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Related Concept Videos

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.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
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Active Filters01:25

Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Correlation01:09

Correlation

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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.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Subatomic Particles03:37

Subatomic Particles

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Dalton was only partially correct about the particles that make up matter. All matter is composed of atoms, and atoms are composed of three smaller subatomic particles: protons, neutrons, and electrons. These three particles account for the mass and the charge of an atom.
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Related Experiment Video

Updated: Feb 8, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter.

Osamu Hirose, Shotaro Kawaguchi, Terumasa Tokunaga

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    |July 11, 2018
    PubMed
    Summary

    We developed SPF-CellTracker, a new method for tracking many cells in 3D image sequences. This spatial particle filter approach improves accuracy by modeling correlated cell movements, reducing tracking errors.

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    Area of Science:

    • Bioimage informatics
    • Neuroscience
    • Computational biology

    Background:

    • Tracking cells in 3D time-lapse microscopy is crucial for understanding biological processes.
    • Existing methods struggle with distinguishing cells and handling correlated movements in dense populations.

    Purpose of the Study:

    • To develop an accurate and efficient multi-cell tracking method for challenging bioimage data.
    • To improve the precision of cell tracking by incorporating movement dependencies.

    Main Methods:

    • Developed SPF-CellTracker, a software suite for multi-cell tracking.
    • Modeled correlated cell movements using a Markov random field.
    • Derived a fast computation algorithm: the spatial particle filter.

    Main Results:

    • SPF-CellTracker demonstrated improved accuracy over standard particle filters.
    • The method effectively reduces cell switching and position coalescence errors.
    • Successfully tracked approximately 120 neuronal nuclei in C. elegans live-imaging data.

    Conclusions:

    • The spatial particle filter method enhances multi-cell tracking accuracy in 3D time-lapse sequences.
    • Accounting for correlated cell movements is key to reducing tracking errors.
    • SPF-CellTracker offers a robust solution for analyzing dense cell populations in neuroscience research.