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P>Computing the magnitude of an earthquake requires correcting for the propagation effects from the source to the receivers. This is often accomplished by performing numerical simulations using a suitable Earth model. In this work, the energy magnitude M(e) is considered and its determination is performed using theoretical spectral amplitude decay functions over teleseismic distances based on the global Earth model AK135Q. Since the high frequency part (above the corner frequency) of the source spectrum has to be considered in computing M(e), the influence of propagation and site effects may not be negligible and they could bias the single station M(e) estimations. Therefore, in this study we assess the inter- and intrastation distributions of errors by considering the M(e) residuals computed for a large data set of earthquakes recorded at teleseismic distances by seismic stations deployed worldwide. To separate the inter- and intrastation contribution of errors, we apply a maximum likelihood approach to the M(e) residuals. We show that the interstation errors (describing a sort of site effect for a station) are within +/- 0.2 magnitude units for most stations and their spatial distribution reflects the expected lateral variation affecting the velocity and attenuation of the Earth's structure in the uppermost layers, not accounted for by the 1-D AK135Q model. The variance of the intrastation error distribution (describing the record-to-record component of variability) is larger than the interstation one (0.240 against 0.159), and the spatial distribution of the errors is not random but shows specific patterns depending on the source-to-station paths. The set of coefficients empirically determined may be used in the future to account for the heterogeneities of the real Earth not considered in the theoretical calculations of the spectral amplitude decay functions used to correct the recorded data for propagation effects.
Soil moisture at the plot or hill-slope scale is an important link between local vadose zone hydrology and catchment hydrology. However, so far only a few methods are on the way to close this gap between point measurements and remote sensing. One new measurement methodology that could determine integral soil moisture at this scale is the aboveground sensing of cosmic-ray neutrons, more precisely of ground albedo neutrons. The present study performed ground albedo neutron sensing (GANS) at an agricultural field in northern Germany. To test the method it was accompanied by other soil moisture measurements for a summer period with corn crops growing on the field and a later autumn-winter period without crops and a longer period of snow cover. Additionally, meteorological data and aboveground crop biomass were included in the evaluation. Hourly values of ground albedo neutron sensing showed a high statistical variability. Six-hourly values corresponded well with classical soil moisture measurements, after calibration based on one reference dry period and three wet periods of a few days each. Crop biomass seemed to influence the measurements only to minor degree, opposed to snow cover which has a more substantial impact on the measurements. The latter could be quantitatively related to a newly introduced field neutron ratio estimated from neutron counting rates of two energy ranges. Overall, our study outlines a procedure to apply the ground albedo neutron sensing method based on devices now commercially available, without the need for accompanying numerical simulations and suited for longer monitoring periods after initial calibration.
Aiming at the stimulation of intrinsic microbial activity, pulses of pure oxygen or pressurized air were recurrently injected into groundwater polluted with chlorobenzene. To achieve well-controlled conditions and intensive sampling, a large, vertical underground tank was filled with the local unconfined sandy aquifer material. In the course of two individual gas injections, one using pure oxygen and one using pressurized air, the mass transfer of individual gas species between trapped gas phase and groundwater was studied. Field data on the dissolved gas composition in the groundwater were combined with a kinetic model on gas dissolution and transport in porous media. Phase mass transfer of individual gas components caused a temporary enrichment of nitrogen, and to a lower degree of methane, in trapped gas leading to the formation of excess dissolved nitrogen levels downgradient from the dissolving gas phase. By applying a novel gas sampling method for dissolved gases in groundwater it was shown that dissolved nitrogen can be used as a partitioning tracer to indicate complete gas dissolution in porous media.
New Public Management
(2011)
New Public Management hat in den vergangenen Jahren die Ansätze und das Verständnis moderner Verwaltungsführung maßgebend beeinflusst.
Stossrichtungen und Grundanliegen dieses Modells wurden zum Teil in die Führungspraxis übernommen und stellen in vielerlei Hinsicht nach wie vor Entwicklungsziele und Leitlinien für die Steuerung und Führung der öffentlichen Verwaltung dar. NPM soll die öffentliche Verwaltung an geforderte Neuausrichtungen anpassen und effizienter gestalten. Ziele und Gestaltung der öffentlichen Verwaltung unter NPM und die dazu notwendigen Instrumente werden in diesem Lehrbuch umfassend und strukturiert erläutert. Besonderes Augenmerk wird dabei auf die Veränderungslinien und -ansätze für die Verwaltungsführung gelegt.
Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.
Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.
Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.
Large-scale co-expression approach to dissect secondary cell wall formation across plant species
(2011)
Plant cell walls are complex composites largely consisting of carbohydrate-based polymers, and are generally divided into primary and secondary walls based on content and characteristics. Cellulose microfibrils constitute a major component of both primary and secondary cell walls and are synthesized at the plasma membrane by cellulose synthase (CESA) complexes. Several studies in Arabidopsis have demonstrated the power of co-expression analyses to identify new genes associated with secondary wall cellulose biosynthesis. However, across-species comparative co-expression analyses remain largely unexplored. Here, we compared co-expressed gene vicinity networks of primary and secondary wall CESAsin Arabidopsis, barley, rice, poplar, soybean, Medicago, and wheat, and identified gene families that are consistently co-regulated with cellulose biosynthesis. In addition to the expected polysaccharide acting enzymes, we also found many gene families associated with cytoskeleton, signaling, transcriptional regulation, oxidation, and protein degradation. Based on these analyses, we selected and biochemically analyzed T-DNA insertion lines corresponding to approximately twenty genes from gene families that re-occur in the co-expressed gene vicinity networks of secondary wall CESAs across the seven species. We developed a statistical pipeline using principal component analysis and optimal clustering based on silhouette width to analyze sugar profiles. One of the mutants, corresponding to a pinoresinol reductase gene, displayed disturbed xylem morphology and held lower levels of lignin molecules. We propose that this type of large-scale co-expression approach, coupled with statistical analysis of the cell wall contents, will be useful to facilitate rapid knowledge transfer across plant species.
Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem.
Results: We first review the shortcomings of the existing generic sampling scheme-switch randomization-and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties.
Spatiotemporal dynamics of the Calvin cycle multistationarity and symmetry breaking instabilities
(2011)
The possibility of controlling the Calvin cycle has paramount implications for increasing the production of biomass. Multistationarity, as a dynamical feature of systems, is the first obvious candidate whose control could find biotechnological applications. Here we set out to resolve the debate on the multistationarity of the Calvin cycle. Unlike the existing simulation-based studies, our approach is based on a sound mathematical framework, chemical reaction network theory and algebraic geometry, which results in provable results for the investigated model of the Calvin cycle in which we embed a hierarchy of realistic kinetic laws. Our theoretical findings demonstrate that there is a possibility for multistationarity resulting from two sources, homogeneous and inhomogeneous instabilities, which partially settle the debate on multistability of the Calvin cycle. In addition, our tractable analytical treatment of the bifurcation parameters can be employed in the design of validation experiments.
Integration of high-throughput data with functional annotation by graph-theoretic methods has been postulated as promising way to unravel the function of unannotated genes. Here, we first review the existing graph-theoretic approaches for automated gene function annotation and classify them into two categories with respect to their relation to two instances of transductive learning on networks - with dynamic costs and with constant costs - depending on whether or not ontological relationship between functional terms is employed. The determined categories allow to characterize the computational complexity of the existing approaches and establish the relation to classical graph-theoretic problems, such as bisection and multiway cut. In addition, our results point out that the ontological form of the structured functional knowledge does not lower the complexity of the transductive learning with dynamic costs - one of the key problems in modern systems biology. The NP-hardness of automated gene annotation renders the development of heuristic or approximation algorithms a priority for additional research.
Analysis of biological networks requires assessing the statistical significance of network-based predictions by using a realistic null model. However, the existing network null model, switch randomization, is unsuitable for metabolic networks, as it does not include physical constraints and generates unrealistic reactions. We present JMassBalance, a tool for mass-balanced randomization and analysis of metabolic networks. The tool allows efficient generation of large sets of randomized networks under the physical constraint of mass balance. In addition, various structural properties of the original and randomized networks can be calculated, facilitating the identification of the salient properties of metabolic networks with a biologically meaningful null model.
Identifying causal links (couplings) is a fundamental problem that facilitates the understanding of emerging structures in complex networks. We propose and analyze inner composition alignment-a novel, permutation-based asymmetric association measure to detect regulatory links from very short time series, currently applied to gene expression. The measure can be used to infer the direction of couplings, detect indirect (superfluous) links, and account for autoregulation. Applications to the gene regulatory network of E. coli are presented.
Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns
(2011)
We conducted a comparative analysis of the root metabolome of six parental maize inbred lines and their 14 corresponding hybrids showing fresh weight heterosis. We demonstrated that the metabolic profiles not only exhibit distinct features for each hybrid line compared with its parental lines, but also separate reciprocal hybrids. Reconstructed metabolic networks, based on robust correlations between metabolic profiles, display a higher network density in most hybrids as compared with the corresponding inbred lines. With respect to metabolite level inheritance, additive, dominant and overdominant patterns are observed with no specific overrepresentation. Despite the observed complexity of the inheritance pattern, for the majority of metabolites the variance observed in all 14 hybrids is lower compared with inbred lines. Deviations of metabolite levels from the average levels of the hybrids correlate negatively with biomass, which could be applied for developing predictors of hybrid performance based on characteristics of metabolite patterns.
Describing the determinants of robustness of biological systems has become one of the central questions in systems biology. Despite the increasing research efforts, it has proven difficult to arrive at a unifying definition for this important concept. We argue that this is due to the multifaceted nature of the concept of robustness and the possibility to formally capture it at different levels of systemic formalisms (e.g, topology and dynamic behavior). Here we provide a comprehensive review of the existing definitions of robustness pertaining to metabolic networks. As kinetic approaches have been excellently reviewed elsewhere, we focus on definitions of robustness proposed within graph-theoretic and constraint-based formalisms.
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.
Aggregation of the Amyloid β (Aβ) peptide to amyloid fibrils is associated with the outbreak of Alzheimer’s disease. Early aggregation intermediates in form of soluble oligomers are of special interest as they are believed to be the major toxic components in the process. These oligomers are of disordered and transient nature. Therefore, their detailed molecular structure is difficult to access experimentally and often remains unknown. In the present work extensive, fully atomistic replica exchange molecular dynamics simulations were performed to study the preaggregated, monomer states and early aggregation intermediates (dimers, trimers) of Aβ(25-35) and Aβ(10-35)-NH2 in aqueous solution. The folding and aggregation of Aβ(25-35) were studied at neutral pH and 293 K. Aβ(25-35) monomers mainly adopt β-hairpin conformations characterized by a β-turn formed by residues G29 and A30, and a β-sheet between residues N27–K28 and I31–I32 in equilibrium with coiled conformations. The β-hairpin conformations served as initial configurations to model spontaneous aggregation of Aβ(25-35). As expected, within the Aβ(25-35) dimer and trimer ensembles many different poorly populated conformations appear. Nevertheless, we were able to distinguish between disordered and fibril-like oligomers. Whereas disordered oligomers are rather compact with few intermolecular hydrogen bonds (HBs), fibril-like oligomers are characterized by the formation of large intermolecular β-sheets. In most of the fibril-like dimers and trimers individual peptides are fully extended forming in- or out-of-register antiparallel β-sheets. A small amount of fibril-like trimers contained V-shaped peptides forming parallel β-sheets. The dimensions of extended and V-shaped oligomers correspond well to the diameters of two distinct morphologies found for Aβ(25-35) fibrils. The transition from disordered to fibril-like Aβ(25-35) dimers is unfavorable but driven by energy. The lower energy of fibril-like dimers arises from favorable intermolecular HBs and other electrostatic interactions which compete with a loss in entropy. Approximately 25 % of the entropic cost correspond to configurational entropy. The rest relates to solvent entropy, presumably caused by hydrophobic and electrostatic effects. In contrast to the transition towards fibril-like dimers the first step of aggregation is driven by entropy. Here, we compared structural and thermodynamic properties of the individual monomer, dimer and trimer ensembles to gain qualitative information about the aggregation process. The β-hairpin conformation observed for monomers is successively dissolved in dimer and trimer ensembles while instead intermolecular β-sheets are formed. As expected upon aggregation the configurational entropy decreases. Additionally, the solvent accessible surface area (SASA), especially the hydrophobic SASA, decreases yielding a favorable solvation free energy which overcompensates the loss in configurational entropy. In summary, the hydrophobic effect, possibly combined with electrostatic effects, yields an increase in solvent entropy which is believed to be one major driving force towards aggregation. Spontaneous folding of the Aβ(10-35)-NH2 monomer was modeled using two force fields, GROMOS96 43a1 and OPLS/AA, and compared to primary NMR data collected at pH 5.6 and 283 K taken from the literature. Unexpectedly, the two force fields yielded significantly different main conformations. Comparison between experimental and calculated nuclear Overhauser effect (NOE) distances is not sufficient to distinguish between the different force fields. Additionally, the comparison with scalar coupling constants suggest that the chosen protonation in both simulations corresponds to a pH lower than in the experiment. Based on this analysis we were unable to determine which force field yields a better description of this system. Dimerization of Aβ(10-35)-NH2 was studied at neutral pH and 300 K. Dimer conformations arrange in many distinct, poorly populated and rather complex alignments or interlocking patterns which are rather stabilized by side chain interactions than by specific intermolecular hydrogen bonds. Similar to Aβ(25-35) dimers, transition towards β-sheet-rich, fibril-like Aβ(10-35) dimers is driven by energy competing with a loss in entropy. Here, transition is mediated by favorable peptide-solvent and solvent-solvent interactions mainly arising from electrostatic interactions.
