Phenolic compounds have been identified as the most common allelochemicals produced by higher plants. Inhibitions of cinnamic acid, its related phenolic derivatives, and abscisic acid (ABA) on seedling growth and seed germination of lettuce were studied.trans-Cinnamic acid, ando-,m-, andp-coumaric acids inhibited the growth of etiolated seedlings of lettuce at concentrations higher than 10–4 M and seed germination above 10–3 M. Coumarin inhibited seedling growth and seed germination at 10–5 M or above. Chlorogenic acid inhibited seedling growth above 10–4 M, but did not inhibit seed germination at 10–5–5×10–3 M. Low concentrations (below 10–3 M) of caffeic and ferulic acids promoted the elongation of hypocotyls, but higher concentrations (over 10–3 M) inhibited seedling growth and seed germination. These phenolic compounds and abscisic acid had additive inhibitory effects both on seedling growth and seed germination. The inhibition on lettuce was reversed by caffeic and ferulic acids at concentrations lower than 10–3 M except for the inhibition of germination by coumarin. These results suggest that in naturetrans-cinnamic acid,o-, m-, p-coumaric acids, coumarin, and chlorogenic acid inhibit plant growth regardless of their concentration. However, caffeic and ferulic acids can either promote or inhibit plant growth according to their concentration. 相似文献
Multimedia Tools and Applications - Almost all existing image encryption algorithms are only suitable for low-resolution images in the standard image library. When they are used to encrypt... 相似文献
Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
