Measure and Probability in Cosmology

General relativity has a Hamiltonian formulation, which formally provides a canonical (Liouville) measure on the space of solutions. In ordinary statistical physics, the Liouville measure is used to compute probabilities of macrostates, and it would seem natural to use the similar measure arising in general relativity to compute probabilities in cosmology, such as the probability that the universe underwent an era of inflation. Indeed, a number of authors have used the restriction of this measure to the space of homogeneous and isotropic universes with scalar field matter (minisuperspace)---namely, the Gibbons-Hawking-Stewart measure---to make arguments about the likelihood of inflation. We argue here that there are at least four major difficulties with using the measure of general relativity to make probability arguments in cosmology: (1) Equilibration does not occur on cosmological length scales. (2) Even in the minisuperspace case, the measure of phase space is infinite and the computation of probabilities depends very strongly on how the infinity is regulated. (3) The inhomogeneous degrees of freedom must be taken into account (we illustrate how) even if one is interested only in universes that are very nearly homogeneous. The measure depends upon how the infinite number of degrees of freedom are truncated, and how one defines "nearly homogeneous." (4) In a universe where the second law of thermodynamics holds, one cannot make use of our knowledge of the present state of the universe to "retrodict" the likelihood of past conditions.Comment: 43 pages, 2 figure

Systematic Variation in Willingness to Pay for Agricultural Land Preservation and Implications for Benefit Transfer: A Meta-Analysis

Despite prior studies examining willingness to pay for farmland preservation there has been no quantitative, systematic analysis of findings across the literature. This paper presents the first statistical meta-analysis of farmland preservation values. Results confirm systematic variations in willingness to pay, with value surfaces corresponding to theoretical expectations. Findings also provide significant insight into the potential for valid meta-analytic, function based benefit transfer. Results suggest, for example, that transfer validity is critically dependent on jurisdictional scale. Transfer errors are modest for community scale farmland preservation, but large for state scale preservation policies in which per acre welfare estimates are small.Land Economics/Use,

Predicting the effectiveness of hepatitis C virus neutralizing antibodies by bioinformatic analysis of conserved epitope residues using public sequence data

Hepatitis C virus (HCV) is a global health issue. Although direct-acting antivirals are available to target HCV, there is currently no vaccine. The diversity of the virus is a major obstacle to HCV vaccine development. One approach toward a vaccine is to utilize a strategy to elicit broadly neutralizing antibodies (bNAbs) that target highly-conserved epitopes. The conserved epitopes of bNAbs have been mapped almost exclusively to the E2 glycoprotein. In this study, we have used HCV-GLUE, a bioinformatics resource for HCV sequence data, to investigate the major epitopes targeted by well-characterized bNAbs. Here, we analyze the level of conservation of each epitope by genotype and subtype and consider the most promising bNAbs identified to date for further study as potential vaccine leads. For the most conserved epitopes, we also identify the most prevalent sequence variants in the circulating HCV population. We examine the distribution of E2 sequence data from across the globe and highlight regions with no coverage. Genotype 1 is the most prevalent genotype worldwide, but in many regions, it is not the dominant genotype. We find that the sequence conservation data is very encouraging; several bNAbs have a high level of conservation across all genotypes suggesting that it may be unnecessary to tailor vaccines according to the geographical distribution of genotypes

Pathway-Based Genomics Prediction using Generalized Elastic Net.

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach

Identification of Conserved Aquilegia Coerulea MicroRNAs and Their Targets

Aquilegia is an emerging model organism that is phylogenetically intermediate between the core eudicot and monocot models, Arabidopsis and Oryza. In this study, we have used a comparative genomics approach to identify 45 Aquilegia microRNAs that comprise 20 separate plant microRNA families. We have predicted 84 targets of these newly identified Aquilegia microRNAs including transcription factors and loci involved in metabolism, stress responses, transport, and auxin signaling. microRNA families from 16 plant species and the newly identified microRNAs from Aquilegia were analyzed in a phylogenetic context revealing 40 distantly conserved microRNA families. In addition to these highly conserved plant microRNA families, several families with disjointed phylogenetic distribution were identified. This study provides a phylogenetically important dataset for plant microRNA evolution studies. The current study is the first to identify miRNAs in a lower eudicot in which comprehensive genomic resources are becoming available.Organismic and Evolutionary Biolog