Tailored large-particle quantum dots with high color purity and excellent electroluminescent efficiency.

High-quality quantum dots (QDs) possess superior electroluminescent efficiencies and ultra-narrow emission linewidths are essential for realizing ultra-high definition QD light-emitting diodes (QLEDs). However, the synthesis of such QDs remains challenging. In this study, we present a facile high-temperature successive ion layer adsorption and reaction (HT-SILAR) strategy for the growth of precisely tailored ZnCdSe/ZnSe shells, and the consequent production of high-quality, large-particle, alloyed red CdZnSe/ZnCdSe/ZnSe/ZnS/CdZnS QDs.

Effect of microwave treatment on the structural and physicochemical properties of amylose partially removed sorghum starch.

This study aimed to probe the influence of amylose in starch granules on starch modification. Part of the amylose from sorghum starch was removed through warm water leaching, and the samples were then microwaved. The effects of treatments on starch structure, physicochemical properties, and digestibility were researched.

Genetics of biliary atresia: Approaches, pathological insights and challenges.

Biliary atresia (BA) is a severe neonatal cholestatic disorder marked by fibro-obliteration of the extrahepatic and intrahepatic bile ducts. It is the most common cause of pediatric end-stage liver disease and the leading indication for liver transplantation in children. There is significant heterogeneity in the etiology, involving various genetic and environmental factors such as viral infection, immune dysregulation and genetic predisposition to defective hepatobiliary development.

When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute.

Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection.

New Progress in Zebrafish Liver Tumor Models: Techniques and Applications in Hepatocellular Carcinoma Research.

Liver tumors represent a serious clinical health problem that threatens human life. Previous studies have demonstrated that the pathogenesis of liver tumors is complex and influenced by various factors, highlighting limitations in both basic pathological research and clinical treatment. Traditional research methods often begin with the discovery of phenomena and gradually progress to the development of animal models and human trials.