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 study, 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. Specifically, to integrate the intra-modality relationship of image patches, we develop a multi-scale histopathological features fusion transformer (MS-Trans). Furthermore, we provide a cross-modal fusion transformer based on cross attention for multi-scale pathological representation and multi-omics representation, which includes RNA-seq expression and copy number alteration (CNA). At the output layer of the TransSurv, we adopt the Cox layer to integrate multi-modal fusion representation with clinical information for end-to-end survival analysis. The experimental results on the Cancer Genome Atlas (TCGA) colorectal cancer cohort demonstrate that the proposed TransSurv outperforms the existing methods and improves the prognosis prediction of colorectal cancer.

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. Herein, we introduce a programmable strategy to construct several distinct peptide-based alignment media by adjusting the amino acid sequence, which allows us to measure independent sets of residual dipolar couplings (RDCs) in a highly efficient and accurate manner. This study opens a new avenue for de novo structure determination of organic compounds without requiring prior structural information.