Trajectory Reconstruction
Trajectory Reconstruction - Related Videos
07:52
Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
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Cited by 27 •
2019
Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. We describe an automated version of skilled reaching with motion tracking and three-dimensional reconstruction of reach trajectories.
01:26
Orthogonal Trajectories
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2026
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Cited by 15 •
2013
A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.
04:24
Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
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Cited by 3 •
2011
We describe a protocol to observe and analyze cell rolling trajectories on asymmetric receptor-patterned substrates. The resulting data are useful for engineering of receptor-patterned substrates for label-free cell separation and analysis.
10:04
Electroporation of the Hindbrain to Trace Axonal Trajectories and Synaptic Targets in the Chick Embryo
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Cited by 9 •
2013
How neuronal networks are established in the embryonic brain is a fundamental question in developmental neurobiology. Here we combined an electroporation technique with novel genetic tools, such as Cre/Lox–plasmids and PiggyBac-mediated DNA transposition system in the avian hindbrain to label dorsal interneurons and track their axonal projections and synaptic targets at various developmental stages.
FAQs
The process links observations from successive imaging frames by matching the same cell or organism over time. Matching must account for changes in position while preserving identity, so that a sequence represents one biological entity rather than a mixture of different objects. Accurate identity linking is essential for calculating meaningful movement patterns and comparing trajectories across experimental conditions.
Sequential observations may contain incomplete locations or measurement noise, so reconstruction estimates positions that are not directly recorded while using the available observations to maintain continuity. This makes it possible to analyze motion despite imperfect imaging data. The reliability of later measurements, such as speed or turning, depends on how well the reconstructed path represents the underlying biological movement.
Reconstructed paths support quantitative measurements of movement, including speed, turning, and migration direction. These features convert linked positions into interpretable descriptions of biological behavior. Researchers can then compare how cells or organisms move under different genetic or environmental conditions, helping identify changes in migration behavior or broader behavioral dynamics.
A typical workflow begins with sequential imaging observations, followed by linking cell or organism positions across frames. The analysis then estimates missing locations and produces continuous paths for individual entities. Researchers can quantify movement features from these paths and compare the resulting patterns among experimental groups, allowing incomplete observations to support structured studies of biological dynamics.
The approach is useful when researchers need to follow movement through biological systems rather than examine isolated positions. Applications described for biology include cell migration, tissue organization, and development. By preserving movement over time, reconstructed paths can reveal how cells or organisms respond to genetic or environmental changes and how their behavior contributes to larger biological patterns.
Trajectory data provide time-resolved descriptions of where cells or organisms move and how their motion changes. In host-pathogen interactions, these paths can help examine movement patterns involving biological participants. More broadly, the measurements can guide mathematical models of biological systems by supplying observed patterns of migration, direction, speed, or turning for comparison with model behavior.