Lower studio dining room, Mooloomba House, Point Lookout, North Stradbroke Island

View through courtyard to lower studio dining room, as seen from upper living area.

Lower studio dining room, Mooloomba House, Point Lookout, North Stradbroke Island

View through courtyard to lower studio dining room, as seen from upper living area.

Mooloomba House, Point Lookout, North Stradbroke Island

North elevation, deck below and belvedere above, as seen from path to beach.

Lower studio dining room, Mooloomba House, Point Lookout, North Stradbroke Island

View through courtyard to lower studio dining room, as seen from upper living area.

Internal cladding to north-east facade, Mooloomba House, Point Lookout, North Stradbroke Island

View of internal cladding to north-east facade as seen from dining studio.

Dining studio, Mooloomba House, Point Lookout, North Stradbroke Island

View through courtyard to dining studio as seen from upper living room.

Mooloomba House, Point Lookout, North Stradbroke Island

North elevation, deck below and belvedere above, as seen from path to beach.

Cladding detail, Mooloomba House, Point Lookout, North Stradbroke Island

Detail view of cladding to upper level dining studio.

Second floor reading area interior, Sunshine Coast University Library, Maroochydore

View of second floor reading area with rigid frames and air-conditioning ducting.

Second floor reading area interior, Sunshine Coast University Library, Maroochydore

View of second floor reading area with rigid frames and air-conditioning ducting.

An improved seeded region growing algorithm

Recently Adams and Bischof (1994) proposed a novel region growing algorithm for segmenting intensity images. The inputs to the algorithm are the intensity image and a set of seeds - individual points or connected components - that identify the individual regions to be segmented. The algorithm grows these seed regions until all of the image pixels have been assimilated. Unfortunately the algorithm is inherently dependent on the order of pixel processing. This means, for example, that raster order processing and anti-raster order processing do not, in general, lead to the same tessellation. In this paper we propose an improved seeded region growing algorithm that retains the advantages of the Adams and Bischof algorithm fast execution, robust segmentation, and no tuning parameters - but is pixel order independent. (C) 1997 Elsevier Science B.V.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.