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Journal articles on the topic 'Neural conduction'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2024

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1

Klimkin, A. V., V. B. Voitenkov, and N. V. Skripchenko. "NEURAL CONDUCTION AT TOURNIQUET TEST." Marine Medicine 3, no. 1 (January 1, 2017): 25–33. http://dx.doi.org/10.22328/2413-5747-2017-3-1-25-33.

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2

Parris, W. C. V., K. E. Misulis, J. R. Kambam, J. J. Franks, and W. D. Dettbarn. "VERAPAMIL POTENTIATES BUPIVACAINE-INDUCED NEURAL CONDUCTION BLOCKADE." Anesthesiology 67, no. 3 (September 1, 1987): A250. http://dx.doi.org/10.1097/00000542-198709001-00250.

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3

Mackel, R., and E. Brink. "Conduction of neural impulses in diabetic neuropathy." Clinical Neurophysiology 114, no. 2 (February 2003): 248–55. http://dx.doi.org/10.1016/s1388-2457(02)00320-6.

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4

Gorza, Luisa, Stefano Schiaffino, and Maurizio Vitadello. "Heart conduction system: a neural crest derivative?" Brain Research 457, no. 2 (August 1988): 360–66. http://dx.doi.org/10.1016/0006-8993(88)90707-x.

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5

Keneskyzy, K., and S. B. Yeskermes. "Метод машинного обучения для обратных задач теплопроводности." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, no. 1(5) (March 26, 2021): 59–64. http://dx.doi.org/10.54309/ijict.2021.05.1.008.

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Investigated in this work is the potential of carrying out inverse problems with linear and non-linear behavior using machine learning methods and the neural network method. With the advent of ma-chine learning algorithms it is now possible to model inverse problems faster and more accurately. In order to demonstrate the use of machine learning and neural networks in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning and neural network algorithms. The trained algorithm is then used to determine the boundary conditions of a problem from assumed meas-urements. The proposed method is tested for the linear/non-linear heat conduction problems in which the boundary conditions are determined by providing three, four, and five temperature measurements. This re-search demonstrates that the proposed fusion of computational mechanics and machine learning is an effec-tive way of tackling complex inverse problems.
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Fukuoka, Yuko, Hiromichi Komori, Shigenori Kawabata, Harunobu Ohkubo, Kenichi Shinomiya, and Omi Terasaki. "Imaging of neural conduction block by neuromagnetic recording." Clinical Neurophysiology 113, no. 12 (December 2002): 1985–92. http://dx.doi.org/10.1016/s1388-2457(02)00345-0.

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7

Bijl, G. K., and F. Veringa. "Neural conduction time and steady-state evoked potentials." Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 62, no. 6 (November 1985): 465–67. http://dx.doi.org/10.1016/0168-5597(85)90059-0.

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8

Chole, Richard A., Timothy E. Hullar, and Lisa G. Potts. "Conductive Component After Cochlear Implantation in Patients With Residual Hearing Conservation." American Journal of Audiology 23, no. 4 (December 2014): 359–64. http://dx.doi.org/10.1044/2014_aja-14-0018.

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Purpose Changes in auditory thresholds following cochlear implantation are generally assumed to be due to damage to neural elements. Theoretical studies have suggested that placement of a cochlear implant can cause a conductive hearing loss. Identification of a conductive component following cochlear implantation could guide improvements in surgical techniques or device designs. The purpose of this study is to characterize new-onset conductive hearing losses after cochlear implantation. Method In a prospective study, air- and bone-conduction audiometric testing were completed on cochlear implant recipients. An air–bone gap equal to or greater than 15 dB HL at 2 frequencies determined the presence of a conductive component. Results Of the 32 patients with preoperative bone-conduction hearing, 4 patients had a new-onset conductive component resulting in a mixed hearing loss, with air-conduction thresholds ranging from moderate to profound and an average air–bone gap of 30 dB HL. One had been implanted through the round window, 2 had an extended round window, and 1 had a separate cochleostomy. Conclusions Loss of residual hearing following cochlear implantation may be due in part to a conductive component. Identifying the mechanism for this conductive component may help minimize hearing loss. Postoperative hearing evaluation should measure both air- and bone-conduction thresholds.
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9

Chen, Ley, S. Askarian, M. Mohammadzaheri, and F. Samadi. "Simulation and Experimental Study of Inverse Heat Conduction Problem." Advanced Materials Research 233-235 (May 2011): 2820–23. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2820.

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In this paper, a neural network method is proposed to solve a one dimensional inverse heat conduction problem (IHCP). The method relies on input/output data of an unknown system to create an intelligent neural network model. Multi layer perceptrons with recurrent properties are utilised in the model. Prepared input/output data are used to train the neural network. Reliable checking processes are also offered to justify the robustness of the method. A numerical sequential function specification (SFS) method is used as another technique to solve the IHCP. The numerical result is compared with that of the proposed method and good agreement is shown between the two methods. However, the numerical method can be only used to solve the IHCP off-line due to the high computation requirement. The proposed neural network method can be used in real-time situations as shown in the experimental tests.
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10

Pitta, Izabela Jardim Rodrigues, Bruno de Siqueira Mietto, Aron dos Santos Camilo, Sergio Luiz Gomes Antunes, Euzenir Nunes Sarno, and Marcia Rodrigues Jardim. "Clinical, Neurophysiological and Histopatological correlations in Pure Neural Leprosy." Brazilian Journal of Case Reports 1, no. 4 (November 5, 2021): 105–13. http://dx.doi.org/10.52600/2763-583x.bjcr.2021.1.4.105-113.

