Τεχνικές ολοκλήρωσης σημασιολογικών γεωχωρικών δεδομένων

95 σ.Στόχος της διπλωματικής ήταν πρώτον ο μετασχηματισμός δεδομένων γεωχωρικού χαρακτήρα σε RDF και δεύτερον η ανάπτυξη τεχνικών διασύνδεσης σημασιολογικών γεωχωρικών δεδομένων. Στο πλαίσιο του πρώτου στόχου αναπτύξαμε το Geosm, ένα εργαλείο για το μετασχηματισμό δεδομένων χαρτογράφησης από το OpenStreetMap σε RDF γράφους σε συμφωνία με το OGC GeoSPARQL πρότυπο. Για την ανάπτυξη του εργαλείου βασιστήκαμε στο υπάρχον εργαλείο Osmosis και στη βιβλιοθήκη Apache Jena, ενώ για τη διατήρηση της συμβατότητας με το LinkedGeoData project κάναμε χρήση των ίδιων RDF λεξιλογίων. Στο πλαίσιο του δεύτερου στόχου αναλύσαμε το θέμα της διασύνδεσης γεωχωρικών σημασιολογικών δεδομένων, εξετάζοντας τις δυνατότητες υπαρχόντων μετρικών ομοιότητας συμβολοσειρών, σχεδιάζοντας νέες μετρικές ομοιότητας γεωμετριών προσαρμοσμένες στις απαιτήσεις των δεδομένων μας και τέλος εκτελώντας ένα ευρύ φάσμα πειραμάτων σε δεδομένα που συλλέξαμε για την ανάλυση της επίδοσης των μετρικών και την εξαγωγή συμπερασμάτων.The objective of this thesis was firstly the transformation of geospatial data to RDF and secondly the development of techniques for the interlinking of semantic geospatial data. In the context of the first goal we developed Geosm, a tool for the transformation of mapping data from OpenStreetMap to RDF graphs in compliance with the OGC GeoSPARQL standard. For the development of the tool we made use of the existing tool Osmosis and of the library Apache Jena, while to ensure compatibility with LinkedGeoData project we made use of the same RDF vocabularies. In the context of the second goal we explored the issue of interlining semantic geospatial data by examining the capabilities of existing string similarity metrics, by designing new metrics for the similarity of geometries in concert to the requirements imposed by our data and lastly by executing a wide range of experiments on data we collected for this purpose in order to analyse the quality of the metrics and draw conclusions concerning their optimal use.Θωμάς Δ. Μαρούλη

Microarray image segmentation using spot morphological model

Abstract-The up-to-date segmentation techniques and software programs for microarray image segmentation require human intervention which in turn may detrimentally affect the biological conclusions reached during microarray experiments. In this paper, an automatic approach for segmenting microarray images, based on the morphological modeling of spots, is presented. The conducted experiments have shown that the proposed approach is very effective even when it is applied to noisy images as well as to images containing spots of various shapes and intensities. Index Terms- Microarrays, Image Analysis, Segmentation I

Unsupervised level set parameterization using multi-scale filtering

This paper presents a novel framework for unsupervised level set parameterization using multi-scale filtering. A standard multi-scale, directional filtering algorithm is used in order to capture the orientation coherence in edge regions. The latter is encoded in entropy-based image `heatmaps', which are able to weight forces guiding level set evolution. Experiments are conducted on two large benchmark databases as well as on real proteomics images. The experimental results demonstrate that the proposed framework is capable of accelerating contour convergence, whereas it obtains a segmentation quality comparable to the one obtained with empirically optimized parameterization

Self-adjusted active contours using multi-directional texture cues

Parameterization is an open issue in active contour research, associated with the cumbersome and time-consuming process of empirical adjustment. This work introduces a novel framework for self-adjustment of region-based active contours, based on multi-directional texture cues. The latter are mined by applying filtering transforms characterized by multi-resolution, anisotropy, localization and directionality. This process yields to entropy-based image “heatmaps”, used to weight the regularization and data fidelity terms, which guide contour evolution. Experimental evaluation is performed on a large benchmark dataset as well as on textured images. Τhe segmentation results demonstrate that the proposed framework is capable of accelerating contour convergence, maintaining a segmentation quality which is comparable to the one obtained by empirically adjusted active contours

Control of human cytomegalovirus replication by liver resident natural killer cells

Natural killer cells are considered to be important for control of human cytomegalovirus– a major pathogen in immune suppressed transplant patients. Viral infection promotes the development of an adaptive phenotype in circulating natural killer cells that changes their anti-viral function. In contrast, less is understood how natural killer cells that reside in tissue respond to viral infection. Here we show natural killer cells resident in the liver have an altered phenotype in cytomegalovirus infected individuals and display increased anti-viral activity against multiple viruses in vitro and identify and characterise a subset of natural killer cells responsible for control. Crucially, livers containing natural killer cells with better capacity to control cytomegalovirus replication in vitro are less likely to experience viraemia post-transplant. Taken together, these data suggest that virally induced expansion of tissue resident natural killer cells in the donor organ can reduce the chance of viraemia post-transplant

The improved ARTEMIS IV multichannel solar radio spectrograph of the University of Athens

We present the improved solar radio spectrograph of the University of Athens operating at the Thermopylae Satellite Telecommunication Station. Observations now cover the frequency range from 20 to 650 MHz. The spectrograph has a 7-meter moving parabola fed by a log-periodic antenna for 100 650 MHz and a stationary inverted V fat dipole antenna for the 20 100 MHz range. Two receivers are operating in parallel, one swept frequency for the whole range (10 spectrums/sec, 630 channels/spectrum) and one acousto-optical receiver for the range 270 to 450 MHz (100 spectrums/sec, 128 channels/spectrum). The data acquisition system consists of two PCs (equipped with 12 bit, 225 ksamples/sec ADC, one for each receiver). Sensitivity is about 3 SFU and 30 SFU in the 20 100 MHz and 100 650 MHz range respectively. The daily operation is fully automated: receiving universal time from a GPS, pointing the antenna to the sun, system calibration, starting and stopping the observations at preset times, data acquisition, and archiving on DVD. We can also control the whole system through modem or Internet. The instrument can be used either by itself or in conjunction with other instruments to study the onset and evolution of solar radio bursts and associated interplanetary phenomena.Comment: Experimental Astronomy, Volume 21, Issue 1, pp.41-5

Knot-isomers of Moebius Cyclacene: How Does the Number of Knots Influence the Structure and First Hyperpolarizability?

Four large ring molecules composed by 15 nitrogen-substituted benzene rings, named as "knot-isomers of Moebius cyclacene", i.e. non-Moebius cyclacenes without a knot (0), Moebius cyclacenes with a knot (1), non-Moebius cyclacenes with two knots (2), and Moebius cyclacenes with three knots (3), are systematically studied for their structures and nonlinear optical properties. The first hyperpolarizability (beta_0) values of these four knot-isomers structures are 4693 (0) < 10484 (2) < 25419 (3) < 60846 au (1). The beta_0 values (60846 for 1, 10484 for 2 and 25419 au for 3) of the knot-isomers with knot(s) are larger than that (4693 au for 0) of the knot-isomer without a knot. It shows that the beta_0 value can be dramatically increases (13 times) by introducing the knot(s) to the cyclacenes structures. It is found that introducing knots to cyclacenes is a new means to enhance the first hyperpolarizability.Comment: 12 pages, 4 figure

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p