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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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VMlib is a C++ library for visualizing spacecraft mission simulations with an emphasis on the execution of observation sequences.
The Rover Sequencing and Visualization Program is a suite of toos for the commanding of planetary rovers which are subject to significant light time delay and thus are unsuitable for tele-operation.
Each phase of the sequence development process had to overcome many operational challenges due to the immense complexity of the spacecraft, tour design, pointing capabilities, flight rules and software development. This paper will address the specific challenges related to each of those complexities and the methods used to overcome them during operation.
The ongoing discoveries of extra-solar planets are unveiling a wide range of terrestrial mass (size) planets around their host stars. In this Letter, we present estimates of habitable zones (HZs) around stars with stellar effective temperatures in the range 2600 K-7200 K, for planetary masses between 0.1M and 5M. Assuming H2O-(inner HZ) and CO2-(outer HZ) dominated atmospheres, and scaling the background N2 atmospheric pressure with the radius of the planet, our results indicate that larger planets have wider HZs than do smaller ones. Specifically, with the assumption that smaller planets will have less dense atmospheres, the inner edge of the HZ (runaway greenhouse limit) moves outward (approx.10% lower than Earth flux) for low mass planets due to larger greenhouse effect arising from the increased H2O column depth. For larger planets, the H2O column depth is smaller, and higher temperatures are needed before water vapor completely dominates the outgoing long-wave radiation. Hence the inner edge moves inward (approx.7% higher than Earth's flux). The outer HZ changes little due to the competing effects of the greenhouse effect and an increase in albedo. New, three-dimensional climate model results from other groups are also summarized, and we argue that further, independent studies are needed to verify their predictions. Combined with our previous work, the results presented here provide refined estimates of HZs around main-sequence stars and provide a step toward a more comprehensive analysis of HZs.
NASA has developed an advanced arrival management capability for terminal controllers, known as Terminal Sequencing and Spacing (TSS). TSS increases use of performance-based navigation (PBN) arrival procedures during periods of high traffic demand. It extends two Federal Aviation Administration's operational systems with terminal metering and controller spacing tools. Sixteen high-fidelity human-in-the-loop simulations, involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of- concept design to fully functional prototype. These simulations modeled arrival procedures at several U.S. airports, incorporated a broad range of traffic demand profiles and wind conditions, and used controllers with extensive operational experience. Two fundamental metrics are evaluated for these simulations: PBN Success Rate and Inter-Arrival Spacing Error. The PBN Success Rate shows a definitive trend when TSS is used. It increases from 42 percent for today's operations to 68 percent for terminal metering only and 92 percent for terminal metering with controller-managed spacing tools. Meanwhile, the Inter-Arrival Spacing Error improves 25 to 35 percent when TSS is used compared to not used. The TSS technology was transferred to the FAA and, and it is targeted for deployment to several busy airports in the U.S. starting in 2018.
Food waste digestate was fed into a sequencing batch reactor (SBR) for Haloferax mediterranei (HM) to produce poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). This SBR was operated uninterruptedly for 450 days to test its stability, during which the cycle time and volume exchange ratio were varied to understand their impacts on the PHBV fermentation performance under ranged organic loading rates (OLR). Results showed that 1) PHBV productivity was proportional to OLR of food waste digestate; 2) substrate and product inhibitions were two limiting factors constraining substrate utilization and PHBV yields; 3) PHBV titer was dependent on the hydraulic retention time of the SBR while a volume exchange ratio lower than 0.5 is unfavorable due to the product inhibitor accumulation. Furthermore, this study for the first time demonstrated that the long-term stability of food waste-fed PHBV production by HM and revealed that inhibition effects could be barriers in SBR limiting the full-scale application of the technology.
The sea cucumber (H. glaberrima) is a species found in the shallow waters near coral reefs and seagrass beds in Puerto Rico. To characterize the microbial taxonomic composition and functional profiles present in the sea cucumber, total DNA was obtained from their intestinal system, fosmid libraries constructed, and subsequent sequencing was performed. The diversity profile displayed that the most predominant domain was Bacteria (76.56 %), followed by Viruses (23.24 %) and Archaea (0.04 %). Within the 11 phyla identified, the most abundant was Proteobacteria (73.16 %), followed by Terrabacteria group (3.20 %) and Fibrobacterota, Chlorobiota, Bacteroidota (FCB) superphylum (1.02 %). The most abundant species were Porvidencia rettgeri (21.77 %), Pseudomonas stutzeri (14.78 %), and Alcaligenes faecalis (5.00 %). The functional profile revealed that the most abundant functions are related to transporters, MISC (miscellaneous information systems), organic nitrogen, energy, and carbon utilization. The data collected in this project on the diversity and functional profiles of the intestinal system of the H. glaberrima provided a detailed view of its microbial ecology. These findings may motivate comparative studies aimed at understanding the role of the microbiome in intestinal regeneration.
Intervertebral disc (IVD) degeneration contributes to disabling back pain. Degeneration can be initiated by injury and progressively leads to an irreversible loss of cells and function. IVD function restoration through cell replacement therapies have had limited success due to knowledge gaps in the critical cell populations important for repair. Here, in this study, we used single cell RNA sequencing to identify the transcriptional changes of IVD resident and infiltrating cell populations from Control and Injured coccygeal IVDs extracted from 12-week-old female C57BL/6J mice 7 days post injury. Clustering, gene ontology, and pseudotime trajectory analyses determined transcriptomic divergences with injury, flow cytometry identified they types of infiltrating immune cells, and immunofluorescence was utilized to define mesenchymal stem cell (MSC) localization. We identified 11 distinct clusters that included IVD, immune, vascular cells, and MSCs. Differential gene expression analysis determined that Outer Annulus Fibrosus, Neutrophils, Saa2-High MSCs, Macrophages, and Krt18 + Nucleus Pulposus (NP) cells were the major drivers of transcriptomic differences between Control and Injured cells. Gene ontology revealed that the most upregulated biological pathways were angiogenesis and T cell-related while wound healing and ECM regulation were downregulated. Pseudotime trajectory analyses revealed that IVD injury directed cells towards increased differentiation in all clusters, except for Krt18 + NP cells which remained in a less mature cell state. Saa2-High and Grem1-High MSCs populations shifted towards more differentiated IVD cells profiles with injury and localized distinctly within the IVD. This study revealed novel MSC populations with the potential to be leveraged for future IVD repair studies.
Biological macromolecular machines occupy a continuum of structural conformations to perform cellular tasks. Mapping this conformational space provides an insight into its functionality. While the cryo-electron microscopy resolution revolution has expanded our ability to characterize the conformational continuums, there are obstacles in structurally characterizing regions of high flexibility. These technical barriers have impeded characterization of flexible ribosomal proteins when the ribosome is interacting with mRNA stem-loop structures such as a frameshifting stimulatory sequence (FSS). Small-angle neutron/X-ray scattering and electron microscopy were used to study ribosomal samples and compared structural differences between a ribosome that is bound to an FSS stem-loop compared to a ribosome bound to linear mRNA. This comparison shows that a large protein stalk elongates by 22% when the 70S interacts with an mRNA stem-loop. Finally, our results suggest that ribosomal proteins have extensive flexibility and may influence important ribosomal mechanisms, such as those that involve FSS.
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatibilization, chemical transformations, and separations. s-BCPs have chain architectures where three or more linear diblock copolymer arms comprised of two chemically distinct linear polymers, e.g., solvophobic and solvophilic chains, are covalently joined at one point. The chemical composition of each of the subunit polymer chains comprising the arms, their molecular weights, and the number of arms can be varied to tailor the surface and interfacial activity of these architecturally unique molecules. Further, this makes identification of the optimal s-BCP design nontrivial as the total number of plausible s-BCP architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with a reinforcement learning-based Monte Carlo tree search (MCTS) to identify s-BCP designs that minimize the interfacial tension between polar and nonpolar solvents. We first validate the MCTS approach for the design of small- and medium-sized s-BCPs and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified by using the configurations obtained from MD simulations. Chemical insights into the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provides important groundwork for future experimental investigation of s-BCPs for various applications.
