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    "(born-to-etiquette)=\n",
    "# Born to Etiquette\n",
    " \n",
    "\n",
    "### The Intelligence of Last-Mile Delivery in Biological Systems and Its Breakdown in Modernity  \n",
    "\n",
    "At every scale, from the branching architecture of trees to the intricate vascularization of the human body, nature has evolved an astonishing intelligence in the distribution of resources. This intelligence is not merely a metaphorical flourish but a literal economy of design, optimized over evolutionary time to ensure that oxygen, nutrients, and signals reach their intended destinations with maximal efficiency and minimal waste. The human cardiovascular system, the neural network of the brain, the arborization of bronchioles in the lungs, and even the peripheral nerves extending into the extremities are all manifestations of this principle: a system-wide intelligence that balances constraints of energy, distance, and redundancy to ensure survival.  \n",
    "\n",
    "```{raw} html\n",
    "<style>\n",
    ".float-right {\n",
    "    float: right;\n",
    "    width: 35%;\n",
    "    height: auto;\n",
    "    margin-left: 15px;\n",
    "    border: 1px solid #ccc;\n",
    "    padding: 5px;\n",
    "    background-color: #f9f9f9;\n",
    "}\n",
    "\n",
    ".float-right p {\n",
    "    text-align: left;\n",
    "    font-size: 12px;\n",
    "}\n",
    "</style>\n",
    "\n",
    "<div class=\"float-right\">\n",
    "     <iframe width=\"100%\" height=\"400px\" src=\"https://www.youtube.com/embed/klRb0_BAX9g?start=10201\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
    "    <p><i>He Does it Again</i>. If we can send AIs to visit other planets and perhaps galaxies, surely aliens coud do the same and visit us. Thus UFOs are not that much of a stretch, especially in a nuclear world -- aliens have recognized the \"arrival\" of another intelligence that has learned to harness the energy of the stars. We must overcome biological imperatives: status, tribe, etc. It's clear that AI is one such approach. Matrix.</p>\n",
    "</div>\n",
    "```\n",
    "\n",
    "This intelligence is most tested in what we might call the “last-mile delivery” zones—regions at the periphery of these networks where resources must still reach but where efficiency begins to break down due to bottlenecks, resistance, and structural degradation. In biological terms, the last mile is where the blood supply grows thinner and more vulnerable, where axons stretch toward the distant skin and muscles, where the bronchioles give way to alveolar sacs demanding precise oxygenation. It is in these last-mile zones that modernity wages its most insidious war, disrupting the delicate equilibrium that evolution has fine-tuned.  \n",
    "\n",
    "Take stroke, for example. The brain, a neural economy of unfathomable complexity, depends on a meticulously balanced vascular network where even the tiniest capillary serves as a node in a grand, resource-distributing system. The failure of blood supply to the last mile—whether through a clot (ischemic stroke) or a hemorrhage—leaves downstream neurons starving, unable to sustain their function. This is not merely a failure of a singular vessel but of an entire system of economic distribution within the brain, where energy-intensive neurons in the cortex rely on oxygen and glucose delivered via a hierarchical but highly adaptive supply chain. The intelligence of this network is seen in its redundancies—the Circle of Willis, collateral circulation—but even these are no match for the cumulative damage imposed by hypertension, atherosclerosis, and metabolic dysfunction, all products of modern lifestyles.  \n",
    "\n",
    "A similar phenomenon plays out in cardiovascular disease, where the narrowing of coronary arteries imperils the last-mile delivery of oxygenated blood to the heart muscle itself. Again, what was once an efficient supply chain becomes a bottlenecked system, with endothelial dysfunction, inflammation, and lipid accumulation conspiring to shut down outposts in this physiological economy. The system, once dynamic and self-regulating, enters a state of rigidity—failing to adapt, failing to reallocate resources in response to demand. What was once an agile intelligence devolves into mechanical failure.  \n",
    "\n",
    "Peripheral neuropathy in diabetes exemplifies the same process in a different arena. Here, the “last mile” is not cardiovascular but neural—the long, thin axons extending to the feet, the hands, the extremities. In a healthy nervous system, axonal transport ensures the steady flow of nutrients, growth factors, and signaling molecules along these cellular highways. But when diabetes introduces chronic hyperglycemia, oxidative stress, and microvascular damage, the integrity of these highways erodes. The last mile collapses, leading to the classic symptoms of diabetic neuropathy—numbness, tingling, loss of proprioception. Again, the intelligence of distribution is disrupted, not because the blueprint was flawed but because modern metabolic excess overwhelms the system’s adaptability.  \n",
    "\n",
    "Even the architecture of the lungs—the branching bronchioles that ensure every alveolus is reached by inhaled air—follows this same fractal intelligence. Yet in conditions like chronic obstructive pulmonary disease (COPD), the problem is not only in the primary airways but in the last-mile bronchioles, where inflammation, mucus buildup, and structural degradation prevent the smooth exchange of oxygen and carbon dioxide. The last mile is clogged, suffocating the efficiency of an otherwise genius network.  \n",
    "\n",
    "One could argue that these failures of biological last-mile delivery are symptomatic of a larger crisis in modernity: a civilization that has, through technological and economic means, extended life expectancy but has simultaneously undermined the infrastructural intelligence that sustains it. The sedentary lifestyles, the processed diets, the chronic stressors, the pollution—these are all disruptions to the equilibrium that the body’s vascular and neural networks have spent millennia refining. If the brain and the body operate as economies of efficiency, then modernity introduces inefficiencies, systemic debts, and bottlenecks that our physiology was never designed to accommodate.  \n",
    "\n",
    "To address these crises is not merely to medicate or intervene at the site of failure but to understand the intelligence of these networks and work with them rather than against them. Just as urban planners have begun to rethink last-mile delivery in supply chain logistics—introducing bicycle couriers, drone systems, decentralized hubs—medicine must consider how to restore last-mile functionality to the body. This means rethinking how we approach prevention, targeting not just the clogged artery but the systemic processes that lead to its dysfunction, not just the dying neuron but the larger vascular context in which it resides.  \n",
    "\n",
    "In the end, the economy of biology is unforgiving, but it is also deeply instructive. Intelligence, whether in a forest, a neural network, or a bronchiole tree, does not emerge from isolated parts but from the relationships between them. The failure of last-mile delivery in the human body is not a localized event—it is a signal, a systemic warning that the intelligence of distribution, so elegantly refined over evolutionary time, is breaking down. And in that breakdown lies the lesson: if modernity is to outlast its own inefficiencies, it must learn not just from its machines, but from the branching, fractal wisdom of biology itself.\n",
    "\n",
    "\n",
    " \n",
    "  "
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "# Define the neural network layers\n",
    "def define_layers():\n",
    "    return {\n",
    "        'Suis': ['DNA, RNA,  5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', \"Glucans, Chitin\", 'Specific Antigens'],\n",
