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Abstract The Altay Prefecture in Xinjiang Uyghur Autonomous Region, China, boasts an abundance source of rock art, with the Dunde Bulaq rock art complex holding paramount significance in the study of local culture. This work employs hyperspectral techniques for data collection and analysis of the Dunde Bulaq I rock art site. Signal decomposition, pigment classification/clustering, and spectral curve analysis were employed to delve deeply into the information of the rock art hyperspectral data. Furthermore, cross-validation was utilized to reveal hidden information within rock art. To comprehend these hidden information, a more profound historical tracing was conducted, employing methods such as literature retrieval and image analysis, uncovering the internal relationship between the site I and the Chemurchek culture. This provides fresh material for studying the Bronze Age cultures within the Altai region.

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image's regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals. [ABSTRACT FROM AUTHOR]

Significance: Comprehensive profiling of the enhancer landscape and 3D genome structure in liposarcoma identifies extensive enhancer-oncogene coamplification and enhancer hijacking events, deepening the understanding of how oncogenes are regulated in cancer., (©2023 The Authors; Published by the American Association for Cancer Research.)