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NASA NTRS2000

An early ethylene up-regulated gene encoding a calmodulin-binding protein involved in plant senescence and death

35S-Labeled calmodulin (CaM) was used to screen a tobacco anther cDNA library. A positive clone (NtER1) with high homology to an early ethylene-up-regulated gene (ER66) in tomato, and an Arabidopsis homolog was isolated and characterized. Based on the helical wheel projection, a 25-mer peptide corresponding to the predicted CaM-binding region of NtER1 (amino acids 796-820) was synthesized. The gel-mobility shift assay showed that the peptide formed a stable complex with CaM only in the presence of Ca(2+). CaM binds to NtER1 with high affinity (K(d) approximately 12 nm) in a calcium-dependent manner. Tobacco flowers at different stages of development were treated with ethylene or with 1-methylcyclopropene for 2 h before treating with ethylene. Northern analysis showed that the NtER1 was rapidly induced after 15 min of exposure to ethylene. However, the 2-h 1-methylcyclopropene treatment totally blocked NtER1 expression in flowers at all stages of development, suggesting that NtER1 is an early ethylene-up-regulated gene. The senescing leaves and petals had significantly increased NtER1 induction as compared with young leaves and petals, implying that NtER1 is developmentally regulated and acts as a trigger for senescence and death. This is the first documented evidence for the involvement of Ca(2+)/CaM-mediated signaling in ethylene action.

Non-NASA Center↗

NASA NTRS1990

Influence of penetrating solar radiation on the heat budget of the equatorial Pacific Ocean

Recent satellite observations of ocean transparency, coupled with climatological surface heat fluxes and ocean density profiles, are used here to show that solar radiation in visible frequencies, usually assumed to be absorbed at the sea surface, in fact penetrates to a significant degree to below the upper mixed layer of the ocean which interacts actively with the atmosphere. The net effect is a reduction of the heat input into the upper layer; for a 20 m-thick mixed layer this is equivalent to an annual reduction in temperature of about 5-10 K. The results provide a natural explanation for the discrepancy between the SSTs predicted by models and those observed.

Lewis, Marlon R.↗

NASA NTRS1985

Aerothermodynamic Heating Analysis of Aerobraking and Aeromaneuvering Orbital Transfer Vehicles

The thermal-protection requirements of two aeroassisted orbital transfer vehicles (AOTVS) are analyzed for return missions between the geosynchronous and Shuttle orbits. One of the designs is a specialized version of a previously proposed generic aerobraking vehicle that Is capable of only delivery-type operations. The other Is a high-lift aeromaneuvering vehicle that is optimized for low Earth orbit sortie missions involving large, multiple plane-inclination changes. The aerothermal environment of the aerobraking vehicle is analyzed using state-of-the-art methods for nonequilibrium-radiative and convective heating that incorporate refinements unique to the configuration. The heating analysis of the aeromaneuvering vehicle required the development of a flowfield model for rarefied-hypersonic flow over a lifting surface at incidence. The predicted aerothermodynamic heating characteristics for both vehicles are correlated with thermal-control

Menees, Gene P.↗

NASA NTRSDate not supplied

Evaluating the Efficacy of Conditional Variational Autoencoders in Generating Synthetic Single Nuclei RNA-Seq Data for Space Biology Research

Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.

