Developing DNA barcoding (matK) primers for marama bean [Tylosema esculentum (Burchell) Schreiber]

DNA barcoding is based on the premise that a short standardized DNA barcoding sequence can distinguish individuals of a species because the genetic variation between species exceeds that within species. Information on genetic variation of breeding materials helps to maintain genetic diversity and sustains long term selection gain. This information is a prerequisite for the genetic improvement of any plant species for effective use of germplasm in breeding and for conservation. Marama bean [Tylosema esculentum (Burchell) Schreiber] is found in the arid, dry parts of Southern Africa and due to the high nutrient value of the seeds and tubers, richness in protein, oil and starch, it is a potential crop for arid areas where few conventional crops can survive. The effective conservation and use of marama bean genetic resources for domestication involves investigating the extent of genetic variation. The matK gene, formerly known as orfK, is emerging as a DNA barcoding gene with potential contribution to plant molecular systematics and evolution. The gene matK, approximately 1500 base pairs (bp), is believed to code for a maturase-related protein based on structural similarities to other such genes. This gene was investigated for potential contribution in genetic variation studies of marama bean and also establishing a barcode for T. esculentum. The matK gene was amplified in marama bean and we reported herein, the first record of sequences of this gene for the species that were found to be related to other legume matK sequences deposited in GenBank. The homology found with Tylosema fassoglensis (trnK gene) and Pisum sativum (matK gene) suggests that an identical region was amplified for Tylosema esculentum. A phylogenetic tree was constructed based on the matK sequences and the results suggest that the matK region can also be used in determining levels of genetic variation and for barcoding.Key words: Marama bean, DNA barcoding, genetic variation, maturase kinase

Anisotropic matern correlation and spatial prediction using REML

The Mat´ern correlation function provides great flexibility for modeling spatially correlated random processes in two dimensions, in particular via a smoothness parameter, whose estimation allows data to determine the degree of smoothness of a spatial process. The extension to include anisotropy provides a very general and flexible class of spatial covariance functions that can be used in a model-based approach to geostatistics, in which parameter estimation is achieved via REML and prediction is within the E-BLUP framework. In this article we develop a general class of linear mixed models using an anisotropic Mat´ern class with an extended metric. The approach is illustrated by application to soil salinity data in a rice-growing field in Australia, and to fine-scale soil pH data. It is found that anisotropy is an important aspect of both datasets, emphasizing the value of a straightforward and accessible approach to modeling anisotropy

A review of geographical distribution of marama bean [Tylosema esculentum (Burchell) Schreiber] and genetic diversity in the Namibian germplasm

Marama bean [Tylosema esculentum (Burchell) Schreiber] occurs naturally in the drier areas of Southern Africa, including Botswana and Namibia. The implementation of molecular breeding is important to advance the process of securing the world’s food supply. The development of molecular markers is vital for mapping important traits that can then be followed in subsequent breeding programs. This study assessed the distribution of marama bean in Namibia and isolated microsatellite regions for microsatellite primer design for the purpose of determining genetic diversity and construction of molecular genetic maps for marama. This will greatly enhance the process ofdomesticating marama bean, currently a wild plant that is still picked wild and unsustainably. The geographical distribution was geo-referenced using Geographic Positioning System (GPS) points andmicrosatellites were isolated from the germplasm using a modified FIASCO technique. The study revealed widespread, but patchy distribution of marama bean in Namibia. Five Marama beanmicrosatellite enriched libraries were created. The initial results provided vital information for the ongoing marama bean conservation function and improvement program

Classification of calorimetric titration plots for alkyltrimethylammonium and alkylpyridinium cationic surfactants in aqueous solutions

Calorimetric titration plots for deaggregation of micelles formed by alkylpyridinium and alkyltrimethylammonium surfactants are classified into three types, A, B and C, depending on the shape of the plot of the enthalpy of dilution as a function of surfactant concentration. For Type A plots the recorded heat of injection q changes sharply between two parts of the titration curve over which the recorded heats are effectively independent of the composition of the solution in the sample cell. For Type B plots, the change is less sharp and both parts of the plot show dependences of heat q on solution composition, a pattern accounted for in terms of solute-solute interactions. Type C plots are complicated, in that no sharp change in q is recorded, the complexity of the plots being accounted for in terms of micelle-monomer equilibria over a range of surfactant concentrations and related enthalpies of deaggregation

Optimizing Umkehr Ozone Profile Retrievals

NOAA Dobson Umkehr ozone profile records have been collected since the 1970s. Umkehr ozone profiles are used to monitor stratospheric ozone recovery predicted to occur by the 2050s. Current operational Dobson Umkehr profile algorithms produce data that have uncertainty on the order of ~ 5 % in the stratosphere. However, when large volcanic eruptions inject aerosols into the stratosphere, the errors can be as large as 70 %. In order to evaluate Umkehr records for aerosol-related and instrumental artifacts, we compare observations with a Hindcast simulation of the NASA Merra-2 Global Modeling Initiative (GMI) Replay (M2GMI, Orbe et al, 2017; Wargan et al, 2018) and Chemistry Transport Model (GMI CTM, Strahan et al, 2013, Strahan et al, 2016). The biases found between the models and observations are summarized for each Dobson calibration and volcanic eruption period, thus providing a reference tool for homogenization of the Umkehr time series and removal of volcanic aerosol errors

Coexistence and Survival in Conservative Lotka-Volterra Networks

Analyzing coexistence and survival scenarios of Lotka-Volterra (LV) networks in which the total biomass is conserved is of vital importance for the characterization of long-term dynamics of ecological communities. Here, we introduce a classification scheme for coexistence scenarios in these conservative LV models and quantify the extinction process by employing the Pfaffian of the network's interaction matrix. We illustrate our findings on global stability properties for general systems of four and five species and find a generalized scaling law for the extinction time

Thermal effects on electron-phonon interaction in silicon nanostructures

Raman spectra from silicon nanostructures, recorded using excitation laser power density of 1.0 kW/cm^2, is employed here to reveal the dominance of thermal effects at temperatures higher than the room temperature. Room temperature Raman spectrum shows only phonon confinement and Fano effects. Raman spectra recorded at higher temperatures show increase in FWHM and decrease in asymmetry ratio with respect to its room temperature counterpart. Experimental Raman scattering data are analyzed successfully using theoretical Raman line-shape generated by incorporating the temperature dependence of phonon dispersion relation. Experimental and theoretical temperature dependent Raman spectra are in good agreement. Although quantum confinement and Fano effects persists, heating effects start dominating at higher temperatures than room tempaerature.Comment: 9 Pages, 3 Figures and 1 Tabl

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments