Purdue Conference on Active Nonproliferation

One major problem with nuclear security measurements involves source identification inthe presence of low signal-to-background ratio. This scenario iscommon to several applications, ranging from radiation identification atportal monitors to radiation source search with unmanned vehicles. In this context of identification of a large variety of sources, including natural and medical sources, sensitive sources of particular interest, but also potentially new/unknown sources for which no reference measurement is available, statistical methods are particularly appealing for their ability to capture the random nature of the measurements. Among them, Bayesian methods form a generic framework allowing for uncertainty quantification and propagation, which is of prime interest for detection (of known and unknown sources), classification, and quantification of smuggled nuclear and radiological materials. We demonstratethe use of Bayesian models for the identificationof mixed gamma sources, measured with organic scintillatorswithinshort acquisition times. We alsocompare the estimation performance using two different materials: liquid EJ-309 and stilbene crystal

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications

Current and Future Treatments in Primary Ciliary Dyskinesia

Primary ciliary dyskinesia (PCD) is a rare genetic ciliopathy in which mucociliary clearance is disturbed by the abnormal motion of cilia or there is a severe reduction in the generation of multiple motile cilia. Lung damage ensues due to recurrent airway infections, sometimes even resulting in respiratory failure. So far, no causative treatment is available and treatment efforts are primarily aimed at improving mucociliary clearance and early treatment of bacterial airway infections. Treatment guidelines are largely based on cystic fibrosis (CF) guidelines, as few studies have been performed on PCD. In this review, we give a detailed overview of the clinical studies performed investigating PCD to date, including three trials and several case reports. In addition, we explore precision medicine approaches in PCD, including gene therapy, mRNA transcript and read-through therapy

Effect of natural gamma background radiation on portal monitor radioisotope unmixing

National security relies on several layers of protection. One of the most important is the traffic control at borders and ports that exploits Radiation Portal Monitors (RPMs) to detect and deter potential smuggling attempts. Most portal monitors rely on plastic scintillators to detect gamma rays. Despite their poor energy resolution, their cost effectiveness and the possibility of growing them in large sizes makes them the gamma-ray detector of choice in RPMs. Unmixing algorithms applied to organic scintillator spectra can be used to reliably identify the bare and unshielded radionuclides that triggered an alarm, even with fewer than 1,000 detected counts and in the presence of two or three nuclides at the same time. In this work, we experimentally studied the robustness of a state-of-the-art unmixing algorithm to different radiation background spectra, due to varying atmospheric conditions, in the 16 $^\circ$C to 28 $^\circ$C temperature range. In the presence of background, the algorithm is able to identify the nuclides present in unknown radionuclide mixtures of three nuclides, when at least 1,000 counts from the sources are detected. With fewer counts available, we found larger differences of approximately 35.9$\%$ between estimated nuclide fractions and actual ones. In these low count rate regimes, the uncertainty associated by our algorithm with the identified fractions could be an additional valuable tool to determine whether the identification is reliable or a longer measurement to increase the signal-to-noise ratio is needed. Moreover, the algorithm identification performances are consistent throughout different data sets, with negligible differences in the presence of background types of different intensity and spectral shape