QUANTIFICATION AND ANALYSIS OF BIOMOLECULES VIA MASS SPECTROMETRY FOR THE UNDERSTANDING, DETECTION, AND TREATMENT OF DISEASE

Mass spectrometry is an incredibly powerful analytical tool capable of analyzing a wide range of biologically relevant molecules like metabolites and proteins. Metabolites, or molecules with a molecular weight under 1500 daltons, are a group of molecules incredibly sensitive to small changes in the body. This characteristic makes them excellent as diagnostic markers of disease. Metabolites also serve as the building blocks of all of the biochemical pathways that make our body function. By observing changes in metabolism caused by disease, mechanisms of disease and, by extension, potential disease treatments can be discerned. Proteins, a key regulator of metabolism and an important group of molecules for drug development, can also be quantified via mass spectrometry, but their large structure presents several analytical challenges. In this dissertation, the applications of mass spectrometry in the study of metabolites as both a diagnostic biomarker and tool for understanding disease is explored. First, in a mouse model for COVID-19, we used mass spectrometry to identify dysregulation of fatty acids, amino acids, and eicosanoids in the lungs. Furthermore, we observed that dysregulation was worse in peripheral lung tissue despite higher viral loads in central lung tissue. Moving beyond metabolites, chapter 3 of this dissertation focuses on the development of methodology to prepare and analyze proteins using a novel acoustic droplet ejection mass spectrometer. By optimizing instrument and analytical parameters, an analytical pipeline was developed for analyzing a small protein (17 kilodaltons), a medium protein (50 kilodaltons) and a large protein (150 kilodaltons) in under one second per sample. This dissertation provides insight on the detection and mechanism of infectious diseases while also providing insight on new methods to quantify drugs via mass spectrometry

How propaganda and intersectionality influenced the American Revolution

The overall research question for this thesis is how both propaganda and intersectionality play their respective roles within the American Revolution era in the American colonies. Many historians have brought forward different aspects of this era, ranging from 1763 through 1783, and give others a more well-rounded perspective of this time. Other historians have also discussed some of the propaganda, while others have focused on bringing forward the discussion of the biographies of those involved, specifically Abigail Adams, Phillis Wheatley and Molly Brant.There was the possibility of not finding the information regarding propaganda and intersectionality playing roles within the American Revolution, and if that had been the case, this would be an entirely different thesis. The research shows that both propaganda and intersectionality played different roles within this era, as primary and secondary sources show the usage of both persuasion through propaganda and the roles that Adams, Wheatley, and Brant found themselves in throughout this time. Both propaganda and intersectionality played roles in decisions that were made as the years continued towards and through the American Revolution. This research is important as it expands the overall knowledge and understanding of the American Revolution era, and how other factors played a role in the outcome of the war

Gender non-conformity in science fiction: interrogating gender as language in The First Sister trilogy

This project seeks to analyze gender through the lens of a language within the First Sister Trilogy. The purpose of this approach is to interrogate the multitude of ways in which gender is performed, experienced, and read, in order to create a consistent and coherent framework with which to articulate the fluid and multifaceted nature of gender. More specifically, it seeks to investigate the ways that science fiction, as well as other forms of speculative fiction, may be used in order to improve reader understanding of gender as a construct, making them more able to grasp and articulate the ways in which gender exists as a fluid and nuanced structure

Faculty and Staff Publications Test Submission

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APPLICATION OF POST-STACK SEISMIC ATTRIBUTES, WELL DYNAMIC DATA, AND MACHINE LEARNING FOR CARBONATE RESERVOIR FACIES AND PRODUCTIVITY PREDICTION FOR UPSTREAM OPTIMIZATION

