Physical Sciences and Mathematics Commons^™

An Application Of An In-Depth Advanced Statistical Analysis In Exploring The Dynamics Of Depression, Sleep Deprivation, And Self-Esteem, Muslihat Gaffari

Electronic Theses and Dissertations

Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, …

High-Dimensional Mediation Analysis Of Multi-Omics Data, Sunyi Chi

Dissertations & Theses (Open Access)

Environmental exposures such as cigarette smoking influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. Rapid development of high-throughput technologies have made mediation analysis of multi-omics data critical to gain groundbreaking insights into the biological mechanisms underlying the disease etiology. This dissertation aims to develop mediation analysis methods that utilize the enormous amount of multi-omics data in assessing mechanisms of disease etiology. It contains three projects where I propose advanced mediation …

Factors Predictive Of The Development Of Surgical Site Infection In Thyroidectomy, A Replication Study Of Myssiorek (2018), Kaitlyn M. Kenig

Capstone Experience

The original study aimed to show that thyroidectomy does not result in surgical site infection (SSI) in most cases, and thus routine prescription of antibiotics is not necessary. The study looked to see what risk factors could predict the incidence of SSI. This would highlight those individuals who were at most risk of developing SSI, and then antibiotics would only be prescribed to these individuals instead of all or most individuals who undergo thyroidectomy.

This study used NSQIP data to look at incidence of SSI and look for risk factors that may be predictive of SSI. Only surgeries that were …

The Performance Of Marginal Modeling Methods For Rare Events With Application To Opioid Overdose Mortality And Morbidity, Shawn Nigam

Theses and Dissertations--Epidemiology and Biostatistics

Opioid misuse is a nationwide epidemic, with Kentucky having one of the highest opioid overdose-related fatality rates across all US states. These rates have increased significantly over the past decade, with particularly large increases during the COVID-19 pandemic. This dissertation aims to study the behavior of these increases and the methods for the marginal modeling of count outcomes related to opioid overdose.

Opioid overdose-related fatality rates in Kentucky increased significantly during the COVID-19 pandemic. In this chapter, we characterize the changes in opioid overdose fatality rates in Kentucky and identify associations between potential factors and fatality rates. County-level opioid overdose …

Multiscale Modelling Of Brain Networks And The Analysis Of Dynamic Processes In Neurodegenerative Disorders, Hina Shaheen

Theses and Dissertations (Comprehensive)

The complex nature of the human brain, with its intricate organic structure and multiscale spatio-temporal characteristics ranging from synapses to the entire brain, presents a major obstacle in brain modelling. Capturing this complexity poses a significant challenge for researchers. The complex interplay of coupled multiphysics and biochemical activities within this intricate system shapes the brain's capacity, functioning within a structure-function relationship that necessitates a specific mathematical framework. Advanced mathematical modelling approaches that incorporate the coupling of brain networks and the analysis of dynamic processes are essential for advancing therapeutic strategies aimed at treating neurodegenerative diseases (NDDs), which afflict millions of …

Tropical Fish Study In Tahiti, French Polynesia, Miranda Brainard, Caitlyn Swango, Paityn Houglan, Richard Londraville

Williams Honors College, Honors Research Projects

In May of 2023, I embarked on an exciting research journey to Moorea, French Polynesia, alongside fellow students and faculty members from the University of Akron and Syracuse University. This expedition was part of the university-sponsored Tropical Vertebrate Biology course, where we delved into the exploration of various tropical species inhabiting the island, including sea urchins, geckos, and my primary focus, the blackspotted rockskipper.

My research team, composed of my co-authors and me, was particularly intrigued by the unique refuge-seeking behavior displayed by blackspotted rockskippers. These amphibious fish are renowned for their remarkable ability to inhabit tide pools and rocky …

Climate Change's Effect On Flow Regime, Alexander Ialenti

Williams Honors College, Honors Research Projects

This project will test to see if there is a percent increase in non-perennial streams sampled from 2003-2021. Using data provided by The Cleveland Metroparks, sampling events will be separated by date, flow regime classification, and rain data. Current literature supports the claim that many perennial streams, streams that flow year-round, will become non-perennial streams over time. This shift is predicted to be caused by a change in rain patterns. Both the interval between rain events and the intensity of rainfall per event are predicted to increase. My hypothesis is that there will be an increase in the percentage of …

Interactions Between Sediment Mechanical Structure And Infaunal Community Structure Following Physical Disturbance, William Cyrus Roger Clemo

<strong> Theses and Dissertations </strong>

Shallow, river-influenced coastal sediments are important for global carbon storage and nutrient cycling and provide a habitat for diverse communities of invertebrates (infauna). Elevated bed shear stress from extreme storms can resuspend, transport, and deposit sediments, disrupting the cohesive structure of muds, and sorting and depositing sand eroded from beaches. These physical disruptions can also resuspend or smother infauna, decreasing abundances and changing community structure. Infaunal activities such as burrowing, tube construction, and feeding can impact sediment structure and stability. However, little is known about how physical disturbance impacts short and long-term sediment habitat suitability and whether disturbance-tolerant infauna influence …

Aspects Of Stochastic Geometric Mechanics In Molecular Biophysics, David Frost

All Dissertations

In confocal single-molecule FRET experiments, the joint distribution of FRET efficiency and donor lifetime distribution can reveal underlying molecular conformational dynamics via deviation from their theoretical Forster relationship. This shift is referred to as a dynamic shift. In this study, we investigate the influence of the free energy landscape in protein conformational dynamics on the dynamic shift by simulation of the associated continuum reaction coordinate Langevin dynamics, yielding a deeper understanding of the dynamic and structural information in the joint FRET efficiency and donor lifetime distribution. We develop novel Langevin models for the dye linker dynamics, including rotational dynamics, based …

Bayesian Strategies For Propensity Score Estimation In Causal Inference., Uthpala I. Wanigasekara

Electronic Theses and Dissertations

Causal inference is a method used in various fields to draw causal conclusions based on data. It involves using assumptions, study designs, and estimation strategies to minimize the impact of confounding variables. Propensity scores are used to estimate outcome effects, through matching methods, stratification, weighting methods, and the Covariate Balancing Propensity Score method. However, they can be sensitive to estimation techniques and can lead to unstable findings. Researchers have proposed integrating weighing with regression adjustment in parametric models to improve causal inference validity. The first project focuses on Bayesian joint and two-stage methods for propensity score analysis. Propensity score modeling …

Causal Inference For The Effect Of Continuous Treatment On Time-To-Event Outcomes And Mediation Analysis On Health Disparities In Observational Studies., Triparna Poddar

Electronic Theses and Dissertations

The dissertation comprises two projects related to causal inference based on observational data. In healthcare research, where abundant observational data such as claims data and electronic records are available, researchers often aim to study the treatment effect and the pathway of that effect. However, estimating treatment effects in observational data presents challenges due to confounding factors. The first project focuses on estimating continuous treatment effects for survival outcomes, while the second concentrates on mediation analysis, allowing the exploration of the pathway of the causal effect. Both projects involve addressing confounding variables. In the first project, I investigate estimation of the …

Bayesian Statistical Modeling Of Spatially Resolved Transcriptomics Data, Xi Jiang

Statistical Science Theses and Dissertations

Spatially resolved transcriptomics (SRT) quantifies expression levels at different spatial locations, providing a new and powerful tool to investigate novel biological insights. As experimental technologies enhance both in capacity and efficiency, there arises a growing demand for the development of analytical methodologies.

