TAXONOMY, MORPHOLOGY, AND RNA-SEQ TRANSCRIPTOMICS OF THE CUBOZOAN ALATINA ALATA, AN EMERGING MODEL CNIDARIAN

Cnidarians are often considered simple animals, but the more than 13,000 estimated species (e.g., corals, hydroids and jellyfish) of the early diverging phylum exhibit a broad diversity of forms, functions and behaviors, some of which are demonstrably complex. In particular, cubozoans (box jellyfish) are cnidarians that have evolved a number of distinguishing features. Some cubozoan species possess complex mating behaviors or particularly potent stings, and all possess well-developed light sensation involving image-forming eyes. Like all cnidarians, cubozoans have specialized subcellular structures called nematocysts that are used in prey capture and defense. The objective of this study is to contribute to the development of the box jellyfish Alatina alata as a model cnidarian. This cubozoan species offers numerous advantages for investigating morphological and molecular traits underlying complex processes and coordinated behavior in free-living medusozoans (i.e., jellyfish), and more broadly throughout Metazoa. First, I provide an overview of Cnidaria with an emphasis on the current understanding of genes and proteins implicated in complex biological processes in a few select cnidarians. Second, to further develop resources for A. alata, I provide a formal redescription of this cubozoan and establish a neotype specimen voucher, which serve to stabilize the taxonomy of the species. Third, I generate the first functionally annotated transcriptome of adult and larval A. alata tissue and apply preliminary differential expression analyses to identify candidate genes implicated broadly in biological processes related to prey capture and defense, vision and the phototransduction pathway and sexual reproduction and gametogenesis. Fourth, to better understand venom diversity and mechanisms controlling venom synthesis in A. alata, I use bioinformatics to investigate gene candidates with dual roles in venom and digestion, and review the biology of prey capture and digestion in cubozoans. The morphological and molecular resources presented herein contribute to understanding the evolution of cubozoan characteristics and serve to facilitate further research on this emerging cubozoan model.

Tying it Together: Education-Focused Community Change Initiatives

Policymakers make many demands of our schools to produce academic success. At the same time, community organizations, government agencies, faith-based institutions, and other groups often are providing support to students and their families, especially those from high-poverty backgrounds, that are meant to impact education but are often insufficient, uncoordinated, or redundant. In many cases, these institutions lack access to schools and school leaders. What’s missing from the dominant education reform discourse is a coordinated education-focused approach that mobilizes community assets to effectively improve academic and developmental outcomes for students. This study explores how education-focused comprehensive community change initiatives (CCIs) that utilize a partnership approach are organized and sustained. In this study, I examine three research questions: 1. Why and how do school system-level community change initiative (CCI) partnerships form? 2. What are the organizational, financial, and political structures that support sustainable CCIs? What, in particular, are their connections to the school systems they seek to impact? 3. What are the leadership functions and structures found within CCIs? How are leadership functions distributed across schools and agencies within communities? To answer these questions, I used a cross-case study approach that employed a secondary data analysis of data that were collected as part of a larger research study sponsored by a national organization. The original study design included site visits and extended interviews with educators, community leaders and practitioners about community school initiatives, one type of CCI. This study demonstrates that characteristics of sustained education-focused CCIs include leaders that are critical to starting the CCIs and are willing to collaborate across institutions, a focus on community problems, building on previous efforts, strategies to improve service delivery, a focus on education and schools in particular, organizational arrangements that create shared leadership and ownership for the CCI, an intermediary to support the initial vision and collaborative leadership groups, diversified funding approaches, and political support. These findings add to the literature about the growing number of education-focused CCIs. The study’s primary recommendation—that institutions need to work across boundaries in order to sustain CCIs organizationally, financially, and politically—can help policymakers as they develop new collaborative approaches to achieving educational goals.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

Mutual Information-based RBM Neural Networks

(Deep) neural networks are increasingly being used for various computer vision and pattern recognition tasks due to their strong ability to learn highly discriminative features. However, quantitative analysis of their classication ability and design philosophies are still nebulous. In this work, we use information theory to analyze the concatenated restricted Boltzmann machines (RBMs) and propose a mutual information-based RBM neural networks (MI-RBM). We develop a novel pretraining algorithm to maximize the mutual information between RBMs. Extensive experimental results on various classication tasks show the eectiveness of the proposed approach.