The study characterizes a conserved RNA structural element named DEAD within DEAD-box helicase genes in land plants, showing that it functions as a sensor of helicase activity to regulate alternative splicing in Arabidopsis thaliana. By modulating the folding of DEAD, the plant balances helicase transcript and protein levels via a negative feedback loop, and loss of this regulation leads to widespread splicing disruptions and severe stress phenotypes.

The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.