Bates Medlin (fifthsushi49)

Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. This is made possible via the introduction of locally-constrained routing and transformation matrix sharing, which reduces the parameter/memory burden and allows for the segmentation of objects at large resolutions. To compensate for the loss of global information in constraining the routing, we propose the concept of "deconvolutional" capsules to create a deep encoder-decoder style network, called SegCaps. We extend the masked reconstruction regularization to the task of segmentation and perform thorough ablation experiments on each component of our method. The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks. To validate our proposed method, we perform experiments segmenting pathological lungs from clinical and pre-clinical thoracic computed tomography (CT) scans and segmenting muscle and adipose (fat) tissue from magnetic resonance imaging (MRI) scans of human subjects' thighs. Notably, our experiments in lung segmentation represent the largest-scale study in pathological lung segmentation in the literature, where we conduct experiments across five extremely challenging datasets, containing both clinical and pre-clinical subjects, and nearly 2000 computed-tomography scans. Our newly developed segmentation platform outperforms other methods across all datasets while utilizing less than 5% of the parameters in the popular U-Net for biomedical image segmentation. Further, we demonstrate capsules' ability to generalize to unseen handling of rotations/reflections on natural images.Although various methods have been tried to study and treat cancer, the cancer remains a major challenge for human medicine today. One important reason for this is the presence of cancer evolution. Cancer evolution is a process in which tumor cells adapt to the external environment, which can suppress the human immune system's ability to recognize and attack tumors, and also reduce the reproducibility of cancer research. Among them, heterogeneity of the tumor provides intrinsic motivation for this process. Recently, with the development of related technologies such as liquid biopsy, more and more knowledge about cancer evolution has been gained and interest in this topic has also increased. Therefore, starting from the causes of tumorigenesis, this paper introduces several tumorigenesis processes and pathways, as well as treatment options for different targets. Intestinal microbiota is a novel drug target of metabolic diseases, especially for those with poor oral bioavailability. Nuciferine, with poor bioavailability, has an anti-hyperlipidemic effect at low dosages. In the present study, we aimed to explore the role of intestinal microbiota in the anti-hyperlipidemic function of nuciferine and identify the key bacterial targets that might confer the therapeutic actions. The contribution of gut microbes in the anti-hyperlipidemic effect of nuciferine was evaluated by conventional and antibiotic-established pseudo-sterile mice. Whole-metagenome shotgun sequencing was used to characterize the changes in microbial communities by various agents. Nuciferine exhibited potent anti-hyperlipidemic and liver steatosis-alleviating effects at the doses of 7.5-30 mg/kg. The beneficial effects of nuciferine were substantially abolished when combined with antibiotics. Metagenomic analysis showed that nuciferine significantly shifted the microbial structure, and the enrichment of Akkermansia muciniphila was closely related to the therapeutic effect of nuciferine. Our results revealed that gut microbiota played an essential role in the anti-hyperlipidemic effect of nuciferine, and enrichment of Akkermansia muciniphila represented a key mechanism through which nuciferine exerted i