Reconstructing interpretable features in computational super-resolution microscopy via regularized latent search
Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderat...
Saved in:
Published in | Biological imaging (Cambridge, England) Vol. 4; pp. e8 - 20 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
England
Cambridge University Press
2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on generative adversarial network (GAN) latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution (HR) image interpretable features. Here, we propose a robust super-resolution (SR) method based on regularized latent search (RLS) that offers an actionable balance between fidelity to the ground truth (GT) and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution (LR) image into a computational SR task performed by deep learning followed by a quantification task performed by a handcrafted algorithm based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the HR details of a specific sample but rather to obtain HR images that preserve explainable and quantifiable differences between conditions. |
---|---|
AbstractList | Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on generative adversarial network (GAN) latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution (HR) image interpretable features. Here, we propose a robust super-resolution (SR) method based on regularized latent search (RLS) that offers an actionable balance between fidelity to the ground truth (GT) and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution (LR) image into a computational SR task performed by deep learning followed by a quantification task performed by a handcrafted algorithm based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the HR details of a specific sample but rather to obtain HR images that preserve explainable and quantifiable differences between conditions. Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on generative adversarial network (GAN) latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution (HR) image interpretable features. Here, we propose a robust super-resolution (SR) method based on regularized latent search (RLS) that offers an actionable balance between fidelity to the ground truth (GT) and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution (LR) image into a computational SR task performed by deep learning followed by a quantification task performed by a handcrafted algorithm based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the HR details of a specific sample but rather to obtain HR images that preserve explainable and quantifiable differences between conditions.Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on generative adversarial network (GAN) latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution (HR) image interpretable features. Here, we propose a robust super-resolution (SR) method based on regularized latent search (RLS) that offers an actionable balance between fidelity to the ground truth (GT) and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution (LR) image into a computational SR task performed by deep learning followed by a quantification task performed by a handcrafted algorithm based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the HR details of a specific sample but rather to obtain HR images that preserve explainable and quantifiable differences between conditions. |
