2022 Publications

Adams, S., & Carré, I. A. (2022). Chromatin Immunoprecipitation Protocol for Circadian Clock Proteins. In D. Staiger, S. Davis, & A. M. Davis (Eds.), Plant Circadian Networks (Vol. 2398, pp. 135–150). Springer US. https://doi.org/10.1007/978-1-0716-1912-4_12
Amos, B., Aurrecoechea, C., Barba, M., Barreto, A., Basenko, E. Y., Bażant, W., Belnap, R., Blevins, A. S., Böhme, U., Brestelli, J., Brunk, B. P., Caddick, M., Callan, D., Campbell, L., Christensen, M. B., Christophides, G. K., Crouch, K., Davis, K., DeBarry, J., … Zheng, J. (2022). VEuPathDB: The eukaryotic pathogen, vector and host bioinformatics resource center. Nucleic Acids Research, 50(D1), D898–D911. https://doi.org/10.1093/nar/gkab929
Amundson, K. K., Borton, M. A., Daly, R. A., Hoyt, D. W., Wong, A., Eder, E., Moore, J., Wunch, K., Wrighton, K. C., & Wilkins, M. J. (2022). Microbial colonization and persistence in deep fractured shales is guided by metabolic exchanges and viral predation. Microbiome, 10(1), 5. https://doi.org/10.1186/s40168-021-01194-8
Arras, P., Frank, P., Haim, P., Knollmüller, J., Leike, R., Reinecke, M., & Enßlin, T. (2022). Variable structures in M87* from space, time and frequency resolved interferometry. Nature Astronomy, 6(2), 259–269. https://doi.org/10.1038/s41550-021-01548-0
Beck, M. A., Liu, C.-Y., Bidinosti, C. P., Henry, C. J., Godee, C. M., & Ajmani, M. (2021). An extensive lab-and field-image dataset of crops and weeds for computer vision tasks in agriculture.
Boeckman, J., Korn, A., Yao, G., Ravindran, A., Gonzalez, C., & Gill, J. (2022). Sheep in wolves’ clothing: Temperate T7-like bacteriophages and the origins of the Autographiviridae. Virology, 568, 86–100. https://doi.org/10.1016/j.virol.2022.01.013
Chávez Montes, R. A., Haber, A., Pardo, J., Powell, R. F., Divisetty, U. K., Silva, A. T., Hernández-Hernández, T., Silveira, V., Tang, H., Lyons, E., Herrera Estrella, L. R., VanBuren, R., & Oliver, M. J. (2022). A comparative genomics examination of desiccation tolerance and sensitivity in two sister grass species. Proceedings of the National Academy of Sciences, 119(5), e2118886119. https://doi.org/10.1073/pnas.2118886119
Chiozzi, G., De Marchi, G., Fasola, M., Ibrahim, K. M., Bardelli, G., Hagos, F., Rocca, F., & Masseti, M. (2022). Insular gazelles of the circum-Arabian seas: Origin, distribution, dwarfism and taxonomy. Mammalian Biology, 102(1), 1–20. https://doi.org/10.1007/s42991-021-00186-3
Dykes, J., Kappenman, K., & Nissen, E. D. (2022). Using DNA Barcoding Methods to Identify Wild Huckleberry, Vaccinium membranaceum , as a Classroom Project. The American Biology Teacher, 84(1), 40–44. https://doi.org/10.1525/abt.2022.84.1.40
Fernandez-Pozo, N., Haas, F. B., Gould, S. B., & Rensing, S. A. (2022). An overview of bioinformatics, genomics and transcriptomics resources for bryophytes. Journal of Experimental Botany, erac052. https://doi.org/10.1093/jxb/erac052
Fitzgerald, C., Vens, C. S., Miller, N., Barker, R., Westphall, M., Lombardino, J., Miao, J., Swanson, S. J., & Gilroy, S. (2022). Using the Automated Botanical Contact Device (ABCD) to Deliver Reproducible, Intermittent Touch Stimulation to Plants. In E. B. Blancaflor (Ed.), Plant Gravitropism (Vol. 2368, pp. 81–94). Springer US. https://doi.org/10.1007/978-1-0716-1677-2_6
Gupta, S., & Rather, S. A. (2022). Innovations in Crop Production: An Amalgamation of Abiotic Stress Physiology and Technology. Plant Abiotic Stress Physiology: Volume 1: Responses and Adaptations, 1.
Heilman, K. A., Dietze, M. C., Arizpe, A. A., Aragon, J., Gray, A., Shaw, J. D., Finley, A. O., Klesse, S., DeRose, R. J., & Evans, M. E. K. (2022). Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty. Global Change Biology, gcb.16038. https://doi.org/10.1111/gcb.16038
Jhu, M.-Y., Farhi, M., Wang, L., Philbrook, R. N., Belcher, M. S., Nakayama, H., Zumstein, K. S., Rowland, S. D., Ron, M., Shih, P. M., & Sinha, N. R. (2022). Heinz-resistant tomato cultivars exhibit a lignin-based resistance to field dodder ( Cuscuta campestris ) parasitism. Plant Physiology, kiac024. https://doi.org/10.1093/plphys/kiac024
Martínez-Alvarez, L., Ramond, J.-B., Vikram, S., León-Sobrino, C., Maggs-Kölling, G., & Cowan, D. (2022). With a pinch of salt: Metagenomic insights into Namib Desert salt pan microbial mats and halites reveal functionally adapted and competitive communities [Preprint]. Microbiology. https://doi.org/10.1101/2022.02.18.481119
Martinson, J. W., Bencic, D. C., Toth, G. P., Kostich, M. S., Flick, R. W., See, M. J., Lattier, D., Biales, A. D., & Huang, W. (2022). De Novo Assembly of the Nearly Complete Fathead Minnow Reference Genome Reveals a Repetitive but Compact Genome. Environmental Toxicology and Chemistry, 41(2), 448–461. https://doi.org/10.1002/etc.5266
Morisse, M., Wells, D. M., Millet, E. J., Lillemo, M., Fahrner, S., Cellini, F., Lootens, P., Muller, O., Herrera, J. M., Bentley, A. R., & Janni, M. (2022). A European perspective on opportunities and demands for field-based crop phenotyping. Field Crops Research, 276, 108371. https://doi.org/10.1016/j.fcr.2021.108371
Nguyen, D. T., Hayes, J. E., Atieno, J., Li, Y., Baumann, U., Pattison, A., Bramley, H., Hobson, K., Roorkiwal, M., Varshney, R. K., Colmer, T. D., & Sutton, T. (2022). The genetics of vigour-related traits in chickpea (Cicer arietinum L.): Insights from genomic data. Theoretical and Applied Genetics, 135(1), 107–124. https://doi.org/10.1007/s00122-021-03954-4
Palos, K. (2022). Identifying Long Noncoding RNAs and Examining Their Functional Roles in Brassicaceae [PhD Thesis]. In ProQuest Dissertations and Theses. https://ezproxy.library.arizona.edu/login?url=https://www.proquest.com/dissertations-theses/identifying-long-noncoding-rnas-examining-their/docview/2626931596/se-2?accountid=8360
Rhoades, N. S., Davies, M., Lewis, S. A., Cinco, I. R., Kohama, S. G., Bermudez, L. E., Winthrop, K. L., Fuss, C., Mattison, J. A., Spindel, E. R., & Messaoudi, I. (2022). Functional, transcriptional, and microbial shifts associated with healthy pulmonary aging: Insights from rhesus macaques [Preprint]. Immunology. https://doi.org/10.1101/2022.02.08.479578
Sams, E. I., Ng, J. K., Tate, V., Claire Hou, Y.-C., Cao, Y., Antonacci-Fulton, L., Belhassan, K., Neidich, J., Mitra, R. D., Cole, F. S., Dickson, P., Milbrandt, J., & Turner, T. N. (2022). From karyotypes to precision genomics in 9p deletion and duplication syndromes. Human Genetics and Genomics Advances, 3(1), 100081. https://doi.org/10.1016/j.xhgg.2021.100081
Satapathy, K., Psaltis, D., Özel, F., Medeiros, L., Dougall, S. T., Chan, C.-K., Wielgus, M., Prather, B. S., Wong, G. N., Gammie, C. F., & others. (2022). The Variability of the Black Hole Image in M87 at the Dynamical Timescale. The Astrophysical Journal, 925(1), 13.
Senn, S., Pangell, K., & Bowerman, A. L. (2022). Metagenomic Insights into the Composition and Function of Microbes Associated with the Rootzone of Datura inoxia. BioTech, 11(1), 1. https://doi.org/10.3390/biotech11010001
Shang, J., & Sun, Y. (2022). CHERRY: A Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model. ArXiv:2201.01018 [Cs, q-Bio]. http://arxiv.org/abs/2201.01018
Spalding, E., Morzinski, K. M., Hinz, P., Males, J., Meyer, M., Quanz, S. P., Leisenring, J., & Power, J. (2022). High-contrast Imaging with Fizeau Interferometry: The Case of Altair*. The Astronomical Journal, 163(2), 62. https://doi.org/10.3847/1538-3881/ac3b5b
Tran, H., Zhang, J., O’Neill, M. M., Ryken, A., Condon, L. E., & Maxwell, R. M. (2022). A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019. Scientific Data, 9(1), 16. https://doi.org/10.1038/s41597-022-01123-w
Tuggle, C. K., Clarke, J., Dekkers, J. C. M., Ertl, D., Lawrence-Dill, C. J., Lyons, E., Murdoch, B. M., Scott, N. M., & Schnable, P. S. (2022). The Agricultural Genome to Phenome Initiative (AG2PI): Creating a shared vision across crop and livestock research communities. Genome Biology, 23(1), 3, s13059-021-02570–02571. https://doi.org/10.1186/s13059-021-02570-1
Wang, L., Lu, Z., Van Buren, P., & Ware, D. (2022). SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data. In D. Edwards (Ed.), Plant Bioinformatics (Vol. 2443, pp. 197–209). Springer US. https://doi.org/10.1007/978-1-0716-2067-0_10
Williams, J. (2022). CyVerse for Reproducible Research: RNA-Seq Analysis. In D. Edwards (Ed.), Plant Bioinformatics (Vol. 2443, pp. 57–79). Springer US. https://doi.org/10.1007/978-1-0716-2067-0_3
Wyman, C. L., Gontijo Silva Maia, L., Yang, L., & Benedito, V. A. (2022). The Model Legume, Medicago truncatula in the Genomic Era: Speeding Up Discoveries in Legume Biology. In S. Sinharoy, Y. Kang, & V. Benedito (Eds.), The Medicago truncatula Genome (pp. 1–9). Springer International Publishing. https://doi.org/10.1007/978-3-030-90757-0_1
Xu, G., Lyu, J., Obata, T., Liu, S., Ge, Y., Schnable, J. C., & Yang, J. (2022). A historically balanced locus under recent directional selection in responding to changed nitrogen conditions during modern maize breeding [Preprint]. Genomics. https://doi.org/10.1101/2022.02.09.479784
Zhang, X., Zhu, Y., Kremling, K. A. G., Romay, M. C., Bukowski, R., Sun, Q., Gao, S., Buckler, E. S., & Lu, F. (2022). Genome-wide analysis of deletions in maize population reveals abundant genetic diversity and functional impact. Theoretical and Applied Genetics, 135(1), 273–290. https://doi.org/10.1007/s00122-021-03965-1
Zhao, Y., Zhang, S., Lv, Y., Ning, F., Cao, Y., Liao, S., Wang, P., & Huang, S. (2022). Optimizing ear-plant height ratio to improve kernel number and lodging resistance in maize (Zea mays L.). Field Crops Research, 276, 108376. https://doi.org/10.1016/j.fcr.2021.108376