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.

The study investigated unexpected leaf spot symptoms in Psa3‑resistant kiwifruit (Actinidia) germplasm, finding that Psa3 was detectable by qPCR and metabarcoding despite poor culturing. Metabarcoding revealed distinct bacterial community shifts in lesions versus healthy tissue, and whole‑genome sequencing identified diverse Pseudomonas spp. that, while not individually more pathogenic, could enhance Psa3 growth, suggesting pathogenic consortia on resistant hosts.