Kernel Estimation of Rate Function for Recurrent Event Data

Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users.

EFFECT OF ELECTRICAL DISCHARGE PATTERN ON SPARK IGNITED FLAME KERNEL DEVELOPMENT

Fuel-lean combustion and exhaust gas recirculation (EGR) in spark ignition engines improve engine efficiency and reduce emission. However, flame initiation becomes more difficult in lean and dilute fuel-air mixture with traditional spark discharge. This research proposal will first provide an intensive review on topics related to spark ignition including properties of electrical discharge, flame kernel behavior and spark ignition modeling and simulation. Focus will be laid on electrical discharge pattern effect as it is showing prospect in extending ignition limits in SI engines. An experimental setup has been built with an optically accessible constant volume combustion vessel. Multiple imaging techniques as well as spectroscopy will be applied. By varying spark discharge patterns, preliminary test results are available on consequent flame kernel development. In addition to experimental investigation of spark plasma and flame kernel development, spark ignition modeling with detailed description of plasma channel is also proposed for this study.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Postmortem magnetic resonance imaging: Reproducing typical autopsy heart measurements

PURPOSE The aim of this study was to evaluate the utility of cardiac postmortem magnetic resonance (PMMR) to perform routine measurements of the ventricular wall thicknesses and the heart valves and to assess if imaging measurements are consistent with traditional autopsy measurements. METHODS In this retrospective study, 25 cases with cardiac PMMR and subsequent autopsy were included. The thicknesses of the myocardial walls as well as the circumferences of all heart valves were measured on cardiac PMMR and compared to autopsy measurements. Paired samples T-test and the Wilcoxon-Signed rank test, were used to compare autopsy and cardiac PMMR measurements. For exploring correlations, the Pearson's Correlation coefficient and the Spearman's Rho test were used. RESULTS Cardiac PMMR measurements of the aortic and pulmonary valve circumferences showed no significant differences from autopsy measurements. The mitral and tricuspid valves circumferences differed significantly from autopsy measurements. Left myocardial and right myocardial wall thickness also differed significantly from autopsy measurements. Left and right myocardial wall thickness, and tricuspid valve circumference measurements on cardiac PMMR and autopsy, correlated strongly and significantly. CONCLUSION Several PMMR measurements of cardiac parameters differ significantly from corresponding autopsy measurements. However, there is a strong correlation between cardiac PMMR measurements and autopsy measurements in the majority of these parameters. It is important to note that myocardial walls are thicker when measured in situ on cardiac PMMR than when measured at autopsy. Investigators using post-mortem MR should be aware of these differences in order to avoid false diagnoses of cardiac pathology based on cardiac PMMR.