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

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Species-specific ribosomal RNA-FISH identifies interspecies cellular-material exchange, active-cell population dynamics and cellular localization of translation machinery in clostridial cultures and co-cultures

ABSTRACT The development of synthetic microbial consortia in recent years has revealed that complex interspecies interactions, notably the exchange of cytoplasmic material, exist even among organisms that originate from different ecological niches. Although morphogenetic characteristics, viable RNA and protein dyes, and fluorescent reporter proteins have played an essential role in exploring such interactions, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to further investigate interactions in synthetic or semisynthetic consortia. Despite its maturity, several challenges exist in using rRNA-FISH as a tool to quantify individual species population dynamics and interspecies interactions using high-throughput instrumentation such as flow cytometry. In this work, we resolve such challenges and apply rRNA-FISH to double and triple co-cultures of Clostridium acetobutylicum, Clostridium ljungdahlii, and Clostridium kluyveri . In pursuing our goal to capture each organism’s population dynamics, we demonstrate dynamic rRNA, and thus ribosome, exchange between the three species leading to the formation of hybrid cells. We also characterize the localization patterns of the translation machinery in the three species, identifying distinct, dynamic localization patterns among them. Our data also support the use of rRNA-FISH to assess the culture’s health and expansion potential, and, here again, our data find surprising differences among the three species examined. Taken together, our study argues for rRNA-FISH as a valuable and accessible tool for quantitative exploration of interspecies interactions, especially in organisms which cannot be genetically engineered or in consortia where selective pressures to maintain recombinant species cannot be used. IMPORTANCE Though dyes and fluorescent reporter proteins have played an essential role in identifying microbial species in co-cultures, we hypothesized that ribosomal RNA-fluorescence in situ hybridization (rRNA-FISH) could be adapted and applied to quantitatively probe complex interactions between organisms in synthetic consortia. Despite its maturity, several challenges existed before rRNA-FISH could be used to study Clostridium co-cultures of interest. First, species-specific probes for Clostridium acetobutylicum and Clostridium ljungdahlii had not been developed. Second, “state-of-the-art” labeling protocols were tedious and often resulted in sample loss. Third, it was unclear if FISH was compatible with existing fluorescent reporter proteins. We resolved these key challenges and applied the technique to co-cultures of C. acetobutylicum , C. ljungdahlii , and Clostridium kluyveri . We demonstrate that rRNA-FISH is capable of identifying rRNA/ribosome exchange between the three organisms and characterized rRNA localization patterns in each. In combination with flow cytometry, rRNA-FISH can capture sub-population dynamics in co-cultures.

Hill, John D.↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

DNA-PAINT Imaging with Hydrogel Imprinting and Clearing

Hydrogel-embedding is a versatile technique in fluorescence microscopy, offering stabilization, optical clearing, and the physical expansion of biological specimens. DNA-PAINT is a super-resolution microscopy approach based on the diffusion and transient binding of fluorescently labeled oligos, but its feasibility in hydrogels has not yet been explored. In this study, we demonstrate that polyacrylamide hydrogels support sufficient diffusion for effective DNA-PAINT imaging. Using acrydite-anchored oligonucleotides imprinted from patterned DNA origami nanostructures and microtubule filaments in fixed cells, we find that hydrogel embedding preserves docking strand positioning at the nanoscale. Sample clearing via protease treatment had minor structural effects on the microtubule structure and enhanced diffusion and accessibility to hydrogel-imprinted docking strands. Our work demonstrates promising potential for diffusion and binding-based fluorescence imaging applications in hydrogel-embedded samples.

DNA origami↗
DOE OSTI2026

The U.S. Agrivoltaic Shading Tool: A National-Scale Interface for Modeling Light and Shade Patterns in Ten Common Agrivoltaic Configurations

Agrivoltaic systems are dual-use configurations that co-locate agriculture and photovoltaic (PV) infrastructure and require careful design to balance crop performance and energy generation. A critical element of agrivoltaic design is the spatial and temporal distribution of irradiance and shade within and around PV arrays. To support research, planning, and stakeholder decision-making, we introduce the U.S. Agrivoltaic Shading Tool, a novel web-based application that delivers high-resolution irradiance and photosynthetically active radiation (PAR) modeling for ten standardized PV configurations across the conterminous United States. The tool leverages the National Laboratory of the Rockies (NLR) System Advisor Model (SAM) to perform detailed irradiance simulations, using meteorological data from the National Solar Radiation Database (NSRDB). Outputs include seasonal, monthly, weekly, and diurnal patterns of available sunlight, amount of shade, irradiance, and PAR at ground level within agrivoltaic system footprints. For a user's selected location, these results are visualized through interactive visualizations, heatmaps, and time-series plots, designed to be accessible to both technical and non-technical users. In addition to facilitating rapid spatial exploration of agrivoltaic light environments, the tool will offer seamless integration with the InSPIRE Agrivoltaics Design and Analysis Model (ADAM). This optional workflow will allow users to port selected site and configuration parameters into a more advanced modeling environment for further customization of structural layouts, crop-system compatibility, power generation, and technoeconomic performance. Finally, to promote open science, the entire dataset will be hosted and available for open access through the OpenEI platform. By standardizing and disseminating high-quality irradiance data and design tools, the U.S. Agrivoltaic Shading Tool supports a wide range of users, including researchers, landowners, energy developers, and policymakers, in evaluating the agronomic and energetic feasibility of agrivoltaic systems across the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Abstract Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Protein folds vs. protein folding: Differing questions, different challenges

