Material transport in the left ventricle with aortic valve regurgitation

This experimental in vitro work investigates material transport properties in a model left ventricle in the case of aortic regurgitation, a valvular disease characterized by a leaking aortic valve and consequently double-jet filling within the elastic left ventricular geometry. This study suggests that material transport phenomena are strongly determined by the motion of the counterrotating vortices driven by the regurgitant aortic and mitral jets. The overall particle residence time appears to be significantly longer with moderate aortic regurgitation, attributed to the dynamics resulting from the timing between the onset of the two jets. Increasing regurgitation severity is shown to be associated with higher frequencies in the time-frequency spectra of the velocity signals at various points in the flow, suggesting nonlaminar mixing past moderate regurgitation. Additionally, a large part of the regurgitant inflow is retained for at least one cardiac cycle. Such an increase in particle residence time accompanied by the occurrence and persistence of a number of attracting Lagrangian coherent structures presents favorable conditions and locations for activated platelets to agglomerate within the left ventricle, potentially posing an additional risk factor for patients with aortic regurgitation

Scaling law of diffusivity generated by a noisy telegraph signal with fractal intermittency

In many complex systems the non-linear cooperative dynamics determine the emergence of self-organized, metastable, structures that are associated with a birth-death process of cooperation. This is found to be described by a renewal point process, i.e., a sequence of crucial birth-death events corresponding to transitions among states that are faster than the typical long-life time of the metastable states. Metastable states are highly correlated, but the occurrence of crucial events is typically associated with a fast memory drop, which is the reason for the renewal condition. Consequently, these complex systems display a power-law decay and, thus, a long-range or scale-free behavior, in both time correlations and distribution of inter-event times, i.e., fractal intermittency. The emergence of fractal intermittency is then a signature of complexity. However, the scaling features of complex systems are, in general, affected by the presence of added white or short-term noise. This has been found also for fractal intermittency. In this work, after a brief review on metastability and noise in complex systems, we discuss the emerging paradigm of Temporal Complexity. Then, we propose a model of noisy fractal intermittency, where noise is interpreted as a renewal Poisson process with event rate rp. We show that the presence of Poisson noise causes the emergence of a normal diffusion scaling in the long-time range of diffusion generated by a telegraph signal driven by noisy fractal intermittency. We analytically derive the scaling law of the long-time normal diffusivity coefficient. We find the surprising result that this long-time normal diffusivity depends not only on the Poisson event rate, but also on the parameters of the complex component of the signal: the power exponent μ of the inter-event time distribution, denoted as complexity index, and the time scale T needed to reach the asymptotic power-law behavior marking the emergence of complexity. In particular, in the range μ < 3, we find the counter-intuitive result that normal diffusivity increases as the Poisson rate decreases. Starting from the diffusivity scaling law here derived, we propose a novel scaling analysis of complex signals being able to estimate both the complexity index μ and the Poisson noise rate rp

Breaking axi-symmetry in stenotic flow lowers the critical transition Reynolds number

Flow through a sinuous stenosis with varying degrees of non-axisymmetric shape variations and at Reynolds number ranging from 250 to 750 is investigated using direct numerical simulation (DNS) and global linear stability analysis. At low Reynolds numbers (Re < 390), the flow is always steady and symmetric for an axisymmetric geometry. Two steady state solutions are obtained when the Reynolds number is increased: a symmetric steady state and an eccentric, non-axisymmetric steady state. Either one can be obtained in the DNS depending on the initial condition. A linear global stability analysis around the symmetric and non-axisymmetric steady state reveals that both flows are linearly stable for the same Reynolds number, showing that the first bifurcation from symmetry to antisymmetry is subcritical. When the Reynolds number is increased further, the symmetric state becomes linearly unstable to an eigenmode, which drives the flow towards the nonaxisymmetric state. The symmetric state remains steady up to Re = 713, while the non-axisymmetric state displays regimes of periodic oscillations for Re ≥ 417 and intermittency for Re & 525. Further, an offset of the stenosis throat is introduced through the eccentricity parameter E. When eccentricity is increased from zero to only 0.3% of the pipe diameter, the bifurcation Reynolds number decreases by more than 50%, showing that it is highly sensitive to non-axisymmetric shape variations. Based on the resulting bifurcation map and its dependency on E, we resolve the discrepancies between previous experimental and computational studies. We also present excellent agreement between our numerical results and previous experimental resultsThis is the author accepted manuscript. The final version is available from AIP via http://dx.doi.org/10.1063/1.493453

Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media

Here we numerically study a model of excitable media, namely, a network with occasionally quiet nodes and connection weights that vary with activity on a short-time scale. Even in the absence of stimuli, this exhibits unstable dynamics, nonequilibrium phases -including one in which the global activity wanders irregularly among attractors- and 1/f noise while the system falls into the most irregular behavior. A net result is resilience which results in an efficient search in the model attractors space that can explain the origin of certain phenomenology in neural, genetic and ill-condensed matter systems. By extensive computer simulation we also address a relation previously conjectured between observed power-law distributions and the occurrence of a "critical state" during functionality of (e.g.) cortical networks, and describe the precise nature of such criticality in the model.Comment: 18 pages, 9 figure

Characterizing the Quantum Confined Stark Effect in Semiconductor Quantum Dots and Nanorods for Single-Molecule Electrophysiology

We optimized the performance of quantum confined Stark effect QCSE based voltage nanosensors. A high throughput approach for single particle QCSE characterization was developed and utilized to screen a library of such nanosensors. Type II ZnSe CdS seeded nanorods were found to have the best performance among the different nanosensors evaluated in this work. The degree of correlation between intensity changes and spectral changes of the excitons emission under applied field was characterized. An upper limit for the temporal response of individual ZnSe CdS nanorods to voltage modulation was characterized by high throughput, high temporal resolution intensity measurements using a novel photon counting camera. The measured 3.5 us response time is limited by the voltage modulation electronics and represents about 30 times higher bandwidth than needed for recording an action potential in a neuron.Comment: 36 pages, 6 figure

QUANTIFYING GAIT ADAPTABILITY: FRACTALITY, COMPLEXITY, AND STABILITY DURING ASYMMETRIC WALKING

Successful walking necessitates modifying locomotor patterns when encountering organism, task, or environmental constraints. The structure of stride-to-stride variance (fractal dynamics) may represent the adaptive capacity of the locomotor system. To date, however, fractal dynamics have been assessed during unperturbed walking. Quantifying gait adaptability requires tasks that compel locomotor patterns to adapt. The purpose of this dissertation was to determine the potential relationship between fractal dynamics and gait adaptability. The studies presented herein represent a necessary endeavor to incorporate both an analysis of gait fractal dynamics and a task requiring adaptation of locomotor patterns. The adaptation task involved walking asymmetrically on a split-belt treadmill, whereby individuals adapted the relative phasing between legs. This experimental design provided a better understanding of the prospective relationship between fractal dynamics and adaptive capacity. Results from the first study indicated there was no association between unperturbed walking fractal dynamics and gait adaptability in young, healthy adults. However, there was an emergent relationship between asymmetric walking fractal dynamics and gait adaptability. Moreover, fractal dynamics increased during asymmetric walking. The second study investigated fractal dynamics and gait adaptability in healthy, active young and older adults. The findings from study 2 showed no differences between young and older adults regarding unperturbed or asymmetric walking fractal dynamics, or gait adaptability performance. The second study provided further evidence for the lack of association between unperturbed fractal dynamics and gait adaptability. Furthermore, study 2 delivered additional support that asymmetric walking not only yields increased fractal scaling values, but also associates with adaptive gait performance in older adults. Finally, while the first two studies explored stride time monofractality during various walking tasks, the third study aimed to understand the potential multifractality, i.e. temporal evolution of fractal dynamics, of unperturbed and asymmetric walking. The results suggest that unperturbed walking is monofractal in nature, while more challenging asymmetric walking reveals multifractal characteristics, and that multifractality does not associate with adaptive gait performance. This dissertation provides preliminary evidence for the lack of relationship between gait adaptability and unperturbed fractal dynamics, and the emergent association between adaptive gait and asymmetric walking fractality

Assessment of long-range correlation in animal behaviour time series: the temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)

The aim of this study was to evaluate the performance of a classical method of fractal analysis, Detrended Fluctuation Analysis (DFA), in the analysis of the dynamics of animal behavior time series. In order to correctly use DFA to assess the presence of long-range correlation, previous authors using statistical model systems have stated that different aspects should be taken into account such as: 1) the establishment by hypothesis testing of the absence of short term correlation, 2) an accurate estimation of a straight line in the log-log plot of the fluctuation function, 3) the elimination of artificial crossovers in the fluctuation function, and 4) the length of the time series. Taking into consideration these factors, herein we evaluated the presence of long-range correlation in the temporal pattern of locomotor activity of Japanese quail ({\sl Coturnix coturnix}) and mosquito larva ({\sl Culex quinquefasciatus}). In our study, modeling the data with the general ARFIMA model, we rejected the hypothesis of short range correlations (d=0) in all cases. We also observed that DFA was able to distinguish between the artificial crossover observed in the temporal pattern of locomotion of Japanese quail, and the crossovers in the correlation behavior observed in mosquito larvae locomotion. Although the test duration can slightly influence the parameter estimation, no qualitative differences were observed between different test durations

Non-Markov stochastic dynamics of real epidemic process of respiratory infections

The study of social networks and especially of the stochastic dynamics of the diseases spread in human population has recently attracted considerable attention in statistical physics. In this work we present a new statistical method of analyzing the spread of epidemic processes of grippe and acute respiratory track infections (ARTI) by means of the theory of discrete non-Markov stochastic processes. We use the results of our last theory (Phys. Rev. E 65, 046107 (2002)) to study statistical memory effects, long - range correlation and discreteness in real data series, describing the epidemic dynamics of human ARTI infections and grippe. We have carried out the comparative analysis of the data of the two infections (grippe and ARTI) in one of the industrial districts of Kazan, one of the largest cities of Russia. The experimental data are analyzed by the power spectra of the initial time correlation function and the memory functions of junior orders, the phase portraits of the four first dynamic variables, the three first points of the statistical non-Markov parameter and the locally averaged kinetic and relaxation parameters. The received results give an opportunity to provide strict quantitative description of the regular and stochastic components in epidemic dynamics of social networks taking into account their time discreteness and effects of statistical memory. They also allow to reveal the degree of randomness and predictability of the real epidemic process in the specific social network.Comment: 18 pages, 8figs, 1 table

Working with children? the probability of mothers exiting the workforce at time of birth

Recent trends in the labor force participation of women have brought much public attention to the issue of women opting out. This paper explores the decision of working women to exit the labor market at a time of major transition—the birth of a child—utilizing linked vital statistics, administrative employer, and state welfare records. The results indicate that, consistent with utility maximization theory, women are not just opting out but rather are accurately assessing the potential opportunity and direct labor market costs of their exit decisions and are making workforce exit decisions based on measurable costs and benefits.Women - Employment