Sarah Golts↗

NASA NTRS1999

Modeling the Activity of Single Genes

The central dogma of molecular biology states that information is stored in DNA, transcribed to messenger RNA (mRNA) and then translated into proteins. This picture is significantly augmentated when we consider the action of certain proteins in regulating transcription. These transcription factors provide a feedback pathway by which genes can regulate one another's expression as mRNA and then as protein. To review: DNA, RNA and proteins have different functions. DNA is the molecular storehouse of genetic information. When cells divide, the DNA is replicated, so that each daughter cell maintains the same genetic information as the mother cell. RNA acts as a go-between from DNA to proteins. Only a single copy of DNA is present, but multiple copies of the same piece of RNA may be present, allowing cells to make huge amounts of protein. In eukaryotes (organisms with a nucleus), DNA is found in the nucleus only. RNA is copied in the nucleus then translocates(moves) outside the nucleus, where it is transcribed into proteins. Along the way, the RNA may be spliced, i.e., may have pieces cut out. RNA then attaches to ribosomes and is translated to proteins. Proteins are the machinery of the cell other than DNA and RNA, all the complex molecules of the cell are proteins. Proteins are specialized machines, each of which fulfills its own task, which may be transporting oxygen, catalyzing reactions, or responding to extracellular signals, just to name a few. One of the more interesting functions a protein may have is binding directly or indirectly to DNA to perform transcriptional regulation, thus forming a closed feedback loop of gene regulation. The structure of DNA and the central dogma were understood in the 50s; in the early 80s it became possible to make arbitrary modifications to DNA and use cellular machinery to transcribe and translate the resulting genes; more recently, genomes (i.e., the complete DNA sequence) of many organisms have been sequenced. This large-scale sequencing began with simple organisms, viruses and bacteria, progressed to eukaryotes such as yeast, and more recently (1998) progressed to a multi-cellular animal, the nematode Caenorhabditis elegans. Sequencers have now moved on to the fruit fly Drosophila melanogaster, whose sequence is slated for completion by the end of 1999. The human genome project is expected to determine the complete sequence of all 3 billion bases of human DNA within the next five years. In the wake of genome-scale sequencing, further instrumentation is being developed to assay gene expression and function on a comparably large scale. Much of the work in computational biology focuses on computational tools used in sequencing, finding genes that are related to a particular gene, finding which parts of the DNA code for proteins and which do not, understanding what proteins will be formed from a given length of DNA, predicting how the proteins will fold from a one-dimensional structure into a three dimensional structure, and so on. Much less computational work has been done regarding the function of proteins. One reason for this is that different proteins function very differently, and so work on protein function is very specific to certain classes of proteins. There are, for example, proteins such enzymes that catalyze various intracellular reactions, receptors that respond to extracellular signals and ion channels that regulate the flow of charged particles into and out of the cell. In this chapter, we will consider a particular class of proteins called transcription factors(TFs), which are responsible for regulating when a certain gene is expressed in a certain cell, which cells it is express in, and how much is expressed. Understanding these processes will involve developing a deeper understanding of transcription, translation, and the cellular processes that control those processes. All of these elements fall under the aegis of gene regulation or more narrowly transcriptional regulation. Some of the key questions in gene regulation are: What genes are expressed in a certain cell at a certain time? How does gene expression differ from cell to cell in a multicellular organism? Which proteins act as transcription factors, i.e., are important in regulating gene expression? From questions like these, we hope to understand which genes are important for various macroscopic processes. Nearly all of the cells of a multicellular organism contain the same DNA. Yet this same genetic information yields a large number of different cell types. The fundamental difference between a neuron and a liver cell, for example, is which genes are expressed. Thus understanding gene regulation is an important step in understanding development. Furthermore, understanding the usual genes that are expressed in cells may give important clues about various diseases. Some diseases, such as sickle cell anemia and cystic fibrosis, are caused by defects in single, non-regulatory genes; others, such as certain cancers, are caused when the cellular control circuitry malfunctions - an understanding of these diseases will involve pathways of multiple interacting gene products. There are numerous challenges in the area of understanding and modeling gene regulation. First and foremost, biologists would like to develop a deeper understanding of the processes involved, including which genes and families of genes are important, how they interact, etc. From a computation point of view, there has been embarrassingly little work done. In this chapter there are many areas in which we can phrase meaningful, non-trivial computational questions, but questions that have not been addressed. Some of these are purely computational (what is a good algorithm for dealing with a model of type X) and others are more mathematical (given a system with certain characteristics, what sort of model can one use? How does one find biochemical parameters from system-level behavior using as few experiments as possible?). In addition to biological and algorithmic problems, there is also the ever-present issue of theoretical biology - what general principles can be derived from these systems, what can one do with models other than just simulate time-courses, what can be deduced about a class of systems without knowing all the details? The fundamental challenge to computationalists and theorists is to add value to the biology - to use models, modeling techniques and algorithms to understand the biology in new ways.

Mjolsness, Eric↗

NASA NTRS2007

Solar Radiation Pressure Estimation and Analysis of a GEO Class of High Area-to-Mass Ratio Debris Objects

A population of deep space objects is thought to be high area-to-mass ratio (AMR) debris having origins from sources in the geosynchronous orbit (GEO) belt. The typical AMR values have been observed to range anywhere from 1's to 10's of m(sup 2)/kg, and hence, higher than average solar radiation pressure effects result in long-term migration of eccentricity (0.1-0.6) and inclination over time. However, the nature of the debris orientation-dependent dynamics also results time-varying solar radiation forces about the average which complicate the short-term orbit determination processing. The orbit determination results are presented for several of these debris objects, and highlight their unique and varied dynamic attributes. Estimation or the solar pressure dynamics over time scales suitable for resolving the shorter term dynamics improves the orbit estimation, and hence, the orbit predictions needed to conduct follow-up observations.

