When studying electrochemical systems, EIS practitioners face the challenge of choosing a relevant equivalent electrical circuit to analyze their measurement data and interpreting the role of its components. In this review, we take a closer look at the use of equivalent electrical circuits (EEC) across various application domains. We aim to aid EIS practitioners in determining and evaluating their EEC-based data analysis methodology in light of recent progress from all EIS application domains. We review EEC usage and its interpretation while additionally providing software to automatically search for equivalent electrical circuits from the relevant application domain literature that fit EIS measurements supplied by users. Finally, we make a comparison of the impedimetric behaviour and circuit modelling approaches of a range of different electrochemical systems and discuss some complementary EIS data analysis strategies.

Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics.

Receptor-binding proteins (RBPs) of bacteriophages initiate the infection of their corresponding bacterial host and act as the primary determinant for host specificity. The ever-increasing amount of sequence data enables the development of predictive models for the automated identification of RBP sequences. However, the development of such models is challenged by the inconsistent or missing annotation of many phage proteins. Recently developed tools have started to bridge this gap but are not specifically focused on RBP sequences, for which many different annotations are available. We have developed two parallel approaches to alleviate the complex identification of RBP sequences in phage genomic data. The first combines known RBP-related hidden Markov models (HMMs) from the Pfam database with custom-built HMMs to identify phage RBPs based on protein domains. The second approach consists of training an extreme gradient boosting classifier that can accurately discriminate between RBPs and other phage proteins. We explained how these complementary approaches can reinforce each other in identifying RBP sequences. In addition, we benchmarked our methods against the recently developed PhANNs tool. Our best performing model reached a precision-recall area-under-the-curve of 93.8% and outperformed PhANNs on an independent test set, reaching an F1-score of 84.0% compared to 69.8%.

Water potential explains water transport in the soil–plant–atmosphere continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale terrestrial biosphere models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed functional–structural plant models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognized links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and above-ground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).