Home About us Contact
|
Home About us Contact | ||
|
|
||
Standardized Regression Coefficients (standardized + regression_coefficient)
Selected AbstractsGood modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysisBIOTECHNOLOGY PROGRESS, Issue 4 2009Gürkan Sin Abstract The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base-consumption were found low compared to the large uncertainty observed in the antibiotic and off-gas CO2 predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass-transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source] Leaf litter nitrogen concentration as related to climatic factors in Eurasian forestsGLOBAL ECOLOGY, Issue 5 2006Chunjiang Liu ABSTRACT Aim, The aim of this study is to determine the patterns of nitrogen (N) concentrations in leaf litter of forest trees as functions of climatic factors, annual average temperature (Temp, °C) and annual precipitation (Precip, dm) and of forest type (coniferous vs. broadleaf, deciduous vs. evergreen, Pinus, etc.). Location, The review was conducted using data from studies across the Eurasian continent. Methods, Leaf litter N concentration was compiled from 204 sets of published data (81 sets from coniferous and 123 from broadleaf forests in Eurasia). We explored the relationships between leaf litter N concentration and Temp and Precip by means of regression analysis. Leaf litter data from N2 -fixing species were excluded from the analysis. Results, Over the Eurasian continent, leaf litter N concentration increased with increasing Temp and Precip within functional groups such as conifers, broadleaf, deciduous, evergreen and the genus Pinus. There were highly significant linear relationships between ln(N) and Temp and Precip (P < 0.001) for all available data combined, as well as for coniferous trees, broadleaf trees, deciduous trees, evergreen trees and Pinus separately. With both Temp and Precip as independent variables in multiple regression equations, the adjusted coefficient of determination () was evidently higher than in simple regressions with either Temp or Precip as independent variable. Standardized regression coefficients showed that Temp had a larger impact than Precip on litter N concentration for all groups except evergreens. The impact of temperature was particularly strong for Pinus. Conclusions, The relationship between leaf litter N concentration and temperature and precipitation can be well described with simple or multiple linear regression equations for forests over Eurasia. In the context of global warming, these regression equations are useful for a better understanding and modelling of the effects of geographical and climatic factors on leaf litter N at a regional and continental scale. [source] ORIGINAL RESEARCH,EPIDEMIOLOGY: Effect of Sexual Function on Health-Related Quality of Life Mediated by Depressive Symptoms in Cardiac Rehabilitation.THE JOURNAL OF SEXUAL MEDICINE, Issue 6 2010Findings of the SPARK Project in 493 Patients ABSTRACT Introduction., Empirical evidence suggests associations between cardiovascular diseases, sexual functioning, depressive symptoms, and quality of life. However, to date, the interrelation of these constructs has not been examined simultaneously in a structural analysis. Aim., To estimate the prevalence of sexual disorders and depressive symptoms and to examine the association between sexual disorders, depressive symptoms, and quality of life in patients in the rehabilitation of cardiovascular disorders. Aim., A postal survey in five German inpatient rehabilitation centers for cardiovascular diseases was conducted. Prevalence of sexual disorders and depressive symptoms were assessed using psychometrically sound instruments. To analyze complex associations, structural equation modeling was used. Main Outcome Measures., For epidemiological questions, proportions with 95% confidence intervals were calculated. The strength of association in structural equation models was expressed as a standardized regression coefficient. Results., Data from 493 patients were analyzed (response rate 22.7%). At least moderate erectile dysfunction proved to be present in 20.3% of men. The prevalence of female sexual dysfunction lay at 43.1%. At least moderate depressive symptoms were present in 14.4% of men and 16.5% of women. A considerable association between sexual functioning and quality of life was found in both sexes, which was largely mediated by depressive symptoms. Major drawbacks of the study are imprecision of the estimates due to limited sample size and questionable generalizability of the findings due to possible self-selection bias. Conclusions., Considering the high prevalence of depressive