Yr Athro Irena Spasic

2026

• Corcoran, P. and Spasic, I. 2026. Balance disclosure in payment channel networks using noiseless privacy. Distributed Ledger Technologies: Research and Practice 5 (4) 43. (10.1145/3770861)
• Corcoran, P. and Spasic, I. 2026. Path planning at the edge of payment channel networks. Distributed Ledger Technologies: Research and Practice 5 (4) 39. (10.1145/3761828)

2025

• Corcoran, P. et al. 2025. A spatial analysis of the use of Bitcoin as a medium of exchange. Financial Innovation 11 127. (10.1186/s40854-025-00871-z)

2024

• Bennett, V. et al. 2024. Assessing the feasibility of using parents' social media conversations to inform burn first aid interventions: mixed methods study. JMIR Formative Research 8 e48695. (10.2196/48695)
• Chopard, D. , Corcoran, P. and Spasić, I. 2024. Word sense disambiguation of acronyms in clinical narratives. Frontiers in Digital Health 6 1282043. (10.3389/fdgth.2024.1282043)
• Liao, Y. , Liu, H. and Spasić, I. 2024. Fine-tuning coreference resolution for different styles of clinical narratives. Journal of Biomedical Informatics 149 104578. (10.1016/j.jbi.2023.104578)
• Liao, Y. , Liu, H. and Spasic, I. 2024. RadCoref: Fine-tuning coreference resolution for different styles of clinical narratives (version 1.0.0). [Online].PhysioNet. (10.13026/z67q-xy65)Available at: https://doi.org/10.13026/z67q-xy65.
• Balaneji, F. , Maringer, D. and Spasic, I. 2024. The power of words: predicting stock market returns with fine-grained sentiment analysis and XGBoost. Presented at: Intelligent Systems Conference (IntelliSys) Amsterdam, The Netherlands 7-8 September 2023. Published in: Arai, K. ed. Intelligent Systems and Applications. Vol. 1.Lecture Notes in Networks and Systems Vol. 822. Springer Nature. , pp.577-596. (10.1007/978-3-031-47721-8_39)
• Liao, Y. et al. 2024. CID at RRG24: Attempting in a conditionally initiated decoding of Radiology Report Generation with clinical entities. Presented at: The 23rd Workshop on Biomedical Natural Language Processing Bangkok, Thailand 16 August 2024. Published in: Demner-Fushman, D. et al., Proceedings of the 23rd Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics. , pp.591-596. (10.18653/v1/2024.bionlp-1.49)
• Liao, Y. et al. 2024. Using information extraction to normalize the training data for automatic radiology report generation. IEEE Access 12 , pp.185103-185116. (10.1109/ACCESS.2024.3504378)

2023

• Miok, K. , Corcoran, P. and Spasic, I. 2023. The value of numbers in clinical text classification. Machine Learning and Knowledge Extraction 5 (3), pp.746-762. (10.3390/make5030040)
• Edmunds, G. et al., 2023. Associations between dog breed and clinical features of mammary epithelial neoplasia in bitches: an epidemiological study of submissions to a single diagnostic pathology centre between 2008-2021. Journal of Mammary Gland Biology and Neoplasia 28 (6)(10.1007/s10911-023-09531-3)
• Corcoran, P. and Spasic, I. 2023. Self-supervised representation learning for geographical data - a systematic literature review. ISPRS International Journal of Geo-Information 12 (2) 64. (10.3390/ijgi12020064)
• Liao, Y. , Liu, H. and Spasic, I. 2023. Deep learning approaches to automatic radiology report generation: A systematic review. Informatics in Medicine Unlocked 39 101273. (10.1016/j.imu.2023.101273)

