Identification of residues important for agonist recognition and activation in GPR40

GPR40 was formerly an orphan G protein-coupled receptor whose endogenous ligands have recently been identified as free fatty acids (FFAs). The receptor, now named FFA receptor 1, has been implicated in the pathophysiology of type 2 diabetes and is a drug target because of its role in FFA-mediated enhancement of glucose-stimulated insulin release. Guided by molecular modeling, we investigated the molecular determinants contributing to binding of linoleic acid, a C18 polyunsaturated FFA, and GW9508, a synthetic small molecule agonist. Twelve residues within the putative GPR40-binding pocket including hydrophilic/positively charged, aromatic, and hydrophobic residues were identified and were subjected to site-directed mutagenesis. Our results suggest that linoleic acid and GW9508 are anchored on their carboxylate groups by Arg183, Asn244, and Arg258. Moreover, His86, Tyr91, and His137 may contribute to aromatic and/or hydrophobic interactions with GW9508 that are not present, or relatively weak, with linoleic acid. The anchor residues, as well as the residues Tyr12, Tyr91, His137, and Leu186, appear to be important for receptor activation also. Interestingly, His137 and particularly His86 may interact with GW9508 in a manner dependent on its protonation status. The greater number of putative interactions between GPR40 and GW9508 compared with linoleic acid may explain the higher potency of GW9508.

Low-rank representation based action recognition

Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.

Goal Recognition through Goal Graph Analysis

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.