Matthew J. Wade

TL;DR: In this article, a discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex.

TL;DR: Overall, GAC proved to be better than sand in controlling microbial growth, by promoting higher bacterial decay rates and hosting less bacterial cells, and showed better performance for putative pathogen control by leaking less Legionella cells into the effluent water.

TL;DR: In this paper, the current state of mathematical modelling in microbial ecology, looking back at the developments that have defined the synergies between the disciplines, and outline some of the existing challenges that motivate us to provide practical models in the hope that greater engagement with empiricists and practitioners in the microbiological domain may be achieved.

TL;DR: The results suggest that incorporating microbial community data with machine learning algorithms can be used for the prediction of feed substrate and for the potential improvement of MFC-based biosensor signal specificity, providing a new use of machine learning techniques that has substantial practical applications in biotechnological fields.

TL;DR: Substrate was the dominant factor in determining performance and diversity: unexpectedly the simple electrogenic substrate delivered a higher diversity than a complex wastewater.