560 research outputs found
Quantification of antithrombin isoform proportions in plasma samples of healthy subjects, sepsis patients, and in antithrombin concentrates
Antithrombin (AT) circulates in plasma in two isoforms, AT-alpha (90-95%) and AT-beta (5-10%). AT isoform proportions were measured in plasma samples of 17 healthy subjects and 26 posttraumatic or postoperative septic patients, as well as in 4 commercially available AT concentrates. Total AT was immune-purified from plasma and concentrates. Micellar electrokinetic chromatography was used to analytically separate and quantify the isoforms. Compared with plasma samples of healthy donors, septic plasmas revealed significantly reduced AT activity (p < 0.001) and beta-isoform content (p < 0.05). AT-beta correlated inversely with urea and creatinine serum concentrations (p < 0.01), indicating a relationship between better renal function and higher beta-isoform content. beta-Isoform neither correlated with age, gender, and 28-day mortality, nor with plasma concentrations of various inflammatory and organ function parameters. The commercial AT concentrate, which is equivalent to the current WHO standard, had an AT-beta content close to that found in plasma of healthy subjects. The availability of this novel quantitative AT isoform assay allows, for the first time, a closer look at the role of AT isoforms in hemostasis and sepsis pathophysiology. Copyright (C) 2002 S. Karger AG, Basel
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Local difference measures between complex networks for dynamical system model evaluation
Power-laws in recurrence networks from dynamical systems
Recurrence networks are a novel tool of nonlinear time series analysis
allowing the characterisation of higher-order geometric properties of complex
dynamical systems based on recurrences in phase space, which are a fundamental
concept in classical mechanics. In this Letter, we demonstrate that recurrence
networks obtained from various deterministic model systems as well as
experimental data naturally display power-law degree distributions with scaling
exponents that can be derived exclusively from the systems' invariant
densities. For one-dimensional maps, we show analytically that is not
related to the fractal dimension. For continuous systems, we find two distinct
types of behaviour: power-laws with an exponent depending on a
suitable notion of local dimension, and such with fixed .Comment: 6 pages, 7 figure
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Identifying causal gateways and mediators in complex spatio-temporal systems
Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
When network and graph theory are used in the study of complex systems, a
typically finite set of nodes of the network under consideration is frequently
either explicitly or implicitly considered representative of a much larger
finite or infinite region or set of objects of interest. The selection
procedure, e.g., formation of a subset or some kind of discretization or
aggregation, typically results in individual nodes of the studied network
representing quite differently sized parts of the domain of interest. This
heterogeneity may induce substantial bias and artifacts in derived network
statistics. To avoid this bias, we propose an axiomatic scheme based on the
idea of node splitting invariance to derive consistently weighted variants of
various commonly used statistical network measures. The practical relevance and
applicability of our approach is demonstrated for a number of example networks
from different fields of research, and is shown to be of fundamental importance
in particular in the study of spatially embedded functional networks derived
from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure
Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability
The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (spline-based) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles
The backbone of the climate network
We propose a method to reconstruct and analyze a complex network from data
generated by a spatio-temporal dynamical system, relying on the nonlinear
mutual information of time series analysis and betweenness centrality of
complex network theory. We show, that this approach reveals a rich internal
structure in complex climate networks constructed from reanalysis and model
surface air temperature data. Our novel method uncovers peculiar wave-like
structures of high energy flow, that we relate to global surface ocean
currents. This points to a major role of the oceanic surface circulation in
coupling and stabilizing the global temperature field in the long term mean
(140 years for the model run and 60 years for reanalysis data). We find that
these results cannot be obtained using classical linear methods of multivariate
data analysis, and have ensured their robustness by intensive significance
testing.Comment: 6 pages, 5 figure
Meereswirtschaft in Europa : rechtliche und ökonomische Rahmenbedingungen für deutsche Unternehmen.
Meeresnutzung; Meeresboden; Meeresbergbau; Seerecht; Erdgasvorkommen; Fischerei; EU-Staaten; Norwegen; Deutschland;
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Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis
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
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