Single-Cell Transcriptomic Profiling Unveils Dynamic Immune Cell Responses during Haemonchus contortus Infection.

BACKGROUND: Haemonchus contortus is a parasite widely distributed in tropical, subtropical, and warm temperate regions, causing significant economic losses in the livestock industry worldwide. However, little is known about the genetics of H. contortus resistance in livestock. In this study, we monitor the dynamic immune cell responses in diverse peripheral blood mononuclear cells (PBMCs) during H. contortus infection in goats through single-cell RNA sequencing (scRNA-Seq) analysis. METHODS AND RESULTS: A total of four Boer goats, two goats with oral infection with the L3 larvae of H. contortus and two healthy goats as controls, were used in the animal test. The infection model in goats was established and validated by the fecal egg count (FEC) test and qPCR analysis of the gene expression of IL-5 and IL-6. Using scRNA-Seq, we identified seven cell types, including T cells, monocytes, natural killer cells, B cells, and dendritic cells with distinct gene expression signatures. After identifying cell subpopulations of differentially expressed genes (DEGs) in the case and control groups, we observed the upregulation of multiple inflammation-associated genes, including NFKBIA and NFKBID. Kyoto Encyclopedia of the Genome (KEGG) enrichment analysis revealed significant enrichment of NOD-like receptor pathways and Th1/Th2 cell differentiation signaling pathways in CD4 T cells DEGs. Furthermore, the analysis of ligand-receptor interaction networks showed a more active state of cellular communication in the PBMCs from the case group, and the inflammatory response associated MIF-(CD74 + CXCR4) ligand receptor complex was significantly more activated in the case group, suggesting a potential inflammatory response. CONCLUSIONS: Our study preliminarily revealed transcriptomic profiling characterizing the cell type specific mechanisms in host PBMCs at the single-cell level during H. contortus infection.

Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images.

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.