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In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
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Curvilinear Motion: Rectangular Components01:23

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Angular variables are introduced in rotational dynamics. Comparing the definitions of angular variables with the definitions of linear kinematic variables, it is seen that there is a mapping of the linear variables to the rotational ones. Linear displacement, velocity, and acceleration have their equivalents in rotational motion, which are angular displacement, angular velocity, and angular acceleration. Similar to the rotational variables, a mapping exists from Newton's second law of motion...
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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Investigating Carboxysome Morphology Dynamics with a Rotationally Invariant Variational Autoencoder.

Miguel Fuentes-Cabrera1, Jonathan K Sakkos2, Daniel C Ducat2,3

  • 1Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.

The Journal of Physical Chemistry. A
|July 26, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning with rotationally invariant variational autoencoders (rVAEs) quantitatively analyzes carboxysome dynamics in cyanobacteria. This method accelerates the study of carboxysome assembly and morphology changes over time.

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Area of Science:

  • Microbiology
  • Biophysics
  • Systems Biology

Background:

  • Carboxysomes are essential bacterial microcompartments crucial for CO2 fixation in cyanobacteria.
  • Understanding carboxysome assembly and dynamics is vital for photosynthetic metabolism.
  • Current microscopy-based analysis of carboxysome morphology is subjective and low-throughput.

Purpose of the Study:

  • To develop and apply deep learning techniques for quantitative analysis of carboxysome dynamics.
  • To evaluate the impact of carboxysome shell remodelling on microcompartment morphology over time.
  • To accelerate the study of carboxysome assembly and dynamics in response to perturbations.

Main Methods:

  • Utilized a Rotationally Invariant Variational Autoencoder (rVAE) for image analysis.
  • Employed a system to control endogenous protein levels in *Synechococcus elongatus* PCC 7942.
  • Analyzed fluorescence microscopy images of cyanobacteria with a carboxysome reporter.

Main Results:

  • rVAEs quantitatively assessed changes in carboxysome number, shape, and size.
  • Demonstrated the ability to tune carboxysomal protein levels in real-time.
  • Successfully correlated shell remodelling with morphological trends.

Conclusions:

  • rVAEs offer a powerful, high-throughput tool for analyzing carboxysome dynamics.
  • This approach can accelerate research into carboxysome assembly and response to stimuli.
  • The methodology may be applicable to other bacterial microcompartments and regulatory processes.