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We report CO2 emission rates and plume δ13C during the July 2023 eruption at Litli Hrútur in the Fagradalsfjall region of the Reykjanes Peninsula. The CO2 emission rates were measured by UAV utilizing a new method of data extrapolation that enables obtaining rapid flux results of dynamic eruption plumes. The δ13C values are consistent with degassing-induced isotopic fractionation of the magma during and after the eruption. Our results show that rapid, real-time CO2 flux measurements coupled with isotopic values of samples collected at the same time provide key insights into the dynamics of volcanic eruptions and have the potential of forecasting the termination of activity. 

Abstract. We report in-plume carbon dioxide (CO2) concentrations and carbon isotope ratios during the 2021 eruption of Tajogaite volcano, island of La Palma, Spain. CO2 measurements inform our understanding of volcanic contributions to the global climate carbon cycle and the role of CO2 in eruptions. Traditional ground-based methods of CO2 collection are difficult and dangerous, and as a result only about 5 % of volcanoes have been directly surveyed. We demonstrate that unpiloted aerial system (UAS) surveys allow for fast and relatively safe measurements. Using CO2 concentration profiles we estimate the total flux during several measurements in November 2021 to be 1.76±0.20×103 to 2.23±0.26×104 t d−1. Carbon isotope ratios of plume CO2 indicate a deep magmatic source, consistent with the intensity of the eruption. Our work demonstrates the feasibility of UASs for CO2 surveys during active volcanic eruptions, particularly for deriving rapid emission estimates. 

T cells are required to clear infection, and T cell motion plays a role in how quickly a T cell finds its target, from initial naive T cell activation by a dendritic cell to interaction with target cells in infected tissue. To better understand how different tissue environments affect T cell motility, we compared multiple features of T cell motion including speed, persistence, turning angle, directionality, and confinement of T cells moving in multiple murine tissues using microscopy. We quantitatively analyzed naive T cell motility within the lymph node and compared motility parameters with activated CD8 T cells moving within the villi of small intestine and lung under different activation conditions. Our motility analysis found that while the speeds and the overall displacement of T cells vary within all tissues analyzed, T cells in all tissues tended to persist at the same speed. Interestingly, we found that T cells in the lung show a marked population of T cells turning at close to 180^o, while T cells in lymph nodes and villi do not exhibit this “reversing” movement. T cells in the lung also showed significantly decreased meandering ratios and increased confinement compared to T cells in lymph nodes and villi. These differences in motility patterns led to a decrease in the total volume scanned by T cells in lung compared to T cells in lymph node and villi. These results suggest that the tissue environment in which T cells move can impact the type of motility and ultimately, the efficiency of T cell search for target cells within specialized tissues such as the lung. 

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.