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Hantaviruses are zoonotic viruses transmitted to humans by persistently infected rodents, giving rise to serious outbreaks of hemorrhagic fever with renal syndrome (HFRS) or of hantavirus pulmonary syndrome (HPS), depending on the virus, which are associated with high case fatality rates. There is only limited knowledge about the organization of the viral particles and in particular, about the hantavirus membrane fusion glycoprotein Gc, the function of which is essential for virus entry. We describe here the X-ray structures of Gc from Hantaan virus, the type species hantavirus and responsible for HFRS, both in its neutral pH, monomeric pre-fusion conformation, and in its acidic pH, trimeric post-fusion form. The structures confirm the prediction that Gc is a class II fusion protein, containing the characteristic beta-sheet rich domains termed I, II and III as initially identified in the fusion proteins of arboviruses such as alpha-and flaviviruses. The structures also show a number of features of Gc that are distinct from arbovirus class II proteins. In particular, hantavirus Gc inserts residues from three different loops into the target membrane to drive fusion, as confirmed functionally by structure-guided mutagenesis on the HPS-inducing Andes virus, instead of having a single "fusion loop". We further show that the membrane interacting region of Gc becomes structured only at acidic pH via a set of polar and electrostatic interactions. Furthermore, the structure reveals that hantavirus Gc has an additional N-terminal "tail" that is crucial in stabilizing the post-fusion trimer, accompanying the swapping of domain III in the quaternary arrangement of the trimer as compared to the standard class II fusion proteins. The mechanistic understandings derived from these data are likely to provide a unique handle for devising treatments against these human pathogens.
Background:
Arising from the relevance of sensorimotor training in the therapy of nonspecific low back pain patients and from the value of individualized therapy, the present trial aims to test the feasibility and efficacy of individualized sensorimotor training interventions in patients suffering from nonspecific low back pain.
Methods and study design:
A multicentre, single-blind two-armed randomized controlled trial to evaluate the
effects of a 12-week (3 weeks supervised centre-based and 9 weeks home-based) individualized sensorimotor exercise program is performed. The control group stays inactive during this period. Outcomes are pain, and pain-associated function as well as motor function in adults with nonspecific low back pain. Each participant is scheduled to five measurement dates: baseline (M1), following centre-based training (M2), following home-based training (M3) and at two follow-up time points 6 months (M4) and 12 months (M5) after M1. All investigations and the assessment of the primary and secondary outcomes are performed in a standardized order: questionnaires – clinical examination – biomechanics (motor function). Subsequent statistical procedures are executed after the examination of underlying assumptions for parametric or rather non-parametric testing.
Discussion:
The results and practical relevance of the study will be of clinical and practical relevance not only for researchers and policy makers but also for the general population suffering from nonspecific low back pain.
Trial registration:
Identification number DRKS00010129. German Clinical Trial registered on 3 March 2016.
Dietary approaches contribute to the prevention and treatment of type 2 diabetes. High protein diets were shown to exert beneficial as well as adverse effects on metabolism. However, it is unclear whether the protein origin plays a role in these effects. The LeguAN study investigated in detail the effects of two high protein diets, either from plant or animal origin, in type 2 diabetic patients. Both diets contained 30 EN% protein, 40 EN% carbohydrates, and 30 EN% fat. Fiber content, glycemic index, and composition of dietary fats were similar in both diets. In comparison to previous dietary habits, the fat content was exchanged for protein, while the carbohydrate intake was not modified. Overall, both high protein diets led to improvements of glycemic control, insulin sensitivity, liver fat, and cardiovascular risk markers without remarkable differences between the protein types.
Fasting glucose together with indices of insulin resistance were ameliorated by both interventions to varying extents but without significant differences between protein types. The decline of HbA1c was more pronounced in the plant protein group, whereby the improvement of insulin sensitivity in the animal protein group. The high protein intake had only slight influence on postprandial metabolism seen for free fatty acids and indices of insulin secretion, sensitivity and degradation. Except for GIP release, ingestion of animal and plant meals did not provoke differential metabolic and hormonal responses despite diverse circulating amino acid levels.
The animal protein diets led to a selective increase of fat-free mass and decrease of total fat mass, which was not significantly different from the plant protein diet. Moreover, the high protein diets potently decreased liver fat content by 42% on average which was linked to significantly diminished lipogenesis, free fatty acids flux and lipolysis in adipose tissue. Moderate decline of circulating liver enzymes was induced by both interventions. The liver fat reduction was associated with improved glucose homeostasis and insulin sensitivity which underlines the protective effect of the diets.
Blood lipid profile improved in all subjects and was probably related to the lower fat intake. Reductions in uric acid and markers of inflammation further argued for metabolic benefits of both high protein diets. Systolic and diastolic blood pressure declined only in the PP group pointing a possible role of arginine.
Kidney function was not altered by high protein consumption over 6 weeks. The rapid decrease of serum creatinine in the PP group was noteworthy and should be further investigated. Protein type did not seem to play a role but long-term studies are warranted to fully elucidate safety of high protein regimen.
