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Abstract Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H²MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H²MM (mpH²MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH²MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, fromYersiniaspecies, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH²MM facilitates the identification and quantification of biomolecular sub-populations and their origin. 

A leading cause of physical injury sustained by elderly persons is the event of unintentionally falling. A delay between the time of fall and the time of medical attention can exacerbate injury if the fall resulted in a concussion, traumatic brain injury, or bone fracture. The authors present a solution capable of finding and tracking, in real-time, the location of an elderly person within an indoor facility, using only existing Wi-Fi infrastructure. This paper discusses the development of an open source software framework capable of finding the location of an individual within 3m accuracy using 802.11 Wi-Fi in good coverage areas. This framework is comprised of an embedded software layer, a Web Services layer, and a mobile application for monitoring the location of individuals, calculated using trilateration, with Kalman filtering employed to reduce the effect of multipath interference. The solution provides a real-time, low cost, extendible solution to the problem of indoor geolocation to mitigate potential harm to elderly persons who have fallen and require immediate medical help. 

High performance computing (HPC) is undergoing significant changes. The emerging HPC applications comprise both compute- and data-intensive applications. To meet the intense I/O demand from emerging data-intensive applications, burst buffers are deployed in production systems. Existing HPC schedulers are mainly CPU-centric. The extreme heterogeneity of hardware devices, combined with workload changes, forces the schedulers to consider multiple resources (e.g., burst buffers) beyond CPUs, in decision making. In this study, we present a multi-resource scheduling scheme named BBSched that schedules user jobs based on not only their CPU requirements, but also other schedulable resources such as burst buffer. BBSched formulates the scheduling problem into a multi-objective optimization (MOO) problem and rapidly solves the problem using a multi-objective genetic algorithm. The multiple solutions generated by BBSched enables system managers to explore potential tradeoffs among various resources, and therefore obtains better utilization of all the resources. The trace-driven simulations with real system workloads demonstrate that BBSched improves scheduling performance by up to 41% compared to existing methods, indicating that explicitly optimizing multiple resources beyond CPUs is essential for HPC scheduling.