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Time-resolved crystallography reveals allosteric communication aligned with molecular breathing
(2019)
A comprehensive understanding of protein function demands correlating structure and dynamic changes. Using time-resolved serial synchrotron crystallography, we visualized half-of-the-sites reactivity and correlated molecular-breathing motions in the enzyme fluoroacetate dehalogenase. Eighteen time points from 30 milliseconds to 30 seconds cover four turnover cycles of the irreversible reaction. They reveal sequential substrate binding, covalent-intermediate formation, setup of a hydrolytic water molecule, and product release. Small structural changes of the protein mold and variations in the number and placement of water molecules accompany the various chemical steps of catalysis. Triggered by enzyme-ligand interactions, these repetitive changes in the protein framework’s dynamics and entropy constitute crucial components of the catalytic machinery.
The supercritical Hopf bifurcation is one of the simplest ways in which a stationary state of a nonlinear system can undergo a transition to stable self-sustained oscillations. At the bifurcation point, a small-amplitude limit cycle is born, which already at onset displays a finite frequency. If we consider a reaction-diffusion system that undergoes a supercritical Hopf bifurcation, its dynamics is described by the complex Ginzburg-Landau equation (CGLE). Here, we study such a system in the parameter regime where the CGLE shows spatio-temporal chaos. We review a type of time-delay feedback methods which is suitable to suppress chaos and replace it by other spatio-temporal solutions such as uniform oscillations, plane waves, standing waves, and the stationary state.
Data assimilation has been an active area of research in recent years, owing to its wide utility. At the core of data assimilation are filtering, prediction, and smoothing procedures. Filtering entails incorporation of measurements' information into the model to gain more insight into a given state governed by a noisy state space model. Most natural laws are governed by time-continuous nonlinear models. For the most part, the knowledge available about a model is incomplete; and hence uncertainties are approximated by means of probabilities. Time-continuous filtering, therefore, holds promise for wider usefulness, for it offers a means of combining noisy measurements with imperfect model to provide more insight on a given state.
The solution to time-continuous nonlinear Gaussian filtering problem is provided for by the Kushner-Stratonovich equation. Unfortunately, the Kushner-Stratonovich equation lacks a closed-form solution. Moreover, the numerical approximations based on Taylor expansion above third order are fraught with computational complications. For this reason, numerical methods based on Monte Carlo methods have been resorted to. Chief among these methods are sequential Monte-Carlo methods (or particle filters), for they allow for online assimilation of data. Particle filters are not without challenges: they suffer from particle degeneracy, sample impoverishment, and computational costs arising from resampling.
The goal of this thesis is to:— i) Review the derivation of Kushner-Stratonovich equation from first principles and its extant numerical approximation methods, ii) Study the feedback particle filters as a way of avoiding resampling in particle filters, iii) Study joint state and parameter estimation in time-continuous settings, iv) Apply the notions studied to linear hyperbolic stochastic differential equations.
The interconnection between Itô integrals and stochastic partial differential equations and those of Stratonovich is introduced in anticipation of feedback particle filters. With these ideas and motivated by the variants of ensemble Kalman-Bucy filters founded on the structure of the innovation process, a feedback particle filter with randomly perturbed innovation is proposed. Moreover, feedback particle filters based on coupling of prediction and analysis measures are proposed. They register a better performance than the bootstrap particle filter at lower ensemble sizes.
We study joint state and parameter estimation, both by means of extended state spaces and by use of dual filters. Feedback particle filters seem to perform well in both cases. Finally, we apply joint state and parameter estimation in the advection and wave equation, whose velocity is spatially varying. Two methods are employed: Metropolis Hastings with filter likelihood and a dual filter comprising of Kalman-Bucy filter and ensemble Kalman-Bucy filter. The former performs better than the latter.
Recent research on proactive work behaviours (PWBs) pointed out that these behaviours can have negative consequences for the proactive individual. We add to this perspective by showing that PWBs may be a source of strain at work and result in elevated time pressure. Challenging the view of time pressure as a challenge stressor, we hypothesize that over the course of work weeks, time pressure will result in less (rather than more) PWB. We investigate these reciprocal effects as within-person, week-level fluctuations of time pressure and PWB based on experience sampling data (N = 52 participants, k = 274 observations). Over the course of three consecutive work weeks, results show a positive lagged effect of PWB in the first week on experiencing time pressure in the second week; in turn, time pressure in the second week had a negative lagged effect on PWB in the third week. Results further suggest that PWB is lowest in work weeks of low time pressure when following a week of high time pressure, indicating a conservation of resources interpretation of the results.
Tikhonov regularization with oversmoothing penalty for linear statistical inverse learning problems
(2019)
In this paper, we consider the linear ill-posed inverse problem with noisy data in the statistical learning setting. The Tikhonov regularization scheme in Hilbert scales is considered in the reproducing kernel Hilbert space framework to reconstruct the estimator from the random noisy data. We discuss the rates of convergence for the regularized solution under the prior assumptions and link condition. For regression functions with smoothness given in terms of source conditions the error bound can explicitly be established.
