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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

CW EC-QCL-based sensor for simultaneous detection of H 2 O, HDO, N 2 O and CH 4 using multi-pass absorption spectroscopy

A sensor system based on a continuous wave, external-cavity quantum-cascade laser (CW EC-QCL) was demonstrated for simultaneous detection of atmospheric H 2 O, HDO, N 2 O and CH 4 using a compact, dense pattern multi-pass gas cell with an effective path-length of 57.6 m. The EC-QCL with a mode-hop-free spectral range of 1225-1285 cm -1 operating at similar to 7.8 mu m was scanned covering four neighboring absorption lines, for H 2 O at 1281.161 cm -1 , HDO at 1281.455 cm -1 , N 2 O at 1281.53 cm -1 and CH 4 at 1281.61 cm -1 . A first-harmonic-normalized wavelength modulation spectroscopy with second-harmonic detection (WMS-2f/1f) strategy was employed for data processing. An Allan-Werle deviation analysis indicated that minimum detection limits of 1.77 ppmv for H 2 O, 3.92 ppbv for HDO, 1.43 ppbv for N 2 O, and 2.2 ppbv for CH 4 were achieved with integration times of 50-s, 50-s, 100-s and 129-s, respectively. In conclusion, experimental measurements of ambient air are also reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2018

Siderophore profiling of co-habitating soil bacteria by ultra-high resolution mass spectrometry

The chemical structure of organic molecules profoundly impacts their interactions with metal ions and mineral phases in soils. Understanding the sources and cycling of metal-chelating compounds is therefore essential for predicting the bioavailability and transport of metals throughout terrestrial environments. We investigate the molecular speciation of organic molecules that solubilize trace metals in calcareous soils from Eastern Washington. Ultra-high performance Fourier transform ion cyclotron resonance mass spectrometry at 21 Tesla enabled fast and confident detection and identification of metal chelators that are produced by microbes that inhabit these soils based on screening for features that match diagnostic metal isotope patterns. We compared two approaches, one based on direct infusion using the incorporation of a rare isotope to validate true iron-binding features, and another based on separation with liquid chromatography and detection of isotopologues with coherent elution profiles. While the isotopic exchange method requires significantly shorter analysis time, nearly twice as many features were observed with liquid chromatography mass spectrometry (LCMS), mostly due to the reduction in ion suppression where major features limit the sensitivity of minor features. In addition, LCMS enabled the collection of higher quality fragmentation spectra and facilitated feature identification. Siderophores belonging to four major classes were identified, including ferrioxamines, pseudobactins, enterobactins, and arthrobactins. Each of these siderophores likely derives from a unique member of the microbial community, and each possesses different chemical characteristics and uptake pathways, likely contributing to fierce competition for iron within these soils. Our results provide insight into the metabolic pathways by which microbes that co-inhabit calcareous soils compete for this essential micronutrient.

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

Material transfers detection with seismic observations

We are exploring the use of data from a seismic network around a research nuclear reactor and isotope production facility at Oak Ridge National Laboratory to study activity patterns related to the transfer of nuclear material. A sensor network with eight seismometers was installed around the High Flux Isotope Reactor and the Radiochemical Engineering Development Center and started operating in October 2019. These data are used to detect and characterize operational events around facility. We are using those data to extract signals related to the movement of vehicles involved in the transport of nuclear materials (e.g., transportation of reactor fuel, targets, and product isotopes). Large vehicles produce mechanical energy that can be observed as seismic signals, either as the result of sound emanating from the vehicle engine or by the generation of surface waves as a response of the ground to the load of the vehicles. In particular, the station located near the entrance gate of the facility displays a clear seismic signature as vehicles cross a metal platform on the ground. This signal is characterized by sharp energy bursts that correspond to the number of axles on the vehicle. We developed and are testing an algorithm to identify sequences of energy bursts to count and characterize vehicles of different sizes. We are also exploring the use of seismic polarization analysis to measure the degree of polarization of the observed signals. These two techniques for detection and characterization of vehicles used for nuclear material transfers will be validated with seismic data from a targeted collection using different vehicles and routes similar to the ones used in real-life scenarios at the High Flux Isotope Reactor.

