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.

Microstructure and mechanical properties of carbon nanotube reinforced cementitious composites developed using a novel dispersion technique

The authors also acknowledge Centre for Textile Science and Technology (University of Minho) and FIBRENAMICS PLATFORMfor providing required conditions for this research. Sincere thanks are also due to Mr. Pedro Samuel Leite and Mr. Carlos Jesus for their kind help in sample preparation and testing.

Enzymatic treatments to improve mechanical properties and surface hydrophobicity of jute fiber membranes

Fiber membranes prepared from jute fragments can be valuable, low cost, and renewable. They have broad application prospects in packing bags, geotextiles, filters, and composite reinforcements. Traditionally, chemical adhesives have been used to improve the properties of jute fiber membranes. A series of new laccase, laccase/mediator systems, and multi-enzyme synergisms were attempted. After the laccase treatment of jute fragments, the mechanical properties and surface hydrophobicity of the produced fiber membranes increased because of the cross-coupling of lignins with ether bonds mediated by laccase. The optimum conditions were a buffer pH of 4.5 and an incubation temperature of 60 °C with 0.92 U/mL laccase for 3 h. Laccase/guaiacol and laccase/alkali lignin treatments resulted in remarkable increases in the mechanical properties; in contrast, the laccase/2,2-azino-bis-(3-ethylthiazoline-6-sulfonate) (ABTS) and laccase/2,6-dimethoxyphenol treatments led to a decrease. The laccase/ guaiacol system was favorable to the surface hydrophobicity of jute fiber membranes. However, the laccase/alkali lignin system had the opposite effect. Xylanase/laccase and cellulase/laccase combined treatments were able to enhance both the mechanical properties and the surface hydrophobicity of jute fiber membranes. Among these, cellulase/laccase treatment performed better; compared to mechanical properties, the surface hydrophobicity of the jute fiber membranes showed only a slight increase after the enzymatic multi-step processes.

HOXA9 as a key regulator in glioma initiation, aggressiveness and response to therapy

Tese de Doutoramento em Ciências da Saúde

Pre-clinical trials for Machado-Joseph Disease: Hypothesis-based and hypothesis-free therapeutic approaches

Tese de Doutoramento em Ciências da Saúde

Stress, cognitive appraisal, and psychological health: testing instruments for health professionals

The job of health professionals, including nurses, is considered inherently stressful (Lee & Wang, 2002; Rutledge et al., 2009), and thus it is important to improve and develop specific measures that are sensitive to the demands that health professionals face. This study analysed the psychometric properties of three instruments that focus on the professional experiences of nurses in aspects related to occupational stress, cognitive appraisal, and mental health issues. The evaluation protocol included the Stress Questionnaire for Health Professionals (SQHP; Gomes, 2014), the Cognitive Appraisal Scale (CAS; Gomes, Faria, & Gonçalves, 2013), and the General Health Questionnaire-12 (GHQ-12; Goldberg, 1972). Validity and reliability issues were considered with statistical analysis (i.e. confirmatory factor analysis, convergent validity, and composite reliability) that revealed adequate values for all of the instruments, namely, a six-factor structure for the SQHP, a five-factor structure for the CAS, and a two-factor structure for the GHQ-12. In conclusion, this study proposes three consistent instruments that may be useful for analysing nurses’ adaptation to work contexts.