Sequential anomaly detection in the presence of noise and limited feedback

This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: 1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations and 2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family. Moreover, the regret against static distributions coincides with the minimax value of the corresponding online strongly convex game. We also prove bounds on the number of mistakes made during the hedging step relative to the best offline choice of the threshold with access to all estimated beliefs and feedback signals. We validate the theory on synthetic data drawn from a time-varying distribution over binary vectors of high dimensionality, as well as on the Enron email dataset. © 1963-2012 IEEE.

Quality control autophagy: a joint effort of ubiquitin, protein deacetylase and actin cytoskeleton.

Autophagy has been predominantly studied as a nonselective self-digestion process that recycles macromolecules and produces energy in response to starvation. However, autophagy independent of nutrient status has long been known to exist. Recent evidence suggests that this form of autophagy enforces intracellular quality control by selectively disposing of aberrant protein aggregates and damaged organelles--common denominators in various forms of neurodegenerative diseases. By definition, this form of autophagy, termed quality-control (QC) autophagy, must be different from nutrient-regulated autophagy in substrate selectivity, regulation and function. We have recently identified the ubiquitin-binding deacetylase, HDAC6, as a key component that establishes QC. HDAC6 is not required for autophagy activation per se; rather, it is recruited to ubiquitinated autophagic substrates where it stimulates autophagosome-lysosome fusion by promoting F-actin remodeling in a cortactin-dependent manner. Remarkably, HDAC6 and cortactin are dispensable for starvation-induced autophagy. These findings reveal that autophagosomes associated with QC are molecularly and biochemically distinct from those associated with starvation autophagy, thereby providing a new molecular framework to understand the emerging complexity of autophagy and therapeutic potential of this unique machinery.