[{"article_processing_charge":"No","day":"01","scopus_import":"1","date_published":"2019-12-01T00:00:00Z","page":"1466-1470","article_type":"original","citation":{"mla":"Garriga, Edgar, et al. “Large Multiple Sequence Alignments with a Root-to-Leaf Regressive Method.” Nature Biotechnology, vol. 37, no. 12, Springer Nature, 2019, pp. 1466–70, doi:10.1038/s41587-019-0333-6.","short":"E. Garriga, P. Di Tommaso, C. Magis, I. Erb, L. Mansouri, A. Baltzis, H. Laayouni, F. Kondrashov, E. Floden, C. Notredame, Nature Biotechnology 37 (2019) 1466–1470.","chicago":"Garriga, Edgar, Paolo Di Tommaso, Cedrik Magis, Ionas Erb, Leila Mansouri, Athanasios Baltzis, Hafid Laayouni, Fyodor Kondrashov, Evan Floden, and Cedric Notredame. “Large Multiple Sequence Alignments with a Root-to-Leaf Regressive Method.” Nature Biotechnology. Springer Nature, 2019. https://doi.org/10.1038/s41587-019-0333-6.","ama":"Garriga E, Di Tommaso P, Magis C, et al. Large multiple sequence alignments with a root-to-leaf regressive method. Nature Biotechnology. 2019;37(12):1466-1470. doi:10.1038/s41587-019-0333-6","ista":"Garriga E, Di Tommaso P, Magis C, Erb I, Mansouri L, Baltzis A, Laayouni H, Kondrashov F, Floden E, Notredame C. 2019. Large multiple sequence alignments with a root-to-leaf regressive method. Nature Biotechnology. 37(12), 1466–1470.","ieee":"E. Garriga et al., “Large multiple sequence alignments with a root-to-leaf regressive method,” Nature Biotechnology, vol. 37, no. 12. Springer Nature, pp. 1466–1470, 2019.","apa":"Garriga, E., Di Tommaso, P., Magis, C., Erb, I., Mansouri, L., Baltzis, A., … Notredame, C. (2019). Large multiple sequence alignments with a root-to-leaf regressive method. Nature Biotechnology. Springer Nature. https://doi.org/10.1038/s41587-019-0333-6"},"publication":"Nature Biotechnology","issue":"12","abstract":[{"text":"Multiple sequence alignments (MSAs) are used for structural1,2 and evolutionary predictions1,2, but the complexity of aligning large datasets requires the use of approximate solutions3, including the progressive algorithm4. Progressive MSA methods start by aligning the most similar sequences and subsequently incorporate the remaining sequences, from leaf-to-root, based on a guide-tree. Their accuracy declines substantially as the number of sequences is scaled up5. We introduce a regressive algorithm that enables MSA of up to 1.4 million sequences on a standard workstation and substantially improves accuracy on datasets larger than 10,000 sequences. Our regressive algorithm works the other way around to the progressive algorithm and begins by aligning the most dissimilar sequences. It uses an efficient divide-and-conquer strategy to run third-party alignment methods in linear time, regardless of their original complexity. Our approach will enable analyses of extremely large genomic datasets such as the recently announced Earth BioGenome Project, which comprises 1.5 million eukaryotic genomes6.","lang":"eng"}],"type":"journal_article","oa_version":"Submitted Version","intvolume":" 37","status":"public","title":"Large multiple sequence alignments with a root-to-leaf regressive method","_id":"7181","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publication_identifier":{"issn":["10870156"],"eissn":["15461696"]},"month":"12","language":[{"iso":"eng"}],"doi":"10.1038/s41587-019-0333-6","project":[{"name":"Characterizing the fitness landscape on population and global scales","call_identifier":"H2020","_id":"26580278-B435-11E9-9278-68D0E5697425","grant_number":"771209"}],"isi":1,"quality_controlled":"1","oa":1,"external_id":{"pmid":["31792410"],"isi":["000500748900021"]},"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894943/"}],"ec_funded":1,"volume":37,"date_created":"2019-12-15T23:00:43Z","date_updated":"2023-09-06T14:32:52Z","related_material":{"record":[{"status":"public","relation":"research_data","id":"13059"}]},"author":[{"full_name":"Garriga, Edgar","last_name":"Garriga","first_name":"Edgar"},{"full_name":"Di Tommaso, Paolo","last_name":"Di Tommaso","first_name":"Paolo"},{"full_name":"Magis, Cedrik","first_name":"Cedrik","last_name":"Magis"},{"last_name":"Erb","first_name":"Ionas","full_name":"Erb, Ionas"},{"full_name":"Mansouri, Leila","last_name":"Mansouri","first_name":"Leila"},{"full_name":"Baltzis, Athanasios","first_name":"Athanasios","last_name":"Baltzis"},{"full_name":"Laayouni, Hafid","first_name":"Hafid","last_name":"Laayouni"},{"full_name":"Kondrashov, Fyodor","orcid":"0000-0001-8243-4694","id":"44FDEF62-F248-11E8-B48F-1D18A9856A87","last_name":"Kondrashov","first_name":"Fyodor"},{"full_name":"Floden, Evan","last_name":"Floden","first_name":"Evan"},{"first_name":"Cedric","last_name":"Notredame","full_name":"Notredame, Cedric"}],"department":[{"_id":"FyKo"}],"publisher":"Springer Nature","publication_status":"published","pmid":1,"year":"2019"},{"article_number":"e51381","ec_funded":1,"file_date_updated":"2020-07-14T12:47:53Z","pmid":1,"year":"2019","department":[{"_id":"SiHi"}],"publisher":"eLife Sciences Publications","publication_status":"published","author":[{"first_name":"Alfredo","last_name":"Llorca","full_name":"Llorca, Alfredo"},{"full_name":"Ciceri, Gabriele","last_name":"Ciceri","first_name":"Gabriele"},{"full_name":"Beattie, Robert J","id":"2E26DF60-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8483-8753","first_name":"Robert J","last_name":"Beattie"},{"last_name":"Wong","first_name":"Fong Kuan","full_name":"Wong, Fong