IAEA coordinated research project on nuclear data for charged-particle monitor reactions and medical isotope production

An IAEA coordinated research project was launched in December 2012 to establish and improve the nuclear data required to characterise charged-particle monitor reactions and extend data for medical radionuclide production. An international team was assembled to undertake work addressing the requirements for more accurate cross-section data over a wide range of targets and projectiles, undertaken in conjunction with a limited number of measurements and more extensive evaluations of the decay data of specific radionuclides. These studies are nearing completion, and are briefly described below

Content and performance of the MiniMUGA genotyping array: A new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research

Mediastinal masses masquerading as common respiratory conditions of childhood:A case series

Introduction: Leukaemia and lymphoma may present with symptoms and signs mimicking common respiratory conditions of childhood such as asthma or croup. The UK National Institute for Clinical Excellence guidelines for referral for suspected cancer state that "the primary healthcare professional should be ready to review the initial diagnosis in patients in whom common symptoms do not resolve as expected" and "must be alert to the possibility of cancer when confronted by unusual symptom patterns" (National Institute for Health and Clinical Excellence, 2005). Results and discussion: A child with an undiagnosed mediastinal mass presenting with signs and symptoms suggestive of asthma or croup may be given oral systemic steroids. We report four such illustrative cases presenting to a single institution within the last 3 years. Conclusion: We highlight key points from the history and examination findings which should lead to review of the original diagnosis, the benefit of early chest X-ray in such cases and the dangers of steroid pretreatment.</p

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold

Road Networks Management under Uncertainty: A stochastic based model

Current pavement management systems (PMS) adopted by the Road Authorities are often very complex and data intensive. Other challenges also faced by Road Authorities in managing road networks include budget constraints and the uncertainty associated in predicting the future performance of pavements. In addition, the emphasis in pavement management has shifted from reconstructing completely new roads towards preservation of existing networks. In many cases, existing PMS do not meet these requirements. Thus, an efficient model that is able to accommodate all of those challenges needs to be developed. This paper outlines the development of a stochastic based PMS that includes a performance prediction model using Markov chains and an optimization model based on Markov Decision Processes (MDP). Combinations of pavement preservation strategies and maintenance budget levels are applied as action criteria in contrast to other stochastic models. Despite the apparent influence of uncertainty in road pavement performance during their service live, stochastic models provide promising results for enhancing current PMS. By analysing historical data, the future behaviour of road pavements under different expenditure levels and combination of routine and periodic maintenance measures can be predicted. From an optimization point of view, the utilization of constrained MDP will potentially result in cost savings. This is due to the optimality principal of the model which is capable of finding a optimal multi-year maintenance policy through the direct inclusion of additional constraints into the optimization problem. Hence, the model considers constraints and incorporates relationships between historical maintenance actions and costs. This paper also presents a methodology for developing rationale for long-term maintenance policies by integrating stochastic based performance prediction and optimization models with the experience of Road Authorities in managing roads networks

IAEA coordinated research project on nuclear data for charged-particle monitor reactions and medical isotope production

An IAEA coordinated research project was launched in December 2012 to establish and improve the nuclear data required to characterise charged-particle monitor reactions and extend data for medical radionuclide production. An international team was assembled to undertake work addressing the requirements for more accurate cross-section data over a wide range of targets and projectiles, undertaken in conjunction with a limited number of measurements and more extensive evaluations of the decay data of specific radionuclides. These studies are nearing completion, and are briefly described below