Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Finite integration methods for isospectral flows

In this paper we consider the approximate computation of isospectral flows based on finite integration methods( FIM) with radial basis functions( RBF) interpolation,a new algorithm is developed. Our method ensures the symmetry of the solutions. Numerical experiments demonstrate that the solutions have higher accuracy by our algorithm than by the second order Runge- Kutta( RK2) method.

Avaliação quantitativa da geodiversidade: desenvolvimento de instrumentos metodológicos com aplicação ao ordenamento do território

Tese de Doutoramento em Ciências (Especialidade de Geologia)

Feature Nets: behavioural modelling of software product lines

Software product lines (SPL) are diverse systems that are developed using a dual engineering process: (a)family engineering defines the commonality and variability among all members of the SPL, and (b) application engineering derives specific products based on the common foundation combined with a variable selection of features. The number of derivable products in an SPL can thus be exponential in the number of features. This inherent complexity poses two main challenges when it comes to modelling: Firstly, the formalism used for modelling SPLs needs to be modular and scalable. Secondly, it should ensure that all products behave correctly by providing the ability to analyse and verify complex models efficiently. In this paper we propose to integrate an established modelling formalism (Petri nets) with the domain of software product line engineering. To this end we extend Petri nets to Feature Nets. While Petri nets provide a framework for formally modelling and verifying single software systems, Feature Nets offer the same sort of benefits for software product lines. We show how SPLs can be modelled in an incremental, modular fashion using Feature Nets, provide a Feature Nets variant that supports modelling dynamic SPLs, and propose an analysis method for SPL modelled as Feature Nets. By facilitating the construction of a single model that includes the various behaviours exhibited by the products in an SPL, we make a significant step towards efficient and practical quality assurance methods for software product lines.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

Methods to solve the equations of motion

"Series title: Springerbriefs in applied sciences and technology, ISSN 2191-530X"

Coronary artery disease, microalbuminuria and lipid profile in patients with non-insulin dependent diabetes mellitus

PURPOSE: To determine the frequency of coronary artery disease, microalbuminuria and the relation to lipid profile disorders, blood pressure and clinical and metabolic features. METHODS: Fifty-five type 2 diabetic patients (32 females, 23 males), aged 59.9±9 years and with known diabetes duration of 11±7.3 years were studied. Coronary artery disease (CAD) was defined as a positive history of myocardial infarction, typical angina, myocardial revascularization or a positive stress testing. Microalbuminuria was defined when two out of three overnight urine samples had a urinary albumin excretion ranging 20 - 200µg/min. RESULTS: CAD was present in 24 patients (43,6%). High blood pressure (HBP) present in 32 patients (58.2%) and was more frequent in CAD group (p=0.05) HBP. Increased the risk of CAD 3.7 times (CI[1.14-12]). Microalbuminuria was present in 25 patients (45.5%) and tended to associate with higher systolic blood pressure (SBP) (p = 0.06), presence of hypertension (p = 0.06) and know diabetes duration (p = 0.08). In the stepwise multiple logistic regression the systolic blood pressure was the only variable that influenced UAE (r = 0.39, r² = 0.14, p = 0.01). The h ypertensive patients had higher cholesterol levels (p = 0.04). CONCLUSION: In our sample the frequency of microalbuminuria, hypertension, hypercholesterolemia and CHD was high. Since diabetes is an independent risk factor for cardiovascular disease, the association of others risk factors suggest the need for an intensive therapeutic intervention in primary and in secundary prevention.

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational intelligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two illustrative Traffic Engineering methods are described, allowing to attain routing configurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Safety and efficacy of angioplasty with intracoronary stenting in patients with unstable coronary syndromes. Comparison with stable coronary syndromes

OBJECTIVE: To assess safety and efficacy of coronary angioplasty with stent implantation in unstable coronary syndromes. METHODS: Retrospective analysis of in-hospital and late evolution of 74 patients with unstable coronary syndromes (unstable angina or infarction without elevation of the ST segment) undergoing coronary angioplasty with stent placement. These 74 patients were compared with 31 patients with stable coronary syndromes (stable angina or stable silent ischemia) undergoing the same procedure. RESULTS: No death and no need for revascularization of the culprit artery occurred in the in-hospital phase. The incidences of acute non-Q-wave myocardial infarction were 1.4% and 3.2% (p=0.6) in the unstable and stable coronary syndrome groups, respectively. In the late follow-up (11.2±7.5 months), the incidences of these events combined were 5.7% in the unstable coronary syndrome group and 6.9% (p=0.8) in the stable coronary syndrome group. In the multivariate analysis, the only variable with a tendency to significance as an event predictor was diabetes mellitus (p=0.07; OR=5.2; 95% CI=0.9-29.9). CONCLUSION: The in-hospital and late evolutions of patients with unstable coronary syndrome undergoing angioplasty with intracoronary stent implantation are similar to those of the stable coronary syndrome group, suggesting that this procedure is safe and efficacious when performed in unstable coronary syndrome patients.

Predictive value of clinical and electrophysiological variables in patients with chronic chagasic cardiomyopathy and nonsustained ventricular tachycardia

OBJECTIVE: Risk stratification of patients with nonsustained ventricular tachycardia (NSVT) and chronic chagasic cardiomyopathy (CCC). METHODS: Seventy eight patients with CCC and NSVT were consecutively and prospectively studied. All patients underwent to 24-hour Holter monitoring, radioisotopic ventriculography, left ventricular angiography, and electrophysiologic study. With programmed ventricular stimulation. RESULTS: Sustained monomorphic ventricular tachycardia (SMVT) was induced in 25 patients (32%), NSVT in 20 (25.6%) and ventricular fibrillation in 4 (5.1%). In 29 patients (37.2%) no arrhythmia was inducible. During a 55.7-month-follow-up, 22 (28.2%) patients died, 16 due to sudden death, 2 due to nonsudden cardiac death and 4 due to noncardiac death. Logistic regression analysis showed that induction was the independent and main variable that predicted the occurrence of subsequent events and cardiac death (probability of 2.56 and 2.17, respectively). The Mantel-Haenszel chi-square test showed that survival probability was significantly lower in the inducible group than in the noninductible group. The percentage of patients free of events was significantly higher in the noninducible group. CONCLUSION: Induction of SMVT during programmed ventricular stimulation was a predictor of arrhythmia occurrence cardiac death and general mortality in patients with CCC and NSVT.

Ambulatory blood pressure monitoring and microalbuminuria in normotensive subjects with insulin-dependent diabetes mellitus

OBJECTIVE: To assess the association between microalbuminuria with ambulatory blood pressure monitoring in normotensive individuals with insulin-dependent diabetes mellitus. METHODS: Thirty-seven patients underwent determination of the rate of urinary excretion of albumin through radioimmunoassay and ambulatory blood pressure monitoring. Their mean age was 26.5±6.7 years, and the mean duration of their disease was 8 (1-34) years. Microalbuminuria was defined as urinary excretion of albumin > or = 20 and <200µg/min in at least 2 out of 3 urine samples. RESULTS: Nine (24.3%) patients were microalbuminuric. Ambulatory blood pressure monitoring in the microalbuminuric patients had higher mean pressure values, mainly the systolic pressure, during sleep as compared with that in the normoalbuminuric patients (120.1±8.3 vs 110.8±7.1 mmHg; p=0.007). The pressure load was higher in the microalbuminuric individuals, mainly the systolic pressure load during wakefulness [6.3 (2.9-45.9) vs 1.6 (0-16%); p=0.001]. This was the variable that better correlated with the urinary excretion of albumin (rS=0.61; p<0.001). Systolic pressure load >50% and diastolic pressure load > 30% during sleep was associated with microalbuminuria (p=0.008). The pressure drop during sleep did not differ between the groups. CONCLUSION: Microalbuminuric normotensive insulin-dependent diabetic patients show greater mean pressure value and pressure load during ambulatory blood pressure monitoring, and these variables correlate with urinary excretion of albumin.

Numerical methods in multibody system dynamics

"Series: Solid mechanics and its applications, vol. 226"