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.

Systematic analysis of innate immune-related genes in the silkworm: Application to antiviral research.

The silkworm, a crucial model organism of the Lepidoptera, offers an excellent platform for investigating the molecular mechanisms underlying the innate immune response of insects toward pathogens. Over the years, researchers worldwide have identified numerous immune-related genes in silkworms. However, these identified silkworm immune genes are not well classified and not well known to the scientific community. With the availability of the latest genome data of silkworms and the extensive research on silkworm immunity, it has become imperative to systematically categorize the immune genes of silkworms with different database IDs. In this study, we present a meticulous organization of prevalent immune-related genes in the domestic silkworm, using the SilkDB 3.0 database as a reliable source for updated gene information. Furthermore, utilizing the available data, we classify the collected immune genes into distinct categories: pattern recognition receptors, classical immune pathways, effector genes and others. In-depth data analysis has enabled us to predict some potential antiviral genes. Subsequently, we performed antiviral experiments on selected genes, exploring their impact on Bombyx mori nucleopolyhedrovirus replication. The outcomes of this research furnish novel insights into the immune genes of the silkworm, consequently fostering advancements in the field of silkworm immunity research by establishing a comprehensive classification and functional understanding of immune-related genes in the silkworm. This study contributes to the broader understanding of insect immune responses and opens up new avenues for future investigations in the domain of host-pathogen interactions.

Single-Nucleus Sequencing of Silkworm Larval Brain Reveals the Key Role of Lysozyme in the Antiviral Immune Response in Brain Hemocytes.

INTRODUCTION: The brain is considered as an immune-privileged organ, yet innate immune reactions can occur in the central nervous system of vertebrates and invertebrates. Silkworm (Bombyx mori) is an economically important insect and a lepidopteran model species. The diversity of cell types in the silkworm brain, and how these cell subsets produce an immune response to virus infection, remains largely unknown. METHODS: Single-nucleus RNA sequencing (snRNA-seq), bioinformatics analysis, RNAi, and other methods were mainly used to analyze the cell types and gene functions of the silkworm brain. RESULTS: We used snRNA-seq to identify 19 distinct clusters representing Kenyon cell, glial cell, olfactory projection neuron, optic lobes neuron, hemocyte-like cell, and muscle cell types in the B. mori nucleopolyhedrovirus (BmNPV)-infected and BmNPV-uninfected silkworm larvae brain at the late stage of infection. Further, we found that the cell subset that exerts an antiviral function in the silkworm larvae brain corresponds to hemocytes. Specifically, antimicrobial peptides were significantly induced by BmNPV infection in the hemocytes, especially lysozyme, exerting antiviral effects. CONCLUSION: Our single-cell dataset reveals the diversity of silkworm larvae brain cells, and the transcriptome analysis provides insights into the immune response following virus infection at the single-cell level.

Single-nucleus sequencing of silkworm larval midgut reveals the immune escape strategy of BmNPV in the midgut during the late stage of infection.

The midgut is an important barrier against microorganism invasion and proliferation, yet is the first tissue encountered when a baculovirus naturally invades the host. However, only limited knowledge is available how different midgut cell types contribute to the immune response and the clearance or promotion of viral infection. Here, single-nucleus RNA sequencing (snRNA seq) was employed to analyze the responses of various cell subpopulations in the silkworm larval midgut to B. mori nucleopolyhedrovirus (BmNPV) infection. We identified 22 distinct clusters representing enteroendocrine cells (EEs), enterocytes (ECs), intestinal stem cells (ISCs), Goblet cell-like and muscle cell types in the BmNPV-infected and uninfected silkworm larvae midgut at 72 h post infection. Further, our results revealed that the strategies for immune escape of BmNPV in the midgut at the late stage of infection include (1) inhibiting the response of antiviral pathways; (2) inhibiting the expression of antiviral host factors; (3) stimulating expression levels of genes promoting BmNPV replication. These findings suggest that the midgut, as the first line of defense against the invasion of the baculovirus, has dual characteristics of "resistance" and "tolerance". Our single-cell dataset reveals the diversity of silkworm larval midgut cells, and the transcriptome analysis provides insights into the interaction between host and virus infection at the single-cell level.

Dual-Stream Fusion Network with ConvNeXtV2 for Pig Weight Estimation Using RGB-D Data in Aisles.

In the field of livestock management, noncontact pig weight estimation has advanced considerably with the integration of computer vision and sensor technologies. However, real-world agricultural settings present substantial challenges for these estimation techniques, including the impacts of variable lighting and the complexities of measuring pigs in constant motion. To address these issues, we have developed an innovative algorithm, the moving pig weight estimate algorithm based on deep vision (MPWEADV). This algorithm effectively utilizes RGB and depth images to accurately estimate the weight of pigs on the move. The MPWEADV employs the advanced ConvNeXtV2 network for robust feature extraction and integrates a cutting-edge feature fusion module. Supported by a confidence map estimator, this module effectively merges information from both RGB and depth modalities, enhancing the algorithm's accuracy in determining pig weight. To demonstrate its efficacy, the MPWEADV achieved a root-mean-square error (RMSE) of 4.082 kg and a mean absolute percentage error (MAPE) of 2.383% in our test set. Comparative analyses with models replicating the latest research show the potential of the MPWEADV in unconstrained pig weight estimation practices. Our approach enables real-time assessment of pig conditions, offering valuable data support for grading and adjusting breeding plans, and holds broad prospects for application.

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.

Single-Nucleus Sequencing of Fat Body Reveals Distinct Metabolic and Immune Response Landscapes in Silkworm Larvae after Bombyx mori Nucleopolyhedrovirus Infection.

The fat body plays a central role in the regulation of the life cycle of insects and acts as the major site for detoxification, nutrient storage, energy metabolism, and innate immunity. However, the diversity of cell types in the fat body, as well as how these cell subsets respond to virus infection, remains largely unknown. We used single-nucleus RNA sequencing to identify 23 distinct clusters representing adipocyte, hemocyte, epithelial cell, muscle cell, and glial cell types in the fat body of silkworm larvae. Further, by analysis of viral transcriptomes in each cell subset, we reveal that all fat body cells could be infected by Bombyx mori nucleopolyhedrovirus (BmNPV) at 72 h postinfection, and that the majority of infected cells carried at least a medium viral load, whereas most cells infected by BmNPV at 24 h postinfection had only low levels of infection. Finally, we characterize the responses occurring in the fat body cell clusters on BmNPV infection, which, on one hand, mainly reduce their metabolic functions, involving energy, carbohydrates, lipids, and amino acids, but, on the other hand, initiate a strong antiviral response. Our single-nucleus RNA sequencing analysis reveals the diversity of insect fat body cells and provides a resource of gene expression profiles for a systems-level understanding of their response to virus infection.

A Pig Mass Estimation Model Based on Deep Learning without Constraint.

The body mass of pigs is an essential indicator of their growth and health. Lately, contactless pig body mass estimation methods based on computer vision technology have gained attention thanks to their potential to improve animal welfare and ensure breeders' safety. Nonetheless, current methods require pigs to be restrained in a confinement pen, and no study has been conducted in an unconstrained environment. In this study, we develop a pig mass estimation model based on deep learning, capable of estimating body mass without constraints. Our model comprises a Mask R-CNN-based pig instance segmentation algorithm, a Keypoint R-CNN-based pig keypoint detection algorithm and an improved ResNet-based pig mass estimation algorithm that includes multi-branch convolution, depthwise convolution, and an inverted bottleneck to improve accuracy. We constructed a dataset for this study using images and body mass data from 117 pigs. Our model achieved an RMSE of 3.52 kg on the test set, which is lower than that of the pig body mass estimation algorithm with ResNet and ConvNeXt as the backbone network, and the average estimation speed was 0.339 s·frame-1 Our model can evaluate the body quality of pigs in real-time to provide data support for grading and adjusting breeding plans, and has broad application prospects.