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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

HIDRA-MAT liquid metal droplet injector for liquid metal applications in HIDRA

A liquid metal droplet injector was explicitly designed for the Hybrid Illinois Device for Research and Applications Material Analysis Test-stand (HIDRA-MAT) at the University of Illinois Urbana-Champaign to prepare liquid metal plasma-facing components (PFCs) for plasma exposure. The design goals were to create a compact, reliable, and robust design that could apply liquid metal droplets in-vacuo to a variety of samples. The injector was designed for liquid metal use, and results discussed pertain to lithium. The injector can produce lithium droplets of consistent size by utilizing programmable piston movement. Here, droplet formation data is presented for four different piston step sizes (0.125 mm, 0.25 mm, 0.5 mm, and 1 mm) at three different nozzle temperatures (185 °C, 230 °C, and 270 °C). Droplets are formed on the injector nozzle tip and a linear shift mechanism vertically translates the droplet to the substrate for application. This design prevents the droplet from inadvertently detaching off the nozzle and falling to the sample. Application of the liquid metal droplet ensures placement consistency on the substrate and helps avoid damage to components in HIDRA-MAT that should not interact with liquid metals. A description of the liquid lithium droplet creation and application results is given and provides additional insight into the cooling and oxidation of lithium droplets under vacuum conditions. A second nozzle was fabricated and demonstrated repeatability in droplet diameter creation having a variance of ±0.14 mm for droplets created from the same number of piston steps. The end-use for the injector on HIDRA-MAT is to apply liquid metals to PFCs and expose them to HIDRA's plasma with subsequent intershot in-vacuo surface analysis in HIDRA-MAT for plasma-material interaction studies.

42 ENGINEERING↗
DOE OSTI2022

Techno-Economic Analysis for Shear Assisted Processing and Extrusion (ShAPE) of High Strength Aluminum Alloys

Aluminum alloy 7075 (AA7075) is a high strength aluminum alloy (HSAL), attractive for applications such as automotive, aviation, aerospace, defense, and marine applications. However, AA7075 has not yet been widely adopted due slow extrusion speed, high energy use, narrow process window, and sensitivity to incipient melting common in conventional extrusion methods. Alternative extrusion methods may overcome these limitations. New research funded by the U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) is exploring the use of a new SPP approach called Shear Assisted Processing and Extrusion (ShAPE) for the manufacture of AA7075 extrusions. Pacific Northwest National Laboratory (PNNL) are leading the development, testing and characterization of ShAPE, which is showing that high speed ShAPE extrusions (e.g., above 12 meters/min) which is significantly faster than the 1-2 meters/min possible with conventional AA7075 extrusions. Initial findings from the economic and energy analysis indicate that AA7075 tubes created with ShAPE use less energy than tubes that are conventionally heated and extruded. The reduction in energy use is primarily a result of using direct chill cast (non-homogenized) billets, eliminating the pre-heating step, and faster extrusion speeds. The TEA model translates CAPEX and O&M costs to a manufacturing cost, and then a minimum sustainable price (MSP) per ton of extruded product for multiple facility capacities. A sample output figure of the TEA model is presented below. It presents results of the preliminary version of the TEA model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Practical Probabilistic Programming

Recent advances in probabilistic programming languages (PPLs) have provided the capability for exact inference: computing a closed-form probability distribution for a given probabilistic program. In particular, the new language Roulette uses a language oriented programming (LOP) approach, wherein analysts build new programming languages on top of a set of primitives provided by Roulette, which then translates these structures into a weighted model counting problem which can be solved by automated reasoning tools. However, because Roulette provides few convenience features, developing these new languages is challenging even for expert users. We developed a standard library of common probability functions for Roulette with the goal of improved usability. This included approximation of continuous probability density functions using discrete probability mass functions. We demonstrated this approach by modeling a cosmic ray striking a RAM controller. We found that Roulette provides a powerful interface for highly expressive probabilistic programs to be generated. In collaboration with the NNSA Advanced Simulation and Computing program, which resulted in development of a tool called Circulette, we were able to model complex circuits expressed in Verilog using probabilistic programs with an expressivity not previously possible. Our research question that motivated the development of a Roulette standard library was to determine whether non-experts could use a PPL to model relevant problems regarding radiation effects on microelectronics. This standard library improved the expressivity of Roulette by implementing common probability density functions, mathematical operators on distributions, and support for empirical distributions. While Roulette is a powerful modeling language, the untyped, LOP approach makes error messages difficult to understand and requires expert aid. We recommend further research on Roulette, especially with its error messages, to enable improved usability. At the same time, this project demonstrated that for users familiar with Roulette and the LOP approach, Roulette provides powerful new capabilities that can be integrated with other Sandia modeling capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Superconductivity in the bilayer Hubbard model: Two Fermi surfaces are better than one

Fully occupied or unoccupied bands in a solid are often considered inert and irrelevant to a material's low-energy properties. But the discovery of enhanced superconductivity in heavily electron-doped FeSe-derived superconductors poses questions about the possible role of incipient bands (those laying close to but not crossing the Fermi level) in pairing. To answer this question, researchers have studied pairing correlations in the bilayer Hubbard model, which has an incipient band for large interlayer hopping t⊥, using many-body perturbation theory and variational methods. They have generally found that superconductivity is enhanced as one of the bands approaches the Lifshitz transition and even when it becomes incipient. Here we address this question using the nonperturbative quantum Monte Carlo (QMC) dynamical cluster approximation (DCA) to study the bilayer Hubbard model's pairing correlations. We find that the model has robust s± pairing correlations in the large t⊥ limit, which can become stronger as one band is made incipient. While this behavior is linked to changes in the effective interaction, we further find that it is counteracted by a suppression of the intrinsic pair-field susceptibility and does not translate to an increased Tc. Our results demonstrate that the highest achievable transition temperatures in the bilayer Hubbard model occur when the system has two bands crossing the Fermi level.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effects of Reactive Power on Photovoltaic Inverter Reliability and Lifetime

This paper performs research on predicting Photovoltaic (PV) inverters reliability and lifetime based on thermal cycling. Thermal cycling is considered the most important stressors in an inverter system. In order to achieve this, a detailed electro-thermal model of the PV inverter will be developed along with their controllers capable of providing voltage support through reactive power. An in-house inverter was built, and a PV inverter model was developed to match the physical inverter. The PV inverter electrothermal model was validated for different ambient temperatures to match the in-house inverter hardware. The in-house inverter was placed inside a thermal chamber to emulate different ambient temperatures and their losses and temperature rises within the system were measured. After the validation of the model, a reduced order model of the inverter will be implemented to translate the mission profile of ambient temperature and solar irradiance into load profile of junction temperatures of the switches. The junction temperature data will be used to identify the reliability indices and hence predict the useful lifetime of the inverter system. Along with the model to predict useful lifetime of the system, the impact of reactive power on the overall reliability of the system will be studied. The key observation in this paper shows that lifetime of the inverter decreases as the operating power factor moves away from unity.

14 SOLAR ENERGY↗
DOE OSTI2022

Vertically Resolved Convective–Stratiform Echo-Type Identification and Convectivity Retrieval for Vertically Pointing Radars

Using data from the airborne HIAPER Cloud Radar (HCR), a partitioning algorithm (ECCO-V) that provides vertically resolved convectivity and convective versus stratiform radar-echo classification is developed for vertically pointing radars. The algorithm is based on the calculation of reflectivity and radial velocity texture fields that measure the horizontal homogeneity of cloud and precipitation features. The texture fields are translated into convectivity, a numerical measure of the convective or stratiform nature of each data point. The convective–stratiform classification is obtained by thresholding the convectivity field. Subcategories of low, mid-, and high stratiform, shallow, mid-, deep, and elevated convective, and mixed echoes are introduced, which are based on the melting-layer and divergence-level altitudes. As the algorithm provides vertically resolved classifications, it is capable of identifying different types of vertically layered echoes, and convective features that are embedded in stratiform cloud layers. Its robustness was tested on data from four HCR field campaigns that took place in different meteorological and climatological regimes. The algorithm was adapted for use in spaceborne and ground-based radars, proving its versatility, as it is adaptable not only to different radar types and wavelengths, but also different research applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Translation of DNA Damage Response Inhibitors as Chemoradiation Sensitizers From the Laboratory to the Clinic

