Correspondence/Rebuttal pubs.acs.org/est
Response to Comment on “Chromosomal Aberrations in Large Japanese Field Mice (Apodemus speciosus) Captured near Fukushima Dai-ichi Nuclear Power Plant”
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model is therefore suggested as the best-fit model for both Figure 3A and Figure 3B. The results of the AIC analyses corroborate our conclusion that the chromosomal aberration frequency in individual mice tended to roughly increase with the estimated dose rates and accumulated doses. Additionally, as we discussed in our article, our results are comparable with the results from in vivo low dose rate exposure experiments.7,8 We also emphasize that our results are consistent with current scientific knowledge about the dose−response relationship of chromosomal aberrations in low dose and low dose rate ranges (UNSCEAR 2000, 2010, 2012; NRC 2006).9−12 Mortazavi et al. are clearly concerned that our article may contribute to the public’s fear of low levels of radiation exposure. Tanaka et al. showed that, in laboratory mice, longterm exposure to a dose rate of 1 mGy d−1, comparable to the dose rates in the moderately and heavily contaminated areas in our study, induced no distinct stochastic effects such as tumor incidence, although the frequency of chromosomal aberrations did increase with dose.8 Our findings of a slight increase in chromosomal aberrations in wild mice inhabiting severely contaminated areas can never be linked to human health effects of radiation, including any in residents of Fukushima exposed to much lower radiation levels than the mice were. Finally, Mortazavi et al. mention, quoting a sentence from our article, “care should be exercised when using unstable chromosomal aberrations as a biomarker of radiation exposure and also when interpreting the results”, that the exercise of such care should certainly lead to caution in interpreting the data presented. We agree with this. However, it is statistically evident that chromosomal aberrations (both unstable aberrations such as dicentrics and stable aberrations such as translocation) were significantly more frequent in the heavily contaminated area than in the noncontaminated control area, even if unstable chromosomal aberrations had declined over time.
e appreciate the opportunity to respond to the comment of Mortazavi et al.1 regarding our recent
article.2 Mortazavi et al. mainly criticize our conclusions that chromosomal aberrations were significantly more common in field mice captured in the heavily contaminated area of Fukushima Prefecture than in mice in the noncontaminated control area, and that the frequency of aberration in individual mice tended to roughly increase with the estimated dose rates and accumulated doses. The reason given for this criticism is that our conclusions were drawn based on very scattered data points. Mortazavi et al. assert that the data points shown in Figure 1 would fit a threshold or biphasic “J” shaped hormetic dose− response model. However, Figure 1 compares chromosomal aberrations in individual sampling areas and is not a dose− response curve, and thus it is meaningless to discuss the dose− response model for Figure 1. It is statistically evident that the frequencies of chromosomal aberrations were significantly higher in the heavily contaminated area than in the noncontaminated control area. We disagree with their claim, which is based on the assumption that “if we only remove the first two top data points from heavily contaminated areas, it is clear that the findings would be changed.” The selection of data to match with one’s explanation with no logical grounds is an unacceptable data manipulation. Figure 3 is dose−response data, and we recognize that the data points are very scattered as Mortazavi et al. point out. This is due to an innate characteristic of chromosomal aberrations that the frequency of aberrations induced by low-dose (rate) exposure varies substantially among individuals, even in the case of laboratory mice that have identical genetic backgrounds and are bred and reared under identical conditions, as discussed in our article. For this reason, it is important to draw conclusions based on statistical analyses and fitting the data to a relevant dose−response model. Although we could fit complex models to our data, a too-complex model that perfectly fitted our data would not fit other data well. To prevent fitting toocomplex models, information criteria that add some complexity penalty to model-fitting are often used during model selection.3,4 We used the Akaike information criterion (AIC), one of the best-known information criteria,5,6 to compare the fittings of dose (rate)−response curves in Figure 3 with linear, linear-quadratic, and threshold models. The AIC value represents how well a model fits the data set, where a relatively low AIC means a better fit than a higher AIC. The AICs calculated for dose (rate)−response data (Figure 3A) were 155.7, 156.9, and 157.7 for the linear model, linear-quadratic model, and threshold model, respectively. The AICs calculated for cumulative dose−response data (Figure 3B) were 156.7, 156.8, and 158.7 for the linear model, linear-quadratic model, and threshold model, respectively. The linear dose−response © 2017 American Chemical Society
Isao Kawaguchi† Kazutaka Doi† Taiki Kawagoshi‡ Yoshihisa Kubota*,‡ †
Center for Radiation Protection Knowledge, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan ‡ Fukushima Project Headquarters, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan Published: June 27, 2017 8198
DOI: 10.1021/acs.est.7b02421 Environ. Sci. Technol. 2017, 51, 8198−8199
Environmental Science & Technology
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Correspondence/Rebuttal
AUTHOR INFORMATION
Corresponding Author
*Phone: +81 43 206 3158; fax: +81 43 251 4853; e-mail:
[email protected]. ORCID
Yoshihisa Kubota: 0000-0002-2807-1535 Notes
The authors declare no competing financial interest.
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REFERENCES
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DOI: 10.1021/acs.est.7b02421 Environ. Sci. Technol. 2017, 51, 8198−8199