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The general public receives approximately half of its exposure to natural radiation through alpha (α)-particles from radon ((222)Rn) gas and its decay progeny. Epidemiological studies have found a positive correlation between exposure to (222)Rn and lung carcinogenesis. An understanding of the transcriptional responses involved in these effects remains limited. In this study, genomic technology was employed to mine for subtle changes in gene expression that may be representative of an altered physiological state. Human lung epithelial cells were exposed to 0, 0.03, 0.3 and 0.9Gy of α-particle radiation. Microarray analysis was employed to determine transcript expression levels 4h and 24h after exposure. A total of 590 genes were shown to be differentially expressed in the α-particle radiated samples (false discovery rate (FDR)≤0.05). Sub-set of these transcripts were time-responsive, dose-responsive and both time- and dose-responsive. Pathway analysis showed functions related to cell cycle arrest, and DNA replication, recombination and repair (FDR≤0.05). The canonical pathways associated with these genes were in relation to pyrimidine metabolism, G2/M damage checkpoint regulation and p53 signaling (FDR≤0.05). Overall, this gene expression profile suggests that α-particle radiation inhibits DNA synthesis and subsequent mitosis, and causes cell cycle arrest.

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A simple in vitro alpha radiation exposure system (ARES) was designed to study the biological effects of alpha particle radiation. The ARES consists of six (241)Am electroplated stainless steel discs with activities averaging 66 kBq and Mylar-based culture dishes to allow the transmission of alpha particles. The dosimetry of the exposure system was calculated using the GEANT4 Monte Carlo simulation toolkit with the source code adapted from the open-source Microbeam example. The average dose rate and linear energy transfer of the system was simulated to be 0.98 ± 0.01 (statistical)(+0.18)( - 0.00) (systematic) Gy h(-1) and 127.4 ± 0.4 (statistical)(+23)( - 0) (systematic) keV µm(-1), respectively. The system was characterized by a comparison of the survival curves of gamma and alpha irradiated cell lines which showed a relative biological effectiveness of 6.3. This is in good agreement with values obtained using other published alpha particle exposure systems. Results show that the ARES provides a simple, cost-effective exposure platform for research into the biological effects of alpha particle radiation using in vitro modelling of cell cultures.

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A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of (131m)Xe, (133)Xe, (133m)Xe, and (135)Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.

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Over 300 daily environmental radioxenon samples were analyzed using French developed SPALAX for automatic sample preparation including high-resolution gamma-spectrometry. The 133Xe sensitivity was significantly better than 1 mBq/m3 (specified criterion for Comprehensive Nuclear-Test-Ban Treaty verification). Radioxenon analysis was extended to include the X-ray region by improved detector window, sample cell design, efficiency calibration, line shape fitting and background analysis. The resulting analysis offers a 4-16 fold improvement in sensitivity for 133mXe and 131mXe, respectively.