The preceding paper described our numerical index of carcinogenic potency, the TD54, and the statistical procedures adopted for estimating it from experimental data. This paper presents the Carcinogenic Potency Database, which includes results of about 3000 long-term, chronic experiments of 770 test compounds. Part II is a discussion of the sources of our data, the rationale for the inclusion of particular experiments and particular target sites, and the conventions adopted in summarizing the literature. Part III is a guide to the plot of results presented in Part IV. A number of appendices are provided to facilitate use of the database. The plot includes information about chronic cancer tests in mammals, such as dose and other aspects of experimental protocol, histopathology and tumor incidence, TD5o and its statistical significance, dose response, author's opinion and literature reference. The plot readily permits comparisons of carcinogenic potency and many other aspects of cancer tests; it also provides quantitative information about negative tests. The range of carcinogenic potency is over 10 million-fold. Part 1: IntroductionThis paper presents the Carcinogenic Potency Database, which includes data on approximately 3000 longterm, chronic animal experiments with about 770 chemicals. The preceding paper (1) described our numerical index of carcinogenic potency, the TD50, and the statistical procedures adopted for estimating it from experimental data. weight/day) which, if administered chronically for the standard lifespan of the species, will halve the probability of remaining tumorless throughout that period. A TD50 can be computed for any particular type of neoplasm, for any particular tissue, or for any combination of these.Part II of this paper discusses the sources of bioassay results and the rationale for including particular experiments and particular target sites in the database. The conventions adopted in summarizing and standardizing the literature are also described. In Part IV we present a plot of the database. In

No abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. . The National Institute of Environmental Health Sciences (NIEHS) and Brogan & Partners are collaborating with JSTOR to digitize, preserve and extend access to Environmental Health Perspectives. This supplement includes results of 280 long-term, chronic experiments of 114 test compounds, and reports the same information about each experiment in the same plot format as the earlier paper: e.g., the species and strain of test animal, the route and duration of compound administration, dose level and other aspects of experimental protocol, histopathology and tumor incidence, TD50 and its statistical significance, dose response, author's opinion about carcinogenicity, and literature reference. While a number of appendices are provided to facilitate use of this supplement, we have not duplicated here the material published earlier. Instead, we refer the reader to the earlier publications (Peto et al. and Gold et al.) for a thorough description of the numerical index of carcinogenic potency (TD50), a guide to the plot of the database, and a discussion of the sources of data, the rationale for the inclusion of particular experiments and particular target sites, and the conventions adopted in summarizing the literature. For 44 of the 114 chemicals reported in this second plot, results of earlier experiments are also given in the first plot; since only 1981-1982 results are reported here, the first plot is required for these repeated compounds. In this paper we also give corrections for errors that appeared in the earlier publication. 11). Appendices 12 and 13 give full bibliographic information for all experiments reported in this plot: the bibliography for the general literature (Appendix 12); and a list of the NCI/NTP Technical Reports (Appendix 13). We are continuing to update the Carcinogenic Po?tency Database with papers published after 1982, and are also attempting to add earlier papers which we overlooked in our literature search. Therefore, we would appreciate information about any tests which the reader notices are missing.

This article is an overview of research on the design and application of a Self-organizing Pattern Retrieval System. This system is a nontraditional form of database for several reasons: (1) The objects (patterns) stored in the database are complex data structures such as graphs, or matrices as opposed to relations, sets, lists or frames; (2) the system attempts to optimize its internal structure for retrieval efficiency by taking advantage of classificatory information (common patterns) it discovers about the patterns in the database; and (3) the system is representation-independent, allowing the user to use whatever representation scheme he desires as long as he provides a small set of I/O routines and comparison functions for the representation. This system has been applied to the organization of molecules and reactions in organic chemistry. The system is able to select valid precursors to molecules based on its knowledge of real-world reactions. It is also able to suggest new chemical reactions based on generalizations from its knowledge base. The system has also been applied to the retrieval, generalization, and classification of chess positions. Its ability to recognize the tactical similarity of positions is compared with that of a chess master. The design objectives of a system for classifying Radio Frequency Interference required by NASA's SET1 (Search for ExtraTerrestrial Intelligence) project is also discussed.

Style is an integral part of natural language in written, spoken or machine generated forms. Humans have been dealing with style in language since the beginnings of language itself, but computers and machine processes have only recently begun to process natural language styles. Automatic processing of styles poses two interrelated challenges: classification and transformation. There have been recent advances in corpus classification, automatic clustering and authorship attribution along many dimensions but little work directly related to writing styles directly and even less in transformation. In this paper we examine relevant literature to define and operationalize a notion of "style" which we employ to designate style markers usable in classification machines. A measurable reading of these markers also helps guide style transformation algorithms. We demonstrate the concept by showing a detectable stylistic shift in a sample piece of text relative to a target corpus. We present ongoing work in building a comprehensive style recognition and transformation system and discuss our results.