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.

A comprehensive multi‑environment trial of 437 maize testcross hybrids derived from 38 MLN‑tolerant lines and 29 testers identified additive genetic effects as the primary driver of grain yield, disease resistance, and drought tolerance. Strong general combining ability and specific combining ability patterns were uncovered, with top hybrids delivering up to 5.75 t ha⁻¹ under MLN pressure while maintaining high performance under optimum and drought conditions. The study provides a framework for selecting elite parents and exploiting both additive and non‑additive effects to develop resilient maize hybrids for sub‑Saharan Africa.