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NASA NTRSDate not supplied

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as instructions for how to download and access the data. The I4 datasets described here re present the first ever comprehensive collection of commercial astronaut data.

Amanda M Saravia-Butler↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
NASA NTRSDate not supplied

Olivine Dissolution and Formation of Secondary phases in Ultramafic Soils

Introduction: Olivine has been proposed as an indicator for the duration of water-rock interaction within Martian rocks and sediments [1-3]. The use of olivine as a mineralogical indicator for past aqueous alteration on Mars requires interpretation of a complex combination of factors including pH, temperature, and composition [5,6]. Here, we examine the persistence of natural olivine within terrestrial ultramafic soils (Fe/Mg-rich, Al-poor) developing under different climatic conditions and the incipient dissolution of emplaced forsterite (Fo) and fayalite (Fa) surfaces to investigate environmental effects on incipient olivine dissolution, olivine persistence in soils, and formation of secondary phases. Methods: Field Sites. We examined olivine weathering and secondary material formation in ultramafic soils at 6 sites in the Klamath Mountains (KM) of northern California with a mean annual temperature of ~12.8℃ and precipitation of ~55.7-95.4 cm/year [7], and soil pH of ~6.5-7.3; 4 sites in the Tablelands (TB) of Newfoundland, Canada with a mean annual temperature of <3.9℃ and precipitation of ~120.0 cm/year [8], and soil pH of ~7.7; and at 3 sites at Pickhandle Gulch (PG), Nevada with a mean annual temperature of ~14.4℃ and precipitation of ~14.1 cm/year [7], and soil pH of ~8.5. Sampling sites span an age range of ~12.1-50+ kya in the Klamath Mountains [9,10] and ~13-30 kya in the Tablelands [11]. Pickhandle Gulch sites are undated. Parent Material and Soil Analyses. Polished thin sections of bulk soil prepared by Wagner Petrographic, Inc were carbon-coated and analyzed on a JEOL 2100 SEM in back-scattered electron mode in the EMIL lab at UNLV and at the 13-ID-E synchrotron beamline at Argonne National Laboratory using µXRF, µXRD, and XAS. Soil and parent material samples were powdered in a Fritsch pulverisette and analyzed by XRD and soil by VNIR. Soil preparation is further described in [12]. Disk Preparation, Burial, and Collection. Fo disks were cut from a column prepared via hot-pressing and Fa disks by sintering synthetic fayalite powder, see [13] for detail. Disks were polished to a 0.25-micron level with diamond grit. Disks were buried in 3 KM soils, 4 TB soils, and 3 PG soils, collected after exactly 365 days, and washed gently with 100% reagent grade ethanol to remove potential adhered soil material. Weathered disks and soil samples were stored in a -20℃ freezer until analysis. Unaltered control disks prepared identically to the buried disks were stored at -20℃ for the duration of the experiment. Disk Analyses. One Fo and Fa disk from each climate zone was analyzed on a variable pressure Zeiss Supra 40VP SEM at Northern Arizona University. A separate Fo and Fa disk from each climate zone was analyzed by XPS using a Physical Electronics VersaProbe II at the Penn State Univ. Materials Characterization Lab after a Na-dodecyl sulfate wash and ozonation to remove carbon contamination as in [14]. VNIR measurements were conducted at Johnson Space Center using an ASD FieldSpec3 under ambient lab conditions on a separate Fo and Fa disk from each climate zone. One separate Fo and Fa control sample was analyzed for each technique for comparison with weathered samples. XPS uncertainty was determined from 5 repeat measurements on controls. Results: Bedrock and Soil Results Olivine is present in the parent material in the KM and TB. Olivine is found in ~12.1 ka KM soils but is absent from all older soils, while persisting into the oldest (>20 ka) TB soil (Figure 1). In both locations, olivine is found as cores surrounded by a serpentine rind (Figure 2). VNIR spectra from the analyzed soils possess strong OH-associated spectral features at ~2.33 µm indicating the presence of Mg-rich phyllosilicates as well as ferric-oxide features at ~0.92 µm in the KM (Figure 3). Primary crystalline silicate grains mostly incorporate Fe2+, while poorly crystalline weathering rinds are best fit by ferric oxide XAS standards (Figure 4). µXRF also shows that Fe and Ni concentrate in weathering rinds and Cr remains within interior silicate grains (Figure 4). Buried Sample Results All Fo surfaces exhibited formation of dissolution features including shallow pitting not observed on controls. Dissolution features were most visually widespread on the KM disk (Figure 5). Leaching of Mg from KM and TB Fo disks was evident from <1.6 Mg/Si ratios measured by XPS (Figure 6). Fe-rich precipitates in SEM (Figure 5) and Fe presence in XPS scans (Figure 6) indicate Fe deposition onto KM and TB Fo disk surfaces. The appearance of a spectral feature at 0.55 µm in the VNIR spectra from the TB Fo suggests this Fe is ferric (Figure 7). The PG Fo appears least altered, with minimal formation of dissolution features in SEM (Figure 5), a Mg/Si ratio inconsistent with leaching (~2) (Figure 6), and VNIR spectra almost identical to the control sample. Analysis of Fa surfaces is ongoing. The higher temperatures and more acidic pH in the KM soils likely drive the faster dissolution of the Fo disks described above. While the TB soils experience greater precipitation than in the KM, the cooler temperatures and more basic soil pH facilitate observable but more limited alteration. The dry climate and basic soil pH at PG lead to minimal dissolution of the PG disk surfaces.

