Digital Commons Network^™

Ai-Driven Deep Learning Techniques In Protein Structure Prediction, Lingtao Chen, Qiaomu Li, Kazi Fahim Ahmad Nasif, Ying Xie, Bobin Deng, Shuteng Niu, Seyedamin Pouriyeh, Zhiyu Dai, Jiawei Chen, Chloe Yixin Xie

2020-Current year OA Pubs

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques …

Development Of Feature Extraction Models To Improve Image Analysis Applications In Cancer, Yu Shi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.

Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these …

The Effects Of Anchor Scheme On The Central And Peripheral Contributions To Performance Fatigability, Perceived Responses And Sensations, And Psychological Factors Following Sustained, Isometric Forearm Flexion Tasks To Failure, Robert Walker Smith

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The Effects of Anchor Scheme on the Central and Peripheral Contributions to Performance Fatigability, Perceived Responses and Sensations, and Psychological Factors Following Sustained, Isometric Forearm Flexion Tasks to Failure Robert W. Smith, Ph.D. University of Nebraska, 2024 Advisor: Terry J. Housh This study assessed time to task failure (TTF), performance fatigability (PF), neuromuscular responses, central and peripheral measures of fatigue, and perceived responses including sensations and psychological factors that contributed to task termination during sustained, isometric forearm flexion tasks anchored to ratings of perceived exertion (RPE) and the torque corresponding to RPE. Fifteen men (age = 21.5 ± 2.3 yrs; …

Occurrence, Inputs, And Ecological Significance Of Antibiotics And Pharmaceuticals In Western Nebraska Streams, Katelyn F. Glause

School of Natural Resources: Dissertations, Theses, and Student Research

Few emerging environmental contaminants are as concerning as antibiotics. Human and animal health benefits greatly from the prudent use of antibiotics, yet we give little thought to environmental release of these biologically active compounds. Environmental occurrence of these and other pharmaceutical compounds must be measured in different environmental compartments such as municipal wastewater and in the vicinity of large animal feeding operations to understand potential effects. This study reports the results of a monitoring study in western Nebraska, with a large population of livestock and smaller but more concentrated population of humans, comparing the relative environmental concentrations and loading from …

Transformer-Based Deep Learning Prediction Of 10-Degree Humphrey Visual Field Tests From 24-Degree Data, Min Shi, Anagha Lokhande, Yu Tian, Yan Luo, Mohammad Eslami, Saber Kazeminasab, Tobias Elze, Lucy Shen, Louis Pasquale, Sarah Wellik, Carlos Gustavo De Moraes, Jonathan Myers, Nazlee Zebardast, David Friedman, Michael Boland, Mengyu Wang

Wills Eye Hospital Papers

PURPOSE: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.

METHODS: We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship …

Exploring The Relationship Of The Noncognitive Variables Of Grit And Mindset In Baccalaureate Nursing Students And Their Impact On Academic Success, Alecia Hollis

Dissertations

The study aimed to evaluate how the noncognitive variables of grit and mindset could impact the academic success of baccalaureate nursing students. With the present and growing shortage of nurses, attrition within nursing programs is cause for concern. Attrition is a multifaceted problem; however, academic failure is one contributing factor. While cognitive factors, such as the standardized aptitude test (SAT), pre-nursing science GPA, pre-nursing GPA, and standardized entrance exams (Al-Alawi et al., 2020; Chisholm-Burns et al., 2021), have traditionally been used for admission criteria, increasing evidence suggests that noncognitive factors, like grit and mindset, play a crucial role in academic …

Prevalence Of Chronic Kidney Disease And Awareness Levels Among Young Adults In Imo And Anambra States, Nigeria, Jacinta Okolie

Walden Dissertations and Doctoral Studies

Chronic kidney disease (CKD) presents a significant public health concern, particularly in developing countries such as Nigeria, where health care resources are limited and awareness remains low. This quantitative study evaluated the prevalence of CKD and awareness levels among young adults in Imo and Anambra States, Nigeria. The relationship between health care access, medication adherence, and CKD diagnosis was also examined. The health belief model and the social ecological model provided the framework for the study. A sample of 800 participants was recruited from in- and outpatient facilities in Imo State and Anambra State, and sociodemographic information, awareness levels, and …

