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 investigates how miR394 influences flowering time in Arabidopsis thaliana by combining transcriptomic profiling of mir394a mir394b double mutants with histological analysis of reporter lines. Bioinformatic analysis identified a novel lncRNA overlapping MIR394B (named MIRAST), and differential promoter activity of MIR394A and MIR394B suggests miR394 fine‑tunes flower development through transcription factor and chromatin remodeler regulation.