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.
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. 95.36.+x
An ADP-ribosyltransferase was found in elongation factor 2 (EF-2) preparations from polyoma virustransformed baby hamster kidney (pyBHK) cells. Like Mono(ADP-ribosyl)ated proteins have been found in a variety of eukaryotic tissues (1-3). These ADP-ribosylated proteins are present in practically every major compartment of the cell (4), suggesting a diversity of biological functions. However, little is known about the identity of these mono-(ADP-ribosyl)ated acceptor proteins and their physiological functions. Moss and Vaughn (5) have described a cytosolic ADP-ribosyltransferase from turkey erythrocytes that catalyzes the mono(ADP-ribosyl)ation of several endogenous proteins and the activation of brain adenylate cyclase. They were the first to suggest that the ADP-ribosyltransferase mechanisms of bacterial toxins, such as cholera toxin and heat-labile enterotoxin of Escherichia coli, are not entirely foreign to vertebrate cells.
Fragment A of diphtheria toxin and Pseudomonas toxin A intoxicate cells by ADP‐ribosylating the diphthamide residue of elongation factor‐2 (EF‐2) resulting in an inhibition of protein synthesis [1–3]. A cellular enzyme from polyoma virus transformed baby hamster kidney (pyBHK) cells ADP‐ribosylates EF‐2 in an identical manner [4]. Here we describe a similar cellular enzyme from beef liver which transfers [adenosine‐14C]ADP‐ribose from NAD to EF‐2. The 14C‐label can be removed from the EF‐2 by snake venom phosphodiesterase as a soluble product which comigrates with AMP on TLC plates, indicating the 14C‐label is present on EF‐2 as monomeric units of ADP‐ribose. Furthermore, the forward transferase reaction catalyzed by the beef liver ADP‐ribosyltransferase is reversible by excess diphtheria toxin fragment A, with the formation of 14C‐labeled NAD, indicating that both transferases ADP‐ribosylate the same site on the diphthamide residue of EF‐2. Thus, beef liver and pyBHK mono(ADP‐ribosyl) transferases both modify the diphthamide residue of EF‐2, in a manner identical to diphtheria toxin fragment A and Pseudomonas toxin A. These results suggest the cellular enzyme is probably ubiquitous among eukaryotic cells.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.