A D Feldman↗
DOE OSTI2025

Whole metagenome sequencing and 16S rRNA gene amplicon analyses reveal the complex microbiome responsible for the success of enhanced in-situ reductive dechlorination (ERD) of a tetrachloroethene-contaminated Superfund site

The North Railroad Avenue Plume (NRAP) Superfund site in New Mexico, USA exemplifies successful chlorinated solvent bioremediation. NRAP was the result of leakage from a dry-cleaning that operated for 37 years. The presence of tetrachloroethene biodegradation byproducts, organohalide respiring genera (OHRG), and reductive dehalogenase (rdh) genes detected in groundwater samples indicated that enhanced reductive dechlorination (ERD) was the remedy of choice. This was achieved through biostimulation by mixing emulsified vegetable oil into the contaminated aquifer. This report combines metagenomic techniques with site monitoring metadata to reveal new details of ERD. DNA extracts from groundwater samples collected prior to and at four, 23 and 39 months after remedy implementation were subjected to whole metagenome sequencing (WMS) and 16S rRNA gene amplicon (16S) analyses. The response of the indigenous NRAP microbiome to ERD protocols is consistent with results obtained from microcosms, dechlorinating consortia, and observations at other contaminated sites. WMS detects three times as many phyla and six times as many genera as 16S. Both techniques reveal abundance changes in Dehalococcoides and Dehalobacter that reflect organohalide form and availability. Methane was not detected before biostimulation but appeared afterwards, corresponding to an increase in methanogenic Archaea. Assembly of WMS reads produced scaffolds containing rdh genes from Dehalococcoides, Dehalobacter, Dehalogenimonas, Desulfocarbo, and Desulfobacula. Anaerobic and aerobic cometabolic organohalide degrading microbes that increase in abundance include methanogenic Archaea, methanotrophs, Dechloromonas, and Xanthobacter, some of which contain hydrolytic dehalogenase genes. Aerobic cometabolism may be supported by oxygen gradients existing in aquifer microenvironments or by microbes that produce O 2 via microbial dismutation. The NRAP model for successful ERD is consistent with the established pathway and identifies new taxa and processes that support this syntrophic process. This project explores the potential of metagenomic tools (MGT) as the next advancement in bioremediation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Computer-based video digitizer analysis of surface extension in maize roots: kinetics of growth rate changes during gravitropism

