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Schultz Polynomials and Their Topological Indices of Jahangir Graphs J2,m
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作者 shaohui wang Mohammad Reza Farahani +1 位作者 M. R. Rajesh Kanna R. Pradeep Kumar 《Applied Mathematics》 2016年第14期1632-1637,共6页
Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological in... Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological index is . In this paper, the Schultz, Modified Schultz polynomials and their topological indices of Jahangir graphs J<sub>2,m</sub> for all integer number m ≥ 3 are calculated. 展开更多
关键词 Molecular Topological Index Schultz Index Schultz Polynomials Jahangir Graphs J2 m
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Completeness Problem of the Deep Neural Networks
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作者 Ying Liu shaohui wang 《American Journal of Computational Mathematics》 2018年第2期184-196,共13页
Hornik, Stinchcombe & White have shown that the multilayer feed forward networks with enough hidden layers are universal approximators. Roux & Bengio have proved that adding hidden units yield a strictly impro... Hornik, Stinchcombe & White have shown that the multilayer feed forward networks with enough hidden layers are universal approximators. Roux & Bengio have proved that adding hidden units yield a strictly improved modeling power, and Restricted Boltzmann Machines (RBM) are universal approximators of discrete distributions. In this paper, we provide yet another proof. The advantage of this new proof is that it will lead to several new learning algorithms. We prove that the Deep Neural Networks implement an expansion and the expansion is complete. First, we briefly review the basic Boltzmann Machine and that the invariant distributions of the Boltzmann Machine generate Markov chains. We then review the θ-transformation and its completeness, i.e. any function can be expanded by θ-transformation. We further review ABM (Attrasoft Boltzmann Machine). The invariant distribution of the ABM is a θ-transformation;therefore, an ABM can simulate any distribution. We discuss how to convert an ABM into a Deep Neural Network. Finally, by establishing the equivalence between an ABM and the Deep Neural Network, we prove that the Deep Neural Network is complete. 展开更多
关键词 AI Universal APPROXIMATORS BOLTZMANN Machine MARKOV CHAIN INVARIANT Distribution COMPLETENESS Deep Neural Network
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The SlWRKY57-SlVQ21/SlVQ16 module regulates salt stress in tomato 被引量:1
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作者 Jilin Ma Chonghua Li +7 位作者 Lulu Sun Xuechun Ma Hui Qiao Wenchao Zhao Rui Yang Susheng Song shaohui wang Huang Huang 《Journal of Integrative Plant Biology》 SCIE CAS CSCD 2023年第11期2437-2455,共19页
Salt stress is a major abiotic stress which severely hinders crop production.However,the regulatory network controlling tomato resistance to salt remains unclear.Here,we found that the tomato WRKY transcription factor... Salt stress is a major abiotic stress which severely hinders crop production.However,the regulatory network controlling tomato resistance to salt remains unclear.Here,we found that the tomato WRKY transcription factor WRKY57 acted as a negative regulator in salt stress response by directly attenuating the transcription of salt-responsive genes(Sl RD29B and Sl DREB2)and an ion homeostasis gene(Sl SOS1).We further identified two VQ-motif containing proteins Sl VQ16 and Sl VQ21as Sl WRKY57-interacting proteins.Sl VQ16 positively,while Sl VQ21 negatively modulated tomato resistance to salt stress.Sl VQ16 and Sl VQ21 competitively interacted with Sl WRKY57 and antagonistically regulated the transcriptional repression activity of Sl WRKY57.Additionally,the Sl WRKY57-Sl VQ21/Sl VQ16 module was involved in the pathway of phytohormone jasmonates(JAs)by interacting with JA repressors JA-ZIM domain(JAZ)proteins.These results provide new insights into how the Sl WRKY57-Sl VQ21/Sl VQ16 module finely tunes tomato salt tolerance. 展开更多
关键词 JASMONATES salt stress TOMATO VQ protein WRKY transcription factor
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双组分系统rcsC基因影响禽致病性大肠杆菌的致病性及相关生物学特性 被引量:2
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作者 王栋 王少辉 +6 位作者 张焕容 刘新 许漩 易正飞 田明星 丁铲 于圣青 《微生物学报》 CAS CSCD 北大核心 2019年第3期468-477,共10页
【目的】双组分系统Rcs感受外界环境变化,并调控细菌的适应性及生存等。本文探讨Rcs双组分系统传感器激酶RcsC对禽致病性大肠杆菌(avian pathogenic Escherichia coli,APEC)相关生物学特性及致病性的影响。【方法】采用Red同源重组的方... 【目的】双组分系统Rcs感受外界环境变化,并调控细菌的适应性及生存等。本文探讨Rcs双组分系统传感器激酶RcsC对禽致病性大肠杆菌(avian pathogenic Escherichia coli,APEC)相关生物学特性及致病性的影响。【方法】采用Red同源重组的方法构建rcsC基因缺失株,并利用互补质粒构建互补株,然后比较野生株、基因缺失株与互补株的生长特性、运动性、生物被膜、凝集沉淀能力、致病力及毒力基因转录水平的差异。【结果】rcsC基因缺失不影响APEC的生长速度,然而,缺失RcsC导致APEC的运动能力升高、生物被膜形成能力降低和凝集能力增强。凝集试验结果显示rcsC基因有助于APEC的凝集沉降。细胞黏附入侵结果表明,rcsC在APEC侵袭DF-1细胞过程中发挥作用,而对黏附能力无影响。动物感染试验结果表明rcsC基因缺失能显著降低APEC的毒力。荧光定量PCR检测结果表明,rcsC基因缺失株中ompA、aatA、fyuA和luxS基因的转录水平均显著降低,而fimC和tsh基因的转录水平显著升高。【结论】RcsC参与调控APEC的运动性、生物被膜形成、凝集沉降和致病力。 展开更多
关键词 禽致病性大肠杆菌 双组分系统 rcsC 生物学特性 致病性
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