Varying the source of dietary proteins did not affect the mTOR pathway in adipose tissue and blood cells under neither acute nor chronic settings. Enhancement of whole-body insulin sensitivity suggested also no alteration of mTOR and no impairment of insulin sensitivity in skeletal muscle.
A remarkable outcome was the extensive reduction of FGF21, critical regulator of metabolic processes, by approximately 50% independently of protein type. Whether hepatic ER-stress, ammonia flux or rather macronutrient preferences is behind this paradoxical finding remains to be investigated in detail.
Unlike initial expectations and previous reports plant protein based diet had no clear advantage over animal proteins. The pronounced beneficial effect of animal protein on insulin homeostasis despite high BCAA and methionine intake was certainly unexpected assuming more complex metabolic adaptations occurring upon prolonged consumption. In addition, the reduced fat intake may have also contributed to the overall improvements in both groups.
Taking into account the above observed study results, a short-term diet containing 30 EN% protein (either from plant or animal origin), 40 EN% carbohydrates, and 30 EN% fat with lower SFA amount leads to metabolic improvements in diabetic patients, regardless of protein source.
The spider mite Tetranychus urticae Koch and the aphid Myzus persicae (Sulzer) both infest a number of economically significant crops, including tomato (Solanurn lycopersicum). Although used for decades to control pests, the impact of green lacewing larvae Chrysoperla carnea (Stephens) on plant biochemistry was not investigated. Here, we used profiling methods and targeted analyses to explore the impact of the predator and herbivore(s)-predator interactions on tomato biochemistry. Each pest and pest -predator combination induced a characteristic metabolite signature in the leaf and the fruit thus, the plant exhibited a systemic response. The treatments had a stronger impact on non-volatile metabolites including abscisic acid and amino acids in the leaves in comparison with the fruits. In contrast, the various biotic factors had a greater impact on the carotenoids in the fruits. We identified volatiles such as myrcene and alpha-terpinene which were induced by pest -predator interactions but not by single species, and we demonstrated the involvement of the phytohormone abscisic acid in tritrophic interactions for the first time. More importantly, C. carnea larvae alone impacted the plant metabolome, but the predator did not appear to elicit particular defense pathways on its own. Since the presence of both C. carnea larvae and pest individuals elicited volatiles which were shown to contribute to plant defense, C. carnea larvae could therefore contribute to the reduction of pest infestation, not only by its preying activity, but also by priming responses to generalist herbivores such as T urticae and M. persicae. On the other hand, the use of C. carnea larvae alone did not impact carotenoids thus, was not prejudicial to the fruit quality. The present piece of research highlights the specific impact of predator and tritrophic interactions with green lacewing larvae, spider mites, and aphids on different components of the tomato primary and secondary metabolism for the first time, and provides cues for further in-depth studies aiming to integrate entomological approaches and plant biochemistry.
Macrocycles based on L-cystine were synthesized by ring-closing metathesis (RCM) and subsequently polymerized by entropy-driven ring-opening metathesis polymerization (ED-ROMP). Monomer conversion reached ∼80% in equilibrium and the produced poly(ester-amine-disulfide-alkene)s exhibited apparent molar masses (Mappw) of up to 80 kDa and dispersities (Đ) of ∼2. The polymers can be further functionalized with acid anhydrides and degraded by reductive cleavage of the main-chain disulfide.
Observed recent and expected future increases in frequency and intensity of climatic extremes in central Europe may pose critical challenges for domestic tree species. Continuous dendrometer recordings provide a valuable source of information on tree stem radius variations, offering the possibility to study a tree's response to environmental influences at a high temporal resolution. In this study, we analyze stem radius variations (SRV) of three domestic tree species (beech, oak, and pine) from 2012 to 2014. We use the novel statistical approach of event coincidence analysis (ECA) to investigate the simultaneous occurrence of extreme daily weather conditions and extreme SRVs, where extremes are defined with respect to the common values at a given phase of the annual growth period. Besides defining extreme events based on individual meteorological variables, we additionally introduce conditional and joint ECA as new multivariate extensions of the original methodology and apply them for testing 105 different combinations of variables regarding their impact on SRV extremes. Our results reveal a strong susceptibility of all three species to the extremes of several meteorological variables. Yet, the inter-species differences regarding their response to the meteorological extremes are comparatively low. The obtained results provide a thorough extension of previous correlation-based studies by emphasizing on the timings of climatic extremes only. We suggest that the employed methodological approach should be further promoted in forest research regarding the investigation of tree responses to changing environmental conditions.
The increase in atmospheric methane concentration, which is determined by an imbalance between its sources and sinks, has led to investigations of the methane cycle in various environments. Aquatic environments are of an exceptional interest due to their active involvement in methane cycling worldwide and in particular in areas sensitive to climate change. Furthermore, being connected with each other aquatic environments form networks that can be spread on vast areas involving marine, freshwater and terrestrial ecosystems. Thus, aquatic systems have a high potential to translate local or regional environmental and subsequently ecosystem changes to a bigger scale. Many studies neglect this connectivity and focus on individual aquatic or terrestrial ecosystems.
