Vegetation and bilateral congenital coronary artery fistulas.

This report describes the case of a man who presented with fever, weight loss, and Streptococcus mitis septicemia. He was found to have congenital bilateral coronary artery fistula, a rare condition. There was no evidence of vegetation on the heart valves on transthoracic or transesophageal echocardiography. Instead, transesophageal echocardiography showed vegetation within the coronary sinus near its entrance into the right atrium. Coronary angiography confirmed the presence of a left circumflex artery to coronary sinus fistula and a right coronary artery to coronary sinus fistula. To the authors' knowledge this combination of findings has never been reported before.

Wiring the olfactory bulb--activity-dependent models of axonal targeting in the developing olfactory pathway.

Two recent experimental studies /20,21/ revealed that odorant-evoked activity-dependent competition is significant in the organisation and maintenance of the olfactory system. In this paper, we investigate the generation of a chemotopic sensory map in the olfactory bulb through three models driven by high-density optical chemosensor arrays which have similar properties to olfactory receptor neurons. By exposing the sensor arrays to various odours, these models were subjected to Hebbian learning to achieve self-organisation, potentially explaining the activity-dependent competition demonstrated by these recent studies. Our final model also predicts a role for periglomerular cells in the formation of the chemotopic sensory map in the olfactory bulb.

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays.

In this paper automatic sensor identification of sensor classes within a high-density randomized array, without a priori knowledge of sensor locations, is demonstrated. Two different fluorescence-based sensor types, with hundreds of replicates each, were randomly distributed into an optical imaging fiber array platform. The sensor element types were vapor-sensitive microspheres with the environmentally-sensitive fluorescent dye Nile Red adsorbed on their surface. Nile Red undergoes spectral changes when exposed to different microenvironmental polarity conditions, e.g. microsphere surface polarity or odor exposure. These reproducible sensor spectral changes, or sensor-response profiles, enable sensors within a randomized array to be grouped into categories by optical decoding methods. Two computational decoding methods (supervised and unsupervised) are introduced; equal classification rates were achieved for both. By comparing sensor responses from a randomized array with those obtained from known (control) arrays, 587 sensors were correctly classified with 99.32% accuracy. Although both methods were equally effective, the unsupervised method, which uses sensor response changes to odor exposure, is a better decoding model for the vapor-sensitive arrays studied, because it relies only on the odor-response profiles. Another decoding technique employed the emission spectra of the sensors and is more applicable to other types of multiplexed fluorescence-based arrays and assays. The sensor-decoding techniques are compared to demonstrate that sensors within high-density optical chemosensor arrays can be positionally-registered, or decoded, with no additional overhead in time or expense other than collecting the sensor-response profiles.