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

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

2020· article· en· W3039955804 sur OpenAlexaboutno aff
Attila Tárnok

Notice bibliographique

RevueCytometry Part A · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueSingle-cell and spatial transcriptomics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)PandemicEditorial boardSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Library scienceWishPublic relationsPolitical scienceComputer scienceOperations researchSociologyMedicineEngineeringInfectious disease (medical specialty)

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,050
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,068
Score d'incertitude au seuil0,227

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,050
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0030,002
Études des sciences et des technologies0,0030,002
Communication savante0,0170,007
Science ouverte0,0030,003
Intégrité de la recherche0,0090,014
Charge utile insuffisante (le modèle a refusé de juger)0,0680,071

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,265
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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