Effortless Mindfulness Toolkit: Easy Meditation Scripts To Begin Each Class., Brett Whysel

Open Educational Resources

The Effortless Mindfulness in the Classroom Toolkit is an open educational resource designed to create happier classrooms, boost student engagement, and foster a sense of community through brief, effective mindfulness exercises. This comprehensive toolkit provides educators with everything they need to seamlessly integrate mindfulness practices into their teaching routines, regardless of prior experience. The toolkit includes introductory slides, 15 ready-to-use meditation scripts, and accompanying audio recordings, covering a variety of mindfulness techniques such as focused breathing, body scans, and loving-kindness meditations. Each meditation is paired with a reflective prompt to deepen student engagement and reinforce the benefits of mindfulness. Additionally, …

Comparative Analysis Of Transfer Learning Strategies For Polyp Detection In Colonoscopy Images Using Yolov8, Fabian Vazquez Jr.

Theses and Dissertations

The early detection of polyps during colonoscopy procedures is crucial for preventing colorectal cancer, a leading cause of cancer-related deaths globally. Traditional methods for polyp detection are often time-consuming and prone to human error. This thesis investigates the effectiveness of transfer learning, the process of taking a pre-trained model that was trained on a large dataset and adapting it to a new, but related task, requiring less data and time for training. This research compares whether the YOLOv8 model trained from scratch on a specific polyp dataset is outperformed by transfer learning methods such as utilizing a pretrained model on …

High Prevalence Of Artifacts In Optical Coherence Tomography With Adequate Signal Strength, Wei-Chun Lin, Aaron Coyner, Charles Amankwa, Abigail Lucero, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

PURPOSE: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment.

METHODS: We conducted a retrospective study on 4555 OCT images from 546 patients, with each image having an acceptable signal strength (≥6). A comprehensive analysis of prevalent OCT artifacts was performed, and five pretrained convolutional neural network models were trained and tested to infer images based on quality.

RESULTS: Our results showed a high prevalence of artifacts in OCT images with acceptable signal strength. Approximately …

Checklist For Reproducibility Of Deep Learning In Medical Imaging, Mana Moassefi, Yashbir Singh, Gian Marco Conte, Bardia Khosravi, Pouria Rouzrokh, Sanaz Vahdati, Nabile Safdar, Linda Moy, Felipe Kitamura, Amilcare Gentili, Paras Lakhani, Nina Kottler, Safwan Halabi, Joseph Yacoub, Yuankai Hou, Khaled Younis, Bradley Erickson, Elizabeth Krupinski, Shahriar Faghani

Department of Radiology Faculty Papers

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey …

Advancing Telehealth Through Artificial Intelligence: Incorporating Emotional Intelligence And Addressing Cybersecurity Challenges, Mahima Rajendra Pulgaonkar

Electronic Theses, Projects, and Dissertations

This culminating experience project explores the integration of Emotional Artificial Intelligence (Emotional AI) into telehealth systems, addressing the dual challenges of enhancing patient care and mitigating cybersecurity risks. The research questions are: (Q1) How can Emotionally Intelligent AI improve telehealth systems' ability to recognize and respond to mental health symptoms? and (Q2) What are the specific cybersecurity challenges associated with AI in telehealth and how can they be mitigated? The findings for each question are: Q1: Emotionally Intelligent AI can significantly enhance telehealth by providing personalized, empathetic interactions that improve patient engagement, adherence to treatment plans, and early detection of …

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.

Consistency Of Advanced Radiation Therapy Qa Methods And Global Auditing Systems, Fre'etta Brooks

Dissertations and Theses (Open Access)

Consistency of Advanced Radiation Therapy QA Methods and Global Auditing Systems

Fre’Etta M.D. Brooks, B.S.

Advisory Professor: Stephen F. Kry, Ph.D.

Abstract

Auditing systems provide a means to verify the implementation and operation of all equipment and radiation parameters used by an institution providing radiation therapy treatments. The systems are important tools that are used to determine if an institution can accurately deliver radiation therapy that requires complex planning in the form of IMRT or VMAT and credential radiotherapy treatment institutions for specific clinical trial protocols; to assure the quality of treatment to all patients. Auditing techniques vary by region …

Influential Factors And Predicting Dose Delivery Accuracy For Imaging And Radiation Oncology Core’S Phantom Program Using Machine Learning, Hunter Mehrens

Dissertations and Theses (Open Access)

IROC’s mission is to help ensure consistent and comparable, high-quality radiotherapy across clinics that participate in national clinical trials. To obtain this mission, IROC’s phantom program provides a third-party end-to-end check of the clinical workflow of a patient receiving radiotherapy. The goal of the phantom audit is to compare the dose delivered to the dose planned by the treatment system ensuring dose delivery accuracy. While IROC’s phantoms are better equipped to catch dose delivery errors compared to a clinic’s QA process, the end-to-end process and reporting of results is time-consuming creating a bottleneck for clinical trial participation. Furthermore, IROC’s passing …

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 …

Experimental And Theoretical Investigation Of The Coordination Of 8-Hydroxquinoline Inhibitors To Biomimetic Zinc Complexes And Histone Deacetylase 8 (Hdac8), Anthony M. Baudino, Harris F. Ciaccio, Michael J. Turski, Xavier A. Akins, Phoebus Sun Cao, Elisa Morales, Roger D. Sommer, Adam R. Johnson, Donald J. Wink, Kyle A. Grice, Kari Stone

Chemistry Department Faculty Articles

Zinc is integral to diverse biological functions, acting catalytically, structurally, and supportively in essential enzyme cycles, despite its limited amounts in the body. Targeting zinc enzymes with potent drugs, such as Vorinostat, demonstrates the therapeutic efficacy of zinc-binding ligands, notably in cutaneous T-cell lymphoma treatments. Our study merges experimental and theoretical approaches to analyze the coordination of 8-hydroxylquinoline (8HQ) inhibitors with biomimetic zinc complexes and human histone deacetylase 8 (HDAC8), a monozinc hydrolase enzyme. Assessing 10 8HQ derivatives for structural and electronic characteristics against these models, we observe minimal inhibition efficacy, corroborated through protein–ligand docking analyses, highlighting the complexities of …

Integrating Machine Learning And Simulation For Resource Planning Of Hospital Systems Based On Predicted Length Of Stay, S M Atikur Rahman

Open Access Theses & Dissertations

Recently Hospital Systems faced a high invasion of patients generated by several events such as health crisis related epidemic (COVID, FLU) or seasonal flows. Hence, managing hospital bed availability and efficiency with proper care is obligatory for addressing the challenges associated with the overburden of patients. However, the Length of stay (LOS) is often increased due to the high patient influx and overcrowding problem occurs within the Hospital. It resolves these issues, it is essential for hospital authority to predict the Patients LOS which is the crucial indicator for the use of medical resources (allocation, utilization of providers and resource) …

Querymate: A Custom Llm Powered By Llamacpp, Pegah Khosravi

Open Educational Resources

No abstract provided.

Challenges Of Insulin-Like Growth Factor-1 Testing, Jieli Li

Faculty, Staff and Students Publications

Insulin-like growth factor 1 (IGF-1), primarily synthesized in the liver, was initially discovered due to its capacity to replicate the metabolic effects of insulin. Subsequently, it emerged as a key regulator of the actions of growth hormone (GH), managing critical processes like cell proliferation, differentiation, and apoptosis. Notably, IGF-1 displays a longer half-life compared to GH, making it less susceptible to factors that may affect GH concentrations. Consequently, the measurement of IGF-1 proves to be more specific and sensitive when diagnosing conditions such as acromegaly or GH deficiency. The recognition of the existence of IGFBPs and their potential to interfere …

Deep Survival Analysis For Interpretable Time-Varying Prediction Of Preeclampsia Risk, Vesela P Kovacheva

Faculty, Staff and Students Publications

Objective: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especially relevant for pregnancy, where the risk for pregnancy-related complications, such as preeclampsia, varies across gestation. Recently, deep learning survival models have shown promise in addressing the limitations of classical models, as the novel models allow for non-proportional risk handling, capturing nonlinear relationships, …

Automatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Modelsautomatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Models, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural …

Artificial Intelligence In Fusion Protein Three-Dimensional Structure Prediction: Review And Perspective

Faculty, Staff and Student Publications

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action …

Patient-Centered Clinical Decision Support Challenges And Opportunities Identified From Workflow Execution Models, Nicole A Gauthreaux, James Swiger, Edwin A Lomotan, Prashila Dullabh

Faculty, Staff and Student Publications

OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.

