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In the living cell, the organization of the complex internal structure relies to a large extent on molecular motors. Molecular motors are proteins that are able to convert chemical energy from the hydrolysis of adenosine triphosphate (ATP) into mechanical work. Being about 10 to 100 nanometers in size, the molecules act on a length scale, for which thermal collisions have a considerable impact onto their motion. In this way, they constitute paradigmatic examples of thermodynamic machines out of equilibrium. This study develops a theoretical description for the energy conversion by the molecular motor myosin V, using many different aspects of theoretical physics. Myosin V has been studied extensively in both bulk and single molecule experiments. Its stepping velocity has been characterized as a function of external control parameters such as nucleotide concentration and applied forces. In addition, numerous kinetic rates involved in the enzymatic reaction of the molecule have been determined. For forces that exceed the stall force of the motor, myosin V exhibits a 'ratcheting' behaviour: For loads in the direction of forward stepping, the velocity depends on the concentration of ATP, while for backward loads there is no such influence. Based on the chemical states of the motor, we construct a general network theory that incorporates experimental observations about the stepping behaviour of myosin V. The motor's motion is captured through the network description supplemented by a Markov process to describe the motor dynamics. This approach has the advantage of directly addressing the chemical kinetics of the molecule, and treating the mechanical and chemical processes on equal grounds. We utilize constraints arising from nonequilibrium thermodynamics to determine motor parameters and demonstrate that the motor behaviour is governed by several chemomechanical motor cycles. In addition, we investigate the functional dependence of stepping rates on force by deducing the motor's response to external loads via an appropriate Fokker-Planck equation. For substall forces, the dominant pathway of the motor network is profoundly different from the one for superstall forces, which leads to a stepping behaviour that is in agreement with the experimental observations. The extension of our analysis to Markov processes with absorbing boundaries allows for the calculation of the motor's dwell time distributions. These reveal aspects of the coordination of the motor's heads and contain direct information about the backsteps of the motor. Our theory provides a unified description for the myosin V motor as studied in single motor experiments.
The Arctic is a particularly sensitive area with respect to climate change due to the high surface albedo of snow and ice and the extreme radiative conditions. Clouds and aerosols as parts of the Arctic atmosphere play an important role in the radiation budget, which is, as yet, poorly quantified and understood. The LIDAR (Light Detection And Ranging) measurements presented in this PhD thesis contribute with continuous altitude resolved aerosol profiles to the understanding of occurrence and characteristics of aerosol layers above Ny-Ålesund, Spitsbergen. The attention was turned to the analysis of periods with high aerosol load. As the Arctic spring troposphere exhibits maximum aerosol optical depths (AODs) each year, March and April of both the years 2007 and 2009 were analyzed. Furthermore, stratospheric aerosol layers of volcanic origin were analyzed for several months, subsequently to the eruptions of the Kasatochi and Sarychev volcanoes in summer 2008 and 2009, respectively. The Koldewey Aerosol Raman LIDAR (KARL) is an instrument for the active remote sensing of atmospheric parameters using pulsed laser radiation. It is operated at the AWIPEV research base and was fundamentally upgraded within the framework of this PhD project. It is now equipped with a new telescope mirror and new detection optics, which facilitate atmospheric profiling from 450m above sea level up to the mid-stratosphere. KARL provides highly resolved profiles of the scattering characteristics of aerosol and cloud particles (backscattering, extinction and depolarization) as well as water vapor profiles within the lower troposphere. Combination of KARL data with data from other instruments on site, namely radiosondes, sun photometer, Micro Pulse LIDAR, and tethersonde system, resulted in a comprehensive data set of scattering phenomena in the Arctic atmosphere. The two spring periods March and April 2007 and 2009 were at first analyzed based on meteorological parameters, like local temperature and relative humidity profiles as well as large scale pressure patterns and air mass origin regions. Here, it was not possible to find a clear correlation between enhanced AOD and air mass origin. However, in a comparison of two cloud free periods in March 2007 and April 2009, large AOD values in 2009 coincided with air mass transport through the central Arctic. This suggests the