Analytical Engineering for Data Stream

The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.

Analytical methods for nano-bio interface interactions

Knowledge on the interactions between engineered nanomaterials(ENMs) and biological systems is critical both for the assessment of biological effects of ENMs and for the rational design of ENM-based products. However, probing the events that occur at the nano-bio interface remains extremely challenging due to their complex and dynamic nature. So far, the understanding of mechanisms underlying nano-bio interactions has been mainly limited by the lack of proper analytical techniques with sufficient sensitivity, selectivity and resolution for characterization of nano-bio interface events. Moreover, many classic bioanalytical methods are not suitable for direct measurement of nano-bio interface interactions. These have made establishing analytical methodologies for systematic and comprehensive study of nano-bio interface one of the most focused areas in nanobiology. In this review we have discussed some representative developments regarding analytical techniques for nano-bio interface characterization, including the improvements of traditional methods and the emergence of powerful new technologies. These developments have allowed ultrasensitive, real-time analysis of interactions between ENMs and biomolecules, transformations of ENMs in biological environment, and impacts of ENMs on living systems on molecular or cellular level.