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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Hydrogen–Deuterium Exchange Mass Spectrometry Identifies Local and Long-Distance Interactions within the Multicomponent Radical SAM Enzyme, PqqE

Interactions among proteins and peptides are essential for many biological activities including the tailoring of peptide substrates to produce natural products. The first step in the production of the bacterial redox cofactor pyrroloquinoline quinone (PQQ) from its peptide precursor is catalyzed by a radical SAM (rSAM) enzyme, PqqE. We describe the use of hydrogen–deuterium exchange mass spectrometry (HDX-MS) to characterize the structure and conformational dynamics in the protein–protein and protein–peptide complexes necessary for PqqE function. HDX-MS-identified hotspots can be discerned in binary and ternary complex structures composed of the peptide PqqA, the peptide-binding chaperone PqqD, and PqqE. Structural conclusions are supported by size-exclusion chromatography coupled to small-angle X-ray scattering (SEC-SAXS). HDX-MS further identifies reciprocal changes upon the binding of substrate peptide and S-adenosylmethionine (SAM) to the PqqE/PqqD complex: long-range conformational alterations have been detected upon the formation of a quaternary complex composed of PqqA/PqqD/PqqE and SAM, spanning nearly 40 Å, from the PqqA binding site in PqqD to the PqqE active site Fe 4 S 4 . Interactions among the various regions are concluded to arise from both direct contact and distal communication. The described experimental approach can be readily applied to the investigation of protein conformational communication among a large family of peptide-modifying rSAM enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The inheritance of anthracnose (Colletotrichum sublineola) resistance in sorghum differential lines QL3 and IS18760

Anthracnose caused by the fungal pathogen C. sublineola is an economically important constraint on worldwide sorghum production. The most effective strategy to safeguard yield is through the introgression of resistance alleles. This requires elucidation of the genetic basis of the different resistance sources that have been identified. In this study, 223 recombinant inbred lines (RILs) derived from crossing anthracnose-differentials QL3 (96 RILs) and IS18760 (127 RILs) with the common susceptible parent PI609251 were evaluated at four field locations in the United States (Florida, Georgia, Texas, and Puerto Rico) for their anthracnose resistance response. Both RIL populations were highly susceptible to anthracnose in Florida and Georgia, while in Puerto Rico and Texas they were segregating for anthracnose resistance response. A genome scan using a composite linkage map of 982 single nucleotide polymorphisms (SNPs) detected two genomic regions of 4.31 and 0.85 Mb on chromosomes 4 and 8, respectively, that explained 10–27% of the phenotypic variation in Texas and Puerto Rico. In parallel, a subset of 43 RILs that contained 67% of the recombination events were evaluated against anthracnose pathotypes from Arkansas (2), Puerto Rico (2) and Texas (4) in the greenhouse. A genome scan showed that the 7.57 Mb region at the distal end of the short arm of chromosome 5 is associated with the resistance response against the pathotype AMP-048 from Arkansas. Comparative analysis identified the genomic region on chromosome 4 overlaps with an anthracnose resistance locus identified in another anthracnose-differential line, SC414-12E, indicating this genomic region is of interest for introgression in susceptible sorghum germplasm. Candidate gene analysis for the resistance locus on chromosome 5 identified an R -gene cluster that has high similarity to another R -gene cluster associated with anthracnose resistance on chromosome 9.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Evidence of a Dwarf Galaxy Stream Populating the Inner Milky Way Halo

Stellar streams produced from dwarf galaxies provide direct evidence of the hierarchical formation of the Milky Way. Here, we present the first comprehensive study of the LMS-1 stellar stream, that we detect by searching for wide streams in the Gaia EDR3 data set using the STREAMFINDER algorithm. This stream was recently discovered by Yuan et al. We detect LMS-1 as a 60° long stream to the north of the galactic bulge, at a distance of ~20 kpc from the Sun, together with additional components that suggest that the overall stream is completely wrapped around the inner Galaxy. Using spectroscopic measurements from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Sloan Digital Sky Survey, and the Apache Point Observatory Galactic Evolution Experiment, we infer that the stream is very metal-poor (<[Fe/H]> = –2.1) with a significant metallicity dispersion (σ [Fe/H] = 0.4), and it possesses a large radial velocity dispersion (σ v = 20 ± 4 km s–1 ). These estimates together imply that LMS-1 is a dwarf galaxy stream. The orbit of LMS-1 is close to polar, with an inclination of 75° to the galactic plane. Both the orbit and metallicity of LMS-1 are remarkably similar to the globular clusters NGC 5053, NGC 5024, and the stellar stream Indus. As a result, these findings make LMS-1 an important contributor to the stellar population of the inner Milky Way halo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Experimental study of the Si 30 ( He 3 , d ) P 31 reaction and thermonuclear reaction rate of Si 30 ( p , γ ) P 31

