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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Remote Sensor Design for Visual Recognition with Convolutional Neural Networks

While deep learning technologies for computer vision have developed rapidly since 2012, modeling of remote sensing systems has remained focused around human vision. In particular, remote sensing systems are usually constructed to optimize sensing cost-quality trade-offs with respect to human image interpretability. While some recent studies have explored remote sensing system design as a function of simple computer vision algorithm performance, there has been little work relating this design to the state-of-the-art in computer vision: deep learning with convolutional neural networks. We develop experimental systems to conduct this analysis, showing results with modern deep learning algorithms and recent overhead image data. Our results are compared to standard image quality measurements based on human visual perception, and we conclude not only that machine and human interpretability differ significantly, but that computer vision performance is largely self-consistent across a range of disparate conditions. This repository contains all the code required to replicate the results of our research paper, and the corresponding docker environment in which that code can be executed. All code is written in Python3 and utilizes the PyTorch library (v1.0) for neural network model training and evaluation. Jupyter notebooks are used to visualize images transformed with our code, in addition to plotting experimental results. We also include the specific parameter files used to execute our experiments, with the intent that these experiments can be replicated.

Zelinski, michaelE.↗
DOE OSTI2020

Soil chemistry data across a catena in the Luquillo Experimental Forest, Puerto Rico

Landscape topography can influence biogeochemical processes across the wet tropical forest soils. This dataset contains soil chemistry data such as pH and dissolved organic carbon (DOC) from April 1, 2017 to May 1, 2018 across a catena in the Luquillo Experimental Forest, Puerto Rico. The attached CSV file contains information on sampling date, location (El Verde field station) in LEF, topographic positions (ridge, slope, and valley, respectively), field replicate number, laboratory replicate number, water extractable pH, CaCl2 extractable pH, and DOC (unit: mg/g). Overall, soil pH values were highest in the valley, followed by slope, and lowest in the ridge topographic positions. However, DOC values followed the opposite trend: ridge>slope>valley. This dataset can facilitate our understanding of the biogeochemical cycling of carbon and nutrient associated with complex topography in wet tropical forest soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the virtual generation of medical images and accurate estimation of radiation dose and other imaging parameters. For this, detailed computational phantoms of the patient anatomy must be utilized and implemented within the radiation transport code. Computational phantoms presently come in one of three format types, and in one of four morphometric categories. Format types include stylized (mathematical equation-based), voxel (segmented CT/MR images), and hybrid (NURBS and polygon mesh surfaces). Morphometric categories include reference (small library of phantoms by age at 50th height/weight percentile), patient-dependent (larger library of phantoms at various combinations of height/weight percentiles), patient-sculpted (phantoms altered to match the patient's unique outer body contour), and finally, patient-specific (an exact representation of the patient with respect to both body contour and internal anatomy). The existence and availability of these phantoms represents a very important advance for the simulation of realistic medical imaging applications using Monte Carlo methods. New Monte Carlo simulation codes need to be thoroughly validated before they can be used to perform novel research. Ideally, the validation process would involve comparison of results with those of an experimental measurement, but accurate replication of experimental conditions can be very challenging. It is very common to validate new Monte Carlo simulations by replicating previously published simulation results of similar experiments. This process, however, is commonly problematic due to the lack of sufficient information in the published reports of previous work so as to be able to replicate the simulation in detail. To aid in this process, the AAPM Task Group 195 prepared a report in which six different imaging research experiments commonly performed using Monte Carlo simulations are described and their results provided. The simulation conditions of all six cases are provided in full detail, with all necessary data on material composition, source, geometry, scoring and other parameters provided. The results of these simulations when performed with the four most common publicly available Monte Carlo packages are also provided in tabular form. The Task Group 195 Report will be useful for researchers needing to validate their Monte Carlo work, and for trainees needing to learn Monte Carlo simulation methods. In this symposium we will review the recent advancements in highperformance computing hardware enabling the reduction in computational resources needed for Monte Carlo simulations in medical imaging. We will review variance reduction techniques commonly applied in Monte Carlo simulations of medical imaging systems and present implementation strategies for efficient combination of these techniques with GPU acceleration. Trade-offs involved in Monte Carlo acceleration by means of denoising and “sparse sampling” will be discussed. A method for rapid scatter correction in cone-beam CT (<5 min/scan) will be presented as an illustration of the simulation speeds achievable with optimized Monte Carlo simulations. We will also discuss the development, availability, and capability of the various combinations of computational phantoms for Monte Carlo simulation of medical imaging systems. Finally, we will review some examples of experimental validation of Monte Carlo simulations and will present the AAPM Task Group 195 Report. Learning Objectives: Describe the advances in hardware available for performing Monte Carlo simulations in high performance computing environments. Explain variance reduction, denoising and sparse sampling techniques available for reduction of computational time needed for Monte Carlo simulations of medical imaging. List and compare the computational anthropomorphic phantoms currently available for more accurate assessment of medical imaging parameters in Monte Carlo simulations. Describe experimental methods used for validation of Monte Carlo simulations in medical imaging. Describe the AAPM Task Group 195 Report and its use for validation and teaching of Monte Carlo simulations in medical imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Evidence for Evolved Stellar Binary Mergers in Observed B-type Blue Supergiants

Blue supergiants are the brightest stars in their host galaxies, and yet their evolutionary status has been a long-standing problem in stellar astrophysics. In this pioneering work, we present a large sample of 59 early B-type supergiants in the Large Magellanic Cloud with newly derived stellar parameters and identify the signatures of stars born from binary mergers among them. We simulate novel 1D merger models of binaries consisting of post main-sequence giants with helium-rich cores (primaries) and main-sequence companions (secondaries), and consider the effects of interaction of the secondary with the core of the primary along with the mixing induced by the merger in the envelope. Thereafter, the evolution of the newborn 17–43 M ⊙ stars is followed until core-carbon depletion, close to their final pre-explosion stage. Unlike stars born alone with comparable masses, stars born from mergers of evolved binaries are blue throughout their core helium-burning phase and replicate the surface gravities and Hertzsprung–Russell diagram positions of most of our sample, thus indicating that B-type supergiants structurally resemble stars born from such mergers. Moreover, the large nitrogen-to-carbon and nitrogen-to-oxygen number ratios, coupled with helium enhancements exhibited by at least half our data sample, is uniquely reproduced by our merger models. Collectively, these findings provide compelling evidence toward the important role of binary mergers in producing the currently observed population of blue supergiants in our Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

NGEE Arctic Plant Traits: Plant Aboveground Biomass, NPP and Traits, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, beginning 2016

Aboveground biomass was determined in twelve plots at the Kougarok hillslope site Kougarok Road, Mile Marker 64 (KG_MM64), Seward Peninsula, Alaska, from samples collected in late July of 2016. The sampled plots were located across six ecotypes present at this site (n=2 replicates per ecotype). Methods employed included destructive harvests to sample understory species (Understory Clip plots) and shrub surveys combined with existing allometric relationships to determine the aboveground biomass of tall shrub species (Shrub Clip Plots). Current year leaves and stems were separated to quantify net primary production of leaves and stems. This data package contains two CSV data files, one pdf and one zip file.The October 2020 update (*.v3) now provides of aboveground biomass and net primary production data (NPP) by plant species and tissue type. The initial release (*.v2) reported just aboveground biomass by plant functional type. Both versions are provided.Foliar specific leaf area, %C, %N, %P, d15N and d13C were measured on an array of vascular plant leaves collected at the Kougarok hillslope site starting in 2016. Leaves were collected during various plant harvests, including understory clip plots, shrub clip plots, nodule biomass plots, N fixation rate measurements and alder point-center-quarter transects. Paired observations from these harvests are also available through the NGEE Arctic data catalogue (see supplemental files).This October 2020 update (*v3) only removed the Ecotype column. No data were changed from initial release (*v2). Both versions are provided.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Matrix multiplication operations with data pre-conditioning in a high performance computing architecture

Mechanisms for performing matrix multiplication operations with data pre-conditioning in a high performance computing architecture are provided. A vector load operation is performed to load a first vector operand of the matrix multiplication operation to a first target vector register. A load and splat operation is performed to load an element of a second vector operand and replicating the element to each of a plurality of elements of a second target vector register. A multiply add operation is performed on elements of the first target vector register and elements of the second target vector register to generate a partial product of the matrix multiplication operation. The partial product of the matrix multiplication operation is accumulated with other partial products of the matrix multiplication operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A small protein inhibits proliferating cell nuclear antigen by breaking the DNA clamp

Proliferating cell nuclear antigen (PCNA) forms a trimeric ring that encircles duplex DNA and acts as an anchor for a number of proteins involved in DNA metabolic processes. PCNA has two structurally similar domains (I and II) linked by a long loop (inter-domain connector loop, IDCL) on the outside of each monomer of the trimeric structure that makes up the DNA clamp. All proteins that bind to PCNA do so via a PCNA-interacting peptide (PIP) motif that binds near the IDCL. A small protein, called TIP, binds to PCNA and inhibits PCNA-dependent activities although it does not contain a canonical PIP motif. The X-ray crystal structure of TIP bound to PCNA reveals that TIP binds to the canonical PIP interaction site, but also extends beyond it through a helix that relocates the IDCL. TIP alters the relationship between domains I and II within the PCNA monomer such that the trimeric ring structure is broken, while the individual domains largely retain their native structure. Small angle X-ray scattering (SAXS) confirms the disruption of the PCNA trimer upon addition of the TIP protein in solution and together with the X-ray crystal data, provides a structural basis for the mechanism of PCNA inhibition by TIP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for An Orphan Gene BOOSTER Enhances Photosynthetic Efficiency and Plant Productivity

