Parameter estimation for the distribution of single cell lag times.

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level. We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

A neuropsychological instrument measuring age-related cerebral decline in older drivers: development, reliability, and validity of MedDrive.

When facing age-related cerebral decline, older adults are unequally affected by cognitive impairment without us knowing why. To explore underlying mechanisms and find possible solutions to maintain life-space mobility, there is a need for a standardized behavioral test that relates to behaviors in natural environments. The aim of the project described in this paper was therefore to provide a free, reliable, transparent, computer-based instrument capable of detecting age-related changes on visual processing and cortical functions for the purposes of research into human behavior in computational transportation science. After obtaining content validity, exploring psychometric properties of the developed tasks, we derived (Study 1) the scoring method for measuring cerebral decline on 106 older drivers aged ≥70 years attending a driving refresher course organized by the Swiss Automobile Association to test the instrument's validity against on-road driving performance (106 older drivers). We then validated the derived method on a new sample of 182 drivers (Study 2). We then measured the instrument's reliability having 17 healthy, young volunteers repeat all tests included in the instrument five times (Study 3) and explored the instrument's psychophysical underlying functions on 47 older drivers (Study 4). Finally, we tested the instrument's responsiveness to alcohol and effects on performance on a driving simulator in a randomized, double-blinded, placebo, crossover, dose-response, validation trial including 20 healthy, young volunteers (Study 5). The developed instrument revealed good psychometric properties related to processing speed. It was reliable (ICC = 0.853) and showed reasonable association to driving performance (R (2) = 0.053), and responded to blood alcohol concentrations of 0.5 g/L (p = 0.008). Our results suggest that MedDrive is capable of detecting age-related changes that affect processing speed. These changes nevertheless do not necessarily affect driving behavior.

Sign of the Times, January 2007

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MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.

The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.