We used a video digitizer system to measure surface extension and curvature in gravistimulated primary roots of maize (Zea mays L.). Downward curvature began about 25 +/- 7 min after gravistimulation and resulted from a combination of enhanced growth along the upper surface and reduced growth along the lower surface relative to growth in vertically oriented controls. The roots curved at a rate of 1.4 +/- 0.5 degrees min-1 but the pattern of curvature varied somewhat. In about 35% of the samples the roots curved steadily downward and the rate of curvature slowed as the root neared 90 degrees. A final angle of about 90 degrees was reached 110 +/- 35 min after the start of gravistimulation. In about 65% of the samples there was a period of backward curvature (partial reversal of curvature) during the response. In some cases (about 15% of those showing a period of reverse bending) this period of backward curvature occurred before the root reached 90 degrees. Following transient backward curvature, downward curvature resumed and the root approached a final angle of about 90 degrees. In about 65% of the roots showing a period of reverse curvature, the roots curved steadily past the vertical, reaching maximum curvature about 205 +/- 65 min after gravistimulation. The direction of curvature then reversed back toward the vertical. After one or two oscillations about the vertical the roots obtained a vertical orientation and the distribution of growth within the root tip became the same as that prior to gravistimulation. The period of transient backward curvature coincided with and was evidently caused by enhancement of growth along the concave and inhibition of growth along the convex side of the curve, a pattern opposite to that prevailing in the earlier stages of downward curvature. There were periods during the gravitropic response when the normally unimodal growth-rate distribution within the elongation zone became bimodal with two peaks of rapid elongation separated by a region of reduced elongation rate. This occurred at different times on the convex and concave sides of the graviresponding root. During the period of steady downward curvature the elongation zone along the convex side extended farther toward the tip than in the vertical control. During the period of reduced rate of curvature, the zone of elongation extended farther toward the tip along the concave side of the root. The data show that the gravitropic response pattern varies with time and involves changes in localized elongation rates as well as changes in the length and position of the elongation zone. Models of root gravitropic curvature based on simple unimodal inhibition of growth along the lower side cannot account for these complex growth patterns.

NASA Discipline Plant Biology↗
DOE OSTI2022

First lithium experiments in HIDRA and evidence of helium retention during quasi-steady-state stellarator plasma operations

Recent experiments in Hybrid Illinois Device for Research and Applications (HIDRA) have had operational discharges between t discharge = 60 and 1000 s using electron cyclotron resonant heating (ECRH) of the plasma. This means that quasi-steady-state plasma discharges reach conditions to study long-pulse plasma material interactions (PMIs). The newly commissioned HIDRA-Material Analysis Test-stand PMI diagnostic is used to place a drop of lithium onto a heated tungsten surface, transfer the sample in-vacuo and expose it in a helium plasma. Helium is of interest as there is an open question to whether lithium will be able to remove helium ash in real fusion devices. The introduction of the W-Li sample in HIDRA resulted in evaporation of lithium into the helium plasma during a 600 s pulse and caused a reduction of over 90% in neutral pressure during the discharge. It was also observed that the plasma density and temperature increased by over 2.5 times. Using spectroscopy and a helium collisional radiative model, the peak temperature and density of the helium plasma can be monitored during the discharge. During lithium evaporation, as significant lithium ionization occurs, there is a 85% drop in the HIDRA vessel neutral pressure, despite a constant flow rate of He gas. This reduction in neutral pressure is supported by spectroscopy data with corresponding reductions in He I line intensities (587 nm, 667 nm, 706 nm, and 728 nm), as well as those of other impurities. At one point in the discharge a lithium plasma is created, as indicated by an increase in Li + emission and a complete reduction in He + emission, but the electron density jumps from ne = 3 × 10 18 m –3 to over ne = 8 × 10 18 m –3 while the core temperature stays relatively constant between Te = 16 eV and 20 eV. Once lithium has completely evaporated from the sample and the majority of the ionized lithium has diffused from the plasma to the vessel walls, pressure and spectroscopy data paired with He collisional radiative model calculations shows a re-establishment of a helium plasma in a low recycling regime. In this regime, the density drops down to ne = 2 × 10 18 m –3 and the electron temperature increases from T e = 20 eV to over T e = 50 eV indicating an increase in helium heating efficiency. This is also indicated by the He+ emission re-establishing and having a higher intensity. Here, we show the results from the first lithium campaign in HIDRA. In the presence of lithium, and in particular when lithium ions are present, the helium disappears from the plasma via an as of yet unknown complex relationship that needs to be further studied. The most likely explanation is that the lithium ions are distributed around the vessel and able to trap helium to the surface turning HIDRA into a large gettering surface. These results have potential implications on future plasma facing component design using liquid lithium for impurity and recycling control using limiters and divertors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1995

Interaction of the terrestrial and atmospheric hydrological cycles in the context of the North American southwest summer monsoon

Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.

