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AI-summarized plant biology research papers from bioRxiv

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Latest 14 Papers

Data-driven mathematical modelling explains altered timing of EARLY FLOWERING 3 in the wheat circadian oscillator

Authors: Upadhyay, A., Rowland-Chandler, J., Stewart-Wood, J., Pingarron-Cardenas, G., Tokuda, I. T., Webb, A. A., Locke, J. C.

Date: 2025-04-08 · Version: 1
DOI: 10.1101/2025.04.08.644541

Category: Plant Biology

Model Organism: Triticum aestivum

AI Summary

The study investigates the altered timing of the core circadian oscillator gene ELF3 in wheat compared to Arabidopsis, revealing that dawn-specific expression in wheat arises from repression by TOC1. An optimized computational model integrating experimental expression data and promoter architecture predicts that wheat’s circadian oscillator remains robust despite this shift, indicating flexibility in plant circadian network design.

circadian rhythm ELF3 wheat TOC1 repression computational modeling

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

Category: Plant Biology

Model Organism: Triticum aestivum

AI Summary

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.

wheat semantic segmentation computer vision deep learning phenotyping

Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement

Authors: Zhang, Z., Ma, S., Yin, M., Zhao, C., Zhao, X., yu, Y., Wang, H., Li, X., Si, Y., Niu, J., Xie, J., Wang, L., Wu, J., Zhang, Y., Zheng, Q., Zheng, S., Jiang, N., Liu, X., Ling, H.-Q., He, F.

Date: 2025-02-23 · Version: 1
DOI: 10.1101/2025.02.18.638840

Category: Plant Biology

Model Organism: Triticum aestivum

AI Summary

RNA‑seq of 328 wheat lines using a pan‑genome reference uncovered over 20,000 additional transcripts beyond the Chinese Spring genome and enabled construction of a pan‑gene eQTL regulatory atlas. Multi‑omics integration identified 231 high‑confidence candidate genes influencing 34 agronomic traits and powdery mildew resistance, with functional validation showing 80% of candidates affecting trait phenotypes via an EMS mutant library.

gene expression variation pan‑genome eQTL analysis introgression wheat

Plant plasticity in the face of climate change - CO2 offsetting effects to warming and water deficit in wheat. A review.

Authors: Gawinowski, M., Chenu, K., Deswarte, J.-C., Launay, M., Bancal, M.-O.

Date: 2025-02-12 · Version: 1
DOI: 10.1101/2025.02.10.637370

Category: Plant Biology

Model Organism: Triticum aestivum

AI Summary

This review compiles experimental studies on wheat to assess how elevated CO₂, higher temperatures, and water deficit interact and affect productivity and water use. By calculating plasticity indices, the authors find that despite CO₂‑induced gains, overall yield generally declines under combined stress, while water consumption often decreases. They highlight the need for more data to improve and validate crop models under future climate scenarios.

elevated CO2 heat stress drought wheat plasticity indices
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