Cost of fear and radiation protection actions: Washington County, Utah and Fukushima, Japan {Comparing case histories}.

Purpose: The purpose of this manuscript is to evaluate the role of regulatory limits and regulatory action on the total impact of nuclear contamination and accidents. While it is important to protect the public from excessive radiation exposures it is also critical to weigh the damage done by implementing regulations against the benefits produced. Two cases: Actions taken as a result of radioactive fallout in Washington County, Utah in 1953 from the atomic bomb testing in Nevada, and the actions implemented post release of radioactive materials into the environment from the damaged nuclear power reactor at Fukushima, Japan, are compared.Materials and methods: The Washington County radiation exposures and doses, resulting from the Nevada nuclear weapons tests, were taken from published reports, papers, and historical records. The protective actions taken were reviewed and reported. Recent publications were used to define the doses following Fukushima. The impact and/or results of sheltering only versus sheltering/evacuation of Washington County and Fukushima are compared.Results: The radiation dose from the fallout in Washington County from the fallout was almost 2-3 three times the dose in Japan, but the regulatory actions were vastly different. In Utah, the minimal action taken, e.g. sheltering in place, had no major impact on the public health or on the economy. The actions in Fukushima resulted in major negative impact precipitated through the fear generated. And the evacuation. The results had adverse human health and wellness consequences and a serious impact on the economy of the Fukushima region, and all of Japan.Conclusions: When evacuation is being considered, great care must be taken when any regulatory actions are initiated based on radiation limits. It is necessary to consider total impact and optimize the actions to limit radiation exposure while minimizing the social, economic, and health impacts. Optimization can help ensure that the protective measures result in more good than harm. It seems clear that organizations who recommend radiation protection guidelines need to revisit the past and current guides in light of the significant Fukushima experience.

Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

BACKGROUND: Better understanding and prediction of progression of Parkinson's disease could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in Parkinson's disease. We also sought to assess the use of these models in the design of treatment trials in Parkinson's disease. METHODS: A Bayesian multivariate predictive inference platform was applied to data from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from patients with Parkinson's disease and healthy controls to construct an ensemble of models to predict the annual rate of change in combined scores from the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and III. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort from the Longitudinal and Biomarker Study in Parkinson's disease (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomised placebo-controlled trials within the out-of-sample LABS-PD cohort. FINDINGS: 117 healthy controls and 312 patients with Parkinson's disease from the PPMI study were available for analysis, and 317 patients with Parkinson's disease from LABS-PD were available for validation. Our model ensemble showed strong performance within the PPMI cohort (five-fold cross-validated R2 41%, 95% CI 35-47) and significant-albeit reduced-performance in the LABS-PD cohort (R2 9%, 95% CI 4-16). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort. These included significant replication of higher baseline MDS-UPDRS motor score, male sex, and increased age, as well as a novel Parkinson's disease-specific epistatic interaction, all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2·9%, 95% CI 1·5-4·3). CSF biomarkers at baseline showed a more modest (0·3%, 95% CI 0·1-0·5) but still significant effect on prediction of motor progression. The simulations (n=5000) showed that incorporating the predicted rates of motor progression (as assessed by the annual change in MDS-UPDRS score) into the final models of treatment effect reduced the variability in the study outcome, allowing significant differences to be detected at sample sizes up to 20% smaller than in naive trials. INTERPRETATION: Our model ensemble confirmed established and identified novel predictors of Parkinson's disease motor progression. Improvement of existing prognostic models through machine-learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment. FUNDING: Michael J Fox Foundation for Parkinson's Research and National Institute of Neurological Disorders and Stroke.

Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins.

The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1® culture, respectively. Considering only linkages that appeared in the consensus network for each culture (exceeding the determined frequency cutoff of ≥ 60%), the resulting Cornell D2 and KB-1® consensus networks maintained 1,105 nodes (genes or conditions) with 974 edges and 1,714 nodes with 1,455 edges, respectively. These consensus networks captured multiple strong and biologically informative relationships. One of the main highlighted relationships shared between these two cultures was a direct edge between the transcript encoding for the major reductive dehalogenase (tceA (D2) or vcrA (KB-1®)) and the transcript for the putative S-layer cell wall protein (DET1407 (D2) or KB1_1396 (KB-1®)). Additionally, transcripts for two key oxidoreductases (a [Ni Fe] hydrogenase, Hup, and a protein with similarity to a formate dehydrogenase, "Fdh") were strongly linked, generalizing a strong relationship noted previously for Dehalococcoides mccartyi strain 195 to multiple strains of Dehalococcoides. Notably, the pangenome array utilized when monitoring the KB-1® culture was capable of resolving signals from multiple strains, and the network inference engine was able to reconstruct gene networks in the distinct strain populations.

Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.

BACKGROUND: Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2 diabetes (T2D). METHODS: Using a novel analytical platform (Reverse Engineering and Forward Simulation [REFS]), we built prediction model ensembles for progression to prediabetes or T2D from an aggregated EHR data sample. REFS relies on a Bayesian scoring algorithm to explore a wide model space, and outputs a distribution of risk estimates from an ensemble of prediction models. We retrospectively followed 24 331 adults for transitions to prediabetes or T2D, 2007-2012. Accuracy of prediction models was assessed using an area under the curve (AUC) statistic, and validated in an independent data set. RESULTS: Our primary ensemble of models accurately predicted progression to T2D (AUC = 0.76), and was validated out of sample (AUC = 0.78). Models of progression to T2D consisted primarily of established risk factors (blood glucose, blood pressure, triglycerides, hypertension, lipid disorders, socioeconomic factors), whereas models of progression to prediabetes included novel factors (high-density lipoprotein, alanine aminotransferase, C-reactive protein, body temperature; AUC = 0.70). CONCLUSIONS: We constructed accurate prediction models from EHR data using a hypothesis-free machine learning approach. Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T2D research. This methodology has potentially valuable downstream applications to personalized medicine and clinical research.

Novel predictive models for metabolic syndrome risk: a "big data" analytic approach.

OBJECTIVES: We applied a proprietary "big data" analytic platform--Reverse Engineering and Forward Simulation (REFS)--to dimensions of metabolic syndrome extracted from a large data set compiled from Aetna's databases for 1 large national customer. Our goals were to accurately predict subsequent risk of metabolic syndrome and its various factors on both a population and individual level. STUDY DESIGN: The study data set included demographic, medical claim, pharmacy claim, laboratory test, and biometric screening results for 36,944 individuals. The platform reverse-engineered functional models of systems from diverse and large data sources and provided a simulation framework for insight generation. METHODS: The platform interrogated data sets from the results of 2 Comprehensive Metabolic Syndrome Screenings (CMSSs) as well as complete coverage records; complete data from medical claims, pharmacy claims, and lab results for 2010 and 2011; and responses to health risk assessment questions. RESULTS: The platform predicted subsequent risk of metabolic syndrome, both overall and by risk factor, on population and individual levels, with ROC/AUC varying from 0.80 to 0.88. We demonstrated that improving waist circumference and blood glucose yielded the largest benefits on subsequent risk and medical costs. We also showed that adherence to prescribed medications and, particularly, adherence to routine scheduled outpatient doctor visits, reduced subsequent risk. CONCLUSIONS: The platform generated individualized insights using available heterogeneous data within 3 months. The accuracy and short speed to insight with this type of analytic platform allowed Aetna to develop targeted cost-effective care management programs for individuals with or at risk for metabolic syndrome.

Characterizing potential exposure to the public from low-level radioactive waste transportation by truck.

To address public concern about potential exposure to gamma radiation from legal-weight low-level radioactive waste truck shipments to the Nevada Test Site, a stationary, automated array of four pressurized ion chambers was established for trucks to pass through. Data were collected from 1,012 of the 2,260 trucks that transported low-level radioactive waste to the Nevada Test Site from February through December 2003. To avoid perception of biasing a potential exposure low, the maximum reading (muR per hour; muR h(-1)) from the array was assigned as the gross measurement value for each truck. [In this article, exposure measurements are reported as Roentgen (R), as this unit is consistent with the data readings of the measurement instruments and has been historically presented to public stakeholders. Subsequently, dose measurements are reported as Roentgen Equivalent Man (rem).] To calculate the "net exposure" for each truck, the average and standard deviation of the maximum background values during the corresponding 12-h period when the truck arrived were subtracted from the gross value. For 483 trucks (47.7%), calculated net exposure values were equal to or less than zero, indicating that the exposure from the truck was indistinguishable from background. An additional 206 trucks (20.4%) had calculated net exposure values ranging between 0.0 and 1.0 muR h(-1). Cumulative exposure scenarios appropriate for rural transportation routes to the Nevada Test Site were developed; however, these scenarios assumed the unlikely case that the same individual was exposed to all of the trucks on that route. Cumulative exposure values were dominated by a small percentage of the trucks with comparatively high values. In communities along transportation routes, the probability of an individual receiving a potential exposure from a single truck may be a more meaningful perspective.