Structures, gelatinization properties and enzyme hydrolyses of starches from transparent and floury grains of rices subjected to field natural extreme high temperature.

Three rice varieties underwent the field natural extreme high temperature (EHT) with daily average temperature over 30 °C from 21 to 89 days after sowing, and had transparent, chalky and floury grains. The structures, gelatinization properties and enzyme hydrolyses of starches from transparent and floury grains were investigated. Compared with control transparent grains, floury grains subjected to EHT markedly decreased the contents of amylose molecules, amylopectin A chains and amylopectin B1 chains and increased the contents of amylopectin B2 and B3+ chains and the average branch-chain length of amylopectin.

A deep learning model for predicting selected organic molecular spectra.

Accurate and efficient molecular spectra simulations are crucial for substance discovery and structure identification. However, the conventional approach of relying on the quantum chemistry is cost intensive, which hampers efficiency. Here we develop DetaNet, a deep-learning model combining E(3)-equivariance group and self-attention mechanism to predict molecular spectra with improved efficiency and accuracy.

An enzyme-powered microRNA discriminator for the subtype-specific diagnosis of breast cancer.

Breast cancer, a disease with highly heterogeneous features, is the most common malignancy diagnosed in people worldwide. Early diagnosis of breast cancer is crucial for improving its cure rate, and accurate classification of the subtype-specific features is essential to precisely treat the disease. An enzyme-powered microRNA (miRNA, RNA = ribonucleic acid) discriminator was developed to selectively distinguish breast cancer cells from normal cells and further identify subtype-specific features.

Application of functional peptides in the electrochemical and optical biosensing of cancer biomarkers.

Early screening and diagnosis are the most effective ways to prevent the occurrence and progression of cancers, thus many biosensing strategies have been developed to achieve economic, rapid, and effective detection of various cancer biomarkers. Recently, functional peptides have been gaining increasing attention in cancer-related biosensing due to their advantageous features of a simple structure, ease of synthesis and modification, high stability, and good biorecognition, self-assembly and antifouling capabilities. Functional peptides can not only act as recognition ligands or enzyme substrates for the selective identification of different cancer biomarkers but also function as interfacial materials or self-assembly units to improve the biosensing performances.