{ "criteria_version": "feasibility_go_no_go_v2", "summary": { "total_problems": 234, "go_now": 6, "needs_specification": 20, "needs_repositioning": 208 }, "ranked_problems": [ { "problem_statement": "The transcriptomic approaches fail to identify and validate novel therapeutic targets beyond already known markers like CXCL2/3 and IL8", "domain": "cardiovascular disease", "subdomain": "CHIP-mediated inflammation and therapeutics", "scope": "medium", "sources": [ "elife-109131v1" ], "best_score": 0.72, "best_tier": "high", "best_confidence": 0.75, "decision_bucket": "go_now", "avg_score": 0.446, "avg_confidence": 0.417, "num_sub_questions": 3, "go_now_count": 1, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 1, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "What are the novel, previously unidentified molecular targets revealed by integrated transcriptomic analysis of TET2-CHIP-mediated cardiovascular disease?", "evidence_needed": "Comprehensive transcriptomic analysis (RNA-seq, single-cell RNA-seq) of TET2-CHIP models focused on identifying targets not previously reported in the literature, with validation by qPCR and protein expression analysis", "complexity": "medium", "composite": 0.72, "raw_score": 0.823, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "high", "decision": "go_now", "breakdown": { "biosafety": 1.0, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "protein", "rna" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "protein expression" ] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "protein", "rna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the novel targets relevant in human TET2-CHIP patients rather than only in mouse models?", "evidence_needed": "Analysis of patient samples (blood, tissue biopsies) from TET2-CHIP patients versus controls, measuring expression levels of novel targets by proteomics, ELISA, or immunohistochemistry, with correlation to cardiovascular disease severity", "complexity": "medium", "composite": 0.352, "raw_score": 0.562, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "elisa" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do these novel targets, when experimentally manipulated, affect monocyte-endothelial interactions and cardiovascular outcomes in TET2-CHIP models?", "evidence_needed": "Functional validation studies using genetic knockdown/knockout or pharmacological inhibition/activation of novel candidate targets in TET2-CHIP models, measuring effects on monocyte adhesion, transmigration, inflammation, and cardiovascular disease phenotypes", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout", "validation" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout", "validation" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Lucifer Yellow dye transfer assay lacks essential controls (cell density, viability) and shows unexplained discrepancy where mutant has less dye but comparable migration distance to control", "domain": "cell biology", "subdomain": "gap junction communication", "scope": "medium", "sources": [ "elife-102453v1" ], "best_score": 0.531, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "go_now", "avg_score": 0.443, "avg_confidence": 0.5, "num_sub_questions": 3, "go_now_count": 1, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 1, "medium_tier_count": 2, "low_tier_count": 0, "sub_question_scores": [ { "question": "Are cell density and viability comparable between control and mutant groups in the dye transfer assay?", "evidence_needed": "Quantification of cell density (cells per area) and viability assays (live/dead staining, MTT, or similar) for both experimental groups at the time of dye transfer", "complexity": "simple", "composite": 0.531, "raw_score": 0.708, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "go_now", "breakdown": { "biosafety": 0.4, "technique": 0.59, "cost": 1.0, "reagent": 0.4, "readiness": 1.0, "tractability": 1.0 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "assay", "culture" ], "measurement_hits": [ "viability" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cell culture" ] }, { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "assay", "culture" ], "measurement_hits": [ "viability" ], "system_hits": [ "cell", "cells" ], "control_hits": [ "control" ], "signal_count": 3 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Why does the mutant group show comparable dye migration distance to control despite having less dye transfer?", "evidence_needed": "Detailed quantitative analysis of dye intensity vs distance profiles, potentially including time-lapse imaging to distinguish migration speed from total transfer efficiency", "complexity": "medium", "composite": 0.407, "raw_score": 0.542, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy", "fluorescence microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "cell" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How was transmission speed quantified between groups, and does it correlate with the observed dye transfer patterns?", "evidence_needed": "Detailed methodological description with validation data showing transmission speed measurements (likely time-lapse imaging with quantification of dye front propagation over time)", "complexity": "medium", "composite": 0.392, "raw_score": 0.522, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Genotype-specific transformation efficiency differences are driven by unknown underlying genetic or epigenetic variation, preventing widespread adoption of plant engineering in non-elite lines.", "domain": "plant genetics", "subdomain": "transformation genetics", "scope": "medium", "sources": [ "doe-plant-transformation-bioenergy-2024" ], "best_score": 0.527, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "go_now", "avg_score": 0.49, "avg_confidence": 0.5, "num_sub_questions": 3, "go_now_count": 1, "needs_specification_count": 2, "needs_repositioning_count": 0, "high_tier_count": 1, "medium_tier_count": 2, "low_tier_count": 0, "sub_question_scores": [ { "question": "Can identified genetic or epigenetic determinants of transformation competence be edited to convert recalcitrant elite lines into transformable lines?", "evidence_needed": "Gene editing experiments targeting candidate loci; transformation efficiency assays in edited lines; stability testing across generations", "complexity": "medium", "composite": 0.527, "raw_score": 0.702, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "go_now", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "gene editing", "transformation" ], "measurement_hits": [ "efficiency", "stability" ], "system_hits": [ "plant", "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "gene editing", "transformation" ], "measurement_hits": [ "efficiency", "stability" ], "system_hits": [ "plant", "gene", "genome" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What genetic loci control transformation efficiency differences between genotypes like TX430 (sorghum), INRA 717-1B4 (poplar), and 'Alamo' (switchgrass) versus recalcitrant genotypes?", "evidence_needed": "QTL mapping or GWAS using crosses between transformable and recalcitrant genotypes; bulk segregant analysis; fine mapping of identified loci", "complexity": "complex", "composite": 0.471, "raw_score": 0.628, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "transformation", "qtl", "gwas" ], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation", "qtl", "gwas" ], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant" ], "control_hits": [ "control", "versus" ], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do epigenetic modifications (DNA methylation, histone modifications) differ between transformable and recalcitrant genotypes in ways that affect transformation efficiency?", "evidence_needed": "Comparative whole-genome bisulfite sequencing; ChIP-seq for histone marks; ATAC-seq to assess chromatin accessibility; functional validation through manipulation of epigenetic modifiers", "complexity": "complex", "composite": 0.471, "raw_score": 0.628, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "transformation", "validation" ], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant", "genome", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation", "validation" ], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant", "genome", "dna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How promoters, enhancers, and cis-regulatory elements (CREs) impact position effects of transgene insertions in bioenergy crops is not understood, and sequences with insulator-like function to dampen ectopic interactions are needed.", "domain": "plant molecular biology", "subdomain": "gene regulation and transgene expression", "scope": "medium", "sources": [ "doe-plant-transformation-bioenergy-2024" ], "best_score": 0.527, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "go_now", "avg_score": 0.446, "avg_confidence": 0.438, "num_sub_questions": 4, "go_now_count": 1, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 1, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Can validated safe harbor loci be identified in bioenergy crop genomes for predictable transgene expression?", "evidence_needed": "Pan-genome analysis to identify conserved intergenic regions; targeted insertion experiments at candidate loci; multi-generational stability testing; expression analysis across tissues and conditions", "complexity": "medium", "composite": 0.527, "raw_score": 0.702, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "go_now", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "transformation", "insertion" ], "measurement_hits": [ "expression", "stability" ], "system_hits": [ "plant", "crop", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation", "insertion" ], "measurement_hits": [ "expression", "stability" ], "system_hits": [ "plant", "crop", "genome" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do genomic position effects vary across different insertion sites in bioenergy crop genomes, and what local chromatin features predict position effects?", "evidence_needed": "Large-scale random or targeted transgene insertion experiments; expression analysis of identical transgenes at different genomic locations; correlation analysis with local chromatin state, gene density, and regulatory element proximity", "complexity": "complex", "composite": 0.471, "raw_score": 0.628, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [ "transformation", "insertion" ], "measurement_hits": [ "expression" ], "system_hits": [ "plant", "crop", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation", "insertion" ], "measurement_hits": [ "expression" ], "system_hits": [ "plant", "crop", "gene" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What DNA sequences function as insulators in bioenergy crops to prevent ectopic interactions between transgenes and endogenous regulatory elements?", "evidence_needed": "Comparative genomics to identify conserved boundary elements; reporter assays testing candidate insulator sequences; functional validation in transformation constructs measuring protection from position effects", "complexity": "medium", "composite": 0.467, "raw_score": 0.623, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [ "transformation", "reporter", "validation" ], "measurement_hits": [], "system_hits": [ "gene", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation", "reporter", "validation" ], "measurement_hits": [], "system_hits": [ "gene", "dna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the genome-wide locations and activities of CREs (enhancers, silencers, insulators) in bioenergy crop genomes?", "evidence_needed": "ATAC-seq and DNase-seq to identify open chromatin regions; ChIP-seq for histone marks associated with regulatory elements; reporter assays to validate enhancer activity; DAP-seq to map transcription factor binding sites", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [ "reporter" ], "measurement_hits": [ "activity", "binding" ], "system_hits": [ "crop", "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter" ], "measurement_hits": [ "activity", "binding" ], "system_hits": [ "crop", "gene", "genome" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Lack of complementary biochemical validation for the structural findings of viral Type 2 IRES recruitment to ribosomal preinitiation complex", "domain": "structural biology", "subdomain": "ribosome-IRES interactions", "scope": "medium", "sources": [ "elife-107788v1" ], "best_score": 0.527, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "go_now", "avg_score": 0.356, "avg_confidence": 0.375, "num_sub_questions": 4, "go_now_count": 1, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 1, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do the structural observations correlate with functional translation activity in cellular or in vitro systems?", "evidence_needed": "In vitro translation assays or cellular reporter assays using wild-type and mutant IRES constructs to measure translation efficiency and validate the functional importance of observed structural features", "complexity": "medium", "composite": 0.527, "raw_score": 0.702, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "go_now", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "reporter" ], "measurement_hits": [ "measure", "efficiency", "activity" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter" ], "measurement_hits": [ "measure", "efficiency", "activity" ], "system_hits": [ "cell" ], "control_hits": [ "wild-type" ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the key binding residues or domains in the IRES that interact with ribosomal components, and do mutations in these regions affect binding affinity?", "evidence_needed": "Mutagenesis studies coupled with binding assays (e.g., surface plasmon resonance, isothermal titration calorimetry, or pull-down experiments) to validate predicted interaction interfaces from the cryo-EM structure", "complexity": "medium", "composite": 0.371, "raw_score": 0.495, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.16, "cost": 0.65, "reagent": 0.4, "readiness": 0.8, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "surface plasmon resonance", "isothermal titration" ] }, { "score": 0.1, "keywords": [ "cryo-em" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the observed IRES-ribosome interactions conserved across different viral Type 2 IRES elements?", "evidence_needed": "Comparative binding and functional studies using IRES elements from different viruses, combined with sequence conservation analysis and mutagenesis of conserved regions", "complexity": "medium", "composite": 0.339, "raw_score": 0.542, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.8, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can the proposed recruitment mechanism be validated through cross-linking mass spectrometry or other orthogonal structural techniques?", "evidence_needed": "Cross-linking mass spectrometry (XL-MS) experiments to identify protein-RNA and protein-protein interactions, or complementary techniques like SAXS or hydrogen-deuterium exchange mass spectrometry to validate the structural model", "complexity": "complex", "composite": 0.187, "raw_score": 0.299, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.16, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "mass spectrometry" ] }, { "score": 0.1, "keywords": [ "saxs" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The concept of 'pachytene genome activation' (PGS) is introduced without proper characterization of which genes are activated, what defines this process, or what mechanisms underlie it", "domain": "genomics", "subdomain": "transcriptional regulation during meiosis", "scope": "medium", "sources": [ "elife-99713v1" ], "best_score": 0.527, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "go_now", "avg_score": 0.349, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 1, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 1, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "What is the complete set of genes that are specifically upregulated during pachytene stage (pachytene genome activation)?", "evidence_needed": "Transcriptomic profiling (RNA-seq or similar) across meiotic stages with focus on pachytene to identify genes with pachytene-specific expression patterns, with validation by RT-qPCR or in situ hybridization", "complexity": "medium", "composite": 0.527, "raw_score": 0.702, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "go_now", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "genome", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "genome", "rna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the functional categories and biological pathways represented by pachytene-activated genes?", "evidence_needed": "Gene ontology and pathway enrichment analysis of pachytene-upregulated genes, with functional validation of representative genes", "complexity": "simple", "composite": 0.369, "raw_score": 0.59, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does pachytene genome activation differ from or relate to previously described transcriptional programs in meiosis?", "evidence_needed": "Comparative analysis with existing literature on meiotic gene expression programs, potentially including comparison with female meiosis or other species", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "genome" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the molecular mechanisms that drive pachytene-specific gene activation?", "evidence_needed": "Chromatin immunoprecipitation studies (ChIP-seq) for transcription factors and histone modifications at pachytene-activated genes, combined with analysis of regulatory elements and potential trans-acting factors", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Quantitative assessment and statistical validation of TNKS1/2 degradation and downstream effects are lacking", "domain": "Cell Biology", "subdomain": "Protein Degradation and Signal Transduction", "scope": "medium", "sources": [ "elife-109052v1" ], "best_score": 0.497, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_specification", "avg_score": 0.347, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the quantitative effect of IWR1-POMA on cell and organoid viability compared to catalytic inhibitors?", "evidence_needed": "Cell viability assays (MTT, CellTiter-Glo, or similar) and organoid viability assays across dose ranges and time courses, with IC50 calculations and statistical comparison between IWR1-POMA and catalytic inhibitors across biological replicates", "complexity": "medium", "composite": 0.497, "raw_score": 0.663, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [ "viability" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cell viability" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [ "viability" ], "system_hits": [ "cell" ], "control_hits": [ "comparison" ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the quantitative effect of IWR1-POMA on destruction complex components and \u03b2-catenin localization as measured by immunofluorescence?", "evidence_needed": "High-content imaging analysis with automated quantification of fluorescence intensity, protein colocalization, and cellular distribution across multiple fields and biological replicates, with statistical comparison to controls", "complexity": "medium", "composite": 0.289, "raw_score": 0.463, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the quantitative extent and reproducibility of TNKS1/2 degradation by IWR1-POMA across biological replicates?", "evidence_needed": "Densitometric quantification of Western blots from multiple independent experiments (n\u22653) with statistical analysis, showing TNKS1/2 protein levels normalized to loading controls across various time points and concentrations", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The biology and genetic mechanisms underlying plant regeneration are not well understood, including somatic embryogenesis, DNA repair, and the molecular basis of recalcitrance.", "domain": "plant biology", "subdomain": "plant regeneration and transformation", "scope": "broad", "sources": [ "doe-plant-transformation-bioenergy-2024" ], "best_score": 0.471, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_specification", "avg_score": 0.403, "avg_confidence": 0.417, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 2, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "What DNA repair pathways are activated during plant transformation and how do they affect transformation efficiency?", "evidence_needed": "Time-course studies of DNA repair gene expression post-transformation; mutant analysis of DNA repair pathway components; chromatin accessibility assays during transformation", "complexity": "complex", "composite": 0.471, "raw_score": 0.628, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "transformation" ], "measurement_hits": [ "efficiency", "expression" ], "system_hits": [ "plant", "gene", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation" ], "measurement_hits": [ "efficiency", "expression" ], "system_hits": [ "plant", "gene", "dna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What genetic and epigenetic factors underlie transformation recalcitrance in specific genotypes?", "evidence_needed": "Comparative genomic and epigenomic analysis between transformable and recalcitrant genotypes; QTL mapping in segregating populations; GWAS studies correlating genetic variants with transformation efficiency", "complexity": "complex", "composite": 0.471, "raw_score": 0.628, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "transformation", "qtl", "gwas" ], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "transformation" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "transformation", "qtl", "gwas" ], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the molecular mechanisms controlling somatic embryogenesis in bioenergy crops?", "evidence_needed": "Transcriptomic and epigenomic profiling during somatic embryogenesis stages; functional validation through gene knockout/overexpression studies; single-cell sequencing to identify cell fate transitions", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout", "validation" ], "measurement_hits": [], "system_hits": [ "cell", "plant", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout", "validation" ], "measurement_hits": [], "system_hits": [ "cell", "plant", "gene" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The authors claim that hypomyelination and neurological phenotypes are caused by reduced sphingosine transport by Spns1 leading to reduced sphingolipid synthesis, but this conclusion is not supported by experimental data and alternative hypotheses are not addressed.", "domain": "biochemistry", "subdomain": "lipid metabolism and lysosomal biology", "scope": "broad", "sources": [ "elife-99913v1" ], "best_score": 0.452, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_specification", "avg_score": 0.282, "avg_confidence": 0.292, "num_sub_questions": 6, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 5, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 3, "sub_question_scores": [ { "question": "Could impaired autophagy or general lysosomal dysfunction, rather than specific sphingosine transport defects, explain the phenotypes?", "evidence_needed": "Assessment of lysosomal function markers (e.g., cathepsin activity, lysosomal pH, LC3/p62 levels), lysosomal morphology by electron microscopy, and autophagy flux measurements in Spns1 knockout brains", "complexity": "medium", "composite": 0.452, "raw_score": 0.603, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [ "activity", "flux" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [ "activity", "flux" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the cellular localization of sphingolipid synthesis altered in the absence of Spns1?", "evidence_needed": "Subcellular fractionation and imaging studies to determine where sphingolipid synthesis occurs in control vs knockout cells/tissues; assessment of sphingolipid synthesis enzyme localization", "complexity": "medium", "composite": 0.407, "raw_score": 0.542, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells", "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells", "enzyme" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is sphingolipid synthesis rate actually reduced in Spns1 knockout brains, or is the observed sphingolipid decrease due to alternative mechanisms?", "evidence_needed": "Metabolic flux analysis using stable isotope labeling (e.g., deuterated sphingosine or serine) to measure de novo sphingolipid synthesis rates; measurement of sphingolipid synthesis enzyme activities and expression levels", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [ "measure", "expression", "flux", "rate" ], "system_hits": [ "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [ "measure", "expression", "flux", "rate" ], "system_hits": [ "enzyme" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Spns1 loss-of-function directly impair sphingosine transport from lysosomes?", "evidence_needed": "Direct sphingosine transport assays using isolated lysosomes from control vs knockout brains, or lysosomal sphingosine accumulation measurements in cells/tissues lacking Spns1", "complexity": "complex", "composite": 0.28, "raw_score": 0.448, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Could the observed phenotypes be caused by lysosomal accumulation of lysophospholipids (LPC, LPE) rather than reduced sphingolipid synthesis?", "evidence_needed": "Experiments to reduce LPC/LPE accumulation independently of sphingolipid levels (e.g., genetic or pharmacological manipulation of lysophospholipid metabolism) and assessment of whether this rescues the phenotype", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does exogenous sphingosine supplementation rescue the hypomyelination or sphingolipid deficiency phenotypes in Spns1 knockout mice?", "evidence_needed": "In vivo sphingosine supplementation experiments with assessment of myelin levels, sphingolipid profiles, and neurological symptoms in treated vs untreated knockout mice", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice", "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Unclear whether observed metabolic changes represent transient adaptations or stable metabolic reprogramming due to reliance on short-term isotope tracing", "domain": "Cancer metabolism", "subdomain": "Metabolic flux analysis", "scope": "medium", "sources": [ "elife-107123v1" ], "best_score": 0.441, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_specification", "avg_score": 0.422, "avg_confidence": 0.438, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 2, "needs_repositioning_count": 2, "high_tier_count": 1, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Do cancer cells maintain the observed metabolic phenotypes when cultured under chronic nutrient limitation over multiple passages?", "evidence_needed": "Serial passaging experiments where cells are maintained in nutrient-limited media for multiple generations, followed by metabolic flux analysis and comparison to short-term adaptation", "complexity": "complex", "composite": 0.441, "raw_score": 0.588, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cell culture" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "cells" ], "control_hits": [ "comparison" ], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the observed changes in mitochondrial respiration and redox state persist under sustained nutrient limitation (e.g., 24-72 hours)?", "evidence_needed": "Long-term isotope tracing experiments (24-72 hours) measuring metabolic fluxes, redox state, and mitochondrial respiration in nutrient-limited conditions across the same cell lines", "complexity": "medium", "composite": 0.352, "raw_score": 0.563, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.45 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "flux" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "flux" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the short-term metabolic flux changes accompanied by corresponding changes in protein expression levels of key metabolic enzymes?", "evidence_needed": "Proteomics or targeted western blot analysis of key enzymes in mitochondrial respiration and redox pathways at multiple timepoints (short-term vs sustained nutrient stress)", "complexity": "medium", "composite": 0.58, "raw_score": 0.663, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.6, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression", "flux" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "protein expression" ] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression", "flux" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do differences in basal metabolic rates across cell lines affect the kinetics of metabolite labeling and flux measurements?", "evidence_needed": "Time-course isotope labeling experiments at multiple timepoints (e.g., 0.5h, 1h, 2h, 4h, 8h) to establish labeling kinetics for each cell line and normalize flux comparisons accordingly", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "metabolite" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The functional role of MDH1-CIT1 interaction in metabolic regulation has not been established; current evidence is correlative only", "domain": "metabolic biochemistry", "subdomain": "metabolon function and regulation", "scope": "medium", "sources": [ "elife-107953v1" ], "best_score": 0.422, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_specification", "avg_score": 0.322, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does specific disruption of MDH1-CIT1 interaction using a peptide inhibitor (Pept1 from Pro354-Pro366 region) alter metabolic flux through the TCA cycle?", "evidence_needed": "13C-labeled glucose metabolic flux analysis comparing wild-type cells to cells treated with Pept1 peptide inhibitor, measuring incorporation into TCA cycle intermediates and products", "complexity": "medium", "composite": 0.422, "raw_score": 0.562, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "flux" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "mass spectrometry" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "flux" ], "system_hits": [ "cells" ], "control_hits": [ "wild-type" ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the cit1\u03943 (Arg362Glu) mutation that perturbs MDH1-CIT1 binding affect mitochondrial respiratory function?", "evidence_needed": "Mitochondrial respiration assays (e.g., Seahorse extracellular flux analysis) comparing wild-type versus cit1\u03943 mutant strains under various metabolic conditions", "complexity": "medium", "composite": 0.377, "raw_score": 0.603, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding", "flux" ], "system_hits": [] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "yeast" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding", "flux" ], "system_hits": [], "control_hits": [ "wild-type", "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is there a direct relationship between MDH1-CIT1 interaction strength and metabolic flux efficiency through the malate-to-citrate conversion?", "evidence_needed": "Combined NanoBiT interaction measurements with simultaneous 13C flux analysis under varying conditions, correlating interaction strength with flux rates", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency", "flux" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency", "flux" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does disruption of MDH1-CIT1 interaction via mutation or peptide inhibition alter TCA cycle metabolite levels in a manner consistent with loss of substrate channeling?", "evidence_needed": "Targeted metabolomics comparing TCA cycle intermediate concentrations in cells with disrupted versus intact MDH1-CIT1 interaction under conditions where the complex normally forms", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells", "metabolite" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The nature of Kr expression in MBNBs (mushroom body neuroblasts) in the KrIf-1 mutant allele is unclear - whether Kr is misexpressed or lost, which is critical for interpreting the neuroblast retention phenotype", "domain": "developmental neurobiology", "subdomain": "Drosophila neurogenesis and gene expression", "scope": "narrow", "sources": [ "elife-106732v1" ], "best_score": 0.419, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.33, "avg_confidence": 0.125, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "Is Kr protein expressed, overexpressed, or lost in MBNBs in the KrIf-1 mutant compared to wild-type?", "evidence_needed": "Immunohistochemistry or fluorescent reporter analysis examining Kr expression specifically in MBNBs in KrIf-1 mutants versus wild-type controls at relevant developmental stages", "complexity": "simple", "composite": 0.419, "raw_score": 0.67, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 1.0, "tractability": 1.0 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "reporter" ], "measurement_hits": [ "expression" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter" ], "measurement_hits": [ "expression" ], "system_hits": [ "protein" ], "control_hits": [ "wild-type", "versus" ], "signal_count": 3 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does the Kr expression pattern in KrIf-1 mutants reconcile with the observation that both loss of Kr and misexpression of Kr produce similar neuroblast retention phenotypes?", "evidence_needed": "Comparison of Kr expression levels and patterns in KrIf-1 versus known Kr loss-of-function and gain-of-function alleles, correlated with neuroblast retention phenotypes", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [ "versus", "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Lack of validation for the specificity and efficacy of the AAV-PV-Cre-GFP approach for selective ErbB4 knockout in PV neurons. The viral approach has not been characterized to demonstrate cell-type-specific gene deletion versus off-target effects.", "domain": "molecular neuroscience", "subdomain": "viral gene targeting", "scope": "medium", "sources": [ "elife-101237v1" ], "best_score": 0.419, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.392, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 2, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "What is the identity and specificity of the AAV-PV-Cre-GFP construct used (e.g., is it using the S5E2 enhancer or another PV-specific promoter)?", "evidence_needed": "Molecular characterization of the viral construct including promoter/enhancer sequences, validation in control experiments showing Cre or reporter expression exclusively in PV-positive cells using immunohistochemistry", "complexity": "simple", "composite": 0.419, "raw_score": 0.67, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 1.0, "tractability": 1.0 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "reporter", "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter", "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "cells" ], "control_hits": [ "control" ], "signal_count": 3 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the AAV approach achieve cell-type-specific knockout of ErbB4 in PV neurons without off-target deletion in non-PV cells?", "evidence_needed": "Single-cell level validation using multiplex in situ hybridization or single-cell RNA-seq showing ErbB4 expression levels in identified PV-positive versus PV-negative cells after viral transduction, combined with immunohistochemistry for PV and Cre recombinase", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "knockout", "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout", "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "rna" ], "control_hits": [ "versus" ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "At the high viral titer used (10^12), what is the extent of non-specific transduction and gene deletion?", "evidence_needed": "Dose-response experiments with different viral titers, cell-type-specific expression analysis, and quantification of Cre expression in PV-negative cell populations", "complexity": "medium", "composite": 0.392, "raw_score": 0.522, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { 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"medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "flux" ], "system_hits": [ "microbial", "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "flux" ], "system_hits": [ "microbial", "plant" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which plant traits are most predictive of methane emission across different ecosystem types and environmental conditions?", "evidence_needed": "Multi-site field studies measuring plant trait variation (aerenchyma, root biomass, exudate chemistry) paired with methane flux measurements; synthesis analysis across existing trait and flux datasets", "complexity": "medium", "composite": 0.302, "raw_score": 0.482, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": 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"signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What combinations of soil properties and microbial communities create persistent spatial hot spots of methane emission?", "evidence_needed": "Spatially intensive field campaigns with co-located measurements of soil physics, chemistry, microbial communities, and methane flux; machine learning analysis identifying key predictor variables; targeted experiments testing identified mechanisms", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { 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will soil methanotroph abundance and activity respond to altered temperature, precipitation, and atmospheric methane concentrations under climate change?", "domain": "microbial ecology", "subdomain": "climate change effects on methane-oxidizing bacteria", "scope": "medium", "sources": [ "doe-ai-methane-cycle-2024" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.335, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Can increased activity of high-affinity methanotrophic bacteria substantially increase the soil sink in key regions like the pan-Arctic?", "evidence_needed": "Validation of model predictions using field measurements of methanotroph abundance, activity, and methane uptake rates across Arctic sites; isotope 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"tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does the abundance and activity of high-affinity versus low-affinity methanotrophs shift with changes in atmospheric methane concentration?", "evidence_needed": "Manipulative experiments exposing soil communities to different atmospheric methane concentrations while monitoring community composition and oxidation rates; field observations across natural methane concentration gradients", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "microbial", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "microbial", "soil" ], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the physiological temperature and moisture optima of different methanotroph functional groups across ecosystems?", "evidence_needed": "Laboratory incubation experiments measuring methane oxidation rates across temperature and moisture gradients for isolates and environmental samples; field measurements correlating methanotroph activity with environmental conditions across climate gradients", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "microbial", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "microbial", "soil" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the morphological and compositional responses of microbial cells when tolerating chemical inhibitors (lignocellulosic inhibitors, substrates, products) and physical stresses during fermentation?", "domain": "microbial physiology", "subdomain": "stress tolerance and cellular responses", "scope": "medium", "sources": [ "doe-genomes-structure-function-2023" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.257, "avg_confidence": 0.125, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "What metabolic rerouting occurs during physical and chemical stress that enables cell survival and continued production?", "evidence_needed": "Metabolic flux analysis using isotope tracing combined with proteomics and transcriptomics in stressed vs. unstressed conditions", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "flux" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "flux" ], "system_hits": [ "cell" ], "control_hits": [ "vs." ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do cell morphology and membrane composition change in response to product toxicity during fermentation?", "evidence_needed": "Time-resolved imaging using electron microscopy and fluorescence microscopy, combined with lipidomics and membrane proteomics under increasing product concentrations", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "microbial", "fermentation" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy", "fluorescence microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "microbial", "fermentation" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the key genes and gene products involved in cellular responses to lignocellulosic inhibitors?", "evidence_needed": "Transcriptomic and proteomic profiling under controlled inhibitor exposure, combined with genetic screens using deletion libraries to identify essential tolerance genes", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "gene" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What cellular machinery is involved in adaptation and evolution to new stress environments?", "evidence_needed": "Adaptive laboratory evolution experiments with genome sequencing at multiple timepoints, combined with phenotypic characterization and multi-omics profiling", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "genome" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The claim that MMV1028806 targets the bc1 complex is based only on indirect evidence (metabolomic signatures and mitochondrial membrane potential changes). Direct biochemical evidence of bc1 complex inhibition is missing, and alternative targets in the electron transport chain or broader mitochondrial metabolism have not been ruled out.", "domain": "parasitology", "subdomain": "mitochondrial biochemistry and drug mechanism of action", "scope": "medium", "sources": [ "elife-102511v1" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.262, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does MMV1028806 directly inhibit bc1 complex enzymatic activity?", "evidence_needed": "Direct enzymatic assay measuring bc1 complex activity in the presence and absence of MMV1028806, using purified enzyme or mitochondrial preparations from Toxoplasma gondii", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "assay" ], "measurement_hits": [ "activity" ], "system_hits": [ "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "assay" ], "measurement_hits": [ "activity" ], "system_hits": [ "enzyme" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does MMV1028806 affect other mitochondrial dehydrogenases that feed electrons into the ETC?", "evidence_needed": "Enzymatic assays testing MMV1028806 activity against dihydroorotate dehydrogenase and other mitochondrial dehydrogenases to rule out alternative targets", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does MMV1028806 inhibit mitochondrial oxygen consumption consistent with ETC disruption?", "evidence_needed": "Oxygen consumption measurements (e.g., using Seahorse analyzer or Clark electrode) in intact parasites or isolated mitochondria treated with MMV1028806", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The functional implications of p53 stabilization for cells subjected to nuclear protein misfolding stress are unclear, particularly given that the small effect on cell cycle arrest is not p53-dependent", "domain": "cell biology", "subdomain": "stress response and cell fate", "scope": "medium", "sources": [ "elife-105021v1" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.325, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "What is the functional role of p53 stabilization in response to nuclear protein misfolding stress?", "evidence_needed": "p53 knockout or knockdown experiments combined with Nuc DD expression; assessment of cell survival, apoptosis, senescence, and DNA damage responses; transcriptional target analysis; rescue experiments with p53 re-expression", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "protein", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "protein", "dna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does p53 activation provide a survival advantage or sensitize cells to nuclear protein misfolding stress?", "evidence_needed": "Long-term cell viability assays comparing wild-type and p53-null cells under Nuc DD stress; clonogenic survival assays; assessment of pro-survival vs pro-apoptotic gene expression; metabolic profiling", "complexity": "medium", "composite": 0.437, "raw_score": 0.583, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression", "viability" ], "system_hits": [ "cell", "cells", "protein", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cell viability" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression", "viability" ], "system_hits": [ "cell", "cells", "protein", "gene" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What mechanisms drive cell cycle arrest in response to nuclear misfolding stress if p53 is not responsible?", "evidence_needed": "Cell cycle analysis in p53-null cells with Nuc DD; analysis of alternative cell cycle regulators (p21, p27, cyclins, CDKs); checkpoint kinase activation studies; identification of p53-independent pathways through genetic screens", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The catalytic mechanism and role of specific residues (G437, A438) in biotin-dependent carboxylases needs to be validated experimentally", "domain": "structural biology", "subdomain": "enzyme catalysis and mechanism", "scope": "medium", "sources": [ "elife-98885v2" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.24, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Are G437 and A438 functionally important catalytic residues in PCC/MCC?", "evidence_needed": "Site-directed mutagenesis of G437 and A438 followed by enzyme activity assays to measure catalytic efficiency compared to wild-type", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "measure", "efficiency", "activity" ], "system_hits": [ "protein", "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "measure", "efficiency", "activity" ], "system_hits": [ "protein", "enzyme" ], "control_hits": [ "wild-type" ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What does higher resolution structural data reveal about the catalytic mechanism compared to previous lower-resolution structures?", "evidence_needed": "Comparative structural analysis identifying new mechanistic features (catalytic water molecules, substrate coordination geometry, conformational changes) not visible in previous structures", "complexity": "medium", "composite": 0.202, "raw_score": 0.323, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.1, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.1, "keywords": [ "cryo-em", "crystallography" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the specific role of biotin in the catalytic reaction mechanism of PCC and MCC?", "evidence_needed": "Structural studies capturing different catalytic intermediates (substrate-bound, biotin-carboxylated intermediate states), combined with kinetic analysis and computational modeling of reaction coordinate", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Most tested compounds showed negative or very weak binding affinities to nsp16-nsp10, leaving the question of what chemical features are required for effective nsp16-nsp10 inhibition unresolved", "domain": "drug discovery", "subdomain": "structure-activity relationships and medicinal chemistry", "scope": "broad", "sources": [ "elife-98310v1" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.263, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What are the key protein-ligand interactions that drive high-affinity binding to nsp16-nsp10?", "evidence_needed": "Comparison of binding modes from multiple crystal structures, computational analysis (molecular dynamics, free energy calculations), and structure-guided mutagenesis to identify critical interaction hotspots", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [ "protein" ], "control_hits": [ "comparison" ], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What modifications to the purine analog scaffold are needed to achieve high-affinity binding to nsp16-nsp10?", "evidence_needed": "Systematic medicinal chemistry campaign testing modifications to purine analogs identified in crystal structures, combined with biophysical binding assays (ITC, SPR, thermal shift) and co-crystal structures of improved analogs", "complexity": "complex", "composite": 0.23, "raw_score": 0.367, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "spr", "itc" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are purine analogs the optimal starting scaffold for nsp16-nsp10 inhibitor development, or should alternative chemotypes be explored?", "evidence_needed": "Fragment screening or virtual screening campaigns with diverse chemical scaffolds, followed by biophysical validation and structural characterization of hits from different chemical classes", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "screening", "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "screening", "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Whether the increased sensitivity (toxicity) to DNMT expression and MMS is accompanied by substantial increases in mutagenicity has not been determined", "domain": "molecular biology", "subdomain": "DNA damage and mutagenesis", "scope": "narrow", "sources": [ "elife-103432v3" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.352, "avg_confidence": 0.25, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 0, "sub_question_scores": [ { "question": "Does DNMT expression combined with MMS treatment increase mutation frequency compared to MMS alone?", "evidence_needed": "Mutation frequency assays (e.g., forward mutation assays, reporter gene assays, or whole genome sequencing) in cells expressing DNMT with and without MMS exposure compared to appropriate controls", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis", "reporter" ], "measurement_hits": [ "expression" ], "system_hits": [ "cells", "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis", "reporter" ], "measurement_hits": [ "expression" ], "system_hits": [ "cells", "gene", "genome" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What types of mutations (point mutations, indels, etc.) are enriched when DNMT expression is combined with oxidative damage?", "evidence_needed": "Mutational spectrum analysis using sequencing-based approaches or specific mutation reporter assays to characterize mutation signatures", "complexity": "medium", "composite": 0.339, "raw_score": 0.542, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.8, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "reporter" ], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter" ], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The authors claim hypomyelination is not caused by changes in oligodendrocyte differentiation, but experimental evidence to support this was not provided.", "domain": "neuroscience", "subdomain": "glial cell biology and myelination", "scope": "medium", "sources": [ "elife-99913v1" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.184, "avg_confidence": 0.375, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "Is the expression of myelin-specific genes altered in oligodendrocytes from Spns1 knockout brains?", "evidence_needed": "Cell-type specific transcriptomics (e.g., single-cell RNA-seq or sorted oligodendrocyte RNA-seq) or in situ hybridization for myelin genes (MBP, PLP, MAG, MOG, CNP)", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "rna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are oligodendrocyte survival and proliferation affected in the absence of Spns1?", "evidence_needed": "Apoptosis assays (TUNEL, cleaved caspase-3) and proliferation markers (Ki67, BrdU incorporation) specifically in oligodendrocyte lineage cells", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are oligodendrocyte precursor cell (OPC) numbers altered in brain-specific Spns1 knockout mice?", "evidence_needed": "Immunohistochemistry or flow cytometry quantification of OPC markers (e.g., PDGFR\u03b1, NG2) in brain sections from control vs knockout mice at multiple developmental timepoints", "complexity": "simple", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "flow cytometry" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is oligodendrocyte maturation and differentiation altered in brain-specific Spns1 knockout mice?", "evidence_needed": "Immunohistochemistry and quantification of oligodendrocyte lineage markers (e.g., Olig2, O4, MBP, PLP) at different developmental stages; analysis of oligodendrocyte maturation stages", "complexity": "medium", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "RNA-seq analysis is biased toward integrins rather than providing comprehensive, unbiased analysis of Osx function in osteocytes, and lacks knockdown data despite using knockout mice in animal studies", "domain": "genomics", "subdomain": "transcriptomics", "scope": "medium", "sources": [ "elife-102453v1" ], "best_score": 0.364, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_specification", "avg_score": 0.333, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 1, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Are transcriptional changes consistent between Osx knockdown and knockout conditions in osteocytes?", "evidence_needed": "RNA-seq or targeted gene expression analysis of Osx knockdown cells/tissues compared alongside knockout samples to validate findings and assess dose-dependency", "complexity": "medium", "composite": 0.364, "raw_score": 0.583, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_specification", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 1.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "gene", "rna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the full spectrum of differentially expressed genes in Osx-deficient osteocytes beyond the integrin family?", "evidence_needed": "Complete differential gene expression analysis from RNA-seq data with GO term enrichment, pathway analysis, and unbiased reporting of all significantly changed genes", "complexity": "simple", "composite": 0.369, "raw_score": 0.59, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do non-integrin pathways contribute significantly to the osteocyte phenotypes observed in Osx mutants?", "evidence_needed": "Functional validation experiments (gain/loss of function) for top differentially expressed genes outside the integrin family, correlated with osteocyte phenotypes", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The claim that synonymous mutations within the p19 promoter region increase Rep DNA packaging activity lacks statistical support and additional experimental evidence", "domain": "Virology", "subdomain": "AAV vector engineering and gene regulation", "scope": "medium", "sources": [ "elife-105040v1" ], "best_score": 0.674, "best_tier": "high", "best_confidence": 0.75, "decision_bucket": "needs_repositioning", "avg_score": 0.499, "avg_confidence": 0.417, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 2, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do synonymous mutations in the p19 promoter affect Rep52/40 protein expression levels?", "evidence_needed": "Western blot analysis comparing Rep52 and Rep40 protein levels between wild-type and synonymous mutant constructs", "complexity": "simple", "composite": 0.674, "raw_score": 0.77, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.6, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "protein expression" ] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "protein" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do synonymous mutations in the p19 promoter affect Rep52/40 transcript levels?", "evidence_needed": "RT-qPCR or Northern blot analysis measuring Rep52/40 mRNA levels in cells transfected with wild-type versus synonymous mutant constructs", "complexity": "simple", "composite": 0.547, "raw_score": 0.73, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cells", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cells", "gene" ], "control_hits": [ "wild-type", "versus" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the synonymous mutations in the p19 promoter region show statistically significant increases in packaging activity with appropriate replication and statistical analysis?", "evidence_needed": "Repeated packaging assays with individual p19 promoter synonymous variants, with sufficient biological replicates and appropriate statistical testing", "complexity": "simple", "composite": 0.275, "raw_score": 0.55, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.4, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The claim that MuSK-IgG KO muscle stem cells are more activated and break quiescence more readily has not been directly tested through activation dynamics measurements", "domain": "Stem cell biology", "subdomain": "Muscle stem cell quiescence and activation", "scope": "medium", "sources": [ "elife-101078v1" ], "best_score": 0.632, "best_tier": "high", "best_confidence": 0.75, "decision_bucket": "needs_repositioning", "avg_score": 0.265, "avg_confidence": 0.667, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 1, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "Do MuSK-IgG KO muscle stem cells exit quiescence more rapidly than controls in vitro?", "evidence_needed": "EdU incorporation assays or live-cell imaging tracking MuSC activation from freshly isolated quiescent cells in culture, comparing full body and MuSC-specific KO models to wild-type controls", "complexity": "medium", "composite": 0.632, "raw_score": 0.723, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cell culture" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the quantitative kinetics of quiescence break in MuSK-IgG KO versus control MuSCs?", "evidence_needed": "Time-course EdU labeling experiments or live-cell imaging with temporal resolution to measure the rate and timing of quiescence exit in both in vitro and in vivo settings", "complexity": "medium", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure", "rate" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure", "rate" ], "system_hits": [ "cell" ], "control_hits": [ "control", "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do MuSK-IgG KO muscle stem cells show increased quiescence exit in vivo in uninjured muscle?", "evidence_needed": "In vivo EdU pulse-chase experiments in unperturbed muscle of genetic mouse models (full body and MuSC-specific MuSK-IgG KO) to quantify spontaneous quiescence exit", "complexity": "medium", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The differential expression profiles and activity levels of individual WNK kinases (WNK1-4) in relevant brain regions (hippocampus, cortex, amygdala) are unknown, and reliance on a single pan-WNK inhibitor with differential efficiency against individual WNKs makes it unclear which specific WNK kinase(s) are responsible for the observed behavioral and metabolic effects.", "domain": "neuroscience", "subdomain": "neuronal signaling and behavior", "scope": "medium", "sources": [ "elife-100097v2" ], "best_score": 0.532, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.279, "avg_confidence": 0.375, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 1, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What are the expression profiles of WNK1, WNK2, WNK3, and WNK4 in hippocampus, cortex, and amygdala?", "evidence_needed": "Gene expression profiling using in situ hybridization, immunohistochemistry, or RNA-seq/qPCR of dissected brain regions; mining of existing brain atlas data (e.g., Allen Brain Atlas)", "complexity": "simple", "composite": 0.532, "raw_score": 0.71, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the activity levels of individual WNK kinases in these brain regions under baseline and insulin-stimulated conditions?", "evidence_needed": "Biochemical assays measuring kinase activity or phosphorylation status of WNK-specific substrates in brain tissue lysates; Western blotting for activated/phosphorylated WNK isoforms", "complexity": "medium", "composite": 0.459, "raw_score": 0.613, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 1.0, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "standard_catalog", "keywords": [ "cell signaling" ] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ], "control_hits": [ "baseline" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do alternative WNK inhibitors with different selectivity profiles recapitulate the behavioral and metabolic phenotypes observed with WNK463?", "evidence_needed": "Behavioral testing (memory, anxiety assays) and metabolic measurements (glucose uptake, insulin signaling) in mice treated with structurally distinct WNK inhibitors", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which specific WNK kinase(s) mediate the observed effects on behavior and CNS glucose metabolism?", "evidence_needed": "Behavioral and metabolic characterization of brain-region-specific or neuron-specific conditional knockout mice for individual WNK kinases (WNK1, WNK2, WNK3, WNK4), or acute knockdown using viral shRNA/siRNA delivery", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "HTT aggregation has not been properly assessed using appropriate methods, and current readouts (fluorescence quantification and high MW species in SDS-PAGE) are not suitable proxies for aggregation", "domain": "protein biochemistry", "subdomain": "protein aggregation and proteostasis", "scope": "narrow", "sources": [ "elife-94739v2" ], "best_score": 0.532, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.384, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 1, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "What is the actual aggregation state of HTT proteins in UBE2D/eff knockdown conditions as measured by biochemical separation methods?", "evidence_needed": "SDD-AGE (semi-denaturing detergent agarose gel electrophoresis) to separate aggregated from soluble HTT; filter retardation assays to quantify insoluble aggregates", "complexity": "simple", "composite": 0.532, "raw_score": 0.71, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "gel electrophoresis", "agarose gel" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the high molecular weight HTT species observed in SDS-PAGE wells truly aggregates or artifacts from cell debris?", "evidence_needed": "Biochemical fractionation to separate aggregates from cell debris before SDS-PAGE; solubility assays using differential centrifugation; detergent extraction protocols to distinguish true aggregates", "complexity": "simple", "composite": 0.344, "raw_score": 0.55, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.6, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "sds-page" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the biophysical properties and dynamics of HTT aggregates in cells with altered UBE2D/eff levels?", "evidence_needed": "FRAP (fluorescence recovery after photobleaching) experiments to assess HTT mobility and aggregate dynamics; additional biophysical characterization of aggregate properties", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The influence of EGFP tag on MORC2 phase separation behavior has not been evaluated", "domain": "Protein Biochemistry", "subdomain": "Phase Separation", "scope": "narrow", "sources": [ "elife-108479v1" ], "best_score": 0.503, "best_tier": "high", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.503, "avg_confidence": 0.5, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 1, "medium_tier_count": 0, "low_tier_count": 0, "sub_question_scores": [ { "question": "Does the EGFP tag alter MORC2 phase separation properties compared to untagged or differently-tagged protein?", "evidence_needed": "Side-by-side comparison of phase separation behavior (critical concentration, condensate dynamics, partition coefficient) between EGFP-tagged, untagged, and alternative small-tag MORC2 variants using in vitro turbidity assays, microscopy, and cellular experiments", "complexity": "simple", "composite": 0.503, "raw_score": 0.67, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "high", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The effects of disease-linked mutations in the N-terminal domain of MORC2 on cellular condensate formation, ATPase activity, and DNA-binding appear inconclusive", "domain": "Structural 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"technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "dna" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "dna" ], "control_hits": [ "vs." ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do disease-linked mutations alter MORC2's DNA binding affinity or specificity?", "evidence_needed": "Quantitative DNA binding assays (EMSA, ITC, fluorescence polarization) comparing wild-type and mutant MORC2 with various DNA substrates, including binding affinity measurements and specificity profiling", "complexity": "medium", "composite": 0.377, "raw_score": 0.502, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "itc" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "dna" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do specific disease-linked mutations quantitatively affect MORC2 condensate formation in cells?", "evidence_needed": "Quantitative imaging analysis of condensate number, size, dynamics (FRAP), and composition for each disease-linked mutant compared to wild-type, with statistical analysis across multiple cells and replicates", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "wild-type" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Key exceptions to the proposed model (MDH1-CIT1 dissociation at low respiratory flux, association at high respiratory flux) suggest the model is oversimplified and requires further work to understand complexities", "domain": "metabolic biochemistry", "subdomain": "metabolon regulation", "scope": "medium", "sources": [ "elife-107953v1" ], "best_score": 0.482, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.263, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does the MDH1-CIT1 interaction follow different regulatory logic under different growth conditions or metabolic states (e.g., fermentation vs. respiration, nutrient excess vs. limitation)?", "evidence_needed": "Comparative analysis of interaction dynamics across distinct physiological states using NanoBiT combined with metabolic profiling and respiratory measurements", "complexity": "medium", "composite": 0.482, "raw_score": 0.643, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "fermentation" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "yeast" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "fermentation" ], "control_hits": [ "vs." ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What causes the observed exceptions where MDH1-CIT1 interaction behavior deviates from the respiratory flux-based model?", "evidence_needed": "Detailed characterization of the exceptional conditions through metabolomics, respiratory measurements, and additional 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"complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are there additional regulatory inputs beyond respiratory flux (e.g., specific metabolites, pH, redox state, protein modifications) that govern MDH1-CIT1 interaction?", "evidence_needed": "Systematic testing of candidate regulatory factors using in vitro MST experiments and in vivo NanoBiT assays under controlled conditions where individual variables are manipulated", "complexity": "medium", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Limited scope of somatic phenotypes examined to establish daf-16 dependence - only a subset of somatic tissues/phenotypes were tested for daf-16 requirement", "domain": "aging biology", "subdomain": "tissue-specific aging and healthspan parameters", "scope": "medium", "sources": [ "elife-97767v1" ], "best_score": 0.482, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": 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"long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is daf-16 required for mitochondrial stress-induced healthspan effects in additional somatic tissues beyond intestine and germline (e.g., muscle, neurons, hypodermis)?", "evidence_needed": "Tissue-specific healthspan assays (e.g., muscle function, neuronal integrity, cuticle integrity) in daf-16 mutant background compared to wild-type with GABAergic isp-1 expression", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [ "wild-type" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The impact of the dCas9-Suntag labeling system on chromatin mobility, genome integrity, and cellular function needs to be validated", "domain": "cell biology", "subdomain": "chromatin dynamics and live-cell imaging", "scope": "medium", "sources": [ "elife-97537v1" ], "best_score": 0.482, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.349, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 0, 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0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "cell", "cells", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "cell", "cells", "dna" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the dCas9-Suntag construct alter chromatin mobility compared to standard chromatin markers like H2B-GFP?", "evidence_needed": "FRAP (Fluorescence Recovery After Photobleaching) analysis comparing 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"signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the presence of sgAlu tagging system affect global chromatin mobility?", "evidence_needed": "Comparative analysis of H2B-GFP mobility measurements (e.g., mean square displacement) between sgAlu-tagged cells and untagged control cells", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 0.8 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "global" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can chemical exchanges between plants and microbes within the rhizosphere be directly measured and visualized to understand plant-microbe interactions?", "domain": "plant-microbe interactions", "subdomain": "rhizosphere chemistry and imaging", "scope": "medium", "sources": [ "doe-genomes-structure-function-2023" ], "best_score": 0.467, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.303, "avg_confidence": 0.312, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "What biosensors or chemical probes can be developed to enable real-time visualization of specific chemical exchanges in the rhizosphere?", "evidence_needed": "Design and testing of genetically encoded biosensors or synthetic chemical probes for key metabolites, validated in vitro and in plant-microbe co-culture systems", "complexity": "medium", "composite": 0.467, "raw_score": 0.623, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ 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"medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "isotope tracing" ], "measurement_hits": [], "system_hits": [ "microbe", "microbial", "plant", "rhizosphere" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "mass spectrometry" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "isotope tracing" ], "measurement_hits": [], "system_hits": [ "microbe", "microbial", "plant", "rhizosphere" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can multimodal imaging approaches (fluorescence, radioisotope, PET, cryo-ET, FISH) be integrated to map plant-microbe interaction hotspots at multiple scales?", "evidence_needed": "Development and validation of correlative imaging workflows on model plant-microbe systems, comparing information obtained at different resolutions and with different modalities", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "microbe", "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": 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{ "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "rhizosphere" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "rhizosphere" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanisms for translational control in dinoflagellates and how specific spliced leaders (SLs) link to organismal functions or mRNA recruitment mechanisms are not established", "domain": "molecular biology", "subdomain": "translational regulation in 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"readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "protein", "rna" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "protein", "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do specific SL sequences affect mRNA recruitment to ribosomes?", "evidence_needed": "In vitro translation experiments with different SL variants; ribosome binding assays; structural studies of SL-ribosome interactions; mutagenesis studies of SL sequences affecting recruitment", "complexity": "complex", "composite": 0.441, "raw_score": 0.588, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.8, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis" ], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which specific biological functions or processes are associated with particular SL sequences?", "evidence_needed": "Functional enrichment analysis of genes containing specific SLs; phenotypic analysis under different conditions; time-course expression studies linking SL usage to cellular states or environmental responses", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the specific mechanisms by which different spliced leaders control translation in dinoflagellates?", "evidence_needed": "Functional studies comparing translation rates of mRNAs with different SL variants; ribosome profiling experiments; reporter assays with SL swaps; analysis of polysome association of different SL-containing mRNAs", "complexity": "complex", "composite": 0.204, "raw_score": 0.408, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "reporter" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanistic basis for p53 stabilization by nuclear-destabilized domain (Nuc DD) is unclear - whether it is entirely caused by diminished nuclear degradation activity or involves additional factors", "domain": "cell biology", "subdomain": "protein quality control and proteostasis", "scope": "medium", "sources": [ "elife-105021v1" ], "best_score": 0.467, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.325, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 1, "sub_question_scores": [ { "question": "Why does gene expression analysis only show differential regulation of the p53 pathway despite stabilization of multiple transcription factors?", "evidence_needed": "RNA-seq or targeted qPCR analysis of downstream targets of c-fos, c-myc, and other stabilized transcription factors; chromatin immunoprecipitation to assess binding of stabilized factors to target gene promoters; activity assays for transcription factors beyond p53", "complexity": "medium", "composite": 0.467, "raw_score": 0.623, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "expression", "binding" ], "system_hits": [ "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "expression", "binding" ], "system_hits": [ "gene", "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Nuc DD cause stabilization of other short-lived transcription factors (c-fos, c-myc) in addition to p53, Nrf1, and Nrf2?", "evidence_needed": "Western blot or quantitative proteomics analysis of multiple short-lived transcription factors (c-fos, c-myc, HIF1\u03b1, etc.) in cells expressing Nuc DD; half-life measurements of these proteins under Nuc DD conditions", "complexity": "simple", "composite": 0.344, "raw_score": 0.55, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.6, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is p53 stabilization by Nuc DD entirely due to limited nuclear proteasome degradation capacity, or are other factors involved?", "evidence_needed": "Proteasome activity assays in nuclear vs cytoplasmic fractions; rescue experiments with proteasome activators; measurement of p53 ubiquitination status; half-life measurements of p53 with and without proteasome inhibitors in Nuc DD conditions", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are different short-lived transcription factors selectively targeted by nuclear vs cytosolic proteasome for degradation?", "evidence_needed": "Subcellular fractionation experiments measuring degradation rates of specific transcription factors in isolated nuclear and cytoplasmic fractions; localization studies of proteasome substrates; proteasome inhibition studies with compartment-specific approaches", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism by which Nuc DD affects the ubiquitin-proteasome system (UPS) in the nucleus is unknown - whether it directly clogs the proteasome or affects assisting factors like chaperones or ubiquitinating enzymes", "domain": "cell biology", "subdomain": "ubiquitin-proteasome system", "scope": "medium", "sources": [ "elife-105021v1" ], "best_score": 0.467, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.361, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Does Nuc DD directly clog or inhibit the nuclear proteasome?", "evidence_needed": "In vitro proteasome activity assays with purified proteasomes and Nuc DD protein; measurements of proteasome subunit composition and assembly; proteasome processivity assays; imaging of proteasome dynamics and aggregation in cells", "complexity": "medium", "composite": 0.467, "raw_score": 0.623, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "cells", "protein" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "cells", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Nuc DD affect nuclear chaperone function or availability?", "evidence_needed": "Measurement of chaperone levels (HSP70, HSP90, etc.) in nuclear fractions; chaperone activity assays; co-immunoprecipitation of Nuc DD with chaperones; analysis of chaperone client protein folding status", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Nuc DD affect ubiquitinating enzyme activity or availability in the nucleus?", "evidence_needed": "Measurement of global ubiquitination patterns in nuclear fractions; activity assays for E1, E2, and E3 enzymes; identification of specific E3 ligases affected by Nuc DD; ubiquitin chain topology analysis", "complexity": "medium", "composite": 0.302, "raw_score": 0.482, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "enzyme" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "global" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Discrepancy between current Coulomb repulsion mechanism and previous findings implicating calpains and calcium in vimentin disassembly under hypotonic challenge", "domain": "cell