Combination therapies with agents targeting the DNA damage response (DDR) offer an opportunity to selectively enhance the therapeutic index of chemoradiation or eliminate use of chemotherapy altogether. The successful translation of DDR inhibitors to clinical use requires investigating both their direct actions as (chemo)radiosensitizers and their potential to stimulate tumor immunogenicity. Beginning with high-throughput screening using both viability and DNA damage-reporter assays, followed by validation in gold-standard radiation colony-forming assays and in vitro assessment of mechanistic effects on the DDR, we describe proven strategies and methods leading to the clinical development of DDR inhibitors both with radiation alone and in combination with chemoradiation. Beyond these in vitro studies, we discuss the impact of key features of human xenograft and syngeneic mouse models on the relevance of in vivo tumor efficacy studies, particularly with regard to the immunogenic effects of combined therapy with radiation and DDR inhibitors. Finally, we describe recent technological advances in radiation delivery (using the small animal radiation research platform) that allow for conformal, clinically relevant radiation therapy in mouse models. This overall approach is critical to the successful clinical development and ultimate Food and Drug Administration approval of DDR inhibitors as (chemo)radiation sensitizers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Emerging protein sequencing technologies: proteomics without mass spectrometry?

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry. Areas Covered: We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture. Expert Opinion: Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Scaling plant responses to high temperature from cell to ecosystem

Plant survival and productivity decrease with exposure to less favourable environments. The negative effects of environmental factors such as water-deficit and salinity stress on vegetation are relatively well studied, primarily due to the large losses in crop yield and forest biomass caused by these stresses. Although the impact of combined heat and drought on plants has received considerable interest in recent years due to climate change, there has been less focus on ascertaining the impact of heat stress alone at a range of temporal and spatial scales. The steady increase in global mean temperatures and (more importantly) variability in extreme temperatures is translating into more frequent and extreme heat stress episodes, which in turn reduce plant productivity. Further, projected increases in both day and night temperatures and the predicted impact of these changes on agricultural and forest productivity have drawn attention to the need to better address the direct effects of heat on plants. In particular, there has been tremendous interest in understanding the short- and long-term effects of heat at the cellular and whole-plant levels, including work on crops of agricultural importance and species from less managed ecosystems. Hence, we made a concerted effort in this Special Issue to capture recent progress in our ability to quantify the impact of heat stress, and identify new tools and approaches that have been developed and implemented to capture a wide range of responses in plants. The series of reviews and research articles presented here captures our current understanding of plant physiological and molecular responses to heat stress, as well as covering phenotyping methods, genetic diversity and potential routes through which plants can acclimate and adapt to warming climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mountain waves can impact wind power generation

Abstract. Mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River basin in the lee of the Cascade Range bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting (WRF) model simulations also produce mountain waves and are compared to satellite, lidar, and sodar observations. Simulated mountain wave wavelengths and wave propagation speeds (group velocities) are analyzed using the fast Fourier transform. We found that not all mountain waves exhibit the same speed and conclude that the speed of propagation, magnitudes of wind speeds, or wavelengths are important parameters for forecasters to recognize the risk for mountain waves and associated large drops or surges in power. When analyzing wind farm power output and nacelle wind speeds, we found that even small oscillations in wind speed caused by mountain waves can induce oscillations between full-rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. For the wind plant analyzed in this paper, mountain-wave-induced fluctuations translate to approximately 11 % of the total wind farm output being influenced by mountain waves. Oscillations in measured wind speeds agree well with WRF simulations in timing and magnitude. We conclude that mountain waves can impact wind turbine and wind farm power output and, therefore, should be considered in complex terrain when designing, building, and forecasting for wind farms.

17 WIND ENERGY↗
DOE OSTI2024

Impact of reactor architecture and design parameters on the performance of microbial electrolysis cells revealed by the electrode potential slope analysis

Microbial electrolysis cells (MECs) are appealing for recovering the chemical energy contained in domestic and industrial liquid wastes as hydrogen gas. Despite several years of research in the field, there is still a lack of critical analysis of how the reactor architecture dictates the electrochemical performance of the cell. In this study, internal resistance and onset voltage from the electrode potential slope analysis (EPS) were used in combination with current density, hydrogen production rate, reactor packing density, electrode spacing, membrane type and composition from 23 different studies to identify the reactor design parameters that primarily govern electrochemical performance of MECs. Using anion exchange membranes resulted in smaller internal resistances (AEM R int = 41± 40 mΩ m 2 ) and larger current density (18 ± 14 A m −2 ) compared to single chamber reactors (SC R int = 68 ± 58 mΩ m 2 ; 22 ± 16 A m −2 ) or MECs with cation exchange membranes (CEM R int = 376 ± 280 mΩ m 2 ; 3.0 ± 2.1 A m −2 ). Higher electrochemical performance for AEM- and SC-MECs translated in larger hydrogen gas production rates (0.122 mL H 2 C −1 for AEM vs 0.117 mL H 2 C −1 for SC), but only when inhibitors against hydrogen scavengers were added in single chamber systems (0.080 mL H 2 C −1 for SC without inhibitors). Following membrane type and composition, maintaining a small electrode spacing was the most critical parameter to improve MEC performance, indicating that the low conductivity of the media primarily limit performance by increasing ohmic resistance. Here, reactor volume and electrode surface area negatively correlated with internal resistance and current density, indicating that better performance of scaled-up reactors can likely be obtained by stacking multiple smaller units rather than just increasing reactor size. Although challenges remain in the implementation of MECs for hydrogen production from liquid wastes, advances in electrochemical engineering of the reactors can facilitate scale up and performance prediction at scale.

Electrochemistry↗
DOE OSTI2022

Machine learning enables interpretable discovery of innovative polymers for gas separation membranes

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementation for the discovery of innovative polymers with ideal performance. Specifically, multitask ML models are trained on experimental data to link polymer chemistry to gas permeabilities of He, H 2 , O 2 , N 2 , CO 2 , and CH 4 . We interpret the ML models and extract valuable insights into the contributions of different chemical moieties to permeability and selectivity. We then screen over 9 million hypothetical polymers and identify thousands that lie well above current performance upper bounds, including hundreds of never-before-seen ultrapermeable polymer membranes with O 2 and CO 2 permeability greater than 10 4 and 10 5 Barrers, respectively. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enabling Extreme Fast-Charging: Challenges at the Cathode and Mitigation Strategies

We report charging lithium-ion batteries (LiBs) in 10 to 15 min via extreme fast-charging (XFC) is important for the widespread adoption of electric vehicles (EVs). Lately, the battery research community has focused on identifying XFC bottlenecks and determining novel design solutions. Like other LiB components, cathodes can present XFC bottlenecks, especially when considering long-term battery life. Therefore, it is necessary to develop a comprehensive understanding of how XFC conditions degrade LiB cathodes. The present article reviews relevant cathode-focused studies and summarizes the current understanding regarding cathode performance and aging issues under XFC conditions. Dominant aging modes and mechanisms are identified at different length-scales with electrochemical correlations for LiNi x Mn y Co z O 2 (NMC)-based cathodes. A range of electrochemical techniques and models provide key insights into cathode performance and life issues. A suite of multimodal and multiscale microscopy and X-ray techniques is surveyed to quantify chemical, structural, and crystallographic NMC-cathode degradation. Cathode cycle-life is scaled to equivalent EV miles to illustrate how cathode degradation translates to real-world scenarios and quantifies cathode-related bottlenecks that hinder XFC adoption. Finally, the article discusses several cathode cycle-life aging mitigation strategies with example case studies and identifies remaining challenges.

