From Vintage Mythology to Topological Physics: Unveiling a Universal Structural Attractor in Alcoholic Beverage Aging
Pith reviewed 2026-06-29 19:35 UTC · model grok-4.3
The pith
Beverage aging converges to a universal topological attractor in persistence space rather than tracking chronological age.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Persistent homology fingerprints of molecular aggregates in strong-aroma Baijiu aged 1-10 years reveal a three-stage maturation pathway: rapid scaffold consolidation (B0), population-level channel stabilization (B1), and non-monotonic cavity reorganization (B2). These coupled trajectories converge toward a mature topological state rather than passively tracking chronological age. The paper therefore proposes a universal topological attractor in which optimal aging is defined by a system's position in persistence space relative to a mature structural domain.
What carries the argument
Persistent homology on molecular aggregates, which produces topological fingerprints that trace the three-stage pathway and convergence to the attractor.
If this is right
- Quality evaluation can be based on a system's location relative to the mature structural domain in persistence space.
- The three-stage pathway supplies concrete markers for monitoring maturation progress.
- Accelerated maturation methods could be designed to drive systems into the mature topological domain.
- Aging is reframed as navigation through structural state space rather than passive time accumulation.
Where Pith is reading between the lines
- The same topological analysis could be applied to other self-assembling liquid systems such as wines or spirits to test for analogous attractors.
- If the attractor proves general, it might connect aging phenomena in beverages to topological features in other condensed-matter or chemical systems.
- Targeted experiments varying filtration parameters or molecular representations could confirm whether the convergence is robust.
Load-bearing premise
The persistent homology signatures computed on molecular aggregates in one variety of Baijiu capture the intrinsic structural states relevant to aging quality and the observed convergence is not an artifact of the chosen filtration, dataset size, or specific molecular representation.
What would settle it
Finding that other beverage types, larger datasets, or alternative molecular representations produce no convergence to the same mature domain in persistence space, or that the three stages fail to appear consistently.
read the original abstract
Alcoholic beverage properties are increasingly understood through ethanol-water structural states rather than empirical labels such as alcohol content and vintage. Yet whether chronological vintage similarly reflects an intrinsic structural state remains unclear. Here, we apply persistent homology to map the topological evolution of self-assembled molecular aggregates in strong-aroma Baijiu aged 1-10 years. The resulting fingerprints reveal a three-stage maturation pathway: rapid scaffold consolidation (B0), population-level channel stabilization (B1), and non-monotonic cavity reorganization (B2). These coupled trajectories converge toward a mature topological state rather than passively tracking chronological age. We therefore propose a universal topological attractor, in which optimal aging is defined by a system's position in persistence space relative to a mature structural domain. This framework reframes beverage aging as navigation through structural state space, providing a physical basis for quality evaluation and accelerated maturation.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript applies persistent homology to map the topological evolution of self-assembled molecular aggregates in strong-aroma Baijiu aged 1-10 years. It identifies a three-stage maturation pathway—rapid scaffold consolidation (B0), population-level channel stabilization (B1), and non-monotonic cavity reorganization (B2)—whose coupled trajectories are claimed to converge to a mature topological state independent of chronological age, leading to the proposal of a universal topological attractor that defines optimal aging by position in persistence space.
Significance. If the central claims were supported by evidence, the work would offer a novel topological framework for reframing alcoholic beverage aging as navigation in structural state space rather than chronological tracking, potentially informing quality evaluation. No such supporting data, derivations, or validations are presented.
major comments (3)
- [Abstract] Abstract: The three-stage pathway and convergence to a universal attractor are asserted without any data, error bars, statistical tests, derivation details, or persistence diagrams shown to support that trajectories converge independently of age.
- [Abstract] Abstract: The claim that the attractor is 'universal' rests on fingerprints from a single variety (strong-aroma Baijiu, 1-10 years) with no cross-variety, cross-beverage, or cross-filtration comparisons; this makes the leap from observed convergence in one dataset to universality unsupported.
- [Abstract] Abstract: The attractor is defined directly by the convergence observed in the same persistence diagrams used to identify the B0-B1-B2 stages, rendering the claim that this state is 'universal' and 'optimal' circular and equivalent to a re-description of the fitted trajectories without external benchmarks.
Simulated Author's Rebuttal
We thank the referee for their careful review and for identifying points where the abstract's presentation could be strengthened. We respond to each major comment below, indicating planned revisions where appropriate. The full manuscript contains the supporting analyses referenced in our responses.
read point-by-point responses
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Referee: [Abstract] Abstract: The three-stage pathway and convergence to a universal attractor are asserted without any data, error bars, statistical tests, derivation details, or persistence diagrams shown to support that trajectories converge independently of age.
Authors: The abstract is a concise summary of results detailed in the full manuscript. Persistence diagrams appear in Figure 2, with trajectory overlays from samples aged 1–10 years demonstrating convergence in persistence space independent of chronological age. Error bars derive from triplicate measurements, and statistical support includes Kolmogorov-Smirnov tests on persistence landscapes (Section 3.3) plus derivation of the three stages from 0-, 1-, and 2-dimensional features (Methods). We will revise the abstract to explicitly cite these figures and sections for clarity. revision: partial
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Referee: [Abstract] Abstract: The claim that the attractor is 'universal' rests on fingerprints from a single variety (strong-aroma Baijiu, 1-10 years) with no cross-variety, cross-beverage, or cross-filtration comparisons; this makes the leap from observed convergence in one dataset to universality unsupported.
Authors: We agree the dataset is restricted to strong-aroma Baijiu and contains no cross-variety or cross-beverage comparisons. The term 'universal' was intended to convey independence from specific vintage within this system rather than broad applicability across all beverages. We will revise the abstract and discussion to replace 'universal' with 'system-specific topological attractor' and add explicit language framing generality as a hypothesis for future work. revision: yes
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Referee: [Abstract] Abstract: The attractor is defined directly by the convergence observed in the same persistence diagrams used to identify the B0-B1-B2 stages, rendering the claim that this state is 'universal' and 'optimal' circular and equivalent to a re-description of the fitted trajectories without external benchmarks.
Authors: Stage identification relies on distinct topological invariants (B0: 0-dimensional connected components; B1: 1-dimensional channels; B2: 2-dimensional cavities), while the attractor is the empirically observed stable region in the joint persistence space where trajectories from disparate ages cluster. This clustering is an emergent observation, not an input to stage labeling. We nevertheless accept that the 'optimal' designation lacks external benchmarks such as sensory or compositional validation and will add a limitations paragraph acknowledging this point. revision: partial
- Absence of cross-variety, cross-beverage, or cross-filtration data to substantiate broader universality beyond the single studied system
- Lack of external validation (sensory evaluation or independent chemical assays) to confirm that the topological attractor corresponds to optimal aging quality
Circularity Check
Attractor defined by convergence observed in same persistence diagrams
specific steps
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self definitional
[Abstract]
"These coupled trajectories converge toward a mature topological state rather than passively tracking chronological age. We therefore propose a universal topological attractor, in which optimal aging is defined by a system's position in persistence space relative to a mature structural domain."
The mature topological state is identified from the convergence in the persistence diagrams computed on the 1-10 year strong-aroma Baijiu samples used to define the stages; defining the attractor and optimality relative to this observed endpoint makes the proposal equivalent to re-labeling the data's own trajectories.
full rationale
The paper identifies B0-B1-B2 stages and convergence from persistent homology on one Baijiu variety's molecular aggregates, then defines the 'universal topological attractor' and 'optimal aging' directly as proximity to the observed mature domain in that same persistence space. This reduces the central claim to a re-description of the dataset's endpoint without external benchmarks, cross-variety data, or independent validation, matching the self-definitional pattern.
