Recent evidence suggests that energy metabolism contributes to molecular mechanisms controlling stem cell identity. For example, human embryonic stem cells (hESCs) receive their metabolic energy mostly via glycolysis rather than mitochondrial oxidative phosphorylation. This suggests a connection of metabolic homeostasis to stemness. Nicotinamide adenine dinucleotide (NAD) is an important cellular redox carrier and a cofactor for various metabolic pathways, including glycolysis. Therefore, accurate determination of NAD cellular levels and dynamics is of growing importance for understanding the physiology of stem cells. Conventional analytic methods for the determination of metabolite levels rely on linear calibration curves. However, in actual practice many two-enzyme cycling assays, such as the assay systems used in this work, display prominently nonlinear behavior. Here we present a diaphorase/lactate dehydrogenase NAD cycling assay optimized for hESCs, together with a mechanism-based, nonlinear regression models for the determination of NAD(+), NADH, and total NAD. We also present experimental data on metabolic homeostasis of hESC under various physiological conditions. We show that NAD(+)/NADH ratio varies considerably with time in culture after routine change of medium, while the total NAD content undergoes relatively minor changes. In addition, we show that the NAD(+)/NADH ratio, as well as the total NAD levels, vary between stem cells and their differentiated counterparts. Importantly, the NAD(+)/NADH ratio was found to be substantially higher in hESC-derived fibroblasts versus hESCs. Overall, our nonlinear mathematical model is applicable to other enzymatic amplification systems.
- MeSH
- Cell Extracts MeSH
- Electrophoresis, Capillary MeSH
- Embryonic Stem Cells metabolism MeSH
- Calibration MeSH
- Humans MeSH
- NAD metabolism MeSH
- Nonlinear Dynamics * MeSH
- Oxazines metabolism MeSH
- Regression Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets.
- MeSH
- Algorithms * MeSH
- Least-Squares Analysis MeSH
- Artificial Intelligence * MeSH
- Publication type
- Journal Article MeSH
When drugs are poorly soluble then, instead of the potentiometric determination of dissociation constants, pH-spectrophotometric titration can be used along with nonlinear regression of the absorbance response surface data. Generally, regression models are extremely useful for extracting the essential features from a multiwavelength set of data. Regression diagnostics represent procedures for examining the regression triplet (data, model, method) in order to check (a) the data quality for a proposed model; (b) the model quality for a given set of data; and (c) that all of the assumptions used for least squares hold. In the interactive, PC-assisted diagnosis of data, models and estimation methods, the examination of data quality involves the detection of influential points, outliers and high leverages, that cause many problems when regression fitting the absorbance response hyperplane. All graphically oriented techniques are suitable for the rapid estimation of influential points. The reliability of the dissociation constants for the acid drug silybin may be proven with goodness-of-fit tests of the multiwavelength spectrophotometric pH-titration data. The uncertainty in the measurement of the pK (a) of a weak acid obtained by the least squares nonlinear regression analysis of absorption spectra is calculated. The procedure takes into account the drift in pH measurement, the drift in spectral measurement, and all of the drifts in analytical operations, as well as the relative importance of each source of uncertainty. The most important source of uncertainty in the experimental set-up for the example is the uncertainty in the pH measurement. The influences of various sources of uncertainty on the accuracy and precision are discussed using the example of the mixed dissociation constants of silybin, obtained using the SQUAD(84) and SPECFIT/32 regression programs.
- MeSH
- Antioxidants analysis chemistry MeSH
- Time Factors MeSH
- Models, Chemical MeSH
- Financing, Organized MeSH
- Hydrogen-Ion Concentration MeSH
- Pharmaceutical Preparations chemistry MeSH
- Least-Squares Analysis MeSH
- Regression Analysis MeSH
- Silymarin analysis chemistry MeSH
- Spectrophotometry methods MeSH
- Drug Stability MeSH
- Titrimetry methods MeSH
The mixed dissociation constants of four non-steroidal anti-inflammatory drugs (NSAIDs) ibuprofen, diclofenac sodium, flurbiprofen and ketoprofen at various ionic strengths I of range 0.003-0.155, and at temperatures of 25 degrees C and 37 degrees C, were determined with the use of two different multiwavelength and multivariate treatments of spectral data, SPECFIT/32 and SQUAD(84) nonlinear regression analyses and INDICES factor analysis. The factor analysis in the INDICES program predicts the correct number of components, and even the presence of minor ones, when the data quality is high and the instrumental error is known. The thermodynamic dissociation constant pK(a)(T) was estimated by nonlinear regression of (pK(a), I) data at 25 degrees C and 37 degrees C. Goodness-of-fit tests for various regression diagnostics enabled the reliability of the parameter estimates found to be proven. PALLAS, MARVIN, SPARC, ACD/pK(a) and Pharma Algorithms predict pK(a) being based on the structural formulae of drug compounds in agreement with the experimental value. The best agreement seems to be between the ACD/pK(a) program and experimentally found values and with SPARC. PALLAS and MARVIN predicted pK(a,pred) values with larger bias errors in comparison with the experimental value for all four drugs.
