Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

Suitability of real-time quantitative PCR to estimate the relative telomere length in European Hake (Merluccius merluccius Linnaeus, 1758)

Telomere length measurement has been proposed as a promising tool to estimate the age of individuals in natural populations. We used real-time quantitative PCR (qPCR) to measure relative telomere length in four tissues (brain, kidney, liver and muscle) of European hake (Merluccius merluccius) in different groups based upon body length an otolith age estimate. We observed a high level of inter-individual differences in the measurements of relative telomere length in hakes of similar age and body length groups. The results of qPCR analysis showed a great variability in all measures and a lack of repeatability and reproducibility with significant statistical differences in the results of the different assays. The paper discusses the technical reasons for the variability in qPCR obtained in this work and by other authors.

Ultrasound imaging to estimate risk of esophageal and vascular puncture after conventional stellate ganglion block

The most common techniques to perform stellate ganglion blocks (SGBs) are the blind C6 approach and the fluoroscopic-controlled paratracheal C7 approach, both after manual dislocation of the large vessels. Complications due to vascular or esophageal puncture have been reported. The goal of this ultrasound imaging study was to determine how frequently hazardous structures are located along the needle path of conventional SGB and to determine the influence of the dislocation maneuver on their position.

Use of Land Surface Temperature to Estimate Surface Energy Fluxes: Contributions of Wilfried Brutsaert and Collaborators

Land surface temperature (LST) plays a key role in governing the land surface energy budget, and measurements or estimates of LST are an integral part of many land surface models and methods to estimate land surface sensible heat (H) and latent heat fluxes. In particular, the LST anchors the potential temperature profile in Monin-Obukhov similarity theory, from which H can be derived. Brutsaert has made important contributions to our understanding the nature of surface temperature measurements as well as the practical but theoretically sound use of LST in this framework. His work has coincided with the wide-spread availability of remotely sensed LST measurements. Use of remotely sensed LST estimates inevitably involves complicating factors, such as: varying spatial and temporal scales in measurements, theory, and models; spatial variability of LST and H; the relationship between measurements of LST and the temperature felt by the atmosphere; and the need to correct satellite-based radiometric LST measurements for the radiative effects of the atmosphere. This paper reviews the progress made in research in these areas by tracing and commenting on Brutsaert's contributions.

Choosing Smoothness Parameters for Smoothing Splines by Minimizing and Estimate of Risk

Smoothing splines are a popular approach for non-parametric regression problems. We use periodic smoothing splines to fit a periodic signal plus noise model to data for which we assume there are underlying circadian patterns. In the smoothing spline methodology, choosing an appropriate smoothness parameter is an important step in practice. In this paper, we draw a connection between smoothing splines and REACT estimators that provides motivation for the creation of criteria for choosing the smoothness parameter. The new criteria are compared to three existing methods, namely cross-validation, generalized cross-validation, and generalization of maximum likelihood criteria, by a Monte Carlo simulation and by an application to the study of circadian patterns. For most of the situations presented in the simulations, including the practical example, the new criteria out-perform the three existing criteria.

Fitting of Mixtures with Unspecified Number of Components Using Cross Validation Distance Estimate

Estimation of the number of mixture components (k) is an unsolved problem. Available methods for estimation of k include bootstrapping the likelihood ratio test statistics and optimizing a variety of validity functionals such as AIC, BIC/MDL, and ICOMP. We investigate the minimization of distance between fitted mixture model and the true density as a method for estimating k. The distances considered are Kullback-Leibler (KL) and “L sub 2”. We estimate these distances using cross validation. A reliable estimate of k is obtained by voting of B estimates of k corresponding to B cross validation estimates of distance. This estimation methods with KL distance is very similar to Monte Carlo cross validated likelihood methods discussed by Smyth (2000). With focus on univariate normal mixtures, we present simulation studies that compare the cross validated distance method with AIC, BIC/MDL, and ICOMP. We also apply the cross validation estimate of distance approach along with AIC, BIC/MDL and ICOMP approach, to data from an osteoporosis drug trial in order to find groups that differentially respond to treatment.