One question in SRT data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process, which could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types …

The Prevalence Of Burnout In Saudi Arabia Dental Hygienists, Nouf Hamad Aldayel

Dental Hygiene Theses & Dissertations

Purpose: The purpose of this pilot study was to assess the prevalence of burnout in Saudi Arabian dental hygienists and identify risk factors associated with burnout. Methods: A descriptive survey design using the Copenhagen Burnout Inventory (CBI) assessed burnout among a convenience sample of n=123 Saudi dental hygienists. The survey was disseminated electronically to 1,000 Saudi Arabian dental hygienists. The CBI measures three subscales: personal, work-related, and client/patient-related burnout on a five-point Likert-type scale. The survey also included six demographic questions, two Likert-type, one “yes/no,” and one openended question, related to burnout. Descriptive statistics, one-way between subject’s ANOVA, independent samples …

Dental Hygiene Students Reported Physiological Symptoms Associated With Wearing An N95 Respirator Mask, Peyton Shea Butler

Dental Hygiene Theses & Dissertations

Purpose: Physiological symptoms and comfort levels while wearing an N95 respiratory mask has not been examined with dental hygienists. The purpose of this study was to investigate dental hygiene students reported physiological symptoms and comfort perception while wearing an N95 respirator mask during patient care appointments. Methods: After IRB approval (IRB #1987754-2), a 16-item questionnaire was distributed through email to a convenience sample of 65 dental hygiene students. Questions assessed respiratory, dermatologic, cardiac, mask mouth and general physiological symptoms, as well as comfort levels. Additionally, participants were asked to respond to demographic questions and one open ended question inquiring about …

Nonparametric Methods For Analysis And Sizing Of Cluster Randomization Trials With Baseline Measurements, Chengchun Yu

Electronic Thesis and Dissertation Repository

Cluster randomization trials are popular in situations where the intervention needs to be implemented at the cluster level, or logistical, financial and/or ethical reason dictates the choice for randomization at the cluster level, or minimization of contamination is needed. It is very common for cluster trials to take measurements before randomization and again at follow-up, resulting in a clustered pretest-posttest design. For continuous outcomes, the cluster-adjusted analysis of covariance approach can be used to adjust for accidental bias and improve efficiency. However, a direct application of this method is nonsensical if the measures are incompatible with an interval scale, yet …

Construction And Performance Optimization Of Bioconjugated Nanosensors For Early Detection Of Breast Cancer And Pro-Inflammatory Diseases, Pooja Gaikwad

Dissertations, Theses, and Capstone Projects

In recent years, nanosensors have emerged as a tool with strong potential in medical diagnostics. Single-walled carbon nanotube (SWCNT) based optical nanosensors have notably garnered interest due to the unique characteristics of their near-infrared fluorescence emission, including tissue transparency, photostability, and various chiralities with discrete absorption and fluorescence emission bands. Additionally, the optoelectronic properties of SWCNT are sensitive to the surrounding environment, which makes them suitable for in vitro and in vivo biosensing. Single-stranded (ss) DNA-wrapped SWCNTs have been reported as optical nanosensors for cancers and metabolic diseases. Breast cancer and cardiovascular diseases are the most common causes of death …

A Review Of Recent Gene Expression-Based And Dna Methylation-Based Mathematical Cell Type Deconvolution Methods, Chenxiao Tian

Arts & Sciences Electronic Theses and Dissertations

In recent years, many cell type deconvolution methods based on DNA methylation data and gene expression data have been developed. Both of these two methods have its special advantages and disadvantages, e.g., DNA methylation-based methods’ data source is usually more stable than gene expression and DNA methylation is easier to measure in FFPE tissues or formalin-fixed paraffin-embedded, while some gene-expression data like scRNA-seq data usually has high cost and complexity. On the other hand, gene expression-based deconvolution methods currently have many more available methods than DNA methylation-based deconvolution methods, which leads to DNA methylation-based methods in many cases can learn …

Genetic Associations Of Alzheimer’S Disease And Mild Cognitive Impairment, Scott Hebert

Masters Theses

Over 6 million people are estimated to have been living with Alzheimer’s Disease (AD) in 2020, with another 12 million living with Mild Cognitive Impairment (MCI). Research has been conducted to evaluate genetic links to AD, but more research is needed on the subject. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been conducting a longitudinal study of AD and MCI since 2004 and offering their data to research teams around the world. Diagnostic and demographic data was collected from participants, as well as data regarding single nucleotide polymorphisms (SNPs). SNP data was transformed to a binary format regarding whether the …

Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross

Masters Theses

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.

In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into …

Development Of A Metapgs For Accurate Prediction Of Osteoporotic Fracture, Xiangxue Xiao

UNLV Theses, Dissertations, Professional Papers, and Capstones

Introduction: Early identification of individuals at high-risk for osteoporotic fractures who may benefit from preventive intervention is essential. However, the predictive accuracy of the currently used fracture risk assessment tool remains suboptimal. The first aim of this research is to construct genome-wide polygenic scores for the femoral neck (PGS_FNBMD_idpred) and total body BMD (PGS_TBBMD_idpred) and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures. The second aim is to validate the predictive performance of two previously established PGSs (PGS_FNBMD_idpred and PGS_TBBMD_idpred) in an external cohort …

Statistical Inference On Lung Cancer Screening Using The National Lung Screening Trial Data., Farhin Rahman

Electronic Theses and Dissertations

This dissertation consists of three research projects on cancer screening probability modeling. In these projects, the three key modeling parameters (sensitivity, sojourn time, transition density) for cancer screening were estimated, along with the long-term outcomes (including overdiagnosis as one outcome), the optimal screening time/age, the lead time distribution, and the probability of overdiagnosis at the future screening time were simulated to provide a statistical perspective on the effectiveness of cancer screening programs. In the first part of this dissertation, a statistical inference was conducted for male and female smokers using the National Lung Screening Trial (NLST) chest X-ray data. A …

Approaches To Detecting And Modeling Over-And Underdispersion In Alternative Count Data Distributions And An Application Of Logistic Regression And Random Forest Modeling To Improve Screening Tools For Tic Disorders In Children, Rebecca C. Wardrop

Theses and Dissertations

This dissertation focuses on theory and application of discrete data methods, particularly approaches to over- and underdispersion relative to the Poisson distribution and an application of random forest and logistic regression modeling. The first chapter derives a score test for over- and underdispersion in the heaped generalized Poisson distribution. Equi-, over-, and underdispersed heaped generalized Poisson and heaped negative binomial data are simulated to evaluate the performance of the score test by comparing the power it achieves to that of Wald and likelihood ratio tests. We find that the score test we derive performs comparably to both the Wald and …

Statistical Methods For Single Cell Sequencing Data Analysis, Fei Qin

Theses and Dissertations

The recent emergence of single cell sequencing (SCS) technology has provided us with single-cell DNA or RNA sequencing (scDNA/RNA-seq) information to investigate cellular evolutionary relationships. Despite many analysis methods have been developed to infer intra-tumor genetic heterogeneity, cluster cellular subclones, detect genetic mutations, and investigate spatially variable (SV) genes, exploring SCS data remains statistically challenging due to its noisy nature.

To identify subclones with scDNA-seq data, many existing studies use an independent statistical model to detect copy number profile in the first step, followed by classical clustering methods for subclone identification in downstream analyses. However, spurious results might be generated …

A Bayesian Spatial Scan Statistic For Normal Data, Laasya Velamakanni

Theses and Dissertations

Scan statistics are useful methods for detecting spatial clustering. While they were initially developed to detect regions with an excess of binomial or Poisson events, spatial scan statistics have been extended to detect hotspots in other types of data including continuous data. They have many applications in different fields such as epidemiology (e.g. detecting disease outbreaks), sociology (e.g. detecting crime hotspots), and environmental health (e.g. detecting high-pollution areas). Spatial scan statistics identify a ‘most likely cluster’ and then use a likelihood ratio test to determine if this cluster is statistically significant. Spatial scan statistics have been extended to the Bayesian …

Utilizing New Technologies To Measure Therapy Effectiveness For Mental And Physical Health, Jonathan Ossie

Dissertations

Mental health is quickly becoming a major policy concern, with recent data reporting increasing and disproportionately worse mental health outcomes, including anxiety, depression, increased substance abuse, and elevated suicidal ideation. One specific population that is especially high risk for these issues is the military community because military conflict, deployment stressors, and combat exposure contribute to the risk of mental health problems.