Hyoscyamine 6β-hydroxylase (H6H) is an Fe(II)- and 2-oxoglutarate-dependent (Fe/2OG) oxygenase that catalyzes the last two steps in the biosynthesis of scopolamine, a prolifically administered anti-nausea drug. After its namesake first reaction, H6H couples the newly installed C6-bonded oxygen to C7 to form the epoxide of scopolamine. Oxoiron(IV) (ferryl) intermediates initiate both reactions by cleaving C–H bonds, but it remains unclear how the enzyme switches target site and promotes (C6)O–C7 coupling in preference to C7 hydroxylation in the second step. In one possible epoxidation mechanism, the C6 oxygen would – analogously to mechanisms proposed for the Fe/2OG halogenases and, in the preceding paper, N-acetylnorloline synthase (LolO) – coordinate as alkoxide to the C7–H-cleaving ferryl intermediate to enable alkoxyl coupling to the ensuing C7 radical. Here we provide structural and kinetic evidence that H6H instead exploits the distinct spatial dependencies of competitive C–H-cleavage (C6 vs C7) and C–O-coupling (oxygen rebound vs cyclization) steps to promote the two-step sequence without substrate coordination or repositioning for the epoxidation step. Structural comparisons of ferryl-mimicking vanadyl complexes of wild-type H6H and a variant that preferentially hydroxylates C7 of 6-hydroxyhyoscyamine suggest that only a modest (~ 10°) shift in the Fe–O–H(C7) approach angle is sufficient to change the outcome. Finally, the observation that, in wild-type H6H, 2 H 2 O solvent also increases the C7-hydroxylation:epoxidation ratio by ~ 8-fold implies that the latter outcome requires cleavage of the alcohol O-H bond, which, unlike in the LolO oxacyclization, is not accomplished in advance of C–H cleavage.
Abstract Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.
T cell states are prognostic in different cancer types. Recent technologies enable joint profiling of T cell RNA and T cell receptor (TCR) sequences at single-cell resolution. Here we present the TCR-RNA Integrating Model (TRIM), a multi-modal variational autoencoder framework that integrates RNA-TCR data and predicts T cell clonality and transcriptional states. TRIM learns a shared representation of the data conditioned on patient, tissue source, and treatment timepoint. We applied TRIM to three independent datasets that included T cells collected before and after checkpoint inhibitor treatment, sourced either from blood and tumor biopsies in patients with head and neck squamous cell carcinoma and colorectal cancer, or from tumor and adjacent tissue in a pan-cancer dataset. In all settings, TRIM accurately predicted intra-tumor T cell clonal expansion and transcriptional status based on T cells from blood or normal tissue before treatment, demonstrating its utility in modeling multimodal T cell data and predicting T cell response to treatment and disease progression.
Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.
Neurofilaments (NFs) are multisubunit, bottlebrush-shaped intermediate filaments abundant in the axonal cytoskeleton. Each NF subunit contains a long intrinsically disordered tail domain, which protrudes from the NF core to form a “brush” surrounding each NF. Precisely how the tails’ variable charge patterns and repetitive phosphorylation sites mediate their conformation within the brush remains an open question in axonal biology. We address this problem by grafting recombinant NF tail protein constructs NF-Light, -Medium, and -Heavy (NFL, NFM, and NFH) to surfaces, yielding protein brushes of defined stoichiometry that can be phosphorylated in vitro. Atomic force microscopy measurements reveal that brush height depends on composition monotonically but not always linearly for binary NFL:NFM or NFL:NFH systems, and that NFM-based brushes are highly extended, while brushes incorporating the much larger NFH are surprisingly compact even after multisite phosphorylation. Complementary self-consistent field theory (SCFT) predicts multilayer brush morphologies for NFM and phosphorylated NFH brushes. Further experiments and SCFT analysis with designed mutants reveal that N-terminal negative charges in the NFH tail repel phosphorylated residues to generate the multilayer morphology, while the C-terminal charge-neutral region contributes to multilayer brush morphology but not total brush height. Charge-shuffled NFM variants show that charge segregation promotes brush collapse near physiological ionic strengths. Collectively, this study supports a role for NFM in establishing a dynamic range for NF brush conformation, lending insight into previous in vitro and in vivo findings. More broadly, this work establishes a platform for dissecting contributions of disordered protein sequence to conformation at interfaces.
