Solid-state NMR study of bioactive binary borosilicate glasses

A series of binary borosilicate glasses prepared by the sol-gel method are shown to be bioactive. Tetraethyl orthosilicate (TEOS) and trimethylborate (TMB) in acidic medium are used to prepare xB(2)O(3)center dot(1-x)SiO2 glass systems for x = 0.045-0.167. The formation of a layer of apatite-like mineral on the glass surface becomes apparent after soaking in simulated body fluid for 48 h. We have measured the B-11-B-11 homonuclear second moments of the borosilicate glasses and inferred that no macroscopic phase separation occurred in our glasses. The B-11 chemical shift data also show that the formation of clustered boroxol rings is negligible in our glass system. Although the bioactivity of our borosilicate glasses is less than that of CaO-SiO2 sol-gel glasses, these simple binary systems could be taken as reference glass systems for the search of new bioactive borosilicate glasses. (C) 2008 Elsevier Ltd. All rights reserved.

Approximations for Binary Gaussian Process Classification

We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results. We examine both 1) the quality of the predictive distributions and 2) the suitability of the different marginal likelihood approximations for model selection (selecting hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods produce good predictive distributions although their marginal likelihood approximations are poor. Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost always the method of choice unless the computational budget is very tight. We also extend existing methods in various ways, and provide unifying code implementing all approaches.

Optimizing the Curie temperature of pseudo-binary RxR2-x ' Fe-17 (R, R ' = rare earth) for magnetic refrigeration

Several pseudo-binary RxR2-x'Fe-17 alloys (with R = Y, Ce, Pr, Gd and Dy) were synthesized with rhombohedral Th2Zn17-type crystal structure determined from x-ray and neutron powder diffraction. The choice of compositions was done with the aim of tuning the Curie temperature (T-C) in the 270 +/- 20 K temperature range, in order to obtain the maximum magneto-caloric effect around room temperature. The investigated compounds exhibit broad isothermal magnetic entropy changes, Delta S-M(T), with moderate values of the refrigerant capacity, even though the values of Delta S-M(Peak) are relatively low compared with those of the R2Fe17 compounds with R = Pr or Nd. The reduction on the Delta S-M(Peak) is explained in terms of the diminution in the saturation magnetization value. Furthermore, the Delta S-M(T) curves exhibit a similar caret-like behavior, suggesting that the magneto-caloric effect is mainly governed by the Fe-sublattice. A single master curve for Delta S-M/Delta S-M(Peak)(T) under different values of the magnetic field change are obtained for each compound by rescaling of the temperature axis.

A Bayesian method for identification of stock mixtures from molecular marker data

Molecular markers have been demonstrated to be useful for the estimation of stock mixture proportions where the origin of individuals is determined from baseline samples. Bayesian statistical methods are widely recognized as providing a preferable strategy for such analyses. In general, Bayesian estimation is based on standard latent class models using data augmentation through Markov chain Monte Carlo techniques. In this study, we introduce a novel approach based on recent developments in the estimation of genetic population structure. Our strategy combines analytical integration with stochastic optimization to identify stock mixtures. An important enhancement over previous methods is the possibility of appropriately handling data where only partial baseline sample information is available. We address the potential use of nonmolecular, auxiliary biological information in our Bayesian model.