202,443 research outputs found
Flow cytometry data (1) from: Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity, Potts et al.
No full textFull associated publication: 'Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity', Potts et al (2023). Raw 18-colour flow cytometry data characterising PBMC derived from cohort of 36 donors, including healthy controls and individuals infected with SARS-CoV-2. Data were utilised to validate upregulation of the markers CEACAM1, CEACAM6, CEACAM8, CD177, CD63 and CD89 in severe COVID-19 and phenotype immune cell populations upregulating these markers. Flow cytometry data were collected over 3 seaparate experiments on a Cytek Aurora spectral flow cytometer and are provided as unmixed and unprocessed .fcs files from the first experiment. These data can be analysed using the widely used FlowJo analysis software. Each data file is titled with indicators for the donor ID (CVXXXX) and whether the sample was stained with the antibody cocktail or left as an unstained control. Full metadata for each donor can be found in the associated publication
Flow cytometry data (3) from: Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity, Potts et al.
No full textFull associated publication: 'Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity', Potts et al (2023). Raw 18-colour flow cytometry data characterising PBMC derived from cohort of 36 donors, including healthy controls and individuals infected with SARS-CoV-2. Data were utilised to validate upregulation of the markers CEACAM1, CEACAM6, CEACAM8, CD177, CD63 and CD89 in severe COVID-19 and phenotype immune cell populations upregulating these markers. Flow cytometry data were collected over 3 separate experiments on a Cytek Aurora spectral flow cytometer and are provided as unmixed and unprocessed .fcs files from the third experiment. These data can be analysed using the widely used FlowJo analysis software. Each data file is titled with indicators for the donor ID (CVXXXX) and whether the sample was stained with the antibody cocktail or left as an unstained control. Full metadata for each donor can be found in the associated publication
Flow cytometry data (2) from: Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity, Potts et al.
No full textFull associated publication: 'Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity', Potts et al (2023). Raw 18-colour flow cytometry data characterising PBMC derived from cohort of 36 donors, including healthy controls and individuals infected with SARS-CoV-2. Data were utilised to validate upregulation of the markers CEACAM1, CEACAM6, CEACAM8, CD177, CD63 and CD89 in severe COVID-19 and phenotype immune cell populations upregulating these markers. Flow cytometry data were collected over 3 seaparate experiments on a Cytek Aurora spectral flow cytometer and are provided as unmixed and unprocessed .fcs files from the second experiment. These data can be analysed using the widely used FlowJo analysis software. Each data file is titled with indicators for the donor ID (CVXXXX) and whether the sample was stained with the antibody cocktail or left as an unstained control. Full metadata for each donor can be found in the associated publication
[Report on Officer's Duties by W. E. Potts]
No full textCarbon copy of report by W. E. Potts. Following the President's assassination, Potts went into the Homicide and Robbery bureau and took affidavits. Potts and B. L. Senkel went to search Lee Harvey Oswald's room and were informed that he had registered as O. H. Lee. Several items were taken from the room after a search warrant arrived. On the 25th of November, F. M. Turner and W. E. Potts questioned Ronald Fischer, who stated a photo of Oswald looked a lot like a man who had been looking out the window of the Texas Book Depository on the day that the President was killed
Supplementary Data from: Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity, Potts et al.
