Psoriasis is a chronic inflammatory skin disease that affects over 3% of the population. Various methods are currently used to evaluate psoriasis severity and to monitor therapeutic response. The PASI system of scoring is widely used for evaluating psoriasis severity. It employs a visual analogue scale to score the thickness, redness (erythema), and scaling of psoriasis lesions. However, PASI scores are subjective and suffer from poor inter- and intra-observer concordance. As an integral part of developing a reliable evaluation method for psoriasis, an algorithm is presented for segmenting scaling in 2-D digital images. The algorithm is believed to be the first to localize scaling directly in 2-D digital images. The scaling segmentation problem is treated as a classification and parameter estimation problem. A Markov random field (MRF) is used to smooth a pixel-wise classification from a support vector machine (SVM) that utilizes a feature space derived from image color and scaling texture. The training sets for the SVM are collected directly from the image being analyzed giving the algorithm more resilience to variations in lighting and skin type. The algorithm is shown to give reliable segmentation results when evaluated with images with different lighting conditions, skin types, and psoriasis types.

No abstract

Thesauri and other types of controlled vocabularies are increasingly re‐engineered into ontologies described using the Web Ontology Language (OWL), particularly in the life sciences. This has led to the perception by some that thesauri are ontologies once they are described by using the syntax of OWL while others have emphasized the need to re‐engineer a vocabulary to use it as ontology. This confusion is rooted in different perceptions of what ontologies are and how they differ from other types of vocabularies. In this article, we rigorously examine the structural differences and similarities between thesauri and meaning‐defining ontologies described in OWL. Specifically, we conduct (a) a conceptual comparison of thesauri and ontologies, and (b) a comparison of a specific thesaurus and a specific ontology in the same subject field. Our results show that thesauri and ontologies need to be treated as 2 orthogonal kinds of models with superficially similar structures. An ontology is not a good thesaurus, nor is a thesaurus a good ontology. A thesaurus requires significant structural and other content changes to become an ontology, and vice versa.

In this paper, concepts from network automata are adapted and extended to model complex biological systems. Specifically, systems of nephrons, the operational units of the kidney, are modelled and the dynamics of such systems are explored. Nephron behaviour can fluctuate widely and, under certain conditions, become chaotic. However, the behaviour of the whole kidney remains remarkably stable and blood solute levels are maintained under a wide range of conditions even when many nephrons are damaged or lost. A network model is used to investigate the stability of systems of nephrons and interactions between nephrons. More sophisticated dynamics are explored including the observed oscillations in single nephron filtration rates and the development of stable ionic and osmotic gradients in the inner medulla which contribute to the countercurrent exchange mechanism. We have used the model to explore the effects of changes in input parameters including hydrostatic and osmotic pressures and concentrations of ions, such as sodium and chloride. The intrinsic nephron control, tubuloglomerular feedback, is included and the effects of coupling between nephrons are explored in two-, eight-and 72-nephron models.

No abstract