We present a calculation of the amide I′ infrared (IR) spectra of the folded, unfolded, and intermediate states of the WW domain Fip35, a model system for β-sheet folding. Using an all-atom molecular dynamics simulation in which multiple folding and unfolding events take place we identify six conformational states and then apply perturbed matrix method quantum-mechanical calculations to determine their amide I′ IR spectra. Our analysis focuses on two states previously identified as Fip35 folding intermediates and suggests that a three-stranded core similar to the folded state core is the main source of the spectroscopic differences between the two intermediates. In particular, we propose a hypothesis for why folding via one of these intermediates was not experimentally observed by IR T-jump.
BACKGROUND: The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to date has investigated how different transfer entropy estimation methods perform in typical biomedical applications featuring small sample size and presence of outliers. METHODS: With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE), and the Darbellay-Vajda (D-V) adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratory-related chemosensitivity to O2 and CO2 induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO2 and PCO2 on minute ventilation (V˙E) before and after administration of domperidone was analyzed. RESULTS: In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for PO2 → VE. In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for PCO2 → VE, in agreement with experimental findings. CONCLUSIONS: Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results of this study suggest that fixed-binning, even with ranking, is too primitive, and although there is no clear winner between KDE and D-V partitioning, the reader should note that KDE requires more computational time and extensive parameter selection than D-V partitioning. We hope this study provides a guideline for selection of an appropriate transfer entropy estimation method.
Background: We present a fundamental theoretical framework for analysis of energy dissipation in any component of the circulatory system and formulate the full energy budget for both venous and arterial circulations. New indices allowing disease-specific subject-to-subject comparisons and disease-to-disease hemodynamic evaluation (quantifying the hemodynamic severity of one vascular disease type to the other) are presented based on this formalism.
Methods and Results: Dimensional analysis of energy dissipation rate with respect to the human circulation shows that the rate of energy dissipation is inversely proportional to the square of the patient body surface area and directly proportional to the cube of cardiac output. This result verified the established formulae for energy loss in aortic stenosis that was solely derived through empirical clinical experience. Three new indices are introduced to evaluate more complex disease states: (1) circulation energy dissipation index (CEDI), (2) aortic valve energy dissipation index (AV-EDI), and (3) total cavopulmonary connection energy dissipation index (TCPC-EDI). CEDI is based on the full energy budget of the circulation and is the proper measure of the work performed by the ventricle relative to the net energy spent in overcoming frictional forces. It is shown to be 4.01 ± 0.16 for healthy individuals and above 7.0 for patients with severe aortic stenosis. Application of CEDI index on single-ventricle venous physiology reveals that the surgically created Fontan circulation, which is indeed palliative, progressively degrades in hemodynamic efficiency with growth (p < 0.001), with the net dissipation in a typical Fontan patient (Body surface area = 1.0 m2) being equivalent to that of an average case of severe aortic stenosis. AV-EDI is shown to be the proper index to gauge the hemodynamic severity of stenosed aortic valves as it accurately reflects energy loss. It is about 0.28 ± 0.12 for healthy human valves. Moderate aortic stenosis has an AV-EDI one order of magnitude higher while clinically severe aortic stenosis cases always had magnitudes above 3.0. TCPC-EDI represents the efficiency of the TCPC connection and is shown to be negatively correlated to the size of a typical "bottle-neck" region (pulmonary artery) in the surgical TCPC pathway (p < 0.05).
Conclusions: Energy dissipation in the human circulation has been analyzed theoretically to derive the proper scaling (indexing) factor. CEDI, AV-EDI, and TCPC-EDI are proper measures of the dissipative characteristics of the circulatory system, aortic valve, and the Fontan connection, respectively.
Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.
Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states and muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization, and stability analysis tools to provide structural insights into the neural control of movement.
Objective. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features. Approach. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks to model the spatial and temporal regularities that underlie multi-muscle activation. Main results. We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches. Significance. This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas, and for improving brain-machine interfaces that rely on myoelectric control signals.
Objective. High morphological variability magnitude (MVM) and microvolt T wave alternans (TWA) within an electrocardiogram (ECG) signifies increased electrical instability and risk of sudden cardiac death. However, the influence of breathing rate (BR), heart rate (HR), and signal-to-noise ratio (SNR) is unknown and may inflate measured values. Approach. We synthesize ECGs with morphologies derived from the Physikalisch-Technische Bundesanstalt Database. We calculate MVM and TWA at varying BRs, HRs and SNRs. We compare the MVM and TWA of signal with versus without breathing at varying HRs and SNRs. We then quantify the percentage of MVM and TWA estimates affected by BR and HR in a healthy population and assess the effect of removing these affected estimates on a method for classifying individuals with and without post-traumatic stress disorder (PTSD). Main results. For signals with high SNR (>15 dB), MVM is significantly increased when BRs are > 9 respirations/minute (rpm) and HRs are < 100 beats/minute (bpm). Increased TWAs are detected for HR/BR pairs of 60/15, 60/30 and 120/30 bpm/rpm. For 18 healthy participants, 8.33% of TWA windows and 66.76% of MVM windows are affected by BR and HR. On average, the number of windows with TWA elevations > 47 μV decreases by 23% after excluding regions with significant BR and HR effect. Adding HR and BR to a morphological variability feature increases the classification performance by 6% for individuals with and without PTSD. Significance. Physiological BR and HR significantly increase MVM and TWA, indicating that BR and HR should be considered separately as confounders. The code for this work has been released as part of an open-source toolbox.
The biomechanical principles underlying the organization of muscle activation patterns during standing balance are poorly understood. The goal of this study was to understand the influence of biomechanical inter-joint coupling on endpoint forces and accelerations induced by the activation of individual muscles during postural tasks. We calculated induced endpoint forces and accelerations of 31 muscles in a 7 degree-of-freedom, three-dimensional model of the cat hindlimb. To test the effects of inter-joint coupling, we systematically immobilized the joints (excluded kinematic degrees of freedom) and evaluated how the endpoint force and acceleration directions changed for each muscle in 7 different conditions. We hypothesized that altered inter-joint coupling due to joint immobilization of remote joints would substantially change the induced directions of endpoint force and acceleration of individual muscles. Our results show that for most muscles crossing the knee or the hip, joint immobilization altered the endpoint force or acceleration direction by more than 90° in the dorsal and sagittal planes. Induced endpoint forces were typically consistent with behaviorally observed forces only when the ankle was immobilized. We then activated a proximal muscle simultaneous with an ankle torque of varying magnitude, which demonstrated that the resulting endpoint force or acceleration direction is modulated by the magnitude of the ankle torque. We argue that this simple manipulation can lend insight into the functional effects of co-activating muscles. We conclude that inter-joint coupling may be an essential biomechanical principle underlying the coordination of proximal and distal muscles to produce functional endpoint actions during motor tasks.
Purpose: Pediatric and adult patients with sickle cell anemia (SCA) are at increased risk of stroke and cerebrovascular accident. In the general adult population, there is a relationship between arterial hemodynamics and pathology; however, this relationship in SCA patients remains to be elucidated. The aim of this work was to characterize circle of Willis hemodynamics in patients with SCA and quantify the impact of viscosity choice on pathophysiologically-relevant hemodynamics measures. Methods: Based on measured vascular geometries, time-varying flow rates, and blood parameters, detailed patient-specific simulations of the circle of Willis were conducted for SCA patients (n = 6). Simulations quantified the impact of patient-specific and standard blood viscosities on wall shear stress (WSS). Results: These results demonstrated that use of a standard blood viscosity introduces large errors into the estimation of pathophysiologically-relevant hemodynamic parameters. Standard viscosity models overpredicted peak WSS by 55% and 49% for steady and pulsatile flow, respectively. Moreover, these results demonstrated non-uniform, spatial patterns of positive and negative WSS errors related to viscosity, and standard viscosity simulations overpredicted the time-averaged WSS by 32% (standard deviation = 7.1%). Finally, differences in shear rate demonstrated that the viscosity choice alters the simulated near-wall flow field, impacting hemodynamics measures. Conclusions: This work presents simulations of circle of Willis arterial flow in SCA patients and demonstrates the importance and feasibility of using a patient-specific viscosity in these simulations. Accurately characterizing cerebrovascular hemodynamics in SCA populations has potential for elucidating the pathophysiology of large-vessel occlusion, aneurysms, and tissue damage in these patients.
Computational fluid dynamic (CFD) simulations are widely utilized to assess Fontan hemodynamics that are related to long-term complications. No previous studies have systemically investigated the effects of using different inlet velocity profiles in Fontan simulations. This study implements real, patient-specific velocity profiles for numerical assessment of Fontan hemodynamics using CFD simulations. Four additional, artificial velocity profiles were used for comparison: (1) flat, (2) parabolic, (3) Womersley, and (4) parabolic with inlet extensions [to develop flow before entering the total cavopulmonary connection (TCPC)]. The differences arising from the five velocity profiles, as well as discrepancies between the real and each of the artificial velocity profiles, were quantified by examining clinically important metrics in TCPC hemodynamics: power loss (PL), viscous dissipation rate (VDR), hepatic flow distribution, and regions of low wall shear stress. Statistically significant differences were observed in PL and VDR between simulations using real and flat velocity profiles, but differences between those using real velocity profiles and the other three artificial profiles did not reach statistical significance. These conclusions suggest that the artificial velocity profiles (2)–(4) are acceptable surrogates for real velocity profiles in Fontan simulations, but parabolic profiles are recommended because of their low computational demands and prevalent applicability.