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  • 2015-2019  (7)
  • 1995-1999  (14)
  • 1
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract. Repetitive occurrence of partial or total upper airway obstruction characterizes several respiratory dysfunctions such as the obstructive sleep apnea syndrome (OSAS). In OSAS patients, pharyngeal collapses are linked to a decrease in upper airway muscle activity during sleep which causes decreased upper airway wall stiffness. Continuous positive airway pressure (CPAP) is recommended as the treatment of choice. Advancements in CPAP therapy require early detection of respiratory events in real time to adapt the level of the applied pressure to airway collapsibility. The forced oscillation technique (FOT) is a noninvasive method which reflects patients' airway patency by measuring respiratory impedance. The aim of this study was to evaluate by a mathematical model of the respiratory system if FOT can provide an early detection index of total or partial upper airway obstruction. Furthermore, the simulation should suggest which characteristic features are relevant for early apnea detection in measured clinical data. The respiratory system has been treated as a series of cylindrical segments. The oropharynx analog of the model allows simulation of upper airway collapse, mimicking the situation in patients with OSAS. We calculated the input impedance for different degrees of upper airway obstruction ranging from unobstructed airways to total occlusion. Furthermore, we simulated different upper airway wall compliances. We compared the simulation with real data. The results of the study suggest that FOT is a valuable tool for assessing the degree of upper airway obstruction in patients with OSAS. Especially, the phase angle of the impedance seems to be a potentially useful tool for early apnea detection by assessing the upper airway wall collapsibility.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 78 (1998), S. 349-357 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract We investigate the relationship between the extensor electromyogram (EMG) and tremor time series in physiological hand tremor by cross-spectral analysis. Special attention is directed to the phase spectrum and the effects of observational noise. We calculate the theoretical phase spectrum for a second-order linear stochastic process and compare the results to measured tremor data recorded from subjects who did not show a synchronized EMG activity in the corresponding extensor muscle. The results show that physiological tremor is well described by the proposed model and that the measured EMG represents a Newtonian force by which the muscle acts on the hand.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 78 (1998), S. 359-368 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract We investigated the relationship between synchronized muscle activity and tremor time series in (enhanced) physiological tremor by cross-spectral analysis. Special attention was directed to the phase spectrum and its potential to clarify the contribution of reflex mechanisms to physiological tremor. The phase spectra are investigated assuming that the electromyogram (EMG) synchronization was caused by a reflex or a central oscillator. Comparing these results to phase spectra of measured data, we found a significant contribution of reflexes. But reflexes only modify existing peaks in the power spectrum. The main agents of physiological tremor are an efferent pace and the resonant behavior of the biomechanical system.
    Type of Medium: Electronic Resource
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  • 4
    Publication Date: 2016-04-08
    Description: Motivation: To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model’s parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality , classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. Results: In this article, reliable and smooth point-wise prediction and confidence bands to assess the model’s uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. Availability and implementation: The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org . Contact: helge.hass@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 5
    Publication Date: 2015-10-21
    Description: : Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications. Availability and implementation: The Data2Dynamics modeling environment is MATLAB based, open source and freely available at http://www.data2dynamics.org . Contact: andreas.raue@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 6
    Publication Date: 2016-05-07
    Description: Author(s): S. Sanna, C. Dues, W. G. Schmidt, F. Timmer, J. Wollschläger, M. Franz, S. Appelfeller, and M. Dähne Rare-earth induced layered structures on the Si(111) surface are investigated by a combined approach consisting of ab initio thermodynamics, electron and x-ray diffraction experiments, angle-resolved photoelectron spectroscopy, and scanning tunneling microscopy. Our density functional theory calcula… [Phys. Rev. B 93, 195407] Published Fri May 06, 2016
    Keywords: Surface physics, nanoscale physics, low-dimensional systems
    Print ISSN: 1098-0121
    Electronic ISSN: 1095-3795
    Topics: Physics
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  • 7
    Publication Date: 2016-11-25
    Description: Different features of the Gulf of Mexico (GOM), such as the Loop Current and warm-core rings, are found to influence monthly-to-seasonal severe weather occurrence in different regions of the United States (US). The warmer (cooler) the GOM sea-surface temperatures (SSTs), the more (less) hail and tornadoes occur during March-May over the southern US. This pattern is reflected physically in boundary layer specific humidity and mixed-layer convective available potential energy, two large-scale atmospheric conditions favorable for severe weather occurrence. This relationship is complicated by interactions between the GOM and El Niño Southern Oscillation (ENSO), but persists when analyzing ENSO neutral conditions. This suggests that the GOM can influence hail and tornado occurrence and provides another source of regional predictability for seasonal severe weather.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 8
    Publication Date: 1997-09-01
    Print ISSN: 0365-0138
    Electronic ISSN: 1286-4846
    Topics: Physics
    Published by EDP Sciences
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  • 9
    Publication Date: 2016-09-02
    Description: Motivation: A major goal of drug development is to selectively target certain cell types. Cellular decisions influenced by drugs are often dependent on the dynamic processing of information. Selective responses can be achieved by differences between the involved cell types at levels of receptor, signaling, gene regulation or further downstream. Therefore, a systematic approach to detect and quantify cell type-specific parameters in dynamical systems becomes necessary. Results: Here, we demonstrate that a combination of nonlinear modeling with L 1 regularization is capable of detecting cell type-specific parameters. To adapt the least-squares numerical optimization routine to L 1 regularization, sub-gradient strategies as well as truncation of proposed optimization steps were implemented. Likelihood-ratio tests were used to determine the optimal regularization strength resulting in a sparse solution in terms of a minimal number of cell type-specific parameters that is in agreement with the data. By applying our implementation to a realistic dynamical benchmark model of the DREAM6 challenge we were able to recover parameter differences with an accuracy of 78%. Within the subset of detected differences, 91% were in agreement with their true value. Furthermore, we found that the results could be improved using the profile likelihood. In conclusion, the approach constitutes a general method to infer an overarching model with a minimum number of individual parameters for the particular models. Availability and Implementation: A MATLAB implementation is provided within the freely available, open-source modeling environment Data2Dynamics. Source code for all examples is provided online at http://www.data2dynamics.org/ . Contact: bernhard.steiert@fdm.uni-freiburg.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 10
    Publication Date: 1999-07-07
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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