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Molecular targeted agents, such as sorafenib, remain the only choice of an antitumor drug for the treatment of advanced hepatocellular carcinoma (HCC). The Notch signaling pathway plays central roles in regulating the cellular injury/stress response, anti-apoptosis, or epithelial-mesenchymal transition process in HCC cells, and is a promising target for enhancing the sensitivity of HCC cells to antitumor agents. The ADAM metalloprotease domain-17 (ADAM-17) mediates the cleavage and activation of Notch protein. In the present study, microRNA-3163 (miR-3163), which binds to the 3′-untranslated region of ADAM-17, was screened using online methods. miRDB and pre-miR-3163 sequences were prepared into lentivirus particles to infect HCC cells. miR-3163 targeted ADAM-17 and inhibited the activation of the Notch signaling pathway. Infection of HCC cells with miR-3163 enhanced their sensitivity to molecular targeted agents, such as sorafenib. Therefore, miR-3163 may contribute to the development of more effective strategies for the treatment of advanced HCC.

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can’t meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3%...

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