“绿色革命”新进展:赤霉素与氮营养双重调控的表观修饰助力水稻高产高效育种

以半矮秆育种为代表的“绿色革命”极大地提高了作物产量,但也带来氮营养利用效率降低的严重问题。“绿色革命”主要基于调控赤霉素的代谢和信号转导而实现。前期的研究发现,赤霉素信号转导关键因子DELLA蛋白通过调控GRF4而负调控氮素的吸收利用,为半矮秆品系氮利用效率低的问题提供了解决方案。最近的一项研究进一步揭示了GA信号途径与氮响应交叉互作的新机制。该研究发现水稻(Oryza sativa)NGR5是氮素调控分蘖数目的一个关键基因,其表达受氮诱导。通过招募PRC2,NGR5对D14和OsSPL14等分蘖抑制基因所在位点进行H3K27me3甲基化修饰,从而抑制其表达。而在半矮秆背景下超表达NGR5可以提高低氮水平下的水稻产量。NGR5同时也被发现为赤霉素受体GID1的一个新靶标,受到其负调控。该研究发现了调控赤霉素信号通路的新机制,并对高产高效的新一代“绿色革命”育种实践具有重要启示。     

Correlation between bilayer destabilization and activity enhancement by diacylglycerols in reconstituted Ca-ATPase vesicles

Using the reconstituted Ca-ATPase vesicles as a model system, we demonstrated that the presence of 1,2-dioleoyl-sn-glycerol (diolein) in the membrane introduces a pronounced enhancement in the Ca-transport function of Ca-ATPase, while the 1,2-dipalmitoyl-sn-glycerol (dipalmitin) does not. We also found by both 31P NMR and freeze-fraction electron microscopy that diolein destabilized lipid bilayers to a greater extent than did dipalmitin. We conclude that the tendency of diacylglycerols to destabilize the phospholipid bilayer is related to their capacity to enhance the activity of the membrane calcium pump.     

Fast, efficient and selective deprotection of the tert-butoxycarbonyl (Boc) group using HCl/dioxane (4 m).

Fast, efficient and selective deprotection of the tert-butoxycarbonyl (Boc) group of various amino acids and peptides was achieved by using hydrogen chloride (4 m) in anhydrous dioxane solution for 30 min at room temperature. In the cases studied in our laboratory, this protocol provided superior selectivity to deprotect Nalpha-Boc groups in the presence of tert-butyl esters and tert-butyl ethers, including thio-tert-butyl ethers, but not phenolic tert-butyl ethers.     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.