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At least 55 records · Page 3Linked to original sources

Can nonlinear dynamics elucidate mechanisms in ecology and epidemiology?

Using recently developed methods in nonlinear dynamics, two hypotheses often advanced to account for recurrent outbreaks of childhood diseases such as measles are investigated. The first, maintenance of otherwise damped oscillations by noise, appears incapable of reproducing essential features of the data. The second, cycles and chaos sustained by seasonal variation in contact rates gives qualitative and quantitative agreement between model and observation. It is concluded that nonlinear dynamics offers a methodology which may allow students of ecology and epidemiology to distinguish between competing mechanistic hypotheses.

Animals↗

Pattern of prolactin diurnal secretion in normal humans: evidence for nonlinear dynamics.

Prolactin (PRL) circadian profiles were analyzed using methods of nonlinear dynamics, directly from the experimental data, by combining in a single time-series (432 measurements), six individual 24-hour PRL profiles (72 measurements per profile, sampling interval = 20 min), obtained from young healthy human volunteers (4 males, 2 females), under basal conditions. Significant autocorrelation exists between any given point of the time series and a limited number of its successors. Fourier analysis showed a dominant frequency of 1 cycle/24 h, without sub-24-hour harmonics. Poincaré section indicated the presence of a fractal attractor and a sketch of the attractor revealed a highly convoluted geometric structure with a conical contour. The box-counting dimension (D0), information dimension (D1) and correlation dimension (D2) of the attractor had low, fractal values, did not differ significantly from each other, and exhibited saturation at an embedding dimension of 2. The evidence taken together suggests that, under basal conditions, the daily changes in the peripheral blood levels of PRL are governed by nonlinear deterministic dynamics, with a dominant rhythm of 1 cycle/day mixed with a higher-frequency, low-amplitude signal.

Adult↗

Nonlinear dynamic characteristics of electroencephalography in a high-dose pilocarpine-induced status epilepticus model.

We applied nonlinear analysis to the results of electroencephalography (EEG) in a pilocarpine-induced status epilepticus (SE) model to characterize nonlinear dynamics according to SE phase. Nine male Sprague-Dawley rats weighing 150-250 g were used. EEG was classified into four phases in addition to baseline EEG (phase 0) as follows: phase 1, discrete seizures; phase 2, continuous ictal discharges; phase 3, early periodic epileptiform discharges (PEDs); and phase 4, late PEDs. High-dose diazepam was administered at phase 4 to terminate SE. Diazepam controlled SE in five rats (group 1), while it failed to stop SE in the rest (group 2). The presence of nonlinearity was determined by time reversal asymmetry statistics using a surrogate data set. The correlation dimension (D(2)) was calculated to characterize the dimensional complexity of each phase of SE. EEG of later phases of SE showed strong nonlinearity, whereas no or only weak nonlinearity was noted at phases 0 and 1. D(2) showed the highest value at phase 0 and decreased progressively. Considering therapeutic responsiveness, D(2) showed significant differences between the two groups at phases 2 and 4. These results suggest that nonlinear dynamic changes in the later SE phases reflect underlying pathophysiological changes that contribute to determining therapeutic responsiveness in the pilocarpine-induced SE model.

Animals↗

Nonlinear dynamics of direction-selective recurrent neural media.

The direction selectivity of cortical neurons can be accounted for by asymmetric lateral connections. Such lateral connectivity leads to a network dynamics with characteristic properties that can be exploited for distinguishing in neurophysiological experiments this mechanism for direction selectivity from other possible mechanisms. We present a mathematical analysis for a class of direction-selective neural models with asymmetric lateral connections. Contrasting with earlier theoretical studies that have analyzed approximations of the network dynamics by neglecting nonlinearities using methods from linear systems theory, we study the network dynamics with nonlinearity taken into consideration. We show that asymmetrically coupled networks can stabilize stimulus-locked traveling pulse solutions that are appropriate for the modeling of the responses of direction-selective neurons. In addition, our analysis shows that outside a certain regime of stimulus speeds the stability of these solutions breaks down, giving rise to lurching activity waves with specific spatiotemporal periodicity. These solutions, and the bifurcation by which they arise, cannot be easily accounted for by classical models for direction selectivity.

