Digital Commons Network^™

Ecological Risk Assessment Of Transgenic Conditional Lethality Systems For Genetic Biocontrol Strategies, Fernan Rodrigo Perez-Galvez

Theses and Dissertations--Entomology

Genetic strategies of insect pest control are receiving increased interest due to recent advances in genetic engineering. By introducing alleles that make males functionally sterile, genetically modified (GM) insects can be used in area-wide pest management programs similar to the Sterile Insect Technique (SIT). Genetic control strategies carry several potential benefits, including improved efficacy and specificity, lower operation costs, and reduced dependence on chemical insecticides, but their use is still controversial despite extensive technical documentation indicating their innocuity to humans and the environment. This hesitation is likely due to the novel nature of the approach.

While the first field applications …

Trauma-Informed Care: A Collaborative Strategy To Address Hiv-Infection Rates Among Black Gay And Bisexual Men, Willie T. Boddie Jr.

DSW Capstone Projects

The Human Immunodeficiency Virus and Acquired Immune Deficiency Syndrome (HIV/AIDS) continues to be an epidemic among gay and bisexual men or men who have sex with men (MSMs). For Black MSMs, the HIV infection rate is disproportionate compared to the number of Black or African American citizens in the U.S. Black MSMs have the highest HIV infection rate in the U.S. This Capstone Project seeks to explore the possible impact of trauma experiences among this group and determine how providing trauma-informed care, along with the standard HIV education and awareness can be used to effectively reduce this disparity and ultimately …

Mitigating Compounded Barriers To Independence: Improving Outcomes Post Emancipation For Foster Youth With Secondary And Tertiary Barriers To Success, Corletha Norman

DSW Capstone Projects

A total of 407,493 children were identified as foster children by the U.S. Department of Health and Human Services, Administration for Children and Families (2021) at the completion of their 2020 fiscal year. The reason for entry into foster care is just as varied as the way in which the children exit. It is expected that many foster youth will experience poor outcomes as a result of their foster care experiences. Poor outcomes continue to be an ever-present undesired rite of passage for foster youth preparing to leave foster care at or after the age of 18. Research conducted over …

Use Of A Nurse-Led Daily Goals Checklist To Improve Multidisciplinary Rounds In The Icu, Marissa Riley

DNP Projects

Research has shown a multidisciplinary rounding approach can improve patient care outcomes in the intensive care unit (ICU). Due to the complexity and wide range of the healthcare team, many patient details and treatment goals may be overlooked during daily patient rounds without a daily goals checklist to help ensure collaboration and communication of the multidisciplinary team. A nurse led daily goals checklist could be useful for framing nurse-provider conversations to promote comprehensive communication and collaboration. The purpose of this project was to improve adherence to a nurse-led daily goals checklist to improve communication and collaboration between the nurses and …

The Impact Of Flooding Events In Kentucky Counties On Preventable Asthma Hospitalizations, Mckaylee Copher

Theses and Dissertations--Public Health (M.P.H. & Dr.P.H.)

Objective: Given the frequency and intensity of flooding, along with the disparities in respiratory illnesses in Kentucky, this study aims to determine if there is an association between flooding and asthma is crucial to the health of the state population.

Methods: Preventable asthma hospitalizations in adults aged 18-39 were obtained from the Kentucky Cabinet of Health and Family Services, Health and Facility Services Data and included the count of admissions of an ICD-9 diagnosis of asthma, as determined by hospital discharge data. FEMA disaster declarations were used to identify 69 counties that had floods during the period of 2008-2015. A …

Sociodemographic Factors Related To Late-Stage Lung Cancer Diagnosis In Kentucky, Kaylee Stickler

Theses and Dissertations--Public Health (M.P.H. & Dr.P.H.)

