Reticuloendotheliosis type C and primate type D oncoretroviruses are members of the same receptor interference group.

The reticuloendotheliosis viruses (REVs), originally isolated from avian species, constitute a group of retroviruses which are more closely related to mammalian retroviruses than to other avian retroviruses. The envelope glycoproteins of members of the REV group display a striking amino acid sequence identity with a group of primate oncoretroviruses which belong to a single receptor interference group and include all of the type D and some type C primate oncoretroviruses. Members of the REV group also have a broad host range which covers most avian cells and some mammalian cells, including those of simian and human origin. In view of this broad host range and the envelope sequence similarities, we investigated the cross-interference pattern between REV and primate virus groups to determine whether they utilized the same receptor. Superinfection experiments using a vector virus containing an Escherichia coli lacZ gene showed that reticuloendotheliosis and simian oncoretroviruses constitute a single receptor interference group on both human and canine cells and indicate that the viruses bind to the same receptor to initiate infection. These results suggest that this receptor binding specificity has been maintained over a wide range of retroviruses and may be responsible for the broad spread of these retroviruses between different orders of vertebrates.     

Determination of the number of superhelical turns by the hyperchromicity of partially denatured covalently-closed DNA molecules

A novel method of determining the number of superhelical turns of a covalently-closed plasmid DNA is described. It relies on the determination of the hyperchromicity, and hence the proportion of unstacked basepairs, of a partially heat-denatured sample which co-migrates during electrophoresis with nicked circular duplex DNA. The values obtained for plasmid pBR beta G DNA at 4 degrees C (-29.8 and -33.5 in the two buffers used) agree closely with the values obtained in parallel by topoisomer band-counting. Our method is less precise than band-counting but is readily applicable to determining the superhelicity of very large DNA molecules. Our results confirm earlier findings that magnesium-containing buffers cause an increase in the duplex winding angle, and hence an increase in the number of negative superhelical turns.     

Artificial light at night affects the timing of roosting by Chimney Swifts

Understanding the impact of anthropogenic threats, such as light pollution, on biodiversity is necessary to establish effective guidelines to protect diminishing wildlife. In this study, we examined the effect of artificial light at night (ALAN) on the roosting behaviour of Chimney Swifts Chaetura pelagica, a highly threatened migratory bird species that lives commensally with humans, where it often breeds and roosts in artificial structures such as chimneys. Although Chimney Swifts are known to use time of sunset in combination with temperature, wind and season to coordinate roost entry, we predicted that high ALAN exposure would override these natural cues and lead to a delayed entry compared with sites with less light pollution. To test this, we examined the effects of ALAN on the start and end times of entry to 21 roosting sites located along a light pollution gradient in New Jersey and the New York Metropolitan area. We found that ALAN was a significant predictor of roosting entry time, with birds entering later in sites with more light pollution. While Chimney Swifts initiated roosting earlier in the summer months compared with the autumn, this effect was absent in areas with high light pollution. These findings highlight the need to determine the causes and consequences of light pollution effects.     

Steady-state kinetic analysis of human ubiquitin-activating enzyme (E1) using a fluorescently labeled ubiquitin substrate

We report the synthesis of fluorescently labeled ubiquitin (Ub) and its use for following ubiquitin transfer to various proteins. Using Oregon green (Og) succinimidyl ester, we prepared a population of Ub mainly labeled by a single Og molecule; greater than 95% of the Og label is associated with Lys 6 of Ub. We demonstrate that Og-Ub is efficiently accepted by Ub-utilizing enzymes, such as the human ubiquitin-activating enzyme (E1). We used this fluorescent substrate to follow the steady-state kinetics of human E1-catalyzed Ub-transfer to the ubiquitin-carrier enzyme Ubc4. In this reaction, E1 uses three substrates: ATP, Ubc4, and Ub. The steady-state kinetics of Og-Ub utilization by E1 is presented. We have also used analytical ultracentrifugation methods to establish that E1 is monomeric under our assay condition (low salt) as well as under physiological condition (150 mM NaCl).     

Generating Boolean networks with a prescribed attractor structure

MOTIVATION: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). RESULTS: The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. AVAILABILITY: The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm     

Superior feature-set ranking for small samples using bolstered error estimation

MOTIVATION: Ranking feature sets is a key issue for classification, for instance, phenotype classification based on gene expression. Since ranking is often based on error estimation, and error estimators suffer to differing degrees of imprecision in small-sample settings, it is important to choose a computationally feasible error estimator that yields good feature-set ranking. RESULTS: This paper examines the feature-ranking performance of several kinds of error estimators: resubstitution, cross-validation, bootstrap and bolstered error estimation. It does so for three classification rules: linear discriminant analysis, three-nearest-neighbor classification and classification trees. Two measures of performance are considered. One counts the number of the truly best feature sets appearing among the best feature sets discovered by the error estimator and the other computes the mean absolute error between the top ranks of the truly best feature sets and their ranks as given by the error estimator. Our results indicate that bolstering is superior to bootstrap, and bootstrap is better than cross-validation, for discovering top-performing feature sets for classification when using small samples. A key issue is that bolstered error estimation is tens of times faster than bootstrap, and faster than cross-validation, and is therefore feasible for feature-set ranking when the number of feature sets is extremely large.