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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

AlphaFold Protein Structure Database for Sequence-Independent Molecular Replacement

Crystallographic phasing recovers the phase information that is lost during a diffraction experiment. Molecular replacement is a commonly used phasing method for crystal structures in the protein data bank. In one form it uses a protein sequence to search a structure database to find suitable templates for phasing. However, sequence information is not always available, such as when proteins are crystallized with unknown binding partner proteins or when the crystal is of a contaminant. The recent development of AlphaFold published the predicted protein structures for every protein from twenty distinct species. In this work, we tested whether AlphaFold-predicted E. coli protein structures were accurate enough to enable sequence-independent phasing of diffraction data from two crystallization contaminants of unknown sequence. Using each of more than 4000 predicted structures as a search model, robust molecular replacement solutions were obtained, which allowed the identification and structure determination of YncE and YadF. Our results demonstrate the general utility of the AlphaFold-predicted structure database with respect to sequence-independent crystallographic phasing.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Changes in gene expression and signal transduction in microgravity

Studies from space flights over the past three decades have demonstrated that basic physiological changes occur in humans during space flight. These changes include cephalic fluid shifts, loss of fluid and electrolytes, loss of muscle mass, space motion sickness, anemia, reduced immune response, and loss of calcium and mineralized bone. The cause of most of these manifestations is not known and until recently, the general approach was to investigate general systemic changes, not basic cellular responses to microgravity. This laboratory has recently studied gene growth and activation of normal osteoblasts (MC3T3-El) during spaceflight. Osteoblast cells were grown on glass coverslips and loaded in the Biorack plunger boxes. The osteoblasts were launched in a serum deprived state, activated in microgravity and collected in microgravity. The osteoblasts were examined for changes in gene expression and signal transduction. Approximately one day after growth activation significant changes were observed in gene expression in 0-G flight samples. Immediate early growth genes/growth factors cox-2, c-myc, bcl2, TGF beta1, bFGF and PCNA showed a significant diminished mRNA induction in microgravity FCS activated cells when compared to ground and 1-G flight controls. Cox-1 was not detected in any of the samples. There were no significant differences in the expression of reference gene mRNA between the ground, 0-G and 1-G samples. The data suggest that quiescent osteoblasts are slower to enter the cell cycle in microgravity and that the lack of gravity itself may be a significant factor in bone loss in spaceflight. Preliminary data from our STS 76 flight experiment support our hypothesis that a basic biological response occurs at the tissue, cellular, and molecular level in 0-G. Here we examine ground-based and space flown data to help us understand the mechanism of bone loss in microgravity.

manned↗
NASA NTRS1998

Effects of microgravity on osteoblast growth

Studies from space flights over the past two decades have demonstrated that basic physiological changes occur in humans during space flight. These changes include cephalic fluid shifts, loss of fluid and electrolytes, loss of muscle mass, space motion sickness, anemia, reduced immune response, and loss of calcium and mineralized bone. The cause of most of these manifestations is not known and until recently, the general approach was to investigate general systemic changes, not basic cellular responses to microgravity. Recently analyzed data from the 1973-1974 Skylabs disclose that there is a rise in the systemic hormone, cortisol, which may play a role in bone loss in flight. In two flights where bone growth was measured (Skylabs 3 and 4), the crew members had a significant loss of calcium accompanied by a rise in 24 hour urinary cortisol during the entire flight period. In ground-based work on osteoblasts, we have demonstrated that equivalent amounts of glucocorticoids can inhibit osteoblast cell growth. In addition, this laboratory has recently studied gene growth and activation of mouse osteoblasts (MC3T3-E1) during spaceflight. Osteoblast cells were grown on glass coverslips, loaded in the Biorack plunger boxes 18 hours before launch and activated 19 hours after launch in the Biorack incubator under microgravity conditions. The osteoblasts were launched in a serum deprived state, activated and collected in microgravity. Samples were collected at 29 hours after sera activation (0-g, n=4; 1-g, n=4). The osteoblasts were examined for changes in gene expression and cell morphology. Approximately one day after growth activation, remarkable differences were observed in gene expression in 0-g and 1-g flight samples. The 0-g activated cells had increased c-fos mRNA when compared to flight 1-g controls. The message of immediate early growth gene, cox-2 was decreased in the microgravity activated cells when compared to ground or 1-g flight controls. Cox-1 was not detected in any of the samples. There were no significant differences in the expression of actin mRNA between the 0-g and 1-g samples. These data indicate that quiescent osteoblasts are slower to enter the cell cycle in microgravity, suggesting that the force of gravity itself may be a significant factor in bone loss in spaceflight. Preliminary data from our STS 76 flight experiment support our hypothesis that a basic biological response occurs at the tissue, cellular, and molecular level in 0-g. Here we examine ground-based and space flown data on osteoblast growth in ground-based experiments mimicking space flight conditions and in microgravity to simulate lack of gravity stress to help us understand the mechanism of bone loss by experiments.

