Lithospheric structure of the Rio Grande rift

A high-resolution, regional passive seismic experiment in the Rio Grande rift region of the southwestern United States has produced new images of upper-mantle velocity structure and crust-mantle topography. Synthesizing these results with geochemical and other geophysical evidence reveals highly symmetric lower-crustal and upper-mantle lithosphere extensional deformation, suggesting a pure-shear rifting mechanism for the Rio Grande rift. Extension in the lower crust is distributed over a region four times the width of the rift's surface expression. Here we propose that the laterally distributed, pure shear extension is a combined effect of low strain rate and a regionally elevated geotherm, possibly abetted by pre-existing lithospheric structures, at the time of rift initiation. Distributed extension in the lower crust and mantle has induced less concentrated vertical mantle upwelling and less vigorous small-scale convection than would have arisen from more localized deformation. This lack of highly focused mantle upwelling may explain a deficit of rift-related volcanics in the Rio Grande rift compared to other major rift systems such as the Kenya rift.     

Saturable transport of amphetamine across the blood-brain barrier

Zusammenfassung Untersuchungen über die die Penetration von Amphetaminen ins Gehirn bestimmenden Faktoren. Entwicklung neuer Aspekte im Hinblick auf den Transport von Amphetaminen über die Blut-Hirn-Schranke mittels eines Trägers.

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Supported in part by USPHS Research Grant No. NSO8884, NINDS.     

A gene-driven approach to the identification of ENU mutants in the mouse

The construction of parallel archives of DNA and sperm from mice mutagenized with ethylnitrosurea (ENU) represents a potentially powerful and rapid approach for identifying point mutations in any gene in the mouse genome. We provide support for this approach and report the identification of mutations in the gene (Gjb2) encoding connexin 26, using archives established from the UK ENU mutagenesis program.     

Low melting point nanocrystalline Sn–Ag solder synthesized by a refined chemical reduction method

The commercial market of Sn-Pb solder is gradually decreasing due to its toxicity, calling for Pb-free substitute materials. Sn-Ag alloy is a potential candidate in terms of good mechanical property. The major problematic issue of using Sn-Ag is their high melting temperature, consequently this study is dedicated to lowering the melt- ing temperature of Sn3.5Ag （wt%） alloy by developing nanomaterials using a chemical reduction approach. The resultant nanocrystalline Sn3.5Ag is characterized by field emission scanning electron microscope. The size dependence of the melting temperature is discussed based on differential scanning calorimetry results. We have reduced the melting temperature to 209.8 ℃ in the nanocrystalline Sn3.5Ag of （32.4± 8.0） nm, compared to ～221 ℃ of the bulk alloy. The results are consistent with the prediction made by a relevant theoretical model, and it is possible to further lower the melting temperature using the chemical reduction approach developed by this study.     

A Comparison of Box—Jenkins and objective methods for determining the order of a non-seasonal ARMA Model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time-series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.