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DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗

DOE OSTI2024

Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Interpretable Net Load Forecasting Using Smooth Multiperiodic Features

We consider the problem of forecasting net load over a horizon such as one day, using a trailing window of past net load values as well as date and time. We focus on three variations on this problem: point forecasts, marginal quantile forecasts, and generating conditional samples of the future value. We propose a method that relies on linear regression using some custom engineered time-based features to capture multiple periodicities, such as daily, weekly, and seasonal, and their interactions. Our proposed models are readily interpretable, and rely on efficient and reliable convex optimization [1] to fit. We illustrate our method on four years worth of hourly net load data, comparing predictions made with various subsets of the features.

Ogut, Mehmet G↗

DOE OSTI2024

A Mid-Century Net-Zero Scenario for the State of Wyoming and its Economic Impacts

Clean hydrogen has the potential to help achieve 10% economy-wide emissions reductions by 2050 relative to 2005, promote energy security and resilience, and develop a new economy in the United States. In 2030, the hydrogen economy could create about 100,000 new jobs to build new capital projects and clean hydrogen infrastructure. The Wyoming Energy Authority recently announced the state’s energy strategy, which establishes a goal of net-zero emissions by 2050. Under all likely scenarios, achieving a mid-century net-zero target will pose challenges and create opportunities for Wyoming’s energy sector. If executed properly, the transition could favorably affect the state’s economy overall in the long term. This research program examines the economic impact of fossil energy production in Wyoming and provides various predictions for future energy mixes to achieve net-zero emissions. Preliminary work suggests that Wyoming-based hydrogen production could have significant economic benefits and job creation implications for Wyoming. This study further assesses Wyoming’s opportunities to create hydrogen-based industries, assess economic impacts, identify knowledge gaps and research needs, and create a Hydrogen Center of Excellence to accelerate commercialization and deployment. This project helped to understand Wyoming's areas of focus for research and development and identified its areas of strength and potential challenges in creating a hydrogen ecosystem. As a result of this study, we estimate that for blue hydrogen produced from coal and gas resources, the overall cost reduction will be driven mainly by the carbon-sequestration tax credit and the improvement in carbon capture. Mature technologies, like SMR and PSA, will make limited contributions. They have no or limited reductions from an additional capacity deployment in future costs. We also understand the importance of continued support from public and private sectors for Carbon Capture and Storage (CCS)-related research, development, and demonstration programs at federal and state levels. The successful and efficient production of blue hydrogen requires a unique blend of energy resources, geology, regulation, law, and infrastructure. Wyoming has the distinction of meeting all these demands. The team also estimates that the availability and command of water resources accessible for hydrogen production are crucial for developing new projects. Water treatment, use, and disposal after treatment will also make projects possible. Primarily, this is relevant for hydrogen made using renewable energy. Wyoming has one of the best wind resource capacity in the nation. Harnessing this resource is challenging due to limited transmission line availability. Hydrogen could become one of the solutions to the stranded resource problem, primarily if the water availability challenge is addressed. Using produced oil & gas water could help to solve the problem. A commonly cited barrier to the expansion of hydrogen markets is the cost associated with constructing new pipelines, which typically require large amounts of capital to develop. Wyoming already possesses much of the export infrastructure needed to connect Wyoming’s hydrogen production with major markets across the West Coast, Pacific Northwest, Midwest, and Front Range regions of the United States, where a large portion of Wyoming’s natural gas is already transported. In addition to transportation by pipeline, rail transportation of hydrogen has also proven feasible. Wyoming uses its extensive railway system to transport large amounts of coal to its export partners across the United States. By using cryogenic or compressed-gas cars, Wyoming has the potential to add hydrogen to its existing network of railroad energy exports. The same technology may also be applied to hydrogen transport via trucks traveling interstate highways. Wyoming’s workforce is ready to meet the demands of clean hydrogen development. Many of the skills and training needed for hydrogen production are the same skills already possessed by Wyoming’s oil & gas and coal workforce. Many government and industry leaders expect clean hydrogen and other low-carbon energy projects to generate significant job growth and to recruit many already-trained oil & gas and coal workers whose jobs may be displaced. As energy companies seek to penetrate the markets for Wyoming hydrogen production, there is a natural mutual benefit to Wyoming’s workers and companies seeking to launch projects with the assistance of a trained workforce. Wyoming’s university and community college system have adopted several programs to ensure that highly qualified engineers and other technically skilled employees continue to graduate with skills to support the development of hydrogen and other innovative energy projects moving forward. Throughout the project, stakeholder outreach and education took many forms, including meetings with several major companies in the industry, collaborating with local government organizations, educational organizations, and national laboratories, tribal outreach and engagement, the sponsoring of several hydrogen-focused projects in many departments throughout the University of Wyoming, and developing a collaboration with international universities. The products of these collaborations consist of working relationships with several companies in the industry, educational institutions, national labs, and local government, as well as strong connections with individuals who will play an essential role in the success of the Hydrogen Energy Research Center.

