[{"language":[{"iso":"eng"}],"doi":"10.1093/bioinformatics/btz841","project":[{"name":"Systematic investigation of epistasis in molecular evolution","call_identifier":"FP7","grant_number":"335980","_id":"26120F5C-B435-11E9-9278-68D0E5697425"}],"isi":1,"quality_controlled":"1","tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)"},"oa":1,"external_id":{"isi":["000538696800054"],"pmid":["31742320"]},"publication_identifier":{"eissn":["1460-2059"],"issn":["1367-4803"]},"month":"03","volume":36,"date_created":"2020-10-11T22:01:14Z","date_updated":"2023-08-22T09:57:29Z","author":[{"full_name":"Esteban, Laura A","first_name":"Laura A","last_name":"Esteban"},{"full_name":"Lonishin, Lyubov R","first_name":"Lyubov R","last_name":"Lonishin"},{"last_name":"Bobrovskiy","first_name":"Daniil M","full_name":"Bobrovskiy, Daniil M"},{"first_name":"Gregory","last_name":"Leleytner","full_name":"Leleytner, Gregory"},{"full_name":"Bogatyreva, Natalya S","last_name":"Bogatyreva","first_name":"Natalya S"},{"full_name":"Kondrashov, Fyodor","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8243-4694","first_name":"Fyodor","last_name":"Kondrashov"},{"full_name":"Ivankov, Dmitry N ","last_name":"Ivankov","first_name":"Dmitry N "}],"department":[{"_id":"FyKo"}],"publisher":"Oxford Academic","publication_status":"published","pmid":1,"year":"2020","acknowledgement":"This work was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013, ERC grant agreement 335980_EinME) and Startup package to the Ivankov laboratory at Skolkovo Institute of Science and Technology. The work was started at the School of Molecular and Theoretical Biology 2017 supported by the Zimin Foundation. N.S.B. was supported by the Woman Scientists Support Grant in Centre for Genomic Regulation (CRG). ","ec_funded":1,"file_date_updated":"2020-10-12T12:02:09Z","date_published":"2020-03-15T00:00:00Z","page":"1960-1962","article_type":"original","citation":{"ama":"Esteban LA, Lonishin LR, Bobrovskiy DM, et al. HypercubeME: Two hundred million combinatorially complete datasets from a single experiment. Bioinformatics. 2020;36(6):1960-1962. doi:10.1093/bioinformatics/btz841","apa":"Esteban, L. A., Lonishin, L. R., Bobrovskiy, D. M., Leleytner, G., Bogatyreva, N. S., Kondrashov, F., & Ivankov, D. N. (2020). HypercubeME: Two hundred million combinatorially complete datasets from a single experiment. Bioinformatics. Oxford Academic. https://doi.org/10.1093/bioinformatics/btz841","ieee":"L. A. Esteban et al., “HypercubeME: Two hundred million combinatorially complete datasets from a single experiment,” Bioinformatics, vol. 36, no. 6. Oxford Academic, pp. 1960–1962, 2020.","ista":"Esteban LA, Lonishin LR, Bobrovskiy DM, Leleytner G, Bogatyreva NS, Kondrashov F, Ivankov DN. 2020. HypercubeME: Two hundred million combinatorially complete datasets from a single experiment. Bioinformatics. 36(6), 1960–1962.","short":"L.A. Esteban, L.R. Lonishin, D.M. Bobrovskiy, G. Leleytner, N.S. Bogatyreva, F. Kondrashov, D.N. Ivankov, Bioinformatics 36 (2020) 1960–1962.","mla":"Esteban, Laura A., et al. “HypercubeME: Two Hundred Million Combinatorially Complete Datasets from a Single Experiment.” Bioinformatics, vol. 36, no. 6, Oxford Academic, 2020, pp. 1960–62, doi:10.1093/bioinformatics/btz841.","chicago":"Esteban, Laura A, Lyubov R Lonishin, Daniil M Bobrovskiy, Gregory Leleytner, Natalya S Bogatyreva, Fyodor Kondrashov, and Dmitry N Ivankov. “HypercubeME: Two Hundred Million Combinatorially Complete Datasets from a Single Experiment.” Bioinformatics. Oxford Academic, 2020. https://doi.org/10.1093/bioinformatics/btz841."