Kuan"},{"first_name":"Giovanni","last_name":"Diana","full_name":"Diana, Giovanni"},{"first_name":"Eleni","last_name":"Serafeimidou-Pouliou","full_name":"Serafeimidou-Pouliou, Eleni"},{"first_name":"Marian","last_name":"Fernández-Otero","full_name":"Fernández-Otero, Marian"},{"full_name":"Streicher, Carmen","id":"36BCB99C-F248-11E8-B48F-1D18A9856A87","last_name":"Streicher","first_name":"Carmen"},{"full_name":"Arnold, Sebastian J.","first_name":"Sebastian J.","last_name":"Arnold"},{"first_name":"Martin","last_name":"Meyer","full_name":"Meyer, Martin"},{"full_name":"Hippenmeyer, Simon","first_name":"Simon","last_name":"Hippenmeyer","id":"37B36620-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2279-1061"},{"last_name":"Maravall","first_name":"Miguel","full_name":"Maravall, Miguel"},{"full_name":"Marín, Oscar","last_name":"Marín","first_name":"Oscar"}],"volume":8,"date_created":"2019-12-22T23:00:42Z","date_updated":"2023-09-06T14:38:39Z","publication_identifier":{"eissn":["2050084X"]},"month":"11","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":{"isi":["000508156800001"],"pmid":["31736464"]},"oa":1,"project":[{"name":"Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development","call_identifier":"H2020","grant_number":"725780","_id":"260018B0-B435-11E9-9278-68D0E5697425"},{"_id":"264E56E2-B435-11E9-9278-68D0E5697425","grant_number":"M02416","call_identifier":"FWF","name":"Molecular Mechanisms Regulating Gliogenesis in the Cerebral Cortex"}],"isi":1,"quality_controlled":"1","doi":"10.7554/eLife.51381","language":[{"iso":"eng"}],"type":"journal_article","abstract":[{"text":"The cerebral cortex contains multiple areas with distinctive cytoarchitectonical patterns, but the cellular mechanisms underlying the emergence of this diversity remain unclear. Here, we have investigated the neuronal output of individual progenitor cells in the developing mouse neocortex using a combination of methods that together circumvent the biases and limitations of individual approaches. Our experimental results indicate that progenitor cells generate pyramidal cell lineages with a wide range of sizes and laminar configurations. Mathematical modelling indicates that these outcomes are compatible with a stochastic model of cortical neurogenesis in which progenitor cells undergo a series of probabilistic decisions that lead to the specification of very heterogeneous progenies. Our findings support a mechanism for cortical neurogenesis whose flexibility would make it capable to generate the diverse cytoarchitectures that characterize distinct neocortical areas.","lang":"eng"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"7202","intvolume":" 8","status":"public","ddc":["570"],"title":"A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture","oa_version":"Published Version","file":[{"creator":"dernst","file_size":2960543,"content_type":"application/pdf","access_level":"open_access","file_name":"2019_eLife_Llorca.pdf","checksum":"b460ecc33e1a68265e7adea775021f3a","date_updated":"2020-07-14T12:47:53Z","date_created":"2020-02-18T15:19:26Z","file_id":"7503","relation":"main_file"}],"scopus_import":"1","has_accepted_license":"1","article_processing_charge":"No","day":"18","citation":{"ama":"Llorca A, Ciceri G, Beattie RJ, et al. A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture. eLife. 2019;8. doi:10.7554/eLife.51381","ista":"Llorca A, Ciceri G, Beattie RJ, Wong FK, Diana G, Serafeimidou-Pouliou E, Fernández-Otero M, Streicher C, Arnold SJ, Meyer M, Hippenmeyer S, Maravall M, Marín O. 2019. A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture. eLife. 8, e51381.","ieee":"A. Llorca et al., “A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture,” eLife, vol. 8. eLife Sciences Publications, 2019.","apa":"Llorca, A., Ciceri, G., Beattie, R. J., Wong, F. K., Diana, G., Serafeimidou-Pouliou, E., … Marín, O. (2019). A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.51381","mla":"Llorca, Alfredo, et al. “A Stochastic Framework of Neurogenesis Underlies the Assembly of Neocortical Cytoarchitecture.” ELife, vol. 8, e51381, eLife Sciences Publications, 2019, doi:10.7554/eLife.51381.","short":"A. Llorca, G. Ciceri, R.J. Beattie, F.K. Wong, G. Diana, E. Serafeimidou-Pouliou, M. Fernández-Otero, C. Streicher, S.J. Arnold, M. Meyer, S. Hippenmeyer, M. Maravall, O. Marín, ELife 8 (2019).","chicago":"Llorca, Alfredo, Gabriele Ciceri, Robert J Beattie, Fong Kuan Wong, Giovanni Diana, Eleni Serafeimidou-Pouliou, Marian Fernández-Otero, et al. “A Stochastic Framework of Neurogenesis Underlies the Assembly of Neocortical Cytoarchitecture.” ELife. eLife Sciences Publications, 2019. https://doi.org/10.7554/eLife.51381."},"publication":"eLife","article_type":"original","date_published":"2019-11-18T00:00:00Z"},{"date_published":"2019-12-01T00:00:00Z","article_type":"original","page":"13734-13746","publication":"FASEB Journal","citation":{"mla":"Klotz, Lisa, et al. “Localization of Group II and III Metabotropic Glutamate Receptors at Pre- and Postsynaptic Sites of Inner Hair Cell Ribbon Synapses.” FASEB Journal, vol. 33, no. 12, FASEB, 2019, pp. 13734–46, doi:10.1096/fj.201901543R.","short":"L. Klotz, O. Wendler, R. Frischknecht, R. Shigemoto, H. Schulze, R. Enz, FASEB Journal 33 (2019) 13734–13746.","chicago":"Klotz, Lisa, Olaf Wendler, Renato Frischknecht, Ryuichi Shigemoto, Holger Schulze, and Ralf Enz. “Localization of Group II and III Metabotropic Glutamate Receptors at Pre- and Postsynaptic Sites of Inner Hair Cell Ribbon Synapses.” FASEB Journal. FASEB, 2019. https://doi.org/10.1096/fj.201901543R.","ama":"Klotz L, Wendler O, Frischknecht R, Shigemoto R, Schulze H, Enz R. Localization of group II and III metabotropic glutamate receptors at pre- and postsynaptic sites of inner hair cell ribbon synapses. FASEB Journal. 