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True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.

Segmentation of prostate magnetic resonance images is of great significance in the interventional diagnosis and treatment of prostate diseases. In this work, the conventional distance regularized level set evolution (DRLSE) model is improved and applied to prostate segmentation. In magnetic resonance image, the prostate boundary near the bladder is often blurred, while that near the urethra is clear, resulting in a poor performance for the traditional gradient information indicator function. In this study, two indicator functions were used to control the evolution of boundary in the clear segment and blurred segment, respectively, to achieve better segmentation. In addition, an energy check term was added to the external energy function to prevent evolution from stopping at a false boundary. This modification could drive the level set to move to regions with large gray level fluctuation and stop evolution at a blurred boundary. Experimental results demonstrated that the performance of prostate segmentation was satisfactory, judging from the Dice similarity coefficient (DSC) which reached an average of 96%.

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.

Bladder cancer is a common tumour worldwide and exhibits a poor prognosis. Fibronectin leucine rich transmembrane protein 2 (FLRT2) is associated with the regulation of multiple tumours; however, its function in human bladder cancer remain unclear. Herein, we found that FLRT2 level was reduced in human bladder cancer and that higher FLRT2 level predicted lower survival rate. FLRT2 overexpression inhibited, while FLRT2 silence facilitated tumour cell growth, migration and invasion. Mechanistic studies revealed that FLRT2 elevated acyl‐CoA synthetase long‐chain family member 4 (ACSL4) expression, increased lipid peroxidation and subsequently facilitated ferroptosis of human bladder cancer cells. In summary, we demonstrate that FLRT2 elevates ACSL4 expression to facilitate lipid peroxidation and subsequently triggers ferroptosis, thereby inhibiting the malignant phenotype of human bladder cancer cells. Overall, we identify FLRT2 as a tumour suppressor gene. [ABSTRACT FROM AUTHOR]