BACKGROUND: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. RESULTS: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely \'has target\', and \'may treat\', are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach. CONCLUSIONS: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.

Spinal cord injury (SCI) triggers several lipid alterations in nervous tissue. It is characterized by extensive demyelination and the inflammatory response leads to accumulation of activated microglia/macrophages, which often transform into foam cells by accumulation of lipid droplets after engulfment of the damaged myelin sheaths. Using an experimental rat model, Raman microspectroscopy was applied to retrieve the modifications of the lipid distribution following SCI. Coherent anti-Stokes Raman scattering (CARS) and endogenous two-photon fluorescence (TPEF) microscopies were used for the detection of lipid-laden inflammatory cells. The Raman mapping of CH2 deformation mode intensity at 1440 cm−1 retrieved the lipid-depleted injury core. Preserved white matter and inflammatory regions with myelin fragmentation and foam cells were localized by specifically addressing the distribution of esterified lipids, i.e., by mapping the intensity of the carbonyl Raman band at 1743 cm−1, and were in agreement with CARS/TPEF microscopy. Principal component analysis revealed that the inflammatory regions are notably rich in saturated fatty acids. Therefore, Raman spectroscopy enabled to specifically detect inflammation after SCI and myelin degradation products.

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Description Logics (DLs) belong to a successful family of knowledge representation formalisms with two key assets: formally well-defined semantics which allows to represent knowledge in an unambiguous way and automated reasoning which allows to infer implicit knowledge from the one given explicitly. This thesis investigates various reasoning techniques for tractable DLs in the EL family which have been implemented in the CEL system. It suggests that the use of the lightweight DLs, in which reasoning is tractable, is beneficial for ontology design and maintenance both in terms of expressivity and scalability. The claim is supported by a case study on the renown medical ontology SNOMED CT and extensive empirical evaluation on several large-scale biomedical ontologies.

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As dental caries is one of the most common diseases, the early and noninvasive detection of carious lesions plays an important role in public health care. Optical coherence tomography (OCT) with its ability of depth-resolved, high-resolution, noninvasive, fast imaging has been previously recognized as a promising tool in dentistry. Additionally, polarization sensitive imaging provides quantitative measures on the birefringent tissue properties and can be utilized for imaging dental tissue, especially enamel and dentin. By imaging three exemplary tooth samples ex vivo with proximal white spot, brown spot, and cavity, we show that the combination of polarization sensitive OCT and the degree of polarization uniformity (DOPU) algorithm is a promising approach for the detection of proximal carious lesions due to the depolarization contrast of demineralized tissue. Furthermore, we investigate different sizes of the DOPU evaluation kernel on the resulting contrast and conclude a suitable value for this application. We propose that DOPU provides an easy to interpret image representation and appropriate contrast for possible future screening applications in early caries diagnostics.

Description Logics (DLs) belong to a successful family of knowledge representation formalisms with two key assets: formally well-defined semantics which allows to represent knowledge in an unambiguous way and automated reasoning which allows to infer implicit knowledge from the one given explicitly. This thesis investigates various reasoning techniques for tractable DLs in the EL family which have been implemented in the CEL system. It suggests that the use of the lightweight DLs, in which reasoning is tractable, is beneficial for ontology design and maintenance both in terms of expressivity and scalability. The claim is supported by a case study on the renown medical ontology SNOMED CT and extensive empirical evaluation on several large-scale biomedical ontologies.

No description available.

An in vitro experiment on the dissolved oxygen transport during liquid ventilation by means of measuring global oxygen concentration fields is presented within this work. We consider the flow in an idealized four generation model of the human airways in a range of peak Reynolds numbers of Re=500–3400 and Womersley numbers of α=3–5. Fluorescence quenching measurements were employed in order to visualize and quantify the oxygen distribution with high temporal and spatial resolution during the breathing cycle. Measurements with varying tidal volumes and oscillating frequencies reveal short living times of characteristic concentration patterns for all parameter variations. Similarities to typical velocity patterns in similar lung models persist only in early phases during each cycle. Concentration gradients are quickly homogenized by secondary motions within the lung model. A strong dependency of peak oxygen concentration on tidal volume is observed with considerably higher relative concentrations for higher tidal volumes.