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The following are publications made possible by ARCC resources. Any UW faculty that would like to highlight the research that has benefited from ARCC resources are invited to contact us.
2023
Parallel shifts in trout feeding morphology suggest rapid adaptation to alpine lake environments. (2023) Combrink, L. L., Rosenthal, W. C., Boyle, L. J., Rick, J. A., Mandeville, E. G., Krist, A. C., Walters, A. W., Wagner, C. E. UBC Research Data doi:http://dx.doi.org/10.14288/1.0434251
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2022
A Bayesian Analysis of Physical Parameters for 783 Kepler Close Binaries: Extreme-mass-ratio Systems and a New Mass Ratio versus Period Lower Limit. (2022) Kobulnicky, H. A., Molnar, L. A., Cook, E. M., Henderson, L. E., American Astronomical Society (262)132 doi: 10.3847/1538-4365
A continental-scale survey of Wolbachia infections in blue butterflies reveals evidence of interspecific transfer and invasion dynamics. Shastry, V., Bell K. L., Burkle, C. A., Fordyce, J. A., Forister, M. A., Gompert, Z., Lebeis, S. L., Lucas, L. K., Marion, Z. H., Nice, C. C.
OpenML-CTR23 – A curated tabular regression benchmarking suite. (2022) Fischer, S., Harutyunyan, L., Feurer, M., Bischl, B.,
The Genetic Population Structure of Lake Tanganyika’s Lates Species Flock, an Endemic Radiation of Pelagic Top Predators. Rick, J.A., Junker, J., Kimirei, I. A., Sweke, E. A., Mosille, J. B., Dinkel C., Mwaiko, S., Seehausen, O., Wagner, C. E., (2022). Journal of Heredity 113(2) 45–159, https://doi.org/10.1093/jhered/esab072
Super-suppression of long phonon mean-free-paths in nano-engineered Si due to heat current anticorrelations. (2022) Hosseini., S. A., Davies, A., Dickey, I., Neophytou, N., Greaney, P. A., de Sousa Oliveira, L. Materials Today Physics., 27(2542-4293). https://doi.org/10.1016/j.mtphys.2022.100719
Trehalose and tardigrade CAHS proteins work synergistically to promote desiccation tolerance. (2022) Nguyen, K., Shraddha, K. C., Gonzales, T., Tapia, H., Boothby, T. C.
Communications Biology, 5: 1046. doi.org/10.1038%2Fs42003-022-04015-2
Whole-Genome Duplication and Host Genotype Affect Rhizosphere Microbial Communities. (2022) Ponsford, J. C., Hubbard, C. J., Harrison, J. G., Maignien, L., Burkle, C. A., Weinig, C. (2022) American Society for Microbiology mSystems, 7(1), https://doi.org/10.1128/msystems.00973-21
Understanding the drivers of dispersal evolution in range expansions and their ecological consequences. (2022) Weiss-Lehman, C., Shaw, A. K., Evol Ecol 36, 181-197 https://doi.org/10.1007/s10682-022-10166-9
Multi-population puma connectivity could restore genomic diversity to at-risk costal populations in California. (2022) Gustafson, K. D., Gagne, R. B., Buchalski, M. R., Vickers, T. W., Riley, S. P., Sikich, J. A., Rudd, J. L., Dellinger, J. A., LaCava, M. E., Ernest, H. B. Evolutionary Applications, 15.2, 286-299. https://doi.org/10.1111/eva.13341
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2021
Linking functional traits and demography to model species-rich communities. (2021)Chalmandrier, L., Hartig, F., Laughlin, D.C. et al. Nat Commun 12, 2724 https://doi.org/10.1038/s41467-021-22630-1
Ecological outcomes of hybridization vary extensively in Catostomus fishes. Mandeville, E. G., Hall, R. O., & Buerkle, C. A. (2021). BioRxiv, 2021.01.20.427472. https://doi.org/10.1101/2021.01.20.427472
Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy. Shastry, V., Adams, P. E., Lindtke, D., Mandeville, E. G., Parchman, T. L., Gompert, Z., & Buerkle, C. A. (2021). Molecular Ecology Resources, 21(5), 1434–1451. https://doi.org/10.1111/1755-0998.13330