Seeds of Col-0 wild type, sig6 T-DNA mutants (CS877785, ABRC), PRL-1-OE, and sig6 T-DNA mutants transfected with PRL-1 (sig6::PRL-1) were planted in 1/2 MS media. Seedlings growth including chlorophyll development defects were investigated across the genotypes. Four-days-old-post-light exposure seedlings were harvested and performed RNAseq analysis with four biological replicates.

Biomass Analytics↗
DOE OSTI2019

Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest

Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N2O). We used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N2O fluxes. Diminished snow cover resulted in increased N2O emissions each year; over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N2O fluxes from agricultural soils subject to wintertime freezing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Onset dynamics of air-water menisci on rock fracture surfaces

Information on menisci dynamics and equilibrium contact angles, θ e , is needed for modeling multiphase flow of fluids in geologic formations. The wettability of fracture faces is relevant to applications such as waterflooding in enhanced oil recovery and evaluation of caprock integrity for geologic carbon sequestration. We investigated the vertical capillary rise of air-water menisci on exposed fracture faces for a sandstone, a shale, and three granites. The experimental design approximated that employed in the Wilhelmy plate method. Replicate fracture faces were prepared by fracturing cylindrical rock cores using the Brazilian method and splitting the fractured cores apart along their fracture planes. Meniscus onset was imaged from a cross-sectional perspective using dynamic neutron radiography, and quantified with change point analysis. An existing model for meniscus onset fitted the experimental data very well (median R 2 = 0.96). Capillary rise was typified by t 2/3 scaling (where t is time), followed by a constant equilibrium height, z e . The average time taken for the menisci to reach z e was 0.39 s, with no differences between rock types. The menisci achieved a greater vertical extent on the fracture faces of the sedimentary rocks as compared to those of the granites. Apparent θ e values were calculated from the best fit estimates of z e using the Wilhelmy plate equation for a smooth and planar vertical surface. Despite the fractal roughness of some of the fracture faces, there was a significant linear relationship (R 2 = 0.80) between the apparent θ e values and intrinsic θ e values measured on polished surfaces of the same rocks using the sessile drop method. The regression equation indicated the apparent θ e values were consistently greater than the intrinsic values. This overestimation may be due to averaging of pixels at the air-water-solid interface, a required step in the image analysis. Further exploration of the range of applicability and measurement accuracy of this proposed new technique would be valuable.

58 GEOSCIENCES↗
DOE OSTI2019

Superluminous supernovae from the Dark Energy Survey

We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I) and one hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES). These SNe, located in the redshift range 0.220 < z < 1.998, represent the largest homogeneously selected sample of SLSN events at high redshift. We present the observed g, r, i, z light curves for these SNe, which we interpolate using Gaussian processes. The resulting light curves are analyzed to determine the luminosity function of SLSNe-I, and their evolutionary time-scales. The DES SLSN-I sample significantly broadens the distribution of SLSN-I light-curve properties when combined with existing samples from the literature. We fit a magnetar model to our SLSNe, and find that this model alone is unable to replicate the behavior of many of the bolometric light curves. We search the DES SLSN-I light curves for the presence of initial peaks prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds that 3 of our 14 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude of <–16, suggesting that such features are not ubiquitous to all SLSN-I events. In conclusion, we also identify a red pre-peak feature within the light curve of one SLSN, which is comparable to that observed within SN2018bsz.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Inferring Viral Dynamics in Chronically HCV Infected Patients from the Spatial Distribution of Infected Hepatocytes

Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, we are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from 4-50 infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Lastly, our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A Pervasive Parallel Processing Framework for Data Visualization and Analysis at Extreme Scale