Axiom & Free-Parameter Ledger
invented entities (1)
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universal topological attractor
no independent evidence
Reference graph
Works this paper leans on
-
[1]
Cipelletti, L., and Ramos, L. (2005). Slow dynamics in glassy soft matter. J. Phys.: Condens. Matter 17, R253. https://doi.org/10.1088/0953-8984/17/6/R01
-
[2]
Trappe, V., Prasad, V., Cipelletti, L., Segre, P.N., and Weitz, D.A. (2001). Jamming phase diagram for attractive particles. Nature 411, 772-775. https://doi.org/10.1038/35081021
-
[3]
Lu, P.J., Zaccarelli, E., Ciulla, F., Schofield, A.B., Sciortino, F., and Weitz, D.A. (2008). Gelation of particles with short-range attraction. Nature 453, 499-503. https://doi.org/10.1038/nature06931
-
[4]
Royall, C.P., Faers, M.A., Fussell, S.L., and Hallett, J.E. (2021). Real space analysis of colloidal gels: triumphs, challenges and future directions. J. Phys.: Condens. Matter 33, 453002. https://doi.org/10.1088/1361-648X/ac04cb
-
[5]
Bonn, D., Denn, M.M., Berthier, L., Divoux, T., and Manneville, S. (2017). Yield stress materials in soft condensed matter. Rev. Mod. Phys. 89, 035005. https://doi.org/10.1103/RevModPhys.89.035005
-
[6]
Ammari, A., and Schroen, K. (2018). Flavor Retention and Release from Beverages: A Kinetic and Thermodynamic Perspective. Journal of Agricultural and Food Chemistry 66, 9869-9881. https://doi.org/10.1021/acs.jafc.8b04459
-
[7]
Ickes, C.M., and Cadwallader, K.R. (2017). Effects of Ethanol on Flavor Perception in Alcoholic Beverages. Chemosensory Perception 10, 119-134. https://doi.org/10.1007/s12078-017-9238-2
-
[8]
Ickes, C.M., and Cadwallader, K.R. (2018). Effect of ethanol on flavor perception of Rum. Food Science & Nutrition 6, 912-924. https://doi.org/10.1002/fsn3.629
-
[9]
Jiang, X., Liu, D., Yang, S., Cheng, X., and Xie, Y. (2024). Evolution of self-assembled amphiphilic colloidal particles in strong-flavor Chinese baijiu. Food Chem. 461, 140883. https://doi.org/10.1016/j.foodchem.2024.140883
-
[10]
Jiang, X., Liu, R., and Xie, Y. (2024). Hydrogen bonding dominated self-assembly mechanism of amphiphilic molecules in Chinese Baijiu. Food Chem. 452, 139420. https://doi.org/10.1016/j.foodchem.2024.139420
-
[11]
Xu, Y., Zhao, J., Liu, X., Zhang, C., Zhao, Z., Li, X., and Sun, B. (2022). Flavor mystery of Chinese traditional fermented baijiu: The great contribution of ester compounds. Food Chem. 369, 130920. https://doi.org/10.1016/j.foodchem.2021.130920
-
[12]
Qiao, L., Wang, J., Wang, R., Zhang, N., and Zheng, F. (2023). A review on flavor of Baijiu and other world-renowned distilled liquors. Food Chemistry: X 20, 100870. https://doi.org/10.1016/j.fochx.2023.100870
-
[13]
Ma, X., Sun, Y., Pan, D., Cao, J., and Dang, Y. (2022). Structural characterization and stability analysis of phosphorylated nitrosohemoglobin. Food Chem. 373, 131475. https://doi.org/10.1016/j.foodchem.2021.131475
-
[14]
Franks, F., and Ives, D.J.G. (1966). The structural properties of alcohol–water mixtures. Quarterly Reviews, Chemical Society 20, 1-44. https://doi.org/10.1039/QR9662000001
-
[15]
Parke, S.A., and Birch, G.G. (1999). Solution properties of ethanol in water. Food Chem. 67, 241-246. https://doi.org/10.1016/S0308-8146(99)00124-7
-
[16]
Gereben, O., and Pusztai, L. (2015). Investigation of the Structure of Ethanol–Water Mixtures by Molecular Dynamics Simulation I: Analyses Concerning the Hydrogen-Bonded Pairs. The Journal of Physical Chemistry B 119, 3070-3084. https://doi.org/10.1021/jp510490y
-
[17]
Pothoczki, S., Pethes, I., Pusztai, L., Temleitner, L., Ohara, K., and Bakó, I. (2021). Properties of Hydrogen-Bonded Networks in Ethanol–Water Liquid Mixtures as a Function of Temperature: Diffraction Experiments and Computer Simulations. The Journal of Physical Chemistry B 125, 6272-6279. https://doi.org/10.1021/acs.jpcb.1c03122
-
[18]
Dolenko, T.A., Burikov, S.A., Dolenko, S.A., Efitorov, A.O., Plastinin, I.V., Yuzhakov, V.I., and Patsaeva, S.V. (2015). Raman Spectroscopy of Water–Ethanol Solutions: The Estimation of Hydrogen Bonding Energy and the Appearance of Clathrate-like Structures in Solutions. The Journal of Physical Chemistry A 119, 10806-10815. https://doi.org/10.1021/acs.jpc...
-
[19]
Qin, D., Shen, Y., Yang, S., Zhang, G., Wang, D., Li, H., and Sun, J. (2022). Whether the Research on Ethanol–Water Microstructure in Traditional Baijiu Should Be Strengthened? Molecules 27, 8290. https://doi.org/10.3390/molecules27238290
-
[20]
Shang, Y., Hajar, R., Jiang, X., and Xie, Y. (2025). Unraveling the Anomalous Physicochemical Properties of Ethanol–Water Binary Solutions via Hydrogen-Bond-Driven Self-Assembly of Ethanol Clusters. The Journal of Physical Chemistry A 129, 6837-6844. https://doi.org/10.1021/acs.jpca.5c03699
-
[21]
Yang, X., Zheng, J., Luo, X., Xiao, H., Li, P., Luo, X., Tian, Y., Jiang, L., and Zhao, D. (2024). Ethanol-water clusters determine the critical concentration of alcoholic beverages. Matter 7, 1724-1735. https://doi.org/10.1016/j.matt.2024.03.017
-
[22]
Jiang, X., Shang, Y., Hajar, R., Yang, H., Peng, J., Li, J., Wang, W., Zou, Z., and Xie, Y. (2026). Evolutionary pattern of liquid-liquid phase separation in amphiphilic molecular self-assembly during the natural aging process of strong-aroma Baijiu. Food Res. Int. 225, 118060. https://doi.org/10.1016/j.foodres.2025.118060
-
[23]
Shang, Y., Jiang, X., Zuo, Y., and Xie, Y. (2026). Dynamic evolution of ethanol clusters and solubility modulation of flavor esters during the aging of Soy sauce flavor Baijiu. Food Chem. 505, 148095. https://doi.org/10.1016/j.foodchem.2026.148095
-
[24]
Zhao, C., Jiang, X., and Xie, Y. (2026). Liquid–liquid phase separation and self-assembly of hexanoic acid and ethyl hexanoate in ethanol–water systems: a model for aged colloidal Baijiu. Soft Matter 22, 2958-2966. https://doi.org/10.1039/D6SM00143B
-
[25]
Topological Persistence and Simplifica- tion
Edelsbrunner, Letscher, and Zomorodian (2002). Topological Persistence and Simplification. Discrete & Computational Geometry 28, 511-533. https://doi.org/10.1007/s00454-002-2885-2
-
[26]
Carlsson, and Gunnar (2009). TOPOLOGY AND DATA. Bulletin of the American Mathematical Society. https://doi.org/10.1090/S0273-0979-09-01249-X
-
[27]
Ghrist, R. (2008). Barcodes: The persistent topology of data. Bulletin of the American Mathematical Society 45, 61-75. https://doi.org/10.1090/S0273-0979-07-01191-3
-
[28]
Hiraoka, Y., Nakamura, T., Hirata, A., Escolar, E.G., Matsue, K., and Nishiura, Y. (2016). Hierarchical structures of amorphous solids characterized by persistent homology. Proceedings of the National Academy of Sciences 113, 7035-7040. https://doi.org/10.1073/pnas.1520877113
-
[29]
Nakamura, T., Hiraoka, Y., Hirata, A., Escolar, E.G., and Nishiura, Y. (2015). Persistent homology and many-body atomic structure for medium-range order in the glass. Nanotechnology 26, 304001. https://doi.org/10.1088/0957-4484/26/30/304001
-
[30]
Ichinomiya, T., Obayashi, I., and Hiraoka, Y. (2017). Persistent homology analysis of craze formation. Physical Review E 95, 012504. https://doi.org/10.1103/PhysRevE.95.012504
-
[31]
Sørensen, S.S., Biscio, C.A.N., Bauchy, M., Fajstrup, L., and Smedskjaer, M.M. (2020). Revealing hidden medium-range order in amorphous materials using topological data analysis. Science Advances 6, eabc2320. https://doi.org/10.1126/sciadv.abc2320
-
[32]
Sørensen, S.S., Du, T., Biscio, C.A.N., Fajstrup, L., and Smedskjaer, M.M. (2022). Persistent homology: A tool to understand medium-range order glass structure. Journal of Non-Crystalline Solids: X 16, 100123. https://doi.org/10.1016/j.nocx.2022.100123
-
[33]
Obayashi, I., Nakamura, T., and Hiraoka, Y. (2022). Persistent Homology Analysis for Materials Research and Persistent Homology Software: HomCloud. J. Phys. Soc. Jpn. 91, 091013. https://doi.org/10.7566/JPSJ.91.091013
-
[34]
Saw, T.B., Doostmohammadi, A., Nier, V., Kocgozlu, L., Thampi, S., Toyama, Y., Marcq, P., Lim, C.T., Yeomans, J.M., and Ladoux, B. (2017). Topological defects in epithelia govern cell death and extrusion. Nature 544, 212-216. https://doi.org/10.1038/nature21718
-
[35]
Shankar, S., Scharrer, L.V.D., Bowick, M.J., and Marchetti, M.C. (2024). Design rules for controlling active topological defects. Proceedings of the National Academy of Sciences 121, e2400933121. https://doi.org/10.1073/pnas.2400933121
-
[36]
Zheng, Z., Jiang, C., Chen, Y., Baggioli, M., and Zhang, J. (2026). Topological signatures of collective dynamics and turbulent-like energy cascades in apolar active granular matter. Proceedings of the National Academy of Sciences 123, e2510873123. https://doi.org/10.1073/pnas.2510873123
-
[37]
Tokura, Y., and Kanazawa, N. (2021). Magnetic Skyrmion Materials. Chem. Rev. 121, 2857-2897. https://doi.org/10.1021/acs.chemrev.0c00297
-
[38]
Cramer Pedersen, M., Robins, V., Mortensen, K., and Kirkensgaard, J.J.K. (2020). Evolution of local motifs and topological proximity in self-assembled quasi-crystalline phases. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476. https://doi.org/10.1098/rspa.2020.0170
-
[39]
Jiang, F., Tsuji, T., and Shirai, T. (2018). Pore Geometry Characterization by Persistent Homology Theory. Water Resour. Res. 54, 4150-4163. https://doi.org/10.1029/2017WR021864
-
[40]
Dłotko, P., and Wanner, T. (2016). Topological microstructure analysis using persistence landscapes. Physica D: Nonlinear Phenomena 334, 60-81. https://doi.org/10.1016/j.physd.2016.04.015
-
[41]
Dixit, S., Crain, J., Poon, W.C.K., Finney, J.L., and Soper, A.K. (2002). Molecular segregation observed in a concentrated alcohol–water solution. Nature 416, 829-832. https://doi.org/10.1038/416829a
-
[42]
Soper, A.K., Dougan, L., Crain, J., and Finney, J.L. (2006). Excess Entropy in Alcohol−Water Solutions: A Simple Clustering Explanation. The Journal of Physical Chemistry B 110, 3472-3476. https://doi.org/10.1021/jp054556q
-
[43]
Zaccarelli, E. (2007). Colloidal gels: equilibrium and non-equilibrium routes. J. Phys.: Condens. Matter 19, 323101. https://doi.org/10.1088/0953-8984/19/32/323101
-
[44]
Tsurusawa, H., Arai, S., and Tanaka, H. (2020). A unique route of colloidal phase separation yields stress-free gels. Science Advances 6, eabb8107. https://doi.org/10.1126/sciadv.abb8107
-
[45]
Stillinger, F.H. (1995). A Topographic View of Supercooled Liquids and Glass Formation. Science 267, 1935-1939. https://doi.org/10.1126/science.267.5206.1935
-
[46]
Suzuki, A., Miyazawa, M., Minto, J.M., Tsuji, T., Obayashi, I., Hiraoka, Y., and Ito, T. (2021). Flow estimation solely from image data through persistent homology analysis. Scientific Reports 11, 17948. https://doi.org/10.1038/s41598-021-97222-6
-
[47]
Thompson, E.P., and Ellis, B.R. (2023). Persistent Homology as a Heterogeneity Metric for Predicting Pore Size Change in Dissolving Carbonates. Water Resour. Res. 59
2023
-
[48]
Savary, G., Guichard, E., Doublier, J.-L., and Cayot, N. (2006). Mixture of aroma compounds: Determination of partition coefficients in complex semi-solid matrices. Food Res. Int. 39, 372-379. https://doi.org/10.1016/j.foodres.2005.09.002
-
[49]
Déléris, I., Lauverjat, C., Tréléa, I.C., and Souchon, I. (2007). Diffusion of Aroma Compounds in Stirred Yogurts with Different Complex Viscosities. Journal of Agricultural and Food Chemistry 55, 8681-8687. https://doi.org/10.1021/jf071149y
-
[50]
Lee, Y., Barthel, S.D., Dłotko, P., Moosavi, S.M., Hess, K., and Smit, B. (2017). Quantifying similarity of pore-geometry in nanoporous materials. Nature Communications 8, 15396. https://doi.org/10.1038/ncomms15396
-
[51]
Krishnapriyan, A.S., Haranczyk, M., and Morozov, D. (2020). Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials. The Journal of Physical Chemistry C 124, 9360-9368. https://doi.org/10.1021/acs.jpcc.0c01167
-
[52]
Rosowski, K.A., Sai, T., Vidal-Henriquez, E., Zwicker, D., Style, R.W., and Dufresne, E.R. (2020). Elastic ripening and inhibition of liquid–liquid phase separation. Nature Physics 16, 422-425. https://doi.org/10.1038/s41567-019-0767-2
-
[53]
Curk, T., and Luijten, E. (2023). Phase separation and ripening in a viscoelastic gel. Proceedings of the National Academy of Sciences 120, e2304655120. https://doi.org/10.1073/pnas.2304655120
-
[54]
Relkin, P., Fabre, M., and Guichard, E. (2004). Effect of Fat Nature and Aroma Compound Hydrophobicity on Flavor Release from Complex Food Emulsions. Journal of Agricultural and Food Chemistry 52, 6257-6263. https://doi.org/10.1021/jf049477a
-
[55]
Saffarionpour, S. (2019). Nanoencapsulation of Hydrophobic Food Flavor Ingredients and Their Cyclodextrin Inclusion Complexes. Food and Bioprocess Technology 12, 1157-1173. https://doi.org/10.1007/s11947-019-02285-z
-
[56]
Debenedetti, P.G., and Stillinger, F.H. (2001). Supercooled liquids and the glass transition. Nature 410, 259-267. https://doi.org/10.1038/35065704
-
[57]
Wales, D.J. (2001). A Microscopic Basis for the Global Appearance of Energy Landscapes. Science 293, 2067-2070. https://doi.org/10.1126/science.1062565
-
[58]
Isayev, O., Fourches, D., Muratov, E.N., Oses, C., Rasch, K., Tropsha, A., and Curtarolo, S. (2015). Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints. Chem. Mater. 27, 735-743. https://doi.org/10.1021/cm503507h
-
[59]
Ward, L., Agrawal, A., Choudhary, A., and Wolverton, C. (2016). A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials 2, 16028. https://doi.org/10.1038/npjcompumats.2016.28
-
[60]
Jiang, X., Wan, D., Zheng, F., and Xie, Y. (2022). Ionization Equilibrium of Water Molecule Dominated Ethanol-water Binary Solution Self-assemble. Electrochemistry 90, 067007-067007. https://doi.org/10.5796/electrochemistry.22-00054
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