BioCC: An Openfree Hypertext Bio Community Cluster for Biology

We present an openfree hypertext (also known as wiki) web cluster called BioCC. BioCC is a novel wiki farm that lets researchers create hundreds of biological web sites. The web sites form an organic information network. The contents of all the sites on the BioCC wiki farm are modifiable by anonymous as well as registered users. This enables biologists with diverse backgrounds to form their own Internet bio-communities. Each community can have custom-made layouts for information, discussion, and knowledge exchange. BioCC aims to form an ever-expanding network of openfree biological knowledge databases used and maintained by biological experts, students, and general users. The philosophy behind BioCC is that the formation of biological knowledge is best achieved by open-minded individuals freely exchanging information. In the near future, the amount of genomic information will have flooded society. BioGG can be an effective and quickly updated knowledge database system. BioCC uses an opensource wiki system called Mediawiki. However, for easier editing, a modified version of Mediawiki, called Biowiki, has been applied. Unlike Mediawiki, Biowiki uses a WYSIWYG (What You See Is What You Get) text editor. BioCC is under a share-alike license called BioLicense (http://biolicense.org). The BioCC top level site is found at http://bio.cc/

Software Development for the Integrated Visualization of Brain Tumor and its Surrounding Fiber Tracts

PURPOSE: The purpose of this study was to implement a software to visualize tumor and its surrounding fiber tracts simultaneously using diffusion tensor imaging and examine the feasibility of our software for investigating the influence of tumor on its surrounding fiber connectivity. MATERIALS AND METHODS: MR examination including T1-weigted and diffusion tensor images of a patient with brain tumor was performed on a 3.0 T MRI unit. We used the skull-striped brain and segmented tumor images for volume/surface rendering and anatomical information from contrast-enhanced T1-weighted images. Diffusion tensor images for the white matter fiber-tractography were acquired using a SE-EPI with a diffusion scheme of 25 directions. Fiber-tractography was performed using the streamline and tensorline methods. To correct a spatial mismatch between T1- weighted and diffusion tensor images, they were coregistered using a SPM. Our software was implemented under window-based PC system. RESULTS: We successfully implemented the integrated visualization of the fiber tracts with tube-like surfaces, cortical surface and the tumor with volume/surface renderings in a patient with brain tumor. CONCLUSION: Our result showed the feasibility of the integrated visualization of brain tumor and its surrounding fiber tracts. In addition, our implementation for integrated visualization can be utilized to navigate the brain for the quantitative analysis of fractional anisotropy to assess changes in the white matter tract integrity of edematic and peri-edematic regions in a number of tumor patients.