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ARRU Phase Picker: Attention Recurrent‐Residual U‐Net for Picking Seismic P‐ and S‐Phase Arrivals. Liao, W., Lee, E., Mu, D., Chen, P., & Rau, R. (2021). Seismological Research Letters, 92(4), 2410–2428. https://doi.org/10.1785/0220200382
Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel Execution, Haniye Kashgarani, and Lars Kotthoff (University of Wyoming): AAAI 2021 Workshop on Meta-Learning
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Rapid synchronized fabrication of vascularized thermosets and composites. Garg, M., Aw, J.E., Zhang, X. et al. Nat Commun 12, 2836 (2021). https://doi.org/10.1038/s41467-021-23054-7
A suite of rare microbes interacts with a dominant, heritable, fungal endophyte to influence plant trait expression. Harrison, J. G., Beltran, L. P., Buerkle, C. A., Cook, D., Gardner, D. R., Parchman, T. L., Poulson, S. R., & Forister, M. L. (2021). BioRxiv, 608729. https://doi.org/10.1101/608729
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The Role of European Starlings (Sturnus vulgaris) in the Dissemination of Multidrug-Resistant Escherichia coli among Concentrated Animal Feeding Operations. Chandler, J.C., Anders, J.E., Blouin, N.A. et al. Sci Rep 10, 8093 (2020). https://doi.org/10.1038/s41598-020-64544-w
Accuracy of de novo assembly of DNA sequences from double-digest libraries varies substantially among software. LaCava, M. E. F., Aikens, E. O., Megna, L. C., Randolph, G., Hubbard, C., & Buerkle, C. A. (2020). Molecular Ecology Resources, 20(2), 360–370. https://doi.org/10.1111/1755-0998.13108https://doi.org/10.1016/j.poly.2020.114461
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Evidence for the amnion-fetal gut-microbial axis in late gestation beef calves. Hummel, G. L., Woodruff, K. L., Austin, K. J., Smith, T. L., & Cunningham-Hollinger, H. C. (2020). Translational Animal Science, 4(Supplement_1), S174–S177. https://doi.org/10.1093/tas/txaa138
Influence of the maternal rumen microbiome on development of the calf meconium and rumen microbiome. Woodruff, K. L., Hummel, G. L., Austin, K. J., Smith, T. L., & Cunningham-Hollinger, H. C. (2020). Translational Animal Science, 4(Supplement_1), S169–S173. https://doi.org/10.1093/tas/txaa136
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Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model. Hu, X., & Douglas, C. C. (2020). In V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, & J. Teixeira (Eds.), Computational Science – ICCS 2020 (pp. 72–85). Springer International Publishing. https://doi.org/10.1007/978-3-030-50433-5_6
A Terminal Rh Methylidene from Activation of CH2Cl2. Morrow, T. J., Gipper, J. R., Christman, W. E., Arulsamy, N., & Hulley, E. B. (2020).Organometallics, 39(13), 2356–2364. https://doi.org/10.1021/acs.organomet.0c00031
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Hover Predictions Using a High-Order Discontinuous Galerkin Off-Body Discretization. Kara, K., Brazell, M. J., Kirby, A. C., Mavriplis, D. J., & Duque, E. P. (2020). In AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-0771
Sensitivity Analysis for Aero-Thermo-Elastic Problems Using the Discrete Adjoint Approach. Kamali, S., Mavriplis, D. J., & Anderson, E. M. (n.d.). In AIAA AVIATION 2020 FORUM. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-3138
Advances in the Pseudo-Time Accurate Formulation of the Adjoint and Tangent Systems for Sensitivity Computation and Design. Padway, E., & Mavriplis, D. J. (2020). In AIAA AVIATION 2020 FORUM. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-3136https://doi.org/10.2514/6.2020-3138
