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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.
Antimicrobial surfaces have broad use in multiple settings including touch surfaces in hospitals, implanted devices, or consumer products. Their aim is to support existing hygiene procedures, and to help combat the increasing threat of antimicrobial resistance. However, concerns have been raised over the potential selection pressure exerted by such surfaces, which might drive the evolution and spread of antimicrobial resistance. In my presentation, I will highlight the risks and knowledge gaps associated with resistance on antimicrobial surfaces by different processes including evolution by de novo mutations and horizontal gene transfer, and species sorting of inherently resistant bacteria dispersed onto antimicrobial surfaces. The latter process has the potential to select for antibiotic resistance via cross-resistance between traits that confer resistance to both the antimicrobial surface coating and antibiotics. Conditions in which antibiotics and antimicrobial coatings are present simultaneously (e.g. implants) will lead to more complex interactions that can either result in the selection for or against antibiotic resistance. We mapped these interactions between several antimicrobials and antibiotics on growth and selection of Pseudomonas aeruginosa. We find prevalent physiological (i.e. synergy and antagonism) and evolutionary (i.e. cross-resistance and collateral sensitivity) combination effects. Understanding these interactions opens the door to tailor therapeutic interventions to select against resistance. In additions, we need new methods and translational studies that investigate resistance development to antimicrobial surfaces under realistic conditions. Therefore, I will present recent developments in our lab on the development of such a method based on existing efficacy standards.
Antimicrobials can exert specific physiological effects when used in combination that are different from those when applied alone. These effects include physiological effects (i.e. synergy, antagonism and suppression) as well as evolutionary effects on the selection of resistant strains (i.e. cross-resistance and collateral sensitivity). While combination effects have been extensively mapped for antibiotic-antibiotic combinations, the combination effects of antibiotics with antimicrobials used as biocides or antiseptics have not been systematically investigated.
Here, we investigated the physiological and evolutionary consequences of combinations of antibiotics (meropenem, gentamicin and ciprofloxacin) and substances used as biocides or antiseptics (octenidine, benzalkonium chloride, cetrimonium bromide, chlorhexidine, povidone-iodine, silver) on growth and selection of Pseudomonas aeruginosa. We find prevalent physiological combination effects with synergy occurring 6 times and antagonism occurring 10 times. The effects are specific to the antibiotic-biocide combination with meropenem showing a tendency for antagonism with biocides (6 of 7), while gentamicin has a tendency for synergy (5 of 7). A particular strong antagonism is apparent for the meropenem-chlorhexidine combination, for which we conducted an in-depth study on the underlying molecular mechanism using RNASeq. Moreover, we find widespread effects of the biocide-antibiotic combinations on selection of P. aeruginosa strains resistant to the antibiotics, including cross-resistance and collateral sensitivity.
In conclusion, antibiotics and biocides or antiseptics exert physiological and evolutionary combination effects on the pathogen P. aeruginosa. These effects have consequences for the efficacy of both types of substances and for the selection of antimicrobial resistant strains in clinical applications with combined exposure (e.g. wound care, coated biomaterials).
SRB are environmentally and industrially important microorganisms. The disadvantage of their metabolic activity (e.g. sulfate reduction) results in the formation of toxic sulfide that leads to microbial influenced corrosion. SRB have been responsible for biocorrosion of ferrous metal. One of mitigation strategy is the use of biocides. However, it has been shown that various bacteria develop antimicrobial resistance due to excessive use of biocides. Thus, a deeper understanding of the evolution of biocide resistance of SRB is necessary. Three commonly used biocides, THPS, BAC, and GLUT were applied to investigate the susceptibility of Desulfovibrio alaskensis G20.The minimum inhibitory and bactericidal concentration and the killing kinetics of the three biocides was determined. These results will be used to conduct evolution experiments to determine the evolution of resistance towards biocides of SRBs. The outcome of this work can be helpful to improve the management of MIC treatments.
Biocides used as disinfectants are important to prevent the transmission of pathogens, especially during the current antibiotic resistance crisis. This crisis is exacerbated by phenotypically tolerant persister subpopulations which can survive transient antibiotic treatment and facilitate resistance evolution. Despite the transient nature of disinfection, knowledge concerning persistence to disinfectants and its link to resistance evolution is currently lacking. Here, we show that E. coli displays persistence against a widely used disinfectant benzalkonium chloride (BAC). Periodic, persister-mediated failure of disinfection rapidly selects for BAC tolerance. BAC tolerance is associated with reduced cell surface charge and mutations in the novel tolerance locus lpxM. Moreover, the fitness cost incurred by BAC tolerance turned into a fitness benefit in the presence of antibiotics, suggesting a selective advantage of BAC-tolerant mutants in antibiotic environments. Our findings provide a mechanistic underpinning for the faithful application of disinfectants to prevent multi-drug-resistance evolution and to steward the efficacy of biocides and antibiotics.
Biocides used as disinfectants are important to prevent the transmission of pathogens, especially during the current antibiotic resistance crisis. This crisis is exacerbated by phenotypically tolerant persister subpopulations which can survive transient antibiotic treatment and facilitate resistance evolution. Despite the transient nature of disinfection, knowledge concerning persistence to disinfectants and its link to resistance evolution is currently lacking. Here, we show that E. coli displays persistence against a widely used disinfectant benzalkonium chloride (BAC). Periodic, persister-mediated failure of disinfection rapidly selects for BAC tolerance. BAC tolerance is associated with reduced cell surface charge and mutations in the novel tolerance locus lpxM. Moreover, the fitness cost incurred by BAC tolerance turned into a fitness benefit in the presence of antibiotics, suggesting a selective advantage of BAC-tolerant mutants in antibiotic environments. Our findings provide a mechanistic underpinning for the faithful application of disinfectants to prevent multi-drug-resistance evolution and to steward the efficacy of biocides and antibiotics.
