The conformational landscape of RNA translational regulators and their potential as drug discovery targets

RNA is an underutilized target for drug discovery. Once thought to be a passive carrier of genetic information, RNA is now known to play a critical role in essentially all aspects of biology including signaling, gene regulation, catalysis, and retroviral infection. It is now well-established that RNA does not exist as a single static structure, but instead populates an ensemble of energetic minima along a free-energy landscape. Knowledge of this structural landscape has become an important goal for understanding its diverse biological functions. In this case, NMR spectroscopy has emerged as an important player in the characterization of RNA structural ensembles, with solution-state techniques accounting for almost half of deposited RNA structures in the PDB, yet the rate of RNA structure publication has been stagnant over the past decade. Several bottlenecks limit the pace of RNA structure determination by NMR: the high cost of isotopic labeling, tedious and ambiguous resonance assignment methods, and a limited database of RNA optimized pulse programs. We have addressed some of these challenges to NMR characterization of RNA structure with applications to various RNA-drug targets. These approaches will increasingly become integral to designing new therapeutics targeting RNA.

NITROUS OXIDE EMISSIONS IN COVER CROP-BASED CORN PRODUCTION SYSTEMS

Nitrous oxide (N2O) is a potent greenhouse gas; the majority of N2O emissions are the result of agricultural management, particularly the application of N fertilizers to soils. The relationship of N2O emissions to varying sources of N (manures, mineral fertilizers, and cover crops) has not been well-evaluated. Here we discussed a novel methodology for estimating precipitation-induced pulses of N2O using flux measurements; results indicated that short-term intensive time-series sampling methods can adequately describe the magnitude of these pulses. We also evaluated the annual N2O emissions from corn-cover crop (Zea mays; cereal rye [Secale cereale], hairy vetch [Vicia villosa], or biculture) production systems when fertilized with multiple rates of subsurface banded poultry litter, as compared with tillage incorporation or mineral fertilizer. N2O emissions increased exponentially with total N rate; tillage decreased emissions following cover crops with legume components, while the effect of mineral fertilizer was mixed across cover crops.

Home Literacy Activities in Latino Immigrant Families: Contributions to Toddlers’ Expressive and Receptive Language Skills

The development of language is a critical component of early childhood, enabling children to communicate their wishes and desires, share thoughts, and build meaning through linguistic interactions with others. A wealth of research has highlighted the importance of children’s early home experiences in fostering language development. This literature emphasizes the importance of a stimulating and supportive home environment in which children are engaged in literacy activities such as reading, telling stories, or singing songs with their parents. This study examined the association between low-income Latino immigrant mothers’ and fathers’ home literacy activities and their children’s receptive and expressive language skills. It also examined the moderating influence of maternal (i.e., reading quality and language quality) and child (engagement during reading, interest in literacy activities) characteristics on this association. This study included observational mother-child reading interactions, child expressive and receptive language assessments, and mother- and father-reported survey data. Controlling for parental education, multiple regression analyses revealed a positive association between home literacy activities and children’s receptive and expressive language skills. The findings also revealed that mothers’ reading quality and children’s engagement during reading (for expressive language skills only) moderated this association. Findings from this study will help inform new interventions, programs, and policies that build on Latino families’ strengths.

Relations among Topic Knowledge, Individual Interest, and Relational Reasoning, and Critical Thinking in Maternity Nursing

Critical thinking in learners is a goal of educators and professional organizations in nursing as well as other professions. However, few studies in nursing have examined the role of the important individual difference factors topic knowledge, individual interest, and general relational reasoning strategies in predicting critical thinking. In addition, most previous studies have used domain-general, standardized measures, with inconsistent results. Moreover, few studies have investigated critical thinking across multiple levels of experience. The major purpose of this study was to examine the degree to which topic knowledge, individual interest, and relational reasoning predict critical thinking in maternity nurses. For this study, 182 maternity nurses were recruited from national nursing listservs explicitly chosen to capture multiple levels of experience from prelicensure to very experienced nurses. The three independent measures included a domain-specific Topic Knowledge Assessment (TKA), consisting of 24 short-answer questions, a Professed and Engaged Interest Measure (PEIM), with 20 questions indicating level of interest and engagement in maternity nursing topics and activities, and the Test of Relational Reasoning (TORR), a graphical selected response measure with 32 items organized in scales corresponding to four forms of relational reasoning: analogy, anomaly, antithesis, and antinomy. The dependent measure was the Critical Thinking Task in Maternity Nursing (CT2MN), composed of a clinical case study providing cues with follow-up questions relating to nursing care. These questions align with the cognitive processes identified in a commonly-used definition of critical thinking in nursing. Reliable coding schemes for the measures were developed for this study. Key findings included a significant correlation between topic knowledge and individual interest. Further, the three individual difference factors explained a significant proportion of the variance in critical thinking with a large effect size. While topic knowledge was the strongest predictor of critical thinking performance, individual interest had a moderate significant effect, and relational reasoning had a small but significant effect. The findings suggest that these individual difference factors should be included in future studies of critical thinking in nursing. Implications for nursing education, research, and practice are discussed.

