The authors introduce S²-PepAnalyst, a web-based tool that leverages plant-specific datasets and advanced machine learning to predict small signaling peptides (SSPs) with 99.5% accuracy and minimal false negatives. By integrating protein language models, geometric‑topological analysis, and reinforcement learning, the tool surpasses existing predictors such as SignalP 6.0 in classifying peptide families like CLE and RALF.

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 identified a major QTL (qDTH3) on chromosome 3 responsible for a 7‑10‑day earlier heading phenotype in the rice line SM93, using QTL‑seq, KASP genotyping, association mapping, and transcriptomic analysis to fine‑map the locus to a 2.53 Mb region and pinpoint candidate genes. SNP markers linked to these genes were proposed as tools for breeding early‑maturing, climate‑resilient rice varieties.

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.