We have developed a three-dimensional (3D) graphene electrode suitable for the immobilization of human sulfite oxidase (hSO), which catalyzes the electrochemical oxidation of sulfite via direct electron transfer (DET). The electrode is fabricated by drop-casting graphene-polyethylenimine (G-P) composites on carbon papers (CPs) precoated with graphene oxide (GO). The negatively charged hSO can be adsorbed electrostatically on the positively charged matrix (G-P) on CP electrodes coated with GO (CPG), with a proper orientation for accelerated DET. Notably, further electrochemical reduction of G-P on CPG electrodes leads to a 9-fold increase of the saturation catalytic current density (j(m)) for sulfite oxidation reaching 24.4 +/- 0.3 mu A to cm(-2), the highest value among reported DET-based hSO bioelectrodes. The increased electron transfer rate plays a dominating role in the enhancement of direct enzymatic current because of the improved electric contact of hSO with the electrode, The optimized hSO bioelectrode shows a significant catalytic rate (k(cat): 25.6 +/- 0.3 s(-1)) and efficiency (k(cat)/K-m: 0.231 +/- 0.003 s(-1) mu M-1) compared to the reported hSO bioelectrodes. The assembly of the hSO bioanode and a commercial platinum biocathode allows the construction of sulfite/O-2 enzymatic biofuel cells (EBFCs) with flowing fuels. The optimized EBFC displays an open-circuit voltage (OCV) of 0.64 +/- 0.01 V and a maximum power density of 61 +/- 6 mu W cm(-2) (122 +/- 12 mW m(-3)) at 30 degrees C, which exceeds the best reported value by more than 6 times.
Enzyme immobilization using nanomaterials offers new approaches to enhanced bioelectrochemical performance and is essential for the preparation of bioelectrodes with high reproducibility and low cost. In this report, we describe the development of new three-dimensional (3D) bioelectrodes by immobilizing a "bioink" of glucose oxidase (GOD) in a matrix of reduced graphene oxides (RGOs), polyethylenimine (PEI), and ferrocene carboxylic acid (FcCOOH) on carbon paper (CP). CP with 3D interwoven carbon fibers serves as a solid porous and electronically conducting skeleton, providing large surface areas and space for loading the bioink and diffusion of substrate molecules, respectively. RGO enhances contact between the GOD-matrix and CP, maintaining high conductivity. The composition of the bioink has been systematically optimized. The GOD bioelectrodes show linearly increasing electrocatalytic oxidation current toward glucose concentration up to 48 mM. A hybrid enzymatic biofuel cell equipped with the GOD bioelectrode as a bioanode and a platinum cathode furthermore registers a maximum power density of 5.1 mu W cm(-2) and an open circuit voltage of 0.40 V at 25 degrees C. The new method reported of preparing a bioelectrode by drop-casting the bioink onto the substrate electrode is facile and versatile, with the potential of application also for other enzymatic bioelectrodes.
Three Strange Spaces: An Ethnographic Study in the Construction of Contemporary Jewish Sacred Spaces
(2019)
Modern health care systems are characterized by pronounced prevention and cost-optimized treatments. This dissertation offers novel empirical evidence on how useful such measures can be. The first chapter analyzes how radiation, a main pollutant in health care, can negatively affect cognitive health. The second chapter focuses on the effect of Low Emission Zones on public heath, as air quality is the major external source of health problems. Both chapters point out potentials for preventive measures. Finally, chapter three studies how changes in treatment prices affect the reallocation of hospital resources. In the following, I briefly summarize each chapter and discuss implications for health care systems as well as other policy areas. Based on the National Educational Panel Study that is linked to data on radiation, chapter one shows that radiation can have negative long-term effects on cognitive skills, even at subclinical doses. Exploiting arguably exogenous variation in soil contamination in Germany due to the Chernobyl disaster in 1986, the findings show that people exposed to higher radiation perform significantly worse in cognitive tests 25 years later. Identification is ensured by abnormal rainfall within a critical period of ten days. The results show that the effect is stronger among older cohorts than younger cohorts, which is consistent with radiation accelerating cognitive decline as people get older. On average, a one-standarddeviation increase in the initial level of CS137 (around 30 chest x-rays) is associated with a decrease in the cognitive skills by 4.1 percent of a standard deviation (around 0.05 school years). Chapter one shows that sub-clinical levels of radiation can have negative consequences even after early childhood. This is of particular importance because most of the literature focuses on exposure very early in life, often during pregnancy. However, population exposed after birth is over 100 times larger. These results point to substantial external human capital costs of radiation which can be reduced by choices of medical procedures. There is a large potential for reductions because about one-third of all CT scans are assumed to be not medically justified (Brenner and Hall, 2007). If people receive unnecessary CT scans because of economic incentives, this chapter points to additional external costs of health care policies. Furthermore, the results can inform the cost-benefit trade-off for medically indicated procedures. Chapter two provides evidence about the effectiveness of Low Emission Zones. Low Emission Zones are typically justified by improvements in population health. However, there is little evidence about the potential health benefits from policy interventions aiming at improving air quality in inner-cities. The chapter ask how the coverage of Low Emission Zones air pollution and hospitalization, by exploiting variation in the roll out of Low Emission Zones in Germany. It combines information on the geographic coverage of Low Emission Zones with rich