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Although neuropathy remains one of the most problematic issues faced by leprosy patients, the evolving process of its findings continues a challenge particularly in pure neural leprosy (PNL). We evaluated neurological examination, nerve conduction studies and histhopathological data of patients with PNL and ulnar neuropathy. Patients with longer duration of symptoms had reduction in the motor conduction velocities and patients with fibrosis in the biopsy had axonal damage in the nerve conduction studies. This suggests that focal demyelination may be present in leprosy patients at the moment of the diagnosis and be related to the duration of the neuropathy.
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11

Warner, M. R., J. M. deTarnowsky, C. C. Whitson, and J. M. Loeb. "Beat-by-beat modulation of AV conduction. II. Autonomic neural mechanisms." American Journal of Physiology-Heart and Circulatory Physiology 251, no. 6 (December 1, 1986): H1134—H1142. http://dx.doi.org/10.1152/ajpheart.1986.251.6.h1134.

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We examined the mechanism by which autonomic neural activity associated with respiration and blood pressure modulates atrioventricular (AV) conduction in conscious dogs. Mongrel dogs were anesthetized and instrumented under sterile conditions to record atrial and ventricular electrograms and blood pressure. In the conscious state, electrocardiogram (ECG), respiration, blood pressure, and electrograms were recorded continuously, and heart rate and AV interval were plotted graphically as a function of time. To delineate the role(s) of sympathetic and parasympathetic activity, AV conduction was studied during abrupt and linear changes in heart rate after administration of atropine, propranolol, or both. In the basal state and after propranolol, AV interval oscillated with respiration both in the absence of atrial pacing and at pacing rates 10–100 beats/min above control. Following atropine, oscillations in AV interval associated with respiration were abolished; however, linear and abrupt heart rate increases resulted in AV conduction changes that were associated with fluctuations in blood pressure. In contrast, after both atropine and propranolol, alterations in blood pressure or respiration did not influence AV conduction and rate-dependent prolongation of AV conduction occurred. We conclude that in the basal state, AV conduction is influenced predominately by changes in parasympathetic activity which is the major determinant of respiratory-related AV interval oscillations; after atropine, sympathetic activity produces fluctuations in both AV conduction and blood pressure; and intrinsic rate-dependent properties of the AV node are modulated continually by both divisions of the autonomic nervous system.
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12

Mackel, R. "Conduction of neural impulses in human mechanoreceptive cutaneous afferents." Journal of Physiology 401, no. 1 (July 1, 1988): 597–615. http://dx.doi.org/10.1113/jphysiol.1988.sp017182.

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13

Pearce, Alan J., Grant S. Rowe, and Douglas G. Whyte. "Neural conduction and excitability following a simple warm up." Journal of Science and Medicine in Sport 15, no. 2 (March 2012): 164–68. http://dx.doi.org/10.1016/j.jsams.2011.09.001.

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14

Heidari, M., and M. Garshasbi. "Using Artificial Neural Networks in Solving Heat Conduction Problems." International Journal of Operations Research 12, no. 1 (2016): 16–20. http://dx.doi.org/10.21307/ijor-2015-003.

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15

Pireethi, R., and R. Balamurugan. "Bridgeless Isolated Cuk PFC Implementation using PID and Neural Controller." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 1 (April 1, 2016): 88. http://dx.doi.org/10.11591/ijeecs.v2.i1.pp88-95.

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<p> The comparision of Bridgeless Isolated Cuk converter using Proportional Integral Derivative (PID) and Neural controller (NC) is presented in this paper. Due to Diode Bridge Rectifier (DBR) conventional converters suffer from high conduction loss. Bridgeless Cuk converter is used to eliminate the issues. Bridgeless Isolated (BLI) Cuk converter is used to withstand high voltage. Compare to Continuous Conduction Mode (CCM) Discontinuous Conduction Mode (DCM) utilize less number of sensors. To obtain unity power factor it is used to work in DCM mode. BLI Cuk Converter is implemented in two controller PID and Neural controller to observe power factor. Performance of BLDC motor is simulated by using MATLAB/SIMULINK software. </p>
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16

Parisi, Vincenzo, Maria Elisa Scarale, Nicole Balducci, Michela Fresina, and Emilio C. Campos. "Electrophysiological Detection of Delayed Postretinal Neural Conduction in Human Amblyopia." Investigative Opthalmology & Visual Science 51, no. 10 (October 1, 2010): 5041. http://dx.doi.org/10.1167/iovs.10-5412.

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17

Newton, J. R. "Manic–depression neural conduction speeds and action potential event dyscorrelation." Medical Hypotheses 52, no. 1 (January 1999): 77–83. http://dx.doi.org/10.1054/mehy.1997.0610.

