Risk assessment in a research laboratory during sol–gel synthesis of nano-TiO2

The occupational risks in the nanotechnology research laboratories are an important topic since a great number of researchers are involved in this area. The risk assessment performed by both qualitative and quantitative methods is a necessary step for the management of the occupational risks. Risk assessment could be performed by qualitative methods that gather consensus in the scientific community. It is also possible to use quantitative methods, based in different technics and metrics, as indicative exposure limits are been settled by several institutions. While performing the risk assessment, the information on the materials used is very important and, if it is not updated, it could create a bias in the assessment results. The exposure to TiO2 nanoparticles risk was assessed in a research laboratory using a quantitative exposure method and qualitative risk assessment methods. It was found the results from direct-reading Condensation Particle Counter (CPC) equipment and the CB Nanotool seem to be related and aligned, while the results obtained from the use of the Stoffenmanager Nano seem to indicate a higher risk level.

Rockburst laboratory tests database - Application of data mining techniques

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.

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.