Life Sciences Commons^™

Impacts Of Environmental And Anthropogenic Stressors On Amphibian Welfare, Diversity, And Distribution In The Upper Missouri River Basin, Kaitlyn Campbell

Dissertations and Theses

Climate change and anthropogenic stressors have contributed to rapid declines among various taxonomic groups; however, amphibian declines have been particularly intense and primarily stemmed from warming temperatures, habitat loss, exposure to contaminants, disease, and their subsequent interactions. Several climate mitigation strategies, like Bioenergy with Carbon Capture and Storage, have been proposed to alleviate the impact of rising temperatures; however, these proposals often fail to recognize and quantify the true impact on fauna, including changes in species distributions. To address this critical gap in knowledge, this research identified current amphibian distributions in the Upper Missouri River Basin and projected distribution changes …

Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy

All Dissertations

Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …

Deep Learning Applications In Medical Bioinformatics, Ziad Omar

Electronic Theses and Dissertations

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural …

The Music Of Rivers: How Climate, Land Use, And Disturbances Tune The Frequencies And Volumes Of Streams Worldwide, Brian Charles Brown

Theses and Dissertations

The amount of water flowing through streams and rivers changes through time. The seasonality and duration of these changes can have profound impacts on human freshwater availability, aquatic habitat, and biogeochemical cycling. Numerous factors are thought to influence streamflow regime, including drainage basin area, temperature, precipitation, and land cover. Few of these qualities have remained untouched, either directly or indirectly, by expanding human activities. Altered climate, sweeping changes to large portions of the earth's surface, and the construction of dams and other infrastructure have fundamentally altered streamflows worldwide. Understanding the nature of these changes, both globally and regionally in the …

A Constitutive-Based Deep Learning Model For The Identification Of Active Contraction Parameters Of The Left Ventricular Myocardium, Igor Augusto Paschoalotte Nobrega

USF Tampa Graduate Theses and Dissertations

Modern breakthroughs in biomedical engineering, computer science, and data mining have created new opportunities for detecting important mechanical properties of soft tissues that can be employed to identify possible signs of diseases or physiological difficulties. However, the scarcity of different mechanical properties obtained through noninvasive testing emphasizes the importance of incorporating authentic biological data into computer models capable of replicating the behavior of soft tissues.

The field of continuum theory of large deformation hyperactivity permits the formulation of highly descriptive mathematical research and computational models capable of perfectly describing the minute mechanical characteristics of soft materials. By including features about …

Gene Selection And Classification In High-Throughput Biological Data With Integrated Machine Learning Algorithms And Bioinformatics Approaches, Abhijeet R Patil

Open Access Theses & Dissertations

With the rise of high throughput technologies in biomedical research, large volumes of expression profiling, methylation profiling, and RNA-sequencing data are being generated. These high-dimensional data have large number of features with small number of samples, a characteristic called the "curse of dimensionality." The selection of optimal features, which largely affects the performance of classification algorithms in machine learning models, has led to challenging problems in bioinformatics analyses of such high-dimensional datasets. In this work, I focus on the design of two-stage frameworks of feature selection and classification and their applications in multiple sets of colorectal cancer data. The first …

Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …

Ensemble Protein Inference Evaluation, Kyle Lee Lucke

Graduate Student Theses, Dissertations, & Professional Papers

The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …

Machine Learning And Bioinformatic Insights Into Key Enzymes For A Bio-Based Circular Economy, Japheth E. Gado

Theses and Dissertations--Chemical and Materials Engineering

The world is presently faced with a sustainability crisis; it is becoming increasingly difficult to meet the energy and material needs of a growing global population without depleting and polluting our planet. Greenhouse gases released from the continuous combustion of fossil fuels engender accelerated climate change, and plastic waste accumulates in the environment. There is need for a circular economy, where energy and materials are renewably derived from waste items, rather than by consuming limited resources. Deconstruction of the recalcitrant linkages in natural and synthetic polymers is crucial for a circular economy, as deconstructed monomers can be used to manufacture …

Applications Of Machine Learning In Microbial Forensics, Ryan B. Ghannam

Dissertations, Master's Theses and Master's Reports

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity.

Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as …