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Journal articles on the topic 'FlowBox'

Author: Grafiati

Published: 28 June 2021

Last updated: 12 February 2022

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1

Aarts, J. M., and L. G. Oversteegen. "Flowbox manifolds." Transactions of the American Mathematical Society 327, no. 1 (January 1, 1991): 449–63. http://dx.doi.org/10.1090/s0002-9947-1991-1042286-8.

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2

Bessa, Mário. "The flowbox theorem for divergence-free Lipschitz vector fields." Comptes Rendus Mathematique 355, no. 8 (August 2017): 881–86. http://dx.doi.org/10.1016/j.crma.2017.07.006.

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3

Ezeofor, Victory ‘Segun, Nathan Bray, Lucy Bryning, Farina Hashmi, Henrik Hoel, Daniel Parker, and Rhiannon Tudor Edwards. "Economic model to examine the cost-effectiveness of FlowOx home therapy compared to standard care in patients with peripheral artery disease." PLOS ONE 16, no. 1 (January 14, 2021): e0244851. http://dx.doi.org/10.1371/journal.pone.0244851.

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Background Critical limb ischaemia is a severe stage of lower limb peripheral artery disease which can lead to tissue loss, gangrene, amputation and death. FlowOx™ therapy is a novel negative-pressure chamber system intended for home use to increase blood flow, reduce pain and improve wound healing for patients with peripheral artery disease and critical limb ischaemia. Methods A Markov model was constructed to assess the relative cost-effectiveness of FlowOx™ therapy compared to standard care in lower limb peripheral artery disease patients with intermittent claudication or critical limb ischaemia. The model used data from two European trials of FlowOx™ therapy and published evidence on disease progression. From an NHS analysis perspective, various FlowOx™ therapy scenarios were modelled by adjusting the dose of FlowOx™ therapy and the amount of other care received alongside FlowOx™ therapy, in comparison to standard care. Results In the base case analysis, consisting of FlowOx™ therapy plus nominal care, the cost estimates were £12,704 for a single dose of FlowOx™ therapy per annum as compared with £15,523 for standard care. FlowOx™ therapy patients gained 0.27 additional quality adjusted life years compared to standard care patients. This equated to a dominant incremental cost-effectiveness ratio per QALY gained. At the NICE threshold WTP of £20,000 and £30,000 per QALY gained, FlowOx™ therapy in addition to standard care had a 0.80 and 1.00 probability of being cost-effectiveness respectively. Conclusions FlowOx™ therapy delivered as a single annual dose may be a cost-effective treatment for peripheral artery disease. FlowOx™ therapy improved health outcomes and reduced treatment costs in this modelled cohort. The effectiveness and cost-effectiveness of FlowOx™ therapy is susceptible to disease severity, adherence, dose and treatment cost. Research assessing the impact of FlowOx™ therapy on NHS resource use is needed in order to provide a definitive economic evaluation.

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4

Gooding, Mark. "Flowol (Data Harvest)." Electronics Education 2002, no. 2 (2002): 33. http://dx.doi.org/10.1049/ee.2002.0028.

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Quintelier, Katrien, Artuur Couckuyt, Annelies Emmaneel, Joachim Aerts, Yvan Saeys, and Sofie Van Gassen. "Analyzing high-dimensional cytometry data using FlowSOM." Nature Protocols 16, no. 8 (June 25, 2021): 3775–801. http://dx.doi.org/10.1038/s41596-021-00550-0.

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Derby, Stephen, Gene Eckert, David Brown, and John McFadden. "The compact FlowBot: a robotic pick and place motion system (patent pending)." Industrial Robot: An International Journal 40, no. 2 (March 2013): 106–10. http://dx.doi.org/10.1108/01439911311297694.

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7

Meekes, Jordy, and Wolter H. J. Hassink. "Flowbca: A Flow-Based Cluster Algorithm in Stata." Stata Journal: Promoting communications on statistics and Stata 18, no. 3 (September 2018): 564–84. http://dx.doi.org/10.1177/1536867x1801800305.

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In this article, we introduce the Stata implementation of a flow-based cluster algorithm, flowbca, written in Mata. The main purpose of flowbca is to identify clusters based on relational data of flows. We illustrate the command by providing multiple examples of applications from the research fields of economic geography, industrial input–output analysis, and social network analysis.

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Arrillaga, J., N. R. Watson, and G. N. Bathurst. "A Multifrequency Power Flowof General Applicability." IEEE Transactions on Power Delivery 19, no. 1 (January 2004): 342–49. http://dx.doi.org/10.1109/tpwrd.2003.820193.

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Bergström, Dan. "Cost Analysis of Innovative Biomass Harvesting Systems for Young Dense Thinnings." Croatian journal of forest engineering 40, no. 2 (July 19, 2019): 221–30. http://dx.doi.org/10.5552/crojfe.2019.552.