The current study focuses on environmental controls of the distribution and aerobic oxidation of methane at the example of two aquatic ecosystems. These ecosystems are Arctic fresh water bodies and the Elbe estuary which represent interfaces between freshwater-terrestrial and freshwater-marine environments, respectively.
Arctic water bodies are significant atmospheric sources of methane. At the same time the methane cycle in Arctic water bodies is strongly affected by the surrounding permafrost environment, which is characterized by high amounts of organic carbon. The results of this thesis indicate that the methane concentrations in Arctic lakes and streams substantially vary between each other being regulated by local landscape features (e.g. floodplain area) and the morphology of the water bodies (lakes, streams and river). The highest methane concentrations were detected in the lake outlets and in a floodplain lake complex. In contrast, the methane concentrations measured at different sites of the Lena River did not vary substantially. The lake complexes in comparison to the Lena River, thus, appear as more individual and heterogeneous systems with a pronounced imprint of the surrounding soil environment. Furthermore, connected with each other Arctic aquatic environments have a large potential to transport methane from methane-rich water bodies such as streams and floodplain lakes to aquatic environments relatively poor in methane such as the Lena River.
Estuaries represent hot spots of oceanic methane emissions. Also, estuaries are intermediate zones between methane-rich river water and methane depleted marine water. Substantiated through this thesis at the example of the Elbe estuary, the methane distribution in estuaries, however, cannot entirely be described by the conservative mixing model i.e. gradual decrease from the freshwater end-member to the marine water end-member. In addition to the methane-rich water from the Elbe River mouth substantial methane input occurs from tidal flats, areas of significant interaction between aquatic and terrestrial environments. Thus, this study demonstrates the complex interactions and their consequences for the methane distribution within estuaries. Also it reveals how important it is to investigate estuaries at larger spatial scales.
Methane oxidation (MOX) rates are commonly correlated with methane concentrations. This was shown in previous research studies and was substantiated by the present thesis. In detail, the highest MOX rates in the Arctic water bodies were detected in methane-rich streams and in the floodplain area while in the Elbe estuary the highest MOX rates were observed at the coastal stations. However, in these bordering environments, MOX rates are affected not only via the regulation through methane concentrations. The MOX rates in the Arctic lakes were shown to be also dependent on the abundance and community composition of methane-oxidising bacteria (MOB), that in turn are controlled by local landscape features (regardless of the methane concentrations) and by the transport of MOB between neighbouring environments. In the Elbe estuary, the MOX rates in addition to the methane concentrations are largely affected by the salinity, which is in turn regulated by the mixing of fresh- and marine waters. The magnitude of the salinity impact on MOX rates thereby depends on the MOB community composition and on the rate of the salinity change.
This study extends our knowledge of environmental controls of methane distribution and aerobic methane oxidation in aquatic environments. It also illustrates how important it is to investigate complex ecosystems rather than individual ecosystems to better understand the functioning of whole biomes.
It is the intention of this study to contribute to further rethinking and innovating in the Microcredit business which stands at a turning point – after around 40 years of practice it is endangered to fail as a tool for economic development and to become a doubtful finance product with a random scope instead. So far, a positive impact of Microfinance on the improvement of the lives of the poor could not be confirmed. Over-indebtment of borrowers due to the pre-dominance of consumption Microcredits has become a widespread problem. Furthermore, a rising number of abusive and commercially excessive practices have been reported.
In fact, the Microfinance sector appears to suffer from a major underlying deficit: there does not exist a coherent and transparent understanding of its meaning and objectives so that Microfinance providers worldwide follow their own approaches of Microfinance which tend to differ considerably from each other.
In this sense the study aims at consolidating the multi-faced and very often confusingly different Microcredit profiles that exist nowadays. Subsequently, in this study, the Microfinance spectrum will be narrowed to one clear-cut objective, in fact away from the mere monetary business transactions to poor people it has gradually been reduced to back towards a tool for economic development as originally envisaged by its pioneers.
Hence, the fundamental research question of this study is whether, and under which conditions, Microfinance may attain a positive economic impact leading to an improvement of the living of the poor.
The study is structured in five parts: the three main parts (II.-IV.) are surrounded by an introduction (I.) and conclusion (V.). In part II., the Microfinance sector is analysed critically aiming to identify the challenges persisting as well as their root causes. In the third part, a change to the macroeconomic perspective is undertaken in oder to learn about the potential and requirements of small-scale finance to enhance economic development, particularly within the economic context of less developed countries. By consolidating the insights gained in part IV., the elements of a new concept of Microfinance with the objecitve to achieve economic development of its borrowers are elaborated.
