TY - JOUR A1 - Altenburg, T. A1 - Giese, S. A1 - Wang, S. A1 - Muth, Thilo A1 - Renard, B.Y. T1 - Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides N2 - Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%. KW - Mass spectrometry KW - Machine learning KW - Deep learning KW - Peptide identification PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-547580 SN - 2522-5839 VL - 4 SP - 378 EP - 388 PB - Springer Nature CY - London AN - OPUS4-54758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tscheuschner, Georg A1 - Kaiser, Melanie N. A1 - Lisec, Jan A1 - Beslic, D. A1 - Muth, Thilo A1 - Krüger, M. A1 - Mages, H. W. A1 - Dorner, B. G. A1 - Knospe, J. A1 - Schenk, J. A. A1 - Sellrie, F. A1 - Weller, Michael G. T1 - MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour N2 - During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used 35 monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied to the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 60 min and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 degrees C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context. KW - Reproducibility KW - Quality control KW - Traceability KW - Peptides KW - Peptide mass fingerprinting KW - Monoclonal antibody KW - Recombinant antibody KW - Identity KW - Antibody identification KW - Sequencing KW - Light chain KW - Mass spectrometry KW - Software KW - Open science KW - Library KW - COVID-19 KW - Corona virus KW - Sequence coverage KW - NIST-mAb 8671 KW - Reference material KW - RBD KW - Spike protein KW - Nucleocapsid KW - Cleavage KW - Tryptic digest KW - MALDI KW - DHAP KW - 2,5-dihydroxyacetophenone KW - Github KW - Zenodo KW - ABID PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-547347 VL - 11 IS - 2 SP - 1 EP - 22 PB - MDPI CY - Basel AN - OPUS4-54734 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - QI-CLOUD Perspektiven zur Entwicklung einer digitalen Qualitätsinfrastruktur N2 - In diesem Vortrag wird die Perspektive einer digitalen Qualitätsinfrastruktur (QI) auf informatischer Seite vorgestellt. Eine zu entwickelnde QI-Cloud ist die Grundlage einer verteilten IT-Plattform über die digitalisierte Prozesse der QI abgewickelt, Daten sicher vorgehalten und ausgetauscht sowie digitale Zertifikate ausgestellt werden können. Dazu werden Methoden wie die Distributed Ledger Technologie sowie Smart Standards beschrieben, die das Potential haben, essentielle technologische Bestandteile einer digital transformierten QI zu werden. T2 - VMPA Tagung CY - Nuremberg, Germany DA - 31.05.2022 KW - Digital quality infrastructure KW - QI-Digital KW - QI-Cloud KW - smart standards PY - 2022 AN - OPUS4-56680 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - Better data quality for man and machine in materials research and testing N2 - Applying data-driven AI systems makes it possible to extract patterns from given data, generate predictions and helps making decisions. Material research and testing holds a plethora of AI-based applications, for example, for the automatized search and synthesis of new materials, the detection of materials defects, or the prediction of process and materials parameters (inverse problems). However, AI algorithms can often only be as good as the training data from which the corresponding models are learned. Therefore, it is also indispensable to develop measures for the standardization and quality assurance of such data. For this purpose, we develop and implement methods from transferring data from various sources into a homogeneous data repository with uniform data descriptions. Through the standardization and corresponding machine-readable interfaces, research data can be made usable and reusable for further data analyses. In addition to the technical implementation of integrative platforms, it is crucial that quality-assured research data management is recognized and implemented as an integral part of daily scientific work. Finally, we provide a vision of how the Federal Institute for Materials Research and Testing can benefit from data-driven AI systems. We discuss early applications and take a peek at future research. T2 - BR50 AI Workshop CY - Online meeting DA - 13.12.2021 KW - Data quality KW - Machine learning KW - Research data management PY - 2021 AN - OPUS4-56681 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Weichert, D. A1 - Kister, Alexander A1 - Houben, S. A1 - Ernis, G. A1 - Wrobel, S. T1 - Robustness in Fatigue Strength Estimation N2 - Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiment. T2 - 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering CY - Washington DC, USA DA - 07.02.2023 KW - Active Learning KW - Gaussian Process KW - Fatigue Strength PY - 2022 AN - OPUS4-56684 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - Benchmarking the current state of metaproteomics : a community driven evaluation of experimental and computational techniques N2 - Metaproteomics has substantially grown over the past years and supplements other omics approaches by bringing valuable functional information, enabling genotype- phenotype linkages and connections to metabolic outputs. Currently, a wide variety of metaproteomic workflows is available, yet their impact on the results remains to be thoroughly assessed. Here, we carried out the first community-driven, multi-lab comparison in metaproteomics: the critical assessment of metaproteome investigation (CAMPI) study. Based on well-established workflows, we evaluated the influence of sample preparation, mass spectrometry acquisition, and bioinformatic analysis using two samples: a simplified, lab-assembled human intestinal model and a human fecal sample. Although bioinformatic pipelines contributed to variability in peptide identification, wet-lab workflows were the most important source of differences between analyses. Overall, these peptide-level differences largely disappeared at the protein group level. Differences were observed between peptide- and protein-centric approaches for the predicted community composition but similar functional profiles were found across workflows. The CAMPI findings demonstrate the robustness of current metaproteomics research and provide a perspective for future benchmarking studies. T2 - ISMB/ECCB 2021 CY - Online meeting DA - 28.07.2021 KW - Mass spectrometry KW - Metaproteomics KW - Benchmarking KW - International lab study KW - Bioanalytics PY - 2021 AN - OPUS4-56685 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kister, Alexander A1 - Wrobel, S. A1 - Wirtz, T. A1 - Paul, N. T1 - Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs N2 - We interpret solving the multi-vehicle routing problem as a team Markov game with partially observable costs. For a given set of customers to serve, the playing agents (vehicles) have the common goal to determine the team-optimal agent routes with minimal total cost. Each agent thereby observes only its own cost. Our multi-agent reinforcement learning approach, the so-called multi-agent Neural Rewriter, builds on the single-agent Neural Rewriter to solve the problem by iteratively rewriting solutions. Parallel agent action execution and partial observability require new rewriting rules for the game. We propose the introduction of a so-called pool in the system which serves as a collection point for unvisited nodes. It enables agents to act simultaneously and exchange nodes in a conflict-free manner. We realize limited disclosure of agent-specific costs by only sharing them during learning. During inference, each agents acts decentrally, solely based on its own cost. First empirical results on small problem sizes demonstrate that we reach a performance close to the employed OR-Tools benchmark which operates in the perfect cost information setting. T2 - Gamification and Multiagent Solutions Workshop (ICLR 2022) CY - Online meeting DA - 29.04.2022 KW - Reinforcement learning KW - Deep Learning KW - Vehicle Routing PY - 2022 AN - OPUS4-56687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Tscheuschner, Georg A1 - Kaiser, Melanie N. A1 - Lisec, Jan A1 - Beslic, D. A1 - Muth, Thilo A1 - Krüger, M. A1 - Mages, H.-W. A1 - Dorner, B. G. A1 - Knospe, J. A1 - Schenk, J. A. A1 - Sellrie, F. A1 - Weller, Michael G. T1 - MALDI-TOF-MS-based identification of monoclonal murine anti-SARS-CoV-2 antibodies within one hour N2 - During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used thirty-five monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied onto the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 45 minutes and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 °C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0 (https://gets.shinyapps.io/ABID/). This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context. KW - SARS-CoV-2 antibody KW - Reproducibility crisis KW - Peptide mass fingerprinting KW - Monoclonal antibody KW - Traceability KW - Antibody identification KW - Identity KW - Antibody light chain KW - MALDI-TOF-MS KW - Trypsin KW - Acidic cleavage KW - Antibody subclass KW - Database KW - Peak overlap KW - ABID KW - Sulfuric acid KW - Online software KW - Sequencing KW - Peptide coverage PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-545182 SN - 2310-287X SP - 1 EP - 24 PB - MDPI CY - Basel AN - OPUS4-54518 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Monavari, M. A1 - Homaeigohar, S. A1 - Fuentes-Chandía, M. A1 - Nawaz, Q. A1 - Monavari, Mehran A1 - Venkatraman, A. A1 - Boccaccini, A. T1 - 3D printing of alginate dialdehyde-gelatin (ADA-GEL) hydrogels incorporating phytotherapeutic icariin loaded mesoporous SiO2-CaO nanoparticles for bone tissue engineering N2 - 3D printing enables a better control over the microstructure of bone restoring constructs, addresses the challenges seen in the preparation of patient-specific bone scaffolds, and overcomes the bottlenecks that can appear in delivering drugs/growth factors promoting bone regeneration. Here, 3D printing is employed for the fabrication of an osteogenic construct made of hydrogel nanocomposites. Alginate dialdehyde-gelatin (ADA-GEL) hydrogel is reinforced by the incorporation of bioactive glass nanoparticles, i.e. mesoporous silica-calcia nanoparticles (MSNs), in two types of drug (icariin) loading. The composites hydrogel is printed as superhydrated composite constructs in a grid structure. The MSNs not only improve the mechanical stiffness of the constructs but also induce formation of an apatite layer when the construct is immersed in simulated body fluid (SBF), thereby promoting cell adhesion and proliferation. The nanocomposite constructs can hold and deliver icariin efficiently, regardless of its incorporation mode, either as loaded into the MSNs or freely distributed within the hydrogel. Biocompatibility tests showed that the hydrogel nanocomposites assure enhanced osteoblast proliferation, adhesion, and differentiation. Such optimum biological properties stem from the superior biocompatibility of ADA-GEL, the bioactivity of the MSNs, and the supportive effect of icariin in relation to cell Proliferation and differentiation. Taken together, given the achieved structural and biological properties and effective drug delivery capability, the hydrogel nanocomposites show promising potential for bone tissue engineering. KW - 3D printed hydrogel KW - ADA-GEL KW - Drug delivery KW - Mesoporous SiO2-CaO nanoparticles KW - Bone tissue engineering PY - 2021 U6 - https://doi.org/10.1016/j.msec.2021.112470 VL - 131 SP - 1 EP - 11 PB - Elsevier B.V. AN - OPUS4-53848 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Van Den Bossche, T. A1 - Arntzen, M. O. A1 - Becher, D. A1 - Benndorf, D. A1 - Eijsink, V. G. H. A1 - Henry, C. A1 - Jagtap, P. D. A1 - Jehmlich, N. A1 - Juste, C. A1 - Kunath, B. J. A1 - Mesuere, B. A1 - Muth, Thilo A1 - Pope, P. B. A1 - Seifert, J. A1 - Tanca, A. A1 - Uzzau, S. A1 - Wilmes, P. A1 - Hettich, R. L. A1 - Armengaud, J. T1 - The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes N2 - Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this feld. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics. KW - Microbiome KW - Metaproteomics KW - Networking KW - Meta-Omics KW - Interactions KW - Education PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-542290 VL - 9 IS - 1 SP - 243 PB - BMC AN - OPUS4-54229 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Van Den Bossche, T. A1 - Kunath, B. A1 - Schallert, K. A1 - Schäpe, S. A1 - Abraham, P. E. A1 - Armengaud, J. A1 - Arntzen, M. Ø. A1 - Bassignani, A. A1 - Benndorf, D. A1 - Fuchs, S. A1 - Giannone, R. J. A1 - Griffin, T. J. A1 - Hagen, L. H. A1 - Halder, R. A1 - Henry, C. A1 - Hettich, R. L. A1 - Heyer, R. A1 - Jagtap, P. A1 - Jehmlich, N. A1 - Jensen, M. A1 - Juste, C. A1 - Kleiner, M. A1 - Langella, O. A1 - Lehmann, T. A1 - Leith, E. A1 - May, P. A1 - Mesuere, B. A1 - Miotello, G. A1 - Peters, S. L. A1 - Pible, O. A1 - Queiros, P. T. A1 - Reichl, U. A1 - Renard, B. Y. A1 - Schiebenhoefer, H. A1 - Sczyrba, A. A1 - Tanca, A. A1 - Trappe, K. A1 - Trezzi, J.-P. A1 - Uzzau, S. A1 - Verschaffelt, P. A1 - von Bergen, M. A1 - Wilmes, P. A1 - Wolf, M. A1 - Martens, L. A1 - Muth, Thilo T1 - Critical Assessment of MetaProteome Investigation (CAMPI): A multi-laboratory comparison of established workflows N2 - Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments. KW - Metaproteomics KW - Mass spectrometry KW - Data science KW - Benchmarking KW - Bioinformatics PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-541220 SN - 2041-1723 VL - 12 SP - 1 EP - 15 PB - Nature Publishing Group CY - London AN - OPUS4-54122 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Anzt, H. A1 - Bach, F. A1 - Druskat, S. A1 - Löffler, F. A1 - Loewe, A. A1 - Renard, B. Y. A1 - Seemann, G. A1 - Struck, A. A1 - Achhammer, E. A1 - Aggarwal, P. A1 - Appel, F. A1 - Bader, M. A1 - Brusch, L. A1 - Busse, C. A1 - Chourdakis, G. A1 - Dabrowski, P. W. A1 - Ebert, P. A1 - Flemisch, B. A1 - Friedl, S. A1 - Fritzsch, B. A1 - Funk, M. D. A1 - Gast, V. A1 - Goth, F. A1 - Grad, J. A1 - Hegewald, J. A1 - Hermann, S. A1 - Hohmann, F. A1 - Janosch, S. A1 - Kutra, D. A1 - Linxweiler, J. A1 - Muth, Thilo A1 - Peters-Kottig, W. A1 - Rack, F. A1 - Raters, F. H. C. A1 - Rave, S. A1 - Reina, G. A1 - Reißig, M. A1 - Ropinski, T. A1 - Schaarschmidt, J. A1 - Seibold, H. A1 - Thiele, J. P. A1 - Uekermann, B. A1 - Unger, S. A1 - Weeber, R. T1 - An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action N2 - Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. KW - Research Software KW - Sustainable Software Development KW - Academic Software KW - Software Infrastructure KW - Software Training KW - Software Licensing PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-522029 VL - 9 SP - 1 EP - 35 AN - OPUS4-52202 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pech, S. A1 - Rehberg, M. A1 - Janke, R. A1 - Benndorf, D. A1 - Genzel, Y. A1 - Muth, Thilo A1 - Sickmann, A. A1 - Rapp, E. A1 - Reichl, U. T1 - Tracking changes in adaptation to suspension growth for MDCK cells: cell growth correlates with levels of metabolites, enzymes and proteins N2 - Adaptations of animal cells to growth in suspension culture concern in particular viral vaccine production, where very specific aspects of virus-host cell interaction need to be taken into account to achieve high cell specific yields and overall process productivity. So far, the complexity of alterations on the metabolism, enzyme, and proteome level required for adaptation is only poorly understood. In this study, for the first time, we combined several complex analytical approaches with the aim to track cellular changes on different levels and to unravel interconnections and correlations. Therefore, a Madin-Darby canine kidney (MDCK) suspension cell line, adapted earlier to growth in suspension, was cultivated in a 1-L bioreactor. Cell concentrations and cell volumes, extracellular metabolite concentrations, and intracellular enzyme activities were determined. The experimental data set was used as the input for a segregated growth model that was already applied to describe the growth dynamics of the parental adherent cell line. In addition, the cellular proteome was analyzed by liquid chromatography coupled to tandem mass spectrometry using a label-free protein quantification method to unravel altered cellular processes for the suspension and the adherent cell line. Four regulatory mechanisms were identified as a response of the adaptation of adherent MDCK cells to growth in suspension. These regulatory mechanisms were linked to the proteins caveolin, cadherin-1, and pirin. Combining cell, metabolite, enzyme, and protein measurements with mathematical modeling generated a more holistic view on cellular processes involved in the adaptation of an adherent cell line to suspension growth. KW - MDCK cell KW - Proteome KW - Metabolism KW - Enzyme activity KW - Suspension growth PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-522070 VL - 105 IS - 5 SP - 1861 EP - 1874 PB - Springer AN - OPUS4-52207 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias A1 - El-Athman, Rukeia T1 - Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods N2 - Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system. T2 - 11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022) CY - Barcelona, Spain DA - 11.07.2022 KW - Research data management KW - Structural health monitoring KW - Artificial intelligence PY - 2022 SN - 978-1-032-35623-5 SN - 978-1-003-32264-1 U6 - https://doi.org/10.1201/9781003322641-127 SP - 1061 EP - 1068 PB - CRC Press CY - Boca Raton AN - OPUS4-55493 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias A1 - El-Athman, Rukeia T1 - Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods N2 - Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system. T2 - 11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022) CY - Barcelona, Spain DA - 11.07.2022 KW - Research data management KW - Structural health monitoring KW - Artificial intelligence PY - 2022 AN - OPUS4-55494 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Verschaffelt, P. A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - Probability based taxonomic profiling of viral and microbiome samples using PepGM and Unipept N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference in samples of unknown taxonomic origin. PepGM uses a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets providing taxonomic confidence scores. To build the graphical model, a list of potentially present taxa needs to be inferred. To this end, we integrate Unipept, which enables the fast querying of potentially present taxa. Together, they allow for taxonomic inference with statistically sound confidence scores. T2 - HUPO 2022 world congress CY - Cancun, Mexico DA - 04.12.2022 KW - Bioinformatics KW - Mass spectrometry KW - Taxonomic analysis KW - Microbiomes PY - 2022 AN - OPUS4-56748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. heuristics. T2 - International conference on systems biology 2022 CY - Berlin, Germany DA - 07.10.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56749 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. heuristics. T2 - European proteomics association conference 2022 CY - Leipzig, Germany DA - 03.04.2022 KW - graphical models KW - Taxonomic inference KW - Bioinformatics KW - Virus protoemics PY - 2022 AN - OPUS4-56750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - European bioinformatics community winter school 2022 CY - Lisbon, Portugal DA - 21.03.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56751 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - HUPO conference 2021 CY - Online meeting DA - 04.12.2021 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2021 AN - OPUS4-56752 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - European bioinformatics community winter school 2022 CY - Lisbon, Portugal DA - 21.03.