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High-throughput microfluidics-based assays can potentially increase the speed and quality of yeast replicative lifespan measurements. One major challenge is to efficiently convert large volumes of time-lapse images into quantitative measurements of cellular lifespans. Here, we address this challenge by prototyping an algorithm that can track cellular division events through family trees of cells. We generated a null distribution using single cells inside microfluidic traps. Based on this null distribution, we prototyped a maximum likelihood algorithm for cell tracking between images at different time-points. We inferred cell family trees through a likelihood based trace-back method. The branching patterns of the cell family trees are then used to infer replicative lifespan of the yeast mother cells. The longest branch of a cell family tree represents the full trajectory of a yeast mother cell. The replicative lifespan of this mother cell can be counted as the number of bifurcating branches of this family tree. In addition, we prototyped a different approach based on summing cells area which improved the replicative lifespan estimation significantly. These generic methods have the potential to accelerate the efficiency and expand the range of quantitative measurement of yeast replicative aging experiments.
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Multiobject Tracking for Thresholded Cell Measurements
In many multiobject tracking applications, including radar and sonar tracking, after prefiltering the received signal, measurement data is typically structured in cells. The cells, e.g., represent different range and bearing values. However, conventional multiobject tracking methods use so-called point measurements. Point measurements are provided by a preprocessing stage that applies a threshold or detector and breaks up the cell’s structure by converting cell indexes into, e.g., range and bearing measurements. We here propose a Bayesian multiobject tracking method that processes measurements that have been thresholded but are still cell-structured. We first derive a likelihood function that systematically incorporates an adjustable detection threshold which makes it possible to control the number of cell measurements. We then propose a Poisson Multi-Bernoulli (PMB) filter based on the likelihood function for cell measurements. Furthermore, we establish a link to the conventional point measurement model by deriving the likelihood function for point measurements with amplitude information (AM) and discuss the PMB filter that uses point measurements with AM. Our numerical results demonstrate the advantages of the proposed PMB filter for thresholded cell measurements compared to the conventional PMB filter for point measurements with and without AM.
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- Award ID(s):
- 2146261
- PAR ID:
- 10564931
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-7377497-6-9
- Page Range / eLocation ID:
- 1 to 8
- Subject(s) / Keyword(s):
- Multiobject tracking, multitarget tracking, Poisson Multi-Bernoulli filtering, random finite sets.
- Format(s):
- Medium: X
- Location:
- Venice, Italy
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/FUSION59988.2024.10706453