Lung cancer is the leading cause of cancer mortality in the United States, and the state of Kentucky has a significant burden of lung cancer morbidity and mortality. The aim of this study is to examine the sociodemographic factors associated with late-stage lung cancer diagnosis in Kentucky. The present study is a retrospective, population-based cohort study that utilizes data from the Kentucky Cancer Registry to examine the associations between late-stage lung cancer diagnosis and sociodemographic factors. Descriptive, bivariate, and multivariable analyses were conducted to estimate the significance of these associations. Significant relationships between the variables of sex, age, Appalachian status, …

Nonoperative Management Of Shoulder Instability, Margie K. Olds, Timothy L. Uhl

Physical Therapy Faculty Publications

Non-operative management following a shoulder dislocation or subluxation remains a challenging and complex task. Accurate diagnosis of the condition, and shared decision-making regarding operative and non-operative management, as well as timing of return to play is required. This clinical concept paper introduces a shoulder instability framework that addresses these fundamental clinical dilemmas. Valid clinical prognostic tools which can predict recurrent shoulder instability are reviewed. The process of shared decision-making within the realm of shoulder instability is also presented. Finally, a framework for progressive rehabilitation that addresses deficits in motor control, strength, and endurance in scapula and shoulder musculature is presented …

Measuring Ground Surface Elevation Changes In A Slow-Moving Colluvial Landslide Using Combinations Of Regional Airborne Lidar, Uav Lidar And Uav Photogrammetric Surveys, Sarah E. Johnson, William C. Haneburg, Lindsey Sebastian Bryson, Matthew M. Crawford

Faculty, Staff, and Affiliated Publications--KGS

Slow-moving, chronically destructive landslides are projected to grow in number globally in response to precipitation increases from climate change, and land disturbances from wildfire, mining and construction. In the Cincinnati and northern Kentucky metropolitan area, USA, landslides develop in colluvium that covers the steep slopes along the Ohio River and its tributaries. Here we quantify elevation changes in a slow-moving colluvial landslide over 14 years using county-wide lidar, uncrewed aerial vehicle (UAV) structure-from-motion (SfM) surveys and a UAV lidar survey. Because the technology and quality differ between surveys, the challenge was to calculate a threshold of detectable change for each …

An Assessment Of Negative Samples And Model Structures In Landslide Susceptibility Characterization Based On Bayesian Network Models, Sahand Khabiri, Matthew M. Crawford, Hudson J. Koch, William C. Haneberg, Yichuan Zhu

Faculty, Staff, and Affiliated Publications--KGS

Landslide susceptibility mapping (LSM) characterizes landslide potential, which is essential for assessing landslide risk and developing mitigation strategies. Despite the significant progress in LSM research over the past two decades, several long-standing issues, such as uncertainties related to training samples and model selection, remain inadequately addressed in the literature. In this study, we employed a physically based susceptibility model, PISA-m, to generate four different non-landslide data scenarios and combine them with mapped landslides from Magoffin County, Kentucky, for model training. We utilized two Bayesian network model structures, Naïve Bayes (NB) and Tree-Augmented Naïve Bayes (TAN), to produce LSMs based on …

Mapping Landslide Susceptibility Over Large Regions With Limited Data, J. B. Woodard, Benjamin B. Mirus, Matthew M. Crawford, D. Or, B. A. Leshchinsky, K. E. Allstadt, N. J. Wood

Faculty, Staff, and Affiliated Publications--KGS

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines …

Prediction Of Mortality And Major Adverse Kidney Events In Critically Ill Patients With Acute Kidney Injury, Javier A. Neyra, Victor M. Ortiz-Soriano, Lucas J. Liu, Taylor D. Smith, Xilong Li, Donglu Xie, Beverly Adams-Huet, Orson W. Moe, Robert D. Toto, Jin Chen

Markey Cancer Center Faculty Publications

Rationale & Objective: Risk prediction tools for assisting acute kidney injury (AKI) management have focused on AKI onset but have infrequently addressed kidney recovery. We developed clinical models for risk stratification of mortality and major adverse kidney events (MAKE) in critically ill patients with incident AKI.

Study Design: Multicenter cohort study.

Setting & Participants: 9,587 adult patients admitted to heterogeneous intensive care units (ICUs; March 2009 to February 2017) who experienced AKI within the first 3 days of their ICU stays.

Predictors: Multimodal clinical data consisting of 71 features collected in the first 3 days of ICU stay.