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56753 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Benner, Philipp T1 - eScience bei Produktprüfung und Zertifizierung N2 - Das eScience Referat unterstützt die Forschungsbereiche der BAM im Bereich Research Data Management und Data Science. Der Vortrag gibt beispielhafte Einblicke in die Aktivitäten der eScience. Insbesondere wird das Projekt QI-Digital vorgestellt, welches als Ziel die Bereitstellung einer digitalen Qualitätsinfrastruktur hat. T2 - EDAP CY - Online meeting DA - 03.11.2021 KW - Produktprüfung KW - Zertifizierung PY - 2021 AN - OPUS4-56692 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Nowatzky, Yannek A1 - Benner, Philipp A1 - Reinert, K. A1 - Muth, Thilo T1 - Mistle: Metaproteomic index and spectral library search engine N2 - Introduction: With the introduction of accurate deep learning predictors, spectral matching applications might experience a renaissance in tandem mass spectrometry (MS/MS) driven proteomics. Deep learning models, e.g., Prosit, predict complete MS/MS spectra from peptide sequences and give the unprecedented ability to accurately predict mass spectra that may arise from any given proteome. However, the amount of spectral data is enormous when querying large search spaces, e.g., metaproteomes composed of many different species. Current spectral library search software, such as SpectraST, is not equipped to meet run time and memory constraints imposed by such large MS/MS databases, covering several millions of peptide spectrum predictions. Methods: Inspired by the fragment index data structure that had been introduced with MSFragger, we implement an efficient peak matching algorithm for computing spectral similarity between query and library spectra. Mistle (Metaproteomic index and spectral library search engine) uses index partitioning and SIMD (Single instruction, multiple data) intrinsics, which greatly improves speed and memory efficiency for searching large spectral libraries. Mistle is written in C++20 and highly parallelized. Results: We demonstrate the efficiency of Mistle on two predicted spectral libraries for the lab-assembled microbial communities 9MM and SIHUMIx. Compared to the spectral library search engine SpectraST, Mistle shows a >10-fold runtime improvement and is also faster than msSLASH, which uses locality-sensitive hashing. Although Mistle is slower than MSFragger, Mistle‘s memory footprint is an order of magnitude smaller. Furthermore, we find evidence that the spectral matching approach to predicted libraries identifies peptides with higher precision. Mistle detects peptides not found by database search via MSFragger and in turn uncovers unnoticed false discoveries among their matches. Conclusion: In this study, we show that predicted spectral libraries can enhance peptide identification for metaproteomics. Mistle provides the means to efficiently search large-scale spectral libraries, highlighted for the microbiota 9MM and SIHUMIx. T2 - HUPO2022 CY - Cancun, Mexico DA - 04.12.2022 KW - Proteomics KW - Mass spectrometry KW - Algorithms KW - Metaproteomics PY - 2022 AN - OPUS4-56695 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Van den Bossche, T. A1 - Verschaffelt, P. A1 - Schallert, K. A1 - Barsnes, H. A1 - Dawyndt, P. A1 - Benndorf, D. A1 - Renard, B. Y. A1 - Mesuere, B. A1 - Martens, L. A1 - Muth, Thilo T1 - Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis N2 - Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the Connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis. Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation. KW - Metaproteomics KW - Bioinformatics KW - Software KW - Pipelines PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.0c00136 VL - 19 IS - 8 SP - 3562 EP - 3566 PB - ACS Publications AN - OPUS4-51331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schiebenhoefer, H. A1 - Schallert, K. A1 - Renard, B. Y. A1 - Trappe, K. A1 - Schmid, E. A1 - Benndorf, D. A1 - Riedel, K. A1 - Muth, Thilo A1 - Fuchs, S. T1 - A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane N2 - Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow. KW - Bioinformatics KW - Protocol KW - Microbial proteomics KW - Software KW - Mass spectrometry KW - Metaproteomics PY - 2020 U6 - https://doi.org/10.1038/s41596-020-0368-7 SN - 1750-2799 VL - 15 IS - 10 SP - 3212 EP - 3239 PB - Nature Publishing Group AN - OPUS4-51335 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kuhring, M. A1 - Doellinger, J. A1 - Nitsche, A. A1 - Muth, Thilo A1 - Renard, B. Y. T1 - TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples N2 - Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt. KW - Bioinformatics KW - Mass spectrometry KW - Microbial proteomics KW - Strain identification KW - MS/MS PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.9b00714 VL - 19 IS - 6 SP - 2501 EP - 2510 PB - ACS AN - OPUS4-50942 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Grossegesse, M. A1 - Hartkopf, F. A1 - Nitsche, A. A1 - Schaade, L. A1 - Doellinger, J. A1 - Muth, Thilo T1 - Perspective on Proteomics for Virus Detection in Clinical Samples N2 - One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted. KW - COVID-19 KW - Mass spectrometry KW - Virus diagnostics KW - Virus detection KW - Targeted mass spectrometry KW - Proteomics PY - 2020 U6 - https://doi.org/10.1021/acs.jproteome.0c00674 SN - 1535-3907 VL - 19 IS - 11 SP - 4380 EP - 4388 PB - ACS AN - OPUS4-51633 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sajulga, R. A1 - Easterly, C. A1 - Riffle, M. A1 - Mesuere, B. A1 - Muth, Thilo A1 - Mehta, S. A1 - Kumar, P. A1 - Johnson, J. A1 - Gruening, B. A1 - Schiebenhoefer, H. A1 - Kolmeder, C. A1 - Fuchs, S. A1 - Nunn, B. A1 - Rudney, J. A1 - Griffin, T. A1 - Jagtap, P. T1 - Survey of metaproteomics software tools for functional microbiome analysis N2 - To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform. KW - Bioinformatics KW - Metaproteomics KW - Mass spectrometry PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-516358 SP - e0241503 AN - OPUS4-51635 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Holstein, Tanja A1 - Kurths, J. A1 - Wiedermann, M. T1 - Optimization of coupling and global collapse in diffusively coupled socio-ecological resource exploitation networks N2 - Single- and multi-layer complex networks have been proven as a powerful tool to study the dynamics within social, technological, or natural systems. An often observed common goal is to optimize these systems for specific purposes by minimizing certain costs while maximizing a desired output. Acknowledging that especially real-world systems from the coupled socio-ecological realm are highly intertwined this work exemplifies that in such systems the optimization of a certain subsystem, e.g. to increase the resilience against external pressure in an ecological network, may unexpectedly diminish the stability of the whole coupled system. For this purpose we utilize an adaptation of a previously proposed conceptual bi-layer network model composed of an ecological network of diffusively coupled resources co-evolving with a social network of interacting agents that harvest these resources and learn each other’s strategies depending on individual success. We derive an optimal coupling strength that prevents collapse in as many resources as possible if one assumes that the agents’ strategies remain constant over time. We then show that if agents socially learn and adapt strategies according to their neighbors’ success, this optimal coupling strength is revealed to be a critical parameter above which the probability for a global collapse in terms of irreversibly depleted resources is high—an effect that we denote the tragedy of the optimizer. We thus find that measures which stabilize the Dynamics within a certain part of a larger co-evolutionary system may unexpectedly cause the emergence of novel undesired globally stable states. Our results therefore underline the importance of holistic approaches for managing socio-ecological systems because stabilizing effects which focus on single subsystems may be counter-beneficial for the system as a whole. KW - Network science KW - Complex systems KW - Socio-ecological models KW - Non linear dynamics KW - Physics KW - Physics and society PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-524176 VL - 23 IS - 3 SP - 033027 PB - IOP Publishing CY - Bristol, England AN - OPUS4-52417 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Johann, Sergej A1 - Mansurova, Maria A1 - Kohlhoff, Harald A1 - Gkertsos, Aris A1 - Neumann, Patrick P. A1 - Bell, Jérémy A1 - Bartholmai, Matthias T1 - Wireless Mobile Sensor Device for in-situ Measurements with Multiple Fluorescent Sensors N2 - This paper describes a wireless mobile prototype able to perform optical measurements by means of a miniaturized spectrometer for low light analysis, e.g. fluorescent sensors. Evaluations, calculations, calibration management and result display are performed by a computer or a standard tablet. The device was designed primarily to detect traces of oil in drinking or ground water and for the analyses of crude oils. However, it can also address a wide range of fluorescent sensors. The fast and user-friendly inspection of water quality or oil properties, as well as the adaptability and mobility, make the device attractive for a variety of users. Further application areas could be easily implemented by adapting the optics and the software (database, data processing and calibration plots, etc.). T2 - IEEE Sensors 2018 CY - New Delhi, India DA - 28.10.2018 KW - Wireless mobile sensor device KW - Fluorescent sensor KW - Embedded system KW - Water quality KW - Oil PY - 2018 AN - OPUS4-46554 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Johann, Sergej A1 - Mansurova, Maria A1 - Kohlhoff, Harald A1 - Gkertsos, Aris A1 - Neumann, Patrick P. A1 - Bell, Jérémy A1 - Bartholmai, Matthias T1 - Wireless Mobile Sensor Device for in-situ Measurements with Multiple Fluorescent Sensors N2 - This paper describes a wireless mobile prototype able to perform optical measurements by means of a miniatur-ized spectrometer for low light analysis, e.g. fluorescent sensors. Evaluations, calculations, calibration management and result display are performed by a computer or a standard tablet. The device was designed primarily to detect traces of oil in drinking or ground water and for the analyses of crude oils. However, it can also address a wide range of fluorescent sensors. The fast and user-friendly inspection of water quality or oil properties, as well as the adaptability and mobility, make the device attractive for a variety of users. Further application areas could be easily imple-mented by adapting the optics and the software (database, data processing and calibration plots, etc.) T2 - IEEE Sensors 2018 CY - New Delhi, India DA - 28.10.2018 KW - Wireless mobile sensor device KW - Fluorescent sensor KW - Embedded system KW - Water quality KW - Oil PY - 2018 SN - 978-1-5386-4707-3 