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RNAseq data for P. putida with vanillate
Illumina sequencing reads from RNA sequencing of vanillate-utilizing strains of Pseudomonas putida, described in Evolution and engineering of pathways for aromatic O-demethylation in Pseudomonas putida KT2440 by A. Bleem, et al. (2024)
Comparison of native and heterologous pathways for aromatic O-demethylation in Pseudmonas putida KT2440 enabled by adaptive laboratory evolution
Comparison of native and heterologous pathways for aromatic O-demethylation in Pseudomonas putida KT2440 enabled by adaptive laboratory evolution
Evolution_and_engineering_of_pathways_for_aromatic_O-demethylation_in_Pseudomonas_putida_KT2440
Biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However,aromatic compoundsderived from lignin commonly contain methoxy groups, andO-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Severalenzymefamilies catalyze aromaticO-demethylation, but they are rarely comparedin vivoto determine an optimal biocatalytic strategy. Here, two pathways for aromaticO-demethylation were compared inPseudomonas putidaKT2440. The native Rieske non-heme ironmonooxygenase(VanAB) and, separately, a heterologous tetrahydrofolate-dependentdemethylase(LigM) were constitutively expressed inP. putida, and the strains were optimized via adaptive laboratory evolution (ALE) withvanillateas a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ∼1.8x faster than those harboring the heterologous LigM pathway.Enzyme kineticsandtranscriptomicsstudies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, thereductasefor vanillateO-demethylase, PP_3494, a global regulator of vanillate catabolism, andfghA, involved in formaldehydedetoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms,yielding a platform strain for efficientO-demethylation of lignin-related aromatic compounds to value-added products. This DOI contains supplementary material associated with the published manuscript.
Performance of a Modular Ton-Scale Pixel-Readout Liquid Argon Time Projection Chamber
The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations.
VERITAS and Multiwavelength Observations of the Blazar B3 2247+381 in Response to an IceCube Neutrino Alert
While the sources of the diffuse astrophysical neutrino flux detected by the IceCube Neutrino Observatory are still largely unknown, one of the promising methods to improve our understanding of them is investigating the potential temporal and spatial correlations between neutrino alerts and the electromagnetic radiation from blazars. We report on the multiwavelength target-of-opportunity observations of the blazar B3 2247+381, taken in response to an IceCube multiplet alert for a cluster of muon neutrino events compatible with the source location between 2022 May 20 and 2022 November 10. B3 2247+381 was not detected with VERITAS during this time period. The source was found to be in a low-flux state in the optical, ultraviolet, and gamma-ray bands for the time interval corresponding to the neutrino event, but was detected in the hard X-ray band with NuSTAR during this period. We find the multiwavelength spectral energy distribution is described well using a simple one-zone leptonic synchrotron self-Compton radiation model. Moreover, assuming the neutrinos originate from hadronic processes within the jet, the neutrino flux would be accompanied by a photon flux from the cascade emission, and the integrated photon flux required in such a case would significantly exceed the total multiwavelength fluxes and the VERITAS upper limits presented here. The lack of flaring activity observed with VERITAS, combined with the low multiwavelength flux levels, as well as the significance of the neutrino excess being at a 3σ level (uncorrected for trials), makes B3 2247+381 an unlikely source of the IceCube multiplet. We conclude that the neutrino excess is likely a background fluctuation.
HD 143811 AB b: A Directly Imaged Planet Orbiting a Spectroscopic Binary in Sco-Cen
We present confirmation of HD 143811 AB b, a substellar companion to spectroscopic binary HD 143811 AB through direct imaging with the Gemini Planet Imager (GPI) and Keck NIRC2. HD 143811 AB was observed as a part of the GPI Exoplanet Survey in 2016 and 2019 and is a member of the Sco-Cen star formation region. The exoplanet is detected ∼430 mas from the host star by GPI. With two GPI epochs and one from Keck/NIRC2 in 2022, we confirm through common proper motion analysis that the object is bound to its host star. We derive an orbit with a semimajor axis of $64^{+32}_{-14}$ au and eccentricity $0.23^{+0.24}_{-0.16}$. Spectral analysis of the GPI H-band spectrum and NIRC2 L′ photometry provides additional proof that this object is a substellar companion. We compare the spectrum of HD 143811 AB b to PHOENIX stellar models and Exo-Radioactive-Convective Equilibrium Model (REM) exoplanet atmosphere models and find that Exo-REM models provide the best fits to the data. From the Exo-REM models, we derive an effective temperature of $1042^{+178}_{-132}$ K for the planet and translate the derived luminosity of the planet to a mass of 5.6 ± 1.1 M Jup assuming hot-start evolutionary models. HD 143811 AB b is the first directly imaged planet around a binary that is not on an ultrawide orbit. Future characterization of this object will shed light on the formation of planets around binary star systems.
Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)
Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.