NASA Experiment Number 9403056↗
NASA NTRSDate not supplied

NASA Open Science Data Repository: Maximizing Spaceflight Bioscience Data

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for data re-analysis and re-use via Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). To address the challenges posed by gaining new knowledge from a vast and diverse amount of biological, health and environmental data in space, the NASA Open Science Data Repository (OSDR - osdr.nasa.gov/bio) plays a crucial role in curating and openly publishing biological data from space-related experiments. Its design incorporates successes and lessons from NASA GeneLab, encompassing not only high-throughput sequencing data but also physiological, phenotypic, and telemetry data. The OSDR makes space biological data FAIR (findable, accessible, interoperable, reusable), and facilitates effective data ingestion, dissemination, and Open Science collaborations. The OSDR also has the capability to integrate human astronaut data with state-of-the-art security and accessibility procedures. We will discuss here several strategies that NASA’s Biological and Physical Science Division have put in place to maximize the return on investment for spaceflight bioscience data.

space biology↗
NASA NTRS2002

Laboratory Astrophysics Needs of the Herschel Space Observatory

The science teams of the Herschel Space Observatory have identified a number of areas where laboratory study is required for proper interpretation of Herschel observational data. The most critical is the collection and compilation of laboratory data on spectral line frequencies, transition probabilities and energy levels for the known astrophysical atomic and molecular species in 670 to 57 micron wavelength range of Herschel. The second most critical need is the compilation of collisional excitation cross sections for the species known to dominate the energy balance in the ISM and the temperature dependent chemical reaction rates. On the theoretical front, chemical and radiative transfer models need to be prepared in advance to assess calibration and identify instrument anomalies. In the next few years there will be a need to incorporate spectroscopists and theoretical chemists into teams of astronomers so that the spectroscopic surveys planned can he properly calibrated and rapidly interpreted once the data becomes available. The science teams have also noted that the enormous prospects for molecular discovery will be greatly handicapped by the nearly complete lack of spectroscopic data for anything not already well known in the ISM. As a minimum, molecular species predicted to exist by chemical models should be subjected to detailed laboratory study to ensure conclusive detections. This has the greatest impact on any astrobiology program that might be proposed for Herschel. Without a significant amount of laboratory work in the very near future Herschel will not be prepared for many planned observations, much less addressing the open questions in molecular astrophysics.

Pearson, J. C.↗
DOE OSTI2023

Maximum respiration rates in hyporheic zone sediments are primarily constrained by organic carbon concentration and secondarily by organic matter chemistry

Abstract. River corridors are fundamental components of the Earth system, and their biogeochemistry can be heavily influenced by processes in subsurface zones immediately below the riverbed, referred to as the hyporheic zone. Within the hyporheic zone, organic matter (OM) fuels microbial respiration, and OM chemistry heavily influences aerobic and anaerobic biogeochemical processes. The link between OM chemistry and respiration has been hypothesized to be mediated by OM molecular diversity, whereby respiration is predicted to decrease with increasing diversity. Here we test the specific prediction that aerobic respiration rates will decrease with increases in the number of unique organic molecules (i.e., OM molecular richness, as a measure of diversity). We use publicly available data across the United States from crowdsourced samples taken by the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our continental-scale analyses rejected the hypothesis of a direct limitation of respiration by OM molecular richness. In turn, we found that organic carbon (OC) concentration imposes a primary constraint over hyporheic zone respiration, with additional potential influences of OM richness. We specifically observed respiration rates to decrease nonlinearly with the ratio of OM richness to OC concentration. This relationship took the form of a constraint space with respiration rates in most systems falling below the constraint boundary. A similar, but slightly weaker, constraint boundary was observed when relating respiration rate to the inverse of OC concentration. These results indicate that maximum respiration rates may be governed primarily by OC concentration, with secondary influences from OM richness. Our results also show that other variables often suppress respiration rates below the maximum associated with the richness-to-concentration ratio. An important focus of future research will identify physical (e.g., sediment grain size), chemical (e.g., nutrient concentrations), and/or biological (e.g., microbial biomass) factors that suppress hyporheic zone respiration below the constraint boundaries observed here.

