Protoplast isolation and transient transformation system for Ginkgo biloba L.

Ginkgo biloba L. has a unique evolutionary status. Owing to its high medicinal and ornamental value, ginkgo has also recently become a research hotspot. However, the large genome and long juvenile period, as well as the lack of an effective genetic transformation system, have hindered gaining a full understanding of the comprehensive functions of ginkgo genes. At present, heterologous expression of genes in model plants is the primary method used in ginkgo-related research; however, these distant plant model relatives limit reliable interpretation of the results for direct applications in ginkgo breeding. To overcome these limitations, in this study, an efficient isolation and transient expression system for ginkgo protoplasts was established. A large number of intact and homogeneous ginkgo mesophyll protoplasts were isolated using 2% cellulase and 0.25% pectinase in 0.4 M mannitol. The activity of these protoplasts remained above 90% even after 24 h. Furthermore, when the concentration of the polyethylene glycol 4000 solution was 30%-40% (w/v), the transformation efficiency of the protoplasts reached 40%. Finally, the reliability of the system was verified using subcellular localization, transient overexpression, and protein interaction experiments with ginkgo genes, thereby providing a technical platform for the identification and analysis of ginkgo gene functions. The proposed method partially compensates for the limitations associated with the lack of a genetic transformation system and provides technical support to expand research on elucidating the functions of ginkgo genes.

TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF.

Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer Unet (TUnet) and local binary energy function model (LBF), TUnet-LBF, for coarse to fine segmentation of retinal vessels. In the coarse segmentation stage, the global topological information of blood vessels is obtained by TUnet. The neural network outputs the initial contour and the probability maps, which are input to the fine segmentation stage as the priori information. In the fine segmentation stage, an energy modulated LBF model is proposed to obtain the local detail information of blood vessels. The proposed model reaches accuracy (Acc) of 0.9650, 0.9681 and 0.9708 on the public datasets DRIVE, STARE and CHASE_DB1 respectively. The experimental results demonstrate the effectiveness of each component in the proposed model.

Strain-stress study of AlxGa1-xN/AlN heterostructures on c-plane sapphire and related optical properties.

This work presents a systematic study of stress and strain of AlxGa1-xN/AlN with composition ranging from GaN to AlN, grown on a c-plane sapphire by metal-organic chemical vapor deposition, using synchrotron radiation high-resolution X-ray diffraction and reciprocal space mapping. The c-plane of the AlxGa1-xN epitaxial layers exhibits compressive strain, while the a-plane exhibits tensile strain. The biaxial stress and strain are found to increase with increasing Al composition, although the lattice mismatch between the AlxGa1-xN and the buffer layer AlN gets smaller. A reduction in the lateral coherence lengths and an increase in the edge and screw dislocations are seen as the AlxGa1-xN composition is varied from GaN to AlN, exhibiting a clear dependence of the crystal properties of AlxGa1-xN on the Al content. The bandgap of the epitaxial layers is slightly lower than predicted value due to a larger tensile strain effect on the a-axis compared to the compressive strain on the c-axis. Raman characteristics of the AlxGa1-xN samples exhibit a shift in the phonon peaks with the Al composition. The effect of strain on the optical phonon energies of the epitaxial layers is also discussed.