Assessment of the properties to characterize the interface between clay brick substrate and strengthening mortar

The behaviour of masonry elements under in-plane and out-of-plane loads can be improved through the application of strengthening systems based on reinforcing overlays. After strengthening, the transition region between the original substrate and the strengthening layer is especially stressed, and premature failure of the strengthened masonry is reached if insufficient interfacial capacity is assured. Therefore, the assessment of the mechanical behaviour of the interface is critical to the development of the masonry strengthening system based on the application of strengthening overlays. In this research a method for the characterization of the interface behaviour between two different materials, a polypropylene fibre reinforced mortar (PFRM) and a ceramic brick used for masonry construction is presented. Direct shear tests were carried out in couplet specimens. Due to the orthotropic nature of the bricks surface, the shear load was applied along three different directions in order to perform an overall estimation of the interface behaviour. The peak and residual shear stresses, as well as the failure modes, were obtained at different levels of the normal stress. Based on these experimental results constitutive laws were assessed for the simulation of the interface mechanical behaviour based on the Mohr and Mohr-Coulomb failure criteria.

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.

Extracting kinetic information from literature with KineticRE

To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.

A review of mycotoxins in food and feed products in Portugal and estimation of probable daily intakes

Mycotoxins are toxic secondary metabolites produced by filamentous fungi that occur naturally in agricultural commodities worldwide. Aflatoxins, ochratoxin A, patulin, fumonisins, zearalenone, trichothecenes and ergot alkaloids are presently the most important for food and feed safety. These compounds are produced by several species that belong to the Aspergillus, Penicillium, Fusarium and Claviceps genera and can be carcinogenic, mutagenic, teratogenic, cytotoxic, neurotoxic, nephrotoxic, estrogenic and immunosuppressant. Human and animal exposure to mycotoxins is generally assessed by taking into account data on the occurrence of mycotoxins in food and feed as well as data on the consumption patterns of the concerned population. This evaluation is crucial to support measures to reduce consumer exposure to mycotoxins. This work reviews the occurrence and levels of mycotoxins in Portuguese food and feed to provide a global overview of this issue in Portugal. With the information collected, the exposure of the Portuguese population to those mycotoxins is assessed, and the estimated dietary intakes are presented.

Dynamic interface model for masonry walls subjected to high strain rate out-of-plane loads

The present study proposes a dynamic constitutive material interface model that includes non-associated flow rule and high strain rate effects, implemented in the finite element code ABAQUS as a user subroutine. First, the model capability is validated with numerical simulations of unreinforced block work masonry walls subjected to low velocity impact. The results obtained are compared with field test data and good agreement is found. Subsequently, a comprehensive parametric analysis is accomplished with different joint tensile strengths and cohesion, and wall thickness to evaluate the effect of the parameter variations on the impact response of masonry walls.

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.

Influence of casting condition on the anisotropy of the fracture properties of Steel Fibre Reinforced Self-Compacting Concrete (SFRSCC)

Identification of the tensile constitutive behaviour of Fibre Reinforced Concrete (FRC) represents an important aspect of the design of structural elements using this material. Although an important step has been made with the introduction of guidance for the design with regular FRC in the recently published fib Model Code 2010, a better understanding of the behaviour of this material is still necessary, mainly for that with self-compacting properties. This work presents an experimental investigation employing Steel Fibre Self-Compacting Concrete (SFRSCC) to cast thin structural elements. A new test method is proposed for assessing the post-cracking behaviour and the results obtained with the proposed test method are compared with the ones resulted from the standard three-point bending tests (3PBT). Specimens extracted from a sandwich panel consisting of SFRSCC layers are also tested. The mechanical properties of SFRSCC are correlated to the fibre distribution by analysing the results obtained with the different tests. Finally, the stress-crack width constitutive law proposed by the fib Model Code 2010 is analysed in light of the experimental results.

Atividade fotossintética do bago de uva (Vitis vinifera, cv. Alvarinho) ao longo do desenvolvimento dos cachos em diferentes microambientes de luz

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

Biotechnological approaches for yield improvement of anticancer alkaloids in the plant Catharanthus roseus

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

Retroanálise de parâmetros geomecânicos baseada em algoritmos de otimização

Dissertação de mestrado integrado em Engenharia Civil

Evaluation of the performance of steel fibre reinforced self-compacting concrete in elevated slab systems; from the material to the structure

Degree of Doctor of Philosophy of Structural/Civil Engineering

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

MET is required for the recruitment of anti-tumoural neutrophils

Mutations or amplification of the MET proto-oncogene are involved in the pathogenesis of several tumours, which rely on the constitutive engagement of this pathway for their growth and survival. However, MET is expressed not only by cancer cells but also by tumour-associated stromal cells, although its precise role in this compartment is not well characterized. Here we show that MET is required for neutrophil chemoattraction and cytotoxicity in response to its ligand hepatocyte growth factor (HGF). Met deletion in mouse neutrophils enhances tumour growth and metastasis. This phenotype correlates with reduced neutrophil infiltration to both the primary tumour and metastatic sites. Similarly, Met is necessary for neutrophil transudation during colitis, skin rash or peritonitis. Mechanistically, Met is induced by tumour-derived tumour necrosis factor (TNF)-a or other inflammatory stimuli in both mouse and human neutrophils. This induction is instrumental for neutrophil transmigration across an activated endothelium and for inducible nitric oxide synthase production upon HGF stimulation. Consequently, HGF/MET-dependent nitric oxide release by neutrophils promotes cancer cell killing, which abates tumour growth and metastasis. After systemic administration of a MET kinase inhibitor, we prove that the therapeutic benefit of MET targeting in cancer cells is partly countered by the pro-tumoural effect arising from MET blockade in neutrophils. Our work identifies an unprecedented role of MET in neutrophils, suggests a potential 'Achilles' heel' of MET-targeted therapies in cancer, and supports the rationale for evaluating anti-MET drugs in certain inflammatory diseases.

Aflatoxins detoxification by gamma irradiation

[Excerpt] Mycotoxins are secondary toxic metabolites of filamentous fungi. Aflatoxins (AFs) are produced to Aspergillus species such as A. flavus and A. parasiticus. These fungi are ubiquitous in nature and usually found on agricultural commodities. Therefore, AFs are encountered in many important foodstuff, including wheat, rice, maize, peanuts, sorghum, pearl millet, spices, oilseeds, tree nuts and milk. Due to the high toxicity of AFs, many methods have been studied to reduce or eliminate these mycotoxins from food and feed. Gamma irradiation is one technology that has been investigated with promising results. The aims of this study were (I) to study the effect of gamma radiation on aflatoxin B1, aflatoxin B2, aflatoxin G1 and aflatoxin G2 (II) to evaluate the effect of the presence of water on AFs degradation during the irradiation process; and (IV) to evaluate the cytotoxicity of radiolytic products formed. (...)