The purpose of this study was to examine the effectiveness of relative fundamental frequency (RFF) in quantifying voice disorder severity and possible change with treatment in individuals with Primary Sjögren's Syndrome (SS). Participants completed twice-daily audio recordings during an ABAB within-subjects experimental study investigating the effects of nebulized saline on voice production in this population. Voice samples of the Rainbow Passage from seven of the eight individuals with Primary SS involved in a larger investigation met inclusion criteria for analysis, for a total of 555 tokens. The results indicated that RFF values for this sample were similar to previously reported RFF values for individuals with voice disorders. RFF values improved with nebulized saline treatment but did not fall within the normal range for typical speakers. These findings were similar to other populations of voice disorders who experienced improvement, but not complete normalization, of RFF with treatment. Patient-based factors, such as age and diagnosis as well as measurement and methodological factors, might affect RFF values. The results from this study indicate that RFF is a potentially useful measure in quantifying voice production and disorder severity in individuals with Primary SS.

Similarity searches are an essential component to most bioinformatic applications. They form the bases of structural motif identification, gene identification, and insights into functional associations. With the rapid increase in the available genetic data through a wide variety of databases, similarity searches are an essential tool for accessing these data in an informative and productive way. In our chapter, we provide an overview of similarity searching approaches, related databases, and parameter options to achieve the best results for a variety of applications. We then provide a worked example and some notes for consideration. Homology detection is one of the most basic and fundamental problems at the heart of bioinformatics. It is central to problems currently under intense investigation in protein structure prediction, phylogenetic analyses, and computational drug development. Currently discriminative methods for homology detection, which are not readily interpretable, are substantially more powerful than their more interpretable counterparts, particularly when sequence identity is very low. Here I present a computational graph-based framework for homology inference using physiochemical amino acid properties which aims to both reduce the gap in accuracy between discriminative and generative methods and provide a framework for easily identifying the physiochemical basis for the structural similarity between proteins. The accuracy of my method slightly improves on the accuracy of PSI-BLAST, the most popular generative approach, and underscores the potential of this methodology given a more robust statistical foundation.

The voice plays a vital role in human communication. The purpose of voice research is to advance the understanding of voice production physics, with the ultimate goal of leading to improved voice care. In this research computational and synthetic vocal fold models were used to explore the role of subglottal geometry in vocal fold vibration. Three specific studies were performed. First, the effect of the inferior vocal fold surface angle on voice production was investigated using a two-dimensional self-oscillating finite element vocal fold model. Varying the inferior angle resulted in significant changes to model vibratory motion, glottal width, flow rate, and energy transfer. The changes were attributed primarily to changes in structural, rather than aerodynamic, factors. Second, subglottic stenosis (SGS) was introduced and parametrically varied in a similar computational model to determine the influence of SGS on vocal fold vibration. High severities of SGS influenced several factors related to vibration, including glottal width, flow rate, flow resistance, and vibration frequency. Subglottal pressure distributions and flow patterns were also affected. Third, the response of a self-oscillating silicone vocal fold model to varying degrees of SGS in an experimental setup was studied. Consistent with the computational SGS study, SGS had an effect on the synthetic model response at high severities. Changes were seen particularly in subglottal pressure and radiated acoustic sound, and consequently glottal efficiency, which may have important implications regarding the effect of SGS on the human voice.

Self-oscillating synthetic vocal fold (VF) models are often used to study human voice production. In this thesis, a method for fabricating multi-layer self-oscillating synthetic VF models using silicone 3D printing is presented. Multi-material 3D printing enables faster fabrication times with more complex geometries than traditional casting methods and builds a foundation for producing VF models with potentially more life-like geometries, materials, and vibratory characteristics. The printing method in this study used a custom dual extruder and slicing software to print UV-curable liquid silicone into a gel-like support matrix. The extruder was fabricated using high-torque stepper motors with high resolution leadscrews for precise extrusion and retraction. The custom slicing software accounted for challenges with printing a low-viscosity uncured silicone and was capable of allowing the user to visually observe the effects of print settings on print paths before finalizing the g-code. Three validation tests were conducted to demonstrate the 3D printer’s ability to print ultra-soft silicone with the desired range of stiffness, change between materials quickly, and print a material stiffness gradient. Two types of VF models were printed in this study, a previously-designed model with multiple distinct layers (“EPI” model), and the same model but with a vertical stiffness gradient (VSG) in the superficial lamina propria layer. The EPI model was chosen to demonstrate the ability to 3D print a multi-layer model, and the VSG model was chosen to demonstrate the ability to print multi-material VFs with geometric and material properties that are difficult to fabricate using traditional casting methods. Sixteen VFs (i.e., eight pairs) of each model type were printed, and their vibratory responses were recorded, including onset pressure, frequency, and glottal width. A micro-CT scanner was used to evaluate the external geometric accuracy of the models. One-centimeter cubes were 3D printed and tensile tested to characterize the material properties of each set of VF models. The material and phonatory properties of both the EPI and VSG VF models were found to be comparable to human data and to previous data acquired using synthetic VF models fabricated via other methods. In this thesis, the 3D printing methodology is summarized, the setup and results of the validation and VF model tests are reported and discussed, and recommendations for future work are provided.

