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RASSF2 is a powerful pro-apoptotic K-Ras effector that is that is inactivated in many tumors via promoter methylation and has been shown to function as a tumor suppressor in lung, colorectal, and breast cell lines. RASSF2 belongs to the Ras-association domain family (RASSF) of proteins, which are able to engage in homo/hetero-dimerization and interact with common binding partners. In the context of acute myelogenous leukemia (AML), RASSF2 is exclusively downregulated in t(8;21) AML, suggesting that its repression may be essential for t(8;21) leukemia development.In order to further characterize RASSF2’s tumor suppressive role in a leukemic context, we performed a CRISPR/Cas9-mediated knockout of RASSF2 in two non-t(8;21) AML cell lines: HL-60 and U937, generating single cell isolated clonal lines that are RASSF2 wild type, heterozygous, and knockout. Among the clonal cell lines, we assayed for changes in proliferation, apoptosis, cell cycle, and differentiation. We observed that heterozygous knockdown of RASSF2 in the U937 cells resulted in significantly higher (p

While much progress has been made in capturing high-quality facial performances using motion capture markers and shape-from-shading, high-end systems typically also rely on rotoscope curves hand-drawn on the image. These curves are subjective and difficult to draw consistently; moreover, ad-hoc procedural methods are required for generating matching rotoscope curves on synthetic renders embedded in the optimization used to determine three-dimensional facial pose and expression. We propose an alternative approach whereby these curves and other keypoints are detected automatically on both the image and the synthetic renders using trained neural networks, eliminating artist subjectivity and the ad-hoc procedures meant to mimic it. More generally, we propose using machine learning networks to implicitly define deep energies which when minimized using classical optimization techniques lead to three-dimensional facial pose and expression estimation.