Mutagenized populations have provided important materials for introducing variation and identifying gene function in plants. In this study, an ethyl methanesulfonate (EMS)‐induced soybean (Glycine max) population, consisting of 21,600 independent M2 lines, was developed. Over 1,000 M4 (5) families, with diverse abnormal phenotypes for seed composition, seed shape, plant morphology and maturity that are stably expressed across different environments and generations were identified. Phenotypic analysis of the population led to the identification of a yellow pigmentation mutant, gyl, that displayed significantly decreased chlorophyll (Chl) content and abnormal chloroplast development. Sequence analysis showed that gyl is allelic to MinnGold, where a different single nucleotide polymorphism variation in the Mg‐chelatase subunit gene (ChlI1a) results in golden yellow leaves. A cleaved amplified polymorphic sequence marker was developed and may be applied to marker‐assisted selection for the golden yellow phenotype in soybean breeding. We show that the newly developed soybean EMS mutant population has potential for functional genomics research and genetic improvement in soybean.

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Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches.

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