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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.

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NASA NTRS2015

NASA Space Radiation Risk Project: Overview and Recent Results

The NASA Space Radiation Risk project is responsible for integrating new experimental and computational results into models to predict risk of cancer and acute radiation syndrome (ARS) for use in mission planning and systems design, as well as current space operations. The project has several parallel efforts focused on proving NASA's radiation risk projection capability in both the near and long term. This presentation will give an overview, with select results from these efforts including the following topics: verification, validation, and streamlining the transition of models to use in decision making; relative biological effectiveness and dose rate effect estimation using a combination of stochastic track structure simulations, DNA damage model calculations and experimental data; ARS model improvements; pathway analysis from gene expression data sets; solar particle event probabilistic exposure calculation including correlated uncertainties for use in design optimization.

Blattnig, Steve R.↗

NASA NTRS2004

cWINNOWER algorithm for finding fuzzy dna motifs

The cWINNOWER algorithm detects fuzzy motifs in DNA sequences rich in protein-binding signals. A signal is defined as any short nucleotide pattern having up to d mutations differing from a motif of length l. The algorithm finds such motifs if a clique consisting of a sufficiently large number of mutated copies of the motif (i.e., the signals) is present in the DNA sequence. The cWINNOWER algorithm substantially improves the sensitivity of the winnower method of Pevzner and Sze by imposing a consensus constraint, enabling it to detect much weaker signals. We studied the minimum detectable clique size qc as a function of sequence length N for random sequences. We found that qc increases linearly with N for a fast version of the algorithm based on counting three-member sub-cliques. Imposing consensus constraints reduces qc by a factor of three in this case, which makes the algorithm dramatically more sensitive. Our most sensitive algorithm, which counts four-member sub-cliques, needs a minimum of only 13 signals to detect motifs in a sequence of length N = 12,000 for (l, d) = (15, 4). Copyright Imperial College Press.

Evaluation Studies↗

NASA NTRS2003

Effects of broad frequency vibration on cultured osteoblasts

Bone is subjected in vivo to both high amplitude, low frequency strain, incurred by locomotion, and to low amplitude, broad frequency strain. The biological effects of low amplitude, broad frequency strain are poorly understood. To evaluate the effects of low amplitude strains ranging in frequency from 0 to 50 Hz on osteoblastic function, we seeded MC3T3-E1 cells into collagen gels and applied the following loading protocols for 3 min per day for either 3 or 7 days: (1) sinusoidal strain at 3 Hz, with 0-3000 microstrain peak-to-peak followed by 0.33 s resting time, (2) "broad frequency vibration" of low amplitude strain (standard deviation of 300 microstrain) including frequency components from 0 to 50 Hz, and (3) sinusoidal strain combined with broad frequency vibration (S + V). The cells were harvested on day 4 or 8. We found that the S + V stimulation significantly repressed cell proliferation by day 8. Osteocalcin mRNA was up-regulated 2.6-fold after 7 days of S + V stimulation, and MMP-9 mRNA was elevated 1.3-fold after 3 days of vibration alone. Sinusoidal stimulation alone did not affect the cell responses. No differences due to loading were observed in alkaline phosphatase activity and in mRNA levels of type I collagen, osteopontin, connexin 43, MMPs-1A, -3, -13. These results suggest that osteoblasts are more sensitive to low amplitude, broad frequency strain, and this kind of strain could sensitize osteoblasts to high amplitude, low frequency strain. This suggestion implies a potential contribution of stochastic resonance to the mechanical sensitivity of osteoblasts. Copyright 2002 Elsevier Science Ltd.

Non-NASA Center↗

NASA NTRS1999

Multi-Parent Clustering Algorithms from Stochastic Grammar Data Models

We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several "parent" clusters. For each data vector the algorithm makes a discrete decision among these alternatives. Thus, a recursive version of this algorithm would place data clusters in a Directed Acyclic Graph rather than a tree. We test the algorithm with synthetic data generated according to the statistical data model. We also illustrate the algorithm using real data from large-scale gene expression assays.

Mjoisness, Eric↗