Trace amount of taurine leveling agent for stable Zn anode.

Aqueous zinc-ion batteries are highly praised for their cost-effectiveness, environmental friendliness, and high safety, making them an ideal choice for next-generation energy storage systems. However, the practical application of Zn metal anodes is constrained by well-known challenges such as dendrite growth and significant interfacial side reactions. This study introduces a trace amount of taurine (TAU) as a leveling additive into the electrolyte to optimize the microstructure of the electrolyte and the anode interface chemistry.

Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval.

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations.

Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval.

Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs).

Stain Standardization Capsule for Application-Driven Histopathological Image Normalization.

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers.