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Sulfuryl fluoride (SO₂F₂) is a synthetic pesticide and a potent greenhouse gas that is accumulating in the global atmosphere. Rising emissions are a concern since SO₂F₂has a relatively long atmospheric lifetime and a high global warming potential. The U.S. is thought to contribute substantially to global SO₂F₂emissions, but there is a paucity of information on how emissions of SO₂F₂are distributed across the U.S., and there is currently no inventory of SO₂F₂emissions for the U.S. or individual states. Here we provide an atmospheric measurement-based estimate of U.S. SO₂F₂emissions using high-precision SO₂F₂measurements from the NOAA Global Greenhouse Gas Reference Network (GGGRN) and a geostatistical inverse model. We find that California has the largest SO₂F₂emissions among all U.S. states, with the highest emissions from southern coastal California (Los Angeles, Orange, and San Diego counties). Outside of California, only very small and infrequent SO₂F₂emissions are detected by our analysis of GGGRN data. We find that California emits 60-85% of U.S. SO₂F₂emissions, at a rate of 0.26 ( ± 0.10) Gg yr⁻¹. We estimate that emissions of SO₂F₂from California are equal to 5.5–12% of global SO₂F₂emissions.

 

Abstract Atmospheric concentrations of methane, a powerful greenhouse gas, have strongly increased since 2007. Measurements of stable carbon isotopes of methane can constrain emissions if the isotopic compositions are known; however, isotopic compositions of methane emissions from wetlands are poorly constrained despite their importance. Here, we use a process-based biogeochemistry model to calculate the stable carbon isotopic composition of global wetland methane emissions. We estimate a mean global signature of −61.3 ± 0.7‰ and find that tropical wetland emissions are enriched by ~11‰ relative to boreal wetlands. Our model shows improved resolution of regional, latitudinal and global variations in isotopic composition of wetland emissions. Atmospheric simulation scenarios with the improved wetland isotopic composition suggest that increases in atmospheric methane since 2007 are attributable to rising microbial emissions. Our findings substantially reduce uncertainty in the stable carbon isotopic composition of methane emissions from wetlands and improve understanding of the global methane budget. 

Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter. 

Small-molecule inhibitors of PD-L1 are postulated to control immune evasion in tumors similar to antibodies that target the PD-L1/PD-1 immune checkpoint axis. However, the identity of targetable PD-L1 inducers is required to develop small-molecule PD-L1 inhibitors. In this study, using chromatin immunoprecipitation (ChIP) assay and siRNA, we demonstrate that vitamin D/VDR regulates PD-L1 expression in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) cells. We have examined whether a VDR antagonist, MeTC7, can inhibit PD-L1. To ensure that MeTC7 inhibits VDR/PD-L1 without off-target effects, we examined competitive inhibition of VDR by MeTC7, utilizing ligand-dependent dimerization of VDR-RXR, RXR-RXR, and VDR-coactivators in a mammalian 2-hybrid (M2H) assay. MeTC7 inhibits VDR selectively, suppresses PD-L1 expression sparing PD-L2, and inhibits the cell viability, clonogenicity, and xenograft growth of AML cells. MeTC7 blocks AML/mesenchymal stem cells (MSCs) adhesion and increases the efferocytotic efficiency of THP-1 AML cells. Additionally, utilizing a syngeneic colorectal cancer model in which VDR/PD-L1 co-upregulation occurs in vivo under radiation therapy (RT), MeTC7 inhibits PD-L1 and enhances intra-tumoral CD8+T cells expressing lymphoid activation antigen-CD69. Taken together, MeTC7 is a promising small-molecule inhibitor of PD-L1 with clinical potential. 

Public spaces like concert stadiums and sporting arenas are ideal venues for AR content delivery to crowds of mobile phone users. Unfortunately, these environments tend to be some of the most challenging in terms of lighting and dynamic staging for vision-based relocalization. In this paper, we introduce FLASH 1 , a system for delivering AR content within challenging lighting environments that uses active tags (i.e., blinking) with detectable features from passive tags (quads) for marking regions of interest and determining pose. This combination allows the tags to be detectable from long distances with significantly less computational overhead per frame, making it possible to embed tags in existing video displays like large jumbotrons. To aid in pose acquisition, we implement a gravity-assisted pose solver that removes the ambiguous solutions that are often encountered when trying to localize using standard passive tags. We show that our technique outperforms similarly sized passive tags in terms of range by 20-30% and is fast enough to run at 30 FPS even within a mobile web browser on a smartphone. 

Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and reveals an important obstacle to incentive design. We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. Moreover, we show a similar result holds for designing cost functions that satisfy the requirements of previous work. With the benefit of hindsight, our results show much of the prior work on strategic classification is causal modeling in disguise. 

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a prediction. This also extends naturally to data in an online stream. Our simple approach leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts.