Mixture Models for Estimating Maximum Blood Flow Velocity.

Objectives: A gaussian mixture model (GMM) was recently developed for estimating the probability density function of blood flow velocity measured with transcranial Doppler ultrasound data. In turn, the quantiles of the probability density function allow one to construct estimators of the "maximum" blood flow velocity. However, GMMs assume gaussianity, a feature that is not omnipresent in observed data.

Earth before life.

Background: A recent study argued, based on data on functional genome size of major phyla, that there is evidence life may have originated significantly prior to the formation of the Earth.

Results: Here a more refined regression analysis is performed in which 1) measurement error is systematically taken into account, and 2) interval estimates (e.g.

A double-gaussian, percentile-based method for estimating maximum blood flow velocity.

Objectives: Transcranial Doppler sonography allows for the estimation of blood flow velocity, whose maximum value, especially at systole, is often of clinical interest. Given that observed values of flow velocity are subject to noise, a useful notion of "maximum" requires a criterion for separating the signal from the noise. All commonly used criteria produce a point estimate (ie, a single value) of maximum flow velocity at any time and therefore convey no information on the distribution or uncertainty of flow velocity.

A method for estimating zero-flow pressure and intracranial pressure.

Background: It has been hypothesized that the critical closing pressure of cerebral circulation, or zero-flow pressure (ZFP), can estimate intracranial pressure (ICP). One ZFP estimation method used extrapolation of arterial blood pressure as against blood-flow velocity. The aim of this study was to improve ICP predictions.