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Fast actuation speed, large-shape deformation and robust responsiveness are critical to synthetic soft actuators. A simultaneous optimization of all these aspects without trade-offs remains unresolved. Here we describe porous polymer actuators that bend in response to acetone vapour (24 kPa, 20 degrees C) at a speed of an order of magnitude faster than the state-of-the-art, coupled with a large-scale locomotion. They are meanwhile multi-responsive towards a variety of organic vapours in both the dry and wet states, thus distinctive from the traditional gel actuation systems that become inactive when dried. The actuator is easy-to-make and survives even after hydrothermal processing (200 degrees C, 24 h) and pressing-pressure (100 MPa) treatments. In addition, the beneficial responsiveness is transferable, being able to turn 'inert' objects into actuators through surface coating. This advanced actuator arises from the unique combination of porous morphology, gradient structure and the interaction between solvent molecules and actuator materials.
These lecture notes are intended as a short introduction to diffusion processes on a domain with a reflecting boundary for graduate students, researchers in stochastic analysis and interested readers. Specific results on stochastic differential equations with reflecting boundaries such as existence and uniqueness, continuity and Markov properties, relation to partial differential equations and submartingale problems are given. An extensive list of references to current literature is included. This book has its origins in a mini-course the author gave at the University of Potsdam and at the Technical University of Berlin in Winter 2013.
Surface displacement at volcanic edifices is related to subsurface processes associated with magma movements, fluid transfers within the volcano edifice and gravity-driven deformation processes. Understanding of associated ground displacements is of importance for assessment of volcanic hazards. For example, volcanic unrest is often preceded by surface uplift, caused by magma intrusion and followed by subsidence, after the withdrawal of magma. Continuous monitoring of the surface displacement at volcanoes therefore might allow the forecasting of upcoming eruptions to some extent. In geophysics, the measured surface displacements allow the parameters of possible deformation sources to be estimated through analytical or numerical modeling. This is one way to improve the understanding of subsurface processes acting at volcanoes. Although the monitoring of volcanoes has significantly improved in the last decades (in terms of technical advancements and number of monitored volcanoes), the forecasting of volcanic eruptions remains puzzling. In this work I contribute towards the understanding of the subsurface processes at volcanoes and thus to the improvement of volcano eruption forecasting. I have investigated the displacement field of Llaima volcano in Chile and of Tendürek volcano in East Turkey by using synthetic aperture radar interferometry (InSAR). Through modeling of the deformation sources with the extracted displacement data, it was possible to gain insights into potential subsurface processes occurring at these two volcanoes that had been barely studied before. The two volcanoes, although of very different origin, composition and geometry, both show a complexity of interacting deformation sources. At Llaima volcano, the InSAR technique was difficult to apply, due to the large decorrelation of the radar signal between the acquisition of images. I developed a model-based unwrapping scheme, which allows the production of reliable displacement maps at the volcano that I used for deformation source modeling. The modeling results show significant differences in pre- and post-eruptive magmatic deformation source parameters. Therefore, I conjecture that two magma chambers exist below Llaima volcano: a post-eruptive deep one and a shallow one possibly due to the pre-eruptive ascent of magma. Similar reservoir depths at Llaima have been confirmed by independent petrologic studies. These reservoirs are interpreted to be temporally coupled. At Tendürek volcano I have found long-term subsidence of the volcanic edifice, which can be described by a large, magmatic, sill-like source that is subject to cooling contraction. The displacement data in conjunction with high-resolution optical images, however, reveal arcuate fractures at the eastern and western flank of the volcano. These are most likely the surface expressions of concentric ring-faults around the volcanic edifice that show low magnitudes of slip over a long time. This might be an alternative mechanism for the development of large caldera structures, which are so far assumed to be generated during large catastrophic collapse events. To investigate the potential subsurface geometry and relation of the two proposed interacting sources at Tendürek, a sill-like magmatic source and ring-faults, I have performed a more sophisticated numerical modeling approach. The optimum source geometries show, that the size of the sill-like source was overestimated in the simple models and that it is difficult to determine the dip angle of the ring-faults with surface displacement data only. However, considering physical and geological criteria a combination of outward-dipping reverse faults in the west and inward-dipping normal faults in the east seem to be the most likely. Consequently, the underground structure at the Tendürek volcano consists of a small, sill-like, contracting, magmatic source below the western summit crater that causes a trapdoor-like faulting along the ring-faults around the volcanic edifice. Therefore, the magmatic source and the ring-faults are also interpreted to be temporally coupled. In addition, a method for data reduction has been improved. The modeling of subsurface deformation sources requires only a relatively small number of well distributed InSAR observations at the earth’s surface. Satellite radar images, however, consist of several millions of these observations. Therefore, the large amount of data needs to be reduced by several orders of magnitude for source modeling, to save computation time and increase model flexibility. I have introduced a model-based subsampling approach in particular for heterogeneously-distributed observations. It allows a fast calculation of the data error variance-covariance matrix, also supports the modeling of time dependent displacement data and is, therefore, an alternative to existing methods.
