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The latest research articles published by EPJ Nonlinear Biomedical Physics2014-11-22T00:00:00Z
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Sliding Window Empirical Mode Decomposition -its performance and qualityBackground:
In analysis of nonstationary nonlinear signals the classical notion of frequency is meaningless. Instead one may use Instantaneous Frequency (IF) that can be interpreted as the frequency of a sine wave which locally fits the signal. IF is meaningful for monocomponent nonstationary signals and may be calculated by Hilbert transform (HT).
Methods:
A multicomponent signal may be decomposed into its monocomponents. Empirical Mode Decomposition (EMD), developed by Norden E. Huang, is a new method of such breaking down of a signal into its monocomponents. EMD combined with HT (called Hilbert-Huang Transform) is a good tool for analyzing nonstationary signals, but unfortunately the traditional EMD algorithm consumes a lot of time and computer resources. I propose a modified EMD algorithm - Sliding Window EMD, SWEMD.
Results:
Proposed algorithm speeds up (about 10 times) the computation with acceptable quality of decomposition.
Conclusions:
Sliding Window EMD algorithm is suitable for decomposition of long signals with high sampling frequency.
http://www.epjnonlinearbiomedphys.com/content/2/1/14
Pawel StepienEPJ Nonlinear Biomedical Physics 2014, null:142014-11-22T00:00:00Zdoi:10.1140/epjnbp/s40366-014-0014-9/content/figures/s40366-014-0014-9-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}142014-11-22T00:00:00ZPDFA multi-compartment pharmacokinetic model of the interaction between paclitaxel and doxorubicinBackground:
In this paper the interactions between paclitaxel, doxorubicin and the metabolic enzyme CYP3A4 are studied using computational models. The obtained results are compared with those of available clinical data sets. Analysis of the drug-enzyme interactions leads to a recommendation of an optimized paclitaxel-doxorubicin drug regime for chemotherapy treatment.
Methods:
A saturable multi-compartment pharmacokinetic model for the multidrug treatment of cancer using paclitaxel and doxorubicin in a combination is developed. The model’s kinetic equations are then solved using standard numerical methods for solving systems of nonlinear differential equations. The parameters were adjusted by fitting to available clinical data. In addition, we studied the interaction of each drug with the metabolic enzyme CYP3A4 through blind docking simulations to demonstrate that these drugs compete for the same metabolic enzyme and to show their molecular mode of binding. This provides a molecular-level justification for the introduction of interaction terms in the kinetic model.
Results:
Using docking simulations we compared the relative binding affinities for the metabolic enzyme of the two chemotherapy drugs. Since paclitaxel binds more strongly to CYP3A4 than doxorubicin, an explanation is given why doxorubicin has no apparent influence upon paclitaxel, while paclitaxel has a profound effect upon doxorubicin. Finally, we studied different time sequences of paclitaxel and doxorubicin concentrations and calculated their AUCs.
Conclusions:
We have found excellent agreement between our model and available empirical clinical data for the drug combination studied here. To support the kinetic model at a molecular level, we built an atomistic three-dimensional model of the ligands interacting with the metabolic enzyme and elucidated the binding modes of paclitaxel and doxorubicin within CYP3A4. Blind docking simulations provided estimates of the corresponding binding energies. The paper is concluded with clinical implications for the administration of the two drugs in combination.
http://www.epjnonlinearbiomedphys.com/content/2/1/13
Kenneth VosAngela MartinMaxine TrimboliLindsay ForestellKhaled BarakatJack TuszynskiEPJ Nonlinear Biomedical Physics 2014, null:132014-11-05T00:00:00Zdoi:10.1140/epjnbp/s40366-014-0013-x/content/figures/s40366-014-0013-x-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}132014-11-05T00:00:00ZXMLRealistic human muscle pressure for driving a mechanical lungBackground:
An important issue in noninvasive mechanical ventilation consists in understanding the origins of patient-ventilator asynchrony for reducing their incidence by adjusting ventilator settings to the intrinsic ventilatory dynamics of each patient. One of the possible ways for doing this is to evaluate the performances of the domiciliary mechanical ventilators using a test bench. Such a procedure requires to model the evolution of the pressure imposed by respiratory muscles, but for which there is no standard recommendations.
Methods:
In this paper we propose a mathematical model for simulating the muscular pressure developed by the inspiratory muscles and corresponding to different patient ventilatory dynamics to drive the ASL 5000 mechanical lung. Our model is based on the charge and discharge of a capacitor through a resistor, simulating contraction and relaxation phases of the inspiratory muscles.
