The Use of Interdisciplinary Approaches to Understand the Biology of Campylobacter jejuni.

Campylobacter jejuni is a bacterial pathogen recognised as a major cause of foodborne illness worldwide. While Campylobacter jejuni generally does not grow outside its host, it can survive outside of the host long enough to pose a health concern. This review presents an up-to-date description and evaluation of biological, mathematical, and statistical approaches used to understand the behaviour of this foodborne pathogen and suggests future avenues which can be explored. Specifically, the incorporation of mathematical modelling may aid the understanding of C. jejuni biofilm formation both outside and inside the host. Predictive studies may be improved by the introduction of more standardised protocols for assessments of disinfection methods and by assessment of novel physical disinfection strategies as well as assessment of the efficiency of plant extracts on C. jejuni eradication. A full description of the metabolic pathways of C. jejuni, which is needed for the successful application of metabolic models, is yet to be achieved. Finally, a shift from animal models (except for those that are a source of human campylobacteriosis) to human-specific data may be made possible due to recent technological advancements, and this may lead to more accurate predictions of human infections.

Power-law and log-normal avalanche size statistics in random growth processes.

We study the avalanche statistics observed in a minimal random growth model. The growth is governed by a reproduction rate obeying a probability distribution with finite mean a[over ¯] and variance v_{a}. These two control parameters determine if the avalanche size tends to a stationary distribution (finite scale statistics with finite mean and variance, or power-law tailed statistics with exponent ∈(1,3]), or instead to a nonstationary regime with log-normal statistics. Numerical results and their statistical analysis are presented for a uniformly distributed growth rate, which are corroborated and generalized by mathematical results. The latter show that the numerically observed avalanche regimes exist for a wide family of growth rate distributions, and they provide a precise definition of the boundaries between the three regimes.

Importance of untested infectious individuals for interventions to suppress COVID-19.

The impact of the extent of testing infectious individuals on suppression of COVID-19 is illustrated from the early stages of outbreaks in Germany, the Hubei province of China, Italy, Spain and the UK. The predicted percentage of untested infected individuals depends on the specific outbreak but we found that they typically represent 60-80% of all infected individuals during the early stages of the outbreaks. We propose that reducing the underlying transmission from untested cases is crucial to suppress the virus. This can be achieved through enhanced testing in combination with social distancing and other interventions that reduce transmission such as wearing face masks. Once transmission from silent carriers is kept under control by these means, the virus could have been fully suppressed through fast isolation and contact tracing of tested cases.

Estimated Dissemination Ratio-A Practical Alternative to the Reproduction Number for Infectious Diseases.

Policymakers require consistent and accessible tools to monitor the progress of an epidemic and the impact of control measures in real time. One such measure is the Estimated Dissemination Ratio (EDR), a straightforward, easily replicable, and robust measure of the trajectory of an outbreak that has been used for many years in the control of infectious disease in livestock. It is simple to calculate and explain. Its calculation and use are discussed below together with examples from the current COVID-19 outbreak in the UK. These applications illustrate that EDR can demonstrate changes in transmission rate before they may be clear from the epidemic curve. Thus, EDR can provide an early warning that an epidemic is resuming growth, allowing earlier intervention. A conceptual comparison between EDR and the commonly used reproduction number is also provided.

Mining whole genome sequence data to efficiently attribute individuals to source populations.

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.

Challenges of biofilm control and utilization: lessons from mathematical modelling.

This article reviews modern applications of mathematical descriptions of biofilm formation. The focus is on theoretically obtained results which have implications for areas including the medical sector, food industry and wastewater treatment. Examples are given as to how models have contributed to the overall knowledge on biofilms and how they are used to predict biofilm behaviour. We conclude that the use of mathematical models of biofilms has demonstrated over the years the ability to significantly contribute to the vast field of biofilm research. Among other things, they have been used to test various hypotheses on the nature of interspecies interactions, viability of biofilm treatment methods or forces behind observed biofilm pattern formations. Mathematical models can also play a key role in future biofilm research. Many models nowadays are analysed through computer simulations and continue to improve along with computational capabilities. We predict that models will keep on providing answers to important challenges involving biofilm formation. However, further strengthening of the ties between various disciplines is necessary to fully use the tools of collective knowledge in tackling the biofilm phenomenon.

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

We present the LiSEQ (Listeria SEQuencing) project, funded by the European Food Safety Agency (EFSA) to compare Listeria monocytogenes isolates collected in the European Union from ready-to-eat foods, compartments along the food chain (e.g. food-producing animals, food-processing environments) and humans. In this article, we report the molecular characterization of a selection of this data set employing whole-genome sequencing analysis. We present an overview of the strain diversity observed in different sampled sources, and characterize the isolates based on their virulence and resistance profile. We integrate into our analysis the global L. monocytogenes genome collection described by Moura and colleagues in 2016 to assess the representativeness of the LiSEQ collection in the context of known L. monocytogenes strain diversity.

