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Uncertainty quantification of multimodal surface wave inversion using artificial neural networks
(2023)
An inversion of surface waves dispersion curves is a non-unique and ill-conditioned problem. The inversion result has a probabilistic nature, which becomes apparent when simultaneously restoring the shear wave (S-wave) velocity and layer thickness. Therefore, the problem of uncertainty quantification is relevant. Existing methods through deterministic or global optimization approaches of uncertainty quantification via posterior probability density (PPD) of the model parameters are not computationally efficient since they demand multiple solutions of the inverse problem. We present an alternative method based on a multi-layer fully connected artificial neural network (ANN). We improve the current uni-modal approach, which is known from publications, to multi-modal inversion. The learned ANN maps the phase velocity dispersion curves to values of the S-wave velocity and layers thickness. To estimate the uncertainties, we adapt the Monte-Carlo simulation strategy and project onto the resulting velocity model both frequency-dependent data noise and inverse operator errors, which are evaluated by the prediction of the training data set. The proposed combination of surface waves data processing methods, configured with each other, provides a novel surface waves multi-modal dispersion data inversion and uncertainty quantification approach. We first test our approach on synthetic experiments for various velocity models: a positive velocity gradient, a low-velocity layer and a high-velocity layer. This is done considering uni-modal inversion at first and then compared to the multi-modal inversion. Afterwards, we apply our approach to field data and compare resulting models with the body S-wave processing by the generalized reciprocal method (GRM). The experiments show high-potential results using ANN yields the possibility to accurately estimate PPD of restored model parameters without a significant computational effort. The PPD-based comparison demonstrates advantages of a multi-modal inversion over uni-modal inversion. The trained ANN provides reasonable model parameters predictions and related uncertainties in real-time.
Pseudomonas aeruginosa is an opportunistic pathogen of considerable medical importance, owing to its pronounced antibiotic tolerance and association with cystic fibrosis and other life-threatening diseases. The aim of this study was to highlight the genes responsible for P. aeruginosa biofilm tolerance to antibiotics and thereby identify potential new targets for the development of drugs against biofilm-related infections. By developing a novel screening approach and utilizing a public P. aeruginosa transposon insertion library, several biofilm-relevant genes were identified. The Pf phage gene (PA0720) and flagellin gene (fliC) conferred biofilm-specific tolerance to gentamicin. Compared with the reference biofilms, the biofilms formed by PA0720 and fliC mutants were completely eliminated with a 4-fold-lower gentamicin concentration. Furthermore, the mreC, pprB, coxC, and PA3785 genes were demonstrated to play major roles in enhancing biofilm tolerance to gentamicin. The analysis of biofilm-relevant genes performed in this study provides important novel insights into the understanding of P. aeruginosa antibiotic tolerance, which will facilitate the detection of antibiotic resistance and the development of antibiofilm strategies against P. aeruginosa.
Materials that support natural biodiversity on their surfaces can compensate for human activities that have a negative impact on nature and thus contribute to a carbon-neutral and nature-positive world. Specifically designing bioreceptive materials which favor the growth of biofilms on their surface is an approach complementing conventional, macroscopic green façades. But what exactly characterizes a bioreceptive substrate and how do biofilm and substrate interact? How and why does a spontaneous colonization and the formation of biofilms take place? What are biofilms and how can they be established in a laboratory setting? How can this existing knowledge be transferred to the artificial stone concrete so that this material can be tuned to increase (or decrease) its bioreceptivity?
This review paper aims at summarizing the existing state of knowledge on bioreceptive concrete and pointing out inconsistencies and contradictions which can only be removed by more interdisciplinary research in the field.
In this study, the wavefield radiated from a building to its surroundings is identified and extracted from M4.6 earthquake recordings collected by sensors installed in a building and on the nearby athletic field in Matera (Italy) using a new approach for soil-structure interaction assessment. The proposed approach for earthquake data analysis combines in an innovative way two methods already used in seismology and engineering seismology: deconvolution and polarization analysis. The approach enables the identification, reconstruction, and characterization of the wavefield radiated from a vibrating building into its surroundings, and the estimation of the amount of energy associated with it. The approach consists of four steps: estimation of the resonant frequencies of the building, deconvolution of the earthquake recordings from a building and its surroundings, identification of the seismic phases, reconstruction of the signal transmitted from the building to its surroundings, and estimation of its energy, and polarization analysis. Analysis of recordings of the M4.6 event highlighted that the motion related to the wavefield radiated from the building to the ground was mostly linearly polarized in the radial and transverse planes, while a clear ellipticity was observed only in the horizontal plane. The wavefield radiated from the building might be dominated by unconventionally polarized surface waves, i.e., quasi-Rayleigh waves or a combination of quasi-Rayleigh and quasi-Love waves. The results indicated that the energy transmitted from the analyzed vibrating building to its surroundings was significant and decreased the ground motion shaking due to the out-of-phase motion.
Biocides are used for a wide range of purposes, including disinfectants or preservatives. They play a major role in the prevention of microbial infections in healthcare and animal husbandry. The use of biocides often leads to the discharge of active biocidal substances into wastewater streams, causing the exposure of wastewater microbial communities to subinhibitory concentrations. In turn, it is known that wastewater treatment plants (WWTP) are hotspots for antibiotic resistant bacteria. Since similar mechanisms confer resistance to biocides and antibiotics, exposure to biocides can result in co-selection of antibiotic resistant bacteria in WWTP Here, we want to investigate co-selection processes of antibiotic resistance in natural WWTP microbial communities upon biocide exposure. Microbial communities were sampled at the WWTP Ruhleben in Berlin and characterized regarding their susceptibility against different clinically relevant antibiotics. To investigate the link between biocide exposure and antibiotic resistance, changes in the susceptibility level after exposure to environmentally relevant concentrations of the commonly used biocide didecyldimethylammonium chloride (DDAC) will be determined by enumerating resistant and non-resistant E. coli on selective plates with and without antibiotics and DDAC. In case of antibiotics, clinical breakpoint concentrations according to EUCAST will be used to discriminate between susceptible and resistant strains. In case of DDAC (and biocides in general), clinical breakpoints do not exist. Therefore, we determined a cut-off concentration at which the majority of naturally-occurring E. coli strains cannot grow anymore based on (I) the MIC (minimal inhibitory concentration) distribution, and (II) by plating wastewater communities onto selective indicator agar plates loaded with increasing DDAC concentration. Additionally, antibiotic cross-resistance will be determined by spotting single colonies, isolated from DDAC-selective plates onto antibiotic plates. The results of our experiments will help to determine selective concentrations and to estimate the risk of antibiotic co-selection and cross-resistance in microbial WWTP communities upon biocide exposure.