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.
Evaluation of combined root exudate and rhizosphere microbiota sampling approaches to elucidate plant-soil-microbe interaction
Authors: Escudero-Martinez, C., Browne, E. Y., Schwalm, H., Santangeli, M., Brown, M., Brown, L., Roberts, D. M., Duff, A. M., Morris, J., Hedley, P. E., Thorpe, P., Abbott, J. C., Brennan, F., Bulgarelli, D., George, T. S., Oburger, E.
The study benchmarked several sampling approaches for simultaneous profiling of root exudates and rhizosphere microbiota in soil-grown barley, revealing consistent exudate chemistry across methods but variation in root morphology and nitrogen exudation. High‑throughput amplicon sequencing and quantitative PCR showed protocol‑specific impacts on microbial composition, yet most rhizosphere-enriched microbes were captured by all approaches. The authors conclude that different protocols provide comparable integrated data, though methodological differences must be aligned with experimental objectives.