Start codon targeted (SCoT) polymorphism reveals genetic diversity in wild and domesticated populations of ramie (Boehmeria nivea L. Gaudich.), a premium textile fiber producing species.

Twenty-four start codon targeted (SCoT) markers were used to assess genetic diversity and population structure of indigenous, introduced and domesticated ramie (Boehmeria nivea L. Gaudich.). A total of 155 genotypes from five populations were investigated for SCoT polymorphism, which produced 136 amplicons with 87.5% polymorphism. Polymorphism information content and resolving power of the SCoT markers were 0.69 and 3.22, respectively. The Indian ramie populations exhibited high SCoT polymorphism (> 50%), high genetic differentiation (GST = 0.27) and moderate gene flow (Nm = 1.34). Analysis of molecular variance identified significant differences for genetic polymorphism among the populations explaining 13.1% of the total variation. The domesticated population exhibited higher genetic polymorphism and heterozygosity compared to natural populations. Cluster analysis supported population genetic analysis and suggested close association between introduced and domesticated genotypes. The present study shows effectiveness of employing SCoT markers in a cross pollinated heterozygous species like Boehmeria, and would be useful for further studies in population genetics, conservation genetics and cultivar improvement.

Analysis of petroleum contaminated soils by spectral modeling and pure response profile recovery of n-hexane.

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r(2) = 0.87, RMSE = 0.580 log10 mg kg(-1), and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r(2) = 0.65 and RMSE 0.261 log10 mg kg(-1)) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane.

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning.

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r(2)=0.91 and RMSE=13.38 µg g(-1) h(-1)) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.