How fish embryos that develop externally survive microbial attacks is poorly understood. Here, we clearly demonstrated that the embryo extract of zebrafish and its early embryo both displayed antimicrobial activity against microbes, including pathogenic Aeromonas hydrophila, and phosvitin (Pv), a nutritional protein abundant in eggs, was related to this antimicrobial activity. We also showed that recombinant Pv (rPv) acted as a pattern recognition receptor capable of recognizing the microbial signature molecules LPS, lipoteichoic acid, and peptidoglycan, as well as binding the Gram-negative and -positive microbes Escherichia coli, A. hydrophila, and Staphylococcus aureus and functioned as an antimicrobial agent capable of killing the microbes. Furthermore, we revealed that its C-terminal 55 residues (Pt5) with the functional sites Arg 242 and Ala 201 / Ile 203 were indispensable for Pv antimicrobial activity. Importantly, microinjection of rPv or Pt5 into early embryos significantly enhanced their resistance to A. hydrophila challenge, and this enhanced bacterial resistance was markedly reduced by coinjection of anti-Pv antibody plus rPv (or Pt5) but not by injection of anti-actin antibody plus rPv. Moreover, the generated mutants with in vitro antimicrobial activity, when injected into the embryos, could also promote their resistance to A. hydrophila, but those without in vitro antimicrobial activity could not. It is thus proposed that Pv participates in the protection of early embryos against pathogenic attacks via binding and disrupting potential pathogens. This work also opens a new way for the study of the immunological roles of yolk proteins in oviparous animals that rely on yolk proteins for embryonic development.
Motion (change) detection is a basic preprocessing step in video processing, which has many application scenarios. One challenge is that deep learning-based methods require high computation power to improve their accuracy. In this paper, we introduce a novel semantic segmentation and lightweight-based network for motion detection, called Real-time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation (MDNet-LBPC). In the feature extraction stage, the most computationally expensive CNN block is replaced with our single linear bottleneck operator to reduce the computational cost. During the decoder stage, our pooling compensation mechanism can supplement the useful motion detection information. To our best knowledge, this is the first work to use the lightweight operator to solve the motion detection task. We show that the acceleration performance of the single linear bottleneck is 5% higher than that of the linear bottleneck, which is more suitable for improving the efficiency of model inference. On the dataset CDNet2014, MDNet-LBPC increases the frames per second (FPS) metric by 123 compared to the suboptimal method FgSegNet_v2, ranking first in inference speed. Meanwhile, our MDNet-LBPC achieves 95.74% on the accuracy metric, which is comparable to the state-of-the-art methods.
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