Annotating metabolite mass spectra with domain-inspired chemical formula transformers

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Advances in neural information processing systems 7

Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works

UMAP: Uniform Manifold Approximation and Projection

McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works

Untitled work

Larian Studios Dev Team (2024) · doi:10.1145/3292500.3330701 · Cited by 8,618 · see citing works

Extended-Connectivity Fingerprints

Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works

SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629 · see citing works

Cancer-associated IDH1 mutations produce 2-hydroxyglutarate

Dang, L., White, D. W., Gross, S., Bennett, B. D., Bittinger, M. A., Driggers, E. M., Fantin, V. R., Jang, H. G., Jin, S., Keenan, M. C., et al. (2009) · Nature · doi:10.1038/nature08617 · Cited by 4,154 · see citing works

PubChem 2019 update: improved access to chemical data

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249 · see citing works

HMDB: the Human Metabolome Database

Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl923 · Cited by 3,219 · see citing works

Transformer-XL: Attentive language models beyond a fixed-length context

Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · see citing works

Oncometabolite 2-Hydroxyglutarate Is a Competitive Inhibitor of α-Ketoglutarate-Dependent Dioxygenases

Xu, W., Yang, H., Liu, Y., Yang, Y., Wang, P., Kim, S.-H., Ito, S., Yang, C., Wang, P., Xiao, M.-T., et al. (2011) · Cancer Cell · doi:10.1016/j.ccr.2010.12.014 · Cited by 2,875 · see citing works

HMDB 5.0: the Human Metabolome Database for 2022

Wishart, D. S., Guo, A., Oler, E., Wang, F., Anjum, A., Peters, H., Dizon, R., Sayeeda, Z., Tian, S., Lee, B. L., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1062 · Cited by 2,558 · see citing works

Self-Training With Noisy Student Improves ImageNet Classification

Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393 · see citing works

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information

Dührkop, K., Fleischauer, M., Ludwig, M., Aksenov, A. A., Melnik, A. V., Meusel, M., Dorrestein, P. C., Rousu, J., & Böcker, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0344-8 · Cited by 2,088 · see citing works

Gut microbiome structure and metabolic activity in inflammatory bowel disease

Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall, A. B., Mallick, H., McIver, L. J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0306-4 · Cited by 1,995 · see citing works

Feature-based molecular networking in the GNPS analysis environment

Nothias-Scaglia, L.-F., Petras, D., Schmid, R., Dührkop, K., Rainer, J., Sarvepalli, A., Protsyuk, I., Ernst, M., Tsugawa, H., Fleischauer, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0933-6 · Cited by 1,658 · see citing works

The KEGG Database

Kanehisa, M. I. (2002) · Novartis Foundation symposium · doi:10.1002/0470857897.ch8 · Cited by 1,387 · see citing works

Metabolomics for Investigating Physiological and Pathophysiological Processes

Wishart, D. S. (2019) · Physiological Reviews · doi:10.1152/physrev.00035.2018 · Cited by 1,246 · see citing works

Integrative analysis of multimodal mass spectrometry data in MZmine 3

Schmid, R., Heuckeroth, S., Korf, A., Smirnov, A., Myers, O., Dyrlund, T. S., Bushuiev, R., Murray, K. J., Hoffmann, N., Lu, M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01690-2 · Cited by 1,241 · see citing works

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Dührkop, K., Shen, H., Meusel, M., Rousu, J., & Böcker, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509788112 · Cited by 1,190 · see citing works

MetFrag relaunched: incorporating strategies beyond in silico fragmentation

Ruttkies, C., Schymanski, E., Wolf, S., Hollender, J., & Neumann, S. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0115-9 · Cited by 1,063 · see citing works

Mass spectral molecular networking of living microbial colonies

Watrous, J. D., Roach, P. J., Alexandrov, T., Heath, B. S., Yang, J. Y., Kersten, R. D., van der Voort, M., Pogliano, K., Gross, H., Raaijmakers, J. M., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203689109 · Cited by 974 · see citing works

Indoleacrylic Acid Produced by Commensal Peptostreptococcus Species Suppresses Inflammation

Wlodarska, M., Luo, C., Kolde, R., d'Hennezel, E. B., Annand, J. W., Heim, C. E., Krastel, P., Schmitt, E. K., Omar, A. S., Creasey, E. A., et al. (2017) · Cell Host & Microbe · doi:10.1016/j.chom.2017.06.007 · Cited by 890 · see citing works

Ensemble of exemplar-SVMs for object detection and beyond

Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865 · see citing works

Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

Dührkop, K., Nothias, L.-F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M. A., Petras, D., Gerwick, W. H., Rousu, J., Dorrestein, P. C., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0740-8 · Cited by 860 · see citing works

Environmental metabolomics: a critical review and future perspectives

Bundy, J. G., Davey, M. P., & Viant, M. R. (2008) · Metabolomics · doi:10.1007/s11306-008-0152-0 · Cited by 817 · see citing works

In silico fragmentation for computer assisted identification of metabolite mass spectra

Wolf, S., Schmidt, S., Müller-Hannemann, M., & Neumann, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-148 · Cited by 698 · see citing works

Detection of gene pathways with predictive power for breast cancer prognosis

Ma, S., & Kosorok, M. R. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-1 · Cited by 691 · see citing works

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Lee, J., Lee, Y., Kim, J., Kosiorek, A. R., Choi, S., & Teh, Y. W. (2018) · arXiv · doi:10.48550/arxiv.1810.00825 · Cited by 691 · see citing works

A comprehensive review and update on ulcerative colitis,

Gajendran, M., Loganathan, P., Jiménez, G., Catinella, A. P., Ng, N., Umapathy, C., Ziade, N., & Hashash, J. G. (2019) · Disease-a-Month · doi:10.1016/j.disamonth.2019.02.004 · Cited by 640 · see citing works

Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians

Sato, Y., Atarashi, K., Plichta, D. R., Arai, Y., Sasajima, S., Kearney, S. M., Suda, W., Takeshita, K., Sasaki, T., Okamoto, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03832-5 · Cited by 632 · see citing works

Global chemical effects of the microbiome include new bile-acid conjugations

Quinn, R. A., Melnik, A. V., Vrbanac, A., Fu, T., Patras, K. A., Christy, M. P., Bodai, Z., Belda-Ferre, P., Tripathi, A., Chung, L. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2047-9 · Cited by 579 · see citing works

Illuminating the dark matter in metabolomics

Silva, R., Dorrestein, P. C., & Quinn, R. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516878112 · Cited by 552 · see citing works

NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W. (2021) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00399 · Cited by 551 · see citing works

Bacteroides-Derived Sphingolipids Are Critical for Maintaining Intestinal Homeostasis and Symbiosis

Brown, E., Ke, X., Hitchcock, D. S., Jeanfavre, S., Ávila-Pacheco, J., Nakata, T., Arthur, T. D., Fornelos, N., Heim, C. E., Franzosa, E. A., et al. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.04.002 · Cited by 539 · see citing works

Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification

Allen, F., Greiner, R., & Wishart, D. S. (2014) · Metabolomics · doi:10.1007/s11306-014-0676-4 · Cited by 430 · see citing works

CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification

Wang, F., Liigand, J., Tian, S., Arndt, D., Greiner, R., & Wishart, D. S. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c01465 · Cited by 412 · see citing works

High-confidence structural annotation of metabolites absent from spectral libraries

Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01045-9 · Cited by 388 · see citing works

Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity

Mills, R. H., Dulai, P. S., Vázquez-Baeza, Y., Sauceda, C., Daniel, N., Gerner, R. R., Batachari, L. E., Malfavon, M., Zhu, Q., Weldon, K., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-021-01050-3 · Cited by 385 · see citing works

PBS3 and EPS1 Complete Salicylic Acid Biosynthesis from Isochorismate in Arabidopsis

Torrens-Spence, M. P., Bobokalonova, A., Carballo, V., Glinkerman, C. M., Pluskal, T., Shen, A. Z., & Weng, J. (2019) · Molecular Plant · doi:10.1016/j.molp.2019.11.005 · Cited by 336 · see citing works

Compositional and Temporal Changes in the Gut Microbiome of Pediatric Ulcerative Colitis Patients Are Linked to Disease Course

Schirmer, M., Denson, L. A., Vlamakis, H. C., Franzosa, E. A., Thomas, S., Gotman, N., Rufo, P. A., Baker, S. S., Sauer, C. G., Markowitz, J. F., et al. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.09.009 · Cited by 295 · see citing works

Machine learning in protein structure prediction

AlQuraishi, M. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.005 · Cited by 252 · see citing works

Commensal microbiota from patients with inflammatory bowel disease produce genotoxic metabolites

Cao, Y., Oh, J., Xue, M., Huh, W. J., Wang, J., González-Hernández, J. A., Rice, T., Martin, A. L., Song, D., Crawford, J. M., et al. (2022) · Science · doi:10.1126/science.abm3233 · Cited by 251 · see citing works

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships

Huber, F., Ridder, L., Verhoeven, S., Spaaks, J. H., Diblen, F., Rogers, S., & van der Hooft, J. J. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008724 · Cited by 248 · see citing works

Database-independent molecular formula annotation using Gibbs sampling through ZODIAC

Ludwig, M., Nothias-Scaglia, L.-F., Dührkop, K., Koester, I., Fleischauer, M., Hoffmann, M., Petras, D., Vargas, F., Morsy, M. R., Aluwihare, L. I., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00234-6 · Cited by 242 · see citing works

Impact of Cigarette Smoking on the Gastrointestinal Tract Inflammation: Opposing Effects in Crohn’s Disease and Ulcerative Colitis

Berkowitz, L., Schultz, B. M., Salazar, G. A., Pardo‐Roa, C., Sebastián, V. P., Álvarez-Lobos, M. M., & Bueno, S. M. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00074 · Cited by 232 · see citing works

MSNovelist: de novo structure generation from mass spectra

Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226 · see citing works

KNApSAcK: A Comprehensive Species-Metabolite Relationship Database

Shinbo, Y., Nakamura, Y., Altaf‐Ul‐Amin, M., Asahi, H., Kurokawa, K., Arita, M., Saito, K., Ohta, D., Shibata, D., & Kanaya, S. (2006) · Springer eBooks · doi:10.1007/3-540-29782-0_13 · Cited by 217 · see citing works

MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra

Huber, F., van der Burg, S., van der Hooft, J. J. J., & Ridder, L. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00558-4 · Cited by 209 · see citing works

Fragmentation trees reloaded

Böcker, S., & Dührkop, K. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0116-8 · Cited by 208 · see citing works

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

Wei, J. N., Belanger, D., Adams, R. P., & Sculley, D. (2019) · ACS Central Science · doi:10.1021/acscentsci.9b00085 · Cited by 202 · see citing works

Computational mass spectrometry for metabolomics: Identification of metabolites and small molecules

Neumann, S., & Böcker, S. (2010) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-010-4142-5 · Cited by 177 · see citing works

Critical Assessment of Small Molecule Identification 2016: automated methods

Schymanski, E., Ruttkies, C., Krauss, M. K. V., Brouard, C., Kind, T., Dührkop, K., Allen, F. R., Vaniya, A., Verdegem, D., Böcker, S., et al. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0207-1 · Cited by 177 · see citing works

Inflammation-associated nitrate facilitates ectopic colonization of oral bacterium Veillonella parvula in the intestine

Rojas‐Tapias, D. F., Brown, E., Temple, E. R., Onyekaba, M. A., Mohamed, A. M., Duncan, K., Schirmer, M., Walker, R. L., Mayassi, T., Pierce, K. A., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01224-7 · Cited by 149 · see citing works

Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction

Tu, Z., & Coley, C. W. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00321 · Cited by 141 · see citing works

matchms - processing and similarity evaluation of mass spectrometry data.

Huber, F., Verhoeven, S., Meijer, C., Spreeuw, H., Castilla, E. M. V., Geng, C., van der Hooft, J. J. J., Rogers, S., Belloum, A. S. Z., Diblen, F., et al. (2020) · The Journal of Open Source Software · doi:10.21105/joss.02411 · Cited by 138 · see citing works

The critical role that spectral libraries play in capturing the metabolomics community knowledge

Bittremieux, W., Wang, M., & Dorrestein, P. C. (2022) · Metabolomics · doi:10.1007/s11306-022-01947-y · Cited by 135 · see citing works

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Tripathi, A., Vázquez‐Baeza, Y., Gauglitz, J. M., Wang, M., Dührkop, K., Nothias-Esposito, M., Acharya, D. D., Ernst, M., van der Hooft, J. J. J., Zhu, Q., et al. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-00677-3 · Cited by 115 · see citing works

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa

Ridder, L., van der Hooft, J. J. J., & Verhoeven, S. (2014) · Mass Spectrometry · doi:10.5702/massspectrometry.s0033 · Cited by 95 · see citing works

An end-to-end deep learning framework for translating mass spectra to de-novo molecules

Litsa, E. E., Chenthamarakshan, V., Das, P., & Kavraki, L. E. (2023) · Communications Chemistry · doi:10.1038/s42004-023-00932-3 · Cited by 89 · see citing works

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra

Shrivastava, A. D., Swainston, N., Samanta, S., Roberts, I., Muelas, M. W., & Kell, D. B. (2021) · Biomolecules · doi:10.3390/biom11121793 · Cited by 87 · see citing works

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Eldjárn, G. H., Ramsay, A., van der Hooft, J. J. J., Duncan, K., Soldatou, S., Rousu, J., Daly, R., Wandy, J., & Rogers, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008920 · Cited by 66 · see citing works

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation

Fan, Z., Alley, A., Ghaffari, K., et al. (2020) · Metabolomics · doi:10.1007/s11306-020-01726-7 · Cited by 64 · see citing works

Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints

Ludwig, M., Dührkop, K., & Böcker, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty245 · Cited by 48 · see citing works

Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN

Aisporna, A., Benton, H. P., Chen, A., Derks, R. J. E., Galano, J., Giera, M. A., & Siuzdak, G. E. (2022) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.1c00343 · Cited by 48 · see citing works

The Critical Assessment of Small Molecule Identification (CASMI): Challenges and Solutions

Schymanski, E., & Neumann, S. (2013) · Metabolites · doi:10.3390/metabo3030517 · Cited by 46 · see citing works

Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID

Shen, H., Zamboni, N., Heinonen, M., & Rousu, J. (2013) · Metabolites · doi:10.3390/metabo3020484 · Cited by 28 · see citing works

Bacterial protease uses distinct thermodynamic signatures for substrate recognition

Bezerra, G. A., Ohara‐Nemoto, Y., Cornaciu, I., Fedosyuk, S., Hoffmann, G., Round, A. R., Márquez, J. A., Nemoto, T., & Djinović‐Carugo, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-03220-y · Cited by 19 · see citing works

Deep kernel learning improves molecular fingerprint prediction from tandem mass spectra

Dührkop, K. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac260 · Cited by 19 · see citing works

Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry DataPreprint

Voronov, G., Lightheart, R., Davison, J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.02980 · Cited by 16 · see citing works

Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo moleculesPreprint

Litsa, E. E., Chenthamarakshan, V., Das, P., et al. (2021) · doi:10.33774/chemrxiv-2021-6rdh6 · Cited by 11 · see citing works

MSNovelist: De novo structure generation from mass spectraPreprint

Stravs, M. A., Dührkop, K., Böcker, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.06.450875 · Cited by 6 · see citing works

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Vitale, G. A., Geibel, C., Minda, V., Wang, M., Aron, A. T., & Petras, D. (2024) · Natural Product Reports · doi:10.1039/d3np00050h · Cited by 66 · see citing works

Structure Damage Detection via the Behavior of Substructure

Wang, J. H., & Liou, C.-C. (1991) · doi:10.1115/detc1991-0371

Frequency-Domain Substructure Isolation for Local Damage Identification

Hou, J., Jankowski, Ł., & Ou, J. (2014) · Advances in Structural Engineering · doi:10.1260/1369-4332.18.1.137 · Cited by 24 · see citing works

The dynamic synthesis of general non-conservative structures from separately identified substructure models

Hale, A. L., & Bergman, L. A. (1985) · Journal of Sound and Vibration · doi:10.1016/0022-460x(85)90286-x · Cited by 13 · see citing works

1314 Development of Vibration Characteristic Optimization System of Large-scale Complicated Structure

Zhao, X. (2005) · Sekkei Kougaku, Shisutemu Bumon Kouenkai kouen rombunshuu/Sekkei Kogaku, Shisutemu Bumon Koenkai koen ronbunshu · doi:10.1299/jsmedsd.2005.15.142

ON THE DESIGN OF THE SUBSTRUCTURE EQUILIBRATED WITH THE SUPERSTRUCTURE OF BEAM BRIDGE

Uchida, I. (1960) · Transactions of the Japan Society of Civil Engineers · doi:10.2208/jscej1949.1960.8