The Effect of Architecture and Shear Stress on Endothelialization of 3D Printed Vascular Networks

Despite significant progress in the field of tissue engineering within the last decade, a number of unsolved problems still remain. One of the most relevant issues is the lack of proper vascularization that limits the size of engineered tissues to smaller than clinically relevant dimensions. In particular, the growth of engineered tissue in vitro within bioreactors is plagued with this challenge. Specifically, the tubular perfusion system bioreactor has been used for large scale bone constructs; however these engineered constructs lack inherent vasculature and quickly develop a hypoxic core, where no nutrient exchange can occur, thus leading to cell death. Through the use of 3D printed vascular templates in conjunction with a tubular perfusion system bioreactor, we attempt to create an endothelial cell monolayer on 3D scaffolds that could potentially serve as the foundation of inherent vasculature within these engineered bone grafts.

TESTING THE JOINT ROLES OF CAREER DECISION SELF-EFFICACY AND PERSONALITY TRAITS IN THE PREDICTION OF CAREER INDECISION

Career decision-making self-efficacy and the Big Five traits of neuroticism, extraversion, and conscientiousness were examined as predictors of career indecision in a sample of 181 undergraduates. Participants completed an online survey. I predicted that the Big Five traits and career decision-making self-efficacy would (a) interrelate moderately and (b) each relate significantly and moderately to career indecision. In addition, I predicted that career decision-making self-efficacy would partially mediate the relationships between the Big Five traits and career indecision, while the Big Five traits were predicted to moderate the relationship between career decision-making self-efficacy and career indecision. Finally, I predicted that career decision-making self-efficacy would account for a greater amount of unique variance in career indecision than the Big Five traits. All predicted correlations were significant. Career decision-making self-efficacy fully mediated the relationship of Extraversion to career indecision and partially mediated the relationships of Neuroticism and Conscientiousness to career indecision. Conscientiousness was found to moderate the relationship of career decision-making self-efficacy to career indecision such that the negative relation between self-efficacy and career indecision was stronger in the presence of high conscientiousness. This study builds upon existing research on the prediction of career indecision by examining potential mediating and moderating relationships.

The Drama of History: Representation and Revolutionaries in Haitian Theater, 1818-1907

Since the beginning of the Haitian theatrical tradition there has been an ineluctable dedication to the representation of Haitian history on stage. Given the rich theatrical archive about Haiti throughout the world, this study considers operas and plays written solely by Haitian playwrights. By delving into the works of Juste Chanlatte, Massillon Coicou, and Vendenesse Ducasse this study proposes a re-reading of Haitian theater that considers the stage as an innovative site for contesting negative and clichéd representations of the Haitian Revolution and its revolutionary leadership. A genre long mired in accusations of mimicking European literary forms, this study proposes a reevaluation of Haitian theater and its literary origins.

The Sugar Factory

Luke Banas is a young video artist who lives illegally in the disused Domino sugar refinery in Williamsburg, Brooklyn. While his art is an attempt to fully record and share his own life story, developers want to tear down the building where he works; a building that’s a monument to his hip neighborhood’s industrial past. The novel’s narrator, Lila Fairfax, is a journalist writing her first feature article about Luke and the fate of the factory. Observant and astute, she soon realizes that, despite his obsessive self-revelation, Luke is hiding a secret. Lila’s rational, detached approach to life is disrupted as, in the course of her reporting, she falls in love with Luke and as a result, learns far more than she anticipated. Though primarily a love story, The Sugar Factory is also an investigation of art, and art’s interaction with commerce, history, and new technology.

Sexism and Imperialism in Mozart's Don Giovanni, Puccini's Madama Butterfly, and Blitzstein's Regina: A Performance Study

This dissertation project comprises three major operatic performances and an accompanying document; a performance study which surveys aspects of sexism and imperialism as represented in three operas written over the last three centuries by examining the implications of prejudice through research as well as through performances of the major roles found in the operas. Mr. Eversole performed the role of Sharpless in the 2014 Castleton Festival production of Madama Butterfly (music by Giacomo Puccini, libretto by Luigi Illica and Giuseppe Giacosa), conducted by Bradley Moore. In 2015, Mr. Eversole sang the title role in four performances of Mozart and Da Ponte’s Don Giovanni with the Maryland Opera Studio at the Clarice Smith Performing Arts Center, conducted by Craig Kier. Also as part of the Maryland Opera Studio 2015-16 season, Mr. Eversole appeared as Oscar Hubbard in four performances of Marc Blitzstein’s Regina, an adaptation of Lillian Hellman’s 1939 play, The Little Foxes. These performances were also conducted by Craig Kier. The accompanying research document discusses significant issues of cultural, geographical, and sexual hegemony as they relate to each opera. It examines the plots and characters of the operas from a postcolonial and feminist perspective, and takes a moral stance against imperialism, sexism, domestic abuse, and in general, the exploitation of women and of the colonized by the socially privileged and powerful. Recordings of all three operas can be accessed at the University of Maryland Hornbake Library. They are: Giacomo Puccini’s Madama Butterfly (the role of Sharpless) July 20, 2014, Castleton Festival production, Bradley Moore, Conductor Castleton, Virginia Wolfgang Amadeus Mozart’s Don Giovanni (title role) November 22nd, 2015, Maryland Opera Studio, Craig Kier, Conductor Clarice Smith Performing Arts Center, UMD Marc Blitzstein’s Regina, (Oscar Hubbard) April 8th, 8016, Maryland Opera Studio, Craig Kier, Conductor Clarice Smith Performing Arts Center, UMD

