Liderança no futebol júnior: relação com medidas subjetivas e objetivas dos atletas

Dissertação de mestrado integrado em Psicologia

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.

Coolhunting: metodologia de pesquisa de tendências de moda in loco

ISSN:2237-2954

Influence of climate on clinical diagnostic dry eye tests: pilot study

Purpose. To analyze dry eye disease (DED) tests and their consistency in similar nonsymptomatic population samples living in two geographic locations with different climates (Continental vs. Atlantic). Methods. This is a pilot study including 14 nonsymptomatic residents from Valladolid (Continental climate, Spain) and 14 sex-matched and similarly aged residents from Braga (Atlantic climate, Portugal); they were assessed during the same season (spring) of two consecutive years. Phenol red thread test, conjunctival hyperemia, fluorescein tear breakup time, corneal and conjunctival staining, and Schirmer test were evaluated on three different consecutive visits. Reliability was assessed using the intraclass correlation coefficient and weighted kappa (J) coefficient for quantitative and ordinal variables, respectively. Results. Fourteen subjects were recruited in each city with a mean (TSD) age of 63.0 (T1.7) and 59.1 (T0.9) years (p = 0.08) in Valladolid and Braga, respectively. Intraclass correlation coefficient and J values of the tests performed were below 0.69 and 0.61, respectively, for both samples, thus showing moderate to poor reliability. Subsequently, comparisons were made between the results corresponding to the middle and higher outdoor relative humidity (RH) visit in each location as there were no differences in mean temperature (p Q 0.75) despite RH values significantly differing (p e 0.005). Significant (p e 0.05) differences were observed between Valladolid and Braga samples on tear breakup time (middle RH visit, 2.76 T 0.60 vs. 5.26 T 0.64 seconds; higher RH visit, 2.61 T 0.32 vs. 5.78 T 0.88 seconds) and corneal (middle RH, 0.64 T 0.17 vs. 0.14 T 0.10; higher RH, 0.60 T 0.22 vs. 0.0 T 0.0) and conjunctival staining (middle RH, 0.61 T 0.17 vs. 0.14 T 0.08; higher RH, 0.57 T 0.15 vs. 0.18 T 0.09). Conclusions. This pilot study provides initial evidence to support that DED test outcomes assessing the ocular surface integrity and tear stability are climate dependent. Future large-sample studies should support these outcomes also in DED patients. This knowledge is fundamental for multicenter clinical trials. Lack of consistency in diagnostic clinical tests for DED was also corroborated. (Optom Vis Sci 2015;92:e284Ye289)

Indoor biological agents: evaluation of primary schools environments

Dissertação de mestrado em Human Engineering

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.

Molecular regulation of flowering induction in Quercus suber

Dissertação de mestrado em Plant Molecular Biology, Biotechnology and Bioentrepeneurship

Microscopy- or DNA-based analyses: Which methodology gives a truer picture of stream-dwelling decomposer fungal diversity?

We assessed aquatic hyphomycete diversity in autumn and spring on oak leaves decomposing in five streams along a gradient of eutrophication in the Northwest of Portugal. Diversity was assessed through microscopy-based (identification by spore morphology) and DNA-based techniques (Denaturing Gradient Gel Electrophoresis and 454 pyrosequencing). Pyrosequencing revealed five times greater diversity than DGGE. About 21% of all aquatic hyphomycete species were exclusively detected by pyrosequencing and 26% exclusively by spore identification. In some streams, more than half of the recorded species would have remained undetected if we had relied only on spore identification. Nevertheless, in spring aquatic hyphomycete diversity was higher based on spore identification, probably because many species occurring in this season are not yet connected to ITS barcodes in genetic databases. Pyrosequencing was a powerful tool for revealing aquatic hyphomycete diversity on decomposing plant litter in streams and we strongly encourage researchers to continue the effort in barcoding fungal species.

Spawning habitat selection by Octopus vulgaris: new insights for a more effective management of this resource

The selection of spawning habitat of a population of Octopus vulgaris that is subject to a small-scale exploitation was studied in the Cíes Islands within the National Park of the Atlantic Islands of Galicia (NW Spain). The technique used was visual censuses by scuba diving. We conducted 93 visual censuses from April 2012 to April 2014. The total swept area was 123.69 ha. Habitat features (season, depth, zone, bottom temperature, swept area, bottom substrate type, and creels fishing impact) were evaluated as predictors of the presence/absence of spawning dens using GAM models. O. vulgaris has a noteworthy preference for spawning in areas with hard bottom substrate and moderate depth (approximately 20 m). The higher density of spawning dens (1.08ha−1) was found in a surveyed area of 50.14ha located in the northeastern part of the northern Cíes Island. We propose to protect the area comprised from Punta Escodelo to Punta Ferreiro between 5 and 30 m depth. This area has a surface of 158 ha equivalent to 5.98% of the total marine area of the Cíes islands. The strengths and weaknesses of a management strategy based on the protection of the species’ spawning habitat are discussed.

Linguagem médica e linguagem televisiva: uma guerra aberta ou uma convivência pacífica?

Dissertação de mestrado em Ciências da Comunicação (área de especialização em Informação e Jornalismo)