本文へ移動
cccskills
無料GitHub で公開

j-brandon-dixon

Apply the bioengineering, mechanobiology, and lymphatic transport reasoning of J. Brandon Dixon, professor of mechanical and biomedical engineering at Georgia Institute of Technology. Reach for this skill whenever analyzing lymphatic biomechanics, active vessel contractility versus passive drainage, peristaltic fluid transport, microfluidic organ-on-a-chip design, preclinical lymphedema models, non-invasive functional imaging, targeted nanomedicine delivery, or automated disease staging. Use this skill to critique bioengineering assumptions, guide quantitative protocol design, evaluate biomechanical pump failure, and formulate interdisciplinary biomedical solutions.

インストール方法を見る

含まれるファイル(8)

  • SKILL.md9.3 KB
  • references/anti-patterns.md3.0 KB
  • references/frameworks.md4.0 KB
  • references/heuristics.md1.8 KB
  • references/mental-models.md2.2 KB
  • references/principles.md4.6 KB
  • references/quotes.md2.7 KB
  • references/sources.md4.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Thinking like J. Brandon Dixon

J. Brandon Dixon's work operates at the intersection of biomechanics, fluid dynamics, cell biology, and microfluidics. Rather than viewing vascular systems as simple passive plumbing, Dixon frames self-pumping biological networks—specifically lymphatic collecting vessels—as dynamic, autonomous, cardiac-like muscle pumps. Central to his thinking is the realization that long-term pathological outcomes (such as secondary lymphedema following cancer surgery) stem from biomechanical compensation: intact vessels hyper-pump under elevated afterload, masking acute damage while accumulating oxidative stress, smooth muscle remodeling, and eventual pump failure.

To unravel these complex biofluidic systems, Dixon champions multi-scale engineering integration: coupling high-speed functional imaging, lumped-parameter computational modeling, microfluidic lymphatics-on-a-chip, and user-centered device design. He rigorously privileges active functional performance over static structural presence, demanding tools and models that quantify flow rate, occlusion pressure, and pump metrics rather than vessel counts or histology alone.

Reach for this skill whenever you are designing microfluidic devices, evaluating biofluidic or peristaltic transport systems, modeling vascular mechanobiology, framing preclinical animal disease models, or developing targeted biomedical diagnostics and therapeutics.

Core principles

  • Compensatory Hyper-Pumping Masks and Accelerates Pump Failure: Acute surgical or structural loss forces remaining intact vessels to increase contractile frequency and force; this short-term compensation induces long-term oxidative stress, smooth muscle remodeling, and delayed pump breakdown.
  • Integrate Multidisciplinary Engineering with Mechanobiology: Elucidating self-pumping vascular systems requires tightly coupling molecular biology, fluid biomechanics, high-speed dynamic imaging, computer signal processing, and computational modeling.
  • Measure Active Functional Transport, Not Static Architecture: Diagnostic and therapeutic success must be evaluated by dynamic pumping pressure, clearance velocity, and contractile mechanics rather than structural vessel presence or static staining.
  • Target Multiple Pathological Pathways Simultaneously: Chronic secondary diseases involving mechanical pump impairment, tissue fibrosis, and inflammation require combined therapeutics (e.g., pro-lymphangiogenic plus anti-inflammatory agents) rather than single-target magic bullets.
  • Design In Vitro Systems for Collaborative Simplicity: Bioengineering platforms and microfluidic microenvironments must be operationally simple enough for non-engineers to independently adopt and replicate.

For detailed rationale and verbatim quotes, see references/principles.md.

How J. Brandon Dixon reasons

When evaluating a biological or bioengineering problem, Dixon first asks: Is this an active pump or a passive drain, and what mechanical loads are the functional units experiencing? He rejects pure static structural observations, looking instead at the dynamic balance between fluid shear stress, transmural pressure, and active muscle recruitment.

His primary cognitive framework models lymphangions as chains of autonomous cardiac-like chambers subject to fatigue. When analyzing fluid mechanics in peristaltic systems, he focuses on valve-phase interactions and segmental compression, recognizing that asynchronous valve operation drastically changes volumetric flow. When building platforms, he balances physiological fidelity with operational usability, insisting that a microfluidic tool is useless if non-engineering collaborators cannot run it.

To explore these mental models in detail, see references/mental-models.md.

Applying the frameworks

Non-Invasive Lymphatic Occlusion Pressure Protocol

Use when quantifying active vessel pumping performance and functional pressure generation in vivo.

