Onion Skin Is Not a Universal Firing Pattern for Spinal Motoneurons: Simulation Study.

Muscle force is modulated by sequential recruitment and firing rates of motor units (MUs). However, discrepancies exist in the literature regarding the relationship between MU firing rates and their recruitment, presenting two contrasting firing-recruitment schemes. The first firing scheme, known as "onion skin", exhibits low-threshold MUs firing faster than high-threshold MUs, forming separate layers akin to an onion. This contradicts the other firing scheme, known as "reverse onion skin" or "after-hyperpolarization (AHP)", with low-threshold MUs firing slower than high-threshold MUs. To study this apparent dichotomy, we employed a high-fidelity computational model that prioritizes physiological fidelity and heterogeneity, allowing versatility in the recruitment of different motoneuron types. Our simulations indicate that these two schemes are not mutually exclusive but rather coexist. The likelihood of observing each scheme depends on factors such as the motoneuron pool activation level, synaptic input activation rates, and MU type. The "onion skin" scheme does not universally govern the encoding rates of MUs but tends to emerge in unsaturated motoneurons (cells firing < their fusion frequency that generates peak force), whereas the "AHP" scheme prevails in saturated MUs (cells firing at their fusion frequency), which is highly probable for S-type MUs. When unsaturated, FF-type MUs always show the "onion skin" scheme, while S-type MUs do not show either one. FR-type MUs are generally similar but show weaker "onion skin" behaviors than FF-type MUs. Our results offer an explanation for the longstanding dichotomy regarding MU firing patterns, shedding light on the factors influencing the firing-recruitment schemes.

Reductions in Motor Unit Firing are Associated with Clinically Meaningful Leg Extensor Weakness in Older Adults.

Weakness, one of the key characteristics of sarcopenia, is a significant risk factor for functional limitations and disability in older adults. It has long been suspected that reductions in motor unit firing rates (MUFRs) are one of the mechanistic causes of age-related weakness. However, prior work has not investigated the extent to which MUFR is associated with clinically meaningful weakness in older adults. Forty-three community-dwelling older adults (mean: 75.4 ± 7.4 years; 46.5% female) and 24 young adults (mean: 22.0 ± 1.8 years; 58.3% female) performed torque matching tasks at varying submaximal intensities with their non-dominant leg extensors. Decomposed surface electromyographic recordings were used to quantify MUFRs from the vastus lateralis muscle. Computational modeling was subsequently used to independently predict how slowed MUFRs would negatively impact strength in older adults. Bivariate correlations between MUFRs and indices of lean mass, voluntary activation, and physical function/mobility were also assessed in older adults. Weak older adults (n = 14) exhibited an approximate 1.5 and 3 Hz reduction in MUFR relative to non-weak older adults (n = 29) at 50% and 80% MVC, respectively. Older adults also exhibited an approximate 3 Hz reduction in MUFR relative to young adults at 80% MVC only. Our model predicted that a 3 Hz reduction in MUFR results in a strength decrement of 11-26%. Additionally, significant correlations were found between slower MUFRs and poorer neuromuscular quality, voluntary activation, chair rise time performance, and stair climb power (r's = 0.31 to 0.43). These findings provide evidence that slowed MUFRs are mechanistically linked with clinically meaningful leg extensor weakness in older adults.

Bifurcation analysis of motoneuronal excitability mechanisms under normal and ALS conditions.

Introduction: Bifurcation analysis allows the examination of steady-state, non-linear dynamics of neurons and their effects on cell firing, yet its usage in neuroscience is limited to single-compartment models of highly reduced states. This is primarily due to the difficulty in developing high-fidelity neuronal models with 3D anatomy and multiple ion channels in XPPAUT, the primary bifurcation analysis software in neuroscience. Methods: To facilitate bifurcation analysis of high-fidelity neuronal models under normal and disease conditions, we developed a multi-compartment model of a spinal motoneuron (MN) in XPPAUT and verified its firing accuracy against its original experimental data and against an anatomically detailed cell model that incorporates known MN non-linear firing mechanisms. We used the new model in XPPAUT to study the effects of somatic and dendritic ion channels on the MN bifurcation diagram under normal conditions and after amyotrophic lateral sclerosis (ALS) cellular changes. Results: Our results show that somatic small-conductance Ca2+-activated K (SK) channels and dendritic L-type Ca2+ channels have the strongest effects on the bifurcation diagram of MNs under normal conditions. Specifically, somatic SK channels extend the limit cycles and generate a subcritical Hopf bifurcation node in the V-I bifurcation diagram of the MN to replace a supercritical node Hopf node, whereas L-type Ca2+ channels shift the limit cycles to negative currents. In ALS, our results show that dendritic enlargement has opposing effects on MN excitability, has a greater overall impact than somatic enlargement, and dendritic overbranching offsets the dendritic enlargement hyperexcitability effects. Discussion: Together, the new multi-compartment model developed in XPPAUT facilitates studying neuronal excitability in health and disease using bifurcation analysis.

Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization.

Internet of Things (IoT) tasks are offloaded to servers located at the edge network for improving the power consumption of IoT devices and the execution times of tasks. However, deploying edge servers could be difficult or even impossible in hostile terrain or emergency areas where the network is down. Therefore, edge servers are mounted on unmanned aerial vehicles (UAVs) to support task offloading in such scenarios. However, the challenge is that the UAV has limited energy, and IoT tasks are delay-sensitive. In this paper, a UAV-based offloading strategy is proposed where first, the IoT devices are dynamically clustered considering the limited energy of UAVs, and task delays, and second, the UAV hovers over each cluster head to process the offloaded tasks. The optimization problem of dynamically determining the optimal number of clusters, specifying the member tasks of each cluster, is modeled as a mixed-integer, nonlinear constraint optimization. A discrete differential evolution (DDE) algorithm with new mutation and crossover operators is proposed for the formulated optimization problem, and compared with the particle swarm optimization (PSO) and genetic algorithm (GA) meta-heuristics. Further, the ant colony optimization (ACO) algorithm is employed to identify the shortest path over the cluster heads for the UAV to traverse. The simulation results validate the effectiveness of the proposed offloading strategy in terms of tasks delays and UAV energy consumption.

High-performance simplification of triangular surfaces using a GPU.

Due to advances in high-performance computing technologies, computer graphics techniques-especially those related to mesh simplification-have been noticeably improved. These techniques, which have a strong impact on many applications, such as geometric modeling and visualization, have been well studied for more than two decades. Recent advances in GPUs have led to significant improvements in terms of speed and interactivity. In this paper, we present a mesh simplification algorithm that benefits from the parallel framework provided by recent GPUs. We customize the halfedge data structure for adaption with the dynamic memory restrictions of CUDA. The proposed algorithm is fully parallelized by employing a lock-free skip priority queue and a set of disjoint regions of the mesh. The proposed technique accelerates the simplification process while preserving the topological properties of the mesh. Some results and comparisons are provided to verify the efficiency of the proposed algorithm.

In-silico development and assessment of a Kalman filter motor decoder for prosthetic hand control.

Up to 50% of amputees abandon their prostheses, partly due to rapid degradation of the control systems, which require frequent recalibration. The goal of this study was to develop a Kalman filter-based approach to decoding motoneuron activity to identify movement kinematics and thereby provide stable, long-term, accurate, real-time decoding. The Kalman filter-based decoder was examined via biologically varied datasets generated from a high-fidelity computational model of the spinal motoneuron pool. The estimated movement kinematics controlled a simulated MuJoCo prosthetic hand. This clear-box approach showed successful estimation of hand movements under eight varied physiological conditions with no retraining. The mean correlation coefficient of 0.98 and mean normalized root mean square error of 0.06 over these eight datasets provide proof of concept that this decoder would improve long-term integrity of performance while performing new, untrained movements. Additionally, the decoder operated in real-time (~0.3 ms). Further results include robust performance of the Kalman filter when re-trained to more severe post-amputation limitations in the type and number of motoneurons remaining. An additional analysis shows that the decoder achieves better accuracy when using the firing of individual motoneurons as input, compared to using aggregate pool firing. Moreover, the decoder demonstrated robustness to noise affecting both the trained decoder parameters and the decoded motoneuron activity. These results demonstrate the utility of a proof of concept Kalman filter decoder that can support prosthetics' control systems to maintain accurate and stable real-time movement performance.