In addition to their role as a source of reduced carbon, sugars may directly or indirectly control a wide range of activities in plant cells, through transcriptional and post-translational regulation. This control has been studied in detail using Arabidopsis thaliana, where genetic analysis offers many possibilities. Much less is known about perennial woody species. For several years, various aspects of sugar sensing and signalling have been investigated in the grape (Vitis vinifera L.) berry, an organ that accumulates high concentrations of hexoses in the vacuoles of flesh cells. Here we review various aspects of this topic: the molecular basis of sugar transport and its regulation by sugars in grapevine; the functional analysis of several sugar-induced genes; the effects of some biotic and abiotic stresses on the sugar content of the berry; and finally the effects of exogenous sugar supply on the ripening process in field conditions. A picture of complex feedback and multiprocess regulation emerges from these data.
Metabolic systems tend to exhibit steady states that can be measured in terms of their concentrations and fluxes. These measurements can be regarded as a phenotypic representation of all the complex interactions and regulatory mechanisms taking place in the underlying metabolic network. Such interactions determine the system's response to external perturbations and are responsible, for example, for its asymptotic stability or for oscillatory trajectories around the steady state. However, determining these perturbation responses in the absence of fully specified kinetic models remains an important challenge of computational systems biology. Structural kinetic modeling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a parameterised representation of the system's Jacobian matrix in which the model parameters encode information about the enzyme-metabolite interactions. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. The parameter space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Because the sampled parameters are equivalent to the elasticities used in metabolic control analysis (MCA), the results are easy to interpret biologically. In this project, the SKM framework was extended by several novel methodological improvements. These improvements were evaluated in a simulation study using a set of small example pathways with simple Michaelis Menten rate laws. Afterwards, a detailed analysis of the dynamic properties of the neuronal TCA cycle was performed in order to demonstrate how the new insights obtained in this work could be used for the study of complex metabolic systems. The first improvement was achieved by examining the biological feasibility of the elasticity combinations created during Monte Carlo sampling. Using a set of small example systems, the findings showed that the majority of sampled SK-models would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion was formulated that mitigates such infeasible models and the application of this criterion changed the conclusions of the SKM experiment. The second improvement of this work was the application of supervised machine-learning approaches in order to analyse SKM experiments. So far, SKM experiments have focused on the detection of individual enzymes to identify single reactions important for maintaining the stability or oscillatory trajectories. In this work, this approach was extended by demonstrating how SKM enables the detection of ensembles of enzymes or metabolites that act together in an orchestrated manner to coordinate the pathways response to perturbations. In doing so, stable and unstable states served as class labels, and classifiers were trained to detect elasticity regions associated with stability and instability. Classification was performed using decision trees and relevance vector machines (RVMs). The decision trees produced good classification accuracy in terms of model bias and generalizability. RVMs outperformed decision trees when applied to small models, but encountered severe problems when applied to larger systems because of their high runtime requirements. The decision tree rulesets were analysed statistically and individually in order to explore the role of individual enzymes or metabolites in controlling the system's trajectories around steady states. The third improvement of this work was the establishment of a relationship between the SKM framework and the related field of MCA. In particular, it was shown how the sampled elasticities could be converted to flux control coefficients, which were then investigated for their predictive information content in classifier training. After evaluation on the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle with respect to their intrinsic mechanisms responsible for stability or instability. The findings showed that several elasticities were jointly coordinated to control stability and that the main source for potential instabilities were mutations in the enzyme alpha-ketoglutarate dehydrogenase.
