We propose a simple and general method to obtain the boundary crossing probability for Brownian motion. This method can be easily extended to higher dimensional of Brownian motion. It also covers certain classes of stochastic processes associated with Brownian motion. The basic idea of the method is based on being able to construct a nite Markov chain such that the boundary crossing probability of Brownian motion is obtained as the limiting probability of the nite Markov chain entering a set of absorbing states induced by the boundary. Numerical results are given to illustrate our method.

We propose a simple and general method to obtain the boundary crossing probability for Brownian motion. This method can be easily extended to higher dimensional of Brownian motion. It also covers certain classes of stochastic processes associated with Brownian motion. The basic idea of the method is based on being able to construct a nite Markov chain such that the boundary crossing probability of Brownian motion is obtained as the limiting probability of the nite Markov chain entering a set of absorbing states induced by the boundary. Numerical results are given to illustrate our method.

Single-particle tracking (SPT) is a method used to study the diffusion of various molecules within the cell. SPT involves tagging proteins with optical labels and observing their individual two-dimensional trajectories with a microscope. The analysis of this data provides important information about protein movement and mechanism, and is used to create multistate biological models. One of the challenges in SPT analysis is the variety of complex environments that contribute to heterogeneity within movement paths. In this thesis, we explore the limitations of current methods used to analyze molecular movement, and adapt analytical methods used in animal movement analysis, such as correlated random walks and first-passage time variance, to SPT data of leukocyte function-associated antigen-1 (LFA-1) integral membrane proteins. We discuss the consequences of these methods in understanding different types of heterogeneity in protein movement behaviour, and provide support to results from current experimental work. / Applied Mathematics

This dissertation presents a theoretical study of arbitrary discretizations of general nonequilibrium and non-steady-state systems. It will be shown that, without requiring the partitions of the phase-space to fulfill certain assumptions, such as culminating in Markovian partitions, a Markov chain can be constructed which has the same macro-change of probability of the occupation of the states as the original process. This is true for any classical and semiclassical system under any discrete or continuous, deterministic or stochastic, Markovian or non-Markovian dynamics. Restricted to classical and semi-classical systems, a formalism is developed which treats the projection of arbitrary (multidimensional) complex systems onto a discrete set of states of an abstract state-space using time and ensemble sampled transitions between the states of the trajectories of the original process. This formalism is then used to develop expressions for the mean first passage time and (in the case of projections resulting in pseudo-one-dimensional motion) for the individual residence times of the states using just the time and ensemble sampled transition rates. The theoretical work is illustrated by several numerical examples of non-linear diffusion processes. Those include the escape over a Kramers potential and a rough energy barrier, the escape from an entropic barrier, the folding process of a toy model of a linear polymer chain and the escape over a fluctuating barrier. The latter is an example of a non- Markovian dynamics of the original process. The results for the mean first passage time and the residence times (using both physically meaningful and non-meaningful partitions of the phase-space) confirms the theory. With an accuracy restricted only by the resolution of the measurement and/or the finite sampling size, the values of the mean first passage time of the projected process agree with those of a direct measurement on the original dynamics and with any available semi-analytical solution.

