scholarly journals Adsorption between Quercetin Derivatives and β-Glucan Studied with a Novel Approach to Modeling Adsorption Isotherms

2020 ◽  
Vol 10 (5) ◽  
pp. 1637 ◽  
Author(s):  
Lidija Jakobek ◽  
Petra Matić ◽  
Šima Kraljević ◽  
Šime Ukić ◽  
Mirta Benšić ◽  
...  
Keyword(s):  
Linear Regression ◽  
Adsorption Isotherms ◽  
Spatial Arrangement ◽  
Experimental Results ◽  
Oh Groups ◽  
Quercetin Derivatives ◽  
Novel Approach ◽  
Non Linear ◽  
Modeling Of Adsorption

Interactions between polyphenols and fibers are important for polyphenol bioactivities, and have been studied in vitro with adsorption process and isotherms. However, the theoretical interpretations of adsorption potentially can be affected by the method of isotherm modeling. The aim was to study the interactions between β-glucan and quercetin derivatives (quercetin-3-glucoside, quercetin-3-galactoside, quercetin-3-rhamnoside) by studying adsorption, and to potentially improve the modeling of adsorption isotherms. Quercetin derivatives were determined by using spectrophotometric method. Experimental results were modeled with Langmuir, Dubinin-Radushkevich, and Hill isotherms using non-linear regression, linear regression, and improved non-linear regression. For improved non-linear regression, code in the R programming language was developed. All quercetin derivatives adsorbed onto the surface of β-glucan. Improved non-linear regression gave somewhat lower errors and may be the most appropriate for adsorption interpretation. According to isotherms obtained with improved regression, it may be suggested that adsorption is higher for rhamnoside and glucoside of quercetin than for quercetin-3-galactoside which agrees with experimental results. Adsorption could be a physical process. The spatial arrangement of hydroxyl (OH) groups on the glycoside part of quercetin could affect the adsorption. In conclusion, a novel approach using improved non-linear regression has been shown to be a useful, novel tool for adsorption interpretation.

scholarly journals Rumen protein degradation rates estimated by non-linear regression analysis of Michaelis–Menten in vitro data

1992 ◽  
Vol 67 (1) ◽  
pp. 27-42 ◽  
Author(s):  
Glen A. Broderick ◽  
Murray K. Clayton
Keyword(s):  
Linear Regression ◽  
Protein Degradation ◽  
Degradation Rate ◽  
Soya Bean ◽  
Protein Sources ◽  
Degradation Rates ◽  
Non Linear ◽  
Bean Meal ◽  
Soya Bean Meal

An in vitro method applying Michaelis–Menten saturation kinetics was developed as an alternative approach for estimating protein degradation rates in the rumen. Non-linear regression (NLR) analysis of the integrated Michaelis–Menten equation yielded fractional degradation rates,kd, from direct estimates of the maximum velocity: Michaelis constant ratio (kd=Vmax:Km). Degradation rates obtained using data from a series of 2 h inhibitor in vitro incubations were respectively 0.989, 0.134, and 0.037 /h for casein, solvent soya-bean meal (SSBM) and expeller soya-bean meal (ESBM). Degradation rates obtained from 2 h incubations had lower standard errors than those obtained using 1 h incubations; 2 h rates were not significantly different from 1 h rates, suggesting end-product inhibition was not significant at 2 h. The NLR Michaelis–Menten method was used to determine degradation rates for twelve protein sources: casein, bovine serum albumin, two samples of lucerne (Medicago sativa) hay, and four samples each of SSBM and ESBM. Statistical analysis of NLR results revealed significant differences among the twelve protein sources. Casein was degraded most rapidly (0.827 /h), and the four ESBM samples most slowly (0.050–0.098 /h). Degradation rate for serum albumin was 0.135 /h; rates for SSBM and lucerne hays ranged from 0.160 to 0.208 /h. Degradation rates estimated using the NLR method were more rapid than those obtained with a limited substrate approach; NLR rates were more consistent with in vivo estimates of rumen protein escape. Greater concentrations of slowly degraded proteins were needed with the NLR method to define curvilinearity of the degradation curve more accurately.Protein degradation rate: Rumen protein escape: Michaelis–Menten kinetics: Non-linear regression

Modeling of Adsorption Isotherms for Hydrogen on Zeolite ZSM-5

2011 ◽  
Vol 391-392 ◽  
pp. 732-736
Author(s):  
Xiao Ming Du
Keyword(s):  
Experimental Data ◽  
High Pressure ◽  
Adsorption Isotherms ◽  
Lattice Theory ◽  
Analytic Solution ◽  
Hydrogen Adsorption ◽  
Experimental Results ◽  
Slit Pores ◽  
Modeling Of Adsorption ◽  
Pressure Hydrogen

We give the analytic solution of Ono-Kondo equation based on lattice theory to describe the supercritical high-pressure hydrogen adsorption inside slit pores of adsorbents, and predict adsorption isotherms for hydrogen on microporous ZSM-5 zeolite at 77K,195K and 293K. The results from prediction are compared with the experimental data. It shows that the model can elucidate the peculiar features in adsorption isotherms of supercritical hydrogen which are fundamentally different from those of standard (IUPAC) classification, and the results from the equation also qualitatively reflect the experimental results.

Evaluating sample stability in the clinical laboratory with the help of linear and non-linear regression analysis

2020 ◽  
Vol 58 (2) ◽  
pp. 188-196 ◽  
Author(s):  
Joachim K.W. Pum
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Study Design ◽  
Clinical Laboratory ◽  
Linear Regression Analysis ◽  
Time Points ◽  
Stability Functions ◽  
Sample Stability ◽  
Non Linear

AbstractAs it is common practice for laboratories to store patient samples for a predefined period, allowing clinicians to request additional tests on previously collected samples, knowledge about sample stability is indispensable for the laboratorian. A common approach to estimating the maximum storage time is to use a discrete study design, measuring the analyte of interest at various time-points and then checking for significant differences with the help of a statistical test, such as Student’s t-test, Wilcoxon’s test or an analysis of variance (ANOVA) test. Because only discrete time intervals are considered, stability data can just be approximated. Alternatively, a continuous study design, as described by the Clinical and Laboratory Standards Institute (CLSI) for performing stability experiments for in vitro diagnostic reagents, can also be adopted by the clinical laboratory to evaluate the stability of biological samples. The major advantage of this approach is that it allows laboratories to define individual stability limits for different medical situations and offers more flexibility when choosing time-points for measurements. The intent of this paper is to demonstrate the evaluation of sample stability in the clinical laboratory with a continuous study design implemented with linear or non-linear regression analysis. Appropriate statistical modeling and acceptance criteria are presented, stability functions are described briefly, and checking the overall validity of the results is discussed.

Mathematical modelling for predicting surface roughness during machining of PEEK CF30 composite

2012 ◽  
Vol 3 (2) ◽  
pp. 87-96
Author(s):  
F. M. Cabrera ◽  
A. Khamlichi ◽  
I. Losilla ◽  
I. Hanafi
Keyword(s):  
Linear Regression ◽  
Broad Class ◽  
Cutting Speed ◽  
Linear Interpolation ◽  
Experimental Results ◽  
Linear Regression Models ◽  
Ether Ether Ketone ◽  
Poly Ether Ether Ketone ◽  
Non Linear ◽  
Ether Ketone

Abstract Considering the increasing prospective regarding the various applications of Poly Ether Ether Ketone reinforced with 30% carbon fibres (PEEK CF30), there is a crucial need to investigate its machinability. In this work, focus is centered on non-linear regression based models that can be built to predict roug hness parameters Ra and Rt associated to turning of PEEK CF30 when using TiN coated cutting tool. Attention was paid to one-variant models that can be proposed to represent the effect of the cutting speed on the surface finish parameters. A broad class of non-linear interpolation models was considered. Their aptness to be used in modelling this particular application was assessed. The identification of the various mathematical models was performed by using experimental results that were obtained from CNC turning of PEEK samples. Based on statistical analysis, all the considered non-linear regression models proved to be highly significant and succeeded to fit adequately the experimental results.

scholarly journals The wasting-associated metabolite succinate disrupts myogenesis and impairs skeletal muscle regeneration

2019 ◽  
Author(s):  
Paige C Arneson ◽  
Kelly A Hogan ◽  
Alexandra M Shin ◽  
Adrienne Samani ◽  
Aminah Jatoi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Muscle Regeneration ◽  
Metabolic Profiling ◽  
Muscle Wasting ◽  
Post Injury ◽  
Feret Diameter ◽  
Regression Curves ◽  
Non Linear ◽  
The Mean

ABSTRACTBackgroundMuscle wasting is a debilitating co-morbidity affecting most advanced cancer patients. Alongside enhanced muscle catabolism, defects in muscle repair/regeneration contribute to cancer-associated wasting. Among the factors implicated in suppression of muscle regeneration are cytokines that interfere with myogenic signal transduction pathways. Less understood is how other cancer/wasting-associated cues, such as metabolites, contribute to muscle dysfunction. This study investigates how the metabolite succinate affects myogenesis and muscle regeneration.MethodsWe leveraged an established ectopic metabolite treatment (cell permeable dimethyl-succinate) strategy to evaluate the ability of intracellular succinate elevation to 1) affect myoblast homeostasis (proliferation, apoptosis), 2) disrupt protein dynamics and induce wasting-associated atrophy, and 3) modulate in vitro myogenesis. In vivo succinate supplementation experiments (2% succinate, 1% sucrose vehicle) were used to corroborate and extend in vitro observations. Metabolic profiling and functional metabolic studies were then performed to investigate the impact of succinate elevation on mitochondria function.ResultsWe found that in vitro succinate supplementation elevated intracellular succinate about 2-fold, and did not have an impact on proliferation or apoptosis of C2C12 myoblasts. Elevated succinate had minor effects on protein homeostasis (∼25% decrease in protein synthesis assessed by OPP staining), and no significant effect on myotube atrophy. Succinate elevation interfered with in vitro myoblast differentiation, characterized by significant decreases in late markers of myogenesis and fewer nuclei per myosin heavy chain positive structure (assessed by immunofluorescence staining). While mice orally administered succinate did not exhibit changes in overall body composition or whole muscle weights, these mice displayed smaller muscle myofiber diameters (∼6% decrease in the mean of non-linear regression curves fit to the histograms of minimum feret diameter distribution), which was exacerbated when muscle regeneration was induced with barium chloride injury. Significant decreases in the mean of non-linear regression curves fit to the histograms of minimum feret diameter distributions were observed 7 days and 28 days post injury. Elevated numbers of myogenin positive cells (3-fold increase) supportive of the differentiation defects observed in vitro were observed 28 days post injury. Metabolic profiling and functional metabolic assessment of myoblasts revealed that succinate elevation caused both widespread metabolic changes and significantly lowered maximal cellular respiration (∼35% decrease).ConclusionsThis study broadens the repertoire of wasting-associated factors that can directly modulate muscle progenitor cell function and strengthens the hypothesis that metabolic derangements are significant contributors to impaired muscle regeneration, an important aspect of cancer-associated muscle wasting.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

Sign in / Sign up

Export Citation Format

Share Document