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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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Support Reactions in Three Dimensions01:27

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Support reactions in three dimensions help maintain the stability and equilibrium of various structures and systems. These reactions prevent the system from translating and rotating, ensuring the design can withstand external forces and perform its intended function efficiently and safely. Some of the supports providing support reactions in three dimensions are discussed below:
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The understanding of the concept of reference frames is essential to discuss relative motion in one or more dimensions. When we say that an object has a certain velocity, we must state the velocity with respect to a given reference frame. In most examples, this reference frame has been Earth. For instance, if a statement reads that a person is sitting in a train moving at 10 m/s east, then it implies that the person on the train is moving relative to the surface of Earth at this velocity,...
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Machines: Problem Solving II01:30

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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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Relative Velocity in Two Dimensions01:11

Relative Velocity in Two Dimensions

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Relative velocity is the velocity of an object as observed from a particular reference frame, or the velocity of one reference frame with respect to another reference frame. The concept of relative velocity can be used to describe motion in two dimensions. Consider a particle P and two reference frames S and S′. The position of the origin of S′ as measured in S is , the position of P as measured in S′ is , and the position of P as measured in S is , which can be evaluated by utilizing...
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Dimensions of Health and Illness01:21

Dimensions of Health and Illness

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The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
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Related Experiment Video

Updated: Feb 15, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions.

Eoghan O'Duibhir1, Jasmin Paris1, Hannah Lawson1

  • 11 Centre for Regenerative Medicine, University of Edinburgh , Edinburgh, United Kingdom .

Assay and Drug Development Technologies
|January 19, 2018
PubMed
Summary

Machine learning enhances brightfield microscopy for leukemia research. This novel platform analyzes leukemic cell growth and differentiation in 3D cultures for drug discovery and diagnostics.

Keywords:
3Depigenetichigh contentleukemiamachine learningphenotypic

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Drug Discovery

Background:

  • Traditional microscopy limits brightfield image analysis.
  • Machine learning (ML) offers new capabilities for image segmentation and object classification.
  • Label-free assays are crucial for studying cell growth and differentiation.

Purpose of the Study:

  • To develop a novel platform for phenotypic drug discovery using ML and label-free assays.
  • To analyze leukemic colony growth and differentiation in 3D cultures.
  • To identify novel drug-induced phenotypes.

Main Methods:

  • Utilized supervised ML for identifying in-focus colonies in 3D methylcellulose gels.
  • Applied unsupervised clustering and principal component analysis (PCA) for phenotype classification.
  • Integrated ML with a label-free assay for leukemic cell analysis.

Main Results:

  • Successfully identified a novel phenotype induced by a leukemia drug candidate.
  • Demonstrated the platform's ability to analyze complex phenotypic profiles.
  • Validated the use of ML for classifying cellular phenotypes in 3D cultures.

Conclusions:

  • The developed platform enables advanced analysis of brightfield images for phenotypic drug discovery.
  • This approach is beneficial for patient-derived 3D cell culture systems.
  • The platform holds potential for both drug discovery and diagnostic applications in leukemia.