2019;33(12):13734-13746. doi:10.1096/fj.201901543R","ista":"Klotz L, Wendler O, Frischknecht R, Shigemoto R, Schulze H, Enz R. 2019. Localization of group II and III metabotropic glutamate receptors at pre- and postsynaptic sites of inner hair cell ribbon synapses. FASEB Journal. 33(12), 13734–13746.","ieee":"L. Klotz, O. Wendler, R. Frischknecht, R. Shigemoto, H. Schulze, and R. Enz, “Localization of group II and III metabotropic glutamate receptors at pre- and postsynaptic sites of inner hair cell ribbon synapses,” FASEB Journal, vol. 33, no. 12. FASEB, pp. 13734–13746, 2019.","apa":"Klotz, L., Wendler, O., Frischknecht, R., Shigemoto, R., Schulze, H., & Enz, R. (2019). Localization of group II and III metabotropic glutamate receptors at pre- and postsynaptic sites of inner hair cell ribbon synapses. FASEB Journal. FASEB. https://doi.org/10.1096/fj.201901543R"},"day":"01","has_accepted_license":"1","article_processing_charge":"No","scopus_import":"1","file":[{"file_size":4766789,"content_type":"application/pdf","creator":"shigemot","file_name":"Klotz et al 2019 EMBO Reports.pdf","access_level":"open_access","date_created":"2020-12-06T17:30:09Z","date_updated":"2020-12-06T17:30:09Z","checksum":"79e3b72481dc32489911121cf3b7d8d0","success":1,"relation":"main_file","file_id":"8922"}],"oa_version":"Submitted Version","status":"public","title":"Localization of group II and III metabotropic glutamate receptors at pre- and postsynaptic sites of inner hair cell ribbon synapses","ddc":["571","599"],"intvolume":" 33","_id":"7179","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","abstract":[{"lang":"eng","text":"Glutamate is the major excitatory neurotransmitter in the CNS binding to a variety of glutamate receptors. Metabotropic glutamate receptors (mGluR1 to mGluR8) can act excitatory or inhibitory, depending on associated signal cascades. Expression and localization of inhibitory acting mGluRs at inner hair cells (IHCs) in the cochlea are largely unknown. Here, we analyzed expression of mGluR2, mGluR3, mGluR4, mGluR6, mGluR7, and mGluR8 and investigated their localization with respect to the presynaptic ribbon of IHC synapses. We detected transcripts for mGluR2, mGluR3, and mGluR4 as well as for mGluR7a, mGluR7b, mGluR8a, and mGluR8b splice variants. Using receptor-specific antibodies in cochlear wholemounts, we found expression of mGluR2, mGluR4, and mGluR8b close to presynaptic ribbons. Super resolution and confocal microscopy in combination with 3-dimensional reconstructions indicated a postsynaptic localization of mGluR2 that overlaps with postsynaptic density protein 95 on dendrites of afferent type I spiral ganglion neurons. In contrast, mGluR4 and mGluR8b were expressed at the presynapse close to IHC ribbons. In summary, we localized in detail 3 mGluR types at IHC ribbon synapses, providing a fundament for new therapeutical strategies that could protect the cochlea against noxious stimuli and excitotoxicity."}],"issue":"12","type":"journal_article","language":[{"iso":"eng"}],"doi":"10.1096/fj.201901543R","isi":1,"quality_controlled":"1","external_id":{"isi":["000507466100054"],"pmid":["31585509"]},"oa":1,"month":"12","publication_identifier":{"eissn":["15306860"]},"date_created":"2019-12-15T23:00:42Z","date_updated":"2023-09-06T14:34:36Z","volume":33,"author":[{"first_name":"Lisa","last_name":"Klotz","full_name":"Klotz, Lisa"},{"full_name":"Wendler, Olaf","first_name":"Olaf","last_name":"Wendler"},{"first_name":"Renato","last_name":"Frischknecht","full_name":"Frischknecht, Renato"},{"full_name":"Shigemoto, Ryuichi","orcid":"0000-0001-8761-9444","id":"499F3ABC-F248-11E8-B48F-1D18A9856A87","last_name":"Shigemoto","first_name":"Ryuichi"},{"full_name":"Schulze, Holger","first_name":"Holger","last_name":"Schulze"},{"last_name":"Enz","first_name":"Ralf","full_name":"Enz, Ralf"}],"publication_status":"published","publisher":"FASEB","department":[{"_id":"RySh"}],"year":"2019","pmid":1,"file_date_updated":"2020-12-06T17:30:09Z"},{"language":[{"iso":"eng"}],"doi":"10.1145/3295500.3356222","conference":{"name":"SC: Conference for High Performance Computing, Networking, Storage and Analysis","end_date":"2019-11-19","location":"Denver, CO, Unites States","start_date":"2019-11-17"},"project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning"}],"isi":1,"quality_controlled":"1","external_id":{"arxiv":["1802.08021"],"isi":["000545976800011"]},"oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/1802.08021","open_access":"1"}],"publication_identifier":{"eissn":["21674337"],"isbn":["9781450362290"],"issn":["21674329"]},"month":"11","date_updated":"2023-09-06T14:37:55Z","date_created":"2019-12-22T23:00:42Z","author":[{"first_name":"Cedric","last_name":"Renggli","full_name":"Renggli, Cedric"},{"full_name":"Ashkboos, Saleh","first_name":"Saleh","last_name":"Ashkboos","id":"0D0A9058-257B-11EA-A937-9341C3D8BC8A"},{"full_name":"Aghagolzadeh, Mehdi","last_name":"Aghagolzadeh","first_name":"Mehdi"},{"full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian"},{"first_name":"Torsten","last_name":"Hoefler","full_name":"Hoefler, Torsten"}],"publisher":"ACM","department":[{"_id":"DaAl"}],"publication_status":"published","year":"2019","ec_funded":1,"article_number":"a11","date_published":"2019-11-17T00:00:00Z","citation":{"ama":"Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM; 2019. doi:10.1145/3295500.3356222","ieee":"C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.","apa":"Renggli, C., Ashkboos, S., Aghagolzadeh, M., Alistarh, D.-A., & Hoefler, T. (2019). SparCML: High-performance sparse communication for machine learning. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. Denver, CO, Unites States: ACM. https://doi.org/10.1145/3295500.3356222","ista":"Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. 