Inverse agonism of the antipsychotic drugs at the D-2 dopamine receptor

The antipsychotic drugs had been assumed to act as antagonists at D-2 dopamine receptors but recently these drugs have been shown to possess inverse agonist properties at this receptor. Inverse agonism may be demonstrated from the ability of these drugs to potentiate forskolin-stimulated cAMP accumulation or to suppress agonist-independent [S-35]GTPgammaS binding. The antipsychotic drugs tested generally appear as full inverse agonists in these assays regardless of chemical or therapeutic class. The mechanism of inverse agonism of the antipsychotic drugs is still unclear but may involve stabilisation of the ground state of the D-2 receptor. (C) 2003 Elsevier Science B.V All rights reserved.

Antipsychotic drug action: antagonism, inverse agonism or partial agonism

The positive, psychotic symptoms of schizophrenia can be treated by antipsychotic drugs and it has been assumed that these are antagonists at the D-2 and D-3 dopamine receptors in the brain. Recently, the D-2/D-3 partial agonist aripiprazole has been introduced as an antipsychotic drug. It has also been realized that, using in vitro assays, the other antipsychotic drugs are in fact inverse agonists at D-2/D-3 dopamine receptors. This raises questions about how these disparate drugs can achieve a similar clinical outcome. In this review, I shall consider the efficacies of these drugs in signalling assays and how these efficacies might affect treatment outcomes. It seems that the treatment outcome might depend on the overall level of cell stimulation, which is in turn dependent on the level of residual dopamine and the efficacy of the drug in signalling assays.

Mechanisms of inverse agonist action at D-2 dopamine receptors

1 Mechanisms of inverse agonist action at the D-2(short) dopamine receptor have been examined. 2 Discrimination of G-protein-coupled and -uncoupled forms of the receptor by inverse agonists was examined in competition ligand-binding studies versus the agonist [H-3]NPA at a concentration labelling both G-protein-coupled and -uncoupled receptors. 3 Competition of inverse agonists versus [H-3] NPA gave data that were fitted best by a two-binding site model in the absence of GTP but by a one-binding site model in the presence of GTP. K-i values were derived from the competition data for binding of the inverse agonists to G-protein-uncoupled and -coupled receptors. K-coupled and K-uncoupled were statistically different for the set of compounds tested ( ANOVA) but the individual values were different in a post hoc test only for (+)-butaclamol. 4 These observations were supported by simulations of these competition experiments according to the extended ternary complex model. 5 Inverse agonist efficacy of the ligands was assessed from their ability to reduce agonist-independent [S-35]GTPγ S binding to varying degrees in concentration-response curves. Inverse agonism by (+)-butaclamol and spiperone occurred at higher potency when GDP was added to assays, whereas the potency of (-)-sulpiride was unaffected. 6 These data show that some inverse agonists ((+)-butaclamol, spiperone) achieve inverse agonism by stabilising the uncoupled form of the receptor at the expense of the coupled form. For other compounds tested, we were unable to define the mechanism.

Inverse effectiveness, multisensory integration, and the bodily self: some statistical considerations

A recent report in Consciousness and Cognition provided evidence from a study of the rubber hand illusion (RHI) that supports the multisensory principle of inverse effectiveness (PoIE). I describe two methods of assessing the principle of inverse effectiveness ('a priori' and 'post-hoc'), and discuss how the post-hoc method is affected by the statistical artefact of,regression towards the mean'. I identify several cases where this artefact may have affected particular conclusions about the PoIE, and relate these to the historical origins of 'regression towards the mean'. Although the conclusions of the recent report may not have been grossly affected, some of the inferential statistics were almost certainly biased by the methods used. I conclude that, unless such artefacts are fully dealt with in the future, and unless the statistical methods for assessing the PoIE evolve, strong evidence in support of the PoIE will remain lacking. (C) 2009 Elsevier Inc. All rights reserved.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

Finding the smallest eigenvalue by the inverse Monte Carlo method with refinement

Finding the smallest eigenvalue of a given square matrix A of order n is computationally very intensive problem. The most popular method for this problem is the Inverse Power Method which uses LU-decomposition and forward and backward solving of the factored system at every iteration step. An alternative to this method is the Resolvent Monte Carlo method which uses representation of the resolvent matrix [I -qA](-m) as a series and then performs Monte Carlo iterations (random walks) on the elements of the matrix. This leads to great savings in computations, but the method has many restrictions and a very slow convergence. In this paper we propose a method that includes fast Monte Carlo procedure for finding the inverse matrix, refinement procedure to improve approximation of the inverse if necessary, and Monte Carlo power iterations to compute the smallest eigenvalue. We provide not only theoretical estimations about accuracy and convergence but also results from numerical tests performed on a number of test matrices.

