1,721,148 research outputs found
Loss of functional large T-antigen and free viral genomes from cells transformed in vitro by polyoma virus after passage in vivo as tumor cells.
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Corrigendum: To Ki or Not to Ki: Re-Evaluating the Use and Potentials of Ki-67 for T Cell Analysis (Front. Immunol., (2021), 12, (653974), 10.3389/fimmu.2021.653974)
No full textIn the original article, there was a mistake in the legend for Figure 1 as published. On the Viable cells, no “dump” gate, “CD16” was written instead of “CD19”. The correct legend appears below. “HD PBMCs were stained with the viability dye eFluor 780 (eF780), the DNA dye Hoechst-33342, and fluorochrome conjugated mAbs against surface markers and Ki-67, as described (16). An example of flow cytometry analysis is shown. (A) Gating of viable single CD8 T cells in 6 steps: 1) DNA-A/-W singlets. Single cells having 2n≤ DNA content ≤4n were selected on the DNA-area (A) versus (vs) DNA-width (W) plot; 2) Time exclusion. Stable acquisition over time (seconds) was monitored on the time vs DNA-A plot and any events collected in case of pressure fluctuations were excluded; 3) Viable cells, no “dump”. Cells expressing CD4, CD14 and CD19, and dead cells were excluded; 4) FSC-A/SSC-A “relaxed” gate. A “relaxed” gate was used on the FSC-A vs SSC-A plot, to include highly activated and cycling lymphocytes (15); 5) CD8 T cells. CD8 T cells were gated on the CD3 versus CD8 plot; 6) Refined singlets. A few remaining doublets composed by one cell sitting on top of another (so called “shadow” doublets) were excluded as Ki-67int/- events having > 2n DNA content (16). This gating strategy was used as a base for the subsequent gates. (B) The following naïve/memory subsets of CD8 T cells were identified: CD45RA+ CCR7+ Naïve, CD45RA- CCR7+ central memory (CM), CD45RA- CCR7- effector memory (EM), and CD45RA+ CCR7- (EMRA). (C) Cell cycle phases of each naïve/memory CD8 T cell subset were defined on DNA-A vs Ki67-A plot as follows: cells in G0 were identified as DNA 2n/Ki67- (bottom left quadrant); cells in G1 as DNA 2n/Ki67+ (upper left quadrant); cells in S-G2/M (or TDS cells) as DNA>2n/Ki67+ (top right quadrant). Unpublished data in relation to (16).” In the original article, there was also amistake in the legend for SupplementaryTable 1 as published. The peptide- HLA-A*02 tetramer list was incorrectly formatted, there was missing information about numbers in the table (they represent average percentages); missing information about the number of mice (panel A) and number of human donors (panel B and C); and a missing citation of original references at the end. The corrected Supplementary Material File is linked below. In the original article, there was also a mistake in Figure 1 as published. There was an incorrect y-axis label in panel A, third graph from left. The corrected Figure 1 appears below. (Figure presented.) The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Organization and expression of integrated polyoma virus DNA sequences in transformed rodent cells.
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To Ki or Not to Ki: Re-Evaluating the Use and Potentials of Ki-67 for T Cell Analysis
Full text linkThis study discusses substantive advances in T cell proliferation analysis, with the aim to provoke a re-evaluation of the generally-held view that Ki-67 is a reliable proliferation marker per se, and to offer a more sensitive and effective method for T cell cycle analysis, with informative examples in mouse and human settings. We summarize recent experimental work from our labs showing that, by Ki-67/DNA dual staining and refined flow cytometric methods, we were able to identify T cells in the S-G2/M phases of the cell-cycle in the peripheral blood (collectively termed “T Double S” for T cells in S-phase in Sanguine: in short “TDS” cells). Without our refinement, such cells may be excluded from conventional lymphocyte analyses. Specifically, we analyzed clonal expansion of antigen-specific CD8 T cells in vaccinated mice, and demonstrated the potential of TDS cells to reflect immune dynamics in human blood samples from healthy donors, and patients with type 1 diabetes, infectious mononucleosis, and COVID-19. The Ki-67/DNA dual staining, or TDS assay, provides a reliable approach by which human peripheral blood can be used to reflect the dynamics of human lymphocytes, rather than providing mere steady-state phenotypic snapshots. The method does not require highly sophisticated “-omics” capabilities, so it should be widely-applicable to health care in diverse settings. Furthermore, our results argue that the TDS assay can provide a window on immune dynamics in extra-lymphoid tissues, a long-sought potential of peripheral blood monitoring, for example in relation to organ-specific autoimmune diseases and infections, and cancer immunotherapy
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