A synthetic Escherichia coli predator–prey ecosystem

We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator–prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner

Geographical trends in research: a preliminary analysis on authors' affiliations

In the last decade, research literature reached an enormous volume with an unprecedented current annual increase of 1.5 million new publications. As research gets ever more global and new countries and institutions, either from academia or corporate environment, start to contribute with their share, it is important to monitor this complex scenario and understand its dynamics. We present a study on a conference proceedings dataset extracted from Springer Nature Scigraph that illustrates insightful geographical trends and highlights the unbalanced growth of competitive research institutions worldwide. Results emerged from our micro and macro analysis show that the distributions among countries of institutions and papers follow a power law, and thus very few countries keep producing most of the papers accepted by high-tier conferences. In addition, we found that the annual and overall turnover rate of the top 5, 10 and 25 countries is extremely low, suggesting a very static landscape in which new entries struggle to emerge. Finally, we highlight the presence of an increasing gap between the number of institutions initiating and overseeing research endeavours (i.e. first and last authors' affiliations) and the total number of institutions participating in research. As a consequence of our analysis, the paper also discusses our experience in working with affiliations: an utterly simple matter at first glance, that is instead revealed to be a complex research and technical challenge yet far from being solved

Complex Derivatives

The intrinsic complexity of the financial derivatives market has emerged as both an incentive to engage in it, and a key source of its inherent instability. Regulators now faced with the challenge of taming this beast may find inspiration in the budding science of complex systems. When financial derivatives were cast in 2002 as latent 'weapons of mass destruction', one might have expected the world at large to sit up and listen — particularly in the wake of subsequent events that led to the financial crisis of 2008. Instead, the derivatives market continues to grow in size and complexity (Fig. 1), spawning a new generation of financial innovations, and raising concerns about its potential impact on the economy as a whole. A derivative instrument is a financial contract between two parties, in which the value of the payoff is derived from the value of another financial instrument or asset, called the underlying entity. In some cases, this contract acts as a kind of insurance: in a credit default swap, for example, a lender might buy protection from a third party to insure against the default of the borrower. However, unlike conventional insurance, in which a person necessarily owns the house she wants to insure, derivatives can be negotiated on any underlying entity — meaning anyone could take out insurance on the house in question. Speculation therefore emerges as another reason to trade in derivatives. By engaging in a speculative derivatives market, players can potentially amplify their gains, which is arguably the most plausible explanation for the proliferation of derivatives in recent years. Needless to say, losses are also amplified. Unlike bets on, say, dice — where the chances of the outcome are not affected by the bet itself — the more market players bet on the default of a country, the more likely the default becomes. Eventually the game becomes a self-fulfilling prophecy, as in a bank run, where if each party believes that others will withdraw their money from the bank, it pays each to do so. More perversely, in some cases parties have incentives (and opportunities) to precipitate these events, by spreading rumours or by manipulating the prices on which the derivatives are contingent — a situation seen most recently in the London Interbank Offered Rate (LIBOR) affair. Proponents of derivatives have long argued that these instruments help to stabilize markets by distributing risk, but it has been shown recently that in many situations risk sharing can also lead to instabilities

Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure

We consider the edge-based compartmental models for infectious disease spread introduced in Part I. These models allow us to consider standard SIR diseases spreading in random populations. In this paper we show how to handle deviations of the disease or population from the simplistic assumptions of Part I. We allow the population to have structure due to effects such as demographic detail or multiple types of risk behavior the disease to have more complicated natural history. We introduce these modifications in the static network context, though it is straightforward to incorporate them into dynamic networks. We also consider serosorting, which requires using the dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here

Retrospective cohort study evaluating clinical, biochemical and pharmacological prognostic factors for prostate cancer progression using primary care data

This is the final version. Available on open access from BMJ Publishing group via the DOI in this recordObjectives – To confirm the association of previously reported prognostic factors with future progression of localised prostate cancer using primary care data and identify new potential prognostic factors for further assessment in prognostic model development and validation. Design – Retrospective cohort study, employing Cox proportional hazards regression controlling for age, PSA, and Gleason score, stratified by diagnostic stage. Setting – Primary care in England Participants – Males with localised prostate cancer diagnosed between 01/01/1987 and 31/12/2016 within the Clinical Practice Research Datalink database, with linked data from the National Cancer Registration and Analysis Service and Office for National Statistics. Primary and Secondary outcomes – Primary outcome measure was prostate cancer mortality. Secondary outcomes measures were all-cause mortality and commencing systematic therapy. Up13 staging after diagnosis was not used as a secondary outcome owing to significant missing data. Results 10,901 males (mean age 74.38 +/- 9.03 years) with localised prostate cancer were followed up for a mean of 14.12 (+/- 6.36) years. 2,331 (21.38%) men underwent systemic therapy and 3,250 (31.65%) died, including 1,250 (11.47%) from prostate cancer. Factors associated with an increased risk of prostate cancer mortality included age; high PSA; current or ex-smoker; ischaemic heart disease; high C-Reactive Protein; high ferritin; low haemoglobin; high blood glucose; and low albumin. Conclusions This study identified several new potential prognostic factors for prostate cancer progression, as well as confirming some known prognostic factors, in an independent primary care data set. Further research is needed to develop and validate a prognostic model for prostate cancer progression.Can Test Collaborative/CRU