Outcomes: (1) …

Life Cycle Assessment Of Air Classification As A Sulfur Mitigation Technology In Pine Residue Feedstocks, Ashlee Edmonson

Theses and Dissertations--Biosystems and Agricultural Engineering

Sulfur accumulation during biofuel production is pollutive, toxic to conversion catalysts, and causes the premature breakdown of processing equipment. Air classification is an effective preprocessing technology for ash and sulfur removal from biomass feedstocks. A life cycle assessment (LCA) sought to understand the environmental impacts of implementing air classification as a sulfur-mitigation technique for pine residues. Energy demand and material balance for preprocessing were simulated using SimaPro and the Argonne National Laboratory’s GREET model, specifically focusing on comparing the global warming potential (GWP) of grid electricity versus bioelectricity scenarios. Overall, the grid electricity scenario had a GWP impact over 7 …

Improving Connectivity For Remote Cancer Patient Symptom Monitoring And Reporting In Rural Medically Underserved Regions, Esther Max-Onakpoya

Theses and Dissertations--Computer Science

Rural residents are often faced with many disparities when compared to their urban counterparts. Two key areas where these disparities are apparent are access to health and Internet services. Improved access to healthcare services has the potential to increase residents' quality of life and life expectancy. Additionally, improved access to Internet services can create significant social returns in increasing job and educational opportunities, and improving access to healthcare. Therefore, this dissertation focuses on the intersection between access to Internet and healthcare services in rural areas. More specifically, it attempts to analyze systems that can be used to improve Internet access …

Multi-Domain Adaptation For Image Classification, Depth Estimation, And Semantic Segmentation, Yu Zhang

Theses and Dissertations--Computer Science

The appearance of scenes may change for many reasons, including the viewpoint, the time of day, the weather, and the seasons. Traditionally, deep neural networks are trained and evaluated using images from the same scene and domain to avoid the domain gap. Recent advances in domain adaptation have led to a new type of method that bridges such domain gaps and learns from multiple domains.

This dissertation proposes methods for multi-domain adaptation for various computer vision tasks, including image classification, depth estimation, and semantic segmentation. The first work focuses on semi-supervised domain adaptation. I address this semi-supervised setting and propose …

Small Approximate Pareto Sets With Quality Bounds, William Bailey

Theses and Dissertations--Computer Science

We present and empirically characterize a general, parallel, heuristic algorithm for computing small ε-Pareto sets. The algorithm can be used as part of a decision support tool for settings in which computing points in objective space is computationally expensive. We use the graph clearing problem, a formalization of indirect organ exchange markets, as a prototypical example setting. We characterize the performance of the algorithm through ε-Pareto set size, ε value provided, and parallel speedup achieved. Our results show that the algorithm's combination of parallel speedup and small ε-Pareto sets is sufficient to be appealing in settings requiring manual review (i.e., …

Multi-Agent Learning For Game-Theoretical Problems, Kshitija Taywade

Theses and Dissertations--Computer Science

Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. …

A Secure And Distributed Architecture For Vehicular Cloud And Protocols For Privacy-Preserving Message Dissemination In Vehicular Ad Hoc Networks, Hassan Mistareehi

Theses and Dissertations--Computer Science

Given the enormous interest in self-driving cars, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs would help solve many of the needs of drivers, law enforcement agencies, traffic management, etc. The contributions of this dissertation are summarized as follows: A Secure and Distributed Architecture for Vehicular Cloud: Ensuring security and privacy is an important issue in the vehicular cloud; if information exchanged between entities is modified by a malicious vehicle, serious consequences such as traffic congestion and accidents can …

Machine-Learning-Powered Cyber-Physical Systems, Enrico Casella

Theses and Dissertations--Computer Science

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety …

Machine Learning Framework For Real-World Electronic Health Records Regarding Missingness, Interpretability, And Fairness, Jing Lucas Liu

Theses and Dissertations--Computer Science

Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations.

This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing …

The Basil Technique: Bias Adaptive Statistical Inference Learning Agents For Learning From Human Feedback, Jonathan Indigo Watson

Theses and Dissertations--Computer Science

We introduce a novel approach for learning behaviors using human-provided feedback that is subject to systematic bias. Our method, known as BASIL, models the feedback signal as a combination of a heuristic evaluation of an action's utility and a probabilistically-drawn bias value, characterized by unknown parameters. We present both the general framework for our technique and specific algorithms for biases drawn from a normal distribution. We evaluate our approach across various environments and tasks, comparing it to interactive and non-interactive machine learning methods, including deep learning techniques, using human trainers and a synthetic oracle with feedback distorted to varying degrees. …

