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Gliomas are some of the most aggressive types of cancers but the blood–brain barrier acts as an obstacle to therapeutic intervention in brain-related diseases. The blood–brain barrier blocks the permeation of potentially toxic compounds into neural tissue through the interactions of brain endothelial cells with glial cells (astrocytes and pericytes) which(More)
Methylene blue (MB) has been shown to slow down the progression of the Alzheimer's disease (AD) and other tauopathies; however distribution of MB into the brain is limited due its high hydrophilicity. In this study, we aimed to prepare novel hydrophobic glutathione coated PLGA nanoparticles to improve bioavailability of MB in the brain. Glutathione coated(More)
We propose a new approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent latent variable models. Further, our approach allows the ensemble to(More)
Elevated levels of systemic and pulmonary leptin are associated with diseases related to lung injury and lung cancer. However, the role of leptin in lung biology and pathology, including the mechanism of leptin gene expression in the pathogenesis of lung diseases, including lung cancer, remains elusive. Here, using conditional deletion of tumor suppressor(More)
Age-related macular degeneration (AMD) is one of the leading causes of blindness in the US affecting millions yearly. It is characterized by intraocular neovascularization, inflammation and retinal damage which can be ameliorated through intraocular injections of glucocorticoids. However, the complications that arise from repetitive injections as well as(More)
Variational approaches are often used to approximate intractable posteriors or nor-malization constants in hierarchical latent variable models. While often effective in practice, it is known that the approximation error can be arbitrarily large. We propose a new class of bounds on the marginal log-likelihood of directed latent variable models. Our approach(More)
Monte-Carlo Tree Search (MCTS) algorithms such as UCT are an attractive online framework for solving planning under uncertainty problems modeled as a Markov Decision Process. However, MCTS search trees are constructed in flat state and action spaces, which can lead to poor policies for large problems. In a separate research thread, domain abstraction(More)