The study utilizes a large collection of fluorescently marked Ds-GFP insertional mutations in haploid maize pollen to link gene disruptions with quantitative fitness effects measured as transmission deviations. By integrating genome-derived features (e.g., codon usage) and expression profiling into interpretable machine learning models, they achieve high predictive performance (auROC >90%) for genes influencing pollen fitness, highlighting expression specificity as a key predictor.

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 estimated genetic parameters for flowering and maturity time in almond (Prunus dulcis) using classical segregation analyses and Bayesian linear mixed models on a pedigree of over 17,500 individuals spanning 30 years. Heritability and repeatability were quantified, breeding values (EBVs) were computed for all genotypes, and trait-specific rankings were generated to improve parental selection. The results provide a foundation for integrating genomic selection into almond breeding programs.