A fast approximate method of identifying paths of allosteric communication in proteins

Fluctuations of the distance between a pair of residues i and j may be correlated with the fluctuations of the distance between another pair k and l. In this case, information may be transmitted among these four residues. Allosteric activity is postulated to proceed through such correlated paths. In this short communication a fast method for calculating correlations among all possible pairs ij and kl leading to a pathway of correlated residues of a protein is proposed. The method is based on the alpha carbon centered Gaussian Network Model. The model is applied to Glutamine Amidotransferase and pathways of allosteric activity are identified and compared with literature. Proteins 2013; 81:1097–1101. © 2013 Wiley Periodicals, Inc.     

hp‐rare 1.0: a computer program for performing rarefaction on measures of allelic richness

The number of alleles in a sample (allelic richness) is a fundamental measure of genetic diversity. However, this diversity measure has been difficult to use because large samples are expected to contain more alleles than small samples. The statistical technique of rarefaction compensates for this sampling disparity. Here I introduce a computer program that performs rarefaction on private alleles and hierarchical sampling designs.     

The effect of changes in habitat conditions on the movement of juvenile Snail Kites Rostrhamus sociabilis

The degradation of habitats due to human activities is a major topic of interest for the conservation and management of wild populations. There is growing evidence that the Florida Everglades ecosystem continues to suffer from habitat degradation. After a period of recovery in the 1990s, the Snail Kite Rostrhamus sociabilis population suffered a substantial decline in 2001 and has not recovered since. Habitat degradation has been suggested as one of the primary reasons for this lack of recovery. As a consequence of the continued degradation of the Everglades, we hypothesized that this would have led to increased movement of juvenile Kites over time, as a consequence of the need to find more favourable habitat. We used multistate mark‐recapture models to compare between‐site movement probabilities of juvenile Snail Kites in the 1990s (1992–95; which corresponds to the period before the decline) and 2000s (2003–06; after the decline). Our analyses were based on an extensive radiotelemetry study (266 birds tracked monthly over the entire state of Florida for a total period of 6 years) and considered factors such as sex and age of marked individuals. There was evidence of increased movement of juvenile Snail Kites during the post‐decline period from most of the wetland regions used historically by Kites. Higher movement rates may contribute to an increase in the probability of mortality of young individuals and could contribute to the observed declines.     

DISTRIBUTION AND POPULATION SIZE OF THE SOUTHERN SEA LION (OTARIA FLAVESCENS) IN CENTRAL AND SOUTHERN CHUBUT,PATAGONIA, ARGENTINA

Population size and distribution of Otaria flavescens in central and southern Chubut, Patagonia were determined by aerial censuses conducted during the breeding season in 1989 and 1995 and outside the season. Pup numbers were corrected by means of a linear regression between pup and adult male (AM) numbers obtained from terrestrial censuses carried out in 1994 and 1996. During the 1989 breeding season, 10,557 sea lions occurred at 29 locations; 2,800 (26.3%) were pups. During the 1995 breeding season, 14,887 animals were censused at 32 sites; 3,311 (22.2%) were pups. The corrected pup number increased to 4,852, indicating that around 32% is lost when counting from photographs. The corrected total number increased to 16,483; this fell to less than one half between breeding seasons (November 1990 and 1995). The 1995 breeding season census represents the highest number since 1972 (8,800) and the widest historical dispersion (37 sites year round contrasting with 13 in the late 1940s and 1972). However, the present number is about one half of the 33,000 censused about 50 yr ago. Using a correction factor developed for the north Paragonian population (1.8 × censused number) the present population size in the area can be estimated at around 29,669 individuals.     

A general approach for two-stage analysis of multilevel clustered non-Gaussian data

In this article, we propose a two-stage approach to modeling multilevel clustered non-Gaussian data with sufficiently large numbers of continuous measures per cluster. Such data are common in biological and medical studies utilizing monitoring or image-processing equipment. We consider a general class of hierarchical models that generalizes the model in the global two-stage (GTS) method for nonlinear mixed effects models by using any square-root-n-consistent and asymptotically normal estimators from stage 1 as pseudodata in the stage 2 model, and by extending the stage 2 model to accommodate random effects from multiple levels of clustering. The second-stage model is a standard linear mixed effects model with normal random effects, but the cluster-specific distributions, conditional on random effects, can be non-Gaussian. This methodology provides a flexible framework for modeling not only a location parameter but also other characteristics of conditional distributions that may be of specific interest. For estimation of the population parameters, we propose a conditional restricted maximum likelihood (CREML) approach and establish the asymptotic properties of the CREML estimators. The proposed general approach is illustrated using quartiles as cluster-specific parameters estimated in the first stage, and applied to the data example from a collagen fibril development study. We demonstrate using simulations that in samples with small numbers of independent clusters, the CREML estimators may perform better than conditional maximum likelihood estimators, which are a direct extension of the estimators from the GTS method.     

An ecological example of the application of projection pursuit to compositional data

A modern statistical technique for the exploratory analysis of multivariate data, called projection pursuit, is described. The method consists of the extraction and display of informative one- or two-dimensional projections of the multivariate dataset. Projection pursuit has much scope for application to ecological datasets. The example in this paper concerns element concentrations in soil samples from a coastal embayment on the east coast of Australia. The application attempts to search for patterns in the soils which could be related to vegetation patterns. The compositional nature of the dataset adds a novel element to the methodology.