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Lou, Y.

Publications and source records attributed to Lou, Y..

2 recordsLinked to original sources

Molecular dissection of early defense signaling underlying volatile-mediated defense priming and herbivore resistance in rice

Herbivore-induced plant volatiles prime plant defenses and resistance. How volatiles are integrated into early defense signaling is not well understood. Furthermore, whether there is a causal relationship between volatile defense priming and herbivore resistance is unclear. Here, we investigated the impact of indole, a common herbivore-induced plant volatile and known defense priming cue, on early defense signaling and herbivore resistance in rice. We show that rice plants infested by Spodoptera frugiperda caterpillars release up to 25 ng*h-1. Exposure to equal doses of synthetic indole enhances rice resistance to S. frugiperda. Screening of early signaling components reveals that indole directly enhances the expression of the receptor like kinase OsLRR-RLK1. Furthermore, indole specifically primes the transcription, accumulation and activation of the mitogen-activated protein kinase OsMPK3 as well as the expression of the downstream WRKY transcription factor OsWRKY70 and several jasmonate biosynthesis genes, resulting in a higher accumulation of jasmonic acid (JA). Using transgenic plants defective in early signaling, we show that OsMPK3 is required, and that OsMPK6 and OsWRKY70 contribute to indole-mediated defense priming of JA-dependent herbivore resistance. We conclude that volatiles can increase herbivore resistance of plants by priming early defense signaling components.

plant biology

Task-Related EEG Source Localization via Graph Regularized Low-Rank Representation Model

To infer brain source activation patterns under different cognitive tasks is an integral step to understand how our brain works. Traditional electroencephalogram (EEG) Source Imaging (ESI) methods usually do not distinguish task-related and spurious non-task-related sources that jointly generate EEG signals, which inevitably yield misleading reconstructed activation patterns. In this research, we argue that the task-related source signal intrinsically has a low-rank property, which is exploited to to infer the true task-related EEG sources location. Although the true task-related source signal is sparse and low-rank, the contribution of spurious sources scattering over the source space with intermittent activation patterns makes the actual source space lose the low-rank property. To reconstruct a low-rank true source, we propose a novel ESI model that involves a spatial low-rank representation and a temporal Laplacian graph regularization, the latter of which guarantees the temporal smoothness of the source signal and eliminate the spurious ones. To solve the proposed model, an augmented Lagrangian objective function is formulated and an algorithm in the framework of alternating direction method of multipliers is proposed. Numerical results illustrate the effectiveness of the proposed method in terms of reconstruction accuracy with high effciency.

bioinformatics