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This bibliography lists 132 reports, articles and other documents introduced into the NASA Scientific and Technical Information Database. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance.
This bibliography lists 536 reports, articles and other documents introduced into the NASA Scientific and Technical Information System Database. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance.
Abiogenesis has long been suspected to require that chemical reaction networks contain multiple autocatalytic cores, but little is known about what features of these networks allow for the gradual accretion of complexity. To identify realistic scenarios for the emergence of life-like properties, we develop the concept of a seed-dependent autocatalytic system (SDAS), which is a subnetwork that can autocatalytically self-maintain given a flux of food, but cannot be initiated by food alone. Rather, initiation of SDASs requires the transient introduction of chemical “seeds”. We show that, depending on the topological relationship of SDASs in a chemical reaction network, a food-driven system can accrete complexity in a historically contingent manner, governed by rare seeding events. We develop new algorithms for detecting and analyzing SDASs in chemical reaction databases and describe parallels between multi-SDAS networks and biological ecosystems. Applying our algorithms to both an abiotic reaction network and a biochemical one, each driven by a set of simple food chemicals, we detect SDASs that are organized as trophic tiers, of which the higher tier can be seeded by relatively simple chemicals if the lower tier is already activated. This indicates that sequential activation of trophically organized SDASs by seed chemicals that are not much more complex than what already exist could be a mechanism of gradual complexification from relatively simple abiotic reactions to more complex life-like systems. Interestingly, in both reaction networks, higher-tier SDASs include chemicals that might alter emergent features of chemical systems and could serve as early targets of selection. Our analysis provides computational tools for analyzing very large chemical/biochemical reaction networks and suggests new approaches to studying abiogenesis in the lab.
This bibliography lists 212 reports, articles, and other documents recently introduced into the NASA Scientific and Technical Information System database. Subject coverage includes the following: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance.
The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.
Understanding how biology changes in response spaceflight and how these changes affect crew and craft is important to the design of safe, robust and effective space missions. Advances in analytical technologies allow large volumes of new data to be generated from space biological experiments, and a growing library of legacy data can be re-analyzed in the context of a greater understanding of biological systems. This enables powerful insights into how biology changes during spaceflight. However, for this to be fully realized additional tools, programs, and communities must be developed. Now a developing suite of interconnected open science resources (GeneLab, the Ames Life Sciences Data Archive, the NASA Biological Institutional Science Collection, the Biospecimen Sharing Program) can be leveraged by dedicated Analysis Working Groups to accelerate the pace of discovery in the space biological sciences.
Development of the Radiation Biology Ontology (RBO) was motivated by the need for a comprehensive, well-structured ontology for encoding radiation biology metadata. The primary use-cases were archiving data in the STORE database (https://www.storedb.org/), the repository for the RadoNorm Project, and in GeneLab (https://genelab.nasa.gov), NASA’s ‘omics database. The scope of radiobiology research ranges from physics to radiation oncology to socio-legal studies; no existing ontology has the necessary breadth or depth. 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 radiation biology data.
The RadLab Project, initiated by the Open Science Data Repository (OSDR) for Space Biology at NASA Ames Research Center (osdr.nasa.gov), aims to be an ongoing compilation of radiation data relevant to human space flight. Sponsored by the NASA Human Research Program, RadLab serves as the latest database in this domain. RadLab is intended to serve the needs of both the space radiation detector and space radiation biology communities. The RadLab architecture is being designed to accommodate data both from low Earth orbit (LEO) and beyond low Earth orbit (BLEO). Our long-term vision entails the establishment of a sustainable database receiving continuous updates through APIs connecting to other databases, as well as individual investigator contributions via a RadLab submission portal, modeled on the OSDR (https://osdr.nasa.gov/bio/submission-sso-login.html). In addition to serving as a resource for space biologists and space radiation physicists, such a database would facilitate the deployment of AI algorithms to study the impact of location in space and within spacecraft on the ambient space radiation field. The ultimate goal is to develop predictive dosimetry algorithms for future BLEO missions.
The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.
Although available paleobiological data indicate that the geographic ranges of marine species are maintained throughout their entire observable durations, other evidence suggests, by contrast, that the ranges of higher taxa expand as they age, perhaps in association with increased species richness. Here, I utilize a database of Ordovician genus occurrences collected from the literature for several paleocontinents to demonstrate that a significant aging of the global biota during the Ordovician Radiation was accompanied by a geographic and environmental expansion of genus ranges. The proportion of genera occurring in two or more paleocontinents in the database, and two or more environmental zones within a six-zone onshore-offshore framework, increased significantly in the Caradocian and Ashgillian. Moreover, widespread genera tended to be significantly older than their endemic counterparts, suggesting a direct link between their ages and their environmental and geographic extents. Expansion in association with aging was corroborated further by demonstrating this pattern directly among genera that ranged from the Tremadocian through the Ashgillian. Taken together, these results are significant not only for what they reveal about the kinetics of a major, global-scale diversification, but also for what they suggest about the interpretation of relationships between diversity trends at the alpha (within-community) and beta (between-community) levels.