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The objective was to analyze three innovative harvesting systems for early thinnings and compare forest-to-industry supply costs. FlowConv consists of a harvester equipped with an innovative continuously cutting, accumulating and bunching head (the FlowCut head), a forwarder and a truck to transport loose tree-parts. FlowFix consists of a harvester equipped with the same cutting head but also a bundling unit (the Fixteri system), plus a forwarder and roundwood truck for biomass transport. FlowCin consists of a new conceptual biomass harvester (the Cintoc system) equipped with the same cutting head and a second crane to pass the cut trees from the front of the machine to a bundling unit at the back, plus the same forwarding and trucking units as in the FlowFix system. Empirical data were used to assess the FlowConv system’s performance, while the FlowFix and FlowCin systems’ performance was simulated. Results indicate that supply costs of the FlowCin system would be 6–10% and 24–29% lower than those of the FlowFix and FlowConv systems, respectively. Thus, it would be more suitable to be equipped with an innovative cutting head, which is up to 100% more efficient than the current commercially available options. Key features of the Cintoc-based system (which minimize possible waiting times during operation) include its buffering cradle and delivery of biomass acquired in two cutting crane cycles to the intermediate delivering crane. The apparent superiority of the FlowCin system is consistent with previous conclusions regarding developments needed to maximize the cost-effectiveness of harvesting young dense stands.

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10

Bücklein, Veit, Alexandra Stein, Benjamin Tast, Thomas Koehnke, Karsten Spiekermann, Francis Lacombe, and Marion Subklewe. "Flowsom: An R-Based Evaluation Strategy for Flow Cytometry-Based Measurable Residual Disease (MRD) Diagnostics in Acute Myeloid Leukemia (AML)." Blood 134, Supplement_1 (November 13, 2019): 4656. http://dx.doi.org/10.1182/blood-2019-129866.

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Patients with Acute Myeloid Leukemia (AML) frequently relapse due to chemorefractory AML cells persisting after intensive chemotherapy at levels below the 5% morphological detection threshold (measurable residual disease, MRD). MRD has been established as an important prognostic factor for relapse-free and overall survival, making it highly relevant for post-remission treatment stratification. In contrast to MRD assessment by molecular techniques, multiparameter flow cytometry (MFC)-based MRD measurements are applicable in more than 95% of AML patients, while still offering a sensitivity of 10-4 to 10-5. Current MFC MRD assessment strategies measure 8-10 fluorochromes in parallel, resulting in a high-dimensional data set. However, evaluation of this data is usually performed by scatterplot-based manual, two-dimensional analysis. This leads to loss of information and significant inter-observer variability in MRD diagnostics. We therefore established a computational data analysis strategy for MFC MRD diagnostics, based on the unsupervised FlowSOM algorithm. By comparison with healthy bone marrow (HBM) data, FlowSOM analysis can identify aberrant (sub-)populations of cells, clustered in nodes (according to similarity of their antigen profile). These nodes can be denoted as "nodes of interest" (NOI) to simplify MRD analysis after clustering. Aim of the project was to establish FlowSOM analysis protocols and retrospectively evaluate their prognostic significance in a cohort of 46 patients with known outcomes. Bone marrow samples of these patients were analyzed at aplasia (day 16 after initiation of induction chemotherapy). Only patients with morphological blast clearance at aplasia were included. Healthy reference FlowSOM trees were established by merging flow data of 17 HBM. Analysis protocols were developed to report individual ("any node" approach) and cumulative ("sum node" approach) differences in NOI percentages when comparing HBM and MRD samples. We then performed FlowSOM MRD analyses in a patient subcohort of 19 AML patients. Importantly, for these analyses, we excluded patients who underwent allogeneic stem cell transplantation in first remission (non-HSCT subcohort). Median follow-up time was 8.3 (range 2-40) months for this subcohort. Receiver operating characteristic (ROC) analyses were used to determine optimal threshold values to differentiate relapse (n=5) and non-relapse (n=14) patients within the cohort. For "sum node" analysis strategies (defining MRD levels as cumulative difference of NOI percentages) a threshold of -2.44% was identified, optimized for Youden (Y) index and diagnostic odds ratio (DOR). For the "any node" strategy (defining MRD levels by the maximum difference of any NOI), a threshold of 0.04%, also optimized for the Y-index and DOR, discriminated best between relapse and non-relapse patients. Relapse-free survival (RFS) was significantly shorter for MRD-positive (MRDpos) patients identified by "sum node" analysis (median 8 months vs. not reached, p=0.016) and tended to be shorter for MRDpos patients by "any node" analysis (median 8 months vs. not reached, p=0.1). When applying the thresholds identified in the non-HSCT cohort to the full set of 46 patients (median follow-up interval 10.6 months, range 2-40), median RFS was not reached for the MRD-negative group (both for "sum node" and "any node" analysis), and was 14 ("sum node", p=0.098) and 14 months ("any node", p=0.360) for the MRDpos patients. Median overall survival for MRDpos patients by "sum node" analysis was 27 months, whereas it was not reached for MRD-negative patients. However, this difference did not reach statistical significance (p=0.335), probably due to the small sample size. Taken together, FlowSOM-based analysis strategies seem well suited to identify patients with MRD positivity after intensive induction chemotherapy. MFC MRD positivity at aplasia, defined by FlowSOM-based analysis, is associated with inferior RFS in retrospective analyses of small patient cohorts. Due to the underlying computational, unsupervised data analysis, FlowSOM-based assessment can be a means to harmonize MFC MRD evaluation. These promising results need to be verified in larger cohorts, with inclusion of post-induction assessments, and should be followed by prospective analyses to delineate the diagnostic validity of FlowSOM for AML MRD diagnostics in clinical trials. Disclosures Subklewe: Janssen: Consultancy; Morphosys: Research Funding; Celgene: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Research Funding; Miltenyi: Research Funding; Oxford Biotherapeutics: Research Funding; Pfizer: Consultancy, Honoraria; AMGEN: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Research Funding.

