Biosentinel: Developing a Space Radiation Biosensor
Bibliographic record
Abstract
Ionizing radiation presents a major challenge to human exploration and long-term residence in space. The deep-space radiation spectrum includes highly energetic particles that generate double strand breaks (DSBs), deleterious DNA lesions that are usually repaired without errors via homologous recombination (HR), a conserved pathway in all eukaryotes. While progress identifying and characterizing biological radiation effects using Earth-based facilities has been significant, no terrestrial source duplicates the unique space radiation environment.We are developing a biosensor-based nanosatellite to fly aboard NASAs Space Launch System Exploration Mission 1, expected to launch in 2017 and reach a 1AU (astronomic unit) heliocentric orbit. Our biosensor (called BioSentinel) uses the yeast S. cerevisiae to measure DSBs in response to ambient space radiation. The BioSentinel strain contains engineered genetic defects that prevent growth until and unless a radiation-induced DSB near a reporter gene activates the yeasts HR repair mechanisms. Thus, culture growth and metabolic activity directly indicate a successful DSB-and-repair event. In parallel, HR-defective and wild type strains will provide survival data. Desiccated cells will be carried within independent culture microwells, built into 96-well microfluidic cards. Each microwell set will be activated by media addition at different time points over 18 months, and cell growth will be tracked continuously via optical density. One reserve set will be activated only in the occurrence of a solar particle event. Biological measurements will be compared to data provided by onboard physical dosimeters and to Earth-based experiments.BioSentinel will conduct the first study of biological response to space radiation outside Low Earth Orbit in over 40 years. BioSentinel will thus address strategic knowledge gaps related to the biological effects of space radiation and will provide an adaptable platform to perform human-relevant measurements in multiple space environments. We hope that it can therefore be used on the ISS, on and around other planetary bodies as well as other exploration platforms as a self-contained system that will allow us to compare and calibrate different radiation environments.BioSentinels results will be critical for improving interpretation of the effects of space radiation exposure, and for reducing the risk associated with long-term human exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".