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 performed transcriptome profiling of Cryptomeria japonica individuals from different geographic origins grown in three common gardens across Japan, assembling 77,212 transcripts guided by the species' genome. Using SNP-based genetic clustering and weighted gene co‑expression network analysis, they identified gene modules whose expression correlated with genetic differentiation, revealing that defense‑related genes are up‑regulated in Pacific‑side populations while terpenoid metabolism genes are higher in Sea‑of‑Japan populations, indicating local adaptation via regulatory changes.

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.