Verification of analytic energy moments for the one-dimensional energy dependent neutron diffusion equation with MCNP5 and Attila-7.1.0

pre-printThe energy dependent neutron diffusion equation (EDNDE) is converted into a moment equation which is solved analytically for the 1-D problem of a bare sphere of pure 235U. The normalized moments 0-5 generated analytically are compared to normalized energy moments, from Monte Carlo N Particle 5 version 1.40 (MCNP5) and Attila-7.1.0-beta version (Attila). The analytic normalized neutron energy moments, fall between the results from MCNP5 (lower bound) and Attila (upper bound) and are accurate compared to MCNP5 neutron energy moments when error in this Monte Carlo simulation are considered. The error range is from 0% to 14%. The Attila moments are less accurate when compared to MCNP5 than the analytical moments derived in this work. The method of moments is shown to be a fast reliable method, compared to either Monte Carlo methods (MCNP5) or 30 multi-energy group methods (Attila)

Validation of energy moments from the one-dimensional energy dependent neutron diffusion equation, MCNP5 and Attila-7.1.0 with the GODIVA experiment

pre-printNormalized neutron energy moments (moments) from the one-dimensional energy dependent neutron diffusion equation (EDNDE), Monte Carlo N Particle 5 version 1.40 (MCNP5) and Attila-7.1.0-beta version (Attila) are validated with the GODIVA experiment (GODIVA). Energy moments 0-5 for all three methods are compared to GODIVA moments. GODIVA moments are measured with two methods. The 1st method is a time of flight (T-O-F) measurement of the average energy (moment 1) of the leaking neutrons from the surface of GODIVA and the 2nd method is from back calculating moments from foil activation analysis of various metal foils at the center of GODIVA. The error range of the EDNDE normalized moments compared to GODIVA is from 0% to 24%. The MCNP5 error range compared to GODIVA is 0-12% and the Attila error range is 0-79%. The method of moments is shown to be a fast reliable method, compared to either Monte Carlo methods (MCNP5) or 30 multi-energy group methods (Attila) with regard to the GODIVA experiment

Practical management of heterogeneous neuroimaging metadata by global neuroimaging data repositories

Rapidly evolving neuroimaging techniques are producing unprecedented quantities of digital data at the same time that many research studies are evolving into global, multi-disciplinary collaborations between geographically distributed scientists. While networked computers have made it almost trivial to transmit data across long distances, collecting and analyzing this data requires extensive metadata if the data is to be maximally shared. Though it is typically straightforward to encode text and numerical values into files and send content between different locations, it is often difficult to attach context and implicit assumptions to the content. As the number of and geographic separation between data contributors grows to national and global scales, the heterogeneity of the collected metadata increases and conformance to a single standardization becomes implausible. Neuroimaging data repositories must then not only accumulate data but must also consolidate disparate metadata into an integrated view. In this article, using specific examples from our experiences, we demonstrate how standardization alone cannot achieve full integration of neuroimaging data from multiple heterogeneous sources and why a fundamental change in the architecture of neuroimaging data repositories is needed instead

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community

The Loss of ATRX Increases Susceptibility to Pancreatic Injury and Oncogenic KRAS in Female But Not Male Mice

Female mice lacking ATRX in the pancreas have increased sensitivity to pancreatic cancer, whereas male mice without ATRX are protected. This study identifies such susceptibility in pancreatic cancer and highlights the need for sex-specific approaches in cancer treatment. BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in North America, accounting for \u3e30,000 deaths annually. Although somatic activating mutations in KRAS appear in 97% of PDAC patients, additional factors are required to initiate PDAC. Because mutations in genes encoding chromatin remodelling proteins have been implicated in KRAS-mediated PDAC, we investigated whether loss of chromatin remodeler.-thalassemia, mental-retardation, X-linked (ATRX) affects oncogenic KRAS\u27s ability to promote PDAC. ATRX affects DNA replication, repair, and gene expression and is implicated in other cancers including glioblastomas and pancreatic neuroendocrine tumors. The hypothesis was that deletion of Atrx in pancreatic acinar cells will increase susceptibility to injury and oncogenic METHODS: Mice allowing conditional loss of Atrx within pancreatic acinar cells were examined after induction of recurrent cerulein-induced pancreatitis or oncogenic KRAS (KRASG12D). Histologic, biochemical, and molecular analysis examined pancreatic pathologies up to 2 months after induction of Atrx deletion. RESULTS: Mice lacking Atrx showed more progressive damage, inflammation, and acinar-to-duct cell metaplasia in response to injury relative to wild-type mice. In combination with KRASG12D, Atrx-deficient acinar cells showed increased fibrosis, inflammation, progression to acinar-to-duct cell metaplasia, and pre-cancerous lesions relative to mice expressing only KRASG12D. This sensitivity appears only in female mice, mimicking a significant prevalence of ATRX mutations in human female PDAC patients. CONCLUSIONS: Our results indicate the absence of ATRX increases sensitivity to injury and oncogenic KRAS only in female mice. This is an instance of a sex-specific mutation that enhances oncogenic KRAS\u27s ability to promote pancreatic intraepithelial lesion formation