Nanoparticle (NP) therapeutics can improve the therapeutic index of chemoradiotherapy (CRT). However, the effect of NP physical properties, such particle size, on CRT is unknown. To address this, we examined the effects of NP size on biodistribution, efficacy and toxicity in CRT. PEG-PLGA NPs (50, 100, 150 nm mean diameters) encapsulating wotrmannin (wtmn) or KU50019 were formulated. These NP formulations were potent radiosensitizers in vitro in HT29, SW480, and lovo rectal cancer lines. In vivo, the smallest particles avoided hepatic and splenic accumulation while more homogeneously penetrating tumor xenografts than larger particles. However, smaller particles were no more effective in vivo. Instead, there was a trend towards enhanced efficacy with medium sized NPs. The smallest KU60019 particles caused more small bowel toxicity than larger particles. Our results showed that particle size significantly affects nanotherapeutics' biodistrubtion and toxicity but does not support the conclusion that smaller particles are better for this clinical application.

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We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.

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