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- 검색키워드
- 저자 : Liyuan Meng (검색결과 3 건)
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The Role of Wheat Germ Agglutinin in the Attachment of Pseudomonas sp. WS32 to Wheat Root
Jian Zhang,Liyuan Meng,Yuanyuan Cao,Huiping Chang,Zhongyou Ma,Leni Sun,Ming Zhang,Xinyun Tang 한국미생물학회 2014 The journal of microbiology Vol.52 No.12
Wheat germ agglutinin (WGA), which is secreted on thesurface of wheat root, has been defined as a protein that reversiblyand non-enzymatically binds to specific carbohydrates. However, little attention has been paid to the functionof WGA in the attachment of bacteria to their host plants. The aim of this study was to investigate the role of WGA inthe attachment of Pseudomonas sp. WS32 to wheat roots. Wheat roots were initially treated with double-distilled water,WGA-H (WGA solution that was heated at 100°C for 15 min)and WGA, independently. Subsequently, the roots were coincubatedwith cell solutions (109 cells/ml). A dilution platemethod using a solid nutrient medium was employed to determinethe adsorption of WS32 to wheat roots. WGA waslabeled with fluorescein isothiocyanate and detected usingthe fluorescent in situ hybridization (FISH) technique. Thenumber of adsorptive WS32 cells on wheat roots was significantlyincreased when the wheat roots were pretreatedwith WGA, compared with the control treatment (p = 0.01). However, WGA-H failed to increase the amount of bacterialcells that attached to the wheat roots because of the lossof its physiological activity. The FISH assay also revealedthat more cells adhered to WGA-treated wheat roots than tocontrol or WGA-H-treated roots. The results indicated thatWGA can mediate Pseudomonas strain WS32’s adherenceto wheat seedling roots. The findings of this study provide abetter understanding of the processes involved in plant-microbe interactions.
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Jian Zhang,Jingyang Liu,Liyuan Meng,Zhongyou Ma,Xinyun Tang,Yuanyuan Cao,Leni Sun 한국미생물학회 2012 The journal of microbiology Vol.50 No.2
Thirty-two isolates were obtained from wheat rhizosphere by wheat germ agglutinin (WGA) labeled with fluorescein isothiocyanate (FITC). Most isolates were able to produce indole acetic acid (65.6%) and siderophores (59.3%), as well as exhibited phosphate solubilization (96.8%). Fourteen isolates displayed three plant growth-promoting traits. Among these strains, two phosphate-dissolving ones, WS29 and WS31, were evaluated for their beneficial effects on the early growth of wheat (Triticum aestivum Wan33). Strain WS29and WS31 significantly promoted the development of lateral roots by 34.9% and 27.6%, as well as increased the root dry weight by 25.0% and 25.6%, respectively, compared to those of the control. Based on 16S rRNA gene sequence comparisons and phylogenetic positions, both isolates were determined to belong to the genus Bacillus. The proportion of isolates showing the properties of plant growth-promoting rhizobacteria (PGPR) was higher than in previous reports. The efficiency of the isolation of PGPR strains was also greatly increased by WGA labeled with FITC. The present study indicated that WGA could be used as an effective tool for isolating PGPR strains with high affinity to host plants from wheat roots. The proposed approach could facilitate research on biofertilizers or biocontrol agents.
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SVM-Based Incremental Learning Algorithm for Large-Scale Data Stream in Cloud Computing
( Ning Wang ),( Yang Yang ),( Liyuan Feng ),( Zhenqiang Mi ),( Kun Meng ),( Qing Ji ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.10
We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today`s data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.
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