The study examined whether colonisation by the arbuscular mycorrhizal fungus Rhizophagus irregularis primes immune responses in barley against the leaf rust pathogen Puccinia hordei. While AMF did not affect disease severity or plant growth, co‑infected leaves showed heightened expression of defence genes and transcriptome reprogramming, including altered protein ubiquitination, indicating a priming mechanism. These results highlight transcriptional and post‑translational pathways through which AMF can enhance barley disease resistance for sustainable crop protection.

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 characterizes telomere repeat binding (TRB) proteins in the model moss Physcomitrium patens, demonstrating that individual PpTRB genes are essential for normal protonemal and gametophore development and that loss of TRBs leads to telomere shortening, mirroring findings in seed plants. Transcriptome analysis of TRB mutants shows altered expression of genes linked to transcription regulation and stimulus response, while subcellular localization confirms nuclear residence and mutual interaction of PpTRBs, underscoring their conserved role in telomere maintenance across land plants.

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 used Arabidopsis thaliana mutants with low (vtc2, vtc4) and high (vtc2/OE-VTC2) ascorbate levels to examine how ascorbate concentration affects gene expression and cellular homeostasis. Transcriptomic analysis revealed that altered ascorbate levels modulate defense and stress pathways, and that TAA1/TAR2‑mediated auxin biosynthesis is required for coping with elevated ascorbate in a light‑dependent manner.

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.

The study characterizes the distinct and overlapping roles of the rice PI paralogs OsMADS2 and OsMADS4 in lodicule specification, flowering time, and floral organ development by analyzing null and double mutants and overexpression lines. Genome-wide binding (ChIP‑seq) and transcriptome (RNA‑seq) analyses identified downstream targets involved in cell division, cell wall remodeling, and osmotic regulation that underpin the observed phenotypes. These findings reveal novel functions for PI paralogs in reproductive development and highlight mechanisms of transcription factor diversification in Oryza sativa.

The Global Wheat Dataset Consortium released a comprehensive semantic segmentation dataset (GWFSS) of wheat organs across developmental stages, comprising 1,096 fully annotated images and 52,078 unannotated images from 11 institutions. Models based on DeepLabV3Plus and Segformer were trained, with Segformer achieving ≈90% mIoU for leaves and spikes but lower precision (54%) for stems, while also enabling weed exclusion and discrimination of necrotic, senescent, and residue tissues.

The study introduces an in-soil fiber Bragg grating (FBG) sensing system that continuously records three-dimensional strain from growing pseudo-roots, enabling non‑destructive monitoring of root architecture. Using two ResNet models, the system predicts root width and depth with over 90% accuracy, and performance improves to 96‑98% after retraining on data from actual corn (Zea mays) roots over a 30‑day period. This prototype demonstrates potential for scalable, real‑time root phenotyping and broader soil environment sensing.