SEARCH · Search NASA

Results for “BIOLOGY”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRS2014

Comparison of Model Calculations of Biological Damage from Exposure to Heavy Ions with Measurements

The space environment consists of a varying field of radiation particles including high-energy ions, with spacecraft shielding material providing the major protection to astronauts from harmful exposure. Unlike low-LET gamma or X rays, the presence of shielding does not always reduce the radiation risks for energetic charged-particle exposure. Dose delivered by the charged particle increases sharply at the Bragg peak. However, the Bragg curve does not necessarily represent the biological damage along the particle path since biological effects are influenced by the track structures of both primary and secondary particles. Therefore, the ''biological Bragg curve'' is dependent on the energy and the type of the primary particle and may vary for different biological end points. Measurements of the induction of micronuclei (MN) have made across the Bragg curve in human fibroblasts exposed to energetic silicon and iron ions in vitro at two different energies, 300 MeV/nucleon and 1 GeV/nucleon. Although the data did not reveal an increased yield of MN at the location of the Bragg peak, the increased inhibition of cell progression, which is related to cell death, was found at the Bragg peak location. These results are compared to the calculations of biological damage using a stochastic Monte-Carlo track structure model, Galactic Cosmic Ray Event-based Risk Model (GERM) code (Cucinotta, et al., 2011). The GERM code estimates the basic physical properties along the passage of heavy ions in tissue and shielding materials, by which the experimental set-up can be interpreted. The code can also be used to describe the biophysical events of interest in radiobiology, cancer therapy, and space exploration. The calculation has shown that the severely damaged cells at the Bragg peak are more likely to go through reproductive death, the so called "overkill".

Kim, Myung-Hee Y.↗

NASA NTRS2019

Biological CubeSats: What Have We Learned so Far and What Is Next?

Since Apollo 17 in 1972, NASA has sent no humans or other biological organisms outside of Earth's protective magnetosphere. Recently, NASA has set its sights on human exploration in deep space, with an ambitous plan to put astronauts back on the Moon by 2024 and to eventually land human missions on Mars. Such missions will require significant countermeasures, likely both technological and biomedical, to protect biology from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms.NASA has launched five biological CubeSat missions into low-Earth orbit (LEO). GeneSat-1 was launched in 2006 to study gene expression and increase our knowledge of how spaceflight affects microbes. Similar life-support technologies were then used in PharmaSat and O/OREOS, which launched in 2009 and 2010, respectively. PharmaSat contained optical systems to examine how yeast cells responded to an antifungal treatment. One of O/OREOS payloads, SESLO (Space Environment Survivability of Living Organisms), housed dormant microorganisms, which were rehydrated on orbit to track alterations to growth and metabolism induced by microgravity and radiation. In 2014, NASA launched SporeSat to study the mechanisms of plant cell gravity sensing using lab-on-a-chip devices. Most recently, in 2017, NASA launched EcAMSat (E. coli AntiMicrobial Satellite), which investigated the effects of microgravity on antibiotic resistance of a pathogenic bacterium. Each one of these missions increased our understanding of the biological effects of spaceflight in LEO, while refining technologies and imparting valuable lessons to the next generation of CubeSats.CubeSats housing translational biological models are therefore ideal for defining the hazards of deep space travel, as they can provide critical data over relevant durations. BioSentinel, a next-generation deep-space CubeSat, is planned to launch as a secondary payload on Artemis 1 in 2020. BioSentinel will study the DNA damage response to deep space radiation in yeast.

Santa Maria, Sergio R.↗

NASA NTRS2023

Data Sharing in Radiation Biology: Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally "Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗

NASA NTRSDate not supplied

GL4U: Using Space Biology Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant multi-omics data via the Open Science Data Repository (OSDR) that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct (training students) and indirect (training educators) approaches. The GL4U pilot programs were conducted in June 2021 (direct training) and 2022 (indirect training). During the pilots, students and educators at Historically Black Colleges and Universities (HBCUs) and Minority Serving Institutions (MSIs) participated in a week-long (direct training) or two-week-long (indirect training) bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze space biology RNA sequencing data from OSDR. During the educator pilot, participants received materials, training, and the necessary compute resources to enable them to run the bootcamp at their home institutions, thereby extending the reach of this initiative. In July 2023, GeneLab is partnering with JPL to expand GL4U to include amplicon sequencing (Amp-Seq) analysis training. During the GL4U Amp-Seq bootcamp, student and educator participants will receive training on how to analyze and interpret Amp-Seq data using the NASA GeneLab data processing pipeline. All bootcamp material, including instructions for requesting compute resources, will be made publicly available on GitHub for educators to teach the GL4U content in subsequent semesters. GL4U provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. We present results from pre- and post-training surveys completed by all participants of the Amp-Seq bootcamp.

