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Radionuclide monitoring for nuclear explosions includes measuring radioactive aerosol and noble gas concentrations in the atmosphere. The International Monitoring System (IMS) of the Comprehensive Nuclear Test-Ban Treaty has made such measurements for decades, revealing much about how atmospheric radioactivity impacts the sensitivity of the network. For example, civilian emissions of radioiodine make a substantial regional impact, but a minor global impact, while civilian radioxenon emissions create major regional and complex global impacts. The impacts are strongly influenced by the minimum releases anticipated to be interesting. The original design of the IMS anticipated relatively large releases, and the current IMS network substantially meets or exceeds the sensitivity needed to detect those levels. Much lower signal levels can be motivated from historical tests. Using a release that corresponds roughly to a one-ton equivalent of fission in the atmosphere rather than the design level of one-kiloton equivalent, the network detection probabilities for 140Ba and 131I are quite good (~ 75%) and for 133Xe is still considerable (~ 45%). Using measured and simulated background concentrations, various possible desired signal levels, and an innovative anomaly threshold, maps of sensitivity and a station ranking are developed for IMS radionuclide stations. These provide a strong motivation for additional experimentation to learn about sources and the potential plusses of new technology.

In the early years of nuclear explosion monitoring, experts used downwind detections with meaningful ratios of radioactive species to identify an explosion. Today’s reality is sparse networks of radionuclide monitoring stations looking for weak signals. Analysts need to discriminate between industrial background radioactivity and nuclear explosion signals, even using the detection of one isotope. Aerosol and xenon measurements potentially related to nuclear tests in 2006 and 2013 announced by the Democratic People’s Republic of Korea and from worldwide civilian background radioactivity are considered when defining radionuclide detection anomalies to objectively guide the use of limited analyst resources and reduce the possibility of not detecting nuclear explosions.

The Groundwater Protection Project at the US Department of Energy Hanford Site in Washington State is currently developing the means to assess the cumulative impact to human and ecological health and the regional economy and cultures from radioactive and chemical waste that will remain at the Hanford Site after the site closes. This integrated system is known as the System Assessment Capability (SAC). The SAC Risk/Impact Module discussed in the article uses media- and time-specific concentrations of contaminants estimated by the transport models of the integrated system to project potential impacts on the ecology of the Columbia River corridor, the health of persons who might live in or use the corridor or the upland Hanford environment, the local economy, and cultural resources. Preliminary Monte Carlo realizations from the SAC modeling system demonstrate the feasibility of large-scale uncertainty analysis of the complex relationships in the environmental transport of contaminants on the one hand and ecological, human, cultural, and economic risk on the other. Initial impact results show very small long-term risks for the 10 radionuclides and chemicals evaluated. The analysis also helps determine science priorities to reduce uncertainty and suggests what actions matter to reduce risks.

This study examined the impacts of reservoir properties on carbon dioxide (CO₂) migration after subsurface injection and evaluated the possibility of characterizing reservoir properties using CO₂ monitoring data such as spatial–temporal distributions of gas pressure, which can be reasonably monitored in practice. The injection reservoir was assumed to be located 1,400–1,500 m below the ground surface such that CO₂ remained in the supercritical state. The reservoir was assumed to contain layers with alternating conductive and resistive properties, which is analogous to actual geological formations such as the Mount Simon Sandstone unit. The CO₂ injection simulation used a cylindrical grid setting in which the injection well was situated at the center of the domain, which extended out 8,000 m from the injection well. The CO₂ migration was simulated using the latest version of the simulator, subsurface transport over multiple phases (the water–salt–CO₂–energy module), developed by Pacific Northwest National Laboratory. A nonlinear parameter estimation and optimization modeling software package, Parameter ESTimation (PEST), is adopted for automated reservoir parameter estimation. The effects of data quality, data worth, and data redundancy were explored regarding the detectability of reservoir parameters using gas pressure monitoring data, by comparing PEST inversion results using data with different levels of noises, various numbers of monitoring wells and locations, and different data collection spacing and temporal sampling intervals. This study yielded insight into the use of CO₂ monitoring data for reservoir characterization and how to design the monitoring system to optimize data worth and reduce data redundancy. The feasibility of using CO₂ saturation data for improving reservoir characterization was also discussed.

Cortical and subcortical gray matter (GM) atrophy may progress differently during the course of Parkinson’s disease (PD). We delineated and compared the longitudinal pattern of these PD-related changes. Structural MRIs and clinical measures were obtained from 76 PD with different disease durations and 70 Controls at baseline, 18-, and 36 months. Both cortical and subcortical (putamen, caudate, and globus pallidus) GM volumes were obtained, compared, and associated with PD clinical measures at baseline. Their volumes and rates of change also were compared among Controls, PDs, and PD subgroups based on duration of illness [≤1 year (PD E ), 1–5 years (PD M ), and >5 years (PD L )]. Compared to Controls, PD subjects displayed smaller cortical GM and striatal (putamen, caudate, ps ≤0.001), volumes at baseline. Cortical GM volumes were negatively associated with disease duration at baseline, whereas striatal volumes were not. PD subjects demonstrated accelerated volume loss in cortical GM ( p  = 0.006), putamen ( p  = 0.034), and caudate ( p  = 0.008) compared to Controls. Subgroup analyses demonstrated that accelerated cortical atrophy reached statistical significance in PD subjects with duration of illness 1–5 years (PD M , p s <0.001) and the trend of accelerated atrophy seemed to persist until later stages, whereas striatal atrophy occurred in PD subjects with PD E ( p  = 0.021 for putamen, p  = 0.005 for caudate) and PD M ( p  = 0.002 for putamen, p  = 0.001 for caudate) that significantly slowed down in PD L (ps for PD L vs PD E or PD M : <0.01). The pattern of GM loss in PD differs in cortical and subcortical regions, with striatal atrophy occurring earlier and extra-striatal cortical atrophy later.

The goal of this study was to address the need for comprehensive reference data regarding maturational and aging effects on regional transverse relaxation rates (R(2)) of the brain in normal humans. Regional R(2)s were measured in twenty-five brain structures from a sample of seventy-seven normal volunteers 9 to 85 years of age. The relationships between regional R(2) and age were determined using generalized additive models, without the constraint of a specified a priori model. Data analysis demonstrated that the brain tissue R(2)-age correlations followed various time courses with both linear and non-linear characteristics depending on the particular brain structure. Most anatomical structures studied exhibited non-linear characteristics, including the amygdala, hippocampus, thalamus, globus pallidus, putamen, caudate nucleus, red nucleus, substantia nigra, orbitofrontal white matter and temporal white matter. Linear trends were detected in occipital white matter and in the genu of corpus callosum. These results indicate the complexity of age-related R(2) changes in the brain while providing normative reference data that can be utilized in clinical examinations and studies utilizing quantitative transverse relaxation.

Background It is challenging for current statistical models to predict clinical progression of Parkinson’s disease (PD) because of the involvement of multi-domains and longitudinal data. Methods Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. Results First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Conclusions Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. Trial registration The data was obtained from Dr. Xuemei Huang’s NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).