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18

Dyball, R. E., R. Grossmann, G. Leng, and K. Shibuki. "Spike propagation and conduction failure in the rat neural lobe." Journal of Physiology 401, no. 1 (July 1, 1988): 241–56. http://dx.doi.org/10.1113/jphysiol.1988.sp017160.

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19

Shiguemori, Elcio H., José DemÍSio S. Da Silva, and Haroldo F. De Campos Velho. "Estimation of initial condition in heat conduction by neural network." Inverse Problems in Science and Engineering 12, no. 3 (June 2004): 317–28. http://dx.doi.org/10.1080/10682760310001598599.

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20

Egger, Robert, Yevhen Tupikov, Margot Elmaleh, Kalman A. Katlowitz, Sam E. Benezra, Michel A. Picardo, Felix Moll, Jörgen Kornfeld, Dezhe Z. Jin, and Michael A. Long. "Local Axonal Conduction Shapes the Spatiotemporal Properties of Neural Sequences." Cell 183, no. 2 (October 2020): 537–48. http://dx.doi.org/10.1016/j.cell.2020.09.019.

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21

Parisi, Vincenzo, Luigi Uccioli, Leoluca Parisi, Gaspare Colacino, Gianluca Manni, Guido Menzinger, and Massimo G. Bucci. "Neural conduction in visual pathways in newly-diagnosed IDDM patients." Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 108, no. 5 (September 1998): 490–96. http://dx.doi.org/10.1016/s0168-5597(98)00026-4.

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22

Zhang, Tao, Cai-jin Wang, Song-yu Liu, Nan Zhang, and Tong-wei Zhang. "Assessment of soil thermal conduction using artificial neural network models." Cold Regions Science and Technology 169 (January 2020): 102907. http://dx.doi.org/10.1016/j.coldregions.2019.102907.

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23

Noori, Rabiya, Daniel Park, John D. Griffiths, Sonya Bells, Paul W. Frankland, Donald Mabbott, and Jeremie Lefebvre. "Activity-dependent myelination: A glial mechanism of oscillatory self-organization in large-scale brain networks." Proceedings of the National Academy of Sciences 117, no. 24 (June 1, 2020): 13227–37. http://dx.doi.org/10.1073/pnas.1916646117.

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Communication and oscillatory synchrony between distributed neural populations are believed to play a key role in multiple cognitive and neural functions. These interactions are mediated by long-range myelinated axonal fiber bundles, collectively termed as white matter. While traditionally considered to be static after development, white matter properties have been shown to change in an activity-dependent way through learning and behavior—a phenomenon known as white matter plasticity. In the central nervous system, this plasticity stems from oligodendroglia, which form myelin sheaths to regulate the conduction of nerve impulses across the brain, hence critically impacting neural communication. We here shift the focus from neural to glial contribution to brain synchronization and examine the impact of adaptive, activity-dependent changes in conduction velocity on the large-scale phase synchronization of neural oscillators. Using a network model based on primate large-scale white matter neuroanatomy, our computational and mathematical results show that such plasticity endows white matter with self-organizing properties, where conduction delay statistics are autonomously adjusted to ensure efficient neural communication. Our analysis shows that this mechanism stabilizes oscillatory neural activity across a wide range of connectivity gain and frequency bands, making phase-locked states more resilient to damage as reflected by diffuse decreases in connectivity. Critically, our work suggests that adaptive myelination may be a mechanism that enables brain networks with a means of temporal self-organization, resilience, and homeostasis.
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24

Davidson, Eric P., Lawrence J. Coppey, Travis L. Kleinschmidt, Christine L. Oltman, and Mark A. Yorek. "Vascular and Neural Dysfunctions in Obese Zucker Rats: Effect of AVE7688." Experimental Diabetes Research 2009 (2009): 1–8. http://dx.doi.org/10.1155/2009/912327.

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The purpose of this study was to determine whether AVE7688 a drug that inhibits both angiotensin converting enzyme and neutral endopeptidase activity protects vascular and nerve functions in an animal model of metabolic syndrome. Obese Zucker rats at 20 weeks of age were treated for 12 weeks with AVE7688. Vasodilation in epineurial arterioles was measured by videomicroscopy and nerve conduction velocity was measured following electrical stimulation. Treatment with AVE7688 improved vascular relaxation in response to acetylcholine and motor and sensory nerve conduction velocity. In obese Zucker rats superoxide levels and nitrotyrosine staining were elevated in the aorta and treatment corrected both conditions. Obese Zucker rats were hypoalgesic in response to a thermal stimulus and demonstrated signs of impaired tactile response and both conditions were significantly improved with treatment. Even though obese Zucker rats are normoglycemic vascular and neural dysfunctions develop with age and can be improved by treatment with AVE7688.
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Chiang, Chia-Chu, and Dominique M. Durand. "Subthreshold Oscillating Waves in Neural Tissue Propagate by Volume Conduction and Generate Interference." Brain Sciences 13, no. 1 (December 30, 2022): 74. http://dx.doi.org/10.3390/brainsci13010074.

