Duality

Semaglutide, a glucagon-like peptide-1 receptor agonist, has been shown to reduce the risk of adverse cardiovascular events in patients with diabetes. Whether semaglutide can reduce cardiovascular risk associated with overweight and obesity in the absence of diabetes is unknown.

Entropy: Wisdom (Streets)
Resources: Vigilance (Owl)
Faustian: Noise (Molecule) vs. Signal (Epitope)
Distributed: Self (Helmet), Negotiable (Shield), Nonself (Spear)
Illusion: Harmony (Lyre)
— Inverted Tree

Pattern recognition and speculation are instinctive and vestigual aspects of our complex neural, endocrine, and immune systems.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Tragedy (Pattern Recognition)': ['Cosmology', 'Geology', 'Biology', 'Ecology', "Symbiotology", 'Teleology'],
        'History (Resources)': ['Resources'],  
        'Epic (Negotiated Identity)': ['Faustian Bargain', 'Islamic Finance'],  
        'Drama (Self vs. Non-Self)': ['Darabah', 'Sharakah', 'Takaful'],  
        "Comedy (Resolution)": ['Cacophony', 'Outside', 'Ukhuwah', 'Inside', 'Symphony']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Resources'],  
        'paleturquoise': ['Teleology', 'Islamic Finance', 'Takaful', 'Symphony'],  
        'lightgreen': ["Symbiotology", 'Sharakah', 'Outside', 'Inside', 'Ukhuwah'],  
        'lightsalmon': ['Biology', 'Ecology', 'Faustian Bargain', 'Darabah', 'Cacophony'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edges
def define_edges():
    return [
        ('Cosmology', 'Resources'),
        ('Geology', 'Resources'),
        ('Biology', 'Resources'),
        ('Ecology', 'Resources'),
        ("Symbiotology", 'Resources'),
        ('Teleology', 'Resources'),
        ('Resources', 'Faustian Bargain'),
        ('Resources', 'Islamic Finance'),
        ('Faustian Bargain', 'Darabah'),
        ('Faustian Bargain', 'Sharakah'),
        ('Faustian Bargain', 'Takaful'),
        ('Islamic Finance', 'Darabah'),
        ('Islamic Finance', 'Sharakah'),
        ('Islamic Finance', 'Takaful'),
        ('Darabah', 'Cacophony'),
        ('Darabah', 'Outside'),
        ('Darabah', 'Ukhuwah'),
        ('Darabah', 'Inside'),
        ('Darabah', 'Symphony'),
        ('Sharakah', 'Cacophony'),
        ('Sharakah', 'Outside'),
        ('Sharakah', 'Ukhuwah'),
        ('Sharakah', 'Inside'),
        ('Sharakah', 'Symphony'),
        ('Takaful', 'Cacophony'),
        ('Takaful', 'Outside'),
        ('Takaful', 'Ukhuwah'),
        ('Takaful', 'Inside'),
        ('Takaful', 'Symphony')
    ]

# Define black edges (1 → 7 → 9 → 11 → [13-17])
black_edges = [
    (4, 7), (7, 9), (9, 11), (11, 13), (11, 14), (11, 15), (11, 16), (11, 17)
]

# Calculate node positions
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph with correctly assigned black edges
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()

    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1

    # Add nodes with new numbered labels and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))

    # Add edges with updated node labels
    edge_colors = {}
    for source, target in edges:
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target)
            edge_colors[(new_source, new_target)] = 'lightgrey'

    # Define and add black edges manually with correct node names
    numbered_nodes = list(mapping.values())
    black_edge_list = [
        (numbered_nodes[3], numbered_nodes[6]),  # 4 -> 7
        (numbered_nodes[6], numbered_nodes[8]),  # 7 -> 9
        (numbered_nodes[8], numbered_nodes[10]), # 9 -> 11
        (numbered_nodes[10], numbered_nodes[12]), # 11 -> 13
        (numbered_nodes[10], numbered_nodes[13]), # 11 -> 14
        (numbered_nodes[10], numbered_nodes[14]), # 11 -> 15
        (numbered_nodes[10], numbered_nodes[15]), # 11 -> 16
        (numbered_nodes[10], numbered_nodes[16])  # 11 -> 17
    ]

    for src, tgt in black_edge_list:
        G.add_edge(src, tgt)
        edge_colors[(src, tgt)] = 'black'

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, 
        edge_color=[edge_colors.get(edge, 'lightgrey') for edge in G.edges],
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    
    plt.title("Self-Similar Micro-Decisions", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 12 Dynamic Capability. The monumental will align adversarial TNF-α, IL-6, IFN-γ with antigens from pathogens of “ancient grudge”, a new mutiny with antiquarian roots. But it will also tokenize PD-1 & CTLA-4 with specific, emergent antigens, while also reappraising “self” to ensure no rogue viral and malignant elements remain unnoticéd.