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NASA NTRS1980

Properties of the Io plasma torus inferred from Voyager EUV data

A physical model for the Io plasma torus is constructed to explain the EUV radiative emission observed by the Voyager UV spectrometer. Electron impact excitation rate coefficients for electronic transitions of S III, S IV, O II and O III are calculated by the method of distorted waves (Davis, Kepple, and Blaha, 1976); these coefficients account for the asymmetric shape of the 686 A feature. It is concluded that the electron gas must have a distribution function with a non-Maxwellian tail. An approximate representation of the distribution function as two temperature components requires a cold component of 3.5-4 eV and density of 2000 per cu cm and a hot component of about 100 eV and density of 50-100 per cu cm to satisfy observational constraints.

Strobel, D. F.↗

NASA NTRS2008

Using Prominence Mass Inferences in Different Coronal Lines to Obtain the He/H Abundance

In a previous study we developed a new technique for deriving prominence mass by observing how much coronal radiation in the Fe XI1 (lambda 195) spectral line is absorbed by prominence material. In the present work we apply this method, which allows us to consider the effects of both foreground and background radiation in our calculations, to a sample of prominences absorbing in a coronal line that ionizes both H and He (h < 504 Angstroms), and a line that ionizes only H (504 Angstroms < lambda < 911 Angstroms). This approach, first suggested by Kucera et al. (1998), permits the determination of the abundance ratio [He I]/[H I] of neutral helium and hydrogen in the prominence. This ratio should depend on how the prominence is formed, on its current thermodynamic state, and on its dynamical evolution. Thus, it may provide useful insights into the formation and evolution of prominences.

Gilbert, Holly↗

DOE OSTI2020

Characterizing Baselines and Change in Gas Hydrate Systems using EM Methods

The objective of this project was to advance our understanding of gas hydrate systems in nature by characterizing their electrical properties in the field and in the laboratory. In the laboratory measurements, methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, inferred to be contaminant NaCl contributing to conduction in hydrate, to a peak conductivity in agreement with peak resistivities observed in well logs through massive hydrate (3,000--10,000 Ωm). The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater, by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which shows good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure-induced dissociation supports previous conclusions that sediment increases dissociation rate. In order to characterize gas hydrate systems in the field, we collected 360 line kilometers of controlled-source electromagnetic data on Walker Ridge 313, Orca Basin (WR100), Mad Dog (GC781), and Green Canyon 955 in the Gulf of Mexico, all areas with known or seismically inferred gas hydrate deposits and which have be drilled or targeted for future drilling. We deep-towed an EM transmitter that generates an alternating electric field which propagates through the seafloor geology. Data were recorded on 6 receivers towed behind the transmitter at distances between 550 and 1550 m. In the presence of conductive geology, the electric fields will be attenuated, and conversely, in resistive geology the fields will be preserved. Our data were inverted using a 2D inversion method that first optimizes the model-data misfit, then finds the smoothest model fitting the data. This ensures that resistivity structures present in the final model are likely necessary. At each of the proposed drilling sites we found increased resistivity, interpreted as increased hydrate concentrations. However, not only were the primary drilling sites not always more resistive than the alternate sites, at WR313 the strongest resistors were not at the locations targeted for drilling.

03 NATURAL GAS↗

DOE OSTI2014

THE EFFECT OF ISOTOPIC SPLITTING ON THE BISECTOR AND INVERSIONS OF THE SOLAR Ca II 854.2 nm LINE

The Ca II 854.2 nm spectral line is a common diagnostic of the solar chromosphere. The average line profile shows an asymmetric core, and its bisector shows a characteristic inverse-C shape. The line actually consists of six components with slightly different wavelengths depending on the isotope of calcium. This isotopic splitting of the line has been taken into account in studies of non-solar stars, but never for the Sun. We performed non-LTE radiative transfer computations from three models of the solar atmosphere and show that the line-core asymmetry and inverse C-shape of the bisector of the 854.2 nm line can be explained by isotopic splitting. We confirm this finding by analyzing observations and showing that the line asymmetry is present irrespective of conditions in the solar atmosphere. Finally, we show that inversions based on the Ca II 854.2 nm line should take the isotopic splitting into account, otherwise the inferred atmospheres will contain erroneous velocity gradients and temperatures.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Ringwoodite growth rates from olivine with ~75 ppmw H 2 O: Metastable olivine must be nearly anhydrous to exist in the mantle transition zone

