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Abstract Complex network theory has focused on properties of networks with real-valued edge weights. However, in signal transfer networks, such as those representing the transfer of light across an interferometer, complex-valued edge weights are needed to represent the manipulation of the signal in both magnitude and phase. These complex-valued edge weights introduce interference into the signal transfer, but it is unknown how such interference affects network properties such as small-worldness. To address this gap, we have introduced a small-world interferometer network model with complex-valued edge weights and generalized existing network measures to define the interferometric clustering coefficient, the apparent path length, and the interferometric small-world coefficient. Using high-performance computing resources, we generated a large set of small-world interferometers over a wide range of parameters in system size, nearest-neighbor count, and edge-weight phase and computed their interferometric network measures. We found that the interferometric small-world coefficient depends significantly on the amount of phase on complex-valued edge weights: for small edge- weight phases, constructive interference led to a higher interferometric small-world coefficient; while larger edge-weight phases induced destructive interference which led to a lower interferometric small-world coefficient. Thus, for the small-world interferometer model, interferometric measures are necessary to capture the effect of interference on signal transfer. This model is an example of the type of problem that necessitates interferometric measures, and applies to any wave-based network including quantum networks. 

Under healthy conditions, the pancreas responds to a glucose challenge by releasing insulin. Insulin suppresses lipolysis in adipose tissue, thereby decreasing plasma glycerol concentration, and it regulates plasma glucose concentration through action in muscle and liver. Insulin resistance (IR) occurs when more insulin is required to achieve the same effects, and IR may be tissue-specific. IR emerges during puberty as a result of high concentrations of growth hormone and is worsened by youth-onset obesity. Adipose, liver, and muscle tissue exhibit distinct dose-dependent responses to insulin in multi-phase hyperinsulinemic-euglycemic (HE) clamps, but the HE clamp protocol does not address potential differences in the dynamics of tissue-specific insulin responses. Changes to the dynamics of insulin responses would alter glycemic control in response to a glucose challenge. To investigate the dynamics of insulin acting on adipose tissue, we developed a novel differential-equations based model that describes the coupled dynamics of glycerol concentrations and insulin action during an oral glucose tolerance test in female adolescents with obesity and IR. We compared these dynamics to the dynamics of insulin acting on muscle and liver as assessed with the oral minimal model applied to glucose and insulin data collected under the same protocol. We found that the action of insulin on glycerol peaks approximately 67 min earlier ( p < 0.001) and follows the dynamics of plasma insulin more closely compared to insulin action on glucose as assessed by the parameters representing the time constants for insulin action on glucose and glycerol ( p < 0.001). These findings suggest that the dynamics of insulin action show tissue-specific differences in our IR adolescent population, with adipose tissue responding to insulin more quickly compared to muscle and liver. Improved understanding of the tissue-specific dynamics of insulin action may provide novel insights into the progression of metabolic disease in patient populations with diverse metabolic phenotypes. 

Abstract Study Objectives We determine if young people with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH) have distinct nocturnal sleep stability phenotypes compared to subjectively sleepy controls. Methods Participants were 5- to 21-year old and drug-naïve or drug free: NT1 (n = 46), NT2 (n = 12), IH (n = 18), and subjectively sleepy controls (n = 48). We compared the following sleep stability measures from polysomnogram recording between each hypersomnolence disorder to subjectively sleepy controls: number of wake and sleep stage bouts, Kaplan–Meier survival curves for wake and sleep stages, and median bout durations. Results Compared to the subjectively sleepy control group, NT1 participants had more bouts of wake and all sleep stages (p ≤ .005) except stage N3. NT1 participants had worse survival of nocturnal wake, stage N2, and rapid eye movement (REM) bouts (p < .005). In the first 8 hours of sleep, NT1 participants had longer stage N1 bouts but shorter REM (all ps < .004). IH participants had a similar number of bouts but better survival of stage N2 bouts (p = .001), and shorter stage N3 bouts in the first 8 hours of sleep (p = .003). In contrast, NT2 participants showed better stage N1 bout survival (p = .006) and longer stage N1 bouts (p = .02). Conclusions NT1, NT2, and IH have unique sleep physiology compared to subjectively sleepy controls, with only NT1 demonstrating clear nocturnal wake and sleep instability. Overall, sleep stability measures may aid in diagnoses and management of these central nervous system disorders of hypersomnolence. 

The human circadian pacemaker entrains to the 24-h day, but interindividual differences in properties of the pacemaker, such as intrinsic period, affect chronotype and mediate responses to challenges to the circadian system, such as shift work and jet lag, and the efficacy of therapeutic interventions such as light therapy. Robust characterization of circadian properties requires desynchronization of the circadian system from the rest-activity cycle, and these forced desynchrony protocols are very time and resource intensive. However, circadian protocols designed to derive the relationship between light intensity and phase shift, which is inherently affected by intrinsic period, may be applied more broadly. To exploit this relationship, we applied a mathematical model of the human circadian pacemaker with a Markov-Chain Monte Carlo parameter estimation algorithm to estimate the representative group intrinsic period for a group of participants using their collective illuminance-response curve data. We first validated this methodology using simulated illuminance-response curve data in which the intrinsic period was known. Over a physiological range of intrinsic periods, this method accurately estimated the representative intrinsic period of the group. We also applied the method to previously published experimental data describing the illuminance-response curve for a group of healthy adult participants. We estimated the study participants’ representative group intrinsic period to be 24.26 and 24.27 h using uniform and normal priors, respectively, consistent with estimates of the average intrinsic period of healthy adults determined using forced desynchrony protocols. Our results establish an approach to estimate a population’s representative intrinsic period from illuminance-response curve data, thereby facilitating the characterization of intrinsic period across a broader range of participant populations than could be studied using forced desynchrony protocols. Future applications of this approach may improve the understanding of demographic differences in the intrinsic circadian period.