Microfinance is a rather sensitive business the great fundamental idea of which is easily corruptible and, additionally, the recipients of which are predestined victims of abuse due to their limited knowledge in finance. It therefore needs to be practiced responsibly, but also according to clear cut definitions of its meaning and objectives all institutions active in the sector should be devoted to comply with. This is especially relevant as the demand for Microfinance services is expected to rise further within the years coming. For example, the recent refugee migration movement towards Europe entails a vast potential for Microfinance to enable these people to make a new start into economic life. This goes to show that Microfinance may no longer mainly be associated with a less developed economic context, but that it will gain importance as a financial instrument in the developed economies, too.
We present a summary on the current status of two inversion algorithms that are used in EARLINET (European Aerosol Research Lidar Network) for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on a manually controlled inversion of optical data which allows for detailed sensitivity studies. The algorithms allow us to derive particle effective radius as well as volume and surface area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light absorption needs to be known with high accuracy. It is an extreme challenge to retrieve the real part with an accuracy better than 0.05 and the imaginary part with accuracy better than 0.005-0.1 or +/- 50 %. Single-scattering albedo can be computed from the retrieved microphysical parameters and allows us to categorize aerosols into high-and low-absorbing aerosols.
On the basis of a few exemplary simulations with synthetic optical data we discuss the current status of these manually operated algorithms, the potentially achievable accuracy of data products, and the goals for future work. One algorithm was used with the purpose of testing how well microphysical parameters can be derived if the real part of the complex refractive index is known to at least 0.05 or 0.1. The other algorithm was used to find out how well microphysical parameters can be derived if this constraint for the real part is not applied.
The optical data used in our study cover a range of Angstrom exponents and extinction-to-backscatter (lidar) ratios that are found from lidar measurements of various aerosol types. We also tested aerosol scenarios that are considered highly unlikely, e.g. the lidar ratios fall outside the commonly accepted range of values measured with Raman lidar, even though the underlying microphysical particle properties are not uncommon. The goal of this part of the study is to test the robustness of the algorithms towards their ability to identify aerosol types that have not been measured so far, but cannot be ruled out based on our current knowledge of aerosol physics.
We computed the optical data from monomodal logarithmic particle size distributions, i.e. we explicitly excluded the more complicated case of bimodal particle size distributions which is a topic of ongoing research work. Another constraint is that we only considered particles of spherical shape in our simulations. We considered particle radii as large as 7-10 mu m in our simulations where the Potsdam algorithm is limited to the lower value. We considered optical-data errors of 15% in the simulation studies. We target 50% uncertainty as a reasonable threshold for our data products, though we attempt to obtain data products with less uncertainty in future work.
Numerous reports of relatively rapid climate changes over the past century make a clear case of the impact of aerosols and clouds, identified as sources of largest uncertainty in climate projections. Earth’s radiation balance is altered by aerosols depending on their size, morphology and chemical composition. Competing effects in the atmosphere can be further studied by investigating the evolution of aerosol microphysical properties, which are the focus of the present work.
The aerosol size distribution, the refractive index, and the single scattering albedo are commonly used such properties linked to aerosol type, and radiative forcing. Highly advanced lidars (light detection and ranging) have reduced aerosol monitoring and optical profiling into a routine process. Lidar data have been widely used to retrieve the size distribution through the inversion of the so-called Lorenz-Mie model (LMM). This model offers a reasonable treatment for spherically approximated particles, it no longer provides, though, a viable description for other naturally occurring arbitrarily shaped particles, such as dust particles. On the other hand, non-spherical geometries as simple as spheroids reproduce certain optical properties with enhanced accuracy. Motivated by this, we adapt the LMM to accommodate the spheroid-particle approximation introducing the notion of a two-dimensional (2D) shape-size distribution.
Inverting only a few optical data points to retrieve the shape-size distribution is classified as a non-linear ill-posed problem. A brief mathematical analysis is presented which reveals the inherent tendency towards highly oscillatory solutions, explores the available options for a generalized solution through regularization methods and quantifies the ill-posedness. The latter will improve our understanding on the main cause fomenting instability in the produced solution spaces. The new approach facilitates the exploitation of additional lidar data points from depolarization measurements, associated with particle non-sphericity. However, the generalization of LMM vastly increases the complexity of the problem. The underlying theory for the calculation of the involved optical cross sections (T-matrix theory) is computationally so costly, that would limit a retrieval analysis to an unpractical point. Moreover the discretization of the model equation by a 2D collocation method, proposed in this work, involves double integrations which are further time consuming. We overcome these difficulties by using precalculated databases and a sophisticated retrieval software (SphInX: Spheroidal Inversion eXperiments) especially developed for our purposes, capable of performing multiple-dataset inversions and producing a wide range of microphysical retrieval outputs.
Hybrid regularization in conjunction with minimization processes is used as a basis for our algorithms. Synthetic data retrievals are performed simulating various atmospheric scenarios in order to test the efficiency of different regularization methods. The gap in contemporary literature in providing full sets of uncertainties in a wide variety of numerical instances is of major concern here. For this, the most appropriate methods are identified through a thorough analysis on an overall-behavior basis regarding accuracy and stability. The general trend of the initial size distributions is captured in our numerical experiments and the reconstruction quality depends on data error level. Moreover, the need for more or less depolarization points is explored for the first time from the point of view of the microphysical retrieval. Finally, our approach is tested in various measurement cases giving further insight for future algorithm improvements.