symptoms and their role as a mediating factor between sexual functioning and quality of life, it is recommended to routinely screen for depression in men and women with cardiac disease. Kriston L, Günzler C, Agyemang A, Bengel J, and Berner MM. Effect of sexual function on health-related quality of life mediated by depressive symptoms in cardiac rehabilitation. Findings of the SPARK project in 493 patients. J Sex Med 2010;7:2044,2055. [source] Using Logistic Regression to Analyze the Sensitivity of PVA Models: a Comparison of Methods Based on African Wild Dog ModelsCONSERVATION BIOLOGY, Issue 5 2001Paul C. Cross Standardized coefficients from the logistic regression analyses indicated that pup survival explained the most variability in the probability of extinction, regardless of whether or not the model incorporated density dependence. Adult survival and the standard deviation of pup survival were the next most important parameters in density-dependent simulations, whereas the severity and probability of catastrophe were more important during density-independent simulations. The inclusion of density dependence decreased the probability of extinction, but neither the abruptness nor the inclusion of density dependence were important model parameters. Results of both relative sensitivity analyses that altered each parameter by 10% of its range and life-stage-simulation analyses of deterministic matrix models supported the logistic regression results, indicating that pup survival and its variation were more important than other parameters. But both conventional sensitivity analysis of the stochastic model which changed each parameter by 10% of its mean value and elasticity analyses indicated that adult survival was more important than pup survival. We evaluated the advantages and disadvantages of using logistic regression to analyze the sensitivity of stochastic population viability models and conclude that it is a powerful method because it can address interactions among input parameters and can incorporate the range of parameter variability, although the standardized regression coefficients are not comparable between studies. Model structure, method of analysis, and parameter uncertainty affect the conclusions of sensitivity analyses. Therefore, rigorous model exploration and analysis should be conducted to understand model behavior and management implications. Resumen: Utilizamos la regresión logística como un método de análisis de sensibilidad par a un modelo de análisis de viabilidad poblacional de perros silvestres Africanos ( Lycaon pictus) y comparamos estos resultados con análisis de sensibilidad convencionales de modelos estocásticos y determinísticos. Coeficientes estandarizados de los análisis de regresión logística indicaron que la supervivencia de cachorros explicaba la mayor variabilidad en la probabilidad de extinción, independientemente de que el modelo incorporara la denso-dependencia. La supervivencia de adultos y la desviación estándar de la supervivencia de cachorros fueron los parámetros que siguieron en importancia en simulaciones de denso-dependencia, mientras que la severidad y la probabilidad de catástrofes fueron más importantes durante simulaciones denso-independientes. La inclusión de la denso dependencia disminuyó la probabilidad de extinción, pero ni la severidad ni la inclusión de denso-dependencia fueron parámetros importantes. Resultados de los análisis de sensibilidad relativa que alteraron cada parámetro en 10% de su rango y análisis de la simulación de etapas de vida de modelos matriciales determinísticos apoyaron los resultados de la regresión logística, indicando que la supervivencia de cachorros y su variación fueron más importantes que otros parámetros. Sin embargo, el análisis de sensibilidad convencional del modelo estocástico que cambiaron cada parámetro en 10% de su valor medio y el análisis de elasticidad indicaron que la supervivencia de adultos fue más importante que la supervivencia de cachorros. Evaluamos las ventajas y desventajas de utilizar la regresión logística para analizar la sensibilidad de modelos estocásticos de viabilidad poblacional y concluimos que es un método poderoso porque puede atender interacciones entre parámetros ingresados e incorporar el rango de variabilidad de parámetros, aunque los coeficientes de regresión estandarizada no son comparables entre estudios. La estructura del modelo, el método de análisis y la incertidumbre en los parámetros afectan las conclusiones del análisis de sensibilidad. Por lo tanto, se debe realizar una rigurosa exploración y análisis del modelo para entender su comportamiento y sus implicaciones en el manejo. [source] | ||||||||||