Materials Informatics for the Development and Discovery of Future Magnetic Materials
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Engineering↗
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Novel cis-regulatory elements as synthetic promoters to drive recombinant protein expression from the Chlamydomonas reinhardtii nuclear genome
Eukaryotic green microalgae represent a sustainable, photosynthetic biotechnology platform for generating high-value products. The model green alga Chlamydomonas reinhardtii has already been used to generate high value bioproducts such as recombinant proteins and terpenoids. However, low, unstable, and variable nuclear transgene expression has limited the ease and speed of metabolic engineering and recombinant protein expression in this system. Here, novel genetic devices for transgene expression in C. reinhardtii have been developed by identifying cis-regulatory DNA elements capable of driving high transgene expression in C. reinhardtii promoters using de novo motif discovery informatics approaches. Thirteen putative motifs were synthesized as concatemers, linked to a common minimal basal promoter, and assayed for their activity to drive expression of a yellow fluorescent protein reporter gene. Following transformation of the vectors into C. reinhardtii by electroporation, in vivo measurements of yellow fluorescent protein expression by flow cytometry revealed that five of the DNA motifs analyzed displayed significantly higher reporter expression compared to the basal promoter control. Two of the concatemerized motifs, despite being much smaller minimal cis-regulatory elements, drove reporter expression at levels approaching that of the conventionally-used AR1 promoter. This analysis provides insight into C. reinhardtii promoter structure and gene regulation, and provides a new toolbox of cis-regulatory elements that can be used to drive transgene expression at a variety of expression levels.
59 BASIC BIOLOGICAL SCIENCES↗
Infrasound and Low-Frequency Acoustics MINOS Briefing
Oral presentation intended for Multi-Informatics for Nuclear Operations Scenarios (MINOS) venture teams describing programmed notification algorithm and ground truth collected at the Advanced Test Reactor.
42 ENGINEERING↗
NASA’s Comprehensive Databases for Materials Selection (MAPTIS) and Low-Gravity Experiments (PSI)
In the realm of advancing technological change the convergence of materials science and scientific inquiry stands as a testament to humanity’s insatiable curiosity. To assist in this endeavor the National Aeronautics and Space Administration (NASA) provides curated access to two unique databases. Physical Sciences Informatics (PSI) is an online database that houses completed physical science reduced-gravity experiments. Whereas Materials and Processes Technical Information System (MAPTIS) contains several other databases that relate to aerospace materials and processes. Equipped with curated access to these databased provided by the NASA scientists and engineers are furnished with invaluable resources needed to propel technological change.
PSI↗
MINOS Infrasound Analysis Synopsis
This report was written as a guide to working with infrasound data collected as part of the Multi-Informatics for Nuclear Operations Scenarios (MINOS)project, an NA-22 funded venture. The main purpose of overall MINOS project is the combination of multiple, disparate data modalities to characterize the operations at a nuclear facility, specifically instrumenting and studying the High-Flux Isotope Reactor (HFIR) and Radiochemical Engineering Development Center (REDC) locate at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are constructed, processed, and dissolved at REDC. REDC also hosts other glove-box and hot-cell type activities.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics (Joint AR) Product
Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate and tight choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. Additionally, EVA operations, aside from the Apollo Lunar surface missions, have predominately focused on maintenance and construction tasks where success criteria are clearly measurable. However, future exploration missions expect to enable crew to carry out scientific objectives in increasingly Earth-independent ways. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. This paper highlights the project’s experience with a product-focused management style and use-case centered systems engineering approach to iteratively design, build, and test. The Joint AR product features were defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, graphics engine software and analog test beds. We outline the defining architectural design decisions, including safety criticality considerations, suit mounting interfaces, computer architectures, and partnership contracting mechanisms. The outcomes of these studies, architecture decisions, and management requirements result in a recommended design which is the Joint AR product. We discuss the evolution, development of these system components, and what work remains. We hope to share a unified understanding of various design decisions and how they impact the future of crew members’ access to data during Lunar and Martian EVAs. This ongoing effort can enable a community-wide discovery process toward realizing necessary AR features and capabilities for future missions.
Augmented Reality↗
Exploration Medical System Demonstration (EMSD) Project
The Exploration Medical System Demonstration (EMSD) is a project under the Exploration Medical Capability (ExMC) element managed by the Human Research Program (HRP). The vision for the EMSD is to utilize ISS as a test bed to show that several medical technologies needed for an exploration mission and medical informatics tools for managing evidence and decision making can be integrated into a single system and used by the on-orbit crew in an efficient and meaningful manner. Objectives: a) Reduce and even possibly eliminate the time required for on-orbit crew and ground personnel (which include Surgeon, Biomedical Engineer (BME) Flight Controller, and Medical Operations Data Specialist) to access and move medical data from one application to another. b) Demonstrate that the on-orbit crew has the ability to access medical data/information using an intuitive and crew-friendly software solution to assist/aid in the treatment of a medical condition. c) Develop a common data management framework and architecture that can be ubiquitously used to automate repetitive data collection, management, and communications tasks for all crew health and life sciences activities.
Chin, Duane↗
The moving target of visualization software for an increasingly complex world
Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.
97 MATHEMATICS AND COMPUTING↗
Data Reliability in BIM and Performance Analytics: A Survey of Contemporary AECO Practice
As awareness around building energy consumption increases, practitioners are encouraged to consider performance aspects regarding the built environment more closely and find ways to improve its efficiency. Improvements in building information modeling (BIM) and building performance simulation (BPS) tools present opportunities to facilitate information communication with a wider range of stakeholders. The building sector can benefit from the integration of performance informatics; however, there has been limited success in utilizing available technologies that promote data integration and management in favor of enriching our knowledge and understanding of buildings as artifacts of information. This phenomenon was investigated by conducting a survey, together with a review of relevant literature, to depict the relevant challenges and opportunities for the architecture, engineering, construction, and owner-operated (AECO) industry, as it undergoes digital transformation, as well as the working practices that have formed around them. It is argued that the current tools available to practitioners do not support effective data serialization between design and analytics processes, affecting the collaboration between team members. Lastly, a series of functional goals are proposed to support a higher level of reliability in the ways information is mobilized, by rethinking the technologies and methods for organizing information systems.
42 ENGINEERING↗
International Space Station Medical Operations
NASA is currently the leader, in conjunction with our Russian counterpart co-leads, of the Multilateral Medical Policy Board (MMPB), the Multilateral Medical Operations Panel (MMOP), which coordinates medical system support for International Space Station (ISS) crews, and the Multilateral Space Medicine Board (MSMB), which medically certifies all crewmembers for space flight on-board the ISS. These three organizations have representatives from NASA, RSA-IMBP (Russian Space Agency- Institute for Biomedical Problems), GCTC (Gagarin Cosmonaut Training Center), ESA (European Space Agency), JAXA (Japanese Space Agency), and CSA (Canadian Space Agency). The policy and strategic coordination of ISS medical operations occurs at this level, and includes interactions with MMOP working groups in Radiation Health, Countermeasures, Extra Vehicular Activity (EVA), Informatics, Environmental Health, Behavioral Health and Performance, Nutrition, Clinical Medicine, Standards, Post-flight Activities and Rehabilitation, and Training. Each ISS Expedition has a lead Crew Surgeon from NASA and a Russian Crew Surgeon from GCTC assigned to the mission. Day-to-day issues are worked real-time by the flight surgeons and biomedical engineers (also called the Integrated Medical Group) on consoles at the MCC (Mission Control Center) in Houston and the TsUP (Center for Flight Control) in Moscow/Korolev. In the future, this may also include mission control centers in Europe and Japan, when their modules are added onto the ISS. Private medical conferences (PMCs) are conducted regularly and upon crew request with the ISS crew via private audio and video communication links from the biomedical MPSR (multipurpose support room) at MCC Houston. When issues arise in the day-to-day medical support of ISS crews, they are discussed and resolved at the SMOT (space medical operations team) meetings, which occur weekly among the International Partners. Any medical or life science issue that is not resolved at the SMOT can be taken to the Mission Management Team meeting, which occurs biweekly from MCC-Houston. This meeting includes the other International Partners and all flight support and console position representatives via teleconference. ISS Crew Surgeons have handled many medical conditions on orbit; including skin rashes, dental abscesses, lacerations, and STT segment EKG changes. Fortunately to date, there have not been any forced medical evacuations from the ISS. This speaks well for the implementation of the primary, secondary and even tertiary prevention strategies invoked by the Integrated Medical Group, as there were several medical evacuations during the previous Russian space stations.