In the study of relativistic jets one of the key open questions is their interaction with the environment on the microscopic level. Here, we study the initial evolution of both electron-proton (e(-)-p(+)) and electron-positron (e(+/-)) relativistic jets containing helical magnetic fields, focusing on their interaction with an ambient plasma. We have performed simulations of "global" jets containing helical magnetic fields in order to examine how helical magnetic fields affect kinetic instabilities such as the Weibel instability, the kinetic Kelvin-Helmholtz instability (kKHI) and the Mushroom instability (MI). In our initial simulation study these kinetic instabilities are suppressed and new types of instabilities can grow. In the e(-)-p(+) jet simulation a recollimation-like instability occurs and jet electrons are strongly perturbed. In the e(+/-) jet simulation a recollimation-like instability occurs at early times followed by a kinetic instability and the general structure is similar to a simulation without helical magnetic field. Simulations using much larger systems are required in order to thoroughly follow the evolution of global jets containing helical magnetic fields.
The relationship between climate and forest productivity is an intensively studied subject in forest science. This Thesis is embedded within the general framework of future forest growth under climate change and its implications for the ongoing forest conversion. My objective is to investigate the future forest productivity at different spatial scales (from a single specific forest stand to aggregated information across Germany) with focus on oak-pine forests in the federal state of Brandenburg. The overarching question is: how are the oak-pine forests affected by climate change described by a variety of climate scenarios. I answer this question by using a model based analysis of tree growth processes and responses to different climate scenarios with emphasis on drought events. In addition, a method is developed which considers climate change uncertainty of forest management planning.
As a first 'screening' of climate change impacts on forest productivity, I calculated the change in net primary production on the base of a large set of climate scenarios for different tree species and the total area of Germany. Temperature increases up to 3 K lead to positive effects on the net primary production of all selected tree species. But, in water-limited regions this positive net primary production trend is dependent on the length of drought periods which results in a larger uncertainty regarding future forest productivity. One of the regions with the highest uncertainty of net primary production development is the federal state of Brandenburg.
To enhance the understanding and ability of model based analysis of tree growth sensitivity to drought stress two water uptake approaches in pure pine and mixed oak-pine stands are contrasted. The first water uptake approach consists of an empirical function for root water uptake. The second approach is more mechanistic and calculates the differences of soil water potential along a soil-plant-atmosphere continuum. I assumed the total root resistance to vary at low, medium and high total root resistance levels. For validation purposes three data sets on different tree growth relevant time scales are used. Results show that, except the mechanistic water uptake approach with high total root resistance, all transpiration outputs exceeded observed values. On the other hand high transpiration led to a better match of observed soil water content. The strongest correlation between simulated and observed annual tree ring width occurred with the mechanistic water uptake approach and high total root resistance. The findings highlight the importance of severe drought as a main reason for small diameter increment, best supported by the mechanistic water uptake approach with high root resistance. However, if all aspects of the data sets are considered no approach can be judged superior to the other. I conclude that the uncertainty of future productivity of water-limited forest ecosystems under changing environmental conditions is linked to simulated root water uptake.
Finally my study aimed at the impacts of climate change combined with management scenarios on an oak-pine forest to evaluate growth, biomass and the amount of harvested timber. The pine and the oak trees are 104 and 9 years old respectively. Three different management scenarios with different thinning intensities and different climate scenarios are used to simulate the performance of management strategies which explicitly account for the risks associated with achieving three predefined objectives (maximum carbon storage, maximum harvested timber, intermediate). I found out that in most cases there is no general management strategy which fits best to different objectives. The analysis of variance in the growth related model outputs showed an increase of climate uncertainty with increasing climate warming. Interestingly, the increase of climate-induced uncertainty is much higher from 2 to 3 K than from 0 to 2 K.
Recently, due to an increasing demand on functionality and flexibility, beforehand isolated systems have become interconnected to gain powerful adaptive Systems of Systems (SoS) solutions with an overall robust, flexible and emergent behavior. The adaptive SoS comprises a variety of different system types ranging from small embedded to adaptive cyber-physical systems. On the one hand, each system is independent, follows a local strategy and optimizes its behavior to reach its goals. On the other hand, systems must cooperate with each other to enrich the overall functionality to jointly perform on the SoS level reaching global goals, which cannot be satisfied by one system alone. Due to difficulties of local and global behavior optimizations conflicts may arise between systems that have to be solved by the adaptive SoS.
This thesis proposes a modeling language that facilitates the description of an adaptive SoS by considering the adaptation capabilities in form of feedback loops as first class entities. Moreover, this thesis adopts the Models@runtime approach to integrate the available knowledge in the systems as runtime models into the modeled adaptation logic. Furthermore, the modeling language focuses on the description of system interactions within the adaptive SoS to reason about individual system functionality and how it emerges via collaborations to an overall joint SoS behavior. Therefore, the modeling language approach enables the specification of local adaptive system behavior, the integration of knowledge in form of runtime models and the joint interactions via collaboration to place the available adaptive behavior in an overall layered, adaptive SoS architecture.
