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Background Epidemiological data show that radiation exposure during childhood is associated with larger cancer risks compared with exposure at older ages. For exposures in adulthood, however, the relative risks of radiation-induced cancer in Japanese atomic bomb survivors generally do not decrease monotonically with increasing age of adult exposure. These observations are inconsistent with most standard models of radiation-induced cancer, which predict that relative risks decrease monotonically with increasing age at exposure, at all ages. Methods We analyzed observed cancer risk patterns as a function of age at exposure in Japanese atomic bomb survivors by using a biologically based quantitative model of radiation carcinogenesis that incorporates both radiation induction of premalignant cells (initiation) and radiation-induced promotion of premalignant damage. This approach emphasizes the kinetics of radiation-induced initiation and promotion, and tracks the yields of premalignant cells before, during, shortly after, and long after radiation exposure. Results Radiation risks after exposure in younger individuals are dominated by initiation processes, whereas radiation risks after exposure at later ages are more influenced by promotion of preexisting premalignant cells. Thus, the cancer site–dependent balance between initiation and promotion determines the dependence of cancer risk on age at radiation exposure. For example, in terms of radiation induction of premalignant cells, a quantitative measure of the relative contribution of initiation vs promotion is 10-fold larger for breast cancer than for lung cancer. Reflecting this difference, radiation-induced breast cancer risks decrease with age at exposure at all ages, whereas radiation-induced lung cancer risks do not. Conclusion For radiation exposure in middle age, most radiation-induced cancer risks do not, as often assumed, decrease with increasing age at exposure. This observation suggests that promotional processes in radiation carcinogenesis become increasingly important as the age at exposure increases. Radiation-induced cancer risks after exposure in middle age may be up to twice as high as previously estimated, which could have implications for occupational exposure and radiological imaging. [ABSTRACT FROM PUBLISHER]

Radioactive contamination of forests by long-lived radionuclides from nuclear accidents such as Chernobyl and Fukushima continues to be studied and quantitatively modeled. Whereas traditional statistical and machine learning (ML) techniques generate predictions by focusing on correlations between variables, quantification of causal effects of radioactivity deposition levels on contamination of plant tissues represents a more fundamental and relevant research goal. Modeling of cause-and-effect relationships is advantageous over standard predictive modeling, particularly by improving the generalizability of results to other situations, where the distributions of variables, including potential confounders, differ from those in the training data. Here we used the state-of-the-art causal forest (CF) algorithm to quantify the causal effect of 137 Cs land contamination after the Fukushima accident on 137 Cs activity concentrations in the wood of four common Japanese forest tree species: Hinoki cypress (Chamaecyparis obtusa), konara oak (Quercus serrata), red pine (Pinus densiflora), and Sugi cedar (Cryptomeria japonica). We estimated the average causal effect for the population, quantified how it was influenced by other environmental variables, and produced effect estimates at the individual level. The estimated causal effect was quite robust to various refutation methods, and was negatively influenced by high mean annual precipitation, elevation, and time after the accident. Wood subtype (e.g. sapwood, heartwood) and tree species made smaller contributions to the causal effect. We believe that causal ML techniques have promising potential in radiation ecology and can usefully expand the toolkit of modeling approaches available to researchers in this field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Radioactive contamination of terrestrial plants was extensively investigated and quantitatively modeled after the Fukushima nuclear power plant accident. This phenomenon, which is important for ecosystem functioning and protection of human health, is influenced by multiple factors, including plant species, time after the accident, and climate. Machine learning algorithms such as random forests (RF) have a record of strong performance on large multi-dimensional data sets, but, to our knowledge, combined data on post-Fukushima plant contamination with radionuclides were not yet subjected to a machine learning analysis. Here we performed such analysis on two large published data sets: (1) 137 Cs activity concentrations in four common Japanese forest tree species. (2) Plant/soil 137 Cs concentration ratios in multiple perennial plant species. The goal was to show the usefulness of machine learning for identifying and quantifying the main trends of 137 Cs contamination in terrestrial plants. Each data set was split randomly into training and testing parts, RF was fitted and tuned on the training parts, and its performance was assessed on the testing parts by three metrics: coefficient of determination (R 2 ), root mean squared error, and mean absolute error. Synthetic noise variables and the Boruta algorithm were used in a customized procedure to identify the most important predictor variables, which consistently outperformed random noise. Good agreement between observations and RF predictions (e.g. R 2 ∼0.9 on testing data) was obtained on both data sets. The effects of the most important predictors (e.g. time after the accident, 137 Cs land contamination level, and plant species) and interactions between them were quantified by partial dependence plots. These results of machine learning analyses of large data collections can help to complement previous modeling efforts, and to clarify the patterns of 137 Cs contamination of plants after the Fukushima accident., (Copyright © 2021 Elsevier Ltd. All rights reserved.)