AFANet: Adaptive feature aggregation for polyp segmentation.

In terms of speed and accuracy, the deep learning-based polyp segmentation method is superior. It is essential for the early detection and treatment of colorectal cancer and has the potential to greatly reduce the disease's overall prevalence. Due to the various forms and sizes of polyps, as well as the blurring of the boundaries between the polyp region and the surrounding mucus, most existing algorithms are unable to provide highly accurate colorectal polyp segmentation. Therefore, to overcome these obstacles, we propose an adaptive feature aggregation network (AFANet). It contains two main modules: the Multi-modal Balancing Attention Module (MMBA) and the Global Context Module (GCM). The MMBA extracts improved local characteristics for inference by integrating local contextual information while paying attention to them in three regions: foreground, background, and border. The GCM takes global information from the top of the encoder and sends it to the decoder layer in order to further investigate global contextual feature information in the pathologic picture. Dice of 92.11 % and 94.76 % and MIoU of 91.07 % and 94.54 %, respectively, are achieved by comprehensive experimental validation of our proposed technique on two benchmark datasets, Kvasir-SEG and CVCClinicDB. The experimental results demonstrate that the strategy outperforms other cutting-edge approaches.

Structure, variation and assembly of body-wide microbiomes in endangered crested ibis Nipponia nippon.

Limited knowledge of bird microbiome in the all-body niche hinders our understanding of host-microbial relationships and animal health. Here, we characterized the microbial composition of the crested ibis from 13 body sites, representing the cloaca, oral, feather and skin habitats, and explored assembly mechanism structuring the bacterial community of the four habitats respectively. The bacterial community characteristics were distinct among the four habitats. The skin harboured the highest alpha diversity and most diverse functions, followed by feather, oral and cloaca. Individual-specific features were observed when the skin and feathers were concentrated independently. Skin and feather samples of multiple body sites from the same individual were more similar than those from different individuals. Although a significant proportion of the microbiota in the host (85.7%-96.5%) was not derived from the environmental microbiome, as body sites became more exposed to the environment, the relative importance of neutral processes (random drift or dispersal) increased. Neutral processes were the most important contributor in shaping the feather microbiome communities (R2 = .859). A higher percentage of taxa (29.3%) on the skin were selected by hosts compared to taxa on other body habitats. This study demonstrated that niche speciation and partial neutral processes, rather than environmental sources, contribute to microbiome variation in the crested ibis. These results enhance our knowledge of baseline microbial diversity in birds and will aid health management in crested ibises in the future.