It has been previously demonstrated that as little as 300 ppmw H 2 O increases wadsleyite and ringwoodite growth rates to magnitudes that are inconsistent with the metastable olivine hypothesis. To further test this hypothesis, we present new ringwoodite growth rate measurements from olivine with ~75 ppmw H 2 O at 18 GPa and 700, 900, and 1100 °C. These growth rates are nearly identical to those from olivine with ~300 ppmw H 2 O, and significantly higher than those from nominally anhydrous olivine. We infer that transformation of olivine with 75-300 ppmw H 2 O is primarily enhanced by hydrolytic weakening of reaction rims, which reduces the elastic strain-energy barrier to growth. We present a new method for fitting nonlinear nominally anhydrous data, to demonstrate that reduction of growth rates by elastic strain energy is an additional requirement for metastable olivine. In conclusion, based on previous thermokinetic modeling, these enhanced growth rates are inconsistent with the persistence of metastable olivine wedges into the mantle transition zone. Metastable persistence of olivine into the mantle transition-zone would therefore require < 75 ppmw H 2 O.

58 GEOSCIENCES↗

DOE OSTI2014

Application of small-angle neutron scattering to the study of forces between magnetically chained monodisperse ferrofluid emulsion droplets

The optical magnetic chaining technique (MCT) developed by Leal-Calderon, Bibette and co-workers in the 1990 s allows precise measurements of force profiles between droplets in monodisperse ferrofluid emulsions. However, the method lacks an in-situ determination of droplet size and therefore requires the combination of separately acquired measurements of droplet chain periodicity versus an applied magnetic field from optical Bragg scattering and droplet diameter inferred from dynamic light scattering (DLS) to recover surface force-distance profiles between the colloidal particles. Compound refractive lens (CRL) focussed small-angle scattering (SANS) MCT should result in more consistent measurements of droplet size (form factor measurements in the absence of field) and droplet chaining period (from structure factor peaks when the magnetic field is applied); and, with access to shorter length scales, extend force measurements to closer approaches than possible by optical measurements. We report on CRL-SANS measurements of monodisperse ferrofluid emulsion droplets aligned in straight chains by an applied field perpendicular to the incident beam direction. Analysis of the scattering from the closely spaced droplets required algorithms that carefully treated resolution and its effect on mean scattering vector magnitudes in order to determine droplet size and chain periods to sufficient accuracy. At lower applied fields scattering patterns indicate structural correlations transverse to the magnetic field direction due to the formation of intermediate structures in early chain growth.

Colloid↗

DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗

NASA NTRS1998

System and method for creating expert systems

A system and method provides for the creation of a highly graphical expert system without the need for programming in code. An expert system is created by initially building a data interface, defining appropriate Mission, User-Defined, Inferred, and externally-generated GenSAA (EGG) data variables whose data values will be updated and input into the expert system. Next, rules of the expert system are created by building appropriate conditions of the rules which must be satisfied and then by building appropriate actions of rules which are to be executed upon corresponding conditions being satisfied. Finally, an appropriate user interface is built which can be highly graphical in nature and which can include appropriate message display and/or modification of display characteristics of a graphical display object, to visually alert a user of the expert system of varying data values, upon conditions of a created rule being satisfied. The data interface building, rule building, and user interface building are done in an efficient manner and can be created without the need for programming in code.

