How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

BACKGROUND: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. RESULTS: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. CONCLUSIONS: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Experimental study of the effects of temperature variation on droplet size and wetness fraction in a low-pressure model steam turbine

The three-stage low-pressure model steam turbine at the Institute of Thermal Turbomachinery and Machinery Laboratory (ITSM) was used to study the impact of three different steam inlet temperatures on the homogeneous condensation process and the resulting wetness topology. The droplet spectrum as well as the particle number concentration were measured in front of the last stage using an optical-pneumatic probe. At design load, condensation starts inside the stator of the second stage. A change in the steam inlet temperature is able to shift the location of condensation onset within the blade row up- or downstream and even into adjoining blade passages, which leads to significantly different local droplet sizes and wetness fractions due to different local expansion rates. The measured results are compared to steady three-dimensional computational fluid dynamics calculations. The predicted nucleation zones could be largely confirmed by the measurements. Although the trend of measured and calculated droplet size across the span is satisfactory, there are considerable differences between the measured and computed droplet spectrum and wetness fractions. © IMechE 2013 Reprints and permissions: sagepub.co.uk/ journalsPermissions.nav.

Analysis of structural health monitoring data from hammersmith flyover

There has recently been considerable research published on the applicability of monitoring systems for improving civil infrastructure management decisions. Less research has been published on the challenges in interpreting the collected data to provide useful information for engineering decision makers. This paper describes some installed monitoring systems on the Hammersmith Flyover, a major bridge located in central London (United Kingdom). The original goals of the deployments were to evaluate the performance of systems for monitoring prestressing tendon wire breaks and to assess the performance of the bearings supporting the bridge piers because visual inspections had indicated evidence of deterioration in both. This paper aims to show that value can be derived from detailed analysis of measurements from a number of different sensors, including acoustic emission monitors, strain, temperature and displacement gauges. Two structural monitoring systems are described, a wired system installed by a commercial contractor on behalf of the client and a research wireless deployment installed by the University of Cambridge. Careful interpretation of the displacement and temperature gauge data enabled bearings that were not functioning as designed to be identified. The acoustic emission monitoring indicated locations at which rapid deterioration was likely to be occurring; however, it was not possible to verify these results using any of the other sensors installed and hence the only method for confirming these results was by visual inspection. Recommendations for future bridge monitoring projects are made in light of the lessons learned from this monitoring case study. © 2014 This work is made available under the terms of the Creative Commons Attribution 4.0 International license,.