biology", "subdomain": "cytoskeleton regulation", "scope": "medium", "sources": [ "elife-99568v1" ], "best_score": 0.467, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.399, "avg_confidence": 0.417, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Can Coulomb repulsion-mediated vimentin disassembly occur independently of calcium and calpain pathways?", "evidence_needed": "Experiments testing vimentin response to charge changes in cells with inhibited calcium signaling or calpain activity, or in cell-free reconstituted systems", "complexity": "medium", "composite": 0.467, "raw_score": 0.623, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "cell-free" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is calcium signaling and calpain activation involved in charge-dependent vimentin disassembly, or are these independent mechanisms?", "evidence_needed": "Experiments measuring calcium levels and calpain activity during charge-dependent vimentin disassembly, with calpain inhibitors and calcium chelators as controls", "complexity": "medium", "composite": 0.444, "raw_score": 0.593, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 1.0, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "standard_catalog", "keywords": [ "cell signaling" ] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Under what conditions does each mechanism (Coulomb repulsion vs. calpain/calcium) predominate in vimentin disassembly?", "evidence_needed": "Systematic comparison of both mechanisms under varied osmotic and ionic conditions, measuring both charge effects and calcium/calpain activation", "complexity": "complex", "composite": 0.286, "raw_score": 0.458, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 1.0, "readiness": 0.3, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "standard_catalog", "keywords": [ "cell signaling" ] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "vs.", "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism by which UBE2D/eff loss-of-function leads to increased poly-ubiquitinated proteins and p62 accumulation is unexplained, and alternative possibilities beyond proteasome disruption have not been explored", "domain": "cell biology", "subdomain": "ubiquitin-proteasome system and protein quality control", "scope": "medium", "sources": [ "elife-94739v2" ], "best_score": 0.467, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.379, "avg_confidence": 0.375, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 4, "low_tier_count": 0, "sub_question_scores": [ { "question": "Does UBE2D/eff loss affect deubiquitinase (DUB) activity or expression, which could explain increased poly-ubiquitinated proteins?", "evidence_needed": "Biochemical assays measuring DUB activity in UBE2D/eff knockdown cells; Western blots or qPCR measuring DUB expression levels; DUB inhibitor rescue experiments", "complexity": "medium", "composite": 0.467, "raw_score": 0.623, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does UBE2D/eff loss affect the expression or function of other ubiquitin-conjugating enzymes that could compensate or be dysregulated?", "evidence_needed": "Proteomic or Western blot analysis of other E2 enzymes; activity assays for related E2 enzymes; epistasis experiments with other E2 knockdowns", "complexity": "medium", "composite": 0.407, "raw_score": 0.542, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.6, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does UBE2D/eff loss affect the function or localization of E3 ligases that regulate protein quality control?", "evidence_needed": "Co-immunoprecipitation experiments to assess E2-E3 interactions; subcellular fractionation to examine E3 ligase localization; activity assays for specific E3 ligases involved in quality control", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "protein" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does UBE2D/eff loss affect autophagy flux, which could explain p62 accumulation independent of proteasome function?", "evidence_needed": "Autophagy flux assays using LC3 turnover with/without lysosomal inhibitors; measurement of autophagosome-lysosome fusion; lysosomal function assays", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Insufficient quantification of vimentin cytoskeleton changes", "domain": "cell biology", "subdomain": "quantitative microscopy", "scope": "narrow", "sources": [ "elife-99568v1" ], "best_score": 0.458, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.458, "avg_confidence": 0.5, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 0, "sub_question_scores": [ { "question": "What quantitative metrics can be established to measure vimentin network integrity and disassembly?", "evidence_needed": "Image analysis quantifying parameters such as network density, filament length, branch points, or fluorescence intensity distribution with statistical analysis", "complexity": "simple", "composite": 0.458, "raw_score": 0.61, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "In vitro studies do not convincingly demonstrate that cells are actually senescent; only senescence markers (p16, SA-\u03b2gal) are measured without evaluating key senescence phenotypes like cell cycle arrest, SASP, or apoptosis resistance", "domain": "Cell Biology", "subdomain": "Cellular senescence", "scope": "medium", "sources": [ "elife-104550v1" ], "best_score": 0.458, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.326, "avg_confidence": 0.188, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "Do TET2-overexpressing beta cell lines undergo actual cell cycle arrest?", "evidence_needed": "Cell cycle analysis using flow cytometry with BrdU or EdU incorporation assays, and Ki67 staining to demonstrate permanent growth arrest in TET2-overexpressing cells", "complexity": "simple", "composite": 0.458, "raw_score": 0.61, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "flow cytometry" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do TET2-overexpressing beta cell lines exhibit the senescence-associated secretory phenotype (SASP)?", "evidence_needed": "Measurement of SASP factors (IL-6, IL-8, MCP-1, etc.) in conditioned media from TET2-overexpressing cells using ELISA or multiplex immunoassays", "complexity": "simple", "composite": 0.369, "raw_score": 0.59, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "elisa" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are TET2-overexpressing beta cell lines resistant to apoptosis, a hallmark of senescent cells?", "evidence_needed": "Apoptosis assays (Annexin V/PI staining, caspase activation) comparing TET2-overexpressing versus control cells under pro-apoptotic conditions", "complexity": "simple", "composite": 0.265, "raw_score": 0.53, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "control", "versus" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does TET2 induce senescence-like changes under metabolically relevant stressors in beta cells?", "evidence_needed": "Evaluation of senescence markers in TET2-overexpressing cells under glucolipotoxic conditions, oxidative stress (H2O2), or DNA damage (bleomycin, doxorubicin)", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The cell-intrinsic versus cell-extrinsic mode of action of MOTS-c has not been determined - all experiments use exogenous MOTS-c peptide, leaving unclear whether endogenous MOTS-c acts within cells or as a secreted factor", "domain": "cell biology", "subdomain": "peptide signaling and mechanism of action", "scope": "medium", "sources": [ "elife-87615v2" ], "best_score": 0.458, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.366, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Is MOTS-c secreted from cells or does it remain intracellular?", "evidence_needed": "Fractionation experiments, secretion assays, or immunofluorescence microscopy to determine subcellular localization and whether MOTS-c is released into culture medium", "complexity": "simple", "composite": 0.458, "raw_score": 0.61, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can cell-intrinsic MOTS-c rescue differentiation phenotypes observed with exogenous treatment?", "evidence_needed": "Genetic complementation experiments comparing exogenous peptide addition versus endogenous expression in MOTS-c deficient cells", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells" ], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does endogenously expressed MOTS-c affect monocyte differentiation in a cell-intrinsic manner?", "evidence_needed": "Overexpression or knockdown/knockout of MOTS-c in monocytes followed by assessment of differentiation markers without adding exogenous peptide", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The requirement for Fc receptor crosslinking in the mechanism needs further functional validation", "domain": "immunology", "subdomain": "antibody therapeutics and Fc receptor biology", "scope": "medium", "sources": [ "elife-106425v1" ], "best_score": 0.452, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.34, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Does blocking or removing Fc receptors eliminate the cytotoxic effect of DuoHexaBody-CD37?", "evidence_needed": "Cytotoxicity assays in cells lacking Fc receptors (genetic knockout or blocking antibodies) compared to wild-type cells, or use of Fc-deficient variants of DuoHexaBody-CD37", "complexity": "medium", "composite": 0.452, "raw_score": 0.603, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cytotoxicity" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is Fc receptor engagement necessary for the direct cytotoxic signaling observed?", "evidence_needed": "Experiments using DuoHexaBody-CD37 variants with mutated Fc regions that cannot bind Fc receptors, assessing downstream signaling and cytotoxicity", "complexity": "medium", "composite": 0.314, "raw_score": 0.503, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cytotoxicity" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does artificial crosslinking of CD37 without Fc engagement recapitulate the cytotoxic effects?", "evidence_needed": "Experiments using Fc-independent crosslinking methods (e.g., chemical crosslinkers, plate-bound antibodies) to determine if CD37 clustering alone is sufficient", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The role of SHP-1 in mediating cytotoxicity requires further functional validation", "domain": "cell biology", "subdomain": "signal transduction and cancer therapeutics", "scope": "medium", "sources": [ "elife-106425v1" ], "best_score": 0.437, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.373, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Does genetic depletion or knockout of SHP-1 reduce or eliminate DuoHexaBody-CD37-induced cytotoxicity?", "evidence_needed": "Loss-of-function experiments using SHP-1 knockdown (siRNA/shRNA) or knockout (CRISPR/Cas9) in DLBCL cell lines followed by cytotoxicity assays with DuoHexaBody-CD37 treatment", "complexity": "medium", "composite": 0.437, "raw_score": 0.583, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cytotoxicity" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does pharmacological inhibition of SHP-1 block DuoHexaBody-CD37-mediated cell death?", "evidence_needed": "Cytotoxicity assays using specific SHP-1 inhibitors in combination with DuoHexaBody-CD37 treatment in DLBCL cells", "complexity": "simple", "composite": 0.369, "raw_score": 0.59, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cytotoxicity" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is SHP-1 activation (phosphatase activity) directly triggered by DuoHexaBody-CD37 binding to CD37?", "evidence_needed": "Biochemical assays measuring SHP-1 phosphatase activity and/or recruitment to CD37 following DuoHexaBody-CD37 treatment, potentially using co-immunoprecipitation and phosphatase activity assays", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "binding" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "binding" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The dynamics of KLC-TPR docking and undocking remain incompletely defined; it is unclear whether both TPR domains engage CC1 simultaneously or in an alternating fashion.", "domain": "Protein biophysics", "subdomain": "Protein-protein interaction dynamics", "scope": "narrow", "sources": [ "elife-109462v1" ], "best_score": 0.427, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.352, "avg_confidence": 0.375, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "What are the kinetics of TPR domain association and dissociation with CC1?", "evidence_needed": "Surface plasmon resonance (SPR) or bio-layer interferometry (BLI) to measure on/off rates, or stopped-flow fluorescence experiments monitoring binding kinetics in real-time", "complexity": "simple", "composite": 0.427, "raw_score": 0.57, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure", "binding" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "surface plasmon resonance", "spr" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure", "binding" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do both TPR domains bind to CC1 simultaneously or do they engage in an alternating/sequential manner?", "evidence_needed": "Single-molecule FRET experiments with dual-labeled constructs to monitor both TPR domains simultaneously, or crosslinking studies to capture simultaneous vs alternating binding states, or NMR/EPR spectroscopy to monitor dynamic interactions", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "nmr" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "RAS proteins could affect protein production at post-transcriptional or post-translational levels, which have not been adequately considered as alternative mechanisms", "domain": "Molecular Biology", "subdomain": "RAS signaling and gene regulation", "scope": "medium", "sources": [ "elife-104320v2" ], "best_score": 0.426, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.38, "avg_confidence": 0.417, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "Can circuit behavior be recapitulated in a cell-free system where post-transcriptional and post-translational regulation by RAS is minimized?", "evidence_needed": "Cell-free expression system with purified RAS proteins to test direct effects on circuit sensor components versus transcription-based readouts", "complexity": "complex", "composite": 0.426, "raw_score": 0.568, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro", "cell-free" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does RasG12D affect mRNA stability or translation efficiency of circuit components?", "evidence_needed": "Measurement of mRNA levels (qRT-PCR) and protein levels (Western blot or flow cytometry) of circuit components in cells with and without RasG12D, calculating translation efficiency", "complexity": "medium", "composite": 0.401, "raw_score": 0.534, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.56, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency", "stability" ], "system_hits": [ "cells", "protein", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "pcr", "qrt-pcr" ] }, { "score": 0.6, "keywords": [ "western blot" ] }, { "score": 0.5, "keywords": [ "flow cytometry" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency", "stability" ], "system_hits": [ "cells", "protein", "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does RasG12D affect post-translational modifications or protein stability of circuit components?", "evidence_needed": "Protein half-life measurements, phosphorylation state analysis, or degradation assays of key circuit proteins in RasG12D-positive vs negative cells", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [ "cell", "cells", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [ "cell", "cells", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism linking TET2-induced changes to decreased H4K16ac levels is not explained; rationale for focusing on H4K16ac is insufficiently discussed", "domain": "Epigenetics", "subdomain": "Histone modifications and chromatin regulation", "scope": "medium", "sources": [ "elife-104550v1" ], "best_score": 0.425, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.346, "avg_confidence": 0.375, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "Does the TET2-PTEN axis regulate MOF activity or expression?", "evidence_needed": "Western blot and qPCR analysis of MOF expression levels in TET2-manipulated cells, MOF enzymatic activity assays, and rescue experiments showing that PTEN modulation affects H4K16ac in a TET2-dependent manner", "complexity": "medium", "composite": 0.425, "raw_score": 0.567, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.72, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] }, { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the mechanistic link between TET2 and H4K16ac changes?", "evidence_needed": "Experiments testing whether TET2 directly interacts with MOF (the H4K16 acetyltransferase) through co-immunoprecipitation, or whether TET2-mediated DNA demethylation affects MOF recruitment or activity at specific genomic loci using ChIP-seq", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "dna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The binding affinity of sangivamycin to nsp16-nsp10 is orders of magnitude weaker than its antiviral activity, leaving the mechanism of antiviral action unresolved", "domain": "antiviral drug discovery", "subdomain": "target validation and mechanism of action", "scope": "medium", "sources": [ "elife-98310v1" ], "best_score": 0.411, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.218, "avg_confidence": 0.312, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does sangivamycin have off-target effects on host cell pathways that contribute to antiviral activity?", "evidence_needed": "Transcriptomics or metabolomics studies in cells treated with sangivamycin, testing in cell-free viral replication systems, or host kinase/methyltransferase inhibition profiling", "complexity": "complex", "composite": 0.411, "raw_score": 0.548, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "cell-free" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the cellular pharmacokinetics and bioavailability of sangivamycin that might explain the discrepancy between cellular EC50 and in vitro Kd?", "evidence_needed": "Cell permeability assays, intracellular accumulation studies, and measurement of intracellular drug concentrations at effective antiviral doses", "complexity": "medium", "composite": 0.339, "raw_score": 0.542, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the actual molecular target of sangivamycin responsible for its antiviral activity against SARS-CoV-2?", "evidence_needed": "Target identification studies using proteomics-based approaches (e.g., thermal proteome profiling, chemical proteomics with clickable analogs), or testing sangivamycin against panels of viral proteins", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "sars-cov" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does sangivamycin inhibit other SARS-CoV-2 proteins beyond nsp16-nsp10?", "evidence_needed": "Biochemical inhibition assays against a panel of SARS-CoV-2 enzymes (e.g., other methyltransferases, proteases, polymerase) with sangivamycin at relevant concentrations", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "sars-cov" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The functional consequences of massive dCas9 binding throughout the genome on cellular physiology and genome organization need to be assessed", "domain": "cell biology", "subdomain": "genome engineering and cellular homeostasis", "scope": "broad", "sources": [ "elife-97537v1" ], "best_score": 0.407, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.29, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "Are there effects on cell cycle progression, proliferation, or cellular stress responses?", "evidence_needed": "Cell cycle analysis by flow cytometry, proliferation assays, and stress marker expression (e.g., p53, heat shock proteins) comparing engineered and wild-type cells", "complexity": "medium", "composite": 0.407, "raw_score": 0.542, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "flow cytometry" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does widespread dCas9 binding affect global gene expression patterns?", "evidence_needed": "RNA-seq comparison between engineered dCas9-sgAlu cells and wild-type cells, with analysis of differentially expressed genes and pathway enrichment", "complexity": "medium", "composite": 0.314, "raw_score": 0.503, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression", "binding" ], "system_hits": [ "cells", "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression", "binding" ], "system_hits": [ "cells", "gene", "rna" ], "control_hits": [ "wild-type", "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "global" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does dCas9 binding impede transcription elongation at genomic loci where Alu elements overlap with genes?", "evidence_needed": "Nascent RNA-seq or GRO-seq to measure elongation rates at genes containing intronic or exonic Alu elements, comparing dCas9-bound and unbound conditions", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure", "binding" ], "system_hits": [ "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure", "binding" ], "system_hits": [ "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does massive dCas9 occupancy alter global chromatin architecture and genome compartmentalization?", "evidence_needed": "Hi-C or other chromatin conformation capture experiments comparing 3D genome organization between dCas9-sgAlu cells and wild-type cells", "complexity": "complex", "composite": 0.174, "raw_score": 0.348, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.3, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": 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} } ] }, { "problem_statement": "AP-MS of ectopically expressed viral proteins may not accurately capture infection-related interactions, as evidenced by discrepancies between transfection-based interaction studies and infection-based studies, including the inability to reproduce Mlec-Nsp2 interactions observed in transfected cells during actual viral infection", "domain": "virology", "subdomain": "virus-host protein interactions", "scope": "medium", "sources": [ "elife-100834v2" ], "best_score": 0.393, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.325, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Can Mlec-Nsp2 interactions be detected during actual MHV infection in mouse cells using alternative methods beyond AP-MS?", "evidence_needed": 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co-purified SAM in the active site complicates electron density map interpretation for ligand binding, and the methods for determining ligand occupancies are not adequately justified", "domain": "structural biology", "subdomain": "crystallography and structure refinement", "scope": "medium", "sources": [ "elife-98310v1" ], "best_score": 0.386, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.35, "avg_confidence": 0.417, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "What is the accurate occupancy of ligands in the crystal structures, and how much of the density represents SAM versus test compounds?", "evidence_needed": "Rigorous occupancy refinement using multiple approaches (e.g., PanDDA, ensemble refinement, comparison with SAM-free structures), validation with 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[], "control_hits": [ "versus", "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can ligand binding modes be unambiguously determined when SAM is pre-bound in the active site?", "evidence_needed": "Use of advanced electron density analysis methods like PanDDA to distinguish ground state SAM from ligand-induced changes, or generation of SAM-free crystals through apo protein preparation or SAM displacement protocols", "complexity": "medium", "composite": 0.332, "raw_score": 0.443, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.1, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.1, "keywords": [ "crystallography" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can the compounds actually displace SAM from the active site or do they bind to a secondary site?", "evidence_needed": "Competition binding experiments using fluorescence polarization or ITC with pre-bound SAM, or crystallographic soaking experiments with varying SAM:ligand ratios and time courses", "complexity": "simple", "composite": 0.331, "raw_score": 0.53, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 1.0, "reagent": 0.4, "readiness": 0.4, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "itc" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The reproducibility and statistical validity of surface biotinylation experiments for CALHM mutants needs to be established through quantitation and replication", "domain": "cell biology", "subdomain": "membrane protein trafficking and localization", "scope": "narrow", "sources": [ "elife-106134v2" ], "best_score": 0.381, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.375, "avg_confidence": 0.25, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 0, "sub_question_scores": [ { "question": "How many independent replicates of the surface biotinylation experiments were performed for CALHM mutants?", "evidence_needed": "Replicated surface biotinylation assays (minimum n=3) with quantification of band intensities and statistical analysis comparing mutant surface expression to wild-type controls", "complexity": "simple", "composite": 0.381, "raw_score": 0.61, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "protein" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the observed difference in surface expression of CALHM mutants statistically significant and reproducible across biological replicates?", "evidence_needed": "Quantitative analysis of surface biotinylation data from multiple independent experiments with statistical testing (e.g., t-test, ANOVA) and error bars/standard deviations", "complexity": "simple", "composite": 0.369, "raw_score": 0.59, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The redox-sensitive disulfide complex formation between UBA7 and UBE2L6 has only been demonstrated with recombinant proteins in vitro, lacking validation with endogenous proteins in cellular contexts or under different cellular conditions that would support the claimed link to oxidative stress and immune regulation.", "domain": "cell biology", "subdomain": "redox signaling and immune regulation", "scope": "medium", "sources": [ "elife-105638v1" ], "best_score": 0.381, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.287, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does modulation of the UBA7\u2022UBE2L6 disulfide complex affect immune responses or ISGylation in cells?", "evidence_needed": "Functional assays measuring ISGylation levels and immune signaling markers (e.g., interferon response genes) when the complex is disrupted (by reducing agents or cysteine mutations) or enhanced (by oxidative conditions) in cell culture", "complexity": "complex", "composite": 0.381, "raw_score": 0.508, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.8, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.8, "keywords": [ "cell culture" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the UBA7\u2022UBE2L6 disulfide complex form with endogenous proteins in cells under physiological conditions?", "evidence_needed": "Detection of the disulfide-linked UBA7\u2022UBE2L6 complex in cells using co-immunoprecipitation followed by non-reducing SDS-PAGE and western blotting, or proximity ligation assays with endogenous protein antibodies", "complexity": "medium", "composite": 0.304, "raw_score": 0.487, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.72, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "ligation" ] }, { "score": 0.6, "keywords": [ "sds-page" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is endogenous UBA7\u2022UBE2L6 disulfide complex formation modulated by oxidative stress conditions?", "evidence_needed": "Cellular experiments comparing complex formation levels under normal conditions versus oxidative stress treatments (e.g., H2O2, diamide) or antioxidant treatments, detected by non-reducing gel electrophoresis or mass spectrometry", "complexity": "medium", "composite": 0.29, "raw_score": 0.465, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.51, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "gel electrophoresis" ] }, { "score": 0.3, "keywords": [ "mass spectrometry" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the UBA7\u2022UBE2L6 disulfide complex formation relevant during infection or inflammatory conditions in cells?", "evidence_needed": "Detection of complex formation in cells challenged with viral or bacterial pathogens, or treated with inflammatory cytokines (e.g., interferon), compared to unstimulated controls", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "It is unclear whether different circuit output levels are due to RAS-dependent pathway activation or simply varied expression levels of RBDCRD-NarX that are nonlinearly amplified by downstream circuit components", "domain": "Synthetic Biology", "subdomain": "Gene circuit engineering", "scope": "medium", "sources": [ "elife-104320v2" ], "best_score": 0.381, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.336, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "How does circuit performance with RAS-dependent promoters compare to a constitutively dimerized NarX control that only relies on transcriptional amplification?", "evidence_needed": "Side-by-side comparison of circuit output using wild-type vs constitutively dimerized NarX, both utilizing RAS-dependent promoters", "complexity": "simple", "composite": 0.381, "raw_score": 0.61, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [ "gene" ], "control_hits": [ "control", "wild-type", "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the RasG12D-responsiveness when NarX proteins are engineered to constitutively dimerize on the membrane?", "evidence_needed": "Expression of constitutively dimerized NarX variants in cells with and without RasG12D, measuring circuit output levels", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does RasG12D alter the input-output transfer function of the NarL-RE transcriptional component independently of upstream sensing?", "evidence_needed": "Dose-response curves of NarL-RE activity in cells with and without RasG12D, varying input NarL levels systematically", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Intervention studies lack direct targeting of monocytes and comparison between TET2 and DNMT3A mutations to demonstrate TET2-CHIP specificity", "domain": "cardiovascular disease", "subdomain": "clonal hematopoiesis of indeterminate potential (CHIP)", "scope": "medium", "sources": [ "elife-109131v1" ], "best_score": 0.381, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.302, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Are monocyte-endothelial interactions specifically altered in TET2-CHIP versus DNMT3A-CHIP?", "evidence_needed": "Side-by-side comparison of adhesion assays, transendothelial migration assays, and co-culture experiments using monocytes from TET2-mutant versus DNMT3A-mutant models with endothelial cells", "complexity": "simple", "composite": 0.381, "raw_score": 0.61, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "versus", "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the observed effects in TET2-CHIP differ from those in DNMT3A-mutant CHIP, demonstrating mutation-specific mechanisms?", "evidence_needed": "Comparative intervention studies using both TET2 knockout/mutant and DNMT3A knockout/mutant cells or models, measuring monocyte-endothelial interactions, inflammatory markers, and cardiovascular phenotypes under identical experimental conditions", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the effects of directly targeting monocytes (rather than endothelial cells) in TET2-CHIP models on cardiovascular outcomes?", "evidence_needed": "Intervention studies using monocyte-specific targeting approaches (e.g., monocyte-specific drug delivery, genetic manipulation, or depletion strategies) in TET2-CHIP animal models or ex vivo systems, measuring cardiovascular disease markers and monocyte-endothelial interactions", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The study uses in vitro bone marrow-derived macrophages rather than testing macrophages isolated from established animal models of APOL1-associated kidney disease in vivo, which may not accurately reflect physiological conditions", "domain": "Immunology", "subdomain": "Macrophage biology in kidney disease", "scope": "medium", "sources": [ "elife-107841v1" ], "best_score": 0.377, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.259, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Are the macrophage responses observed in vitro exaggerated compared to physiological conditions in APOL1-associated kidney disease?", "evidence_needed": "Direct comparison of inflammatory markers, polyamine pathway activity, and lipid accumulation between in vitro BMDMs and kidney macrophages from APOL1 disease models under similar stimulation conditions", "complexity": "medium", "composite": 0.377, "raw_score": 0.603, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do macrophages isolated from kidneys of G1 and G2 APOL1 mouse models show similar inflammation and lipid accumulation patterns as bone marrow-derived macrophages in vitro?", "evidence_needed": "Isolation and characterization of kidney-resident macrophages from G1 and G2 APOL1 mouse models, comparing their inflammatory markers and lipid content to in vitro BMDMs", "complexity": "medium", "composite": 0.339, 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variants modulate macrophage polyamine pathway and lipid metabolism in vivo in kidney disease models?", "evidence_needed": "In vivo assessment of macrophage polyamine pathway activity and lipid accumulation in established APOL1-associated kidney disease animal models, potentially using disease-inducing stimuli", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, 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0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the TPR-undocked open state of kinesin-1 capable of processive movement along microtubules?", "evidence_needed": "Single-molecule motility assays measuring processivity (run length, velocity) of kinesin-1 in the open conformation, potentially using constructs that stabilize the TPR-undocked state or comparing wild-type vs TPR-mutant kinesin", "complexity": "medium", "composite": 0.289, "raw_score": 0.463, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy", "fluorescence microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" 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"tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "cells", "enzyme", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "cells", "enzyme", "metabolite" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the key transporters and metabolic enzymes involved in D-serine and L-serine metabolism expressed at comparable levels in N2a cells versus primary neurons?", "evidence_needed": "Comparative 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[ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Metabolomics methods and datasets are insufficiently described when the manuscript transitions to metabolite-respiration correlations", "domain": "metabolomics", "subdomain": "integrative multi-omics analysis", "scope": "narrow", "sources": [ "elife-108681v1" ], "best_score": 0.369, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.296, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What statistical approaches were used to correlate metabolite levels with respirometry measurements?", "evidence_needed": "Clear description of correlation 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"penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the complete methodological details for metabolite extraction, detection, identification, and quantification used in this study?", "evidence_needed": "Detailed methods section describing sample preparation, analytical platform (e.g., LC-MS, GC-MS), metabolite coverage, normalization procedures, and quality control measures", "complexity": "simple", "composite": 0.265, "raw_score": 0.53, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "metabolite", "sample" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "metabolite", "sample" ], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the full metabolomics dataset including metabolite identities, abundances, time points, and statistical parameters?", "evidence_needed": "Supplementary data tables or repository deposition containing complete metabolomics results with proper annotation and metadata", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "metabolite" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Lack of comparison between mutation profiles from Kenyan cohort and existing large breast cancer genomic datasets from diverse populations", "domain": "Cancer genomics", "subdomain": "Breast cancer comparative genomics", "scope": "medium", "sources": [ "elife-108076v1" ], "best_score": 0.369, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.267, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "How do ZNF gene mutation frequencies in the Kenyan cohort compare to TCGA and METABRIC datasets?", "evidence_needed": "Comparative analysis of mutation frequencies, types, and locations in ZNF217, ZNF703, and ZNF750 between Kenyan samples and TCGA/METABRIC cohorts with statistical testing", "complexity": "simple", "composite": 0.369, "raw_score": 0.59, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do the mutation profiles compare to other African populations, specifically the Nigerian breast cancer cohort?", "evidence_needed": "Side-by-side comparison of mutation spectra between Kenyan and Nigerian breast cancer patients, including population-specific variant analysis", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are there population-specific mutations in ZNF genes that distinguish African cohorts from European/Asian populations?", "evidence_needed": "Population stratification analysis identifying ancestry-specific variants and their frequencies across different ethnic groups", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism explaining why the affinity for the product (m6A) is higher than the substrate is not understood, and the implications of this product inhibition on the enzymatic mechanism need to be explored.", "domain": "biochemistry", "subdomain": "enzyme kinetics and mechanism", "scope": "medium", "sources": [ "elife-104909v1" ], "best_score": 0.341, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.227, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the structural basis for higher product affinity compared to substrate affinity in METTL3-METTL14?", "evidence_needed": "Comparative structural analysis (e.g., crystallography or cryo-EM) of enzyme-substrate vs enzyme-product complexes, combined with binding affinity measurements (e.g., ITC, SPR) for substrate vs product", "complexity": "medium", "composite": 0.341, "raw_score": 0.455, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.16, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "spr", "itc" ] }, { "score": 0.1, "keywords": [ "cryo-em", "crystallography" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "enzyme" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the thermodynamic and kinetic