'Sciencenet'--towards a global search and share engine for all scientific knowledge.

SUMMARY: Modern biological experiments create vast amounts of data which are geographically distributed. These datasets consist of petabytes of raw data and billions of documents. Yet to the best of our knowledge, a search engine technology that searches and cross-links all different data types in life sciences does not exist. We have developed a prototype distributed scientific search engine technology, 'Sciencenet', which facilitates rapid searching over this large data space. By 'bringing the search engine to the data', we do not require server farms. This platform also allows users to contribute to the search index and publish their large-scale data to support e-Science. Furthermore, a community-driven method guarantees that only scientific content is crawled and presented. Our peer-to-peer approach is sufficiently scalable for the science web without performance or capacity tradeoff. AVAILABILITY AND IMPLEMENTATION: The free to use search portal web page and the downloadable client are accessible at: http://sciencenet.kit.edu. The web portal for index administration is implemented in ASP.NET, the 'AskMe' experiment publisher is written in Python 2.7, and the backend 'YaCy' search engine is based on Java 1.6.

Automated feature detection and imaging for high-resolution screening of zebrafish embryos.

The development of automated microscopy platforms has enabled large-scale observation of biological processes, thereby complementing genome scale biochemical techniques. However, commercially available systems are restricted either by fixed-field-of-views, leading to potential omission of features of interest, or by low-resolution data of whole objects lacking cellular detail. This limits the efficiency of high-content screening assays, especially when large complex objects are used as in whole-organism screening. Here we demonstrate a toolset for automated intelligent high-content screening of whole zebrafish embryos at cellular resolution on a standard wide-field screening microscope. Using custom-developed algorithms, predefined regions of interest-such as the brain-are automatically detected. The regions of interest are subsequently imaged automatically at high magnification, enabling rapid capture of cellular resolution data. We utilize this approach for acquiring 3-D datasets of embryonic brains of transgenic zebrafish. Moreover, we report the development of a mold design for accurate orientation of zebrafish embryos for dorsal imaging, thereby facilitating standardized imaging of internal organs and cellular structures. The toolset is flexible and can be readily applied for the imaging of different specimens in various applications.