Atlanta University Center Robert W. Woodruff Library
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A Spelman SIS Student Outside the Backstreet Cultural Museum, circa 2009
A Spelman SIS student poses outside of the decorated wall at the Backstreet Cultural Museum
Tupac Amaru Shakur Collection Conference: Hip Hop Education & Expanding the Archival Imagination
On September 28-29, 2012, the Atlanta University Center Robert W. Woodruff Library and the Tupac Amaru Shakur Foundation presented the Tupac Amaru Shakur Collection Conference: "Hip Hop, Education and Expanding the Archival Imagination." The Tupac Amaru Shakur Conference was designed to combine AUC Woodruff Library's mission to facilitate scholarly research and the Tupac Amaru Shakur Foundation's mission to encourage hip hop curriculum. Works posted to the Library's website from the Tupac Amaru Shakur Collection Conference may be downloaded, archived, and/or printed for noncommercial, educational, and research use. Any further use or dissemination of these works requires the express written permission of the copyright holders
Association of Epithelial Mesenchymal Transition with prostate and breast health disparities
African Americans (AA) have higher death rates due to prostate and breast cancer as compared to Caucasian Americans (CA), and few biomarkers have been associated with this disparity. In our study we investigated whether epithelial-mesenchymal transition (EMT) with a focus on Snail and Cathepsin L (Cat L), could potentially be two markers associated with prostate and breast health disparities. We have previously shown that Snail can increase Cat L protein and activity in prostate and breast cancer. Western blot and real-time PCR analyses showed that mesenchymal protein expression (Snail, vimentin, Cat L) and Cat L activity (shown by zymography) was higher in AA prostate cancer cells as compared to CA normal transformed RWPE-1 prostate epithelial cells, and androgen-dependent cells, and comparable to metastatic CA cell lines. With respect to breast cancer, mesenchymal markers were higher in TNBC compared to non-TNBC cells. The higher mesenchymal marker expression was functionally associated with higher proliferative and migratory rates. Immunohistochemistry showed that both nuclear Snail and Cat L expression was significantly higher in cancer compared to normal for CA and Bahamas prostate patient tissue. Interestingly, AA normal tissue stained higher for nuclear Snail and Cat L that was not significantly different to cancer tissue for both prostate and breast tissue, but was significantly higher than CA normal tissue. AA TNBC tissue also displayed significantly higher nuclear Snail expression compared to CA TNBC, while no significant differences were observed with Luminal A cancer tissue. Therefore, increased EMT in AA compared to CA that may contribute to the more aggressive disease
Two-Level Block Decompositions for Solving Helmholtz Equation via Chebyshev Pseudo Spectral Method
In this paper, we consider solving the Helmholtz equation in the Cartesian domain , subject to homogeneous Dirichlet boundary condition, discretized with the Chebyshev pseudo-spectral method. The main purpose of this paper is to present the formulation of a two-level decomposition scheme for decoupling the linear system obtained from the discretization into independent subsystems. This scheme takes advantage of the homogeneity property of the physical problem along one direction to reduce a 2D problem to several 1D problems via a block diagonalization approach and the reflexivity property along the second direction to decompose each of the 1D problems to two independent subproblems using a reflexive decomposition, effectively doubling the number of subproblems. Based on the special structure of the coefficient matrix of the linear system derived from the discretization and a reflexivity property of the second-order Chebyshev differentiation matrix, we show that the decomposed submatrices exhibits a similar property, enabling the system to be decomposed using reflexive decompositions. Explicit forms of the decomposed submatrices are derived. The decomposition not only yields more efficient algorithm but introduces coarse-grain parallelism. Furthermore, it preserves all eigenvalues of the original matrix