TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer.

Survival analysis is a significant study in cancer prognosis, and the multi-modal data, including histopathological images, genomic data, and clinical information, provides unprecedented opportunities for its development. However, because of the high dimensionality and the heterogeneity of histopathological images and genomic data, acquiring effective predictive characters from these multi-modal data has always been a challenge for survival analysis. In this article, we propose a transformer-based survival analysis model (TransSurv) for colorectal cancer that can effectively integrate intra-modality and inter-modality features of histopathological images, genomic data, and clinical information.

Programmable alignment media from self-assembled oligopeptide amphiphiles for the measurement of independent sets of residual dipolar couplings in organic solvents.

NMR spectroscopy in anisotropic media has emerged as a powerful technique for the structural elucidation of organic molecules. Its application requires weak alignment of analytes by means of suitable alignment media. Although a number of alignment media, that are compatible with organic solvents, have been introduced in the last 20 years, acquiring a number of independent, non-linearly related sets of anisotropic NMR data from the same organic solvent system remains a formidable challenge, which is however crucial for the alignment simulations and deriving dynamic and structural information of organic molecules unambiguously.