Even almost 20 years after the discovery of the prolific pre-salt oil province in SE Brazil offshore, seismic characterization of these complex reservoirs is still a challenging task. In the oil and gas fields, where dozens of wells are available, the seismic inversion-based workflows are proven to be the best for reservoir characterization. However, in exploratory stage areas the reduced number of wells makes seismic inversion non-viable. As a result, solely seismic amplitude-based workflows are strongly affected by ambiguity pitfalls, which delay the appraisal phase, postpone commercial production, and, thus, erode the economic value of the project. As a solution, I propose a machine learning approach that leverages the big amount of dynamic reservoir data in a known field (Mero field in Santos Basin) and easy-to-obtain post-sack 3D seismic attributes, which are common to both production and exploration areas and are available as soon as the seismic processing is done, without requiring wells for calibration like seismic inversion does. Well dynamic data, mainly drill stem tests, are the most reliable information on reservoir productivity before the commercial production and are available during the upstream phase. In this sense, my doctoral research was structured in three main projects: the first one (Chapter 2) on qualitative reservoir modelling using post-stack seismic attributes and unsupervised learning techniques. I used combinations of ten different seismic attributes (interval velocity, amplitude versus offset, complex traces, geometric and voxel-based texture) as inputs for self-organizing maps, generative topographic mapping and k-means clustering algorithms for seismic facies discrimination in the Barra Velha reservoir of the Mero field. The seismic facies models were validated using information from 13 wells which were not used for any training. The best models were obtained with self-organizing maps. The second project (Chapter 3) is on quantitative interpretation using the same attributes and drill stem test data to train supervised learning algorithms for prediction of reservoir productivity of the Barra Velha carbonates in the Mero field. I tested classic supervised learning algorithms (shallow learning) of random forest, support vector machines and K-nearest neighbors for supervised regression of flow capacity and productivity index. I also tested a deep learning algorithm (multi-layer perceptron) to compare its cost-effectiveness to the shallow learning algorithms. For the validation of my predictive models, I used information from 20 blind test wells. The best results were obtained with random forest regression of flow capacity (85% blind test performance). In the third project, I studied how transfer learning techniques can leverage the machine learning training using Mero field data to accurately predict reservoir facies and productivity in other seismic surveys 200 km distant from Mero field: the Bacalhau and Lapa fields, which have production data to validate my predictive models. Using self-organizing maps and random forest algorithms trained with Mero field data, I could accurately predict (80% average performance) the facies distribution and flow capacity values observed in the blind test wells in Bacalhau and Lapa fields. Transfer learning using Mero training proved effective for reservoir de-risking and upstream optimization even when working with multiple seismic surveys. As I used post-stack seismic attributes, well test productivity, and injectivity data from subsurface reservoirs to train our models, this approach can be used in any kind of project such as CCUS, geothermal, and hydrogen storage projects in the context of the energy transition

INVESTIGATION OF THE HABITAT AND CLIMATE DRIVERS OF COLLARED PIKA DISTRIBUTION AND VULNERABILITY

As environmental conditions continue to change at a rapid rate, understanding habitat requirements and climate vulnerabilities is critical for species conservation. The collared pika (Ochotona collaris), an alpine specialist found in Alaska and northern Canada, faces potential threats from climate change, yet its habitat requirements and responses to environmental change remain poorly understood. In this study, we use existing spatial data, combined with observations from new survey efforts, to 1) identify regional variation in collared pika habitat preferences and climate response, and 2) assess the climate change vulnerability of collared pikas and predict changes in distribution linked to future climate scenarios. To identify habitat preferences, we used a model selection approach, comparing climate and habitat characteristics at known collared pika occurrences to those at surrounding areas. Our analysis indicated that collared pika occurrence is primarily driven by the presence of talus, proximity to the talus edge (with pikas preferring areas closer to vegetation), and talus patch size (with pikas preferring small patches). The rangewide model indicated that collared pika occurrence was positively correlated with maximum winter temperature and negatively correlated with maximum summer temperature, annual precipitation, and solar radiation. While preferences for talus characteristics remained consistent rangewide, the effect of climate variables including precipitation and maximum summer temperature varied regionally. To assess climate vulnerability, we combined trait-based and correlative modeling approaches, with the results suggesting the species has high vulnerability to climate change. Our trait-based assessment indicated that collared pikas are moderately susceptible to climate change due to high exposure to changing conditions, high sensitivity to climatic variables, and low adaptive capacity. To predict future changes in distribution, we employed a presence-background species distribution modeling technique to identify the current climatic niche and projected this model into future climate scenarios. Our models suggest a loss of ~55-70% of climatically suitable areas by 2080. Together, these results inform conservation status and strategies for this species and highlight the importance of conducting regional and species-specific analyses

