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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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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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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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Related Experiment Video

Updated: Feb 2, 2026

Biophysical Characterization of Flagellar Motor Functions
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Applying machine learning to the flagellar motor for biosensing.

Tom J Zajdel, Andrew Nam, Jove Yuan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Researchers developed software to analyze Escherichia coli chemotaxis for biosensing. This enables rapid detection and differentiation of chemicals like aspartate and leucine, paving the way for new biosensor technologies.

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    Area of Science:

    • Microbiology and Biotechnology
    • Biosensor Development
    • Computational Biology

    Background:

    • Escherichia coli (E. coli) exhibits chemotaxis, navigating chemical gradients.
    • Chemotaxis performance nears theoretical biosensor limits, yet practical applications are few.
    • Developing functional biosensors from bacterial chemotaxis requires advanced data processing.

    Purpose of the Study:

    • To create software for processing digital microscope images of tethered E. coli.
    • To label and analyze chemotactic responses of multiple E. coli cells simultaneously.
    • To establish a foundation for adaptable chemotaxis-based biosensing.

    Main Methods:

    • Developed image processing software for digital microscopy of E. coli.
    • Recorded hundreds of wild-type E. coli motor responses to aspartate and leucine.
    • Trained support vector classifiers (SVC) to analyze chemotactic responses.

    Main Results:

    • A single E. coli cell classifier achieved 69% accuracy in estimating aspartate concentration (0M, 100nM, 1μM).
    • A single E. coli cell classifier achieved 83% accuracy in differentiating aspartate from leucine.
    • Population-based majority voting improved confidence: N=27 for concentration detection, N=9 for chemical differentiation (95% confidence).

    Conclusions:

    • The developed software and analysis methods enable rapid, high-throughput labeling of chemotaxis.
    • E. coli chemotaxis can be effectively utilized for quantitative chemical detection and differentiation.
    • These findings represent a significant step towards creating practical chemotaxis-based biosensors.