The Demobilized Body: Transgressions of Personal Space and Political Participation

Grounded in the intersection between gender politics and electoral studies, this dissertation examines the demobilizing effects of violations of personal space (in the form of domestic violence, control over mobility, emotional abuse, and sexual harassment) on the propensity to vote. Using quantitative methods across four survey datasets concerning Lebanon, the United States, Morocco, and Yemen, this research concludes that cross-regionally, familial control over mobility reduces the propensity to vote among women. Conversely, mechanisms of empowerment such as education and employment increase the propensity to vote.

The Effect of Attentional Focus on Singing Voice Quality: Towards the Interdisciplinary Experimental Investigation of Singing Pedagogy

Instructional methods employed by teachers of singing are mostly drawn from personal experience, personal reflections, and methods encountered in their own voice training (Welch & Howard, 2005). Even in Academia, singing pedagogy is one of the few disciplines in which research of teaching/learning practice efficacy has not been established (Crocco, et al., 2016). This dissertation argues the reason for this deficit is a lack of operationalization of constructs in singing, which, to date has not been undertaken. The researcher addresses issues of paradigm, epistemology, and methodology to suggest an appropriate model of experimental research towards the assessment of teaching/learning practice efficacy. A study was conducted adapting attentional focus research methodologies to test the effect of attentional focus on singing voice quality in adult novice singers. Based on previous attentional focus studies, it was hypothesized that external focus conditions would result in superior singing voice quality than internal focus conditions. While the hypothesis was partially supported by the data, the researcher welcomed refinement of the suggested research model. It is hoped that new research methodologies will emerge to investigate singing phenomena, yielding data that may be used towards the development of evidence-based frameworks for singing training.

“In The Good Old Days of Long Ago": Echoes of Vaudeville and Minstrelsy in the Music of Uncle Dave Macon

Uncle Dave Macon provided an essential link between nineteenth-century, urban popular stage music (especially the minstrel show and vaudeville) and commercialized country music of the 1920s. He preserved through his recordings a large body of songs and banjo techniques that had their origins in urban-based, nineteenth-century vaudeville and minstrelsy. Like the minstrel and vaudeville performers of the nineteenth century, Macon told jokes and stories, employed attention-grabbing stage gimmicks, marketed himself with boastful or outrageous slogans, and dressed with individual flair. At the same time, Macon incorporated many features from the rural-based folk music of Middle Tennessee. Overall, Macon’s repertoire, musical style, and stage persona (which included elements of the rube, country gentleman, and old man) demonstrated his deep absorption, and subsequent reinterpretation, of nineteenth-century musical traditions. Macon’s career offers a case study in how nineteenth-century performance styles, repertoire, and stage practices became a part of country music in the 1920s. As an artist steeped in two separate, but overlapping, types of nineteenth-century music—stage and folk—Macon was well-positioned to influence the development of the new commercial genre. He brought together several strains of nineteenth-century music to form a modern, twentieth-century musical product ideally suited to the new mass media of records, radio, and film. By tracing Macon’s career and studying his music, we can observe how the cross-currents of rural and popular entertainment during the nineteenth and early twentieth centuries interacted to form the commercial genre we now know as country music.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.