Quantitation of alternatively spliced NMDA receptor NR1 isoform mRNA transcripts in human brain by competitive RT-PCR

The N-methyl-D-aspartate (NMDA)-selective subtype of ionotropic glutamate receptor is of importance in neuronal differentiation and synapse consolidation, activity-dependent forms of synaptic plasticity, and excitatory amino acid-mediated neuronal toxicity [Neurosci. Res. Program, Bull. 19 (1981) 1; Lab. Invest. 68 (1993) 372]. NMDA receptors exist in vivo as tetrameric or pentameric complexes comprising proteins from two families of homologous subunits, designated NR1 and NR2(A-D) [Biochem. Biophys. Res. Commun. 185 (1992) 826]. The gene coding for the human NR1 subunit (hNR1) is composed of 21 exons, three of which (4, 20 and 21) can be differentially spliced to generate a total of eight distinct subunit variants. We detail here a competitive RT-PCR (cRT-PCR) protocol to quantify endogenous levels of hNR1 splice variants in autopsied human brain. Quantitation of each hNR1 splice variant is performed using standard curve methodology in which a known amount of synthetic ribonucleic acid competitor (internal standard) is co-amplified against total RNA. This method can be used for the quantitation of hNR1 mRNA levels in response to acute or chronic disease states, in particular in the glutamatergic-associated neuronal loss observed in Alzheimer's disease [J. Neurochem. 78 (2001) 175]. Furthermore, alterations in hNR1 mRNA expression may be reflected at the translational level, resulting in functional changes in the NMDA receptor. (C) 2003 Elsevier Science B.V. All rights reserved.

Quantitation of human brain GABAA receptor isoforms by competitive RT-PCR

We have developed a competitive RT-PCR assay, adapted from Lewohl et al. [Brain Res. Brain Res. Protoc. 1 (1997) 347]. for the quantitation of GABA, receptor beta isoforms in human brain using an internal standard that shares high sequence homology to the targets. The internal standard is identical to the beta(1) sequence except for a 61 bp deletion and the incorporation of a Hind III restriction enzyme site. Unlike traditional competitive RT-PCR, which requires a range of internal standard concentrations to be titrated against a constant amount of unknown, this method relies on a standard curve for quantitation of each sample and thus permits increased sample throughput. This method is suitable for the quantitation of beta(1), beta(2) and beta(3) isoforms of the GABA(A) receptor in human alcoholic and control brain. (C) 2003 Elsevier Science B.V. All rights reserved.

Implicit vector equilibrium problems with applications

Let X and Y be Hausdorff topological vector spaces, K a nonempty, closed, and convex subset of X, C: K--> 2(Y) a point-to-set mapping such that for any x is an element of K, C(x) is a pointed, closed, and convex cone in Y and int C(x) not equal 0. Given a mapping g : K --> K and a vector valued bifunction f : K x K - Y, we consider the implicit vector equilibrium problem (IVEP) of finding x* is an element of K such that f (g(x*), y) is not an element of - int C(x) for all y is an element of K. This problem generalizes the (scalar) implicit equilibrium problem and implicit variational inequality problem. We propose the dual of the implicit vector equilibrium problem (DIVEP) and establish the equivalence between (IVEP) and (DIVEP) under certain assumptions. Also, we give characterizations of the set of solutions for (IVP) in case of nonmonotonicity, weak C-pseudomonotonicity, C-pseudomonotonicity, and strict C-pseudomonotonicity, respectively. Under these assumptions, we conclude that the sets of solutions are nonempty, closed, and convex. Finally, we give some applications of (IVEP) to vector variational inequality problems and vector optimization problems. (C) 2003 Elsevier Science Ltd. All rights reserved.

