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Anderson-Cook, Christine M.

Publications and source records attributed to Anderson-Cook, Christine M..

Drought conditioning of rhizosphere microbiome influences maize water use traits

Background and Aims: Beneficial plant–microbe interactions can improve plant performance under drought; however, we know less about how drought-induced shifts in microbial communities affect plant traits. Methods: We cultivated Zea mays in fritted clay with soil microbiomes originating from contrasting environments (agriculture or forest) under two irrigation treatments (well-watered or water limited). Using this design, we investigated whether water conditioning was carried forward through the microbiome to affect a subsequent plant cohort that was subjected to either a well-watered or water limited treatment. Results: Regardless of the microbiome-origin, plants inoculated with a microbiome from a water limited legacy had traits that allowed them to avoid stress but conserve water. They produced longer roots to explore soil, generated greater soil dissolved organic carbon, potentially stimulating the microbiome, and slower soil water content loss during drought. A well-watered legacy resulted in plants that delayed permanent stomatal closure and higher photosynthetic nitrogen use efficiency. In plants with a forest-originated microbiome, a well-watered legacy and water treatment also resulted in higher rates of photosynthesis and stomatal conductance. Conclusion: These results demonstrate that soil microbiomes can be developed to influence plant drought performance, impacting crop resilience, using short-term microbial conditioning.

60 APPLIED LIFE SCIENCES↗

Data for training and testing radiation detection algorithms in an urban environment

The detection, identification, and localization of illicit nuclear materials in urban environments is of utmost importance for national security. Most often, the process of performing these operations consists of a team of trained individuals equipped with radiation detection devices that have built-in algorithms to alert the user to the presence nuclear material and, if possible, to identify the type of nuclear material present. To encourage the development of new detection, radioisotope identification, and source localization algorithms, a dataset consisting of realistic Monte Carlo-simulated radiation detection data from a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector moving through a simulated urban environment based on Knoxville, Tennessee, was developed and made public in the form of a Topcoder competition. The methodology used to create this dataset has been verified using experimental data collected at the Fort Indiantown Gap National Guard facility. Realistic signals from special nuclear material and industrial and medical sources are included in the data for developing and testing algorithms in a dynamic real-world background.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