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van Ravenswaay, Jan P., Gideon P. Greyvenstein, Willem M. K. van Niekerk, and Johan T. Labuschagne. "Verification and validation of the HTGR systems CFD code Flownex." Nuclear Engineering and Design 236, no. 5-6 (March 2006): 491–501. http://dx.doi.org/10.1016/j.nucengdes.2005.11.025.

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12

Ahsan, Syeda Maheen, Elisabet Nadal-Melsio, Mark E. Robinson, Richard Syzdlo, Eva Yebra-Fernandez, Holger W. Auner, Maria Atta, et al. "Clustering Analysis of Myeloma Clone Phenotype Is Informative for Disease Heterogeneity and Prognosis at Relapse." Blood 132, Supplement 1 (November 29, 2018): 4492. http://dx.doi.org/10.1182/blood-2018-99-117460.

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Abstract Background: Multiple myeloma (MM) is a remitting-relapsing malignancy with variable clinical outcome. Certain cytogenetic abnormalities, either present at diagnosis or emerged at later stages, predict for poor outcome and highlight the clinical importance of MM genetic heterogeneity. Whole genome and exome sequencing studies reveal a complex intraclonal genetic landscape, organised in linear and branching Darwinian patterns, which evolves in space and time. Clones with a more complex genetic architecture may be more fit to escape treatment and those patients are likely to have a worse clinical outcome. Clinical multicolour flow cytometry (MFC) is routinely used in MM diagnosis and detection of minimal residual disease. Previous studies have shown that MM cell subpopulations with discrete phenotypic features correspond to genetic subclones, therefore it is plausible that MFC data captures clonal heterogeneity. On that basis, we propose that clustering analysis of MM phenotypic subpopulations could be clinically relevant. Methods: We retrospectively analysed clinical MCF data at diagnosis from 44 patients eligible for autologous stem cell transplantation (AutoSCT) and 14 ineligible patients and data from 52 relapsed patients after first AutoSCT. All patients were treated between 2012 - 2018. The 8-colour MCF marker panel included CD138, CD38, CD56, CD45, CD20, CD19, cytoplasmic kappa and lambda light chains (cytLC). Data was analysed in FlowJo software and MM plasma cells were identified as CD38high, CD19-, cytLC+, within their FSC-A/SSC-A physical gate. The gated events were exported in a new fcs file. Clustering analysis was performed in Cytofkit, a R-based Bioconductor package, using the Rphenograph, Cluster-X and FlowSOM algorithms. All fcs files were subjected in the same clustering analysis, but CD56 positive and CD56 negative cases were analysed separately to offset bias from differential CD56 expression. Parameters inserted in the algorithms were FSC-A, CD138, CD38, CD45, CD20 and CD56. The number of clusters was produced by FlowSOM (k=4) and only clusters with size >1% of the total events were accepted. Results: At diagnosis, FlowSOM identified 1 (n=32, 56.1%) or 2 clusters (n=19, 33.3%) in most cases. Three clusters were found only in 5 patients (8.8%) and 4 clusters in 1 patient (1.8%). The number of clusters at diagnosis did not correlate with cytogenetic risk group or ISS. Also, the number of clusters did not predict for depth of response or relapse free survival post AutoSCT. On the contrary, phenotypic patterns at relapse post AutoSCT were more complex, with 1 cluster identified in 2 patients only (3.8%), 2 clusters in 23 (44.2%), 3 clusters in 24 (46.2%) and 4 clusters in 3 patients (5.8%). Patients with >2 clusters (n=27) had a shorter survival post relapse (median 17 months - 95% CI, 7-26.6) compared to those (n=25) with 1-2 phenotypic clusters (median not reached, Log rank p=0.06). A phenotypic cluster characterised by CD138low/- at relapse, was also associated with adverse outcome and higher risk cytogenetics. In 14 patients with available serial samples at diagnosis and relapse we observed 3 patterns of phenotypic evolution: a. sustained pattern, b. change of dominant cluster and c. emergence of completely new subpopulations. These evolving phenotypes resemble changes in clonal composition over time observed in genetic studies. Conclusion: Clinical MCF, in addition to routine diagnostics, can be informative of MM biological and clinical heterogeneity. Particularly in the relapsed setting, complex phenotypic patterns identified by clustering analysis may be of prognostic value. Validation of this preliminary study results in larger patient cohorts or clinical trials, could provide a useful and readily available tool for patient stratification and prognosis. Disclosures Apperley: Novartis: Honoraria, Research Funding, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau; BMS: Honoraria, Speakers Bureau; Incyte: Honoraria, Speakers Bureau. Karadimitris:Celgene: Research Funding; GSK: Research Funding; Gilead: Honoraria.

