Global Flows with Invariant Measures for the Inviscid Modified SQG Equations

We consider the family known as modified or generalized surface quasi-geostrophic equations (mSQG) consisting of the classical inviscid surface quasi-geostrophic (SQG) equation together with a family of regularized active scalars given by introducing a smoothing operator of nonzero but possibly arbitrarily small degree. This family naturally interpolates between the 2D Euler equation and the SQG equation. For this family of equations we construct an invariant measure on a rough $L^2$-based Sobolev space and establish the existence of solutions of arbitrarily large lifespan for initial data in a set of full measure in the rough Sobolev space.Comment: 18 page

Burning questions: a geospatial analysis of fire regime change in Côte d’Ivoire, 1984-2014

Africa has been called the “burn center” of the planet because it is the continent where the greatest proportion of global fire occurs. This widespread yet poorly understood phenomenon holds the key to processes such as land cover change, vegetation change, and the emission of greenhouse gasses. To understand this role, better information about the distribution and drivers of fire is needed. Research in West Africa points to seasonal changes in vegetation burning over the past 30 years. In Côte d’Ivoire, fieldwork at the terroir scale in one savanna region indicates an increase in the proportion of early dry season fires related to the expansion of livestock raising. Since early dry season fires are generally less intense than late dry season fires, a shift toward early season burning will influence vegetation cover and greenhouse gas emissions. But are these shifts apparent at broader scales? How does cattle herding interact with other variables affecting fire? This research investigates the factors affecting fire seasonality at the country level in Côte d’Ivoire. I reconstruct a representative history of fire activity for Côte d’Ivoire using more than 5000 Landsat TM/ETM+ images over the period 1984 to 2014. Active fires are detected in each image using two indices based on the radiance of fire in the shortwave infrared portion of the electromagnetic spectrum. The work assesses the fire regime as represented by active fire in 896 locations covering Côte d’Ivoire. It also investigates the relationship of fire patterns with climate and land use/land cover variables using random forest regression. The independent variables show a strong relationship with fire regularity and a weaker, though important, relationship with timing and density of fires. The results reveal spatial and temporal patterns in fire seasonality over the past 30 years in Côte d’Ivoire. While I conclude that the timing of fire across Côte d’Ivoire has not shown a substantial linear trend over time, the seasonality, density, and regularity of fire has fluctuated over time and space. These variations are related to temperature, rainfall, and pastoralism, among other variables. Improving the understanding of fire regimes in Côte d’Ivoire can shed new light on ongoing debates regarding the impacts of increasing agricultural activity in West Africa on fire, vegetation, and climate change

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction

Clinical Use and Therapeutic Potential of IVIG/SCIG, Plasma-Derived IgA or IgM, and Other Alternative Immunoglobulin Preparations

Intravenous and subcutaneous immunoglobulin preparations, consisting of IgG class antibodies, are increasingly used to treat a broad range of pathological conditions, including humoral immune deficiencies, as well as acute and chronic inflammatory or autoimmune disorders. A plethora of Fab- or Fc-mediated immune regulatory mechanisms has been described that might act separately or in concert, depending on pathogenesis or stage of clinical condition. Attempts have been undertaken to improve the efficacy of polyclonal IgG preparations, including the identification of relevant subfractions, mild chemical modification of molecules, or modification of carbohydrate side chains. Furthermore, plasma-derived IgA or IgM preparations may exhibit characteristics that might be exploited therapeutically. The need for improved treatment strategies without increase in plasma demand is a goal and might be achieved by more optimal use of plasma-derived proteins, including the IgA and the IgM fractions. This article provides an overview on the current knowledge and future strategies to improve the efficacy of regular IgG preparations and discusses the potential of human plasma-derived IgA, IgM, and preparations composed of mixtures of IgG, IgA, and IgM

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Rethinking Nomenclature for Interspecies Cell Fusions

Cell fusions have long enhanced biomedical research. These experimental models, historically referred to as ‘somatic cell hybrids,’ involve combining the plasma membranes of two cells and merging their nuclei within a single cytoplasm. Cell fusion studies that involve human and chimpanzee pluripotent stem cells highlight the need for careful and principled communication. Names matter. How scientists describe cell lines can shape public perception and inform policy. Referring to source cell lines as ‘parental,’ or calling fused cells ‘hybrids’ evokes a reproductive potential that doesn\u27t exist between humans and other species. We propose a precise, versatile, and generalizable nomenclature that describes the contributing species, ploidy, and cell type. For lay audiences, we recommend the term ‘composite cell line’ to distinguish experimentally fused cell lines from natural cell fusion events and actual reproductive hybrids

Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications

Assessment of Ambient Air Toxics and Wood Smoke Pollution among Communities in Sacramento County

Ambient air monitoring and phone survey data were collected in three environmental justice (EJ) and three non-EJ communities in Sacramento County during winter 2016–2017 to understand the differences in air toxics and in wood smoke pollution among communities. Concentrations of six hazardous air pollutants (HAPs) and black carbon (BC) from fossil fuel (BCff) were significantly higher at EJ communities versus non-EJ communities. BC from wood burning (BCwb) was significantly higher at non-EJ communities. Correlation analysis indicated that the six HAPs were predominantly from fossil fuel combustion sources, not from wood burning. The HAPs were moderately variable across sites (coefficient of divergence (COD) range of 0.07 for carbon tetrachloride to 0.28 for m- and p-xylenes), while BCff and BCwb were highly variable (COD values of 0.46 and 0.50). The BCwb was well correlated with levoglucosan (R2 of 0.68 to 0.95), indicating that BCwb was a robust indicator for wood burning. At the two permanent monitoring sites, wood burning comprised 29–39% of the fine particulate matter (PM2.5) on nights when PM2.5 concentrations were forecasted to be high. Phone survey data were consistent with study measurements; the only significant difference in the survey results among communities were that non-EJ residents burn with indoor devices more often than EJ residents