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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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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Adrenergic Receptors: ɑ Subtype01:31

Adrenergic Receptors: ɑ Subtype

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Adrenoceptors are classified into α and ꞵ classes based on their potencies to catecholamine agonists. α-adrenoceptors show the following order of catecholamine potency:
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β-adrenoceptors have varied sensitivities towards adrenaline, noradrenaline, and isoprenaline. The order of agonist potency is as follows:
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Spectroscopic and machine learning approaches for clinical subtyping in systemic sclerosis.

Bartosz Miziołek1,2, Justyna Miszczyk3, Wiesław Paja4

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Fourier-transform infrared (FTIR) spectroscopy can differentiate systemic sclerosis (SSc) subtypes. Machine learning models applied to FTIR spectra show potential for non-invasive disease stratification and biomarker discovery in SSc patients.

Keywords:
Blood biomarkersFourier-transform infrared spectroscopy (FTIR)Machine learningPrincipal Component Analysis (PCA)Systemic sclerosis (SSc)

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Area of Science:

  • Biomedical Spectroscopy
  • Immunodermatology
  • Computational Biology

Background:

  • Systemic sclerosis (SSc) is a complex autoimmune disorder with diverse clinical manifestations.
  • Current diagnostic and stratification methods for SSc can be invasive and time-consuming.
  • Identifying non-invasive tools for early disease detection and subtype classification is crucial.

Purpose of the Study:

  • To investigate the utility of Fourier-transform infrared (FTIR) spectroscopy on whole blood samples for SSc classification.
  • To explore the application of multivariate and machine learning techniques for SSc subtype differentiation.
  • To assess the potential of FTIR spectroscopy as a non-invasive tool for SSc biomarker discovery.

Main Methods:

  • Whole blood samples from SSc patients were analyzed using FTIR spectroscopy.
  • Multivariate analysis, including Principal Component Analysis (PCA), was employed to analyze spectral data.
  • Supervised machine learning models, such as Random Forest (RF), were developed for classification tasks.

Main Results:

  • FTIR spectroscopy revealed subtle but consistent spectral differences in amide I/II and lipid-associated regions.
  • PCA demonstrated clear clustering of samples, indicating distinct spectral profiles.
  • The Random Forest model achieved optimal performance in classifying diffuse versus limited SSc subtypes.

Conclusions:

  • FTIR spectroscopy combined with machine learning shows promise as a non-invasive method for SSc disease stratification.
  • This approach has the potential for biomarker discovery in systemic sclerosis.
  • Further optimization of models and spectral feature extraction is necessary for clinical implementation.