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Hoel, Henrik, and Jonny Hisdal. "The FlowOx device for the treatment of peripheral artery disease: current status and future prospects." Expert Review of Medical Devices 18, no. 3 (March 4, 2021): 217–20. http://dx.doi.org/10.1080/17434440.2021.1895750.

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14

Hayat, Tasawar, S. Najam, and S. Asghar. "ROTATING FLOWOF A GENERALIZED BURGERS' FLUID WITH SLIP CONDITION." Journal of Porous Media 13, no. 9 (2010): 839–45. http://dx.doi.org/10.1615/jpormedia.v13.i9.60.

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15

SHIRAI, MAREAKI. "Measures taken on reopering organ blood flow.On weaning from pump-oxygenators." JOURNAL OF JAPAN SOCIETY FOR CLINICAL ANESTHESIA 11, no. 4 (1991): 414–19. http://dx.doi.org/10.2199/jjsca.11.414.

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16

John Wessley, G. Jims, and Swati Chauhan. "Modeling and Performance Simulation of a Micro Turbojet Engine Using Flownex." Indian Journal of Science and Technology 12, no. 22 (May 1, 2019): 1–5. http://dx.doi.org/10.17485/ijst/2019/v12i22/131030.

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17

Reddy, S. Surender. "Multi-Objective Optimal Power Flowfor a Thermal-Wind-Solar Power System." Journal of Green Engineering 7, no. 4 (2018): 451–76. http://dx.doi.org/10.13052/jge1904-4720.741.

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18

Bashkin, V. A., and I. V. Ezhov. "CIRCULAR CYLINDER IN THE TRANSONIC FLOWOF A VISCOUS PERFECT GAS." TsAGI Science Journal 42, no. 1 (2011): 13–36. http://dx.doi.org/10.1615/tsagiscij.v42.i1.20.

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Koblížek, Miroslav, Anastasia Lebedeva, and Karel Fišer. "flowIO: Flow cytometry standard conformance testing, editing, and export tool." Cytometry Part A 93, no. 8 (August 2018): 848–53. http://dx.doi.org/10.1002/cyto.a.23563.

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20

Rehak, Martin, Michal Pechoucek, Martin Grill, Karel Bartos, Vojtech Krmicek, and Pavel Celeda. "Collaborative approach to network behaviour analysis based on hardware-accelerated FlowMon probes." International Journal of Electronic Security and Digital Forensics 2, no. 1 (2009): 35. http://dx.doi.org/10.1504/ijesdf.2009.023874.

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21

Mason, Kylie D., and Surender Juneja. "Go with the flowfor monitoring response in myeloma with minimal residual disease." Leukemia & Lymphoma 49, no. 2 (January 2008): 177–78. http://dx.doi.org/10.1080/10428190701824601.

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22

Van Gassen, Sofie, Britt Callebaut, Mary J. Van Helden, Bart N. Lambrecht, Piet Demeester, Tom Dhaene, and Yvan Saeys. "FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data." Cytometry Part A 87, no. 7 (January 8, 2015): 636–45. http://dx.doi.org/10.1002/cyto.a.22625.

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23

Shokhirev, Maxim Nikolaievich, and Alexander Hoffmann. "FlowMax: A Computational Tool for Maximum Likelihood Deconvolution of CFSE Time Courses." PLoS ONE 8, no. 6 (June 27, 2013): e67620. http://dx.doi.org/10.1371/journal.pone.0067620.

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24

Hayat, Tasawar, and Muhammad Nawaz. "Magnetohydrodynamic Three-Dimensional Flowof a Second-Grade Fluid with Heat Transfer." Zeitschrift für Naturforschung A 65, no. 8-9 (September 1, 2010): 683–91. http://dx.doi.org/10.1515/zna-2010-8-909.

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An analysis has been carried out for the heat transfer on steady boundary layer flow of a secondgrade fluid bounded by a stretching sheet. The magnetohydrodynamic nature of the fluid is considered in the presence of Hall and ion-slip currents. The nonlinear mathematical problem is computed by a powerful tool, namely, the homotopy analysis method (HAM). A comparative study between the present and existing limiting results is carefully made. Convergence regarding the obtained solution is discussed. Skin friction coefficients and Nusselt number are analyzed. Effects of embedded parameters on the dimensionless velocities and temperature are examined

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Islam, S., X. J. Ran, Q. K. Ghori, and A. M. Siddiqui. "STEADY INCOMPRESSIBLE FLOWOF A COUPLE STRESS FLUID IN A POROUS MEDIUM." Journal of Porous Media 14, no. 5 (2011): 461–66. http://dx.doi.org/10.1615/jpormedia.v14.i5.80.

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Kamar, Bassem, Damyana Bakardzhieva, Samy Ben Naceur, and Sami Ben Naceur. "The Impact of Capital and Foreign Exchange Flowson the Competitiveness of Developing Countries." IMF Working Papers 10, no. 154 (2010): 1. http://dx.doi.org/10.5089/9781455201372.001.

