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SummaryThe mechanistic links between genetic variation and autoantibody production in autoimmune disease remain obscure. Autoimmune lymphoproliferative syndrome (ALPS) is caused by inactivating mutations in FAS or FASL, with autoantibodies thought to arise through failure of FAS-mediated removal of self-reactive germinal center (GC) B cells. Here we show that FAS is in fact not required for this process. Instead, FAS inactivation led to accumulation of a population of unconventional GC B cells that underwent somatic hypermutation, survived despite losing antigen reactivity, and differentiated into a large population of plasma cells that included autoantibody-secreting clones. IgE+ plasma cell numbers, in particular, increased after FAS inactivation and a major cohort of ALPS-affected patients were found to have hyper-IgE. We propose that these previously unidentified cells, designated “rogue GC B cells,” are a major driver of autoantibody production and provide a mechanistic explanation for the linked production of IgE and autoantibodies in autoimmune disease.

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a session-based perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.

Summary The core curriculum in the education of medical informaticians remains a topic of concern and discussion. This paper reports on a survey of medical informaticians with Master’s level credentials that asked about computer science (CS) topics or skills that they need in their employment. All subjects were graduates or ‘‘near-graduates’’ of a single medical informatics Master’s program that they entered with widely varying educational backgrounds. The survey instrument was validated for face and content validity prior to use. All survey items were rated as having some degree of importance in the work of these professionals, with retrieval and analysis of data from databases, database design and web technologies deemed most important. Least important were networking skills and object-oriented design and concepts. These results are consistent with other work done in the field and suggest that strong emphasis on technical skills, particularly databases, data analysis, web technologies, computer programming and general computer science are part of the core curriculum for medical informatics. © 2003 Elsevier Ireland Ltd. All rights reserved.

Two common assumptions held by information retrieval researchers are that searching using Boolean operators is inferior to natural language searching and that results from batch-style retrieval evaluations are generalizable to the real-world searching. We challenged these assumptions in the Text Retrieval Conference (TREC) interactive track, with real users following a consensus protocol to search for an instance recall task. Our results showed that Boolean and natural language searching achieved comparable results and that the results from batch evaluations were not comparable to those obtained in experiments with real users.

For all of us, one side of the brain, right or left, is the dominant hemisphere used to process information. In 9 out of 10 persons, the left temporal lobe becomes the dominant hemisphere. In the remaining one tenth of the population both sides develop at the same rate, forming "dual dominance," or, more rarely, the right side alone becomes highly developed. The left hemisphere of the brain serves as the analytical, sequential, cause and effect processor, whereas the right hemisphere of the brain is the mind's eye, assimilating information simultaneously and processing visual and nonverbal information and sound. Neither hemisphere works independently, and both are used to effectively learn new information. The creative teacher understands this arrangement and can successfully determine when a complex diagram or video is needed to teach a patient, or when only a clear voice is necessary.