Effect of ternary phosphate-based glass compositions on osteoblast and osteoblast-like prolferation, differentiation and death in vitro

There is currently a need to expand the range of graft materials available to orthopaedic surgeons. This study investigated the effect of ternary phosphate based glass (PBG) compositions on the behaviour of osteoblast and osteoblast-like cells. PBGs of the formula in mol% P2O5 (50)-CaO (50-X)-Na2O (X), where X was either 2, 4, 6, 8 or 10 were produced and their influence on the proliferation, differentiation and death in vitro of adult human bone marrow stromal cells (hBMSCs) and human fetal osteoblast 1.19 (HFOB 1.19) cells were assessed. Tissue culture plastic (TCP) and hydroxyapatite (HA) were used as controls. Exposure to PBGs in culture inhibited cell adhesion, proliferation and increased cell death in both cell types studied. There was no significant difference in %cell death between the PBGs which was significantly greater than the controls. However, compared to other PBGs, a greater number of cells was found on the 48 mol% CaO which may have been due to either increased adherence, proliferation or both. This composition was capable of supporting osteogenic proliferation and early differentiation and supports the notion that chemical modification of the glass could to lead to a more biologically compatible substrate with the potential to support osteogenic grafting. Realisation of this potential should lead to the development of novel grafting strategies for the treatment of problematic bone defects.

Estimation of ternary liquid-liquid equilibria for arene/alkane/ionic liquid mixtures using neural networks

Neural network models have been explored for the prediction of the liquid-liquid equilibrium data and aromatic/aliphatic selectivity values. Four ternary systems composed of toluene, heptane, and the ionic liquids 1-ethyl-3-methylimidazolium ethylsulfate, or 1,3-dimethylimidazolium methylsulfate were investigated at 313.2 and 348.2 K.

Joined Spectral Trees for Scalable SPIHT-Based Multispectral Image Compression

In this paper, the compression of multispectral images is addressed. Such 3-D data are characterized by a high correlation across the spectral components. The efficiency of the state-of-the-art wavelet-based coder 3-D SPIHT is considered. Although the 3-D SPIHT algorithm provides the obvious way to process a multispectral image as a volumetric block and, consequently, maintain the attractive properties exhibited in 2-D (excellent performance, low complexity, and embeddedness of the bit-stream), its 3-D trees structure is shown to be not adequately suited for 3-D wavelet transformed (DWT) multispectral images. The fact that each parent has eight children in the 3-D structure considerably increases the list of insignificant sets (LIS) and the list of insignificant pixels (LIP) since the partitioning of any set produces eight subsets which will be processed similarly during the sorting pass. Thus, a significant portion from the overall bit-budget is wastedly spent to sort insignificant information. Through an investigation based on results analysis, we demonstrate that a straightforward 2-D SPIHT technique, when suitably adjusted to maintain the rate scalability and carried out in the 3-D DWT domain, overcomes this weakness. In addition, a new SPIHT-based scalable multispectral image compression algorithm is used in the initial iterations to exploit the redundancies within each group of two consecutive spectral bands. Numerical experiments on a number of multispectral images have shown that the proposed scheme provides significant improvements over related works.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.