SKALE: An Interpretable Multiscale Machine Learning Model for Decoding Phase-Specific Protein Aggregation in Neurodegenerative Proteinopathies

Abstract

Protein aggregation drives proteinopathies ranging from ALS to systemic amyloidosis, yet the multiscale determinants bridging sequence, structure, and kinetics remain elusive. We present SKALE, an interpretable machine learning framework that integrates sequence motifs, AlphaFold-derived structural descriptors, and experimental kinetics to decode aggregation mechanisms. SKALE identifies latent hotspots that evade conventional tools and matches high-performing neural baselines while preserving computational efficiency. In ALS-linked SOD1 G86R, the model isolates a risk region at residues 72–91 where preserved β-sheet geometry coincides with weakened hydrogen bonding to drive nucleation. Similarly, analysis of TDP-43 S332N reveals that a locally unwound helix increases surface exposure, a prediction validated by showing that targeted deletion of model-identified regions significantly reduces cellular aggregation. The framework generalizes to Tau P301L and PRNP variants where it uncovers distal aggregation-prone regions to discriminate pathogenic drivers from neutral mutations. Interpretability analysis further disentangles global from mutation-local mechanisms to reveal that β-sheet propensity acts as a shared determinant while hydrogen bond dynamics define specific routes to nucleation. These findings establish SKALE as a scalable, disease-agnostic engine that combines high-fidelity prediction with biophysical resolution to decode the molecular logic of misfolding and guide therapeutic design.

Keywords

amyotrophic lateral sclerosis / machine learning / protein aggregation / superoxide dismutase 1 / TAR DNA-binding protein 43

References

[1] M. Tsekrekou, M. Giannakou, K. Papanikolopoulou, and G. Skretas, “Protein Aggregation and Therapeutic Strategies in SOD1- and TDP-43-Linked ALS,” Frontiers in Molecular Biosciences 11 (2024): 1383453.

[2] R. C. Hergesheimer, A. A. Chami, D. R. de Assis, et al., “The Debated Toxic Role of Aggregated TDP-43 in Amyotrophic Lateral Sclerosis: A Resolution in Sight?,” Brain 142 (2019): 1176–1194.

[3] E. Feneberg, E. Gray, O. Ansorge, K. Talbot, and M. R. Turner, “Towards a TDP-43-Based Biomarker for ALS and FTLD,” Molecular Neurobiology 55 (2018): 7789–7801.

[4] H. Y. Tan, Y. K. Yong, Y. C. Xue, et al., “cGAS and DDX41-STING Mediated Intrinsic Immunity Spreads Intercellularly to Promote Neuroinflammation in SOD1 ALS Model,” iScience 25 (2022): 104404.

[5] J. Y. Hiew, Y. S. Lim, H. Liu, and C. S. Ng, “Integrated Transcriptomic Profiling Reveals a STING-Mediated Type II Interferon Signature in SOD1-Mutant Amyotrophic Lateral Sclerosis Models,” Communications Biology 8 (2025): 347.

[6] M. Benatar, J. Robertson, and P. M. Andersen, “Amyotrophic Lateral Sclerosis Caused by SOD1 Variants: From Genetic Discovery to Disease Prevention,” Lancet Neurology 24 (2025): 77–86.

[7] J. Jumper, R. Evans, A. Pritzel, et al., “Highly Accurate Protein Structure Prediction With AlphaFold,” Nature 596 (2021): 583–589.

[8] M. Varadi, S. Anyango, M. Deshpande, et al., “AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space With High-Accuracy Models,” Nucleic Acids Research 50 (2022): D439–D444.

[9] M. Akdel, D. E. V. Pires, E. P. Pardo, et al., “A Structural Biology Community Assessment of AlphaFold2 Applications,” Nature Structural & Molecular Biology 29 (2022): 1056–1067.

[10] D. B. Olawade, O. Fapohunda, S. O. Usman, et al., “Artificial Intelligence in Computational and Materials Chemistry: Prospects and Limitations,” Chemistry Africa 8 (2025): 2707–2721.

[11] Y. Qin, Z. Chen, Y. Peng, et al., “Deep Learning Methods for Protein Structure Prediction,” MedComm—Future Medicine 3 (2024): 1–22.

