Guixin Dong

Statistical analysis of the relationship between pre-parturition backfat thickness and reproductive performance using a large dataset of 693 sows (gilts) revealed that both high and low backfat as detrimental to the optimal reproductive performance of sows. Further analysis indicated that the health and reproductive performance of sows are compromised by problems such as excess backfat deposition due to ad libitum feeding and reduced satiety due to restricted feeding. Despite the consensus that dietary fiber (DF) supplementation of sows during gestation improves their health and performance, there is currently disagreement about the appropriate type of DF to select. In this review, the definition of DF is revised by integrating its physicochemical properties and fermentability, and the definition of functional dietary fiber (FDF) applicable to breeding pig breeding is proposed. Additionally, the mechanisms by which FDF regulates sow constipation, satiety, lactational feed intake and reproductive performance are elaborated in detail. This review provides a theoretical basis for selecting reasonable fiber sources for sow production.

Gut microbiome has a profound influence on koalas’ health. Yet, the relationships among the gut bacteriome, virome, antibiotic resistome, and pathogenicity throughout different stages in koala’s life remain elusive. Here, we presented a metagenome-resolved survey of gut microbiome utilizing 75 fecal samples from three groups of captive koalas. The diversity of bacteriome and virome were age-dependent, predominating in adult koalas. Lytic viruses increased with age as lysogenic viruses and bacterial hosts declined, and virus-to-microbe ratios rose, revealing concomitant age-related shifts in microbial communities, though causality remains unresolved. Antibiotic resistance genes (ARGs) were more prevalent in young koalas, unlike in humans, where they accumulate with age. Two ARG-carrying pathogens, Klebsiella pneumoniae and Escherichia coli , were identified and cultured, with K. pneumoniae and E. coli predominating in young koalas. One age-dependent lytic virus infecting K. pneumoniae only detected in young koalas, and two lysogenic viruses infecting E. coli identified the in young and adult koalas. Analyses showed a positive correlation between mobile genetic elements (MGEs) and virulence factors (VFs), which facilitated the widespread dissemination of VFs and impacted health. Collectively, this study advances the understanding of gut microbiome in health, providing solutions to the treatment and management of captive koalas.

The daily behavioral patterns of giraffes reflect their health status and well-being. Behaviors such as licking, walking, standing, and eating are not only essential components of giraffes’ routine activities but also serve as potential indicators of their mental and physiological conditions. This is particularly relevant in captive environments such as zoos, where certain repetitive behaviors may signal underlying well-being concerns. Therefore, developing an efficient and accurate automated behavior detection system is of great importance for scientific management and welfare improvement. This study proposes a multi-behavior automatic detection method for giraffes based on YOLO11-Pose and the spatial-adaptive two-stream network (SATSN). Firstly, YOLO11-Pose is employed to detect giraffes and estimate the keypoints of their mouths. Observation-Centric SORT (OC-SORT) is then used to track individual giraffes across frames, ensuring temporal identity consistency based on the keypoint positions estimated by YOLO11-Pose. In the SATSN, we propose a region-of-interest extraction strategy for licking behavior to extract local motion features and perform daily behavior classification. In this network, the original 3D ResNet backbone in the slow pathway is replaced with a video transformer encoder to enhance global spatiotemporal modeling, while a Temporal Attention (TA) module is embedded in the fast pathway to improve the representation of fast motion features. To validate the effectiveness of the proposed method, a giraffe behavior dataset consisting of 420 video clips (10 s per clip) was constructed, with 336 clips used for training and 84 for validation. Experimental results show that for the detection tasks of licking, walking, standing, and eating behaviors, the proposed method achieves a mean average precision (mAP) of 93.99%. This demonstrates the strong detection performance and generalization capability of the approach, providing robust support for automated multi-behavior detection and well-being assessment of giraffes. It also lays a technical foundation for building intelligent behavioral monitoring systems in zoos.

In computer vision applications, the primary task of object detection is to answer the following question: “What object is present and where is it located?”. However, underwater environments introduce challenges, such as poor lighting, high complexity, and diverse marine organism shapes, leading to missed detections or false positives in deep learning-based algorithms. To improve detection accuracy and robustness, this paper proposes an enhanced YOLOv11-based algorithm for underwater object detection that strengthens the ability to capture both local and global details and global contextual information in complex underwater environments. To better capture local and global features while integrating contextual information, the proposed method introduces several enhancements. The backbone incorporates a DualBottleneck module to enhance feature extraction, replacing the standard bottleneck structure in C3k, thus enhancing the feature extraction and the channel aggregation. The detection head adopts DyHead-GDC, integrating ghost depthwise separable convolution with DyHead for greater efficiency. Furthermore, the ADown module replaces conventional feature extraction and downsampling convolutions, reducing parameters and FLOPs by 14%. The C2PSF module, combining focal modulation and C2, strengthens local feature extraction and global context processing. Additionally, a SCSA module is inserted before the detection head to fully utilize multi-semantic information, improving the detection performance in complex underwater scenes. Experimental results confirm the effectiveness of these improvements. The model achieves 84.2% mAP50 on UTDAC2020, 84.4% on DUO and 86.7% on RUOD, surpassing the baseline by 2.5%, 1.6% and 1.2%, respectively. It remains lightweight, with 6.5 M parameters and a computational cost of 7.1 GFLOPs.