Menschliche Systeme werden seit einiger Zeit modelliert und analysiert auf der Basis der Theorie komplexer Netzwerke. Dies erlaubt es quantitativ zu untersuchen, welche strukturellen und zeitlichen Merkmale eines Systems Ausbreitungsprozesse beeinflussen, z.B. von Informationen oder von Infektionskrankheiten. Im ersten Teil der Arbeit wird untersucht, wie eine modular-hierarchische Struktur von statischen Netzwerken eine schnelle Verbreitung von Signalen ermöglicht. Es werden neue Heuristiken entwickelt um die Random-Walk-Observablen “First Passage Time” und “Cover Time” auf lokal geclusterten Netzwerken zu ermitteln. Vergleiche mit der Approximation eines gemittelten Mediums zeigen, dass das Auftreten der beobachteten Minima der Observablen ein reiner Netzwerkeffekt ist. Es wird weiterhin dargelegt, dass nicht alle modular-hierarchischen Netzwerkmodelle dieses Phänomen aufweisen. Im zweiten Teil werden zeitlich veränderliche face-to-face Kontaktnetzwerke auf ihre Anfälligkeit für Infektionskrankheiten untersucht. Mehrere Studien belegen, dass Menschen vornehmlich Zeit in Isolation oder kleinen, stark verbundenen Gruppen verbringen, und dass ihre Kontaktaktivität einem zirkadianen Rhythmus folgt. Inwieweit diese beiden Merkmale die Ausbreitung von Krankheiten beeinflussen, ist noch unklar. Basierend auf einem neuen Modell wird erstmals gezeigt, dass zirkadian variierende Netzwerke Trajektorien folgen in einem Zustandsraum mit einer strukturellen und einer zeitlichen Dimension. Weiterhin wird dargelegt, dass mit zunehmender Annäherung der zeitlichen Dimension von System und Krankheit die systemische Infektionsanfälligkeit sinkt. Dies steht in direktem Widerspruch zu Ergebnissen anderer Studien, die eine zunehmende Anfälligkeit vorhersagen, eine Diskrepanz, die auf die Ungültigkeit einer weit verbreiteten Approximation zurückzuführen ist. Die hier vorgestellten Ergebnisse implizieren, dass auf dem Gebiet die Entwicklung neuer theoretischer Methoden notwendig ist. / Human systems have been modeled and analyzed on the basis of complex networks theory in recent time. This abstraction allows for thorough quantitative analyses to investigate which structural and temporal features of a system influence the evolution of spreading processes, such as the passage of information or of infectious diseases. The first part of this work investigates how the ubiquitous modular hierarchical structure of static real-world networks allows for fast delivery of messages. New heuristics are developed to evaluate random walk mean first passage times and cover times on locally clustered networks. A comparison to average medium approximations shows that the emergence of these minima are pure network phenomena. It is further found that not all modular hierarchical network models provide optimal message delivery structure. In the second part, temporally varying face-to-face contact networks are investigated for their susceptibility to infection. Several studies have shown that people tend to spend time in small, densely-connected groups or in isolation, and that their connection behavior follows a circadian rhythm. To what extent both of these features influence the spread of diseases is as yet unclear. Therefore, a new temporal network model is devised here. Based on this model, circadially varying networks can for the first time be interpreted as following trajectories through a newly defined systemic state space. It is further revealed that in many temporally varying networks the system becomes less susceptible to infection when the time-scale of the disease approaches the time-scale of the network variation. This is in direct conflict with findings of other studies that predict increasing susceptibility of temporal networks, a discrepancy which is attributed to the invalidity of a widely applied approximation. The results presented here imply that new theoretical advances are necessary to study the spread of diseases in temporally varying networks.

在此篇論文中，我們提出一個慢性Ｂ型肝炎病毒感染病程之數學模型。因為在病症間的轉移機率(Transition probability)是隨著患者的年齡變動，所以在過去的文獻中，已經有學者提出，在疾病轉移機率模型中，應加入國民生命表(Life table)，藉此讓機率模型更符合Ｂ型肝炎病患的生命歷程。但是過去的文獻中，學者並沒有利用加入國民生命表之後疾病模型做進一步的病程分析。在這篇論文當中，我們假設原始的疾病轉移模型是符合馬可夫鏈的性質，並且提出一種加入國民生命表的方法，賦予疾病有年齡相關特性之模型。根據文獻數據和類馬可夫機率性質，我們使用著名的Chapman-Kolmogorov公式計算Ｂ型肝炎的自然病程機率，並畫出病人的生存機率曲線(Survival curve)。文章最後將會藉由兩個例子來介紹此篇論文提出的模型。實驗數據結果證實，此模型不僅提供了一個更精確的方法去分析在病症與死亡間的轉移機率、平均餘命(Life expectancy)、以及在不同年齡的存活機率(Survival probability)，並且可以更進一步的分析且瞭解病情狀態之間的轉移狀況。 / In this thesis, we propose a new mathematical model extending the natural history of hepatitis B virus (HBV) prognosis progression on chronic HBV infection. Since the actual transition probabilities between symptoms are dependent of ages, it has been proposed that the life table should be accommodated to the HBV prognosis progression model so that it can more properly explain the disease progression of the HBV patients. But in the literature, no further disease analysis and applications of it with the life table are discussed. In this thesis, we assume that the original disease progression is described by a Markov model, and propose a new method to combine the HBV progression with the life table so that the proposed model integrates data from the life table and allows the accommodation of age-dependent properties of the target disease. With clinical data based on annual incidence rates, the entire model is Semi-Markov based in nature. Computation methods similar to the celebrated Chapman-Kolmogorov equation can be applied to study the associated probability of each likely trajectory with desired initial ages and health states under the scenarios of natural history and various treatment policies. This method provides a more accurate way to analyze the transitions between symptoms, such as the mean life expectancy or the survival probabilities at different ages. We will give examples to demonstrate the proposed method in this thesis. Numerical results show the proposed model not only provides a more accurate method to analyze the mean life expectancy, the survival probabilities at different ages, and the transition probabilities from symptoms to death but also helps us to understand the transitions between symptoms.

The retina is a highly metabolically active tissue with large demands on the supply of nutrients. Disorders affecting the retina often include some vasculopathy with an impact on retinal circulation. Studies of retinal haemodynamics could thus help to detect, differentiate and diagnose diseases, to monitor changes in disease as well as progression and efficiency of the therapy. The present studies were an attempt to validate and determine the clinical usefulness of a newly developed technique for studying the retinal circulation in human eyes. We used different techniques to evaluate different parameters of retinal circulation. We examined how leukocyte velocity determined with Blue Field Simulation and transit times, mean transite time (MTT) and arterio-venous passage (AVP), and vessel diameter, determined from fluorescein angiograms, together reflects the retinal circulation. MTT was determined with a method based on an Impulse-Response technique, MTTIR. In a study on monkeys we compared our method, together with two conventional methods, with an absolute measurement of retinal blood flow (RBF) determined with labelled microspheres. There was a weak, but not statistically significant, correlation between retinal blood flow and MTTIR (r2 = -0.60, p = 0.06), but no useful correlation between retinal blood flow and either of the other two measures of transit times. In a study on healthy eyes we determined the effect of a physiological provocation, changes in arterial blood gases, on retinal circulation. Breathing pure oxygen or increased level of carbon dioxide in inspired air had no effect on MTT, but oxygen reduced leukocyte velocity and vessel diameter and carbon dioxide increased leukocyte velocity significantly. We concluded that unchanged transit time trough the retinal tissue was not due to a lack of effect of the gas provocation but a result due to concomitant changes in volume and flow. In a study on eyes of patients with diabetic retinopathy we investigated the relation between the extent of retinal circulation changes and the severity of the diabetes retinopathy (DRP). Transit times were relatively unaffected until proliferative DRP (PDRP) developed. In eyes with PDRP both MTTIR and AVP were increased. After panretinal photocoagulation treatment MTTIR returned to normal levels and vessel diameters tended to decrease while leukocyte velocity and AVP remained unchanged. We concluded that the increase in MTTIR in eyes with PDRP is at least partly explained by vessel dilation, causing an increased volume of the retinal vascular bed.

No description available.