Dickinson, Robert E.↗
NASA NTRSDate not supplied

Retrospectives: Current State of Knowledge on the Intersection of Spaceflight Stressors and Microbial Risks to Crew and Craft

OVERVIEW The spaceflight environment has several unique stressors that affect the health of both the crew and the spacecraft. An area of continued, albeit incomplete, study is the interaction of these stressors on microbial populations inherent to both astronauts and spacecraft surfaces and systems. A primary concern is the potential for the spaceflight environment to perturb the phenotype of these populations towards negative outcomes for crew and craft. In order to effectively mitigate these potential risks, they must first be characterized. We performed a retrospective literature analysis to assess the current state of knowledge regarding the affects of ionizing radiation and elevated CO2 on relevant microbial populations. The results of these retrospectives will guide next steps in the decisions of what (if any) further studies should be pursued and to guide decisions of the need for countermeasures. STRESSORS Ionizing radiation. The health risk involved with increased exposure to cosmic radiation has been studied in crew for 35+ years, with human health and cancer risk being the main focus. However, space radiation could also affect both the resident microorganisms aboard the ISS and the normal, healthy astronaut microbiomes that are of direct concern for crew health. A retrospective review of over 250 publications was accomplished looking at the impact of cumulative ionizing radiation doses lower than 3 Gy (chronic or acute) on microbial populations. Elevated CO2. The health risk involved with elevated atmospheric CO2 in spacecraft, primarily focusing on human toxicological risks, is understudied. The current Spaceflight Maximum Allowance Concentration for 24-hour average CO2 is 0.4% (3 mm Hg), which is significantly higher than terrestrial levels (0.04%). Whether these elevated ambient CO2 levels aboard spacecraft influence the diversity and phenotypic responses of the resident microbial communities from both the spacecraft environment (air, surface, water) and crew members (gut, nasal, skin microbiomes) is not known. A retrospective review was accomplished looking at the impact of chronic CO2 exposure up to 0.7% (5 mm Hg) for up to 6 months and acute exposure up to 2.6% (20 mm Hg) for up to 24 hours. CONCLUSIONS: MICROBIOME OF THE BUILT ENVIRONMENT The microbiome of the built spacecraft environment has been sampled consistently over the course of human spaceflight and significant advancements have been made in identifying microbial populations on the ISS. The dominant source of microbes on spacecraft surfaces are human-derived. Once in the spacecraft built environment, the extreme environment selects for features that enhance survival. While efforts to understand potential antibiotic resistance and pathogenicity of ISS isolates is robust, there is little to no understanding of which spaceflight environmental stressors, to include ionizing radiation or elevated CO2, drive the evolutionary trajectory of spacecraft-associated microbial populations. CONCLUSIONS: MICROBE-HOST INTERACTIONS The host-microbiome field has emerged as an important factor in human health on Earth as well in spaceflight. The field is struggling with the complexity of the system under investigation as there is substantial taxonomic and functional heterogeneity in these communities, making it difficult to establish clear stimulus-response dynamics. Taxonomic characterization is the norm; however, the functional role of each community member is key to linking environmental perturbations to potential dysbiosis. For both ionizing radiation and elevated CO2, the likely target of the perturbation is the host tissue, not the microbes themselves.. Any resulting changes to the microbial community composition and/or function is likely a result of adapting to those changes in the host physiology. RECOMMENDATIONS Emphasize functional characterization as opposed to taxonomic characterization of microbial communities. Increase the number of investigations using chronic, spaceflight-relevant doses of ionizing radiation. MoBE studies should move away from observational studies towards predictive modeling of community dynamics. Continue to develop scale-down models, such as tissues-on-a-chip & defined microbial communities. Focus on the crew response to elevated CO2 over MoBE considerations. Assess how direct contact with the hypercapnic environment affects skin microbiome dynamics.