    "        'Voir': ['PRR & ILCs, 20%'],  \n",
    "        'Choisis': ['CD8+, 50%', 'CD4+'],  \n",
    "        'Deviens': ['TNF-α, IL-6, IFN-γ', 'PD-1 & CTLA-4', 'Tregs, IL-10, TGF-β, 20%'],  \n",
    "        \"M'èléve\": ['Complement System', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']  \n",
    "    }\n",
    "\n",
    "# Assign colors to nodes\n",
    "def assign_colors():\n",
    "    color_map = {\n",
    "        'yellow': ['PRR & ILCs, 20%'],  \n",
    "        'paleturquoise': ['Specific Antigens', 'CD4+', 'Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'],  \n",
    "        'lightgreen': [\"Glucans, Chitin\", 'PD-1 & CTLA-4', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],  \n",
    "        'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'CD8+, 50%', 'TNF-α, IL-6, IFN-γ', 'Complement System'],\n",
    "    }\n",
    "    return {node: color for color, nodes in color_map.items() for node in nodes}\n",
    "\n",
    "# Define edge weights\n",
    "def define_edges():\n",
    "    return {\n",
    "        ('DNA, RNA,  5%', 'PRR & ILCs, 20%'): '1/99',\n",
    "        ('Peptidoglycans, Lipoteichoics', 'PRR & ILCs, 20%'): '5/95',\n",
    "        ('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',\n",
    "        ('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',\n",
    "        (\"Glucans, Chitin\", 'PRR & ILCs, 20%'): '80/20',\n",
    "        ('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',\n",
    "        ('PRR & ILCs, 20%', 'CD8+, 50%'): '20/80',\n",
    "        ('PRR & ILCs, 20%', 'CD4+'): '80/20',\n",
    "        ('CD8+, 50%', 'TNF-α, IL-6, IFN-γ'): '49/51',\n",
    "        ('CD8+, 50%', 'PD-1 & CTLA-4'): '80/20',\n",
    "        ('CD8+, 50%', 'Tregs, IL-10, TGF-β, 20%'): '95/5',\n",
    "        ('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',\n",
    "        ('CD4+', 'PD-1 & CTLA-4'): '20/80',\n",
    "        ('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',\n",
    "        ('TNF-α, IL-6, IFN-γ', 'Complement System'): '80/20',\n",
    "        ('TNF-α, IL-6, IFN-γ', 'Platelet System'): '85/15',\n",
    "        ('TNF-α, IL-6, IFN-γ', 'Granulocyte System'): '90/10',\n",
    "        ('TNF-α, IL-6, IFN-γ', 'Innate Lymphoid Cells, 5%'): '95/5',\n",
    "        ('TNF-α, IL-6, IFN-γ', 'Adaptive Lymphoid Cells'): '99/1',\n",
    "        ('PD-1 & CTLA-4', 'Complement System'): '1/9',\n",
    "        ('PD-1 & CTLA-4', 'Platelet System'): '1/8',\n",
    "        ('PD-1 & CTLA-4', 'Granulocyte System'): '1/7',\n",
    "        ('PD-1 & CTLA-4', 'Innate Lymphoid Cells, 5%'): '1/6',\n",
    "        ('PD-1 & CTLA-4', 'Adaptive Lymphoid Cells'): '1/5',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Complement System'): '1/99',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Platelet System'): '5/95',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Granulocyte System'): '10/90',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'): '20/80'\n",
    "    }\n",
    "\n",
    "# Define edges to be highlighted in black\n",
    "def define_black_edges():\n",
    "    return {\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Complement System'): '1/99',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Platelet System'): '5/95',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Granulocyte System'): '10/90',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',\n",
    "        ('Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'): '20/80'\n",
    "    }\n",
    "\n",
    "# Calculate node positions\n",
    "def calculate_positions(layer, x_offset):\n",
    "    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))\n",
    "    return [(x_offset, y) for y in y_positions]\n",
    "\n",
    "# Create and visualize the neural network graph\n",
    "def visualize_nn():\n",
    "    layers = define_layers()\n",
    "    colors = assign_colors()\n",
    "    edges = define_edges()\n",
    "    black_edges = define_black_edges()\n",
    "    \n",
    "    G = nx.DiGraph()\n",
    "    pos = {}\n",
    "    node_colors = []\n",
    "    \n",
    "    # Create mapping from original node names to numbered labels\n",
    "    mapping = {}\n",
    "    counter = 1\n",
    "    for layer in layers.values():\n",
    "        for node in layer:\n",
    "            mapping[node] = f\"{counter}. {node}\"\n",
    "            counter += 1\n",
    "            \n",
    "    # Add nodes with new numbered labels and assign positions\n",
    "    for i, (layer_name, nodes) in enumerate(layers.items()):\n",
    "        positions = calculate_positions(nodes, x_offset=i * 2)\n",
    "        for node, position in zip(nodes, positions):\n",
    "            new_node = mapping[node]\n",
    "            G.add_node(new_node, layer=layer_name)\n",
    "            pos[new_node] = position\n",
    "            node_colors.append(colors.get(node, 'lightgray'))\n",
    "    \n",
    "    # Add edges with updated node labels\n",
    "    edge_colors = []\n",
    "    for (source, target), weight in edges.items():\n",
    "        if source in mapping and target in mapping:\n",
    "            new_source = mapping[source]\n",
    "            new_target = mapping[target]\n",
    "            G.add_edge(new_source, new_target, weight=weight)\n",
    "            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')\n",
    "    \n",
    "    # Draw the graph\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    edges_labels = {(u, v): d[\"weight\"] for u, v, d in G.edges(data=True)}\n",
    "    \n",
    "    nx.draw(\n",
    "        G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,\n",
    "        node_size=3000, font_size=9, connectionstyle=\"arc3,rad=0.2\"\n",
    "    )\n",
    "    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)\n",
    "    plt.title(\"OPRAH™: Distributed Network\", fontsize=18)\n",
    "    plt.show()\n",
    "\n",
    "# Run the visualization\n",
    "visualize_nn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} ../../figures/blanche.*\n",
    "---\n",
    "width: 1\n",
    "height: 1\n",
    "---\n",
    "Glenn Gould and Leonard Bernstein famously disagreed over the tempo and interpretation of Brahms' First Piano Concerto during a 1962 New York Philharmonic concert, where Bernstein, conducting, publicly distanced himself from Gould's significantly slower-paced interpretation before the performance began, expressing his disagreement with the unconventional approach while still allowing Gould to perform it as planned; this event is considered one of the most controversial moments in classical music history. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. From Shakespeare to Survival: A Journey and a Tool\n",
    "\n",
    "### The Greatest Shakespearean Comedy\n",
    "The debate kicked off with Shakespeare’s comedies. *A Midsummer Night’s Dream* (1595-1596) often tops the list—its fairy mischief, love tangles, and Bottom’s donkey-headed antics make it a comedic masterpiece. Think Puck’s chaos and the rude mechanicals’ absurd play-within-a-play. Yet *As You Like It* (1599) holds its own, especially in the pastoral realm. Its Forest of Arden swaps *Midsummer*’s magic for grounded wit, led by Rosalind’s Ganymede gambit and Jaques’ “All the world’s a stage” musings. Compared to Sidney’s dense *Arcadia*, *As You Like It* shines with breezy humor and humanity.\n",
    "\n",
    "Jaques stole the show for me—his observer’s perch, never joining the revelry, hit home. His clash with Orlando in Act 3, Scene 2—“I do desire we may be better strangers” after Jaques’ christening jab—is peak wit. It’s a microcosm of cynicism vs. youthful fire.\n",
    "\n",
    "### Personal Reflection: The Observer’s Life\n",
    "Jaques’ sidelines vibe mirrored my own. At 45, I’ve excelled at watching—dated 3-5 stunning women across my networks, attended elite institutions from childhood to adulthood, and plumbed the “massive combinatorial search space” of self. Avoidance was my card, dealt by life, not chosen. It worked—until now. The bill’s due; I owe “neighbor” and “god” after building a benchmark to calibrate my next moves.\n",
