Variance of ventilation during exercise

Expired gas concentrations were measured during a multibreath washin of He in one female and seven male subjects at rest (seated) and during cycle exercise at work rates of 70–210 W. In a computational model, the ventilation distribution was represented as a log-normal distribution with standard deviation (ςV˙); values of ςV˙ were obtained by fitting the output of the model to the data. At rest, ςV˙ was 0.89 ± 0.18; during exercise, ςV˙ was 0.60 ± 0.13, independent of the level of exercise. These values for the width of the functional ventilation distribution at the scale of the acinus are approximately two times larger than those obtained from anatomic measurements in animals at a scale of 1 cm3. The values for ςV˙, together with data from the literature on the width of the functional ventilation-perfusion distribution, show that ventilation and perfusion are highly correlated at rest, in agreement with anatomic data. The structural sources of nonuniform ventilation and perfusion and of the correlation between them are unknown.

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Ventilation-perfusion distribution in normal subjects

Journal of Applied Physiology ◽

10.1152/japplphysiol.00163.2012 ◽

2012 ◽

Vol 113 (6) ◽

pp. 872-877 ◽

Cited By ~ 8

Author(s):

Kenneth C. Beck ◽

Bruce D. Johnson ◽

Thomas P. Olson ◽

Theodore A. Wilson

Keyword(s):

Regional Distribution ◽

Normal Subjects ◽

Bivariate Distribution ◽

Moderate Exercise ◽

Ventilation Distribution ◽

Expired Gas ◽

The Difference ◽

Log Normal ◽

Log Normal Distribution ◽

Ventilation And Perfusion

Functional values of LogSD of the ventilation distribution (σV̇) have been reported previously, but functional values of LogSD of the perfusion distribution (σq̇) and the coefficient of correlation between ventilation and perfusion (ρ) have not been measured in humans. Here, we report values for σV̇, σq̇, and ρ obtained from wash-in data for three gases, helium and two soluble gases, acetylene and dimethyl ether. Normal subjects inspired gas containing the test gases, and the concentrations of the gases at end-expiration during the first 10 breaths were measured with the subjects at rest and at increasing levels of exercise. The regional distribution of ventilation and perfusion was described by a bivariate log-normal distribution with parameters σV̇, σq̇, and ρ, and these parameters were evaluated by matching the values of expired gas concentrations calculated for this distribution to the measured values. Values of cardiac output and LogSD ventilation/perfusion (V̇a/Q̇) were obtained. At rest, σq̇ is high (1.08 ± 0.12). With the onset of ventilation, σq̇ decreases to 0.85 ± 0.09 but remains higher than σV̇ (0.43 ± 0.09) at all exercise levels. Rho increases to 0.87 ± 0.07, and the value of LogSD V̇a/Q̇ for light and moderate exercise is primarily the result of the difference between the magnitudes of σq̇ and σV̇. With known values for the parameters, the bivariate distribution describes the comprehensive distribution of ventilation and perfusion that underlies the distribution of the V̇a/Q̇ ratio.

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Human body prediction of size and shape: a hormonal framework

10.1101/722777 ◽

2019 ◽

Author(s):

Jeroen van Vugt

Keyword(s):

Standard Deviation ◽

Normal Distribution ◽

Human Body ◽

Prediction Accuracy ◽

Prediction Method ◽

Future Research ◽

Least Squares Regression ◽

Size And Shape ◽

Log Normal ◽

Log Normal Distribution

ABSTRACTTo achieve high prediction accuracy with minimal inputs from online retail respondents, a method was developed and tested to predict the size and shape of the human body in 3D using a hormonal framework. The prediction method is based on geometric morphometrics, image analysis, and kernel partial least squares regression. The inputs required are answers to three closed-ended questions and a passport photo. Prediction accuracy was tested with the 3D body scan dataset of the Civilian American and European Surface Anthropometry Resource project. Results from the test dataset showed that approximately 82% of the error expectations of landmarks followed a log-normal distribution with an expectation of 8.816 mm and standard deviation of 1.180 mm. The remaining 18% of the error expectations of landmarks followed a log-normal distribution with an expectation of 18.454 mm and standard deviation of 8.844 mm, which may herald future research. Benchmarked with another method, the proposed method features much less input. In addition to high accuracy, the method in this paper allows for visualisation of results as real-size meshes in millimeters.

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Theoretical Models for the Quantification of Lung Injury Using Ventilation and Perfusion Distributions

Computational and Mathematical Methods in Medicine ◽

10.1080/17486700802201592 ◽

2009 ◽

Vol 10 (2) ◽

pp. 139-154 ◽

Cited By ~ 2

Author(s):

B. S. Brook ◽

C. M. Murphy ◽

D. Breen ◽

A. W. Miles ◽

D. G. Tilley ◽

...

