Keywords: paralog processing, transcriptome reprogramming, deep learning, Zea mays

Latest 38 Papers

Metabolite profiling reveals slow and uncoordinated adjustment of C4 photosynthesis to sudden changes in irradiance

Authors: Arrivault, S., Medeiros, D. B., Sales, C. R. G., Guenther, M., Kromdijk, J., Fernie, A. R., Stitt, M.

Date: 2025-04-18 · Version: 1

DOI: 10.1101/2025.04.13.648559

Model Organism: Zea mays

C4 photosynthesis irradiance fluctuations metabolic buffering intercellular shuttles Zea mays

Large-scale single-cell profiling of stem cells uncovers redundant regulators of shoot development and yield trait variation

Authors: Xu, X., Passalacqua, M., Rice, B., Demesa-Arevalo, E., Kojima, M., Takebayashi, Y., Harris, B., Sakakibara, H., Gallavotti, A., Gillis, J., Jackson, D.

Date: 2025-04-17 · Version: 2

DOI: 10.1101/2024.03.04.583414

Model Organism: Multi-species

single-cell RNA sequencing shoot stem cells Arabidopsis thaliana Zea mays stem cell regulators

Volatiles from low R:FR-treated maize plants increase the emission of herbivore-induced plant volatiles in their neighbors

Authors: Escobar Bravo, R., Schimmel, B. C. J., Erb, M.

Date: 2025-04-02 · Version: 2

DOI: 10.1101/2024.10.05.616768

Model Organism: Zea mays

low R:FR light Zea mays herbivore‑induced plant volatiles volatile priming plant‑plant communication

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Authors: Wang, Z., Zenkl, R., Greche, L., De Solan, B., Bernigaud Samatan, L., Ouahid, S., Visioni, A., Robles-Zazueta, C. A., Pinto, F., Perez-Olivera, I., Reynolds, M. P., Zhu, C., Liu, S., D'argaignon, M.-P., Lopez-Lozano, R., Weiss, M., Marzougui, A., Roth, L., Dandrifosse, S., Carlier, A., Dumont, B., Mercatoris, B., Fernandez, J., Chapman, S., Najafian, K., Stavness, I., Wang, H., Guo, W., Virlet, N., Hawkesford, M., Chen, Z., David, E., Gillet, J., Irfan, K., Comar, A., Hund, A.

Date: 2025-03-19 · Version: 1

DOI: 10.1101/2025.03.18.642594

Model Organism: Triticum aestivum

wheat semantic segmentation computer vision deep learning phenotyping

High-resolution transcriptional atlas of growing maize shoot organs throughout plant development under well-watered and drought conditions

Authors: Zhang, J., Verbraeken, L., Sprenger, H., Mertens, S., Wuyts, N., Cannoot, B., De Block, J., Demuynck, K., Natran, A., Maleux, K., Merchie, J., Crafts-Brandner, S., Vogel, J., Bruce, W., Inze, D., Maere, S., Nelissen, H.

Date: 2025-03-13 · Version: 1

DOI: 10.1101/2025.03.12.642568

Model Organism: Zea mays

Zea mays leaf and internode development drought stress spatiotemporal transcriptome atlas gene regulatory networks

Fiber Bragg grating based sensing system for non-destructive root phenotyping using ResNet prediction

Authors: Binder, S., Hossain, K., Bucksch, A., Fok, M.

Date: 2025-02-28 · Version: 2

DOI: 10.1101/2024.09.17.613457

Model Organism: Zea mays

root architecture fiber Bragg grating real-time phenotyping deep learning Zea mays

Interactive effect of Moringa oleifera mediated green nanoparticles and arbuscular mycorrhizal fungi on growth, root system architecture, and nutrient uptake in maize (Zea mays L.)

Authors: Ain, Q. u., Hussain, H. A., Rahman, L., Zhang, Q., Rehman, A., Hussain, S., Uddin, S., Imran, A.

Date: 2025-02-25 · Version: 1

DOI: 10.1101/2025.02.23.639791

Model Organism: Zea mays

arbuscular mycorrhizal fungi green nanoparticles Zea mays iron oxide (FeO)zinc oxide (ZnO)

A maize near-isogenic line population designed for gene discovery and characterization of allelic effects

Authors: Zhong, T., Mullens, A., Morales, L., Swarts, K., Stafstrom, W., He, Y., Sermons, S., Yang, Q., Lopez-Zuniga, L. O., Rucker, E., Thomason, W., Nelson, R., Jamann, T. M., Balint-Kurti, P., Holland, J. B.

Date: 2025-02-02 · Version: 1

DOI: 10.1101/2025.01.29.635337

Model Organism: Zea mays

Zea mays near‑isogenic lines genotyping‑by‑sequencing hidden Markov model QTL mapping

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