Hughes, Peter M.↗

DOE OSTI2015

Characterization of plutonium activities in soil - 15429

Plutonium (Pu) contamination in soils at the Nevada National Security Site (NNSS) is characterized using Am-241 activity results from gamma spectroscopy (gamma Am-241) analysis. This is done by inferring the Pu activities using Pu to Am-241 ratios developed from Pu and Am-241 results from alpha spectroscopy isotopic analyses (isotopic Pu and isotopic Am-241). Although the isotopic Pu and Am-241 methods can accurately measure isotopic activities within the sampled material, the amount of soil analyzed is small (typically 1 gram [g] of soil). If there is heterogeneity of particle distribution within the contaminated release site soil, isotopic methods may provide results that are not representative of the contaminated site. The degree of potential heterogeneity needs to be considered before using isotopic analyses to characterize a Pu-contaminated site. Results from various Pu-contaminated sites at the NNSS are presented that demonstrate significant heterogeneity. A larger sample size (∼1,600 g) within a Marinelli container is used for the gamma spectroscopy analysis. This greatly reduces the impact of heterogeneously distributed discrete particles and provides results that are more representative of true contaminant activities at the release site. However, this method cannot provide Pu activities and has the potential to provide less accurate results due to the haphazard location of contaminant particles within the Marinelli container at the time of measurement. The distance of particles from the detector would result in some differential self-absorption of the emissions from radioactive particles. As the magnitude of this problem was not previously understood, a study was conducted to evaluate the variability in Am-241 results due to self-absorption from particle position. Results from this study are shown that demonstrate that this effect provides minimal variability in replicate Am-241 measurements. The method used at the NNSS to characterize Pu activities in soil takes advantage of the accuracy of the alpha spectroscopy isotopic analyses to establish Pu to Am-241 ratios and the more representative gamma spectroscopy results for Am-241 to provide a reliable characterization of Pu activities at contaminant release sites. (authors)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Consequences of CCD imperfections for cosmology determined by weak lensing surveys: from laboratory measurements to cosmological parameter bias

Weak gravitational lensing causes subtle changes in the apparent shapes of galaxies due to the bending of light by the gravity of foreground masses. By measuring the shapes of large numbers of galaxies (millions in recent surveys, up to tens of billions in future surveys) we can infer the parameters that determine cosmology. Imperfections in the detectors used to record images of the sky can introduce changes in the apparent shapes of galaxies, which in turn can bias the inferred cosmological parameters. In this paper we consider the effect of two widely discussed sensor imperfections: tree rings, due to impurity gradients that cause transverse electric fields in the charge-coupled devices (CCDs), and pixel size variation, due to periodic CCD fabrication errors. These imperfections can be observed when the detectors are subject to uniform illumination (flat-field images). We develop methods to determine the spurious shear and convergence (due to the imperfections) from the flat-field images. We calculate how the spurious shear when added to the lensing shear will bias the determination of cosmological parameters. We apply our methods to candidate sensors of the Large Synoptic Survey Telescope (LSST) as a timely and important example, analyzing flat-field images recorded with LSST prototype CCDs in the laboratory. We find that tree rings and periodic pixel size variation present in the LSST CCDs will introduce negligible bias to cosmological parameters determined from the lensing power spectrum, specifically w, Ω{sub m}, and σ{sub 8}.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2012

Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

Column CO2 observations from current and future remote sensing missions represent a major advancement in our understanding of the carbon cycle and are expected to help constrain source and sink distributions. However, data assimilation and inversion methods are challenged by the difference in scale of models and observations. OCO-2 footprints represent an area of several square kilometers while NASA s future ASCENDS lidar mission is likely to have an even smaller footprint. In contrast, the resolution of models used in global inversions are typically hundreds of kilometers wide and often cover areas that include combinations of land, ocean and coastal areas and areas of significant topographic, land cover, and population density variations. To improve understanding of scales of atmospheric CO2 variability and representativeness of satellite observations, we will present results from a global, 10-km simulation of meteorology and atmospheric CO2 distributions performed using NASA s GEOS-5 general circulation model. This resolution, typical of mesoscale atmospheric models, represents an order of magnitude increase in resolution over typical global simulations of atmospheric composition allowing new insight into small scale CO2 variations across a wide range of surface flux and meteorological conditions. The simulation includes high resolution flux datasets provided by NASA s Carbon Monitoring System Flux Pilot Project at half degree resolution that have been down-scaled to 10-km using remote sensing datasets. Probability distribution functions are calculated over larger areas more typical of global models (100-400 km) to characterize subgrid-scale variability in these models. Particular emphasis is placed on coastal regions and regions containing megacities and fires to evaluate the ability of coarse resolution models to represent these small scale features. Additionally, model output are sampled using averaging kernels characteristic of OCO-2 and ASCENDS measurement concepts to create realistic pseudo-datasets. Pseudo-data are averaged over coarse model grid cell areas to better understand the ability of measurements to characterize CO2 distributions and spatial gradients on both short (daily to weekly) and long (monthly to seasonal) time scales