Marcillo, Omar↗
DOE OSTI2022

Regional epithelial cell diversity in the small intestine of pigs

Abstract Understanding regional distribution and specialization of small intestinal epithelial cells is crucial for developing methods to control appetite, stress, and nutrient uptake in swine. To establish a better understanding of specific epithelial cells found across different regions of the small intestine in pigs, we utilized single-cell RNA sequencing (scRNA-seq) to recover and analyze epithelial cells from duodenum, jejunum, and ileum. Cells identified included crypt cells, enterocytes, BEST4 enterocytes, goblet cells, and enteroendocrine (EE) cells. EE cells were divided into two subsets based on the level of expression of the EE lineage commitment gene, NEUROD1. NEUROD1hi EE cells had minimal expression of hormone-encoding genes and were dissimilar to EE cells in humans and mice, indicating a subset of EE cells unique to pigs. Recently discovered BEST4 enterocytes were detected in both crypts and villi throughout the small intestine via in situ staining, unlike in humans, where BEST4 enterocytes are found only in small intestinal villi. Proximal-to-distal gradients of expression were noted for hormone-encoding genes in EE cells and nutrient transport genes in enterocytes via scRNA-seq, demonstrating regional specialization. Regional gene expression in EE cells and enterocytes was validated via quantitative PCR (qPCR) analysis of RNA isolated from epithelial cells of different small intestinal locations. Though many genes had similar patterns of regional expression when assessed by qPCR of total epithelial cells, some regional expression was only detected via scRNA-seq, highlighting advantages of scRNA-seq to deconvolute cell type-specific regional gene expression when compared to analysis of bulk samples. Overall, results provide new information on regional localization and transcriptional profiles of epithelial cells in the pig small intestine.

Agriculture↗
DOE OSTI2024

Spread Spectrum Time Domain Reflectivity for String Monitoring in PV Power Plants (Final Technical Report)

This final report describes the methods and results of applying Spread Spectrum Time Domain Reflectivity (SSTDR) for String Monitoring in PV Power Plants for DE-EE0008169. The project created a new system for both detecting and locating electrical faults in photovoltaic systems. In this work, we address photovoltaic electric faults that are both common and costly. Based on interviews with photovoltaic power plant owners, operators, and maintainers, three types of faults are common and of significant interest: disconnects, ground faults, and arc faults. Disconnects can originate from many sources. They are often due to everyday events, such as lawnmowing (accidentally running over a cable), animals eating through the cables, or degradation that occurs over time due to corrosion or general degradation. Ground faults occur when the cables (for example, due to frayed insolation) connect to the ground, relaying current into the ground. These faults are particularly problematic since the ground faults are often intermittent. That is, ground faults commonly appear during rain storms due to a change in soil conductivity and then disappear when the rain ends. This makes the ground fault difficult to find because while current systems can detect the overall change in voltage and current associated with a ground fault, technicians are necessary to locate the fault. As a result, ground faults may disappear before the technician arrives at the power plant. Hence, locating and fixing ground faults often require multiple trips. We also study arc faults, which can result when nearby conductors create an arc of electrical current through the air. While less common, arc faults can be extremely dangerous. The energetic electrical arc can cause fires and destroy equipment, costing significant damage. Overall all three types of faults cost owners and operators money, either from the destruction of equipment or from technician time. Furthermore, while devices exist for detecting ground faults (ground fault circuit interrupters) and arc faults (arc fault circuit interrupter), these systems only search patterns of electrical current that correspond to each fault. This information cannot be used to locate the fault. In addition, these protection systems experience nuisance trips due to nearby electromagnetic interference, such as from a lawn mower or other motors that produce significant amounts of electromagnetic radiation. Hence, the overall goal of this project is to create an SSTDR tool that provides photovoltaic power plants with more reliable fault detection in addition to the localization of faults. SSTDR works by transmitting electrical signals into the photovoltaic string. Those signals reflect from impedance discontinuities (i.e., disconnects, ground faults, and arc faults). These faults are then detected by measuring the presence of a reflection at the SSTDR and can be located by identifying the location of that reflection in time. In addition, unlike current protection systems, these systems do not experience nuisance trips since their low amplitude, high frequency, and coded signal can by analyzed without interference from the regular operational voltage on the photovoltaic string.