Current conventional weather forecasts are based on high-dimensional numerical models. They are usually only skillful up to a maximum lead time of around 7 days due to the chaotic nature of the climate dynamics and the related exponential growth of model and data initialisation errors. Even the fully detailed medium-range predictions made for instance at the European Centre for Medium-Range Weather Forecasts do not exceed lead times of 14 days, while even longer-range predictions are limited to time-averaged forecast outputs only. Many sectors would profit significantly from accurate forecasts on seasonal time scales without needing the wealth of details a full dynamical model can deliver. In this thesis, we aim to study the potential of a much cheaper data-based statistical approach to provide predictions of comparable or even better skill up to seasonal lead times, using as an examplary forecast target the time until the next occurrence of frost. To this end, we first analyse the properties of the temperature anomaly time series obtained from measured data by subtracting a sinusoidal seasonal cycle, as well as the distribution properties of the first passage times to frost. The possibility of generating additional temperature anomaly data with the same properties by using very simple autoregressive model processes to potentially reduce the statistical fluctuations in our analysis is investigated and ultimately rejected. In a next step, we study the potential for predictability using only conditional first passage time distributions derived from the temperature anomaly time series and confirm a significant dependence of the distributions on the initial conditions. After this preliminary analysis, we issue data-based out-of-sample forecasts for three different prediction targets: The specific date of first frost, the probability of observing frost before summer for forecasts issued in spring, and the full probability distribution of the first passage times to frost. We then study the possibility of improving the forecast quality first by enhancing the stationarity of the temperature anomaly time series and then by adding as an additional input variable the state of the North Atlantic Oscillation on the date the predictions are issued. We are able to obtain significant forecast skill up to seasonal lead times when comparing our results to an unskilled reference forecast. A first comparison between the data-based forecasts and corresponding predictions gathered from a dynamical weather model, necessarily using a lead time of only up to 15 days, shows that our simple statistical schemes are only outperformed (and then only slightly) if further statistical post-processing is applied to the model output. / Aktuelle Wetterprognosen werden mit Hilfe von hochdimensionalen, numerischen Modellen generiert. Durch die dem Klima zugrunde liegende chaotische Dynamik wachsen Modellfehler und Ungenauigkeiten in der Modellinitialisierung exponentiell an, sodass Vorhersagen mit signifikanter Güte üblicherweise nur für eine Vorlaufzeit von maximal sieben Tagen möglich sind. Selbst die detaillierten Prognosen des Europäischen Zentrums für mittelfristige Wettervorhersagen gehen nicht über eine Vorlaufzeit von 14 Tagen hinaus, während noch längerfristigere Vorhersagen auf zeitgemittelte Größen beschränkt sind. Viele Branchen würden signifikant von akkuraten Vorhersagen auf saisonalen Zeitskalen pro-fitieren, ohne das ganze Ausmaß an Details zu benötigen, das von einem vollständigen dynamischen Modell geliefert werden kann. In dieser Dissertation beabsichtigen wir, am Beispiel einer Vorhersage der Zeitdauer bis zum nächsten Eintreten von Frost zu untersuchen, inwieweit deutlich kostengünstigere, datenbasierte statistische Verfahren Prognosen von gleicher oder sogar besserer Güte auf bis zu saisonalen Zeitskalen liefern können. Dazu analysieren wir zunächst die Eigenschaften der Zeitreihe der Temperaturanomalien, die aus den Messdaten durch das Subtrahieren eines sinusförmigen Jahresganges erhalten werden, sowie die Charakteristiken der Wahrscheinlichkeitsverteilungen der Zeitdauer bis zum nächsten Eintreten von Frost. Die Möglichkeit, durch einen einfachen autoregressiven Modellprozess zusätzliche Datenpunkte gleicher statistischer Eigenschaften wie der Temperaturanomalien zu generieren, um die statistischen Fluktuationen in der Analyse zu reduzieren, wird untersucht und letztendlich verworfen. Im nächsten Schritt analysieren wir das Vorhersagepotential, wenn ausschließlich aus den Temperaturanomalien gewonnene bedingte Wahrscheinlichkeitsverteilungen der Wartezeit bis zum nächsten Frost verwendet werden, und können eine signifikante Abhängigkeit der Verteilungen von den Anfangsbedingungen nachweisen. Nach dieser einleitenden Untersuchung erstellen wir datenbasierte Prognosen für drei verschiedene Vorhersagegrößen: Das konkrete Datum, an dem es das nächste Mal Frost geben wird; die Wahrscheinlichkeit, noch vor dem Sommer Frost zu beobachten, wenn die Vorhersagen im Frühjahr ausgegeben werden; und die volle Wahrscheinlichkeitsverteilung der Zeitdauer bis zum nächsten Eintreten von Frost. Anschließend untersuchen wir die Möglichkeit, die Vorhersagegüte weiter zu erhöhen - zunächst durch eine Verbesserung der Stationarität der Temperaturanomalien und dann durch die zusätzliche Berücksichtigung der Nordatlantischen Oszillation als einer zweiten, den Anfangszustand charakterisierenden Variablen im Vorhersageschema. Wir sind in der Lage, im Vergleich mit einem naiven Referenzvorhersageschema eine signifikante Verbesserung der Vorhersagegüte auch auf saisonalen Zeitskalen zu erreichen. Ein erster Vergleich zwischen den datenbasierten Vorhersagen und entsprechenden, aus den dynamischen Wettermodellen gewonnenen Prognosen, der sich notwendigerweise auf eine Vorlaufzeit der Vorhersagen von lediglich 15 Tagen beschränkt, zeigt, dass letztere unsere simplen statistischen Vorhersageschemata nur schlagen (und zwar knapp), wenn der Modelloutput noch einer statistischen Nachbearbeitung unterzogen wird.

Cette thèse comporte un résumé avec des formules mathématiques. Vous pouvez le consulter via le texte intégral du document à la dernière page. / This thesis contains an abstract with mathematical formulae. You can consult it via the complete text of the document in the back page.