Smartphone-enabled optofluidic exosome diagnostic for concussion recovery.

A major impediment to improving the treatment of concussion is our current inability to identify patients that will experience persistent problems after the injury. Recently, brain-derived exosomes, which cross the blood-brain barrier and circulate following injury, have shown great potential as a noninvasive biomarker of brain recovery. However, clinical use of exosomes has been constrained by their small size (30-100 nm) and the extensive sample preparation (>24 hr) needed for traditional exosome measurements. To address these challenges, we developed a smartphone-enabled optofluidic platform to measure brain-derived exosomes. Sample-to-answer on our chip is 1 hour, 10x faster than conventional techniques. The key innovation is an optofluidic device that can detect enzyme amplified exosome biomarkers, and is read out using a smartphone camera. Using this approach, we detected and profiled GluR2+ exosomes in the post-injury state using both in vitro and murine models of concussion.

Machine learning for the New York City power grid.

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce 1) feeder failure rankings, 2) cable, joint, terminator, and transformer rankings, 3) feeder Mean Time Between Failure (MTBF) estimates, and 4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or realtime, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The "rawness" of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid.