BackgroundDeveloping tolerant cultivars by incorporating resistant genes is regarded as a potential strategy for controlling Verticillium wilt that causes severe losses in the yield and fiber quality of cotton.ResultsHere, we identified the gene GbHyPRP1 in Gossypium barbadense, which encodes a protein containing both proline-rich repetitive and Pollen Ole e I domains. GbHyPRP1 is located in the cell wall. The transcription of this gene mainly occurs in cotton roots and stems, and is drastically down-regulated upon infection with Verticillium dahliae. Silencing HyPRP1 dramatically enhanced cotton resistance to V. dahliae. Over-expression of HyPRP1 significantly compromised the resistance of transgenic Arabidopsis plants to V. dahliae. The GbHyPRP1 promoter region contained several putative phytohormone-responsive elements, of which SA was associated with gene down-regulation. We compared the mRNA expression patterns of HyPRP1-silenced plants and the control at the global level by RNA-Seq. A total of 1735 unique genes exhibited significant differential expression. Of these, 79 DEGs involved in cell wall biogenesis and 43 DEGs associated with the production of ROS were identified. Further, we observed a dramatic thickening of interfascicular fibers and vessel walls and an increase in lignin in the HyPRP1-silenced cotton plants compared with the control after inoculation with V. dahliae. Additionally, silencing of HyPRP1 markedly enhanced ROS accumulation in the root tips of cotton inoculated with V. dahliae.ConclusionsTaken together, our results suggest that HyPRP1 performs a role in the negative regulation of cotton resistance to V. dahliae via the thickening of cell walls and ROS accumulation.Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1565-1) contains supplementary material, which is available to authorized users.
Thanks are also owed to Kansas Structural Composites Inc. for generously providing all the test samples. Laboratory assistance of Mr. Raabal El-Amine, Mr. Justin Robinson and Mr. Avinash Vantaram should be acknowledged. I also want to thank my colleagues Mr. An Chen and Mr. Chuanyu Feng for helpful discussions. At last but not least, I want to express deepest thanks to my lovely and beautiful wife Jieming for her endless support and forbearance throughout my academic career. I also must thank my parents and family for their everlasting support and blessings in my life.
This study aims at developing a stochastic hierarchical multimodal hub location modeling framework for cargo delivery systems to capture uncertainty in hub construction cost and travel time at the strategic level. From a ring-star-star type network design perspective, a stochastic model is established to formulate this problem formally via the expected value and chance-constrained programming techniques. In particular, three types of chance constraints are proposed to ensure that the on-time delivery with prespecified confidence levels in their respective layer networks. For normal distributions, the original stochastic model can be reformulated as a crisp equivalent mixed-integer linear programming (MILP) model by invoking the central limit theorem. Since the number of constraints and variables increases drastically with the size of cargo delivery distribution network, a memetic algorithm (MA) is designed. This algorithm incorporates genetic search and local intensification to obtain optimal/near-optimal solutions for realistic instance size within a reasonable time limit. For general distributions, it is difficult to convert the stochastic model into its deterministic counterpart. Hence, a hybrid methodology is further designed by combining the MA and Monte Carlo (MC) simulation to solve the proposed stochastic model. To demonstrate the properties of the proposed model and the performance of the designed algorithm, a series of numerical experiments are set up based on the Civil Aeronautics Board (CAB) and Turkish network data sets. Computational results indicate as the confidence level increases, the airport hubs are located further apart in the cargo delivery distribution network for gaining a greater time advantage. In addition, comparative results demonstrate that the MA algorithm proposed herein performs better than the genetic algorithm (GA) in terms of computing speed and quality of the solution. INDEX TERMS Hierarchical multimodal hub location, cargo delivery systems, stochastic programming, mixed-integer linear programming, memetic algorithm, Monte Carlo simulation.
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