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Related Concept Videos

Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Machines01:19

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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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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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Machines: Problem Solving I01:22

Machines: Problem Solving I

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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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Mechanical Protein Functions01:58

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Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force. 
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Reaction Mechanisms03:06

Reaction Mechanisms

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Chemical reactions often occur in a stepwise fashion, involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs.
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Uncovering EMT-Associated Molecular Mechanisms Through Integrative Transcriptomic and Machine Learning Analyses.

Şehriban Büyükkılıç1,2, Hani Alotaibi1,2, Alexandros G Georgakilas3

  • 1Izmir Biomedicine and Genome Center, 35340 Izmir, Türkiye.

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Summary
This summary is machine-generated.

Epithelial-mesenchymal transition (EMT) drives normal development and cancer. This study reveals distinct gene expression patterns in cancer EMT, including neural programs, and identifies potential biomarkers for tumor invasion.

Keywords:
axonogenesisbiomarker discoverycancercell plasticityepithelial–mesenchymal transitiongene expression profilinggliogenesismachine learningneurogenesis

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Area of Science:

  • Molecular Biology
  • Developmental Biology
  • Cancer Biology

Background:

  • Epithelial-mesenchymal transition (EMT) is crucial for development and cancer progression.
  • EMT drives tumor invasion and metastasis, but differences between physiological and cancer EMT are unclear.

Purpose of the Study:

  • To delineate transcriptional differences between physiological and cancer-associated EMT.
  • To identify and prioritize genes and pathways involved in EMT.
  • To discover potential EMT biomarkers.

Main Methods:

  • Integrative analysis of 89 mouse RNA-sequencing datasets.
  • Transcriptomic profiling, functional enrichment analysis, and machine learning.
  • Comparison of normal and malignant contexts.

Main Results:

  • Shared pathways in normal and cancer EMT include cell adhesion and morphogenesis.
  • Cancer EMT uniquely shows enrichment for neural development programs (neurogenesis, gliogenesis).
  • Machine learning identified candidate EMT biomarkers with higher heterogeneity in cancer.

Conclusions:

  • Distinct transcriptional profiles differentiate malignant from physiological EMT.
  • Neural program enrichment in cancer EMT suggests mechanisms for cellular plasticity.
  • Candidate biomarkers for EMT heterogeneity were identified.