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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.
It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques.
Lake Magadi, East African Rift Valley, is a hyperalkaline and saline soda lake highly enriched in Na+, K+, CO32–, Cl–, HCO3–, and SiO2 and depleted in Ca2+ and Mg2+, where thick evaporite deposits and siliceous sediments have been forming for 100 000 years. The hydrogeochemistry and the evaporite deposits of soda lakes are subjects of growing interest in paleoclimatology, astrobiology, and planetary sciences. In Lake Magadi, different hydrates of sodium carbonate/bicarbonate and other saline minerals precipitate. The precipitation sequence of these minerals is a key for understanding the hydrochemical evolution, the paleoenvironmental conditions of ancient evaporite deposits, and industrial crystallization. However, accurate determination of the precipitation sequence of these minerals was challenging due to the dependency of the different hydrates on temperature, water activity, pH and pCO2, which could induce phase transformation and secondary mineral precipitation during sample handling. Here, we report a comprehensive methodology applied for monitoring the evaporitic mineral precipitation and hydrochemical evolution of Lake Magadi. Evaporation and mineral precipitations were monitored by using in situ video microscopy and synchrotron X-ray diffraction of acoustically levitated droplets. The mineral patterns were characterized by ex situ Raman spectroscopy, X-ray diffraction, and scanning electron microscopy. Experiments were coupled with thermodynamic models to understand the evaporation and precipitation-driven hydrochemical evolution of brines. Our results closely reproduced the mineral assemblages, patterns, and textural relations observed in the natural setting. Alkaline earth carbonates and fluorite were predicted to precipitate first followed by siliceous sediments. Among the salts, dendritic and acicular trona precipitate first via fractional crystallization─reminiscent of grasslike trona layers of Lake Magadi. Halite/villiaumite, thermonatrite, and sylvite precipitate sequentially after trona from residual brines depleted in HCO3–. The precipitation of these minerals between trona crystals resembles the precipitation process observed in the interstitial brines of the trona layers. Thermonatrite precipitation began after trona equilibrated with the residual brines due to the absence of excess CO2 input. We have shown that evaporation and mineral precipitation are the major drivers for the formation of hyperalkaline, saline, and SiO2-rich brines. The discrepancy between predicted and actual sulfate and phosphate ion concentrations implies the biological cycling of these ions. The combination of different in situ and ex situ methods and modeling is key to understanding the mineral phases, precipitation sequences, and textural relations of modern and ancient evaporite deposits. The synergy of these methods could be applicable in industrial crystallization and natural brines to reconstruct the hydrogeochemical and hydroclimatic conditions of soda lakes, evaporite settings, and potentially soda oceans of early Earth and extraterrestrial planets.
The entire process involving the determination of Cl by molecular emission detection in Laser-Induced Breakdown Spectroscopy (LIBS) is thoroughly studied in this paper. This critical evaluation considers how spectra are normalized, how interferences from other molecular species signals are removed, and how signal integration is applied. Moreover, a data treatment protocol is proposed to achieve reliable and accurate Cl determination from the CaCl molecular spectral signal, not requiring the use of more complex numerical approaches. Calcium chloride dihydrate (CaCl2⋅2H2O) and high purity anhydrite samples (CaSO4) are used to optimize the acquisition conditions and data treatment of CaCl emission signal. Using the developed protocol, calibration curves for Cl, covering the concentration range from 0 μg/g to 60,000 μg/g of Cl, are successfully achieved. Finally, the suitability of the proposed methodology for Cl determination is successfully applied in industrial gypsum waste samples, where the results obtained by LIBS are validated using high-resolution molecular absorption spectroscopy (HR-CS-MAS) and potentiometric titration.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
A method, based on electrothermal vaporization (ETV) coupled to inductively coupled plasma optical emission spectrometry (ICP OES), has been optimized for direct solid sampling analysis of soda-lime glass - the most common type of industrially manufactured glass. This method allows fast and reliable quantification of the main elements - Al, Ca, K, Mg, Na, and Si - and trace elements - As, Ba, Cd, Ce, Co, Cr, Cu, Fe, Mn, Mo, Ni, (P), Pb, Sb, (Se), Sn, Sr, Ti, V, Zn, and Zr. In the presented ETV-ICP OES method, calibration is performed predominantly with matrix-free synthetic samples. This metrological advantage is normally not achieved with direct solid sampling methods and is one of the goals of the present study. In a certification interlaboratory comparison for the soda-lime glass CRM BAM-S005c, 2 out of 16 laboratories employed the ETV-ICP OES method. An improved analytical performance was obtained compared with the results of laboratories that used conventional liquid ICP OES. For both methods, the average relative deviations between the laboratory results and certified values as well as the average values of relative standard deviation were with a few exceptions <10%, in most cases even <5%, which indicated high trueness and precision.
