Es wurde ein zweistufiger, indirekter enzymatischer Assay (Fibrinolytische-Kapazität, FC) in zwei Varianten (basal, aktiviert) vorgestellt, der summarisch Störungen der Fibrinolyse erfasst, da in ihn die Gesamtaktivität der Aktivatoren und Inhibitoren des Plasmas einfließt. In der ersten Stufe wird Plasma Urokinase zugeführt, welche mit Plasminogenaktivatorinhibitoren interagiert. Die noch freie Urokinase aktiviert Plasminogen zu Plasmin. Die plasmatischen Antiplasmine, hauptsächlich alpha 2-Antiplasmin, werden oxidativ mit Taurin-Chloramin inaktiviert. Schließlich wird die resultierende Plasminmenge mit einem chromogenen Substrat quantifiziert. In einer zweiten Variante wird die kontaktphasenabhängige Fibrinolyse vorher sehr potent mit Dextransulfat stimuliert. Zur Validierung wurde der Einfluss von PAI-1, Fibrinogen und Plasminogen untersucht. Störgrößen wie Antioxidantien, parenterale Antikoagulantien, Phenprocoumon, Aprotinin, Tranexamsäure, Thrombozyten und Bilirubin wurden ebenfalls untersucht. Zusätzlich wurde der Test anhand eines Normal-, Thrombose- und Schwangerenkollektives sowie zweier kleiner Kollektive (Schwangere und Patienten unter oraler Antikoagulation) im Zeitverlauf untersucht. Beide FC-Varianten bilden dabei die prothrombotischen Faktoren unterschiedlich ab. In der Regressionsanalyse reagiert die basale FC eher auf Veränderungen der PAI-1- und Plasminogenkonzentrationen, die aktivierte FC eher auf Plasminogen und Thrombose. Thrombose wird durch die aktivierte FC besser als durch die basale FC diagnostiziert (beta-Koeffizienten für Thrombose -0,12 vs. -0,26, Zusammenhangsmaß Eta² von FC und Thrombose 5,6% vs. 9,9%, Entscheidungsgrenze (Cut-Off) für Thrombose 33,0% vs. 66,2% für basale bzw. aktivierte FC). Beide FC-Varianten besitzen ähnliche Sensitivität, Spezifität, prädiktive Werte und relative Risikos für Thrombose bei FC-Werten unterhalb der Entscheidungsgrenze. Die Thromboseerkennbarkeit ist für beide Varianten gleichwertig bei einer Übereinstimmung untereinander von 61,3% (Cohen-Kappa-Koeffizient). Bei der Abklärung einer akuten Thrombose ist dieser Fibrinolyse-Globaltest in der Lage, Ursachen innerhalb des fibrinolytischen Systems zu erkennen. / A two-step indirect enzymatic assay (fibrinolytic capacity, FC) was presented in two variations (basal, activated) detecting the total fibrinolytic disturbances by its ability to assess the entire plasmatic activity of activators and inhibitors. In the first step urokinase is added to plasma, which interacts with plasminogen-activator-inhibitors. The remaining urokinase activated plasminogen to plasmin. The plasmatic antiplasmines, mainly alpha 2-antiplasmine were oxidative inhibited with taurine-chloramine. Finally the resulting amount of plasmin was quantified using a chromogenic substrate. In a second variation the contact-phase fibrinolysis was highly stimulated with dextran-sulfate. The influence of PAI-1, fibrinogen and plasminogen were analysed including disturbing substances such as antioxidants, parenteral anticoagulants, phenprocoumon, aprotinine, tranexamic acid, platelets and bilirubine. In addition, validation was performed including healthy individuals, patients with thrombosis and pregnant women and two small cohorts (pregnant women and patients under oral anticoagulation) over time. The prothrombotic factors were differently represented by the two FC-variations. In the regression analysis the basal FC reacted predominantly to alterations in the concentration of PAI-1 and plasminogen. In contrast the activated FC was more likely affected by plasminogen and thrombosis. The activated FC was more sensitive in the detection of thrombosis than the basal FC (with a beta-coefficient for thrombosis -0,12 vs. -0,26, a coefficient of strength of association eta² from FC with thrombosis 5,6% vs. 9,9% and a cut-off for thrombosis 33,0% vs. 66,2% for basal and activated FC respectively). Below these cut-offs both FC-variations had equal sensitivity, specificity, predictive values and relative risks in the detection of thrombosis by FC-values. The ability to detect thrombosis were equally with a correspondence of 61,3% (Cohen-Kappa-coefficient). This fibrinolytic global-test is able to identify the underlying cause within the fibrinolytic system for the a clarification of acute thrombosis.

<div>This dissertation consists of two parts. The first part is a collaborative project with Dr. Szymanski's group in Agronomy at Purdue, to predict protein complex assemblies and interactions. Proteins in the leaf cytosol of Arabidopsis were fractionated using Size Exclusion Chromatography (SEC) and mixed-bed Ion Exchange Chromatography (IEX).</div><div>Protein mass spectrometry data were obtained for the two platforms of separation and two replicates of each. We combine the four data sets and conduct a series of statistical learning, including 1) data filtering, 2) a two-round hierarchical clustering to integrate multiple data types, 3) validation of clustering based on known protein complexes,</div><div>4) mining dendrogram trees for prediction of protein complexes. Our method is developed for integrative analysis of different data types and it eliminates the difficulty of choosing an appropriate cluster number in clustering analysis. It provides a statistical learning tool to globally analyze the oligomerization state of a system of protein complexes.</div><div><br></div><div><br></div><div>The second part examines global hypothesis testing under sparse alternatives and arbitrarily strong dependence. Global tests are used to aggregate information and reduce the burden of multiple testing. A common situation in modern data analysis is that variables with nonzero effects are sparse. The minimum p-value and higher criticism tests are particularly effective and more powerful than the F test under sparse alternatives. This is the common setting in genome-wide association study (GWAS) data. However, arbitrarily strong dependence among variables poses a great challenge towards the p-value calculation of these optimal tests. We develop a latent variable adjusted method to correct minimum p-value test. After adjustment, test statistics become weakly dependent and the corresponding null distributions are valid. We show that if the latent variable is not related to the response variable, power can be improved. Simulation studies show that our method is more powerful than other methods in highly sparse signal and correlated marginal tests setting. We also show its application in a real dataset.</div>