Background: Abundance anomalies in some globular clusters, such as the enhancement of potassium and the depletion of magnesium, can be explained in terms of an earlier generation of stars polluting the presently observed ones. It was shown that the potential range of temperatures and densities of the polluting sites depends on the strength of a few critical reaction rates. The 30 Si(p,γ) 31 P reaction has been identified as one of these important reactions. Purpose: The key ingredient for evaluating the thermonuclear 30 Si(p,γ) 31 P reaction rate is the strength of the resonances which, at low energy, are proportional to their proton width. Therefore, the goal of this work is to determine the proton widths of unbound 31 P states. Method: States in 31 P were studied at the Maier-Leibnitz-Laboratorium using the one-proton 30 Si( 3 He,d) 31 P transfer reaction. Deuterons were detected with the high resolution one quadrupole three dipoles (Q3D) magnetic spectrometer. Angular distribution and spectroscopic factors were extracted for 27 states, and proton widths and resonance strengths were calculated for the unbound states. Results: Several 31 P unbound states have been observed for the first time in a one-proton transfer reaction. Above 20 MK, the 30 Si(p,γ) 31 P reaction rate is now entirely estimated from the observed properties of 31 P states. The reaction rate uncertainty from all resonances other than the E$^{c.m.}_{r}$ = 149 keV resonance has been reduced down to less than a factor of 2 above that temperature. The unknown spin and parity of the E$^{c.m.}_{r}$ = 149 keV resonance dominates the uncertainty in the rate in the relevant temperature range. Conclusion: Here, the remaining source of uncertainty on the 30 Si(p,γ) 31 P reaction rate comes from the unknown spin and parity of the E$^{c.m.}_{r}$ = 149 keV resonance which can change the reaction rate by a factor of 10 in the temperature range of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Single atoms and small clusters of atoms may accompany Au and Pd dendrimer-encapsulated nanoparticles

Here we report the presence of small clusters of atoms (<1 nm) (SCs) and single atoms (SAs) in solutions containing 1–2 nm dendrimer-encapsulated nanoparticles (DENs). Au and Pd DENs were imaged using aberration-corrected scanning transmission electron microscopy (ac-STEM), and energy dispersive spectroscopy (EDS) was used to identify and quantify the SAs/SCs. Two main findings have emerged from this work. First, the presence or absence of SAs/SCs depends on both the terminal functional group of the dendrimer (–NH 2 or –OH) and the elemental composition of the DENs (Au or Pd). Second, dialysis can be used to remove the majority of SAs/SCs in cases where a high density of SAs/SCs are present. The foregoing conclusions provide insights into the mechanisms for Au and Pd DEN synthesis and stability. Ultimately, these results demonstrate the need for careful characterization of systems containing nanoparticles to ensure that SAs/SCs, which may be below the detection limit of most analytical methods, are taken into consideration (especially for catalysis experiments).

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests

Random forests have become popular models used for data driven predictions. As a result, random forests are currently used or being considered for high-consequence mission applications in national security, such as the prediction of yield from optical signals and malware detection. While random forests may provide accurate predictions, the complexity of the algorithm causes a lack of interpretability. Random forests are an ensemble of regression or decision trees. Individual regression and decision trees are interpretable, but ensembles are inherently difficult to interpret due to the compilation of many models. We aim to increase the interpretability of random forests by finding patterns in the ensemble of trees that can be used to “thin” (or remove) trees. As a starting point, in this report, we develop a new distance metric for quantifying the similarity between trees based on their topologies (i.e., shapes). We base the metric on a novel distance metric for graphs that is a proper mathematical distance, is invariant to transformations, has registration between graphs, and computes topological evolutions between graphs. We use the tree distance metric to compute tree statistics such as a “mean tree” and to identify clusters of trees. We apply the developed methodology to a toy dataset and a mission relevant product inspection dataset to demonstrate how the metric can provide insight into random forests. Furthermore, we discuss the limitations of the approach and ideas for future research into how the metric could be used as a thinning tool to develop less complex models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

via machinae : Searching for stellar streams using unsupervised machine learning

ABSTRACT We develop a new machine learning algorithm, via machinae, to identify cold stellar streams in data from the Gaia telescope. via machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, via machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search for line-like features in patches of the sky. In this paper, we describe the via machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the via machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia data set, for example debris flow and globular clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Beyond mass: detecting secondary halo properties with galaxy-galaxy lensing

