Costume Design for Qui Nguyen’s She Kills Monsters

The following thesis details the process of creating costume designs and implementing them in the production of She Kills Monsters which was produced at the University of Arkansas Black Box Theatre in the Fall of 2018. Throughout this thesis I will explain how the costumes went from initial research and conversations with the director to the final products on the stage. The account of this includes an analysis of the script, collages of photos used for inspiration, portrait gallery, research, rough sketches, renderings with fabric swatches, production photos, and my evaluation of the process as a whole

Slowly cycling Rho kinase-dependent actomyosin cross-bridge slippage explains intrinsic high compliance of detrusor smooth muscle

Biological soft tissues are viscoelastic because they display timeindependent pseudoelasticity and time-dependent viscosity. However, there is evidence that the bladder may also display plasticity, defined as an increase in strain that is unrecoverable unless work is done by the muscle. In the present study, an electronic lever was used to induce controlled changes in stress and strain to determine whether rabbit detrusor smooth muscle (rDSM) is best described as viscoelastic or viscoelastic plastic. Using sequential ramp loading and unloading cycles, stress-strain and stiffness-stress analyses revealed that rDSM displayed reversible viscoelasticity, and that the viscous component was responsible for establishing a high stiffness at low stresses that increased only modestly with increasing stress compared with the large increase produced when the viscosity was absent and only pseudoelasticity governed tissue behavior. The study also revealed that rDSM underwent softening correlating with plastic deformation and creep that was reversed slowly when tissues were incubated in a Ca2+ -containing solution. Together, the data support a model of DSM as a viscoelastic-plastic material, with the plasticity resulting from motor protein activation. This model explains the mechanism of intrinsic bladder compliance as slipping cross bridges, predicts that wall tension is dependent not only on vesicle pressure and radius but also on actomyosin cross-bridge activity, and identifies a novel molecular target for compliance regulation, both physiologically and therapeutically

Land cover classification and change during mine reclamation in Northeast Florida using multispectral imagery

Titanium is commonly used in the aerospace industry because it is lightweight and durable in extreme temperatures. The Green Cove Springs Mine, near Green Cove Springs, Florida, was used for mining and processing titanium and other minerals from 1972 until 2009. Since then, the mine area is being reclaimed, or rehabilitated and restored to its natural state, as required by Florida Department of Environmental Protection regulations. In this study, we use high-resolution multispectral satellite and aerial imagery obtained from 2016 to 2022 to examine land cover (LC) changes at the Green Cove Springs Mine during this reclamation period. We use the National Land Cover Database (NLCD; 30-m resolution) to identify regions of no LC change and train a support vector machine classification of LC across the entire mine area at a much finer scale

Online EM Algorithm for Latent Data Models

In this contribution, we propose a generic online (also sometimes called adaptive or recursive) version of the Expectation-Maximisation (EM) algorithm applicable to latent variable models of independent observations. Compared to the algorithm of Titterington (1984), this approach is more directly connected to the usual EM algorithm and does not rely on integration with respect to the complete data distribution. The resulting algorithm is usually simpler and is shown to achieve convergence to the stationary points of the Kullback-Leibler divergence between the marginal distribution of the observation and the model distribution at the optimal rate, i.e., that of the maximum likelihood estimator. In addition, the proposed approach is also suitable for conditional (or regression) models, as illustrated in the case of the mixture of linear regressions model.Comment: Version that includes the corrigendum published in volume 73, part 5 (2011), of the Journal of the Royal Statistical Society, Series B + the correction of a typo in Eqs. (32-33

Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89554/1/j.1541-0420.2011.01607.x.pd

Bayesian power-spectrum inference for Large Scale Structure data

We describe an exact, flexible, and computationally efficient algorithm for a joint estimation of the large-scale structure and its power-spectrum, building on a Gibbs sampling framework and present its implementation ARES (Algorithm for REconstruction and Sampling). ARES is designed to reconstruct the 3D power-spectrum together with the underlying dark matter density field in a Bayesian framework, under the reasonable assumption that the long wavelength Fourier components are Gaussian distributed. As a result ARES does not only provide a single estimate but samples from the joint posterior of the power-spectrum and density field conditional on a set of observations. This enables us to calculate any desired statistical summary, in particular we are able to provide joint uncertainty estimates. We apply our method to mock catalogs, with highly structured observational masks and selection functions, in order to demonstrate its ability to reconstruct the power-spectrum from real data sets, while fully accounting for any mask induced mode coupling.Comment: 25 pages, 15 figure

Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization

We address the inverse problem of cosmic large-scale structure reconstruction from a Bayesian perspective. For a linear data model, a number of known and novel reconstruction schemes, which differ in terms of the underlying signal prior, data likelihood, and numerical inverse extra-regularization schemes are derived and classified. The Bayesian methodology presented in this paper tries to unify and extend the following methods: Wiener-filtering, Tikhonov regularization, Ridge regression, Maximum Entropy, and inverse regularization techniques. The inverse techniques considered here are the asymptotic regularization, the Jacobi, Steepest Descent, Newton-Raphson, Landweber-Fridman, and both linear and non-linear Krylov methods based on Fletcher-Reeves, Polak-Ribiere, and Hestenes-Stiefel Conjugate Gradients. The structures of the up-to-date highest-performing algorithms are presented, based on an operator scheme, which permits one to exploit the power of fast Fourier transforms. Using such an implementation of the generalized Wiener-filter in the novel ARGO-software package, the different numerical schemes are benchmarked with 1-, 2-, and 3-dimensional problems including structured white and Poissonian noise, data windowing and blurring effects. A novel numerical Krylov scheme is shown to be superior in terms of performance and fidelity. These fast inverse methods ultimately will enable the application of sampling techniques to explore complex joint posterior distributions. We outline how the space of the dark-matter density field, the peculiar velocity field, and the power spectrum can jointly be investigated by a Gibbs-sampling process. Such a method can be applied for the redshift distortions correction of the observed galaxies and for time-reversal reconstructions of the initial density field.Comment: 40 pages, 11 figure

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Chromogenic in situ hybridization to detect EGFR gene copy number in cell blocks from fine-needle aspirates of non small cell lung carcinomas and lung metastases from colo-rectal cancer

<p>Abstract</p> <p>Background</p> <p>Several studies demonstrated that epidermal growth factor receptor (EGFR) gene copy number (GCN) correlates to the response to tyrosine kinase inhibitors in non small cell lung cancer (NSCLC) and to anti-EGFR monoclonal antibodies (MoAbs) in metastatic colorectal cancer (CRC). In the presence of lung nodules, cytology is often the only possible diagnostic approach. Chromogenic <it>in situ </it>hybridization (CISH) is an alternative technique to fluorescence <it>in situ </it>hybridization (FISH), but its feasibility in detecting EGFR GCN in cell blocks from fine-needle aspiration cytology (FNAC) of lung nodules has not yet been established.</p> <p>Methods</p> <p>We evaluated the feasibility of CISH on 33 FNAC from 20 primary NSCLC (5 squamous carcinomas, 8 large cell carcinomas and 7 adenocarcinomas) and 13 lung metastases from CRC.</p> <p>Results</p> <p>Of the 33 FNAC analyzed by CISH, 27 (82%) presented a balanced increase in EGFR gene and chromosome 7 number: 10 cases (30%) showed a low polysomy, 15 (45%) a high polysomy and 2 (6%) NSCLC were amplified. No significant differences between NSCLC and CRC lung metastases were found in relation to disomic or polysomic status. In addition, no correlation between EGFR GCN and EGFR immunohistochemical overexpression was found. Furthermore, we compared CISH results with those obtained by FISH on the same samples and we found 97% overall agreement between the two assays (k = 0.78, p < 0.0001). Two cases were amplified with both assays, whereas 1 case of NSCLC was amplified by FISH only. CISH sensitivity was 67%, the specificity and positive predictive value (PPV) was 100%, and the negative predictive value (NPV) was 97%.</p> <p>Conclusions</p> <p>Our study shows that CISH is a valid method to detect EGFR GCN in cell blocks from FNAC of primary NSCLC or metastatic CRC to the lung.</p

Interventions to reduce the public health burden of gambling-related harms: a mapping review

Recognition is growing that gambling, although highly profitable for corporations and governments, is a source of serious and unevenly distributed harm. This recognition has led to demands for public health strategies at the local, national, and international levels. We aimed to identify review-level evidence for interventions to address or prevent gambling-related harms and explore policy implications, using stakeholder consultation to assess the evidence base, identify gaps, and suggest key research questions. We opted for a systematic mapping review and narrative synthesis for all forms of gambling in any setting. We included participants from the whole population, identified gamblers including self-defined, and specific populations at risk (eg, children and young people). We included all outcome measures relating to prevention or treatment of gambling-related harms that were reported by review authors. After duplication, the searches generated 1080 records. Of 43 potential papers, 13 were excluded at the full paper stage and 30 papers were included in the Review. We identified whole-population preventive interventions, such as demand reduction (n=3) and supply reduction (n=4) interventions, and targeted treatment interventions for individuals addicted to gambling, such as therapeutic (n=12), pharmacological (n=5), and self-help or mutual support (n=4) interventions. We also reviewed studies (n=2) comparing these approaches. Interventions to screen, identify, and support individuals at risk of gambling-related harms and interventions to support ongoing recovery and prevent relapse for individuals with a gambling addiction were not represented in the review-level evidence. A public health approach suggests that there are opportunities to reduce gambling-related harms by intervening across the whole gambling pathway, from regulation of access to gambling to screening for individuals at risk and services for individuals with an identified gambling problem. The dearth of evidence for some interventions means that implementation must be accompanied by robust evaluation