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- Artificial neural networks (1)
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Organisationseinheit der BAM
Microorganisms perform multiple metabolic functions that shape the global cycling of elements, health and disease of their host organisms, and biotechnological processes. The rates, at which different metabolic activities are performed by individual cells, can vary between genetically identical cells within clonal populations. While the molecular mechanisms that result in such metabolic heterogeneity have attracted considerable interest, the environmental conditions that shape heterogeneity and its consequences have received attention only in recent years. Here, we review the environmental drivers that lead to metabolic heterogeneity with a focus on nutrient limitation, temporal fluctuations and spatial structure, and the functional consequences of such heterogeneity. We highlight studies using single-cell methods that allow direct investigation of metabolic heterogeneity and discuss the relevance of metabolic heterogeneity in complex microbial communities.
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.