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Subthreshold neural oscillations have been observed in several brain regions and can influence the timing of neural spikes. However, the spatial extent and function of these spontaneous oscillations remain unclear. To study the mechanisms underlying these oscillations, we use optogenetic stimulation to generate oscillating waves in the longitudinal hippocampal slice expressing optopatch proteins. We found that optogenetic stimulation can generate two types of neural activity: suprathreshold neural spikes and subthreshold oscillating waves. Both waves could propagate bidirectionally at similar speeds and go through a transection of the tissue. The propagating speed is independent of the oscillating frequency but increases with increasing amplitudes of the waves. The endogenous electric fields generated by oscillating waves are about 0.6 mV/mm along the dendrites and about 0.3 mV/mm along the cell layer. We also observed that these oscillating waves could interfere with each other. Optical stimulation applied simultaneously at each slice end generated a larger wave in the middle of the tissue (constructive interference) or destructive interference with laser signals in opposite phase. However, the suprathreshold neural spikes were annihilated when they collided. Finally, the waves were not affected by the NMDA blocker (APV) and still propagated in the presence of tetrodotoxin (TTX) but at a significantly lower amplitude. The role of these subthreshold waves in neural function is unknown, but the results show that at low amplitude, the subthreshold propagating waves lack a refractory period allowing a novel analog form of preprocessing of neural activity by interference independent of synaptic transmission.
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Loeb, J. M., and J. M. deTarnowsky. "Integration of heart rate and sympathetic neural effects on AV conduction." American Journal of Physiology-Heart and Circulatory Physiology 254, no. 4 (April 1, 1988): H651—H657. http://dx.doi.org/10.1152/ajpheart.1988.254.4.h651.

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Sympathetic activation increases heart rate (HR) and reduces atrioventricular interval (AVI), whereas atrial pacing alone increases AVI. We sought to differentiate the direct effects of sympathetic activation on atrioventricular (AV) conduction time from the indirect changes associated with concurrent alterations in HR. We recorded electrocardiograms, blood pressure (BP), and intracardiac electrograms from chloralose-anesthetized autonomically decentralized dogs. Beat-by-beat HR and AVI data were collected continuously. Sympathetic stimulation (0.25-2.5 Hz; mean 0.81 Hz) resulted in a HR change of +60 beats/min after 60 s. This tachycardia was associated with a mean decrease in AVI of 22 ms. Computer-driven atrial pacing to reproduce the HR associated with control sympathetic stimulation caused a mean AVI increase of 10 ms. Propranolol (200 micrograms) was then administered via the sinoatrial node artery and sympathetic stimulation repeated. Although HR remained constant, AVI decreased by 14.8 ms. The AVIs associated with an identical HR achieved by two different mechanisms (sympathetic stimulation and atrial pacing) were significantly different. Although removal of the contribution of sympathetically induced HR changes on AV conduction might be expected to result in potentiation of neural effects at the AV node, none was evident. Thus sympathetic activity restricted to the AV node is less effective in influencing AV conduction than the response that occurs when HR changes occur concurrently. Therefore, the opposing actions of HR and sympathetic tone on AV conduction may not be predicted by a simple linear relationship.
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27

Smith, Michael S., and J. Dale Browne. "The Effect of Endothelial Cell Growth Factor on Peripheral Nerve Regeneration." Otolaryngology–Head and Neck Surgery 118, no. 2 (February 1998): 178–82. http://dx.doi.org/10.1016/s0194-5998(98)80007-7.

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Neural regeneration after grafting can be unpredictable. In an effort to enhance the return of function after cable grafting, we studied the effects of an angiogenic factor, endothelial cell growth factor (ECGF), on regenerating nerves. Cable grafts on the sciatic nerve were established in 18 rats and treated with ECGF or a control saline solution. At 5 weeks, nerve conduction studies were performed, and the animals were killed for histologic measurements of graft vascularity and axon counts. A significant increase in vascularity was noted in the treated group versus the control group; neither the axon counts nor the nerve conduction velocities differed significantly between the two groups, although the treated group appeared to show improved neural conduction compared with the control group.
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28

Davis, Scott L., Thad E. Wilson, Andrea T. White, and Elliot M. Frohman. "Thermoregulation in multiple sclerosis." Journal of Applied Physiology 109, no. 5 (November 2010): 1531–37. http://dx.doi.org/10.1152/japplphysiol.00460.2010.

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Multiple sclerosis (MS) is a progressive neurological disorder that disrupts axonal myelin in the central nervous system. Demyelination produces alterations in saltatory conduction, slowed conduction velocity, and a predisposition to conduction block. An estimated 60–80% of MS patients experience temporary worsening of clinical signs and neurological symptoms with heat exposure. Additionally, MS may produce impaired neural control of autonomic and endocrine functions. This review focuses on five main themes regarding the current understanding of thermoregulatory dysfunction in MS: 1) heat sensitivity; 2) central regulation of body temperature; 3) thermoregulatory effector responses; 4) heat-induced fatigue; and 5) countermeasures to improve or maintain function during thermal stress. Heat sensitivity in MS is related to the detrimental effects of increased temperature on action potential propagation in demyelinated axons, resulting in conduction slowing and/or block, which can be quantitatively characterized using precise measurements of ocular movements. MS lesions can also occur in areas of the brain responsible for the control and regulation of body temperature and thermoregulatory effector responses, resulting in impaired neural control of sudomotor pathways or neural-induced changes in eccrine sweat glands, as evidenced by observations of reduced sweating responses in MS patients. Fatigue during thermal stress is common in MS and results in decreased motor function and increased symptomatology likely due to impairments in central conduction. Although not comprehensive, some evidence exists concerning treatments (cooling, precooling, and pharmacological) for the MS patient to preserve function and decrease symptom worsening during heat stress.
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29

Gurjarpadhye, Abhijit, Kenneth W. Hewett, Charles Justus, Xuejun Wen, Harriett Stadt, Margaret L. Kirby, David Sedmera, and Robert G. Gourdie. "Cardiac neural crest ablation inhibits compaction and electrical function of conduction system bundles." American Journal of Physiology-Heart and Circulatory Physiology 292, no. 3 (March 2007): H1291—H1300. http://dx.doi.org/10.1152/ajpheart.01017.2006.

