No abstract

A full-length cDNA (Hv-GR) whose transcript accumulation increased in response to infection by Blumeria graminis DC.f.sp. tritici (Bgt) was isolated from Haynaldia villosa. Southern analysis revealed a single copy of Hv-GR present in H. villosa. This gene encodes a glutathione reductase (GR) with high similarity to chloroplastic GRs from other plant species. Chloroplastic localization of Hv-GR was confirmed by targeting of the green fluorescent protein (GFP)-Hv-GR fusion protein to chloroplasts of epidermal guard cells. Following inoculation with Bgt, transcript accumulation of Hv-GR increased in a resistant line of wheat, but no significant change was observed in a susceptible line. In vivo function of Hv-GR in converting oxidized glutathione (GSSG) to the reduced form (GSH) was verified through heterologous expression of Hv-GR in a yeast GR-deficient mutant. As expected, overexpression of this gene resulted in increased resistance of the mutant to H(2)O(2), indicating a critical role for Hv-GR in protecting cells against oxidative stress. Moreover, overexpression of Hv-GR in a susceptible wheat variety, Triticum aestivum cv. Yangmai 158, enhanced resistance to powdery mildew and induced transcript accumulation of other pathogenesis-related genes, PR-1a and PR-5, through increasing the foliar GSH/GSSG ratio. Therefore, we concluded that a high ratio of GSH to GSSG is required for wheat defense against Bgt, and that chloroplastic GR enzymes might serve as a redox mediator for NPR1 activation.

Graph embedding has been demonstrated to be a powerful tool for learning latent representations for nodes in a graph. However, despite its superior performance in various graph-based machine learning tasks, learning over graphs can raise significant privacy concerns when graph data involves sensitive information. To address this, in this paper, we investigate the problem of developing graph embedding algorithms that satisfy local differential privacy (LDP). We propose LDP-GE, a novel privacy-preserving graph embedding framework, to protect the privacy of node data. Specifically, we propose an LDP mechanism to obfuscate node data and adopt personalized PageRank as the proximity measure to learn node representations. Then, we theoretically analyze the privacy guarantees and utility of the LDP-GE framework. Extensive experiments conducted over several real-world graph datasets demonstrate that LDP-GE achieves favorable privacy-utility trade-offs and significantly outperforms existing approaches in both node classification and link prediction tasks.

Angle-domain common imaging gathers (ADCIGs) are important input data for migration velocity analysis and amplitude variation with angle analysis. Compared with Kirchhoff migration and one-way wave equation migration, reverse time migration (RTM) is the most accurate imaging method in complex areas, such as the subsalt area. We have developed a method to generate ADCIGs from RTM using analytic wavefield propagation and decomposition. To estimate the wave-propagation direction and angle by spatial Fourier transform during the time domain wave extrapolation, we have developed an analytic wavefield extrapolation method. Then, we decomposed the extrapolated source and receiver wavefields into their local angle components (i.e., local plane-wave components) and applied the angle-domain imaging condition to form ADCIGs. Because the angle-domain imaging condition is a convolution imaging condition about the source and receiver propagation angles, it is costly. To increase the efficiency of the angle-domain imaging condition, we have developed a local plane-wave decomposition method using matching pursuit. Numerical examples of synthetic and real data found that this method could generate high-quality ADCIGs. And these examples also found that the computational cost of this approach was related to the complexity of the source and receiver wavefields.