<｜begin▁of▁sentence｜>### Instruction:
In the complex network of hormone interactions within the human body, imagine we have a pathway that mimics a simplified signaling cascade, starting at the hypothalamus (node 1) and moving through various organs and glands (nodes 2 to 5), ultimately stimulating the adrenal glands (node 5). The connections in this model are similar to a chain with consecutive links represented by edges: [(1, 2), (2, 3), (3, 4), (4, 5)].

To better understand how stress—the hypothetical stimulus in this scenario—affects the system, we'd like to determine the relative importance of each link in the signaling pathway. Specifically, can we calculate the influence of each connection, akin to an edge current flow betweenness centrality, when the stress signal initiates at the hypothalamus and concludes its effect at the adrenal glands? This would reveal to us which link (or edge) carries the most critical influence in this hormonal relay system, potentially identifying a key regulatory point that could be a target for therapeutic intervention.
### Response:
import networkx as nx
import numpy as np

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5)])

# Define sources and targets
sources = [1]
targets = [5]

# Calculate edge current flow betweenness centrality for specified node subsets
betweenness_subset = nx.edge_current_flow_betweenness_centrality_subset(
    G,
    sources=sources,
    targets=targets,
    normalized=True,
    weight=None,
    dtype=np.float64,
    solver='lu'
)

print('Edge current flow betweenness centrality subset:', betweenness_subset)
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