For the first time, we report on the performance of biphasic system composed of ethyl lactate, water and inorganic salt (K3PO4, K2HPO4 and K2CO3) for the separation of amino acids (Lphenylalanine, L-tryptophan and L-tyrosine) from their aqueous solutions. Cloud points (solubility curve) and tie-lines for three ternary (ethyl la ctate + water + inorganic salt) systems at 298.2 K and 313.2 K at atmospheric pressure were determined. For certain composition range, these mixture exhibit biphasic systemstop and bottom phases rich in ethyl lactate and salt, respectively. Partition coefficients of amino acids and their extraction efficiencies, as essential parameters for design of any separation process, were measured at two temperatures-298.2 K and 313.2 K. The maximum values of partition coefficients were observed for the system containing K3PO4: 3.5, 3.7 and 11.9 for L-phenylalanine at 313.2 K, L-tyrosine at 298.2 K and L-tryptophan at 313.2 K, respectively. The obtained results clearly showed that the biphasic systems based on ethyl lactate are suitable for the efficient and sustainable recovery of amino acids from solutions with water.

Abstract. The creation of detailed 3D buildings models, and to a greater extent the creation of entire city models, has become an area of considerable research over the last couple of decades. The accurate modeling of buildings has LBS (Location Based Services) applications in entertainment, planning, tourism and e-commerce to name just a few. Many modeling systems created to date require manual correspondences to be made across the image set in order to determine the models 3D structure. This paper describes SAMATS, a Semi-Automated Modeling And Texturing System, which has the capability of producing geometrically accurate and photorealistic building models without the need for manual correspondences by using a set of geo-referenced terrestrial images. This paper gives an overview of SAMATS' components, while describing the Edge Highlighting component and the Intersection Rating step from the Edge Recovery component in detail.

Previous work has investigated the feasibility of using Eigenimage-based enhancement tools to highlight abnormalities on chest X-rays (Butler et al in J Med Imaging Radiat Oncol 52:244-253, 2008). While promising, this approach has been limited by computational restrictions of standard clinical workstations, and uncertainty regarding what constitutes an adequate sample size. This paper suggests an alternative mathematical model to the above referenced singular value decomposition method, which can significantly reduce both the required sample size and the time needed to perform analysis. Using this approach images can be efficiently separated into normal and abnormal parts, with the potential for rapid highlighting of pathology.

No abstract

No abstract