Understanding pollen tube growth dynamics using the Unscented Kalman Filter
Autor: | Asongu L. Tambo, Bir Bhanu, Zhenbiao Yang, Nan Luo, Nolan Ung, Ninad Thakoor |
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Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Artificial Intelligence and Image Processing Pollination Ovary (botany) Apical growth medicine.disease_cause 03 medical and health sciences Artificial Intelligence Pollen medicine Artificial Intelligence & Image Processing Tip growth Electrical and Electronic Engineering Tube (container) Mathematics Mathematical model of tip growth Statistical parameter food and beverages Kalman filter Pollen tubes 030104 developmental biology Signal Processing Cognitive Sciences Pollen tube Computer Vision and Pattern Recognition Biological system Software |
Zdroj: | Pattern Recognition Letters. 72:100-108 |
ISSN: | 0167-8655 |
DOI: | 10.1016/j.patrec.2015.07.016 |
Popis: | Development of a pollen tube growth model based on observable growth characteristics.The use of statistical techniques to estimate model parameters during an experiment.Adapting model parameters so that model tip matches observed tip in videos.Model validation using experimental videos of growing pollen tubes. In the process of pollination, a pollen tube grows from a pollen grain that has fallen on the stigma of a flower. This tube grows towards the ovary of the flower where it will deliver male reproductive material. Knowledge of the dynamics of pollen tube growth will provide a basis for understanding more complex cells that exhibit similar growth behavior. Current pollen tube growth models are a collection of differential equations that represent the level of understanding that biologists have concerning apical growth. Due to their complex nature, these models are not used to verify observed behavior in living cells as seen under a microscope. We present a model that can be used to describe the behavior of growing pollen tube cells in actual experiments. We propose biologically relevant functions based on knowledge of the growth process to explain the dynamics of model parameters. Our model uses an affine transformation to propagate the tip of the cell and statistical parameter estimation to measure necessary parameters during growth. Using experimental videos of pollen tube growth, we show that our model can adapt to various growth scenarios while extracting growth parameters from the videos. |
Databáze: | OpenAIRE |
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