Effects of variable-state neighborhoods for spreading synergystic processes on lattices

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Modelling fungal colonies and communities:challenges and opportunities

This contribution, based on a Special Interest Group session held during IMC9, focuses on physiological based models of filamentous fungal colony growth and interactions. Fungi are known to be an important component of ecosystems, in terms of colony dynamics and interactions within and between trophic levels. We outline some of the essential components necessary to develop a fungal ecology: a mechanistic model of fungal colony growth and interactions, where observed behaviour can be linked to underlying function; a model of how fungi can cooperate at larger scales; and novel techniques for both exploring quantitatively the scales at which fungi operate; and addressing the computational challenges arising from this highly detailed quantification. We also propose a novel application area for fungi which may provide alternate routes for supporting scientific study of colony behaviour. This synthesis offers new potential to explore fungal community dynamics and the impact on ecosystem functioning

Origin of scale-free intermittency in structural first-order phase transitions

Acknowledgments FJPR acknowledges the financial support from the Carnegie Trust. LT acknowledges the financial support from the french ANR grant EVOCRIT.Peer reviewedPostprin

Immunization and Targeted Destruction of Networks using Explosive Percolation

7 pages, 6 figures The authors acknowledge financial support from the Leverhulme Trust (Grant No. VP2-2014-043) and from Horizon2020 (Grant No. 642563 - COSMOS).Peer reviewedPreprintPublisher PD

Reverse engineering of biochar

This study underpins quantitative relationships that account for the combined effects that starting biomass and peak pyrolysis temperature have on physico-chemical properties of biochar. Meta-data was assembled from published data of diverse biochar samples (n = 102) to (i) obtain networks of intercorrelated properties and (ii) derive models that predict biochar properties. Assembled correlation networks provide a qualitative overview of the combinations of biochar properties likely to occur in a sample. Generalized Linear Models are constructed to account for situations of varying complexity, including: dependence of biochar properties on single or multiple predictor variables, where dependence on multiple variables can have additive and/or interactive effects; non-linear relation between the response and predictors; and non-Gaussian data distributions. The web-tool Biochar Engineering implements the derived models to maximize their utility and distribution. Provided examples illustrate the practical use of the networks, models and web-tool to engineer biochars with prescribed properties desirable for hypothetical scenarios

Prominent effect of soil network heterogeneity on microbial invasion

Using a network representation for real soil samples and mathematical models for microbial spread, we show that the structural heterogeneity of the soil habitat may have a very significant influence on the size of microbial invasions of the soil pore space. In particular, neglecting the soil structural heterogeneity may lead to a substantial underestimation of microbial invasion. Such effects are explained in terms of a crucial interplay between heterogeneity in microbial spread and heterogeneity in the topology of soil networks. The main influence of network topology on invasion is linked to the existence of long channels in soil networks that may act as bridges for transmission of microorganisms between distant parts of soil

Estimated Dissemination Ratio -a practical alternative to the Reproduction Number for infectious diseases

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LiSEQ – whole-genome sequencing of a cross-sectional survey of Listeria monocytogenes in ready-to-eat foods and human clinical cases in Europe

Funding information This work was funded by EFSA, contract number C/EFSA/BIOCONTAM/2014/01-CT 1 on “Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE) foods: activity 3, the comparison of isolates from different compartments along the food chain, and from humans using whole genome sequencing (WGS) analysis’, EFSA-Q-2014-00 026. Acknowledgements A. P., T. D. and K. G. are affiliated to the National Institute for Health Research – Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with Public Health England, in collaboration with the University of East Anglia, the University of Oxford and the Quadram Institute. A. P., T. D. and K. G. are based at Public Health England. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.Peer reviewedPublisher PD