these mutations, seven are shared between RBD regions I and II, while three are shared among RBD …

Visual Complexity Of The Time-Frequency Image Pinpoints The Epileptogenic Zone: An Unsupervised Deep-Learning Tool To Analyze Interictal Intracranial Eeg, Sarvagya Gupta

Graduate Masters Theses

Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, continues to pose significant challenges in diagnosis and treatment, particularly among children. Despite substantial advancements in medical technology and treatment modalities, localization of the part of brain that causes seizures (Epileptogenic Zone) remains a difficult task. Intracranial EEG (iEEG) is often used to estimate the epileptogenic zone (EZ) in children with drugresistant epilepsy (DRE) and target it during surgery. Conventionally, iEEG signals are inspected in the time domain by human experts aiming to locate epileptiform activity.

Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review …

Annotation Of Non-Model Species’ Genomes, Taiya Jarva

Master's Theses

The innovations in high throughput sequencing technologies in recent decades has allowed unprecedented examination and characterization of the genetic make-up of both model and non-model species, which has led to a surge in the use of genomics in fields which were previously considered unfeasible. These advances have greatly expanded the realm of possibilities in the fields of ecology and conservation. It is now possible to the identification of large cohorts of genetic markers, including single nucleotide polymorphisms (SNPs) and larger structural variants, as well as signatures of selection and local adaptation. Markers can be used to identify species, define population …

Decoy-Target Database Strategy And False Discovery Rate Analysis For Glycan Identification, Xiaoou Li

Electronic Thesis and Dissertation Repository

In recent years, the technology of glycopeptide sequencing through MS/MS mass spectrometry data has achieved remarkable progress. Various software tools have been developed and widely used for protein identification. Estimation of false discovery rate (FDR) has become an essential method for evaluating the performance of glycopeptide scoring algorithms. The target-decoy strategy, which involves constructing decoy databases, is currently the most popular utilized method for FDR calculation. In this study, we applied various decoy construction algorithms to generate decoy glycan databases and proposed a novel approach to calculate the FDR by using the EM algorithm and mixture model.

Utilizing Natural Language Processing For Automated Clinical Text Review: Identification Of Care Preference Documentation In Patients’ Discharge Summaries, Saksham Arora

Computer Science Senior Theses

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than more appropriately broad population of `seriously ill' patients. This paper addresses this gap by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods in identifying care preference documentation.

In order to capture a broad spectrum of seriously ill patients, we matched decedent patients to non-decedent counterparts by utilizing a propensity score matching, accounting for important variables like age, gender, primary diagnoses and commodities. We trained and fine-tuned Bio_ClinicalBERT …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Leveraging Biomedical Ontological Knowledge To Improve Clinical Term Embeddings, Fuad Hatem Abuzahra

Theses and Dissertations

ABSTRACT Leveraging Biomedical Ontological Knowledge to Improve Clinical Term Embeddings by Fuad Abu Zahra The University of Wisconsin-Milwaukee, 2023 Under the Supervision of Dr. Rohit J. Kate This research is on obtaining and using word embeddings for natural language processing tasks in the biomedical domain. Word embeddings are vector representations of words commonly obtained from large text corpora. This research leverages the biomedical ontology of SNOMED CT as an alternate source for obtaining embeddings for clinical terms. The existing graph-based methods can only give embeddings for concepts (i.e., nodes of the graph) of an ontology, hence we developed a novel …

Deephtlv: A Deep Learning Framework For Detecting Human T-Lymphotrophic Virus 1 Integration Sites, Johnathan Jia, Johnathan Jia

Dissertations & Theses (Open Access)

In the 1980s, researchers found the first human oncogenic retrovirus called human T-lymphotrophic virus type 1 (HTLV-1). Since then, HTLV-1 has been identified as the causative agent behind several diseases such as adult T-cell leukemia/lymphoma (ATL) and a HTLV-1 associated myelopathy or tropical spastic paraparesis (HAM/TSP). As part of its normal replication cycle, the genome is converted into DNA and integrated into the genome. With several hundreds to thousands of unique viral integration sites (VISs) distributed with indeterminate preference throughout the genome, detection of HTLV-1 VISs is a challenging task. Experimental studies typically use molecular biology …

Inverse Probability Weighting In Survival Analysis And Network Analysis, Yukun Lu

Doctoral Dissertations

Inverse probability weighting is a popular technique to accommodate selection bias due to non-random sampling and missing data. In the first chapter, we develop an inverse probability weighted estimator and an augmented inverse probability weighted estimator of regression coefficients for a linear model with randomly censored covariates, when the censoring mechanism may be dependent on the outcome. We investigate the asymptotic properties of both estimators and evaluate their finite sample performance through extensive simulation studies. We apply the proposed methods to an Alzheimer’s disease study. In the second chapter, we present an application of network analysis in a study of …

Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad

Graduate Theses, Dissertations, and Problem Reports

Categorizing neurons into different types to understand neural circuits and ultimately brain function is a major challenge in neuroscience. While electrical properties are critical in defining a neuron, its morphology is equally important. Advancements in single-cell analysis methods have allowed neuroscientists to simultaneously capture multiple data modalities from a neuron. We propose a method to classify neurons using both morphological structure and electrophysiology. Current approaches are based on a limited analysis of morphological features. We propose to use a new graph neural network to learn representations that more comprehensively account for the complexity of the shape of neuronal structures. In …

The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku

Graduate Research Theses & Dissertations

Machine learning and network analyses are powerful modern tools can process and map out connections between large amount of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie …

Statistical Methods For Gene Selection And Genetic Association Studies, Xuewei Cao

Dissertations, Master's Theses and Master's Reports

This dissertation includes five Chapters. A brief description of each chapter is organized as follows.

