The study identifies the serine/arginine-rich splicing factor RS2Z36 as a key regulator of ovary patterning and early fruit morphology in tomato, with loss‑of‑function mutants producing smaller, ellipsoid fruits and elongated pericarp cells. RNA‑seq and proteomic analyses reveal widespread alternative splicing and altered protein abundance, including novel splice‑variant peptides, while mutant pericarps show increased deposition of LM6‑detected arabinan and AGP epitopes.

The study demonstrates that the plastid chaperone CLPC2, but not its paralogue CLPC1, is essential for Arabidopsis responsiveness to microbial volatile compounds and for normal seed and seedling development. Loss of CLPC2 alters the chloroplast proteome, affecting proteins linked to growth, photosynthesis, and embryogenesis, while overexpression of CLPC2 mimics CLPC1 deficiency, highlighting distinct functional roles within the CLP protease complex.

The study functionally characterizes a conserved structured RNA motif (45ABC) in Arabidopsis RBP45 pre‑mRNAs, showing that its sequence and pairing elements mediate a negative auto‑ and cross‑regulatory feedback loop through alternative splicing that produces unproductive isoforms and reduces RBP45 expression. Transcriptome‑wide splicing analysis and phenotypic assessment of rbp45 mutants reveal that RBP45B plays a dominant role and that proper regulation of this motif is essential for root growth and flowering time.

The authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.

The study assessed how well common deep learning models (ResNet, EfficientNet, Inception, MobileNet) generalize across different tomato pest and disease image datasets. While models performed well on the dataset they were trained on, they suffered substantial accuracy drops when applied to other datasets, indicating that architectural changes alone cannot overcome dataset variability. The results highlight the necessity for more diverse, representative training data to improve real-world deployment of PPD diagnostic tools.

The study demonstrates that hyperspectral imaging can non‑destructively differentiate active nitrogen‑fixing root nodules from non‑fixing nodules and root tissue based on distinct spectral signatures. By integrating deep‑learning models, the authors created an automated nodule counting pipeline that works across multiple legume species and growth conditions, eliminating labor‑intensive manual counting and reliably detecting nodules within dense root systems.

A large-scale proteomic study in Arabidopsis thaliana identified over 32,000 isoform-specific peptides, confirming that alternative splicing, particularly intron retention, produces translated protein isoforms. Integrated proteogenomic analysis, SUPPA classification, and AlphaFold modeling revealed structural impacts and a non-linear regulation of transcript and protein abundance, with mutant phenotypes linking splicing to growth, chlorophyll content, and anthocyanin accumulation.

The study introduces the Botanical Spectrum Analyzer (BSA), a GUI that incorporates a modified U‑Net deep neural network for accurate segmentation of plant images from RGB and hyperspectral (VNIR and SWIR) data. BSA was tested on wheat, barley, and Arabidopsis datasets, achieving >99% accuracy and F1‑scores above 98%, and markedly outperformed commercial tools on root segmentation tasks.

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.