panel data on the universe of German hospitals over the period from 2006 to 2016 with precise information on hospital locations and the annual frequency of detailed diagnoses. In order to establish that our estimates of Low Emission Zones’ health impacts can indeed be attributed to improvements in local air quality, we use data from Germany’s official air pollution monitoring system and assign monitor locations to Low Emission Zones and test whether measures of air pollution are affected by the coverage of a Low Emission Zone. Results in chapter two confirm former results showing that the introduction of Low Emission Zones improved air quality significantly by reducing NO2 and PM10 concentrations. Furthermore, the chapter shows that hospitals which catchment areas are covered by a Low Emission Zone, diagnose significantly less air pollution related diseases, in particular by reducing the incidents of chronic diseases of the circulatory and the respiratory system. The effect is stronger before 2012, which is consistent with a general improvement in the vehicle fleet’s emission standards. Depending on the disease, a one-standard-deviation increase in the coverage of a hospitals catchment area covered by a Low Emission Zone reduces the yearly number of diagnoses up to 5 percent. These findings have strong implications for policy makers. In 2015, overall costs for health care in Germany were around 340 billion euros, of which 46 billion euros for diseases of the circulatory system, making it the most expensive type of disease caused by 2.9 million cases (Statistisches Bundesamt, 2017b). Hence, reductions in the incidence of diseases of the circulatory system may directly reduce society’s health care costs. Whereas chapter one and two study the demand-side in health care markets and thus preventive potential, chapter three analyzes the supply-side. By exploiting the same hospital panel data set as in chapter two, chapter three studies the effect of treatment price shocks on the reallocation of hospital resources in Germany. Starting in 2005, the implementation of the German-DRG-System led to general idiosyncratic treatment price shocks for individual hospitals. Thus far there is little evidence of the impact of general price shocks on the reallocation of hospital resources. Additionally, I add to the exiting literature by showing that price shocks can have persistent effects on hospital resources even when these shocks vanish. However, simple OLS regressions would underestimate the true effect, due to endogenous treatment price shocks. I implement a novel instrument variable strategy that exploits the exogenous variation in the number of days of snow in hospital catchment areas. A peculiarity of the reform allowed variation in days of snow to have a persistent impact on treatment prices. I find that treatment price increases lead to increases in input factors such as nursing staff, physicians and the range of treatments offered but to decreases in the treatment volume. This indicates supplier-induced demand. Furthermore, the probability of hospital mergers and privatization decreases. Structural differences in pre-treatment characteristics between hospitals enhance these effects. For instance, private and larger hospitals are more affected. IV estimates reveal that OLS results are biased towards zero in almost all dimensions because structural hospital differences are correlated with the reallocation of hospital resources. These results are important for several reasons. The G-DRG-Reform led to a persistent polarization of hospital resources, as some hospitals were exposed to treatment price increases, while others experienced reductions. If hospitals increase the treatment volume as a response to price reductions by offering unnecessary therapies, it has a negative impact on population wellbeing and public spending. However, results show a decrease in the range of treatments if prices decrease. Hospitals might specialize more, thus attracting more patients. From a policy perspective it is important to evaluate if such changes in the range of treatments jeopardize an adequate nationwide provision of treatments. Furthermore, the results show a decrease in the number of nurses and physicians if prices decrease. This could partly explain the nursing crisis in German hospitals. However, since hospitals specialize more they might be able to realize efficiency gains which justify reductions in input factors without loses in quality. Further research is necessary to provide evidence for the impact of the G-DRG-Reform on health care quality. Another important aspect are changes in the organizational structure. Many public hospitals have been privatized or merged. The findings show that this is at least partly driven by the G-DRG-Reform. This can again lead to a lack in services offered in some regions if merged hospitals specialize more or if hospitals are taken over by ecclesiastical organizations which do not provide all treatments due to moral conviction. Overall, this dissertation reveals large potential for preventive health care measures and helps to explain reallocation processes in the hospital sector if treatment prices change. Furthermore, its findings have potentially relevant implications for other areas of public policy. Chapter one identifies an effect of low dose radiation on cognitive health. As mankind is searching for new energy sources, nuclear power is becoming popular again. However, results of chapter one point to substantial costs of nuclear energy which have not been accounted yet. Chapter two finds strong evidence that air quality improvements by Low Emission Zones translate into health improvements, even at relatively low levels of air pollution. These findings may, for instance, be of relevance to design further policies targeted at air pollution such as diesel bans. As pointed out in chapter three, the implementation of DRG-Systems may have unintended side-effects on the reallocation of hospital resources. This may also apply to other providers in the health care sector such as resident doctors.