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Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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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.
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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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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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Related Experiment Video

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Correlative analysis between ocular surface features and carotid plaque : A multimodal machine learning framework.

Shichen Zhang1, Dinghan Hu1, Le Luo2

  • 1Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, 310018, China; School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, China.

Computer Methods and Programs in Biomedicine
|February 1, 2026
PubMed
Summary
This summary is machine-generated.

Ocular surface imaging offers a non-invasive method for detecting carotid plaques, a key indicator of cardiovascular disease. This study found strong associations between eye image features and plaque presence, aiding early disease screening.

Keywords:
Carotid plaque assessmentCorrelation analysisMachine learningOcular surface image

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Area of Science:

  • Ophthalmology and Cardiovascular Health
  • Medical Imaging and Diagnostics
  • Biomedical Engineering

Background:

  • Carotid plaque diagnosis is crucial for identifying cardiovascular and cerebrovascular diseases.
  • Current diagnostic methods like carotid ultrasound are time-consuming, radiative, expensive, and limit disease progression tracking.
  • There is a need for accessible, non-invasive methods for carotid plaque screening and monitoring.

Purpose of the Study:

  • To investigate the association between carotid plaque and ocular surface image features.
  • To develop a non-invasive screening method for carotid plaques using ocular imaging.
  • To explore the potential of ocular surface image analysis in cardiovascular health assessment.

Main Methods:

  • A multi-dimensional feature analysis of ocular surface images, including texture, frequency domain, and color characteristics.
  • Feature selection, confidence evaluation, and distribution property studies to establish robust associations.
  • Machine learning classifiers and subgroup validation (age, gender) to assess feature robustness and predictive performance.

Main Results:

  • High prediction accuracy achieved in a cohort of 8875 individuals.
  • Electronic health record (EHR) features showed the strongest association with carotid plaque (ORs: 4.35 in males, 2.92 in females).
  • Ocular surface image features (EHR, LBP, GLGCM, GLCM), age, and male gender were strongly associated with carotid plaque.

Conclusions:

  • Ocular surface image analysis provides a practical and non-invasive approach for carotid plaque screening.
  • The identified feature associations and predictive performance support clinical applications, particularly for large-scale population screening.
  • This method has the potential to complement existing diagnostic tools for cardiovascular risk assessment.