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Independent studies indicate that expression of sialylated fucosylated mucins by human carcinomas portends a poor prognosis because of enhanced metastatic spread of tumor cells, that carcinoma metastasis in mice is facilitated by formation of tumor cell complexes with blood platelets, and that metastasis can be attenuated by a background of P-selectin deficiency or by treatment with heparin. The effects of heparin are not primarily due to its anticoagulant action. Other explanations have been suggested but not proven. Here, we bring together all these unexplained and seemingly disparate observations, showing that heparin treatment attenuates tumor metastasis in mice by inhibiting P-selectin-mediated interactions of platelets with carcinoma cell-surface mucin ligands. Selective removal of tumor mucin P-selectin ligands, a single heparin dose, or a background of P-selectin deficiency each reduces tumor cell-platelet interactions in vitro and in vivo . Although each of these maneuvers reduced the in vivo interactions for only a few hours, all markedly reduce long-term organ colonization by tumor cells. Three-dimensional reconstructions by using volume-rendering software show that each situation interferes with formation of the platelet “cloak” around tumor cells while permitting an increased interaction of monocytes (macrophage precursors) with the malignant cells. Finally, we show that human P-selectin is even more sensitive to heparin than mouse P-selectin, giving significant inhibition at concentrations that are in the clinically acceptable range. We suggest that heparin therapy for metastasis prevention in humans be revisited, with these mechanistic paradigms in mind.

This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.

Abstract. In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system's architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands).

Abstract. This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes.

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We describe star and nebula visualization techniques used to create a 3D volumetric visualization of the Orion Nebula. The nebula's ionization layer is modeled first as a surface model, derived from infrared and visible light observations. The surface model is imported into a volume scene graph‐based visualization system that uses procedural volume modeling to simulate the nebula's emissive gas layers. Additional scene graphs model proplyds and shock fronts within the nebula. Stars are rendered using Gaussian spots that are attenuated with distance. Finally, eighty‐six separate volumes are voxelized from these scene graphs, then simultaneously volume rendered.