2022

• Tong, Y. et al. 2022. A data-driven approach for integrating hedonic quality and pragmatic quality in user experience modelling. Journal of Computing and Information Science in Engineering 22 (6) 061002. (10.1115/1.4054155)
• Tong, Y. et al. 2022. Understanding context of use from online customer reviews using BERT. Presented at: IEEE 18th International Conference on Automation Science and Engineering (CASE 2022) Mexico City, Mexico 20-24 August 2022. 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE(10.1109/CASE49997.2022.9926649)
• Rogers, D. et al. 2022. Real-time text classification of user-generated content on social media: Systematic review. IEEE Transactions on Computational Social Systems 9 (4), pp.1154-1166. (10.1109/TCSS.2021.3120138)
• Zunic, A. , Corcoran, P. and Spasic, I. 2022. The case of aspect in sentiment analysis: seeking attention or co-dependency?. Machine Learning and Knowledge Extraction 4 (2), pp.474-487. (10.3390/make4020021)
• Filimonov, M. , Chopard, D. and Spasic, I. 2022. Simulation and annotation of global acronyms. Bioinformatics 38 (11), pp.3136-3138. btac298. (10.1093/bioinformatics/btac298)

2021

• Chopard, D. et al., 2021. Text mining of adverse events in clinical trials: Deep learning approach. JMIR Medical Informatics 9 (12) e28632. (10.2196/28632)
• Muralidaran, V. , Spasic, I. and Knight, D. 2021. A systematic review of unsupervised approaches to grammar induction. Natural Language Engineering 27 (6), pp.647-689. (10.1017/S1351324920000327)
• Knight, D. et al. 2021. Developing computational infrastructure for the CorCenCC corpus - the National Corpus of Contemporary Welsh. Language Resources and Evaluation 55 , pp.789-816. (10.1007/s10579-020-09501-9)
• Zunic, A. , Corcoran, P. and Spasic, I. 2021. Aspect-based sentiment analysis with graph convolution over syntactic dependencies. Artificial Intelligence in Medicine 119 102138. (10.1016/j.artmed.2021.102138)
• Tong, Y. et al. 2021. Integrating hedonic quality for user experience modelling. Presented at: ASME IDETC/CIE 2021 Virtual 17-20 August 2021. 41st Computers and Information in Engineering Conference (CIE) Proceedings. Vol. 2.ASME. (10.1115/DETC2021-69781)
• Corcoran, P. et al. 2021. Creating Welsh language word embeddings. Applied Sciences 11 (15) 6896. (10.3390/app11156896)
• Espinosa-Anke, L. et al. 2021. English–Welsh cross-lingual embeddings. Applied Sciences 11 (14) 6541. (10.3390/app11146541)
• Dunphy, E. et al., 2021. Feasibility randomised controlled trial comparing TRAK-ACL digital rehabilitation intervention plus treatment as usual versus treatment as usual for patients following anterior cruciate ligament reconstruction. BMJ Open Sport and Exercise Medicine 7 (2) e001002. (10.1136/bmjsem-2020-001002)
• Setchi, R. et al. 2021. Artificial intelligence for patent prior art searching. World Patent Information 64 102021. (10.1016/j.wpi.2021.102021)
• Hughes, C. et al., 2021. Leaving no stone unturned: flexible retrieval of idiomatic expressions from a large text corpus. Machine Learning and Knowledge Extraction 3 (1), pp.263-283. (10.3390/make3010013)
• Muralidaran, V. et al. 2021. A practical implementation of a porter stemmer for Welsh. In: Prys, D. ed. Language and Technology in Wales: Volume 1. Bangor: Bangor University. , pp.30-43.
• Palmer, G. et al. 2021. A closer look at Welsh word embeddings. In: Prys, D. ed. Language and Technology in Wales: Volume 1. Bangor: Bangor University. , pp.21-29.

