Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

O objetivo deste trabalho foi alterar o método centroide de avaliação da adaptabilidade e estabilidade fenotípica de genótipos, para deixá-lo com maior sentido biológico e melhorar aspectos quantitativos e qualitativos de sua análise. A alteração se deu pela adição de mais três ideótipos, definidos de acordo com valores médios dos genótipos nos ambientes. Foram utilizados dados provenientes de um experimento sobre produção de matéria seca de 92 genótipos de alfafa (Medicago sativa) realizado em blocos ao acaso, com duas repetições. Os genótipos foram submetidos a 20 cortes, no período de novembro de 2004 a junho de 2006. Cada corte foi considerado um ambiente. A inclusão dos ideótipos de maior sentido biológico (valores médios nos ambientes) resultou em uma dispersão gráfica em forma de uma seta voltada para a direita, na qual os genótipos mais produtivos ficaram próximos à ponta da seta. Com a alteração, apenas cinco genótipos foram classificados nas mesmas classes do método centroide original. A figura em forma de seta proporciona uma comparação direta dos genótipos, por meio da formação de um gradiente de produtividade. A alteração no método mantém a facilidade de interpretação dos resultados para a recomendação dos genótipos presente no método original e não permite duplicidade de interpretação dos resultados.

Agaricus subrufescens Peck is a basidiomycete with immunomodulatory compounds and antitumor activities. This research evaluated the mycochemical composition of A. subrufescens, considering their morphological and physiological stage of maturity, with a particular focus on the development of a traceability process for the formulation of new nutritional products based on fungal foods. The stipes contained a high amount of dry matter (10.33%), total carbohydrate (69.56%), available carbohydrate (63.89%), and energy value (363.97 kcal 100 g⁻¹ DM). The pilei contained a high amount of moisture (90.66%), nitrogen (7.75%), protein (33.96%), ash (8.24), crude fat (2.44%), acid detergent fiber (16.75 g kg⁻¹), neutral detergent fiber (41.82 g kg⁻¹), hemicellulose (25.07 g kg⁻¹), and lignin (9.77 g kg⁻¹). Stipes with mature physiological stage had higher values of dry matter (10.50%), crude fiber (5.94%), total carbohydrate (72.82%), AC (66.88%), and energy value (364.91 kcal 100 g⁻¹ DM). Pilei of the mushrooms in the immature physiological stage had higher values of P (36.83%), N (8.41%), and A (8.44%). Due to the differences between the mycochemical compositions of the morphological parts of mushrooms linked to their physiological stage of maturity, such characteristics have immense potential to be considered for a traceability process. This study can be used for the purpose of providing the consumer with more product diversity, optimizing bioactivities of composts, and allowing farmers an efficient and profitable use of the mushroom biomass.

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

In Brazil the rust caused by Puccinia psidii Winter stands out as the most important disease of eucalyptus. The use of resistant genotypes is the main control method, which makes the detection of markers linked to rust resistance essential to the selection of resistant genotypes. In this study, an F1 progeny of 131 plants from interspecific crossings of Eucalyptus was used to identify markers linked to resistance genes for this pathogen. An integrated map was constructed for linkage group three based on microsatellite markers. For QTL mapping two methodologies based on alleles identical-by-descent (IBD) were used: single marker analysis of Haseman and Elston and the interval mapping procedure of Fulker and Cardon. Both methods showed significant association for the Embra 125 marker.The QTL that explained 42 % of the phenotypic variation was mapped to 0.02 cM of this marker by the Fulker and Cardon. Marker Embra 125 has potential use in assisted selection, thus increasing the efficiency of the selection of resistant genotypes.