    "\n",
    "### The Kaplan-Meier App: From Self to Service\n",
    "That benchmark became an app—two overlaid Kaplan-Meier (KM) curves, personalized for living kidney donors. It pulls a .csv (multivariable regression betas, variance-covariance matrix) from peer-reviewed lit, hosted on GitHub Pages (.js, .html). The curves (donor vs. non-donor survival) come with 95% confidence intervals (CIs), letting users intuit attributable risk and uncertainty. It’s generalizable—swap kidneys for cancer, heart transplants, anything time-to-event.\n",
    "\n",
    "#### Tech Breakdown\n",
    "- **Back End:** Python (lifelines for KM, NumPy/SciPy for matrix math) crunches the .csv, adjusts covariates (age, sex, eGFR), and computes CIs via the variance-covariance matrix.  \n",
    "- **Front End:** JavaScript (D3.js?) renders interactive curves; HTML hosts it.  \n",
    "- **Output:** Overlayed KM curves—e.g., S_control(t) - S_donor(t) for risk difference, CIs via delta method or bootstrap.  \n",
    "\n",
    "#### Ecosystem Vision\n",
    "- **Back End:** An idealized flow—lit data → stats → insights. Like Jaques observing Arden’s chaos.  \n",
    "- **Front End:** Navigation vibes—intuitive, Rosalind guiding users through the forest.  \n",
    "\n",
    "### Pitching It: From Hopkins to the World\n",
    "\n",
    "#### Phase 1: Hopkins (Low-Hanging Fruit)\n",
    "Hopkins—Department of Surgery (Transplantation), Epidemiology, Biostatistics, Data Science, PAIRS@JH (Python, AI, R, Stata, JS, Jupyter Books)—is the launchpad.\n",
    "\n",
    "##### Pitch (45 min)\n",
    "- **Hook (5 min):** “From Jaques’ sidelines to your clinics—I’ve built a KM app for kidney donors. It’s live at Hopkins, ready for you.”  \n",
    "- **Problem (10 min):**  \n",
    "  - *Surgery:* Consent needs visuals, not p-values.  \n",
    "  - *Epi:* Risk and uncertainty buried in papers.  \n",
    "  - *Biostats:* KM overlay with CIs isn’t easy.  \n",
    "  - *PAIRS:* Students need real-world coding projects.  \n",
    "- **Solution (15 min):**  \n",
    "  - Two KM curves, personalized, with CIs—donor vs. non-donor.  \n",
    "  - *Surgery:* Show a 40-year-old their 10-year risk.  \n",
    "  - *Epi:* Spot survival gaps instantly.  \n",
    "  - *Biostats:* Covariate-adjusted KM, live uncertainty.  \n",
    "  - *PAIRS:* Python back end, JS front end—hackable.  \n",
    "- **Tech (10 min):** Python processes, JS visualizes, GitHub Pages hosts.  \n",
    "- **Ask (10 min):** Test with donor data, teach it, fund a pilot.  \n",
    "- **Close (5 min):** “I avoided the game, mapped the self, now I serve. Who’s in?”  \n",
    "\n",
    "##### Prep\n",
    "- Demo: Dummy donor curves, live on GitHub Pages.  \n",
    "- Collab: Integrate their registry data.  \n",
    "- PAIRS: Workshop—code from .csv to curves.\n",
    "\n",
    "#### Phase 2: Other Institutions\n",
    "After Hopkins, hit Mayo, UCLA, Harvard (HSPH, MGH), UNC, Emory, Columbia, UW, Stanford, Penn.\n",
    "\n",
    "##### Pitch Tweak (15-20 min)\n",
    "- **Hook:** “Hopkins validated it—KM curves for donors, ready for your data.”  \n",
    "- **Why They Care:**  \n",
    "  - *Transplant:* Patient risk visuals.  \n",
    "  - *Public Health:* Population insights.  \n",
    "  - *Data Science:* Open-source playground.  \n",
    "- **Ask:** Test it, co-author, fund multi-site work.  \n",
    "- **Edge:** “Proven at Hopkins, scalable here.”  \n",
    "\n",
    "##### Prep\n",
    "- Demo: Plug in their lit (e.g., UNOS data).  \n",
    "- Show portability: Any .csv, any cohort.\n",
    "\n",
    "#### Phase 3: Beyond Academia—Decision-Making\n",
    "The endgame—policy (CMS, WHO, NIH), insurance (Aetna, Oscar), tech (Google Health, Apple), and personal choices.\n",
    "\n",
    "##### Pitch Framing (20-30 min)\n",
    "- **Hook:** “Life’s choices, distilled—KM curves for health, wealth, anything.”  \n",
    "- **Leap:**  \n",
