L1 ⚛ L1-129 ⊙ Testnet

Cryo-EM Single-Particle Analysis (SPA)

Scientific Instrumentation · Near-atomic 3D reconstruction of frozen-hydrated biomolecules

⬇ Download bundle 🚀 Submit to mainnet — earn 100 PWM + 5% royalty

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-129.md

# ⚛ L1 Principle — Cryo-EM Single-Particle Analysis (SPA) **ID:** `L1-129` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Scientific Instrumentation — *Near-atomic 3D reconstruction of frozen-hydrated biomolecules* > **🎯 Problem class:** tomographic inverse with pose · **🧮 Solution space:** 3d coulomb potential > **📡 Carrier:** electron · **🌫 Noise:** shot poisson > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41554213 --- ## 🧠 1. Introduction **Cryo-EM Single-Particle Analysis (SPA)** is a **tomographic inverse with pose** whose unknown lives in **3d coulomb potential** space, within the **Near-atomic 3D reconstruction of frozen-hydrated biomolecules** sub-domain of **Scientific Instrumentation**. Measurements consist of electrons collected by an electron detector via a **weak phase transmission** sensing mechanism. The forward operator applies, in order: L · project · euler operator; L · modulate · ctf operator; pixel-level spatial averaging on the detector. Observations are corrupted by Poisson shot noise from quantum-limited detection. Signal-to-noise per image ~0.01-0.1; collective averaging boosts SNR as sqrt(N). Mismatch: pose-estimation error, CTF miscalibration, conformational heterogeneity (multi-class), preferential orientation. ## ⚙ 2. Forward Model Physical chain: **x** → L · project · euler → L · modulate · ctf → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.modulate.ctf` `L.project.euler` x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.project.euler` | L · project · euler operator | | `L.modulate.ctf` | L · modulate · ctf operator | | `int.spatial` | Pixel-level spatial averaging on the detector | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Scientific Instrumentation | | Sub domain | Near-atomic 3D reconstruction of frozen-hydrated biomolecules | | Carrier | electron | | Problem class | tomographic_inverse_with_pose | | Solution space | 3d_coulomb_potential | | Noise model | shot_poisson | | Integration axis | projection | | Difficulty delta | 5 | | L dag | 3 | ## 📡 4. Measurement Model Signal-to-noise per image ~0.01-0.1; collective averaging boosts SNR as sqrt(N). Mismatch: pose-estimation error, CTF miscalibration, conformational heterogeneity (multi-class), preferential orientation. | Metric | Value | |---|---| | Metric | resolution_angstrom | | Secondary | FSC_0.143 | ## 📏 5. Operating Range (Ω) **Center problem class:** `spa_homogeneous` · **Forward operator:** `spa_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | Dose | — | 30 | | N particle | — | 100000 | | Defocus um | µm | 1.5 | | Pixel angstrom | — | 1 | | Pose estimate error | — | 0 | | Conformational heterogeneity | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | Dose | — | 10 – 100 | | N particle | — | 1000 – 10000000 | | Defocus um | µm | 0.3 – 5.0 | | Pixel angstrom | — | 0.5 – 3.0 | | Ctf miscalibration | — | 0.0 – 500 | | Per particle motion | — | 0.0 – 5 | | Pose estimate error | — | 0.0 – 10 | | Preferential orientation | — | 0.0 – 0.8 | | Conformational heterogeneity | — | 0.0 – 1.0 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 3.5 Å resolution | Metric | Range | |---|---| | Resolution angstrom | 1.5 – 15 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **resolution_angstrom**, with κ = `1000` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xaaa726f4455d5c140143ffbd8c7321773279c57efdff74c29eff1d2d2cd948d3` - **Chain tx hash:** `0x22ccda357f539047c2daa1d41b4447ee79e3bf5561621d4bd8ee90249bdb7c10` - **Chain block:** `41554213` --- ## File Mapping This bundle consists of: `L1-129.md`, `L1-129.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-129.md` | Source of truth — edit this | Human or LLM | | `L1-129.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-129`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

📝 Polish this artifact in your LLM

Download the files on the right.
Paste the Markdown into ChatGPT / Claude / Gemini and edit freely.
Send your edited Markdown back with the prompt below.
Save the regenerated JSON, then submit via /submit.

