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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗
NASA NTRS1972

Image correlation and sampling study

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed.

Popp, D. J.↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Impact of Acid–Base Stimulation Sequence on Mineral Stability for Tight/Impermeable Unconventional Carbonate-Rich Rocks: A Delaware Basin Case Study

We report mineral precipitation due to reactions with injected fluids during unconventional fracture stimulation is a well-recognized problem. The goal of this study is to evaluate secondary mineral precipitation and permeability attenuation under chemical injection scenarios specific to the Delaware basin. Whole cylindrical cores (2.54 cm diameter and 2.54 cm height) and ground shale (150–250 μm) from the carbonate-rich Bone Spring Formation, Delaware Basin TX (Leonardian), were reacted at 80 °C and 85 bar using a hydraulic fracturing fluid (HFF) recipe and an injection sequence typical of the Delaware Basin. The reacted shales and solutions were analyzed using a variety of laboratory- and synchrotron-based techniques to characterize both the chemical and spatial distributions of secondary mineral precipitation and identify changes in permeability and mineralogy. This carbonate-rich shale (>84% calcite) rapidly neutralized the acidic HFF. Synchrotron-based X-ray fluorescence mapping coupled with X-ray absorption spectroscopy (both bulk and micro) showed that most of the iron was in an oxidized form prior to exposure to HFF and that almost all iron(II) became fully oxidized after the reaction. Scanning electron microscopy images of the ground shale samples primarily identified iron(oxyhydr)oxide microcrystals on grain surfaces. A few small isolated iron-rich areas also contained sulfur, suggesting that some pyrite was preserved when isolated within a calcite crystal but that most was oxidized. The rapid neutralization of the acid spearhead in these carbonate-rich samples demonstrates that the acid spearhead is useful for initiating fractures in extremely calcite-rich rocks but does little to enhance rock permeability. This suggests that the impact of the acid spearhead is significantly smaller for carbonate-rich shales compared to clay-rich shales, which has broad implications for acidizing in carbonate-rich shale formations and iron transformations within these shales.

58 GEOSCIENCES↗
DOE OSTI2021

Calibration of the Hα Age–Activity Relation for M Dwarfs

In this work, we calibrate the relationship between Hα emission and M dwarf ages. We compile a sample of 892 M dwarfs with Hα equivalent width (Hα EW) measurements from the literature that are either comoving with a white dwarf of known age (21 stars) or in a known young association (871 stars). In this sample we identify 7 M dwarfs that are new candidate members of known associations. By dividing the stars into active and inactive categories according to their Hα EW and spectral type (SpT), we find that the fraction of active dwarfs decreases with increasing age, and the form of the decline depends on SpT. Using the compiled sample of age calibrators, we find that Hα EW and fractional Hα luminosity (L {sub Hα}/L {sub bol}) decrease with increasing age. Hα EW for SpT ≤ M7 decreases gradually up until ∼1 Gyr. For older ages, we found only two early M dwarfs that are both inactive and seem to continue the gradual decrease. We also found 14 mid-type M dwarfs, out of which 11 are inactive and present a significant decrease in Hα EW, suggesting that the magnetic activity decreases rapidly after ∼1 Gyr. We fit L {sub Hα}/L {sub bol} versus age with a broken power law and find an index of −0.11{sub −0.01}{sup +0.02} for ages ≲776 Myr. The index becomes much steeper at older ages, but a lack of field age-calibrators (≫1 Gyr) leaves this part of the relation far less constrained. Finally, from repeated independent measurements for the same stars, we find that 94% of them have a level of Hα EW variability ≤5 Å at young ages (<1 Gyr).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Miscentring of optical galaxy clusters based on Sunyaev–Zeldovich counterparts

ABSTRACT The ‘miscentring effect’, i.e. the offset between a galaxy cluster’s optically defined centre and the centre of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalogue from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev–Zeldovich cluster catalogue from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \le z \le 1.4$ over an area of 469 deg$^2$. By modelling the distribution of centring offsets in this fiducial sample, we find a miscentred fraction (corresponding to clusters offset by more than 330 kpc) of ∼25 per cent, a value consistent with previous miscentring studies. We examine the image of each miscentred cluster in our sample and identify one of several reasons to explain the miscentring. Some clusters show significant miscentring for astrophysical reasons, i.e. ongoing cluster mergers. Others are miscentred due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentring from the sample, we find a considerably smaller miscentred fraction, $\sim 10~\,\rm per\,cent$. We show that the gravitational lensing signal within 1 Mpc of miscentred clusters is considerably smaller than that of well-centred clusters, and we suggest that the ACT SZ centres are a better estimate of the true cluster potential centroid.

Ding, Jupiter (ORCID:0000000296119799)↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Population genomics of the pathogenic yeast Candida tropicalis identifies hybrid isolates in environmental samples

Candida tropicalis is a human pathogen that primarily infects the immunocompromised. Whereas the genome of one isolate, C. tropicalis MYA-3404, was originally sequenced in 2009, there have been no large-scale, multi-isolate studies of the genetic and phenotypic diversity of this species. Here, we used whole genome sequencing and phenotyping to characterize 77 isolates of C. tropicalis from clinical and environmental sources from a variety of locations. We show that most C. tropicalis isolates are diploids with approximately 2–6 heterozygous variants per kilobase. The genomes are relatively stable, with few aneuploidies. However, we identified one highly homozygous isolate and six isolates of C. tropicalis with much higher heterozygosity levels ranging from 36–49 heterozygous variants per kilobase. Our analyses show that the heterozygous isolates represent two different hybrid lineages, where the hybrids share one parent (A) with most other C. tropicalis isolates, but the second parent (B or C) differs by at least 4% at the genome level. Four of the sequenced isolates descend from an AB hybridization, and two from an AC hybridization. The hybrids are MTLa/α heterozygotes. Hybridization, or mating, between different parents is therefore common in the evolutionary history of C. tropicalis. The new hybrids were predominantly found in environmental niches, including from soil. Hybridization is therefore unlikely to be associated with virulence. In addition, we used genotype-phenotype correlation and CRISPR-Cas9 editing to identify a genome variant that results in the inability of one isolate to utilize certain branched-chain amino acids as a sole nitrogen source.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1980

Lunar highland melt rocks - Chemistry, petrology and silicate mineralogy

A selected suite containing several of the largest samples of lunar highland melt rocks includes impact melt specimens (anorthositic gabbro, low-K Fra Mauro) and volcanic specimens (intermediate-K Fra Mauro). Although previous assumptions of LKFM volcanism have fallen into disfavor, no fatal arguments against this hypothesis have been presented, and the evidence of a possibly 'inherited igneous' olivine-plagioclase cosaturation provides cause for keeping a volcanic LKFM hypothesis viable. Comparisons of silicate mineralogy with melt rock compositions provide information on the specimen's composition and cooling history. Plagioclase-rock compositions can be matched to the experimentally determined equilibria for appropriate samples to identify melt rocks with refractory anorthitic clasts. Olivine-rock compositions indicate that melt rock vitrophyres precipitate anomalously Fe-rich olivine; the cause of this anomaly is not immediately evident. The Al-Ti and Ca-Fe-Mg zonation in pyroxene provide information on relative cooling rates of highland melt rocks, but Cr- and Al-content (where Al-rich low-Ca pyroxene cores are preserved in rapidly cooled samples) can be correlated with composition of the host rock.

Vaniman, D. T.↗
NASA NTRS2018

Direct Characterization of Comets and Asteroids via Cosmic Dust Analysis from the Deep Space Gateway

The Deep Space Gateway (DSG) may provide a platform for direct sampling of a large number of comets and asteroids, through employment of an instrument for characterizing dust from these bodies. Every year, the Earth traverses through debris streams of dust and small particles from comets and asteroids in Earth-crossing orbits, generating short-lived outbursts of meteor activity commonly known as "meteor showers" (Figure 1). The material in each debris stream originates from a distinct parent body, many of which have been identified. By sampling this material, it is possible to quantitatively analyze the composition of a dozen or more comets and asteroids (See Figure 2, following page) without leaving cislunar space.