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27

Hamacher, Horst W., Stephanie Heller, and Benjamin Rupp. "Flow location (FlowLoc) problems: dynamic network flows and location models for evacuation planning." Annals of Operations Research 207, no. 1 (September 3, 2011): 161–80. http://dx.doi.org/10.1007/s10479-011-0953-9.

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28

Goel, Sudhir K. "Nonseparated manifolds and completely unstable flows." International Journal of Mathematics and Mathematical Sciences 10, no. 4 (1987): 745–56. http://dx.doi.org/10.1155/s016117128700084x.

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We define an order structure on a nonseparatedn-manifold. Here, a nonseparated manifold denotes any topological space that is locally Euclidean and has a countable basis; the usual Hausdorff separation property is not required. Our result is that an ordered nonseparatedn-manifoldXcan be realized as an ordered orbit space of a completely unstable continuous flowϕon a Hausdorff(n+1)-manifoldE.

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Strain, Errol, Florian Hahne, Ryan R. Brinkman, and Perry Haaland. "Analysis of High-Throughput Flow Cytometry Data Using plateCore." Advances in Bioinformatics 2009 (October 11, 2009): 1–10. http://dx.doi.org/10.1155/2009/356141.

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Flow cytometry (FCM) software packages from R/Bioconductor, such as flowCore and flowViz, serve as an open platform for development of new analysis tools and methods. We created plateCore, a new package that extends the functionality in these core packages to enable automated negative control-based gating and make the processing and analysis of plate-based data sets from high-throughput FCM screening experiments easier. plateCore was used to analyze data from a BD FACS CAP screening experiment where five Peripheral Blood Mononucleocyte Cell (PBMC) samples were assayed for 189 different human cell surface markers. This same data set was also manually analyzed by a cytometry expert using the FlowJo data analysis software package (TreeStar, USA). We show that the expression values for markers characterized using the automated approach in plateCore are in good agreement with those from FlowJo, and that using plateCore allows for more reproducible analyses of FCM screening data.

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30

Harris, Andrew, and Rowland S. "FLOWGO: a kinematic thermo-rheological model for lava flowing in a channel." Bulletin of Volcanology 63, no. 1 (May 8, 2001): 20–44. http://dx.doi.org/10.1007/s004450000120.

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Vial, Jean Philippe, Nicolas Lechevalier, Francis Lacombe, Pierre-Yves Dumas, Audrey Bidet, Thibaut Leguay, François Vergez, Arnaud Pigneux, and Marie C. Béné. "Unsupervised Flow Cytometry Analysis Allows for an Accurate Identification of Minimal Residual Disease Assessment in Acute Myeloid Leukemia." Cancers 13, no. 4 (February 5, 2021): 629. http://dx.doi.org/10.3390/cancers13040629.

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The assessment of minimal residual disease (MRD) is increasingly considered to monitor response to therapy in hematological malignancies. In acute myeloblastic leukemia (AML), molecular MRD (mMRD) is possible for about half the patients while multiparameter flow cytometry (MFC) is more broadly available. However, MFC analysis strategies are highly operator-dependent. Recently, new tools have been designed for unsupervised MFC analysis, segregating cell-clusters with the same immunophenotypic characteristics. Here, the Flow-Self-Organizing-Maps (FlowSOM) tool was applied to assess MFC-MRD in 96 bone marrow (BM) follow-up (FU) time-points from 40 AML patients with available mMRD. A reference FlowSOM display was built from 19 healthy/normal BM samples (NBM), then simultaneously compared to the patient’s diagnosis and FU samples at each time-point. MRD clusters were characterized individually in terms of cell numbers and immunophenotype. This strategy disclosed subclones with varying immunophenotype within single diagnosis and FU samples including populations absent from NBM. Detectable MRD was as low as 0.09% in MFC and 0.051% for mMRD. The concordance between mMRD and MFC-MRD was 80.2%. MFC yielded 85% specificity and 69% sensitivity compared to mMRD. Unsupervised MFC is shown here to allow for an easy and robust assessment of MRD, applicable also to AML patients without molecular markers.

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Lechevalier, Nicolas, Jean-Philippe Vial, Francis Lacombe, Pierre-Yves Dumas, Audrey Bidet, Thibaut Leguay, Francois Vergez, Arnaud Pigneux, and Marie C. Béné. "Unsupervised Flow Cytometry Analysis: Application to Minimal Residual Disease Detection in a Cohort of 40 Acute Myeloblastic Leukemia Patients with Molecular Markers." Blood 136, Supplement 1 (November 5, 2020): 30. http://dx.doi.org/10.1182/blood-2020-141199.

