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The Impact Of “Multiple Looks” When Performing Survival Analysis, Quentin Eloise

Electronic Theses and Dissertations

Survival analysis is a critical statistical method in healthcare to assess patient treatment effects and disease progression. Another critical area of statistical methodology in health care is the practice of adaptive designs. Adaptive designs allow for interim analyses to take place during a study and various decisions and actions can take place more ethically. This is beneficial for studies that take multiple years to complete and allows administrators and healthcare providers to make sound decisions as early as possible. A challenging aspect of adaptive designs is that the number of interim analyses is known in advance which is applicable in …

Green Synthesis Of Carbonized Chitosan-Fe3o4-Sio2 Nano-Composite For Adsorption Of Heavy Metals From Aqueous Solutions, Dalia A. Ali Eng, Rinad Galal Ali Eng.

Chemical Engineering

Water pollution with heavy metals owing to industrial and agricultural activities have become a critical dilemma to humans, plants as well as the marine environment. Therefore, it is of great importance that the carcinogenic heavy metals present in wastewater to be eliminated through designing treatment technologies that can remove multiple pollutants. A novel green magnetic nano-composite called (Carbonized Chitosan-Fe3O4-SiO2) was synthesized using Co-precipitation method to adsorb a mixture of heavy metal ions included; cobalt (Co2+), nickel (Ni2+) and copper (Cu2+) ions from aqueous solutions. The novelty of this study was the synthesis of a new

nano-composite which was green with …

Estimation Of Integrated Volatility Functionals & Behavior Of The Bandwidth Selector With Kernel Spot Volatility Estimators, Jincheng Pang

Arts & Sciences Graduate Student Theses and Dissertations

In financial economics, semimartingales hold great importance among all the stochastic processes, and the Itô framework, driven by Brownian motion and Poisson random measure, provides a robust way to model a wide range of phenomena, particularly in finance, and therefore, Itô semimartingales are almost universally preferred in practical applications. Based on high-frequency return data, the accurate estimation of the spot volatility of Itô semimartingales is crucial for addressing numerous issues such as hedging, option pricing, risk analysis and portfolio management. In this dissertation, we investigate kernel type estimators and examine two issues concerning the estimation of spot volatility. Firstly, for …

Disparities And Protective Factors In Pandemic-Related Mental Health Outcomes: A Louisiana-Based Study, Ariane L. Rung, Evrim Oral, Tyler Prusisz, Edward S. Peters

School of Public Health Faculty Publications

Introduction: The COVID-19 pandemic has had a wide-ranging impact on mental health. Diverse populations experienced the pandemic differently, highlighting pre-existing inequalities and creating new challenges in recovery. Understanding the effects across diverse populations and identifying protective factors is crucial for guiding future pandemic preparedness. The objectives of this study were to (1) describe the specific COVID-19-related impacts associated with general well-being, (2) identify protective factors associated with better mental health outcomes, and (3) assess racial disparities in pandemic impact and protective factors. Methods: A cross-sectional survey of Louisiana residents was conducted in summer 2020, yielding a sample of 986 Black …

A Machine Learning Based Approach For The Identification Of Fake Bills, Tianyang Lu, Hongyang Pang

Rose-Hulman Undergraduate Mathematics Journal

Fake or counterfeiting currency, which has been around as long as money has existed, is a major economic problem. Since the US dollar is the most popular form of currency globally, it is the most popular currency to counterfeit. The United States Department of Treasury estimates that between $70 million and $200 million in fake bills are in circulation. The Federal Reserve Bank uses special banknote processing systems to count each bill deposited by the bank and examine them for the possibility of counterfeits. These machines have sensors designed to detect general quality of the bills, including paper type, quality …

Uncertainty Quantification In Machine Learning Models Via Gaussian Process Regression: A Comparative Study, Ayorinde E. Olatunde, Weiqi Yue, Pawan K. Tripathi, Roger H. French, Anirban Mondal

