The study presents an optimized Agrobacterium-mediated transformation protocol for bread wheat that incorporates a GRF4‑GIF1 fusion to enhance regeneration and achieve genotype‑independent transformation across multiple cultivars. The approach consistently improves transformation efficiency while limiting pleiotropic effects, offering a versatile platform for functional genomics and gene editing in wheat.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study estimated genetic parameters for flowering and maturity time in almond (Prunus dulcis) using classical segregation analyses and Bayesian linear mixed models on a pedigree of over 17,500 individuals spanning 30 years. Heritability and repeatability were quantified, breeding values (EBVs) were computed for all genotypes, and trait-specific rankings were generated to improve parental selection. The results provide a foundation for integrating genomic selection into almond breeding programs.

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 identifies RAF24, a B4 Raf-like MAPKKK, as a novel regulator of flowering time in Arabidopsis, demonstrating that RAF24 controls the phosphorylation of the ubiquitin ligase HUB2 via SnRK2 kinases, thereby modulating H2Bub1 levels. Phospho‑mimetic and phospho‑ablative HUB2 mutants confirm that phosphorylation at S314 is critical for proper flowering timing.

The study investigates how cytosine methylation influences flowering time under drought stress in Arabidopsis thaliana, using the drm1 drm2 cmt3 triple mutant (ddc). Drought delayed flowering by one day in wild type but two days in ddc, coinciding with overexpression of BBX16/COL7 and BBX17/COL8 and down‑regulation of NF‑YA2, suggesting a trans‑acting methylation‑dependent regulatory network affecting FT induction.

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.