Immunological Methods for the Detection of Binders in Ancient Tibetan Murals

Application of ELISA in Cultural Heritage: Recent Advances and Challenges

Organic residue analyses have long been the primary focus and challenge in the fields of scientific archaeology and cultural heritage. Enzyme-linked immunosorbent assay (ELISA) has emerged as a valuable method for detecting organic residues owing to its high sensitivity and specificity. Organic components have been observed within four categories of archaeological artifacts: mortars, adhesives, animal and plant remains, and daily use artifacts. Therefore, in this article, we critically analyzed the advantages and limitations of ELISA in detecting organic residues by tracking its recent application in the abovementioned categories. The current focus of ELISA applications is on the preparation of customized antibodies, development of multicomponent detection methods, and meeting on-site identification demands. Additionally, understanding organic residue degradation mechanisms and the proper handling of archaeological samples are also key factors in these applications. Integration of ELISA with biomolecular science and electrochemistry has allowed the development of comprehensive detection and analyses. In the future, ELISA will be capable of handling more complex and diverse analyses, revealing highly intricate information from archaeological samples.

Rapid Enrichment and Detection of Silk Residues from Tombs by Double-Antibody Sandwich ELISA Based on Immunomagnetic Beads

The extraction and identification of silk residues in tombs is of great significance for studying the distribution and spread of early silk. However, the complex organic matter in the tomb hinders the accurate identification of silk. In this study, a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) based on immunomagnetic beads (IMBs) was developed for the rapid enrichment and detection of silk residues. The double-antibody sandwich ELISA method established by pairing the IMBs prepared by the silk fibroin monoclonal antibody SF-3 and the silk fibroin monoclonal-labeled antibody bio-SF-1 had the highest detection sensitivity, with a linear detection range of 10 to 10⁴ ng mL^-1 and a detection limit of 5.12 ng mL^-1. This method was excellent in the extraction and analysis of silk residues from archaeological imprints and soil samples and successfully identified silk residues in samples at the final stage of silk degradation (physical invisible silk). The proteomics analysis results demonstrated the feasibility and practicability of this method.

Application of a Modified Generative Adversarial Network in the Superresolution Reconstruction of Ancient Murals

Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image. Finally, the reconstructed high-resolution image is output to complete the design of the generative network. Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image. In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling. Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image. A mural dataset is set up by the current team. Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring. The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.