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Record W3039955804 · doi:10.1002/cyto.a.24179

<scp>COVID</scp>‐19 Initiatives and a New Associate Editor

2020· article· en· W3039955804 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCytometry Part A · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicEditorial boardSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Library scienceWishPublic relationsPolitical scienceComputer scienceOperations researchSociologyMedicineEngineeringInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Welcome to year 41 of Cytometry Part A! In this July issue, I am proud to presenting our latest special issue (1). Guest edited by Susann Müller and Hyun-Dong Chang, it focuses on “Microbial Communities” and explores how cytometry can help us gain deeper insights into interactions between microorganisms and their ecosystems. I would like to thank both colleagues for putting together this great collection of exciting research. At the beginning of this issue, you will find three COVID-19 Fast Track manuscripts. This month, I had hoped to avoid even mentioning COVID-19 and SARS-CoV-2 in my editorial, but it seems inevitable. In response to the pandemic, we started several initiatives that I wish to inform you about. We did it in an attempt to address the demand in the scientific community for rapidly available new knowledge on the pandemic, its biology, diagnosis, and potential therapies. First, a new manuscript format was created and launched in March 2020: the COVID-19 Fast Track. Many journals now have that publication path, but we were among the first journals that made it work. Fast Track for Cytometry Part A is not just a name, it is a reality. I wish to thank our dedicated team of volunteer Fast Track expert reviewers who evaluated many submissions and provided high quality reviews within less than 48 h after invitation. Without their help, we could not have sent the authors a first decision within about a week after submission. Even when we expedite COVID-19 publications, we still want to publish only high-quality, peer-reviewed work, and avoid unpleasant situations like retractions (2) that unfortunately happen in these hectic days. I encourage you to support these efforts by volunteering as a Fast Track reviewer and asking your colleagues to volunteer, too. We are in constant need of experts in all fields of quantitative single-cell analysis, lab technologies, biosafety, and many other areas. We have also created a virtual issue on COVID-19 publications in Cytometry Part A (3) that will be updated regularly. Also, to speed up publication of important research even further, we are beginning to publish the accepted version of manuscripts. The online availability of the version of record, professionally typeset and edited, can take up to several weeks after acceptance. Now, we make available online the peer-reviewed, unedited, accepted article within just a few days after acceptance (see Accepted Articles in Ref. (4)). Importantly, the Accepted Articles are fully citable and have the final and unique identification numbers (DOI). Not only that, you can also find them in literature databases including PubMed and Google Scholar. I wish to express my thanks to the Wiley team in helping me to realize these initiatives so quickly, taking into account these turbulent times. It is a good tradition to welcome new members of our editorial board. The last addition to our journal was Keisuke Goda from Tokyo University in 2019 (5), whose presence helps further internationalize our editorial board team. Today, I wish to welcome our newest member to the team of Associate Editors, Prof. Dr. Xuantao Su from the Shandong University in Jinan, Shandong, China (Fig. 1). Xuantao will be our second Editor form China. Xuantao Su Prof. Xuantao Su started his research in the field of cytometry 10 years ago when he was a Ph.D. student. He obtained his Ph.D. in physics from the University of Alberta in 2008 for a comprehensively interdisciplinary body of research. As a student of physics, he explored the light-scattering simulation from complex biological cells with parallel computation; while from an engineering point of view, he built a microfluid cytometer that measures light scattering from a label-free single cell. Those research experiences strengthened his motivation to explore micro-opto-electro-mechanical systems (MOEMS) in the field of biomedicine when he moved to Shandong University in 2010 and founded the MOEMS Lab. According to Prof. Su, the capability of flow cytometry to generate big data of cell images is well worth being explored in that it may serve as a label-free approach for clinical diagnosis, particularly with the advancements of artificial intelligence in recent years. In an early paper, he found that organelles, such as mitochondria, may contribute to the blob structures in the 2D light-scattering patterns of single cells (6). The cells' light-scattering images contain rich information about cellular structures, carrying the promise for label-free single-cell analysis. In 2015, Xuantao developed pattern recognition cytometry for label-free single-cell analysis by adopting machine learning algorithms for the analysis of light-scattering images of cells (6). Recently, Prof. Su, with his interdisciplinary colleagues at Shandong University, explored the use of artificial intelligence for label-free cytometry, aiming to support clinical diagnosis of cervical cancer (7), leukemia (8), and lung cancer (9). Cytometry integrates the advanced technologies of optics, fluidics, biology, and computation science. Prof. Su has a high motivation to develop the MOEMS technology for next-generation portable cytometers, which are less expensive and of high performance. His first attempt involved building microsized observation windows inside a microfluidic channel, enabling the lens-less observation of single-cell light-scattering images in a fluid stream (10). Prof. Su also developed microscope-based, label-free microfluidic cytometry (11), which was the subject of a featured article by Gary Boas in Photonics Spectra, titled “Optofluidics and the Real World: Technologies Evolve to Meet 21st Century Challenges.” The integration of light excitation with sheath flow is key in cytometry. Concurrently, Xuantao recently developed a disposable 3D hydrodynamic focusing unit, where light sheet microscopy technology couples with the focused fluidic stream for single-cell analysis (12). Prof. Su has been an editorial board member of Cytometry Part A since 2017. He has contributed seven papers to Cytometry Part A as first or senior author in the past 10 years, and has served as reviewer for Cytometry Part A. He was a guest editor for the special issue “Prevalent Cancers in Asia,” published as the first issue of Cytometry Part A in 2020 (13). Prof. Su was selected as a senior member of SPIE in 2019, and is a committee member in the Division of Cell Analysis in the Chinese Society of Bioengineering, and in the Division of Biophotonics in the Chinese Society of Biomedical Engineering. As an Associate Editor, Dr. Su will be responsible for the field of Biomedical MOEMS. As in editor from China, he will also proactively support Cytometry Part A in China by recruiting authors and help to increase readership and promote science and education of quantitative single-cell science.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.265
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it