Cycle killer... qu'est-ce que c'est? On the comparative approximability of hybridization number and directed feedback vertex set

We show that the problem of computing the hybridization number of two rooted binary phylogenetic trees on the same set of taxa X has a constant factor polynomial-time approximation if and only if the problem of computing a minimum-size feedback vertex set in a directed graph (DFVS) has a constant factor polynomial-time approximation. The latter problem, which asks for a minimum number of vertices to be removed from a directed graph to transform it into a directed acyclic graph, is one of the problems in Karp's seminal 1972 list of 21 NP-complete problems. However, despite considerable attention from the combinatorial optimization community it remains to this day unknown whether a constant factor polynomial-time approximation exists for DFVS. Our result thus places the (in)approximability of hybridization number in a much broader complexity context, and as a consequence we obtain that hybridization number inherits inapproximability results from the problem Vertex Cover. On the positive side, we use results from the DFVS literature to give an O(log r log log r) approximation for hybridization number, where r is the value of an optimal solution to the hybridization number problem

19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology.

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here, we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real data sets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators

Effects of Static and Dynamic Hamstring Stretching on Anaerobic Exercise Performance

Please view abstract in the attached PDF file

Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies.

Bayesian Markov chain Monte Carlo explores tree space slowly, in part because it frequently returns to the same tree topology. An alternative strategy would be to explore tree space systematically, and never return to the same topology. In this article, we present an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, which we show to be a good approximation of the high posterior set of tree topologies on the data sets analyzed. Here, "likelihood" of a topology refers to the tree likelihood for the corresponding tree with optimized branch lengths. We call this method "phylogenetic topographer" (PT). The PT strategy is very simple: starting in a number of local topology maxima (obtained by hill-climbing from random starting points), explore out using local topology rearrangements, only continuing through topologies that are better than some likelihood threshold below the best observed topology. We show that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies. By using a nonblocking hash table keyed on unique representations of tree topologies, we avoid visiting topologies more than once across all concurrent threads exploring tree space. We demonstrate that PT can be used directly to approximate a Bayesian consensus tree topology. When combined with an accurate means of evaluating per-topology marginal likelihoods, PT gives an alternative procedure for obtaining Bayesian posterior distributions on phylogenetic tree topologies

Interactions in the microbiome: communities of organisms and communities of genes

YesA central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term ‘community’ is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of ‘Who is there?’ and ‘What are they doing?’ to the mechanistically driven question of ‘How will they respond?

Lumican Expression in Diaphragm Induced by Mechanical Ventilation

Diaphragmatic dysfunction found in the patients with acute lung injury required prolonged mechanical ventilation. Mechanical ventilation can induce production of inflammatory cytokines and excess deposition of extracellular matrix proteins via up-regulation of transforming growth factor (TGF)-β1. Lumican is known to participate in TGF-β1 signaling during wound healing. The mechanisms regulating interactions between mechanical ventilation and diaphragmatic injury are unclear. We hypothesized that diaphragmatic damage by short duration of mechanical stretch caused up-regulation of lumican that modulated TGF-β1 signaling.Male C57BL/6 mice, either wild-type or lumican-null, aged 3 months, weighing between 25 and 30 g, were exposed to normal tidal volume (10 ml/kg) or high tidal volume (30 ml/kg) mechanical ventilation with room air for 2 to 8 hours. Nonventilated mice served as control groups.High tidal volume mechanical ventilation induced interfibrillar disassembly of diaphragmatic collagen fiber, lumican activation, type I and III procollagen, fibronectin, and α-smooth muscle actin (α-SMA) mRNA, production of free radical and TGF-β1 protein, and positive staining of lumican in diaphragmatic fiber. Mechanical ventilation of lumican deficient mice attenuated diaphragmatic injury, type I and III procollagen, fibronectin, and α-SMA mRNA, and production of free radical and TGF-β1 protein. No significant diaphragmatic injury was found in mice subjected to normal tidal volume mechanical ventilation.Our data showed that high tidal volume mechanical ventilation induced TGF-β1 production, TGF-β1-inducible genes, e.g., collagen, and diaphragmatic dysfunction through activation of the lumican