阿佛曼链霉菌的~(12)C离子辐照效应及高产菌株选育；Research on Mutation Breeding of High-producing Strains from Streptomyces Avermitilis Irradiated by Ion Beam of ~(12)C~(6+)

选用12C6+离子辐照诱变阿维菌素B1a产生菌ZJAV-A1,研究其诱变效应。实验结果表明,12C6+离子辐照剂量50Gy时致死率97%,正突变率最高可达到34.2%。通过12C6+离子诱变处理,结合平板培养基及斜面培养基的正突变菌株筛选,最终获得一株稳定性良好,阿维菌素B1a组分产量稳定在4460—4588μg/ml之间,较出发菌株提高11.1%—14.7%的突变株ZJAV-Y1-203。

诱发突变选育小麦新品种的研究探讨；The reseach of wheat variety breeding through radiation

概述了张掖市农科院小麦诱变育种研究的发展历程,介绍了小麦诱变育种的常用方法及辐照处理的参考剂量,并对张掖市小麦诱变育种今后发展的方向进行了探讨。

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.