Impact of a Glyphosate-Tolerant Soybean Line on the Rhizobacteria, Revealed by Illumina MiSeq

( Gui-hua Lu ),( Yin-ling Zhu ),( Ling-ru Kong ),( Jing Cheng ),( Cheng-yi Tang ),( Xiao-mei Hua ),( Fan-fan Meng ),( Yan-jun Pang ),( Rong-wu Yang ),( Jin-liang Qi ),( Yong-hua Yang ) 한국미생물 · 생명공학회 2017 Journal of microbiology and biotechnology Vol.27 No.3

The global commercial cultivation of transgenic crops, including glyphosate-tolerant soybean, has increased widely in recent decades with potential impact on the environment. The bulk of previous studies showed different results on the effects of the release of transgenic plants on the soil microbial community, especially rhizosphere bacteria. In this study, comparative analyses of the bacterial communities in the rhizosphere soils and surrounding soils were performed between the glyphosate-tolerant soybean line NZL06-698 (or simply N698), containing a glyphosate-insensitive EPSPS gene, and its control cultivar Mengdou12 (or simply MD12), by a 16S ribosomal RNA gene (16S rDNA) amplicon sequencing-based Illumina MiSeq platform. No statistically significant difference was found in the overall alpha diversity of the rhizosphere bacterial communities, although the species richness and evenness of the bacteria increased in the rhizosphere of N698 compared with that of MD12. Some influence on phylogenetic diversity of the rhizosphere bacterial communities was found between N698 and MD12 by beta diversity analysis based on weighted UniFrac distance. Furthermore, the relative abundances of part rhizosphere bacterial phyla and genera, which included some nitrogen-fixing bacteria, were significantly different between N698 and MD12. Our present results indicate some impact of the glyphosate-tolerant soybean line N698 on the phylogenetic diversity of rhizosphere bacterial communities together with a significant difference in the relative abundances of part rhizosphere bacteria at different classification levels as compared with its control cultivar MD12, when a comparative analysis of surrounding soils between N698 and MD12 was used as a systematic contrast study.

Synthesis, Structures and Photoluminescent Properties of Two Novel Zinc(II) Compounds Constructed from 5-Sulfoisophthalic Acid

Zhu, Yu-Lan,Tang, Xue-Ling,Ma, Kui-Rong,Chen, Hao,Ma, Feng,Zhao, Lian-Hua Korean Chemical Society 2010 Bulletin of the Korean Chemical Society Vol.31 No.7

Hydrothermal reaction of zinc(II) salts with 5-sulfoisophthalic acid monosodium salt ($NaO_3SC_6H_3$-1,3-(COOH)$_2$, $NaH_2$-SIP) and 1,10-phenanthroline (phen) led to two new compounds, [Zn(phen)$_3$$\cdot2H_2SIP\cdot4H_2O$ (1) and [Zn(phen)$_2(H_2O)_2]\cdot2H_2SIP\cdot2H_2O$ (2). They were characterized by element analysis, IR spectroscopy, thermalgravimetric analysis (TGA), X-ray powder diffraction (XRD), and single-crystal X-ray diffraction. Both compounds 1-2 represent the first example of Zn/phen/SIP system. The Zn (II) ion in 1 is six-coordinated by six nitrogen atoms from three phen molecules, and the $H_2SIP^-$ ligands engage in the formation of hydrogen bond. The Zn(II) ion in 2 is coordinated by four nitrogen atoms from two phen molecules and two oxygen atoms from two water molecules. Moreover, both 1 and 2 are assembled into 3D supramolecular architectures by hydrogen bonds (O-H$\ldots$O) and $\pi-\pi$ interactions. Solvent water molecules occupying voids of the compounds serve as receptors or donors of the extensive O-H$\ldots$O hydrogen bonds.

Nonlinear damage detection using higher statistical moments of structural responses

Ling Yu,Jun-Hua Zhu 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

An integrated method is proposed for structural nonlinear damage detection based on time seriesanalysis and the higher statistical moments of structural responses in this study. It combines the time seriesanalysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clusteringtechniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean ofthe higher statistical moments, and are classified by using the FCM clustering method to achieve nonlineardamage detection. A series of the measured response data, downloaded from the web site of the Los AlamosNational Laboratory (LANL) USA, from a three-storey building structure considering the environmentalvariety as well as different nonlinear damage cases, are analyzed and used to assess the performance of thenew nonlinear damage detection method. The effectiveness and robustness of the new proposed method arefinally analyzed and concluded.

Ultrastructure of Pear Pollen Tubes Challenged with Stylar S-RNase in Vitro

Zhu Qin Liu,Shao Ling Zhang,Guo Hua Xu,Cai Ping Zhao,Jun Wu 한국원예학회 2006 한국원예학회 기타간행물 Vol.- No.-

A MOM-based algorithm for moving force identification: Part II – Experiment and comparative studies

Ling Yu,Tommy H.T. Chan,Jun-hua Zhu 국제구조공학회 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.29 No.2

A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

An eigenspace projection clustering method for structural damage detection

Zhu, Jun-Hua,Yu, Ling,Yu, Li-Li Techno-Press 2012 Structural Engineering and Mechanics, An Int'l Jou Vol.44 No.2

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

A MOM-based algorithm for moving force identification: Part I - Theory and numerical simulation

Yu, Ling,Chan, Tommy H.T.,Zhu, Jun-Hua Techno-Press 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.29 No.2

The moving vehicle loads on a bridge deck is one of the most important live loads of bridges. They should be understood, monitored and controlled before the bridge design as well as when the bridge is open for traffic. A MOM-based algorithm (MOMA) is proposed for identifying the timevarying moving vehicle loads from the responses of bridge deck in this paper. It aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. The moving vehicle loads are described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and further estimated by solving the new system equations developed with the basis functions. A number of responses have been combined, some numerical simulations on single axle, two axle and multiple-axle loads, being either constant or timevarying, have been carried out and compared with the existing time domain method (TDM) in this paper. The illustrated results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent when it is used to identify the moving force from bridge responses.

Nonlinear damage detection using higher statistical moments of structural responses

Yu, Ling,Zhu, Jun-Hua Techno-Press 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

Viewing Differences between Chinese and Foreign Tourism Research and Their Trends by Comparing Articles Published in Tourism Tribune and Annals of Tourism Research

Ying Hua Liu,Ling Ma,Hong Zhu 한국관광산업학회 2007 Tourism Research Vol.25 No.-

An eigenspace projection clustering method for structural damage detection

Jun-hua Zhu,Ling Yu,Li-li Yu 국제구조공학회 2012 Structural Engineering and Mechanics, An Int'l Jou Vol.44 No.2

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.