Author | Gheisari, Marzieh Genovesio, Auguste |
AuthorAffiliation | Institut de Biologie de l’Ecole Normale Supérieure (ENS), PSL Research University , Paris , France |
AuthorAffiliation_xml | – name: Institut de Biologie de l’Ecole Normale Supérieure (ENS), PSL Research University , Paris , France |
Author_xml | – sequence: 1 givenname: Marzieh surname: Gheisari fullname: Gheisari, Marzieh organization: Institut de Biologie de l'Ecole Normale Supérieure (ENS), PSL Research University, Paris, France – sequence: 2 givenname: Auguste orcidid: 0000-0003-1877-5595 surname: Genovesio fullname: Genovesio, Auguste organization: Institut de Biologie de l'Ecole Normale Supérieure (ENS), PSL Research University, Paris, France |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39314829$$D View this record in MEDLINE/PubMed |
BookMark | eNplUk1r3TAQNCWlSdP8gF6KoZde3Got2ZZOpYSkDQQK_YDexFpev-ihZ7mSHEh_feS8NCStLitmZ4edZV4WB5OfqCheA3sPDLoP3-uWc8X4r1qw_KR4VhytULViB4_-h8VJjNtMqRVwBepFccgVByFrdVTM38j4KaawmGSnTWmnRGEOlLB3VI6EaQkUM1wav5uXhMn6CV0Zl5lClVveLStU7qwJPho_35TXFstAm8VhsH9oKB0mmlIZCYO5elU8H9FFOrmvx8XP87Mfp1-qy6-fL04_XVYmrymqkXcNdqZV0Ml-YJxzrLlgtZR8YIOSigERDryWAAyF6Nqxke1ohk6QNNn2cXGx1x08bvUc7A7DjfZo9R3gw0ZjSNY40r0RsiUGQ5-v0gmBjQGOBo0RY9tRm7U-7rXmpd_RYLKbgO6J6NPOZK_0xl9rAAGSyTorvLtXCP73QjHpnY2GnMOJ_BI1Bya7VvBGZerbf6hbv4R88zsWB2iaBjIL9qz16jHQ-LANML3mQ_-Xjzzz5rGNh4m_aeC3kwy5sw |
ContentType | Journal Article |
Copyright | The Author(s) 2024. The Author(s), 2024. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2024 2024 The Author(s) |
Copyright_xml | – notice: The Author(s) 2024. – notice: The Author(s), 2024. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2024 2024 The Author(s) |
DBID | NPM AAYXX CITATION 3V. 7X7 7XB 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M7P PIMPY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.1017/S2633903X24000084 |
DatabaseName | PubMed CrossRef ProQuest Central (Corporate) ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection AUTh Library subscriptions: ProQuest Central ProQuest Natural Science Collection ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection (Proquest) (PQ_SDU_P3) ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni Edition) Biological Science Database Publicly Available Content Database (ProQuest Open Access資料庫) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef Publicly Available Content Database ProQuest Central Student ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic PubMed Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: BENPR name: AUTh Library subscriptions: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 2633-903X |
EndPage | 20 |
ExternalDocumentID | oai_doaj_org_article_bc486e01db314744a5c13acacc4f67e6 10_1017_S2633903X24000084 39314829 |
Genre | Journal Article |
GrantInformation_xml | – fundername: ; grantid: Labex Memolife – fundername: ; grantid: PSL University – fundername: ; grantid: Pseudotime |
GroupedDBID | 09C 09E 0R~ 7X7 8FI 8FJ AANRG AASVR ABUWG ABVZP ACZWT ADAZD ADDNB ADKIL ADVJH AEBAK AEYHU AFKRA AGABE AGBYD AGJUD AHIPN AHRGI ALIPV ALMA_UNASSIGNED_HOLDINGS AQJOH BBNVY BENPR BHPHI BLZWO CCPQU CCQAD CJCSC FYUFA GROUPED_DOAJ HCIFZ HMCUK IKXGN IPYYG M7P M~E NPM OK1 PGMZT PIMPY RCA ROL RPM UKHRP WFFJZ AAYXX CITATION 3V. 7XB 8FE 8FH 8FK AZQEC DWQXO GNUQQ K9. LK8 PQEST PQQKQ PQUKI PRINS 7X8 5PM |
