Deleterious synonymous mutations hitchhike to high frequency in HIV-1 env evolution

Intrapatient HIV-1 evolution is dominated by selection on the protein level in the arms race with the adaptive immune system. When cytotoxic CD8+ T-cells or neutralizing antibodies target a new epitope, the virus often escapes via nonsynonymous mutations that impair recognition. Synonymous mutations do not affect this interplay and are often assumed to be neutral. We analyze longitudinal intrapatient data from the C2-V5 part of the envelope gene (env) and observe that synonymous derived alleles rarely fix even though they often reach high frequencies in the viral population. We find that synonymous mutations that disrupt base pairs in RNA stems flanking the variable loops of gp120 are more likely to be lost than other synonymous changes, hinting at a direct fitness effect of these stem-loop structures in the HIV-1 RNA. Computational modeling indicates that these synonymous mutations have a (Malthusian) selection coefficient of the order of -0.002 and that they are brought up to high frequency by hitchhiking on neighboring beneficial nonsynonymous alleles. The patterns of fixation of nonsynonymous mutations estimated from the longitudinal data and comparisons with computer models suggest that escape mutations in C2-V5 are only transiently beneficial, either because the immune system is catching up or because of competition between equivalent escapes

FFPopSim: An efficient forward simulation package for the evolution of large populations

The analysis of the evolutionary dynamics of a population with many polymorphic loci is challenging since a large number of possible genotypes needs to be tracked. In the absence of analytical solutions, forward computer simulations are an important tool in multi-locus population genetics. The run time of standard algorithms to simulate sexual populations increases as 8^L with the number L of loci, or with the square of the population size N. We have developed algorithms that allow to simulate large populations with a run-time that scales as 3^L. The algorithm is based on an analog of the Fast-Fourier Transform (FFT) and allows for arbitrary fitness functions (i.e. any epistasis) and genetic maps. The algorithm is implemented as a collection of C++ classes and a Python interface.Comment: available from: http://code.google.com/p/ffpopsi

Did Producer Hedging Opportunities in the Live Hog Contract Decline?

The paper assesses the usefulness of selective hedging strategies when combined with forecast techniques in the live hog contract. The use of routine futures and options hedging is not attractive relative to a cash-only strategy. However, forecasting and hedging can contribute to price risk management improvement for risk-averse producers. Consistent with previous research, the results indicate that the live hog contract continues to offer producers attractive pricing opportunities. The findings suggests that the success of the new lean value carcass contract may depend on its ability to attract trading volume from outside the traditional production sector.hedging, forecasting, risk management, live hog futures, lean hog futures

Effects of Tourism on Venice: Commercial Changes over 30 Years

Tourism is becoming one of the most important economic drivers in the urban context. With this in mind, several cities have tried to adapt their economies to satisfy the demands of the influx of tourism. The main consequences of this trend are the re-shaping of urban areas, with particular regard to art cities. This phenomenon is particularly evident in Venice’s historical city centre. In order to better comprehend the changes that have taken place, we have put together a research based analysis of the commercial structure of the city. Particular attention has been given to comparing and contrasting the retail business over the last thirty years.commercial structure, historical city centre, retail, Venice

Complex Dynamics in One-Dimensional Nonlinear Schrödinger Equations with Stepwise Potential

We prove the existence and multiplicity of periodic solutions as well as solutions presenting a complex behavior for the one-dimensional nonlinear Schrödinger equation -ε2u′′+V(x)u=f(u), where the potential V(x) approximates a two-step function. The term f(u) generalizes the typical p-power nonlinearity considered by several authors in this context. Our approach is based on some recent developments of the theory of topological horseshoes, in connection with a linked twist maps geometry, which are applied to the discrete dynamics of the Poincaré map. We discuss the periodic and the Neumann boundary conditions. The value of the term ε>0, although small, can be explicitly estimated

Population genomics of intrapatient HIV evolution

The Human Immunodeficiency Virus 1 (HIV-1) is a rapidly evolving human retrovirus. HIV-1 nucleic acid sequences have been sampled from many pa- tients, with mostly one sequence per patient, to characterize HIV-1 genetics and epidemiology. Ultimately, however, HIV-1 replicates and evolves during single infections that last for several years. In my doctorate I performed whole-genome longitudinal deep sequencing on several HIV-1 patients and developed experimental, theoretical, and computational methods to (i) char- acterize HIV-1 evolution within single infections, (ii) organise and share the collected genomic data with the research community, and (iii) simulate evo- lution of rapidly adapting organisms like HIV-1 in silico. First, I quantified a number of central properties of intrapatient HIV-1 evolution such as genetic diversity, evolutionary rate, linkage disequilibrium, mutation rate, strength and prevalence of positive and purifying selection, and influence of RNA sec- ondary structures. Second, exploiting modern web technologies, I realized a web application that gives other researchers the chance to perform specific analyses on the same data set. Third, I coded a computer package, FFPop- Sim, to simulate the evolution of populations under selection; via a novel algorithm and a cross-language design, it has proven an ideal tool to bridge theoretical predictions and experimental results

ESTIMATING FARM-LEVEL YIELD DISTRIBUTIONS FOR CORN AND SOYBEANS IN ILLINOIS

Many yield modeling approaches have been developed in attempts to provide accurate characterizations of farm-level yield distributions due to the importance of yield uncertainty in crop insurance design and rating, and for managing farm-level risk. Competing existing models of crop yields accommodate varying skewness, kurtosis, and other departures from normality including features such as multiple modes. Recently, the received view of crop yield modeling has been challenged by Just and Weninger who indicate that there is insufficient evidence to reject normality given data limitations and potential methodological shortcomings in controlling for deterministic components (trend) in yields. They point out that past empirical efforts to estimate and validate specific-farm distributional characterizations have been severely hampered by data limitations. As a result, they argue in favor of normality as an appropriate parameterization of crop yields. This paper investigates alternate representations of farm-level crop yield distributions using a unique data set from the University of Illinois Endowment farms, containing same-site yield observations for a relatively long period of time, and under conditions that closely mirror actual farm conditions in Illinois. Results from alternate econometric model specifications controlling for trend effects suggest that a linear trend provides an adequate representation of crop yields at the farm level during the period covered by the estimations. Specification tests based on a linear-trend model suggest significant heteroskedasticity is present in only a few farms, and that the residuals are white noise. With these data, Jarque-Bera normality test results indicate that normality of detrended yield residuals is rejected by a far greater number of fields than would be explained due to randomness. Thus, to further clarify the issue of yield distribution characterizations, more complete goodness-of-fit measures are compared across a larger set of candidate distributions. The results indicate that the Weibull distribution consistently ranks better than the normal distribution both in fields where normality is rejected and in fields where normality is not rejected. The results highlight the fact that failing to reject normality is not the same as identifying normality as a "best" parameterization, and provide guidance for progressing toward better representations of farm-level crop yields.Productivity Analysis, Research Methods/ Statistical Methods, Teaching/Communication/Extension/Profession,