Algorithms and tools for in silico design of cell factories

PhD thesis in Bioengineering

Development of an integral viscoelastic flow solver in OpenFOAM®

The usual high cost of commercial codes, and some technical limitations, clearly limits the employment of numerical modelling tools in both industry and academia. Consequently, the number of companies that use numerical code is limited and there a lot of effort put on the development and maintenance of in-house academic based codes. Having in mind the potential of using numerical modelling tools as a design aid, of both products and processes, different research teams have been contributing to the development of open source codes/libraries. In this framework, any individual can take advantage of the available code capabilities and/or implement additional features based on his specific needs. These type of codes are usually developed by large communities, which provide improvements and new features in their specific fields of research, thus increasing significantly the code development process. Among others, OpenFOAM® multi-physics computational library, developed by a very large and dynamic community, nowadays comprises several features usually only available in their commercial counterparts; e.g. dynamic meshes, large diversity of complex physical models, parallelization, multiphase models, to name just a few. This computational library is developed in C++ and makes use of most of all language capabilities to facilitate the implementation of new functionalities. Concerning the field of computational rheology, OpenFOAM® solvers were recently developed to deal with the most relevant differential viscoelastic rheological models, and stabilization techniques are currently being verified. This work describes the implementation of a new solver in OpenFOAM® library, able to cope with integral viscoelastic models based on the deformation field method. The implemented solver is verified through the comparison of the predicted results with analytical solutions, results published in the literature and by using the Method of Manufactured Solutions.

Evaluation of the DPMFoam solver

[Extrat] Multiphase flows are relevant in several industrial processes, thus the availability of accurate numerical modeling tools, able to support the design of products and processes, is of much significance. OpenFOAM version 2.3.x comprises a multiphase flow solver able to couple Eulerian and Lagrangian phases using the discrete particles method (DPM), the DPMFoam. In this work the DPMFoam solver is assessed by comparing its predictions with analytical results and experimental and simulated data available in the literature. They are results from Goldschmidt’s [1] and Hoomans’s [2] theses and the analytical Ergun equation. The goal was to define accuracy and performance of DPMFoam in general scientific or commercial applications. Obtained results demonstrate a good agreement with the reference simulation data and is within reasonable deviations from the experimental values. (...)

A new integral viscoelastic flow solver in OpenFOAM®

The usual high cost of commercial codes, and some technical limitations, clearly limits the employment of numerical modelling tools in both industry and academia. Consequently, the number of companies that use numerical code is limited and there a lot of effort put on the development and maintenance of in-house academic based codes . Having in mind the potential of using numerical modelling tools as a design aid, of both products and processes, different research teams have been contributing to the development of open source codes/libraries. In this framework, any individual can take advantage of the available code capabilities and/or implement additional features based on his specific needs. These type of codes are usually developed by large communities, which provide improvements and new features in their specific fields of research, thus increasing significantly the code development process. Among others, OpenFOAM® multi-physics computational library, developed by a very large and dynamic community, nowadays comprises several features usually only available in their commercial counterparts; e.g. dynamic meshes, large diversity of complex physical models, parallelization, multiphase models, to name just a few. This computational library is developed in C++ and makes use of most of all language capabilities to facilitate the implementation of new functionalities. Concerning the field of computational rheology, OpenFOAM® solvers were recently developed to deal with the most relevant differential viscoelastic rheological models, and stabilization techniques are currently being verified. This work describes the implementation of a new solver in OpenFOAM® library, able to cope with integral viscoelastic models based on the deformation field method. The implemented solver is verified through the comparison of the predicted results with analytical solutions, results published in the literature and by using the Method of Manufactured Solutions

Improvign mixing processes with the help of numerical tools

In several industrial applications, highly complex behaviour materials are used together with intricate mixing processes, which difficult the achievement of the desired properties for the produced materials. This is the case of the well-known dispersion of nano-sized fillers in a melt polymer matrix, used to improve the nanocomposite mechanical and/or electrical properties. This mixing is usually performed in twin-screw extruders, that promote complex flow patterns, and, since an in loco analysis of the material evolution and mixing is difficult to perform, numerical tools can be very useful to predict the evolution and behaviour of the material. This work presents a numerical based study to improve the understanding of mixing processes. Initial numerical studies were performed with generalized Newtonian fluids, but, due to the null relaxation time that characterize this type of fluids, the assumption of viscoelastic behavior was required. Therefore, the polymer melt was rheologically characterized, and, a six mode Phan-Thien-Tanner and Giesekus models were used to fit the rheological data. These viscoelastic rheological models were used to model the process. The conclusions obtained in this work provide additional and useful data to correlate the type and intensity of the deformation history promoted to the polymer nanocomposite and the quality of the mixing obtained.

Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

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.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Controlo de qualidade e inspeção visual por imagem: definição de uma aplicação para registo de alterações e avaliação de desempenho

Dissertação de mestrado em Engenharia e Gestão da Qualidade

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

Arquitetura distribuída para análises multimodais de conectividade cerebral

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

Analytical evaluation of fining treatments for white wines contaminated by Ochratoxin A

Mycotoxins are toxic secondary metabolites produced by certain molds. Ochratoxin A (OTA) is one of the most relevant. Its chemical structure is a dihydro-isocoumarin connected at the 7-carboxy group to a molecule of L--phenylalanine via an amide bond. OTA in wine is a risk to consumer health [1]. According to the Regulation No. 123/2005 of the European Commission, the maximum limit for OTA in wine is 2 µg/kg [2]. Then, it is important to control its occurrence. So, the aim of this work was to know the effect of different fining agents on OTA removal from white wine.