2019. SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. SC: Conference for High Performance Computing, Networking, Storage and Analysis, a11.","short":"C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, T. Hoefler, in:, International Conference for High Performance Computing, Networking, Storage and Analysis, SC, ACM, 2019.","mla":"Renggli, Cedric, et al. “SparCML: High-Performance Sparse Communication for Machine Learning.” International Conference for High Performance Computing, Networking, Storage and Analysis, SC, a11, ACM, 2019, doi:10.1145/3295500.3356222.","chicago":"Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.” In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019. https://doi.org/10.1145/3295500.3356222."},"publication":"International Conference for High Performance Computing, Networking, Storage and Analysis, SC","article_processing_charge":"No","day":"17","scopus_import":"1","oa_version":"Preprint","title":"SparCML: High-performance sparse communication for machine learning","status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"7201","abstract":[{"lang":"eng","text":"Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed \"data parallel\" distributed across many nodes. Each node's contribution to the overall gradient is summed using a global allreduce. This allreduce is the single communication and thus scalability bottleneck for most machine learning workloads. We observe that frequently, many gradient values are (close to) zero, leading to sparse of sparsifyable communications. To exploit this insight, we analyze, design, and implement a set of communication-efficient protocols for sparse input data, in conjunction with efficient machine learning algorithms which can leverage these primitives. Our communication protocols generalize standard collective operations, by allowing processes to contribute arbitrary sparse input data vectors. Our generic communication library, SparCML1, extends MPI to support additional features, such as non-blocking (asynchronous) operations and low-precision data representations. As such, SparCML and its techniques will form the basis of future highly-scalable machine learning frameworks."}],"type":"conference"},{"type":"research_data_reference","abstract":[{"lang":"eng","text":"Genetic incompatibilities contribute to reproductive isolation between many diverging populations, but it is still unclear to what extent they play a role if divergence happens with gene flow. In contact zones between the \"Crab\" and \"Wave\" ecotypes of the snail Littorina saxatilis divergent selection forms strong barriers to gene flow, while the role of postzygotic barriers due to selection against hybrids remains unclear. High embryo abortion rates in this species could indicate the presence of such barriers. Postzygotic barriers might include genetic incompatibilities (e.g. Dobzhansky-Muller incompatibilities) but also maladaptation, both expected to be most pronounced in contact zones. In addition, embryo abortion might reflect physiological stress on females and embryos independent of any genetic stress. We examined all embryos of >500 females sampled outside and inside contact zones of three populations in Sweden. Females' clutch size ranged from 0 to 1011 embryos (mean 130±123) and abortion rates varied between 0 and100% (mean 12%). We described female genotypes by using a hybrid index based on hundreds of SNPs differentiated between ecotypes with which we characterised female genotypes. We also calculated female SNP heterozygosity and inversion karyotype. Clutch size did not vary with female hybrid index and abortion rates were only weakly related to hybrid index in two sites but not at all in a third site. No additional variation in abortion rate was explained by female SNP heterozygosity, but increased female inversion heterozygosity added slightly to increased abortion. Our results show only weak and probably biologically insignificant postzygotic barriers contributing to ecotype divergence and the high and variable abortion rates were marginally, if at all, explained by hybrid index of females."}],"publisher":"Dryad","department":[{"_id":"NiBa"}],"ddc":["570"],"status":"public","title":"Data from: Is embryo abortion a postzygotic barrier to gene flow between Littorina ecotypes?","_id":"13067","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2019","oa_version":"Published Version","date_created":"2023-05-23T16:36:27Z","date_updated":"2023-09-06T14:48:57Z","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"7205"}]},"author":[{"first_name":"Kerstin","last_name":"Johannesson","full_name":"Johannesson, Kerstin"},{"last_name":"Zagrodzka","first_name":"Zuzanna","full_name":"Zagrodzka, Zuzanna"},{"full_name":"Faria, Rui","last_name":"Faria","first_name":"Rui"},{"full_name":"Westram, Anja M","orcid":"0000-0003-1050-4969","id":"3C147470-F248-11E8-B48F-1D18A9856A87","last_name":"Westram","first_name":"Anja M"},{"first_name":"Roger","last_name":"Butlin","full_name":"Butlin, Roger"}],"article_processing_charge":"No","day":"02","month":"12","tmp":{"short":"CC0 (1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"oa":1,"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.tb2rbnzwk"}],"citation":{"ama":"Johannesson K, Zagrodzka Z, Faria R, Westram AM, Butlin R. Data from: Is embryo abortion a postzygotic barrier to gene flow between Littorina ecotypes? 