We evaluated the use of amplified fragment length polymorphism (AFLP) markers to distinguish genotypes, populations and species of Lolium. Accessions of two species Lolium perenne and Lolium multiflorum and their hybrid Lolium x hybridum, collected by the Institute of Grass and Environmental Research in 1995 from locations across Portugal, were used. The genetic variation within and between populations from the extremes of latitude and altitude was determined and assessed. Three primer pair combinations generated 765 polymorphic bands. Principal coordinate analysis of similarities between 127 plants showed high dimensionality in the data. Axes 1-3 were associated primarily with species differences, axes 4-14 with population differences within species and axis 15 onwards with within population differences. UPGMA analysis confirmed the groupings. The three populations of L. perenne formed a discrete cluster widely separated from all other populations. There were two distinct groups of L. x hybridum, of which one was similar to and overlapped with L. multiflorum and the second formed a distinct cluster. Analyses of individual bands showed that every inter- and intraspecific contrast involved a different sets of bands, again confirming the high dimensionality of the data. No single band was strictly diagnostic of any population or species. Nevertheless, the UPGMA analysis showed little or no overlap between populations. Thus, despite the high ratio of within-to-between population genetic variance, the full AFLP banding pattern of each genotype is a relatively reliable fingerprint diagnostic of its parent population. The high dimensionality implies that many different factors contribute to the differences observed. This adds to the potential value of the methodology, since it implies that there is a reasonably high likelihood of finding bands relevant to a given environmental gradient or other factor influencing the distribution of genetic diversity.
The modeling of complex phenomena such as adsorption often requires the determination of parameters that cannot be directly measured and, therefore, must be estimated. An important point is related to the analysis of the inverse problem as a method of estimating parameters and selecting models. In view of this, this work aims to apply the Monte Carlo method via Markov Chains (MCMC) as a technique for solving the inverse problem of estimating fixed-bed adsorption parameters using analytical models proposed in the literature. In addition, this work aims to select the best model through the statistical metrics Akaike, corrected Akaike and Bayesian Information Criterion. The use of the Bayesian approach allowed the analysis of the convergence of the chains, as well as selected the best model to represent the experimental data obtained from the literature.
The use of the waste of the Bayer process, red mud, is due to its chemical and mineralogical composition that shows a material rich in oxides of iron, titanium and aluminum. Some studies conducted show that this waste can be applied as a source of alternative raw material for concentration and subsequent recovery of titanium compounds from an iron leaching process, which is present in higher amounts, about 30% by weight. To obtain a greater understanding about the leaching kinetics, the information of the kinetic data of this process is very important. In this context, the main objective of this work is the development of a mathematical model that is able to fit the experimental data (conversion / extraction iron, titanium and aluminum) of the leaching process by which is possible to obtain the main kinetic parameters such as the activation energy and the velocity of chemical reactions as well as the controlling step of the process. The development of the mathematical model was based on the model of core decreasing. The obtained model system of ordinary differential equations was able to fit the experimental data obtained from the leaching process, enabling the determination of the controlling step, the rate constants and the activation energies of the leaching process.
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