Kinase CIPK9 integrates glucose and abscisic acid signaling to regulate seed oil metabolism in rapeseed.

Rapeseed (Brassica napus), an important oil crop worldwide, provides large amounts of lipids for human requirements. Calcineurin B-like (CBL)-interacting protein kinase 9 (CIPK9) was reported to regulate seed oil content in the plant. Here, we generated gene-silenced lines through RNA interference biotechnology and loss-of-function mutant bnacipk9 using CRISPR/Cas9 to further study BnaCIPK9 functions in the seed oil metabolism of rapeseeds. We discovered that compared with wild-type (WT) lines, gene-silenced and bnacipk9 lines had substantially different oil contents and fatty acid compositions: seed oil content was improved by 3%-5% and 1%-6% in bnacipk9 lines and gene-silenced lines, respectively; both lines were with increased levels of monounsaturated fatty acids and decreased levels of polyunsaturated fatty acids. Additionally, hormone and glucose content analyses revealed that compared with WT lines the bnacipk9 lines showed significant differences: in bnacipk9 seeds, indoleacetic acid and abscisic acid (ABA) levels were higher; glucose and sucrose contents were higher with a higher hexose-to-sucrose ratio in bnacipk9 mid-to-late maturation development seeds. Furthermore, the bnacipk9 was less sensitive to glucose and ABA than the WT according to stomatal aperture regulation assays and the expression levels of genes involved in glucose and ABA regulating pathways in rapeseeds. Notably, in Arabidopsis (Arabidopsis thaliana), exogenous ABA and glucose imposed on developing seeds revealed the effects of ABA and glucose signaling on seed oil accumulation. Altogether, our results strongly suggest a role of CIPK9 in mediating the interaction between glucose flux and ABA hormone signaling to regulate seed oil metabolism in rapeseed.

Rapid and Reliable Detection of Alkaline Phosphatase by a Hot Spots Amplification Strategy Based on Well-Controlled Assembly on Single Nanoparticle.

The first appeal of clinical assay is always accurate and rapid. For alkaline phosphatase (ALP) monitoring in medical treatment, a rapid, reliable surface-enhanced Raman scattering (SERS) test kit is designed based on a "hot spots" amplification strategy. Consisting of alkyne-tagged Au nanoparticles (NPs), Ag+, and enzyme substrate, the packaged test kit can achieve one-step clinical assay of ALP in human serum within several minutes, while the operation is simple as it directly inputs the sample into the test kit. Here, Ag+ ions are adsorbed onto the surface of Au core due to electrostatic interaction between Ag+ and the negatively charged donor surface, then enzymatic biocatalysis of ALP triggers the reduction of Ag+ and subsequently silver growth occurs on every Au core surface in a controllable manner, forming "hot spots" between the Au core and Ag shell, in which the SERS signal of alkyne Raman reporters would be highly amplified. Meanwhile, ALP mediates a redox reaction of Ag+ as well as the dynamic silver coating process so the increase of SERS intensity is well-controlled and can be recognized with increasing amounts of the targets. Instead of conventional NP aggregation, this leads to a more reproducible result. In particular, the distinct Raman emission from our self-synthesized alkyne reporter is narrow and stable with zero background in the Raman silent region, suffering no optical fluctuation from biosystem inputs and the detection results are therefore reliable with a limit of detection of 0.01 U/L (2.3 pg/mL). Along with ultrahigh stability, this SERS test kit therefore is an important point-of-care candidate for a reliable, efficacious, and highly sensitive detection method for ALP, which potentially decreases the need for time-consuming clinical trials.

A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior.

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.