Amanda M Saravia-Butler↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

NASA NTRSDate not supplied

Developing Autonomous Technologies for Biological Missions to Deep Space

In upcoming biological missions beyond low Earth orbit (LEO), the use of autonomous instrumentation will allow scientists to perform a variety of experiments, including the characterization of the response to different space environments (Moon, Mars, interplanetary space) using biological models like microbes, plants, organoids, and tissue chips. BioSentinel is an ongoing deep space mission, currently at over 50 million kilometers from Earth and the first instrument developed to perform biological experiments beyond LEO. Even though the primary objective of this CubeSat mission was to investigate the effects of the deep space radiation environment on budding yeast, the spacecraft bus (i.e., all the subsystems that support the biological payload like power, thermal, data telemetry, navigation, etc.) can accommodate a variety of biological (and physical) experiments and model organisms. LEIA, an upcoming CLPS mission to the lunar surface, uses a microfluidic and optical instrument based on BioSentinel to study the effects of the lunar environment on different cellular processes and on bioproduction of antioxidants. A new series of science mission concepts are being proposed to be accommodated into platforms like BioSentinel. These missions will investigate the response of a variety of organisms to the deep space environment, including but not limited to single-cell eukaryotes, cyanobacteria, plants (including crops), organoids, and tissue chips. In addition to optical absorbance measurements like the ones performed in BioSentinel (and LEIA), we are investigating the use of fluorescence detection, microscopy, sequencing devices, etc. Thus, instruments like the ones proposed here can be adapted to a variety of platforms like free-flyers, deployable payloads, landers, rovers, and the lunar Gateway. These technologies can be used as steppingstones for establishing a sustained human presence on the Moon and in deep space while providing knowledge for the development of potential countermeasures.

Sergio R Santa Maria↗

NASA NTRS1997

Aerospace Medicine and Biology: A Continuing Bibliography With Indexes

This issue of Aerospace Medicine and Biology, A Continuing Bibliography with Indexes NASA SP-7O11 lists reports, articles, and other documents recently announced in the NASA STI Database. In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, psychological, and environmental effects to which humans are subjected during and following simulated or actual flight in the Earth's atmosphere or in interplanetary space. References describing similar effects on biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. Applied research receives the most emphasis, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion.

BIBLIOGRAPHIES↗

NASA NTRS1997

Space Station Biological Research Project

To meet NASA's objective of using the unique aspects of the space environment to expand fundamental knowledge in the biological sciences, the Space Station Biological Research Project at Ames Research Center is developing, or providing oversight, for two major suites of hardware which will be installed on the International Space Station (ISS). The first, the Gravitational Biology Facility, consists of Habitats to support plants, rodents, cells, aquatic specimens, avian and reptilian eggs, and insects and the Habitat Holding Rack in which to house them at microgravity; the second, the Centrifuge Facility, consists of a 2.5 m diameter centrifuge that will provide acceleration levels between 0.01 g and 2.0 g and a Life Sciences Glovebox. These two facilities will support the conduct of experiments to: 1) investigate the effect of microgravity on living systems; 2) what level of gravity is required to maintain normal form and function, and 3) study the use of artificial gravity as a countermeasure to the deleterious effects of microgravity observed in the crew. Upon completion, the ISS will have three complementary laboratory modules provided by NASA, the European Space Agency and the Japanese space agency, NASDA. Use of all facilities in each of the modules will be available to investigators from participating space agencies. With the advent of the ISS, space-based gravitational biology research will transition from 10-16 day short-duration Space Shuttle flights to 90-day-or-longer ISS increments.

NASA Discipline General Space Life Sciences↗

NASA NTRS2016

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗

NASA NTRS2016

Advancing Small Satellite Electronics Heritage for Microfluidic Biological Experiments

DLR's Eu:CROPIS (Euglena and Combined Regenerative Organic-Food Production in Space) mission, launching in 2017, will carry multiple biological payloads into a sun-synchronous orbit, including NASA Ames' PowerCell experiment. PowerCell will attempt to characterize the viability of synthetic biology at micro-g, Lunar, and Martian gravity levels. PowerCell experiment requirements demand an electronic system similar to previous microfluidic biology payloads, but with an expanded feature set. As such, the system was based on PharmaSat (Diaz-Aguado et al. 2009), a previous successful biology payload from NASA Ames, and improved upon. Newer, more miniaturized electronics allow for greater capability with a lower part count and smaller size. Two identical PowerCell enclosures will fly. Each enclosure contains two separate and identical experiments with a 48-segment optical density measurement system, grow light system, microfluidic system for nutrient delivery and waste flushing, plus thermal control and environmental sensing/housekeeping including temperature, pressure, humidity, and acceleration. Electronics consist of a single Master PCB that interfaces to the spacecraft bus and regulates power and communication, plus LED, Detector, and Valve Manifold PCBs for each experiment. To facilitate ease of reuse on future missions, experiment electronics were designed to be compatible with a standard 3U small sat form factor and power bus, or to interface with a Master power/comm PCB for use in a larger satellite as in the case of PowerCell's flight on Eu:CROPIS.

