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 study maps the in vivo proximity interactome of Arabidopsis SKP1-LIKE 1 (ASK1) under acute abscisic acid (ABA) signaling and prolonged drought using TurboID-based proximity labeling and quantitative proteomics, revealing condition-specific networks that include both canonical SCF modules and diverse noncanonical partners. Overexpression of ASK1 shifts proteome composition toward drought‑protective and ABA‑responsive proteins while repressing immune and ROS‑scavenging pathways, highlighting ASK1 as a hub that integrates SCF‑dependent and independent pathways to reprogram transcription, translation, and proteostasis during stress adaptation.

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.