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Abstract Under the next cycle of target setting under the Paris Agreement, countries will be updating and submitting new nationally determined contributions (NDCs) over the coming year. To this end, there is a growing need for the United States to assess potential pathways toward a new, maximally ambitious 2035 NDC. In this study, we use an integrated assessment model with state-level detail to model existing policies from both federal and non-federal actors, including the Inflation Reduction Act, Bipartisan Infrastructure Law, and key state policies, across all sectors and gases. Additionally, we develop a high-ambition scenario, which includes new and enhanced policies from these actors. We find that existing policies can reduce net greenhouse gas (GHG) emissions by 44% (with a range of 37% to 52%) by 2035, relative to 2005 levels. The high-ambition scenario can deliver net GHG reductions up to 65% (with a range of 59% to 71%) by 2035 under accelerated implementation of federal regulations and investments, as well as state policies such as renewable portfolio standards, EV sales targets, and zero-emission appliance standards. This level of reductions would provide a basis for continued progress toward the country’s 2050 net-zero emissions goal.

Abstract Objective In patients with Dravet syndrome (DS), fenfluramine reduced convulsive seizure frequency and provided clinical benefit in nonseizure endpoints (e.g., executive function, survival). In zebrafish mutant scn1 DS models, chronic fenfluramine treatment preserved neuronal cytoarchitecture prior to seizure onset and prevented gliosis; here, we extend these findings to a mammalian model of DS (Scn1a+/− mice) by evaluating the effects of fenfluramine on neuroinflammation (degenerated myelin, activated microglia) and survival. Methods Scn1a+/− DS mice were treated subcutaneously once daily with fenfluramine (15 mg/kg) or vehicle from postnatal day (PND) 7 until 35‐37. Sagittal brain sections were processed for immunohistochemistry using antibodies to degraded myelin basic protein (D‐MBP) for degenerated myelin, or CD11b for activated (inflammatory) microglia; sections were scored semi‐quantitatively. Apoptotic nuclei were quantified by TUNEL assay. Statistical significance was evaluated by 1‐way ANOVA with post‐hoc Dunnett's test (D‐MBP, CD11b, and TUNEL) or Logrank Mantel‐Cox (survival). Results Quantitation of D‐MBP immunostaining per 0.1 mm2 unit area of the parietal cortex and hippocampus CA3 yielded significantly higher spheroidal and punctate myelin debris counts in vehicle‐treated DS mice than in wild‐type mice. Fenfluramine treatment in DS mice significantly reduced these counts. Activated CD11b + microglia were more abundant in DS mouse corpus callosum and hippocampus than in wild‐type controls. Fenfluramine treatment of DS mice resulted in significantly fewer activated CD11b + microglia than vehicle‐treated DS mice in these brain regions. TUNEL staining in corpus callosum was increased in DS mice relative to wild‐type controls. Fenfluramine treatment in DS mice lowered TUNEL staining relative to vehicle‐treated DS mice. By PND 35‐37, 55% of control DS mice had died, compared with 24% of DS mice receiving fenfluramine treatment (P = 0.0291). Significance This is the first report of anti‐neuroinflammation and pro‐survival after fenfluramine treatment in a mammalian DS model. These results corroborate prior data in humans and animal models and suggest important pharmacological activities for fenfluramine beyond seizure reduction. Plain Language Summary Dravet syndrome is a severe epilepsy disorder that impairs learning and causes premature death. Clinical studies in patients with Dravet syndrome show that fenfluramine reduces convulsive seizures. Additional studies suggest that fenfluramine may have benefits beyond seizures, including promoting survival and improving control over emotions and behavior. Our study is the first to use a Dravet mouse model to investigate nonseizure outcomes of fenfluramine. Results showed that fenfluramine treatment of Dravet mice reduced neuroinflammation significantly more than saline treatment. Fenfluramine‐treated Dravet mice also lived longer than saline‐treated mice. These results support clinical observations that fenfluramine may have benefits beyond seizures.

Abstract We present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.

Abstract Lung cancer risk from exposure to ambient polycyclic aromatic hydrocarbons (PAHs) is expected to change significantly by 2050 compared to 2008 due to changes in climate and emissions. Integrating a global atmospheric chemistry model, a lung cancer risk model, and plausible future emissions trajectories of PAHs, we assess how global PAHs and their associated lung cancer risk will likely change in the future. Benzo(a)pyrene (BaP) is used as an indicator of cancer risk from PAH mixtures. From 2008 to 2050, the population‐weighted global average BaP concentrations under all RCPs consistently exceeded the WHO‐recommended limits, primarily attributed to residential biofuel use. Peaks in PAH‐associated incremental lifetime cancer risk shift from East Asia (4 × 10−5) in 2008 to South Asia (mostly India, 2–4 × 10−5) and Africa (1–2 × 10−5) by 2050. In the developing regions of Africa and South Asia, PAH‐associated lung cancer risk increased by 30–64% from 2008 to 2050, due to increasing residential energy demand in households for cooking, heating, and lighting, the continued use of traditional biomass use, increases in agricultural waste burning, and forest fires, accompanied by rapid population growth in these regions. Due to more stringent air quality policies in developed countries, their PAH lung cancer risk substantially decreased by ∼80% from 2008 to 2050. Climate change is likely to have minor effects on PAH lung cancer risk compared to the impact of emissions. Future policies, therefore, need to consider efficient combustion technologies that reduce air pollutant emissions, including incomplete combustion products such as PAH.

An efficient one-pot synthetic method has been developed for the preparation of bicyclic carbamoyl pyridones from the known common intermediate methyl 5-((2,4-difluorobenzyl)carbamoyl)-1-(2,2-dimethoxyethyl)-3-methoxy-4-oxo-1,4-dihydropyridine-2-carboxylate (8). The scalable protocol is facile and employs readily available reagents, needing only a single purification as the final step. The utility of the approach was demonstrated by preparing a library of HIV-1 integrase strand transfer inhibitors (INSTIs) that differ by the presence or absence of a double bond in the B-ring of the bicyclic carbamoyl pyridines 6 and 7. Several of the analogs show good antiviral potencies in single-round HIV-1 replication antiviral assays and show no cytotoxicity in cell culture assays. In general, the compounds with a B-ring double bond have higher antiviral potencies than their saturated congeners. Our methodology should be applicable to the synthesis of a range of new metal-chelating analogs.

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Integrase strand transfer inhibitors (INSTIs) are a class of antiretroviral compounds that prevent the insertion of a DNA copy of the viral genome into the host genome by targeting the viral enzyme integrase (IN). Dolutegravir (DTG) is a leading INSTI that is given, usually in combination with nucleoside reverse transcriptase inhibitors (NRTIs), to treat HIV-1 infections. The emergence of resistance to DTG and other leading INSTIs is rare. However, there are recent reports suggesting that drug resistance mutations can occur at positions outside the integrase gene either in the HIV-1 polypurine tract (PPT) or in the envelope gene (env). Here, we used single round infectivity assays to measure the antiviral potencies of several FDA-approved INSTIs and non-nucleoside reverse transcriptase inhibitors (NNRTIs) against a panel of HIV-1 PPT mutants. We also tested several of our promising INSTIs and NNRTIs in these assays. No measurable loss in potency was observed for either INSTIs or NNRTIs against the HIV-1 PPT mutants. This suggests that HIV-1 PPT mutants are not able, by themselves, to confer resistance to INSTIs or NNRTIs.

Serial-section electron microscopy (ssEM) is the method of choice for studying macroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so -called connectomes. The data that can comprise of up to 108 individual EM images must be assembled into a volume, requiring seamless 2D registration from physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render Trautman and Saalfeld (2019) services used in the volume assembly of the brain of adult Drosophila melanogaster (Zheng et al. 2018). It achieves high throughput by operating only on image meta-data and transformations. ASAP is modular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (Yin et al. 2020); Microns Consortium et al. (2021) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

Atmospheric carbon dioxide (CO _2 ) concentrations have increased as a direct result of human activity and are at their highest level over the last 2 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO _2 , the fate of anthropogenic CO _2 over time, and the likelihood of a trend in the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model with the novel ability to explicitly track carbon as it flows through the Earth system. We use key model parameters in a Monte Carlo analysis of 15 000 model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much anthropogenic carbon ends up in the atmosphere. Modeled airborne fraction was roughly 52%, consistent with observational studies. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960–2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. However, the percentage of atmospheric CO _2 derived from anthropogenic origins can be much higher because of Earth system feedbacks. We find it peaks at over 90% between 2010–2050. Moreover, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics of anthropogenic CO _2 in the Earth system.