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Retroviral and transgenic lineage-tracing studies have shown that neural crest cells associate with the developing bundles of the ventricular conduction system. Whereas this migration of cells does not provide progenitors for the myocardial cells of the conduction system, the question of whether neural crest affects the differentiation and/or function of cardiac specialized tissues continues to be of interest. Using optical mapping of voltage-sensitive dye, we determined that ventricles from chick embryos in which the cardiac neural crest had been laser ablated did not progress to apex-to-base activation by the expected stage [i.e., Hamburger and Hamilton (HH) 35] but instead maintained basal breakthroughs of epicardial activation consistent with immature function of the conduction system. In direct studies of activation, waves of depolarization originating from the His bundle were found to be uncommon in control hearts from HH34 and HH35 embryos. However, activations propagating from septal base, at or near the His bundle, occurred frequently in hearts from HH34 and HH35 neural crest-ablated embryos. Consistent with His bundle cells maintaining electrical connections with adjacent working myocytes, histological analyses of hearts from neural crest-ablated embryos revealed His bundles that had not differentiated a lamellar organization or undergone a process of compaction and separation from surrounding myocardium observed in controls. Furthermore, measurements on histological sections from optically mapped hearts indicated that, whereas His bundle diameter in control embryos thinned by almost one-half between HH30 and HH34, the His bundle in ablated embryos underwent no such compaction in diameter, maintaining a thickness at HH30, HH32, and HH34 similar to that observed in HH30 controls. We conclude that the cardiac neural crest is required in a novel function involving lamellar compaction and electrical isolation of the basally located His bundle from surrounding myocardium.
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Löhner, Rainald, Harbir Antil, Hamid Tamaddon-Jahromi, Neeraj Kavan Chakshu, and Perumal Nithiarasu. "Deep learning or interpolation for inverse modelling of heat and fluid flow problems?" International Journal of Numerical Methods for Heat & Fluid Flow 31, no. 9 (January 22, 2021): 3036–46. http://dx.doi.org/10.1108/hff-11-2020-0684.

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Purpose The purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour. Design/methodology/approach A series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches. Findings The results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy. Originality/value This is the first time such a comparison has been made.
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Sun, Yang, Abdussalam Elhanashi, Hao Ma, and Mario Rosario Chiarelli. "Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks." Applied Sciences 12, no. 21 (October 30, 2022): 10986. http://dx.doi.org/10.3390/app122110986.

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Optimizing heat conduction layout is essential during engineering design, especially for sensible thermal products. However, when the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model is trained by data-driven methods, which require intensive training samples from numerical simulations and do not effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a physics-driven convolutional neural networks (PD-CNNs) method to infer the physical field solutions for randomly varied loading cases. After that, the particle swarm optimization (PSO) algorithm is used to optimize the sizes, and the positions of the hole masks in the prescribed design domain and the average temperature value of the entire heat conduction field is minimized. The goal of reducing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework predicts field solutions that are highly consistent with conventional simulation results. However, the proposed method generates the solution space without pre-obtained training data. We obtained thermal intensity results for holes 1, hole 2, hole 3, and hole 4 with 0.3948, 0.007, 0.0044, and 0.3939, respectively, by optimization PD-CNN model.
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Kumar, Rajesh, RK Aggarwal, Dhirender Gupta, and Jyoti Dhar Sharma. "Predicting Total Conduction Losses of the Building Using Artificial Neural Network." Energy and Environmental Engineering 1, no. 1 (July 2013): 1–4. http://dx.doi.org/10.13189/eee.2013.010101.

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33

Mostajeran, F., and R. Mokhtari. "DeepBHCP: Deep neural network algorithm for solving backward heat conduction problems." Computer Physics Communications 272 (March 2022): 108236. http://dx.doi.org/10.1016/j.cpc.2021.108236.

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34

Parisi, V. "Neural conduction in the visual pathways in ocular hypertension and glaucoma." Graefe's Archive for Clinical and Experimental Ophthalmology 235, no. 3 (March 1997): 136–42. http://dx.doi.org/10.1007/bf00941720.

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35

Gourdie, R. G., T. Mima, R. P. Thompson, and T. Mikawa. "Terminal diversification of the myocyte lineage generates Purkinje fibers of the cardiac conduction system." Development 121, no. 5 (May 1, 1995): 1423–31. http://dx.doi.org/10.1242/dev.121.5.1423.