Keyword(s):

Statistical Model ◽

Lung Disease ◽

Normal Distribution ◽

Theoretical Models ◽

Tree Model ◽

Flow Through ◽

Left And Right ◽

Log Normal ◽

Log Normal Distribution ◽

Ventilation And Perfusion

This paper describes two approaches to modelling lung disease: one based on a multi-compartment statistical model with a log normal distribution of ventilation perfusion ratio (V˙/Q˙) values; and the other on a bifurcating tree which emulates the anatomical structure of the lung. In the statistical model, the distribution becomes bimodal, when theV˙/Q˙values of a randomly selected number of compartments are reduced by 85% to simulate lung disease. For the bifurcating tree model a difference in flow to the left and right branches coupled with a small random variation in flow ratio between generations results in a log normal distribution of flows in the terminal branches. Restricting flow through branches within the tree to simulate lung disease transforms this log normal distribution to a bi-modal one. These results are compatible with those obtained from experiments using the multiple inert gas elimination technique, where log normal distributions ofV˙/Q˙ratio become bimodal in the presence of lung disease.

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Binary and analog variation of synapses between cortical pyramidal neurons

10.1101/2019.12.29.890319 ◽

2019 ◽

Cited By ~ 15

Author(s):

Sven Dorkenwald ◽

Nicholas L. Turner ◽

Thomas Macrina ◽

Kisuk Lee ◽

Ran Lu ◽

...

Keyword(s):

Normal Distribution ◽

Cortical Neurons ◽

Pyramidal Neurons ◽

Network Models ◽

Pyramidal Cells ◽

Automated Analysis ◽

Neural Network Models ◽

Highly Correlated ◽

Log Normal ◽

Log Normal Distribution

AbstractLearning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al., 2007) by a log-normal distribution (Loewenstein, Kuras and Rumpel, 2011; de Vivo et al., 2017; Santuy et al., 2018). A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris, 1993; Koester and Johnston, 2005; Bartol et al., 2015; Kasthuri et al., 2015; Dvorkin and Ziv, 2016; Bloss et al., 2018; Motta et al., 2019). We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma.

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Statistical Characteristics of Inter-Particle/Void Distance for Particulate Composites

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.312.105 ◽

2006 ◽

Vol 312 ◽

pp. 105-110

Author(s):

Nam Ho Kim ◽

Ho Sung Kim

Keyword(s):

Standard Deviation ◽

Normal Distribution ◽

Gaussian Distribution ◽

Three Dimensional ◽

Statistical Characteristics ◽

Particulate Composites ◽

Void Size ◽

Three Dimensional Models ◽

Log Normal ◽

Log Normal Distribution

Two dimensional statistical characteristics of inter particle/void distance (ID) for various particle/void and dispersion types are studied in relation with toughening of plastics using computer generated three dimensional models. Particle/void size groups adopted were of log-normal distribution. Particles/voids were dispersed at uniform-random. It was found that IDs are (a) of approximately Gaussian distribution but; (b) not of Gaussian distribution for particle/void sizes of bimodal log-normal distribution (created by mixing of two groups of articles/voids). It was also found that the degree of ID uniformity, which can be represented by the inverse of the coefficient of variation, for a single group of log-normally sized particles/voids is not sensitive to standard deviation of particle/void size. Mixing effect on ID characteristics using two groups of log-normally distributed particles/voids with similar mean particle/void diameters was simulated. It was found that, when a significant amount (36 vol %) of particles/voids of a small mean and standard deviation of ID, was mixed with a group of particles/voids of a large mean and standard deviation of ID, mean and standard deviation of ID for the mixture were not substantially lower than those of the group of particles/voids of the large mean and standard deviation of ID. It was also found that the degree of ID uniformity for the mixture of the two groups were lower than those of individual groups, indicating that the mixing has deleterious effect on toughening.

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A Universal Model for the Log-Normal Distribution of Elasticity in Polymeric Gels and Its Relevance to Mechanical Signature of Biological Tissues

Biology ◽

10.3390/biology10010064 ◽

2021 ◽

Vol 10 (1) ◽

pp. 64

Author(s):

Arnaud Millet

Keyword(s):

Elastic Modulus ◽

Normal Distribution ◽

Biological Tissues ◽

Force Microscopy ◽

Polymeric Gels ◽

Atomic Force ◽

Clear Explanation ◽

Log Normal ◽

Log Normal Distribution

The mechanosensitivity of cells has recently been identified as a process that could greatly influence a cell’s fate. To understand the interaction between cells and their surrounding extracellular matrix, the characterization of the mechanical properties of natural polymeric gels is needed. Atomic force microscopy (AFM) is one of the leading tools used to characterize mechanically biological tissues. It appears that the elasticity (elastic modulus) values obtained by AFM presents a log-normal distribution. Despite its ubiquity, the log-normal distribution concerning the elastic modulus of biological tissues does not have a clear explanation. In this paper, we propose a physical mechanism based on the weak universality of critical exponents in the percolation process leading to gelation. Following this, we discuss the relevance of this model for mechanical signatures of biological tissues.

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