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.

The study integrated 16 Arabidopsis thaliana whole‑genome bisulfite sequencing datasets from 13 stress experiments using a unified bioinformatic pipeline to map common and stress‑specific DNA methylation changes. Differentially methylated regions varied by stress type and methylation context, with CG DMRs enriched in gene bodies and CHG/CHH DMRs in transposable elements, some of which overlapped loci prone to stable epimutations. Gene ontology and TE enrichment analyses highlighted shared stress pathways and suggest environmental stress can generate heritable epigenetic variation.