retrospective↗
NASA NTRSDate not supplied

Retrospectives: Intersection of Spaceflight Stressors and Microbial Risk to Crew and Craft

OVERVIEW The spaceflight environment has several unique stressors that affect the health of both the crew and the spacecraft. An area of continued, albeit incomplete, study is the interaction of these stressors on microbial populations inherent to both astronauts and spacecraft surfaces and systems. A primary concern is the potential for the spaceflight environment to perturb the phenotype of these populations towards negative outcomes for crew and craft. In order to effectively mitigate these potential risks, they must first be characterized. We performed a retrospective literature analysis to assess the current state of knowledge regarding the affects of ionizing radiation and elevated CO2 on relevant microbial populations. The results of these retrospectives will guide next steps in the decisions of what (if any) further studies should be pursued and to guide decisions of the need for countermeasures. STRESSORS Ionizing radiation. The health risk involved with increased exposure to cosmic radiation has been studied in crew for 35+ years, with human health and cancer risk being the main focus. However, space radiation could also affect both the resident microorganisms aboard the ISS and the normal, healthy astronaut microbiomes that are of direct concern for crew health. A retrospective review of over 250 publications was accomplished looking at the impact of cumulative ionizing radiation doses lower than 3 Gy (chronic or acute) on microbial populations. Elevated CO2. The health risk involved with elevated atmospheric CO2 in spacecraft, primarily focusing on human toxicological risks, is understudied. The current Spaceflight Maximum Allowance Concentration for 24-hour average CO2 is 0.4% (3 mm Hg), which is significantly higher than terrestrial levels (0.04%). Whether these elevated ambient CO2 levels aboard spacecraft influence the diversity and phenotypic responses of the resident microbial communities from both the spacecraft environment (air, surface, water) and crew members (gut, nasal,skin microbiomes) is not known. A retrospective review was accomplished looking at the impact of chronic CO2 exposure up to 0.7% (5 mm Hg) for up to 6 months and acute exposure up to 2.6% (20 mm Hg) for up to 24 hours. CONCLUSIONS: MICROBIOME OF THE BUILT ENVIRONMENT The microbiome of the built spacecraft environment has been sampled consistently over the course of human spaceflight and significant advancements have been made in identifying microbial populations on the ISS. The dominant source of microbes on spacecraft surfaces are human-derived. Once in the spacecraft built environment,the extreme environment selects for features that enhance survival. While efforts to understand potential antibiotic resistance and pathogenicity of ISS isolates is robust, there is little to no understanding of which spaceflight environmental stressors, to include ionizing radiation or elevated CO2, drive the evolutionary trajectory of spacecraft-associated microbial populations. CONCLUSIONS: MICROBE-HOST INTERACTIONS The host-microbiome field has emerged as an important factor in human health on Earth as well in spaceflight. The field is struggling with the complexity of the system under investigation as there is substantial taxonomic and functional heterogeneity in these communities, making it difficult to establish clear stimulus-response dynamics.Taxonomic characterization is the norm; however, the functional role of each community member is key to linking environmental perturbations to potential dysbiosis. For both ionizing radiation and elevated CO2, the likely target of the perturbation is the host tissue, not the microbes themselves.. Any resulting changes to the microbial community composition and/or function is likely a result of adapting to those changes in the host physiology. RECOMMENDATIONS Emphasize functional characterization as opposed to taxonomic characterization of microbial communities.Increase the number of investigations using chronic, spaceflight-relevant doses of ionizing radiation. MoBE studies should move away from observational studies towards predictive modeling of community dynamics. Continue to develop scale-down models, such as tissues-on-a-chip & defined microbial communities. Focus on the crew response to elevated CO2 over MoBE considerations. Assess how direct contact with the hypercapnic environment affects skin microbiome dynamics.

Countermeasures↗
NASA NTRS1999

Workshop on Extraterrestrial Materials from Cold and Hot Deserts

The workshop was held July 6-8, 1999 before the Meteoritical Society meeting in Johannesburg, South Africa. The venue was Kwa Maritane Resort in the Pilanesburg Game Reserve. Conveners were Ludolf Schultz (Chair, MPI fur Chemie), Ian Franchi (Open University), Arch Reid (University of Houston), and Mike Zolensky (NASA JSC). Extended abstracts will be published as an LPI Technical Report. In the first session, Marvin discussed three African iron meteorites: Cape of Good Hope, Gibeon, and Hoba. Grady presented a statistical analysis of meteorites from hot and cold deserts. Wasson discussed types of Antarctic iron meteorites. Several presentations characterized populations of meteorites from individual desert areas: Libyan Desert (Weber et al.), Nullarbor Region (Bevan et al.), and Mojave Desert (Kring et al. and Verish et al). Pairing among EET87503-group howardites was discussed by Buchanan et al. Based on 14C terrestrial ages of Allan Hills ordinary chondrites, Bland et al. suggested that ice flow may be the principal sink for Antarctic meteorites. The effects of preterrestrial and terrestrial alteration were considered in the second session. Nakamura et al. and Lipschutz discussed asteroidal metamorphism of carbonaceous chondrites. Zolensky presented evidence for preterrestrial halide and sulfide in meteorites. Crozaz and Wadhwa described terrestrial alteration of Dar al Gani 476. Welten and Nishiizumi discussed terrestrial weathering of chondrites from Frontier Mountain, Antarctica. Most of the third session dealt with terrestrial meteorite ages. Based on 14C-10Be ages, Jull et al. discussed the exponential decay in numbers of meteorites with increased age. Nishiizumi et al. concluded that some Allan Hills meteorites have much older terrestrial ages than any meteorites from Lewis Cliffs. Welten et al. discussed terrestrial ages determined by 41Ca/36CI of metal separates from hot desert meteorites. Based on a comparison with large IDPs, Flynn et al. suggested that polar micrometeorites, lost a water-soluble sulfate phase by terrestrial alteration. Nyquist suggested that Type I cosmic spheres from deep sea sediments and polar ice were derived from carbonaceous chondrite-like asteroidal sources. The final session considered noble gases, cosmic ray effects, and thermoluminescence. Calculations presented by Reedy indicate that cosmic ray-produced nuclides are more likely to be preserved in small objects than in larger objects. Murty and Mohapatra. reported that trapped gas in Dar al Gani 476 includes a Martian atmospheric component. Wieler et al. and Scherer et al. reported noble gas abundances in different types of desert meteorites. Patzer and Schultz discussed the influence of terrestrial weathering on cosmic ray exposure ages of enstatite chondrites. Franchi et al. suggested that it is difficult to discriminate whether differences in gas release profiles of lunar meteorites from hot deserts and returned lunar samples are the result of terrestrial weathering or shock metamorphism. Benoit and Sears surveyed natural and induced thermoluminescence of Antarctic ordinary chondrites. Merchel et al. analyzed Saharan meteorites with short or complex exposure histories.

Buchanan, Paul C.↗
DOE OSTI2024

Novel Instrumentation for Advanced Quantum Chromodynamics Studies with Flavor Sensitivity

