Veiled Resentment

On September 12, 2024, Lieff Cabraser Heimann & Bernstein, LLP, alongside Justice Catalyst Law, initiated a significant federal antitrust lawsuit in New York on behalf of Dr. Lucina Uddin, a neuroscience professor representing a proposed class of U.S. scholars and scientists. This legal action targets six major commercial academic journal publishers—Elsevier, Springer Nature, Taylor & Francis, Sage, Wiley, and Wolters Kluwer—accused of conspiring to exploit the academic community for profit, thereby diverting billions of dollars that could have supported scientific research. An amended complaint followed on November 15, 2024, further detailing the allegations. The lawsuit claims that these publishers, dominating over 53 percent of the global peer-reviewed journal market, have engaged in an anticompetitive scheme with three core elements, each deemed unlawful under Section 1 of the Sherman Act. First, they allegedly fixed the price of peer review services at zero, coercing scholars to provide this essential labor without compensation by tying it to their ability to publish in prestigious journals, a critical factor in the “publish or perish” academic environment. Second, they enforced a “Single Submission Rule,” requiring scholars to submit manuscripts to only one journal at a time, reducing competition among publishers, slowing the review process, and stripping authors of bargaining power. Third, a “Gag Rule” prohibits scholars from sharing their research during the often year-long peer review process and, upon publication, frequently requires them to relinquish intellectual property rights without payment, allowing publishers to monopolize and profit from the scientific knowledge.

Fig. 27 Uddin: a Kindred Spirit. Our gh-pages based ecosystem integration & navigation (EIN) framework is a competitive solution to a diagnosis we reached independently of Uddin. Source: Draft Complaint

This scheme, facilitated through the publishers’ control of the International Association of Scientific, Technical, and Medical Publishers (STM), has proven immensely profitable, generating over $10 billion in revenue in 2023 alone, with profit margins surpassing those of companies like Apple and Google. Critics, including NewScientist and Deutsche Bank, have labeled it an “indefensible” and “bizarre triple-pay system,” where taxpayers fund the research, peer review, and subsequent access costs, while publishers reap the benefits without contributing to the science itself. The complaint highlights how this exploitation has triggered a peer review crisis, with unpaid scholars increasingly fatigued and unwilling to review, leading to significant delays in publication—sometimes months or years—hindering scientific progress in fields like cancer research, quantum computing, and climate change solutions. The publishers exacerbate this harm by charging exorbitant Article Processing Charges (APCs), ranging from hundreds to over $10,000 per article, shifting costs onto scholars while claiming these fees support unpaid peer review, further profiting from their anticompetitive practices.

Danger

• Response. Primitive

• Nonself. Dorsal

• Appraisal. Lateral

• Self. Medial

• Optimum. Dynamic

The lawsuit seeks treble damages and injunctive relief to dismantle these unlawful agreements, arguing that they not only injure scholars by suppressing wages and restricting research dissemination but also damage the public interest by delaying critical advancements. Filed in the Eastern District of New York, the case invokes the Sherman and Clayton Acts, asserting that the publishers’ market dominance—owning majorities in most academic disciplines—and their coordinated policies through STM constitute a cartel that stifles competition and innovation. Dr. Uddin, with over 175 published articles and extensive peer review experience, exemplifies the affected class, estimated to number in the hundreds of thousands. The plaintiffs demand a jury trial and aim to hold these publishers accountable, potentially reshaping the academic publishing landscape to better serve science and society rather than corporate profits.

Show code cell source Hide code cell source

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 {
        ('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'
    }

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

# Run the visualization
visualize_nn()

Fig. 28 Pericentral, Dorsal, Lateral, Medial, Cingulo-insular. These networks may be aligned with the five layers of our immunoneural network model to reveal convergence across completely unrelated biological and nonbiological systems. Importantly, the midcingulo-insular network (CIN), the most evolutionarily ancient and most conserved across mammal and nonmammalian animals, emerges as an integral hub in mediating dynamic interactions between other large-scale brain networks. As the output layer it can be viewed as the arbitrar of the “optimizaing funciton”, which turns out to be dynamic – unlike most human systems with fixed policies and credos.