Drug action on anxiety models with special reference to serotonergic mechanisms

A study has been made of drugs acting at 5-HT receptors on animal models of anxiety. An elevated X-maze was used as a model of anxiety for rats and the actions of various ligands for the 5-HT receptor, and its subtypes, were examined in this model. 5-HT agonists, with varying affinities for the 5-HT receptor subtypes, were demonstrated to have anxiogenic-like activity. The 5-HT2 receptor antagonists ritanserin and ketanserin exhibited an anxiolytic-like profile. The new putatuve anxiolytics ipsapirone and buspirone, which are believed to be selective for 5-HT1 receptors, were also examined. The former had an anxiolytic profile whilst the latter was without effect. Antagonism studies showed the anxiogenic response to 8-hydroxy-2-(Di-n-propylamino)tetralin (8-OH-DPAT) to be antagonised by ipsapirone, pindolol, alprenolol and para-chlorophenylalanine, but not by diazepam, ritanserin, metoprolol, ICI118,551 or buspirone. To confirm some of the results obtained in the elevated X-maze the Social Interaction Test of anxiety was used. Results in this test mirrored the effects seen with the 5-HT agonists, ipsapirone and pindolol, whilst the 5-HT2 receptor antagonists were without effect. Studies using operant conflict models of anxiety produced marginal and varying results which appear to be in agreement with recent criticisms of such models. Finally, lesions of the dorsal raphe nucleus (DRN) were performed in order to investigate the mechanisms involved in the production of the anxiogenic response to 8-OH-DPAT. Overall the results lend support to the involvement of 5-HT, and more precisely 5-HT1, receptors in the manifestation of anxiety in such animal models.

A probabilistic model for trust and reputation

This paper concerns the problem of agent trust in an electronic market place. We maintain that agent trust involves making decisions under uncertainty and therefore the phenomenon should be modelled probabilistically. We therefore propose a probabilistic framework that models agent interactions as a Hidden Markov Model (HMM). The observations of the HMM are the interaction outcomes and the hidden state is the underlying probability of a good outcome. The task of deciding whether to interact with another agent reduces to probabilistic inference of the current state of that agent given all previous interaction outcomes. The model is extended to include a probabilistic reputation system which involves agents gathering opinions about other agents and fusing them with their own beliefs. Our system is fully probabilistic and hence delivers the following improvements with respect to previous work: (a) the model assumptions are faithfully translated into algorithms; our system is optimal under those assumptions, (b) It can account for agents whose behaviour is not static with time (c) it can estimate the rate with which an agent's behaviour changes. The system is shown to significantly outperform previous state-of-the-art methods in several numerical experiments. Copyright © 2010, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Non-Markov state model of peptide dynamics

A hidden Markov state model has been applied to classical molecular dynamics simulated small peptide in explicit water. The methodology allows increasing the time resolution of the model and describe the dynamics with the precision of 0.3 ps (comparing to 6 ps for the standard methodology). It also permits the investigation of the mechanisms of transitions between the conformational states of the peptide. The detailed description of one of such transitions for the studied molecule is presented. © 2012 Elsevier B.V. All rights reserved.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

A Markov switching unobserved component analysis of the CDX index term premium

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX index into a permanent and a stationary component. We establish that the inversion of the CDX term premium is induced by sudden changes in the unobserved stationary component, which represents the evolution of the fundamentals underpinning the probability of default in the economy. We find evidence that the monetary policy response from the Fed during the crisis period was effective in reducing the volatility of the term premium. We also show that equity returns make a substantial contribution to the term premium over the entire sample period.