Light from down under

Coral-algae symbiosis is a key feature of tropical corals and is highly dependent on the efficiency with which solar energy is attenuated by the coral. Scleractinian corals are among the most efficient light collectors in nature because of the modulation of the internal light field in the coral skeleton. Interestingly, coral skeleton particles composing the sandy bottoms in reef margins sustain these optical characteristics. In the present study, we examined two free-living coral species - Heterocyathus aequicostatus (Caryophyllidae) and Heteropsammia cochlea (Dendrophylliidae) - common on biogenic coarse carbonate sand of the Great Barrier Reef but absent from fine sand at the same depth. In coarse carbonate sand, light penetrates a few millimeters below the surface and propagates along horizontal distances of a few centimeters. In fine sand, almost all of the light is reflected back to the water column. For photosynthetic sand-dwelling organisms such as the studied species, with over one-third of their surface area facing the substrate, light flux to their underside may be beneficial. A correlation was found between the diameter of these corals and the distance that light may travel in the sand under the coral. Laboratory and field measurements show that the symbiotic algae on the underside of the corallites are photosynthetically active even when the coral is partially buried, implying sufficient light penetration. Other organisms in the study site, such as fungid corals and foraminiferans, with different morphologies, have different light-trapping strategies but are also photosynthesizing on their underside. The importance of the substrate type to the performance of the three main partners of the symbiosis (coral, endosymbiotic algae and a sipunculan worm) is highlighted, and is a striking example of co-evolution

GliomaPredict: a clinically useful tool for assigning glioma patients to specific molecular subtypes

<p>Abstract</p> <p>Background</p> <p>Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine.</p> <p>Results</p> <p>We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework.</p> <p>Conclusions</p> <p>GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decision-making. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas.</p

Coverage Directed Test Generation for Functional Verification Using Bayesian Networks

Functional verification is widely acknowledged as the bottleneck in the hardware design cycle. This paper addresses one of the main challenges of simulation based verification (or dynamic verification), by providing a new approach for Coverage Directed Test Generation (CDG). This approach is based on Bayesian networks and computer learning techniques. It provides an efficient way for closing a feedback loop from the coverage domain back to a generator that produces new stimuli to the tested design. In this paper, we show how to apply Bayesian networks to the CDG problem. Applying Bayesian networks to the CDG framework has been tested in several experiments, exhibiting encouraging results and indicating that the suggested approach can be used to achieve CDG goals