Gas-Phase Temperature Mapping of Evaporating Microdroplets.

Evaporation is a ubiquitous and complex phenomenon of importance to many natural and industrial systems. Evaporation occurs when molecules near the free interface overcome intermolecular attractions with the bulk liquid. As molecules escape the liquid phase, heat is removed, causing evaporative cooling. The influence of evaporative cooling on inducing a temperature difference with the surrounding atmosphere as well as within the liquid is poorly understood. Here, we develop a technique to overcome past difficulties encountered during the study of heterogeneous droplet evaporation by coupling a piezo-driven droplet generation mechanism to a controlled micro-thermocouple to probe microdroplet evaporation. The technique allowed us to probe the gas-phase temperature distribution using a micro-thermocouple (50 µm) in the vicinity of the liquid-vapor interface with high spatial (±10 µm) and temporal (±100 ms) resolution. We experimentally map the temperature gradient formed surrounding sessile water droplets having varying curvature dictated by the apparent advancing contact angle (100° ≲ θ ≲ 165°). The experiments were carried out at temperatures below and above ambient for a range of fixed droplet radii (130 µm ≲ R ≲ 330 µm). Our results provide a primary validation of the centuries-old theoretical framework underpinning heterogeneous droplet evaporation mediated by the working fluid, substrate, and gas thermophysical properties, droplet apparent contact angle, and droplet size. We show that microscale droplets residing on low-thermal-conductivity substrates such as glass absorb up to 8× more heat from the surrounding gas compared to droplets residing on high-thermal-conductivity substrates such as copper. Our work not only develops an experimental understanding of the heat transfer mechanisms governing droplet evaporation but also presents a powerful platform for the study and characterization of liquid-vapor transport at curved interfaces wetting and nonwetting advanced functional surfaces.

Estimating the effects of slicing on the electrophysiological properties of spinal motoneurons under normal and disease conditions.

Although slice recordings from spinal motoneurons (MNs) are being widely used, the effects of slicing on the measured MN electrical properties under normal and disease conditions have not been assessed. Using high-fidelity cell models of neonatal wild-type (WT) and superoxide dismutase-1 (SOD) cells, we examined the effects of slice thickness, soma position within the slice, and slice orientation to estimate the error induced in measured MN electrical properties from spinal slices. Our results show that most MN electrical properties are not adversely affected by slicing, except for cell time constant, cell capacitance, and Ca2+ persistent inward current (PIC), which all exhibited large errors, regardless of the slice condition. Among the examined factors, soma position within the slice appears to be the strongest factor in influencing the magnitude of error in measured MN electrical properties. Transverse slices appear to have the least impact on measured MN electrical properties. Surprisingly, and despite their anatomical enlargement, we found that G85R-SOD MNs experience similar error in their measured electrical properties to those of WT MNs, but their errors are more sensitive to the soma position within the slice than WT MNs. Unless in thick and symmetrical slices, slicing appears to reduce motoneuron type differences. Accordingly, slice studies should attempt to record from MNs at the slice center to avoid large and inconsistent errors in measured cell properties and have valid cell measurements' comparisons. Our results, therefore, offer information that would enhance the rigor of MN electrophysiological data measured from the slice preparation under normal and disease conditions.NEW & NOTEWORTHY Although slice recordings from motoneurons are being widely used, the effects of slicing on the measured motoneuron electrical properties under normal and disease conditions have not been assessed. Using high-fidelity cell models of neonatal WT and SOD cells, we examined the effects of slice thickness, soma position within the slice, and slice orientation. Our results offer information that enhances the rigor of MN electrophysiological data measured from the slice preparation under normal and disease conditions.

A Classification Approach to Recognize the Firing of Spinal Motoneurons in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects the nervous system causing muscle weakness, paralysis, leading to death. Given that abnormalities in spinal motoneuron (MN) excitability begin long before symptoms manifest, developing an approach that could recognize fluctuations in MN firing could help in early diagnosis of ALS. This paper introduces a machine learning approach to discriminate between ALS and normal MN firing. The approach is based on two electrophysiological markers; namely, spiking latency and the spike-triggered average signal. The method is examined using data generated from a computational model under systematic variation of MN properties. Such variations mimic the differential dynamic changes in cellular properties that different MN types experience during ALS progression. Our results demonstrate the ability of the approach to accurately recognize ALS firing patterns across the spectrum of examined variations in MN properties.Clinical Relevance- These results represent a proof of concept for using the proposed machine-learning approach in early diagnosis of ALS.

