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The lymphatic system is a networked structure used by billions of immune cells, including T cells and Dendritic cells, to locate and identify invading pathogens. Dendritic cells carry pieces of pathogens to the nearest lymph node, and T cells travel through the lymphatic vessels and search within lymph nodes to find them. Here we investigate how the topology of the lymphatic network affects the time for this search to be completed. Building on prior work that maps out the human lymphatic network, we develop and extend a method to infer the lymphatic network topology of mice. We compare search times for the modeled and observed topologies and show that they are similar to each other and consistent with observed immune response times. This is relevant for translating immune response times in mice, where most experimental work occurs, into expected immune response times in humans. Our analysis predicts that for large systemic infections, the topology of the lymphatic network allows immune response times to remain fast even as animal mass increases by orders of magnitude. This work advances our understanding of how the structure of the lymphatic network supports the swarm intelligence of the immune system. It also elucidates general principles relating swarm size and organization to search speed. 

We present methods for autonomous collaborative surveying of volcanic CO 2 emissions using aerial robots. CO 2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO 2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO 2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO 2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO 2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization. 

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.