Beside the modeling language, this thesis proposes analysis rules to investigate the modeled adaptive SoS, which enables the detection of architectural patterns as well as design flaws and pinpoints to possible system threats. Moreover, a simulation framework is presented, which allows the direct execution of the modeled SoS architecture. Therefore, the analysis rules and the simulation framework can be used to verify the interplay between systems as well as the modeled adaptation effects within the SoS. This thesis realizes the proposed concepts of the modeling language by mapping them to a state of the art standard from the automotive domain and thus, showing their applicability to actual systems. Finally, the modeling language approach is evaluated by remodeling up to date research scenarios from different domains, which demonstrates that the modeling language concepts are powerful enough to cope with a broad range of existing research problems.
Decreasing groundwater levels in many parts of Germany and decreasing low flows in Central Europe have created a need for adaptation measures to stabilize the water balance and to increase low flows. The objective of our study was to estimate the impact of ditch water level management on stream-aquifer interactions in small lowland catchments of the mid-latitudes. The water balance of a ditch-irrigated area and fluxes between the subsurface and the adjacent stream were modeled for three runoff recession periods using the Hydrus-2D software package. The results showed that the subsurface flow to the stream was closely related to the difference between the water level in the ditch system and the stream. Evapotranspiration during the growing season additionally reduced base flow. It was crucial to stop irrigation during a recession period to decrease water withdrawal from the stream and enhance the base flow by draining the irrigated area. Mean fluxes to the stream were between 0.04 and 0.64 ls(-1) for the first 20 days of the low-flow periods. This only slightly increased the flow in the stream, whose mean was 57 ls(-1) during the period with the lowest flows. Larger areas would be necessary to effectively increase flows in mesoscale catchments.
In many statistical applications, the aim is to model the relationship between covariates and some outcomes. A choice of the appropriate model depends on the outcome and the research objectives, such as linear models for continuous outcomes, logistic models for binary outcomes and the Cox model for time-to-event data. In epidemiological, medical, biological, societal and economic studies, the logistic regression is widely used to describe the relationship between a response variable as binary outcome and explanatory variables as a set of covariates. However, epidemiologic cohort studies are quite expensive regarding data management since following up a large number of individuals takes long time. Therefore, the case-cohort design is applied to reduce cost and time for data collection. The case-cohort sampling collects a small random sample from the entire cohort, which is called subcohort. The advantage of this design is that the covariate and follow-up data are recorded only on the subcohort and all cases (all members of the cohort who develop the event of interest during the follow-up process).
In this thesis, we investigate the estimation in the logistic model for case-cohort design. First, a model with a binary response and a binary covariate is considered. The maximum likelihood estimator (MLE) is described and its asymptotic properties are established. An estimator for the asymptotic variance of the estimator based on the maximum likelihood approach is proposed; this estimator differs slightly from the estimator introduced by Prentice (1986). Simulation results for several proportions of the subcohort show that the proposed estimator gives lower empirical bias and empirical variance than Prentice's estimator.
Then the MLE in the logistic regression with discrete covariate under case-cohort design is studied. Here the approach of the binary covariate model is extended. Proving asymptotic normality of estimators, standard errors for the estimators can be derived. The simulation study demonstrates the estimation procedure of the logistic regression model with a one-dimensional discrete covariate. Simulation results for several proportions of the subcohort and different choices of the underlying parameters indicate that the estimator developed here performs reasonably well. Moreover, the comparison between theoretical values and simulation results of the asymptotic variance of estimator is presented.
Clearly, the logistic regression is sufficient for the binary outcome refers to be available for all subjects and for a fixed time interval. Nevertheless, in practice, the observations in clinical trials are frequently collected for different time periods and subjects may drop out or relapse from other causes during follow-up. Hence, the logistic regression is not appropriate for incomplete follow-up data; for example, an individual drops out of the study before the end of data collection or an individual has not occurred the event of interest for the duration of the study. These observations are called censored observations. The survival analysis is necessary to solve these problems. Moreover, the time to the occurence of the event of interest is taken into account. The Cox model has been widely used in survival analysis, which can effectively handle the censored data. Cox (1972) proposed the model which is focused on the hazard function. The Cox model is assumed to be
λ(t|x) = λ0(t) exp(β^Tx)
where λ0(t) is an unspecified baseline hazard at time t and X is the vector of covariates, β is a p-dimensional vector of coefficient.
In this thesis, the Cox model is considered under the view point of experimental design. The estimability of the parameter β0 in the Cox model, where β0 denotes the true value of β, and the choice of optimal covariates are investigated. We give new representations of the observed information matrix In(β) and extend results for the Cox model of Andersen and Gill (1982). In this way conditions for the estimability of β0 are formulated. Under some regularity conditions, ∑ is the inverse of the asymptotic variance matrix of the MPLE of β0 in the Cox model and then some properties of the asymptotic variance matrix of the MPLE are highlighted. Based on the results of asymptotic estimability, the calculation of local optimal covariates is considered and shown in examples. In a sensitivity analysis, the efficiency of given covariates is calculated. For neighborhoods of the exponential models, the efficiencies have then been found. It is appeared that for fixed parameters β0, the efficiencies do not change very much for different baseline hazard functions. Some proposals for applicable optimal covariates and a calculation procedure for finding optimal covariates are discussed.
Furthermore, the extension of the Cox model where time-dependent coefficient are allowed, is investigated. In this situation, the maximum local partial likelihood estimator for estimating the coefficient function β(·) is described. Based on this estimator, we formulate a new test procedure for testing, whether a one-dimensional coefficient function β(·) has a prespecified parametric form, say β(·; ϑ). The score function derived from the local constant partial likelihood function at d distinct grid points is considered. It is shown that the distribution of the properly standardized quadratic form of this d-dimensional vector under the null hypothesis tends to a Chi-squared distribution. Moreover, the limit statement remains true when replacing the unknown ϑ0 by the MPLE in the hypothetical model and an asymptotic α-test is given by the quantiles or p-values of the limiting Chi-squared distribution. Finally, we propose a bootstrap version of this test. The bootstrap test is only defined for the special case of testing whether the coefficient function is constant. A simulation study illustrates the behavior of the bootstrap test under the null hypothesis and a special alternative. It gives quite good results for the chosen underlying model.
References
P. K. Andersen and R. D. Gill. Cox's regression model for counting processes: a large samplestudy. Ann. Statist., 10(4):1100{1120, 1982.
D. R. Cox. Regression models and life-tables. J. Roy. Statist. Soc. Ser. B, 34:187{220, 1972.
R. L. Prentice. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika, 73(1):1{11, 1986.
The LEA (late embryogenesis abundant) proteins COR15A and COR15B from Arabidopsis thaliana are intrinsically disordered under fully hydrated conditions, but obtain α-helical structure during dehydration, which is reversible upon rehydration. To understand this unusual structural transition, both proteins were investigated by circular dichroism (CD) and molecular dynamics (MD) approaches. MD simulations showed unfolding of the proteins in water, in agreement with CD data obtained with both HIS-tagged and untagged recombinant proteins. Mainly intramolecular hydrogen bonds (H-bonds) formed by the protein backbone were replaced by H-bonds with water molecules. As COR15 proteins function in vivo as protectants in leaves partially dehydrated by freezing, unfolding was further assessed under crowded conditions. Glycerol reduced (40%) or prevented (100%) unfolding during MD simulations, in agreement with CD spectroscopy results. H-bonding analysis indicated that preferential exclusion of glycerol from the protein backbone increased stability of the folded state.
Foam fractionation of surfactant and protein solutions is a process dedicated to separate surface active molecules from each other due to their differences in surface activities. The process is based on forming bubbles in a certain mixed solution followed by detachment and rising of bubbles through a certain volume of this solution, and consequently on the formation of a foam layer on top of the solution column. Therefore, systematic analysis of this whole process comprises of at first investigations dedicated to the formation and growth of single bubbles in solutions, which is equivalent to the main principles of the well-known bubble pressure tensiometry. The second stage of the fractionation process includes the detachment of a single bubble from a pore or capillary tip and its rising in a respective aqueous solution. The third and final stage of the process is the formation and stabilization of the foam created by these bubbles, which contains the adsorption layers formed at the growing bubble surface, carried up and gets modified during the bubble rising and finally ends up as part of the foam layer.
Bubble pressure tensiometry and bubble profile analysis tensiometry experiments were performed with protein solutions at different bulk concentrations, solution pH and ionic strength in order to describe the process of accumulation of protein and surfactant molecules at the bubble surface. The results obtained from the two complementary methods allow understanding the mechanism of adsorption, which is mainly governed by the diffusional transport of the adsorbing protein molecules to the bubble surface. This mechanism is the same as generally discussed for surfactant molecules. However, interesting peculiarities have been observed for protein adsorption kinetics at sufficiently short adsorption times. First of all, at short adsorption times the surface tension remains constant for a while before it decreases as expected due to the adsorption of proteins at the surface. This time interval is called induction time and it becomes shorter with increasing protein bulk concentration. Moreover, under special conditions, the surface tension does not stay constant but even increases over a certain period of time. This so-called negative surface pressure was observed for BCS and BLG and discussed for the first time in terms of changes in the surface conformation of the adsorbing protein molecules. Usually, a negative surface pressure would correspond to a negative adsorption, which is of course impossible for the studied protein solutions. The phenomenon, which amounts to some mN/m, was rather explained by simultaneous changes in the molar area required by the adsorbed proteins and the non-ideality of entropy of the interfacial layer. It is a transient phenomenon and exists only under dynamic conditions.