2020

• Spasic, I. and Button, K. 2020. Patient triage by topic modelling of referral letters: Feasibility study. JMIR Medical Informatics 8 (11) e21252. (10.2196/21252)
• Muralidaran, V. , Spasic, I. and Knight, D. 2020. A cognitive approach to parsing with neural networks. Presented at: International Conference on Statistical Language and Speech Processing (SLSP) Cardiff, UK 14–16 Oct 2020. Statistical Language and Speech Processing. Vol. 12379.Springer Verlag. , pp.71-84. (10.1007/978-3-030-59430-5_6)
• Owen, D. et al. 2020. Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections.[Version 2]. Research Ideas and Outcomes 6 e58030. (10.3897/rio.6.e58030)
• Spasic, I. , Williams, L. and Buerki, A. 2020. Idiom–based features in sentiment analysis: cutting the Gordian knot. IEEE Transactions on Affective Computing 11 (2)(10.1109/TAFFC.2017.2777842)
• Setchi, R. and Spasic, I. 2020. AI-assisted patent prior art searching - feasibility study.
• Spasic, I. and Nenadic, G. 2020. Clinical text data in machine learning: Systematic review. JMIR Medical Informatics 8 (3) e17984. (10.2196/17984)
• Spasic, I. , Uzuner, Ö. and Zhou, L. 2020. Emerging clinical applications of text analytics. International Journal of Medical Informatics 134 103974. (10.1016/j.ijmedinf.2019.103974)
• Button, K. et al. 2020. Using routine referral data for patients with knee and hip pain to improve access to specialist care. BMC Musculoskeletal Disorders 21 66. (10.1186/s12891-020-3087-x)
• Zunic, A. , Corcoran, P. and Spasic, I. 2020. Sentiment analysis in health and wellbeing: A systematic review. JMIR Medical Informatics 8 (1) e16023. (10.2196/16023)
• Espinosa-Anke, L. , Martin-Vide, C. and Spasic, I. eds. 2020. Statistical language and speech processing: 8th International Conference, SLSP 2020, Cardiff, UK, October 14–16, 2020. Springer.
• Dai, X. et al., 2020. The state of the art in implementing machine learning for mobile apps: A survey. Presented at: IEEE SoutheastCon Raleigh, USA 12-15 Mar 2020. , pp.-.
• Zunic, A. , Corcoran, P. and Spasic, I. 2020. Improving the performance of sentiment analysis in health and wellbeing using domain knowledge. Presented at: the Third UK Healthcare Text Analytics Conference (HealTAC) London, UK 22-24 April 2020.

2019

• Spasic, I. et al. 2019. Cohort selection for clinical trials from longitudinal patient records: text mining approach. JMIR Medical Informatics 7 (4) e15980. (10.2196/15980)
• Spasic, I. et al. 2019. KLOSURE: Closing in on open–ended patient questionnaires with text mining. Journal of Biomedical Semantics 10 (S1) 24. (10.1186/s13326-019-0215-3)
• Williams, L. et al. 2019. Comparing the utility of different classification schemes for emotive language analysis. Journal of Classification 36 (3), pp.619-648. (10.1007/s00357-019-9307-0)
• Chopard, D. and Spasic, I. 2019. A deep learning approach to self-expansion of abbreviations based on morphology and context distance. Presented at: SLSP 2019: 7th International Conference on Statistical Language and Speech Processing Ljubljana, Slovenia 14-16 October 2019. Published in: Martín-Vide, C. , Purver, M. and Pollak, S. eds. Statistical Language and Speech Processing: 7th International Conference, SLSP 2019, Ljubljana, Slovenia, October 14–16, 2019, Proceedings. Vol. 11816.Springer. , pp.71-82. (10.1007/978-3-030-31372-2_6)
• Spasic, I. et al. 2019. Unsupervised multi-word term recognition in Welsh. Presented at: Celtic Language Technology Workshop 2019 Dublin, Ireland 19 August 2019. Published in: Lynn, T. et al., Proceedings of the Celtic Language Technology Workshop. European Association for Machine Translation. , pp.1-6.
• Dai, X. et al., 2019. Machine learning on mobile: An on-device inference app for skin cancer detection. Presented at: 2019 4th International Conference on Fog and Mobile Edge Computing Rome, Italy 10-13 June 2019. 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE. , pp.301-305. (10.1109/FMEC.2019.8795362)
• Nieva De La Hidalga, A. et al. 2019. Use of semantic segmentation for increasing the throughput of digitisation workflows for natural history collections. Presented at: Biodiversity_Next 2019 Leiden, The Netherlands 21-25 October 2019. Biodiversity Information Science and Standards. Vol. 3.Vol. e37161. Pensoft Publishers(10.3897/biss.3.37161)

