A Longitudinal Study of Human Exposure to Potential Nuclear Power Plant Risk

This study constructs a potential risk index (PRI) for the 65 U.S.-based commercial nuclear power plant (NPP) sites in relation to their surrounding populations. Four risk levels are defined: low risk, moderate risk, high risk, and very high risk. Discrepancies that exist in the sociodemographic characteristics of the host communities’ populations are examined as sorted by risk-level category. It is found that a greater percentage of minority groups are exposed to the highest levels of risk. In addition, percent “Hispanic” and percent “Other,” a grouping that includes multiracial, mixed, interracial, as well as Hispanic and Latino groups (for example, Mexican, Puerto Rican, Cuban, or Spanish) are categories that show the greatest percent change in both the period 1990–2000 and 2000–2010

Public Exposure to U.S. Commercial Nuclear Power Plants Induced Disasters

This study explores the potential risks associated with the 65 U.S.-based commercial nuclear power plants and the distribution of those risks among the populations of both their respective host communities and of the communities located in outlying areas. First, it starts by examining the racial/ethnic composition of the host community populations, as well as the disparities in socioeconomic status that exist, if any, between the host communities and communities located in outlying areas. Second, it utilizes two independent-sample T tests to identify any differences in the sociodemographic compositions of the two areas. Third, it explores regional demographic trends by looking at the percent change in demographic variables in the host communities and communities located in outlying areas in 1990–2000 and 2000–2010. Findings reveal that during the past two decades more people were exposed to the risks as population living in the host communities increased

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

<p>Abstract</p> <p>Background</p> <p>High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.</p> <p>Results</p> <p>We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.</p> <p>Conclusions</p> <p>Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.</p

Evaluating methods for ranking differentially expressed genes applied to microArray quality control data

<p>Abstract</p> <p>Background</p> <p>Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility.</p> <p>Results</p> <p>We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated <it>t </it>statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra- and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra- and inter-platform reproducibility of WAD was also observed at a higher biological function level.</p> <p>Conclusion</p> <p>These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.</p

PET Imaging of Soluble Yttrium-86-Labeled Carbon Nanotubes in Mice

The potential medical applications of nanomaterials are shaping the landscape of the nanobiotechnology field and driving it forward. A key factor in determining the suitability of these nanomaterials must be how they interface with biological systems. Single walled carbon nanotubes (CNT) are being investigated as platforms for the delivery of biological, radiological, and chemical payloads to target tissues. CNT are mechanically robust graphene cylinders comprised of sp(2)-bonded carbon atoms and possessing highly regular structures with defined periodicity. CNT exhibit unique mechanochemical properties that can be exploited for the development of novel drug delivery platforms. In order to evaluate the potential usefulness of this CNT scaffold, we undertook an imaging study to determine the tissue biodistribution and pharmacokinetics of prototypical DOTA-functionalized CNT labeled with yttrium-86 and indium-111 ((86)Y-CNT and (111)In-CNT, respectively) in a mouse model.The (86)Y-CNT construct was synthesized from amine-functionalized, water-soluble CNT by covalently attaching multiple copies of DOTA chelates and then radiolabeling with the positron-emitting metal-ion, yttrium-86. A gamma-emitting (111)In-CNT construct was similarly prepared and purified. The constructs were characterized spectroscopically, microscopically, and chromatographically. The whole-body distribution and clearance of yttrium-86 was characterized at 3 and 24 hours post-injection using positron emission tomography (PET). The yttrium-86 cleared the blood within 3 hours and distributed predominantly to the kidneys, liver, spleen and bone. Although the activity that accumulated in the kidney cleared with time, the whole-body clearance was slow. Differential uptake in these target tissues was observed following intravenous or intraperitoneal injection.The whole-body PET images indicated that the major sites of accumulation of activity resulting from the administration of (86)Y-CNT were the kidney, liver, spleen, and to a much less extent the bone. Blood clearance was rapid and could be beneficial in the use of short-lived radionuclides in diagnostic applications