08 HYDROGEN↗

DOE OSTI2025

Filer City Biomass Carbon Removal and Storage (BiCRS) Net-Negative Study

NorthStar Clean Energy Company (NorthStar) conducted the Filer City Biomass Carbon Removal and Storage (BiCRS) Net-Negative Study to develop a conceptual design and cost estimate for retrofitting the TES Filer City Station with post-combustion carbon capture technology in Mainstee Michigan. The Filer City BiCRS Net-Negative Study allowed NorthStar and team to confirm the commercial feasibility of a post-combustion carbon capture system applied to biomass-fired boilers. At the time of the study, there were no operating facilities in the world with carbon capture applied to flue gas from woody biomass as proposed for Filer City. Due to the solvent-agnostic nature of B&W’s SolveBright™ technology, the team determined that multiple amines, both traditional and proprietary, would be capable of 95% CO 2 capture efficiency with the steam available from Filer City’s existing boilers after modifications to burn 100% biomass. Based on the cost estimates produced, NorthStar can now confirm the Filer City BiCRS Project is economically viable with a combination of tax credits available from the Inflation Reduction Act of 2022 and high-quality Carbon Dioxide Removal Credits sold on the Voluntary Carbon Market. The Filer City BiCRS project is uniquely positioned to become one of the first projects to capture and sequester large volumes of CO 2 from a biogenic source, removing existing CO 2 from the atmosphere. The unlimited version of the Filer City Biomass Carbon Removal and Storage (BiCRS) Net-Negative Study Final Technical Report is attached.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Pathways for methane emissions and oxidation that influence the net carbon balance of a subtropical cypress swamp

We evaluated the major pathways for methane emissions from wetlands to the atmosphere at four wetland sites in the Big Cypress National Preserve in southwest Florida. Methane oxidation was estimated based on the d13C-CH4 of surface water, porewater, and bubbles to evaluate mechanisms that limit surface water emissions. Spatially-scaled methane fluxes were then compared to organic carbon burial rates. The pathway with the lowest methane flux rate was diffusion from surface waters (3.50 ± 0.22 mmol m-2 d-1). Microbial activity in the surface water environment and/or shallow oxic sediment layer oxidized 26 ± 3% of the methane delivered from anerobic sediments to the surface waters. The highest rates of diffusion were observed at the site with the lowest extent of oxidation. Ebullition flux rates were 2.2 times greater than diffusion and more variable (7.79 ± 1.37 mmol m-2 d-1). Methane fluxes from non-inundated soils were 1.6 times greater (18.4 ± 5.14 mmol m-2 d-1) than combined surface water fluxes. Methane flux rates from cypress knees (emergent cypress tree root structures) were 3.7 and 2.3 times higher (42.0 ± 6.33 mmol m-2 d-1) than from surface water and soils, respectively. Cypress knee flux rates were highest at the wetland site with the highest porewater methane partial pressure, suggesting that the emergent root structures allow methane produced in anaerobic sediment layers to bypass oxidation in aerobic surface waters or shallow sediments. Scaled across the four wetlands, emissions from surface water diffusion, ebullition, non-inundated soils, and knees contributed to 14 ± 2%, 25 ± 6%, 34 ± 10%, and 26 ± 5% of total methane emissions, respectively. When considering only the three wetlands with cypress knees present, knee emissions contributed to 39 ± 5% of the total scaled methane emissions. Finally, the molar ratio of CH4 emissions to OC burial ranged from 0.03 to 0.14 in the wetland centers indicating that all four wetland sites are net sources of atmospheric warming potential on 20-100 yr timescales, but net sinks over longer time scales (500 yr) with the exception of one wetland site that was a net source even over 500 yr time scales.