Although several pharmacological approaches have been employed to combat this epidemic, their efficacy is mixed at best, which has led to novel nonpharmacological approaches. One such approach is Operation Surf, a nonprofit that provides nature-based programs advocating the restorative …

Optimizing Tumor Xenograft Experiments Using Bayesian Linear And Nonlinear Mixed Modelling And Reinforcement Learning, Mary Lena Bleile

Statistical Science Theses and Dissertations

Tumor xenograft experiments are a popular tool of cancer biology research. In a typical such experiment, one implants a set of animals with an aliquot of the human tumor of interest, applies various treatments of interest, and observes the subsequent response. Efficient analysis of the data from these experiments is therefore of utmost importance. This dissertation proposes three methods for optimizing cancer treatment and data analysis in the tumor xenograft context. The first of these is applicable to tumor xenograft experiments in general, and the second two seek to optimize the combination of radiotherapy with immunotherapy in the tumor xenograft …

Inter-Rater Reliability Of Statistics Based On Reconstructed Individual Patient Data From Published Kaplan-Meier Curves, Megan E. Smith

Theses & Dissertations

Introduction: Time-to-event outcomes include two elements: an indicator variable for whether the event has taken place, and the length of time from some origin point to the occurrence of the event of interest. Due to the complexity of these data, secondary analysis methods, such as indirect comparisons and meta-analysis, are easier to perform when individual-level patient data (IPD) is available.

Objectives: In 2021, an R package IPDfromKM was published, which contains an algorithm for reconstructing IPD from a Kaplan-Meier graph. The current research aimed to investigate the reproducibility of the IPDfromKM algorithm.

Methods: Three statisticians (MS, LS, …

Multiple Endpoints In Randomized Controlled Trials: A Review And An Illustration Of The Global Test, Lindsay Cameron

Electronic Thesis and Dissertation Repository

A randomized controlled trial is often used to provide high quality evidence regarding treatment interventions. Due to the complex nature of many diseases, trials usually select multiple primary outcomes to capture the efficacy of the interventions. In this thesis, we conducted a literature search to determine the prevalence of the different types of multiple outcomes that have been used in randomized controlled trials. We also reviewed the corresponding statistical methods used to deal with such outcomes. In addition, we described the benefits of using global tests as a statistical method when there are multiple primary outcomes in order to answer …

Sparse Partitioned Empirical Bayes Ecm Algorithms For High-Dimensional Linear Mixed Effects And Heteroscedastic Regression, Anja Zgodic

Theses and Dissertations

Variable selection methods in both the frequentist and Bayesian frameworks are powerful techniques that provide prediction and inference in high-dimensional linear regression models. These methods often assume independence between observations and normally distributed errors with the same variance. In practice, these two assumptions are often violated. To mitigate this, we develop efficient and powerful Bayesian approaches for linear mixed modeling and heteroscedastic linear regression. These method offers increased flexibility through the development of empirical Bayes estimators for hyperparameters, with computationally efficient estimation through the Expectation Conditional-Minimization (ECM) algorithm. The novelty of these approaches lies in the partitioning and parameter expansion, …

Wernicke's Encephalopathy: Mapping The Risk Factors Throughout The State Of South Carolina, Shannon M. Rychener

Senior Theses

Wernicke’s Encephalopathy is a consistently underrecognized neurodegenerative brain disorder resulting from prolonged thiamine deficiency. Clinical presentation of the disease results from brain lesions attributable to thiamine deficiency. Because these lesions occur in various locations in the cerebral cortex, symptoms can vary significantly. Varied presentation of symptoms, in addition to the lack of a widely accepted biomarker for the disorder cause challenges to clinicians when identifying and diagnosing the disorder. Due to these challenges, healthcare providers must heavily rely on patient history and risk factor prevalence when multiple symptoms of the disorder are present. By mapping the prevalence of the four …