Deep Learning Models For Ct Image Standardization, Md Selim

Theses and Dissertations--Computer Science

Multicentric CT imaging studies often encounter images acquired with scanners from different vendors or using different reconstruction algorithms. This leads to inconsistencies in noise level, sharpness, and edge enhancement, resulting in a lack of homogeneity in radiomic characteristics. These inconsistencies create significant variations in radiomic features and ambiguity in data sharing across different institutions. Therefore, normalizing CT images acquired using non-standardized protocols is vital for decision-making in cross-center large-scale data sharing and radiomics studies. To address this issue, we present four end-to-end deep-learning-based models for CT image standardization and normalization. The first two models require paired training data and can …

Structured Attention For Image Analysis, Xin Xing

Theses and Dissertations--Computer Science

Attention mechanism, an approach to maintain the local and global features over the input, is the crucial element of the Transformer. This dissertation explores structured attention for image analysis, proposing attention-based methods for multi-label learning and Alzheimer’s Disease (AD) diagnosis.
For the multi-label learning task, I present two works under the Vision Transformer (ViT) framework. The first work focuses on supervised learning of multi-label classification. I address the problems of the multi-label classification and propose a model named AssocFormer, which adopts the association module to access the objects’ association relationship to improve the model performance. The second work addresses the …

Hard-Hearted Scrolls: A Noninvasive Method For Reading The Herculaneum Papyri, Stephen Parsons

Theses and Dissertations--Computer Science

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented …

Deep Learning-Based Intrusion Detection Methods For Computer Networks And Privacy-Preserving Authentication Method For Vehicular Ad Hoc Networks, Ayesha Dina

Theses and Dissertations--Computer Science

The incidence of computer network intrusions has significantly increased over the last decade, partially attributed to a thriving underground cyber-crime economy and the widespread availability of advanced tools for launching such attacks. To counter these attacks, researchers in both academia and industry have turned to machine learning (ML) techniques to develop Intrusion Detection Systems (IDSes) for computer networks. However, many of the datasets use to train ML classifiers for detecting intrusions are not balanced, with some classes having fewer samples than others. This can result in ML classifiers producing suboptimal results. In this dissertation, we address this issue and present …

Can Testing The Environment For Severe Acute Respiratory Coronavirus Virus 2 (Sars-Cov-2) Be A Signal For Coronavirus Disease 2019 (Covid-19) Cases Among Nursing Home Staff?, Gabrielle M. Gussin, Raveena D. Singh, Izabela Coimbra Ibraim, Raheeb Saavedra, Thomas T. Tjoa, Micaila Curtis, Kristine P. Nguyen, Ilhem Messaoudi, Susan S. Huang

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Prior to widespread COVID-19 vaccination, nursing homes were required to perform comprehensive weekly testing of staff. After the vaccine became available, testing became symptom-based for vaccinated staff despite continued waves of different severe acute respiratory coronavirus virus 2 (SARS-CoV-2) variants. Nursing homes need pragmatic, affordable strategies to identify SARS-CoV-2–positive staff because financial and job-related pressures drive reluctance to disclose mild symptoms. We sought to determine whether positive environmental samples for SARS-CoV-2 could serve as trigger for comprehensive staff testing in lieu of weekly testing of all staff.

Correlates Of Sars-Cov-2 Anti-Rbd Igg Antibody Titers Among Persons Experiencing Homelessness In Los Angeles, Adeline Nyamathi, Sanghyuk S. Shin, Brianna M. Doratt, Alexandria Jones-Patten, Benissa Salem, Lillian Gelberg, Darlene Lee, Dana Garfin, Kartik Yadav, Alicia H. Chang, Kathryn White, Nicholas Arce, Ilhem Messaoudi

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Objectives: People experiencing homelessness (PEH) have been especially impacted by the COVID-19 pandemic, likely due to increased vulnerabilities stemming from chronic diseases, substance use, and mental health conditions.

Design: A case–control study to assess the presence of antibodies against SARS-CoV-2 among PEH and associations with key variables.

Sample: A convenience sample of 97 PEH in Skid Row, Los Angeles.

Measurements: A structured questionnaire assessing socio-demographic, mental health, drug and alcohol use, health care access, pandemic stress, and other COVID-19-specific questions.