Jones, Jeffrey A.↗
In-orbit performance of the soft X-ray imaging telescope Xtend aboard XRISM
Here, we present a summary of the in-orbit performance of the soft X-ray imaging telescope Xtend onboard the X-Ray Imaging and Spectroscopy Mission (XRISM), based on in-flight observation data, including first-light celestial objects, calibration sources, and results from the cross-calibration campaign with other currently operating X-ray observatories. XRISM/Xtend has a large field of view of ${38{^{\prime }_{.}}5}$ $\times$ ${38{^{\prime }_{.}}5}$, covering an energy range of 0.4–13 keV, as demonstrated by the first-light observation of the galaxy cluster Abell 2319. It also features an energy resolution of 170–180 eV at 6 keV, which meets the mission requirement and enables us to resolve He-like and H-like Fe K$\alpha$ lines. Throughout the observation during the performance verification phase, we confirm that two issues identified in the Soft X-ray Imager (SXI) onboard the previous Hitomi mission—light leakage and crosstalk events—are addressed and suppressed in the case of Xtend. A joint cross-calibration observation of the bright quasar 3C 273 results in an effective area measured to be $\sim$420 cm$^{2}$ at1.5 keV and $\sim$310 cm$^{2}$ at 6.0 keV, which matches values obtained in ground tests. We also continuously monitor the health of Xtend by analyzing overclocking data, calibration source spectra, and day-Earth observations; the readout noise is stable and low, and contamination is negligible even one year after launch. A low background level compared with other major X-ray instruments onboard satellites, combined with the largest grasp ($\Omega _{\rm eff}\sim 60$ cm$^2$ deg$^2$) of Xtend, will not only support Resolve analysis, but also enable significant scientific results on its own. This includes near-future follow-up observations and transient searches in the context of time-domain and multi-messenger astrophysics.
instrumentation: detectors: Xtend↗
Using Low-Cost “Garage Band” Recording Technology for Acquiring High Resolution High-Speed Data
The Oak Ridge National Laboratory (ORNL) has developed and tested a novel system architecture for acquiring high fidelity high-speed data. The approach uses a consumer grade audio recording device that is normally associated with “garage band” recording of music. ORNL has coupled this low-cost data acquisition hardware with computing technology running open-source software. The main advantage of this approach is per-channel cost; an instrument grade data acquisition system typically costs between $800 to $2000 per channel compared to less than $50 per channel for these consumer grade components. Three systems, each featuring four channels, have been deployed for acquiring data from geophones and the electrical supply system that supports the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at ORNL. Each channel samples at 96 kHz at 24-bit resolution. The deployed systems operate continuously 24/7 and produce about 4 terabytes of data per month per system. This paper provides a technical overview of this approach, its implementation, and some preliminary results from qualification testing. This work was conducted in support of the Multi-Informatics for Nuclear Operations Scenarios (MINOS).Keywords: Data acquisition Measurement Informatics
Wetherington Jr, G↗
Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine
The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.
Callum Wayman↗
Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine
The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.
Callum Wayman↗
An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics
Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.
36 MATERIALS SCIENCE↗
A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.
36 MATERIALS SCIENCE↗
Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics System (Joint AR) Product
Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.
Paromita Mitra↗
Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics System (Joint AR) Product
Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) project team at NASA Johnson Space Center (JSC) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. The Joint AR product was defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, and graphics engine software. This paper outlines the defining architectural design decisions, including safety criticality considerations, interfaces, and computer architectures. The outcomes of these studies result in a prototype design which is defined here as the Joint AR product. This work aims to enable a community-wide discussion toward realizing necessary suit-compatible AR features and capabilities for future missions.
Paromita Mitra↗