Process evaluation of the RaDIANT community study: a dialysis facility-level intervention to increase referral for kidney transplantation.

Background: The Reducing Disparities in Access to kidNey Transplantation Community Study (RaDIANT) was an End-Stage Renal Disease (ESRD) Network 6-developed, dialysis facility-level randomized trial testing the effectiveness of a 1-year multicomponent education and quality improvement intervention in increasing referral for kidney transplant evaluation among selected Georgia dialysis facilities.

Methods: To assess implementation of the RaDIANT intervention, we conducted a process evaluation at the conclusion of the intervention period (January-December 2014). We administered a 20-item survey to the staff involved with transplant education in 67 dialysis facilities randomized to participate in intervention activities.

Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation.

We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately.

A fast, accurate, and reliable reconstruction method of the lumbar spine vertebrae using positional MRI.

In vivo measurement of lumbar spine configuration is useful for constructing quantitative biomechanical models. Positional magnetic resonance imaging (MRI) accommodates a larger range of movement in most joints than conventional MRI and does not require a supine position. However, this is achieved at the expense of image resolution and contrast.

ON THE CONVERGENCE OF THE ENSEMBLE KALMAN FILTER.

Convergence of the ensemble Kalman filter in the limit for large ensembles to the Kalman filter is proved. In each step of the filter, convergence of the ensemble sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the ensemble members, and bounds on the ensemble then give convergence.

Data driven computing by the morphing fast Fourier transform ensemble Kalman filter in epidemic spread simulations.

The FFT EnKF data assimilation method is proposed and applied to a stochastic cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF combines spatial statistics and ensemble filtering methodologies into a localized and computationally inexpensive version of EnKF with a very small ensemble, and it is further combined with the morphing EnKF to assimilate changes in the position of the epidemic.