Faculty Scholarship

As the use of Machine learning models in science and engineering continues to increase, there is an increasing need for quantifying the uncertainties inherent in the predictions of these models. The more complex a model is, the more the uncertainties in its predictions increase. Amongst the plethora of methodologies used in quantifying uncertainties lies Gaussian Process Regression (GPR). GPR surmounts some of the popular shortfalls of other state-of-the-art methodologies. Although GPR has some quick wins in its application for uncertainty quantification, it is plagued with some shortfalls, such as scalability issues when the feature space increases as well as an …

Bayesian And Deep Generative Modeling In Immunology, Yuqiu Yang

Statistical Science Theses and Dissertations

Due to the accumulation of a large volume of data of different natures such as sequencing data, proteomics data, and clinical data, statistical methods and deep learning algorithms have become increasingly important in the field of immunology. By leveraging the diverse datasets as well as interdisciplinary knowledge from areas like biology and public health, these quantitative methods have revolutionized this field by providing powerful tools for data analysis, modeling, and prediction. This has led to a deeper understanding of the immune system, accelerated the development of novel therapies, and paved the way for personalized and precision medicine approaches in immunology. …

Bayesian Variational Inference In Keyword Identification And Multiple Instance Classification, Yaofang Hu

Statistical Science Theses and Dissertations

This dissertation investigates (1) Variational Bayesian Semi-supervised Keyword Extraction and (2) Variational Bayesian Multimodal Multiple Instance Classification.

The expansion of textual data, stemming from various sources such as online product reviews and scholarly publications on scientific discoveries, has created a demand for the extraction of succinct yet comprehensive information. As a result, in recent years, efforts have been spent in developing novel methodologies for keyword extraction. Although many methods have been proposed to automatically extract keywords in the contexts of both unsupervised and fully supervised learning, how to effectively use partially observed keywords, such as author-specified keywords, remains an under-explored …

Non-Receptor Tyrosine Kinases: Their Structure And Mechanistic Role In Tumor Progression And Resistance, Abdulaziz M. Eshaq, Thomas W. Flanagan, Sofie Yasmin Hassan, Sara A. Al Asheikh, Waleed A. Al-Amoudi, Simeon Santourlidis, Sarah Lilly Hassan, Maryam O. Alamodi, Marcelo L. Bendhack, Mohammed O. Alamodi, Youssef Haikel, Mossad Megahed, Mohamed Hassan

School of Graduate Studies Faculty Publications

Protein tyrosine kinases (PTKs) function as key molecules in the signaling pathways in addition to their impact as a therapeutic target for the treatment of many human diseases, including cancer. PTKs are characterized by their ability to phosphorylate serine, threonine, or tyrosine residues and can thereby rapidly and reversibly alter the function of their protein substrates in the form of significant changes in protein confirmation and affinity for their interaction with protein partners to drive cellular functions under normal and pathological conditions. PTKs are classified into two groups: one of which represents tyrosine kinases, while the other one includes the …

Mesenchymal Stem Cells In Autoimmune Disease: A Systematic Review And Meta-Analysis Of Pre-Clinical Studies, Hailey N. Swain, Parker D. Boyce, Bradley A. Bromet, Kaiden Barozinksy, Lacy Hance, Dakota Shields, Gayla R. Olbricht, Julie A. Semon

Mathematics and Statistics Faculty Research & Creative Works

Mesenchymal Stem Cells (MSCs) Are of Interest in the Clinic Because of their Immunomodulation Capabilities, Capacity to Act Upstream of Inflammation, and Ability to Sense Metabolic Environments. in Standard Physiologic Conditions, They Play a Role in Maintaining the Homeostasis of Tissues and Organs; However, there is Evidence that They Can Contribute to Some Autoimmune Diseases. Gaining a Deeper Understanding of the Factors that Transition MSCs from their Physiological Function to a Pathological Role in their Native Environment, and Elucidating Mechanisms that Reduce their Therapeutic Relevance in Regenerative Medicine, is Essential. We Conducted a Systematic Review and Meta-Analysis of Human MSCs …

Lactoferrin And Lysozyme To Promote Nutritional, Clinical And Enteric Recovery: A Protocol For A Factorial, Blinded, Placebo-Controlled Randomised Trial Among Children With Diarrhoea And Malnutrition (The Boresha Afya Trial), Ruchi Tiwari, Kirkby Tickell, Emily Yoshioka, Joyce Otieno, Adeel Shah, Barbra Richardson, Lucia Keter, Maureen Okello, Churchil Nyabinda, Indi Trehan

Paediatrics and Child Health, East Africa

Introduction: Children with moderate or severe wasting are at particularly high risk of recurrent or persistent diarrhoea, nutritional deterioration and death following a diarrhoeal episode. Lactoferrin and lysozyme are nutritional supplements that may reduce the risk of recurrent diarrhoeal episodes and accelerate nutritional recovery by treating or preventing underlying enteric infections and/or improving enteric function.

