Isolation and characterization of microsatellite markers for the threatened African endemic tree species Pterocarpus erinaceus Poir.

To study the genetic diversity and structure of the forest species Pterocarpus erinaceus Poir., seventeen polymorphic nuclear microsatellite markers were isolated and characterized, using next-generation sequencing. Three hundred and sixty-five (365) individuals were analyzed within fifteen (15) West African populations. The number of alleles for these loci varied from 4 to 30, and the heterozygosity varied from 0.23 to 0.82. The seventeen (17) primers designed here will allow characterizing the genetic diversity of this threaten species on its natural stands and to better understand the population differentiation mechanisms shaping it.

Dataset of visible-near infrared handheld and micro-spectrometers - comparison of the prediction accuracy of sugarcane properties.

In the dataset presented in this article, sixty sugarcane samples were analyzed by eight visible / near infrared spectrometers including seven micro-spectrometers. There is one file per spectrometer with sample name, wavelength, absorbance data [calculated as log10 (1/Reflectance)], and another file for reference data, in order to assess the potential of the micro-spectrometers to predict chemical properties of sugarcane samples and to compare their performance with a LabSpec spectrometer. The Partial Least Square Regression (PLS-R) algorithm was used to build calibration models. This open access dataset could also be used to test new chemometric methods, for training, etc.

Using spatial information for evaluating the quality of prediction maps from hyperspectral images: A geostatistical approach.

Applying a calibration model onto hyperspectral (HS) images is of great interest because it produces images of chemical or physical properties. HS imaging is widely used in this way in food processing industries for monitoring product quality and process control. In this context, one of the main difficulties in the application of regression models to HS images is to evaluate the error of the obtained predictions, since in a proximal imaging set up, the size of the pixels is usually much smaller than the area required to obtain a wet chemical reference. Moreover, the selection of regression model parameters, such as the number of latent variables (LV) in a partial least squares (PLS) model, can modify the appearance of the prediction maps. The objective of this work is to propose an approach based on geostatistical indices to use spatial information of prediction maps for supporting the evaluation of regression models applied to HS images. This work stablishes a theoretical connection between linear regression model performance estimates and the spatial decomposition of variance in prediction maps, when the ground truth to estimate is spatially structured. This approach was tested in a simulated dataset and two real case studies. Geostatistical indices of the prediction maps were compared to model performance metrics for PLS models with increasing number of LV. The theoretical framework was proven by the results on the simulated dataset. In particular, the evolution of the nugget effect, C0, corresponded with the evolution of the random error of the model. Conversely, the error term of the model related with the slope of the model corresponded with the evolution of the structured variance observed in the prediction maps. On the real case studies, geostatistical indexes, extracted from the prediction maps, allowed to quantitatively evaluate the spatial structure of the estimations and complement the Root Mean Standard Error of Cross Validation (RMSECV) for the choice of optimal number of LV to consider in the model. The main advantage of this approach is that it does not require ground truth values. It could be used as a source of information for supporting the choice of optimum calibration parameters, such as the number of latent variables, or the choice of pre-treatments, complementing the traditional visual inspection of prediction maps with quantitative and objective metrics.

A systems biology view of wood formation in Eucalyptus grandis trees submitted to different potassium and water regimes.

Wood production in fast-growing Eucalyptus grandis trees is highly dependent on both potassium (K) fertilization and water availability but the molecular processes underlying wood formation in response to the combined effects of these two limiting factors remain unknown. E. grandis trees were submitted to four combinations of K-fertilization and water supply. Weighted gene co-expression network analysis and MixOmics-based co-regulation networks were used to integrate xylem transcriptome, metabolome and complex wood traits. Functional characterization of a candidate gene was performed in transgenic E. grandis hairy roots. This integrated network-based approach enabled us to identify meaningful biological processes and regulators impacted by K-fertilization and/or water limitation. It revealed that modules of co-regulated genes and metabolites strongly correlated to wood complex traits are in the heart of a complex trade-off between biomass production and stress responses. Nested in these modules, potential new cell-wall regulators were identified, as further confirmed by the functional characterization of EgMYB137. These findings provide new insights into the regulatory mechanisms of wood formation under stressful conditions, pointing out both known and new regulators co-opted by K-fertilization and/or water limitation that may potentially promote adaptive wood traits.