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The rhythmic contraction of the vertebrate heart is dependent on organized propagation of electrical excitation through the cardiac conduction system. Because both muscle- and neuron-specific genes are co-expressed in cells forming myocardial conduction tissues, two origins, myogenic and neural, have been suggested for this specialized tissue. Using replication-defective retroviruses, encoding recombinant beta-galactosidase (beta-gal), we have analyzed cell lineage for Purkinje fibers (i.e., the peripheral elements of the conduction system) in the chick heart. Functioning myocyte progenitors were virally tagged at embryonic day 3 of incubation (E3). Clonal beta-gal+ populations of cells, derived from myocytes infected at E3 were examined at 14 (E14) and 18 (E18) days of embryonic incubation. Here, we report that a subset of clonally related myocytes differentiates into conductile Purkinje fibers, invariably in close spatial association with forming coronary arterial blood vessels. These beta-gal+ myogenic clones, containing both working myocytes and Purkinje fibers, did not incorporate cells contributing to tissues of the central conduction system (e.g. atrioventricular ring and bundles). In quantitative analyses, we found that whereas the number of beta-gal+ myocyte nuclei per clone more than doubled between E14 and E18, the number of beta-gal+ Purkinje fiber nuclei remained constant.(ABSTRACT TRUNCATED AT 250 WORDS)
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36

Jun, Kimura. "Consequences of Peripheral Nerve Demyelination: Basic and Clinical Aspects." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 20, no. 04 (November 1993): 263–70. http://dx.doi.org/10.1017/s0317167100048162.

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ABSTRACT:The essential function of a nerve fiber centers on the transmission of impulses. Slowing of conduction velocities merely delays conveyance of information but failure of conduction abolishes the function. Rate-dependent block may falsify input by disturbing the neural coding. The diagnosis of conduction block is complicated by phase cancellation and many other physiologic and technical factors, which may mimic the condition. The conduction block may be reversed by increasing action current with such agents as 4-aminopirizine as well as by reducing the threshold for excitation with a sodium - potassium pump inhibitor such as digitalis. Although their mechanisms of action are poorly understood, imunogloblin and imunosuppressive agents are also effective in some clinical entities like multifocal motor neuropathy with persistent conduction block.
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37

Davidson, Eric P., Lawrence J. Coppey, Brian Dake, and Mark A. Yorek. "Treatment of Streptozotocin-Induced Diabetic Rats with Alogliptin: Effect on Vascular and Neural Complications." Experimental Diabetes Research 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/810469.

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We sought to determine the effect of dipeptidyl peptidase IV (DPP-IV) inhibition on streptozotocin diabetes-induced vascular and neural dysfunction. After 4 weeks of untreated diabetes, rats were treated for 12 weeks with Alogliptin (DPP-IV inhibitor). Diabetes caused a slowing of motor and sensory nerve conduction velocity, thermal hypoalgesia, reduction in intraepidermal nerve fiber density in the hindpaw, and impairment in vascular relaxation to acetylcholine and calcitonin gene-related peptide in epineurial arterioles. Treatment significantly improved motor nerve conduction velocity and thermal response latency. Sensory nerve conduction velocity was marginally improved with treatment of diabetic rats, and treatment did not improve the decrease in intraepidermal nerve fiber density. Vascular relaxation by epineurial arterioles to calcitonin gene-related peptide but not acetylcholine was significantly improved with treatment. These studies suggest that some but not all vascular and neural complications associated with type 1 diabetes can be improved with the inhibition of DPP-IV activity.
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38

Hu, Zhengfa, Tian Yue, and Haixia Xiao. "Features Conduction Neural Response and Its Application in Content-Based Image Retrieval." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/3908056.

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A novel image representation is proposed for content-based image retrieval (CBIR). The core idea of the proposed method is to do deep learning for the local features of image and to melt semantic component into the representation through a hierarchical architecture which is built to simulate human visual perception system, and then a new image descriptor of features conduction neural response (FCNR) is constructed. Compared with the classical neural response (NR), FCNR has lower computational complexity and is more suitable for CBIR tasks. The results of experiments on a commonly used image database demonstrate that, compared with those of NR related methods or some other image descriptors that were originally developed for CBIR, the proposed method has wonderful performance on retrieval efficiency and effectiveness.
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39

Coppey, Lawrence, Bao Lu, Craig Gerard, and Mark A. Yorek. "Effect of Inhibition of Angiotensin-Converting Enzyme and/or Neutral Endopeptidase on Neuropathy in High-Fat-Fed C57Bl/6J Mice." Journal of Obesity 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/326806.