The true nature of ordinary matter has always fascinated humankind, whose imagination has been pointed to the right direction since the 5th century BC with the first known atomic theory by Leucippus and Democritus. They described the matter as formed by small, invisible, indivisible, and eternal particles: the atoms. A long investigation followed them, including contributions from several of history?s greatest philosophers and scientists. The progress led to the understanding that atoms have an internal structure consisting of a central nucleus composed of protons and neutrons, surrounded by a cloud of electrons. About 50 years ago, scientists discovered that nucleons also have an internal structure. Through the Deep Inelastic Scattering (DIS) experiments, on which a lepton is scattered off a nucleon, it was possible to access the distribution of partons inside the nucleon along the longitudinal direction defined by the hard-scale probe. It was a crucial step in developing Quantum Chromodynamics (QCD), currently the best theory describing the subatomic matter. Ultimately, DIS was recognized as a limited tool to investigate the nucleon structure because it is mainly sensitive to observables at the probe?s energy scale. In recent decades, theoretical developments have allowed defining soft-scale observables by measuring more complex processes. Reactions such as Semi-Inclusive Deep Inelastic Scattering (SIDIS) with single or di-hadron production, can provide information about the momentum of parton inside the nucleon. In particular, polarized SIDIS allows access to the Transverse-Momentum-Dependent (TMD) parton distributions describing the correlations of the quark and gluon degrees of freedom in transverse momentum and spin. This study can provide three-dimensional imaging of nucleon structure and inner dynamics, as long as the hadronic component in the final state can be measured. Identifying the species of the produced particles provides information on the quark flavor and assumes a crucial role in this context. The thesis describes the work carried out by the author in preparing novel instrumentation for particle identification in the final state of DIS experiments, to support modern analysis of the parton dynamics within the basic confined object (nucleon) with flavor sensitivity. The work has been focused on the CLAS12 spectrometer in operation at Jefferson Laboratory (JLab), Newport News, VA, USA, and the ePIC experiment in preparation for the future Electron-Ion Collider (EIC) at Brookhaven National Laboratory (BNL), Long Island, NY, USA. CLAS12 is a fixed-target experiment using a 12 GeV beam of polarized electrons scattering off polarized nuclear targets. In 2018 and 2022, two Ring Imaging Cherenkov (RICH) detector modules were installed to improve the ?±/K± separation in the high-momentum region (3÷8 GeV/c) of the experiment. The author contributed to the assembly, installation, and com- missioning of the second RICH module, to the efficiency studies of the first module, and to the first SIDIS analysis with high-momentum kaons identified by the RICH. These studies led to the observation of the first spin asymmetry with high-momentum kaons from the CLAS12 experiment, and an initial assessment of the systematic error associated with the hadron identification. ePIC will be the first experiment at the future EIC, the new collider designed to expand the frontiers of QCD. The author was involved in the development of the dual-radiator Ring Imaging Cherenkov (dRICH). This detector will interpolate the measurements of the Cherenkov angles of photons produced by relativistic particles crossing two different radiators to identify charged hadrons. The author contributed to the studies conducted with the dRICH prototype, developing the reconstruction and analysis software and simulations, characterizing the aerogel radiator samples, and being responsible for the tracking system used during the beam tests. The performance obtained by the prototype has been progressively improved and the results are now comparable with the expectation derived from the simulation and satisfy the requirements of the experiment. In Chapter 1, the SIDIS theory is briefly introduced, outlying the connection with the experimental measurement. Chapter 2 includes the description of the CLAS12 RICH, its assembly and installation, and the efficiency study. In Chapter 3, the analysis of the Beam-Spin Asymmetry associated with SIDIS kaons is described. Chapter 4 describes the contribution to the dRICH for EIC, starting from a description of the ePIC experiment and focusing on the studies performed for the dRICH prototype. The conclusions of this work are summarized at the end.

Vallarino, Simone↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Extended Duration: The SIRIUS 21 Crew Perspective

The SIRIUS (Scientific International Research In a Unique terrestrial Station) missions represent a collaborative effort between NASA and Russia’s Institute for Biomedical Problems (IBMP) to conduct a series of long duration isolation and confinement spaceflight analog missions. Three missions of 17-day, 4-month, and 8-month duration (SIRIUS 17, 19, and 21) have been completed at IBMP’s Ground-Based Experimental Complex / Nazemnyy eksperimental'nyy kompleks (NEK) in Moscow, Russia. The international SIRIUS 21 crew comprising representatives from the United States, United Arab Emirates and Russia recently completed the 8-month analog lunar mission. The extended duration mission included simulated lunar transit, orbital, and surface operations with corresponding deep space communication delay, during which the crew participated in nearly 70 studies, eight of which were sponsored by NASA’s Human Research Program. The studies examined the effect of isolation and confinement on the behavioral health of research subjects, and investigated medical countermeasures, team performance, crew dynamics, crew autonomy, food system risks, consequences of confinement and associated physiological stressors. SIRIUS 21 crewmembers also participated in operational tasks such as Rover and CubeSat assembly, simulated lunar sample assessment, VR activities, robotic arm training, environmental systems monitoring, exercise, greenhouse maintenance and 3D printing. Communication with Mission Control was limited to 30-minute periods every two hours. Since access to the internet and email was restricted, simulated ground support provided the Crew’s primary source of daily news and mission information. This panel discussion will include presentations from the US SIRIUS 21 crewmembers – William Brown and Ashley Kowalski – about their experience participating in the mission and science. A facilitated question and answer session will follow with attendees encouraged to ask questions and join in discussion with the SIRIUS 21 crewmembers about their experiences. William Brown came to SIRIUS 21 with experience spread across multiple industries, including the military, defense contracting, healthcare consulting, software engineering, and logistics. He has lived in the Middle East, Central Asia, and Russia. A former Boren Scholar, Brown is fluent in Russian. He holds a Master of International Business degree from the University of South Carolina’s Darla Moore School of Business. Prior to that, he earned a bachelor’s degree in Russian language, literature, and culture from the University of South Carolina. There, he also completed additional undergraduate coursework in computer science. Ashley Kowalski is a Project Leader in The Aerospace Corporation’s International Partnerships Department, where she works with, represents, and provides technical support to the the U.S. Space Force Space Systems Command International Affairs (SSC/IA) office. Through her numerous national and international assignments (Russia, China, and Germany), she has worked on topics related to international space systems, national security space systems, civil systems (including human spaceflight and civil launch projects), space policy, satellite industry analysis, and satellite manufacturing start-ups. She is proficient in Russian and German, and fluent in Polish. Kowalski received her Bachelor of Science and Master of Science degrees in mechanical and aerospace engineering from George Washington University in 2011 and 2012, respectively.

