To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying WiFi hotspots to offload their mobile traffic. However, these ISP-centric approaches for traffic management do not always match the interests of mobile users. Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for WiFi access, they may not wait for WiFi if they are sensitive to delay. To navigate this tradeoff, we develop AMUSE (Adaptive bandwidth Management through USer-Empowerment), a functional prototype of a practical, cost-aware WiFi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and WiFi availability, AMUSE decides which applications to offload to what times of the day. Since nearly all traffic flows from mobile devices are TCP flows, we introduce a new receiver-side bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps' content servers. Through a measurement study of 20 smartphone users' traffic usage traces, we observe that though users already offload a large amount of some application types, our framework can offload a significant additional portion of users' cellular traffic. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and WiFi usage data obtained from a trial with 37 mobile users. Our results show that AMUSE improves user utility; when compared with AMUSE, other offloading algorithms yield 14% and 27% lower user utilities for light and heavy users, respectively. Intelligently managing users' competing interests for cost, throughput, and delay can therefore improve their offloading decisions.

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The present study investigated the protective effect of morin, a natural flavonoid, on the imipenem-induced nephrotoxicity in rabbits. Nephrotoxicity of imipenem was examined after the intravenous administrations of imipenem (200 mg/kg) to rabbits in the presence and the absence of morin (12, 25, 50 mg/kg, p.o.). Cytotoxicity of imipenem was also examined in the presence and the absence of morin (100 microM) by using MDCK cells overexpressing human organic anion transporter 1 and 3 (MDCK/hOAT1 or MDCK/hOAT3). Intravenous dosing of imipenem alone induced severe proximal tubular necrosis in rabbits, however, the concurrent use of morin (25 or 50 mg/kg, p.o.) significantly suppressed the histopathological damage in the kidney induced by imipenem. While imipenem was not cytotoxic in MDCK/hOAT1 cells over the tested concentrations up to 10 mM, it showed significant cellular toxicity with CC(50) of 0.77 mM in MDCK/hOAT3 cells, implying that OAT3 may involve more actively in the imipenem-induced nephrotoxicity. In addition, the cellular toxicity of imipenem decreased by approximately 20 folds in the presence of morin in MDCK/hOAT3 cells. In conclusion, the present study suggests that morin might be beneficial to reduce the nephrotoxicity of imipenem, at least in part, via the inhibition of OAT3-mediated renal excretion of imipenem.

The two largest U.S. wireless ISPs have recently moved towards usage-based pricing to better manage the growing demand on their networks. Yet usage-based pricing still requires ISPs to over-provision capacity for demand at peak times of the day. Time-dependent pricing (TDP) addresses this problem by considering when a user consumes data, in addition to how much is used. We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE. TUBE creates a price-based feedback control loop between an ISP and its end users. On the ISP side, it computes TDP prices so as to balance the cost of congestion during peak periods with that of offering lower prices in less congested periods. On mobile devices, it provides a graphical user interface that allows users to respond to the offered prices either by themselves or using an "autopilot" mode. We conducted a pilot TUBE trial with 50 iPhone or iPad 3G data users, who were charged according to our TDP algorithms. Our results show that TDP benefits both operators and customers, flattening the temporal fluctuation of demand while allowing users to save money by choosing the time and volume of their usage.