SP - 1067 EP - 1070 PB - IEEE CY - New Delhi, India AN - OPUS4-46556 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pauw, Brian Richard A1 - Laskina, Sofya A1 - Naik, Aakash Ashok A1 - Smales, Glen Jacob A1 - George, Janine T1 - “Ultima Ratio”: Multi-scale, high-resolution 3D-FFT scattering pattern simulations N2 - This talk highlights a proof-of-concept that demonstrates the ability to calculate high-resolution Fourier transforms. These can be combined with multi-scale modeling to simulate scattering over a wide range, from small-angle scattering to XRD and PDF. T2 - Shapespyer/MuSSIC launch workshop CY - Didcot, UK DA - 20.02.2023 KW - X-ray scattering KW - Simulation KW - Fourier Transform KW - 3D KW - High resolution KW - Multi-scale PY - 2023 AN - OPUS4-57031 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beslic, D. A1 - Tscheuschner, Georg A1 - Renard, B. Y. A1 - Weller, Michael G. A1 - Muth, Thilo T1 - Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly N2 - Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody–antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69–99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences. KW - De novo peptide sequencing KW - Bioinformatics KW - Benchmarking study KW - Monoclonal antibody KW - Mass spectrometry KW - Sequence coverage KW - Light chains KW - Heavy chains KW - IgG KW - Immunoglobulins KW - Error correction KW - Sequencing algorithm KW - Preprocessing KW - Missing fragmentation sites KW - Deep learning-based tools PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-570363 VL - 24 IS - 1 SP - 1 EP - 12 PB - Oxford University Press AN - OPUS4-57036 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Veh, G. A1 - Lützow, N. A1 - Tamm, J. A1 - Luna, L.V. A1 - Hugonnet, R. A1 - Vogel, Kristin A1 - Geertsema, M. A1 - Clague, J.J. A1 - Korup, O. T1 - Less extreme and earlier outbursts of ice-dammed lakes since 1900 N2 - Episodic failures of ice-dammed lakes have produced some of the largest floods in history, with disastrous consequences for communities in high mountains. Yet, estimating changes in the activity of ice-dam failures through time remains controversial because of inconsistent regional flood databases. Here, by collating 1,569 ice-dam failures in six major mountain regions, we systematically assess trends in peak discharge, volume, annual timing and source elevation between 1900 and 2021. We show that extreme peak flows and volumes (10 per cent highest) have declined by about an order of magnitude over this period in five of the six regions, whereas median flood discharges have fallen less or have remained unchanged. Ice-dam floods worldwide today originate at higher elevations and happen about six weeks earlier in the year than in 1900. Individual ice-dammed lakes with repeated outbursts show similar negative trends in magnitude and earlier occurrence, although with only moderate correlation to glacier thinning8. We anticipate that ice dams will continue to fail in the near future, even as glaciers thin and recede. Yet widespread deglaciation, projected for nearly all regions by the end of the twenty-first century9, may bring most outburst activity to a halt. KW - Outburst floods KW - Bayesian hierachical modelling KW - Trends in extreme events PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-570529 VL - 614 IS - 7949 SP - 701 EP - 707 AN - OPUS4-57052 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores N2 - Motivation: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. Results: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. Availability and implementation: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM. KW - Graphical model KW - Protemics KW - Virus KW - Ayesian models PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-577488 SN - 1367-4803 VL - 39 IS - 5 SP - 1 EP - 11 PB - Oxford University Press CY - Oxford, UK AN - OPUS4-57748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Amariamir, Sasan A1 - Benner, Philipp A1 - George, Janine T1 - Prediction of materials synthesizability N2 - In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials. Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts. This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API. T2 - MSE Congress 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Synthesizability KW - PU Learning KW - Cheminformatics PY - 2022 AN - OPUS4-56731 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Laskina, Sofya T1 - Computing the forward and inverse problem of X-ray scattering N2 - Continuing progress in the field of X-ray scattering methods empowers scientists with new possibilities to capture the most important piece of information about the structure of the sample - its 3D electron density. Although the first methods appeared almost a century ago, recovering the density structure of a sample is still very problematic. Most avail-able imaging techniques transform a 3D electron density of a realspace structure into the 2D Fourier Transform of the intensity of scattered waves in the reciprocal space. This process causes a loss of information. Firstly, instead of a 3D sample, a 2D image is created, and secondly, the phase information of the scattered waves is lost. The latter is known as the ”phase problem” and poses a serious obstacle on a way to recover a 3D electron density. In this work, we draw attention to the problem of forward and inverse Small Angle X-Ray Scattering. In the first, forward, part, we rethink the existing pipelines to computationally simulate such scattering experiments. Although there are efficient implementations of fast Fourier transformation, they often have some drawbacks. For instance, to calculate a 3D fast Fourier transform it is required to place its density in the RAM. For high-resolution structures of size > 1024 3 , this becomes very problematic, as the whole density structure requires more than 16 GB of memory. CUDA solution allows for a very fast and parallelizable implementation of high-resolution data on hundreds of last-generation machines. Such computations are very pricy and inaccessible for most scientists. To bypass this limitation, we propose a solution for a split-up 3D fast Fourier transform, which is implemented as a sequence of 2D and 1D operations. We compare our implementation on the simulated 3D shapes and show the result of a proof-of-concept on 4096 3 Metallorganic framework density structure. In the second, inverse problem, we train an invertible neural network, that given scattering data can predict the shape and its parameters. The architecture is built such, that the inverse problem is learned together with the forward process - the Fourier Transformation. We achieved very good results with this architecture, nonetheless, further testing is required, as the current training set only encompasses three simple shapes: sphere, hard sphere and cylinder. All code to reproduce and analyze the results is available at https: //github.com/sofyalaski/SAXS-simulations. KW - Machine Learning KW - SAXS KW - DFT PY - 2023 SP - 1 EP - 76 PB - Freie Universität Berlin CY - Berlin AN - OPUS4-56798 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nowatzky, Yannek A1 - Benner, Philipp A1 - Reinert, K. A1 - Muth, Thilo T1 - Mistle: bringing spectral library predictions to metaproteomics with an efficient search index N2 - Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Availability and implementation: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle. KW - Mass spectrometry KW - Proteomics KW - Algorithms KW - Metaproteomics PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-579373 SN - 1367-4811 VL - 39 IS - 6 SP - 1 EP - 12 PB - Oxford University Press CY - Oxford, Great Britain AN - OPUS4-57937 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials N2 - Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 AN - OPUS4-57889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Monavari, Mahshid A1 - Homaeigohar, Shahin A1 - Medhekar, Rucha A1 - Nawaz, Qaisar A1 - Monavari, Mehran A1 - Zheng, Kai A1 - Boccaccini, Aldo R. T1 - A 3D-Printed Wound-Healing Material Composed of Alginate Dialdehyde–Gelatin Incorporating Astaxanthin and Borate Bioactive Glass Microparticles N2 - In this study, a wound dressing composed of an alginate dialdehyde−gelatin (ADA-GEL) hydrogel incorporated by astaxanthin (ASX) and 70B (70:30 B2O3/CaO in mol %) borate bioactive glass (BBG) microparticles was developed through 3D printing. ASX and BBG particles sti.ened the composite hydrogel construct and delayed its in vitro degradation compared to the pristine hydrogel construct, mainly due to their cross-linking role, likely arising from hydrogen bonding between the ASX/BBG particles and ADA-GEL chains. Additionally, the composite hydrogel construct could hold and deliver ASX steadily. The composite hydrogel constructs codelivered biologically active ions (Ca and B) and ASX, which should lead to a faster, more e.ective wound-healing process. As shown through in vitro tests, the ASX-containing composite hydrogel promoted fibroblast (NIH 3T3) cell adhesion, proliferation, and vascular endothelial growth factor expression, as well as keratinocyte (HaCaT) migration, thanks to the antioxidant activity of ASX, the release of cell-supportive Ca2+ and B3+ ions, and the biocompatibility of ADA-GEL. Taken together, the results show that the ADA-GEL/BBG/ASX composite is an attractive biomaterial to develop multipurposed wound-healing constructs through 3D printing. KW - General Materials Science PY - 2023 U6 - https://doi.org/10.1021/acsami.2c23252 SP - 1 EP - 12 PB - American Chemical Society (ACS) AN - OPUS4-58548 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nourinejhad Zarghani, Shaheen A1 - Ehlers, Jens A1 - Monavari, Mehran A1 - von Bargen, Susanne A1 - Hamacher, Joachim A1 - Büttner, Carmen A1 - Bandte, Martina T1 - Applicability of Different Methods for Quantifying Virucidal Efficacy Using MENNO Florades and Tomato Brown Rugose Fruit Virus as an Example N2 - After entry of a quarantine/regulated pathogen, infected plants shall be destroyed, and the cultivated area (e.g., greenhouse) shall be disinfected. Therefore, the selection of an effective disinfectant plays an important role. With the availability of different methods for virus quantification, we