Jet Propulsion Laboratory

Evidence suggests the persistence of non-spore-forming Acinetobacter johnsonii in high-stakes controlled and nutrient-limited environments. Here, we investigated the mechanisms underlying this adaptability through a comprehensive genomic analysis of 22 isolates of A. johnsonii from NASA's Payload Hazardous Servicing Facility (PHSF) and one carbapenem-resistant strain (E154408A) from patient colonization in Ireland. Core-genome phylogeny revealed clustering of PHSF-originating isolates in a monophyletic clade divergent from the main species lineage. Species-wide virulence-associated genes and metabolic reconstruction indicated the exclusive presence in PHSF-originating isolates of two complete efflux pumps and a conserved allantoin racemase, suggesting adaptability for multiple environmental stresses. The ubiquity of blaOXA in genomes analyzed (n = 112) and the phenotypically validated multidrug-resistant profile of the E154408A strain highlight A. johnsonii's potential as an antimicrobial resistance (AMR) reservoir. Plasmidome analysis suggested gain/loss events across the monophyletic population and potential AMR acquisition pathways. Genome-to-metagenome mapping identified genomic signatures of A. johnsonii in PHSF >10 years post-initial isolation.IMPORTANCEAcinetobacter johnsonii is increasingly recognized as an emerging human pathogen, with growing evidence of its ability to persist in controlled, high-stakes environments, posing risks as both a persistent environmental contaminant and an antimicrobial resistance (AMR) reservoir. Yet, gaps remain in our understanding of its AMR profile and the mechanisms that enable its enhanced environmental adaptability. This knowledge is necessary in contexts where biological cleanliness is a priority, such as clinical settings and spacecraft assembly facilities' cleanrooms, where contamination of hardware with terrestrial microorganisms is concerning. In this study, we aim to address some of the key knowledge gaps by providing genomic insights into a rare multidrug-resistant clinical isolate and 22 NASA cleanroom isolates that persisted for over a decade in extremely clean conditions. Our findings will help assess the contamination risk of A. johnsonii in high-stakes environments and ultimately strengthen our ability to manage this microbial contaminant across terrestrial and extraterrestrial settings.Cleanroom-derived A. johnsonii genomes show traits consistent with increased adaptability.Genomic signatures of A. johnsonii persisted in the cleanrooms for over 10 years.blaOXA is ubiquitously found in all 112 A. johnsonii genomes analyzed.Isolate E154408A is the first reported patient colonization case by carbapenem-resistant A. johnsonii in Europe.

ABSTRACT Raman spectroscopy is a valuable tool for detecting trace compounds over wide ranges of concentrations but is usually limited to qualitative analysis (e.g., identification) due to the difficulty of determining concentration from Raman peak intensity except under very controlled conditions. This study presents a new quantitative model, derived from first principles, that can be used to estimate the concentration ratio of binary mixtures from their Raman intensities over several orders of magnitude, fully accounting for any nonlinear behaviors introduced by factors such as overlapping peaks and self‐absorption. By training the model on experimental data of mixtures with known concentrations, the empirical parameters describing a particular mixture can be ascertained and then used to predict concentrations in further samples. The efficacy of the model is explored using synthetic datasets representing four scenarios depending on which compounds contribute to each peak. Bootstrapped model training can be used to consider the effects of noise, determine uncertainties for future predictions, and estimate the limits of detection and quantification for any given measurement. Finally, the model's efficacy is tested on experimental data for aqueous solutions of different organic nucleotides at concentration ratios between 0.1 and 1000 ppm, showing that the model works over 4 orders of magnitude and can be used to reliably predict the concentration ratio of test samples to within 0.1 orders of magnitude. This advanced model will improve our ability to estimate and assess concentrations in a wide range of mixed samples, even when their peaks overlap significantly.

Abstract We present a major update to the open-source atmospheric modeling package PICASO , designed for simulating the thermal structure and spectra of hydrogen-rich atmospheres of brown dwarfs and exoplanets. This release, PICASO 4.0 , expands upon the existing radiative-convective equilibrium model framework by incorporating several new capabilities. Key additions include the integration of Virga for self-consistent cloud modeling, new flexible treatments for rainout and cold trapping of volatile species, and support for photochemistry. We also introduce a parameterized energy injection scheme to simulate additional external or internal heating processes. These features are motivated by lessons from recent JWST observations that reveal the prevalence of nonequilibrium chemistry and clouds. We benchmark the new functionalities against previously published results in the literature, including the Sonora Diamondback grid, energy injected atmospheres, patchy cloud models, and other photochemical models of WASP-39b. PICASO continues to be actively developed as an open-source package aimed at enabling reproducible, community-driven atmospheric modeling of all substellar objects.

Compound, consecutive, and cascading hazards increasingly challenge the systemic resilience of critical infrastructure. Among these, the power grid—vital to nearly every facet of society—is uniquely exposed to interdependent stressors from space weather, terrestrial weather, and wildfire. Traditional single-hazard frameworks are insufficient to capture the dynamic, non-linear interactions across these domains thereby being incapable of understanding systemic dynamics and shaping resilience. Drawing on enriched historical event records, statistical network analysis, and sustained engagement with grid operators, we present a new framework for assessing how multi-hazard interactions influence grid resilience. We share event-based storylines—physically self-consistent reconstructions of past events and plausible future pathways—and emergent patterns that reveal both known and previously unrecognized mechanisms of compounding and cascading failure, recovery, and adaptive response. Through investigation of a previously unexamined multi-hazard system (space weather, terrestrial weather, and wildfire) we uncover novel complex behaviors that reframe how resilience and system flourishing can be understood and designed for.Our findings advance methodologies for multi-hazard resilience assessment by integrating physical hazard processes with socio-technical dynamics, including network structure, operator decision-making, and institutional constraints under deep uncertainty. We further outline an emerging global initiative aimed at collective, transdisciplinary action to collect efforts and groups advancing resilience analyses for these ‘wicked problems’ and to collectively explore translating them into adaptive strategies and governance frameworks. By translating multi-hazard insights into actionable knowledge, we offer both methodological tools and institutional pathways for advancing resilience analysis under conditions of deep uncertainty and systemic interdependence.

Urban regions are major contributors to anthropogenic CO₂ emissions, yet satellite-based observations of column-averaged CO₂ remain spatially and temporally sparse, limiting high-resolution urban monitoring. This study presents a machine-learning framework for predicting daily CO₂ over European urban areas at a spatial resolution of 0.02°, integrating satellite NO₂ observations, reanalysis meteorological variables, and surface data. High-density OCO-2 target-mode observations are used as ground truth, enabling robust learning of the relationships between CO₂ and its atmospheric and surface drivers.Two predictive scenarios are evaluated. The first, a sample-based prediction designed primarily for spatial gap filling, achieves an R² of 0.98 ± 0.00 and an RMSE of 0.60 ± 0.02 ppm using 10-fold cross-validation. The second scenario assesses spatiotemporal generalization, yielding an average R² of 0.91 ± 0.02 and RMSE of 1.17 ± 0.14 ppm for temporal transfer, and R² of 0.85 ± 0.09 with RMSE of 1.25 ± 0.20 ppm for spatial transfer across European regions. Independent validation against ground-based CO₂ measurements from the Munich Urban Carbon Column network (MUCCnet) shows strong agreement, with R² values between 0.95 and 0.97 and RMSE ranging from 0.50 to 0.72 ppm.The results demonstrate the potential of the proposed framework to fill observational gaps and generate reliable, high-resolution CO₂ fields over urban environments and their surroundings, supporting improved monitoring of anthropogenic CO₂ emissions where accurate information is most critical.