ID | FETCH-LOGICAL-c2914-f375a7c69178bd0333a23402883d0d98901eead328110a4476f586fcd74e8c903 |
IEDL.DBID | RPM |
ISSN | 2633-903X |
IngestDate | Tue Oct 22 15:15:33 EDT 2024 Tue Sep 24 05:24:48 EDT 2024 Sat Oct 26 02:07:13 EDT 2024 Thu Oct 10 21:50:59 EDT 2024 Fri Aug 23 01:42:02 EDT 2024 Sat Nov 02 12:12:24 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | microscopy diagnostic generative prior super-resolution |
Language | English |
License | The Author(s) 2024. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c2914-f375a7c69178bd0333a23402883d0d98901eead328110a4476f586fcd74e8c903 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0003-1877-5595 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418082/ |
PMID | 39314829 |
PQID | 3103115551 |
PQPubID | 5515559 |
PageCount | 20 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_bc486e01db314744a5c13acacc4f67e6 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11418082 proquest_miscellaneous_3108764359 proquest_journals_3103115551 crossref_primary_10_1017_S2633903X24000084 pubmed_primary_39314829 |
PublicationCentury | 2000 |
PublicationDate | 2024-00-00 |
PublicationDateYYYYMMDD | 2024-01-01 |
PublicationDate_xml | – year: 2024 text: 2024-00-00 |
PublicationDecade | 2020 |
PublicationPlace | England |
PublicationPlace_xml | – name: England – name: Cambridge – name: Cambridge, UK |
PublicationTitle | Biological imaging (Cambridge, England) |
PublicationTitleAlternate | Biol Imaging |
PublicationYear | 2024 |
Publisher | Cambridge University Press |
Publisher_xml | – name: Cambridge University Press |
SSID | ssj0002913919 |
Score | 2.289966 |
Snippet | Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or... |
SourceID | doaj pubmedcentral proquest crossref pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | e8 |
SubjectTerms | Computer applications Deep learning diagnostic generative prior Image processing Localization Methods Microscopy Realism Signal to noise ratio super-resolution |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LixQxEA6yB_Eirrrau6NE8CQ0292VTtJHFZdB0JMLc2vy3B0Ye4d5LOivtyrpGWZU8OI1lUO6UpX6Kqn-irG3svYQo4PSozuVQrautL6VJWCwJvIQagNH1RZf5fRafJ61s4NWX1QTlumBs-IurRNahqr2FmqhhDCtq8E445yIUoVMtl11B8kUncENsV3W3e4ZkziiGwmY38OMiiarxGZ6EIgSX__fQObvtZIHwefqCXs8okb-Pq_2lD0Iw1P28Mv4Lv6MLSmL3HHBDjd8vi8ltIvAY0jsnWsc5i51cRhvAPl6uwyrEkWjAfLvVJ9Hf6r84Pdzw1epU_1q_jN4vkBUOmx4do3n7Prq07eP03LspVA61IUoI6jWKCcxO9PWVwBgGsDcUWvwle80woKARgWNRjxghFAytlpG55UI2qHeztjJcDeEl4yL2IBVwnREFifBGGsriMQrQwencgV7t1Nsv8yUGX2uJVP9H7tQsA-k-v1EYrtOA2gD_WgD_b9soGCT3cb1owuue2qghnAXEWHB3uzF6Dz0ImKGcLdNczAaIGLsCvYi7_N-JdABcaSiRB9ZwNFSjyXD_DYRdGOOWWvEVuf_4-Mu2KMGgVS-9pmwEzSl8AqB0Ma-Tjb_CwMuBto priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSIgLKu_QgozECSkiyTi2c6oKoqqQ4ESlvUV-lpVKdrvbIsGvZ8ZxQhcQVzsHyzPj-eaRbxh7LWsPMTooPZpTKWTrSutbWQI6ayIPoTFw1G3xWZ6eiY-LdpETbtvcVjm9iemh9itHOfK3NA8L0Qs6-KP1ZUlTo6i6mkdo3GZ36qaSpNVqoeYcS0Ocl3U3FTOJKbqRgFE-LKh1skqcpjfcUWLt_xfU_LNj8oYLOtln9zN25MejsB-wW2F4yO5-ytXxR2xNseTECDuc8-XcUGgvAo8hcXhucZm7NMsh5wH59nodNiVuZTXk36hLj_5X-cG_Lw3fpHn1m-XP4PkFYtPhio8G8pidnXz48v60zBMVSod3IcoIqjXKSYzRtPUVAJgGMILUGnzlO43gIKBqQaMRFRghlIytltF5JYJ2eG9P2N6wGsIzxkVswCphOqKMk2CMtRVEYpeh51O5gr2ZLrZfj8QZ_dhRpvq_pFCwd3T184fEeZ0WVpvzPptQb53QMlS1t1ALJYRpXQ3GGedElCrIgh1OguuzIW7732pTsFfzNpoQ1UXMEFbX6Rv0CYgbu4I9HeU8nwQ6IKZU3NE7GrBz1N2dYfk10XRjpFlrRFjP_3-uA3avQaA0pnUO2R4qSXiBQOfKvkza_AuzS_1q priority: 102 providerName: ProQuest |