No full textFull associated publication: 'Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity', Potts et al (2023). Provided supplementary data includes:Table S2. Details of donors used to generate whole-blood RNA-seq data in Bergamaschi et al and re-analysed here for comparison with proteomic data, related to Figures 3 and 4.Table S3. Details of donors analysed by flow cytometry panels in Bergamaschi et al and re-analysed here to determine sample neutrophil contamination, related to Figure 3.Table S5. Functional enrichment analysis of proteomic data, related to Figure 2.Enrichment of functional pathways in clusters of cellular proteins upregulated during COVID. DAVID enrichment terms and corresponding Benjamini-Hochberg-corrected p-values are shown for each cluster in Fig. 2B.Table S6. Interactive spreadsheet of all proteomic and transcriptomic data in the manuscript, related to Figures 2, 3, 4.(A)Interactive searchable spreadsheet containing all data and statistics from whole cellular (WCL), plasma membrane (PM) and RNAseq analyses(B)Proteomic data from all WCL analyses(C)Proteomic data from WCL analyses for proteins quantified across all three WCL experiments(D)Results of statistical tests comparing relative abundance of each protein quantified in WCL analyses.(E)Proteomic data from second PM analysis(F)Proteomic data from all PM analyses(G)Results of statistical tests comparing relative abundance of each protein quantified in second PM analysis.(H)Transcriptomic data from all donors generated in Bergamaschi et al at day 0 timepoint. Data expressed as Log2(RPKM).(I)Transcriptomic data from donors also analysed in proteomic analyses, generated in Bergamaschi et al at day 0 timepoint. Data expressed as Log2(RPKM)
Bulk and boundary scattering in the q-state potts mode
No full textThis thesis is concerned with the properties of 1 + 1 dimensional massive field theories in both infinite and semi-infinite geometries. Chapters 1, 2 and 3 develop the necessary theoretical framework and review existing work by Chim and Zamolodchikov [1] on integrable perturbations of the (bulk) q-state Potts model, the particular model under consideration in this thesis. Chapter 4 consists of a detailed analysis of the bootstrap for this model, during the course of which unexpected behaviour arises. The treatment of 1] has consequently been revised, but further investigation will be necessary before complete understanding of this behaviour can be reached. In the final chapter, attention turns to the imposition of boundary conditions on two dimensional systems. After looking at this from a statistical mechanical point of view, a brief review of boundary conformal held theory and its integrable perturbations is given. This leads once more to a consideration of the q-state Potts model. After summarising [2], where fixed and free boundary conditions are considered, a third and previously untreated boundary condition is discussed
Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models
No full textThis letter considers the problem of computing the Cramer–Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable derivatives. We show here how it is possible to formulate the computation of the bound as a statistical inference problem that can be solve approximately, but with arbitrarily high accuracy, by using a Monte Carlo method. The proposed methodology is successfully applied on the Ising and the Potts models
Bounds on the complex zeros of (Di)Chromatic polynomials and Potts-model partition functions
No full textWe show that there exist universal constants C(r) such that, for all loopless graphs G of maximum degree less than or equal to r, the zeros (real or complex) of the chromatic polynomial P-G(q) lie in the disc \q\ 7.963907r. This result is a corollary of a more general result on the zeros of the Potts-model partition function Z(G)(q. {v(e)}) in the complex antiferromagnetic regime \1 + v(e)\ less than or equal to 1. The proof is based on a transformation of the Whitney-Tutte-Fortuin-Kasteleyn representation of Z(G)(q,:{v(e)}) to a polymer gas. followed by verification of the Dobrushin-Kotecky-Preiss condition for nonvanishing of a polymer-model partition function. We also show that, for all loopless graphs G of second-largest degree less than or equal to r, the zeros of P-G(q) lie in the disc \q\ < C(r)+ 1. Along the way, I give a simple proof of a generalized (multivariate) Brown-Colbourn conjecture on the zeros of the reliability polynomial for the special case of series-parallel graphs
Parsimonious Segmentation of Time Series' by Potts Models
No full textTypical problems in the analysis of data sets like time-series or images crucially rely on the extraction of primitive features based on segmentation. Variational approaches are a popular and convenient framework in which such problems can be studied. We focus on Potts models as simple nontrivial instances. The discussion proceeds along two data sets from brain mapping and functional genomics
Potts, P M, 1782174
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/411369Surname: POTTS. Given Name(s) or Initials: P M. Military Service Number or Last Known Location: 1782174. Missing, Wounded and Prisoner of War Enquiry Card Index Number: SEA-1606.227080
Item: [2016.0049.43633] "Potts, P M, 1782174
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