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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone ( n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Wu, Chao-Yi↗
NASA NTRS2018

Behavior of Langmuir Probes in Non-Equilibrium Plasmas

Langmuir probes are diagnostic tools used to determine electron temperature, number density, and plasma potential. Irving Langmuir first used an electrostatic probe in the 1920s to find these characteristics in ionized gases. Single, double, and triple Langmuir probes are commonly used in plasma diagnostics because of their relative simplicity. In the single probe, a swept voltage is applied between the probe tip and circuit common to acquire a waveform showing the collected current as a function of applied voltage. A double Langmuir probe consists of two tips, both inserted into the plasma, with a voltage applied between them. As this voltage is swept, a current-voltage characteristic is measured. In a triple probe three probe tips are electrically coupled to each other with constant non-swept voltages applied between each of the tips. The voltages are selected to represent three points on the single Langmuir probe I-V curve. Elimination of the voltage sweep makes it possible to measure time-varying plasma properties in transient plasmas. Triple Langmuir probe measurements have been widely employed for various types of plasmas, including pulsed and time-varying plasmas such as those seen in pulsed plasma thrusters (PPTs), dense plasma focus devices, plasma flows, and fusion experiments. The typical Langmuir probe analysis for determining electron temperature and number density of the plasma (for a single, double, or triple Langmuir probe) includes an assumption that the plasma is in thermal equilibrium. While the this assumption may be justified for some applications, it is unlikely that it is fully justifiable for pulsed and time-varying plasmas or for the entire time a plasma device is in use. In the present work, we model the responses of Langmuir probes as they are inserted into a range of simple equilibrium and non-equilibrium plasmas. We return to basic governing equations of probe current collection and compute the current to the probes for a distribution function consisting of two Maxwellian distributions with different temperatures (the two-temperature Maxwellian). A variation of this method is also employed, where one of the Maxwellians is offset from zero (in velocity space) to add a suprathermal beam of electrons to the tail of the main Maxwellian distribution (the bump-on-the-tail distribution function). For a range of parameters in these non-Maxwellian distributions, we compute the current collection to the probes. Comparing the distribution function that was assumed a priori with the plasma density and temperature one would infer when applying standard probe theory to analyze the collected currents serves to illustrate the effect a non- Maxwellian plasma would have on results interpreted using the equilibrium probe current collection theory, allowing us to state the magnitudes of these deviations as a function of the assumed distribution function properties.

Polzin, Kurt A.↗
NASA NTRS2003

Characterization of Downstream Ion Energy Distributions From a High Current Hollow Cathode in a Ring Cusp Discharge Chamber

The presence of energetic ions produced by a hollow cathodes operating at high emission currents (greater than 10 Angstroms) has been documented in the literature. As part of an ongoing effort to uncover the underlying physics of the formation of these ions, ion efflux from a high current hollow cathode operating in an ion thruster discharge chamber was investigated. Using a spherical sector electrostatic energy analyzer located downstream of the discharge cathode, the ion energy distribution over a 0 to 60 eV energy range was measured. The sensitivity of the ion energy distribution function to zenith angle was also assessed at 3 different positions: 0, 15, and 25 degrees. The measurements suggest that the majority of the ion current at the measuring point falls into the analyzer with an energy approximately equal to the discharge voltage. The ion distribution, however, was found to be quite broad. The high energy tail of the distribution function tended to grow with increasing discharge current. Sensitivity of the profiles to flow rate at fixed discharge current was also investigated. A simple model is presented that provides a potential mechanism for the production of ions with energies above the discharge voltage.

