Corn grain has a high starch content and is used as main energy source in ruminant diets. Compared with finely ground corn (FGC), steam-flaked corn (SFC) could improve the milk yield of lactating dairy cows and the growth performance of feedlot cattle, but the detailed mechanisms underlying those finding are unknown. The rumen microbiome breaks down feedstuffs into energy substrates for the host animals, and contributes to feed efficiency. Therefore, the current study was conducted to investigate the ruminal bacterial community changes of heifers fed differently processed corn (SFC or FGC) using 16S rRNA sequencing technologies, and to uncover the detailed mechanisms underlying the high performance of ruminants fed the SFC diet. The results revealed that different processing methods changed the rumen characteristics and impacted the composition of the rumen bacteria. The SFC diet resulted in an increased average daily gain in heifers, an increased rumen propionate concentration and a decreased rumen ammonia nitrogen concentration. The relative abundance of the phylum Firmicutes and Proteobacteria were tended to increase or significantly increased in the heifers fed SFC diet compared with FGC diet. In addition, the relative abundance of amylolytic bacteria of the genera Succinivibrio, Roseburia and Blautia were elevated, and the cellulolytic bacteria (Ruminococcaceae_UCG-014 and Ruminococcaceae_UCG-013) were decreased by the steam flaking method. Spearman correlation analysis between the ruminal bacteria and the microbial metabolites showed that the rumen propionate concentration was positively correlated with genera Succinivibrio and Blautia abundance, but negatively correlated with genera Ruminococcaceae_UCG-014 abundance. Evident patterns of efficient improvement in rumen propionate and changes in rumen microbes to further improve feed conversion were identified. This observation uncovers the potential mechanisms underlying the increased efficiency of the SFC processing method for enhancing ruminant performance.
Simultaneous Localization and Mapping(SLAM) is the basis for many robotic applications. Most SLAM algorithms are based on the assumption that the scene is static. In real-world applications, moving objects are inevitable, which will greatly impact the ego-pose estimation accuracy. This paper presents DyStSLAM, a visual SLAM system with stereo configuration that can efficiently identify moving objects and accomplish dynamic data association. First of all, DyStSLAM extracts feature points, estimates disparity map, and performs instance segmentation simultaneously. Then, the obtained results are combined to estimate the motion confidence and discriminate between moving objects and static ones. A confidence based matching algorithm is proposed to associate dynamic objects and estimate the pose of each moving object. At the same time, static objects are used to estimate the pose of the camera. Finally, after nonlinear optimization, a sparse point cloud map of both static background and dynamic objects is constructed. Compared with ORB-SLAM2, the proposed method outperforms by 31% for ATE on KITTI dataset.
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