Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Grander system: A new technology to reduce surface tension of adhesive systems in dentistry

Background. Reduced surface tension of liquids results in higher surface wetting ability and diffusivity by the substrate. Objectives. The objective of this study was to evaluate the influence of the Grander Technology in reducing the surface tension of adhesive systems. Methods. Two adhesive systems (self-etch and total-etch) were modified by physical contact with the Grander system Flexible unit to revitalize water, for 48 h. Surface tension of adhesive systems and water in normal and grander-modified conditions was measured with a goniometer. Results. The results showed a reduction of surface tension for all conditions grander-modified between 3-15%. Conclusions. Grander Technology was effective in reducing the surface tension of the Single Bond and Clearfil SE Bond adhesive systems. Clinical significance. Grander technology was employed to restructure the molecular structure of water-based adhesive systems, which can increase their wetness capacity and therefore ensure a greater diffusibility.