844 resultados para index membership


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Arkit: 1 arkintunnukseton lehti, M4.

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Arkit: 1 arkintunnukseton lehti, D4.

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Arkit: 1 arkintunnukseton lehti, H4.

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Arkit: 2 arkintunnuksetonta lehteä, B4.

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Arkit: 1 arkintunnukseton lehti, I4 K1.

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Arkit: 1 arkintunnukseton lehti, F4.

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Arkit: 1 arkintunnukseton lehti, C4.

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Arkit: 1 arkintunnukseton lehti, A4.

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Arkit: 1 arkintunnukseton lehti, L4.

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Arkit: 1 arkintunnukseton lehti, E4.

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Arkit: 1 arkintunnukseton lehti, G4 H2.

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Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

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The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

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Pathogens in maize (Zea mays) seeds cause serious problems, such as the loss of their capacity to germinative. The objectives of this study were to identify the optimal period for infection of maize seeds on agar colonized by Fusarium graminearum, when incubated for 4, 8, 16 and 32 h, and to evaluate the effect of the fungus on the germination and vigor of seeds with different infection levels. After the respective incubation periods, the seeds were removed from the culture medium and submitted to the blotter test for 3 min with and without superficial disinfection with 1% solution of sodium hypochlorite. Once the optimal period for seed incubation was identified, seeds from the same sample were again placed on the colonized agar for infection. Germination and vigor tests (accelerated aging and cold test) were performed with a mixture of healthy seeds (placed on PDA medium) and inoculated seeds, resulting in seeds with 0, 20, 40, 60, 80 and 100% rates of infection. The results showed that a period of 32 h was long enough to obtain seeds infected by the pathogen. There were no significant effects of fungal infection on seed germination at any of the infection levels, probably due to the high vigor of the maize seed lot tested. Regarding vigor tests, infection levels differed significantly from the control (0% infection), but there were no significant differences among the infection levels.