Brassica napus BnaC9.DEWAX1 Negatively Regulates Wax Biosynthesis via Transcriptional Suppression of BnCER1-2.

Very-long-chain alkane plays an important role as an aliphatic barrier. We previously reported that BnCER1-2 was responsible for alkane biosynthesis in Brassica napus and improved plant tolerance to drought. However, how the expression of BnCER1-2 is regulated is still unknown. Through yeast one-hybrid screening, we identified a transcriptional regulator of BnCER1-2, BnaC9.DEWAX1, which encodes AP2\ERF transcription factor. BnaC9.DEWAX1 targets the nucleus and displays transcriptional repression activity. Electrophoretic mobility shift and transient transcriptional assays suggested that BnaC9.DEWAX1 repressed the transcription of BnCER1-2 by directly interacting with its promoter. BnaC9.DEWAX1 was expressed predominantly in leaves and siliques, which was similar to the expression pattern of BnCER1-2. Hormone and major abiotic stresses such as drought and high salinity affected the expression of BnaC9.DEWAX1. Ectopic expression of BnaC9.DEWAX1 in Arabidopsis plants down-regulated CER1 transcription levels and resulted in a reduction in alkanes and total wax loads in leaves and stems when compared with the wild type, whereas the wax depositions in the dewax mutant returned to the wild type level after complementation of BnaC9.DEWAX1 in the mutant. Moreover, both altered cuticular wax composition and structure contribute to increased epidermal permeability in BnaC9.DEWAX1 overexpression lines. Collectively, these results support the notion that BnaC9.DEWAX1 negatively regulates wax biosynthesis by binding directly to the BnCER1-2 promoter, which provides insights into the regulatory mechanism of wax biosynthesis in B. napus.

Exploiting Clustering Manifold Structure for Hyperspectral Imagery Super-Resolution.

Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) conventional image into an HR HSI has become a prevalent HSIs super-resolution scheme. However, in most previous works, little attention has been paid on exploiting the underlying manifold structure in the spatial domain of the latent HR HSI. In this paper, we advance a provable prior knowledge that the clustering manifold structure of the latent HSI can be well preserved in the spatial domain of the input conventional image. Inspired by this, we first conduct clustering in the spatial domain of the input conventional image and adopt the intra-cluster self-expressiveness model to implicitly depict the clustering manifold structure, which enables learning the complicated manifold structure via solving a constrained ridge regression model without knowing the exact form of the manifold. Then, we incorporate the learned structure into a variational super-resolution framework to regularize the latent HSI. The resulted framework can be effectively optimized by a standard alternating direction method of multipliers. Since the learned structure can well depict the underlying spatial manifold of the latent HSI, the proposed method shows the state-of-the-art super-resolution performance on two benchmark data sets.