What can you do with 0.1× genome coverage? A case study based on a genome survey of the scuttle fly Megaselia scalaris (Phoridae)

<p>Abstract</p> <p>Background</p> <p>The declining cost of DNA sequencing is making genome sequencing a feasible option for more organisms, including many of interest to ecologists and evolutionary biologists. While obtaining high-depth, completely assembled genome sequences for most non-model organisms remains challenging, low-coverage genome survey sequences (GSS) can provide a wealth of biologically useful information at low cost. Here, using a random pyrosequencing approach, we sequence the genome of the scuttle fly <it>Megaselia scalaris </it>and evaluate the utility of our low-coverage GSS approach.</p> <p>Results</p> <p>Random pyrosequencing of the <it>M. scalaris </it>genome provided a depth of coverage (0.05x0.1x) much lower than typical GSS studies. We demonstrate that, even with extremely low-coverage sequencing, bioinformatics approaches can yield extensive information about functional and repetitive elements. We also use our GSS data to develop genomic resources such as a nearly complete mitochondrial genome sequence and microsatellite markers for <it>M. scalaris</it>.</p> <p>Conclusion</p> <p>We conclude that low-coverage genome surveys are effective at generating useful information about organisms currently lacking genomic sequence data.</p

Governmental Intervention in an Economic Crisis

This paper articulates a framework both for assessing the various government bailouts that took place at the onset of Great Recession and for guiding future rescue efforts when they become necessary. The goals for those engineering a bailout should be to be as transparent as possible, to articulate clearly the reason for the intervention, to respect existing priorities among investors, to exercise control only at the top level where such efforts can be seen by the public, and to exit as soon as possible. By these metrics, some of the recent bailouts should be applauded, while others fell short. We also explore the related question of what level of judicial scrutiny is appropriate for government actions taken during a bailout. We eschew the extremes of no judicial review on the one hand and full recourse to the courts on the other. Courts need to avoid interfering in a time of crisis, yet, when normalcy has returned, they should measure the actions taken against applicable legislative and constitutional requirements

Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series

Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses – increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data

One Size Fits All? Molecular Evidence for a Commonly Inherited Petal Identity Program in Ranunculales

Petaloid organs are a major component of the floral diversity observed across nearly all major clades of angiosperms. The variable morphology and development of these organs has led to the hypothesis that they are not homologous but, rather, have evolved multiple times. A particularly notable example of petal diversity, and potential homoplasy, is found within the order Ranunculales, exemplified by families such as Ranunculaceae, Berberidaceae, and Papaveraceae. To investigate the molecular basis of petal identity in Ranunculales, we used a combination of molecular phylogenetics and gene expression analysis to characterize APETALA3 (AP3) and PISTILLATA (PI) homologs from a total of 13 representative genera of the order. One of the most striking results of this study is that expression of orthologs of a single AP3 lineage is consistently petal-specific across both Ranunculaceae and Berberidaceae. We conclude from this finding that these supposedly homoplastic petals in fact share a developmental genetic program that appears to have been present in the common ancestor of the two families. We discuss the implications of this type of molecular data for long-held typological definitions of petals and, more broadly, the evolution of petaloid organs across the angiosperms.Organismic and Evolutionary Biolog

The Effects of Wealth on Male Reproduction Among Monogamous Hunter-Fisher-Trappers in Northern Siberia

Variability in men’s reproductive success (RS) is partly attributable to the ability of successful men to influence resource flows relevant to the mate choice and reproduction of women. This study explores the effects of variability in resource flows on men’s RS in an indigenous foraging/mixed-economy community in northern Siberia where monogamous marriage norms predominate. A series of material, embodied, and relational wealth indicators are tested as predictors of men’s age-adjusted RS and age at first birth. Material wealth related to hunting, embodied wealth as represented by hunting skill, and relational wealth as represented by numbers of kin are the most consistent predictors of men’s RS. In this monogamous population, the wives of men with more hunting capital and of men rated as better hunters have shorter interbirth intervals, and hunters show strong producer priority. These findings and ethnographic observations appear more consistent with a provisioning model than with a signaling-for-mates model

Nonparametric Dark Energy Reconstruction from Supernova Data

Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian Process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.Comment: 4 pages, 2 figures, accepted for publication in Physical Review Letter

Nonparametric Reconstruction of the Dark Energy Equation of State

A basic aim of ongoing and upcoming cosmological surveys is to unravel the mystery of dark energy. In the absence of a compelling theory to test, a natural approach is to better characterize the properties of dark energy in search of clues that can lead to a more fundamental understanding. One way to view this characterization is the improved determination of the redshift-dependence of the dark energy equation of state parameter, w(z). To do this requires a robust and bias-free method for reconstructing w(z) from data that does not rely on restrictive expansion schemes or assumed functional forms for w(z). We present a new nonparametric reconstruction method that solves for w(z) as a statistical inverse problem, based on a Gaussian Process representation. This method reliably captures nontrivial behavior of w(z) and provides controlled error bounds. We demonstrate the power of the method on different sets of simulated supernova data; the approach can be easily extended to include diverse cosmological probes.Comment: 16 pages, 11 figures, accepted for publication in Physical Review