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Hirling D, Tasnadi E, Caicedo J, Caroprese MV, Sjögren R, Aubreville M, Koos K, Horvath P. Segmentation metric misinterpretations in bioimage analysis. Nat Methods 2024; 21:213-216. [PMID: 37500758 PMCID: PMC10864175 DOI: 10.1038/s41592-023-01942-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 06/06/2023] [Indexed: 07/29/2023]
Abstract
Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.
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Affiliation(s)
- Dominik Hirling
- Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Szeged, Hungary
| | - Ervin Tasnadi
- Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Szeged, Hungary
| | - Juan Caicedo
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Rickard Sjögren
- Sartorius, Corporate Research, Umeå, Sweden
- CellVoyant Technologies Ltd, Bristol, UK
| | | | - Krisztian Koos
- Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
| | - Peter Horvath
- Biological Research Centre, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.
- Single-Cell Technologies Ltd, Szeged, Hungary.
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
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2
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Grexa I, Iván ZZ, Migh E, Kovács F, Bolck HA, Zheng X, Mund A, Moshkov N, Miczán V, Koos K, Horvath P. SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy. Brief Bioinform 2024; 25:bbae029. [PMID: 38483256 PMCID: PMC10938542 DOI: 10.1093/bib/bbae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/20/2023] [Accepted: 01/08/2024] [Indexed: 03/17/2024] Open
Abstract
Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of structural or molecular information. Achieving accurate registration of multimodal images is essential for the correlative analysis process, but it remains a challenging computer vision task with no widely accepted solution. Moreover, supervised registration methods require annotated data produced by experts, which is limited. To address this challenge, we propose a general unsupervised pipeline for multimodal image registration using deep learning. We provide a comprehensive evaluation of the proposed pipeline versus the current state-of-the-art image registration and style transfer methods on four types of biological problems utilizing different microscopy modalities. We found that style transfer of modality domains paired with fully unsupervised training leads to comparable image registration accuracy to supervised methods and, most importantly, does not require human intervention.
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Affiliation(s)
- Istvan Grexa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
- Doctoral School of Interdisciplinary Medicine, University of Szeged, Korányi fasor 10, Szeged 6720 Hungary
| | - Zsanett Zsófia Iván
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
- Doctoral School of Biology, University of Szeged, Közép fasor 52, Szeged 6726 Hungary
| | - Ede Migh
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
| | - Ferenc Kovács
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
- Single-Cell Technologies Ltd, Temesvári körút 62, Szeged 6726, Hungary
| | - Hella A Bolck
- Department of Pathology and Molecular Pathology, University Hospital Zürich, Zürich, Schmelzbergstrasse 12 8091, Switzerland
| | - Xiang Zheng
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Tuborg Havnevej 19 2900 Hellerup, Denmark
| | - Andreas Mund
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Tuborg Havnevej 19 2900 Hellerup, Denmark
| | - Nikita Moshkov
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
| | - Vivien Miczán
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
| | - Krisztian Koos
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári körút 62, Szeged 6726
- Single-Cell Technologies Ltd, Temesvári körút 62, Szeged 6726, Hungary
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Tukholmankatu 8, Helsinki 00014, Finland
- Institute of AI for Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Oberschleißheim Neuherberg, Germany
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Voros C, Bauer D, Migh E, Grexa I, Végh AG, Szalontai B, Castellani G, Danka T, Dzeroski S, Koos K, Piccinini F, Horvath P. Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era. Biosensors (Basel) 2023; 13:187. [PMID: 36831953 PMCID: PMC9953278 DOI: 10.3390/bios13020187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/14/2023] [Accepted: 01/21/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.
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Affiliation(s)
- Csaba Voros
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - David Bauer
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Ede Migh
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Istvan Grexa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Attila Gergely Végh
- Institute of Biophysics, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Balázs Szalontai
- Institute of Biophysics, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Gastone Castellani
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Via G. Massarenti 9, I-40126 Bologna, Italy
| | - Tivadar Danka
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Saso Dzeroski
- Department of Knowledge Technologies, Jozef Stefan Institute, Jamova Cesta 39, SI-1000 Ljubljana, Slovenia
| | - Krisztian Koos
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
| | - Filippo Piccinini
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Via G. Massarenti 9, I-40126 Bologna, Italy
- Scientific Directorate, IRCCS Istituto Romagnolo per lo Studio Dei Tumori (IRST) “Dino Amadori”, Via P. Maroncelli 40, I-47014 Meldola, Italy
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, Hungary
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, FI-00014 Helsinki, Finland
- Single-Cell Technologies Ltd., Temesvári krt. 62, H-6726 Szeged, Hungary
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Diosdi A, Hirling D, Kovacs M, Toth T, Harmati M, Koos K, Buzas K, Piccinini F, Horvath P. Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids. Data Brief 2021; 36:107090. [PMID: 34026984 PMCID: PMC8134717 DOI: 10.1016/j.dib.2021.107090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 02/25/2021] [Accepted: 04/16/2021] [Indexed: 11/20/2022] Open
Abstract
Nowadays, three dimensional (3D) cell cultures are widely used in the biological laboratories and several optical clearing approaches have been proposed to visualize individual cells in the deepest layers of cancer multicellular spheroids. However, defining the most appropriate clearing approach for the different cell lines is an open issue due to the lack of a gold standard quantitative metric. In this article, we describe and share a single-cell resolution 3D image dataset of human carcinoma spheroids imaged using a light-sheet fluorescence microscope. The dataset contains 90 multicellular cancer spheroids derived from 3 cell lines (i.e. T-47D, 5-8F, and Huh-7D12) and cleared with 5 different protocols, precisely ClearT, ClearT2, CUBIC, ScaleA2, and Sucrose. To evaluate image quality and light penetration depth of the cleared 3D samples, all the spheroids have been imaged under the same experimental conditions, labelling the nuclei with the DRAQ5 stain and using a Leica SP8 Digital LightSheet microscope. The clearing quality of this dataset was annotated by 10 independent experts and thus allows microscopy users to qualitatively compare the effects of different optical clearing protocols on different cell lines. It is also an optimal testbed to quantitatively assess different computational metrics evaluating the image quality in the deepest layers of the spheroids.