Over three decades, and counting, of near-surface turbulent flux measurements from the Atmospheric Radiation Measurement (ARM) user facility
Processes mediating the coupling of terrestrial, aquatic, biospheric, and atmospheric systems influence weather, climate, and ecosystem dynamics via transfer of energy, momentum, water, and carbon (or other species). These exchange processes are quantified by measurements of near-surface turbulent fluxes. Understanding processes at these interfaces provides insight toward understanding and predicting current and future states within the Earth system. The Atmospheric Radiation Measurement (ARM) user facility has been conducting measurements of near-surface turbulent fluxes since the early 1990s at long-term fixed locations and shorter-term mobile deployments across the Earth. ARM has utilized two established methods for conducting these measurements: energy balance Bowen ratio (EBBR) and eddy covariance (EC). Primary measurements from the former include sensible and latent heat flux, while the latter also measures fluxes of momentum and carbon (primarily carbon dioxide, with methane fluxes measured at two locations to date). The EBBR systems have been deployed at 22 locations, and, to date, the EC systems have been deployed at over 50 sites, with plans for additional novel site locations in the future. Herein, the history, evolution, and key aspects of these instrument systems are documented, along with information on data quality assurance and post-processing, as well as best use practices. Additionally, three data validation experiments were recently conducted, and their key findings are summarized. Finally, ancillary datasets acquired by ARM, which can contextualize and aid interpretation of the near-surface turbulent flux measurements, are discussed. The datasets described herein include the eddy correlation flux measurement system: 30ECOR (https://doi.org/10.5439/1879993, Sullivan et al., 1997), 30QCECOR (https://doi.org/10.5439/1097546, Gaustad, 2003), ECORSF (https://doi.org/10.5439/1494128, Sullivan et al., 2019a), and associated AmeriFlux and Methane Value-Added Product, AMCMETHANE (https://doi.org/10.5439/1508268, Billesbach, 2011); the energy balance Bowen ratio system: 30EBBR (https://doi.org/10.5439/1023895, Sullivan et al., 1993) and 30BAEBBR (https://doi.org/10.5439/1027268, Gaustad and Xie, 1993); and the carbon dioxide flux measurement system: CO2FLX (https://doi.org/10.5439/1287574, https://doi.org/10.5439/1287575, https://doi.org/10.5439/1287576, Koontz et al., 2015a, b, c; https://doi.org/10.5439/1989774, https://doi.org/10.5439/1989776, https://doi.org/10.5439/1992202, Biraud and Chan, 2002a, b, c). These data can be found by searching the above data stream names at https://adc.arm.gov/discovery/#/results/ (last access: 8 September 2025).
Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide
Ice nucleating particles (INPs) play a critical role in cloud microphysics and precipitation formation, yet long-term, spatially extensive observational datasets remain limited. Here, we present one of the most comprehensive publicly available datasets of immersion-mode INP concentrations using a single analytical method, generated through the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility. INP filter samples have been collected across a broad range of environments – including agricultural plains, Arctic coastlines, high-elevation mountain sites, marine regions, and urban areas – via fixed observatories, mobile facility deployments, and vertically-resolved tethered balloon system operations. We describe the standardized processing and quality assurance pipeline, from filter collection and processing using the Ice Nucleation Spectrometer to final data products archived on the ARM Data Discovery portal. The dataset includes both total INP concentrations and selectively treated samples, allowing for classification of biological, organic, and inorganic INP types. It features a continuous 5-year record of INP measurements from a central U.S. site, with data collection still ongoing. Seasonal and site-specific differences in INP concentrations are illustrated through intercomparisons at −10 and −20 °C, revealing distinct regional sources and atmospheric drivers. We also outline mechanisms for researchers to access existing data, request additional sample analyses, and propose future field campaigns involving ARM INP measurements. This dataset supports a wide range of scientific applications, from observational and mechanistic studies to model development, and provides critical constraints on aerosol-cloud interactions across diverse atmospheric regimes (Creamean et al., 2024, 2020b; https://doi.org/10.5439/1770816).
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains
Gravity waves are a common occurrence in the atmosphere, with a variety of generation mechanisms. Their impact on wind farms has only recently gained attention, with most studies focused on wind farm-induced gravity waves. In this study, the interaction between a wind farm and gravity waves generated by an atmospheric bore event is assessed using multiscale large-eddy simulations. The atmospheric bore is created by a thunderstorm downdraft from a nocturnal mesoscale convective system (MCS). The associated gravity waves impact the wind resource and power production at a nearby wind farm during the American Wake Experiment (AWAKEN) in the US southern Great Plains. A two-domain nested setup (Δx=300 and 20 m) is used in the Weather Research and Forecasting (WRF) model, forced with data from the High-Resolution Rapid Refresh model, to capture both the formation of the bore and its interaction with individual wind turbines. The MCS is resolved on the large outer domain, where the structure of the bore and the associated gravity waves are found to be especially sensitive to parameterized microphysics processes. On the finer inner domain, gravity wave interactions with individual wind turbines are resolved; wake dynamics are captured using a generalized actuator disk parameterization in WRF. The gravity waves are found to have a strong effect on the atmosphere above the wind farm; however, the effect of the waves is more nuanced closer to the surface where there is additional turbulence, both ambient and wake-generated. Notably, the gravity waves modulate the mesoscale environment by weakening and dissipating the preexisting low-level jet, which reduces hub-height wind speed and hence the simulated power output, which is confirmed by the observed supervisory control and data acquisition (SCADA) power data. Additionally, the gravity waves induce local wind direction variations correlated with fluctuations in pressure, which lead to fluctuations in the simulated power output as various turbines within the farm are subjected to waking from nearby turbines.