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    Stan Yamashiro, PhD

    TitleProfessor of Biomedical Engineering and Electrical Engineering Systems
    SchoolViterbi School of Engineering, BME
    DepartmentBiomedical Engineering
    Address1042 Downey Way, DRB 152
    University Park Campus
    Los Angeles California 90089-1111
    Phone+1 213 740 0336
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      Previous work has shown that breathing rate, airflow pattern shape, and the end-expiratory lung volume level can be predicted by an optimal control model based on minimum power expenditure. One prediction which is important to the control of breathing during exercise is a decrease in end-expiratory lung volume level which is graded according to the level of exercise. Such a decrease is consistently observed at the start of exercise and occurs in a feedforward or predictive manner. One of the consequences of a decreased lung volume is lengthening of the diaphragm, which according to the length-tension characteristics of skeletal muscle increases active tension. Lowering of lung volume requires activation of expiratory muscles, and this work is stored as elastic energy which can be recovered during the following inspiration. This constitutes a potential additional ventilatory drive independent of chemical factors. Based on a computer simulation study approximately half of the ventilatory drive during exercisecan be explained in this way. Animal experiments where phasic lung volume changes are artificially imposed do not support this possibility. However, this could be due to lung inflation reflexes which are strong in animals and known to be weak in humans.

      An optimization hypothesis of respiratory control during exercise based on the minimization of a function reflecting both chemical and mechanical costs has been studied by others. Both additive and multiplicative controllers have been derived as optimal from similar cost functions. The purpose of a recent study was to explore the uniqueness of such predictions. Various formulations of controllers compatible with isocapnia were found to yield identical costs as controllers predicted to be optimal. It was concluded that controller predictions based on optimization theory are not unique. Optimization can occur with either an additive or multiplicative controller or any combination of the two which satisfies an isocapnic constraint. A general form of a combined additive-multiplicative controller was derived which was found to be compatible with previously reported experimental data collected during combined CO2 inhalation and exercise.

      One of the difficulties which has hampered progress in research on physiological optimization is the tedium involved in the solution phase. In an attempt to solve this problem, a numerical procedure which allowed the convenient exploration of various optimization hypotheses of breathing pattern regulation was developed. The method was based on the calculus of variations and used a novel technique for the automatic evaluation of all required derivatives. Advantages of this approach included: exact calculation of all derivatives, parsimonious computer code, and speed of execution. By eliminating the need for hand derivation of derivatives, a major reduction was made in the tedium involved in exploring various optimization strategies. Examples were presented of determining the optimal breathing pattern characteristics for minimum work or force(pressure) required for breathing based on linear and noninear models of respiratory mechanics. The developed procedure can be used to predict the optimal volume-time trajectory and breathing frequency which minimizes a criterion function subject to constraints.

      Multiple channel magnetocardiography is potentially useful for the study of the cardiac conduction system. However, normal atrial repolarization occurs simultaneously and obscures the interpretation of the net signal. Magnetocardiographic data in 4 normal subjects at rest and mild exercise were found to exhibit high spatial correlation during atrial activation. Based on measured channel-to-channel covariances, the atrial repolarization signals as measured in channels in the null zone of conduction system activity were used to estimate atrial repolarization in all channels. A linear prediction method was used which was based on the "kriging" estimator of geostatistical theory. Due to high spatial correlations in the limited thoracic region studied, predictions based on a single null channel were found to be adequate. Removal of the atrial component facilitates the beat-by-beat estimation of conduction system propagation times. These results support the feasibility of studying cardiac conduction system changes during rest and exercise using the multiple channel biomagnetometer.


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      Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
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      1. Cole M, Eikenberry S, Kato T, Sandler RA, Yamashiro S, Marmarelis VZ. Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation. J Comput Biol. 2017 Mar; 24(3):229-237. PMID: 27494114.
        View in: PubMed
      2. Yamashiro S, Kato T. Modeling rate sensitivity of exercise transient responses to limb motion. J Appl Physiol (1985). 2014 Oct 1; 117(7):699-705. PMID: 25103968.
        View in: PubMed
      3. Yamashiro S. Non-linear dynamics of human periodic breathing and implications for sleep apnea therapy. Med Biol Eng Comput. 2007 Apr; 45(4):345-56. PMID: 17325827.
        View in: PubMed
      4. Eldridge FL, Morin D, Romaniuk JR, Yamashiro S, Potts JT, Ichiyama RM, Bell H, Phillipson EA, Killian KJ, Jones NL, Nattie E. Supraspinal locomotor centers do/do not contribute significantly to the hyperpnea of dynamic exercise in humans. J Appl Physiol. 2006 May; 100(5):1743-7. PMID: 16614370.
        View in: PubMed
      5. Yamashiro S. Supraspinal locomotor centers do/do not contribute significantly to the hyperpnea of dynamic exercise in humans. J Appl Physiol. 2006 May; 100(5):1744. PMID: 16688862.
        View in: PubMed
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