Foster, John E.↗
DOE OSTI2021

Optimization and supervised machine learning methods for fitting numerical physics models without derivatives

Here, we address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of particular interest is the performance of optimization-based training algorithms when dozens, rather than millions or more, of training data are available and when the expense of the model places limitations on the number of concurrent model evaluations that can be performed. As a case study, we consider the Fayans energy density functional model, which has characteristics similar to many model fitting and calibration problems in nuclear physics. We analyze hyperparameter tuning considerations and variability associated with stochastic optimization algorithms and illustrate considerations for tuning in different computational settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nanoscale Mass Spectrometry Multimodal Imaging via Tip-Enhanced Photothermal Desorption

Materials ranging from adhesives, pharmaceuticals, lubricants, and personal care products are traditionally studied using macroscopic characterization techniques. However, their functionality is in reality defined by details of chemical organization on often noncrystalline matter with characteristic length scales on the order of microns to nanometers. Additionally, these materials are traditionally difficult to analyze using standard vacuum-based approaches that provide nanoscale chemical characterization due to their volatile and beam-sensitive nature. Therefore, approaches that operate under ambient conditions need to be developed that allow probing of nanoscale chemical phenomena and correlated functionality. Here, we demonstrate a tool for probing and visualizing local chemical environments and correlating them to material structure and functionality using advanced multimodal chemical imaging on a combined atomic force microscopy (AFM) and mass spectrometry (MS) system using tip-enhanced photothermal desorption with atmospheric pressure chemical ionization (APCI). We demonstrate enhanced performance metrics of the technique for correlated imaging and point sampling and illustrate the applicability for the analysis of trace chemicals on a human hair, additives in adhesives on paper, and pharmaceuticals samples notoriously difficult to analyze in a vacuum environment. Overall, this approach of correlating local chemical environments to structure and functionality is key to advancing research in many fields ranging from biology, to medicine, to material science.

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

ADAPTIVE GROUP TESTING WITH MISMATCHED MODELS

Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups, instead of testing each individual in the population, can be more efficient. In this work, we consider noisy adaptive group testing design with specific test sensitivity and specificity that select the optimal group given previous test results based on pre-selected utility function. As in prior studies on group testing, we model this problem as a sequential Bayesian Optimal Experimental Design (BOED) to adaptively design the groups for each test. We analyze the required number of group tests when using the updated posterior on the infection status and the corresponding Mutual Information (MI) as our utility function for selecting new groups. More importantly, we study how the potential bias on the ground-truth noise of group tests may affect the group testing sample complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhanced MPM framework with multipatch isogeometric analysis for geotechnical applications

Achieving stable stress solutions at large strains using the Material Point Method (MPM) is challenging due to the accumulation of errors associated with geometry discretization, cell-crossing noise, and volumetric locking. Several simplified attempts exist in the literature to mitigate these errors, including higher-order frameworks. However, the stability of the MPM solution in such frameworks has been limited to simple geometries and the single-phase formulation (i.e., neglecting pore fluid). Although never explored, multipatch isogeometric analysis offers desirable qualities to simulate complex geometries while mitigating errors in the MPM. The degree of required high-order spatial integration has also never been investigated to infer a minimum limit for the stability of the stress solution in MPM. This paper presents a general-purpose numerical framework for simulating stable stresses in porous media, capturing both near incompressibility and multiphase interactions. First, the numerical framework is presented considering Non-Uniform Rational B-splines (NURBS) to perform isogeometric analysis (IGA) in MPM. Additionally, a volumetric strain smoothing algorithm is used to alleviate errors associated with volumetric locking. Second, the manifestation of cell-crossing errors is assessed via a series of problems with orders ranging from linear to cubic interpolation functions. Third, the use of NURBS is investigated and verified for problems with circular geometries. Finally, multipatch analysis is deployed to simulate plane strain and 3D penetration in soils, considering nearly incompressible elastoplastic (total stress) analysis and fully-coupled hydro-mechanical (effective stress) analysis. The stability of the solution is also analyzed for different constitutive models. From the results, it can be concluded that the framework using cubic interpolation functions with strain smoothing is the most convenient, presenting stable stress solutions for a broad range of multiphase geotechnical applications.

