Order Invariance on Decomposable Structures

Order-invariant formulas access an ordering on a structure's universe, but the model relation is independent of the used ordering. Order invariance is frequently used for logic-based approaches in computer science. Order-invariant formulas capture unordered problems of complexity classes and they model the independence of the answer to a database query from low-level aspects of databases. We study the expressive power of order-invariant monadic second-order (MSO) and first-order (FO) logic on restricted classes of structures that admit certain forms of tree decompositions (not necessarily of bounded width). While order-invariant MSO is more expressive than MSO and, even, CMSO (MSO with modulo-counting predicates), we show that order-invariant MSO and CMSO are equally expressive on graphs of bounded tree width and on planar graphs. This extends an earlier result for trees due to Courcelle. Moreover, we show that all properties definable in order-invariant FO are also definable in MSO on these classes. These results are applications of a theorem that shows how to lift up definability results for order-invariant logics from the bags of a graph's tree decomposition to the graph itself.Comment: Accepted for LICS 201

The state-of-the-art determination of urinary nucleosides using chromatographic techniques “hyphenated” with advanced bioinformatic methods

Over the last decade metabolomics has gained increasing popularity and significance in life sciences. Together with genomics, transcriptomics and proteomics, metabolomics provides additional information on specific reactions occurring in humans, allowing us to understand some of the metabolic pathways in pathological processes. Abnormal levels of such metabolites as nucleosides in the urine of cancer patients (abnormal in relation to the levels observed in healthy volunteers) seem to be an original potential diagnostic marker of carcinogenesis. However, the expectations regarding the diagnostic value of nucleosides may only be justified once an appropriate analytical procedure has been applied for their determination. The achievement of good specificity, sensitivity and reproducibility of the analysis depends on the right choice of the phases (e.g. sample pretreatment procedure), the analytical technique and the bioinformatic approach. Improving the techniques and methods applied implies greater interest in exploration of reliable diagnostic markers. This review covers the last 11 years of determination of urinary nucleosides conducted with the use of high-performance liquid chromatography in conjunction with various types of detection, sample pretreatment methods as well as bioinformatic data processing procedures

Identification of Metabolites in the Normal Ovary and Their Transformation in Primary and Metastatic Ovarian Cancer

In this study, we characterized the metabolome of the human ovary and identified metabolic alternations that coincide with primary epithelial ovarian cancer (EOC) and metastatic tumors resulting from primary ovarian cancer (MOC) using three analytical platforms: gas chromatography mass spectrometry (GC/MS) and liquid chromatography tandem mass spectrometry (LC/MS/MS) using buffer systems and instrument settings to catalog positive or negative ions. The human ovarian metabolome was found to contain 364 biochemicals and upon transformation of the ovary caused changes in energy utilization, altering metabolites associated with glycolysis and β-oxidation of fatty acids—such as carnitine (1.79 fold in EOC, p<0.001; 1.88 fold in MOC, p<0.001), acetylcarnitine (1.75 fold in EOC, p<0.001; 2.39 fold in MOC, p<0.001), and butyrylcarnitine (3.62 fold, p<0.0094 in EOC; 7.88 fold, p<0.001 in MOC). There were also significant changes in phenylalanine catabolism marked by increases in phenylpyruvate (4.21 fold; p = 0.0098) and phenyllactate (195.45 fold; p<0.0023) in EOC. Ovarian cancer also displayed an enhanced oxidative stress response as indicated by increases in 2-aminobutyrate in EOC (1.46 fold, p = 0.0316) and in MOC (2.25 fold, p<0.001) and several isoforms of tocopherols. We have also identified novel metabolites in the ovary, specifically N-acetylasparate and N-acetyl-aspartyl-glutamate, whose role in ovarian physiology has yet to be determined. These data enhance our understanding of the diverse biochemistry of the human ovary and demonstrate metabolic alterations upon transformation. Furthermore, metabolites with significant changes between groups provide insight into biochemical consequences of transformation and are candidate biomarkers of ovarian oncogenesis. Validation studies are warranted to determine whether these compounds have clinical utility in the diagnosis or clinical management of ovarian cancer patients

Portraying the ridiculous and outré - Karikaturen von weiblicher Hand in Großbritannien (1750 - 1830)

Die Arbeit thematisiert weibliche Beiträge der graphischen Gattung der humorvollen und satirischen Zeichnung im "Golden Age" der Karikatur in Großbritannien. Ausgehend von den Begünstigungen und relativen Freiheiten weiblicher Bildproduktion in der vorviktorianischen Zeit, setzt die Untersuchung die zeichnerische Aktivität von Frauen in den Kontext von weiblicher "curiosity" und weiblichem "wit" in der Wahrnehmung neuer Interessenlagen der Aufklärung

Mass spectrometric examination of modified nucleosides and their evaluation as breast cancer markers

Die Identifizierung von Substanzen als Tumormarker für Krebserkrankungen steht seit langem im Fokus der Forschung. Viele der etablierten Tumormarker in der klinischen Praxis sind jedoch nicht spezifisch und/oder sensitiv genug und korrelieren nur bei einem Bruchteil der untersuchten Fälle mit der Erkrankung. Andere Marker sind zur Überwachung einer Therapie geeignet, nicht aber für die Diagnose. Insbesondere bei Brustkrebs ist die Diagnose in frühen Stadien schwierig. Modifizierte Nucleoside sind Endprodukte des RNA-Metabolismus und werden aufgrund des gestörten Stoffwechsels bei Tumorerkrankungen in erhöhten Konzentrationen im Urin ausgeschieden, wie in vielen Studien und bei verschiedenen Krebserkrankungen beobachtet wurde. Modifizierte Nucleoside könnten daher eine Alternative zu den etablierten Tumormarkern bieten. Im Rahmen dieser Arbeit wurden modifizierte und unmodifizierte Nucleoside aus Proben biogenen Ursprungs mit Hilfe von verschiedenen massenspektrometrischen Techniken charakterisiert und identifiziert. Für die Trennung der Nucleoside wurde eine HPLC-Methode entwickelt, um die erhaltenen Fraktionen mittels MALDI-TOF-MS und nano-ESI-MS auf bisher unbekannte Nucleoside und sonstige Ribosylderivate zu untersuchen. Die hohe Massengenauigkeit der entwickelten MALDI-TOF-Methode ermöglicht die Berechnung einer Summenformel, wobei durch Datenbanksuche acht Ribosylderivate identifiziert werden konnten. Anschließend wurde eine auto-LC-MS3-Methode mit Ionenfallen-Detektion entwickelt, die auf Informationen zur Struktur von unbekannten Komponenten abzielt. In Urinproben von Brustkrebspatientinnen wurden 38 Ribosylderivate nachgewiesen, von denen 16 in vorhergehenden Arbeiten identifiziert wurden. Sechs weitere wurden im Rahmen dieser Arbeit identifiziert, teilweise durch Vergleich der Retentionszeit und Fragmentierung mit Standardsubstanzen, teilweise durch Vergleich der Fragmentierung mit der des Nucleinbasen-Standards. Für 11 weitere Substanzen konnten aufgrund der Fragmentierung und der Summenformel Strukturvorschläge gemacht werden. Mit der entwickelten auto-LC-MS3-Methode wurden neben den Urinproben Zellkulturüberstände und Mikrosomeninkubationen untersucht. Die Zellkulturüberstände von MCF7-Brustkrebs-Zelllinien wurden mit denen von gesunden MCF10A Brustepithelzellen verglichen. Schließlich wurden die neu identifizierten neben den bekannten Nucleosiden und sonstigen unbekannten Ribosylderivaten auf ihr Potenzial als Tumormarker untersucht. Dazu wurde eine Klassifizierungsstudie unter Einsatz von Methoden der Bioinformatik zur Unterscheidung von Brustkrebspatientinnen und gesunden Probandinnen auf Basis der Konzentrationsverhältnisse der Ribosylderivate im Urin durchgeführt.The identification of compounds that may be used as tumour markers has been in the focus of research for several years. Most of the established tumour markers show either a low sensitivity or specificity and are elevated significantly in only a few of the investigated cases. Other markers are suitable for surveying therapy, but not for cancer diagnosis. Especially in case of breast cancer, the diagnosis in an early state is difficult. Modified nucleosides are end products of RNA metabolism and are excreted in cancer patients' urine in higher concentrations. This was observed in several studies and different types of cancer. Modified nucleosides could therefore be an alternative as tumour markers. In this work, modified and normal nucleosides from biological fluids were characterized and identified using different mass spectrometric methods. These methods were applied for analysing urine samples and cell culture supernatants. For semi-preparative separation of the nucleosides, an HPLC method was developed. The fractions were examined using MALDI-TOF-MS and nano-ESI-MS. A MALDI-TOF-MS-method was developed for measuring with high mass accuracy. Based on the accurate mass, a sum formula may be calculated and a data base search performed. With this method, eight unknown compounds in urine could be identified. Furthermore, an auto-LC-MS3-method with an Ion Trap mass spectrometer was developed for structural information on unknown compounds. With this method, 38 ribosyl derivatives could be detected in urine samples of breast cancer patients. 16 of these nucleosides had been identified before; six were identified by comparison of retention time and fragmentation with standard compounds. For further 11 compounds, structure proposals were made based on the fragmentation and sum formula. The auto-LC-MS3-method was also applied on cell culture supernatants and microsomal incubations. Supernatants of MCF7 breast cancer cells were compared to those of MCF10A epithelial cells. Finally, all identified nucleosides and further potential ribosyl derivatives were examined for their potential as tumour markers. For classification of urine samples of breast cancer patients and healthy volunteers based on the relative concentrations of 32 compounds, a support vector machine algorithm was applied