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Related Concept Videos

Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery.

Hannah G Polikowsky1, Katherine A Drake2

  • 1Cytobank, Inc, Santa Clara, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 12, 2019
PubMed
Summary
This summary is machine-generated.

CITRUS is a supervised machine learning algorithm for analyzing single-cell data. It identifies cell populations and significant changes associated with outcomes, optimizing analysis with data tools.

Keywords:
Biomarker discoveryCITRUSSupervised machine learningviSNE

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in immunology

Background:

  • Single-cell analysis generates high-dimensional data.
  • Identifying cell populations and their characteristics is crucial for understanding biological systems.
  • Supervised machine learning offers a powerful approach for complex biological data analysis.

Purpose of the Study:

  • To introduce CITRUS, a supervised machine learning algorithm for single-cell data analysis.
  • To detail the steps involved in CITRUS: clustering, characterization, and identification of significant features.
  • To provide guidance on optimizing CITRUS through integration with data analysis and visualization tools.

Main Methods:

  • CITRUS employs a supervised machine learning framework.
  • The algorithm includes unsupervised clustering to define cell populations.
  • It characterizes populations and identifies significant markers or frequency changes associated with an outcome.

Main Results:

  • CITRUS can effectively identify distinct cell populations within complex single-cell datasets.
  • The algorithm pinpoints specific cellular characteristics or frequency shifts linked to a defined outcome.
  • Optimization strategies enhance the interpretability and utility of CITRUS findings.

Conclusions:

  • CITRUS provides a robust method for dissecting single-cell data and identifying outcome-associated cell populations.
  • Integrating CITRUS with complementary analysis and visualization tools maximizes its potential.
  • This approach facilitates deeper insights into cellular heterogeneity and its role in biological processes.