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.

Populus VariantDB v3.2 is a searchable database that catalogs sequence polymorphisms across heterozygous poplar genomes, enabling precise design of guide RNAs and primers for CRISPR genome editing in outcrossing tree species. The tool accepts short sequence inputs, supports multiple Populus genomes (including hybrids), and provides customizable search parameters to improve gRNA/primer design accuracy. Its platform‑agnostic and expandable architecture makes it broadly applicable to sequence‑sensitive functional genomics studies.