biology↗

NASA NTRS2018

Next-Generation Fluids-Related Biology Research Utilizing the Zero Gravity Research Facility at NASA Glenn Research Center

In order to gain insight into how cells sense gravity, NASA has developed a platform to investigate the transient (short-term) response of biological cells to a sudden exposure to microgravity. Utilizing NASA's Zero Gravity Research Facility offers unique capabilities and opportunity to investigate this phenomena. A review of current literature indicates that it remains unclear how biological cells sense and respond to gravitational forces. It is believed that a combination of bio-structural and fluid transport mechanisms are at play. This Technical Memorandum outlines a pathway to address this fundamental biological challenge. A series of low-cost systematic drop tower experiments were conducted to observe the biomechanical behavior of cell membranes (such as vestibular hair, endothelial, and bone cells) that are known to exhibit sensitivity to gravity. These experiments demonstrated a useful, low cost experiment concept that can potentially be employed by researchers interested in observing, identifying and characterizing changes in biological cell morphology.

Fluids for Biology↗

NASA NTRSDate not supplied

3D Construction of Biologically Derived Materials

System for the 3D Construction of Biologically Derived Materials, Structures, and Parts NASA has developed a novel approach for macroscale biomaterial production by combining synthetic biology with 3D printing. Cells are biologically engineered to deposit desired materials, such as proteins or metals, derived from locally available resources. The bioengineered cells build different materials in a specified 3D pattern to produce novel microstructures with precise molecular composition, thickness, print pattern, and shape. Scaffolds and reagents can be used for further control over material product. This innovation provides modern design and fabrication techniques for custom-designed organic or organic-inorganic composite biomaterials produced from limited resources. Benefits Conserves resources. Few raw or bulk starting materials needed Enables custom design of diverse materials Fast, portable, macroscale, on-demand manufacturing High-fidelity microstructures Uses commercially available parts Applications Biomaterials, biotechnology Organic-inorganic composite materials On-demand manufacturing In situ resource utilization Space stations Military Infrastructure materials The Technology Once genes for a desired material type, delivery mode, control method and affinity have been chosen, assembling the genetic components and creating the cell lines can be done with well-established synthetic biology techniques. A 3D microdeposition system is used to make a 3D array of these cells in a precise, microstructure pattern and shape. The engineered cells are suspended in a printable 'ink'. The 3D microdeposition system deposits minute droplets of the cells onto a substrates surface in a designed print pattern. Additional printer passes thicken the material. The cell array is fed nutrients and reagents to activate the engineered genes within the cells to create and deposit the desired molecules. These molecules form the designed new material. If desired, the cells may be removed by flushing. The end product is thus a 3D composite microstructure comprising the novel material. This innovation provides a fast, controlled production of natural, synthetic, and novel biomaterials with minimum resource overhead and reduced pre- and post-processing requirements.

3D↗

NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗

NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗

NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗

NASA NTRSDate not supplied

LEIA: An Investigation of Radiation Risks to Biology at the Lunar South Pole

Radiation and reduced gravity pose biological risks to crewed deep space exploration. At the cellular level, radiation damage can be amplified by reduced gravity. Empirical evidence on cellular responses to beyond low Earth orbit (BLEO) environments is imperative to develop effective countermeasures for crew health and in-space biomanufacturing. The Lunar Explorer Instrument for Space Biology Applications (LEIA) project is developing an instrument suite to be delivered to the south polar region of the Moon by the Commercial Lunar Payload Services (CLPS) program. This presentation will provide an overview of the LEIA hardware, experiments, and mission timeline. The LEIA instruments include the BioSensor, the ARES charged particle detector, and the Mini-FND. The BioSensor is an autonomous light emitting diode (LED)-based spectrophotometer and microfluidic incubator. The BioSensor activates yeast cultures and can measure cell growth, metabolic activity, and carotenoid production. The ARES is a Timepix-based charged particle radiation detector that measures dose, dose rate, and linear energy transfer spectra. The Mini-FND is a fast neutron detector that measures albedo neutron flux and energy spectra. Combined, these instruments will be used for yeast genetics experiments to quantify growth, metabolism, and synthetic biology-enabled production of human nutrients, while taking real time measurements of biologically relevant radiation exposure on the lunar surface. These data will be used to test the importance of selected DNA damage repair and reactive oxygen species defense pathways in mitigating cellular damage from lunar surface radiation.

Yeast↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

NASA NTRSDate not supplied

LEIA: An Investigation of Radiation Risks to Biology at the Lunar South Pole

Yeast↗