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On doubling unconditionals
(2019)
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.
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.
Persönlichkeitspsychologisch fundierte Studienorientierung durch onlinebasierte Self-Assessments
(2019)
Bacterial molybdoenzymes are key enzymes involved in the global sulphur, nitrogen and carbon cycles. These enzymes require the insertion of the molybdenum cofactor (Moco) into their active sites and are able to catalyse a large range of redox-reactions. Escherichia coli harbours nineteen different molybdoenzymes that require a tight regulation of their synthesis according to substrate availability, oxygen availability and the cellular concentration of molybdenum and iron. The synthesis and assembly of active molybdoenzymes are regulated at the level of transcription of the structural genes and of translation in addition to the genes involved in Moco biosynthesis. The action of global transcriptional regulators like FNR, NarXL/QP, Fur and ArcA and their roles on the expression of these genes is described in detail. In this review we focus on what is known about the molybdenum- and iron-dependent regulation of molybdoenzyme and Moco biosynthesis genes in the model organism E. coli. The gene regulation in E. coli is compared to two other well studied model organisms Rhodobacter capsulatus and Shewanella oneidensis.
Molybdenum cofactor (Moco) biosynthesis is a complex process that involves the coordinated function of several proteins. In recent years it has become obvious that the availability of iron plays an important role in the biosynthesis of Moco. First, the MoaA protein binds two (4Fe-4S] clusters per monomer. Second, the expression of the moaABCDE and moeAB operons is regulated by FNR, which senses the availability of oxygen via a functional NFe-4S) cluster. Finally, the conversion of cyclic pyranopterin monophosphate to molybdopterin requires the availability of the L-cysteine desulfurase IscS, which is a shared protein with a main role in the assembly of Fe-S clusters. In this report, we investigated the transcriptional regulation of the moaABCDE operon by focusing on its dependence on cellular iron availability. While the abundance of selected molybdoenzymes is largely decreased under iron-limiting conditions, our data show that the regulation of the moaABCDE operon at the level of transcription is only marginally influenced by the availability of iron. Nevertheless, intracellular levels of Moco were decreased under iron-limiting conditions, likely based on an inactive MoaA protein in addition to lower levels of the L-cysteine desulfurase IscS, which simultaneously reduces the sulfur availability for Moco production. IMPORTANCE FNR is a very important transcriptional factor that represents the master switch for the expression of target genes in response to anaerobiosis. Among the FNR-regulated operons in Escherichia coli is the moaABCDE operon, involved in Moco biosynthesis. Molybdoenzymes have essential roles in eukaryotic and prokaryotic organisms. In bacteria, molybdoenzymes are crucial for anaerobic respiration using alternative electron acceptors. This work investigates the connection of iron availability to the biosynthesis of Moco and the production of active molybdoenzymes.
Decoupling of optical properties appears challenging, but vital to get better insight of the relationship between light and fruit attributes. In this study, nine solid phantoms capturing the ranges of absorption (μa) and reduced scattering (μs’) coefficients in fruit were analysed non-destructively using laser-induced backscattering imaging (LLBI) at 1060 nm. Data analysis of LLBI was carried out on the diffuse reflectance, attenuation profile obtained by means of Farrell’s diffusion theory either calculating μa [cm−1] and μs’ [cm−1] in one fitting step or fitting only one optical variable and providing the other one from a destructive analysis. The nondestructive approach was approved when calculating one unknown coefficient non-destructively, while no ability of the method was found to analysis both, μa and μs’, non-destructively. Setting μs’ according to destructive photon density wave (PDW) spectroscopy and fitting μa resulted in root mean square error (rmse) of 18.7% in comparison to fitting μs’ resulting in rmse of 2.6%, pointing to decreased measuring uncertainty, when the highly variable μa was known.
The approach was tested on European pear, utilizing destructive PDW spectroscopy for setting one variable, while LLBI was applied for calculating the remaining coefficient. Results indicated that the optical properties of pear obtained from PDW spectroscopy as well as LLBI changed concurrently in correspondence to water content mainly. A destructive batch-wise analysis of μs’ and online analysis of μa may be considered in future developments for improved fruit sorting results, when considering fruit with high variability of μs’.
The tremendous success of metal-halide perovskites, especially in the field of photovoltaics, has triggered a substantial number of studies in understanding their optoelectronic properties. However, consensus regarding the electronic properties of these perovskites is lacking due to a huge scatter in the reported key parameters, such as work function (Φ) and valence band maximum (VBM) values. Here, we demonstrate that the surface photovoltage (SPV) is a key phenomenon occurring at the perovskite surfaces that feature a non-negligible density of surface states, which is more the rule than an exception for most materials under study. With ultraviolet photoelectron spectroscopy (UPS) and Kelvin probe, we evidence that even minute UV photon fluxes (500 times lower than that used in typical UPS experiments) are sufficient to induce SPV and shift the perovskite Φ and VBM by several 100 meV compared to dark. By combining UV and visible light, we establish flat band conditions (i.e., compensate the surface-state-induced surface band bending) at the surface of four important perovskites, and find that all are p-type in the bulk, despite a pronounced n-type surface character in the dark. The present findings highlight that SPV effects must be considered in all surface studies to fully understand perovskites’ photophysical properties.