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Plant mechanical strength is an important agronomic trait. To understand the molecular mechanism that controls the plant mechanical strength of crops, we characterized the classic rice mutant brittle culm1 (bc1) and isolated BC1 using a map-based cloning approach. BC1, which encodes a COBRA-like protein, is expressed mainly in developing sclerenchyma cells(More)
Kinesins are encoded by a large gene family involved in many basic processes of plant development. However, the number of functionally identified kinesins in rice is very limited. Here, we report the functional characterization of Brittle Culm12 (BC12), a gene encoding a kinesin-4 protein. bc12 mutants display dwarfism resulting from a significant reduction(More)
Cellulose represents the most abundant biopolymer in nature and has great economic importance. Cellulose chains pack laterally into crystalline forms, stacking into a complicated crystallographic structure. However, the mechanism of cellulose crystallization is poorly understood. Here, via functional characterization, we report that Brittle Culm1 (BC1), a(More)
Nanoparticles are regarded as promising carriers for targeted drug delivery and imaging probes. A fundamental understanding of the dynamics of polymeric nanoparticle targeting to receptor-coated vascular surfaces is therefore of great importance to enhance the design of nanoparticles toward improving binding ability. Although the effects of particle size(More)
In this paper, we study the problem of using contextual data points of a data point for its classification problem. We propose to represent a data point as the sparse linear reconstruction of its context, and learn the sparse context to gather with a linear classifier in a supervised way to increase its discriminative ability. We proposed a novel(More)
—In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification(More)
—In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The(More)
The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of(More)