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Studying cell settlement in the three-dimensional structure of synthetic biomaterials over time is of great interest in research and clinical translation for the development of artificial tissues and organs. Tracking cells as physical objects improves our understanding of the processes of migration, homing, and cell division during colonisation of the artificial environment. In this study, the 3D environment had a direct effect on the behaviour of biological objects. Recently, deep learning-based algorithms have shown significant benefits for cell segmentation tasks and, furthermore, for biomaterial design optimisation. We analysed the primary LHON fibroblasts in an artificial 3D environment after adeno-associated virus transduction. Application of these tools to model cell homing in biomaterials and to monitor cell morphology, migration and proliferation indirectly demonstrated restoration of the normal cell phenotype after gene manipulation by AAV transduction. Following the 3Rs principles of reducing the use of living organisms in research, modeling the formation of tissues and organs by reconstructing the behaviour of different cell types on artificial materials facilitates drug testing, the study of inherited and inflammatory diseases, and wound healing. These studies on the composition and algorithms for creating biomaterials to model the formation of cell layers were inspired by the principles of biomimicry. [ABSTRACT FROM AUTHOR]

Atmospheric correction is a necessary step in processing data recorded by spaceborne sensors for cloudless atmosphere, primarily in the visible and near-IR spectral range. In this paper we present a fast and sufficiently accurate method of atmospheric correction based on the analytical solutions of radiative transfer equation (RTE). The proposed analytical equations can be used to calculate the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image, taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface reflectance, and also assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or within the whole frame), the spectral reflectance at the Earth's surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation using non-linear least squares is based on the analytical solution of the direct transfer problem. This enables fast solution of the inverse problem in a very short calculation time. Testing of the method has been performed using the synthetic outgoing radiation spectra at the top of atmosphere, obtained from the LibRadTran code. In addition, we have used the spectra measured by the Hyperion. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate data obtained by our method, a comparative analysis with ground-based measurements of the Radiometric Calibration Network (RadCalNet) was carried out. [ABSTRACT FROM AUTHOR]