ABSTRACT Secondary halo properties beyond mass, such as the mass accretion rate (MAR), concentration, and the half mass scale, are essential in understanding the formation of large-scale structure and dark matter haloes. In this paper, we study the impact of secondary halo properties on the galaxy-galaxy lensing observable, ΔΣ. We build an emulator trained on N-body simulations to model ΔΣ and quantify the impact of different secondary parameters on the ΔΣ profile. We focus on the impact of MAR on ΔΣ. We show that a 3σ detection of variations in MAR at fixed halo mass could be achieved with the Hyper Suprime Cam survey assuming no baryonic effects and a proxy for MAR with scatter <1.5. We show that the full radial profile of ΔΣ depends on secondary properties at fixed halo mass. Consequently, an emulator that can perform full shape fitting yields better than two times improvement upon the constraints on MAR than only using the outer part of the halo. Finally, we highlight that miscentring and MAR impact the radial profile of ΔΣ in a similar fashion, implying that miscentring and MAR need to be modelled jointly for unbiased estimates of both effects. We show that present-day lensing data sets have the statistical capability to place constraints on halo MAR within our assumptions. Our analysis opens up new possibilities for observationally measuring the assembly history of the dark matter haloes that host galaxies and clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Influence of 10 Unique Chemical Elements in Shaping the Distribution of Kepler Planets

The chemical abundances of planet-hosting stars offer a glimpse into the composition of planet-forming environments. To further understand this connection, we make the first ever measurement of the correlation between planet occurrence and chemical abundances for ten different elements (C, Mg, Al, Si, S, K, Ca, Mn, Fe, and Ni). Leveraging data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and Gaia to derive precise stellar parameters (${\sigma }_{{R}_{\star }}\approx 2.3 \% $, ${\sigma }_{{M}_{\star }}\approx 4.5 \% $) for a sample of 1018 Kepler Objects of Interest, we construct a sample of well-vetted Kepler planets with precisely measured radii (${\sigma }_{{R}_{p}}\approx 3.4 \% $). After controlling for biases in the Kepler detection pipeline and the selection function of the APOGEE survey, we characterize the relationship between planet occurrence and chemical abundance as the number density of nuclei of each element in a star's photosphere raised to a power, β. varies by planet type, but is consistent within our uncertainties across all ten elements. For hot planets (P = 1–10 days), an enhancement in any element of 0.1 dex corresponds to an increased occurrence of ≈20% for super-Earths (Rp = 1–1.9 R⊕) and ≈60% for sub-Neptunes (Rp = 1.9–4 R⊕). Trends are weaker for warm (P = 10–100 days) planets of all sizes and for all elements, with the potential exception of sub-Saturns (Rp = 4–8 R⊕). Finally, we conclude this work with a caution to interpreting trends between planet occurrence and stellar age due to degeneracies caused by Galactic chemical evolution and make predictions for planet occurrence rates in nearby open clusters to facilitate demographics studies of young planetary systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A Natural ≳100× Telescope: Discovery of the Strongly Lensed Type II SN 2025mkn at z = 1.37

We present the discovery of SN 2025mkn, a gravitationally lensed Type II supernova. First detected as a blue transient in Zwicky Transient Facility (ZTF), 0. "83 from a z = 0.42 elliptical galaxy, the follow-up SNIFS/UH2.2 m and LRIS/Keck spectra revealed absorption lines at z = 1.371. Later JWST NIRCam imaging shows that the bright transient is a close pair of point sources separated by 0. "07, and a 30 times fainter counterimage opposite the lens, for which NIRSpec reveals strong Hα emission also at z = 1.371. The lightcurves and spectra are consistent with the Type II supernova source being magnified ≳100 times, with ∼250 required to reconcile its luminosity with that of nearby events such as SN 2023ixf. Lens models are consistent with such high magnifications, and always show that the faint image arrived first (undetected in earlier ZTF imaging), consistent with the later spectral phase of this fainter image. A fourth image is also predicted and possibly detected in the NIRSpec data. Lightcurve-based time-delay measurements are not possible due to the first image being the faintest; however, the resolved NIRSpec spectra offer a future opportunity for time-delay cosmography through supernova phase measurements.

