Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource.

The Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA, http://camera.calit2.net/) is a database and associated computational infrastructure that provides a single system for depositing, locating, analyzing, visualizing and sharing data about microbial biology through an advanced web-based analysis portal. CAMERA collects and links metadata relevant to environmental metagenome data sets with annotation in a semantically-aware environment allowing users to write expressive semantic queries against the database. To meet the needs of the research community, users are able to query metadata categories such as habitat, sample type, time, location and other environmental physicochemical parameters. CAMERA is compliant with the standards promulgated by the Genomic Standards Consortium (GSC), and sustains a role within the GSC in extending standards for content and format of the metagenomic data and metadata and its submission to the CAMERA repository. To ensure wide, ready access to data and annotation, CAMERA also provides data submission tools to allow researchers to share and forward data to other metagenomics sites and community data archives such as GenBank. It has multiple interfaces for easy submission of large or complex data sets, and supports pre-registration of samples for sequencing. CAMERA integrates a growing list of tools and viewers for querying, analyzing, annotating and comparing metagenome and genome data.

High-resolution large-scale mosaic imaging using multiphoton microscopy to characterize transgenic mouse models of human neurological disorders.

The thorough characterization of transgenic mouse models of human central nervous system diseases is a necessary step in realizing the full benefit of using animal models to investigate disease processes and potential therapeutics. Because of the labor- and resource-intensive nature of high-resolution imaging, detailed investigation of possible structural or biochemical alterations in brain sections has typically focused on specific regions of interest as determined by the researcher a priori. For example, Parkinson's disease researchers often focus imaging on regions of the brain expected to exhibit pathology such as the substantia nigra and striatum. Because of limitations in acquiring and storing high-resolution imaging data, additional data contained in the specimen is not usually acquired or disseminated/reported to the research community. Here we present a method of imaging large regions of brain at close to the resolution limit of light microscopy using a mosaic imaging technique in conjunction with multiphoton microscopy. These maps are being used to characterize several genetically modified animal models of neurological disease by filling the information "gap" among techniques such as magnetic resonance imaging and electron microscopic analysis.

Active pixel sensor array as a detector for electron microscopy.

A new high-resolution recording device for transmission electron microscopy (TEM) is urgently needed. Neither film nor CCD cameras are systems that allow for efficient 3-D high-resolution particle reconstruction. We tested an active pixel sensor (APS) array as a replacement device at 200, 300, and 400 keV using a JEOL JEM-2000 FX II and a JEM-4000 EX electron microscope. For this experiment, we used an APS prototype with an area of 64 x 64 pixels of 20 microm x 20 microm pixel pitch. Single-electron events were measured by using very low beam intensity. The histogram of the incident electron energy deposited in the sensor shows a Landau distribution at low energies, as well as unexpected events at higher absorbed energies. After careful study, we concluded that backscattering in the silicon substrate and re-entering the sensitive epitaxial layer a second time with much lower speed caused the unexpected events. Exhaustive simulation experiments confirmed the existence of these back-scattered electrons. For the APS to be usable, the back-scattered electron events must be eliminated, perhaps by thinning the substrate to less than 30 microm. By using experimental data taken with an APS chip with a standard silicon substrate (300 microm) and adjusting the results to take into account the effect of a thinned silicon substrate (30 microm), we found an estimate of the signal-to-noise ratio for a back-thinned detector in the energy range of 200-400 keV was about 10:1 and an estimate for the spatial resolution was about 10 microm.