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In this paper the image fusion algorithm based on Contourlet transform and Pulse Coupled Neural Network (PCNN) was proposed to improve the performance of the image fusion in the detection accuracy of obstacles in forests. At the same time, the wavelet transform and the Principal Component Analysis (PCA) were simulated for comparison with the proposed algorithm. Then visible and infrared thermal images were collected in a forest. The experimental results have shown that the fused images using the method proposed provided a better understanding of the reality, enhanced images' clarity and eliminated factors which provided shelters for targets. [ABSTRACT FROM AUTHOR]

Cancer stem cells (CSCs) are considered one of the key contributors to chemoresistance and tumor recurrence. Therefore, the precise identification of reliable CSC markers and clarification of the intracellular signaling involved in CSCs remains a great challenge in fields relating to cancer biology. Here, we implemented a novel chemoresistant prostate cancer patient-derived xenograft (PDX) model in NOD/SCID mice and identified CD54 as a candidate gene among the most highly enriched gene expression profiles in prostate tumors exposed to chronic cisplatin administration. Additional in vitro and in vivo assays showed that CD54 played a critical role in the self-renewal and tumorigenesis of prostate CSCs. Moreover, silencing CD54 greatly reduced the tumorigenesis of prostate cancers both in vitro and in vivo and significantly extended the survival time of tumor-bearing mice in a prostate cancer xenograft model. Dissection of the molecular mechanism revealed that the p38-Notch1 axis was the main downstream signaling pathway in CD54-mediated regulation of CSCs in prostate cancers. Together, these results established that CD54 could be a novel reliable prostate CSC marker and provided a new potential therapeutic target in prostate cancer via CD54-Notch1 signaling.