[12] S. C. Pakhrin, B. Shrestha, B. Adhikari, and D. B. Kc, “Deep Learning-Based Advances in Protein Structure Prediction,” International Journal of Molecular Sciences 22 (2021): 5553.

[13] S. M. Lundberg, G. Erion, H. Chen, et al., “From Local Explanations to Global Understanding With Explainable AI for Trees,” Nature Machine Intelligence 2 (2020): 56–67.

[14] A. V. Ponce-Bobadilla, V. Schmitt, C. S. Maier, S. Mensing, and S. Stodtmann, “Practical Guide to SHAP Analysis: Explaining Supervised Machine Learning Model Predictions in Drug Development,” Clinical and Translational Science 17 (2024): e70056.

[15] A. Alghamdi, D. J. S. Birch, V. Vyshemirsky, and O. J. Rolinski, “Impact of the Flavonoid Quercetin on β-Amyloid Aggregation Revealed by Intrinsic Fluorescence,” Journal of Physical Chemistry B 126 (2022): 7229–7237.

[16] V. R. Tomar, S. Sharma, S. Siddhanta, and S. Deep, “Biophysical and Spectroscopical Insights Into Structural Modulation of Species in the Aggregation Pathway of Superoxide Dismutase 1,” Communications Chemistry 8 (2025): 22.

[17] J. Beerten, J. Van Durme, R. Gallardo, et al., “WALTZ-DB: A Benchmark Database of Amyloidogenic Hexapeptides,” Bioinformatics 31 (2015): 1698–1700.

[18] N. Louros, K. Konstantoulea, M. De Vleeschouwer, M. Ramakers, J. Schymkowitz, and F. Rousseau, “WALTZ-DB 2.0: An Updated Database Containing Structural Information of Experimentally Determined Amyloid-Forming Peptides,” Nucleic Acids Research 48 (2020): D389–D393.

[19] Y. Vander Meersche, J. Diharce, J. Gelly, and T. Galochkina, “Flexibility or Uncertainty? A Critical Assessment of AlphaFold 2 pLDDT,” Structure 33 (2025): 2157–2163.

[20] O. Kovalevskiy, J. Mateos-Garcia, and K. Tunyasuvunakool, “AlphaFold Two Years On: Validation and Impact,” Proceedings National Academy of Science USA 121 (2024): e2315002121.

[21] O. Carugo, “pLDDT Values in AlphaFold2 Protein Models Are Unrelated to Globular Protein Local Flexibility,” Crystals 13 (2023): 1560.

[22] S. Yamashita and Y. Ando, “Genotype-Phenotype Relationship in Hereditary Amyotrophic Lateral Sclerosis,” Translational Neurodegeneration 4 (2015): 13.

[23] T. Holmøy, J. A. Wilson, C. von der Lippe, P. M. Andersen, and P. Berg-Hansen, “G127R: A Novel SOD1 Mutation Associated With Rapidly Evolving ALS and Severe Pain Syndrome,” Amyotrophic Lateral Sclerosis 11 (2010): 478–480.

[24] A. Trovato, F. Seno, and S. C. E. Tosatto, “The PASTA Server for Protein Aggregation Prediction,” Protein Engineering, Design & Selection 20 (2007): 521–523.

[25] I. Walsh, F. Seno, S. C. E. Tosatto, and A. Trovato, “PASTA 2.0: An Improved Server for Protein Aggregation Prediction,” Nucleic Acids Research 42 (2014): W301–W307.

[26] O. Conchillo-Solé, N. S. de Groot, F. X. Avilés, J. Vendrell, X. Daura, and S. Ventura, “AGGRESCAN: A Server for the Prediction and Evaluation of , “Hot Spots” of Aggregation in Polypeptides,” BMC Bioinformatics 8 (2007): 65.

[27] L. François-Moutal, S. Perez-Miller, D. D. Scott, V. G. Miranda, N. Mollasalehi, and M. Khanna, “Structural Insights Into TDP-43 and Effects of Post-Translational Modifications,” Frontiers in Molecular Neuroscience 12 (2019): 301.

[28] L. Lim, Y. Wei, Y. Lu, and J. Song, “ALS-Causing Mutations Significantly Perturb the Self-Assembly and Interaction With Nucleic Acid of the Intrinsically Disordered Prion-Like Domain of TDP-43,” PLOS Biology 14 (2016): e1002338.

[29] M. Mompeán, V. Romano, D. Pantoja-Uceda, et al., “The TDP-43 N-Terminal Domain Structure at High Resolution,” FEBS Journal 283 (2016): 1242–1260.

[30] D. Arseni, M. Hasegawa, A. G. Murzin, et al., “Structure of Pathological TDP-43 Filaments From ALS With FTLD,” Nature 601 (2022): 139–143.

[31] L. Corrado, A. Ratti, C. Gellera, et al., “High Frequency of TARDBP Gene Mutations in Italian Patients With Amyotrophic Lateral Sclerosis,” Human Mutation 30 (2009): 688–694.

[32] G. S. Pesiridis, V. M. Lee, and J. Q. Trojanowski, “Mutations in TDP-43 Link Glycine-Rich Domain Functions to Amyotrophic Lateral Sclerosis,” Human Molecular Genetics 18 (2009): R156–R162.

[33] A. Wood, Y. Gurfinkel, N. Polain, W. Lamont, and S. Lyn Rea, “Molecular Mechanisms Underlying TDP-43 Pathology in Cellular and Animal Models of ALS and FTLD,” International Journal of Molecular Sciences 22 (2021): 4705.

[34] M. I. Sulatsky, A. I. Sulatskaya, O. I. Povarova, I. A. Antifeeva, I. M. Kuznetsova, and K. K. Turoverov, “Effect of the Fluorescent Probes ThT and ANS on the Mature Amyloid Fibrils,” Prion 14 (2020): 67–75.

[35] S. T. Kumar, S. Nazarov, S. Porta, et al., “Seeding the Aggregation of TDP-43 Requires Post-Fibrillization Proteolytic Cleavage,” Nature Neuroscience 26 (2023): 983–996.

[36] Y. Gao, N. Wang, F. Sun, X. Cao, W. Zhang, and J. Yu, “Tau in Neurodegenerative Disease,” Annals of Translational Medicine 6 (2018): 175.

[37] W. Li and J. Li, “Overlaps and Divergences Between Tauopathies and Synucleinopathies: A Duet of Neurodegeneration,” Translational Neurodegeneration 13 (2024): 16.

[38] M. Goedert, “Tau Filaments in Neurodegenerative Diseases,” FEBS Letters 592 (2018): 2383–2391.

[39] Q. Yao, L. Hong, S. Wu, and S. Perrett, “Distinct Microscopic Mechanisms for the Accelerated Aggregation of Pathogenic Tau Mutants Revealed by Kinetic Analysis,” Physical Chemistry Chemical Physics 22 (2020): 7241–7249.

[40] D. Chen, K. W. Drombosky, Z. Hou, et al., “Tau Local Structure Shields an Amyloid-Forming Motif and Controls Aggregation Propensity,” Nature Communications 10 (2019): 2493.

[41] K. H. Shim, N. Sharma, and S. S. A. An, “Prion Therapeutics: Lessons From the Past,” Prion 16 (2022): 265–294.

[42] R. Nafe, C. T. Arendt, and E. Hattingen, “Human Prion Diseases and the Prion Protein—What Is the Current State of Knowledge?,” Translational Neuroscience 14 (2023): 20220315.

[43] C. Zhu and A. Aguzzi, “Prion Protein and Prion Disease at a Glance,” Journal of Cell Science 134 (2021): jcs245605.

[44] T. Barrio, J. Douet, D. Žáková, et al., “Characterization of Prion Strains and Peripheral Prion Infectivity Patterns in E200K Genetic CJD Patients,” Acta Neuropathologica 149 (2025): 62.

[45] C. Delorme, A. Pégat, J. Theuriet, et al., “Demyelinating Neuropathy as the Initial Presentation of Familial E200K Creutzfeldt-Jakob Disease in Two Patients,” Annals of Clinical and Translational Neurology 12 (2025): 653–658.

[46] S. Won, Y. Kim, and B. Jeong, “Elevated E200K Somatic Mutation of the Prion Protein Gene (PRNP) in the Brain Tissues of Patients With Sporadic Creutzfeldt-Jakob Disease (CJD),” International Journal of Molecular Sciences 24 (2023): 14831.

[47] J. C. Watts, M. E. C. Bourkas, and H. Arshad, “The Function of the Cellular Prion Protein in Health and Disease,” Acta Neuropathologica 135 (2018): 159–178.

[48] J. D. Panes, P. Saavedra, B. Pineda, et al., “Prp C as a Transducer of Physiological and Pathological Signals,” Frontiers in Molecular Neuroscience 14 (2021): 762918.

[49] J. Huang, X. Li, W. Liu, et al., “Neutralizing Mutations Significantly Inhibit Amyloid Formation by Human Prion Protein and Decrease Its Cytotoxicity,” Journal of Molecular Biology 432 (2020): 828–844.

[50] Z. Zheng, M. Zhang, Y. Wang, et al., “Structural Basis for the Complete Resistance of the human Prion Protein Mutant G127V to Prion Disease,” Scientific Reports 8 (2018): 13211.

[51] S. Zhou, D. Shi, X. Liu, H. Liu, and X. Yao, “Protective V127 Prion Variant Prevents Prion Disease by Interrupting the Formation of Dimer and Fibril From Molecular Dynamics Simulations,” Scientific Reports 6 (2016): 21804.

[52] E. A. Asante, M. Smidak, A. Grimshaw, et al., “A Naturally Occurring Variant of the human Prion Protein Completely Prevents Prion Disease,” Nature 522 (2015): 478–481.

[53] F. R. Gharemirshamloo, R. Majumder, U. S. Kumar, et al., “Effects of the Pathological E200K Mutation on Human Prion Protein: A Computational Screening and Molecular Dynamics Approach,” Journal of Cellular Biochemistry 124 (2023): 254–265.

[54] L. L. P. Hosszu, R. Conners, D. Sangar, et al., “Structural Effects of the Highly Protective V127 Polymorphism on Human Prion Protein,” Communications Biology 3 (2020): 402.

[55] M. Belgiu and L. Drăguţ, “Random Forest in Remote Sensing: A Review of Applications and Future Directions,” ISPRS Journal of Photogrammetry and Remote Sensing 114 (2016): 24–31.

[56] G. Biau and E. Scornet, “A Random Forest Guided Tour,” TEST: An Official Journal of the Spanish Society of Statistics and Operations Research 25 (2016): 197–227.

[57] J. Lever, M. Krzywinski, and N. Altman, “Logistic Regression,” Nature Methods 13 (2016): 541–542.

[58] M. E. Shipe, S. A. Deppen, F. Farjah, and E. L. Grogan, “Developing Prediction Models for Clinical Use Using Logistic Regression: An Overview,” Journal of Thoracic Disease 11 (2019): S574–S584.

[59] C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A Comparative Analysis of Gradient Boosting Algorithms,” Artificial Intelligence Review 54 (2021): 1937–1967.

[60] H. Nhat-Duc and T. Van-Duc, “Comparison of Histogram-Based Gradient Boosting Classification Machine, Random Forest, and Deep Convolutional Neural Network for Pavement Raveling Severity Classification,” Automation in Construction 148 (2023): 104767.

[61] R. Blagus and L. Lusa, “SMOTE for High-Dimensional Class-Imbalanced Data,” BMC Bioinformatics 14 (2013): 106.

[62] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-Sampling Technique,” JAIR 16 (2002): 321–357.

[63] A. Fernandez, S. Garcia, F. Herrera, and N. V. Chawla, “SMOTE for Learning From Imbalanced Data: Progress and Challenges, Marking the 15-Year Anniversary,” JAIR 61 (2018): 863–905.

[64] M. W. Beck, “Neuralnettools: Visualization and Analysis Tools for Neural Networks,” Journal of Statistical Software 85 (2018): 1–20.

[65] J. Schmidhuber, “Deep Learning in Neural Networks: An Overview,” Neural Networks 61 (2015): 85–117.

[66] Y. Zhou, J. Hua, D. Ding, and Y. Tang, “Interrogating Amyloid Aggregation With Aggregation-Induced Emission Fluorescence Probes,” Biomaterials 286 (2022): 121605.

