General principles of developing novel radioprotective agents for nuclear emergency

With the rapid application of nuclear energy and radiological technology in national economy and military field,the risk of nuclear accidents increases as well.Exposure to irradiation causes severe damages to the human body,and radioprotective agents are required for medical protection.To find the safe and effective radioprotective agents is the key for nuclear emergency.Recently,Toll like receptors(TLRs)and PHD-HIF oxygen sensing signal pathway have been extensively studied for radioprotection.Great progress has been made in this field and a number of radioprotective agents have been found.In this review,we have summarized recent findings of radioprotective roles of TLRs signaling pathway and PHD-HIF signaling pathway and discussed the general principles of developing novel radioprotective agents.These findings will provide opportunities for developing new strategies to prevent IR-induced injuries in public health events.

EU Including: Development of Radiological and Nuclear Training Learning Objectives

Worldwide, about 20 million consignments of radioactive material are transported annually on public roads, railways, aircraft, and ships. About 95% of radioactive consignments are not related to nuclear power. In 2016, a total of 143 incidents of nuclear or other radioactive materials were found to be outside of regulatory control, which occurred in 19 countries. On an international level risk assessment has to account for the potential threats due to millions of radioactive sources in use worldwide and hundreds of tons of military grade U/Pu not under IAEA safeguards. The European Union (EU) has tasked the INCLUDING project consortium, connecting 15 partners from 10 EU Member States, to address this issue and create an innovative cluster for radiological and nuclear (RN) emergencies. The project is coordinated by the Italian Agency for the New Technologies, Energy and Sustainable Economic Development (ENEA). INCLUDING will provide comprehensive training in the RN security sector. Thereby, know-how is enhanced for practitioners in this sector. An important part in this endeavor is the development of radiological- and nuclear training learning objectives. INCLUDING partners involved in this task (Work Package 4) represent companies, organisations and government agencies from Austria, Greece, Italy, Lithuania, Hungary and Portugal. The task has four main objectives: 1) Harmonisation of RN education/training for EU first responders: 2) Identification of main problems in setting norms;3) Developing a training matrix using revised Bloom’s taxonomy;4) Use of the methodology developed for Joint Actions and its application at INCLUDING Cluster Facilities in different EU Member States. The INCLUDING Work Package 4 members have analyzed the EU EDEN Training Matrix and identified gaps in accordance with NATO CBRN training standards related to civil-military cooperation. Furthermore, they analyzed 5 EUHORIZON 2020- and 9 EUFP7-SECURITY projects, and 97 RN training courses offered to the international community by NATO, 6 EU organisations, Qatar, US military- and civilian organisations, and the International Atomic Energy Agency (IAEA). This paper will present these results, which are being used to develop the basic structure for the Learning Objective Catalogue (LOC), comprised of multiple RN-related Learning Objectives for different threat scenarios.

Performance tests of a portable thyroid radioiodine

Objective:To evaluate the effectiveness of a portable thyroid radioiodine meter in on-site monitoring to enhance the monitoring precision of radioiodine inhaled by nuclear medicine staff and patients.Methods:A portable thyroid radioiodine meter was developed to measure the 131I activity in the thyroid.Performance tests of the portable thyroid radioiodine meter were conducted using calibration sources and testing methodologies adhering to national metrological standards.Results:The thyroid radioiodine meter exhibited integral non-linearity,8-h measurement stability,measurement linearity,and an energy resolution of 0.42%,1.05%,1.56%,and 7.11%,respectively for ^(137)Cs at 661.66 keV.Additionally,its minimum detectable activity for ^(131)I at 364.49 keV was approximately 22 Bq(10 min).Conclusion:The developed thyroid radioiodine meter features elevated performance and great potential for application in nuclear medicine and monitoring of responses to nuclear or radiological emergencies.

The Influence of Knowledge,Trust,and Perceived Risk on Firefighters'Preparedness and Willingness to Respond to Nuclear Emergencies:The Case of South Korea

At the site of nuclear accidents,fjrefjghters situated near nuclear power plants(NPPs)assume responsibility for suppressing fjres and rescuing workers from leaked radiation.Their timely responses to nuclear emergencies can prevent or minimize the catastrophic impacts of these accidents.Despite their important role in nuclear emergency management,little is known about the factors that influence fjrefjghters'nuclear emergency responses.We recruited 179 Korean fjrefjghters(96%male)situated near NPPs.We examined whether and how knowledge regarding nuclear accidents and trust in the authorities operating nuclear power plants were related to fjrefjghters'risk perceptions of nuclear accidents to predict their preparedness and willingness to respond to nuclear emergencies.Our results indicate that trust moderated the effect of knowledge on the perceived risk of nuclear accidents,and that the perceived risk mediated the relationship between knowledge and fjrefjghters'preparedness and willingness to respond to nuclear emergencies.The results revealed a pattern of moderated mediation,in which perceived risk mediated the indirect effect of knowledge on preparedness and willingness when the fjrefjghters'trust was low,but not when it was high.

Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning:From prediction to explainability

The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders.