Researchers at the University of Texas have developed a new artificial intelligence model called RiboNN that predicts the translation efficiency of messenger RNA (mRNA) in mammalian cells with approximately twice the accuracy of previous methods. The development of RiboNN involved curating data from over 10,000 experiments measuring mRNA translation efficiency across more than 140 human and mouse cell types. This advancement in computational methods addresses challenges in decoding circular RNA (circRNA) translation and contributes to a broader understanding of gene expression and translational control. Concurrently, several new studies have been published covering topics such as saline-alkali tolerance in high-latitude rice populations, gene expression patterns in major depressive disorder and chronic spontaneous urticaria, novel ATP13A2 variants linked to early-onset parkinsonism, and transcriptomic responses of Atlantic salmon to sea lice under varying thermal conditions.