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Minimal/measurable residual disease (MRD) assessment in the course of acute myeloblastic leukemia (AML) monitoring is a topic that currently reaches undisputable interest. Multiparameter flow cytometry (MFC) appears as a valuable, rapid tool to assess response to therapy. Within the European LeukemiaNet (ELN) a task force has promoted the use of a backbone panel of monoclonal antibodies likely to cover both the most common leukemia associated immunophenotyping patterns (LAIP) and different from normal (DfN) immunophenotypes (Schuurhuis et al., Blood 2018). Classical gating strategies of MRD assessment however rely on supervised/operator-dependent approaches. The development of mass-cytometry has led to the emergence of new unsupervised analysis software. Among them, the FlowSOM (Flow Self Organizing Maps) R-based solution has been recognized as the most efficient, applicable to both mass and classical flow cytometry. Combination of the R-based FlowSOM definition of self-organized minimal spanning trees (MST) and the Kaluza® classical flow cytometry analysis software allows for a combined exploration of a normal bone marrow (NBM) matrix (derived from 19 normal BM samples)together with individual diagnosis and follow-up (FU) samples. This strategy was applied to 40 AML patients treated with a conventional 3+7 regimen for whom at least post induction FU was available, together with targets allowing for molecular MRD assessment (NPM1, RUNX1, CBFB-MYHorBCR-ABL). According to previously published panels (GEIL, Cytometry part B, 2018), two tubes were tested in MFC and results were compared to molecular MRD results for 96 FU time points. All patients could be evaluated in MFC at all time points. Comparison of molecular and MFC data showed an overall good concordance (80.2%), especially for FU1 time points (87.5%) with a sensitivity of 0.89 and a specificity of 0.75. Yet, outcome after induction appeared to be more correlated with MFC-negative MRD. This work also allowed to demonstrate the particular ability of the FlowSOM algorithm to disclose subtle chemoresistant subclones during FU. Moreover, by systematic comparison of normal bone marrow and FU points, MRD could be asserted without ambiguity and regenerating BM identified as such. Of note, this work could be performed on retrospective data, based on the fact that instruments were harmonized as reported (Lacombe et al. Leukemia, 2016) and identical panels were used all along. This makes this strategy easily transferrable to a network of laboratories using consensus settings and panels, as proposed by the European LeukemiaNet (Schuurhuis et al., Blood 2018). All in all, this study shows that unsupervised MFC MRD assessment appears as an accurate tool to appreciate response to chemotherapy in AML, and the FlowSOM + Kaluza method, combined with a systematic diagnostic FU / NBM multicomparison approach, increases the resolving power in the monitoring of MRD compared to the classic LAIP/different from normal approaches as implemented to date. Disclosures Dumas: Abbvie:Honoraria;Jazz-pharmaceutical:Consultancy;Daiichi Sankyo:Consultancy, Research Funding;Astellas:Research Funding, Speakers Bureau.

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33

Alekseev, Dmitrii Pavlovich, and Yurii Vladimirovich Tunik. "HYDROGEN INJECTION INTO A SUPERSONIC FLOWOF AIR IN A CHANNEL WITH RADIAL PYLONS." TsAGI Science Journal 46, no. 6 (2015): 609–18. http://dx.doi.org/10.1615/tsagiscij.v46.i6.70.

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34

Kratochvíl, Miroslav, David Bednárek, Tomáš Sieger, Karel Fišer, and Jiří Vondrášek. "ShinySOM: graphical SOM-based analysis of single-cell cytometry data." Bioinformatics 36, no. 10 (February 12, 2020): 3288–89. http://dx.doi.org/10.1093/bioinformatics/btaa091.

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Abstract Summary ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes. Availability and implementation ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. Supplementary information Supplementary data are available at Bioinformatics online.

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Muksin, A. S., and B. Syaiful. "Simple Simulation Using Coupling between Flownex and LabVIEW Simultaneously in Case of Indonesian Experimental Power Reactor." Journal of Physics: Conference Series 1198, no. 2 (April 2019): 022072. http://dx.doi.org/10.1088/1742-6596/1198/2/022072.

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Harris, Andrew J. L., Maéva Rhéty, Lucia Gurioli, Nicolas Villeneuve, and Raphaël Paris. "Simulating the thermorheological evolution of channel-contained lava: FLOWGO and its implementation in EXCEL." Geological Society, London, Special Publications 426, no. 1 (June 5, 2015): 313–36. http://dx.doi.org/10.1144/sp426.9.

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Molli, Giancarlo, Marco Doveri, Adele Manzella, Livio Bonini, Flavia Botti, Matia Menichini, Domenico Montanari, Eugenio Trumpy, Alino Ungari, and Luca Vaselli. "Surface-subsurface structural architecture and groundwater flowof the Equi Terme hydrothermal area, northern Tuscany Italy." Italian Journal of Geosciences 134, no. 3 (October 2015): 442–57. http://dx.doi.org/10.3301/ijg.2014.25.

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Eldabe, Nabil T. M., G. Saddeek, and Khaled A. Sh Elagamy. "MAGNETOHYDRODYNAMIC FLOWOF A BI-VISCOSITY FLUID THROUGH POROUS MEDIUM IN A LAYER OF DEFORMABLE MATERIAL." Journal of Porous Media 14, no. 3 (2011): 273–83. http://dx.doi.org/10.1615/jpormedia.v14.i3.70.

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Truelove, Edward, Frances Seymour, Janet Matthews, and John G. Gribben. "Deep Phenotypic Analysis Reveals a Monocyte Subpopulation Predictive of Relapse/Refractory Diffuse Large B-Cell Lymphoma." Blood 132, Supplement 1 (November 29, 2018): 2863. http://dx.doi.org/10.1182/blood-2018-99-119756.

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Abstract Background: Diffuse large B cell lymphoma (DLBCL) is the most frequent lymphoma subtype with up to two thirds of patients achieving cure following standard immuno-chemotherapy. However, approximately 10-15% will be primary refractory and a further 20-30% relapse. There is an urgent need for improved biomarkers to identify these poor risk patients at diagnosis who are destined to fail standard therapy so that novel approaches can be considered. The diagnostic peripheral blood monocyte count is known to be predictive of outcome in DLBCL, with higher counts identifying patients with worse outcome but mechanistic understanding is currently lacking. The aim of this study was to investigate the peripheral blood monocyte population driving this phenomenon and we present here a deep phenotypic analysis of the monocyte compartment at diagnosis in patients with extremes of outcome following immuno-chemotherapy. Methods: We identified 112 immuno-chemotherapy treated DLBCL patients with viably cryopreserved peripheral blood mononuclear cells (PBMCs) in our tissue bank. The diagnostic absolute monocyte (AMC) and lymphocyte counts (ALC) were examined as prognostic variables. For phenotypic analysis a subset of 20 patients, 10 with relapse/refractory disease within 12 months of therapy (Poor risk) and 10 with continued complete remission >24 months post therapy (Good risk), were selected. PBMCs from these patients were stained with a panel of 29 metal-tagged antibodies designed to identify immune populations and characterize the monocyte compartment and analyzed using cytometry by time-of-flight (CyTOF2TM). Samples were acquired in batches run with the same healthy donor control PBMCs to ensure consistency of staining. Normalized data were subjected to traditional Boolean gating in Cytobank (www.mrc.cytobank.org) to identify CD45+ live single cells and further analyzed using FlowSOM and CITRUS. Results: In the full 112 patient cohort, a higher diagnostic AMC (≥0.6 x 109/L) predicted worse outcome (5-year OS, 59% vs 74%, p=0.047*). The lymphocyte to monocyte ratio (LMR) was more discriminatory, with a low LMR (<2.6) predicting worse outcome (5-year OS, 49% vs 81%, p=0.0009*). CyTOF analysis of the CD45+ events from all 20 DLBCL patients were clustered in an unsupervised manner with FlowSOM and visualized according to the tSNE algorithm. This identified all expected immune populations with further subdivision into multiple clusters / sub-populations based on protein marker expression. Repeating the FlowSOM analysis by outcome revealed clusters with clear differences between the 2 groups, including striking changes to the monocyte compartment (Figure 1). CITRUS, an algorithm for automated biomarker discovery, was used to further assess the differences in the monocyte compartments between poor risk and good risk DLBCL patients. This identified a monocyte subset, representing approximately 10% of non-lymphoid CD45+ PBMCs, with increased abundance in poor risk patients with relapsed/refractory disease (p=0.0028*, Figure 2). This subset expressed markers including HLA-DR, CD11b, CD13, CD14, CD33, SIRPα (CD172a), CCR2 (CD192, monocyte chemo-attractant protein receptor), CD206 and PD-L1 (CD274). Conclusions: We confirmed the previous finding that the diagnostic AMC is predictive of outcome in our cohort of DLBCL patients and have identified differences in the peripheral blood monocyte compartment between patients with good and poor risk disease. Unsupervised analysis using FlowSOM identified monocyte clusters with greater abundance in poor risk patients. A second high-dimensional single cell data analysis algorithm, CITRUS validated this finding by revealing a stratifying monocyte sub-population that was enriched in poor risk patients. This phenotype of peripheral blood monocytes may account for the adverse nature of a higher AMC at diagnosis. We are now functionally characterizing these monocytes and evaluating their relationship to differences between the lymphoid populations of good and poor risk patients. Disclosures Seymour: Celgene: Research Funding. Gribben:Janssen: Honoraria, Research Funding; Pharmacyclics: Honoraria; Abbvie: Honoraria; Kite: Honoraria; Acerta Pharma: Honoraria, Research Funding; Wellcome Trust: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; NIH: Research Funding; Unum: Equity Ownership; Roche: Honoraria; Novartis: Honoraria; Cancer Research UK: Research Funding; Medical Research Council: Research Funding; TG Therapeutics: Honoraria.

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Fassler, Segismundo, Manik L. Shrestha, and Reimund Mink. "An Integrated Framework for Financial Positions and Flowson a From-Whom-To-Whom Basis: Concepts, Status, and Prospects." IMF Working Papers 12, no. 57 (2012): i. http://dx.doi.org/10.5089/9781463937751.001.

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Sajid, M. "EFFECT OF COUPLE STRESSES ON THE STEADY FLOWOF A SECOND GRADE FLUID THROUGH THE POROUS MEDIUM." Journal of Porous Media 14, no. 2 (2011): 179–86. http://dx.doi.org/10.1615/jpormedia.v14.i2.60.

