MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages.

Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience. In recent years, video-based automatic animal behavior analysis has received widespread attention. However, methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped, with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change. Here, we introduce a novel method, called MonkeyTrail, which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals. The empty background is generated by combining the frame difference method (FDM) and deep learning-based model (YOLOv5). The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques. To test MonkeyTrail performance, we labeled a dataset containing >8 000 video frames with the bounding boxes of macaques under various conditions as ground-truth. Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learning-based methods (YOLOv5 and Single-Shot MultiBox Detector), traditional frame difference method, and naïve background subtraction method. Using MonkeyTrail to analyze long-term surveillance video recordings, we successfully assessed changes in animal behavior in terms of movement amount and spatial preference. Thus, these findings demonstrate that MonkeyTrail enables low-cost, large-scale daily behavioral analysis of macaques.

[Detection of tick and tick-borne pathogen in some ports of Inner Mongolia].

OBJECTIVE: To investigate the distribution, composition and situation of natural infection pathogen of tick species in the main ports of Inner Mongolia. METHODS: All ticks were collected manually with white cloth, from the grassland and searching for the hosts followed by detection of pathogens, with PCR. RESULTS: 1313 ticks identified, belonged to 1 family, 4 geniuses and 7 species in the three surveyed areas, with Dermacentor nuttallia distributed in the Ceke, Mandula and Manzhouli bordering ports. 69.08% of the total species were discovered at Port Ceke, with Rhipicephalus pumilio as the predominant one, which accounted for 74.86%. 5 kinds of tick-borne disease pathogens were detected from ticks in these three bordering ports while only Coxiella burnetii was found at the Port Ceke. In these three ports, the average infection rates of Lyme disease borrelia, Human babesia microti, Spotted fever group Rickettsia, Coxiella burnetii, Ehrlichiosis were 15.08%, 3.35%, 1.98%, 1.07%, 0.99% respectively. The positive rate of tick infected with Borrelia burgdorferi were 13.56%,22.88%,5.00% in the 3 bordering ports, respectively with significant differences. The positive rates of Babesia microti and Spotted fever group Rickettsia infections were also significantly different among these areas. CONCLUSION: The natural infection rates of the above mentioned five kinds of tick-borne pathogens were different in the Ports Ceke, Mandula and Manzhouli.