},"publication":"Bioinformatics","article_processing_charge":"No","has_accepted_license":"1","day":"15","scopus_import":"1","file":[{"file_size":308341,"content_type":"application/pdf","creator":"dernst","file_name":"2020_Bioinformatics_Esteban.pdf","access_level":"open_access","date_created":"2020-10-12T12:02:09Z","date_updated":"2020-10-12T12:02:09Z","checksum":"21d6f71839deb3b83e4a356193f72767","success":1,"relation":"main_file","file_id":"8649"}],"oa_version":"Published Version","intvolume":" 36","ddc":["000","570"],"title":"HypercubeME: Two hundred million combinatorially complete datasets from a single experiment","status":"public","_id":"8645","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"6","abstract":[{"lang":"eng","text":"Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a ‘combinatorially complete dataset’. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199 847 053 unique combinatorially complete genotype combinations of dimensionality ranging from 2 to 12. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data."}],"type":"journal_article"},{"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"external_id":{"isi":["000575539700001"]},"isi":1,"quality_controlled":"1","doi":"10.1088/1478-3975/abb2db","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["14783975"]},"month":"09","year":"2020","acknowledgement":"I would especially like to thank Michael Sixt for encouraging me to think about these problems while working at home due to restrictions in place. I want to thank Nick Barton, Katka Bodova, Matthew Robinson, Simon Rella, Federico Sau, Ivan Prieto, and Pradeep Kumar for useful discussions.","department":[{"_id":"NanoFab"}],"publisher":"IOP Publishing","publication_status":"published","author":[{"orcid":"0000-0001-5145-4609","id":"4515C308-F248-11E8-B48F-1D18A9856A87","last_name":"Merrin","first_name":"Jack","full_name":"Merrin, Jack"}],"volume":17,"date_created":"2020-10-04T22:01:35Z","date_updated":"2023-08-22T09:53:29Z","article_number":"065005","file_date_updated":"2020-10-05T13:53:59Z","citation":{"ama":"Merrin J. Differences in power law growth over time and indicators of COVID-19 pandemic progression worldwide. Physical Biology. 2020;17(6). doi:10.1088/1478-3975/abb2db","ista":"Merrin J. 2020. Differences in power law growth over time and indicators of COVID-19 pandemic progression worldwide. Physical Biology. 17(6), 065005.","apa":"Merrin, J. (2020). Differences in power law growth over time and indicators of COVID-19 pandemic progression worldwide. Physical Biology. IOP Publishing. https://doi.org/10.1088/1478-3975/abb2db","ieee":"J. Merrin, “Differences in power law growth over time and indicators of COVID-19 pandemic progression worldwide,” Physical Biology, vol. 17, no. 6. IOP Publishing, 2020.","mla":"Merrin, Jack. “Differences in Power Law Growth over Time and Indicators of COVID-19 Pandemic Progression Worldwide.” Physical Biology, vol. 17, no. 6, 065005, IOP Publishing, 2020, doi:10.1088/1478-3975/abb2db.","short":"J. Merrin, Physical Biology 17 (2020).","chicago":"Merrin, Jack. “Differences in Power Law Growth over Time and Indicators of COVID-19 Pandemic Progression Worldwide.” Physical Biology. IOP Publishing, 2020. https://doi.org/10.1088/1478-3975/abb2db."},"publication":"Physical Biology","article_type":"original","date_published":"2020-09-23T00:00:00Z","scopus_import":"1","has_accepted_license":"1","article_processing_charge":"Yes (via OA deal)","day":"23","_id":"8597","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","intvolume":" 17","ddc":["510","570"],"status":"public","title":"Differences in power law growth over time and indicators of COVID-19 pandemic progression worldwide","oa_version":"Published Version","file":[{"relation":"main_file","file_id":"8609","date_created":"2020-10-05T13:53:59Z","date_updated":"2020-10-05T13:53:59Z","checksum":"fec9bdd355ed349f09990faab20838a7","success":1,"file_name":"2020_PhysBio_Merrin.pdf","access_level":"open_access","content_type":"application/pdf","file_size":1667111,"creator":"dernst"}],"type":"journal_article","issue":"6","abstract":[{"text":"Error analysis and data visualization of positive COVID-19 cases in 27 countries have been performed up to August 8, 2020. This survey generally observes a progression from early exponential growth transitioning to an intermediate power-law growth phase, as recently suggested by Ziff and Ziff. The occurrence of logistic growth after the power-law phase with lockdowns or social distancing may be described as an effect of avoidance. A visualization of the power-law growth exponent over short time windows is qualitatively similar to the Bhatia visualization for pandemic progression. Visualizations like these can indicate the onset of second waves and may influence social policy.","lang":"eng"}]},{"external_id":{"isi":["000603428000010"],"pmid":["32976770"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"quality_controlled":"1","isi":1,"doi":"10.1016/j.neuron.2020.08.030","language":[{"iso":"eng"}],"month":"12","publication_identifier":{"eissn":["10974199"],"issn":["08966273"]},"year":"2020","acknowledgement":"We thank J. Angibaud for organotypic cultures and R. Chereau and J. Tonnesen for help with the STED microscope; also D. Gonzales and the Neurocentre Magendie INSERM U1215 Genotyping Platform, for breeding management and genotyping. This work was supported by the Wellcome Trust Principal Fellowships 101896 and 212251, ERC Advanced Grant 323113, ERC Proof-of-Concept Grant 767372, EC FP7 ITN 606950, and EU CSA 811011 (D.A.R.); NRW-Rückkehrerpogramm, UCL Excellence Fellowship, German Research Foundation (DFG) SPP1757 and SFB1089 (C.H.); Human Frontiers Science Program (C.H., C.J.J., and H.J.); EMBO Long-Term Fellowship (L.B.); Marie Curie FP7 PIRG08-GA-2010-276995 (A.P.), ASTROMODULATION (S.R.); Equipe FRM DEQ 201 303 26519, Conseil Régional d’Aquitaine R12056GG, INSERM (S.H.R.O.); ANR SUPERTri, ANR Castro (ANR-17-CE16-0002), R-13-BSV4-0007-01, Université de Bordeaux, labex BRAIN (S.H.R.O. and U.V.N.); CNRS (A.P., S.H.R.O., and U.V.N.); HFSP, ANR CEXC, and France-BioImaging ANR-10-INSB-04 (U.V.N.); and FP7 MemStick Project No. 201600 (M.G.S.).","pmid":1,"publication_status":"published","publisher":"Elsevier","department":[{"_id":"HaJa"}],"author":[{"full_name":"Henneberger, Christian","last_name":"Henneberger","first_name":"Christian"},{"full_name":"Bard, Lucie","last_name":"Bard","first_name":"Lucie"},{"full_name":"Panatier, Aude","first_name":"Aude","last_name":"Panatier"},{"full_name":"Reynolds, James P.","first_name":"James P.","last_name":"Reynolds"},{"first_name":"Olga","last_name":"Kopach","full_name":"Kopach, Olga"},{"full_name":"Medvedev, Nikolay I.","last_name":"Medvedev","first_name":"Nikolay I."},{"last_name":"Minge","first_name":"Daniel","full_name":"Minge, Daniel"},{"first_name":"Michel K.","last_name":"Herde","full_name":"Herde, Michel K."},{"first_name":"Stefanie","last_name":"Anders","full_name":"Anders, Stefanie"},{"last_name":"Kraev","first_name":"Igor","full_name":"Kraev, Igor"},{"full_name":"Heller, Janosch P.","last_name":"Heller","first_name":"Janosch P."},{"full_name":"Rama, Sylvain","first_name":"Sylvain","last_name":"Rama"},{"last_name":"Zheng","first_name":"Kaiyu","full_name":"Zheng, Kaiyu"},{"first_name":"Thomas P.","last_name":"Jensen","full_name":"Jensen, Thomas P."},{"full_name":"Sanchez-Romero, Inmaculada","id":"3D9C5D30-F248-11E8-B48F-1D18A9856A87","first_name":"Inmaculada","last_name":"Sanchez-Romero"},{"full_name":"Jackson, Colin J.","first_name":"Colin J.","last_name":"Jackson"},{"full_name":"Janovjak, Harald L","first_name":"Harald L","last_name":"Janovjak","id":"33BA6C30-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8023-9315"},{"last_name":"Ottersen","first_name":"Ole Petter","full_name":"Ottersen, Ole Petter"},{"first_name":"Erlend Arnulf","last_name":"Nagelhus","full_name":"Nagelhus, Erlend Arnulf"},{"full_name":"Oliet, Stephane H.R.","first_name":"Stephane H.R.","last_name":"Oliet"},{"last_name":"Stewart","first_name":"Michael G.","full_name":"Stewart, Michael G."},{"full_name":"Nägerl, U. VAlentin","last_name":"Nägerl","first_name":"U. VAlentin"},{"full_name":"Rusakov, Dmitri A. ","last_name":"Rusakov","first_name":"Dmitri A. "}],"date_created":"2020-10-18T22:01:38Z","date_updated":"2023-08-22T09:59:29Z","volume":108,"file_date_updated":"2020-12-10T14:42:09Z","publication":"Neuron","citation":{"chicago":"Henneberger, Christian, Lucie Bard, Aude Panatier, James P. Reynolds, Olga Kopach, Nikolay I. Medvedev, Daniel Minge, et al. “LTP Induction Boosts Glutamate Spillover by Driving Withdrawal of Perisynaptic Astroglia.” Neuron. Elsevier, 2020. https://doi.org/10.1016/j.neuron.2020.08.030.","short":"C. Henneberger, L. Bard, A. Panatier, J.P. Reynolds, O. Kopach, N.I. Medvedev, D. Minge, M.K. Herde, S. Anders, I. Kraev, J.P. Heller, S. Rama, K. Zheng, T.P. Jensen, I. Sanchez-Romero, C.J. Jackson, H.L. Janovjak, O.P. Ottersen, E.A. Nagelhus, S.H.R. Oliet, M.G. Stewart, U.Va. Nägerl, D.A. Rusakov, Neuron 108 (2020) P919–936.E11.","mla":"Henneberger, Christian, et al. “LTP Induction Boosts Glutamate Spillover by Driving Withdrawal of Perisynaptic Astroglia.” Neuron, vol. 108, no. 5, Elsevier, 2020, p. P919–936.E11, doi:10.1016/j.neuron.2020.08.030.","ieee":"C. Henneberger et al., “LTP induction boosts glutamate spillover by driving withdrawal of perisynaptic astroglia,” Neuron, vol. 108, no. 5. Elsevier, p. P919–936.E11, 2020.","apa":"Henneberger, C., Bard, L., Panatier, A., Reynolds, J. P., Kopach, O., Medvedev, N. I., … Rusakov, D. A. (2020). LTP induction boosts glutamate spillover by driving withdrawal of perisynaptic astroglia. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2020.08.030","ista":"Henneberger C, Bard L, Panatier A, Reynolds JP, Kopach O, Medvedev NI, Minge D, Herde MK, Anders S, Kraev I, Heller JP, Rama S, Zheng K, Jensen TP, Sanchez-Romero I, Jackson CJ, Janovjak HL, Ottersen OP, Nagelhus EA, Oliet SHR, Stewart MG, Nägerl UVa, Rusakov DA. 2020. LTP induction boosts glutamate spillover by driving withdrawal of perisynaptic astroglia. Neuron. 108(5), P919–936.E11.","ama":"Henneberger C, Bard L, Panatier A, et al. LTP induction boosts glutamate spillover by driving withdrawal of perisynaptic astroglia. Neuron. 