Keywords: paralog processing, CO2 enrichment, CO2-fixing enzyme, computer vision, SNP markers

Latest 10 Papers

Automated detection and quantification of two-spotted spider mite life stages using computer vision for high-throughput in vitro assays

Authors: Wiseman, M. S., Woods, J. L., Hartgrave, C. R., Richardson, B. J., Gent, D. H.

Date: 2025-12-10 · Version: 2

DOI: 10.1101/2025.09.14.676116

Model Organism: Tetranychus urticae

Tetranychus urticae high‑throughput phenotyping computer vision YOLO11 object detection resistance breeding

Rubisco Dark Inhibition in Angiosperms Shows a Complex Distribution Pattern

Authors: Nehls-Ramos, C., Carmo-Silva, E., Orr, D. J.

Date: 2025-11-20 · Version: 1

DOI: 10.1101/2025.11.20.689527

Model Organism: Multi-species

Rubisco dark inhibition flowering plants phylogenetic analysis photosynthetic regulation CO2-fixing enzyme

Leveraging Crop Wild Relatives and Ploidy Level for Climate-Resilient Annual Ryegrass

Authors: Mailhos, M. E., Timmers, J., Erickson, J., Caram, N., Messina, C. D., Rios, E. F.

Date: 2025-10-18 · Version: 1

DOI: 10.1101/2025.10.15.682691

Model Organism: Lolium multiflorum

polyploidy climate change Lolium multiflorum CO2 enrichment evapotranspiration

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

Authors: Moore-Pollard, E. R., Ellestad, P., Mandel, J.

Date: 2025-10-13 · Version: 2

DOI: 10.1101/2025.09.14.676143

Model Organism: Multi-species

phylogenomic discordance polyploidy paralog processing Packera historical biogeography

Genomic prediction of disease resistance provides a path to marker assisted restoration in a wetland foundation tree species

Authors: Guo, K., Ahrens, C. W., Chen, S. H., Sandhu, K., Rossetto, M., Jones, A., Tan, C., Borevitz, J. O., Edwards, R. J., Bragg, J. G.

Date: 2025-09-17 · Version: 1

DOI: 10.1101/2025.09.14.675737

Model Organism: Melaleuca quinquenervia

myrtle rust Melaleuca quinquenervia Genome Wide Association Study disease resistance SNP markers

Predicting flowering time using integrated morphophysiological and genomic data with machine learning models

Authors: Babaei, M., Nemati, H., Arouiee, H., Torkamaneh, D.

Date: 2025-08-13 · Version: 1

DOI: 10.1101/2025.08.11.669697

Model Organism: Cannabis sativa

flowering time Cannabis sativa genotyping-by-sequencing machine learning SNP markers

Whole genome sequencing-based multi-locus association mapping for kernel iron, zinc and protein content in groundnut

Authors: Sagar, U. N., Parmar, S., Gangurde, S. S., Sharma, V., Pandey, A. K., Mohinuddin, D. K., Dube, N., Bhat, R. S., John, K., Sreevalli, M. D., Rani, P. S., Singh, K., Varshney, R. K., Pandey, M. K.

Date: 2025-08-05 · Version: 1

DOI: 10.1101/2025.08.04.668427

Model Organism: Arachis hypogaea

groundnut nutritional quality GWAS SNP markers KASP

Evaporative cooling signals for wound healing in plants

Authors: Balem, J. M., Tan, C., Dias, N. C. F., Arnold, M. L., Tran, S., Severns, P. M., Teixeira, P. J. P. L., Li, C., Yang, L.

Date: 2025-05-28 · Version: 1

DOI: 10.1101/2025.05.23.655667

Model Organism: Arabidopsis thaliana

wound healing Arabidopsis thaliana cold-responsive genes CBF transcription factors computer vision

Omics-driven Identification of Candidate Genes and SNP markers in a Major QTL Controlling Early Heading in Rice

Authors: Rao, D., T, N. S., CG, G., Gaur, N., Jamaloddin, M., Maganti, S. M., Raman, M. S., Patel, H. K., Tiwari, S., Sonti, R. V.

Date: 2025-03-27 · Version: 1

DOI: 10.1101/2025.03.25.645207

Model Organism: Oryza sativa

heading date rice (Oryza sativa)QTL-seq SNP markers transcriptomics

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