The evolution of the computing world from teraflop to petaflop has been relatively effortless, with several of the existing programming models scaling effectively to the petascale. The migration to exascale, however, poses considerable challenges. All industry trends infer that the exascale machine will be built using processors containing hundreds to thousands of cores per chip. It can be inferred that efficient concurrency on exascale machines requires a massive amount of concurrent threads, each performing many operations on a localized piece of data. Currently, visualization libraries and applications are based off what is known as the visualization pipeline. In the pipeline model, algorithms are encapsulated as filters with inputs and outputs. These filters are connected by setting the output of one component to the input of another. Parallelism in the visualization pipeline is achieved by replicating the pipeline for each processing thread. This works well for today’s distributed memory parallel computers but cannot be sustained when operating on processors with thousands of cores. Our project investigates a new visualization framework designed to exhibit the pervasive parallelism necessary for extreme scale machines. Our framework achieves this by defining algorithms in terms of worklets, which are localized stateless operations. Worklets are atomic operations that execute when invoked unlike filters, which execute when a pipeline request occurs. The worklet design allows execution on a massive amount of lightweight threads with minimal overhead. Only with such fine-grained parallelism can we hope to fill the billions of threads we expect will be necessary for efficient computation on an exascale machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Transient dynamics of terrestrial carbon storage: Mathematical foundation and numeric examples

Terrestrial ecosystems absorb roughly 30% of anthropogenic CO 2 emissions since preindustrial era, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling, experimental, and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under climate change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Furthermore, this and our other studies have demonstrated that one matrix equation can exactly replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. Moreover, the emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. We also propose that the C storage potential be the targeted variable for research, market trading, and government negotiation for C credits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Value Proposition of UV-Absorbers in PV Module Encapsulation

Various common crystalline silicon cell technologies were exposed to UVA radiation (1.24 Wm-2 nm-1 at 340 nm peak) on the front and back faces at 45 degrees Celsius for periods up to 3000 h, representing about 3 y of solar exposure in Phoenix, Arizona, USA. The resulting degradation of the open-circuit voltage and short-circuit current is presented. Of the various cell types examined, significant levels of degradation were seen in all cases. Less degradation was generally found after UV irradiation of cell fronts and older cell types, whereas a bifacial PERC type exposed on the rear showed about 25% degradation in short circuit current, attributable to lack of a diffused surface field. Selected cells were exposed to UV irradiation with the addition of long pass UV filters to replicate UV-absorbers in encapsulants. Modern cell designs are sensitive to UV-ID because of reduced or eliminated front and back surface field and increased dependence on high quality surface passivation. Single transformation of the independent variable (t, kW h/m^2) could be used to achieve a linear model of the data to extrapolate to 50 y. Solar Advisor Model (SAM) shows appropriate filtering of UV-irradiation can improve LCOE and net present value of plant. Some advanced cell types are seen to be UV-resistant (cell level solutions also exist). Solutions therefore exist on the cell, glass, and encapsulant level. Changes over time in each of these would also need to be considered (solarization, encapsulant browning...).

ENGINEERING,SOLAR ENERGY↗
DOE OSTI2022

Two interaction surfaces between XPA and RPA organize the preincision complex in nucleotide excision repair

The xeroderma pigmentosum protein A (XPA) and replication protein A (RPA) proteins fulfill essential roles in the assembly of the preincision complex in the nucleotide excision repair (NER) pathway. We have previously characterized the two interaction sites, one between the XPA N-terminal (XPA-N) disordered domain and the RPA32 C-terminal domain (RPA32C), and the other with the XPA DNA binding domain (DBD) and the RPA70AB DBDs. Here, we show that XPA mutations that inhibit the physical interaction in either site reduce NER activity in biochemical and cellular systems. Combining mutations in the two sites leads to an additive inhibition of NER, implying that they fulfill distinct roles. Our data suggest a model in which the interaction between XPA-N and RPA32C is important for the initial association of XPA with NER complexes, while the interaction between XPA DBD and RPA70AB is needed for structural organization of the complex to license the dual incision reaction. Integrative structural models of complexes of XPA and RPA bound to single-stranded/double-stranded DNA (ss/dsDNA) junction substrates that mimic the NER bubble reveal key features of the architecture of XPA and RPA in the preincision complex. Most critical among these is that the shape of the NER bubble is far from colinear as depicted in current models, but rather the two strands of unwound DNA must assume a U-shape with the two ss/dsDNA junctions localized in close proximity. Our data suggest that the interaction between XPA and RPA70 is key for the organization of the NER preincision complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Soil viral production count, respiration, and amplicon data

This study aimed to quantify rates of viral production in aridisol soil under conditions as close to natural field soil as possible given the perturbations necessary to manipulate viral abundances. Viruses were removed from soil, then added back to virus-depleted soil to control the initial viral abundances at either 100% (field_abund) or 10% (reduced_abund) of measured field viral abundance to generate treatments with field-relevant and reduced viral infection pressure, respectively. Replicates of batch incubation jars were harvested every 8 hours for 48 hours to enumerate bacteria and viruses by microscopy (n=5) and profile bacterial community composition by 16S rRNA amplicon sequencing (n=3).

Zimmerman, Amy [Pacific Northwest National Laborat↗