Evolution of size-dependent flowering in a variable environment: construction and analysis of a stochastic integral projection model

Understanding why individuals delay reproduction is a classic problem in evolutionary biology. In plants, the study of reproductive delays is complicated because growth and survival can be size and age dependent, individuals of the same size can grow by different amounts and there is temporal variation in the environment. We extend the recently developed integral projection approach to include size- and age-dependent demography and temporal variation. The technique is then applied to a long-term individually structured dataset for Carlina vulgaris, a monocarpic thistle. The parameterized model has excellent descriptive properties in terms of both the population size and the distributions of sizes within each age class. In Carlina, the probability of flowering depends on both plant size and age. We use the parameterized model to predict this relationship, using the evolutionarily stable strategy approach. Considering each year separately, we show that both the direction and the magnitude of selection on the flowering strategy vary from year to year. Provided the flowering strategy is constrained, so it cannot be a step function, the model accurately predicts the average size at flowering. Elasticity analysis is used to partition the size- and age-specific contributions to the stochastic growth rate, λs. We use λs to construct fitness landscapes and show how different forms of stochasticity influence its topography. We prove the existence of a unique stochastic growth rate, λs, which is independent of the initial population vector, and show that Tuljapurkar's perturbation analysis for log(λs) can be used to calculate elasticities

Decision-making strategies: ignored to the detriment of healthcare training and delivery?

Context: People do not always make health-related decisions which reflect their best interest – best interest being defined as the decision they would make if they carefully considered the options and fully understood the information available. A substantial literature has developed in behavioral economics and social psychology that seeks to elucidate the patterns in individual decision-making. While this is particularly relevant to healthcare, the insights from these fields have only been applied in a limited way. To address the health challenges of the twenty-first century, healthcare providers and healthcare systems designers need to more fully understand how individuals are making decisions. Methods:: We provide an overview of the theories of behavioral economics and social psychology that relate to how individuals make health-related decisions. The concentration on health-related decisions leads to a focus on three topics: (1) mental shortcuts and motivated reasoning; (2) implications of time; and (3) implications of affect. The first topic is relevant because health-related decisions are often made in a hurry without a full appreciation of the implications and the deliberation they warrant. The second topic is included because the link between a decision and its health-related outcomes can involve a significant time lag. The final topic is included because health and affect are so often linked. Findings:: The literature reviewed has implications for healthcare training and delivery. Selection for medical training must consider the skills necessary to understand and adapt to how patients make decisions. Training on the insights garnered from behavioral economics and social psychology would better prepare healthcare providers to effectively support their clients to lead healthy lives. Healthcare delivery should be structured to respond to the way in which decisions are made. Conclusions:: These patterns in decision-making call into question basic assumptions our healthcare system makes about the best way to treat patients and deliver care. This literature has implications for the way we train physicians and deliver care

Noise and Nonlinearity in Measles Epidemics: Combining Mechanistic and Statistical Approaches to Population Modeling

We present and evaluate an approach to analyzing population dynamics data using semimechanistic models. These models incorporate reliable information on population structure and underlying dynamic mechanisms but use nonparametric surface-fitting methods to avoid unsupported assumptions about the precise form of rate equations. Using historical data on measles epidemics as a case study, we show how this approach can lead to better forecasts, better characterizations of the dynamics, and better understanding of the factors causing complex population dynamics relative to either mechanistic models or purely descriptive statistical time-series models. The semimechanistic models are found to have better forecasting accuracy than either of the model types used in previous analyses when tested on data not used to fit the models. The dynamics are characterized as being both nonlinear and noisy, and the global dynamics are clustered very tightly near the border of stability (dominant Lyapunov exponent λ < 0). However, locally in state space the dynamics oscillate between strong short-term stability and strong short-term chaos (i.e., between negative and positive local Lyapunov exponents). There is statistically significant evidence for short-term chaos in all data sets examined. Thus the nonlinearity in these systems is characterized by the variance over state space in local measures of chaos versus stability rather than a single summary measure of the overall dynamics as either chaotic or nonchaotic

From scaling up to sustainability in HIV: potential lessons for moving forward

Background: In 30 years of experience in responding to the HIV epidemic, critical decisions and program characteristics for successful scale-up have been studied. Now leaders face a new challenge: sustaining large-scale HIV prevention programs. Implementers, funders, and the communities served need to assess what strategies and practices of scaling up are also relevant for sustaining delivery at scale. Methods: We reviewed white and gray literature to identify domains central to scaling-up programs and reviewed HIV case studies to identify how these domains might relate to sustaining delivery at scale. Results: We found 10 domains identified as important for successfully scaling up programs that have potential relevance for sustaining delivery at scale: fiscal support; political support; community involvement, integration, buy-in, and depth; partnerships; balancing flexibility/adaptability and standardization; supportive policy, regulatory, and legal environment; building and sustaining strong organizational capacity; transferring ownership; decentralization; and ongoing focus on sustainability. We identified one additional potential domain important for programs sustaining delivery at scale: emphasizing equity. Conclusions: Today, the public and private sector are examining their ability to generate value for populations. All stakeholders are aiming to stem the tide of the HIV epidemic. Implementers need a framework to guide the evolution of their strategies and management practices. Greater research is needed to refine the domains for policy and program implementers working to sustain HIV program delivery at scale

