Electronic Theses and DissertationsElectronic theses and dissertations of masters and doctoral students submitted to the Graduate School.http://hdl.handle.net/1803/95992024-03-29T09:04:15Z2024-03-29T09:04:15ZNeural correlates of rhythm in individuals with and without post-stroke aphasiahttp://hdl.handle.net/1803/186552024-02-06T18:06:18Z2023-09-07T00:00:00ZNeural correlates of rhythm in individuals with and without post-stroke aphasia
Aphasia is an acquired communication disorder resulting from damage to language regions of the brain. Speech-language pathologists frequently use rhythmic elements (e.g., tapping to a beat) to facilitate speech output in individuals with aphasia; however, there is very little empirical work on rhythm in aphasia at both a neural and a behavioral level. Even further, there is a need for a comprehensive understanding of the brain regions involved in musical rhythm in a neurotypical population – a requisite for understanding what happens to this network following brain injury. This dissertation begins to address these critical gaps through two main aims to: 1) identify a brain network for musical rhythm in neurotypical adults (Chapter 2) and 2) characterize rhythm abilities, and their relationship to lesion location and language profiles, in individuals with post-stroke aphasia using an individual differences approach (Chapter 3). To achieve the first aim, I conducted a systematic review and meta-analysis of 30 functional magnetic resonance imaging (fMRI) studies of musical rhythm. I found that rhythm is largely represented in a bilateral cortico-subcortical network. To achieve the second aim, a cohort of 33 individuals with chronic, post-stroke aphasia and a comparison group of 29 neurotypical controls completed a battery of rhythm production and perception tasks. Most individuals with aphasia performed within the normal control range, but about one-third did not. Using lesion-symptom mapping, I found that those who struggled with tapping tended to have damage to a left posterior perisylvian region at the crux of the temporal and parietal lobes; this area has been implicated in auditory-motor transformations such as phonological encoding. Additionally, rhythm abilities correlated with overall aphasia severity but not with motor speech. In Chapter 4, I present new ideas and future clinical directions for individualizing aphasia treatment strategies, connecting the present findings with literature on rhythm-based therapies for aphasia. This dissertation provides new, clinically-translational knowledge on how beat synchronization – a basic human capacity – is represented in the brain.
2023-09-07T00:00:00ZDevelopment of Frameworks for Computational Protein Structure Prediction Applications Challenged By Limited Training Datahttp://hdl.handle.net/1803/186542024-02-06T18:05:11Z2023-08-22T00:00:00ZDevelopment of Frameworks for Computational Protein Structure Prediction Applications Challenged By Limited Training Data
Computational structural biology tools are applied to many different systems to answer questions about how protein sequence and protein environment impacts the structure and function of a protein. These protein structure prediction methods rely on existing data to train and test on. Cases where there is little known data are challenging to use generic tools due to the lack of data. We have developed methods for two different cases where there is limited available data: membrane proteins not embedded in flat bilayers and prediction of deletion mutations. We introduce a framework for implicit membrane models of different geometries including a curved membrane, a double membrane, and an ellipsoid membrane mimicking common membrane model systems used in experiments. This framework allows for more accurate prediction on proteins where a flat membrane is not sufficient to represent the membrane environment the protein is in. Towards modeling deletion mutations in proteins, we characterized deletion mutations in a model protein and test computational methods on their ability to predict the effect of those deletion mutations. Further, we show how the computational methods perform on pathogenic deletion mutations.
2023-08-22T00:00:00ZNeural Representations of Flow Data for Visual Analysishttp://hdl.handle.net/1803/186532024-02-06T18:04:37Z2023-11-15T00:00:00ZNeural Representations of Flow Data for Visual Analysis
Flow visualization serves as a crucial tool for scientists and researchers to gain valuable insights into complex flow data. However, with the growing complexity and scale of the numerical simulations, the resulting datasets have expanded considerably, presenting challenges in terms of storage, transmission, and subsequent analysis. Motivated by these challenges, this dissertation explores the integration of neural representations within flow visualization frameworks. By leveraging the latest developments in deep learning, novel approaches are developed to enhance computational efficiency, improve data reduction and enable interactive visual analysis. First, we investigate the fusion of neural representation with visualization tasks. We propose a technique to integrate streamlines within the superresolution framework enabling the generation of high-resolution flow fields that enhance the fidelity of flow visualization. Next, we propose a technique to reconstruct arbitrary flow map samples using an implicit neural representation (INR), providing a compact and accurate representation for comprehensive analysis. To further bridge neural representations and visualization, we propose a technique for incorporating scale information within INRs, offering a compact model that permits filtering, sampling, and progressive representation of data. Finally, a comprehensive framework is proposed for learning neural representation of flow maps that strikes a balance between accuracy, computational efficiency, and scalability. Overall, we demonstrate the potential of neural representations as a tool that scientists and researchers can utilize to extract essential flow insights, manage data effectively and enable accurate visual analysis.
2023-11-15T00:00:00ZPlasma Proteomic Factors Associated with Sepsis Survival Outcomeshttp://hdl.handle.net/1803/186522024-02-06T14:30:47Z2023-08-22T00:00:00ZPlasma Proteomic Factors Associated with Sepsis Survival Outcomes
Sepsis is life-threating organ dysfunction because of an abnormal host response to infection. Patient response is heterogeneous, so better prognostic indicators could help develop more effective treatments. African American/Black patients have higher mortality rates from sepsis than Non-Hispanic White patients, which is partially explained by socioeconomic factors and comorbidities. Molecular level factors are also important, but few studies have included African American/Black patients. With proteomics, thousands of proteins in complex biological samples such as blood plasma can be analyzed to understand molecular level factors driving sepsis survival. Protein differences between Non-Hispanic White and African American/Black sepsis survivors and non-survivors who had either primary intra-abdominal or urinary tract infections were analyzed. Sepsis survivors universally had lower levels of inflammatory proteins than non-survivors, but many proteins were different between sepsis survivors and non-survivors within one racial/ethnic background or primary infection source. Other proteins’ levels were dependent on both racial background and survival outcome. Potential prognostic markers for sepsis survival outcomes were also identified. This work emphasizes the importance of including patients from underrepresented racial/ethnic backgrounds and under-studied primary infection sources to fully understand molecular level factors that contribute to sepsis survival outcomes.
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