Since 1992, surface elevation change estimates of the Antarctic Ice Sheet (AIS) have been derived from satellite radar altimetry. However, large uncertainties remain due to local topography and the time-variable signal penetration into snow and firn. The unprecedented accuracy of measurements from the ICESat-2 satellite laser altimetry mission, launched in 2018, now enables inter-comparison with radar altimetry results. The primary goal of this study is to improve understanding of the uncertainties in AIS volume and mass balance estimates by quantifying how results from ICESat-2 and the CryoSat-2 radar altimetry mission diverge under different processing regimes. To do so, we analyse coincident ICESat-2 and CryoSat-2 measurements over the 6.9 million km² area of the relatively flat and large AIS interior, where topography-related errors are small. We apply a suite of state-of-the-art correction methods to the CryoSat-2 measurements, including multiple retracking algorithms and empirical corrections for the time-variable surface and volume scattering of the radar signal. From April 2019 to October 2024, ICESat-2 observations show a thickening of 97 ± 4 km3 yr−1, coincident with excess snowfall in this period. CryoSat-2 solutions indicate systematically lower thickening rates than ICESat-2. The smallest bias (0.6 ± 1.0 cm yr−1 or 42 km3 yr−1) between the results from the two missions is found when using the AWI-ICENet1 convolutional neural network retracker. One of our hypotheses is that the systematic radar-laser differences might be due to residual errors related to the time-variable radar penetration, particularly affected by the heavy snowfall events in recent years. While further work is needed to test this hypothesis, our study demonstrates both the challenges of resolving subtle, long-term surface mass balance trends using radar altimetry and the value of joint laser-radar analyses for improving AIS volume and mass balance estimates.

Industrial compounds that contain halogens are potent greenhouse gases, and those that contain chlorine and bromine substantially reduce springtime ozone over the poles. As a result, these gases have been phased out by the 1987 Montreal Protocol and its subsequent amendments. However, many persist in the atmosphere due to their long lifetimes and gradual elimination from industry. Chlorofluorocarbons (CFCs) and carbon tetrachloride (CCl4) were scheduled to be fully phased out by 2010 and replaced by hydrochlorofluorocarbons (HCFCs), which will be banned worldwide by 2030. Some halogenated gases, such as perfluorocarbons (PFCs), have global warming potentials thousands of times greater than carbon dioxide but are not controlled by the Montreal Protocol or any other agreement. With so many halogen-containing compounds remaining in the atmosphere at different levels of regulation, it is crucial to continue carefully monitoring their abundances and trends. This can be accomplished using global satellite-based measurements, which have been continuously available for many of these gases as of the early 2000s. To maximize the reliability of these measurements, it is important to validate them through comparisons with other observations. In this study, we compare collocated measurements from three different satellite instruments (ACE-FTS on SCISAT, HIRDLS on Aura, and MIPAS on Envisat) with each other and with independent reference data from balloon-based instruments for CFC-11, CFC-12, CFC-113, HCFC-22, CCl4, and CF4. We find that overall, the satellite instruments perform well, but for certain regions and time periods, there are significant biases that need to be considered throughout any monitoring activities.

During Juno’s extended mission, the spacecraft’s periapsis migrated toward Jupiter’s north pole, enabling high-resolution observations of auroral regions. At microwave wavelengths, these auroral features appear as localized reductions in brightness temperature (“cold spots”). Here we present observations from Juno Perijoves 56–72 using the Microwave Radiometer (MWR). MWR measures thermal emission from Jupiter’s deep atmosphere, which is partially absorbed by ionization produced by precipitating energetic electrons in the ionosphere. By exploiting MWR’s multi-frequency observations, we use the frequency dependence of electron–neutral collisional absorption to probe the vertical extent of auroral ionization at depths well below those accessible to ultraviolet measurements.We find systematic differences among auroral regions. The deepest ionization occurs at the Io footprint, is moderate along the main auroral oval, and is shallowest in the polar cap. Modeling of the multi-frequency absorption indicates that the Io footprint requires a substantial population of precipitating electrons with energies in the tens-of-MeV range, whereas the main oval can be explained without invoking such high-energy electrons. These results place new constraints on the energy and depth of electron precipitation in Jupiter’s aurora and demonstrate the unique capability of MWR multi-frequency measurements to diagnose deep ionospheric structure, complementing ultraviolet, infrared, and radio occultation observations.