Shrimp shapes a resistance trait against vibriosis by memorizing the colonization resistance of intestinal microbiota.

Vibriosis is one of the most serious diseases that commonly occurs in aquatic animals, thus, shaping a steady inherited resistance trait in organisms has received the highest priority in aquaculture. Whereas, the mechanisms underlying the development of such a resistance trait are mostly elusive. In this study, we constructed vibriosis-resistant and susceptible families of the Pacific white shrimp Litopenaeus vannamei after four generations of artificial selection. Microbiome sequencing indicated that shrimp can successfully develop a colonization resistance trait against Vibrio infections. This trait was characterized by a microbial community structure with specific enrichment of a single probiotic species (namely Shewanella algae), and notably, its formation was inheritable and might be memorized by host epigenetic remodeling. Regardless of the infection status, a group of genes was specifically activated in the resistant family through disruption of complete methylation. Specifically, hypo-methylation and hyper-expression of genes related to lactate dehydrogenase (LDH) and iron homeostasis might provide rich sources of specific carbon (lactate) and ions for the colonization of S. algae, which directly results in the reduction of Vibrio load in shrimp. Lactate feeding increased the survival of shrimp, while knockdown of LDH gene decreased the survival when shrimp was infected by Vibrio pathogens. In addition, treatment of shrimp with the methyltransferase inhibitor 5-azacytidine resulted in upregulations of LDH and some protein processing genes, significant enrichment of S. algae, and simultaneous reduction of Vibrio in shrimp. Our results suggest that the colonization resistance can be memorized as epigenetic information by the host, which has played a pivotal role in vibriosis resistance. The findings of this study will aid in disease control and the selection of superior lines of shrimp with high disease resistance.

CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery.

Extracting roads from satellite imagery is a promising approach to update the dynamic changes of road networks efficiently and timely. However, it is challenging due to the occlusions caused by other objects and the complex traffic environment, the pixel-based methods often generate fragmented roads and fail to predict topological correctness. In this paper, motivated by the road shapes and connections in the graph network, we propose a connectivity attention network (CoANet) to jointly learn the segmentation and pair-wise dependencies. Since the strip convolution is more aligned with the shape of roads, which are long-span, narrow, and distributed continuously. We develop a strip convolution module (SCM) that leverages four strip convolutions to capture long-range context information from different directions and avoid interference from irrelevant regions. Besides, considering the occlusions in road regions caused by buildings and trees, a connectivity attention module (CoA) is proposed to explore the relationship between neighboring pixels. The CoA module incorporates the graphical information and enables the connectivity of roads are better preserved. Extensive experiments on the popular benchmarks (SpaceNet and DeepGlobe datasets) demonstrate that our proposed CoANet establishes new state-of-the-art results. The source code will be made publicly available at: https://mmcheng.net/coanet/.