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Affiliation(s)
- Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Doctoral School of Biology, University of Szeged, H-6726 Szeged, Hungary
| | - Dominik Hirling
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, H-6701 Szeged, Hungary
| | - Maria Kovacs
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Doctoral School of Biology, University of Szeged, H-6726 Szeged, Hungary
| | - Maria Harmati
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
| | - Krisztian Koos
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
| | - Krisztina Buzas
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Department of Immunology, Faculty of Medicine, Faculty of Science and Informatics, University of Szeged, H-6720 Szeged, Hungary
| | - Filippo Piccinini
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Via Piero Maroncelli 40, I-47014 Meldola (FC), Italy
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014 Helsinki, Finland
- Single-Cell Technologies Ltd., H-6726 Szeged, Hungary
- Corresponding author.
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Diosdi A, Hirling D, Kovacs M, Toth T, Harmati M, Koos K, Buzas K, Piccinini F, Horvath P. A quantitative metric for the comparative evaluation of optical clearing protocols for 3D multicellular spheroids. Comput Struct Biotechnol J 2021; 19:1233-1243. [PMID: 33717421 PMCID: PMC7907228 DOI: 10.1016/j.csbj.2021.01.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/19/2021] [Accepted: 01/23/2021] [Indexed: 12/18/2022] Open
Abstract
3D multicellular spheroids quickly emerged as in vitro models because they represent the in vivo tumor environment better than standard 2D cell cultures. However, with current microscopy technologies, it is difficult to visualize individual cells in the deeper layers of 3D samples mainly because of limited light penetration and scattering. To overcome this problem several optical clearing methods have been proposed but defining the most appropriate clearing approach is an open issue due to the lack of a gold standard metric. Here, we propose a guideline for 3D light microscopy imaging to achieve single-cell resolution. The guideline includes a validation experiment focusing on five optical clearing protocols. We review and compare seven quality metrics which quantitatively characterize the imaging quality of spheroids. As a test environment, we have created and shared a large 3D dataset including approximately hundred fluorescently stained and optically cleared spheroids. Based on the results we introduce the use of a novel quality metric as a promising method to serve as a gold standard, applicable to compare optical clearing protocols, and decide on the most suitable one for a particular experiment.
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Affiliation(s)
- Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Doctoral School of Biology, University of Szeged, H-6726 Szeged, Hungary
| | - Dominik Hirling
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, H-6701 Szeged, Hungary
| | - Maria Kovacs
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Doctoral School of Biology, University of Szeged, H-6726 Szeged, Hungary
| | - Maria Harmati
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
| | - Krisztian Koos
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
| | - Krisztina Buzas
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Department of Immunology, Faculty of Medicine, Faculty of Science and Informatics, University of Szeged, H-6720 Szeged, Hungary
| | - Filippo Piccinini
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Via Piero Maroncelli 40, I-47014 Meldola, FC, Italy
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), H-6726 Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014 Helsinki, Finland
- Single-Cell Technologies Ltd., H-6726 Szeged, Hungary
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Smith K, Piccinini F, Balassa T, Koos K, Danka T, Azizpour H, Horvath P. Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays. Cell Syst 2019; 6:636-653. [PMID: 29953863 DOI: 10.1016/j.cels.2018.06.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 03/07/2018] [Accepted: 06/01/2018] [Indexed: 01/01/2023]
Abstract
Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.
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Affiliation(s)
- Kevin Smith
- KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary
| | - Krisztian Koos
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary
| | - Tivadar Danka
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary
| | - Hossein Azizpour
- KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland.
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Koos K, Molnár J, Kelemen L, Tamás G, Horvath P. DIC image reconstruction using an energy minimization framework to visualize optical path length distribution. Sci Rep 2016; 6:30420. [PMID: 27453091 PMCID: PMC4958949 DOI: 10.1038/srep30420] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 07/01/2016] [Indexed: 11/28/2022] Open
Abstract
Label-free microscopy techniques have numerous advantages such as low phototoxicity, simple setup and no need for fluorophores or other contrast materials. Despite their advantages, most label-free techniques cannot visualize specific cellular compartments or the location of proteins and the image formation limits quantitative evaluation. Differential interference contrast (DIC) is a qualitative microscopy technique that shows the optical path length differences within a specimen. We propose a variational framework for DIC image reconstruction. The proposed method largely outperforms state-of-the-art methods on synthetic, artificial and real tests and turns DIC microscopy into an automated high-content imaging tool. Image sets and the source code of the examined algorithms are made publicly available.
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Affiliation(s)
- Krisztian Koos
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, BRC, Szeged, Hungary
| | - József Molnár
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, BRC, Szeged, Hungary
| | - Lóránd Kelemen
- Institute of Biophysics, Hungarian Academy of Sciences, BRC, Szeged, Hungary
| | - Gábor Tamás
- MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, BRC, Szeged, Hungary.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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