A secreted phospholipase A_(2) (BmsPLA_(2)) regulates melanization of immunity through BmDDC in the silkworm Bombyx mori

Insect immune-associated phospholipase A_(2) (PLA_(2)) is an important target of pathogen invasion. Melanization, an effective defense response, has significant correlations with other immune responses to coordinate immune attack against invaders. However, the effect of PLA_(2) on melanization has not yet been reported in insects or other arthropods. In this work, we cloned a PLA_(2) gene (BmsPLA_(2)), and its protein had characteristic features of secreted PLA_(2) (sPLA_(2)). After injection of bacteria, BmsPLA_(2) expression and sPLA_(2) activity in hemolymph significantly increased. BmsPLA_(2) fluorescence was transferred from the cytoplasm to the cell membranes of circulating hemocytes. These results indicated that BmsPLA_(2) was related to hemolymph immunity in silkworms. Interestingly, reducing BmsPLA_(2) by RNA interference decreased melanosis (melanistic hemocytes) levels in vivo and in vitro, while BmsPLA_(2) overexpression had the opposite effect. The larval survival and melanization rate in the hemocoel both slowed depending on the PLA_(2) inhibitor dosage. These results demonstrated that BmsPLA_(2) plays a role in melanization during the immune process of silkworms. Surprisingly, the level of BmDDC matched the degree of melanization in various observations. BmDDC expression showed a significant increase, with the peak occurring later than that of BmsPLA_(2) after injection of bacteria, implying that BmsPLA_(2) was activated prior to BmDDC. Moreover, the alteration of BmsPLA_(2) by RNA interference or overexpression led to altered BmDDC levels. These results suggested that BmsPLA_(2) regulates the melanization response in silkworms through BmDDC. Our study proposes a new regulatory mechanism of the melanization response and new directions for understanding the complex immune networks of insects.

TU-Net:U-shaped Structure Based on Transformers for Medical Image Segmentation

Recently,the development of deep learning technology in medical image segmentation has become increasingly mature,and the symmetric U-Net has made breakthrough progress.However,because of the inherent limitations of convolution operations,U-Net has some shortcomings in the interaction of global context information.For this reason,this paper proposes TU-Net based on transformers.TU-Net can strengthen the modeling of global context information,enhance the extraction of detailed information and reduce the computational complexity of the algorithm.In patch embedding,successive convolutional layers with small convolutional kernels are proposed to extract features.Cross Attention-Skip is proposed to complete the fusion of shallow and deep features during the skip connection process.TU-Net is performed on the Synapse dataset to segment eight abdominal organs.The experimental results show that TU-Net is superior to ViT,V-Net,U-Net and Swin-Unet.

AM-PSPNet:Pyramid Scene Parsing Network Based on Attentional Mechanism for Image Semantic Segmentation

In this paper,AM-PSPNet is proposed for image semantic segmentation.AM-PSPNet embeds the efficient channel attention(ECA)module in the feature extraction stage of the convolutional network and makes the network pay more attention to the channels with obvious classification characteristics through end-to-end learning.To recognize the edges of objects and small objects more effectively,AM-PSPNet proposes a deep guidance fusion(DGF)module to generate global contextual attention maps to guide the expression of shallow information.The average crossover ratio of the proposed algorithm on the Pascal VOC 2012 dataset and Cityscapes dataset reaches 78.8%and 69.1%,respectively.Comparedwith the other four network models,the accuracy and average crossover ratio of AM-PSPNet are improved.