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We have demonstrated that treating diet-induced obese (DIO) mice with the vasopeptidase inhibitor ilepatril improved neural function. Vasopeptidase inhibitors block angiotensin-converting enzyme (ACE) and neutral endopeptidase (NEP) activity. We propose that increased activity of ACE and NEP contributes to pathophysiology of DIO. To address this issue C57Bl/6J mice or mice deficient in NEP were fed a high-fat diet and treated with ilepatril, enalapril, ACE inhibitor, or candoxatril, NEP inhibitor, using both prevention and intervention protocols. Endpoints included glucose utilization and neural function determination. In the prevention study glucose tolerance was impaired in DIO C57Bl/6J mice and improved with ilepatril or enalapril. Sensory nerve conduction velocity, thermal nociception, and intraepidermal nerve fiber density were impaired in DIO C57Bl/6J mice and improved with ilepatril or candoxatril. In the intervention study only enalapril improved glucose tolerance. Sensory nerve conduction velocity and intraepidermal nerve fiber density were improved by all three treatments, whereas thermal nociception was improved by ilepatril or candoxatril. In NEP-deficient mice DIO impaired glucose utilization and this was improved with enalapril. Nerve function was not impaired by DIO in NEP-deficient mice. These studies suggest that ACE and NEP play a role in pathophysiology associated with DIO.
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40

Schetinin, Vitaly, Livija Jakaite, Ndifreke Nyah, Dusica Novakovic, and Wojtek Krzanowski. "Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification." International Journal of Neural Systems 28, no. 06 (June 24, 2018): 1750064. http://dx.doi.org/10.1142/s0129065717500642.

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The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.
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41

Parisi, V., G. L. Manni, D. Olzi, F. Oddone, G. Coppola, and M. G. Bucci. "Impaired Neural Conduction in Crossed Visual Pathways in Patients with Ocular Hypertension." European Journal of Ophthalmology 14, no. 6 (November 2004): 486–94. http://dx.doi.org/10.1177/112067210401400607.

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42

Ifergane, Gal, Marina Bersudsky, Leonid Kachko, and Itzhak Wirguin. "Induction of conduction block by Campylobacter jejuni lipopolysaccharides and focal neural insult." Journal of the Neurological Sciences 213, no. 1-2 (September 2003): 11–14. http://dx.doi.org/10.1016/s0022-510x(03)00123-0.

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43

Shih, Chih-Wen, Jui-Pin Tseng, and Chang-Hong Wu. "Absolute Stability and Synchronization in Second-Order Neural Fields with Conduction Delays." SIAM Journal on Applied Dynamical Systems 22, no. 2 (June 7, 2023): 878–917. http://dx.doi.org/10.1137/21m1441109.

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44

MONTGOMERY, RICHARD. "The Importance of Astrocytes in Neural Conduction: A Paradigm Shift in Neuroscience." Wired Neuroscience 1, no. 2 (January 5, 2024): 1–10. http://dx.doi.org/10.62162/1060911.

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Astrocytes, once relegated to the role of mere 'support cells' in the central nervous system, areemerging as key players in neural conduction and overall brain function. This article aims toelucidate the multifaceted roles of astrocytes, exploring why they have been historicallyovershadowed by neurons and how they contribute to the complexity of brain wave patterns.Through a series of computational simulations and a comprehensive review of existing literature,we demonstrate that astrocytes are not just passive bystanders but active participants in neuralsignaling. The article also discusses the implications of astrocyte function for cognitive abilitieslike learning, memory, and attention, as well as their role in pathological conditions like epilepsy.With 15 pertinent references, this article serves as a comprehensive review that challenges theneuron-centric view of brain function and calls for a reevaluation of the roles of astrocytes inneuroscience.
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45

Warner, M. R., and J. M. Loeb. "Beat-by-beat modulation of AV conduction. I. Heart rate and respiratory influences." American Journal of Physiology-Heart and Circulatory Physiology 251, no. 6 (December 1, 1986): H1126—H1133. http://dx.doi.org/10.1152/ajpheart.1986.251.6.h1126.

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We examined the integration of heart rate and neural influences at the atrioventricular (AV) node in conscious dogs. Animals were anesthetized and, under sterile conditions, instrumented to chronically record atrial and ventricular electrograms and blood pressure. In the conscious state, electrocardiogram (ECG), respiration, blood pressure, and electrograms were recorded on a beat-by-beat basis, and heart rate and AV interval were plotted graphically as a function of time. Resting animals exhibited both respiratory sinus arrhythmia and marked oscillations in AV conduction time associated with respiration. During inspiration AV interval was shortened, and during expiration AV interval was prolonged. To obviate the effect of cyclic changes in heart rate, atrial pacing was used to increase heart rate over a wide range both abruptly and linearly. Regardless of the pattern of heart rate change, AV interval oscillated at the respiratory frequency at pacing rates 10-100 beats/min above control. Higher levels of atrial pacing resulted in AV conduction patterns that were correlated with changes in blood pressure. Thus in the conscious dog variations in AV conduction time occur on a beat-by-beat basis in conjunction with respiration; oscillatory activity of AV conduction is not dependent on simultaneous changes in heart rate; and during atrial pacing, autonomic neural activity associated with respiration and blood pressure appears to dynamically modulate AV conduction with respiratory effects predominating at low heart rates and blood pressure effects at high heart rates.
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46

Davidson, Eric, Lawrence Coppey, Bao Lu, Victor Arballo, Nigel A. Calcutt, Craig Gerard, and Mark Yorek. "The Roles of Streptozotocin Neurotoxicity and Neutral Endopeptidase in Murine Experimental Diabetic Neuropathy." Experimental Diabetes Research 2009 (2009): 1–9. http://dx.doi.org/10.1155/2009/431980.

