Establishing Standards for the Integrated Recommendation and Diagnosis System (DRIS) for Irrigated Bean Crops

This article addresses the establishment of integrated diagnostics and recommendation system (DRIS) standards for irrigated bean crops (Phaseolus vulgaris) and compares leaf concentrations and productivity in low- and high-productivity populations. The work was carried out in Santa Fe de Goias, Goias State, Brazil, in the agricultural years 1999/2000 and 2000/2001. For the nutritional diagnosis, leaf samples were collected, and leaf concentrations of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), boron (B), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) were established in 100 commercial bean crops. A database was set up listing the leaf nutrient content and the respective productivities, subdivided into two subpopulations, high and low productivity, using a bean yield value of 3000 kg ha-1 to separate these subpopulations. Sufficiency values found in the high-productivity population matched only for the micronutrients B and Zn. The nutritional balance among the populations studied was coherent and was lower in the high-productivity population. The DRIS standards proposed for irrigated bean farming were efficient in evaluating the nutritional status of the crop areas studied. Calcium, Cu, and S were found to be the least available nutrients, indicating high response potential for the fertilizing using these nutrients.

Development of an open case-based decision-support system for diagnosis in oral pathology

Making diagnoses in oral pathology are often difficult and confusing in dental practice, especially for the lessexperienced dental student. One of the most promising areas in bioinformatics is computer-aided diagnosis, where a computer system is capable of imitating human reasoning ability and provides diagnoses with an accuracy approaching that of expert professionals. This type of system could be an alternative tool for assisting dental students to overcome the difficulties of the oral pathology learning process. This could allow students to define variables and information, important to improving the decision-making performance. However, no current open data management system has been integrated with an artificial intelligence system in a user-friendly environment. Such a system could also be used as an education tool to help students perform diagnoses. The aim of the present study was to develop and test an open case-based decisionsupport system.Methods: An open decision-support system based on Bayes' theorem connected to a relational database was developed using the C++ programming language. The software was tested in the computerisation of a surgical pathology service and in simulating the diagnosis of 43 known cases of oral bone disease. The simulation was performed after the system was initially filled with data from 401 cases of oral bone disease.Results: the system allowed the authors to construct and to manage a pathology database, and to simulate diagnoses using the variables from the database.Conclusion: Combining a relational database and an open decision-support system in the same user-friendly environment proved effective in simulating diagnoses based on information from an updated database.

Integrated multi-trophic culture of Nile tilapia (Oreochromis niloticus) and Amazon river prawn (Macrobrachium amazonicum) in brackish water

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)