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.

In a two-year controlled-environment experiment, diploid and tetraploid individuals of wild-type and cultivar Marshall annual ryegrass (Lolium multiflorum) were grown under elevated CO2 (540 vs 800 ppm) and differing evapotranspiration regimes. Elevated CO2 increased total biomass by 44% across ploidy levels, and tetraploid wild-type plants matched the improved cultivar in growth and forage quality, indicating that chromosome manipulation and wild genetic resources can enhance climate resilience.

The study examined how polyploidy, hybridization, and incomplete lineage sorting affect phylogenetic reconstructions in the genus Packera, evaluating several published paralog‑processing pipelines. Results showed that the choice of orthology and paralog handling methods markedly altered tree topology, time‑calibrated phylogenies, biogeographic histories, and detection of ancient reticulation, underscoring the need for careful methodological selection alongside comprehensive taxon sampling.