Unified Knowledge-Guided Molecular Graph Encoder with multimodal fusion and multi-task learning.

The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.

Ontology of clinical practice guidelines for Integrated Traditional Chinese and Western Medicine.

Objective: Clinical practice guidelines (CPGs) for Integrated Traditional Chinese and Western Medicine (TCM and WM) are important medical documents used to assist medical decision-making and are of great significance for standardizing clinical pathways. However, due to the constraints of text format, it is difficult for Integrated TCM and WM CPGs to play a real role in medical practice. In addition, how to standardize the structure and semantic relationships between Integrated TCM and WM CPG knowledge, and realize the construction of computable, sharable and reliable CPGs, remains an urgent issue to be addressed.

The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.

Implementation of clinical practice guidelines (CPG) is a complex and challenging task. Computer technology, including artificial intelligence (AI), has been explored to promote the CPG implementation. This study has reviewed the main domains where computer technology and AI has been applied to CPG implementation.

Further discussion on the separation of linear and nonlinear components in HRTEM imaging.

A method for separating linear and nonlinear components in high-resolution transmission electron microscope imaging has been reported by Chang (Microscopy, 2016. 65: p. 465-472), and a deep understanding has been obtained as to the variation of the linear and nonlinear components with the sample thickness.