parameters that distinguish substrate vs product binding?", "evidence_needed": "Detailed kinetic analysis including kon/koff rates, equilibrium dissociation constants, and thermodynamic profiling (ITC) of substrate and product binding", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "itc" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does product inhibition play a regulatory role in m6A methylation in cellular contexts?", "evidence_needed": "Cellular assays measuring methylation rates at varying product concentrations, kinetic modeling of product inhibition effects, and potentially in vivo methylation dynamics studies", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The generalizability of findings across different cell lines has not been established", "domain": "cancer biology", "subdomain": "EGFR/RAS/RAF pathway therapy resistance", "scope": "medium", "sources": [ "elife-105719v1" ], "best_score": 0.339, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.288, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do other EGFR-mutant or RAS/RAF-mutant cancer cell lines show similar YAP relocalization upon Afatinib treatment?", "evidence_needed": "In vitro treatment of multiple EGFR/RAS/RAF-mutant cell lines with Afatinib followed by immunofluorescence or subcellular fractionation to assess YAP localization", "complexity": "medium", "composite": 0.339, "raw_score": 0.542, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do other targeted therapies against EGFR/RAS/RAF pathway components induce similar YAP-mediated resistance mechanisms?", "evidence_needed": "Treatment studies with alternative EGFR inhibitors (erlotinib, osimertinib) or MEK/RAF inhibitors across cell line panels with YAP localization and activity measurements", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the relationship between spatial organization and YAP-mediated resistance conserved across different cancer types with EGFR/RAS/RAF pathway dependencies?", "evidence_needed": "Testing of cell lines from different tissue origins (lung, colorectal, pancreatic, etc.) for YAP-dependent resistance patterns and spatial organization effects", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "It is unclear whether all discarded lariat intermediates are exported to the cytoplasm via the mRNA export pathway, or whether some may be targeted for nuclear retention and/or decay instead", "domain": "RNA biology", "subdomain": "splicing and RNA export", "scope": "medium", "sources": [ "elife-94766v1" ], "best_score": 0.339, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.274, "avg_confidence": 0.188, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "Do different types of discarded lariat intermediates show different subcellular localization patterns (cytoplasmic export vs nuclear retention)?", "evidence_needed": "Systematic analysis of multiple endogenous lariat intermediate species using RNA FISH or fractionation followed by RT-qPCR to determine their subcellular distribution", "complexity": "medium", "composite": 0.339, "raw_score": 0.542, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are some discarded lariat intermediates preferentially degraded in the nucleus rather than exported?", "evidence_needed": "Half-life measurements and decay rate analysis of different lariat intermediate species in nuclear vs cytoplasmic fractions, potentially using metabolic labeling approaches or transcriptional shutoff experiments", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do nuclear retention pathways exist that specifically recognize and sequester certain lariat intermediates?", "evidence_needed": "Identification of RNA-binding proteins that associate with retained but not exported lariat intermediates through RNA immunoprecipitation or crosslinking approaches, followed by functional characterization of candidate retention factors", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What sequence or structural features determine whether a lariat intermediate is exported or retained in the nucleus?", "evidence_needed": "Comparative analysis of sequence/structural motifs in exported vs retained lariat intermediates, combined with mutational analysis to test sufficiency of identified features", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Monocyte differentiation experiments use an artificial cell line system (THP-1 with PMA) that does not represent physiological monocyte biology, and conclusions cannot be generalized without validation in primary cells", "domain": "immunology", "subdomain": "monocyte differentiation", "scope": "medium", "sources": [ "elife-87615v2" ], "best_score": 0.339, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.226, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does MOTS-c affect differentiation of primary mouse monocytes in vitro?", "evidence_needed": "Isolation of monocytes from mouse bone marrow or spleen, M-CSF or GM-CSF driven differentiation with or without MOTS-c, assessment of macrophage markers (F4/80, CD11b, CD206)", "complexity": "medium", "composite": 0.339, "raw_score": 0.542, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does MOTS-c affect differentiation of primary human monocytes isolated from peripheral blood?", "evidence_needed": "Isolation of CD14+ monocytes from human blood, treatment with MOTS-c, and assessment of differentiation into macrophages using cell surface markers (CD68, CD163, HLA-DR, etc.)", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.5, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "bsl2_organisms", "keywords": [ "primary human", "human blood" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can MOTS-c effects on monocyte differentiation be validated in vivo?", "evidence_needed": "In vivo monocyte trafficking and differentiation studies in mice treated with MOTS-c, using inflammation models or monocyte fate mapping", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice", "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can interactive and holistic cell models at multiple scales be created by integrating multiple sources of experimental data to map locations, coordination, quantities, and dynamics of proteins and metabolites at organelle and single-cell levels?", "domain": "systems biology", "subdomain": "cell modeling and spatial organization", "scope": "broad", "sources": [ "doe-genomes-structure-function-2023" ], "best_score": 0.336, "best_tier": "medium", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.276, "avg_confidence": 0.375, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 2, "sub_question_scores": [ { "question": "How are multi-enzyme complexes like fatty acid synthase spatially organized, trafficked, and regulated within cells?", "evidence_needed": "Cryo-electron tomography, fluorescence microscopy, and proximity labeling experiments in model organisms, combined with quantitative proteomics to measure stoichiometry and interactions", "complexity": "complex", "composite": 0.336, "raw_score": 0.448, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure" ], "system_hits": [ "cell", "cells", "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy", "fluorescence microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure" ], "system_hits": [ "cell", "cells", "enzyme" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the pathways and dynamics of metabolite and product transport within engineered biosynthetic pathways?", "evidence_needed": "Biosensor-based live-cell imaging combined with metabolic flux analysis and organelle-targeted mass spectrometry", "complexity": "complex", "composite": 0.306, "raw_score": 0.408, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "mass spectrometry" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "cell", "metabolite" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the molecular machinery involved in lipid body formation, maturation, and maintenance, and how do these processes determine organelle size, number, and location?", "evidence_needed": "Time-resolved imaging of lipid body dynamics combined with genetic screens, lipidomics, and proteomics of isolated lipid bodies in engineered strains", "complexity": "complex", "composite": 0.23, "raw_score": 0.367, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" 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"complex", "composite": 0.184, "raw_score": 0.367, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.3, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant", "genome" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What is the value proposition for farmers to adopt biochar amendments, given that higher yields are not universal across all soils and climates?", "domain": "agricultural economics", "subdomain": "biochar adoption and soil amendment economics", "scope": "medium", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.327, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.244, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Under what soil and climate conditions does biochar application result in economically significant yield increases?", "evidence_needed": "Meta-analysis of field trials correlating biochar application rates with yield responses across soil types and climate zones, with economic analysis of 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"computational_only_hits": [] } } }, { "question": "What biochar quality parameters are most predictive of agronomic and economic benefits?", "evidence_needed": "Controlled experiments testing biochar from different biomass sources and pyrolysis conditions, measuring both agronomic outcomes and production costs", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the economic value of biochar's non-yield ecosystem services (water retention, nutrient cycling, carbon sequestration) to farmers?", "evidence_needed": "Economic valuation studies quantifying ecosystem services, including potential payments for ecosystem services or carbon credits, compared to biochar production and application costs", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "yield" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "yield" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the consequences and feedbacks of hydrological alterations in alluvial wetlands on carbon dynamics and regional atmospheric fluxes, particularly greenhouse gas emissions?", "domain": "wetland biogeochemistry", "subdomain": "carbon cycling and greenhouse gas emissions", "scope": "medium", "sources": [ 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0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "microbial", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "microbial", "soil" ], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the current greenhouse gas emission rates from hydrologically altered versus natural alluvial wetlands?", "evidence_needed": "Regional survey of greenhouse gas fluxes across wetland types and hydrological conditions; chamber-based and eddy covariance measurements; remote sensing-based upscaling", "complexity": "medium", "composite": 0.196, "raw_score": 0.393, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.55 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "survey" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, 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"measurement_hits": [], "system_hits": [ "protein", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "experiment" ], "measurement_hits": [], "system_hits": [ "protein", "dna" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does close proximity static tethering affect kinesin slipping behavior compared to optical trap geometry?", "evidence_needed": "Quantitative measurement of slipping frequency and distance in both assay geometries, possibly using high-speed imaging or fluorescence tracking", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "assay" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "assay" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the restricted rotational freedom in DNA tensiometer geometry alter the mechanochemical cycle or force generation of kinesin motors?", "evidence_needed": "Comparative analysis of stepping kinetics, ATP turnover rates, and force generation between static tether and free rotation conditions", "complexity": "complex", "composite": 0.18, "raw_score": 0.288, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.0, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "restricted", "keywords": [ "restricted" ] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Sensor kinetics have not been sufficiently characterized", "domain": "fluorescence imaging", "subdomain": "calcium sensors", "scope": "medium", "sources": [ "elife-107845v1" ], "best_score": 0.327, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.291, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "How do the kinetics of HaloCamp variants compare to existing ER and mitochondrial calcium sensors?", "evidence_needed": "Side-by-side comparison experiments measuring temporal response characteristics of HaloCamp variants versus established sensors (e.g., 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"infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the temporal resolution of the sensors - how fast can they detect calcium transients in live cells?", "evidence_needed": "Live-cell imaging with precisely controlled, rapid calcium transients (e.g., using optogenetic calcium release or pharmacological stimulation with high temporal resolution imaging) to measure rise time and decay kinetics", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the on-rates (kon) and off-rates (koff) of calcium binding for each HaloCamp variant?", "evidence_needed": "Stopped-flow fluorimetry or rapid mixing experiments measuring fluorescence response upon calcium binding and unbinding at different calcium concentrations", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Evidence is missing that neurons born in adulthood from retained neuroblasts functionally incorporate into the mushroom body circuit, which is necessary to distinguish adult neurogenesis from a tumorigenic phenotype", "domain": "developmental neurobiology", "subdomain": 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cell-type-specific markers consistent with normal mushroom body neuron identity rather than aberrant/tumor-like characteristics?", "evidence_needed": "Immunohistochemical or single-cell transcriptomic analysis of adult-born neurons compared to developmentally-born mushroom body neurons, assessing expression of cell-type markers, differentiation markers, and proliferation markers", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the adult-born neurons from retained neuroblasts functionally integrated into mushroom body circuits?", "evidence_needed": "Functional assays such as calcium imaging during sensory stimulation, electrophysiology, or behavioral assays (e.g., learning and memory tests) comparing animals with and without adult-born neurons, potentially using activity-dependent markers", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the neurons generated from retained neuroblasts in adulthood establish proper morphological connections within the mushroom body structure?", "evidence_needed": "Lineage tracing experiments with temporally controlled labeling (e.g., using temporal clones or inducible markers) to mark neurons born specifically in adulthood, combined with neuronal morphology analysis to assess axon and dendrite projections into appropriate mushroom body lobes", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The effects of hyperactivated S127A YAP on Afatinib sensitivity in cell culture are modest, raising questions about the functional significance of the mechanism", "domain": "cancer biology", "subdomain": "YAP signaling in therapy resistance", "scope": "medium", "sources": [ "elife-105719v1" ], "best_score": 0.327, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.319, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 3, "low_tier_count": 0, "sub_question_scores": [ { "question": "What is the dynamic range of YAP activation in the experimental system, and is S127A YAP achieving physiologically relevant activation levels?", "evidence_needed": "Dose-response studies of YAP activity, comparison of S127A expression levels to endogenous YAP activation under resistance conditions, and measurement of YAP target gene expression", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cell", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "cell", "gene" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does spatial organization specifically enhance YAP activity beyond what S127A mutation provides, and is this context-dependent effect measurable?", "evidence_needed": "Comparison of S127A YAP effects on drug resistance in 2D versus 3D culture or in spatially organized versus disorganized tumor contexts", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [ "activity" ], "system_hits": [ "cell" ], "control_hits": [ "versus", "comparison" ], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are there additional YAP regulatory mechanisms beyond S127 phosphorylation that contribute to resistance and could enhance the phenotype?", "evidence_needed": "Testing of constitutively active YAP variants with multiple phosphorylation sites mutated (S127A/S381A or YAP5SA), or expression of upstream YAP activators", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The specificity of sgRNA targeting to Alu elements and the extent of off-target binding needs to be validated", "domain": "genomics", "subdomain": "CRISPR targeting specificity", "scope": "medium", "sources": [ "elife-97537v1" ], "best_score": 0.327, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.278, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the extent of off-target binding by the sgAlu guide RNA?", "evidence_needed": "Genome-wide analysis of dCas9 binding sites using Cut&Run or ChIP-seq with comparison to predicted off-target sites, quantifying the signal-to-noise ratio of on-target vs off-target binding", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "genome", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "genome", "rna" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Was the H3K4me3 Cut&Run control performed in the same engineered cell line to enable proper comparison?", "evidence_needed": "Cut&Run experiments for H3K4me3 performed in both wild-type and dCas9-sgAlu engineered cells to control for cell line-specific effects", "complexity": "simple", "composite": 0.265, "raw_score": 0.53, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "control", "wild-type", "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does testing alternative guide RNAs reduce off-target binding and background signal?", "evidence_needed": "Cut&Run experiments using multiple independent sgRNA designs targeting Alu elements, with comparison of background signals and genomic distribution patterns", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Missing evidence on whether TET2 protein levels and expression correlate with age-associated gene signatures in human and mouse beta cells, and whether TET2 increases with senescence inducers", "domain": "Aging Biology", "subdomain": "Beta cell aging and senescence", "scope": "medium", "sources": [ "elife-104550v1" ], "best_score": 0.327, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.219, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does TET2 expression correlate with age-associated gene signatures in human beta cells at single-cell resolution?", "evidence_needed": "Regression analysis of single-cell RNA-seq data from human islets across age ranges, correlating TET2 expression with established aging/senescence gene signatures specifically in beta cells versus other islet cell types", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "gene", "rna" ], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do senescence-inducing stressors increase TET2 levels in beta cells?", "evidence_needed": "Treatment of beta cells with DNA damage agents (bleomycin, doxorubicin) or oxidative stress (H2O2) followed by measurement of TET2 protein and mRNA levels", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein", "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do TET2 protein levels increase with aging in mouse and human beta cells?", "evidence_needed": "Western blot or quantitative immunofluorescence analysis of TET2 protein in beta cells from young versus aged mice and human islets from donors of different ages", "complexity": "medium", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.6, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "protein" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The physiological relevance of MOTS-c effects on monocyte differentiation and immune function remains unclear and needs to be demonstrated in relevant in vivo contexts", "domain": "physiology", "subdomain": "in vivo immune function", "scope": "broad", "sources": [ "elife-87615v2" ], "best_score": 0.327, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.151, "avg_confidence": 0.188, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "Are endogenous MOTS-c levels altered during immune challenges or aging-related immune dysfunction?", "evidence_needed": "Measurement of MOTS-c expression/levels in immune cells or plasma during infection, inflammation, or in aged versus young subjects", "complexity": "medium", "composite": 0.327, "raw_score": 0.522, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cells" ], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is there a correlation between MOTS-c levels and immune function in human populations?", "evidence_needed": "Clinical studies measuring MOTS-c levels and immune parameters in healthy individuals, aged populations, or patients with immune dysfunction", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does MOTS-c affect monocyte/macrophage function during infection or inflammation in vivo?", "evidence_needed": "In vivo infection models (bacterial, viral) or inflammatory challenge models in animals with or without MOTS-c treatment, measuring immune cell recruitment, pathogen clearance, and outcomes", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does genetic deletion or overexpression of MOTS-c affect immune responses or susceptibility to infection in vivo?", "evidence_needed": "Generation and characterization of MOTS-c knockout or transgenic mice in infection models or inflammatory disease models", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice", "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Cryo-EM maps appear unprocessed without B-factor sharpening, making it unclear whether observed features like glutamine binding are genuine or artifacts", "domain": "structural biology", "subdomain": "cryo-electron microscopy", "scope": "narrow", "sources": [ "elife-108336v1" ], "best_score": 0.321, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.314, "avg_confidence": 0.25, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 0, "sub_question_scores": [ { "question": "Are the observed structural features (e.g., glutamine binding) maintained when proper B-factor sharpening is applied to the cryo-EM maps?", "evidence_needed": "Re-processing of cryo-EM data with B-factor sharpening applied, generating properly sharpened maps that can be compared to unsharpened versions to verify that structural features remain consistent", "complexity": "simple", "composite": 0.321, "raw_score": 0.514, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.22, "cost": 1.0, "reagent": 0.4, "readiness": 0.4, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] }, { "score": 0.1, "keywords": [ "cryo-em" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the density quality at side-chain resolution consistent with the claimed high resolutions (<3 \u00c5) after proper map sharpening?", "evidence_needed": "Analysis of sharpened maps to assess side-chain density quality, local resolution estimates, and model-to-map fit statistics (FSC curves, Q-scores) to validate that resolution claims match interpretable features", "complexity": "simple", "composite": 0.306, "raw_score": 0.49, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.0, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Significant knowledge gaps remain concerning the molecular-scale mechanisms that drive organismal and interkingdom interactions in soils and how these processes scale to shape ecosystem-level dynamics and responses to extreme weather", "domain": "soil ecology", "subdomain": "soil system dynamics and molecular mechanisms", "scope": "broad", "sources": [ "doe-ai-biological-research-2026" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.233, "avg_confidence": 0.125, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do interactions between bacteria, fungi, and other soil organisms affect nutrient cycling and ecosystem function?", "evidence_needed": "Multi-kingdom interaction studies; isotope tracing of nutrient flows; functional gene expression analysis; manipulation experiments", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "expression" ], "system_hits": [ "bacteria", "gene", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "isotope tracing" ], "measurement_hits": [ "expression" ], "system_hits": [ "bacteria", "gene", "soil" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do molecular-scale processes in soils scale up to affect ecosystem-level carbon and nutrient dynamics?", "evidence_needed": "Multi-scale experiments linking molecular measurements to ecosystem fluxes; spatially-resolved soil measurements; modeling integration with validation", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the molecular mechanisms underlying plant-microbe interactions in soil systems?", "evidence_needed": "Root exudate metabolomics; rhizosphere metagenomics and metatranscriptomics; imaging of root-microbe interfaces; molecular characterization of signaling molecules", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbe", "plant", "soil", "rhizosphere" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbe", "plant", "soil", "rhizosphere" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What molecular and community-level mechanisms determine soil ecosystem responses to extreme weather events (drought, flooding, heat)?", "evidence_needed": "Controlled stress experiments; field observations during extreme events; time-resolved multi-omics; physiological measurements", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The self-incompatibility mechanisms operating in major bioenergy crops (switchgrass, Miscanthus, poplar) have not been characterized, preventing development of self-fertilizing lines for breeding programs.", "domain": "plant reproduction", "subdomain": "self-incompatibility systems", "scope": "medium", "sources": [ "doe-plant-transformation-bioenergy-2024" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.316, "avg_confidence": 0.25, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 4, "low_tier_count": 0, "sub_question_scores": [ { "question": "What molecular mechanism of self-incompatibility (gametophytic, sporophytic, or other) operates in switchgrass?", "evidence_needed": "Genetic crosses and pollen tube growth assays; transcriptomic analysis of reproductive tissues; candidate gene identification through comparative genomics with species of known SI mechanisms; functional validation", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "plant", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "plant", "gene" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What molecular mechanism of self-incompatibility operates in Miscanthus?", "evidence_needed": "Genetic crosses and pollen tube growth assays; transcriptomic analysis of reproductive tissues; candidate gene identification; functional validation", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "plant", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "plant", "gene" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What molecular mechanism of self-incompatibility operates in poplar?", "evidence_needed": "Genetic crosses and pollen tube growth assays; transcriptomic analysis of reproductive tissues; candidate gene identification; functional validation", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "plant", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "plant", "gene" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can identified self-incompatibility genes be edited to produce stable self-compatible lines without fitness costs?", "evidence_needed": "Gene editing of SI loci; self-pollination assays; seed set measurements; multi-generation phenotyping for fitness effects", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "gene editing" ], "measurement_hits": [], "system_hits": [ "plant", "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "gene editing" ], "measurement_hits": [], "system_hits": [ "plant", "gene", "genome" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The correlation between MORC2's condensate-forming ability and its gene silencing function requires additional controls and validation", "domain": "Chromatin Biology", "subdomain": "Transcriptional Regulation", "scope": "medium", "sources": [ "elife-108479v1" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.284, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do mutations that specifically disrupt MORC2 condensate formation (without affecting other functions) abolish gene silencing activity?", "evidence_needed": "Loss-of-function experiments using separation-of-function mutants that prevent LLPS but maintain other MORC2 functions, followed by gene expression analysis (RNA-seq or reporter assays)", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "reporter" ], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "reporter" ], "measurement_hits": [ "activity", "expression" ], "system_hits": [ "gene", "rna" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the appropriate controls for demonstrating that condensate formation (rather than just protein localization or abundance) drives gene silencing?", "evidence_needed": "Controls including: non-condensing MORC2 variants with preserved DNA binding, localization controls, and dose-response experiments to separate condensate effects from expression levels", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression", "binding" ], "system_hits": [ "cell", "protein", "gene", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression", "binding" ], "system_hits": [ "cell", "protein", "gene", "dna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is MORC2-mediated gene silencing dependent on condensate formation in cellular contexts?", "evidence_needed": "Gene silencing assays comparing wild-type MORC2 with LLPS-deficient mutants in cells, using appropriate rescue experiments and transcriptional readouts", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "gene" ], "control_hits": [ "wild-type" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The conditions under which MDH1 and CIT1 enzymes interact and whether such interactions are dynamic in response to metabolic cues remain unclear in the native cellular context", "domain": "metabolic biochemistry", "subdomain": "metabolon dynamics and regulation", "scope": "broad", "sources": [ "elife-107953v1" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.259, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What are the complete set of metabolic and cellular conditions that trigger MDH1-CIT1 association versus dissociation in living cells?", "evidence_needed": "Systematic screening of diverse metabolic perturbations (nutrient availability, stress conditions, growth phases) combined with real-time interaction monitoring via NanoBiT or similar proximity assays", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "screening" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "screening" ], "measurement_hits": [ "growth" ], "system_hits": [ "cell", "cells" ], "control_hits": [ "versus" ], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does the MDH1-CIT1 interaction integrate with other TCA cycle enzyme complexes and the broader mitochondrial metabolon network?", "evidence_needed": "Comprehensive protein-protein interaction mapping using proximity labeling (BioID, APEX) combined with metabolic flux analysis to understand network-level organization and regulation", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "protein", "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "protein", "enzyme" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What molecular mechanisms regulate the dynamic assembly and disassembly of the MDH1-CIT1 complex in response to respiratory flux changes?", "evidence_needed": "Investigation of post-translational modifications, allosteric regulators, cofactor availability, and structural changes using proteomics, structural biology, and biochemical reconstitution experiments", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The claimed syntrophic interactions among members within co-occurrence network modules lack functional validation and remain speculative", "domain": "microbial ecology", "subdomain": "microbial interactions and syntrophy", "scope": "medium", "sources": [ "elife-108843v1" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.287, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Can the predicted syntrophic relationships be validated through co-culture experiments?", "evidence_needed": "Laboratory co-culture experiments with isolated or enriched members showing growth/metabolic activity only when grown together, or demonstration of metabolite cross-feeding", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [ "activity", "growth" ], "system_hits": [ "microbial", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [ "activity", "growth" ], "system_hits": [ "microbial", "metabolite" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the predicted syntrophic partners actually co-localize in the sediment environment?", "evidence_needed": "Fluorescence in situ hybridization (FISH), spatial metagenomics, or other microscopy-based methods to demonstrate physical proximity of predicted syntrophic partners", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the predicted metabolic exchanges actually occurring between co-occurring microbes?", "evidence_needed": "Stable isotope probing (SIP) experiments, metabolomics to detect intermediate compounds, or metatranscriptomics/metaproteomics to confirm active expression of predicted metabolic pathways", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Insufficient correlation analysis between mutations and gene expression patterns in ZNF genes", "domain": "Cancer genomics", "subdomain": "Functional genomics", "scope": "medium", "sources": [ "elife-108076v1" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.287, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do mutations in these ZNF genes affect expression of downstream target genes or related pathways?", "evidence_needed": "Pathway analysis and gene expression profiling comparing samples with and without ZNF mutations, including downstream target validation", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 1.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": true, "reasons": [], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "expression" ], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 3 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do specific mutations in ZNF217, ZNF703, or ZNF750 correlate with altered expression levels of these genes?", "evidence_needed": "Statistical correlation analysis between mutation status (type and location) and gene expression levels, including stratification by mutation functional impact", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are there mutation-expression associations that differ between Kenyan patients and other populations?", "evidence_needed": "Comparative genotype-phenotype analysis across populations to identify population-specific or universal mutation-expression relationships", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Accuracy comparison between ONT sequencing-based SV calling in this study versus HPRC assembly-based genome data for overlapping 1000 Genomes Project samples has not been performed", "domain": "genomics", "subdomain": "structural variant detection validation", "scope": "medium", "sources": [ "elife-106115v1" ], "best_score": 0.317, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.263, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Which approach provides higher sensitivity and specificity for SV detection in regions accessible to both methods?", "evidence_needed": "Validation of discordant calls using orthogonal methods (e.g., PCR, optical mapping, or short-read evidence), particularly for SVs called by one method but not the other", "complexity": "complex", "composite": 0.317, "raw_score": 0.508, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 1.0, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "pcr" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the concordance rate of SV calls between this study's ONT-based approach and HPRC assembly-based calls for the same samples?", "evidence_needed": "Direct comparison of SV calls for overlapping samples between the two datasets, including precision, recall, and F1 scores for different SV types and size classes", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What types and sizes of structural variants show discrepancies between the two approaches?", "evidence_needed": "Stratified analysis of concordance by SV type (deletion, insertion, inversion, etc.) and size range, with characterization of SVs unique to each method", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "insertion" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "insertion" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Accurately propagating functional annotations across isofunctional protein families, correcting numerous errors in functional assignments in existing databases, discovering completely novel functions that have not yet been characterized, and considering biological context during annotation since functions vary with context", "domain": "functional genomics", "subdomain": "protein function annotation", "scope": "medium", "sources": [ "doe-ai-biological-research-2026" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.259, "avg_confidence": 0.188, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "How can functional annotations be accurately propagated across isofunctional protein families without introducing errors?", "evidence_needed": "Comparative studies testing annotation transfer methods across validated protein families with known functions; experimental validation of transferred annotations", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the sources and prevalence of errors in current protein functional assignment databases, and how can they be systematically corrected?", "evidence_needed": "Large-scale experimental validation studies of database annotations; systematic comparison with gold-standard experimental data; error pattern analysis", "complexity": "complex", "composite": 0.28, "raw_score": 0.448, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does biological context (organism, tissue, environmental conditions) affect protein function, and how can this be systematically captured in annotations?", "evidence_needed": "Context-dependent functional assays; comparative studies across organisms/conditions; integration of expression data with functional data", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "organism", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "organism", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can completely novel protein functions be discovered for uncharacterized proteins?", "evidence_needed": "High-throughput biochemical assays; structural characterization; phenotypic screens; integration of multiple experimental approaches", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Understanding how a protein's sequence impacts its function at a deep mechanistic level, beyond protein folding", "domain": "protein engineering", "subdomain": "sequence-function relationships", "scope": "broad", "sources": [ "doe-ai-biological-research-2026" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.283, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "How do specific amino acid substitutions affect protein catalytic activity, substrate specificity, and stability?", "evidence_needed": "Deep mutational scanning; high-throughput enzyme kinetics; stability measurements; structure-function studies of variant libraries", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "stability" ], "system_hits": [ "protein", "enzyme" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "stability" ], "system_hits": [ "protein", "enzyme" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the epistatic interactions between mutations that determine protein function?", "evidence_needed": "Combinatorial mutagenesis studies; functional characterization of multi-mutant libraries; fitness landscape mapping", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "mutagenesis" ], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "mutagenesis" ], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do dynamic conformational changes and allostery contribute to sequence-dependent protein function?", "evidence_needed": "Time-resolved structural studies; single-molecule experiments; molecular dynamics validation; allosteric modulator screening", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "screening", "validation" ], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "screening", "validation" ], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Understanding how microbiome systems function, how they are connected to growth conditions, how they respond to perturbations, including individual organism behavior, interspecies interactions, and interactions with the environment, complicated by the fact 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"matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "microbial", "metabolite" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the metabolic capabilities and functional roles of unculturable microorganisms in microbiome systems?", "evidence_needed": "Single-cell genomics; metatranscriptomics; metaproteomics; metabolite tracking; development of novel cultivation methods", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { 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"reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbial", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbial", "soil" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which agricultural management practices (tillage, fertilization, irrigation) most strongly enhance or suppress soil methane oxidation?", "evidence_needed": "Multi-year field experiments comparing methane uptake across 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"reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What high-throughput culturing methods enable isolation of previously unculturable microbes identified through imaging or spectroscopy?", "evidence_needed": "Development and testing of novel culture conditions informed by 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[], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can imaging-guided approaches identify and target novel microbes for high-throughput genomic characterization?", "evidence_needed": "Integration of imaging modalities (fluorescence, Raman) with automated cell sorting and genomic sequencing workflows", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": 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0.4, "readiness": 0.7, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare", "stability" ], "system_hits": [ "crop", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare", "stability" ], "system_hits": [ "crop", "soil" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "At what rate do marginal, weathered soils accumulate carbon under deep-rooted perennials, and when do they approach saturation?", "evidence_needed": "Chronosequence 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"composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do land-draining activities interact with sea level rise to accelerate hydrological impacts on coastal plain ecosystems?", "evidence_needed": "Watershed modeling of drainage effects under SLR scenarios; comparative studies of drained versus undrained coastal areas; hydrological monitoring across management gradients", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The bell-shaped dose-response curve observed with PROTAC treatment lacks explanation and mechanistic understanding", "domain": "Chemical Biology", "subdomain": "PROTAC Mechanism and Optimization", "scope": "narrow", "sources": [ "elife-109052v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.263, "avg_confidence": 0.125, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "Does catalytic auto-inhibition contribute to reduced efficacy at high PROTAC concentrations?", "evidence_needed": "Comparative analysis of degradation efficiency vs catalytic inhibition at various concentrations, potentially using PARylation assays alongside degradation measurements, and comparing to non-catalytic-inhibiting PROTAC controls", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the bell-shaped curve result from the hook effect (target or E3 ligase titration) at high PROTAC concentrations?", "evidence_needed": "Dose-response experiments measuring ternary complex formation at various PROTAC concentrations using biochemical assays (e.g., AlphaLISA, TR-FRET) or cellular target engagement assays, combined with mathematical modeling of binary vs ternary complex formation", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism by which AOX (alternative oxidase) affects mitochondrial energetics and growth needs clarification - specifically whether AOX acts as a true uncoupler or merely bypasses proton-pumping complexes without dissipating the proton motive force", "domain": "mitochondrial biology", "subdomain": "bioenergetics and electron transport", "scope": "medium", "sources": [ "elife-106202v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.24, "avg_confidence": 0.062, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "Does AOX expression alter the mitochondrial membrane potential in Drosophila larvae compared to controls?", "evidence_needed": "Direct measurement of mitochondrial membrane potential (\u0394\u03a8m) using fluorescent dyes (TMRM, JC-1) or electrode-based methods in isolated mitochondria or intact larvae with and without AOX expression", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does ATP production efficiency change with AOX expression compared to chemical uncouplers?", "evidence_needed": "Comparative analysis of ATP/O ratios in isolated mitochondria from AOX-expressing larvae, control larvae, and control larvae treated with chemical uncouplers (e.g., FCCP) or proton leak inducers (e.g., UCPs)", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency", "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency", "expression" ], "system_hits": [], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does AOX activity correlate with changes in the activity or coupling efficiency of proton-pumping complexes (I, III, IV)?", "evidence_needed": "Blue native PAGE analysis, enzymatic assays of individual respiratory complexes, and measurements of their proton-pumping activity in AOX-expressing vs control mitochondria", "complexity": "complex", "composite": 0.204, "raw_score": 0.408, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency", "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency", "activity" ], "system_hits": [], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does AOX affect the proton gradient across the inner mitochondrial membrane independently of its effects on electron flow?", "evidence_needed": "Measurement of proton gradient (\u0394pH) using pH-sensitive fluorescent probes in mitochondria from AOX-expressing vs control larvae, coupled with oxygen consumption measurements to distinguish decoupling from uncoupling", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "There is an unexplained discrepancy where GIGYF2 was not identified as an interactor of MHV Nsp2/4 in human cells by AP-MS, yet its knockdown in mouse cells reduced MHV titers approximately 1000-fold, suggesting functional importance despite lack of detection", "domain": "virology", "subdomain": "virus-host interactions and functional genomics", "scope": "narrow", "sources": [ "elife-100834v2" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.244, "avg_confidence": 0.125, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 1, "sub_question_scores": [ { "question": "Does GIGYF2 physically interact with MHV Nsp2/4 during actual infection in both mouse and human cells?", "evidence_needed": "Co-immunoprecipitation or proximity labeling experiments performed during MHV infection in both mouse and human cell lines, combined with validation by orthogonal methods", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "If GIGYF2 does not directly interact with Nsp2/4, what is the mechanism by which GIGYF2 knockdown reduces MHV titers?", "evidence_needed": "Functional studies examining GIGYF2's role in MHV replication at different stages (entry, replication, assembly, release), RNA-seq or proteomics to identify affected pathways, and rescue experiments", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The conclusion that lipid extraction is rate-limiting in Ups1-mediated phosphatidic acid transfer has not been adequately distinguished from alternative rate-limiting steps (binding or membrane dissociation), particularly under the experimental conditions used (high protein concentrations and less negatively charged phospholipids).", "domain": "biochemistry", "subdomain": "lipid transfer mechanisms", "scope": "medium", "sources": [ "elife-106979v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.287, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "What is the rate-limiting step in Ups1-Mdm35 mediated lipid transfer under varying protein concentrations?", "evidence_needed": "Kinetic lipid transfer assays measuring transfer rates across a range of Ups1-Mdm35 concentrations (from low to high) to determine concentration-dependence and identify which step becomes saturated", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can binding, lipid extraction, and membrane dissociation be kinetically distinguished as separate steps in the Ups1-Mdm35 transfer cycle?", "evidence_needed": "Time-resolved biophysical measurements (e.g., stopped-flow fluorescence, surface plasmon resonance, or FRET-based assays) that can separately monitor protein-membrane binding, lipid acquisition, and membrane release events", "complexity": "complex", "composite": 0.306, "raw_score": 0.408, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.3, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.3, "keywords": [ "surface plasmon resonance" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "binding" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the charge composition of phospholipids in acceptor/donor membranes affect which step is rate-limiting in the transfer cycle?", "evidence_needed": "Comparative lipid transfer assays using membranes with varying phospholipid charge compositions (highly negatively charged vs. neutral) while measuring transfer kinetics", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [], "control_hits": [ "vs." ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The study focuses only on centromere positioning but does not address broader aspects of genome architecture such as individual chromosome territories or A/B compartments, leaving open questions about how the identified factors affect global genome organization", "domain": "cell biology", "subdomain": "nuclear organization and chromatin architecture", "scope": "medium", "sources": [ "elife-108410v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.267, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Do the identified factors that regulate centromere positioning also affect chromosome territory organization in interphase nuclei?", "evidence_needed": "Chromosome painting (FISH-based) or Hi-C analysis to visualize and quantify chromosome territory positioning and boundaries in cells where the identified factors are perturbed", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does perturbation of the identified factors affect other aspects of 3D genome organization beyond centromere clustering (e.g., TAD structure, enhancer-promoter loops)?", "evidence_needed": "High-resolution Hi-C or Micro-C experiments with analysis of TAD boundaries, loop calling, and contact frequency distributions across different genomic scales", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "perturbation" ], "measurement_hits": [], "system_hits": [ "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "perturbation" ], "measurement_hits": [], "system_hits": [ "genome" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the identified factors influence A/B compartment organization genome-wide?", "evidence_needed": "Hi-C or similar chromatin conformation capture experiments followed by compartment calling analysis in control versus perturbed conditions for the identified factors", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "genome" ], "control_hits": [ "control", "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Axonal validation of the sensors is missing or insufficient", "domain": "neuroscience", "subdomain": "neuronal calcium imaging", "scope": "narrow", "sources": [ "elife-107845v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.314, "avg_confidence": 0.25, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 0, "sub_question_scores": [ { "question": "Can HaloCamp variants effectively detect calcium transients in neuronal axons?", "evidence_needed": "Live imaging experiments of HaloCamp variants targeted to axons of cultured neurons, measuring calcium responses to action potentials or axonal stimulation, with validation of proper localization and signal-to-noise ratio", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Unexplained increase in punctae density after ribosome run-off treatments (HHT or Anisomycin) in both WT and FMR1-KO neurons, inconsistent with the resistance to run-off model", "domain": "cell biology", "subdomain": "ribosome dynamics and RNA granules", "scope": "medium", "sources": [ "elife-106692v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.251, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the biological mechanism causing punctae density to increase after translation inhibitor treatment rather than decrease as expected?", "evidence_needed": "Time-course experiments tracking punctae formation and dissolution after HHT/Anisomycin treatment, combined with live-cell imaging to determine whether increases reflect de novo granule formation, granule fusion, or altered granule stability", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the statistical analyses robust when controlling for vastly different sample sizes across experiments?", "evidence_needed": "Reanalysis of data using matched sample sizes through random sampling, bootstrap analysis, or power calculations to determine if significance calls remain consistent across different statistical approaches", "complexity": "simple", "composite": 0.265, "raw_score": 0.53, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "sample" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "sample" ], "control_hits": [ "matched" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the newly formed or persistent punctae after treatment have the same molecular composition as those present before treatment?", "evidence_needed": "Molecular profiling (proteomics, RNA-seq) of granules before and after ribosome run-off treatments to determine if the increased punctae represent the same or different types of structures", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "No functional beta cell data (e.g., glucose-stimulated insulin secretion) is shown to demonstrate that TET2 regulation of the PTEN/MOF axis affects beta cell function", "domain": "Beta Cell Physiology", "subdomain": "Insulin secretion and beta cell function", "scope": "narrow", "sources": [ "elife-104550v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.314, "avg_confidence": 0.25, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 0, "sub_question_scores": [ { "question": "Does TET2 manipulation affect glucose-stimulated insulin secretion in beta cells?", "evidence_needed": "Glucose-stimulated insulin secretion (GSIS) assays in TET2-overexpressing and TET2-knockout beta cell lines or isolated islets, measuring insulin release at low and high glucose concentrations", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism of glucocorticoid-induced \u03b11 adrenoreceptor trafficking observed in an immortalized hypothalamic cell line has not been validated in actual CRH neurons or in intact neural circuits", "domain": "neuroscience", "subdomain": "neuroendocrinology", "scope": "medium", "sources": [ "elife-102783v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.196, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does glucocorticoid treatment induce the same \u03b11 adrenoreceptor trafficking and nitrosylation-dependent mechanisms in primary CRH neurons as observed in the cell line?", "evidence_needed": "Primary culture experiments using isolated CRH neurons from hypothalamus, measuring receptor trafficking, nitrosylation, and norepinephrine responsiveness after glucocorticoid treatment", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the immortalized hypothalamic cells used in the study accurately recapitulate the molecular and functional characteristics of authentic CRH neurons?", "evidence_needed": "Comparative molecular profiling (RNA-seq or proteomics) and functional characterization comparing the cell line to primary CRH neurons or validated CRH neuron markers", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the glucocorticoid-induced desensitization of CRH neurons to norepinephrine preserved in intact hypothalamic circuits in vivo or in ex vivo slice preparations?", "evidence_needed": "Ex vivo hypothalamic slice electrophysiology or in vivo measurements of CRH neuron activity in response to norepinephrine after glucocorticoid treatment; could use calcium imaging or electrophysiological recordings", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The study does not explore CHIP mechanisms in relevant disease contexts such as adipose tissue in diabetic patients", "domain": "metabolic disease", "subdomain": "CHIP in diabetes and adipose tissue", "scope": "medium", "sources": [ "elife-109131v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.28, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 2, "low_tier_count": 1, "sub_question_scores": [ { "question": "What are the transcriptomic signatures of TET2-CHIP in adipose tissue under diabetic conditions?", "evidence_needed": "Single-cell or bulk RNA-sequencing of adipose tissue from diabetic patients or models with and without TET2-CHIP, identifying cell-type-specific and condition-specific gene expression changes", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "gene", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "gene", "rna" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do monocyte-adipocyte interactions differ in TET2-CHIP diabetes compared to diabetes alone or TET2-CHIP alone?", "evidence_needed": "Co-culture experiments or tissue analysis examining interactions between TET2-mutant monocytes/macrophages and adipocytes under diabetic conditions (high glucose, insulin resistance), measuring inflammatory markers, lipid metabolism, and cellular crosstalk", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does TET2-CHIP affect adipose tissue inflammation and function in diabetic conditions?", "evidence_needed": "Analysis of adipose tissue from diabetic TET2-CHIP models or patients, including histology, immune cell infiltration quantification, adipokine profiling, and metabolic function assays compared to non-CHIP diabetic controls", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "No experimental evidence demonstrating the impact of proteasome pathway changes on the Mettl5 circadian phenotype", "domain": "chronobiology", "subdomain": "circadian rhythm regulation and protein degradation", "scope": "medium", "sources": [ "elife-103427v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.259, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does Mettl5 mutation affect PERIOD protein degradation rates through the proteasome?", "evidence_needed": "Protein degradation assays (e.g., cycloheximide chase) measuring PERIOD stability in wildtype vs Mettl5 mutant with and without proteasome inhibition", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does genetic or pharmacological manipulation of proteasome activity rescue or modify the Mettl5 sleep/circadian phenotype?", "evidence_needed": "Sleep behavior analysis in Mettl5 mutant flies with proteasome inhibitors or proteasome component knockdown/overexpression", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Mettl5 loss alter proteasome activity or composition in clock neurons?", "evidence_needed": "Proteasome activity assays and proteasome component expression analysis in relevant neuronal tissues from wildtype vs Mettl5 mutants", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity", "expression" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "No mechanistic connection established between translation efficiency changes observed in ribosome profiling and the Mettl5 circadian phenotypes", "domain": "molecular biology", "subdomain": "translational regulation and circadian clocks", "scope": "medium", "sources": [ "elife-103427v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.254, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "Do changes in translation efficiency correlate with changes in clock protein levels in Mettl5 mutants?", "evidence_needed": "Comparative proteomics or targeted protein quantification of clock components correlated with ribosome profiling translation efficiency measurements", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are circadian clock genes enriched for the codons showing altered usage in Mettl5 mutants?", "evidence_needed": "Computational analysis of codon usage in circadian clock genes compared to genome-wide codon usage patterns; correlation with ribosome profiling data", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "genome" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does altered translation efficiency of specific circadian clock components explain the Mettl5 sleep phenotype?", "evidence_needed": "Translation rate measurements of key clock proteins (PERIOD, TIMELESS, CLOCK, etc.) in wildtype vs Mettl5 mutants; rescue experiments expressing clock genes with optimized codon usage", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency", "rate" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency", "rate" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The claim that microbial assembly is governed by sequential burial needs evidence to rule out alternative explanations such as residual bioturbation and slow porewater advection", "domain": "microbial ecology", "subdomain": "sediment microbiology", "scope": "medium", "sources": [ "elife-108843v1" ], "best_score": 0.314, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.264, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the extent and depth of bioturbation in these salt marsh sediments and does it significantly impact microbial community distribution?", "evidence_needed": "Tracer studies, sediment core imaging (e.g., X-ray CT scanning), bioturbation rate measurements, or analysis of bioturbation indicators (e.g., preserved burrow structures, sediment mixing coefficients)", "complexity": "medium", "composite": 0.314, "raw_score": 0.502, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are microbial communities at different depths temporally distinct (consistent with burial) rather than spatially variant populations of the same age?", "evidence_needed": "Radiocarbon dating of sediment organic matter correlated with microbial community composition, sediment accumulation rate measurements, or molecular clock analysis of microbial populations", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does porewater advection occur at rates that could redistribute microbial communities vertically in the sediment profile?", "evidence_needed": "Porewater velocity measurements, hydraulic conductivity tests, modeling of porewater flow rates, or geochemical tracer studies to track water movement", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Comparison between static DNA tethers and dynamic protein tethers (MAP7 or CAP-GLY domain) on kinesin behavior has not been performed", "domain": "cell biology", "subdomain": "cytoskeletal motor protein regulation", "scope": "medium", "sources": [ "elife-108837v1" ], "best_score": 0.305, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.218, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "What are the mechanistic differences in how static versus dynamic tethers transmit force to kinesin motors?", "evidence_needed": "Structural and biophysical characterization of tether flexibility, force transmission efficiency, and conformational dynamics using molecular dynamics simulations combined with experimental validation", "complexity": "complex", "composite": 0.305, "raw_score": 0.487, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.9, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "efficiency" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "efficiency" ], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do dynamic protein tethers like MAP7 affect kinesin catch-bond behavior compared to static DNA tethers?", "evidence_needed": "DNA tensiometer-style experiments using MAP7 as the tether instead of DNA, measuring detachment kinetics under various force conditions", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein", "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the CAP-GLY domain of p150glued alter kinesin force-dependent detachment kinetics compared to static tethering?", "evidence_needed": "Force spectroscopy experiments using CAP-GLY domain-mediated tethering, comparing detachment rates and catch-bond parameters with DNA tether results", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the direct and indirect effects of wildfires on methane emissions, and how do forest management practices modulate these effects?", "domain": "disturbance ecology", "subdomain": "fire effects on methane cycling", "scope": "medium", "sources": [ "doe-ai-methane-cycle-2024" ], "best_score": 0.302, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.228, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "How do post-fire changes in soil carbon pools and microbial communities affect the spatial pattern and magnitude of methane fluxes over multi-year timescales?", "evidence_needed": "Chronosequence studies or long-term monitoring of methane flux in burned areas paired with soil biogeochemistry and microbial community analysis; controlled mesocosm experiments simulating fire impacts on soil-microbe systems", "complexity": "medium", "composite": 0.302, "raw_score": 0.482, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbe", "microbial", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbe", "microbial", "soil" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term", "multi-year" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do wildfire characteristics (intensity, severity, extent) affect pyrogenic methane emissions during fire events?", "evidence_needed": "Airborne or satellite measurements of methane emissions during active fires with concurrent characterization of fire behavior; controlled burn experiments with continuous methane monitoring", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do fuel reduction treatments affect both immediate pyrogenic emissions and multi-year post-fire methane dynamics?", "evidence_needed": "Comparative field studies measuring methane emissions from treated versus untreated areas during and after fire events; experimental burns with varying fuel loads and methane monitoring", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "multi-year" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What mechanisms determine whether bioenergy crop inputs increase particulate organic matter (POM) versus mineral-associated organic matter (MAOM), and how can management practices be optimized to increase persistent MAOM storage?", "domain": "soil biogeochemistry", "subdomain": "soil organic matter fractionation and stabilization", "scope": "medium", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.302, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.232, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 2, "sub_question_scores": [ { "question": "How do soil mineralogy and texture modify the efficiency of plant input conversion to MAOM?", "evidence_needed": "Multi-site experiments across soil types measuring MAOM formation rates from standardized plant inputs, with detailed mineralogical characterization", "complexity": "medium", "composite": 0.302, "raw_score": 0.482, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.6, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [ "plant", "soil" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "multi-site" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What crop residue C:N ratios and biochemical composition optimize MAOM formation in bioenergy systems?", "evidence_needed": "Field or laboratory incubation experiments testing residues with varying C:N ratios and chemical composition, measuring organic matter fraction distribution over time", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do root structural characteristics (depth, architecture, C:N ratio) versus root exudate composition differentially contribute to POM versus MAOM formation?", "evidence_needed": "Isotope tracer studies tracking carbon from different plant tissues and exudates into soil organic matter fractions, combined with root phenotyping", "complexity": "complex", "composite": 0.184, "raw_score": 0.367, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.3, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "plant", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "plant", "soil" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Combination studies of YAP inhibitors with EGFR/RAS/RAF pathway inhibitors are missing and would strengthen the therapeutic implications", "domain": "cancer biology", "subdomain": "combination therapy for targeted therapy resistance", "scope": "medium", "sources": [ "elife-105719v1" ], "best_score": 0.302, "best_tier": "medium", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.178, "avg_confidence": 0.125, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 1, "low_tier_count": 3, "sub_question_scores": [ { "question": "Do YAP inhibitors synergize with Afatinib to overcome resistance in vitro?", "evidence_needed": "Combination drug treatment studies using verteporfin or other YAP/TEAD inhibitors with Afatinib, including dose-response matrices, synergy scoring (Bliss, Loewe), and long-term colony formation assays", "complexity": "medium", "composite": 0.302, "raw_score": 0.482, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "medium", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does combined YAP and EGFR/RAS/RAF pathway inhibition prevent or delay resistance development?", "evidence_needed": "Long-term culture experiments with sequential or concurrent treatment, monitoring for resistance emergence, and comparison of resistance rates between monotherapy and combination", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "culture" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which YAP inhibitor modality (direct YAP/TEAD inhibitors, upstream regulators like LATS kinases) is most effective in combination with pathway inhibitors?", "evidence_needed": "Comparative studies of different YAP inhibition strategies (small molecules, genetic knockdown, TEAD inhibitors) in combination with Afatinib or other pathway inhibitors", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the combination of YAP inhibitors with EGFR/RAS/RAF inhibitors effective and tolerable in vivo?", "evidence_needed": "Xenograft studies with combination treatment regimens, measuring tumor growth inhibition, survival, toxicity markers, and pharmacodynamic endpoints (YAP target genes, pathway inhibition)", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Evaluation metrics should extend beyond traditional measures of surface air temperature and precipitation to include wind, snow, soil moisture, runoff, circulation, humidity, evapotranspiration, and radiation, but many statistically downscaled data sets do not provide data to support such evaluation, and lack of sufficient training data complicates such extended evaluation.", "domain": "climate science", "subdomain": "climate model evaluation and downscaling", "scope": "broad", "sources": [ "doe-climate-data-products-2024" ], "best_score": 0.292, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.229, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What metrics effectively evaluate the representation of atmospheric circulation, humidity, and radiation fields that govern surface climate in downscaled products?", "evidence_needed": "Development and testing of circulation-based metrics, moisture budget diagnostics, and radiation-surface coupling metrics; validation studies using reanalysis products and observational campaigns", "complexity": "complex", "composite": 0.292, "raw_score": 0.468, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.8, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [ "evaluate" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [ "evaluate" ], "system_hits": [], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the minimum requirements for observational reference datasets (spatial/temporal resolution, uncertainty characterization) to enable robust evaluation of non-temperature/precipitation variables in downscaled products?", "evidence_needed": "Sensitivity studies testing how observational data characteristics affect evaluation metric reliability; inter-comparison of existing observational products for variables beyond temperature and precipitation", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can hybrid statistical-dynamical approaches provide physically consistent multi-variable outputs that enable comprehensive evaluation when pure statistical methods cannot?", "evidence_needed": "Development and testing of hybrid downscaling methods that preserve physical relationships between variables; validation against observations for variable co-variability and process representation", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The methodology and controls for within-animal comparisons in in vivo electrophysiology experiments are missing. Comparing across different animals with genetic manipulations doesn't ensure recording from the same neuronal populations, making it difficult to attribute observed differences specifically to ErbB4 loss.", "domain": "neuroscience", "subdomain": "in vivo electrophysiology", "scope": "medium", "sources": [ "elife-101237v1" ], "best_score": 0.292, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.192, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Can reversible manipulation of ErbB4 signaling reproduce the phenotypes observed in genetic knockout animals?", "evidence_needed": "Acute pharmacological or optogenetic manipulation experiments that reversibly disrupt ErbB4 signaling while recording neural activity and behavioral responses", "complexity": "complex", "composite": 0.292, "raw_score": 0.468, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.8, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the recorded neurons and electrode placements comparable between control and PV-ErbB4-/- animals?", "evidence_needed": "Histological verification of electrode placement locations, documentation of recording depths/layers, and post-hoc cell type identification for all recorded neurons across both genotypes", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the effects of acute ErbB4 inhibition on odor-evoked responses in the same population of neurons before and after drug application?", "evidence_needed": "Within-animal pharmacological manipulation experiments using ErbB4 inhibitors with tetrode recordings from identified neurons before and after drug application, measuring odor-evoked spike activity changes", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Functional validation that the hormone-treated assembloids actually achieve a receptive 'window of implantation' state is missing", "domain": "reproductive biology", "subdomain": "endometrial receptivity", "scope": "medium", "sources": [ "elife-90729v3" ], "best_score": 0.289, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.21, "avg_confidence": 0.188, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 4, "sub_question_scores": [ { "question": "Do the assembloids express validated protein-level markers of endometrial receptivity at the appropriate cellular locations?", "evidence_needed": "Immunofluorescence and Western blot analysis for established WOI markers (e.g., PAEP, LIF, integrin \u03b1v\u03b23, MUC1) with cellular localization and quantification, compared to known receptive endometrial tissue", "complexity": "medium", "composite": 0.289, "raw_score": 0.463, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.6, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can blastocysts successfully attach to and invade the hormone-treated assembloids compared to non-treated controls?", "evidence_needed": "Blastocyst attachment and invasion assays using human or mouse embryos on hormone-treated versus control assembloids, with quantification of attachment rates and invasion depth", "complexity": "complex", "composite": 0.28, "raw_score": 0.448, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 1.