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Guidelines for the use of flow cytometry and cell sorting in immunological studies <sup>*</sup>
Andrea Cossarizza, Hyun‐Dong Chang, Andreas Radbruch, Mübeccel Akdiş, Immanuel Andrä, Francesco Annunziato, Petra Bächer, Vincenzo Barnaba, Luca Battistini, Wolfgang Bauer, Sabine Baumgart, Burkhard Becher, Wolfgang Beisker, Claudia Berek, Alfonso Blanco, Giovanna Borsellino, Philip E. Boulais, Ryan R. Brinkman, Martin Büscher, Dirk H. Busch, Timothy Bushnell, Xuetao Cao, Andrea Cavani, Pratip K. Chattopadhyay, Qingyu Cheng, Sue Chow, Mario Clerici, Anne Cooke, Antonio Cosma, Lorenzo Cosmi, Ana Cumano, Van Duc Dang, Derek Davies, Sara De Biasi, Genny Del Zotto, Silvia Della Bella, Paolo Dellabona, Günnur Deniz, Mark C. Dessing, Andreas Diefenbach, James P. Di Santo, Francesco Dieli, Andreas Dolf, Vera S. Donnenberg, Thomas Dörner, Götz R. A. Ehrhardt, Elmar Endl, Pablo Engel, Britta Engelhardt, Charlotte Esser, Bart Everts, Anita Dreher, Christine S. Falk, Todd A. Fehniger, Andrew Filby, Simon Fillatreau, Marie Follo, Irmgard Förster, John R. Foster, Gemma A. Foulds, Paul S. Frenette, David W. Galbraith, Natalio Garbi, Maria Dolores García‐Godoy, Jens Geginat, Kamran Ghoreschi, Lara Gibellini, Christoph Goettlinger, Carl S. Goodyear, Andrea Gori, Jane L. Grogan, Mor Gross, Andreas Grützkau, Daryl Grummitt, Jonas Hahn, Quirin Hammer, Anja E. Hauser, David L. Haviland, David W. Hedley, Guadalupe Herrera, Martin Herrmann, Falk Hiepe, Tristan Holland, Pleun Hombrink, Jessica P. Houston, Bimba F. Hoyer, Bo Huang, Christopher A. Hunter, Anna Iannone, Hans‐Martin Jäck, Beatriz Jávega, Stipan Jonjić, Kerstin Juelke, Steffen Jung, Toralf Kaiser, Tomáš Kalina, Baerbel Keller, Srijit Khan, Deborah Kienhöfer, Thomas Kroneis, Désirée Kunkel, Christian Kurts, Pia Kvistborg, Joanne Lannigan, Olivier Lantz, Anis Larbi, Salomé LeibundGut‐Landmann, Michael D. Leipold, Megan K. Levings, Virginia Litwin, Yanling Liu, Michael Lohoff, Giovanna Lombardi, Lilly Lopez, Amy E. Lovett‐Racke, Erik Lubberts, Burkhard Ludewig, Enrico Lugli, Holden T. Maecker, Glòria Martrus, Giuseppe Matarese, Christian Maueröder, Mairi McGrath, Iain B. McInnes, Henrik E. Mei, Fritz Melchers, Susanne Melzer, Dirk Mielenz, Kingston H. G. Mills, David Mirrer, Jenny Mjösberg, Jonni S. Moore, Barry Moran, Alessandro Moretta, Lorenzo Moretta, Tim R. Mosmann, Susann Müller, Werner Müller, Christian Münz, Gabriele Multhoff, Luis E. Muñoz, Kenneth M. Murphy, Toshinori Nakayama, Milena Nasi, Christine Neudörfl, John P. Nolan, Sussan Nourshargh, José‐Enrique O’Connor, Wenjun Ouyang, Annette Oxenius, Raghavendra Palankar, Isabel Panse, Pärt Peterson, Christian Peth, Jordi Pétriz, Daisy Philips, Winfried F. Pickl, Silvia Piconese, Marcello Pinti, A. Graham Pockley, Malgorzata J. Podolska, Carlo Pucillo, Sally A. Quataert, Timothy R. D. J. Radstake, Bartek Rajwa, Jonathan A. Rebhahn, Diether Recktenwald, Ester B. M. Remmerswaal, Katy Rezvani, Laura G. Rico, J. Paul Robinson, Chiara Romagnani, Anna Rubartelli, Beate Rückert, Jürgen Ruland, Shimon Sakaguchi, Francisco Sala‐de‐Oyanguren, Yvonne Samstag, Sharon Sanderson, Birgit Sawitzki, Alexander Scheffold, Matthias Schiemann, Frank A. Schildberg, Esther Schimisky, Stephan Schmid, Steffen Schmitt, Kilian Schober, Thomas Schüler, Axel Schulz, Ton N. Schumacher, Cristiano Scottà, T. Vincent Shankey, Anat Shemer, Anna Katharina Simon, Josef Špidlen, Alan M. Stall, Regina Stark, Christina Stehle, Merle Stein, Tobit Steinmetz, Hannes Stockinger, Yousuke Takahama, Attila Tárnok, Zhi‐Gang Tian, Gergely Toldi, Julia Tornack, Elisabetta Traggiai, Joe Trotter, Henning Ulrich, Marlous van der Braber, René A. W. van Lier, Marc Veldhoen, Salvador Vento‐Asturias, Paulo Vieira, David Voehringer, Hans‐Dieter Volk, Konrad von Volkmann, Ari Waisman, Rachael Walker, Michael D. Ward, Klaus Warnatz, Sarah Warth, James Watson, Carsten Watzl, Leonie Wegener, Annika Wiedemann, Jürgen Wienands, Gerald Willimsky, James B. Wing, Peter Wurst, Liping Yu, Alice Yue, Yi Zhao, Susanne Ziegler, Jakob Zimmermann
Bibliographic record
VenueEuropean Journal of Immunology · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and ImmunityUniversité de SherbrookeSimon Fraser UniversityBC Cancer AgencyPrincess Margaret Cancer CentreUniversity of British ColumbiaBC Children's HospitalUniversity of Toronto
FundersNational Institutes of HealthBiotechnology and Biological Sciences Research CouncilKennedy Trust for Rheumatology ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAgence Nationale de la RechercheFrancis Crick InstituteWellcome Trust
KeywordsBiologyFlow cytometrySortingCell sortingMass cytometryComputational biologyImmunologyGeneticsComputer sciencePhenotypeGene
Abstract
1584
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.182
Threshold uncertainty score0.610
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.017 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.182 | 0.219 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.151
GPT teacher head0.332
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations552
Published2017
Admission routes1
Has abstractyes