Control of crystallographic texture from mostly face-on to edge-on is observed for the film morphology of the n-type semicrystalline polymer [N,N-9-bis(2-octyldodecyl)naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diy1]alt-5,59-(2,29-bithiophene)}, P(NDI2OD-T2), when annealing the film to the polymer melting point followed by slow cooling to ambient temperature. A variety of X-ray diffraction analyses, including pole figure construction and Fourier transform peak shape deconvolution, are employed to quantify the texture change, relative degree of crystallinity and lattice order. We find that annealing the polymer film to the melt leads to a shift from 77.5% face-on to 94.6% edge-on lamellar texture as well as to a 2-fold increase in crystallinity and a 40% decrease in intracrystallite cumulative disorder. The texture change results in a significant drop in the electron-only diode current density through the film thickness upon melt annealing while little change is observed in the in-plane transport of bottom gated thin film transistors. This suggests that the texture change is prevalent in the film interior and that either the (bottom) surface structure is different from the interior structure or the intracrystalline order and texture play a secondary role in transistor transport for this material.
Managing open habitats by wild ungulate browsing and grazing a case-study in North-Eastern Germany
(2011)
Question: Can wild ungulates efficiently maintain and restore open habitats?
Location: Brandenburg, NE Germany.
Methods: The effect of wild ungulate grazing and browsing was studied in three successional stages: (1) Corynephorus canescens-dominated grassland; (2) ruderal tall forb vegetation dominated by Tanacetum vulgare; and (3) Pinus sylvestris-pioneer forest. The study was conducted over 3 yr. In each successional stage, six paired 4 m(2)-monitoring plots of permanently grazed versus ungrazed plots were arranged in three random blocks. Removal of grazing was introduced de novo for the study. In each plot, percentage cover of each plant and lichen species and total cover of woody plants was recorded.
Results: Wild ungulates considerably affected successional pathways and species composition in open habitats but this influence became evident in alteration of abundances of only a few species. Grazing effects differed considerably between successional stages: species richness was higher in grazed versus ungrazed ruderal and pioneer forest plots, but not in the Corynephorus sites. Herbivory affected woody plant cover only in the Pioneer forest sites. Although the study period was too short to observe drastic changes in species richness and woody plant cover, notable changes in species composition were still detected in all successional stages.
Conclusion: Wild ungulate browsing is a useful tool to inhibit encroachment of woody vegetation and to conserve a species-rich, open landscape.
The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks - a recently developed approach - are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods.
Photo-induced deformations in azobenzene-containing polymers (azo-polymers) are central to a number of applications, such as optical storage and fabrication of diffractive elements. The microscopic nature of the underlying opto-mechanical coupling is yet not clear. In this study, we address the experimental finding that the scenario of the effects depends on molecular architecture of the used azo-polymer. Typically, opposite deformations in respect to the direction of light polarization are observed for liquid crystalline and amorphous azo-polymers. In this study, we undertake molecular dynamics simulations of two different models that mimic these two types of azo-polymers. We employ hybrid force field modeling and consider only trans-isomers of azobenzene, represented as Gay-Berne sites. The effect of illumination on the orientation of the chromophores is considered on the level of orientational hole burning and emphasis is given to the resulting deformation of the polymer matrix. We reproduce deformations of opposite sign for the two models being considered here and discuss the relevant microscopic mechanisms in both cases.