The underlying mechanisms of breast cancer cells metastasizing to distant sites are complex and multifactorial. Bone sialoprotein (BSP) and αvβ3 integrin were reported to promote the metastatic progress of breast cancer cells, particularly metastasis to bone. Most theories presume that BSP promotes breast cancer metastasis by binding to αvβ3 integrin. Interestingly, we found the αvβ3 integrin decreased in BSP silenced cells (BSPi), which have weak ability to form bone metastases. However, the relevance of their expression in primary tumor and the way they participate in metastasis are not clear. In this study, we evaluated the relationship between BSP, αvβ3 integrin levels, and the bone metastatic ability of breast cancer cells in patient tissues, and the data indicated that the αvβ3 integrin level is closely correlated to BSP level and metastatic potential. Overexpression of αvβ3 integrin in cancer cells could reverse the effect of BSPi in vitro and promote bone metastasis in a mouse model, whereas knockdown of αvβ3 integrin have effects just like BSPi. Moreover, The Cancer Genome Atlas data and RT‐PCR analysis have also shown that SPP1, KCNK2, and PTK2B might be involved in this process. Thus, we propose that αvβ3 integrin is one of the downstream factors regulated by BSP in the breast cancer‐bone metastatic cascade.

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Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems.