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.

Transfer zone of prestressed CFRP reinforcement applied according to NSM technique for strengthening of RC structures

This study presents an experimental program to assess the tensile strain distribution along prestressed carbon fiber reinforced polymer (CFRP) reinforcement flexurally applied on the tensile surface of RC beams according to near surface mounted (NSM) technique. Moreover, the current study aims to propose an analytical formulation, with a design framework, for the prediction of distribution of CFRP tensile strain and bond shear stress and, additionally, the prestress transfer length. After demonstration the good predictive performance of the proposed analytical approach, parametric studies were carried out to analytically evaluate the influence of the main material properties, and CFRP and groove cross section on the distribution of the CFRP tensile strain and bond shear stress, and on the prestress transfer length. The proposed analytical approach can also predict the evolution of the prestress transfer length during the curing time of the adhesive by considering the variation of its elasticity modulus during this period.

Insider threats: the major challenge to security risk management

Security risk management is by definition, a subjective and complex exercise and it takes time to perform properly. Human resources are fundamental assets for any organization, and as any other asset, they have inherent vulnerabilities that need to be handled, i.e. managed and assessed. However, the nature that characterize the human behavior and the organizational environment where they develop their work turn these task extremely difficult, hard to accomplish and prone to errors. Assuming security as a cost, organizations are usually focused on the efficiency of the security mechanisms implemented that enable them to protect against external attacks, disregarding the insider risks, which are much more difficult to assess. All these demands an interdisciplinary approach in order to combine technical solutions with psychology approaches in order to understand the organizational staff and detect any changes in their behaviors and characteristics. This paper intends to discuss some methodological challenges to evaluate the insider threats and its impacts, and integrate them in a security risk framework, that was defined according to the security standard ISO/IEC_JTC1, to support the security risk management process.

Bioethanol production by Saccharomyces cerevisiae, Pichia stipitis and Zymomonas mobilis from delignified coconut fibre mature and lignin extraction according to biorefinery concept

In search to increase the offer of liquid, clean, renewable and sustainable energy in the world energy matrix, the use of lignocellulosic materials (LCMs) for bioethanol production arises as a valuable alternative. The objective of this work was to analyze and compare the performance of Saccharomyces cerevisiae, Pichia stipitis and Zymomonas mobilis in the production of bioethanol from coconut fibre mature (CFM) using different strategies: simultaneous saccharification and fermentation (SSF) and semi-simultaneous saccharification and fermentation (SSSF). The CFM was pretreated by hydrothermal pretreatment catalyzed with sodium hydroxide (HPCSH). The pretreated CFM was characterized by X-ray diffractometry and SEM, and the lignin recovered in the liquid phase by FTIR and TGA. After the HPCSH pretreatment (2.5% (v/v) sodium hydroxide at 180 °C for 30 min), the cellulose content was 56.44%, while the hemicellulose and lignin were reduced 69.04% and 89.13%, respectively. Following pretreatment, the obtained cellulosic fraction was submitted to SSF and SSSF. Pichia stipitis allowed for the highest ethanol yield 90.18% in SSSF, 91.17% and 91.03% were obtained with Saccharomyces cerevisiae and Zymomonas mobilis, respectively. It may be concluded that the selection of the most efficient microorganism for the obtention of high bioethanol production yields from cellulose pretreated by HPCSH depends on the operational strategy used and this pretreatment is an interesting alternative for add value of coconut fibre mature compounds (lignin, phenolics) being in accordance with the biorefinery concept.

Mother’s depression at childbirth does not contribute to the effects of antenatal depression on neonate’s behavioral development

Background: Maternal depression is a worldwide phenomenon that has been linked to adverse developmental outcomes in neonates. Aims: To study the effect of antenatal depression (during the third trimester of pregnancy) on neonate behavior, preference, and habituation to both the mother and a stranger’s face/voice. To analyze mother’s depression at childbirth as a potential mediator or moderator of the relationship between antenatal depression and neonate behavioral development. Method: A sample of 110 pregnant women was divided in 2 groups according to their scores on the Edinburgh Postnatal Depression Scale during pregnancy (EPDS; ≥10, depressed; <10, non-depressed). In the first 5 days after birth, neonatal performance on the Neonatal Behavioral Assessment Scale (NBAS) and in the ‘Preference and habituation to the mother’s face/voice versus stranger’ paradigm was assessed; each mother filled out an EPDS. Results: Neonates of depressed pregnant women, achieved lower scores on the NBASs (regulation of state, range of state, and habituation); did not show a visual/auditory preference for the mother’s face/voice; required more trials to become habituated to the mother’s face/voice; and showed a higher visual/auditory preference for the stranger’s face/voice after habituation compared to neonates of non-depressed pregnant women. Depression at childbirth does not contribute to the effect of antenatal depression on neonatal behavioral development. Conclusion: Depression even before childbirth compromises the neonatal behavioral development. Depression is a relevant issue and should be addressed as a routine part of prenatal health care.