Background: DNA-methylation is a common epigenetic tool which plays a crucial role in gene regulation and is essential for cell differentiation and embryonic development. The placenta is an important organ where gene activity can be regulated by epigenetic DNA modifications, including DNA methylation. This is of interest as, the placenta is the interface between the fetus and its environment, the mother. Exposure to environmental toxins and nutrition during pregnancy may alter DNA methylation of the placenta and subsequently placental function and as a result the phenotype of the offspring. The aim of this study was to develop a reliable method to quantify DNA methylation in large clinical studies. This will be a tool to analyze the degree of DNA methylation in the human placenta in relationship to clinical readouts. Methods: Liquid chromatography-electrospray ionization/multi-stage mass spectrometry (LC-ESI/MS/MS) technique was used for the quantification of the 5dmC/dG ratio in placentas from 248 healthy pregnancies. We were able to demonstrate that this method is a reliable and stable way to determine global placental DNA methylation in large clinical trials. Results/Conclusion: The degree of placental DNA methylation seen in our pilot study varies substantially from 2% to 5%. The clinical implications of this variation need to be demonstrated in adequately powered large studies.
The Isabena catchment (445 km(2)), Spain, features highly diverse spatial heterogeneity in land use, lithology and rainfall. Consequently, the relative contribution in terms of water and sediment yield varies immensely between its subcatchments, and also temporally. This study presents the synthesis of similar to 2.5 years of monitoring rainfall, discharge and suspended sediment concentration (SSC) in the five main subcatchments of the Isabena and its outlet.
Continuous discharge at the subcatchment outlets, nine tipping bucket rainfall and automatic SSC samplers (complemented by manual samples), were collected from June 2011 until November 2013. The water stage records were converted to discharge using a rating curve derived with Bayesian regression. For reconstructing sediment yields, the data from the intermittent SSC sampling needed to be interpolated. We employed non-parametric multivariate regression (Quantile Regression Forests, QRF) using the discharge and rainfall data plus different aggregation levels of these as ancillary predictors. The subsequent Monte Carlo simulations allowed the determination of monthly sediment yields and their uncertainty.
The Isabena catchment shows high erosion dynamics with great variability in space and time, with stark contrasts even between adjacent subcatchments. The natural conditions make water and sediment monitoring and instrumentation very challenging; the measurement of discharge is particularly prone to considerable uncertainties. The QRF method employed for reconstructing sedigraphs and monthly yields proved well suited for the task.
D-Peptides have been attributed pharmacological advantages over regular L-peptides, yet design rules are largely unknown. Based on a designed coiled coil-like D/L heterotetramer, named L-Base/D-Acid, we generated a library offering alternative residues for interaction with the D-peptide. Phage display selection yielded one predominant peptide, named HelixA, that differed at 13 positions from the scaffold helix. In addition to the observed D-/L-heterotetramers, ratio-dependent intermediate states were detected by isothermal titration calorimetry. Importantly, the formation of the selected HelixA/D-Acid bundle passes through fewer intermediate states than L-Base/D-Acid. Back mutation of HelixA core residues to L-Base (HelixLL) revealed that the residues at e/g-positions are responsible for the different intermediates. Furthermore, a Val-core variant (PeptideVV) was completely devoid of binding D-Acid, whereas an Ile-core helix (HelixII) interacted with D-Acid in a significantly more specific complex than L-Base.
Starch synthase (SS) and branching enzyme (BE) establish the two glycosidic linkages existing in starch. Both enzymes exist as several isoforms. Enzymes derived from several species were studied extensively both in vivo and in vitro over the last years, however, analyses of a functional interaction of SS and BE isoforms are missing so far. Here, we present data from in vitro studies including both interaction of leaf derived and heterologously expressed SS and BE isoforms. We found that SSI activity in native PAGE without addition of glucans was dependent on at least one of the two BE isoforms active in Arabidopsis leaves. This interaction is most likely not based on a physical association of the enzymes, as demonstrated by immunodetection and native PAGE mobility analysis of SSI, BE2, and BE3. The glucans formed by the action of SSI/BEs were analysed using leaf protein extracts from wild type and be single mutants (Atbe2 and Atbe3 mutant lines) and by different combinations of recombinant proteins. Chain length distribution (CLD) patterns of the formed glucans were irrespective of SSI and BE isoforms origin and still independent of assay conditions. Furthermore, we show that all SS isoforms (SSI-SSIV) were able to interact with BEs and form branched glucans. However, only SSI/BEs generated a polymodal distribution of glucans which was similar to CLD pattern detected in amylopectin of Arabidopsis leaf starch. We discuss the impact of the SSI/BEs interplay for the CLD pattern of amylopectin.