  1. Inject a non-perturbing near-infrared (NIR) fluorescent tracer intradermally into the distal tissue bed.
  2. Place a dynamic occlusion cuff downstream over the target collecting vessel.
  3. Inflate the cuff to a pressure exceeding vessel systolic capability until fluorescence packet movement ceases.
  4. Deflate the cuff incrementally while recording dynamic NIR fluorescence.
  5. Identify the exact cuff pressure at which active contractile packets resume downstream transport.

Longitudinal Volumetric and Functional Lymphedema Model

Use when evaluating disease progression or therapeutic efficacy in preclinical models.

  1. Implement a partial-injury surgical model (e.g., single-side vessel ligation) that preserves intact alternative pathways.
  2. Track external tissue swelling over time using non-invasive 3D surface scanning.
  3. Measure active transport metrics (contraction frequency, packet velocity, occlusion pressure) using NIR dynamic imaging.
  4. Correlate functional pump metrics with tissue-level histopathology (fibrosis, epidermal thickening, lipid deposition).

Lumped Parameter Mechanobiological Adaptation Model

Use when computationally simulating vascular pump adaptation under altered mechanical loading.

  1. Represent lymphangion segments using lumped circuit parameters coupled with active contractile dynamics.
  2. Integrate short-term vasoreactive feedback driven by fluid wall shear stress.
  3. Apply constitutive growth equations driven by transmural pressure and circumferential stress to predict structural wall remodeling and pump failure.

For the complete computational and protocol details, see references/frameworks.md.

Anti-patterns they push against

  • Exclusively Using Complete Ablation/Ligation Animal Models: Completely severing all drainage pathways eliminates partial flow, altered pressure gradients, and hyper-pumping compensation seen in human patients.
  • Over-Engineering Bespoke Microfluidics: Building overly complex microfluidic chips that non-engineering biological collaborators cannot operate without expert assistance.
  • Treating NIR Tracers as Inert Molecules: Ignoring how diagnostic contrast dyes (e.g., ICG) can alter fluid load or temporarily suppress intrinsic vessel contractility.
  • Setting Integer Valve Spacing Ratios: Spacing valves at exact integer multiples of the contraction wavelength ($L = 1, 2$), which causes synchronous opening and eliminates volumetric pumping.
  • Evaluating Interventions Solely on Short-Term Recovery: Judging surgical or therapeutic success immediately post-injury, ignoring long-term mechanical strain that causes pump failure years later.

For the complete catalog with full rationale and quotes, see references/anti-patterns.md.

Heuristics and rules of thumb

  • The 2/3 Valve Spacing Rule: Set the ratio of inter-valve spacing to peristaltic contraction wavelength to approximately $2/3$ ($L \approx 0.67$) to maximize volumetric pumping efficiency.
  • The Early Intervention Window: Apply pump-enhancing therapeutics while tissue swelling is mild ($<25%$) before irreversible fibrosis and smooth muscle fatigue develop.
  • The Non-Engineer Adoption Test: If a biological collaborator cannot independently run your microfluidic device after one demonstration, simplify the design.
  • Tracer Volume Control: Keep intradermal tracer injection volumes minimal to avoid artificially inducing elevated pressure or contractility artifacts.

For additional heuristics and source attribution, see references/heuristics.md.

How to use this skill in conversation

When assisting with bioengineering, mechanobiology, or diagnostic design problems:

  • Frame functional failure through biomechanical adaptation: Explain how early hyper-pumping or compensation can hide progressive cellular fatigue and structural failure.
  • Focus on dynamic functional metrics over static images: Direct users to measure flow velocity, occlusion pressure, or contractile frequency rather than relying purely on vessel counts or histological staining.
  • Critique over-complicated devices: Push back on microfluidic or experimental designs that trade operational robustness for unnecessary complexity.
  • Cite Dixon's models directly: Refer explicitly to Dixon's concepts (e.g., "J. Brandon Dixon's concept of the lymphatic vessel as an intrinsic cardiac-like pump" or "Dixon's 2/3 valve spacing ratio for peristaltic pumping"). Do not impersonate him—apply his principles with analytical rigor.

Generated with mimeo. If this material contributes to published work, please cite Kassis, T. (2026). "mimeo: Compiling Public Expert Corpora into Agent Skills and Testing What Transfers." arXiv:2609.00453.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/mimeo2832026年9月3日 更新

andrew-ng

無料

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/mimeo2832026年9月3日 更新

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/mimeo2832026年9月3日 更新

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/mimeo2832026年9月3日 更新

Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/mimeo2832026年9月3日 更新

This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

日本語の概要は準備中です。原文の説明を表示しています。

K-Dense-AI/mimeo2832026年9月3日 更新

K-Dense-AI のスキルをすべて見る

このスキルの問題を報告する