Abstract ↔ claims
run not observed · v1 provisional awaiting approvalWhich claims does the abstract carry, and which does it drop?
for A deep learning pipeline for mapping in situ network-level neurovascular coupling in multi-photon fluorescence microscopy · this layer across all papers · json
Provisional
This layer needs a corpus-scope decision that has not been ruled on yet, so what it produces would change if the decision changed. It waits on claim-format, relation-vocab.
Awaiting approval
Waiting for approval. That is a statement about the record, not about whether anyone has read this: people read the corpus without stamping what they read, and only a stamp leaves a trace. Approval is an operation on a version, not a step of its own — it is recorded against the version it was granted to, so running this layer again does not carry it forward.
What this layer produced
Abstract mapped to claims
The paper's abstract is shown with each sentence linked to the claim(s) it represents in the dependency graph. Hover or click a sentence to highlight the corresponding claim cards. Below: what the graph contains that the abstract leaves out, and vice versa.
1Functional hyperemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the activity of the surrounding neurons. 2Although functional hyperemia has been extensively studied at the level of both tissue and individual vessels, vascular network-level coordination remains largely unknown. 3To bridge this gap, we developed a deep learning-based pipeline that uses two-photon fluorescence microscopy images of cerebral microcirculation to enable automated reconstruction and quantification of the geometric changes across the microvascular network, comprising hundreds of interconnected blood vessels, pre and post-activation of the neighboring neurons. 4The pipeline’s utility was demonstrated in the Thy1-ChR2 optogenetic mouse model, where we observed network-wide vessel radius changes to depend on the photostimulation intensity, with both dilations and constrictions occurring across the cortical depth, at an average of 16.1±14.3 μm (mean ± SD) away from the most proximal neuron for dilations; and at 21.9±14.6 μm away for constrictions. 5We observed a significant heterogeneity of the vascular radius changes within vessels, with radius adjustment varying by an average of 24 ± 28% of the resting diameter, likely reflecting the heterogeneity of the distribution of contractile cells on the vessel walls. 6A graph theory-based network analysis revealed that the assortativity of adjacent blood vessel responses rose by 152 ± 65% at 4.3 mW/mm² of blue photostimulation vs. the control, with a 4% median increase in the efficiency of the capillary networks during this level of blue photostimulation in relation to the baseline. 7Interrogating individual vessels is thus not sufficient to predict how the blood flow is modulated in the network. 8Our pipeline, enables tracking of the microvascular network geometry over time, relating caliber adjustments to vessel wall-associated cells’ state, and mapping network-level flow distribution impairments in experimental models of disease.
- H1.P1.4 unetr-outperforms-ilastik-hd95 fig3 UNETR ensemble segmentation shows significantly better HD95 surface distance than ilastik for both vessel and neuron channels (p<0.05 Wilcoxon signed-rank), while ilastik over-segments vessels with high recall (0.89±0.19) but low precision (0.37±0.33), evaluated on nine test images (507×507×250 µm) from six held-out mice.
- H1.P2.1 artery-dilates-venule-unchanged-at-low-power fig7A At 1.1 mW/mm² 458 nm stimulation, a sample artery dilated 1.33±0.86 µm (p<1e-4) and a sample capillary dilated 0.42±0.39 µm (p<1e-4), while a sample venule showed no significant radius change (p=0.22), demonstrating vessel-type heterogeneity in optogenetic neurovascular responses.
- H1.P2.9 wt-controls-no-blue-green-difference app1fig9 Wild-type C57BL/6J mice (n=4) show no statistically distinguishable capillary radius distributions following blue (458 nm) versus green (552 nm) photostimulation, confirming that vascular responses in Thy1-ChR2 mice are ChR2-specific and not attributable to photothermal or non-specific light effects.
- E1 novas3d-generalizes-qualitatively-ood app1fig12, app1fig13 The NOVAS3D segmentation model produces qualitatively reasonable vessel segmentations on out-of-distribution data including a different mouse strain (C57BL/6), a different species (Fischer rat), and a different microscope modality (light-sheet fluorescence microscopy, Miltenyi UltraMicroscope Blaze), without retraining.
- Sc3 responder-threshold-2sd-untested app1fig14 Vessels are classified as responders if their radius change exceeds twice the baseline standard deviation (2×σ); this threshold is not sensitivity-tested in the main analysis, though an alternative 10% threshold is shown in Appendix 1—figure 14 with qualitatively similar results.
How it is defined
A model answers this layer, so the prompt is the layer. It is reproduced below from the committed file, and it is a declared input — editing it makes every run that used it stale.
The declaration names this path and the repository does not have it. An input that does not exist hashes to nothing, so it cannot make a run stale — the layer is declared to depend on something it is not in fact tracking.
Artifacts
Versions
From the run ledger. There is no changelog beside it to keep in step.
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v1 · 2026-04-20 · unrecorded backfilled from the artifact
backfilled from the artifact on disk
This layer across the corpus
Across the corpus
9 run not observed · 1 stale·a paper links to its own cell, where this layer's output for it is rendered
Inputs and outputs
- Reads, besides its dependencies
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- extract/prompts/abstract-map.md · declared, and not in the repository — it hashes to nothing, so it cannot make a run stale
- Produces
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- site/src/data/abstract-mapping/{paper}.json
One per paper — the table above links each one that exists.
- Views
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- document — rendered above, from the artifact itself
- comparison — on the cell page, two versions aligned by the matcher, wherever the ledger holds more than one
Running it
The command comes from the declaration, so this text and what actually runs cannot
diverge. pipeline.py run also runs the unmet dependencies first.
python3 scripts/pipeline.py run <paper> abstract-map
Underneath, that runs cd extract && python3 -m claim_graphs.cli abstract-map --paper {paper}.