investigated the application of quantitative ELISA (qELISA), RT-qPCR (reverse transcription-quantitative polymerase chain reaction), and bioassays for the quantification of disinfectant efficacy. Therefore, we estimated the titer reduction in tomato brown rugose fruit virus (ToBRFV), a regulated pathogen, in plant sap and on germ carriers after treatment with MENNO Florades 4% for 16 h. The virus load before and after the treatment was measured with the mentioned methods. The RT-qPCR and qELISA methods showed very low efficacy in the presence of the disinfectant. Although bioassays are time-consuming, need purified particles for establishing the quantification models, and are less sensitive than RT-qPCR, they were able to quantify the differences in virus titer in the presence/absence of disinfectant. Interestingly, the bioassays reached at least the lower limit sensitivity of a qELISA. By being less sensitive to the presence of the disinfectant, bioassays proved to be the only technique for the determination of the disinfectant efficacy against ToBRFV on different germ carriers as well as on virus-infected plant sap. KW - Plant Science KW - Ecology KW - Evolution KW - Behavior and Systematics PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-585497 VL - 12 IS - 4 SP - 1 EP - 18 PB - MDPI AG AN - OPUS4-58549 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nourinejhad Zarghani, Shaheen A1 - Monavari, Mehran A1 - Ehlers, Jens A1 - Hamacher, Joachim A1 - Büttner, Carmen A1 - Bandte, Martina T1 - Comparison of Models for Quantification of Tomato Brown Rugose Fruit Virus Based on a Bioassay Using a Local Lesion Host N2 - Considering the availability of serological and molecular biological methods, the bioassay has been paled into insignificance, although it is the only experimental method that can be used to demonstrate the infectivity of a virus. We compared goodness-of-fit and predictability power of five models for the quantification of tomato brown rugose fruit virus (ToBRFV) based on local lesion assays: the Kleczkowski model, Furumoto and Mickey models I and II, the Gokhale and Bald model (growth curve model), and the modified Poisson model. For this purpose, mechanical inoculations onto Nicotiana tabacum L. cv. Xanthi nc and N. glutionosa L. with defined virus concentrations were first performed with half-leaf randomization in a Latin square design. Subsequently, models were implemented using Python software and fitted to the number of local lesions. All models could fit to the data for quantifying ToBRFV based on local lesions, among which the modified Poisson model had the best prediction of virus concentration in spike samples based on local lesions, although data of individual indicator plants showed variations. More accurate modeling was obtained from the test plant N. glutinosa than from N. tabacum cv. Xanthi nc. The position of the half-leaves on the test plants had no significant effect on the number of local lesions. KW - Plant Science KW - Ecology KW - Evolution KW - Behavior and Systematics PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-585506 VL - 11 IS - 24 SP - 1 EP - 16 PB - MDPI AN - OPUS4-58550 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Valdestilhas, Andre A1 - Bayerlein, Bernd A1 - Moreno Torres, Benjamin A1 - Jan Zia, Ghezal Ahmad A1 - Muth, Thilo T1 - The Intersection Between Semantic Web and Materials Science N2 - The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE. KW - Linked open data KW - Materials science KW - Ontology KW - Semantic web PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-575506 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-57550 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp T1 - A Quantum-Chemical Bonding Database for Solid-State Materials N2 - A deep insight into the chemistry and nature of individual chemical bonds is essential for understanding materials. Bonding analysis is expected to provide important features for large-scale data analysis and machine learning of material properties. Such information on chemical bonds can be calculated using the LOBSTER (www.cohp.de) software package, which post-processes data from modern density functional theory computations by projecting plane wave-based wave functions onto a local atomic orbital basis. We have performed bonding analysis on 1520 compounds (insulators and semiconductors) using a fully automated workflow combining the VASP and LOBSTER software packages. We then automatically evaluated the data with LobsterPy (https://github.com/jageo/lobsterpy) and provide results as a database. The projected densities of states and bonding indicators are benchmarked on VASP projections and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features. T2 - Sommersymposium des Fördervereins Chemieolympiade CY - Online meeting DA - 15.04.2023 KW - Automation KW - Chemical Bonds KW - DFT KW - Quantum Chemistry PY - 2023 AN - OPUS4-57310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Albakri, Bashar A1 - Kister, Alexander A1 - Favaro, M. T1 - Robustness in Fatigue Strength Estimation N2 - Researchers study the properties of polymeric membranes - an important building block of batteries – by first measuring its impedance spectra and then determining a so called equivalent electronic circuit (EEC), that is an electronic circuit that approximately reproduces these measurements. Determining EEC is not completely automatized since it includes the search for the topology of the electronic circuit. This search is an inverse problem, since the formulas for determining the impedance spectra given the circuit topology and parameters are known while there are no formulas for the invers direction. To solve this invers problem, the original problem is split into two parts. The first part is to determine the topology of the circuit and the second part is to determine the parameters of the circuit. For the first part a Convolutional Neural Network Classifier is trained on a simulated data set, where the simulator is an implementation of the formulas for determining the impedance spectra given the complete description of the electronic circuit. After the topology is determined, the parameters of the electronic circuit are found by minimizing the error between the observed spectra and the spectra corresponding to these parameters. This minimization is a global optimization. T2 - Mathematical and Statistical Methods for Metrology; MSMM 2023 CY - Turin, Italy DA - 30.05.2023 KW - Equivalent Electrical Circuits KW - Electrochemical Impedance Spectroscopy KW - Neural Networks KW - Global Optimization PY - 2023 AN - OPUS4-57677 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 8) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852823 PB - Zenodo CY - Geneva AN - OPUS4-57448 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1) N2 - This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database. Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7794811 PB - Zenodo CY - Geneva AN - OPUS4-57439 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 2) N2 - This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database. Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7821727 PB - Zenodo CY - Geneva AN - OPUS4-57440 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 1) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) Refer to README.md file instructions to reproduce the data. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852082 PB - Zenodo CY - Geneva AN - OPUS4-57441 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 2) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852107 PB - Zenodo CY - Geneva AN - OPUS4-57442 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 3) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852791 PB - Zenodo CY - Geneva AN - OPUS4-57443 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 4) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852798 PB - Zenodo CY - Geneva AN - OPUS4-57444 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 5) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852806 PB - Zenodo CY - Geneva AN - OPUS4-57445 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 6) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852808 PB - Zenodo CY - Geneva AN - OPUS4-57446 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 7) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 U6 - https://doi.org/10.5281/zenodo.7852820 PB - Zenodo CY - Geneva AN - OPUS4-57447 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pauw, Brian Richard A1 - Laskina, Sofya A1 - Naik, Aakash Ashok A1 - Smales, Glen Jacob A1 - George, Janine A1 - Breßler, Ingo A1 - Benner, Philipp T1 - "Ultima Ratio": Simulating wide-range X-ray scattering and diffraction N2 - We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the scattering intensity, may offer a novel pathway for determining the degree of crystallinity. The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from Q < 0.01 1/nm up to Q < 150 1/nm, with a resolution of 0.16 Angstrom. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to 8000^3 voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-Q behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts. KW - X-ray KW - Simulation KW - 3D Fourier Transform KW - High resolution KW - XRD KW - SAXS KW - PDF KW - Total scattering KW - X-ray scattering KW - Metal organic framework KW - Electron density map KW - FFT PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-572067 VL - Cornell University SP - 1 EP - 12 PB - Ithaca, NY AN - OPUS4-57206 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pauw, Brian Richard A1 - Laskina, Sofya A1 - Naik, Aakash Ashok A1 - Smales, Glen Jacob A1 - George, Janine A1 - Breßler, Ingo A1 - Benner, Philipp T1 - Jupyter notebook and VASP calculation details accompanying the manuscript: "Ultima Ratio: Simulating wide-range X-ray scattering and diffraction" N2 - ## Summary: This notebook and associated datasets (including VASP details) accompany a manuscript available on the ArXiv (https://doi.org/10.48550/arXiv.2303.13435) and hopefully soon in a journal as short communication as well. Most of the details needed to understand this notebook are explained in that paper with the same title as above. For convenience, the abstract is repeated here: ## Paper abstract: We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is presented coupled to the scattering intensity, may offer a novel pathway for determining the degree of crystallinity. The ``Ultima Ratio'' strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from $Q<0.01$\,$\mathrm{nm}^{-1}$ up to $Q\approx150$\,$\mathrm{nm}^{-1}$, with a resolution of 0.16\,\AA. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to $8000^3$ voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-$Q$ behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts. KW - X-ray KW - Simulation KW - Scattering KW - MOUSE KW - Nanomaterials KW - XRD KW - SAXS KW - PDF KW - total scattering KW - 3D Fourier Transform KW - High Resolution KW - FFT PY - 2023 UR - https://doi.org/10.48550/arXiv.2303.13435 U6 - https://doi.org/10.5281/zenodo.7764044 PB - Zenodo CY - Geneva AN - OPUS4-57207 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - VIDEO A1 - Pauw, Brian Richard T1 - "Ultima Ratio": Multi-scale, high-resolution 3D-FFT scattering pattern simulations N2 - This talk highlights a proof-of-concept that demonstrates the ability to calculate high-resolution Fourier transforms. These can be combined with multi-scale modeling to simulate scattering over a wide range, from small-angle scattering to XRD and PDF. The preprint documenting this is available on the ArXiv here: https://doi.org/10.48550/arXiv.2303.13435 The Jupyter notebook, VASP calculation details and MOUSE measured scattering patterns are available from this Zenodo repository: https://dx.doi.org/10.5281/zenodo.7764045 KW - Video KW - Simulation KW - High-resolution KW - Fourier Transform KW - 3D FFT KW - Nanomaterial KW - Metal organic framework KW - MOF KW - SAXS KW - XRD KW - PDF KW - X-ray diffraction KW - Pair distribution function KW - Small-angle X-ray scattering PY - 2023 UR - https://www.youtube.com/watch?v=lEApkOqR5e8 PB - YouTube, LLC CY - San Bruno, CA, USA AN - OPUS4-57212 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Athman, Rukeia A1 - Bresch, Harald A1 - Muth, Thilo A1 - Rädler, Jörg A1 - Rühle, Bastian T1 - Datenmanagement im Nanomaterial-Labor: Einsatz von ELN-Software zur digitalen Transformation des Laboralltags N2 - Angesichts der zunehmenden Digitalisierung und dem Einsatz datenintensiver Methodiken in der Wissenschaft stehen Forschende vor der Herausforderung, stetig wachsende Datenmengen nachvollziehbar zu dokumentieren, langfristig zu speichern und für Dritte nachnutzbar zu machen. Um diesen Anforderungen gerecht zu werden, bietet sich die Nutzung von Software-Lösungen an, welche Forschungsdatenmanagement mit der digitalen Dokumentation von Laborinventar und Experimenten in elektronischen Laborbüchern (engl. electronic lab notebooks (ELN)) verknüpfen. KW - NanoPlattform KW - BAM Data Store KW - OpenBIS KW - Forschungsdatenmanagement KW - Research data management KW - Elektronisches Laborbuch KW - Electronic lab notebook (ELN) PY - 2023 UR - https://analyticalscience.wiley.com/content/article-do/datenmanagement-im-nanomaterial-labor SN - 0016-3538 IS - 9 SP - 24 EP - 26 PB - Wiley Analytical Science CY - Weinheim AN - OPUS4-59600 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - El-Athman, Rukeia A1 - Rädler, Jörg A1 - Löhmann, Oliver A1 - Ariza, Angela A1 - Muth, Thilo T1 - The BAM Data Store N2 - As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE. T2 - 1st Conference on Research Data Infrastructure DA - 12.09.2023 KW - Research Data Infrastructure KW - Electronic Lab Notebook (ELN) KW - openBIS KW - Research Data Management PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-596032 VL - 1 SP - 1 EP - 5 AN - OPUS4-59603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lisec, Jan A1 - Recknagel, Sebastian A1 - Prinz, Carsten A1 - Vogel, Kristin A1 - Koch, Matthias A1 - Becker, Roland T1 - eCerto—versatile software for interlaboratory data evaluation and documentation during reference material production N2 - The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials. KW - Reference material KW - Statistics KW - Software KW - Collaborative trial PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-591851 SP - 1 EP - 9 PB - Springer Science and Business Media LLC AN - OPUS4-59185 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Maack, Stefan A1 - Benner, Philipp A1 - Kröll, Mirco A1 - Prager, Jens A1 - Daum, Werner A1 - Casperson, Ralf A1 - Heckel, Thomas A1 - Spaltmann, Dirk A1 - et al., ED - Wahlster, W. ED - Winterhalter, C. T1 - Deutsche Normungsroadmap künstliche Intelligenz Ausgabe 2 N2 - Im Auftrag des Bundesministeriums für Wirtschaft und Klimaschutz haben DIN und DKE im Januar 2022 die Arbeiten an der zweiten Ausgabe der Deutschen Normungsroadmap Künstliche Intelligenz gestartet. In einem breiten Beteiligungsprozess und unter Mitwirkung von mehr als 570 Fachleuten aus Wirtschaft, Wissenschaft, öffentlicher Hand und Zivilgesellschaft wurde damit der strategische Fahrplan für die KI-Normung weiterentwickelt. Koordiniert und begleitet wurden diese Arbeiten von einer hochrangigen Koordinierungsgruppe für KI-Normung und -Konformität. Mit der Normungsroadmap wird eine Maßnahme der KI-Strategie der Bundesregierung umgesetzt und damit ein wesentlicher Beitrag zur „KI – Made in Germany“ geleistet. Die Normung ist Teil der KI-Strategie und ein strategisches Instrument zur Stärkung der Innovations- und Wettbewerbsfähigkeit der deutschen und europäischen Wirtschaft. Nicht zuletzt deshalb spielt sie im geplanten europäischen Rechtsrahmen für KI, dem Artificial Intelligence Act, eine besondere Rolle. KW - Digitalisierung KW - Künstliche Intelligenz KW - Data Sciences KW - Normung Roadmap PY - 2022 SP - 1 EP - 448 AN - OPUS4-56576 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Abad Andrade, Carlos Enrique A1 - You, Zengchao A1 - Richter, Silke A1 - Benner, Philipp A1 - Recknagel, Sebastian T1 - The use of reference materials to improve the calibration strategy in glow discharge optical emission spectroscopy with machine learning N2 - Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect. Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed. Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail. T2 - Winter Conference on Plasma Chemistry 2022 CY - Tucson, AZ, USA DA - 17.01.2022 KW - GDOES KW - Machine learning KW - Reference materials KW - Calibration KW - Cooper PY - 2022 AN - OPUS4-56497 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Vogel, Kristin A1 - Richter, Silke T1 - Die Berechnung von Unsicherheiten zertifizierter Werte von Referenzmaterialien und deren Nutzung zur Berechnung von Verfahrensmessunsicherheiten N2 - Die Angabe von Unsicherheiten bei zertifizierten Werten von Referenzmaterialien ist von entscheidender Bedeutung. Die korrekte Einbindung der Unsicherheiten zur Berechnung von Verfahrensmessunsicherheiten ist wesentlich für die Gewährleistung der Genauigkeit und Zuverlässigkeit von Messungen. In diesem Vortrag werden die verschiedenen Einflussfaktoren auf die Unsicherheit zertifizierter Werte gemäß ISO Guide 35 dargestellt. Dabei werden insbesondere die Charakterisierung, Homogenität und Stabilität als entscheidende Faktoren für die Bestimmung der Unsicherheit eines Referenzmaterials betrachtet. Abschließend wird das Konzept anhand eines konkreten Beispiels veranschaulicht, um die praktische Anwendung und die Auswirkungen auf die Berechnung von Verfahrensmessunsicherheiten zu verdeutlichen. T2 - 324. PTB-Seminar Berechnung der Messunsicherheit – Empfehlungen für die Praxis CY - Berlin, Germany DA - 22.05.2023 KW - Referenzmaterialien KW - Unsicherheit PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-598418 UR - https://www.ptb.de/cms/fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/324_PTB_Seminar/Vortraege/324-PTB-Seminar_Unsicherheiten_zertifizierter_Werte.pdf UR - https://www.ptb.de/cms/ptb/fachabteilungen/abt8/fb-84/ag-842/seminare/324-ptb-seminar-berechnung-der-messunsicherheit-empfehlungen-fuer-die-praxis.html AN - OPUS4-59841 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Heidenreich, Sebastian A1 - Bär, Markus A1 - Klauenberg, Katy A1 - Elster, Clemens A1 - Harris, Peter A1 - Lines, Keith A1 - Hippolyte, Jean-Laurent A1 - George, Indhu A1 - Wright, Louise A1 - Cox, Maurice A1 - Rhodes, Susan A1 - Zilberti, Luca A1 - Manzin, Alessandra A1 - Pennecchi, Francesca A1 - Kok, Gertjan A1 - van der Veen, Adriaan A1 - Fischer, Nicolas A1 - Sousa, Joao Alves A1 - Vogel, Kristin A1 - Bosnjakovic, Alen A1 - Ellison, Stephen T1 - Strategic Agenda, European Metrology Network for Mathematics and Statistics N2 - This document constitutes the Strategic Research Agenda (SRA) for the European Metrology Network for Mathematics and Statistics in Metrology (EMN Mathmet). The EMN Mathmet is an alliance of European National Metrology Institutes (NMIs), Designated Institutes (DIs) and an EMN Partner that aims to strengthen research and cooperation in the field. The SRA has been developed within a European project (EMPIR 18NET05 MATHMET) to promote and support the network. The SRA was developed based on a consultation process with stakeholders and the strategies of individual NMIs and DIs, and in alignment with the EURAMET 2030 strategy. As a key result, the SRA defines a long-term research goal: the EMN Mathmet will coordinate research to strengthen the trust in algorithms, software tools and data to underpin digital transformation. For this purpose, new emerging research topics where algorithms, software tools and data play a significant role were identified: (i) Artificial Intelligence and Machine Learning, and (ii) Computational Modelling and Virtual Metrology. The foundation for the development of these new topics is given by the traditional focus on (iii) Data Analysis and Uncertainty Evaluation. The SRA characterises the future needs and challenges in the field of mathematics and statistics in metrology and provides an outline of how the EMN Mathmet can meet these new emerging requirements. KW - Research Agenda KW - Metrology KW - Mathematics and Statistics PY - 2023 UR - https://www.euramet.org/european-metrology-networks/mathmet/strategy/strategic-research-agenda SN - 978-3-942992-78-7 SP - 1 EP - 68 PB - EURAMET e.V. CY - Braunschweig AN - OPUS4-59526 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Weichert, Dorina A1 - Kister, Alexander A1 - Volbach, Peter A1 - Houben, Sebastian A1 - Trost, Marcus A1 - Wrobel, Stefan T1 - Explainable production planning under partial observability in high-precision manufacturing N2 - Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners. KW - Explainability KW - Manufacturing KW - Reinforcement Learning KW - Monte Carlo tree search KW - Partially observable Markov decision process PY - 2023 U6 - https://doi.org/10.1016/j.jmsy.2023.08.009 SN - 0278-6125 VL - 70 SP - 514 EP - 524 PB - Elsevier Ltd. CY - Southfield AN - OPUS4-58963 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ghasem Zadeh Khorasani, Media A1 - Albakri, Bashar A1 - Heimann, Jan T1 - Federated SHM: a Novel Collaborative ML Approach to Overcome Data Scarcity and Data Privacy Concerns in StructuralHealth Monitoring N2 - The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities. Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations. In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence. Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems. T2 - QI Digital Forum CY - Berlin, Germany DA - 10.10.2023 KW - Machine learning KW - Federated learning KW - Structural health monitoring KW - Artificial intelligence KW - Hydrogen safety KW - Data privacy PY - 2023 AN - OPUS4-59415 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Monavari, Mehran A1 - Prellwitz, Matthias A1 - Muth, Thilo A1 - Eichstädt, Sascha A1 - Koch, Claudia T1 - Quality-X: A Federated Digital Ecosystem for the Future Quality Infrastructure N2 - Harmonized and interoperable national Quality Infrastructure (QI) systems are essential for fostering cooperation, promoting mutual trust, and facilitating trade. The true potential of the QI is realized when its elements and actors are seamlessly integrated into a cohesive digital QI ecosystem. Recent developments towards industrial international data spaces enable such an ecosystem but require the integration of QI principles. Recognizing the lack of such a platform, Quality-X aims at setting the stage for the implementation of a QI ecosystem in international data spaces (IDS), GAIA-X and related German and European projects dedicated to secure data sharing. Quality-X is not about the construction of a platform; it is the creation of an inclusive QI ecosystem with harmonized interfaces. Instead of imposing rigid data structures, it prioritizes interoperability. Through the utilization of Decentralized Identifiers (DIDs), Verifiable Credentials, and Identity Hubs, Quality-X seeks seamless interactions across diverse service provider systems. This white paper introduces the concept and vision of Quality-X and discusses the general prerequisites for integrating QI processes within data spaces. Further on, we introduce existing testbeds, which will serve as an experimental proving ground for exploring various use cases related to the implementation of the vision of a QI-Digital. +++ Harmonisierte und interoperable nationale Qualitätsinfrastrukturen (QI) sind für die Förderung der Zusammenarbeit, des gegenseitigen Vertrauens und der Erleichterung des Handels unerlässlich. Das wahre Potenzial der QI kommt zum Tragen, wenn ihre Elemente und Akteure nahtlos in ein kohärentes digitales QI-Ökosystem integriert werden. Die jüngsten Entwicklungen hin zu industriellen internationalen Datenräumen ermöglichen ein solches Ökosystem, erfordern jedoch die Integration von QI-Prinzipien. Angesichts des Fehlens einer solchen Plattform zielt Quality-X darauf ab, die Voraussetzungen für die Umsetzung eines QI-Ökosystems in internationalen Datenräumen (IDS), GAIA-X und verwandten deutschen und europäischen Projekten zum sicheren Datenaustausch zu schaffen. Bei Quality-X geht es nicht um den Aufbau einer Plattform, sondern um die Schaffung eines umfassenden QI-Ökosystems mit harmonisierten Schnittstellen. Anstatt starre Datenstrukturen aufzuerlegen, steht die Interoperabilität im Vordergrund. Durch die Verwendung von dezentralen Identifikatoren (DIDs), überprüfbaren Berechtigungsnachweisen und Identitäts-Hubs strebt Quality-X eine nahtlose Interaktion zwischen verschiedenen Systemen von Dienstleistern an. Dieses Whitepaper stellt das Konzept und die Vision von Quality-X vor und erörtert die allgemeinen Voraussetzungen für die Integration von QI-Prozessen in Datenräumen. Darüber hinaus stellen wir bestehende Testbeds vor, die als experimentelles Versuchsfeld für die Erforschung verschiedener Anwendungsfälle im Zusammenhang mit der Umsetzung der Vision einer QI-Digital dienen sollen. KW - Quality Assurance KW - Quality Infrastructure KW - Data Spaces KW - Digitalization KW - Quality Management KW - Interoperability KW - Verification KW - Identification KW - Certificate KW - Datenräume KW - Qualitätsinfrastruktur KW - Gaia-X KW - Manufacturing-X KW - Catena-X KW - Datenökosystem KW - Qualitätssicherung KW - Qualitätsmanagement PY - 2023 UR - https://www.qi-digital.de/fileadmin/user_upload/website/publikationen/1022_Brosch%C3%BCre_Quality-X_v4.pdf SP - 1 CY - Berlin AN - OPUS4-59354 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Arikan, Muzaffer A1 - Muth, Thilo T1 - Integrated multi-omics analyses of microbial communities: A review of the current state and future directions N2 - Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions. KW - Microbiome KW - Multi-omics KW - Data integration PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-580785 SP - 1 EP - 17 PB - Royal Society of Chemistry AN - OPUS4-58078 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Walke, D. A1 - Micheel, D. A1 - Schallert, K. A1 - Muth, Thilo A1 - Broneske, D. A1 - Saake, G. A1 - Heyer, R. T1 - The importance of graph databases and graph learning for clinical applications N2 - The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. KW - Graph databases KW - Graph learning KW - Review KW - RDF PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-580858 SN - 1758-0463 SP - 1 EP - 20 AN - OPUS4-58085 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Muth, Thilo T1 - MassIVE MSV000089620 - Critical Assessment of MetaProteome Investigation (CAMPI): A multi-lab comparison of established workflows N2 - Metaproteomics, the study of the collective proteome within a microbial ecosystem, has substantially grown over the past few years. This growth comes from the increased awareness that it can powerfully supplement metagenomics and metatranscriptomics analyses. Although metaproteomics is more challenging than single-species proteomics, its added value has already been demonstrated in various biosystems, such as gut microbiomes or biogas plants. Because of the many challenges, a variety of metaproteomics workflows have been developed, yet it remains unclear what the impact of the choice of workflow is on the obtained results. Therefore, we set out to compare several well-established workflows in the first community-driven, multi-lab comparison in metaproteomics: the critical assessment of metaproteome investigation (CAMPI) study. In this benchmarking study, we evaluated the influence of different workflows on sample preparation, mass spectrometry acquisition, and bioinformatic analysis on two samples: a simplified, lab-assembled human intestinal sample and a complex human fecal sample. We find that the same overall biological meaning can be inferred from the metaproteome data, regardless of the chosen workflow. Indeed, taxonomic and functional annotations were very similar across all sample-specific data sets. Moreover, this outcome was consistent regardless of whether protein groups or peptides, or differences at the spectrum or peptide level were used to infer these annotations. Where differences were observed, those originated primarily from different wet-lab methods rather than from different bioinformatic pipelines. The CAMPI study thus provides a solid foundation for benchmarking metaproteomics workflows, and will therefore be a key reference for future method improvement. [doi:10.25345/C5SX64D9M] [dataset license: CC0 1.0 Universal (CC0 1.0)] KW - Benchmarking KW - Data set PY - 2021 U6 - https://doi.org/10.25345/c5sx64d9m PB - University of California CY - San Diego AN - OPUS4-58087 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Monavari, M. A1 - Medhekar, R. A1 - Nawaz, Q. A1 - Monavari, Mehran A1 - Fuentes‐Chandía, M. A1 - Homaeigohar, S. A1 - Boccaccini, A. R. T1 - A 3D printed bone tissue engineering scaffold composed of alginate dialdehyde-gelatine reinforced by lysozyme loaded cerium doped mesoporous silica-calcia nanoparticles N2 - A novel biomaterial comprising alginate dialdehyde-gelatine (ADA-GEL) hydrogel augmented by lysozyme loaded mesoporous cerium doped silica-calcia nanoparticles (Lys-Ce-MSNs) is 3D printed to create bioactive scaffolds. Lys-Ce-MSNs raise the mechanical stiffness of the hydrogel composite scaffold and induce surface apatite mineralization, when the scaffold is immersed in simulated body fluid (SBF). Moreover, the scaffolds can co-deliver bone healing (Ca and Si) and antioxidant ions (Ce), and Lys to achieve antibacterial (and potentially anticancer) properties. The nanocomposite hydrogel scaffolds can hold and deliver Lys steadily. Based on the in vitro results, the hydrogel nanocomposite containing Lys assured improved pre-osteoblast cell (MC3T3-E1) proliferation, adhesion, and differentiation, thanks to the biocompatibility of ADA-GEL, bioactivity of Ce-MSNs, and the stabilizing effect of Lys on the scaffold structure. On the other hand, the proliferation level of MG63 osteosarcoma cells decreased, likely due to the effect of Lys. Last but not least, cooperatively, alongside gentamicin (GEN), Lys brought about a proper antibacterial efficiency to the hydrogel nanocomposite scaffold against gram-positive and gram-negative bacteria. Taken together, ADA-GEL/Lys-Ce-MSN nanocomposite holds great promise for 3D printing of multifunctional hydrogel bone tissue engineering (BTE) scaffolds, able to induce bone regeneration, address infection, and potentially inhibit tumor formation and growth. KW - Materials chemistry KW - Polymers and plastics KW - Biomaterials KW - Bioengineering KW - Biotechnology KW - 3D printing KW - Bone tissue engineering KW - Drug delivery KW - Lysozyme KW - Mesoporous SiO2-CaO nanoparticles PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-581238 