A study of the formation of amorphous calcium phosphate and hydroxyapatite on melt quenched Bioglass(A (R)) using surface sensitive shallow angle X-ray diffraction

Melt quenched silicate glasses containing calcium, phosphorous and alkali metals have the ability to promote bone regeneration and to fuse to living bone. These glasses, including 45S5 Bioglass(A (R)) [(CaO)(26.9)(Na2O)(24.4)(SiO2)(46.1)(P2O5)(2.6)], are routinely used as clinical implants. Consequently there have been numerous studies on the structure of these glasses using conventional diffraction techniques. These studies have provided important information on the atomic structure of Bioglass(A (R)) but are of course intrinsically limited in the sense that they probe the bulk material and cannot be as sensitive to thin layers of near-surface dissolution/growth. The present study therefore uses surface sensitive shallow angle X-ray diffraction to study the formation of amorphous calcium phosphate and hydroxyapatite on Bioglass(A (R)) samples, pre-reacted in simulated body fluid (SBF). Unreacted Bioglass(A (R)) is dominated by a broad amorphous feature around 2.2 A...(-1) which is characteristic of sodium calcium silicate glass. After reacting Bioglass(A (R)) in SBF a second broad amorphous feature evolves similar to 1.6 A...(-1) which is attributed to amorphous calcium phosphate. This feature is evident for samples after only 4 h reacting in SBF and by 8 h the amorphous feature becomes comparable in magnitude to the background signal of the bulk Bioglass(A (R)). Bragg peaks characteristic of hydroxyapatite form after 1-3 days of reacting in SBF.

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.