To determine the significance of differences between clonal libraries of environmental rRNA gene sequences, differences between homologous coverage curves, C X (D), and heterologous coverage curves, C XY (D), were calculated by a Cramér-von Mises-type statistic and compared by a Monte Carlo test procedure. This method successfully distinguished rRNA gene sequence libraries from soil and bioreactors and correctly failed to find differences between libraries of the same composition.The sequencing of 16S rRNA genes from clone libraries of DNAs from environmental samples has led to a wealth of information concerning prokaryotic diversity. However, in addition to methodological problems in producing libraries representative of the environmental sample (for a review, see reference 8), this approach is also limited by the difficulty in comparing libraries and determining if they are significantly different.This problem can be addressed quantitatively by application of the formula for coverage as described by Good (4). Let X be a collection of sequences, such as a library of 16S rRNA genes. Define the "homologous" coverage of X (or C X ) by a sample from X to be C X ϭ 1 Ϫ (N X /n), where N X is the number of unique sequences in the sample (i.e., sequences without a replicate) and n is the total number of sequences. In practice, the definition of N X depends upon the criteria used to define uniqueness. For instance, McCaig et al. (6) considered sequences without a homolog of Ն97% similarity to be unique. Other authors have used Ն99% sequence similarity as the criterion. In principle, uniqueness can be defined at any level of sequence similarity or evolutionary distance (D) and a "homologous coverage curve," or C X (D), can be generated by plotting C X versus D (Fig. 1). The coverage curve then describes how well the sample represents the entire library X at various levels of relatedness. Typically, coverage might be low at high levels of relatedness (low values of D), indicating that only a small fraction of the sequences representing unique species are, in fact, sampled. In contrast, coverage might be much higher at low levels of relatedness, indicating that representatives of most of the deep phylogenetic groups present in X are found in the sample.While C X is the "homologous coverage" of X by a sample of X, it is also possible to calculate a "heterologous coverage" of X (or C XY ) by a sample Y from another collection of sequences by the following formula: (D)] to be similar. Thus, a test for differences between these coverage curves is also a test for differences between X and Y. To determine if the coverage curves C X (D) and C XY (D) are significantly different, the distance between the two curves are first calculated by using the Cramér-von Mises test statistic (7):where D increases in increments of 0.01. If X ϭ Y, then ⌬C XY should not be significantly different than a ⌬C calculated after randomly shuffling sequences between the two samples, X and Y. Typically, the sequences are randomly shuffled a large number...

Long generation times limit species' rapid evolution to changing environments. Trees provide critical global ecosystem services, but are under increasing risk of mortality because of climate change-mediated disturbances, such as insect outbreaks. The extent to which disturbance changes the dynamics and strength of selection is unknown, but has important implications on the evolutionary potential of tree populations. Using a 40-y-old Pinus ponderosa genetic experiment, we provide rare evidence of context-dependent fluctuating selection on growth rates over time in a long-lived species. Fast growth was selected at juvenile stages, whereas slow growth was selected at mature stages under strong herbivory caused by a mountain pine beetle (Dendroctonus ponderosae) outbreak. Such opposing forces led to no net evolutionary response over time, thus providing a mechanism for the maintenance of genetic diversity on growth rates. Greater survival to mountain pine beetle attack in slow-growing families reflected, in part, a host-based life-history trade-off. Contrary to expectations, genetic effects on tree survival were greatest at the peak of the outbreak and pointed to complex defense responses. Our results suggest that selection forces in tree populations may be more relevant than previously thought, and have implications for tree population responses to future environments and for tree breeding programs. fluctuating selection | growth-survival trade-offs | selection response | Pinus ponderosa | Dendroctonus ponderosae U nderstanding the dynamics of selection over time is fundamental for understanding life-history evolution (1) and predicting evolutionary change under climate change (2, 3). To date, such understanding is based almost exclusively on data for relatively short-lived species (4, 5), but virtually nonexistent for long-lived organisms, such as trees (ref. 6; but see ref. 7). Trees and forests provide critical ecological and commercial services, including impacts on global carbon cycles, species diversity, water quality, and climate regulation (8). Recent forest mortality (9, 10) highlights the importance of understanding how climate change and climate change-driven disturbances may impact forests (11, 12). Trees may live for hundreds of years and experience varying selection pressures associated with fluctuating climate (13), disturbance regimes (14), and biotic interactions (15), all of which may be magnified under climate change. The extent to which these events may change the strength and direction of selection and contribute to the maintenance of genetic diversity and evolutionary potential is unknown. Of special relevance are insect outbreaks, a biotic interaction expected to increase with climate change (16) but unaccounted for in models to predict the evolutionary potential of tree populations (17, 18). Mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins) is a native, irruptive forest insect in western North America that uses numerous Pinus species as hosts. Via pheromone-mediated mass attacks th...