2018

• Hannah, C. , Spasic, I. and Corcoran, P. 2018. A computational model of pedestrian road safety: the long way round is the safe way home. Accident Analysis & Prevention 121 , pp.347-357. (10.1016/j.aap.2018.06.004)
• Bannister, C. A. et al. 2018. A genetic programming approach to development of clinical prediction models: a case study in symptomatic cardiovascular disease. PLoS ONE 13 (9) e0202685. (10.1371/journal.pone.0202685)
• Button, K. et al. 2018. An evaluation of TRAK physiotherapy self management intervention development and delivery for knee conditions [Abstract]. Osteoarthritis and Cartilage 26 (S1) S336. (10.1016/j.joca.2018.02.667)
• Button, K. et al. 2018. Improving access to care and treatment for patients with hip and knee pain at the interface between primary and secondary care [Abstract]. Osteoarthritis and Cartilage 26 (S1), pp.S268-S269. (10.1016/j.joca.2018.02.546)
• Hannah, C. , Spasic, I. and Corcoran, P. 2018. Modelling pedestrian safety with respect to road traffic crashes by estimating the safety of paths. Presented at: GIScience Research UK Conference Leicester, England 18-20 April 2018.
• Spasic, I. et al. 2018. Head to head: Semantic similarity of multi-word terms. IEEE Access 6 , pp.20545-20557. (10.1109/ACCESS.2018.2826224)
• Preece, A. et al. 2018. Sentinel: a co-designed platform for semantic enrichment of social media streams. IEEE Transactions on Computational Social Systems 5 (1), pp.118-131. (10.1109/TCSS.2017.2763684)
• Spasic, I. 2018. Acronyms as an integral part of multi–word term recognition - A token of appreciation. IEEE Access 6 , pp.8351-8363. (10.1109/ACCESS.2018.2807122)
• Button, K. et al. 2018. Integrating self-management support for knee injuries into routine clinical practice: TRAK intervention design and delivery. Musculoskeletal Science and Practice 33 , pp.53-60. (10.1016/j.msksp.2017.11.002)
• Spasic, I. et al. 2018. Closing in on open-ended patient questionnaires with text mining. Presented at: UK Healthcare Text Analytics Conference (HealTAC) Manchester, UK 18-19 April 2018.

2017

• Dunphy, E. et al., 2017. Acceptability of a digital healthintervention alongside physiotherapy to support patients following anterior cruciateligament reconstruction. BMC Musculoskeletal Disorders 18 471. (10.1186/s12891-017-1846-0)
• Neale, S. et al. 2017. The CorCenCC crowdsourcing app: a bespoke tool for the user-driven creation of the national corpus of contemporary Welsh. Presented at: The 9th International Corpus Linguistics Conference Birmingham, UK 24-28 July 2017.
• Dai, X. , Spasic, I. and Andres, F. 2017. A framework for automated rating of online reviews against the underlying topics. Presented at: ACM Southeast Conference Kennesaw State University, Georgia, USA 13-15 April 2017. ACM SE '17 Proceedings of the SouthEast Conference. New York: ACM. , pp.164-167. (10.1145/3077286.3077291)

2016

• Button, K. and Spasic, I. 2016. Web based interventions for self-management of rehabilitation. Presented at: Enhanced Recovery after Surgery (ERAS) UK Conference Cardiff, UK 4th November.

2015

• Williams, L. et al. 2015. The role of idioms in sentiment analysis. Expert Systems with Applications 42 (21), pp.7375-7385. (10.1016/j.eswa.2015.05.039)
• Moore, S. C. et al. 2015. All-Wales Licensed Premises Intervention (AWLPI): a randomised controlled trial of an intervention to reduce alcohol-related violence. Public Health Research 3 (10)(10.3310/phr03100)
• Spasic, I. et al. 2015. KneeTex: An ontology-driven system for information extraction from MRI reports. Journal of Biomedical Semantics 6 34. (10.1186/s13326-015-0033-1)
• Button, K. et al. 2015. The clinical effectiveness of self-care interventions with an exercise component to manage knee conditions: a systematic review. The Knee 22 (5), pp.360-371. (10.1016/j.knee.2015.05.003)
• Evans, K. et al. 2015. Dynamically reconfigurable workflows for time-critical applications. Presented at: SC15: The International Conference for High Performance Computing, Networking, Storage, and Analysis New York City, NY, USA 16-19 November 2015. WORKS '15 Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science. , pp.1-10. (10.1145/2822332.2822339)
• Spasic, I. et al. 2015. TRAK app suite: A web-based intervention for delivering standard care for the rehabilitation of knee conditions. JMIR Research Protocols 4 (4) e122. (10.2196/resprot.4091)

