DOI: 10.5281/zenodo.17206965
title: Uncropped Film Scans and Analysis Data Addressing PubPeer Query on JNC 2011 vs JNC 2009_Socodato et al.

description: This dataset addresses allegations related to cross‑paper similarities between Paper 1 (2009, J Neurochem) and Paper 2 (2011, J Neurochem): (i) Fig. 2d (AKT) vs Fig. 4e (AKT), and (ii) Fig. 3b/5a (pCREB & AKT) vs Fig. 4c. We re‑analyzed the uncropped films with a lane‑agnostic, ECC‑aligned computer‑vision pipeline.

The PubPeer pages showing the annotated panels are attached for context.

 

Concern 1 — Paper 1, Fig. 2d (AKT) vs Paper 2, Fig. 4e (AKT)

Lane mapping tested: 2d‑L1 ↔ 4e‑L2, 2d‑L2 ↔ 4e‑L3, 2d‑L3 ↔ 4e‑L4.

Defaults: full‑height search; band ≈ 8% of image height; ECC alignment; uncropped films.

Results (per‑lane metrics, ECC‑aligned):





2d‑L1 ↔ 4e‑L2: NCC −0.170, SSIM 0.579, Edge‑Hamming 0.094, Profile_r 0.648, Residual_corr −0.052.




2d‑L2 ↔ 4e‑L3: NCC 0.909, SSIM 0.983, Edge‑Hamming 0.152, Profile_r 0.729, Residual_corr −0.026.




2d‑L3 ↔ 4e‑L4: NCC 0.276, SSIM 0.982, Edge‑Hamming 0.191, Profile_r 0.427, Residual_corr −0.092.

(ORB inlier ratio ≈ 0 for all three.) 



Interpretation. One pair (2d‑L2 ↔ 4e‑L3) shows high structural similarity (NCC≈0.91, SSIM≈0.98), but fails geometric/edge criteria: Edge‑Hamming remains high (0.152), ORB inliers ≈ 0, and residual correlations are negative. In our calibrated framework, true re‑use requires concordance across structure + edge + local geometry; here, the signal does not reach a multi‑metric duplication signature. We therefore do not find evidence that AKT bands were re‑used between Fig. 2d (2009) and Fig. 4e (2011).   

 

Concern 2 — “Very similar” pCREB & AKT between Paper 1 (Fig. 3b, Fig. 5a) and Paper 2 (Fig. 4c)

Protocol: strictly protein‑matched comparisons; full‑height search; band ≈ 8%H; ECC alignment; uncropped films.

AKT (3b lanes 2 & 3 → 4c all lanes, best hits):





Target L2 best at 4c‑L1 — NCC 0.239, SSIM 0.874, Edge‑Hamming 0.234.




Target L3 best at 4c‑L5 — NCC 0.277, SSIM 0.765, Edge‑Hamming 0.288.

(ORB inlier ratio 0.0 in both best pairs.)



 

pCREB (3b lanes 2 & 3 → 4c and 5a, best hits):





3b → 4c: L2 best NCC 0.330, SSIM 0.846; L3 best NCC 0.276, SSIM 0.943.




3b → 5a: L2 best NCC 0.577, SSIM 0.931; L3 best NCC 0.681, SSIM 0.914.



 

Calibration vs. non‑match baselines (percentiles).

For the AKT 3b→4c screen, the “best” hits fall inside the negative distributions: e.g., for 4c‑AKT target L2, NCC and SSIM sit at the 100th percentile of negatives, Edge‑Hamming ≈ 89th, Profile_r ≈ 100th; for target L3, SSIM ≈ 33rd and Edge‑Hamming ≈ 33rd percentile. (See percentiles.csv and best_pairs.csv.)

Interpretation. The closest cross‑paper matches remain definitively non‑matches by calibrated criteria: structural similarity alone is insufficient without edge/geometry concordance, and all “best” pairs lie within their negative baselines. These datasets are independent films that share some gross morphology but diverge in micro‑texture and edge geometry.   

 

What the metrics mean





NCC (−1…+1): normalized cross‑correlation after ECC; +1 identical.




SSIM (0…1): structural similarity; values ~0.98 can occur without geometric agreement.




Edge‑Hamming (0 identical → higher = different): Hamming distance of binarized edge maps; small values indicate edge‑shape identity.




Profile_r (0…1): correlation of vertical intensity profiles; insensitive to micro‑texture differences.