Title | Reconstructing interpretable features in computational super-resolution microscopy via regularized latent search |
URI | https://www.ncbi.nlm.nih.gov/pubmed/39314829 https://www.proquest.com/docview/3103115551 https://www.proquest.com/docview/3108764359 https://pubmed.ncbi.nlm.nih.gov/PMC11418082 https://doaj.org/article/bc486e01db314744a5c13acacc4f67e6 |
Volume | 4 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Na9wwEB2SFEovJf2Mm3RRoaeCs7ZHluRjEhJCISGUBvZmJFlODbveZTcpJL--I9ledpueerVsENKT54309Abgq0grrGuLcUXLKeYit7GpchEjBWtvHuLLwHm1xbW4vOXfJ_lkB8RwFyaI9q1pjtvp7LhtfgVt5WJmx4NObHxzdUYcPlUUu8a7sEsI3cjR_f83806XaTEcYXp_6Ewg5fY48YLJJDiZbgSh4NX_L4L5t05yI_Bc7MPrnjGyk65nb2DHtW_h5VV_Jv4OFj6DHHxg2zvWrGWEZupY7YJz54oeMxsqOPS7f2z1sHDLmJp68LGZ1-b5WyqP7Hej2TJUqV82T65iU2Kk7T3rlsV7uL04_3l2Gfd1FGJLY8HjGmWupRWUmSlTJYioM6S8USmskqpQRAkcAQozRVxAcy5FnStR20pypyyN2wfYa-etOwDG6wyN5LrwRnECtTYmwdp7yvifprQRfBsGtlx0dhllpyOT5bNZiODUD_36Re90HR7Ml3dlP9-lsVwJl6SVwZRLznVuU9RWW8trIZ2I4GiYuLJffqvSF08jqktsMIIv62ZaOP40RLdu_hDeoUhAbLGI4GM3z-ueYIHeH5Va1BYCtrq63UJYDebcAzY__f-nh_AqI-rUbfQcwR4ByH0m6nNvRoT3iRzBi9Pz65sfo7CBMAro_wOeTQin |
link.rule.ids | 230,315,730,783,787,867,888,2109,4032,12069,21401,27936,27937,27938,31732,31733,33757,33758,43323,43818,53805,53807,74080,74637 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSNAL4k2ggJE4IUUkGcd2TggQ1QJtT620t8ivlJVKdrvbIsGvZ8ZxQhcQVzsHyzPj-eaRbxh7JUsPXecg92hOuZC1y62vZQ7orIk8hMbAUbfFkZydiM_zep4SbpvUVjm-ifGh9ktHOfI3NA8L0Qs6-Ler85ymRlF1NY3QuM5uEA8X6bmaqynHUhHnZdmMxUxiiq4kYJQPc2qdLCKn6RV3FFn7_wU1_-yYvOKC9u-w2wk78neDsO-ya6G_x24epur4fbaiWHJkhO1P-WJqKLRngXchcnhucJm7OMsh5QH55nIV1jluJTXk36hLj_5X-cG_Lwxfx3n168XP4PkZYtP-gg8G8oCd7H88_jDL00SF3OFdiLwDVRvlJMZo2voCAEwFGEFqDb7wjUZwEFC1oNKICowQSna1lp3zSgTt8N4esp1-2YfHjIuuAquEaYgyToIx1hbQEbsMPZ_KZez1eLHtaiDOaIeOMtX-JYWMvaernz4kzuu4sFyftsmEWuuElqEovYVSKCFM7UowzjgnOqmCzNjeKLg2GeKm_a02GXs5baMJUV3E9GF5Gb9Bn4C4scnYo0HO00mgAWJKxR29pQFbR93e6RdfI003RpqlRoT15P_nesFuzY4PD9qDT0dfnrLdCkHTkOLZYzuoMOEZgp4L-zxq9i-uhwBg |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB7BVqq4IN4EChiJE1LUJHZs54QodFVeqwpRaW-R40e7Uskuuy0S_HpmEid0AXG1LcWyZzzf2F--AXghc8dDsDx16E6pkKVNG1fKlGOwJvEQKgNHbIuZPDoR7-flPPKfNpFWOZyJ3UHtlpbuyPepHhaiFwzw-yHSIo7fTl-tvqVUQYpeWmM5jeuwowR-ZwI7B4ez48_jjUtBCph5NTxtkm50ITnm_HxORMqsUzi9Epw6Df9_Ac8_-ZNXAtL0FtyMSJK97rf-Nlzz7R3Y_RTfyu_CijLLQR-2PWWLkV7YnHsWfKfoucFmZrvKDvFWkG0uV36dYlc0SvaVOHv098oP9n1h2LqrXr9e_PSOnSNSbS9Y7y734GR6-OXNURrrK6QW10KkgavSKCsxY9ONyzjnpuCYT2rNXeYqjVDBo6HxQiNGMEIoGUotg3VKeG1x3e7DpF22_iEwEQreKGEqEpCT3JimyXggrRk6TJVN4OWwsPWql9Goe36Zqv_ahQQOaOnHgaSA3TUs16d1dKi6sUJLn-Wu4blQQpjS5txYY60IUnmZwN6wcXV0y03924gSeD52o0PRK4lp_fKyG4MRAlFklcCDfp_HmfCKk24q9ugtC9ia6nZPuzjrRLsx78w14q1H_5_XM9hFs64_vpt9eAw3CkRQ_X3PHkzQXvwTREAXzdNo2r8AE0kGAw |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Reconstructing+interpretable+features+in+computational+super-resolution+microscopy+via+regularized+latent+search&rft.jtitle=Biological+imaging+%28Cambridge%2C+England%29&rft.au=Gheisari%2C+Marzieh&rft.au=Genovesio%2C+Auguste&rft.date=2024&rft.pub=Cambridge+University+Press&rft.eissn=2633-903X&rft.volume=4&rft_id=info:doi/10.1017%2FS2633903X24000084&rft.externalDBID=HAS_PDF_LINK |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2633-903X&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2633-903X&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2633-903X&client=summon |