Results: We found high anti-receptor binding domain (RBD) IgG titers among five of 15 PEH who reported no prior COVID-19 …

Maternal Obesity Blunts Antimicrobial Responses In Fetal Monocytes, Suhas Sureshchandra, Brianna M. Doratt, Norma Mendza, Oleg Varlamov, Monica Rincon, Nicole E. Marshall, Ilhem Messaoudi

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Maternal pre-pregnancy (pregravid) obesity is associated with adverse outcomes for both mother and offspring. Amongst the complications for the offspring is increased susceptibility and severity of neonatal infections necessitating admission to the intensive care unit, notably bacterial sepsis and enterocolitis. Previous studies have reported aberrant responses to LPS and polyclonal stimulation by umbilical cord blood monocytes that were mediated by alterations in the epigenome. In this study, we show that pregravid obesity dysregulates umbilical cord blood monocyte responses to bacterial and viral pathogens. Specifically, interferon-stimulated gene expression and inflammatory responses to respiratory syncytial virus (RSV) and E. coli were significantly …

Sars-Cov-2 Infection And Seroconversion Rates In Healthcare Providers Prior To Covid-19 Vaccine Rollout, Sanghyuk S. Shin, Miriam Bender, Delphine C. Malherbe, Hannah Vasquez, Brianna M. Doratt, Ilhem Messaoudi

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Objective: A 6-month longitudinal surveillance study of asymptomatic healthcare providers (HCP) was carried out at a large urban academic medical center in the United States to assess whether their job occupation with higher exposure risks to SARS-CoV-2 would equate with higher risk of contracting COVID-19 at the beginning of the pandemic before COVID-19 vaccines were available.

Methods: A longitudinal cohort study design was used to collect and analyze immunological and virological monitoring data and self-report survey assessments of personal protective equipment (PPE) availability, adherence to infection control guidelines, and time spent on COVID-19 wards.

Results: Among 289 eligible participants, SARS-CoV-2 …

Single-Dose Vsv-Based Vaccine Protects Cynomolgus Macaques From Disease After Taï Forest Virus Infection, Paige Fletcher, Kyle L. O'Donnell, Brianna M. Doratt, Delphine C. Malherbe, Chad S. Clancy, Joseph F. Rhoderick, Friederike Feldmann, Patrick W. Hanley, Thomas G. Ksiazek, Thomas W. Geisbert, Ilhem Messaoudi, Andrea Marzi

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Taï Forest virus (TAFV) is a lesser-known ebolavirus that causes lethal infections in chimpanzees and is responsible for a single human case. Limited research has been done on this human pathogen; however, with the recent emergence of filoviruses in West Africa, further investigation and countermeasure development against this virus is warranted. We developed a vesicular stomatitis virus (VSV)-based vaccine expressing the TAFV glycoprotein as the viral antigen and assessed it for protective efficacy in nonhuman primates (NHPs). Following a single high-dose vaccination, NHPs developed antigen-specific binding and neutralizing antibodies as well as modest T cell responses. Importantly, all vaccinated NHPs …

Transcriptional Profiling Of Immune Responses In Nhps After Low-Dose, Vsv-Based Vaccination Against Marburg Virus, Cecilia A. Prator, Brianna M. Doratt, Kyle L. O'Donnell, Justin Lack, Amanda N. Pinski, Stacy M. Ricklefs, Craig A. Martens, Ilhem Messaoudi, Andrea Marzi

Microbiology, Immunology, and Molecular Genetics Faculty Publications

Infection with Marburg virus (MARV), the causative agent of Marburg virus disease (MVD), results in haemorrhagic disease and high case fatality rates (>40%) in humans. Despite its public health relevance, there are no licensed vaccines or therapeutics to prevent or treat MVD. A vesicular stomatitis virus (VSV)-based vaccine expressing the MARV glycoprotein (VSV-MARV) is currently in clinical development. Previously, a single 10 million PFU dose of VSV-MARV administered 1–5 weeks before lethal MARV challenge conferred uniform protection in nonhuman primates (NHPs), demonstrating fast-acting potential. Additionally, our group recently demonstrated that even a low dose VSV-MARV (1000 PFU) protected NHPs …