Effects of cyclic adenosine 3,5-monophosphate on photoreceptor disc shedding and retinomotor movement. Inhibition of rod shedding and stimulation of cone elongation.

As a test of the hypothesis that cyclic nucleotides play a role in the regulation of retinomotor movements and disc shedding in the photoreceptor-pigment epithelial complex, we have used an in vitro eyecup preparation that sustains both disc shedding and cone retinomotor movements, Eyecups were prepared in white light from animals in which both shedding and cone movement had been blocked by 4 d of constant-light treatment. In eyecups incubated for 3 h in light, disc shedding was negligible and cones remained in the light-adapted (contracted) position. In eyecups incubated in darkness, however, a massive shedding response (dominated by rod photoreceptors) was induced, and at the same time cone photoreceptors elongated to their dark-adapted position. In eyecups incubated in light dbcAMP promoted cone elongation and thus mimicked darkness; the dbcAMP effect was potentiated by the phosphodiesterase inhibitors papaverine and 3-isobutylmethylxanthine. In eyecups incubated in darkness, on the other hand, both phosphodiesterase inhibitors and dbcAMP reduced the phagosome content of the pigment epithelium. The effects of dbcAMP on the cone elongation and rod shedding appear to be specific in that dbcGMP, adenosine, and adenosine 5-monophosphate had no significant effect. Our results suggest that cAMP plays a role in the regulation of both retinomotor movements and disc shedding

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

The motivation for this paper is to investigate the use of a promising class of neural network models, Psi Sigma (PSI), when applied to the task of forecasting the one-day ahead value at risk (VaR) of the oil Brent and gold bullion series using open–high–low–close data. In order to benchmark our results, we also consider VaR forecasts from two different neural network designs, the multilayer perceptron and the recurrent neural network, a genetic programming algorithm, an extreme value theory model along with some traditional techniques such as an ARMA-Glosten, Jagannathan, and Runkle (1,1) model and the RiskMetrics volatility. The forecasting performance of all models for computing the VaR of the Brent oil and the gold bullion is examined over the period September 2001–August 2010 using the last year and half of data for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms such as the Christoffersen tests, the violation ratio and our proposed loss function that considers not only the number of violations but also their magnitude. Our results show that the PSI outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations with small magnitude