58 GEOSCIENCES↗
NASA NTRS1974

Recommended radiative property data for Venusian entry calculations

A compilation of experimental and calculated data on the radiative properties species important in Venusian entry is presented. Molecular band systems, atomic lines, free-bound, and free-free continua are considered for the principal radiating species of shock heated carbon dioxide. A limited amount of data pertinent to the species in the ablation layer is also included. The assumption is made that the Venus atmosphere so closely approximates pure CO2 that the inviscid layer radiation is due almost entirely to thermally excited CO2. The only exception is the inclusion of data on the Violet band system of CN. Recommendations are made as to best property values for radiative heating calculations. A review of the basic equations and the relationships of the various emission-absorption gas porperties is also included.

Jones, J. J.↗
DOE OSTI2022

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.

09 BIOMASS FUELS↗
DOE OSTI2020

Solvent influence on non-adiabatic interfacial electron transfer at conductive oxide electrolyte interfaces

The kinetics for interfacial electron transfer (ET) from a transparent conductive oxide (tin-doped indium oxide, ITO, Sn:In 2 O 3 ) to molecular acceptors 4-[N,N-di(p-tolyl)amino]benzylphosphonic acid, TPA, and [Ru II (bpy) 2 (4,4' -(PO 3 H 2 ) 2 -bpy)] 2+ , RuP, positioned at variable distances within and beyond the electric double layer (EDL), were quantified in benzonitrile and methanol by nanosecond absorption spectroscopy as a function of the thermodynamic driving force, -ΔG ° . Relevant ET parameters such as the rate constant, k et , reorganization energy, λ, and electronic coupling, Hab, were extracted from the kinetic data. Overall, k et increased as the distance between the molecular acceptor and the conductor decreased. For redox active molecules within the Helmholtz planes of the EDL, k et was nearly independent of -ΔG° , consistent with a negligibly small λ value. Rips–Jortner analysis revealed a non-adiabatic electron transfer mechanism consistent with H ab < 1 cm -1 . The data indicate that the barrier for electron transfer is greatly diminished at the conductor–electrolyte interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2016

GeneLab for High Schools: Data Mining for the Next Generation

Modern biological sciences have become increasingly based on molecular biology and high-throughput molecular techniques, such as genomics, transcriptomics, and proteomics. NASA Scientists and the NASA Space Biology Program have aimed to examine the fundamental building blocks of life (RNA, DNA and protein) in order to understand the response of living organisms to space and aid in fundamental research discoveries on Earth. In an effort to enable NASA funded science to be available to everyone, NASA has collected the data from omics studies and curated them in a data system called GeneLab. Whilst most college-level interns, academics and other scientists have had some interaction with omics data sets and analysis tools, high school students often have not. Therefore, the Space Biology Program is implementing a new Summer Program for high-school students that aims to inspire the next generation of scientists to learn about and get involved in space research using GeneLabs Data System. The program consists of three main components core learning modules, focused on developing students knowledge on the Space Biology Program and Space Biology research, Genelab and the data system, and previous research conducted on model organisms in space; networking and team work, enabling students to interact with guest lecturers from local universities and their fellow peers, and also enabling them to visit local universities and genomics centers around the Bay area; and finally an independent learning project, whereby students will be required to form small groups, analyze a dataset on the Genelab platform, generate a hypothesis and develop a research plan to test their hypothesis. This program will not only help inspire high-school students to become involved in space-based research but will also help them develop key critical thinking and bioinformatics skills required for most college degrees and furthermore, will enable them to establish networks with their peers and connections with university Professors that may help them achieve their educational goals.

genelab↗
DOE OSTI2024

A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction

Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Yang, Yuxin↗
NASA NTRS1980

Calculation of multiple scattered radiation in clean atmospheres

Calculations of the multiple-scattered solar radiation in the earth's atmosphere are compared with telephotometer measurements of the sky intensity obtained during the EPA-sponsored Visibility Impairment by Sulfate Transport and Transformation in the Atmosphere experiment. The doubling and adding calculation technique, in which the reflection and transmission properties of the atmosphere are computed by initially calculating such properties for the case of very thin layers and then adding adjacent layers together, is described and compared with data for two cloudless sky conditions. The first is typical of clean tropospheric conditions, and the second represents an ultraclean atmosphere in which surface aerosol scattering is much lower than molecular scattering. Good agreement is found between the computations and the measurement data.

Bergstrom, R. W.↗
NASA NTRS1975

Interstellar molecular abundances toward omicron Persei

Extensive scanning of the B1 III star omicron Per has been done with the Copernicus telescope-spectrometer in an effort to determine column densities or meaningful upper limits for several molecules expected to be relatively abundant in H I regions of moderate density. The combination of radio H II, OH, and CO emission-line data with the optical absorption measurements indicates that omicron Per is embedded in the near edge of a dense molecular cloud and that the material seen in front of the star must be close to it. Molecules detected in ultraviolet absorption are H2, HD, CO, and possibly OH. The CO rotational temperature is between 2.2 and 3.8 K. The observed column densities and upper limits from Copernicus and ground-based data are compared with recent models for molecular formation in diffuse clouds.

Snow, T. P., Jr.↗
DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗
NASA NTRS1982

The energetic molecular outflow near AFGL 961 - Millimeter-wave and infrared observations

Sensitive millimeter-wave C-12O line observations and near-infrared hydrogen recombination line observations of AFGL 961 are reported and analyzed. The observations show that the high-velocity flow has a maximum extent of at least six arcmin or 1.9 pc at the distance of AFGL 961. The flow is anisotropic and the emission profiles are asymmetric in velocity. The mass of the gas involved in the flow is determined to be approximately 19 solar masses, and the kinetic energy of this gas is estimated to be about 8 x 10 to the 46th ergs. These observations are interpreted as evidence that an energetic bipolar outflow of molecular gas is occurring near AFGL 961. Comparison of infrared recombination line data, radio continuum, and CO observations shows that a strong, ionized, stellar wind would not be a sufficient force to drive the outflow. The effects of the outflow on the surrounding molecular cloud was also investigated.

Lada, C. J.↗
NASA NTRSDate not supplied

Investigating Molecular Responses to Space Radiation for Biological Missions Beyond Low Earth Orbit

As we plan crewed missions to the Moon, Mars, and beyond, it is essential to understand how persistent exposure to deep space radiation affects biology. Unlike on the International Space Station (ISS), where crew support and sample return are possible, experiments for long-duration missions require autonomous systems with no sample return. Human cells would be ideal biosensors, but limitations in culture methods, extended prelaunch storage, and long flight durations make it difficult to keep human cells alive. Unlike other model systems, yeast can survive the constraints of long-duration spaceflight. Despite a billion years of evolution separating yeast from humans, we share homology in hundreds of genes important for basic cell function, including responses to DNA damage. Thus, yeast are excellent biosensors for detecting types/extent of damage induced by space radiation. BioSentinel is NASA’s latest biological CubeSat, and first interplanetary space bioscience mission. BioSentinel is launching on Artemis 1, the first flight of NASA’s Space Launch System, in 2022. The BioSensor payload within BioSentinel contains two yeast strains. The wild type serves as a control for health and normal DNA damage repair (DDR). The rad51 deletion mutant is defective for DDR and will undergo alterations to growth and metabolism as it accumulates radiation damage. Changes in growth and metabolic activity will be measured using a 3-color LED detection system and the metabolic redox dye alamarBlue®. Preliminary tests indicate a significant change in alamarBlue responses to space-like, low-dose ionizing radiation. We will discuss these findings in five parts – Introduction to NASA’s biological CubeSats and BioSentinel (presented by Sergio Santa Maria), analysis of flight data from the ISS mission (presented by Kylie Akiyama), preliminary molecular responses to space radiation (presented here), a deeper dive into those pathways (presented by Kyra Keenan), and characterizing stress response through redox potential data (presented by Diana Gentry).

Lauren Courtney Liddell↗
DOE OSTI2021

Further Investigations of Racemic and Chiral Molecular Sieves of the STW Topology

Large single crystals of pure-silica STW-type molecular sieves are prepared using 1,2,3,4,5-pentamethylimidazolium (12345PMI) and 2-ethyl-1,3,4-trimethylimidazolium (2E134TMI) as organic structure-directing agents (OSDAs) and α-amino acid additives. The effects of the amount and type of amino acid on the crystallization of pure-silica STW materials are investigated. Room-temperature single-crystal X-ray diffractometry shows that 12345PMI better fits the STW cage than 2E134TMI and is fully consistent with computed simulations. The synthesis of an enantioenriched R-STW-type molecular sieve having a Si/Ge ratio of 2.2 is achieved using a chiral, dicationic OSDA that contains two stereocenters. Furthermore, the conformation of the chiral OSDA in the chiral framework has been obtained from a Rietveld refinement using synchrotron powder diffraction data and is the first structure refinement of an enantioenriched, inorganic molecular sieve prepared from a pure, asymmetric OSDA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