The study integrates genome, transcriptome, and chromatin accessibility data from 380 soybean accessions to dissect the genetic and regulatory basis of symbiotic nitrogen fixation (SNF). Using GWAS, TWAS, eQTL mapping, and ATAC-seq, the authors identify key loci, co‑expression modules, and regulatory elements, and validate the circadian clock gene GmLHY1b as a negative regulator of nodulation via CRISPR and CUT&Tag. These resources illuminate SNF networks and provide a foundation for soybean improvement.

The study introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.

The study presents a deep‑learning pipeline that uses state‑of‑the‑art convolutional neural networks to automatically estimate the establishment of perennial groundcovers in agricultural research plots from smartphone images. By employing region‑of‑interest markers and deploying the models on AWS SageMaker with a lightweight Django web interface, the approach provides fast, objective, and reproducible assessments that can be adopted by researchers and growers across the Midwest.

The study generated a phenotypic dataset for 550 Lactuca accessions, including 20 wild relatives, and applied an iterative two‑step GWAS using a jointly processed SNP set for cultivated lettuce (L. sativa) and its wild progenitor (L. serriola) to dissect trait loci. Known and novel QTLs for anthocyanin accumulation, leaf morphology, and pathogen resistance were identified, with several L. serriola‑specific QTLs revealing unique genetic architectures, underscoring the breeding value of wild lettuce species.

The study used chlorophyll fluorescence imaging to map non-photochemical quenching (NPQ) gradients along barley leaf axes and found heat stress attenuates NPQ induction, revealing spatial heterogeneity in stress responses. Genome‑wide association and transcriptomic analyses identified candidate genes, notably HORVU.MOREX.r3.3HG0262630, that mediate region‑specific heat responses, highlighting pathways for improving cereal heat resilience.

The study identifies the transcription factor MdBRC1 as a key inhibitor of bud growth during the ecodormancy phase in apple (Malus domestica), directly regulating dormancy‑associated genes and interacting with the flowering promoter MdFT2 to modulate bud break. Comparative transcriptomic analysis and gain‑of‑function experiments in poplar demonstrate that MdFT2 physically binds MdBRC1, attenuating its repressive activity and acting as a molecular switch for the transition to active growth.

The study used an interpretable convolutional neural network to predict ABA responsiveness from proximal promoter sequences in Arabidopsis thaliana, revealing both known ABF-binding motifs and novel regulatory elements. Model performance was boosted by advanced data augmentation, and predicted regulatory regions were experimentally validated using reporter lines, confirming the inferred cis‑regulatory code.

The study introduced full-length SOC1 genes from maize and soybean, and a partial SOC1 gene from blueberry, into tomato plants under constitutive promoters. While VcSOC1K and ZmSOC1 accelerated flowering, all three transgenes increased fruit number per plant mainly by promoting branching, and transcriptomic profiling revealed alterations in flowering, growth, and stress‑response pathways.

The study applied Spatial Analysis of Field Trials with Splines (SpATS) and Neighbor Genome-Wide Association Study (Neighbor GWAS) to barley field data, revealing that neighboring genotypes contribute to spatial variation in disease damage. Neighbor GWAS identified variants on chromosome 7H that modestly affect net form net blotch and scald resistance, suggesting that genotype mixtures could mitigate pest damage.

The study presents GenoRetriever, an interpretable deep learning framework trained on STRIPE-seq data from soybean and other crops, that predicts transcription start site locations and usage by identifying 27 core promoter motifs. Validation using in silico motif insertions, saturation mutagenesis, and CRISPR‑Cas9 promoter editing demonstrates high predictive accuracy and reveals domestication‑driven motif usage shifts and lineage‑specific effects. The tool is provided via a web server for promoter analysis and design, offering a new resource for plant functional genomics and crop improvement.