Phylogeographic Analysis Reveals Multiple International transmission Events Have Driven the Global Emergence of Escherichia coli O157:H7.

BACKGROUND: Shiga toxin-producing Escherchia coli (STEC) O157:H7 is a zoonotic pathogen that causes numerous food and waterborne disease outbreaks. It is globally distributed, but its origin and the temporal sequence of its geographical spread are unknown. METHODS: We analyzed whole-genome sequencing data of 757 isolates from 4 continents, and performed a pan-genome analysis to identify the core genome and, from this, extracted single-nucleotide polymorphisms. A timed phylogeographic analysis was performed on a subset of the isolates to investigate its worldwide spread. RESULTS: The common ancestor of this set of isolates occurred around 1890 (1845-1925) and originated from the Netherlands. Phylogeographic analysis identified 34 major transmission events. The earliest were predominantly intercontinental, moving from Europe to Australia around 1937 (1909-1958), to the United States in 1941 (1921-1962), to Canada in 1960 (1943-1979), and from Australia to New Zealand in 1966 (1943-1982). This pre-dates the first reported human case of E. coli O157:H7, which was in 1975 from the United States. CONCLUSIONS: Inter- and intra-continental transmission events have resulted in the current international distribution of E. coli O157:H7, and it is likely that these events were facilitated by animal movements (eg, Holstein Friesian cattle). These findings will inform policy on action that is crucial to reduce the further spread of E. coli O157:H7 and other (emerging) STEC strains globally.

Immunization and Targeted Destruction of Networks using Explosive Percolation.

A new method ("explosive immunization") is proposed for immunization and targeted destruction of networks. It combines the explosive percolation (EP) paradigm with the idea of maintaining a fragmented distribution of clusters. The ability of each node to block the spread of an infection (or to prevent the existence of a large cluster of connected nodes) is estimated by a score. The algorithm proceeds by first identifying low score nodes that should not be vaccinated or destroyed, analogously to the links selected in EP if they do not lead to large clusters. As in EP, this is done by selecting the worst node (weakest blocker) from a finite set of randomly chosen "candidates." Tests on several real-world and model networks suggest that the method is more efficient and faster than any existing immunization strategy. Because of the latter property it can deal with very large networks.

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.

Effects of local and global network connectivity on synergistic epidemics.

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Effect of disorder on condensation in the lattice gas model on a random graph.

The lattice gas model of condensation in a heterogeneous pore system, represented by a random graph of cells, is studied using an exact analytical solution. A binary mixture of pore cells with different coordination numbers is shown to exhibit two phase transitions as a function of chemical potential in a certain temperature range. Heterogeneity in interaction strengths is demonstrated to reduce the critical temperature and, for large-enough degreeS of disorder, divides the cells into ones which are either on average occupied or unoccupied. Despite treating the pore space loops in a simplified manner, the random-graph model provides a good description of condensation in porous structures containing loops. This is illustrated by considering capillary condensation in a structural model of mesoporous silica SBA-15.

Zero-temperature random-field Ising model on a bilayered Bethe lattice.

The zero-temperature random-field Ising model is solved analytically for magnetization versus external field for a bilayered Bethe lattice. The mechanisms of infinite avalanches which are observed for small values of disorder are established. The effects of variable interlayer interaction strengths on infinite avalanches are investigated. The spin-field correlation length is calculated and its critical behavior is discussed. Direct Monte Carlo simulations of spin-flip dynamics are shown to support the analytical findings. We find, paradoxically, that a reduction of the interlayer bond strength can cause a phase transition from a regime with continuous magnetization reversal to a regime where magnetization exhibits a discontinuity associated with an infinite avalanche. This effect is understood in terms of the proposed mechanisms for the infinite avalanche.

Capillary condensation in one-dimensional irregular confinement.

A lattice-gas model with heterogeneity is developed for the description of fluid condensation in finite sized one-dimensional pores of arbitrary shape. Mapping to the random-field Ising model allows an exact solution of the model to be obtained at zero-temperature, reproducing the experimentally observed dependence of the amount of fluid adsorbed in the pore on external pressure. It is demonstrated that the disorder controls the sorption for long pores and can result in H2-type hysteresis. Finite-temperature Metropolis dynamics simulations support analytical findings in the limit of low temperatures. The proposed framework is viewed as a fundamental building block of the theory of capillary condensation necessary for reliable structural analysis of complex porous media from adsorption-desorption data.

Mechanisms of evolution of avalanches in regular graphs.

A mapping of avalanches occurring in the zero-temperature random-field Ising model to life periods of a population experiencing immigration is established. Such a mapping allows the microscopic criteria for the occurrence of an infinite avalanche in a q-regular graph to be determined. A key factor for an avalanche of spin flips to become infinite is that it interacts in an optimal way with previously flipped spins. Based on these criteria, we explain why an infinite avalanche can occur in q-regular graphs only for q>3 and suggest that this criterion might be relevant for other systems. The generating function techniques developed for branching processes are applied to obtain analytical expressions for the durations, pulse shapes, and power spectra of the avalanches. The results show that only very long avalanches exhibit a significant degree of universality.

