Investigation of the Attachment of Circulating Endothelial Cells to a Cell Probe: Combined Experimental and Numerical Study

Circulating endothelial cells (CECs) are a reliable biomarker for cardiovascular diseases (CVDs). A major unresolved challenge limiting the widespread use of CECs for the diagnosis and monitoring of CVDs is their unreliable detection. This problem is mainly attributed to the low sample volume (5-10 mL) of commonly used ex vivo CEC isolation methods. To overcome this limitation, the BMProbe for the in vivo isolation of CECs is proposed. It consists of a twisted medical flat wire with a polymer-coated surface functionalized with anti-CD105 antibodies. A combined experimental and numerical study is performed to investigate which flow conditions lead to an increased cell attachment to the probe's surface. Endothelial cells are solved in a dextran solution and circulated in a flow system containing the BMProbes. Microscopic images of the attached CECs are taken. In addition, the experiments are simulated using a computational fluid dynamics (CFD) flow solver to quantify the flow conditions at the probe's surface. The microscopic images are superimposed with the CFD data to investigate the influence of wall shear rate and wall normal rate on the attachment of CECs to the probe. Most of all attached cells (85.5%) are found in areas of negative wall normal rate

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Image Compression for Geological Mapping and Novelty Detection

We describe an image-comparison technique of Heidemann and Ritter [4,5] that uses image compression, and is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously-observed texture. This image-comparison technique has been implemented and tested using our Astrobiology Phone-cam system, which employs Bluetooth communication to send images to a local laptop server in the field for the image-compression analysis. We tested the system in a field site displaying a heterogeneous suite of sandstones, limestones, mudstones and coalbeds. Some of the rocks are partly covered with lichen. The imagematching procedure of this system performed very well with data obtained through our field test, grouping all images of yellow lichens together and grouping all images of a coal bed together, and giving a 91% accuracy for similarity detection. Such similarity detection could be employed to make maps of different geological units. The novelty-detection performance of our system was also rather good (a 64% accuracy). Such novelty detection may become valuable in searching for new geological units, which could be of astrobiological interest. By providing more advanced capabilities for similarity detection and novelty detection, this image-compression technique could be useful in giving more scientific autonomy to robotic planetary rovers, and in assisting human astronauts in their geological exploration

matching of prior textures by image compression for geological mapping and novelty detection

We describe an image-comparison technique of Heidemann and Ritter (2008a, b), which uses image compression, and is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously observed texture. This image-comparison technique has been implemented and tested using our Astrobiology Phone-cam system, which employs Bluetooth communication to send images to a local laptop server in the field for the image-compression analysis. We tested the system in a field site displaying a heterogeneous suite of sandstones, limestones, mudstones and coal beds. Some of the rocks are partly covered with lichen. The image-matching procedure of this system performed very well with data obtained through our field test, grouping all images of yellow lichens together and grouping all images of a coal bed together, and giving 91% accuracy for similarity detection. Such similarity detection could be employed to make maps of different geological units. The novelty-detection performance of our system was also rather good (64% accuracy). Such novelty detection may become valuable in searching for new geological units, which could be of astrobiological interest. The current system is not directly intended for mapping and novelty detection of a second field site based on image- compression analysis of an image database from a first field site, although our current system could be further developed towards this end. Furthermore, the image-comparison technique is an unsupervised technique that is not capable of directly classifying an image as containing a particular geological feature; labelling of such geological features is done post facto by human geologists associated with this study, for the purpose of analysing the system's performance. By providing more advanced capabilities for similarity detection and novelty detection, this image-compression technique could be useful in giving more scientific autonomy to robotic planetary rovers, and in assisting human astronauts in their geological exploration and assessment