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Related Concept Videos

Line Loss01:10

Line Loss

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Major Losses in Pipes01:28

Major Losses in Pipes

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When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
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Minor Losses in Pipes01:25

Minor Losses in Pipes

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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Simplified Drop-seq workflow with minimized bead loss using a bead capture and processing microfluidic chip.

Marjan Biočanin1, Johannes Bues1, Riccardo Dainese1

  • 1Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. bart.deplancke@epfl.ch and Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Lab on a Chip
|March 29, 2019
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Summary
This summary is machine-generated.

This study enhances the Drop-seq method for single-cell RNA sequencing, improving flexibility and efficiency. Optimized droplet microfluidics reduce bead loss, making single-cell analysis more accessible.

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Area of Science:

  • Biotechnology
  • Genomics
  • Molecular Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution analysis of cellular heterogeneity.
  • Droplet microfluidics, particularly the Drop-seq platform, enables high-throughput, cost-effective scRNA-seq.
  • The original Drop-seq method faces limitations, including significant bead loss and lack of flexibility.

Purpose of the Study:

  • To systematically re-engineer and optimize the Drop-seq platform.
  • To enhance the flexibility and efficiency of droplet-based single-cell analysis.
  • To address limitations of the original Drop-seq setup, such as bead loss.

Main Methods:

  • Re-designing the dropleting device for compatibility with air-pressure systems and syringe pumps.
  • Developing an accompanying chip for post-encapsulation bead processing.
  • Implementing systematic re-engineering and optimization of the Drop-seq workflow.

Main Results:

  • The re-designed dropleting device offers increased platform flexibility.
  • The new chip significantly improves cell processing efficiency and simplifies the workflow.
  • Overall optimization leads to a more efficient and adaptable Drop-seq version.

Conclusions:

  • The optimized Drop-seq platform provides a more flexible and efficient solution for scRNA-seq.
  • These improvements aim to increase accessibility and reduce limitations in single-cell analysis.
  • The enhanced method facilitates deeper understanding of cellular heterogeneity.