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Metabolic profiling of Echinostoma caproni and Schistosoma mansoni in their definitive and intermediate hosts

100%

O’Sullivan C. , Fried B. , Sherma J.

nr 1

This review examines metabolic profiling of Schistosoma mansoni and Echinostoma caproni in their definitive and intermediate hosts. The earlier coverage of the literature on metabolic profiling was reviewed by Wang et al. 2010, Advances in Parasitology, 73, 373–404 and covered mainly studies using proton nuclear magnetic resonance spectroscopy. The methods focused upon in our review are mainly chromatographic. In the studies reviewed, various metabolites were analyzed in hosts infected with either E. caproni or S. mansoni and compared to the uninfected controls.

An analysis of the performance of genetic programming for realised volatility forecasting

100%

Yin Z. , O’Sullivan C. , Brabazon A.

2016

tom Vol. 6, No. 3

155--172

Traditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.