MPB_2024v15n1

Molecular Plant Breeding 2024, Vol.15, No.1, 27-33 http://genbreedpublisher.com/index.php/mpb 33 Miryeganeh M., 2021, Plants’ epigenetic mechanisms and abiotic stress, Genes, 12(8): 1106. https://doi.org/10.3390/genes12081106 PMid:34440280 PMCid:PMC8394019 Sharma M., Kumar P., Verma V., Sharma R., Bhargava B., and Irfan M., 2022, Understanding plant stress memory response for abiotic stress resilience: molecular insights and prospects, Plant Physiol. Biochem., 179: 10-24. https://doi.org/10.1016/j.plaphy.2022.03.004 PMid:35305363 Singh R., and Prasad M., 2021, Big genomic data analysis leads to more accurate trait prediction in hybrid breeding for yield enhancement in crop plants, Plant Cell Reports, 40: 2009-2011. https://doi.org/10.1007/s00299-021-02761-x PMid:34309724 Souza M., Koo-Oshima S., Kahil T., Wada Y., Qadir M., Jewitt G., Cudennec C., Uhlenbrook S., and Zhang L., 2021, Food and agriculture, The United Nations World Water Development Report. Stack S., Shearer L., Lohmiller L., and Anderson L., 2020, Preparing maize synaptonemal complex spreads and sequential immunofluorescence and fluorescence in situ hybridization, Methods in Molecular Biology, 2061: 79-115. https://doi.org/10.1007/978-1-4939-9818-0_8 PMid:31583655 Teressa T., Semahegn Z., and Bejiga T., 2021, Multi environments and genetic-environmental interaction (GxE) in plant breeding and its challenges: a review article, International Journal of Research Studies in Agricultural Sciences, 7(4): 11-18. https://doi.org/10.20431/2454-6224.0704002 Vu T.V., Doan D.T.H., Kim J., Sung Y.W., Tran M.T., Song Y.J., Das S., and Kim J.Y., 2021, CRISPR/Cas‐based precision genome editing via microhomology‐mediated end joining, Plant Biotechnology Journal, 19(2): 230-239. https://doi.org/10.1111/pbi.13490 PMid:33047464 PMCid:PMC7868975 Wallace J., Rodgers-Melnick E., and Buckler E., 2018, On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics, Annual Review of Genetics, 52: 421-444. https://doi.org/10.1146/annurev-genet-120116-024846 PMid:30285496 Wang Z., Jiang Y., Liu Z., Tang X., and Li H., 2022, Machine learning and ensemble learning for transcriptome data: principles and advances, 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), pp.676-683. https://doi.org/10.1109/AEMCSE55572.2022.00137 Wen G., Vanheusden M., Leen V., Rohand T., Vandereyken K., Voet T., and Hofkens J., 2021, A universal labeling strategy for nucleic acids in expansion microscopy, Journal of the American Chemical Society, 143(34): 13782-13789. https://doi.org/10.1021/jacs.1c05931 PMid:34424689 Weyler J., Milioto A., Falck T., Behley J., and Stachniss C., 2021, Joint plant instance detection and leaf count estimation for in-field plant phenotyping, IEEE Robotics and Automation Letters, 6: 3599-3606. https://doi.org/10.1109/LRA.2021.3060712 Yang Y., Saand M., Huang L., Abdelaal W., Zhang J., Wu Y., Li J., Sirohi M., and Wang F., 2021, Applications of multi-omics technologies for crop improvement, Frontiers in Plant Science, 12: 563953. https://doi.org/10.3389/fpls.2021.563953 PMid:34539683 PMCid:PMC8446515

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