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      Modeling Biphasic Environmental Decay of Pathogens and Implications for Risk Analysis

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          Abstract

          As the appreciation for the importance of the environment in infectious disease transmission has grown, so too has interest in pathogen fate and transport. Fate has been traditionally described by simple exponential decay, but there is increasing recognition that some pathogens demonstrate a biphasic pattern of decay—fast followed by slow. While many have attributed this behavior to population heterogeneity, we demonstrate that biphasic dynamics can arise through a number of plausible mechanisms. We examine the identifiability of a general model encompassing three such mechanisms: population heterogeneity, hardening off, and the existence of viable-but-not-culturable states. Although the models are not fully identifiable from longitudinal sampling studies of pathogen concentrations, we use a differential algebra approach to determine identifiable parameter combinations. Through case studies using Cryptosporidium and Escherichia coli, we show that failure to consider biphasic pathogen dynamics can lead to substantial under- or overestimation of disease risks and pathogen concentrations, depending on the context. More reliable models for environmental hazards and human health risks are possible with an improved understanding of the conditions in which biphasic die-off is expected. Understanding the mechanisms of pathogen decay will ultimately enhance our control efforts to mitigate exposure to environmental contamination.

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          Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

          Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. Supplementary data are available at Bioinformatics online.
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            Dormancy contributes to the maintenance of microbial diversity.

            Dormancy is a bet-hedging strategy used by a variety of organisms to overcome unfavorable environmental conditions. By entering a reversible state of low metabolic activity, dormant individuals become members of a seed bank, which can determine community dynamics in future generations. Although microbiologists have documented dormancy in both clinical and natural settings, the importance of seed banks for the diversity and functioning of microbial communities remains untested. Here, we develop a theoretical model demonstrating that microbial communities are structured by environmental cues that trigger dormancy. A molecular survey of lake ecosystems revealed that dormancy plays a more important role in shaping bacterial communities than eukaryotic microbial communities. The proportion of dormant bacteria was relatively low in productive ecosystems but accounted for up to 40% of taxon richness in nutrient-poor systems. Our simulations and empirical data suggest that regional environmental cues and dormancy synchronize the composition of active communities across the landscape while decoupling active microbes from the total community at local scales. Furthermore, we observed that rare bacterial taxa were disproportionately active relative to common bacterial taxa, suggesting that microbial rank-abundance curves are more dynamic than previously considered. We propose that repeated transitions to and from the seed bank may help maintain the high levels of microbial biodiversity that are observed in nearly all ecosystems.
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              Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework

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                Author and article information

                Journal
                Environ Sci Technol
                Environ. Sci. Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                23 January 2017
                21 February 2017
                : 51
                : 4
                : 2186-2196
                Affiliations
                []Department of Epidemiology, University of Michigan , 1415 Washington Heights, Ann Arbor, Michigan 48109, United States
                []Department of Environmental Health Sciences, University of California Berkeley , 50 University Hall, Berekely, California 94720, United States
                Author notes
                Article
                10.1021/acs.est.6b04030
                5789392
                28112914
                8f421c3d-7950-4abc-9853-b791ea1fc61b
                Copyright © 2017 American Chemical Society

                This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

                History
                : 10 August 2016
                : 23 January 2017
                : 16 January 2017
                Categories
                Article
                Custom metadata
                es6b04030
                es-2016-04030j

                General environmental science
                General environmental science

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