25 ENERGY STORAGE↗
DOE OSTI2020

Chapter 2: Genetics of Unstudied Thermophiles for Industry

Thermophilic organisms hold great potential for industry due to their numerous advantages in biotechnological applications such as higher reaction rate, higher substrate loading, decreased susceptibility to reaction contamination, energy savings in industrial fermentations, and ability to express thermostable proteins that can be utilized in many important industrial processes. Bioprospecting for thermophiles will continue to reveal new enzymatic and metabolic paradigms with industrial applicability. In order to translate these paradigms to production scale, routine methods for microbial genetic engineering are needed, yet remain to be developed in many newly isolated thermophiles. Major challenges and recent developments in the establishment of reliable genetic systems in thermophiles are discussed. Here, we use a hyperthermophilic, cellulolytic bacterium, Caldicellulosiruptor bescii, as a case study to demonstrate the development of a genetic system for an industrially useful thermophile, describing in detail methods for transformation, genetic tool utilization, and chromosomal modification using targeted gene deletion and insertion techniques.

93 SC - Biological and Environmental Research (BER↗
DOE OSTI2025

Thermal and kinematic properties of ejecta in SN1987A revealed by XRISM

We present an analysis of high-resolution spectra from the shock-heated plasmas in SN 1987A, based on an observation using the Resolve instrument onboard the X-Ray Imaging and Spectroscopy Mission (XRISM). The 1.7–10 keV Resolve spectra are accurately represented by a single-component, plane-parallel shock plasma model, with a temperature of $2.84_{-0.08}^{+0.09}$ keV and an ionization parameter of $2.64_{-0.45}^{+0.58}$ × $10^{11}\,\,{\rm s\,\, cm}^{-3}$. The Resolve spectra are also well reproduced by the 3D magneto-hydrodynamic simulation presented by Orlando et al. (2020, A&A, 636, A22) suggesting substantial contribution from the ejecta. The metal abundances obtained with Resolve align with the Large Magellanic Cloud value, indicating that the X-rays in 2024 originate from “non-metal-rich” shock-heated ejecta and the reverse shock has not reached the inner metal-rich region of ejecta. Doppler widths of the atomic lines from Si, S, and Fe correspond to velocities of 1500–1700 km s$^{-1}$, where the thermal broadening effects in this non-metal-rich plasma are negligible. Therefore, the line broadening seen in Resolve spectra is determined by the large bulk motion of ejecta. For reference, we determined a $90\%$ upper limit on non-thermal emission from a pulsar wind nebula at $4.3 \times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$ in the 2–10 keV range, aligning with NuSTAR findings by Greco et al. (2022, ApJ, 931, 132). Additionally, we searched for the $^{44}$Sc K line feature and found a $1\sigma$ upper limit of $1.0 \times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$, which translates to an initial $^{44}$Ti mass of approximately $2 \times 10^{-4}\, M_{\odot }$, consistent with previous X-ray to soft gamma-ray observations (Boggs et al. 2015, Science, 348, 670; Grebenev et al. 2012, Nature, 490, 373; Leising 2006, ApJ, 651, 1019).

ISM: supernova remnants↗
DOE OSTI2022

Targeting KDM4 for treating PAX3-FOXO1–driven alveolar rhabdomyosarcoma

Chimeric transcription factors drive lineage-specific oncogenesis but are notoriously difficult to target. Alveolar rhabdomyosarcoma (RMS) is an aggressive childhood soft tissue sarcoma transformed by the pathognomonic Paired Box 3–Forkhead Box O1 (PAX3-FOXO1) fusion protein, which governs a core regulatory circuitry transcription factor network. Here, we show that the histone lysine demethylase 4B (KDM4B) is a therapeutic vulnerability for PAX3-FOXO1 + RMS. Genetic and pharmacologic inhibition of KDM4B substantially delayed tumor growth. Suppression of KDM4 proteins inhibited the expression of core oncogenic transcription factors and caused epigenetic alterations of PAX3-FOXO1–governed superenhancers. Combining KDM4 inhibition with cytotoxic chemotherapy led to tumor regression in preclinical PAX3-FOXO1 + RMS subcutaneous xenograft models. In summary, we identified a targetable mechanism required for maintenance of the PAX3-FOXO1–related transcription factor network, which may translate to a therapeutic approach for fusion-positive RMS.

Cell Biology↗