A production of Paul Baker's Hamlet ESP

This thesis is a record of a UNC-G, Summer Repertory Theatre production of Hamlet ESP by Paul Baker, performed in Taylor Theatre on June 19, 20, 22, 24, 26, and 23, and July 1, 3, and 5 of 1974. The first chapter provides introductory analysis: of the play "Hamlet," the play in adaptation, and detailed analysis of the characters, setting, lights and costumes. A discussion of considerations influencing the choice of the script is also included. The Promptbook comprises the second chapter. In it are notations of movement, composition, business, and characterization, as well as cues for sound, lights, special effects, and curtains. Photographs and diagrams are integrated to demonstrate composition, focus, and picturization. In the third chapter are critical evaluations of the audience response and the acting, along with discussion of technical considerations which weighed in the over-all success of the production, and/or the limitations of that success

Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends

Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections.Influenza activity data from 2003-04 through 2007-08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson's correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003-04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90).This analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003-04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data

<p>Abstract</p> <p>Background</p> <p>The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.</p> <p>Results</p> <p>Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for <it>eve </it>mRNA pattern formation in the <it>Drosophila melanogaster </it>blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same <it>cis</it>-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.</p> <p>Conclusions</p> <p>Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.</p

Purification and characterization of native human insulin-like growth factor binding protein-6

Insulin-like growth factor binding proteins (IGFBPs) are key regulators of insulin-like growth factor (IGF) mediated signal transduction and thereby can profoundly influence cellular phenotypes and cell fate. Whereas IGFBPs are extracellular proteins, intracellular activities were described for several IGFBP family members, such as IGFBP-3, which can be reinternalized by endocytosis and reaches the nucleus through routes that remain to be fully established. Within the family of IGFBPs, IGFBP-6 is unique for its specific binding to IGF-II. IGFBP-6 was described to possess additional IGF-independent activities, which have in part been attributed to its translocation to the nucleus; however, cellular uptake of IGFBP-6 was not described. To further explore IGFBP-6 functions, we developed a new method for the purification of native human IGFBP-6 from cell culture supernatants, involving a four-step affinity purification procedure, which yields highly enriched IGFBP-6. Whereas protein purified in this way retained the capacity to interact with IGF-II and modulate IGF-dependent signal transduction, our data suggest that, unlike IGFBP-3, human IGFBP-6 is not readily internalized by human tumor cells. To summarize, this work describes a novel and efficient method for the purification of native human insulin-like growth factor binding protein 6 (IGFBP-6) from human cell culture supernatants, applying a four-step chromatography procedure. Intactness of purified IGFBP-6 was confirmed by IGF ligand Western blot and ability to modulate IGF-dependent signal transduction. Cellular uptake studies were performed to further characterize the purified protein, showing no short-term uptake of IGFBP-6, in contrast to IGFBP-3