[67] P. Hanczyc and P. Fita, “Laser Emission of Thioflavin T Uncovers Protein Aggregation in Amyloid Nucleation Phase,” ACS Photonics 8 (2021): 2598–2609.

[68] F. Hsu, G. Park, and Z. Guo, “Key Residues for the Formation of aβ42 Amyloid Fibrils,” ACS Omega 3 (2018): 8401–8407.

[69] K. Chouchane, I. Pignot-Paintrand, F. Bruckert, and M. Weidenhaupt, “Visible Light-Induced Insulin Aggregation on Surfaces via Photoexcitation of Bound Thioflavin T,” Journal of Photochemistry and Photobiology B: Biology 181 (2018): 89–97.

[70] C. Xue, T. Y. Lin, D. Chang, and Z. Guo, “Thioflavin T as an Amyloid Dye: Fibril Quantification, Optimal Concentration and Effect on Aggregation,” Royal Society Open Science 4 (2017): 160696.

[71] M. M. Wördehoff and W. Hoyer, “α-Synuclein Aggregation Monitored by Thioflavin T Fluorescence Assay,” Bio Protocol 8 (2018): e2941.

[72] J. Estaun-Panzano, M. Arotcarena, and E. Bezard, “Monitoring α-Synuclein Aggregation,” Neurobiology of Disease 176 (2023): 105966.

[73] V. V. Kushnirov, A. A. Dergalev, and A. I. Alexandrov, “Proteinase K Resistant Cores of Prions and Amyloids,” Prion 14 (2020): 11–19.

[74] M. A. Pastrana, G. Sajnani, B. Onisko, et al., “Isolation and Characterization of a Proteinase K-Sensitive PrPSc Fraction,” Biochemistry 45 (2006): 15710–15717.

[75] S. Cronier, N. Gros, M. Tattum, et al., “Detection and Characterization of Proteinase K-Sensitive Disease-Related Prion Protein With Thermolysin,” Biochemical Journal 416 (2008): 297–305.

[76] V. B. Chen, W. B. Arendall, J. J. Headd, et al., “MolProbity: All-Atom Structure Validation for Macromolecular Crystallography,” Acta Crystallographica Section D, Biological Crystallography 66 (2010): 12–21.

[77] I. W. Davis, A. Leaver-Fay, V. B. Chen, et al., “MolProbity: All-Atom Contacts and Structure Validation for Proteins and Nucleic Acids,” Nucleic Acids Research 35 (2007): 375–383.

[78] R. McGibbon, K. Beauchamp, M. Harrigan, et al., “MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories,” Biophysical Journal 109 (2015): 1528–1532.

[79] J. Ribeiro, C. Ríos-Vera, F. Melo, and A. Schüller, “Calculation of Accurate Interatomic Contact Surface Areas for the Quantitative Analysis of Non-Bonded Molecular Interactions,” Bioinformatics 35 (2019): 3499–3501.

[80] E. N. Baker and R. E. Hubbard, “Hydrogen Bonding in Globular Proteins,” Progress in Biophysics and Molecular Biology 44 (1984): 97–179.

[81] N. Ferruz, S. Schmidt, and B. Höcker, “ProteinTools: A Toolkit to Analyze Protein Structures,” Nucleic Acids Research 49 (2021): W559–W566.

[82] S. Zhang, J. M. Krieger, Y. Zhang, et al., “ProDy 2.0: Increased Scale and Scope After 10 Years of Protein Dynamics Modelling With Python,” Bioinformatics 37 (2021): 3657–3659.

[83] F. B. Naughton, I. Alibay, J. Barnoud, et al., “MDAnalysis 2.0 and Beyond: Fast and Interoperable, Community Driven Simulation Analysis,” Biophysical Journal 121 (2022): 272a–273a.

[84] V. V. Verma, S. Vimal, M. K. Mishra, and V. K. Sharma, “A Comprehensive Review on Structural Insights Through Molecular Visualization: Tools, Applications, and Limitations,” Journal of Molecular Modeling 31 (2025): 173.

[85] M. Ohue, “MEGADOCK-on-Colab: An Easy-to-Use Protein-Protein Docking Tool on Google Colaboratory,” BMC Research Notes 16 (2023): 229.