2019. doi:10.5061/DRYAD.TB2RBNZWK","ista":"Johannesson K, Zagrodzka Z, Faria R, Westram AM, Butlin R. 2019. Data from: Is embryo abortion a postzygotic barrier to gene flow between Littorina ecotypes?, Dryad, 10.5061/DRYAD.TB2RBNZWK.","apa":"Johannesson, K., Zagrodzka, Z., Faria, R., Westram, A. M., & Butlin, R. (2019). Data from: Is embryo abortion a postzygotic barrier to gene flow between Littorina ecotypes? Dryad. https://doi.org/10.5061/DRYAD.TB2RBNZWK","ieee":"K. Johannesson, Z. Zagrodzka, R. Faria, A. M. Westram, and R. Butlin, “Data from: Is embryo abortion a postzygotic barrier to gene flow between Littorina ecotypes?” Dryad, 2019.","mla":"Johannesson, Kerstin, et al. Data from: Is Embryo Abortion a Postzygotic Barrier to Gene Flow between Littorina Ecotypes? Dryad, 2019, doi:10.5061/DRYAD.TB2RBNZWK.","short":"K. Johannesson, Z. Zagrodzka, R. Faria, A.M. Westram, R. Butlin, (2019).","chicago":"Johannesson, Kerstin, Zuzanna Zagrodzka, Rui Faria, Anja M Westram, and Roger Butlin. “Data from: Is Embryo Abortion a Postzygotic Barrier to Gene Flow between Littorina Ecotypes?” Dryad, 2019. https://doi.org/10.5061/DRYAD.TB2RBNZWK."},"doi":"10.5061/DRYAD.TB2RBNZWK","date_published":"2019-12-02T00:00:00Z"},{"article_number":"641","file_date_updated":"2020-07-14T12:47:54Z","year":"2019","publisher":"BMC","department":[{"_id":"DaAl"}],"publication_status":"published","author":[{"last_name":"Aganezov","first_name":"Sergey","full_name":"Aganezov, Sergey"},{"full_name":"Zban, Ilya","first_name":"Ilya","last_name":"Zban"},{"id":"2980135A-F248-11E8-B48F-1D18A9856A87","first_name":"Vitalii","last_name":"Aksenov","full_name":"Aksenov, Vitalii"},{"full_name":"Alexeev, Nikita","first_name":"Nikita","last_name":"Alexeev"},{"first_name":"Michael C.","last_name":"Schatz","full_name":"Schatz, Michael C."}],"volume":20,"date_created":"2019-12-29T23:00:46Z","date_updated":"2023-09-06T14:51:06Z","publication_identifier":{"eissn":["14712105"]},"month":"12","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":["000511618800007"]},"quality_controlled":"1","isi":1,"doi":"10.1186/s12859-019-3208-4","language":[{"iso":"eng"}],"type":"journal_article","abstract":[{"text":"Background: Many cancer genomes are extensively rearranged with highly aberrant chromosomal karyotypes. Structural and copy number variations in cancer genomes can be determined via abnormal mapping of sequenced reads to the reference genome. Recently it became possible to reconcile both of these types of large-scale variations into a karyotype graph representation of the rearranged cancer genomes. Such a representation, however, does not directly describe the linear and/or circular structure of the underlying rearranged cancer chromosomes, thus limiting possible analysis of cancer genomes somatic evolutionary process as well as functional genomic changes brought by the large-scale genome rearrangements.\r\n\r\nResults: Here we address the aforementioned limitation by introducing a novel methodological framework for recovering rearranged cancer chromosomes from karyotype graphs. For a cancer karyotype graph we formulate an Eulerian Decomposition Problem (EDP) of finding a collection of linear and/or circular rearranged cancer chromosomes that are determined by the graph. We derive and prove computational complexities for several variations of the EDP. We then demonstrate that Eulerian decomposition of the cancer karyotype graphs is not always unique and present the Consistent Contig Covering Problem (CCCP) of recovering unambiguous cancer contigs from the cancer karyotype graph, and describe a novel algorithm CCR capable of solving CCCP in polynomial time. We apply CCR on a prostate cancer dataset and demonstrate that it is capable of consistently recovering large cancer contigs even when underlying cancer genomes are highly rearranged.\r\n\r\nConclusions: CCR can recover rearranged cancer contigs from karyotype graphs thereby addressing existing limitation in inferring chromosomal structures of rearranged cancer genomes and advancing our understanding of both patient/cancer-specific as well as the overall genetic instability in cancer.","lang":"eng"}],"_id":"7214","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","intvolume":" 20","title":"Recovering rearranged cancer chromosomes from karyotype graphs","ddc":["570"],"status":"public","file":[{"checksum":"7a30357efdcf8f66587ed495c0927724","date_updated":"2020-07-14T12:47:54Z","date_created":"2020-01-02T16:10:58Z","file_id":"7221","relation":"main_file","creator":"dernst","content_type":"application/pdf","file_size":1917374,"access_level":"open_access","file_name":"2019_BMCBioinfo_Aganezov.pdf"}],"oa_version":"Published Version","scopus_import":"1","article_processing_charge":"No","has_accepted_license":"1","day":"17","citation":{"chicago":"Aganezov, Sergey, Ilya Zban, Vitalii Aksenov, Nikita Alexeev, and Michael C. Schatz. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.” BMC Bioinformatics. BMC, 2019. https://doi.org/10.1186/s12859-019-3208-4.","short":"S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, M.C. Schatz, BMC Bioinformatics 20 (2019).","mla":"Aganezov, Sergey, et al. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.” BMC Bioinformatics, vol. 20, 641, BMC, 2019, doi:10.1186/s12859-019-3208-4.","apa":"Aganezov, S., Zban, I., Aksenov, V., Alexeev, N., & Schatz, M. C. (2019). Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. BMC. https://doi.org/10.1186/s12859-019-3208-4","ieee":"S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, and M. C. Schatz, “Recovering rearranged cancer chromosomes from karyotype graphs,” BMC Bioinformatics, vol. 20. BMC, 2019.","ista":"Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. 2019. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 20, 641.","ama":"Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 2019;20. doi:10.1186/s12859-019-3208-4"},"publication":"BMC Bioinformatics","article_type":"original","date_published":"2019-12-17T00:00:00Z"},{"publication":"Bioengineering","citation":{"ista":"Merrin J. 2019. Frontiers in microfluidics, a teaching resource review. Bioengineering. 6(4), 109.","ieee":"J. Merrin, “Frontiers in microfluidics, a teaching resource review,” Bioengineering, vol. 6, no. 4. MDPI, 2019.","apa":"Merrin, J. (2019). Frontiers in microfluidics, a teaching resource review. Bioengineering. MDPI. https://doi.org/10.3390/bioengineering6040109","ama":"Merrin J. Frontiers in microfluidics, a teaching resource review. Bioengineering. 2019;6(4). doi:10.3390/bioengineering6040109","chicago":"Merrin, Jack. “Frontiers in Microfluidics, a Teaching Resource Review.” Bioengineering. MDPI, 2019. https://doi.org/10.3390/bioengineering6040109.","mla":"Merrin, Jack. “Frontiers in Microfluidics, a Teaching Resource Review.” Bioengineering, vol. 6, no. 4, 109, MDPI, 2019, doi:10.3390/bioengineering6040109.","short":"J. Merrin, Bioengineering 6 (2019)."},"article_type":"review","date_published":"2019-12-03T00:00:00Z","scopus_import":"1","day":"03","has_accepted_license":"1","article_processing_charge":"Yes","_id":"7225","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","ddc":["620"],"status":"public","title":"Frontiers in microfluidics, a teaching resource review","intvolume":" 6","file":[{"creator":"dernst","content_type":"application/pdf","file_size":2660780,"access_level":"open_access","file_name":"2019_Bioengineering_Merrin.pdf","checksum":"80f1499e2a4caccdf3aa54b137fd99a0","date_created":"2020-01-07T14:49:59Z","date_updated":"2020-07-14T12:47:54Z","file_id":"7243","relation":"main_file"}],"oa_version":"Published Version","type":"journal_article","abstract":[{"lang":"eng","text":"This is a literature teaching resource review for biologically inspired microfluidics courses\r\nor exploring the diverse applications of microfluidics. The structure is around key papers and model\r\norganisms. While courses gradually change over time, a focus remains on understanding how\r\nmicrofluidics has developed as well as what it can and cannot do for researchers. As a primary\r\nstarting point, we cover micro-fluid mechanics principles and microfabrication of devices. A variety\r\nof applications are discussed using model prokaryotic and eukaryotic organisms from the set\r\nof bacteria (Escherichia coli), trypanosomes (Trypanosoma brucei), yeast (Saccharomyces cerevisiae),\r\nslime molds (Physarum polycephalum), worms (Caenorhabditis elegans), flies (Drosophila melangoster),\r\nplants (Arabidopsis thaliana), and mouse immune cells (Mus musculus). Other engineering and\r\nbiochemical methods discussed include biomimetics, organ on a chip, inkjet, droplet microfluidics,\r\nbiotic games, and diagnostics. While we have not yet reached the end-all lab on a chip,\r\nmicrofluidics can still be used effectively for specific applications."}],"issue":"4","external_id":{"isi":["000505590000024"],"pmid":["31816954"]},"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":"10.3390/bioengineering6040109","language":[{"iso":"eng"}],"month":"12","publication_identifier":{"eissn":["23065354"]},"year":"2019","pmid":1,"publication_status":"published","publisher":"MDPI","department":[{"_id":"NanoFab"}],"author":[{"full_name":"Merrin, Jack","last_name":"Merrin","first_name":"Jack","orcid":"0000-0001-5145-4609","id":"4515C308-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2023-09-06T14:52:49Z","date_created":"2020-01-05T23:00:45Z","volume":6,"article_number":"109","file_date_updated":"2020-07-14T12:47:54Z"},{"publication_identifier":{"issn":["0302-9743"],"isbn":["978-3-0302-9399-4"],"eissn":["1611-3349"]},"month":"08","doi":"10.1007/978-3-030-29400-7_23","conference":{"name":"Euro-Par: European Conference on Parallel Processing","location":"Göttingen, Germany","start_date":"2019-08-26","end_date":"2019-08-30"},"language":[{"iso":"eng"}],"external_id":{"isi":["000851061400023"]},"isi":1,"quality_controlled":"1","author":[{"last_name":"Koval","first_name":"Nikita","id":"2F4DB10C-F248-11E8-B48F-1D18A9856A87","full_name":"Koval, Nikita"},{"orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"},{"full_name":"Elizarov, Roman","first_name":"Roman","last_name":"Elizarov"}],"volume":11725,"date_created":"2020-01-05T23:00:46Z","date_updated":"2023-09-06T14:53:59Z","year":"2019","department":[{"_id":"DaAl"}],"publisher":"Springer Nature","publication_status":"published","article_processing_charge":"No","day":"13","scopus_import":"1","date_published":"2019-08-13T00:00:00Z","citation":{"ieee":"N. Koval, D.-A. Alistarh, and R. Elizarov, “Scalable FIFO channels for programming via communicating sequential processes,” in 25th Anniversary of Euro-Par, Göttingen, Germany, 2019, vol. 11725, pp. 317–333.","apa":"Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In 25th Anniversary of Euro-Par (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-29400-7_23","ista":"Koval N, Alistarh D-A, Elizarov R. 2019. Scalable FIFO channels for programming via communicating sequential processes. 25th Anniversary of Euro-Par. Euro-Par: European Conference on Parallel Processing, LNCS, vol. 11725, 317–333.","ama":"Koval N, Alistarh D-A, Elizarov R. Scalable FIFO channels for programming via communicating sequential processes. In: 25th Anniversary of Euro-Par. Vol 11725. Springer Nature; 2019:317-333. doi:10.1007/978-3-030-29400-7_23","chicago":"Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.” In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29400-7_23.","short":"N. Koval, D.-A. Alistarh, R. Elizarov, in:, 25th Anniversary of Euro-Par, Springer Nature, 2019, pp. 317–333.","mla":"Koval, Nikita, et al. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.” 25th Anniversary of Euro-Par, vol. 11725, Springer Nature, 2019, pp. 317–33, doi:10.1007/978-3-030-29400-7_23."},"publication":"25th Anniversary of Euro-Par","page":"317-333","abstract":[{"lang":"eng","text":"Traditional concurrent programming involves manipulating shared mutable state. Alternatives to this programming style are communicating sequential processes (CSP) and actor models, which share data via explicit communication. These models have been known for almost half a century, and have recently had started to gain significant traction among modern programming languages. The common abstraction for communication between several processes is the channel. Although channels are similar to producer-consumer data structures, they have different semantics and support additional operations, such as the select expression. Despite their growing popularity, most known implementations of channels use lock-based data structures and can be rather inefficient.\r\n\r\nIn this paper, we present the first efficient lock-free algorithm for implementing a communication channel for CSP programming. We provide implementations and experimental results in the Kotlin and Go programming languages. Our new algorithm outperforms existing implementations on many workloads, while providing non-blocking progress guarantee. Our design can serve as an example of how to construct general communication data structures for CSP and actor models. "}],"type":"conference","alternative_title":["LNCS"],"oa_version":"None","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"7228","intvolume":" 11725","status":"public","title":"Scalable FIFO channels for programming via communicating sequential processes"},{"doi":"10.1109/ITSC.2019.8917514","date_published":"2019-11-28T00:00:00Z","conference":{"name":"ITSC: Intelligent Transportation Systems Conference","location":"Auckland, New Zealand","start_date":"2019-10-27","end_date":"2019-10-30"},"language":[{"iso":"eng"}],"external_id":{"isi":["000521238102050"]},"citation":{"chicago":"Osang, Georg F, James Cook, Alex Fabrikant, and Marco Gruteser. “LiveTraVeL: Real-Time Matching of Transit Vehicle Trajectories to Transit Routes at Scale.” In 2019 IEEE Intelligent Transportation Systems Conference. IEEE, 2019. https://doi.org/10.1109/ITSC.2019.8917514.","short":"G.F. Osang, J. Cook, A. Fabrikant, M. Gruteser, in:, 2019 IEEE Intelligent Transportation Systems Conference, IEEE, 2019.","mla":"Osang, Georg F., et al. “LiveTraVeL: Real-Time Matching of Transit Vehicle Trajectories to Transit Routes at Scale.” 2019 IEEE Intelligent Transportation Systems Conference, 8917514, IEEE, 2019, doi:10.1109/ITSC.2019.8917514.","apa":"Osang, G. F., Cook, J., Fabrikant, A., & Gruteser, M. (2019). LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale. In 2019 IEEE Intelligent Transportation Systems Conference. Auckland, New Zealand: IEEE. https://doi.org/10.1109/ITSC.2019.8917514","ieee":"G. F. Osang, J. Cook, A. Fabrikant, and M. Gruteser, “LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale,” in 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand, 2019.","ista":"Osang GF, Cook J, Fabrikant A, Gruteser M. 2019. LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale. 2019 IEEE Intelligent Transportation Systems Conference. ITSC: Intelligent Transportation Systems Conference, 8917514.","ama":"Osang GF, Cook J, Fabrikant A, Gruteser M. LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale. In: 2019 IEEE Intelligent Transportation Systems Conference. IEEE; 2019. doi:10.1109/ITSC.2019.8917514"},"publication":"2019 IEEE Intelligent Transportation Systems Conference","quality_controlled":"1","isi":1,"publication_identifier":{"isbn":["9781538670248"]},"article_processing_charge":"No","day":"28","month":"11","scopus_import":"1","author":[{"orcid":"0000-0002-8882-5116","id":"464B40D6-F248-11E8-B48F-1D18A9856A87","last_name":"Osang","first_name":"Georg F","full_name":"Osang, Georg F"},{"full_name":"Cook, James","first_name":"James","last_name":"Cook"},{"full_name":"Fabrikant, Alex","first_name":"Alex","last_name":"Fabrikant"},{"first_name":"Marco","last_name":"Gruteser","full_name":"Gruteser, Marco"}],"oa_version":"None","date_updated":"2023-09-06T14:50:28Z","date_created":"2019-12-29T23:00:47Z","_id":"7216","year":"2019","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","department":[{"_id":"HeEd"}],"publisher":"IEEE","publication_status":"published","title":"LiveTraVeL: Real-time matching of transit vehicle trajectories to transit routes at scale","status":"public","abstract":[{"lang":"eng","text":"We present LiveTraVeL (Live Transit Vehicle Labeling), a real-time system to label a stream of noisy observations of transit vehicle trajectories with the transit routes they are serving (e.g., northbound bus #5). In order to scale efficiently to large transit networks, our system first retrieves a small set of candidate routes from a geometrically indexed data structure, then applies a fine-grained scoring step to choose the best match. Given that real-time data remains unavailable for the majority of the world’s transit agencies, these inferences can help feed a real-time map of a transit system’s trips, infer transit trip delays in real time, or measure and correct noisy transit tracking data. This system can run on vehicle observations from a variety of sources that don’t attach route information to vehicle observations, such as public imagery streams or user-contributed transit vehicle sightings.We abstract away the specifics of the sensing system and demonstrate the effectiveness of our system on a \"semisynthetic\" dataset of all New York City buses, where we simulate sensed trajectories by starting with fully labeled vehicle trajectories reported via the GTFS-Realtime protocol, removing the transit route IDs, and perturbing locations with synthetic noise. Using just the geometric shapes of the trajectories, we demonstrate that our system converges on the correct route ID within a few minutes, even after a vehicle switches from serving one trip to the next."