Animals↗

MZ twinning: chance or determinism? An essay in nonlinear dynamics (chaos).

Classically, researchers considered monozygotic twinning (MZT) a random phenomenon. This paper tests the hypothesis with the aid of nonlinear dynamics techniques. The latter can tell true randomness from chance-like variation. Chaos, the endpoint of the threshold state of a nonlinear deterministic system, can mimic constrained randomness. From a practical standpoint, recognizing chaos in a time series data set means that the paradigmatic multifactorial model of causation is essentially ruled out. Specifically, time series of MZ, DZ, and single maternities were analysed. First, spectral analysis was used to uncover periodicities embedded in the series. Second, a singular value decomposition was undertaken to reduce noise from the series. Third, phase space attractors were drawn up that describe the 'asymptotic' trajectory of the system at any time. Results suggested that DZ, MZ, and single maternities shared a similar 32-year periodicity. Owing to two interwoven similar periodicities, the single-maternity cycle kinetics proved to be faster than that of DZ's. The MZ series was the only one to display secondary interacting harmonics, thus eliciting a rather unusual trajectory in the bidimensional phase space. The MZ time points were not spread in a haphazard fashion; on the contrary, a fine structure was present that did not reduce to a limit cycle such as the one characterizing the DZ- or the single-maternity trajectory. It was concluded that a complex nonlinear dynamic underlies MZ twinning. Therefore, calling for extrinsic causes to account for what appears to be random variation overtime would be pointless. MZ twinning should rather be traced to a limited number of intrinsic and deterministic interacting system components. The most likely candidates are presented and discussed.

Bias↗

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

BACKGROUND: We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. RESULTS: We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. CONCLUSION: Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.

Algorithms↗

Using distributed nonlinear dynamics for public key encryption.

We introduce a new method for asymmetric (public key/private key) encryption exploiting properties of nonlinear dynamical systems. A high-dimensional dissipative nonlinear dynamical system is distributed between transmitter and receiver, so we call the method distributed dynamics encryption (DDE). The transmitter dynamics is public, and the receiver dynamics is hidden. A message is encoded by modulation of parameters of the transmitter, and this results in a shift of the overall system attractor. An unauthorized receiver does not know the hidden dynamics in the receiver and cannot decode the message. We present an example of DDE using a coupled map lattice.

Journal Article↗

Perturbation and nonlinear dynamic analyses of voices from patients with unilateral laryngeal paralysis.

This study used perturbation methods (eg, jitter and shimmer) and nonlinear dynamic methods (eg, phase space reconstruction and correlation dimension) to analyze sustained voices generated by normal subjects and patients with unilateral laryngeal paralysis. We found that normal and pathological voices had low-dimensional dynamic characteristics. For nearly periodic voices, jitter and shimmer values of pathological voices from patients with unilateral laryngeal paralysis were significantly different from normal voices. For nearly periodic and aperiodic voices, the correlation dimensions of pathological voices were statistically higher than normal voices. Receiver operating characteristic analysis was used to evaluate the diagnostic performances of jitter, shimmer, and correlation dimension. High sensitivity and specificity of these three acoustic analyses in distinguishing unilateral laryngeal paralysis patients from normal subjects were found. We concluded that combining traditional perturbation analysis and nonlinear dynamic analysis might provide efficient descriptions of pathological voices and represent a valuable tool for clinical diagnosis of laryngeal paralysis.

Adolescent↗

On nonlinear dynamics of three-dimensional astrophysical disks.

Nonlinear processes concerned with different aspects of nonlinear dynamics of astrophysical disks--structures, flows, turbulence--are reviewed. Special attention is paid to the influence of the three dimentionality of disks on their nonlinear behavior.

Journal Article↗

Type III intermittency: a nonlinear dynamic model of EEG burst suppression.

Burst suppression electroencephalograms from 9 comatose patients have been studied using nonlinear dynamic techniques. These EEG records show many dynamical features characteristic of nonlinear systems, including sensitive dependence on initial conditions, self-organization, similarity across scales, and intermittency. Histograms of burst durations showed an asymmetric distribution with a decreasing tail of increasing duration. Interpreting the histograms from the standpoint of intermittency classifications of iterated dynamical maps, the absence of any conspicuous maximal cut-off duration suggests a type III intermittency. The power-law exponent of the decreasing tail is -3/2 for type III intermittency in the large scale sample size limit, and we have found the EEGs to be consistent with type III intermittency behavior. We have also developed a nonlinear algorithm which models burst suppression pattern based on a low dimensional return map. Burst suppression pattern appears to be the constrained activity of a nonlinear dynamical system at the transition to chaos.

Adolescent↗

Nonlinear dynamic analysis of voices before and after surgical excision of vocal polyps.

Phase space reconstruction, correlation dimension, and second-order entropy, methods from nonlinear dynamics, are used to analyze sustained vowels generated by patients before and after surgical excision of vocal polyps. Two conventional acoustic perturbation parameters, jitter and shimmer, are also employed to analyze voices before and after surgery. Presurgical and postsurgical analyses of jitter, shimmer, correlation dimension, and second-order entropy are statistically compared. Correlation dimension and second-order entropy show a statistically significant decrease after surgery, indicating reduced complexity and higher predictability of postsurgical voice dynamics. There is not a significant postsurgical difference in shimmer, although jitter shows a significant postsurgical decrease. The results suggest that jitter and shimmer should be applied to analyze disordered voices with caution; however, nonlinear dynamic methods may be useful for analyzing abnormal vocal function and quantitatively evaluating the effects of surgical excision of vocal polyps.

Adult↗

Nonlinear dynamics stability measurements of locomotion in healthy greyhounds.

OBJECTIVE: To characterize normal locomotion of dogs, using nonlinear dynamic stability measurements to analyze two-dimensional kinematic data. ANIMALS: 5 healthy, orthopedically sound Greyhounds. PROCEDURE: Data were studied by sequentially constructing phase plane portraits from the angular velocity and displacement data; creating first-return (Poincaré) maps from periodically sampled data; and evaluating the dynamic stability of the gait, using Floquet multipliers calculated from the assembled data. Retroreflective markers were placed on the left craniodorsal aspect of the iliac spine, greater trochanter, lateral epicondyle of the femur, lateral malleolus, and fifth metatarsophalangeal joint. Each dog was repeatedly led at a trot along a 10-m runway. Data were collected, using a video-based, two-dimensional motion measurement and analysis system. Dogs were considered a nonlinear system and were represented by the joint angular displacements and velocities. Phase plane portraits and first-return maps were constructed to analyze the smoothed data. The Floquet theory was then used to investigate the local stability of critical points of the discrete map. RESULTS: The femorotibial joint had the highest angular velocity, ranging from -2.5 to 4.9 radians/s. Tarsal joint velocity ranged from -2.7 to 3.2 radians/s, and the coxofemoral angle had the lowest range of -2.2 to 2.2 radians/s. The points on the first-return maps converged to the 45 degrees diagonal line and were clustered together. The largest Floquet multiplier averaged 0.452, which characterized the stability of this population and will be used to draw a comparison between this and future work. CONCLUSIONS: Nonlinear dynamics can be effectively used to analyze two-dimensional kinematic data from animal models to quantify the dynamic stability of animal locomotion through precise mathematical measurements. The method is general and can be applied to normal or abnormal gaits. CLINICAL RELEVANCE: Point mapping and quantitative measurement of joint movement have several advantages associated with the application to animal and human locomotion. The clinician can visually distinguish the normal gait pattern from abnormal patterns to assist in the diagnosis of musculoskeletal abnormalities (diseases).

Animals↗

Nonlinear dynamics applied to blood pressure control.

Hypertension is a very frequent disease, known to trigger a range of severe cardiovascular problems. The elucidation of its pathophysiology requires investigation of the mechanisms responsible for the maintenance of blood pressure in the normal system, and their possible failure in hypertension. Some of these control mechanisms display nonlinear features, indicating that the blood pressure signal might be characterized by nonlinear dynamics. Our aim was thus to investigate the nonlinear properties of the blood pressure signal under normal conditions, and in a cardiovascular system prone to hypertension. Blood pressure was investigated in young spontaneously hypertensive rats (SHR), versus their age-matched normotensive progenitors (WKY). The correlation dimension was computed as quantification of blood pressure control complexity. The parameters required for the calculation procedure of the correlation dimension were carefully determined. The results were tested with surrogate data statistics. assuming linear autocorrelated Gaussian noise as the null hypothesis. Non-integer correlation dimension values were found in both strains, with lower values for SHR than for WKY, in particular following alpha-blockade. In all cases, a statistically significant difference was found between the real and surrogate data. These results show that the nonlinear dynamics parameter D, can be used to detect differences in BP control between prehypertensive SHR and WKY rats as early as 6-7 weeks after birth.

Animals↗

Coarse nonlinear dynamics and metastability of filling-emptying transitions: water in carbon nanotubes.

Using a coarse-grained molecular dynamics (CMD) approach we study the apparent nonlinear dynamics of water molecules filling or emptying carbon nanotubes as a function of system parameters. Different levels of the pore hydrophobicity give rise to tubes that are empty, water-filled, or fluctuate between these two long-lived metastable states. The corresponding coarse-grained free-energy surfaces and their hysteretic parameter dependence are explored by linking MD to continuum fixed point and bifurcation algorithms. The results are validated through equilibrium MD simulations.

Algorithms↗

Nonlinear dynamic systems evaluation of rhythmic' eye movements (optokinetic nystagmus).

The last decade has seen a surge in the study of nonlinear dynamical behavior in physiologic systems. In this paper, some of the computational techniques most commonly used to investigate the nonlinear dynamics of these systems are described. Applications to eye movement analysis are included, including the validation of a mathematical model of optokinetic nystagmus (OKN) eye movements. OKN appears to have some nonlinear and deterministic component, along with significant randomness. Fast phase starting and ending points are somewhat predictable (deterministic), while so-called 'exceptional events' analysis shows that they also have a large random component. Surrogate data methods suggest that the population of slow and fast phases in OKN is more important than any specific relationship between adjacent slow and fast phases. Analysis of a statistical model for fast phase intervals indicates that the model data are slightly more random than the actual OKN.

Computational Biology↗

Relapse as a nonlinear dynamic system: application to patients with alcohol use disorders.

Understanding the relapse process is one of the most important issues in addictive behaviors research. To date, most studies have taken a linear approach toward predicting relapse based on risk factors. Nonlinear dynamical systems theory can be used to describe processes that are not adequately modeled using a linear approach. In particular, the authors propose that catastrophe theory, a subset of nonlinear dynamical systems theory, can be used to describe the relapse process in addictive behaviors. Two small prospective studies using 6-month follow-ups of patients with alcohol use disorders (inpatient, n = 51; outpatient, n = 43) illustrate how cusp catastrophe theory may be used to predict relapse. Results from these preliminary studies indicate that a cusp catastrophe model has more predictive utility than traditional linear models.

Adult↗

Large quantum nonlinear dynamic susceptibility of single-molecule magnets.

The nonlinear dynamical response of Mn12 single-molecule magnets is experimentally found to be very large, quite insensitive to the spin-lattice coupling constant, and displaying peaks reversed with respect to classical superparamagnets. It is shown that these features are caused by the strong field dependence of the relaxation rate due to the detuning of energy levels between which tunneling takes place. The nonlinear susceptibility technique, previously overlooked, is thus proposed as a privileged probe to ascertain the occurrence of quantum effects in mesoscopic magnetic systems.

Journal Article↗

A dynamic nonlinear time domain model for reconstruction and compression of cardiovascular signals with application to telemedicine.

A new nonlinear time domain model is proposed in this paper for signals of cardiovascular origin. An equation of the dynamic nonlinear model has been obtained by considering a masking function, which is modulated by a harmonic series with the baseline drift incorporated into the model. Signal reconstruction using model parameters has established the effectiveness of the model for signal compression. Improvement has been effected by using neural networks for reducing the time for optimizing the initial parameters. An improved adaptive optimization step size algorithm has also been implemented. Results show that the technique is able to provide reasonable compression with low error between the original and reconstructed signals. One of the main advantages of the model is its potential of being used for compression of many different types of biosignals transmitted in parallel. Incorporation of the compression model into a telemedicine system has led to considerable saving in transmission time for patient data.

Algorithms↗