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We demonstrated that inhibition of neutral endopeptidase (NEP), a protease that degrades vaso- and neuroactive peptides, improves vascular and neural function in diabetic animal models. In this study we explored the role of NEP in neuropathy related to either insulin-deficient diabetes or diet-induced obesity using NEP deficient (−/−) mice. Initial studies showed that streptozotocin, in the absence of subsequent hyperglycemia, did not induce nerve conduction slowing or paw thermal hypoalgesia. Glucose disposal was impaired in both C57Bl/6 and NEP −/− mice fed a high fat diet. Thermal hypoalgesia and nerve conduction slowing were present in both streptozotocin-diabetic and high fat fed C57Bl/6 mice but not in NEP −/− mice exposed to either streptozotocin-induced diabetes or a high fat diet. These studies suggest that streptozotocin does not induce neurotoxicity in mice and that NEP plays a role in regulating nerve function in insulin-deficient diabetes and diet-induced obesity.
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47

Segura, M. J., O. M. Genovese, Elsa Segura, Olga P. Sanz, and R. E. P. Sica. "Central motor conduction in human chronic Chagas' disease." Arquivos de Neuro-Psiquiatria 52, no. 1 (March 1994): 29–31. http://dx.doi.org/10.1590/s0004-282x1994000100005.

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The possible involvement of spinal alpha motor neurons, dorsal root ganglia and sensory fibers in human chronic Chagas' disease was previously demonstrated. More recently neuropsychological and sensory evoked potentials studies suggest the existence of central nervous system abnormalities in these patients. We assessed the state of central motor pathways in 46 patients with chronic Chagas' disease and 30 healthy volunteers by means of percutaneous cortical and spinal electrical stimulation. No significative slowness in pyramidal tracts (PT) conduction was found when comparing both groups. Neither any individual patient exhibited abnormally delayed PT conduction values beyond the upper normal limit of the healthy volunteers. These results suggest that, in contrast with other neural systems, the large myelinated PT fibers are usually spared in human chronic Chagas' disease.
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48

Zhou, Yi, Yufan Chen, Yongbao Ma, and Hongqing Liu. "A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor." Sensors 20, no. 18 (September 5, 2020): 5050. http://dx.doi.org/10.3390/s20185050.

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The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm.
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49

Del Vecchio, Alessandro, Francesco Negro, Francesco Felici, and Dario Farina. "Associations between motor unit action potential parameters and surface EMG features." Journal of Applied Physiology 123, no. 4 (October 1, 2017): 835–43. http://dx.doi.org/10.1152/japplphysiol.00482.2017.

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The surface interference EMG signal provides some information on the neural drive to muscles. However, the association between neural drive to muscle and muscle activation has long been debated with controversial indications due to the unavailability of motor unit population data. In this study, we clarify the potential and limitations of interference EMG analysis to infer motor unit recruitment strategies with an experimental investigation of several concurrently active motor units and of the associated features of the surface EMG. For this purpose, we recorded high-density surface EMG signals during linearly increasing force contractions of the tibialis anterior muscle, up to 70% of maximal force. The recruitment threshold (RT), conduction velocity (MUCV), median frequency (MDFMU), and amplitude (RMSMU) of action potentials of 587 motor units from 13 individuals were assessed and associated with features of the interference EMG. MUCV was positively associated with RT ( R2 = 0.64 ± 0.14), whereas MDFMU and RMSMU showed a weaker relation with RT ( R2 = 0.11 ± 0.11 and 0.39 ± 0.24, respectively). Moreover, the changes in average conduction velocity estimated from the interference EMG predicted well the changes in MUCV ( R2 = 0.71), with a strong association to ankle dorsiflexion force ( R2 = 0.81 ± 0.12). Conversely, both the average EMG MDF and RMS were poorly associated with motor unit recruitment. These results clarify the limitations of EMG spectral and amplitude analysis in inferring the neural strategies of muscle control and indicate that, conversely, the average conduction velocity could provide relevant information on these strategies. NEW & NOTEWORTHY The surface EMG provides information on the neural drive to muscles. However, the associations between EMG features and neural drive have been long debated due to unavailability of motor unit population data. Here, by using novel highly accurate decomposition of the EMG, we related motor unit population behavior to a wide range of voluntary forces. The results fully clarify the potential and limitation of the surface EMG to provide estimates of the neural drive to muscles.
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50

R., Meena Devi, and L. Premalatha. "Efficient bridgeless SEPIC converter fed PMBLDC motor using artificial neural network." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 3025. http://dx.doi.org/10.11591/ijece.v9i4.pp3025-3031.

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<p>In this paper, a new design of Bridgeless SEPIC (Single Ended Primary Inductance converter) with Artificial neural network (ANN) fed PMBLDC Motor drive is proposed to improve Power Factor. The proposed converter has single switching device of MOSFET, so the switching losses is reduced.ANN is used to achieve the higher power factor and fixed dc link voltage. Also the ANN methodology the time taken for computation is less since there is no mathematical model. The output voltage depends on the switching frequency of the MOSFET. The BLSEPIC act as a buck operation in continuous conduction mode. Detailed converter analysis, equivalent circuit and closed-loop analysis are presented for 36V, 120W, 1500rpm BLDC Motor drive. This proposed converter produces low conduction loss, low total harmonic reduction, low settling time and high power factor reaching near-unity. All the simulation work is verified with MATLAB – Simulink.</p>
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