Identification of an AnnexinB9 involve in white spot syndrome virus infection in red claw crayfish Cherax quadricarinatus.

Annexin (Anx) family protein is a highly conserved protein family that plays important roles in immune defense of vertebrates and invertebrates against invading pathogens. In this study, a novel Anx was cloned and characterized from the red claw crayfish, Cherax quadricarinatus. The Open Reading Frame of CqAnxB9 consisted of 930 nucleotide bases pair and encoded 309 amino acids. The CqAnxB9 protein contained three repeat Anx domains and a typical KGLGT sequence. Tissue expression analysis showed that the expression levels of CqAnxB9 were mainly expressed in the intestine, hepatopancreas and hemocytes. After WSSV challenge, CqAnxB9 expression was up-regulated in the hematopoietic tissue (Hpt) cells. Moreover, knockdown of CqAnxB9 inhibited WSSV replication and VP28 expression, suggesting that CqAnxB9 plays a positive role in WSSV infection. Further studies revealed that recombinant CqAnxB9 protein was found to bind to the viral envelop protein VP28. All these findings indicate that new-found CqAnxB9 is likely to promote WSSV infection in crustaceans, which provides a better understanding of the pathogenesis of WSSV.

ShrimpSeg: a local-global structure for the mantis shrimp point cloud segmentation network with contextual reasoning.

To quantify the architecture and select the ideal ideotype, it is vital to accurately measure the dimension of each part of the mantis shrimp. Point clouds have become increasingly popular in recent years as an efficient solution. However, the current manual measurement is labor intensive and costly and has high uncertainty. Automatic organ point cloud segmentation is a prerequisite and core step for phenotypic measurements of mantis shrimps. Nevertheless, little work focuses on mantis shrimp point cloud segmentation. To fill this gap, this paper develops a framework for automated organ segmentation of mantis shrimps from multiview stereo (MVS) point clouds. First, a Transformer-based MVS architecture is applied to generate dense point clouds from a set of calibrated phone images and estimated camera parameters. Next, an improved point cloud segmentation (named ShrimpSeg) that exploits both local and global features based on contextual information is proposed for organ segmentation of mantis shrimps. According to the evaluation results, the per-class intersection over union of organ-level segmentation is 82.4%. Comprehensive experiments demonstrate the effectiveness of ShrimpSeg, outperforming other commonly used segmentation methods. This work may be helpful for improving shrimp phenotyping and intelligent aquaculture at the level of production-ready.

Semantic-Based Building Extraction from LiDAR Point Clouds Using Contexts and Optimization in Complex Environment.

The extraction of buildings has been an essential part of the field of LiDAR point clouds processing in recent years. However, it is still challenging to extract buildings from huge amount of point clouds due to the complicated and incomplete structures, occlusions and local similarities between different categories in a complex environment. Taking the urban and campus scene as examples, this paper presents a versatile and hierarchical semantic-based method for building extraction using LiDAR point clouds. The proposed method first performs a series of preprocessing operations, such as removing ground points, establishing super-points and using them as primitives for subsequent processing, and then semantically labels the raw LiDAR data. In the feature engineering process, considering the purpose of this article is to extract buildings, we tend to choose the features extracted from super-points that can describe building for the next classification. There are a portion of inaccurate labeling results due to incomplete or overly complex scenes, a Markov Random Field (MRF) optimization model is constructed for postprocessing and segmentation results refinement. Finally, the buildings are extracted from the labeled points. Experimental verification was performed on three datasets in different scenes, our results were compared with the state-of-the-art methods. These evaluation results demonstrate the feasibility and effectiveness of the proposed method for extracting buildings from LiDAR point clouds in multiple environments.