Assessment of the performance of steel fibre reinforced self-compacting concrete in elevated slabs

The reinforcement mechanisms at the cross section level assured by fibres bridging the cracks in steel fibre reinforced self-compacting concrete (SFRSCC) can be significantly amplified at structural level when the SFRSCC is applied in structures with high support redundancy, such is the case of elevated slab systems. To evaluate the potentialities of SFRSCC as the fundamental material of elevated slab systems, a ¼ scale SFRSCC prototype of a residential building was designed, built and tested. The extensive experimental program includes material tests for characterizing the relevant properties of SFRSCC, as well as structural tests for assessing the performance of the prototype at serviceability and ultimate limit conditions. Three distinct approaches where adopted to derive the constitutive laws of the SFRSCC in tension that were used in finite element material nonlinear analysis to evaluate the reliability of these approaches in the prediction of the load carrying capacity of the prototype.

Effect of the degree of porosity on the performance of poly(vinylidene fluoride-trifluoroethylene)/poly(ethylene oxide) blend membranes for lithium-ion battery separators

Porous polymer membranes based on poly(vinylidene fluoride-trifluoroethylene)/poly(ethylene oxide) copolymers, P(VDF-TrFE)/PEO, are prepared through the, from partial to total, elimination of PEO, leading to interconnected micropores in the polymer blends. Electrolyte uptake, thermal and mechanical properties depend on the amount of PEO present in the polymer blend. Further, the degree of crystallinity of PEO and the elastic modulus (E´) of the polymer blend decrease with increasing PEO removal. Electrical properties of the polymer blend membranes are influenced by the porosity and are dominated by diffusion. The temperature dependence of ionic conductivity follows the Arrhenius behavior. It is the highest for the membranes with a volume fraction of pores of 44% (i.e, 90% PEO removal), reaching a value of 0.54 mS.cm-1 at room temperature. Battery performance was determined by assembling Li/C-LiFePO4 swagelok cells. The polymer blends with 90% PEO removal exhibit rate (124 mAhg-1 at C/5 and 47 mAhg-1 at 2C) and cycling capabilities suitable for lithium ion battery applications.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

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.

The choroid plexus transcriptome reveals changes in type I and II interferon responses in a mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a marked decline in cognition and memory function. Increasing evidence highlights the essential role of neuroinflammatory and immune-related molecules, including those produced at the brain barriers, on brain immune surveillance, cellular dysfunction and amyloid beta (Aß) pathology in AD. Therefore, understanding the response at the brain barriers may unravel novel pathways of relevance for the pathophysiology of AD. Herein, we focused on the study of the choroid plexus (CP), which constitutes the blood-cerebrospinal fluid barrier, in aging and in AD. Specifically, we used the PDGFB-APPSwInd (J20) transgenic mouse model of AD, which presents early memory decline and progressive Aß accumulation, and littermate age-matched wild-type (WT) mice, to characterize the CP transcriptome at 3, 5-6 and 11-12months of age. The most striking observation was that the CP of J20 mice displayed an overall overexpression of type I interferon (IFN) response genes at all ages. Moreover, J20 mice presented a high expression of type II IFN genes in the CP at 3months, which became lower than WT at 5-6 and 11-12months. Importantly, along with a marked memory impairment and increased glial activation, J20 mice also presented a similar overexpression of type I IFN genes in the dorsal hippocampus at 3months. Altogether, these findings provide new insights on a possible interplay between type I and II IFN responses in AD and point to IFNs as targets for modulation in cognitive decline.

Systems biology approaches for the design of novel Saccharomyces cerevisiae winemaking strains for enhanced flavour compounds synthesis

Tese de Doutoramento em Biologia Ambiental e Molecular