Technology-Supported Storytelling (TSST) Strategy in Virtual World for Multicultural Education

Learning culture through stories is an effective way for multicultural education, since stories are one of the most powerful and personal ways that we learn about the world. Storytelling, the process of telling stories, is a form of communication and a universal expression of culture. With the development of technology, storytelling emerges out of diverse ways. This study explores the storytelling in virtual worlds for multicultural education, and devises a Technology-Supported storytelling (TSST) strategy by examining and considering the characteristics of virtual worlds which could be incorporated into the storytelling, and then uses this strategy to teach Korean culture to students with different culture background. With this innovative TSST strategy in virtual world, this study expects to provide a guide to practice for teaching multicultural in digital era

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York

Public Census Data on CD-ROM at Lawrence Berkeley Laboratory

The Comprehensive Epidemiologic Data Resource (CEDR) and Populations at Risk to Environmental Pollution (PAREP) projects, of the Information and Computing Sciences Division (ICSD) at Lawrence Berkeley Laboratory (LBL), are using public socioeconomic and geographic data files which are available to CEDR and PAREP collaborators via LBL's computing network. At this time 72 CD-ROM diskettes (approximately 37 gigabytes) are on line via the Unix file server cedrcd.lbl.gov''. Most of the files are from the US Bureau of the Census, and many of these pertain to the 1990 Census of Population and Housing. All the CD-ROM diskettes contain documentation in the form of ASCII text files. In addition, printed documentation for most files is available for inspection at University of California Data and Technical Assistance (UC DATA), tel. (510) 642-6571, or the UC Documents Library, tel. (510) 642-2569, both located on the UC Berkeley Campus. Many of the CD-ROM diskettes distributed by the Census Bureau contain software for PC compatible computers, for easily accessing the data. Shared access to the data is maintained through a collaboration among the CEDR and PAREP projects at LBL, and UC DATA, and the UC Documents Library. LBL is grateful to UC DATA and the UC Documents Library for the use of their CD-ROM diskettes. Shared access to LBL facilities may be restricted in the future if costs become prohibitive. Via the Sun Network File System (NFS), these data can be exported to Internet computers for direct access by the user's application program(s). Due to the size of the files, this access method is preferred over File Transfer Protocol (FTP) access. Please contact Deane Merrill (dwmerrill lbl.gov) if you wish to make use of the data

Density Equalizing Map Projections (Cartograms) in Public Health Applications

In studying geographic disease distributions, one normally compares rates among arbitrarily defined geographic subareas (e.g. census tracts), thereby sacrificing some of the geographic detail of the original data. The sparser the data, the larger the subareas must be in order to calculate stable rates. This dilemma is avoided with the technique of Density Equalizing Map Projections (DEMP){copyright}. Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease risk is constant. On the transformed map, the statistical analysis of the observed distribution is greatly simplified. Even for sparse distributions, the statistical significance of a supposed disease cluster can be calculated with validity. The DEMP algorithm was applied to a data set previously analyzed with conventional techniques; namely, 401 childhood cancer cases in four counties of California. The distribution of cases on the transformed map was analyzed visually and statistically. To check the validity of the method, the identical analysis was performed on 401 artificial cases randomly generated under the assumption of uniform risk. No statistically significant evidence for geographic non-uniformity of rates was found, in agreement with the original analysis performed by the California Department of Health Services