MPB_2024v15n1

Molecular Plant Breeding 2024, Vol.15, No.1, 27-33 http://genbreedpublisher.com/index.php/mpb 32 References Adem M., Feyissa T., Beyene D., and Journals S., 2017, Application of precise genome editing in plants, Journal of Plant Sciences, 2: 1-9. https://doi.org/10.25177/JPS.2.1.2 Alexander W., 2018, A history of genome editing in Saccharomyces cerevisiae, Yeast, 35: 355-360. https://doi.org/10.1002/yea.3300 PMid:29247562 Ashapkin V., Kutueva L., Aleksandrushkina N., and Vanyushin B., 2020, Epigenetic mechanisms of plant adaptation to biotic and abiotic stresses, International Journal of Molecular Sciences, 21(20): 7457. https://doi.org/10.3390/ijms21207457 PMid:33050358 PMCid:PMC7589735 Chen Q.M., Zheng B.Y., Chenu K., Hu P.C., and Chapman S.C., 2022, Unsupervised plot-scale LAI phenotyping via UAV-based imaging, modelling, and machine learning, Plant Phenomics, 4(1): 196-214. https://doi.org/10.34133/2022/9768253 PMid:35935677 PMCid:PMC9317541 Dutta S., Jiang J., Ghosh S., Patel S., Bhikadiya C., Lowe R., Voigt M., Goodsell D., Zardecki C., and Burley S., 2022, An idea to explore: how an interdisciplinary undergraduate course exploring a global health challenge in molecular detail enabled science communication and collaboration in diverse audiences, Biochemistry and Molecular Biology Education, 51(2): 137-145. https://doi.org/10.1002/bmb.21699 PMid:36495283 Harfouche A., Jacobson D., Kainer D., Romero J., Harfouche A., Mugnozza G., Moshelion M., Tuskan G., Keurentjes J., and Altman A., 2019, Accelerating climate resilient plant breeding by applying next-generation artificial intelligence, Trends in Biotechnology, Trends in Biotechnology, 37(11): 1217-1235. https://doi.org/10.1016/j.tibtech.2019.05.007 PMid:31235329 Heinemann A., Matta D., Fernandes I., Fritsche‐Neto R., and Costa-Neto G., 2022, Enviromic prediction is useful to define the limits of climate adaptation: a case study of common beans in Brazi, 286: 108628. https://doi.org/10.1016/j.fcr.2022.108628 Jamil I., Remali J., Azizan K., Muhammad N., Arita M., Goh H., and Aizat W., 2020, Systematic multi-omics integration (MOI) approach in plant systems biology, Frontiers in Plant Science, 11: 944. https://doi.org/10.3389/fpls.2020.00944 PMid:32754171 PMCid:PMC7371031 Juma B., Mweu C., Piero M., and Mbinda W., 2021, CRISPR/Cas genome editing: a frontier for transforming precision cassava breeding, African Journal of Biotechnology, 20: 237-250. https://doi.org/10.5897/AJB2021.17344 Kashyap S., Agarwala N., and Sunkar R., 2023, Understanding plant stress memory traits can provide a way for sustainable agriculture, Plant Sci., 340: 111954. https://doi.org/10.1016/j.plantsci.2023.111954 PMid:38092267 Khan M., Wang S., Wang J., Ahmar S., Saeed S., Khan S., Xu X., Chen H., Bhat J., and Feng X., 2022, Applications of artificial intelligence in climate-resilient smart-crop breeding, International Journal of Molecular Sciences, 23(19): 11156. https://doi.org/10.3390/ijms231911156 PMid:36232455 PMCid:PMC9570104 Kim K., Kang Y., and Kim C., 2020, Application of genomic big data in plant breeding: past, present, and future, Plants, 9(11): 1454. https://doi.org/10.3390/plants9111454 PMid:33126607 PMCid:PMC7694055 Kuriakose S., Pushker R., and Hyde E., 2020, Data-driven decisions for accelerated plant breeding, Cereal Crops, 1: 89-119. https://doi.org/10.1007/978-3-030-41866-3_4 Lee S., Lee H., Lee C., Ha S., Park H., Lee S., Kwon Y., Jeung J., and Mo Y., 2023, Multi-environment trials and stability analysis for yield-related traits of commercial rice cultivars, Agriculture, 13(2): 256. https://doi.org/10.3390/agriculture13020256 Lucic B., Wegner J., Stanic M., Jost K., and Lusic M., 2021, 3D Immuno-DNA fluorescence in situ hybridization (FISH) for detection of HIV-1 and cellular genes in primary CD4+ T Cells, Methods in Molecular Biology, 2157: 239-249. https://doi.org/10.1007/978-1-0716-0664-3_14 PMid:32820408 Mao C., Lee M., Jhan J., Halpern A., Woodworth M., Glaser A., Chozinski T., Shin L., Pippin J., Shankland S., Liu J., and Vaughan J., 2020, Feature-rich covalent stains for super-resolution and cleared tissue fluorescence microscopy, Science Advances, 6(22): eaba4542. https://doi.org/10.1126/sciadv.aba4542 PMid:32518827 PMCid:PMC7253160

RkJQdWJsaXNoZXIy MjQ4ODYzMg==