Investigation into low power of a 2D Inverse Discrete CosineTransform (IDCT) in FPGAs

Design for low power in FPGA is rather limited since technology factors affecting power are either fixed or limited for FPGA families. This paper investigates opportunities for power savings of a pipelined 2D IDCT design at the architecture and logic level. We report power consumption savings of over 25% achieved in FPGA circuits obtained from clock gating implementation of optimizations made at the algorithmic level(1).

Kinetics and mechanism of the interconversion of inverse bicontinuous cubic mesophases

This paper describes time-resolved x-ray diffraction data monitoring the transformation of one inverse bicontinuous cubic mesophase into another, in a hydrated lipid system. The first section of the paper describes a mechanism for the transformation that conserves the topology of the bilayer, based on the work of Charvolin and Sadoc, Fogden and Hyde, and Benedicto and O'Brien in this area. We show a pictorial representation of this mechanism, in terms of both the water channels and the lipid bilayer. The second section describes the experimental results obtained. The system under investigation was 2:1 lauric acid: dilauroylphosphatidylcholine at a hydration of 50% water by weight. A pressure-jump was used to induce a phase transition from the gyroid (Q(II)(G)) to the diamond (Q(II)(D)) bicontinuous cubic mesophase, which was monitored by time-resolved x-ray diffraction. The lattice parameter of both mesophases was found to decrease slightly throughout the transformation, but at the stage where the Q(II)(D) phase first appeared, the ratio of lattice parameters of the two phases was found to be approximately constant for all pressure-jump experiments. The value is consistent with a topology-preserving mechanism. However, the polydomain nature of our sample prevents us from confirming that the specific pathway is that described in the first section of the paper. Our data also reveal signals from two different intermediate structures, one of which we have identified as the inverse hexagonal (H-II) mesophase. We suggest that it plays a role in the transfer of water during the transformation. The rate of the phase transition was found to increase with both temperature and pressure-jump amplitude, and its time scale varied from the order of seconds to minutes, depending on the conditions employed.

Compact cosmic ray detector for unattended atmospheric ionisation monitoring

Two vertical cosmic ray telescopes for atmospheric cosmic ray ionization event detection are compared. Counter A, designed for low power remote use, was deployed in the Welsh mountains; its event rate increased with altitude as expected from atmospheric cosmic ray absorption. Independently, Counter B’s event rate was found to vary with incoming particle acceptance angle. Simultaneous colocated comparison of both telescopes exposed to atmospheric ionization showed a linear relationship between their event rates.

Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

In this article a simple and effective controller design is introduced for the Hammerstein systems that are identified based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The controller is composed by computing the inverse of the B-spline approximated nonlinear static function, and a linear pole assignment controller. The contribution of this article is the inverse of De Boor algorithm that computes the inverse efficiently. Mathematical analysis is provided to prove the convergence of the proposed algorithm. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Issues in the assimilation of the sources of a trace gas by inverse modelling

The problem of reconstructing the (otherwise unknown) source and sink field of a tracer in a fluid is studied by developing and testing a simple tracer transport model of a single-level global atmosphere and a dynamic data assimilation system. The source/sink field (taken to be constant over a 10-day assimilation window) and initial tracer field are analysed together by assimilating imperfect tracer observations over the window. Experiments show that useful information about the source/sink field may be determined from relatively few observations when the initial tracer field is known very accurately a-priori, even when a-priori source/sink information is biased (the source/sink a-priori is set to zero). In this case each observation provides information about the source/sink field at positions upstream and the assimilation of many observations together can reasonably determine the location and strength of a test source.