From Lower Town to St. Cloud State: Geophysical Survey of an Evolving Urban Landscape 1869-2019

In 2019 a team of graduate students from the Cultural Resource Management Masters program, led by Rob Mann, PhD., Professor of Anthropology, under took a ground penetrating radar survey of critical sites on the St. Cloud State University campus. These were sites that had played a role in shaping the development of the University. The project was funded by a graduate student research grant from St. Cloud State University

K-space data processing for Magnetic Resonance Elastography (MRE)

International audienceObject: Magnetic Resonance Elastography (MRE) requires substantial data processing based on phase image reconstruction, wave enhancement and inverse problem solving. The objective of this study is to propose a new, fast MRE method based on MR raw data processing, particularly adapted to applications requiring fast MRE measurement or high elastogram update rate.Material and Methods: The proposed method allows measuring tissue elasticity directly from raw data without prior phase image reconstruction and without phase unwrapping. Experimental feasibility is assessed both in a gelatin phantom and in the liver of a porcine model in vivo. Elastograms are reconstructed with the raw MRE method and compared to those obtained using conventional MRE. In a third experiment, changes in elasticity are monitored in real-time in a gelatin phantom during its solidification by using both conventional MRE and raw MRE.Results: The raw MRE method shows promising results by providing similar elasticity values to the ones obtained with conventional MRE methods while decreasing the number of processing steps and circumventing the delicate step of phase unwrapping. Limitations of the proposed method are the influence of the magnitude on the elastogram and the requirement for a minimum number of phase offsets.Conclusion: This study demonstrates the feasibility of directly reconstructing elastograms from raw data

Report for closeout of DE-FC02-96SF21260 - United Steelworkers Former Worker Program - Screening Exams 1996-2006

Medical Screening and surveillance of former DOE worker

Quantum dynamics of a fully-blockaded Rydberg atom ensemble

Classical simulation of quantum systems plays an important role in the study of many-body phenomena and in the benchmarking and verification of quantum technologies. Exact simulation is often limited to small systems because the dimension of the Hilbert space increases exponentially with the size of the system. For systems that possess a high degree of symmetry, however, classical simulation can reach much larger sizes. Here, we consider an ensemble of strongly interacting atoms with permutation symmetry, enabling the simulation of dynamics of hundreds of atoms at arbitrarily long evolution times. The system is realized by an ensemble of three-level atoms, where one of the levels corresponds to a highly excited Rydberg state. In the limit of all-to-all Rydberg blockade, the Hamiltonian is invariant under permutation of the atoms. Using techniques from representation theory, we construct a block-diagonal form of the Hamiltonian, where the size of the largest block increases only linearly with the system size. We apply this formalism to derive efficient pulse sequences to prepare arbitrary permutation-invariant quantum states. Moreover, we study the quantum dynamics following a quench, uncovering a parameter regime in which the system thermalizes slowly and exhibits pronounced revivals. Our results create new opportunities for the experimental and theoretical study of large interacting and nonintegrable quantum systems

Interventional MR Elastography for MRI-Guided Percutaneous Procedures

International audiencePURPOSE : MRI-guided thermal ablations require reliable monitoring methods to ensure complete destruction of the diseased tissue while avoiding damage to the surrounding healthy tissue. Based on the fact that thermal ablations result in substantial changes in biomechanical properties, interventional MR elastography (MRE) dedicated to the monitoring of MR-guided thermal therapies is proposed here. METHODS : Interventional MRE consists of a needle MRE driver, a fast and interactive gradient echo pulse sequence with motion encoding, and an inverse problem solver in real-time. This complete protocol was tested in vivo on swine and the ability to monitor elasticity changes in real-time was assessed in phantom. RESULTS : Thanks to a short repetition time, a reduction of the number of phase-offsets and the use of a sliding window, one refreshed elastogram was provided every 2.56 s for an excitation frequency of 100 Hz. In vivo elastograms of swine liver were successfully provided in real-time during one breath-hold. Changes of elasticity were successfully monitored in a phantom during its gelation with the same elastogram frame rate. CONCLUSION : This study demonstrates the ability of detecting elasticity changes in real-time and providing elastograms in vivo with interventional MRE that could be used for the monitoring of thermal ablations

Cyclic AMP-specific phosphodiesterase, PDE8A1, is activated by protein kinase A-mediated phosphorylation

The cyclic AMP-specific phosphodiesterase PDE8 has been shown to play a pivotal role in important processes such as steroidogenesis, T cell adhesion, regulation of heart beat and chemotaxis. However, no information exists on how the activity of this enzyme is regulated. We show that under elevated cAMP conditions, PKA acts to phosphorylate PDE8A on serine 359 and this action serves to enhance the activity of the enzyme. This is the first indication that PDE8 activity can be modulated by a kinase, and we propose that this mechanism forms a feedback loop that results in the restoration of basal cAMP levels. (C) 2012 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserve

Risk of stomach cancer in Aotearoa/New Zealand: A Māori population based case-control study.

Māori, the indigenous people of New Zealand, experience disproportionate rates of stomach cancer, compared to non-Māori. The overall aim of the study was to better understand the reasons for the considerable excess of stomach cancer in Māori and to identify priorities for prevention. Māori stomach cancer cases from the New Zealand Cancer Registry between 1 February 2009 and 31 October 2013 and Māori controls, randomly selected from the New Zealand electoral roll were matched by 5-year age bands to cases. Logistic regression was used to estimate odd ratios (OR) and 95% confidence intervals (CI) between exposures and stomach cancer risk. Post-stratification weighting of controls was used to account for differential non-response by deprivation category. The study comprised 165 cases and 480 controls. Nearly half (47.9%) of cases were of the diffuse subtype. There were differences in the distribution of risk factors between cases and controls. Of interest were the strong relationships seen with increased stomach risk and having >2 people sharing a bedroom in childhood (OR 3.30, 95%CI 1.95-5.59), testing for H pylori (OR 12.17, 95%CI 6.15-24.08), being an ex-smoker (OR 2.26, 95%CI 1.44-3.54) and exposure to environmental tobacco smoke in adulthood (OR 3.29, 95%CI 1.94-5.59). Some results were attenuated following post-stratification weighting. This is the first national study of stomach cancer in any indigenous population and the first Māori-only population-based study of stomach cancer undertaken in New Zealand. We emphasize caution in interpreting the findings given the possibility of selection bias. Population-level strategies to reduce the incidence of stomach cancer in Māori include expanding measures to screen and treat those infected with H pylori and a continued policy focus on reducing tobacco consumption and uptake

DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record

Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable and reliable models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher created clinical ML models into a widely used electronic medical record (EMR) system. We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within EMR software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating twelve ML models triggered by clinician button-clicks in Stanford Health Care's production instance of Epic. Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. By describing DEPLOYR, we aim to inform ML deployment best practices and help bridge the model implementation gap