Keywords: paralog processing, ascorbate, transcriptomic analysis, wheat, deep learning, image‑based phenotyping

Latest 35 Papers

Molecular basis of delayed leaf senescence induced by short-term treatment with low phosphate in rice

Authors: Martin-Cardoso, H., Bundo, M., Garcia-Molina, A., San Segundo, B.

Date: 2026-01-24 · Version: 1

DOI: 10.64898/2026.01.23.701354

Model Organism: Oryza sativa

phosphate deficiency leaf senescence Oryza sativa CRISPR/Cas9 transcriptomic analysis

Deep learning of fossil pollen morphology reveals 25,000 years of ecological change in East African grasslands

Authors: Adaime, M.-E., Kong, S., Urban, M. A., Street-Perrott, F. A., Verschuren, D., Punyasena, S. W.

Date: 2026-01-22 · Version: 3

DOI: 10.1101/2024.09.23.612957

Model Organism: General

deep learning grass pollen morphology C3/C4 plant ratios paleo‑vegetation reconstruction super‑resolution microscopy

Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning

Authors: Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., Rehush, N.

Date: 2026-01-21 · Version: 1

DOI: 10.64898/2026.01.18.700158

Model Organism: General

deep learning point cloud reconstruction alpha‑shape coniferous tree crowns geometry-aware transformer

Physics-Informed Neural Network Methods for Predicting Plant Height Development

Authors: Shao, Y., van Eeuwijk, F., Peeters, C., Zumsteg, O., Athanasiadis, I., van Voorn, G.

Date: 2026-01-14 · Version: 1

DOI: 10.64898/2026.01.14.699475

Model Organism: Triticum aestivum

Physics-Informed Neural Network logistic ODE Long Short-Term Memory plant height prediction wheat

Evolution of HMA-integrated tandem kinases accompanied by expansion of target pathogens

Authors: Asuke, S., Tagle, A. G., Hyon, G.-S., Koizumi, S., Murakami, T., Horie, A., Niwamoto, D., Katayama, E., Shibata, M., Takahashi, Y., Islam, M. T., Matsuoka, Y., Yamaji, N., Shimizu, M., Terauchi, R., Hisano, H., Sato, K., Tosa, Y.

Date: 2025-12-16 · Version: 1

DOI: 10.64898/2025.12.15.692859

Model Organism: Multi-species

tandem kinase proteins HMA domain disease resistance barley wheat

Targeting granule initiation and amyloplast structure to create giant starch granules in wheat

Authors: McNelly, R., Esch, L., Ngai, Q. Y., Pohan, K., Stringer, R., Fahy, B., Warren, F., Seung, D.

Date: 2025-12-15 · Version: 1

DOI: 10.64898/2025.12.12.693964

Model Organism: Triticum aestivum

starch granule size PARC6 BGC1 wheat giant starch granules

Glycosylated diterpenes associate with early containment of Fusarium culmorum infection across wheat (Triticum aestivum L.) genotypes under field conditions

Authors: Pieczonka, S. A., Dick, F., Bentele, M., Ramgraber, L., Prey, L., Kupczyk, E., Seidl-Schulz, J., Hanemann, A., Noack, P. O., Asam, S., Schmitt-Kopplin, P., Rychlik, M.

Date: 2025-12-04 · Version: 1

DOI: 10.64898/2025.12.02.691979

Model Organism: Triticum aestivum

wheat Fusarium head blight deoxynivalenol untargeted metabolomics diterpene conjugates

Semi-automated image analysis of root architecture and early root development in faba bean and white clover and genomic estimation of breeding values and correlations

Authors: Nagy, I., Kristensen, P. S., Malinowska, M., Nielsen, L. K., Schiemann, A., Rolund, N., Andersen, S. U., Asp, T.

Date: 2025-12-03 · Version: 1

DOI: 10.64898/2025.12.01.691692

Model Organism: Multi-species

leguminous protein crops climate‑resilient breeding root architecture image‑based phenotyping genomic estimated breeding values

Key molecular and cellular events of table olive fruit abscission zone formation during natural maturation and after ethephon treatment

Authors: Wang, M., Santos, E., Eagle, S., Chernikova, A., Zhang, S., Tran, P., Marino, G., Jernstedt, J., Niederholzer, F., Wheeler-Dykes, B., Wilkop, T., Ferguson, L., Drakakaki, G.

Date: 2025-11-12 · Version: 1

DOI: 10.1101/2025.11.10.687704

Model Organism: Olea europaea

abscission zone Olea europaea transcriptomic analysis cell wall remodeling alkalinization

Cross-dataset evaluation of deep learning models for plant pest and disease diagnosis

Authors: Sun, J.

Date: 2025-11-03 · Version: 2

DOI: 10.1101/2024.10.07.617111

Model Organism: Solanum lycopersicum

deep learning cross-dataset generalization tomato pest and disease dataset variability model architecture

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