Background: A growing body of evidence has revealed that the mammalian genome is organized into hierarchical layers that are closely correlated with and may even be causally linked with variations in gene expression. Recent studies have characterized chromatin organization in various porcine tissues and cell types and compared them among species and during the early development of pigs. However, how chromatin organization differs among pig breeds is poorly understood. Results: In this study, we investigated the 3D genome organization and performed transcriptome characterization of two adipose depots (upper layer of backfat [ULB] and greater omentum [GOM]) in wild boars and Bama pigs; the latter is a typical indigenous pig in China. We found that over 95% of the A/B compartments and topologically associating domains (TADs) are stable between wild boars and Bama pigs. In contrast, more than 70% of promoter-enhancer interactions (PEIs) are dynamic and widespread, involving over a thousand genes. Alterations in chromatin structure are associated with changes in the expression of genes that are involved in widespread biological functions such as basic cellular functions, endocrine function, energy metabolism and the immune response. Approximately 95% and 97% of the genes associated with reorganized A/B compartments and PEIs in the two pig breeds differed between GOM and ULB, respectively. Conclusions: We reported 3D genome organization in adipose depots from different pig breeds. In a comparison of Bama pigs and wild boar, large-scale compartments and TADs were mostly conserved, while fine-scale PEIs were extensively reorganized. The chromatin architecture in these two pig breeds was reorganized in an adipose depot-specific manner. These results contribute to determining the regulatory mechanism of phenotypic differences between Bama pigs and wild boar.

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.

The giant panda (Ailuropoda melanoleuca) is a relict species endemic to China. Captive breeding has boosted its population. However, some captive individuals exhibit stereotypic behavior. Though extensive research showing that host behavior can be influenced by gut microbiota via the gut-brain axis, the gut microbiota related to stereotypic behavior of giant pandas has not been reported. To explore gut microbial composition and functional characteristics of stereotypically behaving (SB) giant pandas, the behavior of 15 captive giant pandas was observed, and their feces were analyzed using 16S ribosomal RNA (rRNA) sequencing (n = 145) and metagenomic sequencing (n = 45). Our study revealed distinct gut microbial diversity, composition, and functions between SB giant pandas and normal controls, indicating gut microbial dysbiosis in SB giant pandas. Serratia sp. Se-RSBMAAmG was significantly enriched in SB giant pandas, five Clostridium species and Lactococcus garvieae exhibited upward trends in SB individuals, whereas the probiotic Enterococcus hirae showed the opposite trend compared with those in normal controls. The Clostridium bacteria may affect propionate metabolism and the gamma-aminobutyric acidergic (GABAergic) synapse pathway, potentially affecting the emergence of stereotypic behavior. These findings suggest a link between gut microbiota and stereotypic behavior of giant pandas, providing novel insights for their conservation.

The giant panda (Ailuropoda melanoleuca) is a relict species endemic to China. Captive breeding has boosted its population. However, some captive individuals exhibit stereotypic behavior. Though extensive research showing that host behavior can be influenced by gut microbiota via the gut-brain axis, the gut microbiota related to stereotypic behavior of giant pandas has not been reported. To explore gut microbial composition and functional characteristics of stereotypically behaving (SB) giant pandas, the behavior of 15 captive giant pandas was observed, and their feces were analyzed using 16S ribosomal RNA (rRNA) sequencing (n = 145) and metagenomic sequencing (n = 45). Our study revealed distinct gut microbial diversity, composition, and functions between SB giant pandas and normal controls, indicating gut microbial dysbiosis in SB giant pandas. Serratia sp. Se-RSBMAAmG was significantly enriched in SB giant pandas, five Clostridium species and Lactococcus garvieae exhibited upward trends in SB individuals, whereas the probiotic Enterococcus hirae showed the opposite trend compared with those in normal controls. The Clostridium bacteria may affect propionate metabolism and the gamma-aminobutyric acidergic (GABAergic) synapse pathway, potentially affecting the emergence of stereotypic behavior. These findings suggest a link between gut microbiota and stereotypic behavior of giant pandas, providing novel insights for their conservation.

The giant panda, a rare and iconic species endemic to China, has attracted significant attention from both domestic and international researchers due to its crucial ecological role, unique cultural value, and distinct evolutionary history. While substantial progress has been made in the field of individual identification, behavior recognition remains underdeveloped, facing challenges such as the lack of dynamic temporal features and insufficient extraction of behavioral characteristics. To address these challenges, we propose the Swin-Panda model, which leverages transfer learning based on the Video Swin Transformer architecture within the mmaction2 framework. In addition, we introduce two novel modules: the Comprehensive Perception Auxiliary Module and the Spatiotemporal Shift Attention Module. These modules facilitate the extraction of local and spatiotemporal information, allowing the model to more effectively capture the behavioral and movement patterns of giant pandas. Experimental results on the PACV-8 dataset demonstrate that our model achieves an accuracy of 88.02%, outperforming several benchmark models. This approach significantly enhances behavior recognition accuracy, thereby contributing to the advancement of panda welfare and species conservation efforts.