14 SOLAR ENERGY↗
DOE OSTI2018

Detecting outliers in streaming time series data from ARM distributed sensors

The Atmospheric Radiation Measurement (ARM) Data Center at ORNL collects data from a number of permanent and mobile facilities around the globe. The data is then ingested to create high level scientific products. High frequency streaming measurements from sensors and radar instruments at ARM sites require high degree of accuracy to enable rigorous study of atmospheric processes. Outliers in collected data are common due to instrument failure or extreme weather events. Thus, it is critical to identify and flag them. We employed multiple univariate, multivariate and time series techniques for outlier detection methods and studied their effectiveness. First, we examined Pearson correlation coefficient which is used to measure the pairwise correlations between variables. Singular Spectrum Analysis (SSA) was applied to detect outliers by removing the anticipated annual and seasonal cycles from the signal to accentuate anomalies. K-means was applied for multivariate examination of data from collection of sensors to identify any deviation from expected and known patterns and identify abnormal observation. The Pearson correlation coefficient, SSA and K-means methods were later combined together in a framework to detect outliers through a range of checks. We applied the developed method to data from meteorological sensors at ARM Southern Great Plains site and validated against existing database of known data quality issues.

Lu, Yuping↗
DOE OSTI2013

Method of detecting genetic deletions identified with chromosomal abnormalities

Methods and compositions for staining based upon nucleic acid sequence that employ nucleic acid probes are provided. Said methods produce staining patterns that can be tailored for specific cytogenetic analyzes. Said probes are appropriate for in situ hybridization and stain both interphase and metaphase chromosomal material with reliable signals. The nucleic acids probes are typically of a complexity greater tha 50 kb, the complexity depending upon the cytogenetic application. Methods and reagents are provided for the detection of genetic rearrangements. Probes and test kits are provided for use in detecting genetic rearrangements, particlularly for use in tumor cytogenetics, in the detection of disease related loci, specifically cancer, such as chronic myelogenous leukemia (CML) and for biological dosimetry. Methods and reagents are described for cytogenetic research, for the differentiation of cytogenetically similar ut genetically different diseases, and for many prognostic and diagnostic applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) for accurate detection and counting of RNA copies in single cells

Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes. This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2 - 2/Ins2 ). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Two Pathways for Glutamate Biosynthesis in the Syntrophic Bacterium Syntrophus aciditrophicus

Here, the anaerobic metabolism of crotonate, benzoate, and cyclohexane carboxylate by Syntrophus aciditrophicus grown syntrophically with Methanospirillum hungatei provides a model to study syntrophic cooperation. Recent studies revealed that S. aciditrophicus contains Re -citrate synthase but lacks the common Si-citrate synthase. To establish whether the Re-citrate synthase is involved in glutamate synthesis via the oxidative branch of the Krebs cycle, we have used [1- 13 C]acetate and [1- 14 C]acetate as well as [ 13 C]bicarbonate as additional carbon sources during axenic growth of S. aciditrophicus on crotonate. Our analyses showed that labeled carbons were detected in at least 14 amino acids, indicating the global utilization of acetate and bicarbonate. The labeling patterns of alanine and aspartate verified that pyruvate and oxaloacetate were synthesized by consecutive carboxylations of acetyl coenzyme A (acetyl-CoA). The isotopomer profile and 13 C nuclear magnetic resonance (NMR) spectroscopy of the obtained [ 13 C]glutamate, as well as decarboxylation of [ 14 C]glutamate, revealed that this amino acid was synthesized by two pathways. Unexpectedly, only the minor route used Re -citrate synthase (30 to 40%), whereas the majority of glutamate was synthesized via the reductive carboxylation of succinate. This symmetrical intermediate could have been formed from two acetates via hydration of crotonyl-CoA to 4-hydroxybutyryl-CoA. 4-Hydroxybutyrate was detected in the medium of S. aciditrophicus when grown on crotonate, but an active hydratase could not be measured in cell extracts, and the annotated 4-hydroxybutyryl-CoA dehydratase (SYN_02445) lacks key amino acids needed to catalyze the hydration of crotonyl-CoA. Besides Clostridium kluyveri , this study reveals the second example of a microbial species to employ two pathways for glutamate synthesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Detection of long-range entanglement in gapped quantum spin liquids by local measurements

Topological order, reflected in long-range patterns of entanglement, is quantified by the topological entanglement entropy γ. We show that for gapped quantum spin liquids it is possible to extract γ using two-spin local correlators. We demonstrate our method for the gapped Z 2 Kitaev spin liquid on a honeycomb lattice with anisotropic interactions. We show that the γ = ln⁡2 for Z 2 topological order can be simply extracted from local two-spin correlators across two different bonds that involve only Majorana fermions, with an accuracy comparable to or higher than the Kitaev-Preskill construction. This implies that even though the ground state can be factorized into the product of Majorana and gauge sectors, the different superselection sectors of Z 2 gauge theory determined by global Wilson loop operators can be reflected locally in the Majorana sector.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Preliminary Evidence for a Virial Shock around the Coma Galaxy Cluster

