OVERCOMING INTERCULTURAL CHALLENGES IN INTERPRETING: A CASE STUDY ON CHINESE-ENGLISH CONFERENCE INTERPRETING

This exploratory case study examines the role of culture in Chinese-English conference interpreting. Given that there has been a lack of empirical research in understanding the role of culture in conference interpreting through the lens of intercultural communication frameworks, we know relatively little about conference interpreters’ experiences with intercultural communication challenges. This project helps address this research gap by investigating the types of intercultural communication challenges that Chinese-English conference interpreters experience and their strategies in managing those challenges. This study hears the voices of both professionals and postgraduate interpreting students. A total number of 27 participants were recruited for this research. Twenty professional conference interpreter were interviewed and seven interpreting students were organized for a focus group discussion. Grounded theory was used to analyze the participants’ observations and strategies in managing intercultural communication challenges when doing Chinese-English conference interpreting. The data analysis process led to the emergence of two procedural guidelines and one process – Interpreters’ Intercultural Mediation Process. The two procedural guidelines offer guidance for the interpreters to provide the most appropriate and effective service: meet with the clients beforehand and be prepared to offer intercultural insights when consulted. Interpreters are found to follow the Interpreters’ Intercultural Mediation Process to decide when and how to mediate intercultural communication challenges at work. This Process includes four criteria, seven intercultural challenges, and seven coping strategies. This study offers theoretical and applied contributions to our understanding of the role of culture in interpreting. By jointly applying frameworks from intercultural communication and interpreting studies to examine the conference interpreting process, this case study makes great efforts to connect the field of intercultural communication with the field of interpreting studies. This study identifies the types of intercultural differences that would lead to challenges in Chinese-English conference interpreting. It also contributes to the call for a cultural turn in interpreting studies. By learning the two procedural guidelines, conference interpreters can be better prepared for their work. By following the Interpreters’ Intercultural Mediation Process, conference interpreters can better anticipate and manage the intercultural challenges at work. This study also offers guidance on tailoring intercultural communication courses for postgraduate interpreting training programs.

“PROSPERING BECAUSE THAT’S ITS HISTORY”: BLACK RESILIENCE AND HONORS DEVELOPMENT IN HIGHER EDUCATION: MORGAN STATE UNIVERSITY AND THE STATE OF MARYLAND, 1867-1988

This study explores the origins and development of honors education at a Historically Black College and University (HBCU), Morgan State University, within the context of the Maryland higher education system. During the last decades, public and private institutions have invested in honors experiences for their high-ability students. These programs have become recruitment magnets while also raising institutional academic profiles, justifying additional campus resources. The history of higher education reveals simultaneous narratives such as the tension of post-desegregated Black colleges facing uncertain futures; and the progress of the rise and popularity of collegiate honors programs. Both accounts contribute to tracing seemingly parallel histories in higher education that speaks to the development of honors education at HBCUs. While the extant literature on honors development at Historically White Institutions (HWIs) of higher education has gradually emerged, our understanding of activity at HBCUs is spotty at best. One connection of these two phenomena is the development of honors programs at HBCUs. Using Morgan State University, I examine the role and purpose of honors education at a public HBCU through archival materials and oral histories. Major unexpected findings that constructed this historical narrative beyond its original scope were the impact of the 1935/6 Murray v Pearson, the first higher education desegregation case. Other emerging themes were Morgan’s decades-long efforts to resist state control of its governance, Maryland’s misuse of Morrill Act funds, and the border state’s resistance to desegregation. Also, the broader histories of Black education, racism, and Black citizenship from Dred Scott and Plessy, the 1863 Emancipation Proclamation to Brown, inform this study. As themes are threaded together, Critical Race Theory provides the framework for understanding the emerging themes. In the immediate wake of the post-desegregation era, HBCUs had to address future challenges such as purpose and mission. Competing with HWIs for high-achieving Black students was one of the unanticipated consequences of the Brown decision. Often marginalized from higher education research literature, this study will broaden the research repository of honors education by documenting HBCU contributions despite a challenging landscape.

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.