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Béné, Marie C., Olof Axler, Despoina Violidaki, Francis Lacombe, Mats Ehinger, and Anna Porwit. "Definition of Erythroid Differentiation Subsets in Normal Human Bone Marrow Using FlowSOM Unsupervised Cluster Analysis of Flow Cytometry Data." HemaSphere 5, no. 1 (December 21, 2020): e512. http://dx.doi.org/10.1097/hs9.0000000000000512.

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Jana, M., S. L. Maji, S. Das, and Rabindra N. Jana. "UNSTEADY FLOWOF VISCOUS FLUID THROUGH A POROUS MEDIUM BOUNDED BY A POROUS PLATE IN A ROTATING SYSTEM." Journal of Porous Media 13, no. 7 (2010): 645–53. http://dx.doi.org/10.1615/jpormedia.v13.i7.60.

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N. V. Varghese. "Education and Migration:." International Journal of African Higher Education 8, no. 2 (May 23, 2021): 103–17. http://dx.doi.org/10.6017/ijahe.v8i2.13481.

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The Indian diaspora consists of low- and semi-skilled migrants mainly tothe Middle-East; migration of the highly-skilled to developed countries;and cross-border students who seek employment and remain in their hostcountries. India initially viewed the migration of the best educated fromits prestigious institutions as ‘brain drain’. However, with the reverse flowof these professionals, the diaspora came to be seen as ‘brain gain’. Thehighly-skilled Indian diaspora assumed positions of responsibility in thecorporate world, in academia (including Nobel laureates), and in the politicaland social spheres in some host countries, thereby enhancing India’simage abroad. Key words: India, skilled migration, human aspirations, brain drain, braingain

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Lacombe, Francis, Nicolas Lechevalier, Jean Philippe Vial, and Marie C. Béné. "An R‐Derived FlowSOM Process to Analyze Unsupervised Clustering of Normal and Malignant Human Bone Marrow Classical Flow Cytometry Data." Cytometry Part A 95, no. 11 (October 2, 2019): 1191–97. http://dx.doi.org/10.1002/cyto.a.23897.

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Reddy Kukutla, Pol, and BVSSS Prasad. "Network analysis of a coolant flow performance for the combined impingement and film cooled first-stage of high pressure gas turbine nozzle guide vane." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 6 (April 16, 2018): 1977–89. http://dx.doi.org/10.1177/0954410018767290.

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The present paper describes a system-level thermo-fluid network analysis for the secondary air system analysis of a typically film-cooled nozzle guide vane with multiple actions of jet impingement. The one-dimensional simulation was done with the help of the commercially available Flownex 2015 software. The system-level thermo-fluid network results were validated with both the computational fluid dynamics results and experimentally available literature. The entire nozzle guide vane geometry was first mapped to a thermo-fluid network model and the pressure conditions at different nodes. The discharge and heat transfer coefficients obtained from the Ansys FLUENT were specified as inputs to the thermo-fluid network model. The results show that the one-dimensional simulation of the coolant mass flow rates and jet Nusselt number values are in good agreement with the three-dimensional computational fluid dynamics results.

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Wantim, M. N., M. Kervyn, G. G. J. Ernst, M. A. del Marmol, C. E. Suh, and P. Jacobs. "Numerical experiments on the dynamics of channelised lava flows at Mount Cameroon volcano with the FLOWGO thermo-rheological model." Journal of Volcanology and Geothermal Research 253 (March 2013): 35–53. http://dx.doi.org/10.1016/j.jvolgeores.2012.12.003.

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Guenther, F., A. L. Klibanov, E. Ferrante, C. Bode, and C. von zur Muhlen. "CMR2009: 1.07: An ultrasound contrast agent targeted towards p-selectin detects activated platelets at supra-arterial shear flowex vivo." Contrast Media & Molecular Imaging 4, no. 6 (November 2009): 262–63. http://dx.doi.org/10.1002/cmmi.302.

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Costa, Bruno Ferreira, and Géssica Teles. "THE ROLE OF CITIES IN WELCOMING REFUGEES." Diacrítica 31, no. 3 (May 28, 2019): 153–81. http://dx.doi.org/10.21814/diacritica.392.

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In recent years there has been a significant increase in the volume of migratory flowson a global scale, especially with the European Community area as the final destination.In fact, the European community’s space due to democratic stability and to itsliving conditions, has become a magnet for this new wave of refugees from countriesinvolved in various armed conflicts in North Africa and the Middle East. /is influxof migrants challenges all levels of government, especially local governments, who are at the forefront of receiving and welcoming refugees. /e present study focuseson the recent refugee crisis in Europe, as well as on how European cities, namelyParis and Berlin, worked to integrate a significant number of refugees, consideringthe different approaches and strategies to host this flow of refugees. Based on a comparativeand descriptive study, we will try to trace the political strategies adopted atthe local level in the reception of refugees, allowing to map the conditions for theeffective integration of these citizens in the European space.

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Swery, Elinor E., Reinhold Meier, Stepan V. Lomov, Klaus Drechsler, and Piaras Kelly. "Predicting permeability based on flow simulations and textile modelling techniques: Comparison with experimental values and verification of FlowTex solver using Ansys CFX." Journal of Composite Materials 50, no. 5 (April 9, 2015): 601–15. http://dx.doi.org/10.1177/0021998315579927.

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