Benzylideneoxymorphone: A New Lead for Development of Bifunctional Mu/Delta Opioid Receptor Ligands

Opioid analgesic tolerance remains a considerable drawback to chronic pain management. The finding that concomitant administration of delta opioid receptor (DOR) antagonists attenuates the development of tolerance to mu opioid receptor (MOR) agonists has led to interest in producing bifunctional MOR agonist/DOR antagonist ligands. Herein, we present 7-benzylideneoxymorphone (6, UMB 246) displaying MOR partial agonist/DOR antagonist activity, representing a new lead for designing bifunctional MOR/DOR ligands

Estimated and observed performance of a neutron SNM portal monitor for vehicles

In July 1987, we completed our development of a neutron-detection- based vehicle SNM portal monitor with a conference paper presented at the annual meeting. The paper described the neutron vehicle portal (NVP), described source-response measurements made with it at Los Alamos, and gave our estimate of the monitor`s potential performance. Later, in December 1988, we had a chance to do a performance test with the monitor in a plant environment. This paper discusses how our original performance estimate should vary in different circumstances, and it uses the information to make a comparison between the monitor`s estimated and actual performance during the 1988 performance testing

Lump correction and identification in the combined thermal/epithermal neutron (CTEN) method

The authors present a model for self shielding in lumps of fissile material in active-neutron assays. The model combines the formula for self-attenuation of gamma-ray in lumpy sources with the multi-group analysis techniques used in neutron transport calculations. Models for thin foils and for spheres are examined in terms of error multiplication in determining lump corrections and the basic accuracy of the model

Political brand image: an investigation into the operationalisation of the external orientation of David Cameron’s Conservative brand

This paper seeks to address the limited understanding of how to operationalise the external brand image of a political brand. More specifically, this research critically assesses the transfer potential of the six variables of brand image by Bosch, Venter, Han and Boshoff to deconstruct the UK Conservative Party brand from the perspective of young people aged 18–24 years during the 2010 UK General Election campaign. This research demonstrates the applicability of the six variables otherwise known as the ‘brand image framework’ to the political environment. However, the application of the brand image framework in its original conceptualisation proved problematic. Many of the brand image variables were clarified, rearticulated and simplified to address the political context. This refined conceptualisation provided an in-depth understanding of how to investigate the political brand image of David Cameron’s Conservative Party. This study addresses the paucity of research that operationalises external brand image and provides practitioners and academics within and beyond the context of political branding a mechanism to understand the external orientation of brands. This research may also be used by political and non-political brands as a basis to explore external brand image and compare its consistency with internal brand identity

Learning intrinsic excitability in medium spiny neurons

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parametrization of individual ion channels on the neuronal activation function. We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal variability on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how variability and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic variability determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.Comment: 20 pages, 8 figure

Evidence for Pervasive Adaptive Protein Evolution in Wild Mice

The relative contributions of neutral and adaptive substitutions to molecular evolution has been one of the most controversial issues in evolutionary biology for more than 40 years. The analysis of within-species nucleotide polymorphism and between-species divergence data supports a widespread role for adaptive protein evolution in certain taxa. For example, estimates of the proportion of adaptive amino acid substitutions (alpha) are 50% or more in enteric bacteria and Drosophila. In contrast, recent estimates of alpha for hominids have been at most 13%. Here, we estimate alpha for protein sequences of murid rodents based on nucleotide polymorphism data from multiple genes in a population of the house mouse subspecies Mus musculus castaneus, which inhabits the ancestral range of the Mus species complex and nucleotide divergence between M. m. castaneus and M. famulus or the rat. We estimate that 57% of amino acid substitutions in murids have been driven by positive selection. Hominids, therefore, are exceptional in having low apparent levels of adaptive protein evolution. The high frequency of adaptive amino acid substitutions in wild mice is consistent with their large effective population size, leading to effective natural selection at the molecular level. Effective natural selection also manifests itself as a paucity of effectively neutral nonsynonymous mutations in M. m. castaneus compared to humans

Genetic Crossovers Are Predicted Accurately by the Computed Human Recombination Map

Hotspots of meiotic recombination can change rapidly over time. This instability and the reported high level of inter-individual variation in meiotic recombination puts in question the accuracy of the calculated hotspot map, which is based on the summation of past genetic crossovers. To estimate the accuracy of the computed recombination rate map, we have mapped genetic crossovers to a median resolution of 70 Kb in 10 CEPH pedigrees. We then compared the positions of crossovers with the hotspots computed from HapMap data and performed extensive computer simulations to compare the observed distributions of crossovers with the distributions expected from the calculated recombination rate maps. Here we show that a population-averaged hotspot map computed from linkage disequilibrium data predicts well present-day genetic crossovers. We find that computed hotspot maps accurately estimate both the strength and the position of meiotic hotspots. An in-depth examination of not-predicted crossovers shows that they are preferentially located in regions where hotspots are found in other populations. In summary, we find that by combining several computed population-specific maps we can capture the variation in individual hotspots to generate a hotspot map that can predict almost all present-day genetic crossovers

An Approximate Bayesian Estimator Suggests Strong, Recurrent Selective Sweeps in Drosophila

The recurrent fixation of newly arising, beneficial mutations in a species reduces levels of linked neutral variability. Models positing frequent weakly beneficial substitutions or, alternatively, rare, strongly selected substitutions predict similar average effects on linked neutral variability, if the product of the rate and strength of selection is held constant. We propose an approximate Bayesian (ABC) polymorphism-based estimator that can be used to distinguish between these models, and apply it to multi-locus data from Drosophila melanogaster. We investigate the extent to which inference about the strength of selection is sensitive to assumptions about the underlying distributions of the rates of substitution and recombination, the strength of selection, heterogeneity in mutation rate, as well as the population's demographic history. We show that assuming fixed values of selection parameters in estimation leads to overestimates of the strength of selection and underestimates of the rate. We estimate parameters for an African population of D. melanogaster (ŝ∼2E−03, ) and compare these to previous estimates. Finally, we show that surveying larger genomic regions is expected to lend much more discriminatory power to the approach. It will thus be of great interest to apply this method to emerging whole-genome polymorphism data sets in many taxa

The Case of the Fickle Fingers: How the PRDM9 Zinc Finger Protein Specifies Meiotic Recombination Hotspots in Humans

Recent discoveries have revealed the central role of PRDM9 in mammalian recombination. The precise function of this protein, however, remains poorly understood, as do the causes for its rapid evolution and its role in reproductive isolation

Ancestral Informative Marker Selection and Population Structure Visualization Using Sparse Laplacian Eigenfunctions

Identification of a small panel of population structure informative markers can reduce genotyping cost and is useful in various applications, such as ancestry inference in association mapping, forensics and evolutionary theory in population genetics. Traditional methods to ascertain ancestral informative markers usually require the prior knowledge of individual ancestry and have difficulty for admixed populations. Recently Principal Components Analysis (PCA) has been employed with success to select SNPs which are highly correlated with top significant principal components (PCs) without use of individual ancestral information. The approach is also applicable to admixed populations. Here we propose a novel approach based on our recent result on summarizing population structure by graph Laplacian eigenfunctions, which differs from PCA in that it is geometric and robust to outliers. Our approach also takes advantage of the priori sparseness of informative markers in the genome. Through simulation of a ring population and the real global population sample HGDP of 650K SNPs genotyped in 940 unrelated individuals, we validate the proposed algorithm at selecting most informative markers, a small fraction of which can recover the similar underlying population structure efficiently. Employing a standard Support Vector Machine (SVM) to predict individuals' continental memberships on HGDP dataset of seven continents, we demonstrate that the selected SNPs by our method are more informative but less redundant than those selected by PCA. Our algorithm is a promising tool in genome-wide association studies and population genetics, facilitating the selection of structure informative markers, efficient detection of population substructure and ancestral inference