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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Networks and interfaces as catalysts for polymer materials innovation

Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Beyond the Hype: An Evaluation of Commercially Available Machine-Learning-Based Malware Detectors

There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). Consequently, efficacy of malware detectors is opaque, inhibiting end users from making informed decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four prominent commercial malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously and never-before-seen files? Is purchasing a network-level malware detector worth the cost? To investigate, we tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone & Bridges. Although the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool might still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files—37% of malware, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given. Code for future use of the cost model is provided.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Hormonal changes associated with the transition between nursing and natural fasting in northern elephant seals (Mirounga angustirostris)

To better interpret previously described hormonal changes observed during the natural postweaning fast (2-3 months) endured by pups of the northern elephant seal (Mirounga angustirostris), we compared plasma cortisol, thyroid hormones, and leptin in pups (n=5) measured during nursing and fasting periods. Blood samples were taken at four times; early (9 days postpartum) and late (18-22 days postpartum) nursing, and early (second week postweaning) and late (eighth week postweaning) fasting. Plasma cortisol increased 39% between early and late nursing and almost 4-fold by late fasting. After the early nursing period, cortisol and body mass were negatively correlated (y=28.3-0.19 x; R=0.569; p=0.027). Total thyroxine (tT(4)), free T(4) (fT(4)), total triiodothyronine (tT3) and reverse T(3) (rT(3)) were greatest at early nursing and reduced by late nursing and remained so throughout the fast, with the exception of tT(4), which increased between late nursing (17.7+/-2.1 ng mL(-1)) and late fasting (30.1+/-2.8 ng mL(-1)) periods. Leptin remained unaltered among the four sampling periods and was not correlated with body mass. Pups appear to exhibit a shift in the relationship between cortisol and body mass suggesting a potential role for cortisol in the regulation of body fat. The higher concentrations of tT(3) and tT(4) during early nursing may reflect enhanced growth and development during this period, however the increase late in fasting is likely physiologically insignificant and an artifact of reduced metabolic clearance of these hormones. Transition of the pups from nursing to fasting states is characterized by a striking lack of change in cortisol, thyroid hormones, and leptin suggesting that any metabolic alterations associated with this transition may occur independent of these hormones.

NASA Discipline Regulatory Physiology↗
NASA NTRS1997

Effect of fluid ingestion on orthostatic responses following acute exercise

Orthostatic tolerance is impaired following an acute bout of exercise. This study examined the effect of fluid ingestion following treadmill exercise in restoring the cardiovascular responses to an orthostatic stress. Five men (age, 29.6 +/- 3.4 yrs) were exposed to a graded lower body negative (LBNP) pressure protocol (0 to -50 mmHg) during euhydration without exercise (C), 20 minutes after exercise dehydration (D), 20 minutes after exercise and fluid ingestion (FI20), and 60 minutes after exercise and fluid ingestion (FI60). Fluid ingestion (mean +/- SE) consisted of water-ingestion equivalent to 50% of the body weight lost during exercise (520 +/- 15 ml). Exercise dehydration resulted in significantly higher heart rates (119 +/- 8 vs 82 +/- 7 bpm), lower systolic blood pressures (95 +/- 1.7 vs 108 +/- 2.3 mmHg), a smaller increase in leg circumference (3.7 +/- 4 vs 6.9 +/- 1.0 mm), and an attenuated increase in total peripheral resistance (2.58 +/- 1.2 vs 4.28 +/- 0.9 mmHg/L/min) at -50 mmHg LBNP compared to the C condition. Fluid ingestion (both 20 and 60), partially restored the heart rate, systolic blood pressure, and total peripheral resistance responses to LBNP, but did not influence the change in leg circumference during LBNP (4 +/- 0.3 for R20 and 2.8 +/- 0.4 mm for R60). These data illustrate the effectiveness of fluid ingestion on improving orthostatic responses following exercise, and suggest that dehydration is a contributing factor to orthostatic intolerance following exercise.

NASA Center JSC↗
DOE OSTI2022

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2025

Cyclometallated Co(III) Complexes with Lowest-Energy Charge Transfer Excited States Accessible with Visible Light