[86] J. Ludwiczak, A. Winski, and S. Dunin-Horkawicz, “Localpdb – A Python Package to Manage Protein Structures and Their Annotations,” Bioinformatics 38 (2022): 2633–2635.

[87] F. V. Ryzhkov, Y. E. Ryzhkova, and M. N. Elinson, “Machine Learning: Python Tools for Studying Biomolecules and Drug Design,” Molecular Diversity 29 (2025): 3789–3824.

[88] M. L. Hekkelman, D. Á. Salmoral, A. Perrakis, and R. P. Joosten, “DSSP 4: FAIR Annotation of Protein Secondary Structure,” Protein Science 34 (2025): e70208.

[89] S. Gorelov, A. Titov, O. Tolicheva, A. Konevega, and A. Shvetsov, “DSSP in GROMACS: Tool for Defining Secondary Structures of Proteins in Trajectories,” Journal of Chemical Information and Modeling 64 (2024): 3593–3598.

[90] J. K. Chhipi-Shrestha, M. Yoshida, and S. Iwasaki, “Filter Trapping Protocol to Detect Aggregated Proteins in Human Cell Lines,” STAR Protocols 3 (2022): 101571.

[91] K. Jung, J. Lee, V. Gupta, and G. Cho, “Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation,” Frontiers in Psychology 10 (2019): 2215.

[92] P. Kabaila, D. Farchione, S. Alhelli, and N. Bragg, “The Effect of a Durbin–Watson Pretest on Confidence Intervals in Regression,” Statistica Neerlandica 75 (2021): 4–23.

[93] J. Hao and T. K. Ho, “Machine Learning Made Easy: A Review of Scikit-Learn Package in Python Programming Language,” Journal of Educational and Behavioral Statistics 44 (2019): 348–361.

[94] S. Raschka, J. Patterson, and C. Nolet, “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence,” Information 11 (2020): 193.

[95] M. Oeller, R. Kang, R. Bell, H. Ausserwöger, P. Sormanni, and M. Vendruscolo, “Sequence-Based Prediction of pH-Dependent Protein Solubility Using CamSol,” Brief Bioinformatics 24 (2023): 1–7.

[96] P. Sormanni and M. Vendruscolo, “Protein Solubility Predictions Using the CamSol Method in the Study of Protein Homeostasis,” Cold Spring Harbor Perspectives in Biology 11 (2019): a033845.

[97] R. Zambrano, M. Jamroz, A. Szczasiuk, J. Pujols, S. Kmiecik, and S. Ventura, “AGGRESCAN3D (A3D): Server for Prediction of Aggregation Properties of Protein Structures,” Nucleic Acids Research 43 (2015): W306–W313.

[98] P. Kouba, P. Kohout, F. Haddadi, et al., “Machine Learning-Guided Protein Engineering,” ACS Catalysis 13 (2023): 13863–13895.

[99] A. Estaña, M. Ghallab, P. Bernadó, and J. Cortés, “Investigating the Formation of Structural Elements in Proteins Using Local Sequence-Dependent Information and a Heuristic Search Algorithm,” Molecules 24 (2019): 1150.

[100] H. M. Berman, J. Westbrook, Z. Feng, et al., “The Protein Data Bank,” Nucleic Acids Research 28 (2000): 235–242.

[101] S. K. Burley, H. M. Berman, G. J. Kleywegt, J. L. Markley, H. Nakamura, and S. Velankar, “Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive,” Methods in Molecular Biology 1607 (2017): 627–641.

[102] M. Varadi, D. Bertoni, P. Magana, et al., “AlphaFold Protein Structure Database in 2024: Providing Structure Coverage for Over 214 Million Protein Sequences,” Nucleic Acids Research 52 (2024): D368–D375.

[103] S. Ahmad and A. Sarai, “PSSM-Based Prediction of DNA Binding Sites in Proteins,” BMC Bioinformatics 6 (2005): 33.

[104] S. Mitternacht, “FreeSASA: An Open Source C Library for Solvent Accessible Surface Area Calculations,” F1000Research 5 (2016): 189.

RIGHTS & PERMISSIONS

2026 The Author(s). Aggregate published by SCUT, AIEI, and John Wiley & Sons Australia, Ltd.