A Long-Term Video Tracking Method for Group-Housed Pigs.

Pig tracking provides strong support for refined management in pig farms. However, long and continuous multi-pig tracking is still extremely challenging due to occlusion, distortion, and motion blurring in real farming scenarios. This study proposes a long-term video tracking method for group-housed pigs based on improved StrongSORT, which can significantly improve the performance of pig tracking in production scenarios. In addition, this research constructs a 24 h pig tracking video dataset, providing a basis for exploring the effectiveness of long-term tracking algorithms. For object detection, a lightweight pig detection network, YOLO v7-tiny_Pig, improved based on YOLO v7-tiny, is proposed to reduce model parameters and improve detection speed. To address the target association problem, the trajectory management method of StrongSORT is optimized according to the characteristics of the pig tracking task to reduce the tracking identity (ID) switching and improve the stability of the algorithm. The experimental results show that YOLO v7-tiny_Pig ensures detection applicability while reducing parameters by 36.7% compared to YOLO v7-tiny and achieving an average video detection speed of 435 frames per second. In terms of pig tracking, Higher-Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTP), and Identification F1 (IDF1) scores reach 83.16%, 97.6%, and 91.42%, respectively. Compared with the original StrongSORT algorithm, HOTA and IDF1 are improved by 6.19% and 10.89%, respectively, and Identity Switch (IDSW) is reduced by 69%. Our algorithm can achieve the continuous tracking of pigs in real scenarios for up to 24 h. This method provides technical support for non-contact pig automatic monitoring.

An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model.

Pig counting is an important task in pig sales and breeding supervision. Currently, manual counting is low-efficiency and high-cost and presents challenges in terms of statistical analysis. In response to the difficulties faced in pig part feature detection, the loss of tracking due to rapid movement, and the large counting deviation in pig video tracking and counting research, this paper proposes an improved pig counting algorithm (Mobile Pig Counting Algorithm with YOLOv5xpig and DeepSORTPig (MPC-YD)) based on YOLOv5 + DeepSORT model. The algorithm improves the detection rate of pig body parts by adding two different sizes of SPP networks and using SoftPool instead of MaxPool operations in YOLOv5x. In addition, the algorithm includes a pig reidentification network, a pig-tracking method based on spatial state correction, and a pig counting method based on frame number judgment on the DeepSORT algorithm to improve pig tracking accuracy. Experimental analysis shows that the MPC-YD algorithm achieves an average precision of 99.24% in pig object detection and an accuracy of 85.32% in multitarget pig tracking. In the aisle environment of the slaughterhouse, the MPC-YD algorithm achieves a correlation coefficient (R2) of 98.14% in pig counting from video, and it achieves stable pig counting in a breeding environment. The algorithm has a wide range of application prospects.

Evaluation of Duck Egg Hatching Characteristics with a Lightweight Multi-Target Detection Method.

Since it is difficult to accurately identify the fertilization and infertility status of multiple duck eggs on an incubation tray, and due to the lack of easy-to-deploy detection models, a novel lightweight detection architecture (LDA) based on the YOLOX-Tiny framework is proposed in this paper to identify sterile duck eggs with the aim of reducing model deployment requirements and improving detection accuracy. Specifically, the method acquires duck egg images through an acquisition device and augments the dataset using rotation, symmetry, and contrast enhancement methods. Then, the traditional convolution is replaced by a depth-wise separable convolution with a smaller number of parameters, while a new CSP structure and backbone network structure are used to reduce the number of parameters of the model. Finally, to improve the accuracy of the network, the method includes an attention mechanism after the backbone network and uses the cosine annealing algorithm in training. An experiment was conducted on 2111 duck eggs, and 6488 duck egg images were obtained after data augmentation. In the test set of 326 duck egg images, the mean average precision (mAP) of the method in this paper was 99.74%, which was better than the 94.92% of the YOLOX-Tiny network before improvement, and better than the reported prediction accuracy of 92.06%. The number of model parameters was only 1.93 M, which was better than the 5.03 M of the YOLOX-Tiny network. Further, by analyzing the concurrent detection of single 3 × 5, 5 × 7 and 7 × 9 grids, the algorithm achieved a single detection number of 7 × 9 = 63 eggs. The method proposed in this paper significantly improves the efficiency and detection accuracy of single-step detection of breeder duck eggs, reduces the network size, and provides a suitable method for identifying sterile duck eggs on hatching egg trays. Therefore, the method has good application prospects.

Hypothermia-Modulating Matrix Elasticity of Injured Brain Promoted Neural Lineage Specification of Mesenchymal Stem Cells.

Both chemical and physical microenvironments appear to be important for lineage specification of umbilical cord mesenchymal stem cells (UCMSCs). However, physical factors such as the elastic modulus in traumatic brain injury (TBI) are seldom studied. Intracranial hypertension and cerebral edema after TBI may change the brain's physical microenvironment, which inhibits neural lineage specification of transplanted UCMSCs. The purpose of this study is to investigate the potential regulatory effect of mild hypothermia on the elastic modulus of the injured brain. First, we found that more UCMSCs grown on gels mimicking the elastic modulus of the brain (0.5 kPa) differentiated into neural cells, which were verified with the formation of branched cells and the expression of neural markers. Then, UCMSCs were transplanted into TBI rats, and we observed that mild hypothermia resulted in the differentiation of more neurons and astrocytes from transplanted UCMSCs. To demonstrate that more neural specification of UCMSCs was due to the regulation of the elastic modulus, we monitored intracranial pressure and cerebral edema. The results showed that mild hypothermia significantly reduced intracranial pressure and brain water content, indicating modulation of the elastic modulus by mild hypothermia. An examination with atomic force microscopy (AFM) in a cell injury model in vitro further verified hypothermia-regulated elastic modulus. In this study, we found a novel role of mild hypothermia in modulating the elastic modulus of the injured brain, resulting in the promotion of neural lineage specification of UCMSCs, which suggested that the combination of mild hypothermia had more advantages in cell-based therapy after TBI.

Microvascular Decompression for Hemimasticatory Spasm: A Case Report and Review of the Literature.

BACKGROUND: Hemimasticatory spasm is a rare clinical entity characterized by involuntary and paroxysmal contractions of the jaw-closing muscles on 1 side of the face. Although its cause is not fully known, vascular compression of the trigeminal nerve has been speculated. CASE DESCRIPTION: Here, we report 1 case of hemimasticatory spasm that was cured by microvascular decompression of the motor branch of the trigeminal nerve; a relevant literature review was also performed. CONCLUSIONS: Hemimasticatory spasm is a rare disease that may be recalcitrant to conservative medical therapy, and vascular decompression of the trigeminal nerve may be required to relieve the spasm.