Exposure to ionizing radiation during long-duration space missions is expected to cause short-term illness and increase long-term risk of cancer for astronauts. Radiation-induced free radicals overload the antioxidant defense mechanisms and lead to cellular damage at the membrane, enzyme, and chromosome levels. A large number of radioprotective agents were screened, but most had significant side effects. But there is increasing evidence that significant radioprotective benefit is achieved by increasing the dietary intake of foods with high antioxidant potential. Early plant-growing systems for space missions will be limited in both size and volume to minimize power and mass requirements. These systems will be well suited to producing plants containing high concentrations of bioprotective antioxidants. This project explored whether the production of bioprotective compounds could be increased by altering the lighting system, without increasing the space or power requirements for production, and evaluated the effects of environmental conditions (light quantity, light quality, and carbon dioxide [CO2] concentration) on the production of bioprotective compounds in lettuce, which provide a biological countermeasure for radiation exposure. The specific deliverables were to develop a database of bioprotectant compounds in plants that are suitable for use on longduration space missions, develop protocols for maintaining and increasing bioprotectant production under light emitting diodes (LEDs), recommend lighting requirements to produce dietary countermeasures of radiation, and publish results in the Journal of the American Society for Horticultural Science.
Forward GEOS-5 AGCM simulations of CO2, with transport constrained by analyzed meteorology for 2009-2010, are examined. The CO2 distributions are evaluated using AIRS upper tropospheric CO2 and ACOS-GOSAT total column CO2 observations. Different combinations of surface C02 fluxes are used to generate ensembles of runs that span some uncertainty in surface emissions and uptake. The fluxes are specified in GEOS-5 from different inventories (fossil and biofuel), different data-constrained estimates of land biological emissions, and different data-constrained ocean-biology estimates. One set of fluxes is based on the established "Transcom" database and others are constructed using contemporary satellite observations to constrain land and ocean process models. Likewise, different approximations to sub-grid transport are employed, to construct an ensemble of CO2 distributions related to transport variability. This work is part of NASA's "Carbon Monitoring System Flux Pilot Project,"
The Green's function for the transport of ions of high charge and energy is utilized with a nuclear fragmentation database to evaluate dose, dose equivalent, and RBE for C3H1OT1/2 cell survival and neoplastic transformation as a function of depth in soft tissue. Such evaluations are useful to estimates of biological risk for high altitude aircraft, space operations, accelerator operations, and biomedical applications.
The Green's function for the transport of ions of high charge and energy is utilized with a nuclear fragmentation database to evaluate dose, dose equivalent, and RBE for C3H10T1/2 cell survival and neo-plastic transformation as function of depth in soft tissue. Such evaluations are useful to estimates of biological risk for high altitude aircraft, space operations, accelerator operations, and biomedical application.
The genome of Methanocaldococcus (Methanococcus) jannaschii DSM 2661 was the first Archaeal genome to be sequenced in 1996. Subsequent sequence-based annotation cycles led to its first metabolic reconstruction in 2005. Leveraging new experimental results and function assignments, we have now re-annotated M. jannaschii, creating an updated resource with novel information and testable predictions in a pathway-genome database available at BioCyc.org. This reannotation effort has resulted in 652 function assignments with enzyme roles, accounting for a third of the total protein-coding entries for this genome. The updated resource includes 883 reactions, 540 enzymes, and 142 individual pathways. Despite notable progress in computational genomics, more than a third of the genome remains functionally uncharacterized. The publicly available MjCyc pathway-genome database holds great potential for the wider community to conduct research on the biology of methanogenic Archaea.
Low-temperature (77 K) absorption and fluorescence spectra of 12 naturally occurring photosynthetic tetrapyrrole macrocycles have been recorded in a frozen glass (2-methyltetrahydrofuran). The compounds encompass distinct chromophore classes: porphyrin, chlorophyll c 2 ; chlorin, chlorophylls a, b, d, f and bacteriochlorophylls c, d, e, f; and bacteriochlorin, bacteriochlorophylls a, b, g. The spectra are compared with those of the same pigment in liquid solution (predominantly 2-methyltetrahydrofuran) at room temperature (293 K). The measured Stokes shifts at 77 K across the 12 macrocycles range from ~30 to 300 cm −1 . The spectral data in digital form are made available as part of the PhotochemCAD databases. Literature searches have revealed extensive published data for Chl a (often in biological matrices) but at best rather limited data for less common macrocycles. The availability of a systematic collection of curated spectral data collected at low temperature should be useful for a variety of assessments, including reconstruction of absorption spectra of (bacterio)chlorophyll-containing protein complexes, vibrational analysis of absorption and fluorescence spectra, and calculations where knowledge of energy levels is important.
NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). The GeneLab Data Systems (GLDS) version 4.0 will be available on October 1st 2019, and will provide the latest in terms of professional state-of-the-art bioinformatics platform for the space biology and radiation community to upload their data into an omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. Started in 2015 as a repository designed to archive omics data from space experiments, GeneLab has expanded its scope to all ionizing radiation omics experiments conducted on the ground and has put considerable effort in providing carefully characterized radiation metadata on all dataset. GeneLab is also providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome) to help users explore important questions: 1) Which genes or proteins are expressed differently in space for various living organisms? 2) What specific DNA mutations or epigenetic changes happen in space or after exposure to ionizing radiation? and 3) How does genetics affect these responses? Processed data available on GeneLab are derived by standard data analysis workflows vetted by hundreds of scientists who volunteered to join one of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). In this presentation, we will discuss how to bridge the gap between irradiation studies performed on earth and biological experiments conducted in space since the early 1990's. We will discuss how radiation dosimetry was estimated for datasets derived from samples collected during the Space Shuttle era or on the International Space Station. Finally, we will address future strategies regarding dose monitoring in future missions into space, inter-agency efforts to unify data under one umbrella, and knowledge dissemination across the radiation research community and the space biology community.