Modeling gamma radiation exposure rates using geologic and remote sensing data to locate radiogenic anomalies.

Aerial Gamma-Ray Surveys (GRS) are ideal for tracking anthropogenic gamma radiation releases and transport. The interpretation of a GRS can be complicated by natural gamma-ray sources such as atmospheric radon, cosmic rays, geologic materials, and even the survey equipment itself. Some of these complicating factors can be accounted for or corrected by calibration or mathematic techniques. Real-time algorithms that attempt to enhance potential radiogenic anomalies over background are also in use. However, natural geology is a source of significant background gamma-ray production and neither mathematical corrections nor real-time algorithmic approaches directly account for geology and geochemistry. In this study, we advance techniques to predict geologic background exposure rates using rapid and practical methods which can be achieved in the field. In addition we generate models that focus specifically on highlighting radiogenic anomalies for emergency response or further investigation. Predictive models developed in this study were generally able to predict background with medians of ± 1.0 µR/h compared to measured data, and were also able to highlight anomalous areas even where radiation exposure rates were within the range of natural background.

Modeling background radiation in Southern Nevada.

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials by creating a high resolution background model. The intention is for this method to be used in an emergency response scenario where the background radiation environment is unknown. Two study areas in Southern Nevada have been modeled using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas that are homogenous in terms of K, U, and Th, referred to as background radiation units, are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by the Department of Energy's Remote Sensing Lab - Nellis, allowing for the refinement of the technique. By using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide and define radiation background units within alluvium, successful models have been produced for Government Wash, north of Lake Mead, and for the western shore of Lake Mohave, east of Searchlight, NV.

Monte Carlo simulations of the gamma-ray exposure rates of common rocks.

Monte Carlo simulations have been performed to model the gamma ray emission and attenuation properties of common rocks. In geologic materials, 40K, 238U, and 232Th are responsible for most gamma ray production. If the concentration of these radioelements and attenuation factors such as degree of water saturation are known, an estimate of the gamma-ray exposure rate can be made. The results show that there are no significant differences in gamma-ray screening between major rock types. If the total number of radionuclide atoms are held constant then the major controlling factor is density of the rock. Finally, the thickness of regolith or soil overlying rock can be estimated by modeling the exposure rate if the radionuclide contents of both materials are known.

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona.

This study compares high resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (National Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.

Rational design of protein-based MRI contrast agents.

We describe the rational design of a novel class of magnetic resonance imaging (MRI) contrast agents with engineered proteins (CAi.CD2, i = 1, 2,..., 9) chelated with gadolinium. The design of protein-based contrast agents involves creating high-coordination Gd(3+) binding sites in a stable host protein using amino acid residues and water molecules as metal coordinating ligands. Designed proteins show strong selectivity for Gd(3+) over physiological metal ions such as Ca(2+), Zn(2+), and Mg(2+). These agents exhibit a 20-fold increase in longitudinal and transverse relaxation rate values over the conventional small-molecule contrast agents, e.g., Gd-DTPA (diethylene triamine pentaacetic acid), used clinically. Furthermore, they exhibit much stronger contrast enhancement and much longer blood retention time than Gd-DTPA in mice. With good biocompatibility and potential functionalities, these protein contrast agents may be used as molecular imaging probes to target disease markers, thereby extending applications of MRI.

Rational design of a calcium-binding protein.

Calcium ions play key roles as structural components in biomineralization and as a second messenger in signaling pathways. We have introduced a de novo designed calcium-binding site into the framework of a non-calcium-binding protein, domain 1 of CD2. The resulting protein selectively binds calcium over magnesium with calcium-binding affinity comparable to that of natural extracellular calcium-binding proteins (K(d) of 50 microM). This experiment is the first successful metalloprotein design that has a high coordination number (seven) metal-binding site constructed into a beta-sheet protein. Our results demonstrate the feasibility of designing a single calcium-binding site into a host protein, taking into account only local properties of a calcium-binding site obtained by a survey of natural calcium-binding proteins and chelators. The resulting site exhibits strong metal selectivity, suggesting that it should now be feasible to understand and manipulate signaling processes by designing novel calcium-modulated proteins with specifically desired functions and to affect their stability.