58 GEOSCIENCES↗
DOE OSTI2023

Multiscale spatial analysis of fracture nodes in two dimensions

Spatial arrangement of fractures as a function of scale is an important component of fracture quantification for inferential and predictive modeling. Available methods that analyze fracture spatial arrangement are based on one-dimensional spacing data; therefore, they are limited to semi-parallel fractures. Such methods cannot be applied to fracture networks in higher dimensions, particularly when fractures have different orientations. Here to characterize fracture arrangements in two dimensions, we propose using Ripley’s K-function, as a method of point pattern analysis, to quantify spatial arrangement of fracture nodes. Fracture nodes, such as barycenters, intersection points, and tips, are point-based representations of fracture locations and connectivity within the fracture network. We introduce formulations for isotropic as well as directional analyses of spatial arrangement. In addition, we derive formulations for edge correction in circular and rectangular study domains. Finally, we demonstrate applications of Ripley’s K-function on two natural fracture datasets. Our proposed method supports quantification and characterization of fracture spatial arrangements that allow practitioners to build representative models of fractures in the subsurface.

02 PETROLEUM↗
DOE OSTI2020

Powering the Blue Economy - Ocean Observing Use Cases Report

The "Powering the Blue Economy" (PBE) Report identified ocean observing and autonomous underwater vehicle (AUV) recharge as near-term markets with significant potential for marine energy integration when specified qualitative and quantitative assessment criteria were used. The ocean observing market (including AUVs) scored high relative to other markets analyzed in the PBE Report with respect to market size, marine renewable energy (MRE) compatibility with functional requirements, competitive positioning (i.e., MRE versus other power sources), path to market, and alignment to the Department of Energy Water Power Technologies Office (WPTO) and other agencies' objectives. Based on this scoring, Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL) pursued the development of five use cases that represent a spectrum of ocean observation platforms, fixed and mobile, to explore the present and future power needs that might be satisfied by marine energy. The use cases were described and analyzed for this potential by examining current missions and future needs, evaluating the mix of renewable energy sources that might satisfy those needs at sea, and understanding the overall cost drivers that might enable this application.

14 SOLAR ENERGY↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
DOE OSTI2024

Learning Functions Varying along a Central Subspace

Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of an s-Hölder function in $R^D$ which varies along a central subspace of dimension $d$ while $d \ll D$. A direct approximation of $f$ in $R^D$ with an accuracy $\varepsilon$ requires the number of samples in the order of $\varepsilon^{-(2s+D)/s}$. In this paper, we analyze the generalized contour regression (GCR) algorithm for the estimation of the central subspace and use piecewise polynomials for function approximation. GCR is among the best estimators for the central subspace, but its sample complexity is an open question. In this paper, we partially answer this questions by proving that if a variance quantity is exactly known, GCR leads to a mean squared estimation error of $O(n^{-1})$ for the central subspace. The estimation error of this variance quantity is also given in this paper. The mean squared regression error of $f$ is proved to be in the order of $(n/\log n)^{-\frac{2s}{2s+d}}$, where the exponent depends on the dimension of the central subspace instead of the ambient space . This result demonstrates that GCR is effective in learning the low-dimensional central subspace. We also propose a modified GCR with improved efficiency. Here, the convergence rate is validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2019

Behavior of Triple Langmuir Probes in Non-Equilibrium Plasmas

A model of current collection in Langmuir probes is used to investigate the effect representative non- equilibrium plasmas under various conditions have on the electron temperature and number density that would be calculated through analysis of the probe collection characteristics. The model uses the distribution function to calculate the charged particle flux to a probe and then, for fixed applied voltages, the current values that satisfy continuity in the probes are determined. The triple probe is not scanned in voltage, so there is no practical way using experimental triple probe data to determine if the plasma is in equilibrium. As a consequence, a triple probe analysis typically relies on the assumption that the plasma is in equilibrium. Proceeding from this point, the numerically-generated non-equilibrium triple probe data are analyzed assuming that the plasma is in equilibrium, with the data compared to the initial distribution function inputs of plasma temperature and number density to determine the effect the non-equilibrium distribution has on plasma measurements. The temperature and number density are both significantly affected when a fraction of the particles in the distribution are shifted from the equilibrium configuration into the non-equilibrium part of the distribution function. For all instances studied, the computed electron temperature and number density are extremely sensitive to small deviations from equilibrium ( ≤ 5% of the plasma shifted into the non-equilibrium function). Shifting more of the plasma into the non-equilibrium distribution beyond this initial level does not produce a significant additional shift in the computed plasma properties.