Epidemic Enhancement in Partially Immune Populations

We observe that a pathogen introduce/pmcdata/journal/plosone/2-2007/1/ingest/pmcmod/sgml/pone.0000165.xmld into a population containing individuals with acquired immunity can result in an epidemic longer in duration and/or larger in size than if the pathogen were introduced into a naive population. We call this phenomenon “epidemic enhancement,” and use simple dynamical models to show that it is a realistic scenario within the parameter ranges of many common infectious diseases. This finding implies that repeated pathogen introduction or intermediate levels of vaccine coverage can lead to pathogen persistence in populations where extinction would otherwise be expected

Success of a suicidal defense strategy against infection in a structured habitat

Pathogen infection often leads to the expression of virulence and host death when the host-pathogen symbiosis seems more beneficial for the pathogen. Previously proposed explanations have focused on the pathogen's side. In this work, we tested a hypothesis focused on the host strategy. If a member of a host population dies immediately upon infection aborting pathogen reproduction, it can protect the host population from secondary infections. We tested this "Suicidal Defense Against Infection" (SDAI) hypothesis by developing an experimental infection system that involves a huge number of bacteria as hosts and their virus as pathogen, which is linked to modeling and simulation. Our experiments and simulations demonstrate that a population with SDAI strategy is successful in the presence of spatial structure but fails in its absence. The infection results in emergence of pathogen mutants not inducing the host suicide in addition to host mutants resistant to the pathogen

The effects of climatic fluctuations and extreme events on running water ecosystems

Most research on the effects of environmental change in freshwaters has focused on incremental changes in average conditions, rather than fluctuations or extreme events such as heatwaves, cold snaps, droughts, floods or wildfires, which may have even more profound consequences. Such events are commonly predicted to increase in frequency, intensity and duration with global climate change, with many systems being exposed to conditions with no recent historical precedent. We propose a mechanistic framework for predicting potential impacts of environmental fluctuations on running water ecosystems by scaling up effects of fluctuations from individuals to entire ecosystems. This framework requires integration of four key components: effects of the environment on individual metabolism, metabolic and biomechanical constraints on fluctuating species interactions, assembly dynamics of local food webs and mapping the dynamics of the meta-community onto ecosystem function. We illustrate the framework by developing a mathematical model of environmental fluctuations on dynamically assembling food webs. We highlight (currently limited) empirical evidence for emerging insights and theoretical predictions. For example, widely supported predictions about the effects of environmental fluctuations are: high vulnerability of species with high per capita metabolic demands such as large-bodied ones at the top of food webs; simplification of food web network structure and impaired energetic transfer efficiency; reduced resilience and top-down relative to bottom-up regulation of food web and ecosystem processes. We conclude by identifying key questions and challenges that need to be addressed to develop more accurate and predictive bio-assessments of the effects of fluctuations, and implications of fluctuations for management practices in an increasingly uncertain world

Structural Perturbations to Population Skeletons: Transient Dynamics, Coexistence of Attractors and the Rarity of Chaos

Simple models of insect populations with non-overlapping generations have been instrumental in understanding the mechanisms behind population cycles, including wild (chaotic) fluctuations. The presence of deterministic chaos in natural populations, however, has never been unequivocally accepted. Recently, it has been proposed that the application of chaos control theory can be useful in unravelling the complexity observed in real population data. This approach is based on structural perturbations to simple population models (population skeletons). The mechanism behind such perturbations to control chaotic dynamics thus far is model dependent and constant (in size and direction) through time. In addition, the outcome of such structurally perturbed models is [almost] always equilibrium type, which fails to commensurate with the patterns observed in population data.We present a proportional feedback mechanism that is independent of model formulation and capable of perturbing population skeletons in an evolutionary way, as opposed to requiring constant feedbacks. We observe the same repertoire of patterns, from equilibrium states to non-chaotic aperiodic oscillations to chaotic behaviour, across different population models, in agreement with observations in real population data. Model outputs also indicate the existence of multiple attractors in some parameter regimes and this coexistence is found to depend on initial population densities or the duration of transient dynamics. Our results suggest that such a feedback mechanism may enable a better understanding of the regulatory processes in natural populations

Cooperation and Contagion in Web-Based, Networked Public Goods Experiments

A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of web-based experiments, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous theoretical work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial seed players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation were contagious only to direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs