University of California, Davis

About: University of California, Davis is a education organization based out in Davis, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 78770 authors who have published 180033 publications receiving 8064158 citations. The organization is also known as: UC Davis & UCD.

Reproducibility in density functional theory calculations of solids

Abstract: The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. We report the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements.

Toxicity of nanomaterials

Abstract: Nanoscience has matured significantly during the last decade as it has transitioned from bench top science to applied technology. Presently, nanomaterials are used in a wide variety of commercial products such as electronic components, sports equipment, sun creams and biomedical applications. There are few studies of the long-term consequences of nanoparticles on human health, but governmental agencies, including the United States National Institute for Occupational Safety and Health and Japan's Ministry of Health, have recently raised the question of whether seemingly innocuous materials such as carbon-based nanotubes should be treated with the same caution afforded known carcinogens such as asbestos. Since nanomaterials are increasing a part of everyday consumer products, manufacturing processes, and medical products, it is imperative that both workers and end-users be protected from inhalation of potentially toxic NPs. It also suggests that NPs may need to be sequestered into products so that the NPs are not released into the atmosphere during the product's life or during recycling. Further, non-inhalation routes of NP absorption, including dermal and medical injectables, must be studied in order to understand possible toxic effects. Fewer studies to date have addressed whether the body can eventually eliminate nanomaterials to prevent particle build-up in tissues or organs. This critical review discusses the biophysicochemical properties of various nanomaterials with emphasis on currently available toxicology data and methodologies for evaluating nanoparticle toxicity (286 references).

A Nonparametric Approach to Estimating Divergence Times in the Absence of Rate Constancy

Abstract: A new method for estimating divergence times when evolutionary rates are variable across lineages is proposed. The method, called nonparametric rate smoothing (NPRS), relies on minimization of ancestor-descendant local rate changes and is motivated by the likelihood that evolutionary rates are autocorrelated in time. Fossil information pertaining to minimum and/or maximum ages of nodes in a phylogeny is incorporated into the algorithms by constrained optimization techniques. The accuracy of NPRS was examined by comparison to a clock-based maximum-likelihood method in computer simulations. NPRS provides more accurate estimates of divergence times when (1) sequence lengths are sufficiently long, (2) rates are truly nonclocklike, and (3) rates are moderately to highly autocorrelated in time. The algorithms were applied to estimate divergence times in seed plants based on data from the chloroplast rbcL gene. Both constrained and unconstrained NPRS methods tended to produce divergence time estimates more consistent with paleobotanical evidence than did clock-based estimates.

Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

Abstract: Summary. Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AICc, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, AICc can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AICc avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other 'classical' approaches (such as generalized cross-validation or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AICc-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.