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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.
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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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Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Sumeet Pal Singh1, Sharan Janjuha2,3, Samata Chaudhuri4,5

  • 1Center for Molecular and Cellular Bioengineering, TU Dresden, Dresden, 01307, Germany. sumeet_pal.singh@tu-dresden.de.

Scientific Reports
|November 23, 2018
PubMed
Summary
This summary is machine-generated.

Scientists developed GERAS (GEnetic Reference for Age of Single-cell), a machine learning tool that accurately determines cellular age from transcriptomes. This framework aids in identifying aging-related genes and opens avenues for preventive therapies against age-related diseases.

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Area of Science:

  • Cellular and Molecular Biology
  • Biotechnology
  • Computational Biology

Background:

  • Cellular aging contributes to age-related diseases, but current methods for assessing cellular age are limited.
  • Accurate detection of cellular age is crucial for developing preventive therapies against age-related conditions.

Purpose of the Study:

  • To develop a machine learning framework, GERAS (GEnetic Reference for Age of Single-cell), for accurate classification of cellular chronological age using transcriptomic data.
  • To assess the impact of factors like calorie intake and BMI on cellular aging.
  • To identify novel molecular factors associated with beta-cell aging.

Main Methods:

  • Implementation of GERAS, a machine learning framework analyzing single-cell transcriptomes.
  • Validation of GERAS accuracy (>90%) on zebrafish and human pancreatic cells.
  • Assessment of GERAS robustness against biological and technical noise.

Main Results:

  • GERAS accurately classifies the chronological stage of zebrafish and human pancreatic cells.
  • The framework identified impacts of calorie intake and BMI on cellular aging.
  • GERAS identified 'junba' as a key factor for maintaining juvenile beta-cell proliferation.

Conclusions:

  • GERAS provides a robust, accurate method for determining cellular age from transcriptomes.
  • The framework facilitates the identification of novel aging-associated genes and molecular factors.
  • This approach has significant implications for understanding and intervening in age-related diseases.