Here, we describe a fast and simple extraction method for the determination of per- and polyfluorinated alkyl substances (PFASs) utilizing extractable organic fluorine (EOF) sum parameter analysis and high resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) in soil samples. After extraction and separation of inorganic and organic fluorinated compounds, organically bound fluorinated compounds were indirectly determined via the molecular absorption of gallium mono-fluoride at 211.248 nm. The implementation of the decisions of the “Stockholm Convention on persistent organic pollutants” as well as the “Protocol on Persistent Organic Pollutants of the Convention on Long-Range Transboundary Air Pollution” of the UNECE include the reduction respectively the elimination of PFASs in the environment. Currently, regulations aim to target single compounds (mostly C8-PFAS). But the PFAS group includes over 4700 substances, which are potentially persistent and toxic, thus target analytical approaches are not suitable for a holistic approach investigating the PFAS pollutant situation. Furthermore, forbidden PFASs are substituted by short-chain PFASs, thus the number of unknow substances is steadily increasing. For this reason, sum parameter approaches are more suitable to investigate and assess the pollution situation as well as derive exposure limits. Our developed analytical method was successfully applied to determine PFASs in soil samples from a polluted site. In view of steadily increasing numbers of PFAS substances, our method will make an important contribution in assessing the pollution situation as well as support policy makers in deriving exposure limits for PFAS in the future.
Per- and polyfluorinated alkyl substances (PFASs) are a substance class of over 4730 individual compounds. Several PFASs are extremely persistent, bioaccumulative and toxic. The analysis of PFASs is challenging because of their different chemical and physical properties as well as the high number of target substances. Target-based approaches (e.g., LC-MS/MS) are limited to the availability of analytical grade standards and are not suitable for the analysis of new/unknown PFASs and transformation products. Therefore, PFAS sum parameter methods become increasingly important to indicate a realistic PFAS pollution level.
PFAS sum parameters compromise the proportion of organically bound fluorine that can either be extracted (EOF) or adsorbed to activated carbon (AOF). For the instrumental analysis of such sum parameters, a fluorine selective detector is needed. High resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) is a sensitive and highly selective tool for indirect fluorine determination. The method is based on the in situ formation of diatomic gallium-mono fluoride (GaF) in a graphite furnace at a temperature of 1550°C. The molecular absorption of GaF can be detected at its most sensitive wavelength at 211.248 nm providing limits of quantification of c(F) 2.7 µg/L.
Here, we describe a fast and simple extraction method for the determination of the EOF using HR-CS-GFMAS in soil samples. Common approaches for the EOF determination use solid phase extraction (SPE). To omit the bias of this time consuming and expensive step we optimized a fast and simple SPE-free extraction method. The developed extraction method consists of a liquid-solid extraction using acidified methanol. Comparison of the method with and without an SPE clean-up step revealed a drastic underestimation of EOF concentrations using SPE. In the next step, the applicability of our method was tested for other solid matrices. In view of steadily increasing numbers of PFASs, our method will make an important contribution in assessing the pollution situation as well as support policy makers in deriving exposure limits for PFASs in the future.
Here, we describe a fast and simple extraction method for the determination of per- and polyfluorinated alkyl substances (PFASs) utilizing extractable organic fluorine (EOF) sum parameter analysis and high resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) in soil samples. After extraction and separation of inorganic and organic fluorinated compounds, organically bound fluorinated compounds were indirectly determined via the molecular absorption of gallium mono-fluoride at 211.248 nm. The implementation of the decisions of the “Stockholm Convention on persistent organic pollutants” as well as the “Protocol on Persistent Organic Pollutants of the Convention on Long-Range Transboundary Air Pollution” of the UNECE include the reduction respectively the elimination of PFASs in the environment. Currently, regulations aim to target single compounds (mostly C8-PFAS). But the PFAS group includes over 4700 substances, which are potentially persistent and toxic, thus target analytical approaches are not suitable for a holistic approach investigating the PFAS pollutant situation. Furthermore, forbidden PFASs are substituted by short-chain PFASs, thus the number of unknow substances is steadily increasing. For this reason, sum parameter approaches are more suitable to investigate and assess the pollution situation as well as derive exposure limits. Our developed analytical method was successfully applied to determine PFASs in soil samples from a polluted site. In view of steadily increasing numbers of PFAS substances, our method will make an important contribution in assessing the pollution situation as well as support policy makers in deriving exposure limits for PFAS in the future.
