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Determine which flight-associated signals are consistent across tissue-handling strata and two published processing outputs, and which should be held for validation before entering a space-resilience target list.\n\n## Challenge details\nThe SpaceBio project prioritizes clinical targets for space resilience. A prerequisite is separating candidate spaceflight biology from sample-handling and processing sensitivity. OSD-48 contains matched flight/ground samples collected either upon euthanasia or from frozen carcasses and two processed RNA-seq result tables distinguished by the rRNArm processing label. Use the deposited results rather than generating new RNA-seq analyses or models. Read the study protocols to state what the processing variants actually mean; the filename alone is insufficient evidence for a mechanism.\n\nThe core comparisons are flight minus ground within each handling stratum, in each processing output: four aligned contrasts per gene. Also retain carcass minus upon-euthanasia contrasts within flight and within ground to characterize handling-associated signals. The study's small strata and common underlying samples limit inference: consistency is not independent replication, and a differing significance label does not establish an interaction.\n\n## What you need to submit (Deliverables)\n- Exact scientific input bytes, optionally losslessly gzipped, plus `provenance.json` recording the uncompressed input checksums, URLs and accessed versions. Include the sample table and relevant metadata as flat extracted text files. Do not include a nested archive in the Submission; the source metadata ZIP may be unpacked into the required flat files.\n- `samples.csv`: map all 14 RNA-seq samples to flight/ground, handling group and source protocol. Identify unequal stratum sizes, any documented pairing/batch information and relevant unknowns.\n- `aligned-results.csv.gz`: every deposited gene identifier in either processing output, with presence/absence flags, source row/column, harmonized log2 fold changes, reported adjusted p values and missing-value reasons for the four core contrasts and four handling contrasts. Preserve the source's identifiers and one-to-many annotations without silently deduplicating into gene symbols. Check the reciprocal contrast columns for direction consistency and report discrepancies.\n- `analysis.py` or `analysis.R`, `robustness.csv.gz`, and `summary.csv`: align the two processing outputs; calculate each core contrast's direction, sign concordance across processing and handling, absolute effect differences, and sets satisfying reported adjusted p<0.05 and absolute log2 fold change at least 0.5 or at least 1.0. For each threshold report intersection/union counts and Jaccard similarities across processing within a stratum and across strata within a processing output. Define empty-set and zero-effect handling. Record robust, processing-sensitive, handling-sensitive and indeterminate categories using an explicit reproducible rule; categories need not be mutually exclusive if the rule says so. No minimum number of “robust” hits is required.\n- `sensitivity.svg` or `sensitivity.png`: effect-comparison plots with matched axes, labeled processing/handling groups, and a clear indication of missing/unmatched records.\n- `decision.md`: produce a bounded shortlist of up to ten candidates warranting validation, or a defensible empty shortlist, with exact identifiers, computed evidence and the validation needed. Explain how handling effects, unequal precision and normalization choices change confidence. Discuss why direction discordance is not automatically a proven interaction, why agreement is not independent replication, and why mouse liver expression cannot establish human drug efficacy or microgravity-specific causality. Recommend a concrete next validation contrast capable of resolving the leading uncertainty.\n- `README.md`: source/version details, protocol interpretation, dependencies and a command reproducing every numeric artifact from the included inputs. This task needs no raw sequence data, paid service, GPU, astronaut records or laboratory work.\n\n## Inputs, Materials and References\nPublic inputs verified on 17 September 2026:\n1. NASA OSD-48, *Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data*: https://osdr.nasa.gov/bio/repo/data/studies/OSD-48 . Public file inventory: https://visualization.osdr.nasa.gov/biodata/api/v2/dataset/OSD-48/files/ . Download links are supplied by this inventory.\n2. `GLDS-48_rna_seq_differential_expression_GLbulkRNAseq.csv`, SHA-256 `4222e8eadf931df8962280b86ae739bbbbb05a7772469133ed6fd6a1495ba161`, 35,945,532 uncompressed bytes.\n3. `GLDS-48_rna_seq_differential_expression_rRNArm_GLbulkRNAseq.csv`, SHA-256 `54824223bcd9cde30df3f3b19969ee8fbcd91f8ef4cc6d048579d8a53bfab73c`, 35,938,380 uncompressed bytes.\n4. `GLDS-48_rna_seq_SampleTable_GLbulkRNAseq.csv`, SHA-256 `965dfda9f00d39e201564d9d2642b7b22c60b14cbf8c52ee014ce61b46dd956e`, 866 bytes.\n5. `OSD-48_metadata_OSD-48-ISA.zip`, SHA-256 `f68496f8a6361e2fefe8c4000be73f22808dc15c507c786d3fce458536b5ffb3`, 203,905 bytes. Read `i_Investigation.txt`, `s_OSD-48.txt` and the RNA-seq assay metadata therein; other assays are out of scope. The published API documentation is https://visualization.osdr.nasa.gov/biodata/api/ . No credential is required.\n\nContextual primary paper: *Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact*, PMC7870178: https://pmc.ncbi.nlm.nih.gov/articles/PMC7870178/ . This supplies the space-biology rationale and does not replace the fixed data or establish a required conclusion.\n\nProject: https://openlabs-git-codex-openlabs-elgora-adapter-bio-xyz.vercel.app/projects/a1b93be9-a7ef-45b2-8bad-fd3b48e79bd0 . Independent contribution without project-owner endorsement.\n\n## Acceptance Criteria\n1. Scientific input checksums match after decompression, metadata map is complete and correct, and all deposited gene IDs remain accounted for. Source formatting, absent genes and undefined p values are handled explicitly.\n2. Contrast direction is verified against reciprocal columns and source metadata; all comparisons consistently use flight-minus-ground or carcass-minus-upon-euthanasia as specified. No cross-stratum comparison is substituted for a matched core contrast.\n3. Code reproduces aligned effects, threshold classifications, set sizes, Jaccard values and category rules within 0.000001 absolute tolerance before rounding. Reported adjusted p values are preserved, not relabeled as newly corrected pooled tests. Missing values are not counted as nonsignificant measured results.\n4. Plots and shortlist agree with the numeric outputs and every selected or excluded example has traceable evidence. No forced positive hit list or new compound prediction is required.\n5. The interpretation distinguishes sensitivity from causal proof, processing agreement from replication, and small-stratum precision from interaction evidence. It makes a concrete validation decision relevant to the project while explicitly limiting human, clinical and exposure-specific claims.\n\n## How is the winner selected?\nOnly submissions satisfying all criteria are eligible. Prefer fewer material data or scientific errors, then the strongest defensible connection between sensitivity results and the validation shortlist, then better reproducibility and ambiguity handling. Remaining ties go to earlier on-chain submission timestamp, then lower numeric submission ID. 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