Methods and analysis: In this factorial, blinded, placebo-controlled randomised trial, we aim to determine the efficacy of lactoferrin and lysozyme supplementation in decreasing diarrhoea incidence and improving nutritional recovery in Kenyan children convalescing from comorbid diarrhoea and wasting. Six hundred children aged 6–24 months with …

Assessing Gtfs Accuracy, Gregory L. Newmark

Mineta Transportation Institute

The promised benefits of the General Transit Feed Specification (GTFS) Schedule and Realtime standards are dependent on the underlying quality of the data. Despite this fundamental reliance, there has been relatively little research on techniques and strategies to assess GTFS accuracy. The need for such assessment is growing as federal and state governments increasingly require transit agencies to make these data available to the public. This research fills this gap by presenting a suite of methods and metrics to assess the temporal accuracy of GTFS Realtime and the spatial accuracy of GTFS Schedule feeds. The temporal assessment demonstrates an approach …

Two New Baseball Performance Statistics, Charles H. Smith

Faculty/Staff Personal Papers

Ever since I was a small child I have been interested in both statistics and baseball, so I guess it was inevitable I would eventually find a way to put the two together. In this short note I'd like to suggest a pair of measures that I feel might be useful in interpreting quality of play: one focusing more on hitting, the other on pitching. Let's start with the one concerning hitting.

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Application Of Machine Learning Algorithms In Healthcare, Dwaipayan Mukhopadhyay

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) is a subset of artificial intelligence that has made substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few fields of healthcare. Here we provide a brief overview of machine learning-based approaches and learning algorithms. Second, we discuss a general procedure of ML and review some studies presented in ML application for several healthcare fields. We also briefly discuss the risks and challenges of ML application to healthcare.This dissertation also consists of four different cases in healthcare where we have applied ML techniques on real life data sets. …

Using Digitized Building And Weather Records To Improve The Accuracy Of Ground To Roof Snow Load Ratio Estimations, Gideon Parry

All Graduate Theses and Dissertations, Fall 2023 to Present

Reliability target loads refer to the amount of accumulated snow a roof needs to be able to support to ensure that the probability of collapse is sufficiently low. Since ground snow weight, or load, is much easier to measure than roof snow load, models for roof snow loads rely on ground snow load measurements along with a statistical model that estimates roof snow retention as a ratio of the measured ground snow load. This thesis focuses on improving the roof snow retention model using data from Canadian case studies that include information about building geometry and local wind speeds. This …

Emotionality Stigma Scale: Measurement Development, Reliability, And Validity., Hayley D. Seely

Electronic Theses and Dissertations

Emotions are biological responses to stimuli that allow individuals to derive meaning, appraise experiences, and prepare to respond. However, individuals perceive emotions differently based on emotion socialization which not only dictates the way emotions are viewed and managed but also has been directly linked with mental health outcomes. Furthermore, research shows emotion socialization is informed by demographic variables such as gender such that the expectations of emotionality differ; where women are taught to express emotions, men are taught to conceal. Given the societal rules regarding emotionality, it is possible that emotionality stigma – the stigma around the experience and expression …

Bayesian Approaches In Multi-State Markov Models And High Dimensional Time-To-Event Data., Yuchen Han

Electronic Theses and Dissertations

This dissertation consists of two projects. The first one involves nonparametric methods on Continuous Time Markov Chains (CTMCs). The second one is centered around Bayesian shrinkage models for detecting prognostic and predictive biomarkers in high-dimensional clinical data. Both these projects build on methods from across the frequentist and Bayesian paradigm to offer novel solutions. In the first project, we aim to model the nonlinear effects of continuous variables within multistate framework in a non-parametrically by appealing to the rich mathematical framework of Reproducing Kernel Hilbert Spaces (RKHS). Then we adapted the classical Representer Theorem to penalized (squared norm) log-likelihood which …