Prompt to give your LLM

Read the attached Markdown (L1-129.md).
Regenerate L1-129.json so every field matches the Markdown.
Preserve the schema documented in the File Mapping footer at the bottom of the MD.
Output each file in its own fenced code block tagged with the filename.

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Spec 1 child ▾

L2-129-001 Cryo-EM SPA Homogeneous Spec

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-129",
  "chain_block": 41554213,
  "chain_hash": "0xaaa726f4455d5c140143ffbd8c7321773279c57efdff74c29eff1d2d2cd948d3",
  "chain_tx_hash": "0x22ccda357f539047c2daa1d41b4447ee79e3bf5561621d4bd8ee90249bdb7c10",
  "domain": "Scientific Instrumentation",
  "hardness_fn": {
    "delta": 5,
    "kappa": 1000,
    "metric": "resolution_angstrom",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "resolution_angstrom",
    "regime": "Signal-to-noise per image ~0.01-0.1; collective averaging boosts SNR as sqrt(N). Mismatch: pose-estimation error, CTF miscalibration, conformational heterogeneity (multi-class), preferential orientation.",
    "secondary": "FSC_0.143"
  },
  "physics_fingerprint": {
    "L_DAG": 3.0,
    "carrier": "electron",
    "difficulty_delta": 5,
    "domain": "Scientific Instrumentation",
    "integration_axis": "projection",
    "noise_model": "shot_poisson",
    "primitives": [
      "L.project.euler",
      "L.modulate.ctf",
      "int.spatial"
    ],
    "problem_class": "tomographic_inverse_with_pose",
    "sensing_mechanism": "weak_phase_transmission",
    "solution_space": "3d_coulomb_potential",
    "sub_domain": "Near-atomic 3D reconstruction of frozen-hydrated biomolecules",
    "title": "Cryo-EM Single-Particle Analysis (SPA)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "spa_forward",
      "heterogeneity_aware_forward"
    ],
    "allowed_omega_dimensions": [
      "N_particle",
      "pixel_angstrom",
      "defocus_um",
      "dose",
      "pose_estimate_error",
      "ctf_miscalibration",
      "conformational_heterogeneity",
      "preferential_orientation",
      "per_particle_motion"
    ],
    "allowed_problem_classes": [
      "spa_homogeneous",
      "spa_heterogeneous",
      "ab_initio_model"
    ],
    "center_spec": {
      "epsilon_fn_center": "3.5 \u00c5 resolution",
      "forward_operator": "spa_forward",
      "input_format": "particle_stack",
      "omega": {
        "N_particle": 100000,
        "conformational_heterogeneity": 0.0,
        "defocus_um": 1.5,
        "dose": 30,
        "pixel_angstrom": 1.0,
        "pose_estimate_error": 0.0
      },
      "problem_class": "spa_homogeneous"
    },
    "epsilon_bounds": {
      "resolution_angstrom": [
        1.5,
        15
      ]
    },
    "omega_bounds": {
      "N_particle": [
        1000,
        10000000
      ],
      "conformational_heterogeneity": [
        0.0,
        1.0
      ],
      "ctf_miscalibration": [
        0.0,
        500
      ],
      "defocus_um": [
        0.3,
        5.0
      ],
      "dose": [
        10,
        100
      ],
      "per_particle_motion": [
        0.0,
        5
      ],
      "pixel_angstrom": [
        0.5,
        3.0
      ],
      "pose_estimate_error": [
        0.0,
        10
      ],
      "preferential_orientation": [
        0.0,
        0.8
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Near-atomic 3D reconstruction of frozen-hydrated biomolecules",
  "title": "Cryo-EM Single-Particle Analysis (SPA)"
}

🎁

Earn PWM by promoting this to mainnet

• 100 PWM one-time grant on mainnet promotion
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Submit to mainnet →

How: Download the bundle above → polish the Markdown in ChatGPT / Claude / Gemini → submit your version → founder reviews → mainnet.