NMR in the characterization of heavy residual procedural streams

The main objective of this work was to monitor a set of physical-chemical properties of heavy oil procedural streams through nuclear magnetic resonance spectroscopy, in order to propose an analysis procedure and online data processing for process control. Different statistical methods which allow to relate the results obtained by nuclear magnetic resonance spectroscopy with the results obtained by the conventional standard methods during the characterization of the different streams, have been implemented in order to develop models for predicting these same properties. The real-time knowledge of these physical-chemical properties of petroleum fractions is very important for enhancing refinery operations, ensuring technically, economically and environmentally proper refinery operations. The first part of this work involved the determination of many physical-chemical properties, at Matosinhos refinery, by following some standard methods important to evaluate and characterize light vacuum gas oil, heavy vacuum gas oil and fuel oil fractions. Kinematic viscosity, density, sulfur content, flash point, carbon residue, P-value and atmospheric and vacuum distillations were the properties analysed. Besides the analysis by using the standard methods, the same samples were analysed by nuclear magnetic resonance spectroscopy. The second part of this work was related to the application of multivariate statistical methods, which correlate the physical-chemical properties with the quantitative information acquired by nuclear magnetic resonance spectroscopy. Several methods were applied, including principal component analysis, principal component regression, partial least squares and artificial neural networks. Principal component analysis was used to reduce the number of predictive variables and to transform them into new variables, the principal components. These principal components were used as inputs of the principal component regression and artificial neural networks models. For the partial least squares model, the original data was used as input. Taking into account the performance of the develop models, by analysing selected statistical performance indexes, it was possible to conclude that principal component regression lead to worse performances. When applying the partial least squares and artificial neural networks models better results were achieved. However, it was with the artificial neural networks model that better predictions were obtained for almost of the properties analysed. With reference to the results obtained, it was possible to conclude that nuclear magnetic resonance spectroscopy combined with multivariate statistical methods can be used to predict physical-chemical properties of petroleum fractions. It has been shown that this technique can be considered a potential alternative to the conventional standard methods having obtained very promising results.

Molecular markers for diabetic nephropathy

Type 2 diabetes is one of the most common metabolic disorders in the world. Globally, the prevalence of this disorder is predicted to increase, along with the risk of developing diabetic related complications. One of those complications is diabetic nephropathy, defined by a progressive increase in proteinuria and a gradual decline in renal function. Approximately 25% to 30% of type 2 diabetic individuals develop this complication. However, its underlying genetic mechanisms remain unclear. Thus, the aim of this study is to contribute to the discovery of the genetic mechanisms involved in the development and progression of diabetic nephropathy, through the identification of relevant genetic variants in Portuguese type 2 diabetic individuals. The exomes of 36 Portuguese type 2 diabetic individuals were sequenced on the Ion ProtonTM Sequencer. From those individuals, 19 did not present diabetic nephropathy, being included in the control group, while the 17 individuals that presented the diabetic complication formed the case group. A statistical analysis was then performed to identify candidate common genetic variants, as well as genes accumulating rare variants that could be associated with diabetic nephropathy. From the search for common variants in the study population, the statistically significant (p-value ≤ 0.05) variants rs1051303 and rs1131620 in the LTBP4 gene, rs660339 in UCP2, rs2589156 in RPTOR, rs2304483 in the SLC12A3 gene and rs10169718 present in ARPC2, were considered as the most biologically relevant to the pathogenesis of diabetic nephropathy. The variants rs1051303 and rs1131620, as well as the variants rs660339 and rs2589156 were associated with protective effects in the development of the complication, while rs2304483 and rs10169718 were considered risk variants, being present in individuals with diagnosed diabetic nephropathy. In the rare variants approach, the genes with statistical significance (p-value ≤ 0.05) found, the STAB1 gene, accumulating 9 rare variants, and the CUX1 gene, accumulating 2 rare variants, were identified as the most relevant. Both genes were considered protective, with the accumulated rare variants mainly present in the group without the renal complication. The present study provides an initial analysis of the genetic evidence associated with the development and progression of diabetic nephropathy, and the results obtained may contribute to a deeper understanding of the genetic mechanisms associated with this diabetic complication.