TY - JOUR A1 - Afantitis, A. A1 - Melagraki, G. A1 - Isigonis, P. A1 - Tsoumanis, A. A1 - Varsou, D. D. A1 - Valsami-Jones, E. A1 - Papadiamantis, A. A1 - Ellis, L.-J. A. A1 - Sarimveis, H. A1 - Doganis, P. A1 - Karatzas, P. A1 - Tsiros, P. A1 - Liampa, I. A1 - Lobaskin, V. A1 - Greco, D. A1 - Serra, A. A1 - Kinaret, P. A. S. A1 - Saarimäki, L. A. A1 - Grafström, R. A1 - Kohonen, P. A1 - Nymark, P. A1 - Willighagen, E. A1 - Puzyn, T. A1 - Rybinska-Fryca, A. A1 - Lyubartsev, A. A1 - Jensen, K. A. A1 - Brandenburg, J. G. A1 - Lofts, S. A1 - Svendsen, C. A1 - Harrison, S. A1 - Maier, D. A1 - Tamm, K. A1 - Jänes, J. A1 - Sikk, L. A1 - Dusinska, M. A1 - Longhin, E. A1 - Rundén-Pran, E. A1 - Mariussen, E. A1 - El Yamani, N. A1 - Unger, Wolfgang A1 - Radnik, Jörg A1 - Tropsha, A. A1 - Cohen, Y. A1 - Lesczynski, J. A1 - Hendren, C. O. A1 - Wiesner, M. A1 - Winkler, D. A1 - Suzuki, N. A1 - Yoon, T. H. A1 - Choi, J.-S. A1 - Sanabria, N. A1 - Gulumian, M. A1 - Lynch, I. T1 - NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment JF - Computational and Structural Biotechnology Journal N2 - Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational ‘safe-by-design’ approaches to facilitate NM commercialization. KW - Nanoinformatics KW - Hazard assessment KW - (Quantitative) Structure-Active Relationships KW - Safe-by-design KW - Predictive modelling PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-505952 DO - https://doi.org/10.1016/j.csbj.2020.02.023 VL - 18 SP - 583 EP - 602 PB - Elsevier B.V. AN - OPUS4-50595 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Greco, A. A1 - Starostin, V. A1 - Karapanagiotis, C. A1 - Hinderhofer, A. A1 - Gerlach, A. A1 - Pithan, L. A1 - Liehr, Sascha A1 - Schreiber, Frank A1 - Kowarik, Stefan T1 - Fast fitting of reflectivity data of growing thin films using neural networks JF - Journal of Applied Crystallography N2 - 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. KW - Artificial neural networks KW - X-ray reflectivity PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-498997 DO - https://doi.org/10.1107/S1600576719013311 SN - 1600-5767 VL - 52 SP - 1342 EP - 1347 PB - Wiley AN - OPUS4-49899 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -