Comportamento de fontes de resistência em relação à mosca do sorgo.

A mosca do sorgo Contarinia sorghicola (Coq.1898) e a praga mais importante para a cultura do sorgo, visando a producao de graos. No atual estagio de pesquisa o metodo de controle mais promissor em relacao a essa praga e o de resistencia de plantas. Existem, na literatura, trabalhos indicando fontes de resistencia de diferentes origens. Segundo dados obtidos no Brasil, a melhor fonte tem sido AF-28. O objetivo principal do presente trabalho foi avaliar uma colecao de materiais citados como resistentes, que fazem parte do banco de germoplasma de sorgo do CNPMS, ao lado de progenies F-6 do cruzamento entre AF28 e a linhagem Tx 2536. Para este estudo, foram realizados dois ensaios em condicoes de campo. Um em Ribeirao Preto, SP, sob infestacao natural e outro em Sete Lagoas, MG, com infestacao artificial. No primeiro caso, quando o campo apresentava-se florescido, cerca de 50 plantas, por entrada, foram etiquetadas segundo o estagio de florescimento da panicula assim classificado: O inicio de florescimento; 1 de 1-20%; 2 de 21-40%; 3 de 41-60%; 4 de 61-80%; 5 de 81-100% de florescimento. Apos a granacao, os dados da mosca foram estimados pela escala visual de notas de Wiseman e McMillian (1968), que varia de 0 (zero) a 10 (dez). As entradas foram comparadas entre si em cada estagio de florescimento. No segundo ensaio, dois tipos de infestacao artificial foram realizados: primeiramente, utilizando-se de gaiolas, confinou-se cerca de 200 adultos em cada panicula florescida, em tres repeticoes, para cada entrada..

Resultados do ensaio nacional de sorgo granífero - 1975/76 e 1976/77.

Sorgo granifero; Variedade; Sorghum bicolor; Varieties.bitstream/item/43238/1/Bol-tec-1.pd

A Divide-and-Conquer Approach Towards Understanding Deep Networks

Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that interpretation, understanding and correcting architectures is difficult as there is no general theory for deep neural network design. Previously, precision learning was proposed to fuse deep architectures and traditional approaches. Deep networks constructed in this way benefit from the original known operator, have fewer parameters, and improved interpretability. However, they do not yield state-of-the-art performance in all applications. In this paper, we propose to analyze deep networks using known operators, by adopting a divide-and-conquer strategy to replace network components, whilst retaining networks performance. The task of retinal vessel segmentation is investigated for this purpose. We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators. The results indicate that a combination of a trainable guided filter and a trainable version of the Frangi filter yields a performance at the level of U-Net (AUC 0.974 vs. 0.972) with a tremendous reduction in parameters (111, 536 vs. 9, 575). In addition, the trained layers can be mapped back into their original algorithmic interpretation and analyzed using standard tools of signal processing

Genetic resistance to soil chemical toxicities and deficiencies.

Breeding new crop cultivars for adaptation to stress-related phenomena due to soil chemical toxicity and deficiency is a complex process. Data from nutrient culture trials, in which seedling plants are stressed with a deficiency or excess of mineral elements, do not correlate well with those from similar field stress conditions using the same germplasm. Further, evaluating segregating populations in nutrient culture can result in little or no genetic gain due to selection. Field screening efforts are plagued with genotype x environmental interactions caused by a multitude of biotic and abiotic factors. Selecting the proper level of stress for field evaluations and maintaining this level in a dynamically changeable medium like soil can be difficult. Genetic improvement of sorghum under field conditions similar to those encountered by farmers, however, has nearly always been obtained