The Gene Regulatory Cascade Linking Proneural Specification with Differentiation in Drosophila Sensory Neurons

Temporal expression profiling of sensory precursor cells reveals how the atonal proneural transcription factor regulates a specialized neuronal differentiation pathway

Analysis of stress gradients in physical vapour deposition multilayers by X-ray diffraction at fixed depth intervals

The objective of this article is to develop and apply a model for the design and evaluation of X-ray diffraction experiments to measure phase-specific residual stress profiles in multilayer systems. Using synchrotron radiation and angle-dispersive diffraction, the stress measurements are performed on the basis of the sin2[psi] method. Instead of the traditional [Omega] or [chi] mode, the experiments are carried out by a simultaneous variation of the goniometer angles [chi], [Omega] and [varphi]G to ensure that the penetration and information depth and the measuring direction [varphi] remain unchanged when the polar angle [psi] is varied. The applicability of this measuring and evaluation strategy is demonstrated by the example of a multilayer system consisting of Ti and TiAlN layers, alternately deposited on a steel substrate by means of physical vapour deposition

Predictor Selection for Model Averaging

When a number of distinct models is available for prediction, choice of a single model can offer unstable results. In regression, stochastic search variable selection with Bayesian model averaging is a solution for this robustness issue but utilizes very many predictors. Here we look at Bayesian model averaging that incorporates variable selection for prediction and use decision theory in the context of the multivariate general linear model with continuous covariates. We obtain similar mean square errors of prediction but with a greatly reduced predictor space that helps model interpretation. The paper summarises some results from Brown et al. (2001b). Here we provide a new example by applying the results to the selection of wavelet coefficients when regressing constituents of biscuit doughs on near-infrared spectra. In the example the number of predictors greatly exceeds the number of observation