Intrinsic chaos and external noise in population dynamics

We address the problem of the relative importance of the intrinsic chaos and the external noise in determining the complexity of population dynamics. We use a recently proposed method for studying the complexity of nonlinear random dynamical systems. The new measure of complexity is defined in terms of the average number of bits per time-unit necessary to specify the sequence generated by the system. This measure coincides with the rate of divergence of nearby trajectories under two different realizations of the noise. In particular, we show that the complexity of a nonlinear time-series model constructed from sheep populations comes completely from the environmental variations. However, in other situations, intrinsic chaos can be the crucial factor. This method can be applied to many other systems in biology and physics.Comment: 13 pages, Elsevier styl

Understanding Terrorist Organizations with a Dynamic Model

Terrorist organizations change over time because of processes such as recruitment and training as well as counter-terrorism (CT) measures, but the effects of these processes are typically studied qualitatively and in separation from each other. Seeking a more quantitative and integrated understanding, we constructed a simple dynamic model where equations describe how these processes change an organization's membership. Analysis of the model yields a number of intuitive as well as novel findings. Most importantly it becomes possible to predict whether counter-terrorism measures would be sufficient to defeat the organization. Furthermore, we can prove in general that an organization would collapse if its strength and its pool of foot soldiers decline simultaneously. In contrast, a simultaneous decline in its strength and its pool of leaders is often insufficient and short-termed. These results and other like them demonstrate the great potential of dynamic models for informing terrorism scholarship and counter-terrorism policy making.Comment: To appear as Springer Lecture Notes in Computer Science v2: vectorized 4 figures, fixed two typos, more detailed bibliograph

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

We consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on their behavior or other measurable responses. We show that, generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular we demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects, and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and their choices, even when there is no intrinsic affinity between them. We also suggest some possible constructive responses to these results.Comment: 27 pages, 9 figures. V2: Revised in response to referees. V3: Ditt

Transmission phenotype of mycobacterium tuberculosis strains is mechanistically linked to induction of distinct pulmonary pathology

In a study of household contacts (HHC), households were categorized into High (HT) and Low (LT) transmission groups based on the proportion of HHC with a positive tuberculin skin test. The Mycobacterium tuberculosis (Mtb) strains from HT and LT index cases of the households were designated Mtb-HT and Mtb-LT, respectively. We found that C3HeB/FeJ mice infected with Mtb-LT strains exhibited significantly higher bacterial burden compared to Mtb-HT strains and also developed diffused inflammatory lung pathology. In stark contrast, a significant number of mice infected with Mtb-HT strains developed caseating granulomas, a lesion type with high potential to cavitate. None of the Mtb-HT infected animals developed diffused inflammatory lung pathology. A link was observed between increased in vitro replication of Mtb-LT strains and their ability to induce significantly high lipid droplet formation in macrophages. These results support that distinct early interactions of Mtb-HT and Mtb-LT strains with macrophages and subsequent differential trajectories in pathological disease may be the mechanism underlying their transmission potential.publishersversionpublishe

Integrating evolution into ecological modelling: accommodating phenotypic changes in agent based models.

PMCID: PMC3733718This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Evolutionary change is a characteristic of living organisms and forms one of the ways in which species adapt to changed conditions. However, most ecological models do not incorporate this ubiquitous phenomenon. We have developed a model that takes a 'phenotypic gambit' approach and focuses on changes in the frequency of phenotypes (which differ in timing of breeding and fecundity) within a population, using, as an example, seasonal breeding. Fitness per phenotype calculated as the individual's contribution to population growth on an annual basis coincide with the population dynamics per phenotype. Simplified model variants were explored to examine whether the complexity included in the model is justified. Outputs from the spatially implicit model underestimated the number of individuals across all phenotypes. When no phenotype transitions are included (i.e. offspring always inherit their parent's phenotype) numbers of all individuals are always underestimated. We conclude that by using a phenotypic gambit approach evolutionary dynamics can be incorporated into individual based models, and that all that is required is an understanding of the probability of offspring inheriting the parental phenotype

Natural variation in immune responses to neonatal mycobacterium bovis bacillus calmette-guerin (BCG) vaccination in a cohort of Gambian infants

Background There is a need for new vaccines for tuberculosis (TB) that protect against adult pulmonary disease in regions where BCG is not effective. However, BCG could remain integral to TB control programmes because neonatal BCG protects against disseminated forms of childhood TB and many new vaccines rely on BCG to prime immunity or are recombinant strains of BCG. Interferon-gamma (IFN-) is required for immunity to mycobacteria and used as a marker of immunity when new vaccines are tested. Although BCG is widely given to neonates IFN- responses to BCG in this age group are poorly described. Characterisation of IFN- responses to BCG is required for interpretation of vaccine immunogenicity study data where BCG is part of the vaccination strategy. Methodology/Principal Findings 236 healthy Gambian babies were vaccinated with M. bovis BCG at birth. IFN-, interleukin (IL)-5 and IL-13 responses to purified protein derivative (PPD), killed Mycobacterium tuberculosis (KMTB), M. tuberculosis short term culture filtrate (STCF) and M. bovis BCG antigen 85 complex (Ag85) were measured in a whole blood assay two months after vaccination. Cytokine responses varied up to 10 log-fold within this population. The majority of infants (89-98% depending on the antigen) made IFN- responses and there was significant correlation between IFN- responses to the different mycobacterial antigens (Spearman’s coefficient ranged from 0.340 to 0.675, p=10-6-10-22). IL-13 and IL-5 responses were generally low and there were more non-responders (33-75%) for these cytokines. Nonetheless, significant correlations were observed for IL-13 and IL-5 responses to different mycobacterial antigens Conclusions/Significance Cytokine responses to mycobacterial antigens in BCG-vaccinated infants are heterogeneous and there is significant inter-individual variation. Further studies in large populations of infants are required to identify the factors that determine variation in IFN- responses