Dendritic distributions of L-type Ca2+ and SKL channels in spinal motoneurons: a simulation study.

Persistent inward currents are important to motoneuron excitability and firing behaviors and also have been implicated in excitotoxicity. In particular, L-type Ca2+ channels, usually located on motoneuron dendrites, play a primary role in amplification of synaptic inputs. However, recent experimental findings on L-type Ca2+ channel behaviors challenge some fundamental assumptions that have been used in interpreting experimental and computational modeling data. Thus, the objectives of this study were to incorporate recent experimental data into an updated, high-fidelity computational model in order to explain apparent inconsistencies and to better elucidate the spatial distributions, expression patterns, and functional roles of L-type Ca2+ and SKL channels. Specifically, the updated model incorporated asymmetric channel activation/deactivation kinetics, depolarization-dependent facilitation, randomness in channel gating, and coactivation of SKL channels. Our simulation results suggest that L-type Ca2+ and SKL channels colocalize primarily on distal dendrites of motoneurons in a punctate expression. Also, punctate expression, as opposed to a homogeneous expression, provides high synaptic current amplification, limits bistability and firing rates, and robustly regulates the Ca2+ persistent inward current, thereby reducing risk of excitotoxicity. The hysteresis and bistability observed experimentally in current-voltage and frequency-current relationships result from the L-type Ca2+ channels' distal location and intrinsic warm-up. Accordingly, our results indicate that punctate expression of L-type Ca2+ and SKL channels is a potent mechanism for regulating excitability, which would provide a strong neuroprotective effect. Our results could provide broader insights into the functional significance of warm-up and punctate expression of ion channels to regulation of cell excitability.NEW & NOTEWORTHY Recent experimental findings on L-type Ca2+ channels challenge fundamental assumptions used in interpreting experimental and computational modeling data. Here, we incorporated recent experimental data into an updated, high-fidelity computational model to explain apparent inconsistencies and better elucidate the distributions, expression patterns, and functional roles of L-type Ca2+ and SKL channels. Our results indicate that punctate expression of L-type Ca2+ and SKL channels is a potent mechanism for regulating motoneuron excitability, providing a strong neuroprotective effect.

The Mechanistic Basis for Successful Spinal Cord Stimulation to Generate Steady Motor Outputs.

Electrical stimulation of the spinal cord is a promising rehabilitation intervention to restore/augment motor function after spinal cord injury (SCI). Combining sensory feedback with stimulation of remaining motor circuits has been shown to be a prerequisite for the functional improvement of SCI patients. However, little is known about the cellular mechanisms potentially underlying this functional benefit in the injured spinal cord. Here, we combine computer simulations with an isolated whole-tissue adult mouse spinal cord preparation to examine synaptic, cellular, and system potentials measured from single motoneurons and ventral roots. The stimulation protocol included separate and combined activation of the sensory inputs (evoked by dorsal root stimulation) and motor inputs (evoked by stimulation of spinal cord tissue) at different frequencies, intensities, and neuromodulatory states. Our data show that, while sensory inputs exhibit short-term depression in response to a train of stimulation, motor inputs exhibit short-term facilitation. However, the concurrent activation of both inputs elicits a stronger and steadier motor output. This effect is enhanced by the application of pharmacological neuromodulators. Furthermore, sensorimotor excitatory postsynaptic potentials (EPSPs) summate sublinearly (i.e., their combination produces an excitatory potential smaller than the sum of the excitatory potentials they would individually produce). However, ventral root compound action potentials (CoAPs) summate supralinearly generating much higher outputs. Computer simulations revealed that the contrasting summation and disproportionality in plasticity between the excitatory postsynaptic potentials (EPSPs) and CoAPs result from the motoneuronal firing threshold acting as an amplitude-selective filter. Together, these results provide the mechanistic basis for the cellular processes contributing to the generation of steady motor outputs using spinal stimulation. This data has great potential to guide the design of more effective stimulation protocols in SCI patients.