scholarly journals The use of numerical methods in the design of a formula that returns the surface of the leaves of Phragmites australis (Cav.) Trin. Ex Steud.

2020 ◽  
Vol 20 (1) ◽  
pp. 33-39
Author(s):  
Katarzyna Krzyżanowska ◽  
Paweł Krzyżanowski
Keyword(s):  
Linear Regression ◽  
Phragmites Australis ◽  
Measurement Errors ◽  
Numerical Models ◽  
Common Reed ◽  
Small Samples ◽  
Real Surface ◽  
Maximum Width ◽  
Average Accuracy ◽  
Leaf Dimensions

AbstractThe paper presents the results of calculations and a verification of numerical models developed for estimating the surface of leaves of the common reed (Phragmites australis (Cav.) Trin. Ex Steud.). The research sample consisted of 137 leaves collected from the rush zone of Lake Raduńskie Górne in 2018. The total area of leaves obtained for testing was 1932.3 cm2. To derive a formula that returns the surface of common reed foliage regression models were used – MLR (Multiple Linear Regression) and SLR (Stepwise Linear Regression). It has been shown that the measurement of basic leaf dimensions (i.e. length – L, mid-width – WM and maximum width – WX) makes it possible to define an empirical formula which, with an average accuracy of 99.9%, allows the real surface of leaves to be estimated. The modelling results were compared with formulas currently used in practice, and the measurement errors were determined using these formulas. It has been shown that the formulas used to date are subject to RMSE to the value of 1.19-2.52. The application of the developed formula (A = 0.4486 – 0.046 L + 7.9267 WM – 5.8121 WX + 0.5853 L • WX) will significantly reduce errors in leaf surface estimation (RMSE = 0.86) and thus the amount of reed transpiration and evapotranspiration, especially in the case of handling small samples (number of leaves and measurements).

scholarly journals A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dana Vaknin ◽  
Guy Amit ◽  
Amir Bashan
Keyword(s):  
Gene Regulation ◽  
Measurement Errors ◽  
Numerical Models ◽  
Expression Profiles ◽  
Small Sample ◽  
Biological Variability ◽  
Technical Variability ◽  
Gene Coordination ◽  
Cell To Cell Variability ◽  
Errors Analysis

AbstractRecent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.

scholarly journals Osmotically inactive sodium and potassium storage: lessons learned from the Edelman and Boling data

2016 ◽  
Vol 311 (3) ◽  
pp. F539-F547 ◽  
Author(s):  
Minhtri K. Nguyen ◽  
Dai-Scott Nguyen ◽  
Minh-Kevin Nguyen
Keyword(s):  
Linear Regression ◽  
Measurement Errors ◽  
Linear Regression Analysis ◽  
Sodium Concentration ◽  
Lessons Learned ◽  
Dynamic Changes ◽  
Data Set ◽  
Storage Pool ◽  
Total Body ◽  
Sodium And Potassium

Because changes in the plasma water sodium concentration ([Na+]pw) are clinically due to changes in the mass balance of Na+, K+, and H2O, the analysis and treatment of the dysnatremias are dependent on the validity of the Edelman equation in defining the quantitative interrelationship between the [Na+]pw and the total exchangeable sodium (Nae), total exchangeable potassium (Ke), and total body water (TBW) (Edelman IS, Leibman J, O'Meara MP, Birkenfeld LW. J Clin Invest 37: 1236–1256, 1958): [Na+]pw = 1.11(Nae + Ke)/TBW − 25.6. The interrelationship between [Na+]pw and Nae, Ke, and TBW in the Edelman equation is empirically determined by accounting for measurement errors in all of these variables. In contrast, linear regression analysis of the same data set using [Na+]pw as the dependent variable yields the following equation: [Na+]pw = 0.93(Nae + Ke)/TBW + 1.37. Moreover, based on the study by Boling et al. (Boling EA, Lipkind JB. 18: 943–949, 1963), the [Na+]pw is related to the Nae, Ke, and TBW by the following linear regression equation: [Na+]pw = 0.487(Nae + Ke)/TBW + 71.54. The disparities between the slope and y-intercept of these three equations are unknown. In this mathematical analysis, we demonstrate that the disparities between the slope and y-intercept in these three equations can be explained by how the osmotically inactive Na+ and K+ storage pool is quantitatively accounted for. Our analysis also indicates that the osmotically inactive Na+ and K+ storage pool is dynamically regulated and that changes in the [Na+]pw can be predicted based on changes in the Nae, Ke, and TBW despite dynamic changes in the osmotically inactive Na+ and K+ storage pool.

scholarly journals Title: Influences of reduced iron and magnesium on growth and photosynthetic performance of Phragmites australis subsp. americanus (North American common reed)

2017 ◽  
Vol 137 ◽  
pp. 30-38 ◽  
Author(s):  
Kevin G. Willson ◽  
Angela N. Perantoni ◽  
Zachary C. Berry ◽  
Matthew I. Eicholtz ◽  
Yvette B. Tamukong ◽  
...  
Keyword(s):  
Phragmites Australis ◽  
North American ◽  
Common Reed ◽  
Photosynthetic Performance

STUDY ON COMMON REED(PHRAGMITES AUSTRALIS) OVERGROWTH CONTROL BY SUNLIGHT COVER IN A SMALL RIVER

2020 ◽  
Vol 76 (7) ◽  
pp. III_355-III_360
Author(s):  
Yohei YANAGI ◽  
Masahiko SEKINE ◽  
Ariyo KANNO ◽  
Kousuke MATSUDA
Keyword(s):  
Phragmites Australis ◽  
Common Reed ◽  
Small River
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