Identification of a novel adenovirus in liver tissue sample of the Great Himalayan leaf-nosed bat (Hipposideros armiger).

Bats are important reservoirs for many zoonotic viruses. To explore and monitor potential novel viruses carried by bats, 21 liver samples of bats (Hipposideros armiger) were collected from Yunnan Province in southern China. Only one (4.8%) of all models was detected with adenovirus. The whole genome strain obtained by the viral metagenomics method combined with PCR was temporarily named YN01. The complete genome of YN01 was 37,676 bp, with a G + C content of 55.20% and 28 open reading frames. Phylogenetic analysis indicated that the strain YN01 can be classified as genus Mastadenovirus and was the most similar to the adenovirus isolated from Rhinolophus sinicus in China in 2016. The analysis is needed to verify the possibility of cross-species transmission. This virological investigation has increased our understanding of the ecology of bat-borne viruses in this area and provided a reference for possible future infectious diseases.

Identification of novel anelloviruses in the blood of giant panda (Ailuropoda melanoleuca).

In recent years, the continuous development of metagenomics has revealed that in addition to the digestive tract, some viruses are also common in mammalian blood. To explore and monitor potential novel viruses, in April 2015, a blood sample was collected from a healthy captive giant panda at the Chengdu Research Base of Giant Panda Breeding in Sichuan Province, China. The genomes of 25 different anelloviruses containing the complete ORF1 region have been identified. The BLASTp results showed that the amino acid sequence identity of these viruses with the best match in GenBank ranged from 27.15% to 41.29%. Based on phylogenetic analysis and SDT (Species Demarcation Tool) analysis of the complete ORF1 regions of these 25 viruses, these sequences were deduced to represent one or several novel virus genera or species. This virological study has increased our understanding of the diversity of anelloviruses in the blood of giant pandas, but further laboratory analysis is needed to verify its possible pathogenicity.

Cluster-Locating Algorithm Based on Deep Learning for Silicon Pixel Sensors.

The application of silicon pixel sensors provides an excellent signal-to-noise ratio, spatial resolution, and readout speed in particle physics experiments. Therefore, high-performance cluster-locating technology is highly required in CMOS-sensor-based systems to compress the data volume and improve the accuracy and speed of particle detection. Object detection techniques using deep learning technology demonstrate significant potential for achieving high-performance particle cluster location. In this study, we constructed and compared the performance of one-stage detection algorithms with the representative YOLO (You Only Look Once) framework and two-stage detection algorithms with an RCNN (region-based convolutional neural network). In addition, we also compared transformer-based backbones and CNN-based backbones. The dataset was obtained from a heavy-ion test on a Topmetal-M silicon pixel sensor at HIRFL. Heavy-ion tests were performed on the Topmetal-M silicon pixel sensor to establish the dataset for training and validation. In general, we achieved state-of-the-art results: 68.0% AP (average precision) at a speed of 10.04 FPS (Frames Per Second) on Tesla V100. In addition, the detection efficiency is on the same level as that of the traditional Selective Search approach, but the speed is higher.