Freedom in Fetters#
CG-BEST. This is our bequest according to cosmology, geology, biology, ecology, symbiotology, teleology
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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 {
'Suis': ['DNA, RNA, 5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', "Glucans, Chitin", 'Specific Antigens'],
'Voir': ['PRR & ILCs, 20%'],
'Choisis': ['CD8+, 50%', 'CD4+'],
'Deviens': ['TNF-α, IL-6, IFN-γ', 'PD-1 & CTLA-4', 'Tregs, IL-10, TGF-β, 20%'],
"M'èléve": ['Complement System', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['PRR & ILCs, 20%'],
'paleturquoise': ['Specific Antigens', 'CD4+', 'Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'],
'lightgreen': ["Glucans, Chitin", 'PD-1 & CTLA-4', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],
'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'CD8+, 50%', 'TNF-α, IL-6, IFN-γ', 'Complement System'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights
def define_edges():
return {
('DNA, RNA, 5%', 'PRR & ILCs, 20%'): '1/99',
('Peptidoglycans, Lipoteichoics', 'PRR & ILCs, 20%'): '5/95',
('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',
('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',
("Glucans, Chitin", 'PRR & ILCs, 20%'): '80/20',
('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',
('PRR & ILCs, 20%', 'CD8+, 50%'): '20/80',
('PRR & ILCs, 20%', 'CD4+'): '80/20',
('CD8+, 50%', 'TNF-α, IL-6, IFN-γ'): '49/51',
('CD8+, 50%', 'PD-1 & CTLA-4'): '80/20',
('CD8+, 50%', 'Tregs, IL-10, TGF-β, 20%'): '95/5',
('CD4+', 'TNF-α, IL-6, IFN-γ'): '5/95',
('CD4+', 'PD-1 & CTLA-4'): '20/80',
('CD4+', 'Tregs, IL-10, TGF-β, 20%'): '51/49',
('TNF-α, IL-6, IFN-γ', 'Complement System'): '80/20',
('TNF-α, IL-6, IFN-γ', 'Platelet System'): '85/15',
('TNF-α, IL-6, IFN-γ', 'Granulocyte System'): '90/10',
('TNF-α, IL-6, IFN-γ', 'Innate Lymphoid Cells, 5%'): '95/5',
('TNF-α, IL-6, IFN-γ', 'Adaptive Lymphoid Cells'): '99/1',
('PD-1 & CTLA-4', 'Complement System'): '1/9',
('PD-1 & CTLA-4', 'Platelet System'): '1/8',
('PD-1 & CTLA-4', 'Granulocyte System'): '1/7',
('PD-1 & CTLA-4', 'Innate Lymphoid Cells, 5%'): '1/6',
('PD-1 & CTLA-4', 'Adaptive Lymphoid Cells'): '1/5',
('Tregs, IL-10, TGF-β, 20%', 'Complement System'): '1/99',
('Tregs, IL-10, TGF-β, 20%', 'Platelet System'): '5/95',
('Tregs, IL-10, TGF-β, 20%', 'Granulocyte System'): '10/90',
('Tregs, IL-10, TGF-β, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
('Tregs, IL-10, TGF-β, 20%', 'Adaptive Lymphoid Cells'): '20/80'
}
# Define edges to be highlighted in black
def define_black_edges():
return {
('DNA, RNA, 5%', 'PRR & ILCs, 20%'): '1/99',
('Peptidoglycans, Lipoteichoics', 'PRR & ILCs, 20%'): '5/95',
('Lipopolysaccharide', 'PRR & ILCs, 20%'): '20/80',
('N-Formylmethionine', 'PRR & ILCs, 20%'): '51/49',
("Glucans, Chitin", 'PRR & ILCs, 20%'): '80/20',
('Specific Antigens', 'PRR & ILCs, 20%'): '95/5',
}
# 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
def visualize_nn():
layers = define_layers()
colors = assign_colors()
edges = define_edges()
black_edges = define_black_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), weight in edges.items():
if source in mapping and target in mapping:
new_source = mapping[source]
new_target = mapping[target]
G.add_edge(new_source, new_target, weight=weight)
edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
# Draw the graph
plt.figure(figsize=(12, 8))
edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
nx.draw(
G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
plt.title("OPRAH™ aAPCs", fontsize=18)
plt.show()
# Run the visualization
visualize_nn()
#
Fig. 17 In the grand tapestry of existence, the dance of Apollo and Dionysus bequeaths us a legacy written in the stars and stone, cells and symbiosis, ecosystems and purpose. Cosmology whispers of a universe born in chaos, its ordered galaxies spiraling from a wild singularity. Geology etches time into the Earth’s crust, each layer a testament to upheaval and stillness entwined. Biology gifts us variability, the fractal branching of life defying any singular script, while ecology binds us in webs of interdependence, a chorus of voices both discordant and harmonious. Symbiotology reveals our essence as collaborative beings, hosts to countless others in a shared becoming. And teleology, ever elusive, hints at a direction—not a destination, but a striving, a bequest of meaning forged in the tension between order and entropy. This is our inheritance, a CG-BEST mosaic: not a clock to wind or a cloud to chase, but a living fractal, infinite in its finite frame, urging us to embrace both the lyre and the wine.#