In Chapter One, we propose a signed bipartite genotype and phenotype network (GPN) by linking phenotypes and genotypes based on the statistical associations. It provides a new insight to investigate the genetic architecture among multiple correlated phenotypes and explore where phenotypes might be related at a higher level of cellular and organismal organization. We show that multiple phenotypes association studies by considering the proposed network are improved by incorporating the genetic information into the phenotype clustering.

In Chapter Two, we first illustrate the proposed GPN …

Multiscale Molecular Modeling Studies Of The Dynamics And Catalytic Mechanisms Of Iron(Ii)- And Zinc(Ii)-Dependent Metalloenzymes, Sodiq O. Waheed

Dissertations, Master's Theses and Master's Reports

Enzymes are biological systems that aid in specific biochemical reactions. They lower the reaction barrier, thus speeding up the reaction rate. A detailed knowledge of enzymes will not be achievable without computational modeling as it offers insight into atomistic details and catalytic species, which are crucial to designing enzyme-specific inhibitors and impossible to gain experimentally. This dissertation employs advanced multiscale computational approaches to study the dynamics and reaction mechanisms of non-heme Fe(II) and 2-oxoglutarate (2OG) dependent oxygenases, including AlkB, AlkBH2, TET2, and KDM4E, involved in DNA and histone demethylation. It also focuses on Zn(II) dependent matrix metalloproteinase-1 (MMP-1), which helps …

Prediction Of Sumoylation Sites In Proteins From Language Model Representations, Evgenii Sidorov

Dissertations, Master's Theses and Master's Reports

Sumoylation is an essential post-translational modification intimately involved in a diverse range of eukaryotic cellular mechanisms and plays a significant role in DNA repair. Some researchers hypothesize that a high level of SUMOylation events in cancer cells improves cells' chances for survival under stress conditions by regulating tumor-related proteins.

This study belongs to a booming field of harnessing computational power to the domain of life. Prediction of protein structure, its molecular function, and the design of new drugs are just a few examples of the applications within this exciting area of research. By leveraging computational power, researchers can analyze vast …

Machine Learning Methods For Prediction Of Human Infectious Virus And Imputation Of Hla Alleles, Xiaoqing Gao

Dissertations, Master's Theses and Master's Reports

This dissertation contains three Chapters. The following is a concise description of each Chapters.

In Chapter 1, we introduced the Random Forest, a machine learning method, to foresee whether a virus is capable of infecting humans. The Covid pandemic informs us the importance of predicting the ability of a zoonotic virus that can infect humans from its genomic sequence. We used the -mer with and as features of a virus to predict if it can affect humans. We further employed the Boruta algorithm to select the important features, then fed those important features into the Random Forest method to train …

Applying Data Science And Machine Learning To Understand Health Care Transition For Adolescents And Emerging Adults With Special Health Care Needs, Lisamarie Turk

Nursing ETDs

A problem of classification places adolescents and emerging adults with special health care needs among the most at risk for poor or life-threatening health outcomes. This preliminary proof-of-concept study was conducted to determine if phenotypes of health care transition (HCT) for this vulnerable population could be established. Such phenotypes could support development of future studies that require data classifications as input. Mining of electronic health record data and cluster analysis were implemented to identify phenotypes. Subsequently, a machine learning concept model was developed for predicting acute care and medical condition severity. Three clusters were identified and described (Cluster 1, n …

Dynamics Of Redox-Driven Molecular Processes In Local And Systemic Plant Immunity, Philip Berg

Theses and Dissertations

The work here presents two main parts. In the first part, chapters 1 – 3 focus on dynamical systems modeling in plant immunity, whereas chapters 4 – 6 describe contributions to computational modeling and analysis of proteomics and genomics data. Chapter 1 investigates dynamical and biochemical patterns of reversibly oxidized cysteines (RevOxCys) during effector-triggered immunity (ETI) in Arabidopsis, examines the regulatory patterns associated with Arabidopsis thimet oligopeptidase 1 and 2’s (TOP1 and TOP2), roles in the RevOxCys events during ETI, and analyzes the redox phenotype of the top1top2 mutant. The second chapter investigates the peptidome dynamics during ETI …

Improving Adjacency List Storage Methods For Polypeptide Similarity Analysis, Arianna Swensen

Honors Theses

Protein design is a complex biomolecular and computational problem. Working on increasingly large protein folding problems requires an improvement in current analysis methods available. This work first discusses various methods of protein design, including de novo protein design, which is the primary focus of this thesis. Then, a new approach utilizing a B+ tree to effectively store and query a graph of keys and vertices is proposed in order to store the number of times two polypeptides are considered to be similar. This approach is found to have a reduction in time complexity from current mapping methods and thus provides …

Improved Computational Prediction Of Function And Structural Representation Of Self-Cleaving Ribozymes With Enhanced Parameter Selection And Library Design, James D. Beck

Boise State University Theses and Dissertations

Biomolecules could be engineered to solve many societal challenges, including disease diagnosis and treatment, environmental sustainability, and food security. However, our limited understanding of how mutational variants alter molecular structures and functional performance has constrained the potential of important technological advances, such as high-throughput sequencing and gene editing. Ribonuleic Acid (RNA) sequences are thought to play a central role within many of these challenges. Their continual discovery throughout all domains of life is evidence of their significant biological importance (Weinreb et al., 2016). The self-cleaving ribozyme is a class of noncoding Ribonuleic Acid (ncRNA) that has been useful for …

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …