Plant hormones play central roles in plant growth, developmental processes, and plant response to biotic and abiotic stresses. On the one hand, plant hormones may allocate limited resources to the most serious stresses; on the other hand, the crosstalks among multiple plant hormone signaling regulate the balance between plant growth and defense. Many studies have reported the mechanism of crosstalks between jasmonic acid (JA) and other plant hormones in plant growth and stress responses. Based on these studies, this paper mainly reviews the crosstalks between JA and other plant hormone signaling in regulating the balance between plant growth and defense response. The suppressor proteins JASMONATE ZIM DOMAIN PROTEIN (JAZ) and MYC2 as the key components in the crosstalks are also highlighted in the review. We conclude that JA interacts with other hormone signaling pathways [such as auxin, ethylene (ET), abscisic acid (ABA), salicylic acid (SA), brassinosteroids (BRs), and gibberellin (GA)] to regulate plant growth, abiotic stress tolerance, and defense resistance against hemibiotrophic pathogens such as Magnaporthe oryzae and Pseudomonas syringae. Notably, JA may act as a core signal in the phytohormone signaling network.

The effector proteins secreted by a pathogen not only promote virulence and infection of the pathogen, but also trigger plant defense response. Therefore, these proteins could be used as important genetic resources for transgenic improvement of plant disease resistance. Magnaporthe oryzae systemic defense trigger 1 (MoSDT1) is an effector protein. In this study, we compared the agronomic traits and blast disease resistance between wild type (WT) and MoSDT1 overexpressing lines in rice. Under control conditions, MoSDT1 transgenic lines increased the number of tillers without affecting kernel morphology. In addition, MoSDT1 transgenic lines conferred improved blast resistance, with significant effects on the activation of callose deposition, reactive oxygen species (ROS) accumulation and cell death. On the one hand, overexpression of MoSDT1 could delay biotrophy–necrotrophy switch through regulating the expression of biotrophy-associated secreted protein 4 (BAS4) and Magnaporthe oryzaecell death inducing protein 1 (MoCDIP1), and activate plant defense response by regulating the expression of Bsr-d1, MYBS1, WRKY45, peroxidase (POD), heat shock protein 90 (HSP90), allenoxide synthase 2 (AOS2), phenylalanine ammonia lyase (PAL), pathogenesis-related protein 1a (PR1a) in rice. On the other hand, overexpression of MoSDT1 could increase the accumulation of some defense-related primary metabolites such as two aromatic amino acids (L-tyrosine and L-tryptohan), 1-aminocyclopropane carboxylic acid, which could be converted to ethylene, vanillic acid and L-saccharopine. Taken together, overexpression of MoSDT1 confers improved rice blast resistance in rice, through modulation of callose deposition, ROS accumulation, the expression of defense-related genes, and the accumulation of some primary metabolites.

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The electronic and charge transport properties of peri-xanthenoxanthene (PXX) and its phenyl-substitued derivative (Ph-PXX) are explored via quantum chemical calculations. To gain a better understanding of the physical properties of PXX, a comparative study is performed for its analogue, that is, anthanthrene. By employing Marcus electron transfer theory coupled with an incoherent charge hopping and diffusion model, we estimate the charge mobilties of PXX and Ph-PXX. Our calculated results indicate that the introduction of a heteroatom (oxygen) at the reactive sites of anthanthrene can stabilize the extended π-system and improve the effiecient charge injection in electronic devices. The phenyl substitution of PXX makes a remarkable change of charge transport characteristics from a p-type semiconductor to an n-type semiconductor, which shed light on molecular design for an n-type semiconductor through simple chemical structural modification.

The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-theart algorithms. INDEX TERMS Complex networks, influential nodes, local structure, neighbor contribution.

In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs’ movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs.

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Motivation Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming, and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types. Results In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by graph neural network (GNN) model. Finally, a multilayer with three fully connected (FC) layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on 6 association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct 5-fold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve (AUC) scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations. Availability and Implementation Data and source codes are available at https://github.com/27167199/PDMDA