Fries, M.↗
DOE OSTI2025

BONCAT-Live for isolation and cultivation of active environmental bacteria

In diverse environments, microbes drive a myriad of processes, from geochemical and nutrient cycling to interspecies interactions, including associations with plants and animals. Their physiological state is dynamic and impacted by abiotic and biotic conditions, responding to environmental fluctuations by changes in cellular metabolism, according to their genetic potential. Molecular, cellular, and genomic approaches can identify and measure microbial responses and adaptation to environmental changes in native communities. However, isolating individual microbial cells that respond to specific changes for cultivation has been difficult. To address this, we implemented a novel bacterial isolation approach (BONCAT-Live) by integrating bio-orthogonal non-canonical amino acid tagging (BONCAT) in diverse native communities, with isolation and cultivation of cells responding to specific stimuli, at different time scales. In frozen Arctic permafrost samples, we identified and isolated dormant bacteria that become active after thawing under native or nutrient-enriched conditions. From the Populus tree rhizosphere, we isolated strains that thrive under high concentrations of root exudates that act as defense compounds and nutrients. In the human microbiome, we identified and isolated bacteria that rapidly proliferated when exposed to metabolites provided by the host or other co-occurring microbes. Further characterization of isolated bacterial strains will provide opportunities for in-depth determination of how these microbes adapt to changes in their environments, individually and as part of model communities.

Analytical Methods↗
DOE OSTI2020

Increasing the census of ultracool dwarfs in wide binary and multiple systems using Dark Energy Survey DR1 and Gaia DR2 data

ABSTRACT We present the discovery of 255 binary and 6 multiple system candidates with wide (> 5 arcsec) separation composed by ultracool dwarfs (UCDs) companions to stars, plus nine double ultracool dwarf systems. These systems were selected based on common distance criteria. About 90 per cent of the total sample has proper motions available and 73 per cent of the systems also satisfy a common proper motion criterion. The sample of ultracool candidates was taken from the Dark Energy Survey (DES) and the candidate stellar primaries are from Gaia DR2 and DES data. We compute chance alignment probabilities in order to assess the physical nature of each pair. We find that 174 candidate pairs with Gaia DR2 primaries and 81 pairs with a DES star as a primary have chance alignment probabilities $\lt 5{{\ \rm per\ cent}}$. Only nine candidate systems composed of two UCDs were identified. The sample of candidate multiple systems is made up of five triple systems and one quadruple system. The majority of the UCDs found in binaries and multiples are of early L type and the typical wide binary fraction over the L spectral types is $2\!-\!4{{\ \rm per\ cent}}$. Our sample of candidate wide binaries with UCDs as secondaries constitutes a substantial increase over the known number of such systems, which are very useful to constrain the formation and evolution of UCDs.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

Carbon and Oxygen Isotope Measurements of Ordinary Chondrite (OC) Meteorites from Antarctica Indicate Distinct Carbonate Species Using a Stepped Acid Extraction Procedure

The purpose of this study is to characterize the stable isotope values of terrestrial, secondary carbonate minerals from five Ordinary Chondrite (OC) meteorites collected in Antarctica. These samples were identified and requested from NASA based upon their size, alteration history, and collection proximity to known Martian meteorites. They are also assumed to be carbonate-free before falling to Earth. This research addresses two questions involving Mars carbonates: 1) characterize terrestrial, secondary carbonate isotope values to apply to Martian meteorites for isolating in-situ carbonates, and 2) increase understanding of carbonates formed in cold and arid environments with Antarctica as an analog for Mars. Two samples from each meteorite, each approximately 0.5 grams, were crushed and dissolved in pure phosphoric acid for 3 sequential reactions: a) R times 0 for 1 hour at 30 degrees Centigrade (fine calcite extraction), b) R times 1 for 18 hours at 30 degrees Centigrade (course calcite extraction), and c) R times 2 for 3 hours at 150 degrees Centigrade (siderite and/or magnesite extraction). CO (sub 2) was distilled by freezing with liquid nitrogen from each sample tube, then separated from organics and sulfides with a TRACE GC using a Restek HayeSep Q 80/100 6 foot 2 millimeter stainless column, and then analyzed on a Thermo MAT 253 Isotope Ratio Mass Spectrometer (IRMS) in Dual Inlet mode. This system was built at NASA/JSC over the past 3 years and proof-tested with known carbonate standards to develop procedures, assess yield, and quantify expected error bands. Two distinct species of carbonates are found: 1) calcite, and 2) non-calcite carbonate (future testing will attempt to differentiate siderite from magnesite). Preliminary results indicate the terrestrial carbonates are formed at approximately sigma (sup 13) C equal to plus 5 per mille, which is consistent with atmospheric CO (sub 2) sigma (sup 13) C equal to minus 7 per mille and fractionation of plus12 per mille based upon polar temperature of -20 degrees Centigrade. The oxygen values fractionate sigma (sup 18) O equal to minus 10-20 per mille lighter between the R times 0 and R times 1 reactions at 30 degrees Centigrade. The carbonate oxygen isotope measurements are consistently heavier than expected with meteoric water and temperatures from Antarctica, perhaps due to secondary carbonate formation during curation in Houston, TX.

Evans, Michael E.↗
DOE OSTI2022

How galaxies populate halos in extreme density environments: An analysis of the halo occupation distribution in SDSS

Recent works have shown that the properties of galaxy populations in simulated dark matter halos vary with large-scale environments. These results suggest a variation in the halo occupation distribution (HOD) in extreme density environments since the dynamical and astrophysical conditions prevailing in these regions may significantly affect the formation and evolution of their halos and residing galaxies, influencing the mean number of satellite galaxies. To analyse these effects, we identify cosmic voids and future virialised structures (FVS) in the Sloan Digital Sky Server Data Release 12 (SDSS-DR12) and estimate the HOD within these super-structures using group catalogues as dark matter halo proxies. Our goal is to use observational galaxy data to characterise the HOD within cosmic voids and FVS, explore the different properties of these galaxies populations and compare them with the general results outside these super-structures. We use a publicly available observational galaxy catalogue with information on redshifts, positions, magnitudes and other astrophysical features to build a volume complete galaxy sample and identify cosmic voids and FVS. Using a publicly available galaxy group catalogue as a proxy to dark matter halos, we compute the HOD within both types of super-structures for different absolute magnitude thresholds. We also study the dependence on the results on the main void and FVS properties, density and volume. We also analysed the main characteristics of the stellar content of galaxies inside these extreme-density regions such as the mean stellar age and the galaxy light concentration index. In all cases, we compare the results with those derived from the Field sample, defined by objects outside both types of environments. Inside cosmic voids, we find a strong decrease in HOD concerning the Field results. In the most extreme cases, the mean number of satellites fall to ~ 50%. Inside FVS, the HOD shows a significant increase to the Field, with a ~ 40% excess in the mean number of satellites. These results are present for the different galaxy luminosity ranges explored. In both environments, the differences with respect to the Field increases for the extreme values of the density environments. However, we obtain no signs of variations related to intrinsic characteristics of the super-structures, indicating that the effects depend mainly on the density of the large-scale environment. In addition, we find that the cumulative distribution of the mean age of stars of the central galaxy also varies in the different regions, this suggests that the history of the formation of the dark matter halos may be different. Finally, we explore the HOD for the 25% youngest (oldest) galaxies, based on the mean age of their stars. We find that for the low-mass groups the youngest galaxies are only present inside voids and are generally central galaxies. On the other hand, for the high-mass groups the FVS environments show the same increase in the HOD concerning the Field as previously mentioned. We find that cosmic voids lack a significant fraction of galaxies with the oldest stellar population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Orthologs of human circulating miRNAs associated with hepatocellular carcinoma are elevated in mouse plasma months before tumour detection

Research examining the potential for circulating miRNA to serve as markers for preneoplastic lesions or early-stage hepatocellular carcinoma (HCC) is hindered by the difficulties of obtaining samples from asymptomatic individuals. As a surrogate for human samples, we identified hub miRNAs in gene co-expression networks using HCC-bearing C3H mice. We confirmed 38 hub miRNAs as associated with HCC in F2 hybrid mice derived from radiogenic HCC susceptible and resistant founders. When compared to a panel of 12 circulating miRNAs associated with human HCC, two had no mouse ortholog and 7 of the remaining 10 miRNAs overlapped with the 38 mouse HCC hub miRNAs. Using small RNA sequencing data generated from serially collected plasma samples in F2 mice, we examined the temporal levels of these 7 circulating miRNAs and found that the levels of 4 human circulating markers, miR-122-5p, miR-100-5p, miR-34a-5p and miR-365-3p increased linearly as the time approaching HCC detection neared, suggesting a correlation of miRNA levels with oncogenic progression. Estimation of change points in the kinetics of the 4 circulating miRNAs suggested the changes started 17.5 to 6.8 months prior to HCC detection. These data establish these 4 circulating miRNAs as potential sentinels for preneoplastic lesions or early-stage HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks

Identifying the molecular systems and proteins that modify the progression of Alzheimer’s disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2013

'Direct' Gas-Phase Metallicities, Stellar Properties, and Local Environments of Emission-Line Galaxies at Redshifts Below 0.90

Using deep narrow-band (NB) imaging and optical spectroscopy from the Keck telescope and the Multi Mirror Telescope (MMT), we identify a sample of 20 emission-line galaxies (ELGs) at z = 0.065-0.90 where the weak auroral emission line, [O iii] lambda4363, is detected at >=3sigma. These detections allow us to determine the gas-phase metallicity using the "direct" method. With electron temperature measurements, and dust attenuation corrections from Balmer decrements, we find that 4 of these low-mass galaxies are extremely metal-poor with 12+log(O/H) <= 7.65 or one-tenth solar. Our most metal-deficient galaxy has 12+log(O/H)= 7.24(+0.45 / −0.30) (95% confidence), similar to some of the lowest metallicity galaxies identified in the local universe. We find that our galaxies are all undergoing significant star formation with average specific star formation rate (SFR) of (100 Myra)(exp −1), and that they have high central SFR surface densities (average of 0.5 Solar M / yr/ sq. kpc). In addition, more than two-thirds of our galaxies have between one and four nearby companions within a projected radius of 100 kpc, which we find is an excess among star-forming galaxies at z =0.4 -- 0.85. We also find that the gas-phase metallicities for a given stellar mass and SFR lie systematically lower than the local stellar M-Z-(SFR) relation by approx. = 0.2 dex (2 sigma significance). These results are partly due to selection effects, since galaxies with strong star formation and low metallicity are more likely to yield [O iii] lambda4363 detections. Finally, the observed higher ionization parameter and high electron density suggest that they are lower redshift analogs to typical z approx. > 1 galaxies.

Direct↗
NASA NTRS2005

Isolation and Identification of Microorganisms in JSC Mars-1 Simulant Soil

Microorganisms were isolated and identified in samples of JSC Mars-1, a Mars simulant soil. JSC Mars-1 is an altered volcanic ash from a cinder cone south of Mauna Kea, Hawaii. This material was chosen because of its similarity to the Martian soil in physical and chemical composition. The soil was obtained by excavating 40 cm deep in a vegetated area to prevent contamination. In previous studies, bacteria from this soil has been isolated by culturing on different types of media, including minimal media, and using biochemical techniques for identification. Isolation by culturing is successful only for a small percentage of the population. As a result, molecular techniques are being employed to identify microorganisms directly from the soil without culturing. In this study, bacteria were identified by purifying and sequencing the DNA encoding the 16s ribosomal RNA (16s rDNA). This gene is well conserved in species and demonstrates species specificity. In addition, biofilm formation, an indicator of microbial life, was studied with this soil. Biofilms are microbial communities consisting of microbes and exopolysaccharides secreted by them. This is a protective way of life for the microbes as they are more resistant to environmental pressures.

Mendez, Claudia↗