occurrence of aerosol transformation processes during the aerosol transport to Ny-Ålesund. Measurements on 4 April 2009 revealed maximum AOD values of up to 0.12 and aerosol size distributions changing with altitude. This and other performed case studies suggest the differentiation between three aerosol event types and their origin: Vertically limited aerosol layers in dry air, highly variable hygroscopic boundary layer aerosols and enhanced aerosol load across wide portions of the troposphere. For the spring period 2007, the available KARL data were statistically analyzed using a characterization scheme, which is based on optical characteristics of the scattering particles. The scheme was validated using several case studies. Volcanic eruptions in the northern hemisphere in August 2008 and June 2009 arose the opportunity to analyze volcanic aerosol layers within the stratosphere. The rate of stratospheric AOD change was similar within both years with maximum values above 0.1 about three to five weeks after the respective eruption. In both years, the stratospheric AOD persisted at higher rates than usual until the measurements were stopped in late September due to technical reasons. In 2008, up to three aerosol layers were detected, the layer structure in 2009 was characterized by up to six distinct and thin layers which smeared out to one broad layer after about two months. The lowermost aerosol layer was continuously detected at the tropopause altitude. Three case studies were performed, all revealed rather large indices of refraction of m = (1.53–1.55) - 0.02i, suggesting the presence of an absorbing carbonaceous component. The particle radius, derived with inversion calculations, was also similar in both years with values ranging from 0.16 to 0.19 μm. However, in 2009, a second mode in the size distribution was detected at about 0.5 μm. The long term measurements with the Koldewey Aerosol Raman LIDAR in Ny-Ålesund provide the opportunity to study Arctic aerosols in the troposphere and the stratosphere not only in case studies but on longer time scales. In this PhD thesis, both, tropospheric aerosols in the Arctic spring and stratospheric aerosols following volcanic eruptions have been described qualitatively and quantitatively. Case studies and comparative studies with data of other instruments on site allowed for the analysis of microphysical aerosol characteristics and their temporal evolution.
The Greenland Ice Sheet (GIS) contains enough water volume to raise global sea level by over 7 meters. It is a relic of past glacial climates that could be strongly affected by a warming world. Several studies have been performed to investigate the sensitivity of the ice sheet to changes in climate, but large uncertainties in its long-term response still exist. In this thesis, a new approach has been developed and applied to modeling the GIS response to climate change. The advantages compared to previous approaches are (i) that it can be applied over a wide range of climatic scenarios (both in the deep past and the future), (ii) that it includes the relevant feedback processes between the climate and the ice sheet and (iii) that it is highly computationally efficient, allowing simulations over very long timescales. The new regional energy-moisture balance model (REMBO) has been developed to model the climate and surface mass balance over Greenland and it represents an improvement compared to conventional approaches in modeling present-day conditions. Furthermore, the evolution of the GIS has been simulated over the last glacial cycle using an ensemble of model versions. The model performance has been validated against field observations of the present-day climate and surface mass balance, as well as paleo information from ice cores. The GIS contribution to sea level rise during the last interglacial is estimated to be between 0.5-4.1 m, consistent with previous estimates. The ensemble of model versions has been constrained to those that are consistent with the data, and a range of valid parameter values has been defined, allowing quantification of the uncertainty and sensitivity of the modeling approach. Using the constrained model ensemble, the sensitivity of the GIS to long-term climate change was investigated. It was found that the GIS exhibits hysteresis behavior (i.e., it is multi-stable under certain conditions), and that a temperature threshold exists above which the ice sheet transitions to an essentially ice-free state. The threshold in the global temperature is estimated to be in the range of 1.3-2.3°C above preindustrial conditions, significantly lower than previously believed. The timescale of total melt scales non-linearly with the overshoot above the temperature threshold, such that a 2°C anomaly causes the ice sheet to melt in ca. 50,000 years, but an anomaly of 6°C will melt the ice sheet in less than 4,000 years. The meltback of the ice sheet was found to become irreversible after a fraction of the ice sheet is already lost – but this level of irreversibility also depends on the temperature anomaly.