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Development and Validation of a High-Fidelity Aero-Thermo-Elastic Analysis Capability. Kamali, S., Mavriplis, D. J., & Anderson, E. M. (2020). In AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-1449
An implicit block ILU smoother for preconditioning of Newton–Krylov solvers with application in high-order stabilized finite-element methods. Ahrabi, B. R., & Mavriplis, D. J. (2020). Computer Methods in Applied Mechanics and Engineering, 358, 112637. https://doi.org/10.1016/j.cma.2019.112637
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Novel hybrid finds a peri-urban niche: Allen’s Hummingbirds in southern California. Godwin, B. L., LaCava, M. E. F., Mendelsohn, B., Gagne, R. B., Gustafson, K. D., Love Stowell, S. M., Engilis, A., Tell, L. A., & Ernest, H. B. (2020). Conservation Genetics, 21(6), 989–998. https://doi.org/10.1007/s10592-020-01303-4
Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys. Arora, G., & Aidhy, D. S. (2020). Metals, 10(8), 1072. https://doi.org/10.3390/met10081072
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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018). Proceedings of the National Academy of Sciences, 115(25), E5716–E5725. https://doi.org/10.1073/pnas.1719367115
Machine learning to classify animal species in camera trap images: Applications in ecology. Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., Vercauteren, K. C., Snow, N. P., Halseth, J. M., Salvo, P. A. D., Lewis, J. S., White, M. D., Teton, B., Beasley, J. C., Schlichting, P. E., Boughton, R. K., Wight, B., Newkirk, E. S., Ivan, J. S., Odell, E. A., Brook, R. K., … Miller, R. S. (2019). Methods in Ecology and Evolution, 10(4), 585–590. https://doi.org/10.1111/2041-210X.13120
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Influence of maternal factors on the rumen microbiome and subsequent host performance. Cunningham, H. C., Austin, K. J., & Cammack, K. M. (2018). I Translational Animal Science, 2(suppl_1), S101–S105. https://doi.org/10.1093/tas/txy058
Mode of delivery influence on the early calf rumen microbiome. Cunningham, H. C., K. J. Austin, K. T. Carpenter, S. R. Powell, and K. M. Cammack. (2018) Accepted. (Abstr.) Poster presented at Rowett-INRA Gut Microbiology: No longer the forgotten organ. June, 2018. Aberdeen, Scotland.
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The plant circadian clock influences rhizosphere community structure and function. Hubbard, C. J., Brock, M. T., van Diepen, L. T., Maignien, L., Ewers, B. E., & Weinig, C. (2018). The ISME Journal, 12(2), 400–410. https://doi.org/10.1038/ismej.2017.172
Rhizosphere microbes and host plant genotype influence the plant metabolome and reduce insect herbivory. Hubbard, C. J., Li, B., McMinn, R., Brock, M. T., Maignien, L., Ewers, B. E., Kliebenstein, D., & Weinig, C. (2018). BioRxiv, 297556. https://doi.org/10.1101/297556
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Classical interatomic potential for quaternary Ni–Fe–Cr–Pd solid solution alloys. Bonny, G., Chakraborty, D., Pandey, S., Manzoor, A., Castin, N., Phillpot, S. R., & Aidhy, D. S. (2018). Modelling and Simulation in Materials Science and Engineering, 26(6), 065014. https://doi.org/10.1088/1361-651X/aad2e7
Effect of atomic order/disorder on vacancy clustering in concentrated NiFe alloys. Chakraborty, D., Harms, A., Ullah, M. W., Weber, W. J., & Aidhy, D. S. (2018). Computational Materials Science, 147, 194–203. https://doi.org/10.1016/j.commatsci.2018.02.011
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