University of Iowa

About: University of Iowa is a education organization based out in Iowa City, Iowa, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 49229 authors who have published 109171 publications receiving 5021465 citations. The organization is also known as: UI & The University of Iowa.

Perforant pathway changes and the memory impairment of Alzheimer's disease.

Abstract: The perforant pathway is a large neuronal projection that arises from layers II and III of the entorhinal cortex of the parahippocampal gyrus. It is the principal source of cortical input to the hippocampal formation. In 11 cases of Alzheimer's disease, we have found that neurofibrillary tangles develop in the cells of origin of the perforant pathway. In addition, the termination zone of the perforant pathway, in the outer two thirds of the molecular layer of the dentate gyrus, contains a distinct layer of neuritic plaques. None of the 8 control subjects had such changes. These profound alterations effectively disconnect the hippocampal formation from the association and limbic cortices. Because of the central role of the hippocampus and parahippocampal gyrus in learning, it is likely that pathological changes in the perforant pathway, by precluding normal hippocampal operation, account for some aspects of the memory impairment in Alzheimer's disease.

Clinical practice guidelines for the management of hypertension in the community: a statement by the American Society of Hypertension and the International Society of Hypertension.

Abstract: Clinical practice guidelines for the management of hypertension in the community a statement by the American society of hypertension and the International society of hypertension

Contemporary Bayesian Econometrics and Statistics

Abstract: Preface. 1. Introduction. 1.1 Two Examples. 1.1.1 Public School Class Sizes. 1.1.2 Value at Risk. 1.2 Observables, Unobservables, and Objects of Interest. 1.3 Conditioning and Updating. 1.4 Simulators. 1.5 Modeling. 1.6 Decisionmaking. 2. Elements of Bayesian Inference. 2.1 Basics. 2.2 Sufficiency, Ancillarity, and Nuisance Parameters. 2.2.1 Sufficiency. 2.2.2 Ancillarity. 2.2.3 Nuisance Parameters. 2.3 Conjugate Prior Distributions. 2.4 Bayesian Decision Theory and Point Estimation. 2.5 Credible Sets. 2.6 Model Comparison. 2.6.1 Marginal Likelihoods. 2.6.2 Predictive Densities. 3. Topics in Bayesian Inference. 3.1 Hierarchical Priors and Latent Variables. 3.2 Improper Prior Distributions. 3.3 Prior Robustness and the Density Ratio Class. 3.4 Asymptotic Analysis. 3.5 The Likelihood Principle. 4. Posterior Simulation. 4.1 Direct Sampling,. 4.2 Acceptance and Importance Sampling. 4.2.1 Acceptance Sampling. 4.2.2 Importance Sampling. 4.3 Markov Chain Monte Carlo. 4.3.1 The Gibbs Sampler. 4.3.2 The Metropolis-Hastings Algorithm. 4.4 Variance Reduction. 4.4.1 Concentrated Expectations. 4.4.2 Antithetic Sampling. 4.5 Some Continuous State Space Markov Chain Theory. 4.5.1 Convergence of the Gibbs Sampler. 4.5.2 Convergence of the Metropolis-Hastings Algorithm. 4.6 Hybrid Markov Chain Monte Carlo Methods. 4.6.1 Transition Mixtures. 4.6.2 Metropolis within Gibbs. 4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo. 5. Linear Models. 5.1 BACC and the Normal Linear Regression Model. 5.2 Seemingly Unrelated Regressions Models. 5.3 Linear Constraints in the Linear Model. 5.3.1 Linear Inequality Constraints. 5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection. 5.4 Nonlinear Regression. 5.4.1 Nonlinear Regression with Smoothness Priors. 5.4.2 Nonlinear Regression with Basis Functions. 6. Modeling with Latent Variables. 6.1 Censored Normal Linear Models. 6.2 Probit Linear Models. 6.3 The Independent Finite State Model. 6.4 Modeling with Mixtures of Normal Distributions. 6.4.1 The Independent Student-t Linear Model. 6.4.2 Normal Mixture Linear Models. 6.4.3 Generalizing the Observable Outcomes. 7. Modeling for Time Series. 7.1 Linear Models with Serial Correlation. 7.2 The First-Order Markov Finite State Model. 7.2.1 Inference in the Nonstationary Model. 7.2.2 Inference in the Stationary Model. 7.3 Markov Normal Mixture Linear Model. 8. Bayesian Investigation. 8.1 Implementing Simulation Methods. 8.1.1 Density Ratio Tests. 8.1.2 Joint Distribution Tests. 8.2 Formal Model Comparison. 8.2.1 Bayes Factors for Modeling with Common Likelihoods. 8.2.2 Marginal Likelihood Approximation Using Importance Sampling. 8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling. 8.2.4 Density Ratio Marginal Likelihood Approximation. 8.3 Model Specification. 8.3.1 Prior Predictive Analysis. 8.3.2 Posterior Predictive Analysis. 8.4 Bayesian Communication. 8.5 Density Ratio Robustness Bounds. Bibliography. Author Index. Subject Index.

Rapid local acceleration of relativistic radiation-belt electrons by magnetospheric chorus

Abstract: Recent analysis of satellite data obtained during the 9 October 2012 geomagnetic storm identified the development of peaks in electron phase space density, which are compelling evidence for local electron acceleration in the heart of the outer radiation belt, but are inconsistent with acceleration by inward radial diffusive transport. However, the precise physical mechanism responsible for the acceleration on 9 October was not identified. Previous modelling has indicated that a magnetospheric electromagnetic emission known as chorus could be a potential candidate for local electron acceleration, but a definitive resolution of the importance of chorus for radiation-belt acceleration was not possible because of limitations in the energy range and resolution of previous electron observations and the lack of a dynamic global wave model. Here we report high-resolution electron observations obtained during the 9 October storm and demonstrate, using a two-dimensional simulation performed with a recently developed time-varying data-driven model, that chorus scattering explains the temporal evolution of both the energy and angular distribution of the observed relativistic electron flux increase. Our detailed modelling demonstrates the remarkable efficiency of wave acceleration in the Earth's outer radiation belt, and the results presented have potential application to Jupiter, Saturn and other magnetized astrophysical objects.