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52. Brassinosteroids Mediate Endogenous Phytohormone Metabolism to Alleviate High Temperature Injury at Panicle Initiation Stage in Rice

Author

Chen Yanhua, Wang Yaliang, Chen Huizhe, Xiang Jing, Zhang Yikai, Wang Zhigang, Zhu Defeng, and Zhang Yuping

Subjects
rice, high temperature, panicle initiation stage, phytohormone metabolism, physiological and biochemical indices, Plant culture, SB1-1110
Abstract

High temperatures cause physiological and biochemical changes and significantly affect young panicle development of rice (Oryza sativa L.). Brassinosteroids play important roles in enhancing crop stress resistance. In this study, we subjected rice cultivars Huanghuazhan (heat-resistant) and IR36 (heat-sensitive) to high temperature (HT, 40 ºC) or normal temperature (NT, 33 ºC) for 7 d at the panicle initiation stage, in conjunction with application of 24-epibrassinolide [EBR, a synthetic brassinolide (BR)] or brassinazole (BRZ, a BR biosynthesis inhibitor) at the beginning of the treatments. HT exacerbated spikelet degeneration and inhibited young panicle growth, which were partially prevented by EBR application, while BRZ application aggravated the reduction in spikelet number. HT decreased the contents of BR, active cytokinins (aCTK), active gibberellins (aGA) and indole-3-acetic acid (IAA), but increased the content of abscisic acid (ABA) in young panicles. The activities of key enzymes involved in sucrose hydrolysis, glycolysis and the tricarboxylic acid cycle in young panicles were decreased with the change of endogenous hormone levels under HT. In addition, the contents of H2O2 and malondialdehyde (MDA) were increased and the activities of antioxidant enzymes were decreased in young panicles. Exogenous application of EBR induced the expression of phytohormone biosynthesis-related genes and down-regulated the expression of phytohormone catabolism-related genes to increase the contents of endogenous BR, aCTK, aGA and ABA, thus promoting the decomposition and utilization of sucrose in young panicles, enhancing the activities of superoxide dismutase, catalase and peroxidase, and reducing the accumulation of H2O2 and MDA in young panicles, whereas application of BRZ had the opposite physiological effects. These results showed that brassinosteroids mediate endogenous phytohormone metabolism to alleviate HT injury at the panicle initiation stage in rice.

Published
2023
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60. A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors

Author

Tang, Zhiri, Zhu, Ruohua, Hu, Ruihan, Chen, Yanhua, Wu, Edmond Q., Wang, Hao, He, Jin, Huang, Qijun, and Chang, Sheng

Subjects
Computer Science - Emerging Technologies
Abstract

With the development of research on novel memristor model and device, neural networks by integrating various memristor models have become a hot research topic recently. However, state-of-the-art works still build such neural networks using drift memristor only. Furthermore, some other related works are only applied to a few individual applications including pattern recognition and edge detection. In this paper, a novel kind of multilayer neural network is proposed, in which diffusion and drift memristor models are applied to construct a system merging image preprocessing and pattern recognition. Specifically, the entire network consists of two diffusion memristive cellular layers for image preprocessing and one drift memristive feedforward layer for pattern recognition. Experimental results show that good recognition accuracy of noisy MNIST is obtained due to the fusion of image preprocessing and pattern recognition. Moreover, owing to high-efficiency in-memory computing and brief spiking encoding methods, high processing speed, high throughput, and few hardware resources of the entire network are achieved., Comment: Accepted by IEEE Transactions on Cognitive and Developmental Systems

Published
2019

61. Fully Memristive Spiking-Neuron Learning Framework and its Applications on Pattern Recognition and Edge Detection

Author

Tang, Zhiri, Chen, Yanhua, Ye, Shizhuo, Hu, Ruihan, Wang, Hao, He, Jin, Huang, Qijun, and Chang, Sheng

Subjects
Computer Science - Emerging Technologies
Abstract

Fully memristive spiking-neuron learning framework, which uses drift and diffusion memristor models as axon and dendrite respectively, becomes a hot topic recently with the development of memristor devices. Normally, some other devices like resistor or capacitor are still necessary on recent works of fully memristive learning framework. However, theoretically, one neuron needs axon and dendrite only, which makes technique process simpler and learning framework more similar to biologic brain. In this paper, a fully memristive spiking-neuron learning framework is introduced, in which a neuron structure is just built of one drift and one diffusion memristive models. To verify it merits, a feedforward neural network for pattern recognition and a cellular neural network for edge detection are designed. Experiment results show that compared to other memristive neural networks, our framework's the processing speed is much faster and the hardware resource is saved in pattern recognition due to its simple structure. Further due to the dynamic filtering function of diffusion memristor model in our learning framework, its peak signal noise ratio (PSNR) is much higher than traditional algorithms in edge detection., Comment: Accepted by Neurocomputing

Published
2019
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99. Modelling financial market based on econometric and complex network analysis

Author

Chen, Yanhua, Pantelous, Athanasios A., and Zuev, Konstantin M.

Subjects
620
Abstract

The complex network theory, especially, the economic and financial networks have offered a new approach that emphasizes the complexities and interdependencies in the financial market. In this thesis, I am interested in combining the complex network theory and econometric measures, namely, cointegration and error correction models (ECM) to reveal the internal connectedness structure of the financial market and their dynamic characteristics evolution over time. The initial Chapters 1–2 present an introduction, background knowledge as well as the methodology has been applied throughout the thesis. Chapters 3–5 introduce corresponding specific problems leading up to their solutions. Specifically, in Chapter 3, we examine the dynamic evolution of short-term correlation, long-term cointegration and ECM-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. Chapter 4 seeks to incorporate the long-run cointegration and short-run error correction mechanisms to build up the financial networks to quantify the connectedness across 46 stock markets worldwide from January 2007 to June 2017. By constructing the static ECM-based global stock market network, the topological structure reflects the regional integrated and segmented stock markets. The dynamic international stock market further reveals the time-varying properties of both error correction effects and long-run equilibrium relations amongst 46 stock markets during periods of financial turmoil and implementation of the QEs in the Fed, BoE, BoJ, and ECB, respectively. In Chapter 5, we analyze the financial effects of Brexit-vote on the stocks traded on the London Stock Exchange (FTSE 100 and FTSE Mid250 Index). Specifically, we construct corresponding British stock networks using the ECM models to investigate the short-run self-correction mechanisms as well as long-run equilibrium amongst stocks from sectoral-level before and after the Brexit-vote. To extract most strongly related interactions from the British stock networks, the minimal spanning tree (MST) and hierarchical clustering analysis are applied for filtering network and to detect the taxonomy and hierarchical topological structure based our proposed Jaccard distance metric. Each chapter is followed by a mini-conclusion. In the end, we summarize our results and conclude the thesis by presenting some research directions based on our findings in Chapter 6.

Published
2018
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