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "safe_organisms", "keywords": [ "in vitro" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control", "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do the assembloids exhibit functional decidualization capacity characteristic of receptive endometrium?", "evidence_needed": "Decidualization assays with cAMP/progesterone treatment, measuring secretion of decidualization markers (prolactin, IGFBP1) and morphological changes in stromal cells", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the temporal dynamics of receptivity markers in assembloids consistent with the known in vivo WOI timeline?", "evidence_needed": "Time-course analysis of WOI marker expression over the hormone treatment period, compared to synchronized human endometrial samples or established temporal profiles", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Unclear rationale for focusing only on three ZNF genes when whole-exome sequencing data is available for comprehensive analysis", "domain": "Cancer genomics", "subdomain": "Whole-exome analysis", "scope": "medium", "sources": [ "elife-108076v1" ], "best_score": 0.285, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.223, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do mutation frequencies of established breast cancer genes (e.g., TP53, PIK3CA, BRCA1/2) compare to literature in this population?", "evidence_needed": "Mutation frequency analysis of known breast cancer genes with statistical comparison to published cohorts and literature values", "complexity": "simple", "composite": 0.285, "raw_score": 0.57, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.5, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the broader landscape of driver mutations and recurrently mutated genes in this Kenyan breast cancer cohort?", "evidence_needed": "Whole-exome analysis identifying significantly mutated genes, driver mutations, and comparison with known breast cancer genes using tools like MutSig or dNdScv", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are there novel or population-specific cancer driver genes in the Kenyan cohort beyond the ZNF family?", "evidence_needed": "Unbiased genome-wide analysis for recurrently mutated genes, followed by functional prediction and comparison with cancer gene databases", "complexity": "complex", "composite": 0.184, "raw_score": 0.367, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.3, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene", "genome" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can we characterize and model spatial heterogeneity in large-scale bioreactors (10,000+ L) where gradients in pressure, oxygen, carbon dioxide, and nutrients impact microbial productivity?", "domain": "bioprocess engineering", "subdomain": "large-scale fermentation and digital twins", "scope": "medium", "sources": [ "doe-amber-ai-ml-bioenergy-2023" ], "best_score": 0.28, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.235, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do spatial gradients in process conditions affect microbial metabolism and productivity at different scales?", "evidence_needed": "Experiments comparing microbial performance in well-mixed small reactors vs. large reactors with characterized gradients, using omics and metabolic flux analysis", "complexity": "complex", "composite": 0.28, "raw_score": 0.448, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbial" ], "control_hits": [ "vs." ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What novel sensor configurations can capture local process conditions at multiple spatial locations within large bioreactors?", "evidence_needed": "Development and validation of sensor arrays measuring key parameters (O2, CO2, pH, glucose, pressure) at different heights and radial positions in pilot-scale bioreactors", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can digital twin models combining computational fluid dynamics and metabolic modeling accurately predict productivity in large-scale bioreactors?", "evidence_needed": "Validation studies comparing digital twin predictions against measured productivity data from commercial-scale bioreactors across different operating conditions", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The YAP relocalization mechanism identified in xenograft models was not validated in vivo, and the potential mechanism was not tested in animal models", "domain": "cancer biology", "subdomain": "in vivo therapy resistance mechanisms", "scope": "medium", "sources": [ "elife-105719v1" ], "best_score": 0.28, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.139, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Does the hyperactivated S127A YAP mutant alter Afatinib sensitivity in xenograft models?", "evidence_needed": "Xenograft studies comparing tumor growth and drug response between wild-type YAP and S127A YAP-expressing cells treated with Afatinib, including tumor volume measurements and survival analysis", "complexity": "complex", "composite": 0.28, "raw_score": 0.448, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cells" ], "control_hits": [ "wild-type" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does YAP knockdown or inhibition in vivo enhance Afatinib efficacy in xenograft models?", "evidence_needed": "Xenograft experiments with YAP-depleted cells or pharmacological YAP inhibition combined with Afatinib treatment, measuring tumor growth, pharmacodynamic markers, and resistance development", "complexity": "complex", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [ "cells" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can the spatial organization-dependent YAP signaling be recapitulated and manipulated in three-dimensional in vivo tumor contexts?", "evidence_needed": "In vivo imaging or histological analysis of YAP localization in tumors with different spatial organizations, potentially using orthotopic or PDX models with intravital imaging", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Validation that the same entities (neuronal RNA granules) are being analyzed across different experimental modalities", "domain": "cell biology", "subdomain": "neuronal RNA granules", "scope": "medium", "sources": [ "elife-106692v1" ], "best_score": 0.277, "best_tier": "low", 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using correlative light and electron microscopy (CLEM) or proximity-based proteomics", "complexity": "complex", "composite": 0.242, "raw_score": 0.388, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.35, "reagent": 0.4, "readiness": 0.3, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy", "fluorescence microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "protein", "rna" ], "control_hits": [ "versus", "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do granules identified by different methods respond similarly to experimental perturbations?", "evidence_needed": "Parallel tracking of granule dynamics across different detection modalities under identical perturbations (e.g., drug treatments, genetic manipulations) with quantitative comparison of responses", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Lack of reproducibility in experimental results", "domain": "cell biology", "subdomain": "cytoskeleton dynamics", "scope": "medium", "sources": [ "elife-99568v1" ], "best_score": 0.277, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.249, "avg_confidence": 0.125, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the statistical variation across biological replicates for vimentin disassembly measurements under different ionic conditions?", "evidence_needed": "Repeated experiments with multiple biological replicates showing quantification of vimentin disassembly with statistical analysis", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { 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"eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Osx and Col1\u03b11 marker co-localization may not be specific to osteocytes, potentially including periosteum cells and osteoblasts, which could confound cell type identification and interpretation", "domain": "bone biology", "subdomain": "cell type identification", "scope": "narrow", "sources": [ "elife-102453v1" ], "best_score": 0.277, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.277, "avg_confidence": 0.25, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Can osteocytes be definitively distinguished from periosteum cells and osteoblasts in the Osx/Col1\u03b11 co-staining analysis?", "evidence_needed": "Multi-marker immunofluorescence or immunohistochemistry using osteocyte-specific markers (e.g., Dmp1, Sost/sclerostin, E11/podoplanin) alongside Osx and Col1\u03b11, with spatial analysis to distinguish embedded osteocytes from surface osteoblasts and periosteal cells", "complexity": "medium", "composite": 0.277, "raw_score": 0.443, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The hypothesis that additional tetranucleotide repeats (GCTC) in the p19 region enhance packaging has not been experimentally tested", "domain": "Virology", "subdomain": "AAV vector engineering and cis-regulatory elements", "scope": "narrow", "sources": [ "elife-105040v1" ], "best_score": 0.275, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.275, "avg_confidence": 0.0, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do synthetic variants containing additional GCTC tetranucleotide repeats in the p19 region show enhanced AAV packaging activity?", "evidence_needed": "Rational design and synthesis of Rep constructs with varying numbers of GCTC repeats, followed by AAV packaging assays to measure titer", "complexity": "simple", "composite": 0.275, "raw_score": 0.55, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.4, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "measure", "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "measure", "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The base quality of ONT long-read sequencing data appears lower than expected, potentially due to pore version 9.4.1, and needs characterization and explanation", "domain": "genomics", "subdomain": "long-read sequencing quality control", "scope": "medium", "sources": [ "elife-106115v1" ], "best_score": 0.275, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.254, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How does the observed base quality compare to typical ONT pore version 9.4.1 performance benchmarks?", "evidence_needed": "Comparative analysis with published quality metrics from other studies using the same pore version, including base accuracy rates and quality score distributions", "complexity": "simple", "composite": 0.275, "raw_score": 0.55, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.4, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the actual base quality distribution of the ONT sequencing data generated in this study?", "evidence_needed": "Quantitative analysis of per-base quality scores across the dataset, 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"penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the lower-than-expected base quality impact the accuracy of structural variant calling?", "evidence_needed": "Analysis of SV calling accuracy stratified by base quality, validation of called SVs using orthogonal methods, and comparison of SV detection sensitivity at different quality thresholds", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, 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0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell", "organism" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell", "organism" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do low copy number molecules (transcription factors, signaling molecules, regulatory RNAs) affect cellular phenotypes and decision-making?", "evidence_needed": "Single-cell measurements of molecule numbers; single-molecule imaging; correlation of molecular fluctuations with phenotypic outcomes", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], 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"details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell", "microbial", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "cell", "microbial", "genome" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can metabolic models of methanogens and methanotrophs be validated and improved when isolates are not available?", "evidence_needed": "Combined AI-metabolic modeling approaches validated against metatranscriptomic and metaproteomic data from natural communities; flux balance analysis integrated with environmental metabolite measurements", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "metabolite" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How do interspecific microbial interactions and microbe-microbe dependencies control methane production and consumption rates?", "domain": "microbial ecology", "subdomain": "syntrophy and microbial interactions", "scope": "medium", "sources": [ "doe-ai-methane-cycle-2024" ], "best_score": 0.267, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.236, "avg_confidence": 0.167, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What are the mechanisms and quantitative rates of electron transfer between anaerobic methanotrophic archaea and their syntrophic bacterial partners?", "evidence_needed": "Single-cell and microcolony-scale electrochemical measurements; proteomics and metabolomics of co-cultures under varying environmental conditions; genetic perturbation studies of electron transfer pathways", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "perturbation" ], "measurement_hits": [], "system_hits": [ "cell", "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "perturbation" ], "measurement_hits": [], "system_hits": [ "cell", "microbial" ], "control_hits": [], 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"system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [ "microbial" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do non-methanogenic community members affect methanogenesis rates through substrate provision or competition?", "evidence_needed": "Synthetic community experiments with defined co-cultures measuring metabolite exchange and methane production; isotope-labeled substrate tracing in complex communities; genome-scale metabolic modeling of community interactions", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "genome", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "genome", "metabolite" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can we invert AI-derived spectral signatures (infrared, Raman, etc.) to discover the underlying chemistry, genes, and biochemical states that provide predictive power for cell states and phenotypes?", "domain": "analytical chemistry", "subdomain": "spectroscopy and machine learning", "scope": "medium", "sources": [ "doe-amber-ai-ml-bioenergy-2023" ], "best_score": 0.267, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.217, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What is the relationship between spectral features and gene expression states during fermentation?", "evidence_needed": "Time-series experiments collecting simultaneous spectroscopy and transcriptomics data during fermentation, with correlation analysis and causal modeling", "complexity": "complex", "composite": 0.267, "raw_score": 0.428, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.6, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "fermentation" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "gene", "fermentation" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can stereoisomers detected by near-infrared spectroscopy be linked to specific enzymatic activities or metabolic states?", "evidence_needed": "Controlled fermentation experiments with known stereoisomer-producing pathways, comparing spectroscopic signatures with targeted biochemical assays", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "fermentation" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "fermentation" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can machine learning models trained on paired spectroscopy-metabolomics data accurately predict metabolite concentrations from spectral signatures?", "evidence_needed": "Training datasets linking high-resolution infrared/Raman spectra to comprehensive metabolomics profiles across diverse cell states, validated with independent test sets", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "metabolite" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "metabolite" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can the functions of proteins be linked with their sequence and genomic context, structure and dynamics, metabolic pathways, and interactions with other biomolecules in the context of cellular organelles and cells?", "domain": "structural biology", "subdomain": "protein function prediction and 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"readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Top2 reduction extend lifespan in mammalian models?", "evidence_needed": "Lifespan and healthspan studies in Top2 heterozygous or conditional knockout mice; physiological and molecular aging biomarkers in mammalian models", 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} ] }, { "problem_statement": "Potential confounding effects of auxin (IAA) treatment itself on lifespan need to be ruled out, as auxin exposure timing can independently affect lifespan", "domain": "experimental methodology", "subdomain": "auxin-inducible degron system controls", "scope": "narrow", "sources": [ "elife-97829v1" ], "best_score": 0.265, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.265, "avg_confidence": 0.0, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "Does auxin treatment at different temporal windows independently affect lifespan in control animals without the degron system?", "evidence_needed": "Lifespan assays with auxin treatment at the same temporal windows used in the experimental groups, but in wild-type animals or animals lacking the 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"best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.229, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Would including Alzheimer's disease participants increase sample sizes sufficiently to alter between-group differences in age-epigenetic age correlations?", "evidence_needed": "Correlation analysis between chronological age and epigenetic age in each ancestry group including AD participants; comparison of effect sizes with and without AD participants", "complexity": "simple", "composite": 0.265, "raw_score": 0.53, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.3, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "sample" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "sample" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do correlations between chronological age and epigenetic age vary systematically with sample size when controlling for age range?", "evidence_needed": "Power analysis and resampling studies comparing correlations across different sample sizes within the same age range; comparison with matched sample sizes from original Horvath dataset", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "sample" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "sample" ], "control_hits": [ "comparison", "matched" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does age range restriction (60-90 years vs 0-100 years) affect the correlation between chronological and epigenetic age across different ancestry groups?", "evidence_needed": "Analysis of clock performance across matched age ranges in diverse populations; simulation studies examining how age range affects correlation coefficients", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "matched" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The claim that Znhit1 KO cells arrest during late pachytene (after H1t appearance but before diplotene) represents an unprecedented arrest stage for meiotic knockouts that requires stronger histological evidence for substaging", "domain": "reproductive biology", "subdomain": "spermatogenesis and meiosis", "scope": "medium", "sources": [ "elife-99713v1" ], "best_score": 0.255, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.226, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What is the progression of diplotene markers in Znhit1 KO cells to confirm arrest occurs before this stage?", "evidence_needed": "Immunofluorescence or immunohistochemistry for diplotene-specific markers (e.g., chromosome desynapsis markers, specific histone modifications) in Znhit1 KO testes", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the specific morphological and molecular markers that define late pachytene substages in spermatocytes?", "evidence_needed": "Detailed histological analysis using stage-specific markers (beyond H1t) including chromosome morphology, sex body characteristics, and additional stage-specific proteins to precisely substage the arrest point", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the Znhit1 KO arrest occur at a distinct late pachytene stage compared to known midpachytene (stage IV) arrests?", "evidence_needed": "Comparative histological analysis with known midpachytene arrest mutants using multiple temporal markers and ultrastructural analysis to distinguish between midpachytene and late pachytene arrest", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The specificity of corticosterone effects via glucocorticoid receptors has not been confirmed through pharmacological blockade experiments", "domain": "neuroscience", "subdomain": "neuroendocrinology/pharmacology", "scope": "narrow", "sources": [ "elife-102783v1" ], "best_score": 0.255, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.255, "avg_confidence": 0.0, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Can the effects of corticosterone on \u03b11 adrenoreceptor trafficking and CRH neuron desensitization be blocked by glucocorticoid receptor antagonists?", "evidence_needed": "Repeated key experiments (receptor trafficking assays, nitrosylation measurements, norepinephrine response assays) in the presence of glucocorticoid receptor antagonists (e.g., RU486/mifepristone) to demonstrate receptor-specific effects", "complexity": "simple", "composite": 0.255, "raw_score": 0.51, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.2, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can the FLUXNET-CH4 dataset and similar observational networks be expanded to better represent tropical ecosystems and reduce uncertainties in global methane flux estimates?", "domain": "ecosystem ecology", "subdomain": "observational networks and data gaps", "scope": "narrow", "sources": [ "doe-ai-methane-cycle-2024" ], "best_score": 0.253, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.234, "avg_confidence": 0.0, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "What are the priority tropical ecosystem types and geographic locations for establishing new long-term methane flux monitoring sites?", "evidence_needed": "Gap analysis comparing current FLUXNET-CH4 site distribution with global wetland extent and emission estimates; uncertainty quantification showing which regions contribute most to model uncertainty", "complexity": "simple", "composite": 0.253, "raw_score": 0.505, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.4, "tractability": 0.55 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "gap analysis", "monitoring sites", "site distribution" ], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "simple", "penalties": [ "multi_year_or_scale", "infrastructure", "non_bench_emphasis" ], "long_scale_hits": [ "long-term", "global" ], "infrastructure_hits": [ "monitoring sites", "site distribution" ], "computational_only_hits": [ "gap analysis" ] } } }, { "question": "What are the key barriers (logistical, financial, technical) to establishing long-term tropical methane flux monitoring, and how can they be overcome?", "evidence_needed": "Survey of existing tropical research infrastructure; cost-benefit analysis of different monitoring approaches; pilot studies demonstrating feasible approaches", "complexity": "medium", "composite": 0.216, "raw_score": 0.432, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "survey" ], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale", "non_bench_emphasis" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [ "survey" ] } } } ] }, { "problem_statement": "The hypothesis that lipophilicity determines bradyzoite-targeting activity is not convincingly supported because the comparison is made to inactive compounds rather than to compounds that target tachyzoites alone. The appropriate control comparison is missing.", "domain": "pharmacology", "subdomain": "drug discovery and structure-activity relationships", "scope": "narrow", "sources": [ "elife-102511v1" ], "best_score": 0.245, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.245, "avg_confidence": 0.0, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do bradyzoicidal and dual-acting compounds have statistically significant higher lipophilicity than compounds that are only active against tachyzoites?", "evidence_needed": "Statistical comparison of lipophilicity values (e.g., logP) between bradyzoite-active compounds (bradyzoicidal and dual-acting) versus tachyzoite-only compounds from the same screen", "complexity": "simple", "composite": 0.245, "raw_score": 0.49, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.1, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus", "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Incomplete experimental controls for daf-16/FOXO3a dependence experiments - missing positive controls (empty vector and isp-1 alone) when testing daf-16 mutant effects on intestinal and germline phenotypes", "domain": "aging biology", "subdomain": "mitochondrial stress signaling and FOXO-dependent longevity", "scope": "narrow", "sources": [ "elife-97767v1" ], "best_score": 0.245, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.245, "avg_confidence": 0.0, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "Do isp-1 mutations alone (without daf-16 mutation) produce the expected lifespan and healthspan effects in intestinal tissues?", "evidence_needed": "Lifespan and healthspan assays comparing wild-type, isp-1 mutant, daf-16 mutant, and isp-1;daf-16 double mutant animals with appropriate empty vector controls", "complexity": "simple", "composite": 0.245, "raw_score": 0.49, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.1, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "wild-type" ], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do isp-1 mutations alone (without daf-16 mutation) produce the expected phenotypes in germline tissues?", "evidence_needed": "Germline phenotype assays (e.g., reproductive span, germline integrity markers) comparing wild-type, isp-1 mutant, daf-16 mutant, and isp-1;daf-16 double mutant animals", "complexity": "simple", "composite": 0.245, "raw_score": 0.49, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.1, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "wild-type" ], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Unclear statistical analysis and reporting for odor discrimination behavioral tests. Ambiguity about whether reported significance comes from main effects, interactions, or post hoc tests, and whether multiple comparison corrections were applied.", "domain": "behavioral neuroscience", "subdomain": "biostatistics", "scope": "narrow", "sources": [ "elife-101237v1" ], "best_score": 0.245, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.245, "avg_confidence": 0.0, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "What do the reported F and p values represent in the odor discrimination tests - main effects, interactions, or post hoc comparisons?", "evidence_needed": "Complete reporting of ANOVA results including main effects, interactions, and explicitly stated post hoc tests with multiple comparison corrections (e.g., Bonferroni, Tukey) for tests conducted across multiple days or conditions", "complexity": "simple", "composite": 0.245, "raw_score": 0.49, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.1, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Understanding and reducing interlaboratory and interexperimental variability in biological data, and identifying which types of biological data are most susceptible to discrepancies caused by different laboratory protocols", "domain": "experimental biology", "subdomain": "data reproducibility and standardization", "scope": "medium", "sources": [ "doe-ai-biological-research-2026" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.214, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What standardized protocols, controls, and quality metrics can minimize inter-laboratory variability for key biological measurements?", "evidence_needed": "Development and testing of standard operating procedures; validation across multiple laboratories; implementation of reference materials and controls", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the major sources of technical variability in common multi-omics measurements (genomics, transcriptomics, proteomics, metabolomics) across laboratories?", "evidence_needed": "Inter-laboratory comparison studies using identical samples; systematic variation of protocol parameters; quantification of batch effects", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which biological measurement types and experimental protocols are most versus least robust to inter-laboratory variation?", "evidence_needed": "Large-scale reproducibility studies across multiple labs and platforms; statistical analysis of variance components", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Further foundational research is needed on infrastructure engineering issues like fermenters and cooling technologies, improving the engineering of non-model microorganisms, and broader work on metrology and standards which are critical for scaling.", "domain": "synthetic biology", "subdomain": "biomanufacturing infrastructure", "scope": "medium", "sources": [ "oecd-synthetic-biology-2025" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.21, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What metrology standards and measurement protocols are required for reproducible scaling of synthetic biology processes?", "evidence_needed": "Systematic analysis of measurement variability across labs and scales, development and validation of standardized protocols, inter-laboratory comparison studies", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What specific improvements in fermenter design and cooling technologies are needed to enable large-scale biomanufacturing?", "evidence_needed": "Engineering studies comparing different fermenter designs and cooling systems at pilot and industrial scale, measuring efficiency, cost, and scalability metrics", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the key barriers to engineering non-model microorganisms and how can they be overcome?", "evidence_needed": "Comparative studies of genetic engineering tools across model and non-model organisms, development and validation of new toolkits for specific non-model species", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Little research has been done on the best way to combine information from multiple methods of climate data production, especially for combining statistical and dynamical products, as well as combining different dynamical products whose domains overlap.", "domain": "climate science", "subdomain": "regional climate modeling and downscaling", "scope": "medium", "sources": [ "doe-climate-data-products-2024" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.209, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Does ensemble averaging of multiple downscaling products reduce extreme-value information compared to individual products, and how can this be avoided?", "evidence_needed": "Statistical analysis comparing extreme value distributions (e.g., return periods, tail behavior) in ensemble means versus individual products; validation against observed extreme events", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How should overlapping dynamical downscaling products from different regional climate models be combined for a given geographic region?", "evidence_needed": "Intercomparison studies evaluating different weighting schemes based on model performance metrics, domain configuration, and resolution; validation against independent observational datasets", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the optimal methods for combining statistical and dynamical downscaling products to preserve uncertainty information and extreme values?", "evidence_needed": "Comparative analysis studies testing different ensemble combination methods (e.g., simple averaging, weighted averaging, Bayesian model averaging) against observational data, evaluating performance metrics for extremes, means, and uncertainty quantification", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "High-quality observational data sets underpin any climate data product, but uncertainties around these products need to be quantified since they translate to corresponding uncertainties in future impact projections. The need for continuous improvement arises from unconstrained choices in gridding, managing data outages, changes in measurement technology, instrument relocation, and other requirements to produce long-term, high-temporal-and-spatial-resolution fields.", "domain": "climate science", "subdomain": "observational climatology and uncertainty quantification", "scope": "medium", "sources": [ "doe-climate-data-products-2024" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.186, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do different gridding methods and interpolation choices propagate uncertainty into observational climate products used for model training and evaluation?", "evidence_needed": "Ensemble-based studies creating multiple observational products using different gridding algorithms; quantitative comparison of resulting uncertainty envelopes; cross-validation against withheld stations", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do observational uncertainties in training data cascade through the downscaling process to affect uncertainties in future climate projections?", "evidence_needed": "Uncertainty propagation experiments using perturbed observational ensembles as training data; quantification of resulting spread in downscaled projections; comparison with other uncertainty sources", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the magnitude of uncertainty introduced by changes in measurement technology, station relocations, and data gaps in long-term observational records, and how does this affect downscaling and bias correction?", "evidence_needed": "Homogenization studies quantifying step changes and trends from metadata; sensitivity experiments showing how observational uncertainties affect downscaled product skill and bias correction performance", "complexity": "complex", "composite": 0.154, "raw_score": 0.308, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Missing validation that mutated 18S rRNA is present in the ribo-seq dataset and enriches/de-enriches as predicted", "domain": "molecular biology", "subdomain": "ribosome profiling and rRNA analysis", "scope": "medium", "sources": [ "elife-109257v1" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.214, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Are mutated rRNAs detectable in the ribo-seq dataset?", "evidence_needed": "Direct sequencing analysis of rRNA reads from ribo-seq data to identify and quantify mutant versus wild-type 18S rRNA sequences", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [], "control_hits": [ "wild-type", "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do mutated ribosomes enrich at predicted sites based on the hypothesis?", "evidence_needed": "Quantitative analysis showing differential enrichment of mutant rRNA-containing ribosomes at specific mRNA positions (e.g., C-rich sequences) compared to wild-type ribosomes", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "wild-type" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do mutated ribosomes de-enrich at predicted sites?", "evidence_needed": "Complementary analysis showing depletion of mutant rRNA-containing ribosomes at appropriate control regions", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Readthrough analysis needed instead of peak height analysis to properly assess translational control at stop codons", "domain": "molecular biology", "subdomain": "translation termination and ribosome profiling analysis", "scope": "medium", "sources": [ "elife-109257v1" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.236, "avg_confidence": 0.0, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "What is the stop codon readthrough efficiency in wild-type versus mutant conditions?", "evidence_needed": "Quantitative readthrough assays measuring the ratio of ribosomes continuing past stop codons versus terminating, potentially using dual-luciferase reporters or analysis of ribosome occupancy downstream of stop codons", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [], "control_hits": [ "wild-type", "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does readthrough correlate with peak height measurements at stop codons?", "evidence_needed": "Comparative analysis between ribosome peak height at stop codons and actual readthrough quantification to validate whether peak height is an appropriate proxy for termination efficiency", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "efficiency" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "It is unclear whether observed metabolic shifts generalize to sleep loss broadly or are specific to fmn and sss mutants, as sleep deprivation experiments were not performed", "domain": "chronobiology", "subdomain": "sleep and circadian metabolism", "scope": "medium", "sources": [ "elife-108681v1" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.211, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Do wild-type flies show similar metabolic shifts when subjected to acute sleep deprivation (without genetic mutation)?", "evidence_needed": "Sleep deprivation experiment (several hours) in wild-type Drosophila with metabolomics and respirometry measurements compared to non-deprived controls", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "experiment" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "experiment" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "wild-type" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the metabolic phenotypes observed in fmn and sss mutants due to sleep loss itself or to mutation-specific effects on circadian/metabolic pathways?", "evidence_needed": "Comparative study measuring metabolic shifts in sleep-deprived wild-type flies versus fmn and sss mutants under normal conditions to determine overlap in metabolic signatures", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "wild-type", "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do other sleep or circadian mutants show the same metabolic misalignment patterns as fmn and sss?", "evidence_needed": "Respirometry and metabolomics analysis of additional sleep/circadian mutant lines to test generalizability", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Conflicting results between increasing cholinergic neuron activity (this study) and inhibiting cholinergic neuron activity (Zullo et al. 2019) both extending lifespan need to be resolved - whether this is due to different neuronal subsets (acr-2 vs unc-17 expressing neurons) having opposite effects, or whether both hypo- and hyper-signaling can extend lifespan", "domain": "neuroscience", "subdomain": "neuronal signaling and longevity", "scope": "medium", "sources": [ "elife-97829v1" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.218, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Can both insufficient and excessive cholinergic signaling extend lifespan (U-shaped dose-response relationship)?", "evidence_needed": "Dose-response