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Description
This study was conducted as part of an underlying initiative to elucidate the mechanism of action of natural antibacterial clay minerals for application as therapeutic agents for difficult-to-treat infections such as methicillin-resistant Staphylococcus aureus (MRSA)-derived skin lesions and Buruli ulcer. The goal of this investigation was to determine whether exposure to the leachate of an antibacterial clay mineral, designated as CB, produced DNA double-strand breaks (DSBs) in Escherichia coli. A neutral comet assay for bacterial cells was adapted to assess DSB levels upon exposure to soluble antimicrobial compounds. Challenges involved with the adaptation process included comet visualization and data collection. To appropriately account for antimicrobial-mediated strand fragmentation, suitable control reactions comprised of exposures to water, ethanol, kanamycin, and bleomycin were developed and optimized for the assay. Bacterial exposure to CB resulted in significantly longer comet lengths compared to negative control exposures, suggesting that CB killing activity involves the induction of DNA DSBs. The results of this investigation further characterize the antimicrobial mechanisms associated with a particular clay mineral mixture. The adapted comet assay protocol described herein functions as an effective tool to assess double-strand fragmentation resulting from exposure to soluble antimicrobial compounds and to visually compare results from experimental and control reactions.
ContributorsSolanky, Dipesh (Author) / Haydel, Shelley (Thesis director) / Stout, Valerie (Committee member) / Adusumilli, Sarojini (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
The deficiency of American primary and secondary schools as compared to schools worldwide has long been documented. The Teaching Gap highlights exactly where our problems might lie, and lays out a plan for how to deal with it. Progress would be slow, but tangible. Our country, however, seems to prefer vast and immediate overhauls that have historically failed (see: New math, etc.). If we had implemented the changes in The Teaching Gap in the decade in which it was written, we would be seeing results by now. Instead, every change we make gets reverted. The Common Core State Standards will prove over the next few years to be either another one of these attempts or a large step in the right direction. It might finally be the latter, as its creation was informed by practices that work best in every state in the US, as well as high- performing countries around the world.
ContributorsMcKee, Emily (Author) / Sande, V. Carla (Thesis director) / Ashbrook, Mark (Committee member) / Schroeder, Darcy (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
Identifying associations between genotypes and gene expression levels using next-generation technology has enabled systematic interrogation of regulatory variation underlying complex phenotypes. Understanding the source of expression variation has important implications for disease susceptibility, phenotypic diversity, and adaptation (Main, 2009). Interest in the existence of allele-specific expression in autosomal genes evolved with the increased awareness of the important role that variation in non-coding DNA sequences can play in determining phenotypic diversity, and the essential role parent-of-origin expression has in early development (Knight, 2004). As new implications of high-throughput sequencing are conceived, it is becoming increasingly important to develop statistical methods tailored to large and formidably complex data sets in order to maximize the biological insights derived from next-generation sequencing experiments. Here, a Bayesian hierarchical probability model based on the beta-binomial distribution is proposed as a possible approach for quantifying allele-specific expression from whole genome (WGS) and whole transcriptome (RNA-seq) data. Pipeline for the analysis of WGS and RNA-seq data sets from ten samples was developed and implemented, while allele-specific expression (ASE) was quantified from both haplotypes using individuals heterozygous at the tested variants utilizing the described methodology. Both computational and statistical framework applied accurately quantified ASE, achieving high reproducibility of already described allele-specific genes in the literature. In conclusion, described methodology provides a solid starting point for quantifying allele specific expression across whole genomes.
ContributorsMalenica, Ivana (Author) / Craig, David (Thesis director) / Rosenberg, Michael (Committee member) / Szelinger, Szabolcs (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12