2020;108(5):P919-936.E11. doi:10.1016/j.neuron.2020.08.030"},"article_type":"original","page":"P919-936.E11","date_published":"2020-12-09T00:00:00Z","scopus_import":"1","day":"09","has_accepted_license":"1","article_processing_charge":"No","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"8674","title":"LTP induction boosts glutamate spillover by driving withdrawal of perisynaptic astroglia","ddc":["570"],"status":"public","intvolume":" 108","oa_version":"Published Version","file":[{"file_size":7518960,"content_type":"application/pdf","creator":"dernst","file_name":"2020_Neuron_Henneberger.pdf","access_level":"open_access","date_updated":"2020-12-10T14:42:09Z","date_created":"2020-12-10T14:42:09Z","checksum":"054562bb50165ef9a1f46631c1c5e36b","success":1,"relation":"main_file","file_id":"8939"}],"type":"journal_article","abstract":[{"lang":"eng","text":"Extrasynaptic actions of glutamate are limited by high-affinity transporters expressed by perisynaptic astroglial processes (PAPs): this helps maintain point-to-point transmission in excitatory circuits. Memory formation in the brain is associated with synaptic remodeling, but how this affects PAPs and therefore extrasynaptic glutamate actions is poorly understood. Here, we used advanced imaging methods, in situ and in vivo, to find that a classical synaptic memory mechanism, long-term potentiation (LTP), triggers withdrawal of PAPs from potentiated synapses. Optical glutamate sensors combined with patch-clamp and 3D molecular localization reveal that LTP induction thus prompts spatial retreat of astroglial glutamate transporters, boosting glutamate spillover and NMDA-receptor-mediated inter-synaptic cross-talk. The LTP-triggered PAP withdrawal involves NKCC1 transporters and the actin-controlling protein cofilin but does not depend on major Ca2+-dependent cascades in astrocytes. We have therefore uncovered a mechanism by which a memory trace at one synapse could alter signal handling by multiple neighboring connections."}],"issue":"5"},{"isi":1,"quality_controlled":"1","project":[{"name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411"},{"grant_number":"P29902","_id":"26031614-B435-11E9-9278-68D0E5697425","name":"Quantum rotations in the presence of a many-body environment","call_identifier":"FWF"},{"_id":"2688CF98-B435-11E9-9278-68D0E5697425","grant_number":"801770","call_identifier":"H2020","name":"Angulon: physics and applications of a new quasiparticle"}],"external_id":{"isi":["000581681000001"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"language":[{"iso":"eng"}],"doi":"10.1038/s42005-020-00445-8","month":"10","publication_identifier":{"issn":["2399-3650"]},"publication_status":"published","department":[{"_id":"MiLe"}],"publisher":"Springer Nature","year":"2020","acknowledgement":"This work has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 754411 (A.G.V. and A.G.). M.L. acknowledges support by the Austrian Science Fund (FWF), under project No. P29902-N27, and by the European Research Council (ERC) Starting\r\nGrant No. 801770 (ANGULON).","date_created":"2020-10-13T09:48:59Z","date_updated":"2023-08-22T09:58:46Z","volume":3,"author":[{"last_name":"Ghazaryan","first_name":"Areg","orcid":"0000-0001-9666-3543","id":"4AF46FD6-F248-11E8-B48F-1D18A9856A87","full_name":"Ghazaryan, Areg"},{"full_name":"Lemeshko, Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6990-7802","first_name":"Mikhail","last_name":"Lemeshko"},{"last_name":"Volosniev","first_name":"Artem","orcid":"0000-0003-0393-5525","id":"37D278BC-F248-11E8-B48F-1D18A9856A87","full_name":"Volosniev, Artem"}],"article_number":"178","file_date_updated":"2020-10-14T15:16:28Z","ec_funded":1,"article_type":"original","publication":"Communications Physics","citation":{"ista":"Ghazaryan A, Lemeshko M, Volosniev A. 2020. Filtering spins by scattering from a lattice of point magnets. Communications Physics. 