Mechanisms of agonist action at D-2 dopamine receptors

In this study, we investigated the biochemical mechanisms of agonist action at the G protein-coupled D-2 dopamine receptor expressed in Chinese hamster ovary cells. Stimulation of guanosine 5'-O-(3-[S-35]thio) triphosphate ([S-35]GTPgammaS) binding by full and partial agonists was determined at different concentrations of [S-35]GTPgammaS (0.1 and 10 nM) and in the presence of different concentrations of GDP. At both concentrations of [S-35]GTPgammaS, increasing GDP decreased the [S-35]GTPgammaS binding observed with maximally stimulating concentrations of agonist, with partial agonists exhibiting greater sensitivity to the effects of GDP than full agonists. The relative efficacy of partial agonists was greater at the lower GDP concentrations. Concentration-response experiments were performed for a range of agonists at the two [S-35]GTPgammaS concentrations and with different concentrations of GDP. At 0.1 nM [S-35]GTPgammaS, the potency of both full and partial agonists was dependent on the GDP concentration in the assays. At 10 nM [S-35]GTPgammaS, the potency of full agonists exhibited a greater dependence on the GDP concentration, whereas the potency of partial agonists was virtually independent of GDP. We concluded that at the lower [S-35]GTPgammaS concentration, the rate-determining step in G protein activation is the binding of [S-35]GTPgammaS to the G protein. At the higher [S-35]GTPgammaS concentration, for full agonists, [S-35]GTPgammaS binding remains the slowest step, whereas for partial agonists, another (GDP-independent) step, probably ternary complex breakdown, becomes rate-determining.

Generative topographic mapping applied to clustering and visualization of motor unit action potentials

The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA). (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Teachers’ action research in a culture of performativity

In a culture of performativity, action research offers teachers an opportunity to step back and reflect on their practice. This paper reports on a collaborative project carried out between a university and a secondary school in England, in which the university staff supported an action research project within the school. Five school teachers volunteered to engage in this project. They were given an introduction to action research and were assigned a university researcher to support them. Despite the common input and a common school culture, the teachers engaged in very different models of action research. This article reports on two teachers whose approaches were dissimilar. It examines these differences and suggests that they can be explained by considering the teachers’ different responses to a performativity culture.

Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

The analysis step of the (ensemble) Kalman filter is optimal when (1) the distribution of the background is Gaussian, (2) state variables and observations are related via a linear operator, and (3) the observational error is of additive nature and has Gaussian distribution. When these conditions are largely violated, a pre-processing step known as Gaussian anamorphosis (GA) can be applied. The objective of this procedure is to obtain state variables and observations that better fulfil the Gaussianity conditions in some sense. In this work we analyse GA from a joint perspective, paying attention to the effects of transformations in the joint state variable/observation space. First, we study transformations for state variables and observations that are independent from each other. Then, we introduce a targeted joint transformation with the objective to obtain joint Gaussianity in the transformed space. We focus primarily in the univariate case, and briefly comment on the multivariate one. A key point of this paper is that, when (1)-(3) are violated, using the analysis step of the EnKF will not recover the exact posterior density in spite of any transformations one may perform. These transformations, however, provide approximations of different quality to the Bayesian solution of the problem. Using an example in which the Bayesian posterior can be analytically computed, we assess the quality of the analysis distributions generated after applying the EnKF analysis step in conjunction with different GA options. The value of the targeted joint transformation is particularly clear for the case when the prior is Gaussian, the marginal density for the observations is close to Gaussian, and the likelihood is a Gaussian mixture.