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Topographic effects on Cs-137 concentrations in a forested area were quantitatively examined using 58 soil core samples collected in a village in Fukushima, Japan, which was directly impacted by the radioactive plume emitted during the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. In this study, five topographic parameters and two soil properties were evaluated as controls on the soil Cs-137 concentration using generalized additive models (GAMs), a flexible statistical method for evaluating the functional dependencies of multiple parameters. GAMs employing soil dry bulk density, mass water content, and elevation explained 54 % of the observed concentrations of Cs-137 within this landscape, whereas GAMs employing elevation, slope, and upslope distance explained 47 % of the observed concentrations, which provide strong evidence of topographic effects on Cs-137 concentrations in soils. The model fit analysis confirmed that the topographic effects are strongest when multiple topographic parameters and soil properties are included. The ability of each topographic feature to predict Cs-137 concentrations was influenced by the resolution of the digital elevation models. The movement of Cs-137 into the subsurface in this area near Fukushima was faster in comparison to regions affected by the Chernobyl Nuclear Power Plant accident. These results suggest that the effects of topographic parameters should be considered carefully in the use of anthropogenic radionuclides as environmental tracers and in the assessment of current and future environmental risks due to nuclear power plant accidents.

This research collected forest soil samples from Fukushima, Japan, where the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident contaminated the land. The purpose of this study was to examine how the local topography influenced the radiocesium (Cs-137) accumulation patterns in soils over the years since the accident. As an analytical method, the general additive model (GAM) was used to determine at what percentages the topographic parameters explain Cs-137 contamination levels down to a depth of 30 cm. For comparison, topographic parameters were extracted from both 1 m and 10 m digital evaluation models (DEMs). The effects of topography were compared with the effects of the soil water content and dry soil bulk density. An additional Tukey’s honestly significant difference (HSD) test was conducted to determine the significance of the hillslope aspect and vegetation cover differences on concentration predictions. The results showed that, at this study site, topographic parameters extracted from the 10 m DEM better predicted Cs-137 levels. The models with a single topographic parameter did not explain Cs-137 levels higher than 30 %. However, combining the parameters improved the explanation percentages. The relative influences of topographic parameters and soil properties were similar throughout the soil depth, showing their subsurface co-functionalities for Cs-137 concentration levels. Tukey’s HSD test results showed the inter-effects of topography and vegetation cover differences. The results of this study indicate that the selection of topographic parameters, as well as the chosen methods of their extractions, have implications for physical models assessing radionuclide contamination levels.

Forests provide valuable water and nutrient resources to farming activities as well as places for various leisure activities. However, decontaminating forests in the aftermath of a massive radionuclide contamination event presents challenges because of the topography and the difficulty of collecting a large number of field samples. Achieving accurate remote measurements also can be hindered by the canopy cover. In Fukushima, Japan, where elevated radioactive fallout occurred following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in March 2011, about 70 percent of the land is covered with forests. Soil samples collected in a forest in Iitate Village, Fukushima, beginning in 2016 through 2018 still contained an average 98200 Bq/kg of Cs-137 in the top 4 cm depth (std. dev. 114100 Bq/kg). Thus, decontamination and identification of Cs-137 distribution patterns in those forests is still a pressing issue. However, soil types in that region, 1000 mm annual precipitation with intense rain during typhoons, and microtopography have presented challenges to understanding how Cs-137 behaves in those forests. In this study, six topographic parameters were computed from 1-m and 10-m resolution DEMs and the relationships between those parameters and soil water content and bulk density were systematically analyzed for their effects on Cs-137 concentration levels. As the first analytical step, correlation indices and the generalized additive models (GAM) analysis were conducted on those parameters. The results show that not all topographic effects are apparent in the correlation analysis, yet the results can be improved when mixed with other parameters in GAM models. Overall the effect of topographic parameters on Cs-137 levels is DEM resolution-dependent while individual soil properties indicate a strong relationship. Also, it was found that depending on the analysis depth, correlation levels and significance of those parameters in GAM models fluctuate. As the second step, Cs-137 levels were extrapolated to a larger area in the study site to understand further the connections between topography and soil properties. The results, including the limitations and proposals for future forest decontamination, will be presented in the session. Understanding how Cs-137 moves and accumulates in forests, especially immediately after contamination, is critical to avoiding the negative impacts on the environment by decontamination measures and to protecting lowlands from harmful radioactivity levels. This study contributes to the radionuclide research field by presenting an example of data analysis processes using field sampled data.

Biomedical Ontologies integrate diverse biomedical data and enable intelligent data-mining and help translate basic research into useful clinical knowledge. We present the RNA Ontology (RNAO), an ontology for integrating diverse RNA data, including RNA sequences and sequence alignments, three-dimensional structures, and biochemical and functional data. For example, individual atomic resolution RNA structures have broader significance as representatives of classes of homologous molecules, which can differ significantly in sequence while sharing core structural features and common roles or functions. Thus, structural data gain value by being linked to homologous sequences in genomic data and databases of sequence alignments. Likewise, the value of genomic data is enhanced by annotation of shared structural features, especially when these can be linked to specific functions. Moreover, the significance of biochemical, functional and mutational analyses of RNA molecules are most fully understood when linked to molecular structures and phylogenies. To achieve these goals, RNAO provides logically rigorous definitions of the components of RNA primary, secondary and tertiary structure and the relations between these entities. RNAO is being developed to comply with the developing standards of the Open Biomedical Ontologies (OBO) Consortium. The RNAO can be accessed at http://code.google.com/p/rnao/.

The effectiveness of PCR inhibitor removal by silica membranes in combination with the Amplicor Mycobacterium tuberculosis kit was analyzed for 655 respiratory and nonrespiratory specimens. The overall inhibition rate was reduced from 12.5%, when applying the Amplicor kit alone, to 1.1% with the addition of silica membrane DNA purification.