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Ames, IA, United States

Iowa State University of Science and Technology, more commonly known as Iowa State University, Iowa State, or ISU, a Land grant of the Iowa university system, is a public land-grant and space-grant research university located in Ames, Iowa, United States. Until 1959 it was known as the Iowa State College of Agriculture and Mechanic Arts.Founded in 1858 and coeducational from its start, Iowa State became the nation’s first designated land-grant institution when the Iowa Legislature accepted the provisions of the 1862 Morrill Act on September 11, 1862, making Iowa the first state in the nation to do so. Iowa State's academic offerings are administered today through eight colleges, including the graduate college, that offer over 100 bachelor's degree programs, 112 master's degree programs, and 83 at the Ph.D. level, plus a professional degree program in Veterinary Medicine.ISU is classified as a Research University with very high research activity by the Carnegie Foundation for the Advancement of Teaching. The university is a group member of the prestigious American Association of Universities and the Universities Research Association, and a charter member of the Big 12 Conference. Wikipedia.


Travesset A.,Iowa State University
Science | Year: 2011

A set of simple rules is used to design and control the self-assembly of nanoparticles into complex structures. Source


Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (Kmult) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of Kmult remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K mult and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in highdimensional data. Statistical properties of Kmult were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that Kmult provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders. © The Author(s) 2014. Source


Adams D.C.,Iowa State University
Systematic Biology | Year: 2013

In recent years, likelihood-based approaches have been used with increasing frequency to evaluate macroevolutionary hypotheses of phenotypic evolution under distinct evolutionary processes in a phylogenetic context (e.g., Brownian motion, Ornstein-Uhlenbeck, etc.), and to compare one or more evolutionary rates for the same phenotypic trait along a phylogeny. It is also of interest to determine whether one trait evolves at a faster rate than another trait. However, to date no study has compared phylogenetic evolutionary rates between traits using likelihood, because a formal approach has not yet been proposed. In this article, I describe a new likelihood procedure for comparing evolutionary rates for two or more phenotypic traits on a phylogeny. This approach compares the likelihood of a model where each trait evolves at a distinct evolutionary rate to the likelihood of a model where all traits are constrained to evolve at a common evolutionary rate. The method can also account for within-species measurement error and within-species trait covariation if available. Simulations revealed that the method has appropriate Type I error rates and statistical power. Importantly, when compared with existing approaches based on phylogenetically independent contrasts and methods that compare confidence intervals for model parameters, the likelihood method displays preferable statistical properties for a wide range of simulated conditions. Thus, this likelihood-based method extends the phylogenetic comparative biology toolkit and provides evolutionary biologists with a more powerful means of determining when evolutionary rates differ between phenotypic traits. Finally, I provide an empirical example illustrating the approach by comparing rates of evolution for several phenotypic traits in Plethodon salamanders. Evolutionary rates; macroevolution; morphological evolution; phenotype; phylogenetic comparative method; phylogeny. © 2012 The Author(s). Source


Johnston D.C.,Iowa State University
Advances in Physics | Year: 2010

The response of the worldwide scientific community to the discovery in 2008 of superconductivity at Tc = 26 K in the Fe-based compound LaFeAsO1-xFx has been very enthusiastic. In short order, other Fe-based superconductors with the same or related crystal structures were discovered with Tc up to 56 K. Many experiments were carried out and theories formulated to try to understand the basic properties of these new materials and the mechanism for Tc. In this selective critical review of the experimental literature, we distill some of this extensive body of work, and discuss relationships between different types of experiments on these materials with reference to theoretical concepts and models. The experimental normal-state properties are emphasized, and within these the electronic and magnetic properties because of the likelihood of an electronic/magnetic mechanism for superconductivity in these materials. © 2010 Taylor & Francis. Source


Abbott K.C.,Iowa State University
Ecology Letters | Year: 2011

Understanding how dispersal influences the dynamics of spatially distributed populations is a major priority of both basic and applied ecologists. Two well-known effects of dispersal are spatial synchrony (positively correlated population dynamics at different points in space) and dispersal-induced stability (the phenomenon whereby populations have simpler or less extinction-prone dynamics when they are linked by dispersal than when they are isolated). Although both these effects of dispersal should occur simultaneously, they have primarily been studied separately. Herein, I summarise evidence from the literature that these effects are expected to interact, and I use a series of models to characterise that interaction. In particular, I explore the observation that although dispersal can promote both synchrony and stability singly, it is widely held that synchrony paradoxically prevents dispersal-induced stability. I show here that in many realistic scenarios, dispersal is expected to promote both synchrony and stability at once despite this apparent destabilising influence of synchrony. This work demonstrates that studying the spatial and temporal impacts of dispersal together will be vital for the conservation and management of the many communities for which human activities are altering natural dispersal rates. © 2011 Blackwell Publishing Ltd/CNRS. Source

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