The study models maize flowering time plasticity using a physiological reaction norm derived from multi-environment trial data, revealing genotype-specific differences in temperature-driven development and photoperiod perception. It introduces an envirotyping metric that shows genotypes can experience markedly different photoperiods even within the same environment, and demonstrates distinct adaptive strategies between tropical and temperate germplasm.

The study evaluated 11 intermediate phenotypes linked to stalk lodging resistance in a diverse panel of 566 maize (Zea mays L.) inbred lines across four environments, preserving individual stalk identity to capture plant-level variation. This high-density phenotypic dataset enabled statistical genomics, predictive modeling, and machine learning to uncover genetic factors underlying lodging resistance, offering insights applicable to other grass species.

The study used comparative transcriptomics to examine how Fusarium oxysporum isolates with different lifestyles on angiosperms regulate effector genes during infection of the non‑vascular liverwort Marchantia polymorpha. Core effector genes on fast core chromosomes are actively expressed in the bryophyte host, while lineage‑specific effectors linked to angiosperm pathogenicity are silent, and disruption of a compatibility‑associated core effector alters the expression of other core effectors, highlighting conserved fungal gene networks across plant lineages.

The study generated a temporal physiological and metabolomic map of leaf senescence in diverse maize inbred lines differing in stay‑green phenotype, identifying 84 metabolites associated with senescence and distinct metabolic signatures between stay‑green and non‑stay‑green lines. Integration of metabolite data with genomic information uncovered 56 candidate genes, and reverse‑genetic validation in maize and Arabidopsis demonstrated conserved roles for phenylpropanoids such as naringenin chalcone and eriodictyol in regulating senescence.

The study utilizes explainable artificial intelligence (XAI) combined with machine learning to assess how inter‑annual weather variability influences oilseed sunflower yields across the United States from 1976 to 2022. Key climate predictors, especially summer maximum temperature and total precipitation, were identified, and predictive models were projected under various Shared Socioeconomic Pathways to 2080, revealing region‑specific yield declines.

The study generated a high-quality genome assembly for Victoria cruziana and used comparative transcriptomics to identify anthocyanin biosynthesis genes and their transcriptional regulators that are differentially expressed between white and light pinkish flower stages. Differential expression of structural genes (VcrF3H, VcrF35H, VcrDFR, VcrANS, VcrarGST) and transcription factors (VcrMYB123, VcrMYB-SG6_a, VcrMYB-SG6_b, VcrTT8, VcrTTG1) correlates with the observed flower color change.

The study demonstrates that RNA extracted from herbarium specimens can be used to generate high‑quality transcriptomes, comparable to those from fresh or silica‑dried samples. By assembling and comparing transcriptomes across specimen types, the authors validated a plant immune receptor synthesized from a 1956 collection, proving archival RNA’s utility for functional genomics. These findings challenge the prevailing view that herbarium RNA is unsuitable for transcriptomic analyses.