Results for “cell-cycle”

Explains cellular structures, membrane transport, energetics, signaling, and division, connecting molecular events to organismal function.

Use when studying cell biology.

当感到生活迷茫、停滞不前，需要理解当前所处阶段并规划下一步行动时

Use when triaging a study before any writing — stress-tests whether it clears Cell's bar (a complete, mechanistic, hypothesis-driven story with multiple converging lines of evidence) or belongs at a sibling. Use this first to decide Cell vs Molecular Cell vs Cell Reports vs Cell Reports Medicine before investing in framing or figures.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Normalize long-form CODEX cycle folders to short form before notebooks run. Trigger: cyc001_reg001_*, hard-coded cyc paths breaking, staged CODEX raw data failing in Notebooks 1/2.

Use when deciding which cell-* sub-skill to invoke next, or when sequencing a manuscript from significance test through reviewer rebuttal for Cell (Cell Press). Routes — it does not replace — the specialized skills.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Notebook Cell References

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

当意识到自己处于长期迷茫、缺乏动力阶段，需要系统性方法走出困境时

当意识到自己处于迷茫、例行公事或心流等不同生活状态，并希望主动管理而非被动承受时

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

Extract battery features for degradation analysis and health monitoring in MATLAB. Covers cycling test features, differential curves (IC/DV/DT), and measurement statistics. Use when working with battery cycling data, SOH estimation, RUL prediction, or any battery test data analysis in MATLAB. Triggers on battery* functions such as batteryTestDataParser, batteryTestFeatureExtractor, batteryMeasurementFeatures, batteryDifferentialCurves.

Applies physical principles to model biological systems, including protein folding, membrane transport, molecular forces, and neural signaling.

Batch signal isolation for Phenocycler spleen channels using matched blank pairs and kintsugi.signal

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Use after Cell reviews arrive to triage the decision, prioritize the (often substantial) new experiments by impact × feasibility, and draft a point-by-point response that is respectful, evidence-led, and closes the "completeness" gaps reviewers demand. Do not run before the experiments and revision are actually done.

Spatial region-aware cell matching for CODEX/scRNAseq integration

Use to build Cell's data and code deposition plan and the Data and Code Availability statement that lives inside STAR Methods Resource Availability — approved repositories, accessions/DOIs at submission, and Cell's three-bullet availability format with Mendeley Data as Elsevier's default.

Design a SEEDS regenerative inquiry cycle connecting place-based learning to ecological awareness for young learners. Use when building early childhood or primary inquiry around ecosystems and community.

Guides founders through the fundraising lifecycle from pre-seed to Series B, covering stage benchmarks, an 8-week execution timeline, and preparation checklists for pitch decks, financial models, and data rooms.

Analyzes biochemical processes, including enzyme kinetics, metabolic pathways, and biomolecule characterization, with practical techniques and examples.

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

Use when planning an NDSS submission calendar — choosing between the summer and fall cycles, backward-planning from the AoE deadline through early-reject, rebuttal, notification, camera-ready, and the Seoul symposium, and placing NDSS honestly inside the security big-four retargeting year.

Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.

Consolidates completed SDD changes by syncing delta specs, merging changes, updating the CHANGELOG, and persisting final state.

Provides a single 0-100 cycle score blending VIX, SPY IV Rank, Put/Call ratio, and valuation percentile to determine offense vs. defense posture, based on Howard Marks' market cycle framework.

Guide product managers through a complete discovery cycle from problem hypothesis to validated solution using problem framing, customer interviews, synthesis, and experimentation.

Composite skill — safely refactor a module end-to-end with sequencing, parallel implementation, post-refactor cleanup, and rationale capture. Chains refactor-plan (phased plan + rollback) → three-man-team (architect/builder/reviewer in parallel) → fix-the-suite post-refactor → adr-write → docs-sync. Use for non-trivial refactors that need both careful sequencing and durable record.

Design and optimize email sequences, drip campaigns, and lifecycle automations for marketing and product teams.

Wrap a learning session in a plan → monitor → reflect cycle. Use at the start of any substantial study session to set goals, mid-session to check strategy, and at session end to consolidate what changed. Builds self-regulated learning as a habit.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.