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Rapidly Varying Flow01:24

Rapidly Varying Flow

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Uniform Depth Channel Flow01:27

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Flow Cytometry01:23

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow.

Sandro G Contaldo1, Luca Alessandri2, Iacopo Colonnelli1

  • 1Department of Computer Science, University of Turin, Turin, Italy.

Methods in Molecular Biology (Clifton, N.J.)
|December 10, 2022
PubMed
Summary
This summary is machine-generated.

This chapter details running cell subpopulation discovery algorithms on cloud high-performance computing (HPC) infrastructure using the StreamFlow framework for single-cell RNA sequencing (scRNA-seq) data analysis.

Keywords:
Cloud computingHPC environmentSingle-cell RNA sequencingStreamFlow

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of biological systems.
  • Current scRNA-seq platforms can process thousands of cells per run, generating massive datasets.
  • Efficient computational pipelines are crucial for analyzing large-scale scRNA-seq data.

Purpose of the Study:

  • To describe the efficient execution of cell subpopulation discovery algorithms on cloud-HPC infrastructure.
  • To demonstrate the integration of these algorithms within the rCASC framework.
  • To highlight the utility of the StreamFlow framework for scientific workflows.

Main Methods:

  • Utilizing microfluidic approaches for single-cell isolation.
  • Employing container-native runtime support via the StreamFlow framework.
  • Executing cell subpopulation discovery algorithms on cloud-HPC environments.

Main Results:

  • Demonstrated efficient execution of scRNA-seq analysis algorithms on cloud-HPC.
  • Showcased the integration of algorithms within the rCASC framework.
  • Validated the StreamFlow framework for cloud/HPC scientific workflows.

Conclusions:

  • Cloud-HPC infrastructure, powered by frameworks like StreamFlow, can efficiently handle large-scale scRNA-seq data analysis.
  • This approach facilitates the discovery of cell subpopulations at unprecedented resolution.
  • The described methods enhance the accessibility and scalability of advanced bioinformatics analyses.