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NASA NTRS2006

NASA Radiation Track Image GUI for Assessing Space Radiation Biological Effects

The high-charge high-energy (HZE) ion components of the galactic cosmic rays when compared to terrestrial forms of radiations present unique challenges to biological systems. In this paper we present a deoxyribonucleic acid (DNA) breakage model to visualize and analyze the impact of chromatin domains and DNA loops on clustering of DNA damage from X rays, protons, and HZE ions. Our model of DNA breakage is based on a stochastic process of DNA double-strand break (DSB) formulation that includes the amorphous model of the radiation track and a polymer model of DNA packed in the cell nucleus. Our model is a Monte-Carlo simulation based on a randomly located DSB cluster formulation that accomodates both high- and low-linear energy transfer radiations. We demonstrate that HZE ions have a strong impact on DSB clustering, both along the chromosome length and in the nucleus volume. The effects of chromosomal domains and DNA loops on the DSB fragment-size distribution and the spatial distribution of DSB in the nucleus were studied. We compare our model predictions with the spatial distribution of DSB obtained from experiments. The implications of our model predictions for radiation protection are discussed.

Ponomarev, Artem L.↗

NASA NTRS2010

Modeling Common-Sense Decisions in Artificial Intelligence

A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation of the dynamical models in a parameterized form reduces the task of common-sense-based decision making to a solution of the following hetero-associated-memory problem: store a set of m predetermined stochastic processes given by their probability distributions in such a way that when presented with an unexpected change in the form of an input out of the set of M inputs, the coupled motormental dynamics converges to the corresponding one of the m pre-assigned stochastic process, and a sample of this process represents the decision.

Zak, Michail↗

NASA NTRS2011

Interannual Variation in Phytoplankton Concentration and Community in the Pacific Ocean

Climate events such as El Nino have been shown to have an effect on the biology of our ocean. Because of the lack of data, we still have very little knowledge about the spatial and temporal effect these climate events may have on biological marine systems. In this study, we used the NASA Ocean Biogeochemical Model (NOBM) to assess the interannual variability in phytoplankton community in the Pacific Ocean between 1998 and 2005. In the North Central and Equatorial Pacific Ocean, changes in the Multivariate El Nino Index were associated with changes in phytoplankton composition. The model identified an increase in diatoms of approx.33 % in the equatorial Pacific in 1999 during a La Nina event. This increase in diatoms coincided with a decrease of approx.11 % in cyanobacteria concentration. The inverse relationship between cyanobacteria and diatoms concentration was significant (p<0.05) throughout the period of study. The use of a numerical model allows us to assess the impact climate variability has on key phytoplankton groups known to lead to contrasting food chain at a spatial and temporal resolution unachievable when relying solely on in-situ observations.

Rousseaux, C. S.↗

NASA NTRS2016

GeneLab for High Schools: Data Mining for the Next Generation

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

genelab↗

NASA NTRS2021

Advancing Space Life Science Research Using Drosophila Melanogaster

As we embark on exploring deep space, it is imperative to unravel the impacts of such unique and dynamic environments on biological systems. Multiple studies using D. melanogaster, a well-established spaceflight model, have paved the way towards our understanding of spaceflight-associated health risks. Further, Drosophila quantitative genomic experiments along with improved automated hardware systems will enable efficient and effective identification of the underlying genetic and molecular determinants capable of physiological adaptation to endure harsh space environments.

Quantitative genomics↗

NASA NTRS2002

The M2 Proton Channel of Influenza Virus: How Does It Work?

The transport of protons across membranes is an essential process for both bioenergetics of modem cells and the origins of cellular life. All living systems make use of proton gradients across cell walls to convert environmental energy into a high-energy chemical compound, adenosine triphosphate (ATR), synthesized from adenosine diphosphate. ATR, in turn, is used as a source of energy to drive many cellular reactions. The ubiquity of this process in biology suggests that even the earliest cellular systems were relying on proton gradient for harvesting environmental energy needed to support their survival and growth. In contemporary cells, proton transfer is assisted by large, complex proteins embedded in membranes. The issue addressed in this study was: how the same process can be accomplished with the aid of similar, but much simpler molecules that could have existed in the protobiological milieu? The model system used in the study contained a bilayer membrane made of phospholipid, dimyristoylphosphatidylcholine (DMPC), which is a good model of the biological membranes focusing cellular boundaries. Both sides of the bilayer were surrounded by water which simulated the environment inside and outside the cell. Embedded in the membrane was a fragment of the Influenza-A M2 protein and enough sodium counterions to maintain system neutrality. This protein has been shown to exhibit remarkably high rates of proton transport and, therefore, is an excellent model to study the formation of proton gradients across membranes. The Influenza M2 protein is 97 amino acids in length, but a fragment 25 amino acids long, which contains a transmembrane domain of 19 amino acids flanked by 3 amino acids on each side, is sufficient to transport protons. Four identical protein fragments, each folded into a helix, aggregate to form small channels spanning the membrane. Protons are conducted through a narrow pore in the middle of the channel in response to applied voltage. This channel is large enough to contain water molecules, and is normally filled with water. In analogy to the mechanism of proton transfer in some other channels, it has been postulated that protons are translocated along the network of water molecules filling the pore of the channel. This mechanism, however, must involve an additional, important step because the channel contains four histidine amino acid residues, one from each of the helices, which are sufficiently large to occlude the pore and interrupt the water network. The histidine residues ensure channel selectivity by blocking transport of small such as sodium or potassium. They have been also implicated in gating protons due to the ability of each histidine to become positively charged by accepting an additional proton. Two mechanisms of gating have been proposed. In one mechanism, all four histidines acquire an additional proton and, due to repulsion between their positive charges, move away from one another, thus opening the channel. The alternative mechanism relies of the ability of protons to move between different atoms in a molecule (tautomerization). Thus, a proton is captured on one side of the gate while another proton is released from the opposite side, and the molecule returns to the initial state through tautomerization. The simulations were designed to test these two mechanisms. Large-scale, atomic-level molecular dynamics simulations of the channel, in which the histidine residues were in different protonation states revealed that all intermediate states of the system involved in the tautomerization mechanism are structurally stable and the arrangement of water molecules in the channel is conducive to the proton transport. In contrast, in the four-protonated state, postulated to exist in the gate-opening mechanism, the electrostatic repulsion between the histidine residues appears to be so large that the channel looses its structural integrity and one helix moves away from the remaining three. This result indicates that such a mechanism of proton transport is unlikely. The simulations revealed that translocation along a network of water molecules in the channel and tautomerization of the histidine residues in the M2 proteins in the most likely mechanism of proton transport. The results not only explain how a remarkably simple protein system can efficiently aid in the formation of proton gradients across cell walls, but also suggest how this system can be genetically re-engineered to become a directional, reversible proton pump. Such a pump can provide energy to laboratory-built models of simple cellular systems. If they were successfully constructed it would greatly advance our understanding of the beginnings of life and find important applications in medicine and pharmacology.

Pohorille, Andrew↗

NASA NTRS2001

Proton Transport

The transport of protons across membranes is an essential process for both bioenergetics of modern cells and the origins of cellular life. All living systems make use of proton gradients across cell walls to convert environmental energy into a high-energy chemical compound, adenosine triphosphate (ATP), synthesized from adenosine diphosphate. ATP, in turn, is used as a source of energy to drive many cellular reactions. The ubiquity of this process in biology suggests that even the earliest cellular systems were relying on proton gradient for harvesting environmental energy needed to support their survival and growth. In contemporary cells, proton transfer is assisted by large, complex proteins embedded in membranes. The issue addressed in this Study was: how the same process can be accomplished with the aid of similar but much simpler molecules that could have existed in the protobiological milieu? The model system used in the study contained a bilayer membrane made of phospholipid, dimyristoylphosphatidylcholine (DMPC) which is a good model of the biological membranes forming cellular boundaries. Both sides of the bilayer were surrounded by water which simulated the environment inside and outside the cell. Embedded in the membrane was a fragment of the Influenza-A M$_2$ protein and enough sodium counterions to maintain system neutrality. This protein has been shown to exhibit remarkably high rates of proton transport and, therefore, is an excellent model to study the formation of proton gradients across membranes. The Influenza M$_2$ protein is 97 amino acids in length, but a fragment 25 amino acids long. which contains a transmembrane domain of 19 amino acids flanked by three amino acids on each side. is sufficient to transport protons. Four identical protein fragments, each folded into a helix, aggregate to form small channels spanning the membrane. Protons are conducted through a narrow pore in the middle of the channel in response to applied voltage. This channel is large enough to contain water molecules. and is normally filled with water. In analogy to the mechanism of proton transfer in some other channels, it has been postulated that protons are translocated along the network of water molecules filling the pore of the channel. This mechanism, however, must involve an additional important step because the channel contains four histidine amino acid residues, one from each of the helices, which are sufficiently large to occlude the pore and interrupt the water network. The histidine residues ensure channel selectivity by blocking transport of small ions, such as sodium or potassium. They have been also implicated in gating protons due to the ability of each histidine to become positively charged by accepting an additional proton. Two mechanisms of gating have been proposed. In one mechanism, all four histidines acquire an additional proton and, due to repulsion between their positive charges, move away from one another, thus opening the channel. The alternative mechanism relies of the ability of protons to move between different atoms in a molecule (tautomerization). Thus, a proton is captured on one side of the gate while another proton is released from the opposite side, and the molecule returns to the initial state through tautomerization. The simulations were designed to test these two mechanisms. Large-scale, atomic-level molecular dynamics simulations of the channel with the histidine residues in different protonation states revealed that all intermediate states of the system involved in the tautomerization mechanism are structurally stable and the arrangement of water molecules in the channel is conducive to the proton transport. In contrast, in the four-protonated state, postulated to exist in the gate-opening mechanism, the electrostatic repulsion between the histidine residues appears to be so large that the channel loses its structural integrity and one helix moves away from the remaining three. Additional information is contained within the original extended abstract.

Pohorille, Andrew↗

NASA NTRS1973

An investigation of a sterile access technique for the repair and adjustment of sterile spacecraft

A description is presented of a unique system for the sterilization and sterile repair of spacecraft and the results of a test program designed to assess the biological integrity and engineering reliability of the system. This trailer-mounted system, designated the model assembly sterilizer for testing (MAST), is capable of the dry-heat sterilization of spacecraft and/or components less than 2.3 meters in diameter at temperatures up to 433 K and the steam sterilization of components less than 0.724 meter in diameter. Sterile access to spacecraft is provided by two tunnel suits, called the bioisolator suit systems (BISS), which are contiguous with the walls of the sterilization chambers. The test program was designed primarily to verify the biological and engineering reliability of the MAST system by processing simulated space hardware. Each test cycle simulated the initial sterilization of a spacecraft, sterile repair of a failed component, removal of the spacecraft from the MAST for mating with the bus, and a sterile recycle repair.

Farmer, F. H.↗

NASA NTRS2011

Overview of NASA's Carbon Monitoring System Flux-Pilot Project

NASA's space-based observations of physical, chemical and biological parameters in the Earth System along with state-of-the-art modeling capabilities provide unique capabilities for analyses of the carbon cycle. The Carbon Monitoring System is developing an exploratory framework for detecting carbon in the environment and its changes, with a view towards contributing to national and international monitoring activities. The Flux-Pilot Project aims to provide a unified view of land-atmosphere and ocean-atmosphere carbon exchange, using observation-constrained models. Central to the project is the application of NASA's satellite observations (especially MODIS), the ACOS retrievals of the JAXA-GOSAT observations, and the "MERRA" meteorological reanalysis produced with GEOS-S. With a primary objective of estimating uncertainty in computed fluxes, two land- and two ocean-systems are run for 2009-2010 and compared with existing flux estimates. An transport model is used to evaluate simulated CO2 concentrations with in-situ and space-based observations, in order to assess the realism of the fluxes and how uncertainties in fluxes propagate into atmospheric concentrations that can be more readily evaluated. Finally, the atmospheric partial CO2 columns observed from space are inverted to give new estimates of surface fluxes, which are evaluated using the bottom-up estimates and independent datasets. The focus of this presentation will be on the science goals and current achievements of the pilot project, with emphasis on how policy-relevant questions help focus the scientific direction. Examples include the issue of what spatio-temporal resolution of fluxes can be detected from polar-orbiting satellites and whether it is possible to use space-based observations to separate contributions to atmospheric concentrations of (say) fossil-fuel and biological activity

Pawson, Steven↗

DOE OSTI2023

Latent Dirichlet Allocation modeling of environmental microbiomes

Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Evaluation of Late Effects of Heavy-Ion Radiation on Mesenchymal Stem Cells

The overall objective of this recently funded study is to utilize well-characterized model test systems to assess the impact of pluripotent stem cell differentiation on biological effects associated with high-energy charged particle radiation. These stem cells, specifically mesenchymal stem cells (MSCs), have the potential for differentiation into bone, cartilage, fat, tendons, and other tissue types. The characterization of the regulation mechanisms of MSC differentiation to the osteoblastic lineage by transcription factors, such as Runx2/Cbfa1 and Osterix, and osteoinductive proteins such as members of the bone morphogenic protein family are well established. More importantly, for late biological effects, MSCs have been shown to contribute to tissue restructuring and repair after tissue injury. The complex regulation of and interactions between inflammation and repair determine the eventual outcome of the responses to tissue injury, for which MSCs play a crucial role. Additionally, MSCs have been shown to respond to reactive oxygen species, a secondary effector of radiation, by differentiating. With this, we hypothesized that differentiation of MSCs can alter or exacerbate the damage initiated by radiation, which can ultimately lead to late biological effects of misrepair/fibrosis which may ultimately lead to carcinogenesis. Currently, studies are underway to examine high-energy X-ray radiation at low and high doses, approximately 20 and 200 Rad, respectively, on cytogenetic damage and gene modulation of isolated MSCs. These cells, positive for MSC surface markers, were obtained from three persons. In vitro cell samples were harvested during cellular proliferation and after both cellular recovery and differentiation. Future work will use established in vitro models of increasing complexity to examine the value of traditional 2D tissue-culture techniques, and utilize 3D in vitro tissue culture techniques that can better assess late effects associated with radiation.

Gonda, S.R.↗

NASA NTRS1992

Biological weighting function for the inhibition of phytoplankton photosynthesis by ultraviolet radiation

Severe reduction of stratospheric ozone over Antarctica has focused increasing concern on the biological effects of ultraviolet-B (UVB) radiation (280 to 320 nanometers). Measurements of photosynthesis from an experimental system, in which phytoplankton are exposed to a broad range of irradiance treatments, are fit to an analytical model to provide the spectral biological weighting function that can be used to predict the short-term effects of ozone depletion on aquatic photosynthesis. Results show that UVA (320 to 400 nanometers) significantly inhibits the photosynthesis of a marine diatom and a dinoflagellate, and that the effects of UVB are even more severe. Application of the model suggests that the Antarctic ozone hole might reduce near-surface photosynthesis by 12 to 15 percent, but less so at depth. The experimental system makes possible routine estimation of spectral weightings for natural phytoplankton.

Cullen, John J.↗

DOE OSTI2024

Organic layer thickness and carbon concentration in burned and unburned sites, Seward Peninsula, AK, 2022

Measurements associated with organic layer samples collected from naturally burned (1971, 2002, 2015, 2019) and unburned sites at the Kougarok Fire Complex, Seward Peninsula, AK, 2022. Here, a discontinuous permafrost underlies an arctic tundra ecosystem. Measurements include elemental carbon and nitrogen concentrations and stocks, organic layer thickness, and thaw depth. There are five files in *.csv format with one data file and four data description files including data dictionary, methods, terminology, and file-level metadata. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Maps of plant functional type (PFT), PFT fractional cover, and uncertainty derived from AVIRIS-NG data, 2019, Seward Peninsula

Remote sensing maps of plant functional type (PFT), PFT fractional cover (fCover), and fCover uncertainty maps derived from NASA’s Airborne Visible / Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380 ~ 2510 nm) was collected as a part of the collaboration between NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE’s Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps for flights over Teller, Kougarok, and Council watersheds created using eight AVIRIS-NG imagery collected in July, 2019. The map data and metadata are provided as image (ENVI, *.png) and text (*.txt, *hdr) formats. Additional supporting map quicklooks are provided as GIS *.kml files. Detailed description of the methods for each map are provided in Yang et al., (2023). These datasets are provided in support of Yang et al., (In revision) “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization

The complexity and variability of biological data has promoted the increased use of machine learning methods to understand processes and predict outcomes. These same features complicate reliable, reproducible, interpretable, and responsible use of such methods, resulting in questionable relevance of the derived. outcomes. Here we systematically explore challenges associated with applying machine learning to predict and understand biological processes using a well- characterized in vitro experimental system. We evaluated factors that vary while applying machine learning classifers: (1) type of biochemical signature (transcripts vs. proteins), (2) data curation methods (pre- and post-processing), and (3) choice of machine learning classifier. Using accuracy, generalizability, interpretability, and reproducibility as metrics, we found that the above factors significantly mod- ulate outcomes even within a simple model system. Our results caution against the unregulated use of machine learning methods in the biological sciences, and strongly advocate the need for data standards and validation tool-kits for such studies.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2014

The Role of Phytoplankton Dynamics in the Seasonal and Interannual Variability of Carbon in the Subpolar North Atlantic - a Modeling Study

We developed an ecosystem/biogeochemical model system, which includes multiple phytoplankton functional groups and carbon cycle dynamics, and applied it to investigate physical-biological interactions in Icelandic waters. Satellite and in situ data were used to evaluate the model. Surface seasonal cycle amplitudes and biases of key parameters (DIC, TA, pCO2, air-sea CO2 flux, and nutrients) are significantly improved when compared to surface observations by prescribing deep water values and trends, based on available data. The seasonality of the coccolithophore and "other phytoplankton" (diatoms and dinoflagellates) blooms is in general agreement with satellite ocean color products. Nutrient supply, biomass and calcite concentrations are modulated by light and mixed layer depth seasonal cycles. Diatoms are the most abundant phytoplankton, with a large bloom in early spring and a secondary bloom in fall. The diatom bloom is followed by blooms of dinoflagellates and coccolithophores. The effect of biological changes on the seasonal variability of the surface ocean pCO2 is nearly twice the temperature effect, in agreement with previous studies. The inclusion of multiple phytoplankton functional groups in the model played a major role in the accurate representation of CO2 uptake by biology. For instance, at the peak of the bloom, the exclusion of coccolithophores causes an increase in alkalinity of up to 4 μmol kg(sup −1) with a corresponding increase in DIC of up to 16 μmol kg(sup −1). During the peak of the bloom in summer, the net effect of the absence of the coccolithophores bloom is an increase in pCO2 of more than 20 μatm and a reduction of atmospheric CO2 uptake of more than 6 mmolm(sup −2) d(sup −1). On average, the impact of coccolithophores is an increase of air-sea CO2 flux of about 27 %. Considering the areal extent of the bloom from satellite images within the Irminger and Icelandic Basins, this reduction translates into an annual mean of nearly 1500 tonnes C yr(sup −1).

Earth Resources↗

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 NTRS2015

The Search for Life in the Universe

Each recent report of liquid water existing elsewhere in the solar system has reverberated through the international press and excited the imagination of humankind. We have come to realize that where there is liquid water on Earth, virtually no matter what the physical conditions, there is life. Dr. Lynn Rothschild, an evolutionary biologist known for her work on life in extreme environments and a founder of the field of astrobiology, tells us about intriguing new data. At NASA's Ames Research Center in Moffett Field, CA, she leads a program in synthetic biology and works with researchers in industry, government agencies around the world and in academia, including at Brown and Stanford University. The prevalence of potential abodes for life in our solar system and beyond, the survival of microbes in the space environment, modeling of the potential for transfer of life between celestial bodies, and advances in synthetic biology suggest that life could be more common than previously thought. Are we truly "alone"?

Water↗