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Abstract. This paper studies the subspace segmentation problem which aims to segment data drawn from a union of multiple linear subspaces. Recent works by using sparse representation, low rank representation and their extensions attract much attention. If the subspaces from which the data drawn are independent or orthogonal, they are able to obtain a block diagonal affinity matrix, which usually leads to a correct segmentation. The main differences among them are their objective functions. We theoretically show that if the objective function satisfies some conditions, and the data are sufficiently drawn from independent subspaces, the obtained affinity matrix is always block diagonal. Furthermore, the data sampling can be insufficient if the subspaces are orthogonal. Some existing methods are all special cases. Then we present the Least Squares Regression (LSR) method for subspace segmentation. It takes advantage of data correlation, which is common in real data. LSR encourages a grouping effect which tends to group highly correlated data together. Experimental results on the Hopkins 155 database and Extended Yale Database B show that our method significantly outperforms stateof-the-art methods. Beyond segmentation accuracy, all experiments demonstrate that LSR is much more efficient.

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Maintaining an appropriate balance of carbon to nitrogen metabolism is essential for rice growth and yield. Glutamine synthetase is a key enzyme for ammonium assimilation. In this study, we systematically analyzed the growth phenotype, carbon-nitrogen metabolic status and gene expression profiles in GS1;1-, GS1;2-overexpressing rice and wildtype plants. Our results revealed that the GS1;1-, GS1;2-overexpressing plants exhibited a poor plant growth phenotype and yield and decreased carbon/nitrogen ratio in the stem caused by the accumulation of nitrogen in the stem. In addition, the leaf SPAD value and photosynthetic parameters, soluble proteins and carbohydrates varied greatly in the GS1;1-, GS1;2-overexpressing plants. Furthermore, metabolite profile and gene expression analysis demonstrated significant changes in individual sugars, organic acids and free amino acids, and gene expression patterns in GS1;1-, GS1;2-overexpressing plants, which also indicated the distinct roles that these two GS1 genes played in rice nitrogen metabolism, particularly when sufficient nitrogen was applied in the environment. Thus, the unbalanced carbon-nitrogen metabolic status and poor ability of nitrogen transportation from stem to leaf in GS1;1-, GS1;2-overexpressing plants may explain the poor growth and yield.

While barley (Hordeum vulgare L.) is the most sensitive species to Al toxicity among small-grain crops, variation in Al resistance between cultivars does exist. We examined the mechanism responsible for differential Al resistance in 21 barley varieties. Citrate was secreted from the roots in response to Al stress. A positive correlation between citrate secretion and Al resistance [(root elongation with Al)/(root elongation without Al)] and a negative correlation between citrate secretion and Al content of root apices, were obtained, suggesting that citrate secretion from the root apices plays an important role in excluding Al and thereby detoxifying Al. The Al-induced secretion of citrate was characterized using an Al-resistant variety (Sigurdkorn) and an Al-sensitive variety (Kearney). In Sigurdkorn, Al-induced secretion of citrate occurred within 20 min, and the secretion did not increase with increasing external Al concentration. The Al-induced citrate secretion ceased at low temperature (6°C) and was inhibited by anion-channel inhibitors. Internal citrate content of root apices was increased by Al exposure in Sigurdkorn, but was not affected in Kearney. The activity of citrate synthase was unaffected by Al in both Al-resistant and Al-sensitive varieties. The secretion rate of organic acid anions from barley was the lowest among wheat, rye and triticale.