Galaxy clusters, the largest gravitationally bound objects in the universe, are thought to grow by accreting mass from their surroundings through large-scale virial shocks. Due to electron acceleration in such a shock, it should appear as a γ-ray, hard X-ray, and radio ring, elongated toward the large-scale filaments feeding the cluster, coincident with a cutoff in the thermal Sunyaev–Zel’dovich (SZ) signal. However, no such signature was found until now, and the very existence of cluster virial shocks has remained a theory. We find preliminary evidence for a large γ-ray ring of ∼5 Mpc minor axis around the Coma cluster, elongated toward the large-scale filament connecting Coma and Abell 1367, detected at the nominal 2.7σ confidence level (5.1σ using control signal simulations). The γ-ray ring correlates both with a synchrotron signal and with the SZ cutoff, but not with Galactic tracers. The γ-ray and radio signatures agree with analytic and numerical predictions if the shock deposits ∼1% of the thermal energy in relativistic electrons over a Hubble time and ∼1% in magnetic fields. The implied inverse Compton and synchrotron cumulative emission from similar shocks can contribute significantly to the diffuse extragalactic γ-ray and low-frequency radio backgrounds. Our results, if confirmed, reveal the prolate structure of the hot gas in Coma, the feeding pattern of the cluster, and properties of the surrounding large-scale voids and filaments. The anticipated detection of such shocks around other clusters would provide a powerful new cosmological probe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Thin-film topological insulator-ferromagnet heterostructures for terahertz detection

An atomically thin topological insulator is investigated theoretically for long-wavelength photodetection when it interacts with a magnetic material. Through the coupling between top and bottom surfaces as well as the exchange interaction with the proximate ferromagnet, the distribution of optically excited carriers exhibits unique patterns that depend sensitively on the frequency of the incoming light. This effect results in the generation of strong nonzero photocurrent, leading potentially to room-temperature detection of far-infrared/THz radiation with the advantage of low noise and fast response. The ease of frequency tuning by an external electrical bias offers an added versatility in the realistic implementation.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Rain footprints on C-band synthetic aperture radar images of the ocean - Revisited

It is well known that rain leaves footprints on the sea surface that sometimes become visible on synthetic aperture radar (SAR) images. Rain cells can easily be detected on SAR images at all radar frequencies when they are associated with a downdraft pattern. But rain cells are not always associated with downdraft and rain can also occur in other forms, as stratified rain, rain bands, and squall lines. It turns out that radar signatures of rain at C-band are much more complex than at L- or X-band radar and that it is particularly difficult to identify unambiguously rain events on C-band SAR images acquired over the ocean. This is because C-band lies in the transition region where raindrops impinging onto the sea surface can increase (usually) or decrease the backscattered radar power and where volume scattering and attenuation by rain drops in the atmosphere are not always negligible (at very high rain rates). In order to get an insight into the physical mechanisms causing the C-band radar signatures of rain, we first revisit results obtained from historic laboratory and field experiments and multi-frequency/multi-polarization SAR data acquired during the SIR-C/X-SAR spaceshuttle mission in 1994. Then we analyze several C-band SAR images acquired by the European satellites Envisat and Sentinel-1A, and the Canadian satellite Radarsat-2 and compare them, whenever possible, with quasi-coincident and collocated weather radar images. The observational data show that, at low to medium rain rates, the main physical mechanism causing C-band radar signatures of rain is Bragg scattering at ring waves generated by the rain drops impinging onto the sea surface, which increase the radar backscatter. However, areas of increased radar backscatter are often accompanied by adjacent areas of decreased radar backscatter, which is due to attenuation of the Bragg waves by turbulence also generated by the impinging rain drops. Furthermore, we present a full-polarimetric Radarsat-2 SAR image of a rain cell together with a polarimetric decomposition analysis, which shows that the C-band radar signature of a rain cell is caused by surface scattering. The observation show that radar signatures of rain cells often contain segments, where the co-polarized as well the cross-polarized radar backscatter are strongly enhanced, which indicates non-Bragg scattering contributions to the scattering process. Furthermore, the polarimetric decomposition analysis shows that the C-band radar signature of a rain cell is dominated by surface scattering. Possible mechanisms, like scattering at splash products, are discussed. Whether the normalized radar cross section (NRCS) due to rain is increased or decreased depends on rain rate, wind speed, incidence angle, and history of the rain event. At low to moderate wind speeds (< 10 ms - 1 ) and low to medium high rain rates (< 50 mm h - 1 ), the NRCS is usually increased by up to 8 dB, and at high wind speeds (> 10 m s - 1 ) and low to high rain rates (but < 50 mm h - 1 ), the NRCS is usually decreased by up to 3 dB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Salivary mycobiome dysbiosis and its potential impact on bacteriome shifts and host immunity in oral lichen planus

The biodiversity of the mycobiome, an important component of the oral microbial community, and the roles of fungal–bacterial and fungal–immune system interactions in the pathogenesis of oral lichen planus (OLP) remain largely uncharacterized. In this study, we sequenced the salivary mycobiome and bacteriome associated with OLP. First, we described the dysbiosis of the microbiome in OLP patients, which exhibits lower levels of fungi and higher levels of bacteria. Significantly higher abundances of the fungi Candida and Aspergillus in patients with reticular OLP and of Alternaria and Sclerotiniaceae_unidentified in patients with erosive OLP were observed compared to the healthy controls. Aspergillus was identified as an “OLP-associated” fungus because of its detection at a higher frequency than in the healthy controls. Second, the co-occurrence patterns of the salivary mycobiome–bacteriome demonstrated negative associations between specific fungal and bacterial taxa identified in the healthy controls, which diminished in the reticular OLP group and even became positive in the erosive OLP group. Moreover, the oral cavities of OLP patients were colonized by dysbiotic oral flora with lower ecological network complexity and decreased fungal–Firmicutes and increased fungal–Bacteroidetes sub-networks. Third, several keystone fungal genera (Bovista, Erysiphe, Psathyrella, etc.) demonstrated significant correlations with clinical scores and IL-17 levels. Thus, we established that fungal dysbiosis is associated with the aggravation of OLP. Fungal dysbiosis could alter the salivary bacteriome or may reflect a direct effect of host immunity, which participates in OLP pathogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Neutron-capture nucleosynthesis in the first stars

Recent studies suggest that metal-poor stars enhanced in carbon but containing low levels of neutron-capture elements may have been among the first to incorporate the nucleosynthesis products of the first generation of stars. We have observed 16 stars with enhanced carbon or nitrogen using the MIKE Spectrograph on the Magellan Telescopes at Las Campanas Observatory and the Tull Spectrograph on the Smith Telescope at McDonald Observatory. We present radial velocities, stellar parameters, and detailed abundance patterns for these stars. Strontium, yttrium, zirconium, barium, europium, ytterbium, and other heavy elements are detected. In four stars, these heavy elements appear to have originated in some form of r-process nucleosynthesis. In one star, a partial s-process origin is possible. The origin of the heavy elements in the rest of the sample cannot be determined unambiguously. The presence of elements heavier than the iron group offers further evidence that zero-metallicity rapidly rotating massive stars and pair instability supernovae did not contribute substantial amounts of neutron-capture elements to the regions where the stars in our sample formed. If the carbon- or nitrogen-enhanced metal-poor stars with low levels of neutron-capture elements were enriched by products of zero-metallicity supernovae only, then the presence of these heavy elements indicates that at least one form of neutron-capture reaction operated in some of the first stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Observables for recoil identification in high-definition Gas Time Projection Chambers

Directional detection of nuclear recoils is broadly desirable in nuclear and particle physics. At low recoil energies, this capability may be used to confirm the cosmological origin of a dark matter signal, to penetrate the so-called neutrino floor, or to distinguish between different neutrino sources. Gas Time Projection Chambers (TPCs) can enable directional recoil detection if the readout granularity is sufficiently high, as is the case when micro-pattern gaseous detectors (MPGDs) are utilized. A key challenge in such detectors is identifying and rejecting background electron recoil events caused by gamma rays from radioactive contaminants in the detector materials and the environment. We define new observables that can distinguish electron and nuclear recoils, even at keV-scale energies, based on the simulated ionization's topology. Here we perform a simulation study that shows these observables outperform the traditionally used discriminant, dE/dx, by up to three orders of magnitude. Furthermore, these new observables work well even at ionization energies well below 10keV and remain robust even in the regime where directionality fails.

79 ASTRONOMY AND ASTROPHYSICS↗