Assessment of the microbial community in the cathode compartment of a plant microbial fuel cell

Introduction: In plant microbial fuel cells (plant-MFCs) living plants and microorganisms form an electrochemical unit able to produce clean and sustainable electricity from solar energy. It is reasonable to assume that besides the bacteria in the anode compartment also the cathode compartment plays a crucial role for a stable high current producing plant-MFC. In this study we aim to identify dominant bacterial species in the cathode compartment of the plant-MFC

Uptake of methionine sulfoximine by some N2 fixing bacteria, and its effect on ammonium transport

AbstractThe N2 fixing bacteria Klebsiella pneumoniae, Azospirillum brasilense, Rhodopseudomonas sphaeroides and Rhodospirillum rubrum, but not Azotobacter vinelandii accumulate the glutamine analogue methionine sulfoximine in the cell. In the accumulating cells methionine sulfoximine inhibits ammonium transport. Accumulation and inhibition are prevented by glutamine

A modeling-based evaluation of isothermal rebreathing for breath gas analyses of highly soluble volatile organic compounds

Isothermal rebreathing has been proposed as an experimental technique for estimating the alveolar levels of hydrophilic volatile organic compounds (VOCs) in exhaled breath. Using the prototypic test compound acetone we demonstrate that the end-tidal breath profiles of such substances during isothermal rebreathing show characteristics that contradict the conventional pulmonary inert gas elimination theory due to Farhi. On the other hand, these profiles can reliably be captured by virtue of a previously developed mathematical model for the general exhalation kinetics of highly soluble, blood-borne VOCs, which explicitly takes into account airway gas exchange as major determinant of the observable breath output. This model allows for a mechanistic analysis of various rebreathing protocols suggested in the literature. In particular, it clarifies the discrepancies between in vitro and in vivo blood-breath ratios of hydrophilic VOCs and yields further quantitative insights into the physiological components of isothermal rebreathing.Comment: 21 page

Physiological modeling of isoprene dynamics in exhaled breath

Human breath contains a myriad of endogenous volatile organic compounds (VOCs) which are reflective of ongoing metabolic or physiological processes. While research into the diagnostic potential and general medical relevance of these trace gases is conducted on a considerable scale, little focus has been given so far to a sound analysis of the quantitative relationships between breath levels and the underlying systemic concentrations. This paper is devoted to a thorough modeling study of the end-tidal breath dynamics associated with isoprene, which serves as a paradigmatic example for the class of low-soluble, blood-borne VOCs. Real-time measurements of exhaled breath under an ergometer challenge reveal characteristic changes of isoprene output in response to variations in ventilation and perfusion. Here, a valid compartmental description of these profiles is developed. By comparison with experimental data it is inferred that the major part of breath isoprene variability during exercise conditions can be attributed to an increased fractional perfusion of potential storage and production sites, leading to higher levels of mixed venous blood concentrations at the onset of physical activity. In this context, various lines of supportive evidence for an extrahepatic tissue source of isoprene are presented. Our model is a first step towards new guidelines for the breath gas analysis of isoprene and is expected to aid further investigations regarding the exhalation, storage, transport and biotransformation processes associated with this important compound.Comment: 14 page

Superconducting quantum simulator for topological order and the toric code

Topological order is now being established as a central criterion for characterizing and classifying ground states of condensed matter systems and complements categorizations based on symmetries. Fractional quantum Hall systems and quantum spin liquids are receiving substantial interest because of their intriguing quantum correlations, their exotic excitations and prospects for protecting stored quantum information against errors. Here we show that the Hamiltonian of the central model of this class of systems, the Toric Code, can be directly implemented as an analog quantum simulator in lattices of superconducting circuits. The four-body interactions, which lie at its heart, are in our concept realized via Superconducting Quantum Interference Devices (SQUIDs) that are driven by a suitably oscillating flux bias. All physical qubits and coupling SQUIDs can be individually controlled with high precision. Topologically ordered states can be prepared via an adiabatic ramp of the stabilizer interactions. Strings of qubit operators, including the stabilizers and correlations along non-contractible loops, can be read out via a capacitive coupling to read-out resonators. Moreover, the available single qubit operations allow to create and propagate elementary excitations of the Toric Code and to verify their fractional statistics. The architecture we propose allows to implement a large variety of many-body interactions and thus provides a versatile analog quantum simulator for topological order and lattice gauge theories

Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks

The calibration of constitutive models from full-field data has recently gained increasing interest due to improvements in full-field measurement capabilities. In addition to the experimental characterization of novel materials, continuous structural health monitoring is another application that is of great interest. However, monitoring is usually associated with severe time constraints, difficult to meet with standard numerical approaches. Therefore, parametric physics-informed neural networks (PINNs) for constitutive model calibration from full-field displacement data are investigated. In an offline stage, a parametric PINN can be trained to learn a parameterized solution of the underlying partial differential equation. In the subsequent online stage, the parametric PINN then acts as a surrogate for the parameters-to-state map in calibration. We test the proposed approach for the deterministic least-squares calibration of a linear elastic as well as a hyperelastic constitutive model from noisy synthetic displacement data. We further carry out Markov chain Monte Carlo-based Bayesian inference to quantify the uncertainty. A proper statistical evaluation of the results underlines the high accuracy of the deterministic calibration and that the estimated uncertainty is valid. Finally, we consider experimental data and show that the results are in good agreement with a Finite Element Method-based calibration. Due to the fast evaluation of PINNs, calibration can be performed in near real-time. This advantage is particularly evident in many-query applications such as Markov chain Monte Carlo-based Bayesian inference

Endophytic root colonization of gramineous plants by Herbaspirillum frisingense

Herbaspirillum frisingense is a diazotrophic betaproteobacterium isolated from C4-energy plants, for example Miscanthus sinensis. To demonstrate endophytic colonization unequivocally, immunological labeling techniques using monospecific polyclonal antibodies against two H. frisingense strains and green fluorescent protein (GFP)-fluorescence tagging were applied. The polyclonal antibodies enabled specific in situ identification and very detailed localization of H. frisingense isolates Mb11 and GSF30T within roots of Miscanthus×giganteus seedlings. Three days after inoculation, cells were found inside root cortex cells and after 7 days they were colonizing the vascular tissue in the central cylinder. GFP-tagged H. frisingense strains could be detected and localized in uncut root material by confocal laser scanning microscopy and were found as endophytes in cortex cells, intercellular spaces and the central cylinder of barley roots. Concerning the production of potential plant effector molecules, H. frisingense strain GSF30T tested positive for the production of indole-3-acetic acid, while Mb11 was shown to produce N-acylhomoserine lactones, and both strains were able to utilize 1-aminocyclopropane-1-carboxylate (ACC), providing an indication of the activity of an ACC-deaminase. These results clearly present H. frisingense as a true plant endophyte and, although initial greenhouse experiments did not lead to clear plant growth stimulation, demonstrate the potential of this species for beneficial effects on the growth of crop plant

Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics

In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are starting to emerge and can also be addressed in a parameter estimation framework. These developments call for a new unified perspective, which is able to cover both traditional parameter estimation methods and novel approaches in which the state variables or the model structure itself are inferred as well. Adopting concepts discussed in the inverse problems community, we distinguish between all-at-once and reduced approaches. With this general framework, we are able to structure a large portion of the literature on parameter estimation in computational mechanics - and we can identify combinations that have not yet been addressed, two of which are proposed in this paper. We also discuss statistical approaches to quantify the uncertainty related to the estimated parameters, and we propose a novel two-step procedure for identification of complex material models based on both frequentist and Bayesian principles. Finally, we illustrate and compare several of the aforementioned methods with mechanical benchmarks based on synthetic and real data