Abstract

Autophagy is an important catabolic process that delivers cytoplasmic material to the lysosome for degradation. Autophagy promotes cell survival by elimination of damaged organelles and proteins aggregates, as well as by facilitating bioenergetic homeostasis. Although autophagy has been considered a cell survival mechanism, recent studies have shown that autophagy can promote cell death. The core mechanisms that control autophagy are conserved between yeast and humans, but animals also possess genes that regulate autophagy that are not present in yeast. These regulatory differences may be explained by the need to control autophagy in a cell context-specific manner in multicellular animals, such as during cell survival and cell death. Autophagy was thought to be a bulk cytoplasmic degradation mechanism, but recent studies have shown that specific cargo is recruited for degradation. This suggests the possibility that either cell survival or death may be regulated by selective autophagic clearance of cytoplasmic material. Here we summarize the mechanisms that regulate autophagy and how they may contribute to cell survival and death.

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Matrix decomposition methods represent a data matrix as a product of two smaller matrices: one containing basis vectors that represent meaningful concepts in the data, and another describing how the observed data can be expressed as combinations of the basis vectors. Decomposition methods have been studied extensively, but many methods return real-valued matrices. If the original data is binary, the interpretation of the basis vectors is hard. We describe a matrix decomposition formulation, the Discrete Basis Problem. The problem seeks for a Boolean decomposition of a binary matrix, thus allowing the user to easily interpret the basis vectors. We show that the problem is computationally difficult and give a simple greedy algorithm for solving it. We present experimental results for the algorithm. The method gives intuitively appealing basis vectors. On the other hand, the continuous decomposition methods often give better reconstruction accuracies. We discuss the reasons for this behavior.

No abstract