Long-term landscape changes in vegetation structure: fire management in the wetlands of KwaMbonambi,South Africa

In wetlands the effects of fire on vegetation dynamics are somewhat uncertain. A change detection analysis in the herbaceous wetlands of KwaMbonambi, South Africa, which were subject to frequent fires, revealed that in 1937 the study area comprised grassland (69%), herbaceous wetland (25%), indigenous swamp forest (4%) and tree plantations (1%). However, by 1970, tree plantations occupied 78% of the landscape and grasslands and herbaceous wetlands had declined to 9% and 6%, respectively, whereas indigenous swamp forest had increased to 6%. By 2009 tree plantations had been removed from the wetland areas. Despite this opportunity for herbaceous wetlands to recover their historical extent, they decreased to only 2%, mostly changing to indigenous swamp forest or to an herbaceous/fern (Stenochlaena tenuifolia)/woodland mosaic. Fire records showed suppression of fire to be an important contributing factor, particularly in wetlands that had been disturbed by tree plantations, although subsequently removed. A pilot burning experiment revealed that S. tenuifolia did not inhibit fire. It is therefore practicable to increase fire frequency to prevent the mosaic developing into forest. A conceptual model of the influence of fire regime on wetland vegetation type is presented and priorities for further research on wetlands and fire are recommended.     

Chlorophenylpiperazine analogues as high affinity dopamine transporter ligands

Selective σ₂ ligands continue to be an active target for medications to attenuate the effects of psychostimulants. In the course of our studies to determine the optimal substituents in the σ₂-selective phenyl piperazines analogues with reduced activity at other neurotransmitter systems, we discovered that 1-(3-chlorophenyl)-4-phenethylpiperazine actually had preferentially increased affinity for dopamine transporters (DAT), yielding a highly selective DAT ligand.     

A sigma-1 receptor selective analogue of BD1008. A potential substitute for (+)-opioids in sigma receptor binding assays

A simple, achiral monoamine sigma-1 (sigma1) receptor selective ligand (sigma2Ki/sigma1Ki>2000) is described, which could replace the chiral (+)-pentazocine or dextrallorphan as a sigma1 masking agent in sigma2 binding assays.     

IS YOUR PHYLOGENY INFORMATIVE? MEASURING THE POWER OF COMPARATIVE METHODS

Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo-based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the pmc ("Phylogenetic Monte Carlo") package for the R programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods.     

Scrambling eggs: meiotic drive and the evolution of female recombination rates

Theories to explain the prevalence of sex and recombination have long been a central theme of evolutionary biology. Yet despite decades of attention dedicated to the evolution of sex and recombination, the widespread pattern of sex differences in the recombination rate is not well understood and has received relatively little theoretical attention. Here, we argue that female meiotic drivers--alleles that increase in frequency by exploiting the asymmetric cell division of oogenesis--present a potent selective pressure favoring the modification of the female recombination rate. Because recombination plays a central role in shaping patterns of variation within and among dyads, modifiers of the female recombination rate can function as potent suppressors or enhancers of female meiotic drive. We show that when female recombination modifiers are unlinked to female drivers, recombination modifiers that suppress harmful female drive can spread. By contrast, a recombination modifier tightly linked to a driver can increase in frequency by enhancing female drive. Our results predict that rapidly evolving female recombination rates, particularly around centromeres, should be a common outcome of meiotic drive. We discuss how selection to modify the efficacy of meiotic drive may contribute to commonly observed patterns of sex differences in recombination.     

DISENTANGLING THE EFFECTS OF GEOGRAPHIC AND ECOLOGICAL ISOLATION ON GENETIC DIFFERENTIATION

Populations can be genetically isolated both by geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user‐friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte data sets.     

N-arylalkylpiperidines as high-affinity sigma-1 and sigma-2 receptor ligands: phenylpropylamines as potential leads for selective sigma-2 agents

To delineate the differences between the structural requirements necessary for recognition at sigma-1 and sigma-2 receptors, a range of phenethyl- and phenylpropylpiperidines were evaluated in binding assays. Phenethylpiperidines were found to favor sigma-1 receptors, whereas phenylpropylpiperidines tend to favor sigma-2 receptors. It appears that phenylpropylamine is a potential pharmacophore for selective sigma-2 ligands.     

A federated design for a neurobiological simulation engine: the CBI federated software architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components.     

Deconstructing 14-phenylpropyloxymetopon: minimal requirements for binding to mu opioid receptors

A series of phenylpropyloxyethylamines and cinnamyloxyethylamines were synthesized as deconstructed analogs of 14-phenylpropyloxymetopon and analyzed for opioid receptor binding affinity. Using the Conformationally Sampled Pharmacophore modeling approach, we discovered a series of compounds lacking a tyrosine mimetic, historically considered essential for μ opioid binding. Based on the binding studies, we have identified the optimal analogs to be N-methyl-N-phenylpropyl-2-(3-phenylpropoxy)ethanamine, with 1520 nM, and 2-(cinnamyloxy)-N-methyl-N-phenethylethanamine with 1680 nM affinity for the μ opioid receptor. These partial opioid structure analogs will serve as the novel lead compounds for future optimization studies.