Surfactant-mediated control of colloid pattern assembly and attachment strength in evaporating droplets.

This study demonstrates that the pattern assembly and attachment strength of colloids in an evaporating sessile droplet resting on a smooth substrate can be controlled by adding nonionic solutes (surfactant) to the solution. As expected, increasing the surfactant concentration leads to a decrease in initial surface tension of the drop, σ(0). For the range of initial surface tensions investigated (39-72 mN m(-1)), three distinct deposition patterns were produced: amorphous stains (σ(0) = 63-72 mN m(-1)), coffee-ring stains (σ(0) = 48-53 mN m(-1)), and concentric rings (σ(0) = 39-45 mN m(-1)). A flow-displacement system was used to measure the attachment strength of the dried colloids. Characteristic drying regimes associated with the three unique pattern formations are attributed to abrupt transitions of contact line dynamics during evaporation. The first transition from slipping- to pinned-contact line was found to be a direct result of the competition between mechanical instability of the droplet and the friction generated by pinned colloids at the contact line. The second transition from pinned- to recurrent-stick-rip-slip-contact line was caused by repeated liquid film rupturing from evaporation-intensified surfactant concentration. Data from flow-displacement tests indicate that attachment strength of dried particles is strongest for amorphous stains (lowest surfactant concentration) and weakest for concentric rings (highest surfactant concentration). The mechanism behind these observations was ascribed to the formation and adsorption of micelles onto colloid and substrate surfaces as the droplet solution evaporates. The range of attachment forces observed between the colloids and the solid substrate were well captured by extended-DLVO interactions accounting for van der Waals attraction, electric double layer repulsion, and micelle-protrusion repulsion. Both empirical and theoretical results suggest that an increasingly dense layer of adsorbed micellar-protrusions on colloid and substrate surfaces acts as a physical barrier that hinders strong van der Waals attractive interactions at close proximity. Thereby, colloid stains dried at higher surfactant concentrations are more easily detached from the substrate when dislodging forces are applied than stains dried at lower surfactant concentrations.

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

A theoretical framework for the description of susceptible-infected-removed (SIR) spreading processes with synergistic transmission of infection on a lattice is developed. The model incorporates explicitly the effects of time-dependence of the state of the hosts in the neighborhood of transmission events. Exact solution of the model shows that time-dependence of the state of nearest neighbors of recipient hosts is a key factor for synergistic spreading processes. It is demonstrated that the higher the connectivity of a lattice, the more prominent is the effect of synergy on spread.

Prediction of invasion from the early stage of an epidemic.

Predictability of undesired events is a question of great interest in many scientific disciplines including seismology, economy and epidemiology. Here, we focus on the predictability of invasion of a broad class of epidemics caused by diseases that lead to permanent immunity of infected hosts after recovery or death. We approach the problem from the perspective of the science of complexity by proposing and testing several strategies for the estimation of important characteristics of epidemics, such as the probability of invasion. Our results suggest that parsimonious approximate methodologies may lead to the most reliable and robust predictions. The proposed methodologies are first applied to analysis of experimentally observed epidemics: invasion of the fungal plant pathogen Rhizoctonia solani in replicated host microcosms. We then consider numerical experiments of the susceptible-infected-removed model to investigate the performance of the proposed methods in further detail. The suggested framework can be used as a valuable tool for quick assessment of epidemic threat at the stage when epidemics only start developing. Moreover, our work amplifies the significance of the small-scale and finite-time microcosm realizations of epidemics revealing their predictive power.

The effect of heterogeneity on invasion in spatial epidemics: from theory to experimental evidence in a model system.

Heterogeneity in host populations is an important factor affecting the ability of a pathogen to invade, yet the quantitative investigation of its effects on epidemic spread is still an open problem. In this paper, we test recent theoretical results, which extend the established "percolation paradigm" to the spread of a pathogen in discrete heterogeneous host populations. In particular, we test the hypothesis that the probability of epidemic invasion decreases when host heterogeneity is increased. We use replicated experimental microcosms, in which the ubiquitous pathogenic fungus Rhizoctonia solani grows through a population of discrete nutrient sites on a lattice, with nutrient sites representing hosts. The degree of host heterogeneity within different populations is adjusted by changing the proportion and the nutrient concentration of nutrient sites. The experimental data are analysed via Bayesian inference methods, estimating pathogen transmission parameters for each individual population. We find a significant, negative correlation between heterogeneity and the probability of pathogen invasion, thereby validating the theory. The value of the correlation is also in remarkably good agreement with the theoretical predictions. We briefly discuss how our results can be exploited in the design and implementation of disease control strategies.