RESULTS AND DISCUSSION: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new …

Challenges In Lncrna Biology: Views And Opinions, Ekaterina G Derevyanchuk, Tatiana P Shkurat, Jeffrey A Loeb, Ivan Martinez, Leonard Lipovich

Faculty, Staff and Student Publications

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity.

A Cell Cycle-Aware Network For Data Integration And Label Transferring Of Single-Cell Rna-Seq And Atac-Seq

Faculty, Staff and Student Publications

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell …

Canine Parasites In Southern Nevada Urban Dog Parks: Paravec Study, Miklo Azrael A. Alcala

UNLV Theses, Dissertations, Professional Papers, and Capstones

While dogs are one of humans’ greatest companions, they also serve as hosts for a multitude of parasites. Urban dog parks serve as environments where the transmission of diseases is likely to occur. Exposure to infective agents, contained within the soil, intermediate hosts (e.g., rodents, rabbits), and feces, can directly affect the health of dogs and their owners. Other factors that can further influence canine health include age, previous diagnoses of parasites/diseases, and migration. Many dog owners visit dog parks to encourage socialization and exercise. Such activities are meant to improve health, but unawareness of microbiological organisms can lead to …

The Development Of Environmental Dna (Edna) Sampling For Arbovirus Vector Surveillance In Southern Nevada, Katharine Brooke Major

UNLV Theses, Dissertations, Professional Papers, and Capstones

Vector-borne diseases account for 17% of all infectious diseases worldwide and are responsible for more than 700,000 deaths annually. Two-thirds of the global population are at risk of arthropod-borne RNA viruses (arboviruses), spread by mosquitoes, ticks, and sandflies, with these pathogens accounting for 37% of all emerging human diseases. Arboviruses are becoming increasingly important causes of neurological disease in the United States, presenting a significant public health problem. In the absence of available vaccines and efficacious treatment options, accurate, rapid, and cost-effective surveillance of arbovirus vectors is critical for monitoring species distribution and mitigating disease transmission risk. Currently, mosquito vector …

Implementing Radformation At Personalized Radiation Oncology: Enhancing Efficiency And Precision, Devin J. Peart

UNLV Theses, Dissertations, Professional Papers, and Capstones

RadMachine is a data quality management application implemented at Personalized Radiation Oncology (PRO) to streamline quality assurance (QA) tests. It offers various functions assisting medical physicists in a radiation oncology clinic, including inputting, analyzing, managing, exporting, and monitoring QA data. Physicists create test lists following the recommendations provided by the American Association of Medical Physics (AAPM). These lists track results for units such as the Varian Edge, Varian BRAVOS, and Siemens SOMATOM go.Open Pro CT scanner. Ancillary equipment used for testing also has assigned test lists to store calibration factors and receive calibration expiration reminders. The primary goal of utilizing …

Molecular Mechanisms Of Exercise-Stimulated Resolution Of Inflammation And Its Dietary Dependence., Ernesto H. Pena Calderin

Electronic Theses and Dissertations

Diet-induced obesity and adipose tissue (AT) inflammation that ensues sets the stage for systemic metabolic inflammation, disrupted insulin signaling, and eventual type 2 diabetes (T2D) development. At the center of this pathology lies a disruption in endogenous inflammation resolution pathways characterized by an imbalance in lipid mediator production (greater abundance of pro-inflammatory to pro-resolving lipids). Additionally, obese states are characterized by adrenergic deficiency, especially in response to physical activity. Inflammatory processes have been experimentally mitigated by either the pharmacological inhibition of pro-inflammatory signals (i.e., Leukotriene B₄ (LTB₄) and its receptor BLT1, which establishes a potent chemotactic stimulus …