- MeSH
- Anti-Inflammatory Agents, Non-Steroidal chemistry MeSH
- Models, Chemical MeSH
- Financing, Organized MeSH
- Hydrogen-Ion Concentration MeSH
- Least-Squares Analysis MeSH
- Molecular Structure MeSH
- Nonlinear Dynamics MeSH
- Solubility MeSH
- Spectrophotometry methods MeSH
- Thermodynamics MeSH
- Titrimetry methods MeSH
Potentiometric and spectrophotometric pH-titration of the multiprotic cytostatics bosutinib for dissociation constants determination were compared. Bosutinib treats patients with positive chronic myeloid leukemia. Bosutinib exhibits four protonatable sites in a pH range from 2 to 11, where two pK are well separated (ΔpK>3), while the other two are near dissociation constants. In the neutral medium, bosutinib occurs in the slightly water soluble form LH that can be protonated to the soluble cation LH4(3+). The molecule LH can be dissociated to still difficultly soluble anion L(-). The set of spectra upon pH from 2 to 11 in the 239.3-375.0nm was divided into two absorption bands: the first one from 239.3 to 290.5nm and the second from 312.3 to 375.0nm, which differ in sensitivity of chromophores to a pH change. Estimates of pK of the entire set of spectra were compared with those of both absorption bands. Due to limited solubility of bosutinib the protonation in a mixed aqueous-methanolic medium was studied. In low methanol content of 3-6% three dissociation constants can be reliably determined with SPECFIT/32 and SQUAD(84) and after extrapolation to zero content of methanol they lead to pKc1=3.43(12), pKc2=4.54(10), pKc3=7.56(07) and pKc4=11.04(05) at 25°C and pKc1=3.44(06), pKc2=5.03(08) pKc3=7.33(05) and pKc4=10.92(06) at 37°C. With an increasing content of methanol in solvent the dissociation of bosutinib is suppressed and the percentage of LH3(2+) decreases and LH prevails. From the potentiometric pH-titration at 25°C the concentration dissociation constants were estimated with ESAB pKc1=3.51(02), pKc2=4.37(02), pKc3=7.97(02) and pKc4=11.05(03) and with HYPERQUAD: pKc1=3.29(12), pKc2=4.24(10), pKc3=7.95(07) and pKc4=11.29(05).
- MeSH
- Aniline Compounds analysis chemistry MeSH
- Quinolines analysis chemistry MeSH
- Cytostatic Agents analysis chemistry MeSH
- Hydrogen-Ion Concentration MeSH
- Least-Squares Analysis MeSH
- Nonlinear Dynamics * MeSH
- Nitriles analysis chemistry MeSH
- Potentiometry MeSH
- Spectrophotometry methods MeSH
- Publication type
- Journal Article MeSH
... Linear Regression and the Logistic Regression Model 1 -- 1.1 Regression Assumptions 4 -- 1.2 Nonlinear ... ... Summary Statistics for Evaluating the Logistic -- Regression Model 17 -- 2.1 R2, F, and Sums of Squared ... ... 36 -- 2.5 Conclusion: Summary Measures for Evaluating the -- Logistic Regression Model 41 -- 3. ... ... Interpreting the Logistic Regression Coefficients 41 -- 3.1 Statistical Significance in Logistic Regression ... ... Polytomous Logistic Regression and Alternatives to -- Logistic Regression 91 -- 5.1 Polytomous Nominal ...
Sage university papers series Quantitative applications in t he social sciences ; No. 07-106
2nd ed. viii, 111 s. : il.
In the Czech Hydrometeorological Institute (CHMI) there exists a unique set of meteorological measurements consisting of the values of vertical atmospheric levels of beta and gamma radiation. In this paper a stochastic data-driven model based on nonlinear regression and on nonhomogeneous Poisson process is suggested. In the first part of the paper, growth curves were used to establish an appropriate nonlinear regression model. For comparison we considered a nonhomogeneous Poisson process with its intensity based on growth curves. In the second part both approaches were applied to the real data and compared. Computational aspects are briefly discussed as well. The primary goal of this paper is to present an improved understanding of the distribution of environmental radiation as obtained from the measurements of the vertical radioactivity profiles by the radioactivity sonde system.
In this study, affinity capillary electrophoresis (ACE) and quantum mechanical density functional theory (DFT) calculations were combined to investigate non-covalent binding interactions between the hexaarylbenzene-based receptor (R) and alkali metal ions, Rb(+) and Cs(+) , in methanol. The apparent binding (stability) constants (K(b) ) of the complexes of receptor R with alkali metal ions in the methanolic medium were determined by ACE from the dependence of effective electrophoretic mobility of the receptor R on the concentration of Rb(+) and Cs(+) ions in the BGE using a non-linear regression analysis. The receptor R formed relatively strong complexes both with rubidium (log K(b) =4.04±0.21) and cesium ions (log K(b) =3.72±0.22). The structural characteristics of the above alkali metal ion complexes with the receptor R were described by ab initio density functional theory calculations. These calculations have shown that the studied cations bind to the receptor R because they synergistically interact with the polar ethereal fence and with the central benzene ring via cation-π interaction.
