Immune defense systems are indispensable for living organisms. Within an immune network, problems with any given link can impact the normal life activities of an organism. Amphioctopus fangsiao is a cephalopod that exists widely throughout the world’s oceans. Because of its nervous system and locomotive organs, it has become increasingly studied in recent years. Vibrio anguillarum is one of the most common pathogenic bacteria in aquaculture organisms. It is highly infectious and can infect almost all aquaculture organisms. V. anguillarum infection can cause many adverse biological phenomena, including tissue bleeding. Study the immune response after V. anguillarum infection would help us to understand the molecular mechanisms of immune response in aquaculture organisms. In this research, we infected the primary incubation A. fangsiao with V. anguillarum for 24 h. We analyzed gene expression in A. fangsiao larvae via transcriptome profiles at 0, 4, 12, and 24 h after hatching, and 1,385, 734, and 6,109 differentially expressed genes (DEGs) were identified at these three time points. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to identify immune-related DEGs. Protein–protein interaction networks were constructed to examine interactions between immune-related genes. Twenty hub genes involved in multiple KEGG signaling pathways or with multiple protein–protein interaction relationships were identified, and their differential expression verified by quantitative RT-PCR. We first studied V. anguillarum infection of A. fangsiao larvae by means of protein–protein interaction networks. The results provide valuable genetic resources for understanding immunity in molluscan larvae. These data serve as a theoretical basis for the artificial breeding of A. fangsiao.

Intelligent detection of locomotive signal light and railway pedestrians is of great significance to the safe operation of locomotives, especially under bad illumination conditions. Due to the highly complicated operational environment of locomotives, it is relatively difficult to apply the deep neural networkbased object detection methods in the recognition of locomotive signal light and railway pedestrians. This work, for the first time, proposes a real-time detection method based on improved YOLOv4 to recognize locomotive signal light and railway pedestrians, in which the Region-of-Interest is combined with YOLOv4 to improve the detection precision of pedestrians on railway tracks. Most importantly, for the first time, we establish a dataset called detection of locomotive signal light and railway pedestrians (DLSLRP), which is dedicated to the training, validation, and testing of related convolutional neural networks. We validated the proposed method on the DLSLRP dataset, the experiments suggest that our method can detect locomotive signal light and railway pedestrians with high speed and precision under different illumination conditions. The mAP of our method reaches 93.52%, and the average detection speed achieves 25 FPS.

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