3, 178.","ieee":"A. Ghazaryan, M. Lemeshko, and A. Volosniev, “Filtering spins by scattering from a lattice of point magnets,” Communications Physics, vol. 3. Springer Nature, 2020.","apa":"Ghazaryan, A., Lemeshko, M., & Volosniev, A. (2020). Filtering spins by scattering from a lattice of point magnets. Communications Physics. Springer Nature. https://doi.org/10.1038/s42005-020-00445-8","ama":"Ghazaryan A, Lemeshko M, Volosniev A. Filtering spins by scattering from a lattice of point magnets. Communications Physics. 2020;3. doi:10.1038/s42005-020-00445-8","chicago":"Ghazaryan, Areg, Mikhail Lemeshko, and Artem Volosniev. “Filtering Spins by Scattering from a Lattice of Point Magnets.” Communications Physics. Springer Nature, 2020. https://doi.org/10.1038/s42005-020-00445-8.","mla":"Ghazaryan, Areg, et al. “Filtering Spins by Scattering from a Lattice of Point Magnets.” Communications Physics, vol. 3, 178, Springer Nature, 2020, doi:10.1038/s42005-020-00445-8.","short":"A. Ghazaryan, M. Lemeshko, A. Volosniev, Communications Physics 3 (2020)."},"date_published":"2020-10-09T00:00:00Z","scopus_import":"1","day":"09","has_accepted_license":"1","article_processing_charge":"Yes","status":"public","ddc":["530"],"title":"Filtering spins by scattering from a lattice of point magnets","intvolume":" 3","_id":"8652","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file":[{"checksum":"60cd35b99f0780acffc7b6060e49ec8b","success":1,"date_created":"2020-10-14T15:16:28Z","date_updated":"2020-10-14T15:16:28Z","relation":"main_file","file_id":"8662","file_size":1462934,"content_type":"application/pdf","creator":"dernst","access_level":"open_access","file_name":"2020_CommPhysics_Ghazaryan.pdf"}],"oa_version":"Published Version","type":"journal_article","abstract":[{"lang":"eng","text":"Nature creates electrons with two values of the spin projection quantum number. In certain applications, it is important to filter electrons with one spin projection from the rest. Such filtering is not trivial, since spin-dependent interactions are often weak, and cannot lead to any substantial effect. Here we propose an efficient spin filter based upon scattering from a two-dimensional crystal, which is made of aligned point magnets. The polarization of the outgoing electron flux is controlled by the crystal, and reaches maximum at specific values of the parameters. In our scheme, polarization increase is accompanied by higher reflectivity of the crystal. High transmission is feasible in scattering from a quantum cavity made of two crystals. Our findings can be used for studies of low-energy spin-dependent scattering from two-dimensional ordered structures made of magnetic atoms or aligned chiral molecules."}]},{"volume":11,"date_updated":"2023-08-22T10:18:17Z","date_created":"2020-10-18T22:01:35Z","author":[{"full_name":"Sznurkowska, Magdalena K.","last_name":"Sznurkowska","first_name":"Magdalena K."},{"full_name":"Hannezo, Edouard B","id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6005-1561","first_name":"Edouard B","last_name":"Hannezo"},{"first_name":"Roberta","last_name":"Azzarelli","full_name":"Azzarelli, Roberta"},{"last_name":"Chatzeli","first_name":"Lemonia","full_name":"Chatzeli, Lemonia"},{"full_name":"Ikeda, Tatsuro","first_name":"Tatsuro","last_name":"Ikeda"},{"full_name":"Yoshida, Shosei","last_name":"Yoshida","first_name":"Shosei"},{"full_name":"Philpott, Anna","first_name":"Anna","last_name":"Philpott"},{"first_name":"Benjamin D","last_name":"Simons","full_name":"Simons, Benjamin D"}],"department":[{"_id":"EdHa"}],"publisher":"Springer