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Item Investigation of the cellular pathology underlying the optic neuropathy in a mouse model of familial Dysautonomia(Montana State University - Bozeman, College of Agriculture, 2023) Schultz, Anastasia Mardell; Chairperson, Graduate Committee: R. Steven Stowers; This is a manuscript style paper that includes co-authored chapters.Familial dysautonomia (FD) is a rare, recessive, progressive autosomal disorder that affects the nervous system. This neurological disorder is caused by a splice mutation in the Elongator complex I (ELP1) gene. The mutation results in a tissue-specific reduction of ELP1 protein due to unstable mRNA targeted for nonsense-mediated decay. ELP1 is a highly conserved scaffolding protein and core subunit of the six-subunit Elongator complex required for normal translation, neuronal development, and survival. Insufficient ELP1 leads to the developmental death of neurons in the peripheral and autonomic nervous systems in addition to central and peripheral nervous system neurodegeneration. Patients suffer from congenital and progressive neuropathies, such as cardiovascular dysfunction, reduced peripheral sensory function, poor growth, and digestive and respiratory problems. Outside of the risk of death in early adulthood, one of the most debilitating conditions affecting patients' quality of life is progressive blindness marked by continual loss of retinal ganglion cells (RGCs). Within the FD community, there is a concerted effort to develop treatments to prevent the loss of RGCs, thereby improving patients' quality of life. This study aims (1) to elucidate mechanisms underlying the death of RGCs in the absence of Elp1 and (2) to obtain pre-clinical intervention data that can eventually be translated into therapeutics for rescuing RGCs in FD. Using histology and confocal microscopy in conjunction with biochemistry, this study provides evidence for disrupted cellular homeostasis and inflammation preceding RGC death, and as the disease progresses, the retinal cells fail to mount a correct stress response to restore neuronal homeostasis. Furthermore, this study provides first-of-its-kind pre-clinical data using targeted gene therapies to rescue RGCs. Understanding the biological crosstalk and signaling mechanisms underlying the death of RGCs in the absence of Elp1 will allow for more targeted and effective therapeutics that will benefit not only the FD community but also individuals affected by other retinal diseases and neurological diseases that result from a faulty Elongator complex. This study provides a novel characterization of the FD retina and establishes baseline methods to further investigate rescuing RGCs.Item A customizable artificial auditory fovea(Montana State University - Bozeman, College of Engineering, 2018) Casebeer, Christopher Ness; Chairperson, Graduate Committee: Ross K. SniderWe, as humans, can separate and attend to audio sources in mixtures of sounds and noise. We can listen distinctly to a friend at a party in a sea of background noise and conversations. Human auditory neurology exceeds even state-of-the-art audio algorithms. How are we able to do this? This dissertation takes inspiration from biology to frame a novel audio processing front-end. Neurobiology shows that auditory neurons isolate signal onsets, timing, frequency, amplitude and modulation characteristics. Why is it then that many standard processing methods choose to ignore this information or make the assumption that machine learning will extract it regardless of input processing? This dissertation uses time-frequency analysis principles towards building a new front-end aimed at preserving these fine temporal and spectral details of the original signal to improve audio system detection and recognition. The system allows keeping the fine frequency and time characteristics of a signal during analysis, while allowing customization of how much and where this resolution is kept. Like biology, this front-end can dedicate resources to detecting important signal events. It can over represent or foveate regions of the time-frequency plane that are important to the signal processing task at hand. These fine details are hypothesized to help enable audio learning algorithms to detect the fine nuances that distinguish musical instruments, determine the characteristics of a specific persons voice, or even detect the emotional state of a person. This customizable auditory fovea aims to mimic the powerful detection capability found in biology which is in contrast to standard methods in audio signal processing.