Here, we describe an optimized fast and simple extraction method for the determination of per- and polyfluorinated alkyl substances (PFASs) in soils utilizing high resolution-continuum source-graphite furnace molecular absorption spectrometry (HR–CS–GFMAS). To omit the bias of the solid phase extraction (SPE) step commonly used during the analysis of extractable organically bound fluorine (EOF) we optimized a fast and simple SPE-free extraction method. The developed extraction method consists of a liquid-solid extraction using acidified methanol without any additional SPE. Four extraction steps were representative to determine a high proportion of the EOF (>80% of eight extractions). Comparison of the optimized method with and without an additional SPE clean-up step revealed a drastic underestimation of EOF concentrations using SPE. Differences of up to 94% were observed which were not explainable by coextracted inorganic fluoride. Therefore, not only a more accurate but also a more economic as well as ecologic method (bypassing of unnecessary SPE) was developed. The procedural limit of quantification (LOQ) of the developed method was 10.30 μg/kg which was sufficient for quantifying EOF concentrations in all tested samples. For future PFAS monitoring and potential regulative decisions the herein presented optimized extraction method can offer a valuable contribution.
Diclofenac (DCF) is a non-steroidal anti-inflammatory drug (NSAID) with analgetic, anti-inflammatory, and antipyretic properties. The mechanism of action of diclofenac, like that of other NSAIDs, involves inhibition of cyclooxygenase (COX-1 and COX-2). Another pharmacological effect is preventing of prostaglandin synthesis in vitro. Prostaglandins are mediators of inflammation, because diclofenac is an inhibitor of prostaglandin synthesis.
A method has been developed to analyse for diclofenac (DCF) in the milk.
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer‐based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented
algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
The Scaled Boundary Finite Element Method (SBFEM) for prismatic structures is an efficient method for the simulation of acoustic behavior. Hence a further development of the method is of great interest. The wave propagation can be calculated for isotropic and anisotropic materials in solids. As for many applications the acoustic behavior in fluids and the behavior in case of fluid-structure interaction (FSI) is subject of research, the implementation of a fluid model in SBFEM for prismatic structures is needed. In case of FSI the coupling between fluid and solid domains can be performed without additional effort when describing both domains in the same variables. Hence a displacement-based fluid description is used. As the discretized formulation leads to spurious modes, a penalty method to suppress the unphysical behavior is chosen. To validate the derived model a comparison with analytical solutions of purely fluid domains is made. As to verify that in case of FSI the model shows the right behavior, dispersion curves of water-filled pipes are calculated and compared to results obtained with Comsol.
The Scaled Boundary Finite Element Method is known as an efficient method for the simulation of ultrasonic wave propagation. As to investigate acoustic wave behavior in case of fluid‐structure interaction, a fluid model is implemented in the SBFEM for prismatic structures. To omit coupling terms a displacement‐based formulation is used. Spurious modes, which occur in the solution, are suppressed using a penalty parameter. To verify this formulation dispersion curves obtained with Comsol Multiphysics are compared to results of SBFEM. The results of both methods are in very good agreement
A general method to carry out the fluorination of metal oxides with poly(tetrafluoroethylene) (PTFE, Teflon) waste by spark plasma sintering (SPS) on a minute scale with Teflon is reported. The potential of this new approach is highlighted by the following results. i) The tantalum oxyfluorides Ta3O7F and TaO2F are obtained from plastic scrap without using toxic or caustic chemicals for fluorination. ii) Short reaction times (minutes rather than days) reduce the process time the energy costs by almost three orders of magnitude. iii) The oxyfluorides Ta3O7F and TaO2F are produced in gram amounts of nanoparticles. Their synthesis can be upscaled to the kg range with industrial sintering equipment. iv) SPS processing changes the catalytic properties: while conventionally prepared Ta3O7F and TaO2F show little catalytic activity, SPS-prepared Ta3O7F and TaO2F exhibit high activity for photocatalytic oxygen evolution, reaching photoconversion efficiencies up to 24.7% and applied bias to photoconversion values of 0.86%. This study shows that the materials properties are dictated by the processing which poses new challenges to understand and predict the underlying factors.