We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗
DOE OSTI2021

Indexing habitat opportunity for juvenile anadromous fishes in tidal-fluvial wetland systems

In intertidal wetlands, annual habitat use by migrating juvenile salmon is limited by both physical and biological constraints. Physical limits are set by inundation patterns and water quality parameters, while biological bounds are determined by species specific migration timing and seasonal residency behaviors. We developed a new metric called the fish habitat opportunity index (FHOI) to quantify the total time per calendar year that salmon could access intertidal sites, based on periods of inundation and juvenile salmon seasonal migration. The index was applied using data collected at a recently reconnected tidal freshwater wetland area during 2006-2009. We found the wetland to be accessible to different salmon species for different lengths of time and periods of the year. On average, chum salmon (Oncorhynchus keta) had remarkably consistent opportunities to use marsh habitat over the 4 years sampled, while opportunities were more variable for Chinook (O. tshawytscha), and coho salmon (O. kisutch). Inundation varied nonlinearly with tidal height, and fish had access to the productive marsh edge for approximately 40-50% of the time during periods of access. The FHOI is widely applicable to wetland systems throughout the tidal fluvial continuum, and provides a metric for assessing fish habitat opportunity that is more realistic than simple wetted area calculations. Finally, the FHOI affords a method of a priori comparison among potential restoration sites to help resource managers predict the likely relative benefit to juvenile salmon.

wetland restoration, estuary, habitat opportunity,↗
DOE OSTI2020

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Abstract Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

Azodi, Christina B.↗
DOE OSTI2023

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python’s iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).

14 SOLAR ENERGY↗
DOE OSTI2025

Sim2Real Autonomous Robotic Exploration [Poster]

Autonomous robots offer promising solutions for exploration in environments that are inaccessible or hazardous to humans. Despite this, physical training of such robots is often constrained by safety risks, high cost or limited accessibility. This project presents an end-to-end simulation to reality pipeline leveraging Nvidia Isaac Sim and Boston Dynamics' Spot to enable autonomous navigation in indoor environments. A reinforcement learning policy is first trained using Nvidia Isaac Lab to establish Spot's locomotion pattern. Virtual LiDAR sensors are then integrated to perform SLAM-based navigation using simulated odometry. Finally, the simulated navigation scheme is transferred to a physical Spot robot to inspect and record images of a real-world room by repeating the learnt trajectory. The proposed framework highlights the potential of scalable training in simulation and reliable deployment in physical environments. Future directions include dynamic trajectory generation in unseen and challenging environments and integration of environmental sensing like temperature, radiation or humidity via sensor and material simulation.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage

The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Interface-Driven Chiral Magnetism in Ultrathin Metallic Ferromagnets: Towards Skyrmion Spintronics

Magnetic skyrmions are localized chiral magnetic textures in the form of nanoscale vortices or bubbles that are topologically protected from being ‘unwound’. Their topological nature gives rise to rich behaviors including ordered lattice formation, emergent electrodynamics and robust current-driven displacement at remarkably low current densities. However, magnetic skyrmions have so far been restricted to just a few materials and observed only at low temperatures, limiting the experimental accessibility and technological application of these unique topological objects. This project aimed to realize magnetic skyrmions at room temperature in a new class of engineered chiral ferromagnets that exploit broken mirror symmetry at interfaces to generate helical magnetic order. Ultrathin magnetic heterostructures were engineered and patterned into laterally-confined nanostructures in which skyrmions were stabilized, manipulated, and detected for the first time under ambient conditions. Static and dynamic properties were examined using advanced x-ray imaging to provide a fundamental understanding of topological magnetic phases. The interactions between magnetic skyrmions and electron charge and spin currents were studied to identify fundamental new physics and enable device applications. These experimental studies, supported by detailed modeling, will help launch a new subfield of skyrmion-based spintronics in which individual skyrmions can be used to encode, store, and transport information in high-performance, low power memory and logic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electric Vehicle Charging Demand in the Chicago Metropolitan Area through 2030

This report outlines the collaborative efforts between Argonne National Laboratory and Exelon in advancing the Agent-Based Transportation Energy Analysis Model (ATEAM). Aligning with ComEd’s beneficial electrification plan, this study developed eight scenarios to access the temporal and spatial distribution of charging load and demand stemming from the widespread adoption of battery electric vehicles (BEV) adoption, augmented public charging infrastructure deployment, and increased multi-unit dwelling (MUD) charging availability. Enhancements to the ATEAM model encompassed the simulation of multiple days of travel behavior, estimation of total public charging infrastructure needs, user interface refinements, and output tracking at both vehicle and charging station levels. The total electricity consumption for residential and public charging to support over 800,000 BEVs in Chicago in 2029 is projected at approximately 10.2 GWh. Enhanced MUD home charging accessibility (70%) amplifies the home charging load in the study area by 1.5% compared to the baseline scenario (10%). The widespread adoption of BEVs reduces peak charging loads, owing to their inclusion across households with diverse income levels, thus fostering a more dispersed charging activity pattern. However, widespread BEV adoption increases the peak home charging load in areas with lower median household incomes, reflecting a higher BEV concentration in these locales and, subsequently, heightened peak charging demands. In the Widespread BEV adoption scenario, fewer census tracts exhibit elevated peak loads for combined home and public charging, indicating a more even distribution of charging demand across the study area. Predominantly, peak loads for combined charging—both home and public— occur between 2 p.m. and 10 p.m. across all scenarios, encompassing the majority of census tracts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Abstract Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

59 BASIC BIOLOGICAL SCIENCES↗