Residual_corr (−1…+1): correlation of difference images; values near 0/negative argue against duplication.




ORB inlier ratio (0…1): fraction of keypoint matches surviving RANSAC; ≈0 indicates no coherent geometric mapping.



 
description: Methods / Code (how to use it).

Download the script:



cv_metrics_reproduction.py


 

What the script does (core pieces).



Lane detection: column‑projection peaks on the inverted image; lanes ordered left→right.

Lane windows: a fixed half‑width (default 40 px).

Band finder: vertical sliding window of height band_ratio × H (default 0.08×H) picks the band center by maximal inverted intensity.

Alignment: ECC (Euclidean) aligns A‑band to B‑band.

Metrics (after alignment):



NCC: zero‑lag Pearson correlation on normalized intensities (−1…+1).

SSIM_proxy: skimage.metrics.ssim (0…1).

Edge‑Hamming: Hamming distance on Canny edge maps, normalized by edge union (0 identical → higher = more different).

Profile_r: Pearson r of row‑mean (vertical) intensity profiles.

Residual_corr: correlation of high‑frequency residuals (image − Gaussian blur; σ=2).

ORB_inlier_ratio: fraction of ORB matches surviving RANSAC homography.



 

CLI usage (exact commands).



Concern 5 — fixed lane pairs (e.g., 2d‑AKT ↔ 4e‑AKT)


python cv_metrics_reproduction.py \

  --A /path/to/paper1_fig2d_AKT.tif --B /path/to/paper2_fig4e_AKT.tif \

  --nlanesA 3 --nlanesB 4 --lane_half_width 40 --band_ratio 0.08 \

  pairs --pairs A1:B2 A2:B3 A3:B4 --out_csv pair_metrics.csv

 



Output: pair_metrics.csv with rows like


A L1 ↔ B L2, NCC, SSIM_proxy, Edge_Hamming, Profile_r, Residual_corr, ORB_inlier_ratio.

 



Concern 6 — scan best matches for target lanes (e.g., 3b lanes 2 & 3 vs 4c all lanes)


python cv_metrics_reproduction.py \

  --A /path/to/paper1_fig3b_pCREB.tif --B /path/to/paper2_fig4c_pCREB.tif \

  --nlanesA 3 --nlanesB 5 --lane_half_width 40 --band_ratio 0.08 \

  scan --targets 2 3 --out_csv best_pairs.csv

 



Then compute negative‑baseline percentiles for those best hits:


python cv_metrics_reproduction.py \

  --A /path/to/paper1_fig3b_pCREB.tif --B /path/to/paper2_fig4c_pCREB.tif \

  --nlanesA 3 --nlanesB 5 --lane_half_width 40 --band_ratio 0.08 \

  calibrate --best_csv best_pairs.csv --targets 2 3 --out_csv percentiles.csv --n_random 18



Repeat the same scan + calibrate for 3b AKT → 4c AKT, and 3b pCREB → 5a pCREB (adjust --A, --B, lane counts).


 

Script outputs (CSV schema).



pair_metrics.csv:


pair, NCC, SSIM_proxy, Edge_Hamming, Profile_r, Residual_corr, ORB_inlier_ratio



best_pairs.csv:


target_lane, comp_lane, NCC, SSIM_proxy, Edge_Hamming, Profile_r, Residual_corr, ORB_inlier_ratio



percentiles.csv (optional):


target_lane, metric, value, neg_percentile  (negatives from B off‑lane random windows; by default 18 samples per target).

 

Programmatic use (import and call).

from cv_metrics_reproduction import run_pair_table, scan_best_pairs, calibrate_negatives

 

# Concern 5

run_pair_table("2d_AKT.tif","4e_AKT.tif", [(1,2),(2,3),(3,4)], "pair_metrics.csv",

               nlanes_A=3, nlanes_B=4, lane_half_width=40, band_ratio=0.08)

 

# Concern 6 (example: 3b pCREB → 4c pCREB)

best = scan_best_pairs("3b_pCREB.tif","4c_pCREB.tif", [2,3], "best_pairs.csv",

                       nlanes_A=3, nlanes_B=5, lane_half_width=40, band_ratio=0.08)

calibrate_negatives("3b_pCREB.tif","4c_pCREB.tif", [2,3], best, "percentiles.csv",

                    nlanes_A=3, nlanes_B=5, lane_half_width=40, band_ratio=0.08, n_random=18)

 

 
description: CONDITIONS OF ACCESS AND USE

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