S. E. Whiting↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
NASA NTRS2017

Analysis of Mixed Aryl/Alkyl Esters by Pyrolysis Gas Chromatography-Mass Spectrometry in the Presence of Perchlorate

Mars is an important target for Astrobiology. A key goal of the MSL mission was to determine whether Mars was habitable in the past, a que-tion that has now been definitely determined to be yes. Another key goal for Mars exploration is to understand the origin and distribution of organic material on Mars; this question is being addressed by the SAM instrument on MSL, and will also be informed by two upcoming Mars exploration missions, ExoMars and Mars 2020. These latter two missions have instrumentation capable of detecting and characterize organic molecules. Over the next decade, these missions will analyze organics in surface, near-surface and sub-surface samples. Each mission has the capability to analyze organics by different methods (pyrolysis gas chromatography-mass spectrometry [py-GC-MS]; laser desorption and thermal volatilization GC-MS; and Raman spectroscopy). Plausibly extraterrestrial organics were recently discovered by the Mars Science Laboratory (MSL), providing an important first step towards understanding the organic inventory on Mars [1]. The compounds detected were chlorobenzenes and chloroalkanes, but it was argued that chlorination of these compounds occurred during pyrolysis of samples containing unchlorinated organics in the presence of perchlorate. A recent report analyzed a suite of aromatic (benzene, toluene, benzoic acid, phthalic acid, and mellitic acid) and aliphatic (acetic acid, propane, propanol, and hexane) by pyrolysis under SAM-like conditions in the presence of perchlorate to attempt to constrain possible precursor molecules for the organic molecules detected on Mars. For aromatic compounds, the aromatic acids all readily produced SAM-relevant chlorobenzes, whereas benzene and toluene did not. This observation suggests that the chlorobenzene detected on Mars could have derived from compounds like mellitic acid, consistent with the previous hypothesis by Benner et al. [3]. Among the aliphatic molecules, it was shown that pyrolysis of alkanes and alcohols in the presence of perchlorates produced polychlorine containing chloro-alkanes similar to what was observed on Mars. Surpris-ingly, however, similar treatment of acetic acid pro-duced chloroketones, instead, and no chloroalkanes were reported. This suggests that the chloroalkanes detected in the Sheepbed mudstone were not derived from aliphatic carboxylic acids, but instead were from more reduced alcohols or even alkanes, or perhaps were degradation products of more complicated organic material. Because organics analyses on mars will rely heavily on py-GC-MS of perchlorate-containing samples over the next decade, it is important to understand the fate of organic molecules of biotic and abiotic origin under such conditions. In this work we begin a series of experiments to improve our understanding of products generated during py-GC-MS of increasingly complex organic molecules (esters, amides, peptides, nucleic acids, fatty acids) in the presence of perchlorate.

Burton, A. S.↗