The classification of high-frequency marine mammal vocalizations often faces challenges due to the limitations of acoustic features, which are sensitive to mid-to-low frequencies but offer low resolution in high-frequency ranges. Additionally, single-channel networks can restrict overall classification performance. To tackle these challenges, we introduce MT-Resformer, an innovative dual-channel model with a multi-scale framework designed for classifying marine mammal vocalizations. Our approach introduces a feature fusion strategy that combines the constant-Q spectrogram with Mel filter-based spectrogram features, effectively overcoming the low resolution of Mel spectrograms in high frequencies. The MT-Resformer model incorporates two key components: a multi-scale parallel residual network (MResNet) and a Transformer network channel. The model employs a multi-level neural perceptron (MLP) to dynamically regulate the weighting of the two channels, enabling flexible feature fusion. Experimental findings validate the proposed approach, yielding classification accuracies of 99.17% on the Watkins dataset and 95.22% on the ChangLong dataset. These results emphasize its outstanding performance.

In order to optimize diets for Bengal tiger cubs and improve their health condition and survival rates, we conducted microbiota and metabolomics analyses on fecal samples from Bengal tiger cubs fed goat and dog milk replacer formulae. The results showed that there were significant differences in fecal microorganisms and metabolites between the two groups. At the phylum level, the major components of the microbial composition in the feces of cubs were Firmicutes, Actinobacteriota, Proteobacteria, Bacteroidota and Fusobacteriota. In addition, the abundance of gut microbiota varied significantly between the two groups of tiger cubs. The fecal microbiota of the tiger cubs fed dog milk replacer powder exhibited an increase in probiotic bacteria (Anaerostipes and Clostridium_scindens) (p < 0.05), and the microbial community tended to be more balanced. Metabolomics data further elucidated that feeding different milk formulae significantly affected the fecal metabolites and metabolic pathways in the Bengal tiger cubs. In the dog milk replacer powder group, 76 metabolites were up-regulated (p < 0.05), and 278 metabolites were down-regulated (p < 0.05), particularly affecting the metabolism of vitamin D3, vitamin B5, isoleucine, valine, phenylalanine and oleic acid. At the same time, 19 metabolic pathways were affected (p < 0.05), including the amino acid metabolism, lipid metabolism and nucleotide metabolism pathways. In conclusion, this study confirms that milk formula composition affects the gut microbiota and metabolism of Bengal tiger cubs. These findings may provide new insights into how different milk powder formulae and dietary strategies influence the regulation of gut microbiota and overall health in Bengal tiger cubs.

The Yangtze finless porpoises (Neophocaena asiaeorientalis asiaeorientalis) living in different environments display significant differences in behavior and physiology. To compare and analyze gene expression differences between an ex situ population and a controlled environment population of the Yangtze finless porpoise, we sequenced the transcriptome of blood tissues living in a semi-natural reserve and an artificial facility, respectively. We identified 6860 differentially expressed genes (DEGs), of which 6603 were up-regulated and 257 were down-regulated in the controlled environment vs ex situ comparison. GO and KEGG enrichment analysis showed that the up-regulated genes in the controlled environment population were significantly associated with glucose metabolism, amino acid metabolism, and the nervous system, while those up-regulated in the ex situ population were significantly associated with energy supply and biosynthesis. Further analysis showed that metabolic and hearing-related genes were significantly affected by changes in the environment, and key metabolic genes such as HK, PFK, IDH, and GLS and key hearing-related genes such as OTOA, OTOF, SLC38A1, and GABBR2 were identified. These results suggest that the controlled environment population may have enhanced glucose metabolic ability via activation of glycolysis/gluconeogenesis, the TCA cycle, and inositol phosphate metabolism, while the ex situ population may meet higher energy requirements via enhancement of the amino acid metabolism of the liver and muscle and oxidative phosphorylation. Additionally, the acoustic behavior and auditory-related genes of Yangtze finless porpoise may show responsive changes and differential expression under different environment conditions, and thus the auditory sensitivity may also show corresponding adaptive characteristics. This study provides a new perspective for further exploration of the responsive changes of the two populations to various environments and provides a theoretical reference for further improvements in conservation practices for the Yangtze finless porpoise.