Parameter estimation of a respiratory control model from noninvasive carbon dioxide measurements during sleep

2007 ◽  
Vol 24 (2) ◽  
pp. 225-249 ◽  
Author(s):  
T. Aittokallio ◽  
M. Gyllenberg ◽  
O. Polo ◽  
A. Virkki
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
Respiratory Control ◽  
Control Model

scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2014 ◽  
Vol 11 (2) ◽  
pp. 217-235 ◽  
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere–ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

scholarly journals Parameter estimation method of F0 control model for singing voices

2008 ◽  
Author(s):  
Yasunori Ohishi ◽  
Hirokazu Kameoka ◽  
Kunio Kashino ◽  
Kazuya Takeda
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Control Model ◽  
Parameter Estimation Method

Basic Oscillating Mechanism of Cheyne-Stokes Breathing

1956 ◽  
Vol 187 (2) ◽  
pp. 395-398 ◽  
Author(s):  
Arthur C. Guyton ◽  
Jack W. Crowell ◽  
John W. Moore
Keyword(s):  
Carbon Dioxide ◽  
Control System ◽  
Transit Time ◽  
Perfusion Pressure ◽  
Respiratory Control ◽  
Delay System ◽  
Carbon Dioxide Concentrations ◽  
Respiratory Centers ◽  
Cheyne Stokes Breathing ◽  
The Brain

Cheyne-Stokes breathing has been induced in 30 dogs by inserting a circulatory delay system between the heart and the brain to prolong the transit time of blood from the lungs to the brain. The duration of each cycle of Cheyne-Stokes breathing increased proportionately with the volume of the delay system and decreased as the perfusion pressure to the brain was increased. Periodic variations in oxygen and carbon dioxide concentrations in the blood were found to be in appropriate phase to stimulate the respiratory centers at the time of maximal ventilation. This supports the theory that Cheyne-Stokes breathing is due to oscillation of the respiratory control system.

OPTIMAL RESPIRATORY CONTROL SIMULATION AND COMPARATIVE STUDY OF HYPERCAPNIC VENTILATORY RESPONSES TO EXTERNAL DEAD SPACE LOADING

2014 ◽  
Vol 14 (02) ◽  
pp. 1450014 ◽  
Author(s):  
SHYAN-LUNG LIN ◽  
NAI-REN GUO ◽  
TSUNG-CHI CHEN
Keyword(s):  
Dead Space ◽  
Research Effort ◽  
Human Subjects ◽  
Respiratory Control ◽  
Motor Command ◽  
Control Model ◽  
Ventilatory Responses ◽  
Chemical Stimuli ◽  
Experimental Findings ◽  
Relationship Of

There has been considerable research effort regarding ventilatory responses to breathing with an imposed external dead space, and inhalation of fixed levels of CO 2 by human subjects. A human respiratory control model incorporating the optimality hypothesis can successfully demonstrate ventilatory responses to both chemical stimuli and muscular exercise. In this study, to verify the model behavior of the optimal chemical–mechanical respiratory control model, we simulated the ventilatory control under dead space loading and CO 2 inhalation. The simulation was provided by a LabVIEW® based human respiratory control simulator and signal monitoring system. The dead space measurement was described with two distinct models, derived from Gray and Coon, and predicted behaviors with corresponding ventilatory responses were investigated and compared with experimental findings. While both dead space models produced satisfactory predictions on simulated optimal [Formula: see text] versus Pa CO 2, [Formula: see text] versus Pa CO 2, F versus PI CO 2, VT versus PI CO 2, VD-total versus VT, VD- total /VT versus VT, [Formula: see text] versus VT and [Formula: see text] versus VT relationships, Gray's model provided better correlation and more consistent results throughout most of the ventilatory responses. The study of relative behavior of respiratory signals and comparative relationship of the ventilator responses between dead space loading during rest and CO 2 inhalation will certainly provide valuable understanding of increases in central respiratory motor command output of human respiratory control, which is also associated with Dyspnea on exertion, and give potential clinical perspective to realize the impaired ability to excrete CO 2 in patients diagnosed with acute respiratory distress syndrome.

scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2013 ◽  
Vol 10 (8) ◽  
pp. 13753-13802
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere-ecosystem carbon dioxide fluxes are well-constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency ecosystem model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely-sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all of the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

scholarly journals Experimental Validation of a Respiratory Control Model

2018 ◽  
Vol 32 (S1) ◽  
Author(s):  
Casey O. Diekman ◽  
Peter J. Thomas ◽  
Christopher G. Wilson
Keyword(s):  
Experimental Validation ◽  
Respiratory Control ◽  
Control Model
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