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Polynomial Regression Models (polynomial + regression_models)
Selected AbstractsOptimization of Rosmarinic Acid Production by Lavandula vera MM Plant Cell Suspension in a Laboratory BioreactorBIOTECHNOLOGY PROGRESS, Issue 2 2005Atanas I. Pavlov The all-round effect of dissolved oxygen concentration, agitation speed, and temperature on the rosmarinic acid production by Lavandula veraMM cell suspension was studied in a 3-L laboratory bioreactor by means of the modified Simplex method. Polynomial regression models were elaborated for description of the process of rosmarinic acid production (Y) in the bioreactor as a consequence of the variation of the dissolved oxygen (X1) concentration between 10% and 50%; agitation (X2) between 100 and 400 rpm; and temperature (X3) between 22 and 30 °C. The optimization made it possible to establish the optimal conditions for the biosynthesis of rosmarinic acid by L. veraMM: dissolved oxygen (X1*), 50% of air saturation; agitation (X2*), 400 rpm; and temperature (X3*), 29.9 °C, where maximal yield (Ymax) of 3489.4 mg/L of rosmarinic acid was achieved (2 times higher compared with the shake-flasks cultivation). [source] DYNAMIC MODELING OF RETORT PROCESSING USING NEURAL NETWORKSJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 2 2002C. R. CHEN ABSTRACT Two neural network approaches , a moving-window and hybrid neural network , which combine neural network with polynomial regression models, were used for modeling F(t) and Qv(t) dynamic functions under constant retort temperature processing. The dynamic functions involved six variables: retort temperature (116,132C), thermal diffusivity (1.5,2.3 × 10,7m2/s), can radius (40,61 mm), can height (40,61 mm), and quality kinetic parameters z (15,39C) and D (150,250 min). A computer simulation designed for process calculations of food thermal processing systems was used to provide the fundamental data for training and generalization of ANN models. Training data and testing data were constructed by both second order central composite design and orthogonal array, respectively. The optimal configurations of ANN models were obtained by varying the number of hidden layers, number of neurons in hidden layer and learning runs, and a combination of learning rules and transfer function. Results demonstrated that both neural network models well described the F(t) and Qv(t) dynamic functions, but moving-window network had better modeling performance than the hybrid ANN models. By comparison of the configuration parameters, moving-window ANN models required more neurons in the hidden layer and more learning runs for training than the hybrid ANN models. [source] Global transcript profiling of primary stems from Arabidopsis thaliana identifies candidate genes for missing links in lignin biosynthesis and transcriptional regulators of fiber differentiationTHE PLANT JOURNAL, Issue 5 2005Jürgen Ehlting Summary Different stages of vascular and interfascicular fiber differentiation can be identified along the axis of bolting stems in Arabidopsis. To gain insights into the metabolic, developmental, and regulatory events that control this pattern, we applied global transcript profiling employing an Arabidopsis full-genome longmer microarray. More than 5000 genes were differentially expressed, among which more than 3000 changed more than twofold, and were placed into eight expression clusters based on polynomial regression models. Within these, 182 upregulated transcription factors represent candidate regulators of fiber development. A subset of these candidates has been associated with fiber development and/or secondary wall formation and lignification in the literature, making them targets for functional studies and comparative genomic analyses with woody plants. Analysis of differentially expressed phenylpropanoid genes identified a set known to be involved in lignin biosynthesis. These were used to anchor co-expression analyses that allowed us to identify candidate genes encoding proteins involved in monolignol transport and monolignol dehydrogenation and polymerization. Similar analyses revealed candidate genes encoding enzymes that catalyze missing links in the shikimate pathway, namely arogenate dehydrogenase and prephenate aminotransferase. [source] Birth weight charts for gestational age in 63 620 healthy infants born in Peruvian public hospitals at low and at high altitudeACTA PAEDIATRICA, Issue 3 2009Gustavo F Gonzales Abstract Aim: To construct distribution curves for birth weight, length and head circumference using a large sample of infants born at low (150 m) and high (3000,4400 m) altitude. Methods: Cross-sectional analysis of a perinatal database. All live singleton deliveries from public hospitals during 2001,2006 (gestational age from 26 to 42 weeks) with no history of perinatal deaths or smoking and no current obstetric complications (n = 63 620) were included. Fractional polynomial regression models were used to smooth curves for each gestational age. Results: Mean and median birth weight differences between those born at low and high altitudes reached statistical significance after 35 and 33 weeks, respectively. Values of the 10th percentile were higher at low altitude from 36 weeks, whereas values at the 90th percentile were different from 34 weeks. In the Peruvian growth curves, birth weight was greater at each gestational age than in the curves derived by Lubchenco. Conclusion: Altitude affects growth patterns; these growth standards will provide useful references for the care of the newborn in highland populations. In addition, the data have implications for the antepartum management of pregnant patients undergoing sonographic evaluation of fetal weight in whom new definitions of what represents small or large for gestational age in utero can result in differences in time or mode of delivery. [source] | ||||||||||