Strong gravitational lensing↗
DOE OSTI2021

Biomimetic catalysts of iron-based metal–organic frameworks with high peroxidase-mimicking activity for colorimetric biosensing

The field of metal–organic framework (MOF)-based biomimetic catalysts has achieved great progress but is still in its infancy. The systematic investigation of the tailored construction of MOF-based biomimetic catalysts is required for further development. Herein, two iron-based MOFs, namely, [(Fe 3 O) 2 (H 2 O) 4 (HCOO)(L) 2 ] n (HUST-5: H 6 L = hexakis(4-formylphenoxy) cyclotriphosphazene; HUST = Huazhong University of Science and Technology) and [(Fe 3 O)(H 2 O) 3 (L)] n (HUST-7) have been fabricated through the assembly of different iron clusters and hexa-carboxylate ligand under the control of the added acid species. The two MOFs exhibit distinct secondary building units (SBUs) and topological structures, which could be used as biomimetic catalysts for the systematic comparisons of structural characteristics and the catalytic activity. Both MOFs possess catalytic activity similar to that of natural peroxidases towards the catalysis of the oxidation of a variety of substrates. Significantly, HUST-5 and HUST-7 can effectively catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) by H 2 O 2 accompanied by significant colorimetric biosensing. With same compositions, different catalytic performances were obtained due to differences in the porous structures and characteristics of SBUs in two Fe-MOFs, which was also validated by theoretical calculation results. Furthermore, the phenomenon of colorimetric biosensing could be significantly suppressed by the addition of ascorbic acid (AA) during the oxidation process of TMB. It was observed from these findings that a facile colorimetric biosensing platform for detecting H 2 O 2 and ascorbic acid has been successfully explored. Therefore, this work provides another unique perspective for the tailor-made preparation of stable MOF-based peroxidase mimics with excellent catalytic performance and colorimetric biosensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cosmological cross-correlations and nearest neighbour distributions

ABSTRACT Cross-correlations between data sets are used in many different contexts in cosmological analyses. Recently, k-nearest neighbour cumulative distribution functions (kNN-CDF) were shown to be sensitive probes of cosmological (auto) clustering. In this paper, we extend the framework of NN measurements to describe joint distributions of, and correlations between, two data sets. We describe the measurement of joint kNN-CDFs, and show that these measurements are sensitive to all possible connected N-point functions that can be defined in terms of the two data sets. We describe how the cross-correlations can be isolated by combining measurements of the joint kNN-CDFs and those measured from individual data sets. We demonstrate the application of these measurements in the context of Gaussian density fields, as well as for fully non-linear cosmological data sets. Using a Fisher analysis, we show that measurements of the halo-matter cross-correlations, as measured through NN measurements are more sensitive to the underlying cosmological parameters, compared to traditional two-point cross-correlation measurements over the same range of scales. Finally, we demonstrate how the NN cross-correlations can robustly detect cross-correlations between sparse samples – the same regime where the two-point cross-correlation measurements are dominated by noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Single-Cell Genomics of Novel Actinobacteria With the Wood–Ljungdahl Pathway Discovered in a Serpentinizing System

Serpentinite-hosted systems represent modern-day analogs of early Earth environments. In these systems, water-rock interactions generate highly alkaline and reducing fluids that can contain hydrogen, methane, and low-molecular-weight hydrocarbons-potent reductants capable of fueling microbial metabolism. In this study, we investigated the microbiota of Hakuba Happo hot springs (~50°C; pH~10.5–11), located in Nagano (Japan), which are impacted by the serpentinization process. Analysis of the 16S rRNA gene amplicon sequences revealed that the bacterial community comprises Nitrospirae (47%), “Parcubacteria” (19%), Deinococcus-Thermus (16%), and Actinobacteria (9%), among others. Notably, only 57 amplicon sequence variants (ASV) were detected, and fifteen of these accounted for 90% of the amplicons. Among the abundant ASVs, an early-branching, uncultivated actinobacterial clade identified as RBG-16-55-12 in the SILVA database was detected. Ten single-cell genomes (average pairwise nucleotide identity: 0.98–1.00; estimated completeness: 33–93%; estimated genome size: ~2.3 Mb) that affiliated with this clade were obtained. Taxonomic classification using single copy genes indicates that the genomes belong to the actinobacterial class-level clade UBA1414 in the Genome Taxonomy Database. Based on metabolic pathway predictions, these actinobacteria are anaerobes, capable of glycolysis, dissimilatory nitrate reduction and CO 2 fixation via the Wood–Ljungdahl (WL) pathway. Several other genomes within UBA1414 and two related class-level clades also encode the WL pathway, which has not yet been reported for the Actinobacteria phylum. For the Hakuba actinobacterium, the energy metabolism related to the WL pathway is likely supported by a combination of the Rnf complex, group 3b and 3d [NiFe]-hydrogenases, [FeFe]-hydrogenases, and V-type (H + /Na + pump) ATPase. The genomes also harbor a form IV ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO) complex, also known as a RubisCO-like protein, and contain signatures of interactions with viruses, including clustered regularly interspaced short palindromic repeat (CRISPR) regions and several phage integrases. This is the first report and detailed genome analysis of a bacterium within the Actinobacteria phylum capable of utilizing the WL pathway. The Hakuba actinobacterium is a member of the clade UBA1414/RBG-16-55-12, formerly within the group “OPB41.” We propose to name this bacterium ‘Candidatus Hakubanella thermoalkaliphilus.'

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DEMNUni: the imprint of massive neutrinos on the cross-correlation between cosmic voids and CMB lensing

Cosmic voids are a powerful probe of cosmology and are one of the core observables of upcoming galaxy surveys. The cross-correlations between voids and other large-scale structure tracers such as galaxy clustering and galaxy lensing have been shown to be very sensitive probes of cosmology and among the most promising to probe the nature of gravity and the neutrino mass. However, recent measurements of the void imprint on the lensed Cosmic Microwave Background (CMB) have been shown to be in tension with expectations based on LCDM simulations, hinting to a possibility of non-standard cosmological signatures due to massive neutrinos. In this work we use the DEMNUni cosmological simulations with massive neutrino cosmologies to study the neutrino impact on voids selected in photometric surveys, as well as on the void-CMB lensing cross-correlation. We show how the void properties observed in this way (size function, profiles) are affected by the presence of massive neutrinos compared to the neutrino massless case, and show how these can vary as a function of the selection method of the void sample. Further, we comment on the possibility for massive neutrinos to be the source of the aforementioned tension. Finally, we identify the most promising setup to detect signatures of massive neutrinos in the voids-CMB lensing cross-correlation and define a new quantity useful to distinguish among different neutrino masses by comparing future observations against predictions from simulations including massive neutrinos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Regional Characteristics of Observable Foreshocks

Abstract Measures of foreshock occurrence are systematically examined using earthquake catalogs for eight regions (Italy, southern California, northern California, Costa Rica, Onshore Japan, Alaska, Turkey, and Greece) after imposing a magnitude ≥3.0 completeness level. Foreshocks are identified using three approaches: a magnitude-dependent space + fixed-time windowing method, a nearest-neighbor clustering method, and a modified magnitude-dependent space + variable-time windowing method. The method with fixed-time windows systematically yields higher counts of foreshocks than the other two clustering methods. We find similar counts of foreshocks across the three methods when the magnitude aperture is equalized by including only earthquakes in the magnitude range M*−2≤ M< M*, in which M* is the mainshock magnitude. For most of the catalogs (excluding Italy and southern California), the measured b-values of the foreshocks of all region-specific mainshocks are lower by 0.1–0.2 than b-values of respective aftershocks. Allowing for variable-time windows results in relatively high probabilities of having at least one foreshock in Italy (∼43%–56%), compared to other regional catalogs. Foreshock probabilities decrease to 14%–41% for regions such as Turkey, Greece, and Costa Rica. Similar trends are found when requiring at least five foreshocks in a sequence to be considered. Estimates of foreshock probabilities for each mainshock are method dependent; however, consistent regional trends exist regardless of method, with regions such as Italy and southern California producing more observable foreshocks than Turkey and Greece. Some regions with relatively high background seismicity have comparatively low probabilities of detectable foreshock activity when using methods that account for variable background, possibly due to depletion of near-failure fault conditions by background activity.

Geochemistry & Geophysics↗