Dynamic Prediction Of Disease Progression With Longitudinal Data, Wenhao Li

Dissertations and Theses (Open Access)

Dynamic prediction plays a pivotal role in clinical research, especially when forecasting time-to-event outcomes based on evolving longitudinal data. This process often leverages the integration of longitudinal and time-to-event data through joint modeling, a prevalent technique. Alongside joint modeling, landmark modeling stands as another key approach in the realm of longitudinal studies. These methodologies are instrumental in dynamically predicting clinical events by utilizing predictor variables measured over time, up until the moment predictions are made. Within this framework, Chapter 2 addresses the challenge of comparing joint modeling and landmark modeling for dynamic prediction in longitudinal studies, introducing a novel algorithm …

A Two-Part Validation Study Of The Sexual Minority Identity Emotion Scale, Henrietta Kadi Tettey-Tawiah

Graduate Theses and Dissertations

A number of existing studies indicate that there is some correlation between pride and shame and various behavioral health outcomes like anxiety, depression, and self-harm in the general population. Research also confirms that this relationship is true among sexual minorities as well. This study sets itself up to interrogate this assertion vis-à-vis sexual minority adolescents (SMAs). Consequently, confirmatory factor analysis (CFA) and differential item functioning (DIF) detection analysis were performed on the 35-item Sexual Minority Identity Emotion Scale (SMIES) for SMAs. A tree-based modeling technique, specifically conditional inference trees (CIT), was then employed to explore and make predictions about the …

Sparse Neural Network To Enhance Performance Under Limited Parameter Constraints., Nailah Rawnaq

Graduate Theses and Dissertations

Over the past decade, the widespread adoption of deep neural networks has been a breakthrough driven by significant computational advancements. Additionally, the number of parameters of those models is exponentially increasing for performing complex tasks and achieving better performance. However, in most practical cases, often there are constraints in the number of parameters due to limited resources in storage size and computational cost. Network pruning can lead to an optimal solution to this problem. In this thesis, I present supporting evidence to the hypothesis that higher sparsity leads to better performance for a convolution-based neural network. I perform performance studies …

Hierarchical Spatial Abundance Models For Migratory Shorebirds, Md Shahbaz Alam

Graduate Theses and Dissertations

Predicting the distribution and abundance of migratory shorebirds is crucial for effective conservation planning. This research applies hierarchical spatial models to predict counts and spatial variations of three shorebird species: Semipalmated sandpiper (sesa), Ruddy turnstone (rutu), and Whimbrel (whim). Different versions of the Poisson, Negative Binomial, and Hurdle regression models are employed to tackle specific data characteristics, such as overdispersion and excess zeros. Model comparisons are performed in terms of likelihood measures and cross-validation. The Hurdle model for sesa and rutu and the Negative Binomial model for whim effectively captured spatial patterns, highlighting potential hotspots. Mean predictive count further emphasized …

High Fat Diet & Social Isolation: Interactive Effects On Pain, Cognition, & Neuroinflammation, Ian M. Campuzano

Research Psychology Theses

Prior research has established a role for both social isolation and exposure to high fat Western diets in altering a range of behaviors from reduced memory performance to increased depression-like behaviors. The present study scrutinizes the interplay among these variables during the peri-adolescent developmental phase, utilizing Long-Evans rats as the experimental model. Our overarching hypothesis is that rats exposed to either social isolation, a high-fat diet, or both will result in heightened pain sensitivity, diminished cognitive flexibility, and increased neuroinflammatory responses within brain regions implicated in sociability, cognition, memory, and pain processing. Behavioral flexibility will be assessed using a maze-based …

Exploring The Diagnostic Potential Of Radiomics-Based Pet Image Analysis For T-Stage Tumor Diagnosis, Victor Aderanti

Electronic Theses and Dissertations

Cancer is a leading cause of death globally, and early detection is crucial for better

outcomes. This research aims to improve Region Of Interest (ROI) segmentation

and feature extraction in medical image analysis using Radiomics techniques

with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including

PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance …

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, …

Interpregnancy Interval And Adverse Perinatal Outcomes: A Within-Individual Comparative Method, Maria Sevoyan, Marco Geraci, Edward A. Frongillo, Jihong Liu, Nansi S. Boghossian

Faculty Publications

Background and Aim: Previously observed associations between interpregnancy interval (IPI) and perinatal outcomes using a between-individual method may be confounded by unmeasured maternal factors. This study aims to examine the association between IPI and adverse perinatal outcomes using within-individual comparative analyses. Methods: We studied 10,647 individuals from the National Institute of Child Health and Human Development Consecutive Pregnancies Study in Utah with ≥3 liveborn singleton pregnancies. We matched two IPIs per individual and used conditional logistic regression to examine the association between IPI and adverse perinatal outcomes, including preterm birth (PTB, < 37 weeks’ gestation), small-for-gestational-age (SGA, < 10th percentile of sex-specific birthweight for gestational age), low birthweight (LBW, < 2,500 g), and neonatal intensive care unit (NICU) admission. Point and 95% confidence interval (CI) estimates were adjusted for factors that vary across pregnancies within individuals. Results: CIs did not unequivocally support either an increase or a decrease in the odds of PTB (adjusted odds ratio [aOR]: 1.31, 95% CI: 0.87, 1.96), SGA (aOR: 0.81, 95% CI: 0.51, 1.28), LBW (aOR: 1.59, 95% CI: 0.90, 2.80), or NICU admission (aOR: 0.96, 95% CI: 0.66, 1.40) for an IPI < 6 months compared to 18–23-months IPI (reference), and neither did the CIs for the aOR of IPIs of 6–11 and 12–18 months compared to the reference. In contrast, an IPI ≥24 months was associated with increased odds of LBW (aOR: 1.66, 95% CI: 1.03, 2.66 for 24–29 months; aOR: 2.27, 95% CI: 1.21, 4.29 for 30–35 months; and aOR: 2.09, 95% CI: 1.17, 3.72 for ≥36 months). Conclusions: Using a within-individual comparative method, we did not find evidence that a short IPI compared to the recommended IPI of 18–23 months was associated with increased odds of PTB, SGA, LBW, and NICU admission. IPI ≥ 24 months was associated with increased odds of delivering an LBW infant.

The Fourth International Workshop On Smart Data For Blockchain And Distributed Ledger (Sdbd '24), Feida Zhu, Jian Pei, Michael Zeller, Bingxue Zhang

Research Collection School Of Computing and Information Systems

With the advent of Bitcoin, a cryptographically-enabled peer-to-peer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Gan With Skip Patch Discriminator For Biological Electron Microscopy Image Generation, Nishith Ranjon Roy

Graduate Theses and Dissertations

GAN models have been successfully used for image generation in various sections such as real-life objects like human faces, cars, animal faces, landscapes, etc. This work focuses on biological electron microscopy (EM) image generation. Unlike other real-life objects, biological EM images are obtained through electron microscopy techniques to study biological specimens. Electron microscopy offers high resolution and magnification capabilities, making it a powerful tool for visualizing biological structures at the nanoscale. However, using GAN models for biological EM image generation poses challenges due to the complex and unique arrangements of biological structures and the sparse and asymmetrical patterns in EM …

Robust Multivariate Estimation And Inference With The Minimum Density Power Divergence Estimator, Ebenezer Nkum

Open Access Theses & Dissertations

The estimation of the location vector and scatter matrix plays a crucial role in many multivariate statistical methods. However, the classical likelihood-based estimation is greatly influenced by outliers, potentially leading to unreliable decisions. Hence, a fundamental challenge in multivariate statistics is to develop robust alternatives that can maintain performancein the presence of outliers and deviations from the assumed data distribution. Unfortunately, methods with good global robustness often substantially sacrifice efficiency. To address this, we propose the adoption of Minimum Density Power Divergence (MDPD) estimation, a well-established robust technique known for its efficiency and statistical robustness to outliers and model violations. …