Sensitivity monitoring of Phakopsora pachyrhizi populations to triazoles in Brazil.

Asian soybean rust (ASR) reported in 2001 in South America spread to Brazilian producing areas and is considered one of the main diseases of the crop. Fungicides used for the control belong to QoI (strobilurins) and SBI (azole) compounds. A weaker efficacy of straight azole was observed at some regions in the end of the crop season 2006/2007. To determine whether the problem observed was due to the resistance, a sensitivity monitoring test was carried out in 2008/2009 to detect possible changes in the EC50 values of the fungus population. The test was done according to FRAC methodology. Leaves samples infected with Phakopsora pachyrhizi were sent from nine Brazilian states, in a total of 36 populations, and the spores collected were inoculated in detached leaves treated with fungicides. The triazoles tested were cyproconazole, metconazole, tebuconazole, and prothioconazole (0; 0.125; 0.25; 0.5; 1.0; 2.0; 4.0; 8.0; 16.0; 32.0 ppm). Disease severity was evaluated 15 days after inoculation. The EC50 values were estimated by Proc Probit, SAS®. Differences in EC50 values among the populations were statistically significant (P < 0.01). The EC50 for cyproconazole and metconazole ranged from 0.06 to 1.37 ppm and from 0.02 to 3.89 ppm, respectively. For tebuconazole, EC50 ranged from 0.02 to 1.28 ppm. For prothioconazole, there wasn’t a distribution of EC50 values because, with 0.25 ppm, the populations tested didn’t develop symptoms of ASR. The results showed an oscillation of EC50 values in the P. pachyrhizi population from different locations during the crop season

Instalação da lavoura de soja: época, cultivares, espaçamento e população de plantas.

bitstream/CNPSO-2009-09/27618/1/circtec51.pd

Automatic learning of pre-miRNAs from different species.

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools

Potencial de dano causado por Dichelops melacanthus e Euschistus heros (Hemiptera: Pentatomidae) em plantas de milho.

Conteúdo do volume 2: Ácaros; Biologia, fisiologia, morfologia; Controle biológico com bactérias entomopatogênicas; Controle biológico com fungos entomopatológicos; Controle biológico com nematoides; Controle biológico com parasitoides; Controle biológico com predadores; Ecologia e biodiversidade; Educação e etnoentomologia; Entomologia florestal; Entomologia Forense; Entomologia médica e veterinária; Entomologia molecular; Manejo integrado de pragas; Organismos geneticamente modificados; Plantas inseticidas; Polinização; Pragas quarentenárias e invasivas; Resistência de insetos a táticas de controle; Resistência de plantas a insetos; Semioquímicos e comportamento; Sistemática e taxonomia; Tecnologia de aplicação; Controle biológico com vírus entomopatogênicos; Controle químico