Anticoagulant resistance in Norway rats (Rattus norvegicus Berk.) in Kent - a VKORC1 single nucleotide polymorphism, tyrosine139phenylalanine, new to the UK

A sample of 10 Norway rats (Rattus norvegicus) was taken for DNA resistance testing from an agricultural site in Kent where applications of the anticoagulant rodenticide bromadiolone had been unsuccessful. All animals tested were homozygous for the single nucleotide VKORC1 polymorphism tyrosine139phenylalanine, or Y139F. This is a common resistance mutation found extensively in France and Belgium but not previously in the UK. Y139F confers a significant level of resistance to first-generation anticoagulants, such as chlorophacinone, and to the second-generation compound bromadiolone. Another compound widely used in the UK, difenacoum, is also thought to be partially resisted by rats which carry Y139F. A silent VKORC1 mutation was also found in all rats tested. The presence of a third important VKORC1 mutation which confers resistance to anticoagulant rodenticides in widespread use in the UK, the others being Y139C and L120Q, further threatens the ability of pest control practitioners to deliver effective rodent control.

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.