In this lecture the safety related properties og hydrogen compared to other fuel gases and the explosion protection measures of avoiding flammable mixtures, avoiding ignition sources and mitigating the consequences of explosions when handling hydrogen and hydrogen mixtures are presented.
The Joint European Summer School JESS 2021 addresses these issues by offering high quality graduate level courses on selected topics of vehicle technology, innovation & business development, safe handling of hydrogen, and modelling. The course content is tailored to the needs of a diverse audience: newcomers to the field, experienced students, and young professionals working at the forefront of fuel cell and hydrogen applications.
Materials in contact with the environment release e.g., metal-ions, elemental species and/or (nano-)particles. Once these species and/or particles are released, they are ingested by organisms and cells and thus, might have a negative impact on the environment. Thus, identification as well as quantification of potentially harmful substances is of utmost importance and highly needed to assess ecotoxicological impact of (emerging) pollutants.
The oral presentation provides an overview on the power of elemental analytical techniques, in particular ICP-MS as well as HR-CS-GFMAS in environmental research. Current research topics from Division 1.1 @ BAM - Inorganic trace analysis will be highlighted:
i) Elemental Speciation & Isotope analysis - new tools:
Among elemental species separation and quantification, one of the main challenges in environmental elemental speciation analysis is the distinction between anthropogenic and natural elemental species. The on-line combination of elemental speciation and isotope analysis combines “the best from both worlds” - species specific isotopic information becomes available.
As an application example the analysis of current anti-fouling agents via CE/MCICP-MS will be highlighted.
ii) HR-CS-GFMAS for PFC analysis:
Per- and polyfluorinated compounds (PFC) are emerging contaminants in particular in soil and surface water samples. Due to the large number of compounds (>4700), target analytical methods are not sufficient and sum parameter methods for organically bound fluorine are highly needed.
High resolution-continuum source-graphite furnace molecular absorption spectroscopy (HR-CS-GFMAS) based methods for organically bound fluorine analysis will be presented. Application examples (soil and surface water) will be highlighted.
iii) Single cell-ICP-ToF-MS - ecotox. assessment:
Single cell and single organism analysis for e.g. ecotoxicological/medicinal assessment are hot topics in the research field of ICP-MS. In particular ICP-ToF-MS is a powerful, emerging techniques in terms of single cell/particle analysis.
Automated single cell/diatom-ICP-ToF-MS as a potential tool in ecotoxicological testing will be presented.
By far most of the current nanoparticle (NP) research is dealing with (quasi-) spherical and/or monodisperse particles. However, many NPs used in industrial applications are rather aspherical and polydisperse. This inhomogeneity considerably hampers their characterization and, particularly, the accurate determination of the nanoparticle size. In order to overcome this problem and to promote the availability of standardized size measurement methods, it is crucial to develop and establish (candidate) reference materials with inhomogeneous size (distribution), aspherical shape as well as agglomerated or aggregated particles.
Therefore, a new set of NPs including Au-, SiO2 , and TiO2-particles is investigated. The range of properties comprises polydisperse spherical, bimodal spherical, rod-like, acicular, bipyramidal, sheet-like as well as cubic NPs. With respect to a good traceability of the measurements, size and size distributions of the candidate reference materials are determined using microscopic methods like scanning electron microscopy (SEM), transmission electron microscopy (TEM), scanning electron microscopy in transmission mode (STEM-in-SEM), atomic force microscopy (AFM) as well as small angle X-ray scattering (SAXS) as an ensemble technique. The development of protocols for sample preparation is of particular importance to obtain a homogeneous dispersion of the NPs on a substrate. Further, approaches for signal modelling for all the methods above are being developed. The initiation of two VAMAS (www.vamas.org/twa34/index.html) inter-laboratory comparisons on bipyramidal titania and bimodal silica with different modal concentration ratios will be also highlighted.
T cells need to adapt their cellular metabolism for effector cell differentiation. This relies on alterations in mitochondrial physiology. Which signals and molecules regulate those alterations remains unclear. We recently reported, that the mitochondrial protein TCAIM inhibits activation-induced changes in mitochondrial morphology and function and thus, CD effector T cell formation. Using conditional TCAIM knock-in (KI) and knockout (KO) mice, w now show that it also applies to CD8+ T cells and more importantly, delineate the molecular processes in mitochondria by which TCAIM controls effector cell differentiation. TCAIM KI resulted in reduced activation-induced HIF1α protein expression. Metabolomics and transcriptional data in combination with mathematical flux modeling revealed an impaired induction of anabolic pathways, especially of the mevalonate pathway and cholesterol biosynthesis in TCAIM KI CD8+ T cells. Addition of cholesterol completely rescued HIF1α protein expression, activation and proliferation of TCAIM KI CD8+ T cells. At the molecular level, TCAIM delayed activation-induced mitochondria-ER contact (MERC) formation by binding to MERC promoting proteins such as RMD3 and VDAC2. In summary, we demonstrate that TCAIM suppresses effector cell differentiation by inhibiting MERC formation, which induce HIF1α-mediated increase in cellular metabolism and cholesterol biosynthesis.