Keywords: paralog processing, deep learning, ascorbate, spatial transcriptomics, SWI3, GWAS, DDM1

Latest 42 Papers

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

A drought stress-induced MYB transcription factor regulates pavement cell shape in leaves of European aspen (Populus tremula)

Authors: Liu, S., Doyle, S. M., Robinson, K. M., Rahneshan, Z., Street, N. R., Robert, S.

Date: 2026-01-16 · Version: 1

DOI: 10.64898/2026.01.16.699252

Model Organism: Populus tremula

leaf pavement cells Populus tremula MYB305a GWAS drought stress

Wheat diversity reveals new genomic loci and candidate genes for vegetation indices using genome-wide association analysis

Authors: Rustamova, S., Jahangirov, A., Leon, J., Naz, A. A., Huseynova, I.

Date: 2026-01-14 · Version: 1

DOI: 10.64898/2026.01.14.699455

Model Organism: Triticum aestivum

Triticum aestivum drought stress spectral vegetation indices GWAS QTL hotspot

Regulation of spikelet number during wheat spike development

Authors: Li, C., Li, K., Zhang, C., Dubcovsky, J.

Date: 2026-01-08 · Version: 1

DOI: 10.64898/2026.01.07.698073

Model Organism: Triticum aestivum

spikelet number per spike wheat inflorescence development florigen (FT1)spatial transcriptomics single‑cell analysis

GWAs reveals SUBER GENE1-mediated suberization via Type One Phosphatases

Authors: Han, J.-P., Lefebvre-Legendre, L., Yu, J., Capitao, M. B., Beaulieu, C., Gully, K., Shukla, V., Wu, Y., Boland, A., Nawrath, C., Barberon, M.

Date: 2025-12-12 · Version: 2

DOI: 10.1101/2025.05.06.652434

Model Organism: Arabidopsis thaliana

suberin deposition Arabidopsis thaliana GWAS SBG1 TOPP phosphatases

Integrating image-based phenotyping and GWAS to map tolerance to Spittlebug nymphs in interspecific Urochloa grasses

Authors: Espitia-Buitrago, P., Perea, C., Mejia-Medina, J. C., Hernandez, L. M., Castiblanco, V., Ryan, C., De Vega, J. J., Jauregui, R. N.

Date: 2025-11-05 · Version: 1

DOI: 10.1101/2025.11.03.686394

Model Organism: Urochloa spp.

Urochloa spittlebug tolerance GWAS QTL image-based phenotyping

Development alters genotype-environment interactions and shapes adaptation in Arabidopsis

Authors: Lawrence-Paul, E. H., Janakiraman, J., Lawrence-Paul, M. R., Ben-Zeev, R., Xu, Y., Penn, A., Lasky, J. R.

Date: 2025-11-03 · Version: 2

DOI: 10.1101/2025.05.13.653704

Model Organism: Arabidopsis thaliana

vegetative phase change drought adaptation genotype-by-environment interaction GWAS developmental trade‑offs

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

Spatiotemporal regulation of arbuscular mycorrhizal symbiosis at cellular resolution

Authors: Chancellor, T., Ferreras-Garrucho, G., Akmakjian, G. Z., Montero, H., Bowden, S. L., Hope, M., Wallington, E., Bhattacharya, S., Korfhage, C., Bailey-Serres, J., Paszkowski, U.

Date: 2025-11-02 · Version: 1

DOI: 10.1101/2025.10.31.685811

Model Organism: Oryza sativa

arbuscular mycorrhizal symbiosis spatial transcriptomics TRAP-seq arbuscule development nutrient transport

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