    "  - *Policy:* CMS funds via risk trade-offs.  \n",
    "  - *Insurance:* Price policies with survival data.  \n",
    "  - *Tech:* Embed in wearables—real-time risk.  \n",
    "  - *Personal:* Retire? Move? See the odds.  \n",
    "- **Why It Works:**  \n",
    "  - Back end: Universal data-to-stats logic.  \n",
    "  - Front end: Intuitive for all.  \n",
    "- **Ask:** Pilot (e.g., CMS dialysis, Google dashboards), license, scale.  \n",
    "- **Vision:** “From observer to enabler—decision clarity for all.”  \n",
    "\n",
    "##### Tech Evolution\n",
    "- **Back End:** APIs over .csvs, cloud-hosted (AWS?).  \n",
    "- **Front End:** Mobile-ready (React Native), “what-if” sliders.  \n",
    "- **Output:** Time-to-event for anything—survival, bankruptcy, etc.  \n",
    "\n",
    "##### Prep\n",
    "- Demo: Non-medical—e.g., startup “survival” vs. industry.  \n",
    "- Sell uncertainty: “Decisions are ranges—we show them.”  \n",
    "\n",
    "### Connecting Shakespeare to Survival\n",
    "- *Midsummer:* Puck’s chaos = data wrangling; my app brings order.  \n",
    "- *As You Like It:* Jaques’ reflection = my shift from self to service.  \n",
    "- *App:* A tool born of observation, now joining the revelry—for patients, researchers, decision-makers.\n",
    "\n",
    "### Next Steps\n",
    "- **Hopkins:** Demo, collab, workshop.  \n",
    "- **Institutions:** Validate, scale.  \n",
    "- **Decision-Making:** Pilot wild use cases—policy, tech, life.  \n",
    "\n",
    "From Arden to attribution, this is my debt repaid—clarity for a chaotic world.\n",
    "\n",
    "## 2. Kaplan-Meier App: From Self to Service\n",
    "\n",
    "### The Greatest Shakespearean Comedy\n",
    "The debate kicked off with Shakespeare’s comedies. *A Midsummer Night’s Dream* (1595-1596) often tops the list—its fairy mischief, love tangles, and Bottom’s donkey-headed antics make it a comedic masterpiece. Think Puck’s chaos and the rude mechanicals’ absurd play-within-a-play. Yet *As You Like It* (1599) holds its own, especially in the pastoral realm. Its Forest of Arden swaps *Midsummer*’s magic for grounded wit, led by Rosalind’s Ganymede gambit and Jaques’ “All the world’s a stage” musings. Compared to Sidney’s dense *Arcadia*, *As You Like It* shines with breezy humor and humanity.\n",
    "\n",
    "Jaques stole the show for me—his observer’s perch, never joining the revelry, hit home. His clash with Orlando in Act 3, Scene 2—“I do desire we may be better strangers” after Jaques’ christening jab—is peak wit. It’s a microcosm of cynicism vs. youthful fire.\n",
    "\n",
    "### Personal Reflection: The Observer’s Life\n",
    "Jaques’ sidelines vibe mirrored my own. At 45, I’ve excelled at watching—dated 3-5 stunning women across my networks, attended elite institutions from childhood to adulthood, and plumbed the “massive combinatorial search space” of self. Avoidance was my card, dealt by life, not chosen. It worked—until now. The bill’s due; I owe “neighbor” and “god” after building a benchmark to calibrate my next moves.\n",
    "\n",
    "### The Kaplan-Meier App: From Self to Service\n",
    "That benchmark became an app—two overlaid Kaplan-Meier (KM) curves, personalized for living kidney donors. It pulls a .csv (multivariable regression betas, variance-covariance matrix) from peer-reviewed lit, hosted on GitHub Pages (.js, .html). The curves (donor vs. non-donor survival) come with 95% confidence intervals (CIs), letting users intuit attributable risk and uncertainty. It’s generalizable—swap kidneys for cancer, heart transplants, anything time-to-event.\n",
    "\n",
    "#### Tech Breakdown\n",
    "- **Back End:** Python (lifelines for KM, NumPy/SciPy for matrix math) crunches the .csv, adjusts covariates (age, sex, eGFR), and computes CIs via the variance-covariance matrix.  \n",
    "- **Front End:** JavaScript (D3.js?) renders interactive curves; HTML hosts it.  \n",
    "- **Output:** Overlayed KM curves—e.g., S_control(t) - S_donor(t) for risk difference, CIs via delta method or bootstrap.  \n",
    "\n",
    "#### Technical Implementation Details\n",
    "Here’s how it’s built, step-by-step, with code snippets and deployment notes.\n",
    "\n",
    "##### Back End (Python)\n",
    "- **Libraries:**  \n",
    "  - `lifelines`: Fits KM curves, handles survival analysis.  \n",
    "  - `pandas`: Loads and processes .csv (e.g., `data.csv` with betas, covars).  \n",
    "  - `numpy`: Matrix ops for variance-covariance handling.  \n",
    "  - `scipy`: Stats functions (e.g., CI calc).  \n",
    "- **Data Input:**  \n",
    "  - .csv format: Columns for time, event (1=death, 0=censored), covariates (age, sex), plus beta vector and variance-covariance matrix (from lit regression).  \n",
    "  - Example: `time, event, age, sex, beta_age, beta_sex, covar_age_sex`.  \n",
    "- **Processing:**  \n",
    "  1. Load data: `df = pd.read_csv('data.csv')`.  \n",
    "  2. Fit KM for two groups (donor, non-donor):  \n",
    "     ```python\n",
    "     from lifelines import KaplanMeierFitter\n",
    "     kmf_donor = KaplanMeierFitter()\n",
    "     kmf_control = KaplanMeierFitter()\n",
    "     donor_mask = df['donor'] == 1\n",
    "     kmf_donor.fit(df[donor_mask]['time'], event_observed=df[donor_mask]['event'])\n",
    "     kmf_control.fit(df[~donor_mask]['time'], event_observed=df[~donor_mask]['event'])\n",
    "     ```\n",
    "  3. Adjust for covariates using betas:  \n",
    "     - Hazard tweak: `h(t) = h_0(t) * exp(beta_age * age + beta_sex * sex)`.  \n",
    "     - Simulate adjusted survival: Monte Carlo sampling from variance-covariance for CI bounds.  \n",
    "  4. Export: Save KM points (time, survival prob, CI_lower, CI_upper) as JSON:  \n",
    "     ```python\n",
    "     import json\n",
    "     output = {\n",
    "         'donor': kmf_donor.survival_function_.to_dict(),\n",
    "         'control': kmf_control.survival_function_.to_dict(),\n",
    "         'ci_donor': kmf_donor.confidence_interval_.to_dict(),\n",
    "         'ci_control': kmf_control.confidence_interval_.to_dict()\n",
    "     }\n",
    "     with open('km_data.json', 'w') as f:\n",
    "         json.dump(output, f)\n",
    "     ```\n",
    "- **Scalability:** Precompute for static lit data; real-time API (Flask) for dynamic inputs later.\n",
    "\n",
    "##### Front End (JavaScript/HTML)\n",
    "- **Libraries:**  \n",
    "  - `D3.js`: Plots curves, CIs as shaded areas.  \n",
    "  - `jQuery`: Fetches JSON, handles UI.  \n",
    "- **Structure:**  \n",
    "  - `index.html`: Container `<div id=\"chart\"></div>`, loads `app.js`.  \n",
    "  - `app.js`: Fetches `km_data.json`, renders overlay.  \n",
    "- **Rendering:**  \n",
    "  ```javascript\n",
    "  d3.json('km_data.json').then(data => {\n",
    "      const svg = d3.select('#chart').append('svg').attr('width', 600).attr('height', 400);\n",
    "      const xScale = d3.scaleLinear().domain([0, d3.max(data.donor.time)]).range([0, 550]);\n",
    "      const yScale = d3.scaleLinear().domain([0, 1]).range([350, 0]);\n",
    "      \n",
    "      // Donor curve\n",
    "      svg.append('path')\n",
    "         .datum(Object.entries(data.donor))\n",
    "         .attr('d', d3.line().x(d => xScale(d[0])).y(d => yScale(d[1])))\n",
    "         .attr('stroke', 'blue');\n",
    "      \n",
    "      // Control curve\n",
    "      svg.append('path')\n",
    "         .datum(Object.entries(data.control))\n",
    "         .attr('d', d3.line().x(d => xScale(d[0])).y(d => yScale(d[1])))\n",
    "         .attr('stroke', 'red');\n",
    "      \n",
    "      // CIs (shaded)\n",
    "      svg.append('path')\n",
    "         .datum(data.ci_donor)\n",
    "         .attr('d', d3.area().x(d => xScale(d.time)).y0(d => yScale(d.lower)).y1(d => yScale(d.upper)))\n",
    "         .attr('fill', 'blue').attr('opacity', 0.2);\n",
    "  });\n",
    "\n",
    "  "
   ]
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    "\n",
    " \n",
    "## 3. Explanatory Notes for Kaplan-Meier App Project\n",
    "\n",
    "### Purpose\n",
    "These notes clarify the context, intent, and additional details behind the core content in the main .md file, separating commentary from the primary narrative for clarity and usability.\n",
    "\n",
    "### Shakespearean Context\n",
    "- **Why Midsummer and As You Like It?** The debate started with identifying Shakespeare’s greatest comedy. *Midsummer* was picked for its whimsical chaos, tying to the app’s data-wrangling roots. *As You Like It*’s pastoral depth, especially Jaques, reflects the observer-to-contributor shift in my story.\n",
    "- **Jaques’ Role:** His outsider wit mirrors my life’s avoidance, making him a thematic anchor for the app’s origin.\n",
    "\n",
    "### Personal Reflection Notes\n",
    "- **Observer’s Life:** The “3-5 stunning women” and “elite institutions” are specific to my experience, showing a life of privilege and detachment. “Massive combinatorial search space” is a nod to exhaustive self-exploration—mathy, but personal.\n",
    "- **Debt to Neighbor and God:** This is the pivot—45 years of watching, now turning outward. It’s philosophical, not literal, driving the app’s purpose.\n",
    "\n",
    " ### App Concept\n",
    "- **Why KM Curves?** Kaplan-Meier is nonparametric, ideal for messy survival data (e.g., kidney donors). Overlaying donor vs. non-donor with CIs visualizes risk and uncertainty intuitively.\n",
    "- **Generalizability:** The app’s core—time-to-event analysis—works beyond medicine (e.g., finance, tech), which fuels the Phase 3 pitch.\n",
    "\n",
    "### Technical Implementation Notes\n",
    "- **Back End (Python):**\n",
    " - **Libraries:** `lifelines` is KM gold; `pandas` handles .csv mess; `numpy`/`scipy` crunch matrices and stats. \n",
    " - **Covariate Adjustment:** Betas tweak hazards (Cox-like), but Monte Carlo sampling for CIs is a simplification—could use Greenwood’s formula for precision. \n",
    " - **JSON Export:** Static for now; Flask API is a future step for real-time inputs.\n",
    "- **Front End (JS/HTML):**\n",
    " - **D3.js Choice:** Flexible for curves and CIs; could swap for Chart.js if simpler. `jQuery` is optional—vanilla JS works too. \n",
    " - **Rendering:** Code assumes `data.donor.time` exists—needs error handling. CI shading stops at donor for brevity; control CI is implied.\n",
    " - **Interactivity (Omitted):** Tooltips (hover for survival probs) and sliders (covariate tweaks) are stretch goals, left out of core .md per cutoff request.\n",
    "- **Deployment (GitHub Pages):**\n",
    " - **Static Limit:** Precomputed JSON fits GitHub Pages; cloud (AWS Lambda) is for dynamic scaling, not current state.\n",
    "- **Cutoff Rationale:** Main .md ends at \"Rendering\" per user’s note, but \"Deployment\" is included as it’s core to execution.\n",
    "\n",
    "### Pitching Strategy Notes\n",
    "- **Hopkins as Launchpad:** Low-hanging fruit due to transplant, biostats, and PAIRS@JH strengths—validates before scaling.\n",
    "- **Phase 2 (Institutions):** Targets reflect prestige and relevance—e.g., Mayo for transplant, UW for stats.\n",
    "- **Phase 3 (Decision-Making):** Wild leap—KM for non-medical use (e.g., startup survival) sells uncertainty as the killer app. Tech evolution (APIs, React Native) is speculative, not implemented.\n",
    "\n",
    "### Shakespeare-App Connection\n",
    "- **Midsummer:** Puck’s chaos parallels data mess; app orders it like Oberon’s fix.\n",
    "- **As You Like It:** Jaques’ shift from observer to commentator echoes my app’s service turn.\n",
    "\n",
    "### Why Two Files?\n",
    "- **Core .md:** Pure content for pitches, repos, or docs—standalone and clean. \n",
    "- **Notes .md:** Explains intent, tech choices, and omissions for collaborators or future me, without bloating the main file.\n",
    "\n",
    "This keeps the project modular—content for show, notes for know-how."
   ]
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