Nature","publication_status":"published","pmid":1,"year":"2020","file_date_updated":"2020-10-19T11:27:46Z","article_number":"5037","language":[{"iso":"eng"}],"doi":"10.1038/s41467-020-18837-3","isi":1,"quality_controlled":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"pmid":["33028844"],"isi":["000577244600003"]},"oa":1,"publication_identifier":{"eissn":["20411723"]},"month":"10","oa_version":"Published Version","file":[{"file_id":"8677","relation":"main_file","success":1,"checksum":"0ecc0eab72d2d50694852579611a6624","date_updated":"2020-10-19T11:27:46Z","date_created":"2020-10-19T11:27:46Z","access_level":"open_access","file_name":"2020_NatureComm_Sznurkowska.pdf","creator":"dernst","file_size":5540540,"content_type":"application/pdf"}],"intvolume":" 11","ddc":["570"],"title":"Tracing the cellular basis of islet specification in mouse pancreas","status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"8669","abstract":[{"lang":"eng","text":"Pancreatic islets play an essential role in regulating blood glucose level. Although the molecular pathways underlying islet cell differentiation are beginning to be resolved, the cellular basis of islet morphogenesis and fate allocation remain unclear. By combining unbiased and targeted lineage tracing, we address the events leading to islet formation in the mouse. From the statistical analysis of clones induced at multiple embryonic timepoints, here we show that, during the secondary transition, islet formation involves the aggregation of multiple equipotent endocrine progenitors that transition from a phase of stochastic amplification by cell division into a phase of sublineage restriction and limited islet fission. Together, these results explain quantitatively the heterogeneous size distribution and degree of polyclonality of maturing islets, as well as dispersion of progenitors within and between islets. Further, our results show that, during the secondary transition, α- and β-cells are generated in a contemporary manner. Together, these findings provide insight into the cellular basis of islet development."}],"type":"journal_article","date_published":"2020-10-07T00:00:00Z","article_type":"original","citation":{"ama":"Sznurkowska MK, Hannezo EB, Azzarelli R, et al. Tracing the cellular basis of islet specification in mouse pancreas. Nature Communications. 2020;11. doi:10.1038/s41467-020-18837-3","apa":"Sznurkowska, M. K., Hannezo, E. B., Azzarelli, R., Chatzeli, L., Ikeda, T., Yoshida, S., … Simons, B. D. (2020). Tracing the cellular basis of islet specification in mouse pancreas. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-18837-3","ieee":"M. K. Sznurkowska et al., “Tracing the cellular basis of islet specification in mouse pancreas,” Nature Communications, vol. 11. Springer Nature, 2020.","ista":"Sznurkowska MK, Hannezo EB, Azzarelli R, Chatzeli L, Ikeda T, Yoshida S, Philpott A, Simons BD. 2020. Tracing the cellular basis of islet specification in mouse pancreas. Nature Communications. 11, 5037.","short":"M.K. Sznurkowska, E.B. Hannezo, R. Azzarelli, L. Chatzeli, T. Ikeda, S. Yoshida, A. Philpott, B.D. Simons, Nature Communications 11 (2020).","mla":"Sznurkowska, Magdalena K., et al. “Tracing the Cellular Basis of Islet Specification in Mouse Pancreas.” Nature Communications, vol. 11, 5037, Springer Nature, 2020, doi:10.1038/s41467-020-18837-3.","chicago":"Sznurkowska, Magdalena K., Edouard B Hannezo, Roberta Azzarelli, Lemonia Chatzeli, Tatsuro Ikeda, Shosei Yoshida, Anna Philpott, and Benjamin D Simons. “Tracing the Cellular Basis of Islet Specification in Mouse Pancreas.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-18837-3."},"publication":"Nature Communications","has_accepted_license":"1","article_processing_charge":"No","day":"07","scopus_import":"1"}]