}],"type":"conference","article_number":"8917514"},{"abstract":[{"text":"Piecewise Barrier Tubes (PBT) is a new technique for flowpipe overapproximation for nonlinear systems with polynomial dynamics, which leverages a combination of barrier certificates. PBT has advantages over traditional time-step based methods in dealing with those nonlinear dynamical systems in which there is a large difference in speed between trajectories, producing an overapproximation that is time independent. However, the existing approach for PBT is not efficient due to the application of interval methods for enclosure-box computation, and it can only deal with continuous dynamical systems without uncertainty. In this paper, we extend the approach with the ability to handle both continuous and hybrid dynamical systems with uncertainty that can reside in parameters and/or noise. We also improve the efficiency of the method significantly, by avoiding the use of interval-based methods for the enclosure-box computation without loosing soundness. We have developed a C++ prototype implementing the proposed approach and we evaluate it on several benchmarks. The experiments show that our approach is more efficient and precise than other methods in the literature.","lang":"eng"}],"alternative_title":["LNCS"],"type":"conference","oa_version":"Preprint","intvolume":" 11750","status":"public","title":"Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty","_id":"7231","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","article_processing_charge":"No","day":"13","scopus_import":"1","date_published":"2019-08-13T00:00:00Z","page":"123-141","citation":{"ista":"Kong H, Bartocci E, Jiang Y, Henzinger TA. 2019. Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty. 17th International Conference on Formal Modeling and Analysis of Timed Systems. FORMATS: Formal Modeling and Analysis of Timed Systems, LNCS, vol. 11750, 123–141.","ieee":"H. Kong, E. Bartocci, Y. Jiang, and T. A. Henzinger, “Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty,” in 17th International Conference on Formal Modeling and Analysis of Timed Systems, Amsterdam, The Netherlands, 2019, vol. 11750, pp. 123–141.","apa":"Kong, H., Bartocci, E., Jiang, Y., & Henzinger, T. A. (2019). Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty. In 17th International Conference on Formal Modeling and Analysis of Timed Systems (Vol. 11750, pp. 123–141). Amsterdam, The Netherlands: Springer Nature. https://doi.org/10.1007/978-3-030-29662-9_8","ama":"Kong H, Bartocci E, Jiang Y, Henzinger TA. Piecewise robust barrier tubes for nonlinear hybrid systems with uncertainty. In: 17th International Conference on Formal Modeling and Analysis of Timed Systems. Vol 11750. Springer Nature; 2019:123-141. doi:10.1007/978-3-030-29662-9_8","chicago":"Kong, Hui, Ezio Bartocci, Yu Jiang, and Thomas A Henzinger. “Piecewise Robust Barrier Tubes for Nonlinear Hybrid Systems with Uncertainty.” In 17th International Conference on Formal Modeling and Analysis of Timed Systems, 11750:123–41. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29662-9_8.","mla":"Kong, Hui, et al. “Piecewise Robust Barrier Tubes for Nonlinear Hybrid Systems with Uncertainty.” 17th International Conference on Formal Modeling and Analysis of Timed Systems, vol. 11750, Springer Nature, 2019, pp. 123–41, doi:10.1007/978-3-030-29662-9_8.","short":"H. Kong, E. Bartocci, Y. Jiang, T.A. Henzinger, in:, 17th International Conference on Formal Modeling and Analysis of Timed Systems, Springer Nature, 2019, pp. 123–141."},"publication":"17th International Conference on Formal Modeling and Analysis of Timed Systems","volume":11750,"date_updated":"2023-09-06T14:55:15Z","date_created":"2020-01-05T23:00:47Z","author":[{"first_name":"Hui","last_name":"Kong","id":"3BDE25AA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-3066-6941","full_name":"Kong, Hui"},{"full_name":"Bartocci, Ezio","first_name":"Ezio","last_name":"Bartocci"},{"full_name":"Jiang, Yu","last_name":"Jiang","first_name":"Yu"},{"full_name":"Henzinger, Thomas A","last_name":"Henzinger","first_name":"Thomas A","orcid":"0000−0002−2985−7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"publisher":"Springer Nature","department":[{"_id":"ToHe"}],"publication_status":"published","year":"2019","publication_identifier":{"eissn":["1611-3349"],"isbn":["978-3-0302-9661-2"],"issn":["0302-9743"]},"month":"08","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-29662-9_8","conference":{"end_date":"2019-08-29","location":"Amsterdam, The Netherlands","start_date":"2019-08-27","name":"FORMATS: Formal Modeling and Analysis of Timed Systems"},"project":[{"_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23","call_identifier":"FWF","name":"Rigorous Systems Engineering"},{"name":"Game Theory","call_identifier":"FWF","_id":"25863FF4-B435-11E9-9278-68D0E5697425","grant_number":"S11407"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211"}],"quality_controlled":"1","isi":1,"external_id":{"arxiv":["1907.11514"],"isi":["000611677700008"]},"oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/1907.11514","open_access":"1"}]}]