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Description
Premise of the study: Land-plant plastid genomes have only rarely undergone significant changes in gene content and order. Thus, discovery of additional examples adds power to tests for causes of such genome-scale structural changes.
Methods: Using next-generation sequence data, we assembled the plastid genome of saguaro cactus and probed the nuclear

Premise of the study: Land-plant plastid genomes have only rarely undergone significant changes in gene content and order. Thus, discovery of additional examples adds power to tests for causes of such genome-scale structural changes.
Methods: Using next-generation sequence data, we assembled the plastid genome of saguaro cactus and probed the nuclear genome for transferred plastid genes and functionally related nuclear genes. We combined these results with available data across Cactaceae and seed plants more broadly to infer the history of gene loss and to assess the strength of phylogenetic association between gene loss and loss of the inverted repeat (IR).
Key results: The saguaro plastid genome is the smallest known for an obligately photosynthetic angiosperm (∼113 kb), having lost the IR and plastid ndh genes. This loss supports a statistically strong association across seed plants between the loss of ndh genes and the loss of the IR. Many nonplastid copies of plastid ndh genes were found in the nuclear genome, but none had intact reading frames; nor did three related nuclear-encoded subunits. However, nuclear pgr5, which functions in a partially redundant pathway, was intact.
Conclusions: The existence of an alternative pathway redundant with the function of the plastid NADH dehydrogenase-like complex (NDH) complex may permit loss of the plastid ndh gene suite in photoautotrophs like saguaro. Loss of these genes may be a recurring mechanism for overall plastid genome size reduction, especially in combination with loss of the IR.
ContributorsSanderson, Michael J. (Author) / Copetti, Dario (Author) / Burquez, Alberto (Author) / Bustamante, Enriquena (Author) / Charboneau, Joseph L. M. (Author) / Eguiarte, Luis E. (Author) / Kumar, Sudhir (Author) / Lee, Hyun Oh (Author) / Lee, Junki (Author) / McMahon, Michelle (Author) / Steele, Kelly (Author) / Wing, Rod (Author) / Yang, Tae-Jin (Author) / Zwickl, Derrick (Author) / Wojciechowski, Martin (Author) / College of Integrative Sciences and Arts (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2015-07-01
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Description
Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.
Created2015-12-01
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Description
Background
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic,

Background
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
Created2011-03-24