The study investigates the evolutionary shift from archegonial to embryo‑sac reproduction by analyzing transcriptomes of Ginkgo reproductive organs and related species. It reveals that the angiosperm pollen‑tube guidance module MYB98‑CRP‑ECS is active in mature Ginkgo archegonia and that, while egg cell transcription is conserved, changes in the fate of other female gametophyte cells drove the transition, providing a molecular framework for this major reproductive evolution.

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 used comparative proteomics to examine how the emerging 15ADON/3ANX chemotype of Fusarium graminearum affects protein expression in both wheat and the fungus. It identified a core wheat proteome altered by infection, chemotype‑specific wheat proteins, and fungal proteins linked to virulence and ergosterol biosynthesis, revealing distinct molecular responses influencing disease severity.

The study examined three fruit morphotypes of the desert shrub Haloxylon ammodendron, revealing distinct germination performances under salt and drought stress. Proteomic analysis identified 721 differentially expressed proteins, particularly between the YP and PP morphotypes, linking stress‑responsive protein abundance to rapid germination in YP and delayed germination in PP as contrasting adaptive strategies. The findings suggest that fruit polymorphism facilitates niche differentiation and informs germplasm selection for desert restoration.

The study tracked molecular changes in plastoglobules and thylakoids of Zea mays B73 during heat stress and recovery, revealing increased plastoglobule size, number, and adjacent lipid droplets over time. Proteomic and lipidomic analyses uncovered up‑regulation of specific plastoglobule proteins and alterations in triacylglycerol, plastoquinone derivatives, and phytol esters, suggesting roles in membrane remodeling and oxidative defense. These insights highlight plastoglobule‑associated pathways as potential targets for enhancing heat resilience in maize.

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.

A comparative physiological study of persimmon cultivars with flat (Hiratanenashi) and round (Koushimaru) fruit shapes revealed that differences in cell proliferation, cell shape, and size contribute to shape variation. Principal component analysis of elliptic Fourier descriptors tracked shape changes, while histology and transcriptome profiling identified candidate genes, including a WOX13 homeobox gene, potentially governing fruit shape development.

The study identifies the serine/threonine protein kinase CIPK14/SNRK3.15 as a regulator of sulfate‑deficiency responses in Arabidopsis thaliana seedlings, with mutants showing diminished early adaptive and later salvage responses under sulfur starvation. While snrk3.15 mutants exhibit no obvious phenotype under sufficient sulfur, the work provides a novel proteomic dataset comparing wild‑type and mutant seedlings under sulfur limitation.

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.