SN - 1616-5195 VL - 22 IS - 9 SP - 1 EP - 15 PB - Wiley-VCH CY - Weinheim AN - OPUS4-58123 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Muth, Thilo T1 - The promise of mass spectrometry-based virus proteomics: taking a peek at current bioinformatics applications and limitations N2 - Driven by recent technological advances and the need for improved viral diagnostic applications, mass spectrometry-based proteomics comes into play for detecting viral pathogens accurately and efficiently. However, the lack of specific algorithms and software tools presents a major bottleneck for analyzing data from host-virus samples. For example, accurate species- and strain-level classification of a priori unidentified organisms remains a very challenging task in the setting of large search databases. Another prominent issue is that many existing solutions suffer from the protein inference issue, aggravated because many homologous proteins are present across multiple species. One of the contributing factors is that existing bioinformatic algorithms have been developed mainly for single-species proteomics applications for model organisms or human samples. In addition, a statistically sound framework was lacking to accurately assign peptide identifications to viral taxa. In this presentation, an overview is given on current bioinformatics developments that aim to overcome the above-mentioned issues using algorithmic and statistical methods. The presented methods and software tools aim to provide tailored solutions for both discovery-driven and targeted proteomics for viral diagnostics and taxonomic sample profiling. Furthermore, an outlook is provided on how the bioinformatic developments might serve as a generic toolbox, which can be transferred to other research questions, such as metaproteomics for profiling microbiomes and identifying bacterial pathogens. T2 - European Virus Bioinformatics Center in-silico lecture series CY - Online meeting DA - 28.02.2022 KW - Mass spectrometry KW - Data science KW - Virus detection KW - Bioinformatic algorithms PY - 2022 AN - OPUS4-56682 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - El-Athman, Rukeia T1 - The BAM Data Store – an institutional RDM framework for Materials Science and Engineering N2 - In view of the increasing digitization of research and the use of data-intensive measurement and analysis methods, research institutions and their staff are faced with the challenge of documenting a constantly growing volume of data in a comprehensible manner, archiving them for the long term, and making them available for discovery and re-use by others in accordance with the FAIR principles. At BAM, we aim to facilitate the integration of research data management (RDM) strategies during the whole research cycle from the creation and standardized description of materials datasets to their publication in open repositories. To this end, we present the BAM Data Store, a central system for internal RDM that fulfills the heterogenous demands of materials science and engineering labs. The BAM Data Store is based on openBIS, an open-source software developed by the ETH Zurich that has originally been created for life science laboratories but that has since been deployed in a variety of research domains. The software offers a browser-based user interface for the digital representation of lab inventory entities (e.g., samples, chemicals, instruments, and protocols) and an electronic lab notebook for the standardized documentation of experiments and analyses. To investigate whether openBIS is a suitable framework for the BAM Data Store, we carried out a pilot phase during which five research groups with employees from 16 different BAM divisions were introduced to the software. The pilot groups were chosen to represent a diverse array of domain use cases and RDM requirements (e.g., small vs big data volume, heterogenous vs structured data types) as well as varying levels of prior IT knowledge on the users’ side. Overall, the results of the pilot phase are promising: While the creation of custom data structures and metadata schemas can be time-intensive and requires the involvement of domain experts, the system offers specific benefits in the form of a simplified documentation and automation of research processes, as well as constituting a basis for data-driven analysis. In this way, heterogeneous research workflows in various materials science research domains could be implemented, from the synthesis and characterization of nanomaterials to the monitoring of engineering structures. In addition to the technical deployment and the development of domain-specific metadata standards, the pilot phase also highlighted the need for suitable institutional infrastructures, processes, and role models. An institute-wide rollout of the BAM Data Store is currently being planned. T2 - Analytica Conference 2022 CY - Munich, Germany DA - 21.06.2022 KW - BAM Data Store KW - Forschungsdatenmanagement KW - Research data management KW - OpenBIS PY - 2022 AN - OPUS4-55139 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Simon A1 - Benner, Philipp A1 - Vogel, Kristin A1 - Hofmann-Böllinghaus, Anja T1 - Ein Modell zur Ausbreitung von Waldbränden - schnellere Vorhersagen durch den Einsatz von maschinellem Lernen, Fernerkundung und Copernicus-Daten N2 - Weltweit stellen Waldbrände eine große Bedrohung für Umwelt, Wirtschaft und menschliches Leben dar. Der fortschreitende Klimawandel verstärkt Trockenheit und Dürre, wodurch die Größe und Intensität von Waldbränden sowie das daraus resultierende Gefahrenpotential zusätzlich erhöht werden. Um im Risikofall eine schnelle und effektive Waldbrandbekämpfung zu gewährleisten, sollen Einsatzkräfte in Zukunft von KI-basierten Ausbreitungsmodellen unterstützt werden. Der Einsatz von maschinellem Lernen ermöglicht dabei schnelle und zielgerichtete Ausbreitungsvorhersagen in Echtzeit, die in den Prozess der Brandbekämpfung als zusätzliche Information einfließen können. Im Rahmen des durch das EU-Förderprogram Horizon 2020 finanzierte Projekt TREEADS wird ein solches Modell für Europa entwickelt. Dazu wird mit verschiedenen Satellitendaten des Copernicus-Programms und der NASA ein auf räumlicher und zeitlicher Ebene aufgelöster Datensatz zu europäischen Waldbränden aufgebaut. Zu den erfassten Waldbränden werden wichtigen Faktoren, die Einfluss auf die Brandausbreitung haben, ergänzt. Dazu gehören das von Copernicus bereitgestellte, digitale Geländemodell Europas und verschiedene, von Sentinel-2 Messungen abgeleitete Informationen zu Vegetation und Landnutzung. Dieser 2D-Trainingsdatensatz zu realen Waldbränden wird durch die zeitlich hochaufgelösten, meteorologischen Reanalyse-Produkte – Copernicus European Regional ReAnalysis (CERRA) und ERA5-Land – vervollständigt und ist die Grundlage für ein Modell zur Ausbreitungsvorhersage von Waldbränden. Methoden des Maschinellen Lernens können komplexe Muster in den gesammelten Daten erkennen und so zu einer verbesserten Vorhersage der Brandausbreitung beitragen. Die vielfältigen, von Copernicus bereitgestellten Daten sind dabei ein unabdingbarer Bestandteil des Modells, mit dem den Einsatzkräften ein zusätzliches Tool für den effizienten Einsatz ihrer limitierten Ressourcen zur Verfügung steht. T2 - Nationales Forum für Fernerkundung und Copernicus 2024 CY - Berlin, Germany DA - 19.03.2024 KW - Maschinelles Lernen KW - Waldbrand KW - Geoinformatik KW - Fernerkundung PY - 2024 AN - OPUS4-59840 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd A1 - Weinberger, Katharina A1 - Schilling, Markus T1 - Innovations- Plattform Material Digital N2 - Mit der Initiative MaterialDigital fördert das BMBF seit dem letzten Jahr ein wichtiges Instrument zur Digitalisierung der Materialwissenschaft und Werkstofftechnik in Deutschland. In der ersten Phase dieser Initiative wird seit Juli 2019 die Plattform MaterialDigital von einem Konsortium aufgestellt. Ziel dieser Plattform ist der Aufbau eines virtuellen Materialdatenraums, um zusammen mit allen Interessenten die Systematisierung des Umgangs mit Werkstoffdaten voranzutreiben. In einer zweiten Phase werden akademische F&E-Projekte voraussichtlich Anfang 2021 gefördert. In jedem dieser Projekte wird an einer konkreten Fragestellung das Themenfeld „Digitalisierung der Materialwissenschaft und Werkstofftechnik“ adressiert und multidisziplinär bearbeitet. In einer dritten Phase werden im Rahmen industriegeführter vorwettbewerblicher Verbundprojekte mit der gleichen Zielsetzung gefördert. T2 - Workshop - “Datenmanagement in der Additiven Fertigung” CY - Online meeting DA - 27.10.2020 KW - Digitalisierung KW - Plattform Material Digital KW - Virtueller Materialdatenraum KW - Vernetzung KW - Ontologien PY - 2020 AN - OPUS4-51609 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Portella, Pedro Dolabella A1 - Skrotzki, Birgit A1 - Muth, Thilo T1 - Die Digitalisierung der Materialwissenschaft und Werkstofftechnik - ein Überblick N2 - Die Bereitstellung von zuverlässigen Werkstoffdaten stand für die Werkstoffprüfung seit dem 19. Jahrhundert in zentraler Stelle. Mit der zunehmender Digitalisierung unserer Gesellschaft gewinnt das Datenmanagement insbesondere im Hinblick auf die Modellierungs- und Simulationsaktivitäten an Bedeutung. In unserem Beitrag beschreiben wir die gegenwärtigen Aktivitäten in Deutschland – insbesondere die NFDI-MatWerk und die BMBF-Initiative MaterialDigital – sowie in Europa und Übersee. Abschließend heben wir die Auswirkungen auf die technische und akademische Aus- und Weiterbildung. T2 - DVM - Tagung Werkstoffprüfung CY - Online meeting DA - 03.12.2020 KW - Werkstoffdaten KW - Digitalisierung KW - Materialwissenschaft PY - 2020 AN - OPUS4-52044 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Skrotzki, Birgit A1 - Muth, Thilo A1 - Portella, Pedro Dolabella T1 - Interconnecting data repositories: The Platform MaterialDigital (BMBF) N2 - Presentation of the Platform MaterialDigital. Presentation of the needs and challenges in materials science and engineering. T2 - 3rd EMMC International Workshop - EMMC CY - Online meeting DA - 02.03.2021 KW - Digitization KW - Standardization KW - Ontology PY - 2021 AN - OPUS4-52247 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-546729 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -