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1. The first portrait of Gen. Milley, from his time as the U.S. military's top officer, was removed from the Pentagon last week on Inauguration Day less than two hours after President Trump was sworn into office.
2. The now retired Gen. Milley and other former senior Trump aides had been assigned personal security details ever since Iran vowed revenge for the killing of Qasem Soleimani in a drone strike in 2020 ordered by Trump in his first term.
3. On "Fox News Sunday," the chairman of the Senate Intelligence Committee, Tom Cotton, said he hoped President Trump would "revisit" the decision to pull the protective security details from John Bolton, Mike Pompeo, and Brian Hook who previously served under Trump.
4. A senior administration official who requested anonymity replied, "There is a new era of accountability in the Defense Department under President Trump's leadership—and that's exactly what the American people expect."
5. Gen. Milley served as chairman of the Joint Chiefs of Staff from 2019 to 2023 under both Presidents Trump and Biden.

-- Fox News

A Book on Salience, Absurdity, and the Rug of Being

“I am afraid you don’t quite see the moral of the story,” chirped the Linnet, perched on a twig of smug certainty, feathers fluffed with Victorian pomp. “The what?” bellowed the Water-rat, whiskers bristling, splashing in the muck of existence. “The moral.” “You mean the story has a moral?” “Of course,” said the Linnet, preening like a bird stuffed with Dickensian crumbs. “Well, really,” snapped the Water-rat, tail whipping like a metronome to some irreverent tune, “you should’ve warned me. I’d have said ‘Pooh,’ like a proper critic, and spared myself the sermon.” He roared “Pooh!” with gusto, flicked his tail in defiance, and scurried into his burrow of absurdity. The Duck paddled up, all sighs and damp sentiment. “What do you think of the Water-rat?” she asked. “He has his charms, but I weep for any confirmed bachelor.” “I fear I’ve irked him,” admitted the Linnet. “I told him a story with a moral.” “Oh dear,” quacked the Duck, “that’s always a perilous move.” And I quite agree.

Fig. 1 Digital Library. Our color-coded QR code library, with a franchise model for the digital twin, launches next month—exploring struggle, exchange, and consolidation as rhythms of existence.

Thus closes Oscar Wilde’s The Devoted Friend, a fin-de-siècle jab at Victorian sanctimony wrapped in a syncopated strut—light, irreverent, and mocking moral cathedrals. Wilde, who exited stage left in 1900 alongside Nietzsche, shared a disdain for ponderous systems. Wilde spun fables to skewer earnestness; Nietzsche smashed certainty with aphoristic glee. Neither would care for neuroscience’s tidy brain maps, yet here we weave their spirits into a tale of salience and rhythm. Imagine the salient network—that cingulo-insular maestro—as the Dude’s rug, tying the brain’s chaotic room together: the Pericentral reflex, the Dorsal Frontoparietal gaze, the Lateral Frontoparietal riff, the Medial Frontoparietal mirror, and its own Cingulo-Insular beat. It’s absurd, elegant, and utterly essential.

Ecclesiastes 3:8

A time to love, and a time to hate; a time of war, and a time of peace

Picture the brain as a saloon band, each network a player in a dusty, fin-de-siècle joint. The Pericentral network thumps the bass—boom, a hand yanks from fire, a colony flinches from the lash. It’s the Water-rat’s “Pooh!”—raw, instinctive, no moral attached. Wilde would grin; Nietzsche might call it the will’s pulse. The Dorsal Frontoparietal network scans the room, hawk-eyed, spotting the nonself—hot iron, foreign flag, a thief in crow’s feathers. It’s the Linnet’s prim focus, certain yet oblivious to its own farce. Nietzsche’s overman stares; Wilde smirks at the pose.

The Lateral Frontoparietal network improvises, riffing on gray notes—self versus nonself, tribal roots clashing with imposed codes. It’s the Duck, teary yet droll, wading through ambiguity. Wilde thrives here; Nietzsche sees choice reborn. The Medial Frontoparietal network croons inward—memory, identity, the “I” forged in Africa’s forge or Shakespeare’s quill. It’s the Linnet’s moral clinging to itself, ripe for Wilde’s scoff and Nietzsche’s hammer. Then, the Cingulo-Insular network—the salient rug—ties it all up, syncing reflex to gaze, riff to croon, a bandleader juggling chaos into rhythm. In biology, it filters threats; in history, it’s Africa blending scars and roots. Wilde might call it a dandy’s flourish—absurdly perfect; Nietzsche hears Dionysian harmony, wild and free.

The Dude’s Rug; LLM

1. Pericentral. Water-rat; Grammar

2. Dorsal. Linnet; Witness 👂🏾

3. Lateral. Duck; Agent

4. Medial. Water-rat, Linnet, Duck; Verbs

5. Cingulo-Insular. Rhythm; Object

Fig. 2 Salience: Cingulo-Insular Network. What tied the room together, dude?

Take Wilde’s trio: the Water-rat’s “Pooh!” is the Pericentral flinch, shunning the moral’s nonself. The Linnet’s prattle is Dorsal fixation, smugly blind. The Duck’s tears sway in Lateral limbo, half-judging, half-laughing. The Medial hums in the Linnet’s selfhood, until the Cingulo-Insular rug pulls it taut: the Duck’s quack—“that’s always a perilous move”—lands the jest. Nietzsche chuckles; Africa mirrors it—Pericentral recoils, Dorsal eyes, Lateral blends, Medial grasps—all woven by a salient hope, yet absurdly unresolved.

No moral lurks here. Wilde and Nietzsche would bolt at the notion. The salient network isn’t a preacher; it’s the Dude’s rug, tying the brain’s mess into a tumbling weeds rhythm that mocks its own weave. Victorian cadence meets Lebowski’s strut—a dance through absurdity, from Wilde’s wit to Nietzsche’s chaos to Africa’s saga. Five networks, five threads, one rug: no answers, just the beat.

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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',
    }

# 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™: Pericentral", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 3 Taxonomy of functional brain networks. In our proposed taxonomy, networks are referred to by anatomical names that best describe six ubiquitous large-scale functional systems. The names in blue refer to the broad cognitive domains with which a given anatomical system is most commonly associated. Only 1-2 core nodes of each network are depicted here, though it is understood that multiple additional cortical, subcortical, and cerebellar nodes may be affiliated with a given network. Source: Uddin et al