Complex network theory provides an elegant and powerful framework to statistically investigate the topology of local and long range dynamical interrelationships, i.e., teleconnections, in the climate system. Employing a refined methodology relying on linear and nonlinear measures of time series analysis, the intricate correlation structure within a multivariate climatological data set is cast into network form. Within this graph theoretical framework, vertices are identified with grid points taken from the data set representing a region on the the Earth's surface, and edges correspond to strong statistical interrelationships between the dynamics on pairs of grid points. The resulting climate networks are neither perfectly regular nor completely random, but display the intriguing and nontrivial characteristics of complexity commonly found in real world networks such as the internet, citation and acquaintance networks, food webs and cortical networks in the mammalian brain. Among other interesting properties, climate networks exhibit the "small-world" effect and possess a broad degree distribution with dominating super-nodes as well as a pronounced community structure. We have performed an extensive and detailed graph theoretical analysis of climate networks on the global topological scale focussing on the flow and centrality measure betweenness which is locally defined at each vertex, but includes global topological information by relying on the distribution of shortest paths between all pairs of vertices in the network. The betweenness centrality field reveals a rich internal structure in complex climate networks constructed from reanalysis and atmosphere-ocean coupled general circulation model (AOGCM) surface air temperature data. Our novel approach uncovers an elaborately woven meta-network of highly localized channels of strong dynamical information flow, that we relate to global surface ocean currents and dub the backbone of the climate network in analogy to the homonymous data highways of the internet. This finding points to a major role of the oceanic surface circulation in coupling and stabilizing the global temperature field in the long term mean (140 years for the model run and 60 years for reanalysis data). Carefully comparing the backbone structures detected in climate networks constructed using linear Pearson correlation and nonlinear mutual information, we argue that the high sensitivity of betweenness with respect to small changes in network structure may allow to detect the footprints of strongly nonlinear physical interactions in the climate system. The results presented in this thesis are thoroughly founded and substantiated using a hierarchy of statistical significance tests on the level of time series and networks, i.e., by tests based on time series surrogates as well as network surrogates. This is particularly relevant when working with real world data. Specifically, we developed new types of network surrogates to include the additional constraints imposed by the spatial embedding of vertices in a climate network. Our methodology is of potential interest for a broad audience within the physics community and various applied fields, because it is universal in the sense of being valid for any spatially extended dynamical system. It can help to understand the localized flow of dynamical information in any such system by combining multivariate time series analysis, a complex network approach and the information flow measure betweenness centrality. Possible fields of application include fluid dynamics (turbulence), plasma physics and biological physics (population models, neural networks, cell models). Furthermore, the climate network approach is equally relevant for experimental data as well as model simulations and hence introduces a novel perspective on model evaluation and data driven model building. Our work is timely in the context of the current debate on climate change within the scientific community, since it allows to assess from a new perspective the regional vulnerability and stability of the climate system while relying on global and not only on regional knowledge. The methodology developed in this thesis hence has the potential to substantially contribute to the understanding of the local effect of extreme events and tipping points in the earth system within a holistic global framework.
Supernovae are known to be the dominant energy source for driving turbulence in the interstellar medium. Yet, their effect on magnetic field amplification in spiral galaxies is still poorly understood. Analytical models based on the uncorrelated-ensemble approach predicted that any created field will be expelled from the disk before a significant amplification can occur. By means of direct simulations of supernova-driven turbulence, we demonstrate that this is not the case. Accounting for vertical stratification and galactic differential rotation, we find an exponential amplification of the mean field on timescales of 100Myr. The self-consistent numerical verification of such a “fast dynamo” is highly beneficial in explaining the observed strong magnetic fields in young galaxies. We, furthermore, highlight the importance of rotation in the generation of helicity by showing that a similar mechanism based on Cartesian shear does not lead to a sustained amplification of the mean magnetic field. This finding impressively confirms the classical picture of a dynamo based on cyclonic turbulence.