experiments examining lifespan across a gradient of cholinergic neuron activity levels, from severe inhibition through baseline to hyperactivation", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [ "baseline" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do acr-2-expressing neurons and unc-17-expressing neurons represent different neuronal subsets with distinct or opposite effects on longevity?", "evidence_needed": "Cell-type specific manipulation experiments comparing lifespan effects of activating vs inhibiting acr-2-expressing neurons versus unc-17-expressing neurons separately, with characterization of neuronal overlap between these populations", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does disrupting neurotransmission balance alone (regardless of direction) extend lifespan?", "evidence_needed": "Comparative lifespan studies with bidirectional manipulations (both activation and inhibition) of the same neuronal population, plus assessment of downstream neurotransmission balance markers", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Need to distinguish whether the key result is about stochastic population dynamics per se versus variance in reproductive success changing through time, and to clarify the conditions under which effective population sizes will be substantially impacted by stochastic population dynamics", "domain": "population genetics", "subdomain": "stochastic population dynamics and coalescent theory", "scope": "medium", "sources": [ "elife-99990v3" ], "best_score": 0.241, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.214, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do the predictions of the GH model compare to those of Kaj and Krone (2003) under exchangeable populations with stochastic dynamics where variance does not change with time?", "evidence_needed": "Direct mathematical comparison or simulation showing whether GH model converges to the same coalescent as Kaj and Krone (2003) under their specified conditions", "complexity": "medium", "composite": 0.241, "raw_score": 0.482, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the model predict different outcomes when variance in reproductive success changes through time compared to when it remains constant?", "evidence_needed": "Comparative modeling or mathematical analysis showing coalescent outcomes under constant versus time-varying variance in reproductive success, holding other parameters constant", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Under what specific conditions (parameter ranges, population dynamics regimes) do stochastic population dynamics substantially impact effective population size in the GH model versus standard Cannings models?", "evidence_needed": "Systematic parameter sweep analysis or analytical derivation identifying threshold conditions where deviations from standard coalescent predictions become significant", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Unknown data reproducibility and replicability are major challenges; few biological and environmental studies are replicated within the same laboratory, and fewer still across multiple laboratories", "domain": "experimental biology", "subdomain": "reproducibility and replication", "scope": "medium", "sources": [ "doe-ai-biological-research-2026" ], "best_score": 0.235, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.222, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What standards for experimental documentation, reagent tracking, and protocol specification enable reproducible biological research?", "evidence_needed": "Development and testing of detailed reporting standards; pilot studies using enhanced documentation; assessment of reproducibility improvements", "complexity": "simple", "composite": 0.235, "raw_score": 0.47, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.0, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the actual rate of reproducibility for key experimental findings in biological and environmental sciences within and across laboratories?", "evidence_needed": "Large-scale replication studies of published findings; systematic attempts to reproduce key results using original protocols", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What experimental factors (protocol details, reagent sources, environmental conditions, operator expertise) most critically affect reproducibility?", "evidence_needed": "Systematic variation studies; detailed documentation and comparison of successful vs. failed replications; identification of critical parameters", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control", "vs.", "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How do aerosol properties vary between remote ocean regions, traditional shipping lanes, and coastal sites, and what are the environmental controls on this variability?", "domain": "atmospheric science", "subdomain": "marine aerosol characterization", "scope": "medium", "sources": [ "doe-marine-aerosols-clouds-2024" ], "best_score": 0.235, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.202, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "To what extent do coastal aerosol measurements serve as valid proxies for marine aerosol properties over the open ocean?", "evidence_needed": "Paired measurements of aerosol properties at coastal sites and along ship tracks extending into open ocean, with statistical analysis of differences by air mass type and meteorological conditions", "complexity": "simple", "composite": 0.235, "raw_score": 0.47, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 1.0, "reagent": 0.4, "readiness": 0.0, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the dominant sources and environmental controls on aerosol properties in different ocean regions?", "evidence_needed": "Measurements of aerosol properties combined with back trajectory calculations, satellite observations, and meteorological data to link aerosol characteristics to sources and environmental conditions", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the background state of oceanic aerosol and its variability within, near, and outside shipping lanes?", "evidence_needed": "Long-term measurements of aerosol physical and optical properties along transects through shipping lanes and remote ocean regions, combined with trajectory analysis to identify aerosol sources", "complexity": "medium", "composite": 0.181, "raw_score": 0.362, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Inconsistent mRNA sets analyzed across different experiments may influence outcomes", "domain": "bioinformatics", "subdomain": "ribosome profiling data analysis", 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"computational_only_hits": [] } } } ] }, { "problem_statement": "Concerns about the analysis of empirical results on reproductive variance were raised but not addressed by the authors", "domain": "population genetics", "subdomain": "empirical reproductive variance analysis", "scope": "narrow", "sources": [ "elife-99990v3" ], "best_score": 0.235, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.235, "avg_confidence": 0.0, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "What are the specific methodological or interpretive issues with the empirical reproductive variance analysis that need to be resolved?", "evidence_needed": "The original reviewer comments would need to be examined to identify the specific concerns; likely involves re-analysis of empirical data, additional 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"question": "What objective identification algorithms for weather features (heat domes, atmospheric rivers, mesoscale convective systems) are most appropriate for evaluating climate model projections at regional scales?", "evidence_needed": "Intercomparison studies testing different feature detection algorithms on reanalysis and observational data; sensitivity analysis of algorithm parameters on detection statistics", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", 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0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the longevity benefits mediated through DNA damage response pathways or alternative mechanisms?", "evidence_needed": "Genetic epistasis experiments with DNA damage response mutants; quantification of DNA damage markers in Top2-reduced animals; longevity analysis in DNA repair pathway mutants with Top2 reduction", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which downstream transcriptional changes are necessary and sufficient for the longevity phenotype?", "evidence_needed": "RNA-seq time course after Top2 reduction; targeted manipulation of key downstream genes to test necessity and sufficiency for lifespan extension", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the causal relationship between Top2 reduction and the observed epigenetic landscape changes?", "evidence_needed": "Temporal profiling of epigenetic changes following Top2 reduction; rescue experiments restoring specific epigenetic marks to determine if they reverse longevity benefits", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The safety and potential trade-offs of Top2 reduction need to be assessed, particularly regarding DNA damage accumulation and cancer risk", "domain": "aging biology", "subdomain": "healthspan and disease risk", "scope": "medium", "sources": [ "elife-103979v1" ], "best_score": 0.231, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.199, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Are there threshold effects where too much Top2 reduction becomes detrimental?", "evidence_needed": "Dose-response curves with varying levels of Top2 reduction; identification of optimal Top2 activity levels for healthspan extension", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "activity" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does chronic Top2 reduction lead to accumulation of unresolved DNA topological stress or DNA damage?", "evidence_needed": "Longitudinal measurements of DNA damage markers (\u03b3H2AX, 53BP1 foci); chromosomal aberration analysis; DNA supercoiling assays in aged Top2-reduced animals", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Top2 reduction affect cancer incidence or tumor development in aging animals?", "evidence_needed": "Lifetime tumor burden analysis; histopathological examination of aged animals; cancer incidence scoring across lifespan", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The specific role of macrophages in the pathogenesis of APOL1-associated kidney diseases is not clearly articulated, making the significance of studying macrophages in this context insufficiently justified", "domain": "Nephrology", "subdomain": "APOL1-associated kidney disease pathogenesis", "scope": "medium", "sources": [ "elife-107841v1" ], "best_score": 0.231, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.193, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Are macrophages with APOL1 variants present and active in kidneys of patients or animal models with APOL1-associated kidney disease?", "evidence_needed": "Histological analysis and immunostaining of kidney tissue from APOL1 disease patients or animal models to quantify macrophage infiltration and activation status", "complexity": "medium", "composite": 0.231, "raw_score": 0.463, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "quantify" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the contribution of macrophage inflammation to kidney damage in APOL1-associated kidney disease?", "evidence_needed": "Macrophage depletion or conditional knockout studies in APOL1 kidney disease models to assess impact on disease progression and kidney injury markers", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does macrophage lipid accumulation and inflammation through the polyamine pathway directly contribute to kidney dysfunction in APOL1-associated disease?", "evidence_needed": "Interventional studies targeting the polyamine pathway or macrophage lipid metabolism in APOL1 kidney disease models, measuring kidney function outcomes", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can we predict microbial strain performance when scaling from milliliter shake flasks to bioreactors (2L and larger), given that microbes undergo genotypical and phenotypical changes that lead to fitness-related mutations and lower productivity?", "domain": "bioprocess engineering", "subdomain": "microbial fermentation scale-up", "scope": "medium", "sources": [ "doe-amber-ai-ml-bioenergy-2023" ], "best_score": 0.221, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.19, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What are the specific genotypical changes that occur in engineered microbial strains during scale-up from shake flasks to 2L bioreactors?", "evidence_needed": "Comparative genomic sequencing of microbial populations at different time points during shake flask vs. bioreactor cultivation, coupled with fitness assays to identify mutation accumulation patterns", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial" ], "control_hits": [ "vs." ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can real-time monitoring methods detect early indicators of fitness-related mutations before they impact productivity?", "evidence_needed": "Development and validation of real-time monitoring platforms (spectroscopy, imaging) coupled with reference measurements from sequencing and omics to establish predictive signatures", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What phenotypical changes correlate with productivity losses during bioreactor cultivation?", "evidence_needed": "Multi-omics profiling (transcriptomics, proteomics, metabolomics) of cultures at shake flask and bioreactor scales, with parallel productivity measurements to identify phenotypic markers of productivity decline", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How do biochar particles distribute among soil organic matter fractions (POM versus MAOM) over decadal timescales, and what controls this distribution?", "domain": "soil biogeochemistry", "subdomain": "biochar fate and persistence in soils", "scope": "narrow", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.221, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.211, "avg_confidence": 0.0, "num_sub_questions": 2, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 2, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 2, "sub_question_scores": [ { "question": "What biochar properties (particle size, surface area, ash content) predict association with MAOM versus POM fractions?", "evidence_needed": "Laboratory fractionation studies with biochars of varying properties, tracking their distribution into soil organic matter fractions", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [ "versus" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the distribution of pyrogenic carbon across POM and MAOM fractions in soils 10+ years after biochar application?", "evidence_needed": "Long-term field experiments with isotopically labeled biochar or benzene polycarboxylic acid molecular markers to trace biochar carbon into different soil fractions", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How do management practices such as prescribed fire and thinning affect regeneration of valuable hardwood species and interact with disturbance regimes to influence ecosystem recovery trajectories?", "domain": "forest ecology", "subdomain": "forest management and regeneration", "scope": "medium", "sources": [ "doe-land-atmosphere-southeast-2024" ], "best_score": 0.221, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.195, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What are the optimal prescribed fire frequencies and intensities for promoting white oak and other valuable hardwood regeneration?", "evidence_needed": "Replicated fire frequency and intensity experiments; long-term monitoring of seedling establishment and growth under different fire regimes; seed germination and seedling survival studies", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "growth" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do thinning intensity and timing interact with fire to influence hardwood regeneration success?", "evidence_needed": "Factorial experiments with thinning and fire treatments; measurements of light availability, competition, and regeneration density; long-term stand development monitoring", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do management interventions affect ecosystem recovery trajectories following natural disturbances?", "evidence_needed": "Comparative studies of managed versus unmanaged sites after disturbances; monitoring of vegetation succession, carbon cycling, and other ecosystem functions; modeling of recovery pathways", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The mechanism by which DNA damage induces increases in cellular ROS has not been adequately contextualized with respect to prior findings in organisms lacking DNA methylation", "domain": "cell biology", "subdomain": "oxidative stress and DNA damage response", "scope": "medium", "sources": [ "elife-103432v3" ], "best_score": 0.221, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.202, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Does the presence of 5-methylcytosine alter the relationship between DNA damage and ROS production compared to unmethylated DNA?", "evidence_needed": "Direct comparison of ROS production in response to equivalent DNA damage in cells with and without cytosine methylation; potentially using demethylating agents or methylation-deficient controls", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "dna" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the sources of ROS production when DNMT-expressing cells are exposed to oxidative damage agents?", "evidence_needed": "Experiments using specific inhibitors of different ROS sources (mitochondria, NADPH oxidases, etc.) or genetic knockouts to identify which cellular compartments/enzymes contribute to ROS elevation", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Is the ROS increase upon DNA damage in DNMT-expressing cells mechanistically similar to or distinct from ROS increases in organisms naturally lacking DNA methylation (like S. cerevisiae)?", "evidence_needed": "Comparative studies examining ROS production pathways, timing, and magnitude in DNMT-expressing vs non-expressing systems upon DNA damage; pharmacological or genetic inhibition of specific ROS-generating pathways", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "dna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "cells", "dna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The validity of using Alu elements as a proxy for A compartments and euchromatin dynamics needs to be established", "domain": "chromatin biology", "subdomain": "genome organization and compartmentalization", "scope": "medium", "sources": [ "elife-97537v1" ], "best_score": 0.221, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.186, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Are there specific genomic regions or chromatin states where Alu elements do not serve as accurate proxies for euchromatin?", "evidence_needed": "Genome-wide comparison of Alu element locations with Hi-C derived A/B compartment annotations, active chromatin marks, and gene density, identifying discordant regions", "complexity": "medium", "composite": 0.221, "raw_score": 0.443, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.3, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene", "genome" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene", "genome" ], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do Alu elements accurately represent A compartment chromatin dynamics across different genomic contexts?", "evidence_needed": "Comparative analysis of chromatin mobility measurements between Alu-labeled regions and other validated A compartment markers (e.g., active histone marks, RNA Pol II regions), including correlation analysis of dynamic parameters", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "genome", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "genome", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Why does H2B mobility correlate with H2B density independent of Alu density, and what does this imply about using Alu as a proxy?", "evidence_needed": "Systematic analysis comparing chromatin dynamics measurements using Alu labeling versus H2B labeling across regions with varying Alu and H2B densities, with multivariate regression to separate contributions", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can AI/ML approaches predict the performance of downstream processing (DSP) unit operations and identify optimal process pathways using chemical, rheological, and physical properties of fermentation cultures?", "domain": "bioprocess engineering", "subdomain": "downstream processing and separation", "scope": "medium", "sources": [ "doe-amber-ai-ml-bioenergy-2023" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.173, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What physical and chemical properties of fermentation broths are most predictive of performance in specific DSP unit operations (centrifugation, filtration, extraction, etc.)?", "evidence_needed": "Systematic characterization of fermentation broths (rheology, particle size distribution, chemical composition) correlated with measured DSP performance metrics across multiple unit operations", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "fermentation" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "fermentation" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can machine learning models trained on lab-scale DSP data predict optimal operating conditions and performance at pilot and commercial scales?", "evidence_needed": "Multi-scale DSP experiments with comprehensive process monitoring, using lab-scale data to train models validated against pilot and commercial-scale outcomes", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can AI/ML identify novel or unconsidered separation methods for biofuels and bioproducts by analyzing product properties and available unit operations?", "evidence_needed": "Development of knowledge graphs linking product properties to separation principles, coupled with generative AI approaches to suggest novel process configurations validated experimentally", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How can we develop data-efficient AI/ML approaches (generative models or low-N models) that can make accurate predictions from limited experimental data in synthetic biology applications?", "domain": "machine learning", "subdomain": "low-data regime learning for biology", "scope": "medium", "sources": [ "doe-amber-ai-ml-bioenergy-2023" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.192, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What minimal set of experimental measurements provides sufficient information for accurate low-N models to predict variant effects?", "evidence_needed": "Active learning experiments systematically testing different data acquisition strategies and sample sizes to identify minimum data requirements for various prediction tasks", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein", "sample" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein", "sample" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Can transfer learning from related biological systems reduce the data requirements for training models in new systems?", "evidence_needed": "Transfer learning experiments training models on data-rich source organisms/systems and fine-tuning on target organisms/systems with limited data", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What generative model architectures can effectively learn from abundant unlabeled biological sequence data to improve predictions on scarce labeled data?", "evidence_needed": "Benchmarking studies comparing generative pre-training approaches against standard supervised learning on protein/strain engineering tasks with limited labeled examples", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "strain", "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "strain", "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How much crop residue can be removed from soils while maintaining soil health across different soil types and conditions?", "domain": "soil science", "subdomain": "agricultural residue management", "scope": "medium", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.195, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What is the minimum residue retention threshold for erosion-prone soils to maintain soil health?", "evidence_needed": "Field experiments measuring soil health indicators (organic matter, structure, erosion rates) across gradients of residue removal rates on erosion-prone soils over multiple years", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do climate factors (precipitation, temperature) modify sustainable residue removal thresholds?", "evidence_needed": "Comparative studies across climate zones measuring soil health responses to residue removal, or modeling studies integrating climate and soil data", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What soil properties (texture, mineralogy, organic matter content) predict maximum sustainable residue removal rates?", "evidence_needed": "Multi-site field trials correlating soil physical and chemical properties with soil health outcomes under different residue removal scenarios", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "multi-site" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the long-term impacts (beyond 10 years) of biomass willow production on soil carbon storage and belowground biomass across different sites and cultivars?", "domain": "bioenergy crop production", "subdomain": "short-rotation woody crops and soil carbon dynamics", "scope": "medium", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.192, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What happens to soil carbon and belowground biomass when biomass willow crops are terminated and land is transitioned to other uses?", "evidence_needed": "Field studies tracking soil carbon dynamics through willow crop establishment, production, and termination phases", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does belowground biomass accumulation vary among willow cultivars and how does this affect net carbon balance?", "evidence_needed": "Multi-site, multi-cultivar trials measuring belowground biomass at multiple time points, integrated with life 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"infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does soil carbon change in biomass willow systems over complete multi-decade crop cycles (7+ harvest cycles)?", "evidence_needed": "Long-term field experiments (20+ years) measuring soil carbon stocks at multiple depths across complete willow crop lifecycles", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the target microbial taxa and functional genes that regulate persistent soil organic carbon storage in bioenergy cropping systems, and how can they be managed?", "domain": "soil microbiology", "subdomain": "microbial controls on soil carbon stabilization", "scope": "medium", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.183, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What management practices (crop selection, tillage, amendments) most effectively promote microbial communities that enhance persistent carbon storage?", "evidence_needed": "Field experiments testing management practices with integrated microbiome and soil carbon measurements, ideally across multiple sites", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "crop", "soil" ] } }, "biosafety": { "tier": "default", "keywords": 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0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbe", "plant", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbe", "plant", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which fungal and bacterial taxa are most strongly associated with formation of persistent soil organic carbon in bioenergy crop soils?", "evidence_needed": "Multi-site microbiome surveys correlating microbial community composition with soil carbon persistence metrics (e.g., MAOM content, carbon mean residence time), validated with manipulation experiments", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "crop", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", 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land use types and soil characteristics indicate net carbon loss risk when converting to bioenergy production?", "evidence_needed": "Meta-analysis of existing land use change studies combined with soil property data to develop predictive models for carbon outcomes", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can indirect land use change (leakage) from bioenergy crop establishment be quantified and minimized?", "evidence_needed": "Regional economic and agricultural modeling studies tracking land use displacement effects, validated with remote sensing and ground-truth data", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "crop" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the soil carbon trajectories for different land use transitions to bioenergy crops over multi-decadal timescales?", "evidence_needed": "Long-term studies or paired-site comparisons measuring soil carbon stocks through land use transitions, including legacy effects of previous land use", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How will soil carbon kinetics and decomposition rates change under future climate warming 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0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How should carbon accounting frameworks incorporate temperature-dependent changes in soil carbon persistence?", "evidence_needed": "Modeling studies integrating temperature sensitivity data with climate projections to estimate effective persistence of stored carbon under future scenarios", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What microbial and physical mechanisms mediate temperature sensitivity of soil carbon decomposition, and how do these vary with management?", "evidence_needed": "Mechanistic studies combining soil warming experiments with microbiome analysis, enzyme assays, and physical protection measurements", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "enzyme", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "enzyme", "soil" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the optimal management strategies for integrating biochar application with crop residue harvesting to maintain or increase soil carbon stocks in bioenergy feedstock production systems?", "domain": "agricultural management", "subdomain": "integrated biochar and residue management", "scope": "medium", "sources": [ "doe-soil-carbon-bioenergy-2023" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.187, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What biochar application rates are required to offset soil carbon losses from residue removal at different removal intensities?", "evidence_needed": "Field experiments testing factorial combinations of residue removal rates and biochar application rates, measuring soil carbon stocks over multiple years", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": 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0.372, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.55 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "techno-economic analysis" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "non_bench_emphasis" ], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [ "techno-economic analysis" ] } } }, { "question": "How do soil type and climate modify the effectiveness of biochar in compensating for residue removal?", "evidence_needed": "Multi-site field trials across soil types and climate zones with integrated residue removal and biochar application treatments", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { 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"question": "Are stem cell populations affected by Top2 reduction in ways that contribute to extended healthspan?", "evidence_needed": "Stem cell functional assays; proliferation and differentiation capacity measurements; lineage tracing in Top2-reduced animals", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does Top2 reduction have differential effects on aging across different tissues?", "evidence_needed": "Tissue-specific Top2 knockdown/knockout experiments with tissue-specific lifespan and healthspan measurements; comparative analysis of molecular changes across tissues", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Which cell types are primarily responsible for the systemic aging benefits?", "evidence_needed": "Cell-type-specific Top2 reduction using conditional genetics; single-cell RNA-seq and single-cell ATAC-seq of aged tissues with and without Top2 reduction", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell", "rna" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The relationship between dapF and BamB mutations needs to be clarified - additive effects of two deleterious mutations do not necessarily demonstrate genetic linkage or functional pathway connection", "domain": "bacterial genetics", "subdomain": "genetic interaction analysis", "scope": "medium", "sources": [ "elife-99955v1" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.197, "avg_confidence": 0.0, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 4, "sub_question_scores": [ { "question": "Does suppression analysis support a direct functional link between dapF and BamB pathways?", "evidence_needed": "Suppressor screens to identify mutations that rescue the double mutant phenotype; testing whether overexpression of one gene can compensate for defects in the other", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are the phenotypic effects of dapF and BamB mutations on the same or independent cellular processes?", "evidence_needed": "Detailed phenotypic characterization of single and double mutants including cell morphology, 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"tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do dapF and BamB mutations show epistasis (non-additive interaction) or simple additivity when combined?", "evidence_needed": "Quantitative fitness measurements of single mutants (dapF, BamB) and double mutant (dapF/BamB) to calculate deviation from expected additive effects; epistatic miniarray profiling (E-MAP) or synthetic genetic array (SGA) analysis", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do dapF and BamB physically or functionally interact at the molecular level?", "evidence_needed": "Co-immunoprecipitation experiments, bacterial two-hybrid assays, or proximity labeling to test for protein-protein interactions; biochemical assays to test if DapF and BamB affect the same cellular process or substrate", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Inadequate controls for vimentin disassembly experiments", "domain": "cell biology", "subdomain": "cytoskeleton dynamics", 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"eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does vimentin behave under osmotic stress conditions that don't alter net charge?", "evidence_needed": "Experiments using iso-osmotic solutions with varied ionic compositions to separate osmotic from charge effects", "complexity": "medium", "composite": 0.211, "raw_score": 0.423, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Insufficient statistical power and inappropriate statistical testing for behavioral experiments. Sample size of n=3 mice per group is inadequate, and the statistical approach (paired t-test for between-group comparisons) appears incorrect.", "domain": "behavioral neuroscience", "subdomain": "experimental design and statistics", "scope": "narrow", "sources": [ "elife-101237v1" ], "best_score": 0.211, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.211, "avg_confidence": 0.0, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Is the behavioral phenotype in go/no-go discrimination robust with adequate sample size and appropriate statistical testing?", "evidence_needed": "Replication of behavioral experiments with adequately powered sample sizes (power analysis-based), using appropriate between-group statistical tests (unpaired tests for comparing genotypes), and clear 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"computational_only_hits": [] } } } ] }, { "problem_statement": "How can models resolve discrepancies between bottom-up process-based estimates and top-down atmospheric inversion estimates of wetland methane emissions?", "domain": "Earth system modeling", "subdomain": "methane budget reconciliation", "scope": "broad", "sources": [ "doe-ai-methane-cycle-2024" ], "best_score": 0.204, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.187, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How can improved representation of hot spots, hot moments, and ebullition in models reduce bottom-up versus top-down discrepancies?", "evidence_needed": "Model experiments incorporating stochastic or high-resolution representations of temporal and spatial variability; 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[], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What uncertainties in atmospheric transport, hydroxyl radical concentrations, and inversion methods contribute to top-down estimate uncertainties?", "evidence_needed": "Multi-model intercomparison of atmospheric inversions; sensitivity studies of transport model parameters; validation campaigns with intensive atmospheric sampling", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, 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"uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [ "regulatory" ], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "vs." ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What resource requirements (staff, budget, training) would enable regulatory bodies to keep pace with synthetic biology innovation?", "evidence_needed": "Modeling of future product pipelines, assessment of review capacity, benchmarking against other technology sectors, cost-benefit analysis of different resource allocation scenarios", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "policy", "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What specific knowledge gaps exist in current regulatory bodies regarding emerging synthetic biology technologies?", "evidence_needed": "Systematic assessment of regulatory agency capabilities, surveys of regulatory scientists, gap analysis between emerging technologies and current regulatory expertise", "complexity": "medium", "composite": 0.186, "raw_score": 0.372, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.55 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "gap analysis", "policy", "regulatory" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "non_bench_emphasis" ], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [ "gap analysis" ] } } } ] }, { "problem_statement": "It would be useful to consider how plausible the envisioned futures of synthetic biology are under real-world circumstances (such as neoliberal policies in capitalism) and realistically understand how they could lead to harm (such as perverse incentives and unintended consequences).", "domain": "synthetic biology", "subdomain": "technology assessment and responsible innovation", "scope": "medium", "sources": [ "oecd-synthetic-biology-2025" ], "best_score": 0.204, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.167, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What alternative (non-technological or hybrid) solutions exist for societal challenges targeted by synthetic biology, and how do they compare?", "evidence_needed": "Comparative effectiveness studies of technological vs. non-technological interventions, systems analysis of holistic solutions, multistakeholder assessment of solution pathways", "complexity": "complex", "composite": 0.204, "raw_score": 0.408, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "policy" ], "signals": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "compare" ], "system_hits": [], "control_hits": [ "vs." ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the potential unintended consequences of large-scale synthetic biology deployment in different economic and policy contexts?", "evidence_needed": "Case studies of past biotechnology transitions, scenario modeling of different deployment pathways, stakeholder impact assessments, economic modeling of market effects", "complexity": "complex", "composite": 0.149, "raw_score": 0.298, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.25 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "policy", "scenario modeling", "stakeholder" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [ "non_bench_emphasis" ], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [ "scenario modeling" ] } } }, { "question": "Under what real-world conditions do the promised benefits of synthetic biology fail to materialize or become inequitably distributed?", "evidence_needed": "Historical analysis of biotechnology deployment, modeling of access barriers, equity assessments across different socioeconomic contexts, comparative policy analysis", "complexity": "complex", "composite": 0.149, "raw_score": 0.298, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.25 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "comparative policy analysis", "policy" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [ "non_bench_emphasis" ], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [ "comparative policy analysis" ] } } } ] }, { "problem_statement": "Current synthetic biological circuits only function under controlled lab conditions and for a limited time. Improved robustness is needed to deal with environmental fluctuations and evolutionary pressures for real-world applications.", "domain": "synthetic biology", "subdomain": "genetic circuit engineering", "scope": "medium", "sources": [ "oecd-synthetic-biology-2025" ], "best_score": 0.204, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.167, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What design principles enable robust circuit function across diverse environmental contexts?", "evidence_needed": "Comparative analysis of circuit architectures under stress conditions, development of robust design frameworks, validation in multiple environmental contexts", "complexity": "complex", "composite": 0.204, "raw_score": 0.408, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the key mechanisms causing synthetic circuit failure under environmental fluctuations?", "evidence_needed": "Systematic testing of circuits under varying environmental conditions (temperature, pH, nutrients, etc.), identification of failure modes, mechanistic studies of circuit degradation", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How can synthetic circuits be designed to resist evolutionary pressures over extended time periods?", "evidence_needed": "Long-term evolution experiments with engineered circuits, identification of evolutionary escape mechanisms, development and testing of evolutionary stabilization strategies", "complexity": "complex", "composite": 0.144, "raw_score": 0.288, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "long-term" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "How to achieve statistically grid-converged LES solutions for atmospheric conditions with strongly suppressed turbulence length scales (e.g., stable stratification in free troposphere, stable boundary layers, entrainment zones)", "domain": "atmospheric physics", "subdomain": "large eddy simulation and turbulence modeling", "scope": "medium", "sources": [ "doe-les-workshop-2023" ], "best_score": 0.204, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.2, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do subfilter-scale closure models need to be modified to account for stable stratification effects in coarser-resolution LES?", "evidence_needed": "Comparison of SFS model performance against grid-converged high-resolution LES benchmarks across varying stability regimes; a priori and a posteriori testing of modified closure schemes", "complexity": "complex", "composite": 0.204, "raw_score": 0.408, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.5, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "stability" ], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the dominant 3D turbulence dynamics and energy transfer mechanisms in stably stratified atmospheric conditions at meter-scale resolution?", "evidence_needed": "Analysis of grid-converged LES data including energy spectra, structure functions, momentum and energy transfer across scales, comparison with Direct Numerical Simulation where possible", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What minimum grid resolution is required to achieve statistical grid convergence in stably stratified free troposphere surrounding convective clouds?", "evidence_needed": "Systematic LES grid refinement studies at resolutions from 10m down to ~1m, comparing turbulence statistics (TKE spectra, dissipation rates, flux profiles) across resolutions until convergence is demonstrated", "complexity": "complex", "composite": 0.194, "raw_score": 0.388, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.4, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "flux" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Progress in biodata sharing is hindered by infrastructure bottlenecks such as lack of standardization and need for curation, with broader assessment needed of existing incentives and structures especially across borders.", "domain": "synthetic biology", "subdomain": "data infrastructure and governance", "scope": "medium", "sources": [ "oecd-synthetic-biology-2025" ], "best_score": 0.201, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.177, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What incentive structures effectively promote data sharing in synthetic biology while maintaining data ownership and control?", "evidence_needed": "Comparative analysis of different data sharing models, surveys of researcher attitudes and behaviors around data sharing, case studies of successful data sharing initiatives", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "governance", "incentive structures", "policy" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the key technical barriers to biodata standardization across synthetic biology research groups?", "evidence_needed": "Survey and analysis of current data formats and standards used across labs, identification of incompatibilities, pilot studies implementing common standards", "complexity": "medium", "composite": 0.186, "raw_score": 0.372, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.55 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "survey" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "non_bench_emphasis" ], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [ "survey" ] } } }, { "question": "What frameworks enable trustworthy cross-border biodata sharing while addressing biosecurity concerns?", "evidence_needed": "Analysis of existing international data sharing agreements, development and testing of new governance frameworks, security assessments of data sharing platforms", "complexity": "complex", "composite": 0.144, "raw_score": 0.288, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "cross-border", "governance", "international law" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "cross-border" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the impacts of extreme weather events and compound disturbances on surface fluxes, land-atmosphere interactions, and biogeochemical cycles in southeastern ecosystems, including greenhouse gas emissions and boundary layer characteristics?", "domain": "disturbance ecology", "subdomain": "extreme events and ecosystem function", "scope": "broad", "sources": [ "doe-land-atmosphere-southeast-2024" ], "best_score": 0.201, "best_tier": "low", 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disturbance gradients", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do species-specific hydraulic strategies and stress responses influence ecosystem-level water and carbon fluxes following disturbance?", "evidence_needed": "Species-level hydraulic trait measurements; sap flow and gas exchange measurements across species during and after disturbances; upscaling from tree-level to stand-level fluxes", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "plant" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], 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"subdomain": "alternative stable states and regime shifts", "scope": "medium", "sources": [ "doe-land-atmosphere-southeast-2024" ], "best_score": 0.201, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.176, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Which disturbance types or combinations are most likely to trigger regime shifts in southeastern ecosystems?", "evidence_needed": "Historical analysis of ecosystem transitions; controlled experiments with single versus compound disturbances; monitoring of sites experiencing different disturbance histories", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What feedback mechanisms stabilize alternative ecosystem states and prevent recovery?", "evidence_needed": "Manipulation experiments attempting to reverse state changes; measurements of soil properties, seed banks, and microbial communities in alternative states; restoration experiments testing limiting factors", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "plant", "soil" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "microbial", "plant", "soil" ], "control_hits": 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"missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "comparison" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the typical supersaturation values in marine clouds across different oceanic regions?", "evidence_needed": "Direct measurements of in-cloud supersaturation over diverse marine regions to validate and constrain parameterizations used in climate models", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Under what meteorological conditions is supersaturation primarily controlled by updraft velocity versus CCN concentration?", "evidence_needed": "Coordinated measurements of aerosol properties (size, hygroscopicity, CCN), cloud properties (droplet number, supersaturation), and boundary layer dynamics (updraft velocity) to determine dominant controls under different conditions", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The transcriptomic analysis of MOTS-c effects in macrophages from young versus old mice shows differences that appear to be primarily age-related rather than MOTS-c-dependent, making the conclusions unclear and requiring better experimental design to separate age and treatment effects", "domain": "genomics", "subdomain": "transcriptomics and aging", "scope": "medium", "sources": [ "elife-87615v2" ], "best_score": 0.201, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.189, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What transcriptomic changes are specifically attributable to MOTS-c treatment independent of age?", "evidence_needed": "Proper statistical analysis with appropriate controls (young+vehicle, young+MOTS-c, old+vehicle, old+MOTS-c) using interaction terms to identify MOTS-c-specific effects versus age effects", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", 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"composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The relationship between genetic ancestry (as a continuous variable) and methylation clock accuracy has not been quantitatively assessed using local ancestry fractions", "domain": "epigenetics", "subdomain": "DNA methylation clock portability", "scope": "medium", "sources": [ "elife-105343v1" ], "best_score": 0.201, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.194, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Is clock accuracy linearly related to ancestry proportion or are there threshold effects?", "evidence_needed": "Non-linear modeling of clock accuracy across the full spectrum of admixture proportions; comparison of linear vs non-linear models", "complexity": "medium", "composite": 0.201, "raw_score": 0.403, "confidence": 0.0, "uncertainty_penalty": 0.5, 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prediction accuracy across individuals?", "evidence_needed": "Regression analysis of non-European local ancestry fraction against measures of prediction accuracy (e.g., absolute error, correlation between predicted and actual age) across the genetic ancestry spectrum", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do different methylation clocks show different patterns of accuracy decline across the ancestry spectrum?", "evidence_needed": "Clock-specific regression analyses comparing how prediction accuracy varies with ancestry fraction for multiple clock types", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], 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"question": "How does spatial heterogeneity of land cover affect boundary layer height and cloud formation?", "evidence_needed": "Field measurements of boundary layer dynamics across heterogeneous landscapes paired with remote sensing observations of cloud formation patterns; controlled comparison of sites with varying degrees of land cover heterogeneity", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true 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} }, { "question": "What funding structures and requirements promote responsible research practices including transparent risk disclosure and best practice sharing?", "evidence_needed": "Analysis of current funding requirements and their effects, design and testing of alternative funding models, assessment of impact on research practices", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system", "policy_or_infrastructure_or_modeling_without_bench_plan" ], "blocker_hits": [ "funding structures", "policy" ], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": 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0, "low_tier_count": 3, "sub_question_scores": [ { "question": "How do empirically-derived autoconversion parameterizations perform outside the limited regimes for which they were originally developed?", "evidence_needed": "Systematic evaluation of multiple autoconversion schemes across wide ranges of liquid water content, droplet concentration, and turbulence conditions using particle-resolved simulations or observations as reference", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { 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0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How does the decoupling index depend on surface fluxes, cloud properties, and large-scale forcing (inversion strength, advection)?", "evidence_needed": "Multi-year dataset of surface meteorology, cloud-base height from ceilometer, and large-scale meteorological conditions to enable statistical classification and analysis of decoupling under different environmental conditions", "complexity": "medium", "composite": 0.181, "raw_score": 0.362, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "multi-year" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The causal role of immune signaling pathways and associated phenotypes (e.g., monocyte fraction) in obesity or metabolic disease has not been established - alternative explanation that these are consequences rather than causes of dyslipidemia has not been ruled out", "domain": "immunometabolism", "subdomain": "immune system involvement in metabolic disease", "scope": "medium", "sources": [ "elife-108435v1" ], "best_score": 0.191, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.179, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Do changes in immune signaling pathways and monocyte fraction precede or follow the development of dyslipidemia?", "evidence_needed": "Temporal/longitudinal studies tracking immune markers and lipid profiles over time during disease development to establish temporal sequence", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do immune signaling changes occur independently of dyslipidemia or only in its presence?", "evidence_needed": "Studies examining immune phenotypes in models where dyslipidemia is prevented or reversed, or in genetic backgrounds with dissociated lipid and immune phenotypes", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does experimental manipulation of immune signaling pathways or monocyte populations causally affect obesity or metabolic disease outcomes?", "evidence_needed": "Interventional studies using genetic knockouts, antibody depletion, or pharmacological inhibition of immune pathways to test whether modulating these pathways affects disease susceptibility", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The quantitative extent to which meQTLs influence methylation clock portability across ancestries is unclear, particularly given variation in meQTL ascertainment biases and the wide variation in meQTL-affected sites across different clocks", "domain": "epigenetics", "subdomain": "methylation quantitative trait loci (meQTLs)", "scope": "medium", "sources": [ "elife-105343v1" ], "best_score": 0.191, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.172, "avg_confidence": 0.0, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 4, "sub_question_scores": [ { "question": "How does the number and proportion of meQTL-affected CpG sites in a clock correlate with that clock's portability across ancestries?", "evidence_needed": "Quantitative analysis correlating the number/proportion of meQTL-affected sites per clock with ancestry-specific prediction errors or correlation coefficients", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do ancestry-specific allele frequency differences at meQTL sites quantitatively predict clock inaccuracy?", "evidence_needed": "Modeling study relating allele frequency differences at meQTL loci between ancestries to observed differences in clock performance metrics", "complexity": "medium", "composite": 0.191, "raw_score": 0.383, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does the effect of meQTLs on clock portability vary depending on which ancestry populations the meQTLs were originally ascertained in?", "evidence_needed": "Comparative analysis of clock accuracy using only meQTLs ascertained in European vs African vs multi-ancestry populations; assessment of ascertainment bias effects", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the individual contribution of each meQTL-affected site to clock inaccuracy in different ancestry groups?", "evidence_needed": "Site-by-site analysis removing individual meQTL-affected CpGs and measuring impact on clock performance; effect size quantification for each meQTL across ancestries", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Disconnect between ex vivo and in vivo findings regarding mitral cell firing rates. Slice recordings show elevated spontaneous activity in knockout animals, but this is not observed under physiological in vivo conditions, raising questions about the relevance of slice findings and the in vivo activity patterns of PV interneurons.", "domain": "neuroscience", "subdomain": "systems and cellular neuroscience", "scope": "medium", "sources": [ "elife-101237v1" ], "best_score": 0.174, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.104, "avg_confidence": 0.25, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "When during the odor processing cycle is PV-mediated inhibition recruited, and does this timing differ between genotypes?", "evidence_needed": "Simultaneous recordings of PV interneurons and mitral/tufted cells during odor presentation, analyzing temporal dynamics of inhibition recruitment and its relationship to sensory-evoked responses", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cells" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Why do ex vivo changes in mitral cell excitability not translate to in vivo baseline firing rate differences?", "evidence_needed": "Comparative analysis of network state in slice versus in vivo conditions, measurement of factors that differ between preparations (neuromodulator levels, circuit integrity, synaptic drive), and assessment of compensatory mechanisms that might normalize firing rates in vivo", "complexity": "complex", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "rate" ], "system_hits": [ "cell" ], "control_hits": [ "baseline", "versus" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the spontaneous firing rates of PV interneurons in vivo during baseline and odor-evoked conditions in control versus PV-ErbB4-/- animals?", "evidence_needed": "In vivo recordings specifically targeting PV interneurons (using optogenetic tagging or other identification methods) during awake behavioral tasks, quantifying baseline and task-related firing rates in both genotypes", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "control", "baseline", "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Osx knockout mice show decreased osteocyte dendritic network, but it's unclear whether phenotypes result from dendritic network defects or reduced osteocyte numbers, and how this affects overall bone health parameters", "domain": "bone biology", "subdomain": "osteocyte cell biology", "scope": "medium", "sources": [ "elife-102453v1" ], "best_score": 0.174, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.108, "avg_confidence": 0.333, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Can the bone health phenotypes be attributed specifically to dendritic network defects independent of osteocyte number changes?", "evidence_needed": "Comparative analysis correlating dendritic network parameters with bone health metrics while controlling for osteocyte density, or rescue experiments restoring dendritic network function", "complexity": "complex", "composite": 0.174, "raw_score": 0.347, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are osteocyte numbers reduced in Osx knockout mice, which could confound interpretation of dendritic network phenotypes?", "evidence_needed": "Quantitative histomorphometry to count osteocyte numbers per bone area in knockout vs control mice, potentially with osteocyte-specific markers", "complexity": "simple", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.5, "cost": 1.0, "reagent": 0.4, "readiness": 0.7, "tractability": 1.0 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the bone structural and mechanical parameters (bone thickness, bone strength, other metrics) in Osx knockout mice compared to controls?", "evidence_needed": "Bone morphometry analysis (micro-CT for bone thickness, volume, density) and biomechanical testing (three-point bending or compression testing for bone strength) on Osx knockout vs control mice", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.5, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [], "control_hits": [ "control" ], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The finding that external entrainment is essential for maintaining energy homeostasis under constant darkness (despite intact clock) lacks cross-species validation and may not translate to mammals", "domain": "chronobiology", "subdomain": "circadian entrainment and energy homeostasis", "scope": "medium", "sources": [ "elife-108681v1" ], "best_score": 0.164, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.127, "avg_confidence": 0.083, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Is the requirement for external entrainment to maintain metabolic homeostasis conserved across species or specific to Drosophila?", "evidence_needed": "Comparative study across multiple species (insects, rodents, potentially primates) measuring metabolic alignment under entrained versus free-running conditions", "complexity": "complex", "composite": 0.164, "raw_score": 0.328, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.1, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "versus" ], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What are the mechanistic differences between Drosophila and mammalian circadian systems that might explain species-specific responses to loss of external entrainment?", "evidence_needed": "Molecular and physiological characterization of circadian-metabolic coupling mechanisms in both Drosophila and mammalian systems, with focus on differences in peripheral clock robustness and entrainment pathways", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do mammalian models show metabolic misalignment under constant darkness despite having intact circadian clocks?", "evidence_needed": "Metabolomics and energy expenditure measurements in wild-type mammalian models (e.g., mice) under light-dark cycles versus constant darkness conditions", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.1, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [ "wild-type", "versus" ], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Conservation of the m6A-mediated bridge between 18S rRNA and RPL24 has not been examined in Drosophila", "domain": "RNA biology", "subdomain": "ribosomal RNA modification and ribosome structure", "scope": "narrow", "sources": [ "elife-103427v1" ], "best_score": 0.155, "best_tier": "low", "best_confidence": 0.25, "decision_bucket": "needs_repositioning", "avg_score": 0.155, "avg_confidence": 0.25, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Is the m6A-RPL24 interaction structurally conserved in Drosophila ribosomes?", "evidence_needed": "Structural analysis (cryo-EM or homology modeling) of Drosophila ribosome examining the m6A site and RPL24 interface; biochemical interaction studies (crosslinking, co-immunoprecipitation) between m6A-modified 18S rRNA and Drosophila RPL24", "complexity": "complex", "composite": 0.155, "raw_score": 0.248, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.1, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [ { "score": 0.1, "keywords": [ "cryo-em" ] } ], "defaulted": false }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "What are the roles of CCN, ice nucleating particles, and secondary ice processes in determining cold cloud properties over marine regions?", "domain": "atmospheric science", "subdomain": "ice nucleation and cold cloud microphysics", "scope": "broad", "sources": [ "doe-marine-aerosols-clouds-2024" ], "best_score": 0.154, "best_tier": "low", "best_confidence": 0.0, "decision_bucket": "needs_repositioning", "avg_score": 0.154, "avg_confidence": 0.0, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "What are the concentrations and sources of ice nucleating particles in different marine regions, particularly in cold cloud environments?", "evidence_needed": "INP measurements across different marine environments (e.g., Arctic shipping lanes, Southern Ocean) combined with aerosol composition analysis and back-trajectory analysis to identify sources", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "How do CCN properties affect cold cloud development and glaciation in marine environments?", "evidence_needed": "Coordinated measurements of aerosol properties, cloud phase, and cloud microphysical properties in cold marine clouds, combined with modeling studies", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "What is the frequency and impact of secondary ice production processes in marine cold clouds?", "evidence_needed": "In-situ cloud microphysical measurements in cold marine clouds to detect signatures of secondary ice processes, combined with ice particle concentration and morphology measurements", "complexity": "complex", "composite": 0.154, "raw_score": 0.307, "confidence": 0.0, "uncertainty_penalty": 0.5, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.4, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "default", "keywords": [] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "A direct link between exercise and CILP-mediated inhibition of ferroptosis via Keap1-Nrf2 pathway is not established because ferroptosis studies were performed in vitro while exercise effects were demonstrated in an OA animal model", "domain": "Exercise physiology and cartilage biology", "subdomain": "Osteoarthritis mechanobiology and ferroptosis", "scope": "medium", "sources": [ "elife-103178v1" ], "best_score": 0.088, "best_tier": "low", "best_confidence": 0.75, "decision_bucket": "needs_repositioning", "avg_score": 0.072, "avg_confidence": 0.438, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 4, "sub_question_scores": [ { "question": "Is the Keap1-Nrf2 pathway activated by exercise in vivo in the OA model and does this correlate with CILP levels?", "evidence_needed": "In vivo measurement of Nrf2 nuclear translocation, Keap1-Nrf2 interaction, and downstream Nrf2 target genes in cartilage from exercised vs non-exercised animals, with correlation analysis to CILP expression", "complexity": "medium", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 1.0, "readiness": 0.6, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "standard_catalog", "keywords": [ "cell signaling" ] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does exercise directly modulate CILP expression in chondrocytes in vivo in the OA animal model?", "evidence_needed": "In vivo experiments measuring CILP expression levels in cartilage tissue from exercised vs non-exercised OA animals using immunohistochemistry, Western blot, or qPCR on isolated chondrocytes", "complexity": "medium", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.72, "cost": 0.65, "reagent": 0.4, "readiness": 0.4, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "animal model", "in vivo" ] }, "technique": { "matched": [ { "score": 1.0, "keywords": [ "qpcr" ] }, { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does exercise reduce ferroptosis markers in chondrocytes in vivo in the OA model?", "evidence_needed": "In vivo assessment of ferroptosis markers (lipid peroxidation, iron accumulation, GPX4 levels, etc.) in cartilage tissue from exercised vs non-exercised OA animals", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.2, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Does CILP manipulation (overexpression or knockdown) in the OA animal model during exercise alter ferroptosis and the Keap1-Nrf2 pathway in vivo?", "evidence_needed": "In vivo gain-of-function and loss-of-function studies using viral vectors or genetic models to manipulate CILP in exercised OA animals, measuring ferroptosis markers and Keap1-Nrf2 signaling", "complexity": "complex", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.2, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "animal model", "in vivo" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [ "gene" ], "control_hits": [], "signal_count": 1 }, "tractability": { "complexity": "complex", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Osteocyte morphology varies between metaphysis and mid-shaft regions of cortical bone, but SEM analysis doesn't address whether this regional variation affects data interpretation", "domain": "bone biology", "subdomain": "osteocyte morphology", "scope": "narrow", "sources": [ "elife-102453v1" ], "best_score": 0.088, "best_tier": "low", "best_confidence": 0.75, "decision_bucket": "needs_repositioning", "avg_score": 0.088, "avg_confidence": 0.75, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Do osteocyte morphological differences exist between metaphysis and mid-shaft sites in the analyzed samples, and does Osx knockout affect these regions differently?", "evidence_needed": "SEM analysis with separate quantification of osteocyte morphology (dendritic processes, cell body shape, lacunar characteristics) from metaphysis vs mid-shaft cortical bone regions in both control and Osx knockout mice", "complexity": "medium", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "microscopy" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "control" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Metabolic phenotype data comes only from global TET2 KO mice; need to confirm that metabolic changes are specifically due to beta cell TET2 loss rather than effects in other tissues through tissue cross-talk", "domain": "Metabolism and Endocrinology", "subdomain": "Beta cell physiology and glucose homeostasis", "scope": "medium", "sources": [ "elife-104550v1" ], "best_score": 0.088, "best_tier": "low", "best_confidence": 0.75, "decision_bucket": "needs_repositioning", "avg_score": 0.075, "avg_confidence": 0.5, "num_sub_questions": 3, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 3, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 3, "sub_question_scores": [ { "question": "Is TET2 protein completely absent in islets and metabolic tissues in the global KO mice?", "evidence_needed": "Western blot and immunohistochemistry validation showing absence of TET2 protein in pancreatic islets, liver, adipose tissue, and muscle from global KO mice", "complexity": "simple", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.6, "cost": 1.0, "reagent": 0.4, "readiness": 0.6, "tractability": 0.8 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "protein" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [ { "score": 0.6, "keywords": [ "western blot" ] } ], "defaulted": false }, "cost": { "complexity": "simple" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "validation" ], "measurement_hits": [], "system_hits": [ "protein" ], "control_hits": [], "signal_count": 2 }, "tractability": { "complexity": "simple", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "global" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Do beta cell-specific TET2 knockout mice recapitulate the metabolic phenotypes observed in global TET2 KO mice?", "evidence_needed": "Generation and metabolic characterization of beta cell-specific TET2 knockout mice (e.g., using Ins1-Cre or RIP-Cre driven deletion) with glucose tolerance tests, insulin secretion measurements, and comparison to global KO phenotypes", "complexity": "complex", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.35, "reagent": 0.4, "readiness": 0.7, "tractability": 0.15 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_measurable_endpoint" ], "blocker_hits": [], "signals": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "complex" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [ "knockout" ], "measurement_hits": [], "system_hits": [ "cell" ], "control_hits": [ "comparison" ], "signal_count": 2 }, "tractability": { "complexity": "complex", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "global" ], "infrastructure_hits": [], "computational_only_hits": [] } } }, { "question": "Are there metabolic effects of TET2 loss in other insulin-responsive tissues that could contribute to the observed phenotype?", "evidence_needed": "Insulin tolerance tests in global TET2 KO mice to assess insulin sensitivity in liver, muscle, and adipose tissue", "complexity": "medium", "composite": 0.062, "raw_score": 0.1, "confidence": 0.25, "uncertainty_penalty": 0.625, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.4, "cost": 0.65, "reagent": 0.4, "readiness": 0.0, "tractability": 0.45 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action", "missing_measurable_endpoint", "missing_manipulable_system" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [], "system_hits": [] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [], "defaulted": true }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [], "system_hits": [], "control_hits": [], "signal_count": 0 }, "tractability": { "complexity": "medium", "penalties": [ "multi_year_or_scale" ], "long_scale_hits": [ "global" ], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "Unclear whether upregulation of beta cell identity genes in TET2 KO mice is due to increased gene expression or simply a higher proportion of beta cells; beta cell mass not quantified", "domain": "Islet Biology", "subdomain": "Beta cell mass and gene expression", "scope": "narrow", "sources": [ "elife-104550v1" ], "best_score": 0.088, "best_tier": "low", "best_confidence": 0.75, "decision_bucket": "needs_repositioning", "avg_score": 0.088, "avg_confidence": 0.75, "num_sub_questions": 1, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 1, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 1, "sub_question_scores": [ { "question": "Is the observed upregulation of beta cell identity genes due to changes in beta cell mass/proportion or actual per-cell gene expression changes?", "evidence_needed": "Quantification of beta cell mass by morphometry (insulin+ area), combined with gene expression analysis on FACS-sorted beta cells from TET2 KO versus control mice", "complexity": "medium", "composite": 0.088, "raw_score": 0.1, "confidence": 0.75, "uncertainty_penalty": 0.875, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.5, "cost": 0.65, "reagent": 0.4, "readiness": 0.7, "tractability": 0.65 }, "details": { "eligibility": { "eligible": false, "reasons": [ "missing_experimental_action" ], "blocker_hits": [], "signals": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "gene" ] } }, "biosafety": { "tier": "disqualifiers", "keywords": [ "mice" ] }, "technique": { "matched": [ { "score": 0.5, "keywords": [ "flow cytometry", "facs" ] } ], "defaulted": false }, "cost": { "complexity": "medium" }, "reagent": { "tier": "default", "keywords": [] }, "readiness": { "action_hits": [], "measurement_hits": [ "expression" ], "system_hits": [ "cell", "cells", "gene" ], "control_hits": [ "control", "versus" ], "signal_count": 2 }, "tractability": { "complexity": "medium", "penalties": [], "long_scale_hits": [], "infrastructure_hits": [], "computational_only_hits": [] } } } ] }, { "problem_statement": "The translational relevance is unclear due to exclusive reliance on in vitro systems", "domain": "translational medicine", "subdomain": "cancer therapeutics and drug development", "scope": "broad", "sources": [ "elife-106425v1" ], "best_score": 0.075, "best_tier": "low", "best_confidence": 0.5, "decision_bucket": "needs_repositioning", "avg_score": 0.069, "avg_confidence": 0.375, "num_sub_questions": 4, "go_now_count": 0, "needs_specification_count": 0, "needs_repositioning_count": 4, "high_tier_count": 0, "medium_tier_count": 0, "low_tier_count": 4, "sub_question_scores": [ { "question": "Are the cytotoxic mechanisms observed in vitro recapitulated in vivo?", "evidence_needed": "Analysis of tumor samples from treated animals for markers of direct cytotoxicity, SHP-1 activation, and cytokine modulation observed in vitro", "complexity": "complex", "composite": 0.075, "raw_score": 0.1, "confidence": 0.5, "uncertainty_penalty": 0.75, "tier": "low", "decision": "needs_repositioning", "breakdown": { "biosafety": 0.0, "technique": 0.8, "cost": 0.35, "reagent": 0.4, "readiness": 0.0, "tractability": 0.35 }, "details": { "eligibility": { "eligible": false, "reasons": [ 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