The giant panda (Ailuropoda melanoleuca) is a vulnerable mammal herbivore living wild in central China. Viral infections have become a potential threat to the health of these endangered animals, but limited information related to these infections is available. Using a viral metagenomic approach, we surveyed viruses in the feces, nasopharyngeal secretions, blood, and different tissues from a wild giant panda that died from an unknown disease, a healthy wild giant panda, and 46 healthy captive animals. The previously uncharacterized complete or near complete genomes of four viruses from three genera in Papillomaviridae family, six viruses in a proposed new Picornaviridae genus (Aimelvirus), two unclassified viruses related to posaviruses in Picornavirales order, 19 anelloviruses in four different clades of Anelloviridae family, four putative circoviruses, and 15 viruses belonging to the recently described Genomoviridae family were sequenced. Reflecting the diet of giant pandas, numerous insect virus sequences related to the families Iflaviridae, Dicistroviridae, Iridoviridae, Baculoviridae, Polydnaviridae, and subfamily Densovirinae and plant viruses sequences related to the families Tombusviridae, Partitiviridae, Secoviridae, Geminiviridae, Luteoviridae, Virgaviridae, and Rhabdoviridae; genus Umbravirus, Alphaflexiviridae, and Phycodnaviridae were also detected in fecal samples. A small number of insect virus sequences were also detected in the nasopharyngeal secretions of healthy giant pandas and lung tissues from the dead wild giant panda. Although the viral families present in the sick giant panda were also detected in the healthy ones, a higher proportion of papillomaviruses, picornaviruses, and anelloviruses reads were detected in the diseased panda. This viral survey increases our understanding of eukaryotic viruses in giant pandas and provides a baseline for comparison to viruses detected in future infectious disease outbreaks. The similar viral families detected in sick and healthy giant pandas indicate that these viruses result in commensal infections in most immuno-competent animals.

Niviventer is a genus of white-bellied rats that are among the most common rodents in the Indo-Sundaic region. The taxonomy of the genus has undergone extensive revisions and remains controversial. The current phylogeny is unresolved and was developed primarily on the basis of mitochondrial genes. Identification is extremely difficult, and a large number of GenBank sequences seem to be problematic. We extensively sampled specimens of Niviventer in China and neighboring northern Vietnam, including topotypes of the most reported species (n = 6), subspecies (n = 8), and synonyms (n = 4). We estimated phylogenetic relationships on the basis of one mitochondrial and three nuclear genes, using concatenation and coalescent-based approaches. We also employed molecular species delimitation approaches to test the existence of cryptic and putative new species. Our phylogeny was finely resolved, especially for the N. confucianus-like species. Our data provided the first support for N. brahma and N. eha as sister species, an assignment that is congruent with their morphological similarities. Species delimitation analyses provided new insight into species diversity and systematics. Three geographic populations of N. confucianus and one of N. fulvescens were supported as genetically distinct in our species delimitation analyses, while three recognized species (N. coninga, N. huang, and N. lotipes) were not strongly supported as distinct. Our results suggested that several genetically distinct species may be contained within the species currently known as N. confucianus and N. fulvescens. In addition, the results of Bayesian Phylogenetics and Phylogeography (BPP) for N. coninga, N. huang, and N. lotipes indicated that either inter-specific gene flow had occurred or imperfect taxonomy was present. Morphological examinations and morphometric analyses are warranted to examine the molecular results.

Hyperspectral images are a valuable tool for remotely sensing important characteristics of a variety of landscapes, including water quality and the status of marine disasters. However, hyperspectral data are rare or expensive to obtain, which has spurred interest in low-cost, fast methods for reconstructing hyperspectral data from much more common RGB images. We designed a novel algorithm to achieve this goal using multi-scale atrous convolution residual network (MACRN). The algorithm includes three parts: low-level feature extraction, high-level feature extraction, and feature transformation. The high-level feature extraction module is composed of cascading multi-scale atrous convolution residual blocks (ACRB). It stacks multiple modules to form a depth network for extracting high-level features from the RGB image used as an input. The algorithm uses jump connection for residual learning, and the final high-level feature combines the output of the low-level feature extraction module and the output of the cascaded atrous convolution residual block element by element, so as to prevent gradient dispersion and gradient explosion in the deep network. Without adding too many parameters, the model can extract multi-scale features under different receptive fields, make better use of the spatial information in RGB images, and enrich the contextual information. As a proof of concept, we ran an experiment using the algorithm to reconstruct hyperspectral Sentinel-2 satellite data from the northern coast of Australia. The algorithm achieves hyperspectral spectral reconstruction in 443nm-2190nm band with less computational cost, and the results are stable. On the Realworld dataset, the reconstruction error MARE index is less than 0.0645, and the reconstruction time is less than 9.24S. Therefore, in the near infrared band, MACRN reconstruction accuracy is significantly better than other spectral reconstruction algorithms. MACRN hyperspectral reconstruction algorithm has the characteristics of low reconstruction cost and high reconstruction accuracy, and its advantages in ocean spectral reconstruction are more obvious.

The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.

Global biodiversity is facing serious threats. However, knowledge of the genomic consequences of recent rapid population declines of wild organisms is limited. Do populations experiencing recent rapid population decline have the same genomic status as wild populations that experience long-term declines? Yellow-breasted Bunting (Emberiza aureola) is a critically endangered species that has been experiencing a recent rapid population decline. To answer the question, we assembled and annotated the whole genome of Yellow-breasted Bunting. Furthermore, we found high genetic diversity, low linkage disequilibrium, and low proportion of long runs of homozygosity in Yellow-breasted Bunting, suggesting that the populations following recent rapid declines have different genomic statuses from the population that experienced long-term population decline.

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.