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Im Rahmen der von der BAM und anderen Partnern ins Leben gerufenen Initiative „Digitale Qualitätsinfrastruktur“ liegt der Fokus u.a. auf der Entwicklung von neuen Zertifizierungsworkflows. Dabei ist eine besondere Bedeutung dem Zusammenführen von Operational Technology (OT) und Informationstechnik (IT) beizumessen. Zur Datenintegration aus der Sensor-Feldebene einer Versuchs-wasserstofftankstelle wurde eine solche Infrastruktur zunächst in einer Laborumgebung aufgebaut.
McSAS3 is a refactored software package for fitting large batches of (X-ray or Neutron) scattering data. It uses a Monte-Carlo acceptance-rejection algorithm to optimize model parameters - ideal for analysis of size-disperse scatterers.
The refactored code can exploit multiprocessing, traceably stores (multiple) results in the output file, and allows for re-histogramming of previous optimizations. Besides analysis of large batches, it can also be integrated in automated data processing pipelines.
The live demonstration will show how to use the software, what its limitations are, and what outcomes can look like for batches of results.
A software toolbox is introduced that addresses several needs common to computed tomography (CT). Built for the WIPANO CTSimU project to serve as the reference implementation for its image processing and evaluation tasks, it provides a Python 3 interface that is adaptable to many conceivable applications. Foremost, the toolbox features a pipeline architecture for sequential 2D image processing tasks, such as flat field corrections and image binning, and enables the user to create their own processing modules. Beyond that, it provides means to measure line profiles and image quality assessment algorithms to calculate modulation transfer functions (MTF) or to determine the interpolated basic spatial resolution (iSRb) using a duplex wire image. It can also be used to calculate projection matrices for the reconstruction of scans with arbitrary industrial CT geometries and trajectories. The CTSimU project defined a framework of projection- and volume-based test scenarios for the qualification of radiographic simulation software towards its use in dimensional metrology. The toolbox implements the necessary evaluation routines and generates reports for all projection-based tests.
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.
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.
The SPONGE
(2020)
The test material EDS-TM001 together with an accompanying software package, “EDX spectrometer check”, have been made available in 2009 by BAM to be employed by EDS (energy-dispersive X-ray spectrometer) users to check the performance of an EDS attached to the SEM. Particularly for test laboratories operating under accreditation schemes like ISO/IEC 17025, a periodical control of the critical instrumental parameters in end-user laboratories is required. With EDS-TM001 or EDS-TM002 (second generation) test material, this periodical check is simplified to the acquisition of only one 10 kV spectrum. The software “EDX spectrometer check” is destined to evaluate automatically this spectrum and determine the performance of the EDS in terms of energy resolution and calibration, as well as possible alteration of low-energy Efficiency due to detector contamination. Energy resolution can be compared with the specified values according to the international ISO standard ISO 15632:2012. EDS-TM is a synthetic material consisting of a 6 μm thick layer of C, Al, Mn, Cu and Zr deposited on a steel (in case of EDS-TM001) or silicon (in case of EDS-TM002) substrate. The chemical composition of EDS-TM was chosen such as to give nearly equal intensities of the low energy lines in a 10 kV spectrum, thus, making it very sensitive against spectrometer efficiency changes. Meanwhile, about 150 laboratories use the EDS-TM001 or EDS-TM002 test material for the periodical check of their EDS. A detailed description of the test material and software together with examples of application was published recently. New results and gained experiences will be presented as well. When the FWHM of the X-ray lines in the EDS-TM spectrum are determined, the spectrum background must be subtracted accurately. The applied physical background subtraction procedure is robust and takes into account the transmission of the detector window. While the previous version considers only Moxtek AP windows, the new version includes selection of silicon Nitride window and the case of windowless detector. Moreover, the new version allows importing of spectra in Bruker spx-format and EMSA/MSA files from EDAX TEAM software.
Windowless detectors have been also tested demonstrating long-term stability after repeated heating and cooling cycles. In this case, the appropriate Background subtraction is decisive for accurate detector characterization. Detailed results will be presented.
Small-angle scattering (SAS) offers a reliable route to characterize the nanostructure of large amounts of material with a minimum of tedium, for example, easily extracting size distributions and volume fractions. There are a variety of analysis programs available while the evaluation of SAS measurements has been dominated by the classical curve fitting approach. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for SAS data analysis and has been available and used for many years. The latest developments will be presented and a scattering function of a mass fractal model of branched polymers in solution is provided as an example for implementing a plug-in.
Alternatively to classical curve fitting, part two presents the latest developments of the user-friendly open-source Monte Carlo regression package McSAS. The form-free Monte Carlo nature of McSAS means, it is not necessary to provide further restrictions on the mathematical form of the parameter distribution: without prior knowledge, McSAS is able to extract complex multimodal or odd- shaped parameter distributions from SAS data. The headless mode is presented by an example of operation within interactive programming environments such as a Jupyter notebook.