Metagenomic analysis reveals taxonomic and functional diversity of microbial communities on the deteriorated wall paintings of Qinling Tomb in the Southern Tang Dynasty, China.

The microbial colonization on ancient murals attracts more and more attention since the threaten by microorganisms was first reported in Lascaux, Spain. However, the biodeterioration or biodegradation of mural paintings resulted by microorganisms is not clear yet. Especially the biological function of microbial communities in different conditions remained largely unaddressed. The two mausoleums of the Southern Tang Dynasty are the largest group of emperor mausoleums during the Five Dynasties and Ten Kingdoms period in China, which are of great significance to the study of the architecture, imperial mausoleum systems and art in the Tang and Song Dynasties. To make clear the species composition and metabolic functions of different microbial communities (MID and BK), we analyzed the samples from the wall paintings in one of the two mausoleums of the Southern Tang Dynasty with metagenomics method. The result showed totally 55 phyla and 1729 genera were detected in the mural paintings. The two microbial community structure were similar with the dominance of Proteobacteria, Actinobacteria and Cyanobacteria. However, the species abundance presented a significant difference between two communities at genus level --- MID is Lysobacter, Luteimonas are predominant in MID while Sphingomonas and Streptomyces are popular in BK, which is partially attributed to the different substrate materials of murals. As a result, the two communities presented the different metabolic patterns that MID community was mainly participated in the formation of biofilm as well as the degradation of exogenous pollutants while the BK was predominantly related to the photosynthesis process and biosynthesis of secondary metabolites. Taken together, these findings indicated the effect of environmental factor on the taxonomic composition and functional diversity of the microbial populations. The installation of artificial lighting needs to be considered carefully in the future protection of cultural relics.

Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a novel imaging technique can show the patient's condition more objectively, and several characteristics of carpal tunnel syndrome have been found. However, various image sequences, heavy artifacts, small lesion characteristics, high volume of imagine reading, and high difficulty in MRI interpretation limit its application in clinical practice. With the development of automatic image segmentation technology, the algorithm has great potential in medical imaging. The challenge is that the segmentation target is too small, and there are two categories of images with the proximal border of the carpal tunnel as the boundary. To meet the challenge, we propose an end-to-end deep learning framework called Deep CTS to segment the carpal tunnel from the MR image. The Deep CTS consists of the shape classifier with a simple convolutional neural network and the carpal tunnel region segmentation with simplified U-Net. With the specialized structure for the carpal tunnel, Deep CTS can segment the carpal tunnel region efficiently and improve the intersection over union of results. The experimental results demonstrated that the performance of the proposed deep learning framework is better than other segmentation networks for small objects. We trained the model with 333 images, tested it with 82 images, and achieved 0.63 accuracy of intersection over union and 0.17 s segmentation efficiency, which indicate great promise for the clinical application of this algorithm.

A deep learning approach for medical waste classification.

As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.

Phosphorylation of Ago2 is required for its role in DNA double-strand break repair.

Repair of DNA double-strand break (DSB) is critical for the maintenance of genome integrity. A class of DSB-induced small RNAs (diRNAs) has been shown to play an important role in DSB repair. In humans, diRNAs are associated with Ago2 and guide the recruitment of Rad51 to DSB sites to facilitate repair by homologous recombination (HR). Ago2 activity has been reported to be regulated by phosphorylation under normal and hypoxic conditions. However, the role of Ago2 phosphorylation in DNA damage repair is unexplored. Here, we show that S672, S828, T830, and S831 of human Ago2 are phosphorylated in response to ionizing radiation (IR). S672A mutation of Ago2 leads to significant reduction in Rad51 foci formation and HR efficiency. We further show that defective association of Ago2 S672A variant with DSB sites, instead of defects in diRNA and Rad51 binding, may account for decreased Rad51 foci formation and HR efficiency. Our study reveals a novel regulatory mechanism for the function of Ago2 in DNA repair.