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- 저자 : Tang Xiaobing (검색결과 2 건)
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Xiaobing Wang,Danyan Tang,Wei Wang 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.3
Pseudomonas protegens is a gram-negative bacterium with an excellent biological control effect. Compared to standard cells growing in NaCl-free media, the ability of the hyperosmotic cells (450 mM NaCl) to resist high temperatures and freezing was significantly improved. It is of great significance to apply the P. protegens to elaborate on the hyperosmotic adaptation mechanism. RNA-seq was used to sequence P. protegens cultured with 0 mM and 450 mM NaCl. Comparative transcriptomic analyses of the different treatments were performed using gene ontology and the Kyoto encyclopedia of genes and genome. The results revealed that hyperosmotic stress had prominent impacts on the genes involving in multiple cellular functions. The hyperosmotic environment significantly affected carbohydrate, energy, and amino acid metabolism, as well as membrane system and cell motility. Our findings indicated that P. protegens adopted a series of approaches, including the high cytoplasmic concentrations of potassium ions and the uptake or synthesis of osmoprotectants, for surviving hyperosmotic stress. Among these, trehalose and proline synthesis appeared to be an important method to withstand prolonged hyperosmotic stress in P. protegens. These data provide crucial resource that may determine specific responses to the hyperosmotic environment in P. protegens.
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An Image Retrieval Method Based on Visual Dictionary and Saliency Region
Xu Gongwen,Xu Lina,Li Xiaomei,Tang Xiaobing,Wang Xiaoyan,Xu Chunxiu 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.7
An image retrieval method based on the combination of visual dictionary and region of saliency was proposed in this paper, which aims to increase the accuracy of image retrieval. The image is divided into sampled blocks and the low-level features are extracted from these image blocks. Then a variety of features vectors are taken as the input vector for learning its corresponding visual dictionary respectively by non-negative sparse coding. Spatial information is added into the sparse representations of image by proposing the saliency polling method, and the similarity measure between sparse representation vectors is defined as SED (Squared Euclidean Distance), which considering the same nonzero entries and Euclidean distance of vectors at the same time. Results of experiment carried on Corel and Caltech datasets showed that this method can effectively improve the accuracy of image retrieval compared with the methods of single visual dictionary.
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