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

Contrasts: Quartets and Art Songs of the Nineteenth Century

The nineteenth-century Romantic era saw the development and expansion of many vocal and instrumental forms that had originated in the Classical era. In particular, the German lied and French mélodie matured as art forms, and they found a kind of equilibrium between piano and vocal lines. Similarly, the nineteenth-century piano quartet came into its own as a form of true chamber music in which all instruments participated equally in the texture. Composers such as Robert Schumann, Johannes Brahms, and Gabriel Fauré offer particularly successful examples of both art song and piano quartets that represent these genres at their highest level of artistic complexity. Their works have become the cornerstones of the modern collaborative pianist’s repertoire. My dissertation explored both the art songs and the piano quartets of these three composers and studied the different skills needed by a pianist performing both types of works. This project included the following art song cycles: Robert Schumann’s Dichterliebe, Gabriel Fauré’s Poème d’un Jour, and Johannes Brahms’ Zigeunerlieder. I also performed Schumann’s Piano Quartet in E-flat Major, Op. 47, Fauré’s Piano Quartet in C minor, Op. 15, and Brahms’ Piano Quartet in G minor, Op. 25. My collaborators included: Zachariah Matteson, violin and viola; Kristin Bakkegard, violin; Molly Jones, cello; Geoffrey Manyin, cello; Karl Mitze, viola; Emily Riggs, soprano, and Matthew Hill, tenor. This repertoire was presented over the course of three recitals on February 13, 2015, December 11, 2015, March 25, 2016 at the University of Maryland’s Gildenhorn Recital Hall. These recitals can be found in the Digital Repository at the University of Maryland (DRUM).

BLACK SURVIVAL POLITICS: ORGANIZED MOBILIZATION STRATEGIES IN AFRICAN AMERICAN COMMUNITIES TO END THE HIV/AIDS EPIDEMIC

The purpose of this study is to examine organizational patterns of African American activism in response the HIV/AIDS epidemic. Given their political, economic, and social disenfranchisement, African Americans have historically developed protest and survival strategies to respond to the devaluation of their lives, health, and well-being. While Black protest strategies are typically regarded as oppositional and transformative, Black survival strategies have generally been conceptualized as accepting inequality. In the case of HIV/AIDS, African American religious and non-religious organizations were less likely to deploy protest strategies to ensure the survival and well-being of groups most at risk for HIV/AIDS—such as African American gay men and substance abusers. This study employs a multiple qualitative case study analysis of four African American organizations that were among the early mobilizers to respond to HIV/AIDS in Washington D.C. These organizations include two secular or community-based organizations and two Black churches or faith-based organizations. Given the association of HIV/AIDS with sexual sin and social deviance, I postulated that Black community-based organizations would be more responsive to the HIV/AIDS-related needs and interests of African Americans than their religious counterparts. More specifically, I expected that Black churches would be more conservative (i.e. maintain paternalistic heteronormative sexual standards) than the community-based organizations. Yet findings indicate that the Black churches in this study were more similar than different than the community-based organizations in their strategic responses to HIV/AIDS. Both the community-based organizations and Black churches drew upon three main strategies in ways that politicalize the struggle for Black survival—or what I regard as Black survival politics. First, Black survival strategies for HIV/AIDS include coalition building at the intersection of multiple systems of inequality, as well as on the levels of identity and community. Second, Black survival politics include altering aspects of religious norms and practices related to sex and sexuality. Third, Black survival politics relies on the resources of the government to provide HIV/AIDS related programs and initiatives that are, in large part, based on the gains made from collective action.

Absolutely Continuous Spectrum for Parabolic Flows/Maps

This work is devoted to creating an abstract framework for the study of certain spectral properties of parabolic systems. Specifically, we determine under which general conditions to expect the presence of absolutely continuous spectral measures. We use these general conditions to derive results for spectral properties of time-changes of unipotent flows on homogeneous spaces of semisimple groups regarding absolutely continuous spectrum as well as maximal spectral type; the time-changes of the horocycle flow are special cases of this general category of flows. In addition we use the general conditions to derive spectral results for twisted horocycle flows and to rederive spectral results for skew products over translations and Furstenberg transformations.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.

The Space Between

I approach my practice through the truth that art is inseparable from reality. Reducing art to a single idea is an unnatural limitation because the creative process and its manifestations result from many parallel ideas, instincts, emotions and reflections. In the following, I trace the central sources of the inspiration for work and attempt to bridge the experiential and intuitive processes that concurrently fuel my creative process.