The development of chromatography for the characterization of protein interactions: a personal perspective

This article reviews the progress of a personal endeavour to develop chromatography as a quantitative procedure for the determination of reaction stoichiometries and equilibrium constants governing protein interactions. As well as affording insight into an aspect of chromatography with which many protein chemists are unfamiliar, it shows the way in which minor adaptations of conventional chromatographic practices have rendered the technique one of the most powerful methods available for the characterization of interactions. That pathway towards quantification is followed from the introduction of frontal gel filtration for the study of protein self-association to the characterization of ligand binding by the biosensor variant of quantitative affinity chromatography.

Designing competitive international compensation programs

Over the past 25 years, expatriate managers have voiced increased disenchantment with their compensation packages whíle abroad. This paper takes a prescriptive approach, outlíning severa I elements of a successful human resources strategy and stressing key ingredients of effective international compensation programs. Particular ettention is given to the adherence of cultural values and distrlbutive justice when working across nations and cultures.

Equilibrium self-assembly of colloids with distinct interaction sites: Thermodynamics, percolation, and cluster distribution functions

We calculate the equilibrium thermodynamic properties, percolation threshold, and cluster distribution functions for a model of associating colloids, which consists of hard spherical particles having on their surfaces three short-ranged attractive sites (sticky spots) of two different types, A and B. The thermodynamic properties are calculated using Wertheim's perturbation theory of associating fluids. This also allows us to find the onset of self-assembly, which can be quantified by the maxima of the specific heat at constant volume. The percolation threshold is derived, under the no-loop assumption, for the correlated bond model: In all cases it is two percolated phases that become identical at a critical point, when one exists. Finally, the cluster size distributions are calculated by mapping the model onto an effective model, characterized by a-state-dependent-functionality (f) over bar and unique bonding probability (p) over bar. The mapping is based on the asymptotic limit of the cluster distributions functions of the generic model and the effective parameters are defined through the requirement that the equilibrium cluster distributions of the true and effective models have the same number-averaged and weight-averaged sizes at all densities and temperatures. We also study the model numerically in the case where BB interactions are missing. In this limit, AB bonds either provide branching between A-chains (Y-junctions) if epsilon(AB)/epsilon(AA) is small, or drive the formation of a hyperbranched polymer if epsilon(AB)/epsilon(AA) is large. We find that the theoretical predictions describe quite accurately the numerical data, especially in the region where Y-junctions are present. There is fairly good agreement between theoretical and numerical results both for the thermodynamic (number of bonds and phase coexistence) and the connectivity properties of the model (cluster size distributions and percolation locus).

Dynamic equilibrium through reinforcement learning

Reinforcement Learning is an area of Machine Learning that deals with how an agent should take actions in an environment such as to maximize the notion of accumulated reward. This type of learning is inspired by the way humans learn and has led to the creation of various algorithms for reinforcement learning. These algorithms focus on the way in which an agent’s behaviour can be improved, assuming independence as to their surroundings. The current work studies the application of reinforcement learning methods to solve the inverted pendulum problem. The importance of the variability of the environment (factors that are external to the agent) on the execution of reinforcement learning agents is studied by using a model that seeks to obtain equilibrium (stability) through dynamism – a Cart-Pole system or inverted pendulum. We sought to improve the behaviour of the autonomous agents by changing the information passed to them, while maintaining the agent’s internal parameters constant (learning rate, discount factors, decay rate, etc.), instead of the classical approach of tuning the agent’s internal parameters. The influence of changes on the state set and the action set on an agent’s capability to solve the Cart-pole problem was studied. We have studied typical behaviour of reinforcement learning agents applied to the classic BOXES model and a new form of characterizing the environment was proposed using the notion of convergence towards a reference value. We demonstrate the gain in performance of this new method applied to a Q-Learning agent.

Unit Commitment in a Competitive and Emission Constrained Environment

This paper is on the unit commitment problem, considering not only the economic perspective, but also the environmental perspective. We propose a bi-objective approach to handle the problem with conflicting profit and emission objectives. Numerical results based on the standard IEEE 30-bus test system illustrate the proficiency of the proposed approach.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.