Kelecy, Tom↗

NASA NTRS2011

Demagnetization Tests Performed on a Linear Alternator for a Stirling Power Convertor

Demagnetization temperature of a linear alternator (LA) can be accurately predicted through an analytical Maxwell model. The M-H characteristics of the alternator magnets must be known. Vendor data are given for cube-shaped magnets, and the shape of a LA magnet may affect its magnetic properties. At GRC, M-H data are directly measured for each LA magnet. This method was validated using TDC alternator tests on the Alternator Test Rig. The analytical Maxwell modeling was utilized on a different style linear alternator to predict demagnetization temperatures for the Advanced Stirling Convertor.

Geng, Steven M.↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Machine Learning↗

NASA NTRS1996

The Use of Steady and Pulsed Detonations for Propulsion Systems

Objectives of the ODWE concept studies are: demonstrate the feasibility of the oblique detonation wave engine (ODWE) for hypersonic propulsion; demonstrate the existance and stability of an oblique detonation wave in hypersonic wind tunnels; develop engineering codes which predict the performance characteristics of the ODWE including specific impulse and thrust coefficients for various operating conditions; develop multi-dimensional computer codes which can model all aspects of the ODWE including fuel injection, mixing, ignition, combustion and expansion with fully detailed chemical kinetics and turbulence models; and validate the codes with experimental data use the simulations to predict the ODWE performance for conditions not easily obtained in wind tunnels.

Adelman, Henry G.↗

NASA NTRS1995

Chimeric Plant Calcium/Calmodulin-Dependent Protein Kinase Gene with a Neural Visinin-Like Calcium-Binding Domain

Calcium, a universal second messenger, regulates diverse cellular processes in eukaryotes. Ca-2(+) and Ca-2(+)/calmodulin-regulated protein phosphorylation play a pivotal role in amplifying and diversifying the action of Ca-2(+)- mediated signals. A chimeric Ca-2(+)/calmodulin-dependent protein kinase (CCaMK) gene with a visinin-like Ca-2(+)- binding domain was cloned and characterized from lily. The cDNA clone contains an open reading frame coding for a protein of 520 amino acids. The predicted structure of CCaMK contains a catalytic domain followed by two regulatory domains, a calmodulin-binding domain and a visinin-like Ca-2(+)-binding domain. The amino-terminal region of CCaMK contains all 11 conserved subdomains characteristic of serine/threonine protein kinases. The calmodulin-binding region of CCaMK has high homology (79%) to alpha subunit of mammalian Ca-2(+)/calmodulin-dependent protein kinase. The calmodulin-binding region is fused to a neural visinin-like domain that contains three Ca-2(+)-binding EF-hand motifs and a biotin-binding site. The Escherichia coli-expressed protein (approx. 56 kDa) binds calmodulin in a Ca-2(+)-dependent manner. Furthermore, Ca-45-binding assays revealed that CCaMK directly binds Ca-2(+). The CCaMK gene is preferentially expressed in developing anthers. Southern blot analysis revealed that CCaMK is encoded by a single gene. The structural features of the gene suggest that it has multiple regulatory controls and could play a unique role in Ca-2(+) signaling in plants.

Patil, Shameekumar↗

NASA NTRS1989

The effects of simulated hypogravity on murine bone marrow cells

Mouse bone marrow cells grown in complete medium at unit gravity were compared with a similar population cultured in conditions that mimic some aspects of microgravity. After the cells adjusted to the conditions that simulated microgravity, they proliferated as fetal or oncogenic populations; their numbers doubled in twelve hour periods. Differentiated subpopulations were depleted from the heterogeneous mixture with time and the undifferentiated hematopoietic stem cells increased in numbers. The cells in the control groups in unit gravity and those in the bioreactors in conditions of microgravity were monitored under a number of parameters. Each were phenotyped as to cell surface antigens using a panel of monoclonal antibodies and flow cytometry. Other parameters compared included: pH, glucose uptake, oxygen consumption and carbon-dioxide production. Nuclear DNA was monitored by flow cytometry. Functional responses were studied by mitogenic stimulation by various lectins. The importance of these findings should have relevance to the space program. Cells should behave predictably in zero gravity; specific populations can be eliminated from diverse populations and other populations isolated. The availability of stem cell populations will enhance both bone marrow and gene transplant programs. Stem cells will permit developmental biologists study the paths of hematopoiesis.

Lawless, Desales↗

NASA NTRS1987

One-fiftieth scale model studies of 40-by 80-foot and 80-by 120-foot wind tunnel complex at NASA Ames Research Center

The features of a 1/50-scale model of the National Full-Scale Aerodynamics Complex are first described. An overview is then given of some results from the various tests conducted with the model to aid in the design of the full-scale facility. It was found that the model tunnel simulated accurately many of the operational characteristics of the full-scale circuits. Some characteristics predicted by the model were, however, noted to differ from previous full-scale results by about 10%.

Schmidt, Gene I.↗

NASA NTRS2012

Demagnetization Tests Performed on a Linear Alternator for a Stirling Power Convertor

The NASA Glenn Research Center (GRC) is conducting in-house research on rare-earth permanent magnets and linear alternators to assist in developing free-piston Stirling convertors for radioisotope space power systems and for developing advanced linear alternator technology. This research continues at GRC, but, with the exception of Advanced Stirling Radioisotope Generator references, the work presented in this paper was conducted in 2005. A special arc-magnet characterization fixture was designed and built to measure the M-H characteristics of the magnets used in Technology Demonstration Convertors developed under the 110-W Stirling Radioisotope Generator (SRG110) project. This fixture was used to measure these characteristics of the arc magnets and to predict alternator demagnetization temperatures in the SRG110 application. Demagnetization tests using the TDC alternator on the Alternator Test Rig were conducted for two different magnet grades: Sumitomo Neomax 44AH and 42AH. The purpose of these tests was to determine the demagnetization temperatures of the magnets for the alternator under nominal loads. Measurements made during the tests included the linear alternator terminal voltage, current, average power, magnet temperatures, and stator temperatures. The results of these tests were found to be in good agreement with predictions. Alternator demagnetization temperatures in the Advanced Stirling Convertor (ASC-developed under the Advanced Stirling Radioisotope Generator project) were predicted as well because the prediction method had been validated through the SRG110 alternator tests. These predictions led to a specification for maximum temperatures of the ASC pressure vessel.

Geng, Steven M.↗

NASA NTRS1998

A natural view of microbial biodiversity within hot spring cyanobacterial mat communities

This review summarizes a decade of research in which we have used molecular methods, in conjunction with more traditional approaches, to study hot spring cyanobacterial mats as models for understanding principles of microbial community ecology. Molecular methods reveal that the composition of these communities is grossly oversimplified by microscopic and cultivation methods. For example, none of 31 unique 16S rRNA sequences detected in the Octopus Spring mat, Yellowstone National Park, matches that of any prokaryote previously cultivated from geothermal systems; 11 are contributed by genetically diverse cyanobacteria, even though a single cyanobacterial species was suspected based on morphologic and culture analysis. By studying the basis for the incongruity between culture and molecular samplings of community composition, we are beginning to cultivate isolates whose 16S rRNA sequences are readily detected. By placing the genetic diversity detected in context with the well-defined natural environmental gradients typical of hot spring mat systems, the relationship between gene and species diversity is clarified and ecological patterns of species occurrence emerge. By combining these ecological patterns with the evolutionary patterns inherently revealed by phylogenetic analysis of gene sequence data, we find that it may be possible to understand microbial biodiversity within these systems by using principles similar to those developed by evolutionary ecologists to understand biodiversity of larger species. We hope that such an approach guides microbial ecologists to a more realistic and predictive understanding of microbial species occurrence and responsiveness in both natural and disturbed habitats.

Review↗

NASA NTRS2000

End-To-End Risk Assesment: From Genes and Protein to Acceptable Radiation Risks for Mars Exploration

The human exploration of Mars will impose unavoidable health risks from galactic cosmic rays (GCR) and possibly solar particle events (SPE). It is the goal of NASA's Space Radiation Health Program to develop the capability to predict health risks with significant accuracy to ensure that risks are well below acceptable levels and to allow for mitigation approaches to be effective at reasonable costs. End-to-End risk assessment is the approach being followed to understand proton and heavy ion damage at the molecular, cellular, and tissue levels in order to predict the probability of the major health risk including cancer, neurological disorders, hereditary effects, cataracts, and acute radiation sickness and to develop countermeasures for mitigating risks.

Cucinotta, Francis A.↗