Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture

"This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the Journal of Plant Nutrition 2015 March, available online at: http://www.tandfonline.com/10.1080/01904167.2014.934474."Cape gooseberry (Physalis peruviana L.) is a solanaceous plant. The growth and time-course of nutrient accumulation of the plant and its partitioning between roots, stems, leaves, and fruits were examined. The study was conducted analyzing two nutrient solutions in soilless culture under greenhouse conditions during two consecutive seasons. The macronutrient contents were analyzed. On average, the yield was 8.9 t.ha(-1). Growth of the plant until 90 d after transplanting obeys an exponential function of time and the relative growth rate for this period was determined. Nitrogen (N) was the element that showed the highest concentration, corresponding to leaves (4.67%), followed by potassium (K) in stems (4.46%). The highest accumulations of N, phosphorous (P), calcium (Ca), and magnesium (Mg) were found in leaves and of K in the stems. Potassium showed the highest nutrient accumulation (29 g.plant(-1)) and the highest specific uptake rate.Torres Rubio, JF.; Pascual Seva, N.; San Bautista Primo, A.; Pascual España, B.; López Galarza, SV.; Alagarda Pardo, J.; Maroto Borrego, JV. (2015). Growth and nutrient absorption of Cape Gooseberry (Physalis Peruviana L.) in soilless culture. Journal of Plant Nutrition. 38(4):485-496. doi:10.1080/01904167.2014.934474S485496384Bellaloui, N., & Brown, P. H. (1998). Plant and Soil, 198(2), 153-158. doi:10.1023/a:1004343031242Bennett, J. P., Oshima, R. J., & Lippert, L. F. (1979). Effects of ozone on injury and dry matter partitioning in pepper plants. 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Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown