Deep Learning with Microfluidics for Biotechnology.

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural networks (ANNs) leveraging modern graphics processing units (GPUs) have enabled the rapid analysis of structured input data - sequences, images, videos - to predict complex outputs with unprecedented accuracy. While there have been early successes in flow cytometry, for example, the extensive potential of pairing microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges remains largely untapped. Here we provide a roadmap to integrating deep learning and microfluidics in biotechnology laboratories that matches computational architectures to problem types, and provide an outlook on emerging opportunities.

Measuring and Mitigating the Costs of Attentional Switches in Active Network Monitoring for Cybersecurity.

OBJECTIVE: The authors seek to characterize the behavioral costs of attentional switches between points in a network map and assess the efficacy of interventions intended to reduce those costs. BACKGROUND: Cybersecurity network operators are tasked with determining an appropriate attentional allocation scheme given the state of the network, which requires repeated attentional switches. These attentional switches may result in temporal performance decrements, during which operators disengage from one attentional fixation point and engage with another. METHOD: We ran two experiments where participants identified a chain of malicious emails within a network. All interactions with the system were logged and analyzed to determine if users experienced disengagement and engagement delays. RESULTS: Both experiments revealed significant costs from attentional switches before (i.e., disengagement) and after (i.e., engagement) participants navigated to a new area in the network. In our second experiment, we found that interventions aimed at contextualizing navigation actions lessened both disengagement and engagement delays. CONCLUSION: Attentional switches are detrimental to operator performance. Their costs can be reduced by design features that contextualize navigations through an interface. APPLICATION: This research can be applied to the identification and mitigation of attentional switching costs in a variety of visual search tasks. Furthermore, it demonstrates the efficacy of noninvasive behavioral monitoring for inferring cognitive events.