Comparison of Venous Blood Gas and Biochemical Parameters in Sunda Pangolin (Manis javanica) and Chinese Pangolin (Manis pentadactyla) before and after Isoflurane Anesthesia.

Venous blood gas analytes are commonly examined in animals, and the results may be important when evaluating the overall health status of an animal. Pangolins are critically endangered mammals, and there is limited information on their physiological reference values in the literature. The aim of this study was to analyze venous blood gas and biochemical parameters before and during isoflurane anesthesia in wild healthy Sunda and Chinese pangolins. The results obtained showed that the blood gas index trends of the two pangolin species before and after isoflurane anesthesia were the same. After anesthesia, the partial pressure of carbon dioxide (pCO2), partial pressure of oxygen (pO2), total carbon dioxide (CO2), mean blood bicarbonate (HCO3-), extracellular fluid compartment (BEecf) base excess and the mean blood glucose (Glu) levels of both pangolin species showed a significant increase compared to the pre-anesthesia period. In contrast, the mean blood potassium (K+), lactate (Lac) and mean blood pH levels were significantly lower. No significant differences in the mean blood sodium (Na+) or blood ionized calcium (iCa) levels were observed during anesthesia. This study is important for future comparisons and understanding the health status of this endangered species.

Energy budgets of captive Chinese pangolins (Manis pentadactyla).

The Chinese pangolin is an endangered species, and ex situ conservation and captive rescue are important conservation measures. This requires reliable information on nutritional energy requirements and expenditure characteristics. However, we lack sufficient knowledge of their energy physiology to determine their energy requirements for maintenance and growth. An open-flow respirometry system was used to measure the resting metabolic rate (RMR) and the daily energy expenditure (DEE) of Chinese pangolins (Manis pentadactyla), and the dietary digestive energy was measured. The average RMR in Chinese pangolins was 3.23 ml O2 kg-1 min-1 at an ambient temperature (Ta) of 24.5-30°C, which was only 73.0% of the expected value based on body mass (BM). The average DEE values were 744.9 kJ day-1 in animals with BM >3 kg and 597.3 kJ day-1 in those with BM <3 kg, which were only 52.4% and 60.6% of the predicted values, respectively. The RMR and DEE levels of the Chinese pangolin were lower than those of similar-sized eutherian mammals and close to those of anteaters. These characteristics suggest that the Chinese pangolin has a low demand for energy in its diet. Although metabolic level data alone cannot be used to calculate the energy requirements of each Chinese pangolin, we believe they can provide a tangible reference for the relocation of Chinese pangolins. These results provide a scientific basis for future research on the physiology and ecology of endangered wildlife such as the Chinese pangolin.

Holstein Cattle Face Re-Identification Unifying Global and Part Feature Deep Network with Attention Mechanism.

In precision dairy farming, computer vision-based approaches have been widely employed to monitor the cattle conditions (e.g., the physical, physiology, health and welfare). To this end, the accurate and effective identification of individual cow is a prerequisite. In this paper, a deep learning re-identification network model, Global and Part Network (GPN), is proposed to identify individual cow face. The GPN model, with ResNet50 as backbone network to generate a pooling of feature maps, builds three branch modules (Middle branch, Global branch and Part branch) to learn more discriminative and robust feature representation from the maps. Specifically, the Middle branch and the Global branch separately extract the global features of middle dimension and high dimension from the maps, and the Part branch extracts the local features in the unified block, all of which are integrated to act as the feature representation for cow face re-identification. By performing such strategies, the GPN model not only extracts the discriminative global and local features, but also learns the subtle differences among different cow faces. To further improve the performance of the proposed framework, a Global and Part Network with Spatial Transform (GPN-ST) model is also developed to incorporate an attention mechanism module in the Part branch. Additionally, to test the efficiency of the proposed approach, a large-scale cow face dataset is constructed, which contains 130,000 images with 3000 cows under different conditions (e.g., occlusion, change of viewpoints and illumination, blur, and background clutters). The results of various contrast experiments show that the GPN outperforms the representative re-identification methods, and the improved GPN-ST model has a higher accuracy rate (up by 2.8% and 2.2% respectively) in Rank-1 and mAP, compared with the GPN model. In conclusion, using the Global and Part feature deep network with attention mechanism can effectively ameliorate the efficiency of cow face re-identification.