Synthetic peptide pools are used in antigen-specific T-cell assays, which are an important part in vaccine and immunotherapeutic clinical trials. As the analytical characterization is challenging due to the similarity of the single peptides or is expensive due to isotope labeled standards, usually only a pre-characterization of the single peptides is performed. However, a regular quality control of the peptide mix would be highly desirable. Therefore, a cost-efficient high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) method for quality control of a model peptide pool is developed.
Peptides were synthesized using peptides&elephants proprietary libraries of individual peptides (LIPS) technology and purified by reversed-phase chromatography to > 90% each. The lyophilized single peptides were combined to a model peptide pool and analyzed by reversed-phase high-performance capillary liquid chromatography coupled to an orbitrap mass spectrometer. Separation was performed on a capillary reversed phase column (2 μm, ID x L 300 μm x 150 mm) with a linear gradient of acetonitrile + 0,05% trifluoroacetic acid.
After optimizing the injection mode, the gradient elution, the temperature and the additives a model peptide pool was separated. The extracted ion chromatogram (XIC) was studied to confirm the exact masses.
By combination of capillary HPLC and HRMS a new cost-efficient quality control method could be developed for the separation and identification of complex synthetic peptide pools.
The common characterization and identification of pollen is a time-consuming task that mainly relies on microscopic determination of the genus-specific pollen morphology. A variety of spectroscopic and spectrometric approaches have been proposed to develop a fast and reliable pollen identification using specific molecular information. Amongst them, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) shows a high potential for the successful investigation of such complex biological samples. Based on optimized MALDI sample preparation using conductive carbon tape, the application of multivariate statistics (e.g. principal components analysis, PCA) yields an enormous improvement concerning taxonomic classification of pollen species compared to common microscopic techniques.
Since multivariate evaluation of the recorded mass spectra is of vital importance for classification, it’s helpful to implement the applied sequence of standard Matlab functions into a graphical user interface (GUI). In this presentation, a stand-alone application (GUI) is shown, which provides multiple functions to perform fast multivariate analysis on multiple datasets. The use of a GUI enables a first overview on the measured dataset, conducts spectral pretreatment and can give classification information based on HCA and PCA evaluation. Moreover, it can be used to improve fast spectral classification and supports the development of a simple routine method to identify pollen based on mass spectrometry.
The very strong Lewis acid aluminium chlorofluo-ride (ACF) was loaded with anhydrous HF. The interactionbetween the surface of the catalyst and HF was investigatedusing a variety of characterization methods, which revealed he formation of polyfluorides. Moreover, the reactivity ofthe HF-loaded ACF towards the hydrofluorination of alkyneswas studied.
This paper presents an approach to the automatic enrichment of finite elements in the vicinity of a stress singularity. The enrichment consists of semi-analytical singular modes constructed using the Scaled Boundary Finite Element Method (SBFEM).
In contrast to analytical methods, the SBFEM provides modes for inhomogeneous and anisotropic materials without additional effort. The finite element basis can be of arbitrary order and remains unaltered by the enrichment. The approach requires enrichment in only one layer of elements around a node. Due to the compatibility of SBFEM with FEM, there is no Need for transitional elements, and there are no parasitic terms. The approach is tested for several benchmark problems. The stress intensity factors are computed based on techniques inspired by the SBFEM. The proposed procedure is compared to a Standard finite element implementation and shows a significant improvement in the error of the displacement field for problems involving singular stresses.
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology1 let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators over the last five years. Combined with universal, automat-ed data correction pipelines, as well as our analysis and simulation software, this led to more than 40 papers2 in the last 5 years with just over 2 full-time staff members.
While this approach greatly improved the consistency of the results, the consistency of the samples and sample series provided by the users was less reliable nor necessarily reproducible. To address this issue, we built an EPICS-controlled, modular synthesis platform to add to our laboratory. To date, this has prepared over 1200 additional (Metal-Organic Framework) samples for us to meas-ure, analyse and catalogue. By virtue of the automation, the synthesis of these samples is automat-ically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases alongside the morphological results obtained from the automated X-ray scat-tering analysis.
Having developed these proof-of-concepts, we find that the consistency of results are greatly im-proved by virtue of their reproducibility, hopefully adding to the reliability of the scientific findings as well. Additionally, the nature of the experiments has changed greatly, with much more emphasis on preparation and careful planning. This talk will discuss the advantages and disadvantages of this highly integrated approach and will touch upon upcoming developments.