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Abstract The marine tintinnid ciliate Amphorides quadrilineata is a feeding-current feeder, creating flows for particle encounter, capture and rejection. Individual-level behaviors were observed using high-speed, high-magnification digital imaging. Cells beat their cilia backward to swim forward, simultaneously generating a feeding current that brings in particles. These particles are then individually captured through localized ciliary reversals. When swimming backward, cells beat their cilia forward (=ciliary reversals involving the entire ring of cilia), actively rejecting unwanted particles. Cells achieve path-averaged speeds averaging 3–4 total lengths per second. Both micro-particle image velocimetry and computational fluid dynamics were employed to characterize the cell-scale flows. Forward swimming generates a feeding current, a saddle flow vector field in front of the cell, whereas backward swimming creates an inverse saddle flow vector field behind the cell; these ciliary flows facilitate particle encounter, capture and rejection. The model-tintinnid with a full-length lorica achieves an encounter rate Q ~29% higher than that without a lorica, albeit at a ~142% increase in mechanical power and a decrease in quasi-propulsive efficiency (~0.24 vs. ~ 0.38). It is also suggested that Q can be approximated by π(W/2 + l)2U, where W, l and U represent the lorica oral diameter, ciliary length and swimming speed, respectively.
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Video Dataset of Respiratory Cilia Motion Phenotypes
Respiratory cilia are important components in the lung defense mechanism. The coordinated beating of cilia cleans the airways of pathogens and foreign particles. We present a large-scale validation dataset of cilia motion for characterizing ciliary function. Ciliary beat frequency (CBF) is provided as benchmark metrics. The video dataset of cilia motion phenotypes contains four categories: temperatures, drugs and ACE2 manipulation. Under each category, mouse trachea samples were treated with different stimuli and imaged with a high-speed video microscope to acquire cilia motion. In addition, we generate ground truth masks labeling ciliary area for image segmentation. This validation dataset can serve as a benchmark for the computer vision community to develop models for analyzing ciliary beat pattern. This video dataset contains 872 videos and their ground-truth masks with the ciliary area labeled. The videos were recorded at 250 frames per second for 1 second. The image size is 800x800. Each pixel is 0.07987 μm. The csv file contains the CBF values of each video.
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- Award ID(s):
- 1845915
- PAR ID:
- 10528496
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- Cilia motion, CBF, image segmentation
- Format(s):
- Medium: X
- Location:
- Zenodo
- Right(s):
- Creative Commons Attribution 4.0 International; Open Access
- Institution:
- University of Georgia
- Sponsoring Org:
- National Science Foundation
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