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We report the discovery of 11 high-velocity H I clouds at Galactic latitudes of 25°–30°, likely embedded in the Milky Way’s nuclear wind. The clouds are detected with deep Green Bank Telescope 21 cm observations of a 3.2° × 6.2° field around QSO 1H1613-097, located behind the northern Fermi Bubble. Our measurements reach 3sigma limits on NHI as low as 3.1 × 10^17/cm^2, more than twice as sensitive as previous HI studies of the bubbles. The clouds span −180 ≤v_LSR≤ −90 km/s and are the highest-latitude 21 cm high-velocity cloud detected inside the bubbles. Eight clouds are spatially resolved, showing coherent structures with sizes of 4–28 pc, peak column densities of log HI = 17.9–18.7, and HI masses up to 1470M⊙. Several exhibit internal velocity gradients. Their presence at such high latitudes is surprising, given the short expected survival times for clouds expelled from the Galactic center. These objects may be fragments of a larger cloud disrupted by interaction with the surrounding hot gas. 

Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing (10–100 m) between them. In this Letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep-learning-driven superresolution of data events. These “improved” events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional “virtual” optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies. Published by the American Physical Society2025 

We introduce a probabilistic technique for full-waveform inversion, using variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging. 

The industry is experiencing significant changes due to artificial intelligence (AI) and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of “digital twins” for subsurface monitoring and control. 

ABSTRACT The degree of cyclization, or ring index (RI), in archaeal glycerol dibiphytanyl glycerol tetraether (GDGT) lipids was long thought to reflect homeoviscous adaptation to temperature. However, more recent experiments show that other factors (e.g., pH, growth phase, and energy flux) can also affect membrane composition. The main objective of this study was to investigate the effect of carbon and energy metabolism on membrane cyclization. To do so, we cultivatedAcidianussp. DS80, a metabolically flexible and thermoacidophilic archaeon, on different electron donor, acceptor, and carbon source combinations (S⁰/Fe³⁺/CO₂, H₂/Fe³⁺/CO₂, H₂/S⁰/CO₂, or H₂/S⁰/glucose). We show that differences in energy and carbon metabolism can result in over a full unit of change in RI in the thermoacidophileAcidianussp. DS80. The patterns in RI correlated with the normalized electron transfer rate between the electron donor and acceptor and did not always align with thermodynamic predictions of energy yield. In light of this, we discuss other factors that may affect the kinetics of cellular energy metabolism: electron transfer chain (ETC) efficiency, location of ETC reaction components (cytoplasmicvs.extracellular), and the physical state of electron donors and acceptors (gasvs.solid). Furthermore, the assimilation of a more reduced form of carbon during heterotrophy appears to decrease the demand for reducing equivalents during lipid biosynthesis, resulting in lower RI. Together, these results point to the fundamental role of the cellular energy state in dictating GDGT cyclization, with those cells experiencing greater energy limitation synthesizing more cyclized GDGTs. IMPORTANCESome archaea make unique membrane-spanning lipids with different numbers of five- or six-membered rings in the core structure, which modulate membrane fluidity and permeability. Changes in membrane core lipid composition reflect the fundamental adaptation strategies of archaea in response to stress, but multiple environmental and physiological factors may affect the needs for membrane fluidity and permeability. In this study, we tested howAcidianussp. DS80 changed its core lipid composition when grown with different electron donor/acceptor pairs. We show that changes in energy and carbon metabolisms significantly affected the relative abundance of rings in the core lipids of DS80. These observations highlight the need to better constrain metabolic parameters, in addition to environmental factors, which may influence changes in membrane physiology in Archaea. Such consideration would be particularly important for studying archaeal lipids from habitats that experience frequent environmental fluctuations and/or where metabolically diverse archaea thrive. 

Modern-day reservoir management and monitoring of geologic carbon storage increasingly call for costly time-lapse seismic data collection. We demonstrate how techniques from graph theory can be used to optimize acquisition geometries for low-cost sparse 4D seismic data. Based on midpoint-offset-domain connectivity arguments, our algorithm automatically produces sparse nonreplicated time-lapse acquisition geometries that favor wavefield recovery. 

Archaea adjust the number of cyclopentane rings in their glycerol dibiphytanyl glycerol tetraether (GDGT) membrane lipids as a homeostatic response to environmental stressors such as temperature, pH, and energy availability shifts. However, archaeal expression patterns that correspond with changes in GDGT composition are less understood. Here we characterize the acid and cold stress responses of the thermoacidophilic crenarchaeonSaccharolobus islandicusREY15A using growth rates, core GDGT lipid profiles, transcriptomics and proteomics. We show that both stressors result in impaired growth, lower average GDGT cyclization, and differences in gene and protein expression. Transcription data revealed differential expression of the GDGT ring synthasegrsBin response to both acid stress and cold stress. Although the GDGT ring synthase encoded bygrsBforms highly cyclized GDGTs with ≥5 ring moieties,S. islandicus grsBupregulation under acidic pH conditions did not correspond with increased abundances of highly cyclized GDGTs. Our observations highlight the inability to predict GDGT changes from transcription data alone. Broader analysis of transcriptomic data revealed thatS. islandicusdifferentially expresses many of the same transcripts in response to both acid and cold stress. These included upregulation of several biosynthetic pathways and downregulation of oxidative phosphorylation and motility. Transcript responses specific to either of the two stressors tested here included upregulation of genes related to proton pumping and molecular turnover in acid stress conditions and upregulation of transposases in cold stress conditions. Overall, our study provides a comprehensive understanding of the GDGT modifications and differential expression characteristic of the acid stress and cold stress responses inS. islandicus.