Higher-order gravitational wave modes from quasi-circular, spinning, non-precessing binary-black-hole (BBH) mergers encode rich information about the nonlinear dynamics of strong-field gravity. We present a transformer-based sequence-completion surrogate that, given an early-inspiral segment, forecasts the subsequent late inspiral, merger, and ringdown. The intended applications are (i) patching or completing expensive or interrupted numerical-relativity (NR) simulations and (ii) providing late-time cross-checks and rapid hybridization studies. The training set is built from the NRHybSur3dq8 surrogate, which provides spherical-harmonic modes up to $\ell$ ≤ 4 (excluding (4, 0) and (4,±1), and including (5, 5)) for mass ratios q ≤ 8, dimensionless spin components s$^{z}_{1,2}$ ϵ[–0.8, 0.8], and inclination angles θ ϵ [0, π]. Waveforms are supplied on the interval t ϵ [–5000M, –100 M) and the model autoregressively generates the plus and cross polarizations (h + , h x ) on t ϵ [–100 M, 130M]. Training on the Delta supercomputer with 16 NVIDIA A100 GPUs required ~15 h on more than 14 million hybrid waveforms. Evaluation on a held-out test set of 840,000 samples yields mean and median overlaps of 0.996 and 0.997, respectively, with respect to the surrogate ground truth.
Jordan is located on an important spot along the Mediterranean and Black Sea Flyway. Hundreds of migratory bird species have been identified stopping over in Jordan during spring and autumn migratory seasons. Compared to mammals and economically important birds, the microbiomes of wild bird species are severely understudied. Gut microbial composition is a valuable source of information that reflects food preferences, foraging behavior, and the risk of pathogen transmission to humans and other animals. In this study, we assessed the microbiome composition of three species of migrating passerines (willow warblers, lesser whitethroats, and common reed warblers) captured during the spring migration stopover in Jordan in 2023. A total of 59 fecal samples were selected evenly from the three species and subjected to 16S sequencing and microbiome analysis. Our objectives were to determine the diversity of bacteria in these three species, assess the amount of intra- and inter-specific variation, and detect pathogenic genera and species that could pose health risks to humans, domestic animals, and wildlife. Bacteria mainly belonged to the phyla Proteobacteria (62%), Actinobacteriota (18%), Firmicutes (13%), Cyanobacteria (5%), and Bacteroidota (1%). The results reveal that lesser whitethroats had the greatest variation in bacterial genus richness, Shannon diversity, and microbial composition compared to willow warblers and common reed warblers. The three bird species harbored several pathogenic genera and species, including Campylobacter, Enterococcus, Escherichia-Shigella, Mycoplasma, Rickettsia, Clostridium perfringens, and Vibrio cholerae. We suggest further investigation to understand the relationship between migratory behavior and their gut microbiome. We advocate for the use of advanced molecular techniques to characterize the pathogens found in migratory birds that might have public and environmental health impacts in addition to economic loss.
The discovery and engineering of new enzymes is important across the bioeconomy, with diverse applications from foods to pharmaceuticals, sensors to agriculture. However, enzyme engineering, in particular machine learning-guided engineering, is hampered by a lack of data. Currently there exists no database designed to capture and interpret datasets created in this domain, nor are there easy analysis and visualisation tools. We developed the Enzyme Engineering Database to provide a centralized resource and an online analysis tool to consolidate sequence-function data from enzyme engineering campaigns, thereby making three contributions: (i) a database into which researchers can deposit public data, (ii) visualisation and analysis tools for protein engineers to analyse their own data or compare enzyme variants to other engineering campaigns, and (iii) a gold-standard dataset for benchmarking automated extraction along with the first large language model extraction pipeline specific for enzyme engineering campaigns. The Enzyme Engineering Database is accessible at http://enzengdb.org/.