Learning Components of Computational Models from Texts

The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine

Electron Microscopy of Endothelial Cell - Biopolymer Interaction

Vascular endothelial cells form a natural antithrombogenic lining on all blood vessels. Replacement or bypass of small diameter blood vessels with artificial polymeric grafts has not been clinically acceptable due to the thrombogenic nature of polymeric material. One approach to improving the patency of vascular prosthetic devices has been the establishment of endothelial monolayers on the blood flow surface using the technique known as seeding. Scanning electron microscopy has been a major tool in evaluating the interaction of endothelial cells with polymeric surfaces resulting in a basic understanding of forces and structures regulating endothelium-polymer interactions. In-vitro and in-vivo studies have established the feasibility of using endothelial cell seeding technology in human clinical trials. This tutorial describes the development of endothelial cell seeding technology and illustrates how scanning electron microscopic evaluations have furthered our understanding of endothelial cell-polymer interactions

Extracellular Fibrils of Pathogenic Yeast Cryptococcus gattii Are Important for Ecological Niche, Murine Virulence and Human Neutrophil Interactions

Cryptococcus gattii, an emerging fungal pathogen of humans and animals, is found on a variety of trees in tropical and temperate regions. The ecological niche and virulence of this yeast remain poorly defined. We used Arabidopsis thaliana plants and plant-derived substrates to model C. gattii in its natural habitat. Yeast cells readily colonized scratch-wounded plant leaves and formed distinctive extracellular fibrils (40–100 nm diameter ×500–3000 nm length). Extracellular fibrils were observed on live plants and plant-derived substrates by scanning electron microscopy (SEM) and by high voltage- EM (HVEM). Only encapsulated yeast cells formed extracellular fibrils as a capsule-deficient C. gattii mutant completely lacked fibrils. Cells deficient in environmental sensing only formed disorganized extracellular fibrils as apparent from experiments with a C. gattii STE12α mutant. C. gattii cells with extracellular fibrils were more virulent in murine model of pulmonary and systemic cryptococcosis than cells lacking fibrils. C. gattii cells with extracellular fibrils were also significantly more resistant to killing by human polymorphonuclear neutrophils (PMN) in vitro even though these PMN produced elaborate neutrophil extracellular traps (NETs). These observations suggest that extracellular fibril formation could be a structural adaptation of C. gattii for cell-to-cell, cell-to-substrate and/or cell-to- phagocyte communications. Such ecological adaptation of C. gattii could play roles in enhanced virulence in mammalian hosts at least initially via inhibition of host PMN– mediated killing

Knowledge-Based Modeling and Simulation of Diseases with Highly Differentiated Clinical Manifestations

Abstract. This paper presents the cognitive model of gastroesophageal reflux disease (GERD) developed for the Maryland Virtual Patient simulation and mentoring environment. GERD represents a class of diseases that have a large number of clinical manifestations. Our model at once manages that complexity while offering robust automatic function in response to open-ended user actions. This ontologically grounded model is largely based on script-oriented representations of causal chains reflecting the actual physiological processes in virtual patients. A detailed description of the GERD model is presented along with a high-level description of the environment for which it was developed