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 used Weighted Gene Correlation Network Analysis (WGCNA) and GENIE3 to construct co‑expression modules and gene regulatory networks (GRNs) in barley subjected to Fusarium head blight and drought stress. Integration of these approaches highlighted overlapping regulatory patterns, pinpointing WRKY transcription factors as central to FHB response, while bHLH and NAC family members showed stress‑specific roles. Promoter motif enrichment further validated predicted TF‑target interactions, offering candidate regulators for future functional validation.

The researchers performed a large‑scale field trial with 105 wheat (Triticum aestivum) genotypes inoculated by Fusarium culmorum, combining quantitative deoxynivalenol (DON) profiling and untargeted metabolomics to uncover molecular signatures of infection. Sesquiterpene‑derived metabolites tracked toxin accumulation, whereas glycosylated diterpene conjugates were enriched in low‑DON samples, indicating a potential defensive metabolic pathway.

Introgression of a short arm segment of chromosome 3G from the wild relative Triticum timopheevii into hexaploid wheat confers strong type II Fusarium head blight resistance, improves grain weight retention, and reduces deoxynivalenol accumulation. A similar introgression on chromosome 7At also enhances type II resistance, indicating these chromosomal segments have potential for breeding disease‑resistant wheat cultivars.

The study used integrated transcriptomic, proteomic, and metabolomic analyses to characterize barley head defence responses to Fusarium culmorum infection. It identified coordinated upregulation of gene‑protein pairs and associated defence metabolites, notably aromatic amino‑acid‑derived compounds such as tryptamine, serotonin, and hordatines. These findings highlight aromatic secondary metabolism as a consistent resistance mechanism across barley varieties with type II resistance.

The study used comparative proteomics to examine how the emerging 15ADON/3ANX chemotype of Fusarium graminearum affects protein expression in both wheat and the fungus. It identified a core wheat proteome altered by infection, chemotype‑specific wheat proteins, and fungal proteins linked to virulence and ergosterol biosynthesis, revealing distinct molecular responses influencing disease severity.

Twenty-five oat genotypes were field‑tested across Germany for resistance to three Fusarium species, with fungal biomass quantified by species‑specific qPCR revealing highest infection by F. poae. Microscopic analysis showed genotype‑dependent variation in trichome size and density, which correlated positively with Fusarium graminearum biomass, suggesting trichomes influence susceptibility and that resistance is partly cross‑species but mainly species‑specific.