2014

• Spasic, I. et al. 2014. Text mining of cancer-related information: review of current status and future directions. International Journal of Medical Informatics 83 (9), pp.605-623. (10.1016/j.ijmedinf.2014.06.009)
• Bhachu, L. et al., 2014. Mobile application KneeCare to support knee rehabilitation. Presented at: Science and Information Conference (SAI) London, UK 27-29 August 2014. 2014 Science and Information Conference. IEEE. , pp.447-451. (10.1109/SAI.2014.6918226)
• Bannister, C. A. et al. 2014. Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease. Value in Health 17 (3), pp.A200-A201. (10.1016/j.jval.2014.03.1171)
• Bannister, C. et al. 2014. External validation of the UKPDS risk engine in incident type 2 diabetes: a need for new type 2 diabetes-specific risk equations. Diabetes Care 37 (2), pp.537-545. (10.2337/dc13-1159)
• Moore, S. C. et al. 2014. All-Wales licensed premises intervention (AWLPI): a randomised controlled trial to reduce alcohol-related violence. BMC Public Health 14 (1), pp.-. 21. (10.1186/1471-2458-14-21)

2013

• Button, K. et al. 2013. Development of iKnee: A web based application using biomechanical data to optomize knee rehabilitation within the home environment. Presented at: The 32nd Scientific Meeting of the Physiotherapy Research Society Cardiff, UK 9 April 2013.
• Button, K. et al. 2013. TRAK ontology: defining standard care for the rehabilitation of knee conditions. Journal of Biomedical Informatics 46 (4), pp.615-625. (10.1016/j.jbi.2013.04.009)
• Greenwood, M. et al. 2013. Automatic extraction of personal experiences from patients' blogs: A case study in chronic obstructive pulmonary disease. Presented at: Third International Conference on Social Computing and its Applications Karlsruhe, Germany 30th September - 2nd October 2013.
• Smallbone, K. et al., 2013. A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Letters 587 (17), pp.2832-2841. (10.1016/j.febslet.2013.06.043)
• Spasic, I. et al. 2013. FlexiTerm: a flexible term recognition method. Journal of Biomedical Semantics 4 27. (10.1186/2041-1480-4-27)

2012

• Spasic, I. et al. 2012. A naïve Bayes approach to classifying topics in suicide notes. Biomedical Informatics Insights 5 (Supp 1), pp.87-97. (10.4137/BII.S8945)
• Burnap, P. et al. 2012. Protecting patient privacy in distributed collaborative healthcare environments by retaining access control of shared information. Presented at: 2012 International Conference on Collaboration Technologies and Systems (CTS) Denver, CO, USA 21-25 May 2012. Published in: Smari, W. W. and Charles, F. eds. 2012 International Conference on Collaboration Technologies and Systems (CTS). Vol. 14.Los Alamitos, CA: IEEE. , pp.490-497. (10.1109/CTS.2012.6261095)

2010

• Dada, J. O. et al., 2010. SBRML: a markup language for associating systems biology data with models. Bioinformatics 26 (7), pp.932-938. (10.1093/bioinformatics/btq069)
• Li, P. et al., 2010. Systematic integration of experimental data and models in systems biology. BMC Bioinformatics 11 582. (10.1186/1471-2105-11-582)
• Masseroli, M. , Paton, N. W. and Spasic, I. 2010. Chapter 15: Search Computing and the Life Sciences. Lecture Notes in Computer Science 5950 , pp.291-306. (10.1007/978-3-642-12310-8_15)
• Spasic, I. et al. 2010. Medication information extraction with linguistic pattern matching and semantic rules. Journal of the American Medical Informatics Association 17 (5), pp.532-535. (10.1136/jamia.2010.003657)
• Swainston, N. et al., 2010. Integrative information management for systems biology. Lecture Notes in Computer Science 6254 , pp.164-178. (10.1007/978-3-642-15120-0_13)

2009

• Brown, M. et al., 2009. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 134 (7), pp.1322-1332. (10.1039/b901179j)
• Spasic, I. et al. 2009. KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways. Bioinformatics 25 (11), pp.1404-1411. (10.1093/bioinformatics/btp175)
• Yang, H. et al., 2009. A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries. JAMIA Journal of the American Medical Informatics Assocation 16 (4), pp.596-600. (10.1197/jamia.M3096)
• Yang, H. et al., 2009. A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries. Journal of The American Medical Informatics Association 16 (4), pp.596-600. (10.1197/jamia.M3096)

2008

• Spasic, I. et al. 2008. Facilitating the development of controlled vocabularies for metabolomics technologies with text mining. BMC Bioinformatics 9 (Supp 5) S5. (10.1186/1471-2105-9-S5-S5)
• Dunn, W. B. et al., 2008. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. International Journal of Epidemiology 37 (S1), pp.i23-i30. (10.1093/ije/dym281)
• Herrgard, M. J. et al., 2008. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology 26 (10), pp.1155-1160. (10.1038/nbt1492)

2007

• Dunn, W. B. et al., 2007. Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics 3 (4), pp.413-426. (10.1007/s11306-007-0063-5)
• Sansone, S. et al., 2007. Metabolomics standards initiative: ontology working group work in progress. Metabolomics 3 (3), pp.249-256. (10.1007/s11306-007-0069-z)

2006

• Spasic, I. et al. 2006. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 7 281. (10.1186/1471-2105-7-281)
• Wilkinson, S. J. , Spasic, I. and Ellis, D. I. 2006. Genomes to systems 3. Metabolomics 2 (3), pp.165-170. (10.1007/s11306-006-0030-6)

2005

• Brown, M. et al., 2005. A metabolome pipeline: from concept to data to knowledge. Metabolomics 1 (1), pp.39-51. (10.1007/s11306-005-1106-4)
• Kell, D. B. et al., 2005. Metabolic footprinting and systems biology: the medium is the message. Nature Reviews. Microbiology 3 (7), pp.557-565. (10.1038/nrmicro1177)
• Spasic, I. and Ananiadou, S. 2005. A flexible measure of contextual similarity for biomedical terms. Pacific Symposium on Biocomputing 10 , pp.197-208.
• Spasic, I. et al. 2005. Text mining and ontologies in biomedicine: making sense of raw text. Briefings in Bioinformatics 6 (3), pp.239-251. (10.1093/bib/6.3.239)
• Spasic, I. , Ananiadou, S. and Tsujii, J. I. 2005. MaSTerClass: a case-based reasoning system for the classification of biomedical terms. Bioinformatics 21 (11), pp.2748-2758. (10.1093/bioinformatics/bti338)

2004

• Nenadic, G. , Spasic, I. and Ananiadou, S. 2004. Mining term similarities from corpora. Terminology 10 (1), pp.55-80. (10.1075/term.10.1.04nen)
• Spasic, I. and Ananiadou, S. 2004. Using automatically learnt verb selectional preferences for classification of biomedical terms. Journal of Biomedical Informatics 37 (6), pp.483-497. (10.1016/j.jbi.2004.08.002)

2003

• Nenadic, G. , Spasic, I. and Ananiadou, S. 2003. Terminology-driven mining of biomedical literature. Bioinformatics 19 (8), pp.938-943. (10.1093/bioinformatics/btg105)

2002

• Nenadic, G. et al., 2002. Terminology-driven literature mining and knowledge acquisition in biomedicine. International Journal of Medical Informatics 67 (1-3), pp.33-48. (10.1016/S1386-5056(02)00055-2 |)