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.

Measurement of the inclusive jet cross-section in proton--proton collisions at s√=7 TeV using 4.5 fb−1 of data with the ATLAS detector

The inclusive jet cross-section is measured in proton--proton collisions at a centre-of-mass energy of 7 TeV using a data set corresponding to an integrated luminosity of 4.5 fb−1 collected with the ATLAS detector at the Large Hadron Collider in 2011. Jets are identified using the anti-kt algorithm with radius parameter values of 0.4 and 0.6. The double-differential cross-sections are presented as a function of the jet transverse momentum and the jet rapidity, covering jet transverse momenta from 100 GeV to 2 TeV. Next-to-leading-order QCD calculations corrected for non-perturbative effects and electroweak effects, as well as Monte Carlo simulations with next-to-leading-order matrix elements interfaced to parton showering, are compared to the measured cross-sections. A quantitative comparison of the measured cross-sections to the QCD calculations using several sets of parton distribution functions is performed.

Investigating a galactomannan gel obtained from Cassia grandis seeds as immobilizing matrix for Cramoll lectin

Characterization, with emphasis on the rheological properties, of Cassia grandis seeds galactomannan gel containing immobilized Cramoll 1,4 is presented. The gels, with and without immobilized Cramoll 1,4, were evaluated along time by rheometry, pH, color, microbial contamination and lectin hemagglutinating activity (HA). Rheological determinations confirmed the gels to be very stable up to 30 days with variations occurring after this period. Rheological data also showed that the gel/Cramoll 1,4 immobilizing matrix loses its elastic modulus substantially after 60 days. Both gels presented no microbial contamination as well as a pH close to neutral. Colorimetric parameters demonstrated the gels transparency with occasional yellowness. The opacity of the galactomannan gel did not change significantly along the study; the same did not occur for the gel with immobilized Cramoll 1,4 as a statistically significant reduction of its opacity was observed. In what concerns immobilized Cramoll 1,4HA, up to 90% of its initial HA was maintained after 20 days, with a decrease to 60% after 60 days. These results combined with the thickening and stabilizing characteristics of the galactomannan gel make this gel a promising immobilizing matrix for Cramoll 1,4 that can be further exploited for clinical and cosmetic applications.