Results:
Our resulting equations were used to produce 420 time series of the muscle pressure with various contraction velocities, amplitudes and shapes, in order to represent the inter-patient variability clinically observed. All these dynamics depend on two parameters, the ventilatory frequency and the mouth occlusion pressure.
Conclusion:
Based on the equation of the respiratory movement and its electrical analogy, the respiratory muscle pressure was simulated with more consistency in regards of physiological evidences than those provided by the ASL 5000 software. The great variability in the so-produced inspiratory efforts can cover most of realistic patho-physiological conditions.
http://www.epjnonlinearbiomedphys.com/content/2/1/7
Emeline FresnelJean-François MuirChristophe LetellierEPJ Nonlinear Biomedical Physics 2014, null:72014-08-19T12:00:00Zdoi:10.1140/epjnbp/s40366-014-0007-8/content/figures/s40366-014-0007-8-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}72014-08-19T12:00:00ZXMLLumping Izhikevich neuronsWe present the construction of a planar vector field that yields the firing rate of a bursting Izhikevich neuron can be read out, while leaving the sub-threshold behavior intact. This planar vector field is used to derive lumped formulations of two complex heterogeneous networks of bursting Izhikevich neurons. In both cases, the lumped model is compared with the spiking network. There is excellent agreement in terms of duration and number of action potentials within the bursts, but there is a slight mismatch of the burst frequency. The lumped model accurately accounts for both intrinsic bursting and post inhibitory rebound potentials in the neuron model, features which are absent in prevalent neural mass models.
http://www.epjnonlinearbiomedphys.com/content/2/1/6
Sid VisserStephan Van GilsEPJ Nonlinear Biomedical Physics 2014, null:62014-05-12T00:00:00Zdoi:10.1140/epjnbp19/content/figures/epjnbp19-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}62014-05-12T00:00:00ZXMLExtracting novel information from neuroimaging data using neural fieldsWe showcase three case studies that illustrate how neural fields can be useful in the analysis of neuroimaging data. In particular, we argue that neural fields allow one to: (i) compare evidences for alternative hypotheses regarding neurobiological determinants of stimulus-specific response variability; (ii) make inferences about between subject variability in cortical function and microstructure using non-invasive data and (iii) estimate spatial parameters describing cortical sources, even without spatially resolved data.
http://www.epjnonlinearbiomedphys.com/content/2/1/5
Dimitris PinotsisKarl FristonEPJ Nonlinear Biomedical Physics 2014, null:52014-05-09T00:00:00Zdoi:10.1140/epjnbp18/content/figures/epjnbp18-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}52014-05-09T00:00:00ZXMLAttractor and saddle node dynamics in heterogeneous neural fieldsBackground:
We present analytical and numerical studies on the linear stability of spatially non-constant stationary states in heterogeneous neural fields for specific synaptic interaction kernels.
Methods:
The work shows the linear stabiliy analysis of stationary states and the implementation of a nonlinear heteroclinic orbit.
Results:
We find that the stationary state obeys the Hammerstein equation and that the neural field dynamics may obey a saddle-node bifurcation. Moreover our work takes up this finding and shows how to construct heteroclinic orbits built on a sequence of saddle nodes on multiple hierarchical levels on the basis of a Lotka-Volterra population dynamics.
Conclusions:
The work represents the basis for future implementation of meta-stable attractor dynamics observed experimentally in neural population activity, such as Local Field Potentials and EEG.
http://www.epjnonlinearbiomedphys.com/content/2/1/4
Peter beim GrabenAxel HuttEPJ Nonlinear Biomedical Physics 2014, null:42014-05-09T00:00:00Zdoi:10.1140/epjnbp17/content/figures/epjnbp17-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}42014-05-09T00:00:00ZXMLTravelling waves in models of neural tissue: from localised structures to periodic wavesWe consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength we unravel the organizing centers of the bifurcation diagram for fronts and pulses, with a mixture of exact analysis for a Heaviside firing rate function and novel numerical schemes otherwise. These schemes, for non-local models with space-dependent delays, further allow for the construction and continuation of periodic waves. We use them to construct the dispersion curve – wave speed as a function of period – and find that they can be oscillatory and multi-valued, suggesting bistability of periodic waves. A kinematic theory predicts the onset of wave instabilities at stationary points in the dispersion curve, leading to period doubling behaviour, and is confirmed with direct numerical simulations. We end with a discussion of how the construction of dispersion curves may allow a useful classification scheme of neural field models for epileptic waves.PACS codesPrimary 87.19.lj; 87.19.le; 87.19.lq; 87.19.lf
http://www.epjnonlinearbiomedphys.com/content/2/1/3
Hil MeijerStephen CoombesEPJ Nonlinear Biomedical Physics 2014, null:32014-03-06T00:00:00Zdoi:10.1140/epjnbp16/content/figures/epjnbp16-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}32014-03-06T00:00:00ZXMLAssociative learning and self-organization as basic principles for simulating speech acquisition, speech production, and speech perceptionBackground:
Quantitative neural models of speech acquisition and speech processing are rare.
Methods:
In this paper, we describe a neural model for simulating speech acquisition, speech production, and speech perception. The model is based on two important neural features: associative learning and self-organization. The model describes an SOM-based approach to speech acquisition, i.e. how speech knowledge and speaking skills are learned and stored in the context of self-organizing maps (SOMs).
Results:
The model elucidates that phonetic features, such as high-low, front-back in the case of vowels, place and manner or articulation in the case of consonants and stressed vs. unstressed for syllables, result from the ordering of syllabic states at the level of a supramodal phonetic self-organizing map. After learning, the speech production and speech perception of speech items results from the co-activation of neural states within different cognitive and sensorimotor neural maps.
Conclusion:
This quantitative model gives an intuitive understanding of basic neurobiological principles from the viewpoint of speech acquisition and speech processing.
http://www.epjnonlinearbiomedphys.com/content/2/1/2
Bernd KrögerJim KannampuzhaEmily KaufmannEPJ Nonlinear Biomedical Physics 2014, null:22014-02-10T00:00:00Zdoi:10.1140/epjnbp15/content/figures/epjnbp15-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}22014-02-10T00:00:00ZXMLModeling domain formation of MARCKS and protein kinase C at cellular membranesBackground:
Phosphorylation and dephosphorylation of proteins are mechanisms of activation and deactivation which regulate many cellular processes. Both mechanisms have been usually described in well mixed environments. MARCKS is a protein which binds to the membrane by electrostatic interaction. It is translocated from the membrane and phosphorylated by Protein Kinase C. Back in the cytoplasm the translocated MARCKS proteins are dephosphorylated by the enzyme phosphatase and can reattach to the membrane. These three processes (membrane binding, translocation, dephosphorylation) give rise to a cyclic dynamics known as the myristoyl-electrostatic switch.
Methods:
We employ a reaction-diffusion model for the concentrations of MARCKS and PKC in the cell in a circular domain.
Results:
Herein, we start from a reaction-diffusion model taking into account mass conservation of the MARCKS proteins. Then, we extend the model by including the dynamics of binding and unbinding of PKC enzymes, which are in turn activated by spikes of calcium. Furthermore, we show that the model fits previous experimental results well and predicts, in addition, the formation of domains with high concentration of MARCKS proteins at the membrane.
Conclusions:
We have developed a simple model of binding, phosphorylation and desphosphorylation for MARCKS protein. The main prediction emerging from numerical simulations of the model is the spontaneous appearance of domains of high concentration of membrane proteins.
http://www.epjnonlinearbiomedphys.com/content/2/1/1
Sergio AlonsoMarkus BärEPJ Nonlinear Biomedical Physics 2014, null:12014-01-30T00:00:00Zdoi:10.1140/epjnbp14/content/figures/epjnbp14-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}12014-01-30T00:00:00ZXMLProsody and synchronization in cognitive neuroscienceWe introduce our methodological study with a short review of the main literature on embodied language, including some recent studies in neuroscience. We investigated this component of natural language using Recurrence Quantification Analysis (RQA). RQA is a relatively new statistical methodology, particularly effective in complex systems. RQA provided a reliable quantitative description of recurrences in text sequences at the orthographic level. In order to provide examples of the potential impact of this methodology, we used RQA to measure structural coupling and synchronization in natural and clinical verbal interactions. Results show the efficacy of this methodology and possible implications.
http://www.epjnonlinearbiomedphys.com/content/1/1/6
Franco OrsucciRoberta PetrosinoGiulia PaoloniLuca CanestriElio ConteMario RedaMario FulcheriEPJ Nonlinear Biomedical Physics 2013, null:62013-09-23T00:00:00Zdoi:10.1140/epjnbp13/content/figures/epjnbp13-toc.gifEPJ Nonlinear Biomedical Physics2195-0008${item.volume}62013-09-23T00:00:00ZXML