Die Arbeit beschreibt die Analyse von Beobachtungen zweier Sonnenflecken in zweidimensionaler Spektro-Polarimetrie. Die Daten wurden mit dem Fabry-Pérot-Interferometer der Universität Göttingen am Vakuum-Turm-Teleskop auf Teneriffa erfasst. Von der aktiven Region NOAA 9516 wurde der volle Stokes-Vektor des polarisierten Lichts in der Absorptionslinie bei 630,249 nm in Einzelaufnahmen beobachtet, und von der aktiven Region NOAA 9036 wurde bei 617,3 nm Wellenlänge eine 90-minütige Zeitserie des zirkular polarisierten Lichts aufgezeichnet. Aus den reduzierten Daten werden Ergebniswerte für Intensität, Geschwindigkeit in Beobachtungsrichtung, magnetische Feldstärke sowie verschiedene weitere Plasmaparameter abgeleitet. Mehrere Ansätze zur Inversion solarer Modellatmosphären werden angewendet und verglichen. Die teilweise erheblichen Fehlereinflüsse werden ausführlich diskutiert. Das Frequenzverhalten der Ergebnisse und Abhängigkeiten nach Ort und Zeit werden mit Hilfe der Fourier- und Wavelet-Transformation weiter analysiert. Als Resultat lässt sich die Existenz eines hochfrequenten Bandes für Geschwindigkeitsoszillationen mit einer zentralen Frequenz von 75 Sekunden (13 mHz) bestätigen. In größeren photosphärischen Höhen von etwa 500 km entstammt die Mehrheit der damit zusammenhängenden Schockwellen den dunklen Anteilen der Granulen, im Unterschied zu anderen Frequenzbereichen. Die 75-Sekunden-Oszillationen werden ebenfalls in der aktiven Region beobachtet, vor allem in der Lichtbrücke. In den identifizierten Bändern oszillatorischer Power der Geschwindigkeit sind in einer dunklen, penumbralen Struktur sowie in der Lichtbrücke ausgeprägte Strukturen erkennbar, die sich mit einer Horizontalgeschwindigkeit von 5-8 km/s in die ruhige Sonne bewegen. Diese zeigen einen deutlichen Anstieg der Power, vor allem im 5-Minuten-Band, und stehen möglicherweise in Zusammenhang mit dem Phänomen der „Evershed-clouds“. Eingeschränkt durch ein sehr geringes Signal-Rausch-Verhältnis und hohe Fehlereinflüsse werden auch Magnetfeldvariationen mit einer Periode von sechs Minuten am Übergang von Umbra zu Penumbra in der Nähe einer Lichtbrücke beobachtet. Um die beschriebenen Resultate zu erzielen, wurden bestehende Visualisierungsverfahren der Frequenzanalyse verbessert oder neu entwickelt, insbesondere für Ergebnisse der Wavelet-Transformation.
In order to investigate the temporal characteristics of cognitive processing, we apply multivariate phase synchronization analysis to event-related potentials. The experimental design combines a semantic incongruity in a sentence context with a physical mismatch (color change). In the ERP average, these result in an N400 component and a P300-like positivity, respectively. The synchronization analysis shows an effect of global desynchronization in the theta band around 288ms after stimulus presentation for the semantic incongruity, while the physical mismatch elicits an increase of global synchronization in the alpha band around 204ms. Both of these effects clearly precede those in the ERP average. Moreover, the delay between synchronization effect and ERP component correlates with the complexity of the cognitive processes.
In biological cells, the long-range intracellular traffic is powered by molecular motors which transport various cargos along microtubule filaments. The microtubules possess an intrinsic direction, having a 'plus' and a 'minus' end. Some molecular motors such as cytoplasmic dynein walk to the minus end, while others such as conventional kinesin walk to the plus end. Cells typically have an isopolar microtubule network. This is most pronounced in neuronal axons or fungal hyphae. In these long and thin tubular protrusions, the microtubules are arranged parallel to the tube axis with the minus ends pointing to the cell body and the plus ends pointing to the tip. In such a tubular compartment, transport by only one motor type leads to 'motor traffic jams'. Kinesin-driven cargos accumulate at the tip, while dynein-driven cargos accumulate near the cell body. We identify the relevant length scales and characterize the jamming behaviour in these tube geometries by using both Monte Carlo simulations and analytical calculations. A possible solution to this jamming problem is to transport cargos with a team of plus and a team of minus motors simultaneously, so that they can travel bidirectionally, as observed in cells. The presumably simplest mechanism for such bidirectional transport is provided by a 'tug-of-war' between the two motor teams which is governed by mechanical motor interactions only. We develop a stochastic tug-of-war model and study it with numerical and analytical calculations. We find a surprisingly complex cooperative motility behaviour. We compare our results to the available experimental data, which we reproduce qualitatively and quantitatively.
Mass accretion onto compact objects through accretion disks is a common phenomenon in the universe. It is seen in all energy domains from active galactic nuclei through cataclysmic variables (CVs) to young stellar objects. Because CVs are fairly easy to observe, they provide an ideal opportunity to study accretion disks in great detail and thus help us to understand accretion also in other energy ranges. Mass accretion in these objects is often accompanied by mass outflow from the disks. This accretion disk wind, at least in CVs, is thought to be radiatively driven, similar to O star winds. WOMPAT, a 3-D Monte Carlo radiative transfer code for accretion disk winds of CVs is presented.