Linear energy transfer dependence of the effects of carbon ion beams on adventitious shoot regeneration from in vitro leaf explants of Saintpaulia ionahta

Purpose: To determine the effects of carbon ion beams with five different linear energy transfer (LET) values on adventitious shoots from in vitro leaf explants of Saintpaulia ionahta Mauve cultivar with regard to tissue increase, shoots differentiation and morphology changes in the shoots. Materials and methods: In vitro leaf explant samples were irradiated with carbon ion beams with LET values in the range of 31 similar to 151 keV/mu m or 8 MeV of X-rays (LET 0.2 keV/mu m) at different doses. Fresh weight increase, surviving fraction and percentage of the explants with regenerated malformed shoots in all the irradiated leaf explants were statistically analysed. Results: The fresh weight increase (FWI) and surviving fraction (SF) decreased dramatically with increasing LET at the same doses. In addition, malformed shoots, including curliness, carnification, nicks and chlorophyll deficiency, occurred in both carbon ion beam and X-ray irradiations. The induction frequency with the former, however, was far more than that with the X-rays. Conclusions: This work demonstrated the LET dependence of the relative biological effectiveness (RBE) of tissue culture of Saintpaulia ionahta according to 50% FWI and 50% SF. After irradiating leaf explants with 5 Gy of a 221 MeV carbon ion beam having a LET value of 96 keV/mu m throughout the sample, a chlorophyll-deficient (CD) mutant, which could transmit the character of chlorophyll deficiency to its progeny through three continuous tissue culture cycles, and plantlets with other malformations were obtained.

Effects of ion beam irradiation on adventitious shoot regeneration from in vitro leaf explants of Saintpaulia ionahta

The effects of 960 MeV carbon ion beam and 8 MeV X-ray irradiation on adventitious shoots from in vitro leaf explants of two different Saintpaulia ionahta (Mauve and Indikon) cultivars were studied with regard to tissue increase, shoots differentiation and morphology changes in the shoots. The experimental results showed that the survival fraction of shoot formation for the Mauve and Indikon irradiated with the carbon ion beam at 20 Gy were 0.715 and 0.600, respectively, while those for both the cultivars exposed to the Xray irradiation at the same dose were 1.000. Relative biological effectiveness (RBE) of Mauve with respect to X-ray was about two. Secondly, the percentage of regenerating explants with malformed shoots in all Mauve regenerating explants irradiated with carbon ion beam at 20 Gy accounted for 49.6%, while that irradiated with the same dose of X-ray irradiation was only 4.7%; as for Saintpatdia ionahta Indikon irradiated with 20 Gy carbon ion beam, the percentage was 43.3%, which was higher than that of X-ray irradiation. Last, many chlorophyll deficient and other varieties of mutants were obtained in this study. Based on the results above, it can be concluded that the effect of mutation induction by carbon ion beam irradiation on the leaf explants of Saintpaulia ionahta is better than that by X-ray irradiation; and the optimal mutagenic dose varies from 20 Gy to 25 Gy for carbon ion beam irradiation.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.