On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging

We propose a mathematical study of the statistics of chlorophyll fluorescence indices. While most of the literature assumes Gaussian distributions for these indices, we demonstrate their fundamental non-Gaussian nature. Indeed, while the noise in the raw fluorescence images can be assumed as Gaussian additive, the deterministic ratio between them produces nonlinear non-Gaussian distributions. We investigate the states in which this non-Gaussianity can affect the statistical estimation when wrongly approached with linear estimators. We provide an expectation–maximization estimator adapted to the non-Gaussian distributions. We illustrate the interest of this estimator with simulations from images of chlorophyll fluorescence indices.. We demonstrate the benefits of our approach by comparison with the standard Gaussian assumption. Our expectation–maximization estimator shows low estimation errors reaching seven percent for a more pronounced deviation from Gaussianity compared to Gaussianity assumptions estimators rising to more than 70 percent estimation error. These results show the importance of considering rigorous mathematical estimation approaches in chlorophyll fluorescence indices. The application of this work could be extended to various vegetation indices also made up of a ratio of Gaussian distributions

A continuum decorrelation of the variables; Application to multivariate regression

A continuum standardization, called alpha M-standardization, of a dataset whereby the variances of the variables and their correlations are gradually shaded off is proposed. It is tightly connected to the Mahalanobis distance. After investigating its properties, it is used to set up a continuum approach to predict one data set from another. It turned out that this continuum strategy is tightly linked to an approach called PLS power regression [1,2]. It is also shown that this strategy of analysis is broad enough to encompass PLS regression and redundancy analysis. An illustration on the basis of a two case studies is outlined

On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging

We propose a mathematical study of the statistics of chlorophyll fluorescence indices. While most of the literature assumes Gaussian distributions for these indices, we demonstrate their fundamental non-Gaussian nature. Indeed, while the noise in the raw fluorescence images can be assumed as Gaussian additive, the deterministic ratio between them produces nonlinear non-Gaussian distributions. We investigate the states in which this non-Gaussianity can affect the statistical estimation when wrongly approached with linear estimators. We provide an expectation–maximization estimator adapted to the non-Gaussian distributions. We illustrate the interest of this estimator with simulations from images of chlorophyll fluorescence indices.. We demonstrate the benefits of our approach by comparison with the standard Gaussian assumption. Our expectation–maximization estimator shows low estimation errors reaching seven percent for a more pronounced deviation from Gaussianity compared to Gaussianity assumptions estimators rising to more than 70 percent estimation error. These results show the importance of considering rigorous mathematical estimation approaches in chlorophyll fluorescence indices. The application of this work could be extended to various vegetation indices also made up of a ratio of Gaussian distributions

Maternal protein restriction during lactation induces early and lasting plasma metabolomic and hepatic lipidomic signatures of the offspring in a rodent programming model

International audiencePerinatal undernutrition affects not only fetal and neonatal growth but also adult health outcome, as suggested by the metabolic imprinting concept. However, the exact mechanisms underlying offspring metabolic adaptations are not yet fully understood. Specifically, it remains unclear whether the gestation or the lactation is the more vulnerable period to modify offspring metabolic flexibility. We investigated in a rodent model of intrauterine growth restriction (IUGR) induced by maternal protein restriction (R) during gestation which time window of maternal undernutrition (gestation, lactation or gestation-lactation) has more impact on the male offspring metabolomics phenotype. Plasma metabolome and hepatic lipidome of offspring were characterized through suckling period and at adulthood using liquid chromatography-high-resolution mass spectrometry. Multivariate analysis of these fingerprints highlighted a persistent metabolomics signature in rats suckled by R dams, with a clear-cut discrimination from offspring fed by control (C) dams. Pups submitted to a nutritional switch at birth presented a metabolomics signature clearly distinct from that of pups nursed by dams maintained on a consistent perinatal diet. Control rats suckled by R dams presented transiently higher branched-chain amino acid (BCAA) oxidation during lactation besides increased fatty acid (FA) β-oxidation, associated with preserved insulin sensitivity and lesser fat accretion that persisted throughout their life. In contrast, IUGR rats displayed permanently impaired β-oxidation, associated to increased glucose or BCAA oxidation at adulthood, depending on the fact that pups experienced slow postnatal or catch-up growth, as suckled by R or C dams, respectively. Taken together, these findings provide evidence for a significant contribution of the lactation period in metabolic programming