Ott, L.↗

DOE OSTI2016

Extracting host galaxy dispersion measure and constraining cosmological parameters using fast radio burst data

The excessive dispersion measures (DMs) and high Galactic latitudes of fast radio bursts (FRBs) hint toward a cosmological origin of these mysterious transients. Methods of using measured DM and redshift z to study cosmology have been proposed, but one needs to assume a certain amount of DM contribution from the host galaxy (DM{sub HG}) in order to apply those methods. We introduce a slope parameter β(z)≡dln〈DM{sub E}〉/dlnz (where DM{sub E} is the observed DM subtracting the Galactic contribution), which can be directly measured when a sample of FRBs have z measured. We show that 〈DM{sub HG}〉 can be roughly inferred from β and the mean values, (〈DM{sub E}〉)-bar and z-bar , of the sample. Through Monte Carlo simulations, we show that the mean value of local host galaxy DM, 〈DM{sub HG,loc}〉, along with other cosmological parameters (mass density Ω{sub m} in the ΛCDM model, and the IGM portion of the baryon energy density Ω{sub b}f{sub IGM}), can be independently measured through Markov Chain Monte Carlo fitting to the data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Rigorous Representation of Transporters as Allosteric Machines Enables a Quantitative Understanding of their Functional Mechanisms

The sodium-coupled transporters are prototypical molecular machines of the cell that maintain communication with the environment by moving substances across the membrane. How they use energy to perform the transfer of substrates against their chemical gradient, i.e., by sodium symport along its gradient, is of special biophysical interest. New structural information, and powerful computational methods applied to the analysis of extensive molecular dynamics trajectories, make it possible to incorporate mechanistic elements of ion and substrate transport in structure-informed allosteric models that can be interrogated for specific and quantitative inferences.

Biophysics↗

DOE OSTI2016

Measurement of bacterial replication rates in microbial communities

Culture-independent microbiome studies have increased our understanding of the complexity and metabolic potential of microbial communities. However, to understand the contribution of individual microbiome members to community functions, it is important to determine which bacteria are actively replicating. We developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates. The algorithm calculates an index of replication (iRep) based on the sequencing coverage trend that results from bi-directional genome replication from a single origin of replication. We apply this method to show that microbial replication rates increase after antibiotic administration in human infants. We also show that uncultivated, groundwater-associated, Candidate Phyla Radiation bacteria only rarely replicate quickly in subsurface communities undergoing substantial changes in geochemistry. Our method can be applied to any genome-resolved microbiome study to track organism responses to varying conditions, identify actively growing populations and measure replication rates for use in modeling studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Spectro-thermometry of M dwarfs and their candidate planets: Too hot, too cool, or just right?

We use moderate-resolution spectra of nearby late K and M dwarf stars with parallaxes and interferometrically determined radii to refine their effective temperatures, luminosities, and metallicities. We use these revised values to calibrate spectroscopic techniques to infer the fundamental parameters of more distant late-type dwarf stars. We demonstrate that, after masking out poorly modeled regions, the newest version of the PHOENIX atmosphere models accurately reproduce temperatures derived bolometrically. We apply methods to late-type hosts of transiting planet candidates in the Kepler field, and calculate effective temperature, radius, mass, and luminosity with typical errors of 57 K, 7%, 11%, and 13%, respectively. We find systematic offsets between our values and those from previous analyses of the same stars, which we attribute to differences in atmospheric models utilized for each study. We investigate which of the planets in this sample are likely to orbit in the circumstellar habitable zone. We determine that four candidate planets (KOI 854.01, 1298.02, 1686.01, and 2992.01) are inside of or within 1σ of a conservative definition of the habitable zone, but that several planets identified by previous analyses are not (e.g., KOI 1422.02 and KOI 2626.01). Only one of the four habitable-zone planets is Earth sized, suggesting a downward revision in the occurrence of such planets around M dwarfs. These findings highlight the importance of measuring accurate stellar parameters when deriving parameters of their orbiting planets.

79 ASTRONOMY AND ASTROPHYSICS↗