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Lv, Yang,Qiao, Xuewei,Zhao, Liang,Ren, Fazheng The Korean Society for Microbiology and Biotechnol 2015 Journal of microbiology and biotechnology Vol.25 No.6
Bifidobacterium longum subsp. longum BBMN68, isolated from centenarians in Guangxi, China, has been proved to be a promising probiotic strain for its health benefits. In this study, the biodistribution of this strain in the rat gut was first investigated using the quantitative realtime PCR assay and propidium monoazide. Strain-specific primers were originally designed based on the BBMN68 genome sequence. Healthy rats were orally inoculated with either a single dose of BBMN68 (10<sup>10</sup> colony-forming units/kg), or with one dose per day for 7 days and bacterial concentrations were analyzed in detail from the intestinal contents and feces of four different gut locations, including stomach, small intestine, colon, and rectum. Results indicated that strain BBMN68 could overcome the rigors of passage through the upper gastrointestinal tract and transiently accumulate in the colon, even though survival in the stomach and small intestine was not high. A good level of BBMN8 could stay in vivo for 72 h following a 7-day oral administration, and a daily administration is suggested for a considerable and continuous population of BBMN68 to be maintained in the host intestine.
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Wei Song,Shuanghui Zou,Yifei Tian,Su Sun,Simon Fong,조경은,Lvyang Qiu 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.6
Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmannedground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surroundingterrain reconstruction crucially influences decision making in UGVs. To increase the processing speed ofenvironment information analysis, we develop a CPU-GPU hybrid system of automatic environmentperception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists ofthree functional modules, namely, multi-sensor data collection and pre-processing, environment perception,and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processingfunction registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motioninformation into a global terrain model after filtering redundant and noise data according to the redundancyremoval principle. In the environment perception module, the registered discrete points are clustered intoground surface and individual objects by using a ground segmentation method and a connected componentlabeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversedand obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibratesthe projection matrix between the mounted LiDAR and cameras to map the local point clouds onto thecaptured video images. Texture meshes and color particle models are used to reconstruct the ground surfaceand objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallelcomputation method to implement the applied computer graphics and image processing algorithms in parallel.
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Song, Wei,Zou, Shuanghui,Tian, Yifei,Sun, Su,Fong, Simon,Cho, Kyungeun,Qiu, Lvyang Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.6
Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.
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