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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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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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In biological systems, most metabolic pathways are interconnected. The cellular respiration processes that convert glucose to ATP—such as glycolysis, pyruvate oxidation, and the citric acid cycle—tie into those that break down other organic compounds. As a result, various foods—from apples to cheese to guacamole—end up as ATP. In addition to carbohydrates, food also contains proteins and lipids—such as cholesterol and fats. All of these organic compounds are used...
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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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EEG connectivity features associated with fibromyalgia revealed by machine learning.

Jean Li1, Jeremiah D Deng2, Divya Adhia1

  • 1Department of Surgery and Critical Care, University of Otago, Dunedin, New Zealand.

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Machine learning identified electroencephalography (EEG) connectivity features that detect fibromyalgia with 99.57% accuracy. These brain-based markers offer new avenues for diagnosis and treatment of fibromyalgia.

Keywords:
AIEEGfibromyalgiamachine learningpain

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Fibromyalgia is a complex chronic pain disorder with unclear brain mechanisms.
  • Objective diagnostic markers for fibromyalgia are lacking, necessitating reliance on subjective patient reporting.

Purpose of the Study:

  • To identify objective, connectivity-based electroencephalography (EEG) features for diagnosing fibromyalgia.
  • To leverage machine learning for data-driven discovery of fibromyalgia biomarkers from EEG data.

Main Methods:

  • Utilized a data-driven approach with machine learning on raw EEG signals from 463 participants.
  • Performed moderate pre-processing, extracted spectral connectivity features, and applied feature importance analyses.
  • Validated the identified features using an independent dataset of 48 participants.

Main Results:

  • Five specific gamma-band EEG connectivity features (Fz-Cz, Pz-P4, Fz-C3, Cz-P4, Cz-Pz) accurately detected fibromyalgia.
  • Achieved a diagnostic accuracy of 99.57% in distinguishing fibromyalgia patients from healthy controls.
  • Demonstrated the generalizability of these features across different EEG acquisition devices.

Conclusions:

  • EEG-based functional connectivity analysis, powered by machine learning, provides a highly accurate method for fibromyalgia detection.
  • These findings offer novel insights into the neurophysiological underpinnings of fibromyalgia.
  • The identified biomarkers hold potential for future non-invasive neuromodulation and neurofeedback therapies for fibromyalgia.