Interactively Fusing Global and Local Features for Benign and Malignant Classification of Breast Ultrasound Images.

Objective: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its automatic classification can reduce subjectivity. However, current convolutional neural networks (CNNs) face challenges in capturing global features, while vision transformer (ViT) networks have limitations in effectively extracting local features. Therefore, this study aimed to develop a deep learning method that enables the interaction and updating of intermediate features between CNN and ViT to achieve high-accuracy BUS image classification.

tRNA-Ser-UGA efficiently promotes the rapid release of duck hepatitis A virus from infected enterocytes and its remote dissemination to hepatocytes.

Enterocytes are a necessary portal for fecal-oral transmission of viruses, including duck hepatitis A virus (DHAV), that act on the absorption of amino acids (AAs). We note that the rapid death of ducklings caused by DHAV is likely due to its rapid release from enterocytes. However, the underlying mechanism driving the release of DHAV remains poorly understood.

Development and validation of a core competency scale for nursing internship supervisors.

Background: Clinical practicums are a crucial part of nursing education wherein nursing internship supervisors (NIS) play a vital role in facilitating hands-on experience. However, many NIS start their teaching roles without adequate educational training, despite the importance of this task. Therefore, the aim of this study was to develop and validate a reliable and credible core competency scale for NIS.

On-demand Reprogrammable Mechanical Metamaterial Driven by Structure Performance Relations.

The physical reprogrammability of metamaterials provides unprecedented opportunities for tailoring changeable mechanical behaviors. It is envisioned that metamaterials can actively, precisely, and rapidly reprogram their performances through digital interfaces toward varying demands. However, on-demand reprogramming by integration of physical and digital merits still remains less explored.