Polzin, Kurt A.↗
NASA NTRS1981

Water frost and ice - The near-infrared spectral reflectance 0.65-2.5 microns

The spectral reflectance of water frost and frost on ice as a function of temperature and grain size is presented with 1-1/2% spectral resolution in the 0.65- to 2.5-micron wavelength region. The well-known 2.0-, 1.65-, and 1.5-micron solid water absorption bands are precisely defined along with the little studied 1.25-micron band and the previously unidentified (in reflectance) 1.04-, 0.90-, and 0.81-micron absorption bands. The 1.5-microns band complex is quantitatively analyzed using a nonlinear least squares algorithm to resolve the band into four Gaussian components as a function of grain size and temperature. It is found that the 1.65-micron component, which was thought to be a good temperature sensor, is highly grain-size dependent and poorly suited to temperature sensing. Another Gaussian component appears to show a dependence of width on grain size while being independent of temperature. The relative apparent band depths are different for frost layers on ice than for thick layers of frost and may explain the apparent band depths seen in many planetary reflectance spectra.

Clark, R. N.↗
DOE OSTI2020

Ambient Mass Spectrometry Rapid Analysis Methods for Trace Inorganics

The ability to rapidly detect and characterize the chemistry of trace quantities of inorganic species has multiple potential applications, including nuclear safeguards, nuclear forensics, and environmental monitoring. Some mass spectrometric methods currently exist, but they are costly and time-intensive, often destroying chemical information in the process of ionization. Ambient mass spectrometry is a relatively new analytical method in which samples are introduced to the mass spectrometer from atmospheric pressure, rather than from high vacuum. Ambient MS can operate with gentler ionization methods such as electrospray ionization (ESI) and paper-spray ionization (PSI), reducing fragmentation and preserving the chemical information of the analyte. This may be useful for determining the intended purpose of material in a sample. Strontium, part of nuclear fallout, is analyzed here to test these new analytical methods for viability. Electrospray ionization mass spectrometry functions by spraying the sample as a solution through a needle charged to a few kV. A nebulizing gas flows out along with the sample to create the spray before droplets enter a drying chamber with heated drying gas to remove the solvent molecules. From there bare analyte ions enter the mass spectrometer. This method has the advantage of relatively fast sample preparation and stable ionization. More stable ionization produces higher-quality data. Paper-spray ionization mass spectrometry operates by applying sample to a paper triangle. The paper triangle is then electrified in a metal clamp to a few kV before spray solvent is applied. The spray solvent transports analyte in the sample to the tip, where it is ionized and enters the mass spectrometer. One advantage of this method is reduced sample preparation - an analyst can even swipe a surface and cut a triangle directly from the swipe. The data analysis serves to rapidly process tens of samples all at once. The mass spectrometer outputs a file containing a few hundred spectra for each sample. The pipeline processes each one to identify peaks of interest. A full palette of data analysis metrics are generated, including plots of the raw data and plots of calculated values such as isotopic ratios. The pipeline filters the raw data to remove noise and background peaks, as well as peaks representing detector saturation. A heat map is generated to show the overall spectrum in mass-time- intensity space. Post-processing, we obtained the desired calibration curves, demonstrating that both ESI-MS and PSI-MS are viable methods for quantifying trace amounts of inorganics while retaining their chemical speciation information. ESI-MS demonstrated a linear response region of 100 ppb to 20 ppm, with a limit-of-detection of around 100 ppb. At top left is an example spectrum with the {sup 88}SrNO{sub 3} peak identified to show the retention of chemical speciation. PSI-MS showed a linear response region of around 1 ppb to 1 ppm, with a limit-of-detection of near 1 ppb. PSI-MS produced more erratic results than ESI-MS, likely due to the variability in ionization resulting from manual positioning of the paper triangle near the MS inlet. Future efforts will focus on improving the repeatability of PSI-MS as well as analyzing other species of interest such as cerium and uranium. Improvements will also be made to create more reliable data extraction and intelligent data filtering.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1992

Automated support for experience-based software management

To effectively manage a software development project, the software manager must have access to key information concerning a project's status. This information includes not only data relating to the project of interest, but also, the experience of past development efforts within the environment. This paper describes the concepts and functionality of a software management tool designed to provide this information. This tool, called the Software Management Environment (SME), enables the software manager to compare an ongoing development effort with previous efforts and with models of the 'typical' project within the environment, to predict future project status, to analyze a project's strengths and weaknesses, and to assess the project's quality. In order to provide these functions the tool utilizes a vast corporate memory that includes a data base of software metrics, a set of models and relationships that describe the software development environment, and a set of rules that capture other knowledge and experience of software managers within the environment. Integrating these major concepts into one software management tool, the SME is a model of the type of management tool needed for all software development organizations.

