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 examined how single and repeated mechanical disturbances (whole‑pot drops) affect leaf folding in Mimosa pudica, using chlorophyll fluorescence to track photosystem II efficiency and transcriptome profiling to identify responsive genes. A single drop mainly up‑regulated flavonoid biosynthesis genes, whereas multiple drops triggered broader biotic and abiotic stress pathways, indicating a shift in the plant’s gene regulatory network under repeated stress.

The study shows that inoculating Arabidopsis thaliana with the plant‑growth‑promoting bacterium Enterobacter sp. SA187 markedly boosts root and shoot biomass under elevated CO₂, accompanied by altered nitrogen and carbon content and reshaped phytohormone signaling. Transcriptomic and metabolomic analyses reveal activation of salicylic acid, jasmonic acid, and ethylene pathways and enhanced primary metabolism, while the ethylene‑insensitive ein2‑1 mutant demonstrates that the growth benefits are ethylene‑dependent.

The study examined soybean (Glycine max) responses to simultaneous drought and Asian soybean rust infection using combined transcriptomic and metabolomic analyses. Weighted Gene Co-expression Network Analysis identified stress-specific gene modules linked to metabolites, while Copula Graphical Models uncovered sparse, condition‑specific networks, revealing distinct molecular signatures for each stress without overlapping genes or metabolites. The integrative approach underscores a hierarchical, modular defense architecture and suggests targets for breeding multi‑stress resilient soybeans.

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 identifies the pseudokinase CRN in Medicago truncatula as a regulator of inflorescence meristem branching and a negative modulator of root interactions with arbuscular mycorrhizal (AM) fungi, operating partially independently of the AM autoregulation CLE peptide MtCLE53. Transcriptomic profiling of crn mutant roots reveals disruptions in nutrient, symbiosis, and stress signaling pathways, highlighting the multifaceted role of MtCRN in plant development and environmental interactions.

The study examined how varying temperature regimes, including cold deprivation and early cold exposure, affect dormancy onset and maintenance in sweet cherry (Prunus avium) flower buds. Phenological monitoring combined with transcriptomic analyses revealed that temperature drives dormancy progression, identifying specific genes and pathways responsive to cold, and uncovering a distinct shallow dormancy phase induced by cold deprivation with a unique molecular signature.

The study combined cell biology, transcriptomics, and ionomics to reveal that zinc deficiency reduces root apical meristem size while preserving meristematic activity and local Zn levels, leading to enhanced cell elongation and differentiation in Arabidopsis thaliana. ZIP12 was identified as a highly induced gene in the zinc‑deficient root tip, and zip12 mutants displayed impaired root growth, altered RAM structure, disrupted Zn‑responsive gene expression, and abnormal metal partitioning, highlighting ZIP12’s role in maintaining Zn homeostasis and meristem function.

The study profiled root transcriptomes of Arabidopsis wild type and etr1 gain-of-function (etr1-3) and loss-of-function (etr1-7) mutants under ethylene or ACC treatment, identifying 4,522 ethylene‑responsive transcripts, including 553 that depend on ETR1 activity. ETR1‑dependent genes encompassed ethylene biosynthesis enzymes (ACO2, ACO3) and transcription factors, whose expression was further examined in an ein3eil1 background, revealing that both ETR1 and EIN3/EIL1 pathways regulate parts of the network controlling root hair proliferation and lateral root formation.

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.