We evaluated a commercial point-of-care circulating cathodic antigen (POC-CCA) test for assessing Schistosoma mansoni infection prevalence in areas at risk. Overall, 4,405 school-age children in Cameroon, Côte d'Ivoire, Ethiopia, Kenya, and Uganda provided urine for POC-CCA testing and stool for Kato-Katz assays. By latent class analysis, one POC-CCA test was more sensitive (86% versus 62%) but less specific (72% versus ∼100%) than multiple Kato-Katz smears from one stool. However, only 1% of POC-CCA tests in a non-endemic area were false positives, suggesting the latent class analysis underestimated the POC-CCA specificity. Multivariable modeling estimated POC-CCA as significantly more sensitive than Kato-Katz at low infection intensities (< 100 eggs/gram stool). By linear regression, 72% prevalence among 9–12 year olds by POC-CCA corresponded to 50% prevalence by Kato-Katz, whereas 46% POC-CCA prevalence corresponded to 10% Kato-Katz prevalence. We conclude that one urine POC-CCA test can replace Kato-Katz testing for community-level S. mansoni prevalence mapping.

BackgroundEcological momentary assessment (EMA) assesses individuals’ current experiences, behaviors, and moods as they occur in real time and in their natural environment. EMA studies, particularly those of longer duration, are complex and require an infrastructure to support the data flow and monitoring of EMA completion.ObjectiveOur objective is to provide a practical guide to developing and implementing an EMA study, with a focus on the methods and logistics of conducting such a study.MethodsThe EMPOWER study was a 12-month study that used EMA to examine the triggers of lapses and relapse following intentional weight loss. We report on several studies that informed the implementation of the EMPOWER study: (1) a series of pilot studies, (2) the EMPOWER study’s infrastructure, (3) training of study participants in use of smartphones and the EMA protocol and, (4) strategies used to enhance adherence to completing EMA surveys.ResultsThe study enrolled 151 adults and had 87.4% (132/151) retention rate at 12 months. Our learning experiences in the development of the infrastructure to support EMA assessments for the 12-month study spanned several topic areas. Included were the optimal frequency of EMA prompts to maximize data collection without overburdening participants; the timing and scheduling of EMA prompts; technological lessons to support a longitudinal study, such as proper communication between the Android smartphone, the Web server, and the database server; and use of a phone that provided access to the system’s functionality for EMA data collection to avoid loss of data and minimize the impact of loss of network connectivity. These were especially important in a 1-year study with participants who might travel. It also protected the data collection from any server-side failure. Regular monitoring of participants’ response to EMA prompts was critical, so we built in incentives to enhance completion of EMA surveys. During the first 6 months of the 12-month study interval, adherence to completing EMA surveys was high, with 88.3% (66,978/75,888) completion of random assessments and around 90% (23,411/25,929 and 23,343/26,010) completion of time-contingent assessments, despite the duration of EMA data collection and challenges with implementation.ConclusionsThis work informed us of the necessary preliminary steps to plan and prepare a longitudinal study using smartphone technology and the critical elements to ensure participant engagement in the potentially burdensome protocol, which spanned 12 months. While this was a technology-supported and -programmed study, it required close oversight to ensure all elements were functioning correctly, particularly once human participants became involved.