Digital Commons Network^™

Applying Data Science And Machine Learning To Understand Health Care Transition For Adolescents And Emerging Adults With Special Health Care Needs, Lisamarie Turk

Nursing ETDs

A problem of classification places adolescents and emerging adults with special health care needs among the most at risk for poor or life-threatening health outcomes. This preliminary proof-of-concept study was conducted to determine if phenotypes of health care transition (HCT) for this vulnerable population could be established. Such phenotypes could support development of future studies that require data classifications as input. Mining of electronic health record data and cluster analysis were implemented to identify phenotypes. Subsequently, a machine learning concept model was developed for predicting acute care and medical condition severity. Three clusters were identified and described (Cluster 1, n …

Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud

Dissertations

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. …

Narrative Review: Food Image Use For Machine Learnings’ Function In Dietary Assessment And Real Time Nutrition Feedback And Education, Jason Fee

Masters Theses, 2020-current

Technology has played a key role in advancing the health and agriculture sectors to improve obesity rates, diseasecontrol, food waste, and overall health disparities. However, these health and lifestyle determinants continue to plague theUnited States population. While new technologies have been and are currently being developed to address these concerns, they may not be practical for the general population. Utilizing machine learning advancement in food recognition using smartphone technology may be a means to improve the dietary component of nutrition assessments while providing valuable nutrition feedback. This narrative review was conducted to assess the current state of the literature on …

Quantifying Floral Resource Availability Using Unmanned Aerial Systems And Machine Learning Classifications To Predict Bee Community Structure, Jesse Anjin Tabor

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Bees are important for agricultural and non-agricultural ecosystems because they pollinate both wild plants and commercial crops. Flowers provide pollen and nectar resources that bees use to survive and reproduce. Measuring the relationship between the floral community and bee community may help apiarists and land managers to make informed decisions in managing wild and domesticated bee species. Manual methods to describe and count flowering vegetation is costly in time and personnel. Unmanned aerial vehicle (UAV) technology may be an efficient way to describe and count flowering vegetation on a large scale. UAVs with classification analysis and ground transect surveys were …

Improved Computational Prediction Of Function And Structural Representation Of Self-Cleaving Ribozymes With Enhanced Parameter Selection And Library Design, James D. Beck

Boise State University Theses and Dissertations

Biomolecules could be engineered to solve many societal challenges, including disease diagnosis and treatment, environmental sustainability, and food security. However, our limited understanding of how mutational variants alter molecular structures and functional performance has constrained the potential of important technological advances, such as high-throughput sequencing and gene editing. Ribonuleic Acid (RNA) sequences are thought to play a central role within many of these challenges. Their continual discovery throughout all domains of life is evidence of their significant biological importance (Weinreb et al., 2016). The self-cleaving ribozyme is a class of noncoding Ribonuleic Acid (ncRNA) that has been useful for …

Machine Learning Models For Human Synapse Genomics, Anqi Wei

All Dissertations

In the central nervous system, synapses are essential junctions that connect neurons and play important roles in neurotransmission and synaptic plasticity. While there are many challenges in human synapse genomics, machine learning techniques, which are capable of mining and interpreting large amounts of genomic data, may be utilized to facilitate the functional studies of human synapses. In this study, we have developed machine learning models for human synapse genomics to address several biological problems.

RNA localization plays an important role at the synapse, allowing local protein synthesis required for synaptic plasticity during brain development. Previous studies were conducted in mice …

Investigating Students' Interpretations Of Ecological Food Webs, Christopher Neil Grissett

USF Tampa Graduate Theses and Dissertations

To better prepare undergraduate students for current and future biological challenges, scientists, educators and researchers in the Vision and Change report recommended five core conceptual areas essential for the improvement of biological literacy, one of which is biological systems. Systems are an identified core concept that may help promote biological literacy. One example of a system that students have difficulty understanding is an ecological food web, which consists of parts or components interacting with one another to perform a given phenomenon. The intricacies of this system tend to confuse students and can produce naïve conceptions that could hinder future learning …

Using Machine Learning To Identify Neural Mechanisms Underlying The Development Of Cognition In Children And Adolescents With Adhd, Brian Pho

Electronic Thesis and Dissertation Repository

Childhood and adolescence are marked by improvements to cognition and by the emergence of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). What neural mechanisms are associated with cognitive development in ADHD? In this study, I applied machine learning models to functional connectivity profiles to identify patterns of network connectivity that predict various cognitive abilities in a group of participants ages 6 to 16 with ADHD. The models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children ages 6 to 11, but not adolescents. Furthermore, the models identified connections with the default mode, memory retrieval, and …

Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn

Theses and Dissertations

With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …

Determining The Effects Of Elevated Carbon Dioxide On Soil Acidification, Cation Depletion, And Soil Inorganic Carbon And Mapping Soil Carbons Using Artificial Intelligence, Jannatul Ferdush

Theses and Dissertations

Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO₂ affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO₂ and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO₂ effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils …

What I Talk About When I Talk About Integration Of Single-Cell Data, Yang Xu

Doctoral Dissertations

Over the past decade, single-cell technologies evolved from profiling hundreds of cells to millions of cells, and emerged from a single modality of data to cover multiple views at single-cell resolution, including genome, epigenome, transcriptome, and so on. With advance of these single-cell technologies, the booming of multimodal single-cell data creates a valuable resource for us to understand cellular heterogeneity and molecular mechanism at a comprehensive level. However, the large-scale multimodal single-cell data also presents a huge computational challenge for insightful integrative analysis. Here, I will lay out problems in data integration that single-cell research community is interested in and …

Improving The Ribozyme Toolbox: From Structure-Function Insights To Synthetic Biology Applications, Jessica Michelle Roberts

Boise State University Theses and Dissertations

Self-cleaving ribozymes are a naturally occurring class of catalytically active RNA molecules which cleave their own phosphate backbone. In nature, self-cleaving ribozymes are best known for their role in processing concatamers of viral genomes into monomers during viral replication in some RNA viruses, but to a lesser degree have also been implicated in mRNA regulation and processing in bacteria and eukaryotes. In addition to their biological relevance, these RNA enzymes have been harnessed as important biomolecular tools with a variety of applications in fields such as bioengineering. Self-cleaving ribozymes are relatively small and easy to generate in the lab using …

Refining, Testing, And Applying Thermal Species Distribution Models To Enhance Ecological Assessments, Donald J. Benkendorf

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The temperature of streams and rivers is changing rapidly in response to a variety of human activities. This rapid change is concerning because the abundances and distributions of many aquatic species in streams and rivers are strongly associated with temperature. Linking observations of temperature effects on species distributions with observations of temperature effects on fitness is important for improving confidence that temperature (and not some other variable) is causing the distributions we observe. Furthermore, producing accurate models of temperature effects on species distributions may allow us to develop tools to diagnose whether or not thermal pollution has impaired aquatic life. …

Full-Body Biomechanical Characterization Of Children With Hypermobile Ehlers-Danlos Syndrome During Gait And Activities Of Daily Living, Anahita Alahmoradiqashqai

Theses and Dissertations

Hypermobile Ehlers-Danlos syndrome (hEDS) is an inherited connective tissue disorder, often under-diagnosed, and presenting with frequent chronic pain and severe musculoskeletal symptoms that can drastically reduce the quality of life during one’s life span. While there are limited quantitative approaches in the literature on adult movements, the biomechanics of movements during activities of daily living (ADLs) in children have not been investigated comprehensively. Therefore, the primary purpose of this dissertation was to characterize the biomechanics of the musculoskeletal system and investigate the biomechanics of hEDS by quantifying joint dynamics and muscle activations during ADLs and gait in the pediatric population. …

Using Fine-Scale Aquatic Habitat Data To Construct Dreissenid Sdms In The Laurentian Great Lakes, Grace C. Henderson

USF Tampa Graduate Theses and Dissertations

The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing …

Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley

Graduate Theses, Dissertations, and Problem Reports

This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a …

Optimising The Carcass Merit Of Irish Beef Cattle Using Genetic And Non-Genetic Information At The Animal And Herd Level, David Kenny

Theses

Failure of beef carcasses to achieve desirable carcass specifications represent inefficiencies within the supply chain, namely greater carcass processing costs and the inability of the resulting primal cuts to conform to high-value market specifications. Analysis of a representative sample of prime Irish beef cattle conducted in this thesis determined that 59% of cattle fail to achieve the desired carcass specifications of the supply chain at slaughter. The objective of this thesis was to use readily available information to define strategies that could help to reduce this statistic. Firstly, the likelihood of Irish beef carcasses achieving the desired carcass specifications was …

Fine Scale Mapping Of Laurentian Mixed Forest Natural Habitat Communities Using Multispectral Naip And Uav Datasets Combined With Machine Learning Methods, Parth P. Bhatt

Dissertations, Master's Theses and Master's Reports

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment …

Framework For The Evaluation Of Perturbations In The Systems Biology Landscape And Inter-Sample Similarity From Transcriptomic Datasets — A Digital Twin Perspective, Mariah Marie Hoffman

Dissertations and Theses

One approach to interrogating the complexities of human systems in their well-regulated and dysregulated states is through the use of digital twins. Digital twins are virtual representations of physical systems that are descriptive of an individual's state of health, an object fundamentally related to precision medicine. A key element for building a functional digital twin type for a disease or predicting the therapeutic efficacy of a potential treatment is harmonized, machine-parsable domain knowledge. Hypothesis-driven investigations are the gold standard for representing subsystems, but their results encompass a limited knowledge of the full biosystem. Multi-omics data is one rich source of …

Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu

Honors Theses and Capstones

COVID-19 caused state and nation-wide lockdowns, which altered human foot traffic, especially in restaurants. The seafood sector in particular suffered greatly as there was an increase in illegal fishing, it is made up of perishable goods, it is seasonal in some places, and imports and exports were slowed. Foot traffic data is useful for business owners to have to know how much to order, how many employees to schedule, etc. One issue is that the data is very expensive, hard to get, and not available until months after it is recorded. Our goal is to not only find covariates that …