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Dual antiplatelet therapy (DAPT) after percutaneous coronary intervention is necessary for preventing stent thrombosis and ensuring long-term cardiovascular protection. It is important to be thoughtful in balancing thrombotic and bleeding risk when deciding therapy regimen and duration. Revascularization randomized trials have studied many specific, at-risk populations; however, women remain underrepresented. Although there is evidence to support shortened DAPT duration in women, especially those at higher bleeding risk or on chronic oral anticoagulation, we need trials specifically enrolling and focusing on women.

Dynamic ensembles hold great promise in advancing RNA-targeted drug discovery. Here we subjected the transactivation response element (TAR) RNA from human immunodeficiency virus type-1 to experimental high-throughput screening against ~100,000 drug-like small molecules. Results were augmented with 170 known TAR-binding molecules and used to generate sublibraries optimized for evaluating enrichment when virtually screening a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics simulations. Ensemble-based virtual screening scores molecules with an area under the receiver operator characteristic curve of ~0.85–0.94 and with ~40–75% of all hits falling within the top 2% of scored molecules. The enrichment decreased significantly for ensembles generated from the same molecular dynamics simulations without input NMR data and for other control ensembles. The results demonstrate that experimentally determined RNA ensembles can significantly enrich libraries with true hits and that the degree of enrichment is dependent on the accuracy of the ensemble.

We consider the use of fuel cells for powering data centers, based on benefits in reliability, capital and operational costs, and reduced environmental emissions. Using fuel cells effectively in data centers introduces several challenges and we highlight key research questions for designing a fuel cell based data center power distribution system. We analyze a specific configuration in the design space to quantify the cost benefits for a large scale data center, for the most mature and commonly deployed fuel cell technology, achieving over 20% reduction in costs using conservative projections.

A computer system often has multiple power management modules controlling diffierent power knobs. Uncoordinated operation of these knobs not only leads to suboptimal operation but may also cause unsafe behaviors. Coordination methods have thus been proposed to jointly control the power knobs and performance. However, in many systems, such joint design is not feasible due to lack of visibility into all modules to be coordinated. This occurs, for instance, in commodity software that runs on multiple platforms, andemerging cloud hosted applications that operate on platforms outside developers' control and alongside unknown other workloads. We propose an approach for semantics-free coordination where power-performance management can be performed within each module without semantic knowledge regarding other modules.

Significant opportunities for power optimization exist at application design stage and are not yet fully exploited by system and application designers. We describe the challenges developers face in optimizing software for energy efficiency by exploiting application-level knowledge. To address these challenges, we propose the development of automated tools that profile the energy usage of various resource components used by an application and guide the design choices accordingly. We use a preliminary version of a tool we have developed to demonstrate how automated energy profiling helps a developer choose between alternative designs in the energy-performance trade-off space.

The use of environmental energy is now emerging as a feasible energy source for embedded and wireless computing systems such as sensor networks where manual recharging or replacement of batteries is not practical. However, energy supply from environmental sources is highly variable with time. Further, for a distributed system, the energy available at its various locations will be different. These variations strongly influence the way in which environmental energy is used. We present a harvesting theory for determining performance in such systems. First we present a model for characterizing environmental sources. Second, we state and prove two harvesting theorems that help determine the sustainable performance level from a particular source. This theory leads to practical techniques for scheduling processes in energy harvesting systems. Third, we present our implementation of a real embedded system that runs on solar energy and uses our harvesting techniques. The system adjusts its performance level in response to available resources. Fourth, we propose a localized algorithm for increasing the performance of a distributed system by adapting the process scheduling to the spatio-temporal characteristics of the environmental energy in the distributed system. While our theoretical intuition is based on certain abstractions, all the scheduling methods we present are motivated solely from the experimental behavior and resource constraints of practical sensor networking systems.

Packet network based telephony has become an attractive application because of many advantages it offers in terms of costs and services. This article discusses some of the major technical challenges in the implementation of IP telephony. One of the challenges in IP telephony is that of delay jitter in the received audio. The problem of jitter in delays needs to be handled even in the newer networks having high bandwidth and low delays. In this paper, we discuss the jitter problem in detail and provide an overview of various methods used to tackle it. These include a new method proposed by the authors which offers performance gains in terms of packet loss and jitter over existing methods.

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Motion may be used in sensor networks to change the network configuration for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resource overhead, hence we exploit motility, a constrained form of mobility, which has very low overheads but provides significant reconfiguration potential. We present an architecture that allows each node in the network to learn the medium and phenomenon characteristics. We describe a quantitative metric for sensing performance that is concretely tied to real sensor and medium characteristics, rather than assuming an abstract range based model. The problem of determining the desirable network configuration is expressed as an optimization of this metric. We present a distributed optimization algorithm which computes a desirable network configuration, and adapts it to environmental changes. The relationship of the proposed algorithm to simulated annealing and incremental subgradient descent based methods is discussed. A key property of our algorithm is that convergence to a desirable configuration can be proved even though no global coordination is involved. A network protocol to implement this algorithm is discussed, followed by simulations and experiments on a laboratory test bed.