The Co(III) complexes, cis-[Co(ppy) 2 (L)]PF 6 , where ppy = 2-phenylpyridine and L = bpy (2,2′-bipyridine; 1), phen (1,10-phenanthroline; 2), and DAP (1,12-diazaperylene; 3), are reported and their photophysical properties were investigated to evaluate their potential as sensitizers for applications that include solar energy conversion schemes and photoredox catalysis. Calculations show that cyclometallation in the cis-[Co(ppy) 2 (L)]PF 6 series affords strong Co(dπ)/ppy(π) orbital interactions that result in a Co/ppy(π*) highest occupied molecular orbital (HOMO) and a lowest unoccupied molecular orbital (LUMO) localized on the diimine ligand, L(π*). Complexes 1–3 exhibit relatively invariant oxidation potentials, whereas the reduction event is dependent on the identity of the diimine ligand, L, consistent with the theoretical predictions. For 3 a broad Co/ppy(π*) → L(π*) metal/ligand-to-ligand charge transfer (ML-LCT) absorption band is observed in CH 3 CN with a maxima at 507 nm, extending beyond 600 nm. Upon excitation of the 1ML-LCT transition, transient absorption features consistent with the population of a 3 ML-LCT excited state with lifetimes, τ, of 3.0 ps, 4.6 and 42 ps for 1, 2 and 3 in CH 3 CN respectively are observed. Upon irradiation with 505 nm, 3 is able to reduce methyl viologen (MV 2+ ), an electron acceptor commonly in photocatalytic schemes. Here, to our knowledge, 3 represents the first heteroleptic molecular Co(III) complex that combines cyclometallation with a diimine ligand with lowest-lying metal-to-ligand charge transfer excited states able to undergo photoinduced charge transfer with low-energy green light. As such, the structural design of 3 represents an important step toward d6 photosensitizers based on earth abundant metals.

Energy↗
NASA NTRS1993

Factors influencing dry season ozone distributions over the tropical South Atlantic

Airborne measurements of trace gas and aerosol species were obtained in the lower troposphere (less than 5 km) over the western Atlantic Ocean between 13 deg S and 40 deg N during the August/September 1990 NASA Chemical Instrument Test and Evaluation (CITE 3) experiment. The largest background O3 mixing ratios, averaging 35 and 70 ppbv within the mixed layer (ML) and free troposphere (FT; altitudes greater than 2.4 km), respectively, were found over the tropical South Atlantic. Several competing processes were observed to regulate O3 budgets in this region. Within the ML, rapid photochemical destruction produced a diurnal O3 variation of 8 ppbv and an O3/altitude gradient between the surface and 5 km of almost 10 ppbv (O3)/km. ML O3 concentrations were replenished by atmospheric downwelling which occurred at rates of up to and exceeding 1 cm/s. Ozone values within the subsiding FT air were enriched both by long-range transport of O3 produced photochemically within biomass combustion plumes and the downward propagation of dry, upper tropospheric air masses. Overall, the tropospheric O3 column below 3.3 km averaged 13.5 Dobson units (DU) over the South Atlantic region, which is 8-9 DU higher than observed during CITE 3 ferry flights over the northern tropical Atlantic Ocean or measured by ozonesondes over coastal Brazil during the wet season. An examination of simultaneous dew point and combustion tracer (e.g., CO) measurements suggests that the dry subsiding layers and biomass burning layers make approximately equal contributions to the observed O3 enhancement.

Anderson, B. E.↗
DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2023

Atomic-scale modeling of the dissolution of oxidized platinum nanoparticles in an explicit water environment

Pt nanoparticles (NPs) are currently being investigated for use in fuel cells; however, Pt NP oxidation as a function of size, morphology, and temperature is not well understood or currently quantified. In this study, the stability and dissolution of oxidized platinum NPs is examined via classical molecular dynamics in an explicit water environment. The NPs considered range in size from 1.35 to 2.92 nm in diameter and included five different monolayer (ML) coverages of O*. The simulations were performed at 300, 450, and 600 K with the many-body, reactive third-generation charge-optimized many-body or COMB3 potentials and examine the kinetics of NP dissolution in water. The Pt–O layer, which reduces the kinetic activity for Pt atom dissolution, is projected to make dissolution more favorable for O* MLs smaller than 0.5. The simulations further indicate that the Pt NPs' kinetic rates of dissolution are slowed by an increase in the number of adsorbed species caused by the dissociation of water molecules at the reconstructed facets of the Pt NPs. Furthermore, these findings quantify the effect of oxygen and temperature on the stability and dissolution of oxidized platinum NP in an explicit water environment similar to the conditions in fuel cells and electrocatalysis.

08 HYDROGEN↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
NASA NTRS2000

Quantifying ataxia: ideal trajectory analysis--a technical note

We describe a quantitative method to assess repeated stair stepping stability. In both the mediolateral (ML) and anterioposterior (AP) directions, the trajectory of the subject's center of mass (COM) was compared to an ideal sinusoid. The two identified sinusoids were unique in each direction but coupled. Two dimensionless numbers-the mediolateral instability index (IML) and AP instability index (IAP)-were calculated using the COM trajectory and ideal sinusoids for each subject with larger index values resulting from less stable performance. The COM trajectories of nine nonimpaired controls and six patients diagnosed with unilateral or bilateral vestibular labyrinth hypofunction were analyzed. The average IML and IAP values of labyrinth disorder patients were respectively 127% and 119% greater than those of controls (p<0.014 and 0.006, respectively), indicating that the ideal trajectory analysis distinguishes persons with labyrinth disorder from those without. The COM trajectories also identify movement inefficiencies attributable to vestibulopathy.

Non-NASA Center↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance

Abstract This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and L-shaped distractors. They were tasked with categorizing each image as target present or target absent. In Experiment 1, they performed this task without the aid of ML outputs. In Experiments 2–5, they were shown images with bounding boxes, representing the output of an ML model. The outputs could be correct (hits and correct rejections), or they could be erroneous (false alarms and misses). Experiment 2 manipulated the overall accuracy of these mock ML outputs. Experiment 3 manipulated the proportion of different types of errors. Experiments 4 and 5 manipulated the importance of specific types of stimuli or model errors, as well as the framing of the task in terms of human or model performance. These experiments showed that model misses were consistently harder for participants to detect than model false alarms. In general, as the model’s performance increased, human performance increased as well, but in many cases the participants were more likely to overlook model errors when the model had high accuracy overall. Warning participants to be on the lookout for specific types of model errors had very little impact on their performance. Overall, our results emphasize the importance of considering human cognition when determining what level of model performance and types of model errors are acceptable for a given task.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Effects of acute fresh water exposure on water flux rates and osmotic responses in Kemp's ridley sea turtles (Lepidochelys kempi)

Water flux rates and osmotic responses of Kemp's Ridley sea turtles (Lepidochelys kempi) acutely exposed to fresh water were quantified. Salt-water adapted turtles were exposed to fresh water for 4 d before being returned to salt water. During the initial salt water phase, absolute and relative water flux rates were 1.2+/-0.1 l d(-1) and 123.0+/-6.8 ml kg(-1) d(-1), respectively. When turtles were exposed to fresh water, rates increased by approximately 30%. Upon return to salt water, rates decreased to original levels. Plasma osmolality, Na(+), K(+), and Cl(-) decreased during exposure to fresh water, and subsequently increased during the return to salt water. The Na(+):K(+) ratio was elevated during the fresh water phase and subsequently decreased upon return to salt water. Aldosterone and corticosterone were not altered during exposure to fresh water. Elevated water flux rates during fresh water exposure reflected an increase in water consumption, resulting in a decrease in ionic and osmotic concentrations. The lack of a change in adrenocorticoids to acute fresh water exposure suggests that adrenal responsiveness to an hypo-osmotic environment may be delayed in marine turtles when compared to marine mammals.

Non-programmatic↗
NASA NTRS1997

Influence of simulated microgravity on the longevity of insect-cell culture

Simulated microgravity within the NASA High Aspect Rotating-Wall Vessel (HARV) provides a quiescent environment to culture fragile insect cells. In this vessel, the duration of stationary and death phase for cultures of Spodoptera frugiperda cells was greatly extended over that achieved in shaker-flask controls. For both HARV and control cultures, S. frugiperda cells grew to concentrations in excess of 1 x 10(7) viable cells ml-1 with viabilities greater than 90%. In the HARV, stationary phase was maintained 9-15 days in contrast to 4-5 days in the shaker flask. Furthermore, the rate of cell death was reduced in the HARV by a factor of 20-90 relative to the control culture and was characterized with a death rate constant of 0.01-0.02 day-1. Beginning in the stationary phase and continuing in the death phase, there was a significant decrease in population size in the HARV versus an increase in the shaker flask. This phenomenon could represent cell adaptation to simulated microgravity and/or a change in the ratio of apoptotic to necrotic cells. Differences observed in this research between the HARV and its control were attributed to a reduction in hydrodynamic forces in the microgravity vessel.

Bioreactors↗
DOE OSTI2026

DLK1 is a GATA1s-driven dependency and therapeutic target in Down syndrome–associated myeloid leukemia

Children with Down syndrome have a markedly increased risk of developing myeloid leukemia. Although having an excellent prognosis, 10% to 20% develop relapsed or refractory disease with poor survival, highlighting the need for new targeted approaches. The pathogenesis of myeloid leukemia of Down syndrome (ML-DS) is tightly linked to fetal hematopoiesis and mutations in GATA1, generating the truncated GATA1 short (GATA1s) isoform. We identified Delta-like noncanonical Notch ligand 1 (DLK1) as a direct GATA1s target. DLK1, a paternally imprinted transmembrane protein, is highly expressed in fetal liver CD34 + cells but absent in adult hematopoiesis, making it an attractive immunotherapeutic target. Chromatin profiling revealed GATA1s occupancy at a distal enhancer within the DLK1-DIO3 locus, driving aberrant DLK1 upregulation in ML-DS. Functional studies demonstrated that DLK1 is a leukemia dependency, as its genetic ablation impaired proliferation and engraftment, induced apoptosis, and altered Notch and β-catenin signaling. Therapeutically, a DLK1-directed antibody-drug conjugate–induced selective cytotoxicity, abrogated colony formation, and significantly prolonged survival in refractory ML-DS patient-derived xenograft (PDX) models, achieving durable remissions at higher doses. These findings establish DLK1 as a leukemia-specific vulnerability and provide preclinical proof-of-concept for DLK1-targeted therapies in ML-DS and other leukemias with fetal-like expression programs.

Biological and medical sciences↗