The experiments dedicated to the local velocity of rising air bubbles in solutions were performed in a broad range of BLG concentration, pH and ionic strength. Additionally, rising bubble experiments were done for surfactant solutions in order to validate the functionality of the instrument. It turns out that the velocity of a rising bubble is much more sensitive to adsorbing molecules than classical dynamic surface tension measurements. At very low BLG or surfactant concentrations, for example, the measured local velocity profile of an air bubble is changing dramatically in time scales of seconds while dynamic surface tensions still do not show any measurable changes at this time scale. The solution’s pH and ionic strength are important parameters that govern the measured rising velocity for protein solutions. A general theoretical description of rising bubbles in surfactant and protein solutions is not available at present due to the complex situation of the adsorption process at a bubble surface in a liquid flow field with simultaneous Marangoni effects. However, instead of modelling the complete velocity profile, new theoretical work has been started to evaluate the maximum values in the profile as characteristic parameter for dynamic adsorption layers at the bubble surface more quantitatively.
The studies with protein-surfactant mixtures demonstrate in an impressive way that the complexes formed by the two compounds change the surface activity as compared to the original native protein molecules and therefore lead to a completely different retardation behavior of rising bubbles. Changes in the velocity profile can be interpreted qualitatively in terms of increased or decreased surface activity of the formed protein-surfactant complexes. It was also observed that the pH and ionic strength of a protein solution have strong effects on the surface activity of the protein molecules, which however, could be different on the rising bubble velocity and the equilibrium adsorption isotherms. These differences are not fully understood yet but give rise to discussions about the structure of protein adsorption layer under dynamic conditions or in the equilibrium state.
The third main stage of the discussed process of fractionation is the formation and characterization of protein foams from BLG solutions at different pH and ionic strength. Of course a minimum BLG concentration is required to form foams. This minimum protein concentration is a function again of solution pH and ionic strength, i.e. of the surface activity of the protein molecules. Although at the isoelectric point, at about pH 5 for BLG, the hydrophobicity and hence the surface activity should be the highest, the concentration and ionic strength effects on the rising velocity profile as well as on the foamability and foam stability do not show a maximum. This is another remarkable argument for the fact that the interfacial structure and behavior of BLG layers under dynamic conditions and at equilibrium are rather different. These differences are probably caused by the time required for BLG molecules to adapt respective conformations once they are adsorbed at the surface.
All bubble studies described in this work refer to stages of the foam fractionation process. Experiments with different systems, mainly surfactant and protein solutions, were performed in order to form foams and finally recover a solution representing the foamed material. As foam consists to a large extent of foam lamella – two adsorption layers with a liquid core – the concentration in a foamate taken from foaming experiments should be enriched in the stabilizing molecules. For determining the concentration of the foamate, again the very sensitive bubble rising velocity profile method was applied, which works for any type of surface active materials. This also includes technical surfactants or protein isolates for which an accurate composition is unknown.
Molecular paleoclimate reconstructions over the last 9 ka from a peat sequence in South China
(2016)
To achieve a better understanding of Holocene climate change in the monsoon regions of China, we investigated the molecular distributions and carbon and hydrogen isotope compositions delta C-13 and delta D values) of long-chain n-alkanes in a peat core from the Shiwangutian SWGT) peatland, south China over the last 9 ka. By comparisons with other climate records, we found that the delta C-13 values of the long-chain n-alkanes can be a proxy for humidity, while the dD values of the long-chain n-alkanes primarily recorded the moisture source dD signal during 9-1.8 ka BP and responded to the dry climate during 1.8-0.3 ka BP. Together with the average chain length ACL) and the carbon preference index CPI) data, the climate evolution over last 9 ka in the SWGT peatland can be divided into three stages. During the first stage 9-5 ka BP), the delta C-13 values were depleted and CPI and Paq values were low, while ACL values were high. They reveal a period of warm and wet climate, which is regarded as the Holocene optimum. The second stage 5-1.8 ka BP) witnessed a shift to relatively cool and dry climate, as indicated by the more positive delta C-13 values and lower ACL values. During the third stage 1.8-0.3 ka BP), the delta C-13, delta D, CPI and Paq values showed marked increase and ACL values varied greatly, implying an abrupt change to cold and dry conditions. This climate pattern corresponds to the broad decline in Asian monsoon intensity through the latter part of the Holocene. Our results do not support a later Holocene optimum in south China as suggested by previous studies.
The speciation of 2-Mercaptopyridine in aqueous solution has been investigated with nitrogen 1s Near Edge X-ray Absorption Fine Structure spectroscopy and time dependent Density Functional Theory. The prevalence of distinct species as a function of the solvent basicity is established. No indications of dimerization towards high concentrations are found. The determination of different molecular structures of 2-Mercaptopyridine in aqueous solution is put into the context of proton-transfer in keto-enol and thione-thiol tautomerisms. (C) 2016 The Authors. Published by Elsevier B.V.
More effort — more results
(2016)
The development of 'omics' technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of 'omics' data integration together with an overview of available statistical methods and tools.