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Die vorliegende Arbeit untersucht die Politik der Zentralbankunabhängigkeit (ZBU) am Beispiel der Türkei. Im Mittelpunkt der Arbeit stehen theoretische und empirische Fragen und Probleme, die sich im Zusammenhang mit der ZBU stellen und anhand der türkischen Geldpolitik diskutiert werden. Ein zentrales Ziel der Arbeit besteht darin, zu untersuchen, ob und inwiefern die türkische Zentralbank nach Erlangung der de jure institutionellen Unabhängigkeit tatsächlich als unabhängig und entpolitisiert eingestuft werden kann. Um diese Forschungsfrage zu beantworten, werden die institutionellen Bedingungen, die Ziele und die Regeln, nach denen sich die türkische Geldpolitik richtet, geklärt. Anschließend wird empirisch überprüft, ob die geldpolitische Praxis der CBRT sich an dem offiziell vorgegebenen Regelwerk orientiert. Die Hauptthese dieser Arbeit lautet, dass die formelle Unabhängigkeit der CBRT und die regelorientierte Geldpolitik nicht mit einer Entpolitisierung der Geldpolitik in der Türkei gleichzusetzen ist. Als Alternative schlägt die vorliegende Studie vor, den institutionellen Status der CBRT als einen der relativen Autonomie zu untersuchen. Auch eine de jure unabhängige Zentralbank kann sich nicht von politischen Eingriffen abkoppeln, wie das Fallbeispiel Türkei zeigen wird.
We show that the codifference is a useful tool in studying the ergodicity breaking and non-Gaussianity properties of stochastic time series. While the codifference is a measure of dependence that was previously studied mainly in the context of stable processes, we here extend its range of applicability to random-parameter and diffusing-diffusivity models which are important in contemporary physics, biology and financial engineering. We prove that the codifference detects forms of dependence and ergodicity breaking which are not visible from analysing the covariance and correlation functions. We also discuss a related measure of dispersion, which is a nonlinear analogue of the mean squared displacement.
Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.
Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.
We show that the codifference is a useful tool in studying the ergodicity breaking and non-Gaussianity properties of stochastic time series. While the codifference is a measure of dependence that was previously studied mainly in the context of stable processes, we here extend its range of applicability to random-parameter and diffusing-diffusivity models which are important in contemporary physics, biology and financial engineering. We prove that the codifference detects forms of dependence and ergodicity breaking which are not visible from analysing the covariance and correlation functions. We also discuss a related measure of dispersion, which is a nonlinear analogue of the mean squared displacement.
Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as ‘superstatistics’ or ‘diffusing diffusivity’. Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models.Westart from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion.
Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as ‘superstatistics’ or ‘diffusing diffusivity’. Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models.Westart from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion.
Background:
Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked
to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research
comprising manifold, diverse and time
‑consuming experiments, the findings can be fostered by reusing and combin‑
ing existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible
provided that the collected observations are equipped with an adequate set of metadata. So far there have been no
common standards governing phenotypic data description, which hampered data exchange and reuse.
Results:
In this paper we propose the guidelines for proper handling of the information about plant phenotyping
experiments, in terms of both the recommended content of the description and its formatting. We provide a docu‑
ment called “Minimum Information About a Plant Phenotyping Experiment”, which specifies what information about
each experiment should be given, and a Phenotyping Configuration for the ISA
‑Tab format, which allows to practically
organise this information within a dataset. We provide examples of ISA
‑Tab
‑formatted phenotypic data, and a general
description of a few systems where the recommendations have been implemented.
Conclusions:
Acceptance of the rules described in this paper by the plant phenotyping community will help to
achieve findable, accessible, interoperable and reusable data.
The Proteasome Acts as a Hub for Plant Immunity and Is Targeted by Pseudomonas Type III Effectors
(2016)
Recent evidence suggests that the ubiquitin-proteasome system is involved in several aspects of plant immunity and that a range of plant pathogens subvert the ubiquitin-proteasome system to enhance their virulence. Here, we show that proteasome activity is strongly induced during basal defense in Arabidopsis (Arabidopsis thaliana). Mutant lines of the proteasome subunits RPT2a and RPN12a support increased bacterial growth of virulent Pseudomonas syringae pv tomato DC3000 (Pst) and Pseudomonas syringae pv maculicola ES4326. Both proteasome subunits are required for pathogen-associated molecular pattern-triggered immunity responses. Analysis of bacterial growth after a secondary infection of systemic leaves revealed that the establishment of systemic acquired resistance (SAR) is impaired in proteasome mutants, suggesting that the proteasome also plays an important role in defense priming and SAR. In addition, we show that Pst inhibits proteasome activity in a type III secretion-dependent manner. A screen for type III effector proteins from Pst for their ability to interfere with proteasome activity revealed HopM1, HopAO1, HopA1, and HopG1 as putative proteasome inhibitors. Biochemical characterization of HopM1 by mass spectrometry indicates that HopM1 interacts with several E3 ubiquitin ligases and proteasome subunits. This supports the hypothesis that HopM1 associates with the proteasome, leading to its inhibition. Thus, the proteasome is an essential component of pathogen-associated molecular pattern-triggered immunity and SAR, which is targeted by multiple bacterial effectors.