Cell division in plant cells requires the deposition of a new cell wall between the two daughter cells. The assembly of this plate requires the coordinated movement of cargo vesicles whose size is below the diffraction-limited resolution of the optical microscope. We combined high spatial and temporal resolution confocal laser scanning microscopy with advanced imageprocessing tools and fluorescence fluctuation methods and distinguished three distinct phases during cell plate expansion in tobacco (Nicotiana tabacum) 'Bright Yellow-2' cells: massive delivery of preexisting vesicles to a disk-shaped region at the equatorial plane precedes a primary rapid expansion phase followed by a secondary, slow expansion phase during which the extremity of the circular plate seeks contact with the mother wall and brings about the separation of the two portions of cytoplasm. Different effects of pharmacological inhibition emphasize the distinct nature of the assembly and expansion mechanisms characterizing these phases.
BackgroundIn many laboratories, researchers store experimental data on their own workstation using spreadsheets. However, this approach poses a number of problems, ranging from sharing issues to inefficient data-mining. Standard spreadsheets are also error-prone, as data do not undergo any validation process. To overcome spreadsheets inherent limitations, a number of proprietary systems have been developed, which laboratories need to pay expensive license fees for. Those costs are usually prohibitive for most laboratories and prevent scientists from benefiting from more sophisticated data management systems.ResultsIn this paper, we propose the EnzymeTracker, a web-based laboratory information management system for sample tracking, as an open-source and flexible alternative that aims at facilitating entry, mining and sharing of experimental biological data. The EnzymeTracker features online spreadsheets and tools for monitoring numerous experiments conducted by several collaborators to identify and characterize samples. It also provides libraries of shared data such as protocols, and administration tools for data access control using OpenID and user/team management. Our system relies on a database management system for efficient data indexing and management and a user-friendly AJAX interface that can be accessed over the Internet. The EnzymeTracker facilitates data entry by dynamically suggesting entries and providing smart data-mining tools to effectively retrieve data. Our system features a number of tools to visualize and annotate experimental data, and export highly customizable reports. It also supports QR matrix barcoding to facilitate sample tracking.ConclusionsThe EnzymeTracker was designed to be easy to use and offers many benefits over spreadsheets, thus presenting the characteristics required to facilitate acceptance by the scientific community. It has been successfully used for 20 months on a daily basis by over 50 scientists. The EnzymeTracker is freely available online at http://cubique.fungalgenomics.ca/enzymedb/index.html under the GNU GPLv3 license.
To facilitate the integration and querying of genomics data, a number of generic data warehousing frameworks have been developed. They differ in their design and capabilities, as well as their intended audience. We provide a comprehensive and quantitative review of those genomic data warehousing frameworks in the context of large-scale systems biology. We reviewed in detail four genomic data warehouses (BioMart, BioXRT, InterMine and PathwayTools) freely available to the academic community. We quantified 20 aspects of the warehouses, covering the accuracy of their responses, their computational requirements and development efforts. Performance of the warehouses was evaluated under various hardware configurations to help laboratories optimize hardware expenses. Each aspect of the benchmark may be dynamically weighted by scientists using our online tool BenchDW (http://warehousebenchmark.fungalgenomics.ca/benchmark/) to build custom warehouse profiles and tailor our results to their specific needs.
a b s t r a c tData classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality and on influenza using the Google Flu Trends database.
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