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<p>Vocal fold vibration results in voice production. Optimal hydration levels contribute to self-sustained vocal fold vibration and preservation of voice quality. Adequate hydration is implicated as a factor in maintaining voice and preventing voice problems. Voice problems affect up to one third of adults during their lifetime. But whether altered hydration state adversely affects vocal fold biology and biomechanics is still unclear. To untangle the effects of systemic dehydration on vocal fold biology, we developed a water restriction protocol on lab animals that can translate to humans. Our results showed that dehydration induced by restricted water access downregulated the gene expression of IL-1α and desmoglein-1, upregulated the gene expression level of hyaluronidase-2, and downregulated hyaluronic acid (HA).</p> <p>Clinically, hydration treatments are hypothesized to maintain the viscoelastic properties of vocal folds. However, our understanding of the relationship between vocal fold tissue hydration level and biomechanical properties is still evolving. To investigate the effects of dehydration on biomechanical properties we used an ex vivo experimental design. We hypothesized that the optimal stiffness of vocal folds would be impacted after dehydration via losing both water and HA, but that the stiffness properties would recover through rehydration. To test this hypothesis, we experimentally treated porcine vocal fold samples using two different approaches: 1) immersion in hypertonic solution (15% NaCl in ddH2O) and PBS sequentially to mimic dehydration and rehydration, and 2) incubation with hyaluronidase (Hyal) to mimic HA loss during dehydration. Our results showed that loss of water increased tissue stiffness and could be recovered through rehydration in a certain degree. In addition, loss of HA increased tissue stiffness. </p> <p>In While dehydration decreases total body blood volume, different tissues and organs of the body may be impacted in different ways from dehydration. Therefore, it is important to investigate the hemodynamic alterations during changes to hydration status. Magnetic resonance angiography (MRA) and ultrasound imaging were employed to identify the delicate vascular geometry and hemodynamics of the laryngeal blood supply. Animals underwent both MRA and ultrasound imaging at baseline, dehydration and rehydration time points. Our results showed that dehydration impacted the blood supply to larynx. This blood supply was restored through rehydration treatment.</p> <p>Overall, this research has been successful in establishing a mild dehydration animal model, providing evidence from gene and protein levels that dehydration affects cytokine production and extracellular matrix components (ECM) in vocal fold, demonstrating the vocal fold tissue biomechanical behavior after dehydration and loss of HA, and offering a combination application of MRA and ultrasound imaging to study vascular geometry and hemodynamics of the blood supply to the vocal fold region.</p>

<p>Rapid solidification is a well established method to produce novel materials with improved mechanical or electrical properties. The sharp-interface kinetics of rapid solidification for a binary alloy is summarized. A Phase Field model mapping to this sharp interface model is summarized and solved by a new adaptive mesh refinement algorithm. Simulation results are consistent with experiments: The solidification velocity increases in power-law like fashion at low undercooling and approximately linearly at high undercooling; The solid/liquid interface undergoes a transition from four-fold dendritic to circular crystal structures; Solute trapping emerges and the solute partitioning approaches unity as the solidification velocity increases. Our Phase Field simulations are the first self -consistent predictions of velocity selection and morphological selection at both low and high undercoolings and also the first independent check of the solute trapping model in two dimensions.</p> / Thesis / Master of Applied Science (MASc)

The human voice is a product of an intricate biophysical system. The complexity of this system enables a rich variety of possible sounds, but at the same time poses great challenges for quantitative voice analysis. For example, the vocal folds can vibrate in several different ways, leading to variations in the acoustic output. Because the vocal folds are relatively inaccessible, such variations are often difficult to account for. This work proposes a novel method for extracting non-invasively information on the vibratory state of the human vocal folds. Such information is important for creating a more complete voice analysis scheme. Invasive methods are undesirable because they often disturb the subjects and/or the studied phenomena, and they are also impractical in terms of accessibility and cost. A useful frame of reference for voice analysis is the Voice Range Profile (VRP). The 3 dimensional form of the VRP can be used to depict any phonatory metric over the 2 dimensional plane defined by the fundamental frequency of phonation (x-axis) and the sound pressure level (y-axis). The primary goal of this work was to incorporate information on the vibratory state of the vocal folds into the Voice Range Profile (e.g., as a color change). For this purpose, a novel method of analysis of the electroglottogram (EGG) was developed, using techniques from machine learning (clustering) and nonlinear time series analysis (sample entropy estimation). The analysis makes no prior assumptions on the nature of the EGG signal and does not rely on its absolute amplitude or frequency. Unlike time-domain methods, which typically define thresholds for quantifying EGG cycle metrics, the proposed method uses information from the entire cycle of each period. The analysis was applied in a variety of experimental conditions (constant vowel with different vibratory states, constant vibratory state and different vowels, constant vowel and vibratory state with varying lung volume) and the magnitude of effect on the EGG short-term spectrum was estimated for each of these conditions. It was found that the short-term spectrum of the EGG signal sufficed to discriminate between different phonatory configurations, such as modal and falsetto voice. It was found also that even supposedly purely articulatory changes could be traced in the spectrum of the EGG signal. Finally, possible pedagogical and clinical applications of the method are discussed. / <p>QC 20140609</p> / FonaDyn