Ward, Nicholas D.↗

DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Multi-century dynamics of the climate and carbon cycle under both high and net negative emissions scenarios

Abstract. Future climate projections from Earth system models (ESMs) typically focus on the timescale of this century. We use a set of five ESMs and one Earth system model of intermediate complexity (EMIC) to explore the dynamics of the Earth's climate and carbon cycles under contrasting emissions trajectories beyond this century to the year 2300. The trajectories include a very-high-emissions, unmitigated fossil-fuel-driven scenario, as well as a mitigation scenario that diverges from the first scenario after 2040 and features an “overshoot”, followed by a decrease in atmospheric CO2 concentrations by means of large net negative CO2 emissions. In both scenarios and for all models considered here, the terrestrial system switches from being a net sink to either a neutral state or a net source of carbon, though for different reasons and centered in different geographic regions, depending on both the model and the scenario. The ocean carbon system remains a sink, albeit weakened by carbon cycle feedbacks, in all models under the high-emissions scenario and switches from sink to source in the overshoot scenario. The global mean temperature anomaly is generally proportional to cumulative carbon emissions, with a deviation from proportionality in the overshoot scenario that is governed by the zero emissions commitment. Additionally, 23rd century warming continues after the cessation of carbon emissions in several models in the high-emissions scenario and in one model in the overshoot scenario. While ocean carbon cycle responses qualitatively agree in both globally integrated and zonal mean dynamics in both scenarios, the land models qualitatively disagree in zonal mean dynamics, in the relative roles of vegetation and soil in driving C fluxes, in the response of the sink to CO2, and in the timing of the sink–source transition, particularly in the high-emissions scenario. The lack of agreement among land models on the mechanisms and geographic patterns of carbon cycle feedbacks, alongside the potential for lagged physical climate dynamics to cause warming long after CO2 concentrations have stabilized, points to the possibility of surprises in the climate system beyond the 21st century time horizon, even under relatively mitigated global warming scenarios, which should be taken into consideration when setting global climate policy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The Essential Role of Nuclear Energy in Achieving Economy-wide Net-Zero Solutions

Governments and private industry around the world have established aggressive goals to achieve net-zero emissions for the power, industrial, and transportation sectors by 2050. These aggressive goals demand immediate action if we are to be successful, and they require us to think more holistically about our clean energy options. Programs within the U.S. Department of Energy (DOE) are addressing these holistic solutions. Traditionally, electricity generation and management and meeting energy demands for industry and transportation are considered independently. As we seek to achieve net-zero, we need to reassess energy demands. When we consider overall energy use, only one-third is in the form of electricity. Additional energy demands are in the form of heat or steam for industrial processes, as well as transportation. These sectors are much harder to abate, and electrification may not be the best option. Reducing environmental emissions at an affordable cost, while maintaining grid reliability and resilience, will require us to use all of the clean energy resources that we have available. That means coordinating the use of nuclear, renewables, and fossil fuels with carbon capture to meet growing demands for electricity, industrial applications, and mobility. The DOE Office of Nuclear Energy (DOE-NE) program on Integrated Energy Systems (IES) is led by researchers at Idaho National Laboratory (INL) and work is conducted in partnership with an array of other DOE laboratories, industry, and academia. The primary focus of IES research is to assess the technical and economic potential of IES to enhance the flexibility and utilization of nuclear reactors working alongside renewable generators to meet an array of energy demands—thereby maximizing the utilization of clean energy resources across all energy sectors. Various energy applications and product streams beyond electricity are being evaluated, ranging from generation of potable water to production of hydrogen, fertilizers, synthetic fuels, and chemicals. The DOE-NE program additionally partners with the Hydrogen and Fuel Cell Technologies Office under the DOE Office of Energy Efficiency and Renewable Energy to jointly fund the development of analysis tools, technologies, and nuclear-integrated hydrogen demonstration projects. This presentation highlights the wide array of R&D being conducted across multiple DOE-funded programs to develop and deploy nuclear-based IES that will be key to achieving our net-zero goals. By working with key collaborators in the nuclear industry, analytical studies are now becoming a reality in demonstration projects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Advancing Nuclear Energy to Support Economy-wide Net-zero Solutions: Challenges and Opportunities