Valett, Jon D.↗
NASA NTRS1990

Dielectric function of InGaAs in the visible

Measurements are reported of the dielectric function of thermodynamically stable In(x)Ga(1-x)As in the composition range 0.3 equal to or less than X = to or less than 0.7. The optically thick samples of InGaAs were made by molecular beam epitaxy (MBE) in the range 0.4 = to or less than X = to or less than 0.7 and by metal-organic chemical vapor deposition (MOCVD) for X = 0.3. The MBE made samples, usually 1 micron thick, were grown on semi-insulating InP and included a strain release structure. The MOCVD sample was grown on GaAs and was 2 microns thick. The dielectric functions were measured by variable angle spectroscopic ellipsometry in the range 1.55 to 4.4 eV. The data was analyzed assuming an optically thick InGaAs material with an oxide layer on top. The thickness of this layer was estimated by comparing the results for the InP lattice matched material, i.e., X = 0.53, with results published in the literature. The top oxide layer mathematically for X = 0.3 and X = 0.53 was removed to get the dielectric function of the bare InGaAs. In addition, the dielectric function of GaAs in vacuum, after a protective arsenic layer was removed. The dielectric functions for X = 0, 0.3, and 0.53 together with the X = 1 result from the literature to evaluate an algorithm for calculating the dielectric function of InGaAs for an arbitrary value of X(0 = to or less than X = to or less than 1) were used. Results of the dielectric function calculated using the algorithm were compared with experimental data.

Alterovitz, S. A.↗
NASA NTRS1990

Dielectric function of InGaAs in the visible

Measurements are reported of the dielectric function of thermodynamically stable In(x)Ga(1-x)As in the composition range 0.3 equal to or less than X = to or less than 0.7. The optically thick samples of InGaAs were made by molecular beam epitaxy (MBE) in the range 0.4 = to or less than X = to or less than 0.7 and by metal-organic chemical vapor deposition (MOCVD) for X = 0.3. The MBE made samples, usually 1 micron thick, were grown on semi-insulating InP and included a strain release structure. The MOCVD sample was grown on GaAs and was 2 microns thick. The dielectric functions were measured by variable angle spectroscopic ellipsometry in the range 1.55 to 4.4 eV. The data was analyzed assuming an optically thick InGaAs material with an oxide layer on top. The thickness of this layer was estimated by comparing the results for the InP lattice matched material, i.e., X = 0.53, with results published in the literature. The top oxide layer mathematically for X = 0.3 and X = 0.53 was removed to get the dielectric function of the bare InGaAs. In addition, the dielectric function of GaAs in vacuum, after a protective arsenic layer was removed. The dielectric functions for X = 0, 0.3, and 0.53 together with the X = 1 result from the literature to evaluate an algorithm for calculating the dielectric function of InGaAs for an arbitrary value of X (0 = to or less than X = to or less than 1) were used. Results of the dielectric function calculated using the algorithm were compared with experimental data.

Alterovitz, S. A.↗
DOE OSTI2021

Metagenomic strategies identify diverse integron–integrase and antibiotic resistance genes in the Antarctic environment

The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder. We identified 53 ARGs in 15 metagenomes, but only four were classified as GCs, one in MTG12 metagenome (Continental Antarctica), encoding an aminoglycoside-modifying enzyme (AAC(6´)acetyltransferase) and the other three in CS1 metagenome (Maritime Antarctica). One of these genes encodes a class D β-lactamase (blaOXA-205) and the other two are located in the same contig. One is part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.

59 BASIC BIOLOGICAL SCIENCES↗