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Related Concept Videos

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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.
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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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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Image analysis and machine learning in digital pathology: Challenges and opportunities.

Anant Madabhushi1, George Lee1

  • 1Department of Biomedical Engineering, Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH 44106-7207, United State.

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|July 18, 2016
PubMed
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Digital pathology enables extracting sub-visual features from whole slide images for improved disease prediction. Advanced computational tools are crucial for analyzing this big data and integrating it with other omics data for precision medicine.

Keywords:
Deep learningDigital pathologyOmicsRadiology

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Area of Science:

  • Computational pathology
  • Digital pathology image analysis
  • Precision medicine

Background:

  • Whole slide imaging generates large datasets, presenting research opportunities in image computing.
  • Prognostic data is embedded in pathology images, offering potential for improved disease aggressiveness prediction.
  • Existing image analysis tools are insufficient for high-resolution digitized whole slide images.

Discussion:

  • Review of computational image analysis tools for predictive modeling in digital pathology.
  • Exploration of handcrafted and deep learning approaches for tissue classification and object detection.
  • Discussion on fusing radiology and pathology images with molecular omics data for enhanced prognostic prediction.

Key Insights:

  • Mining sub-visual features from digital pathology slides can improve quantitative disease modeling.
  • New handcrafted feature approaches enhance predictive modeling of tissue appearance.
  • Deep learning schemes show promise for object detection and tissue classification.

Outlook:

  • Development of powerful tools for combining features across multiple length scales is needed.
  • Integration of digital pathology with radiology and omics data offers significant potential for predictive modeling.
  • Overcoming technical and computational challenges is key for future quantitation of histopathology.