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis

When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Methods of forming near-net shape structures from non-stoichiometric oxides

A method of forming a near-net shape structure comprises forming a structure comprising non-stoichiometric metal oxide comprising at least one metal and less than a stoichiometric amount of oxygen, and electrochemically reducing the non-stoichiometric metal oxide in an electrochemical cell to form a structure having a near-net shape and comprising the at least one metal having less than about 1,500 ppm oxygen. Related methods of forming a non-stoichiometric metal oxide by sintering, annealing, or additive manufacturing, and forming a near-net shape structure from the non-stoichiometric metal oxide, as well as related electrochemical cells are also disclosed.

Tripathy, Prabhat K.↗

DOE OSTI2023

Improved net carbon budgets in the US Midwest through direct measured impacts of enhanced weathering

Abstract Terrestrial enhanced weathering (EW) through the application of Mg‐ or Ca‐rich rock dust to soil is a negative emission technology with the potential to address impacts of climate change. The effectiveness of EW was tested over 4 years by spreading ground basalt (50 t ha −1 year −1 ) on maize/soybean and miscanthus cropping systems in the Midwest US. The major elements of the carbon budget were quantified through measurements of eddy covariance, soil carbon flux, and biomass. The movement of Mg and Ca to deep soil, released by weathering, balanced by a corresponding alkalinity flux, was used to measure the drawdown of CO 2 , where the release of cations from basalt was measured as the ratio of rare earth elements to base cations in the applied rock dust and in the surface soil. Basalt application stimulated peak biomass and net primary production in both cropping systems and caused a small but significant stimulation of soil respiration. Net ecosystem carbon balance (NECB) was strongly negative for maize/soybean (−199 to −453 g C m −2 year −1 ) indicating this system was losing carbon to the atmosphere. Average EW (102 g C m −2 year −1 ) offset carbon loss in the maize/soybean by 23%–42%. NECB of miscanthus was positive (63–129 g C m −2 year −1 ), indicating carbon gain in the system, and EW greatly increased inorganic carbon storage by an additional 234 g C m −2 year −1 . Our analysis indicates a co‐deployment of a perennial biofuel crop (miscanthus) with EW leads to major wins—increased harvested yields of 29%–42% with additional carbon dioxide removal (CDR) of 8.6 t CO 2 ha −1 year −1 . EW applied to maize/soybean drives a CDR of 3.7 t CO 2 ha −1 year −1 , which partially offsets well‐established carbon losses from soil from this crop rotation. EW applied in the US Midwest creates measurable improvements to the carbon budgets perennial bioenergy crops and conventional row crops.

enhanced weathering↗

DOE OSTI2021

Redefining Net-Zero Water for Resilient Aquifers

Groundwater has long been a critical source of potable water, supplying one-third of all global water withdrawals and providing strategic reserves in times of drought (Konikow, 2012; Famiglietti, 2014). The risk in relying on groundwater is that it is often pumped at rates far greater than it can be naturally replenished. With increasing population and a changing climate, the depletion of potable water supplies has become an ever-growing concern (Srinivasan et al., 2012). Recently, the concept of “resilience” has been applied to groundwater, leaving many questions to be explored in terms of what resilience should mean for aquifers (Foster and MacDonald, 2014; Grönwall and Oduro-Kwarteng, 2017). The term is commonly used to indicate resilience to impacts of climate change specifically, but here we consider resilience in the context of more general water insecurity. Relatedly, the concept of “sustainable yield” has emerged in an attempt to determine a metric that can ensure the long-term resilience of groundwater systems, but the definition is fraught with inherent uncertainties and its quantification poses challenges (Rudestam and Langridge, 2014; Grönwall and Oduro-Kwarteng, 2017). At the heart of these concepts is a focus on sustainable management of aquifers. A sustainable approach to managing groundwater resources usually involves balancing long-term abstraction with the long-term recharge taking into account the needs for environmental flows and implementing drought-preparedness measures (Robins et al. 2006; Foster and MacDonald, 2014). At a high level, a sustainable balance of groundwater abstraction versus aquifer recharge may be considered “Net Zero” (NZ) or “Net Positive” (NP) in cases where recharge is equal to or in excess of abstraction, respectively. However, NZ/NP water management programs that address these concerns have varied definitions of compliance, which include considerations for the application at hand (ILFI, 2020; USGBC, 2020). Locations with high risk of low or poor water supply may be particularly interested in pursuing NZ/NP water. The AQUEDUCT Water Risk Atlas (World Resources Institute, 2019) indicates long-term availability, providing risk levels for quantity and quality of water at specific locations across the globe, for existing and predicted future conditions. This paper follows the U.S. Department of Energy (DOE) Federal Energy Management Program (FEMP) NZ water definition adopted by the U.S. Army for use at their installations, including Army Reserve Centers (ARCs). This definition also relates to the U.S. Army’s current focus on water resilience, which includes short-term and long-term availability of water resources.

net zero water, HUC12, Saipan, American Samoa, Mau↗

DOE OSTI2025

Roadmap to reach global net-zero emissions for developing regions by 2085

As climate change intensifies, determining a developing region’s role in achieving net-zero emissions worldwide is crucial. However, regional efforts, considering historical emissions, remain underexplored. Here, we assess energy system changes, technology adoption, and investments needed for developing regions, including five major- and minor-emitting nations. Our analysis, using an integrated assessment model, shows a large gap in regional efforts toward global net-zero emissions, stemming from the necessary shift of energy systems to low-carbon resources. The use of new technologies, like electric vehicles, hydrogen, and carbon capture, varies by region, with the highest adoption required between 2020 and 2030. Financing this shift needs an average gross domestic product (GDP) investment rise of 0.464% in minor-emitting regions and up to 2.1% in major-emitting regions by 2085. Our results could guide policies and support setting quantifiable targets for developing nations. The findings are key to facilitating strategic technology use and finance mobilization to achieve a carbon-neutral future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Nonnegligible cascading impacts of global urban expansion on net primary productivity

Accelerated global urban expansion not only directly occupies surrounding ecosystems, but also induces cascading losses of natural vegetation elsewhere through cropland displacement. Yet, how such effects alter the net primary productivity (NPP) worldwide remains unclear. Here, we quantified the direct and cascading impacts of global urban expansion on terrestrial NPP from 1992 to 2020 and projected the impacts under the shared socioeconomic pathways framework by 2100. We found that global urban expansion caused a cascading loss of 29.2 to 63.9 Tg C/year of terrestrial NPP in the historical period (1992–2020), accounting for 13–29% of the total direct NPP loss. Instead, our projections indicate that during 2020–2100, mainly due to the increased relocation of displaced croplands to low-productive ecosystems, the cascading impacts gradually change from negative to positive, leading to a net NPP increase. Such an increase may offset up to 7% of the total direct NPP loss, better balancing crop compensation with NPP maintenance. Our findings highlight the unexpected large cascading impacts of urban expansion on the carbon cycle and stress the importance of regulating land transitions to curtail land-use emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Data for Aboveground rather than belowground productivity drives variability in Miscanthus x giganteus net primary productivity

This dataset contains the data used for the publication “Aboveground rather than belowground productivity drives variability in Miscanthus x giganteus net primary productivity”. This dataset contains Miscanthus x giganteus biomass, carbon, and nitrogen tissue data for aboveground and belowground plant parts collected in 2021 for three different sites in Iowa with three different nitrogen application rates. Data at the Iowa sites were collected via biometric hand harvesting, belowground excavations, and soil coring both in-clump and beside-clump. Data were collected at two collection timepoints to calculate the contributions of belowground parts to Miscanthus x giganteus net primary productivity. This dataset also includes Miscanthus x giganteus and Switchgrass soil coring and excavation data collected in 2012 at the University of Illinois Urbana Champaign Energy Farm.

Belowground Biomass↗