Repairing the Catalog: Using Reparative Description for Indigenous Subjects

I was really inspired by The Tribal Nations in Oklahoma Metadata presentation during the 2023 Metadata Justice in Oklahoma Libraries and Archives Symposium. I was inspired to add proper tribal names to all records with “Five Civilized Tribes” as a subject heading. But then, as I read more about subject headings, I realized that Indians of North America was a problematic heading, and I wanted to help mitigate the harm that that subject might do. So, I began adding subject headings “Indigenous people of North America” to all records in Cameron University Library's catalog with Indians as a subject heading. And then, during the 2024 Metadata Justice Symposium, I was inspired by the Searching the Past, Finding the Present: Identifying Contemporary Tribal Communities in Gilcrease Museum's Rare Books Collection presentation, and I went back to the beginning and started all over again, with a more detailed and nuanced spreadsheet. I will explain my process, showing before and after records in the ILS and OPAC, and explaining my spreadsheet. I will explain how I created an alternate vocabulary to use in place of subject headings with "Indian" -- I'm adding Indigenous people to the subjects of each record in the catalog with "Indians" as a subject heading, and then I've been adding the correct tribal names for any tribal nation specified in the subjects (like, Navajo Indians, gets subject headings: Diné Indians (Navajo) ; Navajo Nation) I will ask for feedback on how to continue the work after the project itself is done. How do I "train" anyone following me to keep up the practice

Examining the Predictors of STEM Degree Obtainment Within Social Cognitive Career Theory for Native American Undergraduate Students

This dissertation examines the predictors of STEM degree attainment among Native American undergraduate students using the Social Cognitive Career Theory (SCCT) framework. Leveraging a longitudinal dataset comprising Native American, Asian American, and White undergraduates, this study identifies the SCCT constructs most predictive of educational outcomes, including graduation with a STEM degree, graduation with a non-STEM degree, and earning no degree. Using elastic net multinomial regression, the analysis evaluates the influence of self-efficacy, outcome expectations, supports, barriers, and other SCCT components on these outcomes. The methodology incorporates measurement invariance testing to ensure the validity of cross-group comparisons of SCCT constructs. By integrating institutional records and self-reported survey data, this research addresses critical gaps in understanding the factors that facilitate or hinder STEM success for Native American students. The findings provide actionable insights for developing targeted interventions to support STEM persistence among underrepresented groups, with implications for broader educational policies and practices

PREDICTING STELLAR AGES FROM CHEMICAL ABUNDANCES WITH XGBOOST: A MACHINE LEARNING APPROACH TO GALACTIC ARCHAEOLOGY

Estimating stellar ages remains a central challenge in astrophysics, particularly for stars outside the narrow evolutionary phases where traditional methods like isochrone fitting are reliable. This study explores the use of supervised machine learning, specifically, the Extreme Gradient Boosting (XGBoost) algorithm, to infer stellar ages from chemical abundance ratios derived from the GALAH DR4 spectroscopic survey. A training set of 20,303 Main Sequence Turn-Off stars was constructed using strict quality cuts and age labels obtained via Bayesian isochrone fitting. The XGBoost model was trained on 15 elemental abundance ratios and tuned through exhaustive hyperparameter optimization with five-fold cross-validation. The final model achieves a root mean squared error (RMSE) of 1.38 Gyr on a held-out test set, generalizes well across stellar populations not seen during training, and reproduces known chemical evolution trends such as the anti-correlation between [Fe/H] and age and the positive correlation between [$\alpha$/Fe] and age. Feature importance analysis reveals that [Mg/Fe], [Ca/Fe], and [Y/Fe] are the most informative predictors of age, consistent with theoretical nucleosynthetic expectations. Residual analyses expose a tendency for the model to regress predictions toward the mean age in underrepresented regions, particularly for very young and very old stars. These results demonstrate that machine learning can extract robust age information from chemical abundance patterns and extend age-dating to stellar populations for which traditional techniques break down. This work highlights the power of data-driven approaches in galactic archaeology and paves the way for scalable age estimation in next-generation spectroscopic surveys