Reports of the AAAI 2019 spring symposium series

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates

Micro-computed tomography (μ-CT) as a potential tool to assess the effect of dynamic coating routes on the formation of biomimetic apatite layers on 3D-plotted biodegradable polymeric scaffolds

This work studies the influence of dynamic biomimetic coating procedures on the growth of bonelike apatite layers at the surface of starch/polycaprolactone (SPCL) scaffolds produced by a 3D-plotting technology. These systems are newly proposed for bone Tissue Engineering applications. After generating stable apatite layers through a sodium silicate-based biomimetic methodology the scaffolds were immersed in Simulated Body Fluid solutions (SBF) under static, agitation and circulating flow perfusion conditions, for different time periods. Besides the typical characterization techniques, Micro-Computed Tomography analysis (μ-CT) was used to assess scaffold porosity and as a new tool for mapping apatite content. 2D histomorphometric analysis was performed and 3D virtual models were created using specific softwares for CT reconstruction. By the proposed biomimetic routes apatite layers were produced covering the interior of the scaffolds, without compromising their overall morphology and interconnectivity. Dynamic conditions allowed for the production of thicker apatite layers as consequence of higher mineralizing rates, when comparing with static conditions. μ-CT analysis clearly demonstrated that flow perfusion was the most effective condition in order to obtain well-defined apatite layers in the inner parts of the scaffolds. Together with SEM, this technique was a useful complementary tool for assessing the apatite content in a non-destructive way