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Although human reshaping of the nitrogen (N) cycle is well established, contributions of individual N sources to riverine and coastal eutrophication are less certain. Urban N fluxes are potentially substantial, particularly from sewer overflows. Results from four longitudinal surveys in rivers in and around the city of Pittsburgh, Pennsylvania, were used to characterize N chemistry and isotopic composition and were compared with LOADEST‐model‐derived total N (TN) flux budgets from three urban areas along the Ohio River (Pittsburgh, Pennsylvania; Cincinnati, Ohio; and Louisville, Kentucky). Triple nitrate isotopes reveal that riverine nitrate in the Pittsburgh region is dominated by wastewater inputs despite high atmospheric deposition rates. Our budget estimates demonstrate that the magnitude of urban N yields is comparable to yields reported for agricultural watersheds and that these high urban N yields cannot consist of permitted, point‐source discharges alone. Our results reveal that nonpoint sources in urban systems represent an important but overlooked source of TN to overall riverine budgets.

 

The spectacular outbursts of energy associated with supernovae (SNe) have long motivated research into their potentially hazardous effects on Earth and analogous environments. Much of this research has focused primarily on the atmospheric damage associated with the prompt arrival of ionizing photons within days or months of the initial outburst, and the high-energy cosmic rays that arrive thousands of years after the explosion. In this study, we turn the focus to persistent X-ray emission, arising in certain SNe that have interactions with a dense circumstellar medium and observed months and/or years after the initial outburst. The sustained high X-ray luminosity leads to large doses of ionizing radiation out to formidable distances. We assess the threat posed by these X-ray-luminous SNe for Earth-like planetary atmospheres; our results are rooted in the X-ray SN observations from Chandra, Swift-XRT, XMM-Newton, NuSTAR, and others. We find that this threat is particularly acute for SNe showing evidence of strong circumstellar interaction, such as Type IIn explosions, which have significantly larger ranges of influence than previously expected and lethal consequences up to ∼50 pc away. Furthermore, X-ray-bright SNe could pose a substantial and distinct threat to terrestrial biospheres and tighten the Galactic habitable zone. We urge follow-up X-ray observations of interacting SNe for months and years after the explosion to shed light on the physical nature and full-time evolution of the emission and to clarify the danger that these events pose for life in our galaxy and other star-forming regions.

 

Abstract Background Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation. Objective The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures. Methods We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures. Results We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings. 

We present a catalog of results of gamma-ray observations made by VERITAS, published from 2008 to 2020. VERITAS is a ground based imaging atmospheric Cherenkov telescope observatory located at the Fred Lawrence Whipple Observatory (FLWO) in southern Arizona, sensitive to gamma-ray photons with energies in the range of ∼ 100 GeV - 30 TeV. Its observation targets include galactic sources such as binary star systems, pulsar wind nebulae, and supernova remnants, extragalactic sources like active galactic nuclei, star forming galaxies, and gamma-ray bursts, and some unidentified objects. The catalog includes in digital form all of the high-level science results published in 112 papers using VERITAS data and currently contains data on 57 sources. The catalog has been made accessible via GitHub and at NASA's HEASARC. 

It is often important to determine the source of a microbial strain. Examples include tracking a bacterium linked to a disease epidemic, contaminating the food supply, or used in bioterrorism. Strain identification and tracking are generally approached by using cultivation-based or relatively nonspecific gene fingerprinting methods. Genomic methods have the ability to distinguish strains, but this approach typically has been restricted to isolates or relatively low-complexity communities. We demonstrate that strain-resolved metagenomics can be applied to extremely complex soil samples. We genotypically defined a soil-associated bacterium and identified it as a contaminant. By linking together snapshots of the bacterial genome over time, it was possible to estimate how long the contaminant had been diverging from a likely source population. The results are congruent with the derivation of the bacterium from a strain isolated in Germany and sequenced a decade ago and highlight the utility of metagenomics in strain tracking. 

Annually, half of all plant-derived carbon is added to soil where it is microbially respired to CO 2 . However, understanding of the microbiology of this process is limited because most culture-independent methods cannot link metabolic processes to the organisms present, and this link to causative agents is necessary to predict the results of perturbations on the system. We collected soil samples at two sub-root depths (10–20 cm and 30–40 cm) before and after a rainfall-driven nutrient perturbation event in a Northern California grassland that experiences a Mediterranean climate. From ten samples, we reconstructed 198 metagenome-assembled genomes that represent all major phylotypes. We also quantified 6,835 proteins and 175 metabolites and showed that after the rain event the concentrations of many sugars and amino acids approach zero at the base of the soil profile. Unexpectedly, the genomes of novel members of the Gemmatimonadetes and Candidate Phylum Rokubacteria phyla encode pathways for methylotrophy. We infer that these abundant organisms contribute substantially to carbon turnover in the soil, given that methylotrophy proteins were among the most abundant proteins in the proteome. Previously undescribed Bathyarchaeota and Thermoplasmatales archaea are abundant in deeper soil horizons and are inferred to contribute appreciably to aromatic amino acid degradation. Many of the other bacteria appear to breakdown other components of plant biomass, as evidenced by the prevalence of various sugar and amino acid transporters and corresponding hydrolyzing machinery in the proteome. Overall, our work provides organism-resolved insight into the spatial distribution of bacteria and archaea whose activities combine to degrade plant-derived organics, limiting the transport of methanol, amino acids and sugars into underlying weathered rock. The new insights into the soil carbon cycle during an intense period of carbon turnover, including biogeochemical roles to previously little known soil microbes, were made possible via the combination of metagenomics, proteomics, and metabolomics.