Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

On the Quantification of Cellular Velocity Fields.

ABSTRACT: The application of flow visualization in biological systems is becoming increasingly common in studies ranging from intracellular transport to the movements of whole organisms. In cell biology, the standard method for measuring cell-scale flows and/or displacements has been particle image velocimetry (PIV); however, alternative methods exist, such as optical flow constraint. Here we review PIV and optical flow, focusing on the accuracy and efficiency of these methods in the context of cellular biophysics. Although optical flow is not as common, a relatively simple implementation of this method can outperform PIV and is easily augmented to extract additional biophysical/chemical information such as local vorticity or net polymerization rates from speckle microscopy.

SUBMITTER: Vig DK

PROVIDER: S-EPMC4833777 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

On the Quantification of Cellular Velocity Fields.

Vig Dhruv K DK Hamby Alex E AE Wolgemuth Charles W CW

Biophysical journal 20160401 7

The application of flow visualization in biological systems is becoming increasingly common in studies ranging from intracellular transport to the movements of whole organisms. In cell biology, the standard method for measuring cell-scale flows and/or displacements has been particle image velocimetry (PIV); however, alternative methods exist, such as optical flow constraint. Here we review PIV and optical flow, focusing on the accuracy and efficiency of these methods in the context of cellular b ...[more]

PMID: 27074673

Similar Datasets

Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields.

Project description:BackgroundInflux and clearance of substances in the brain parenchyma occur by a combination of diffusion and convection, but the relative importance of these mechanisms is unclear. Accurate modeling of tracer distributions in the brain relies on parameters that are partially unknown and with literature values varying by several orders of magnitude. In this work, we rigorously quantified the variability of tracer distribution in the brain resulting from uncertainty in diffusion and convection model parameters.MethodsUsing the convection-diffusion-reaction equation, we simulated tracer distribution in the brain parenchyma after intrathecal injection. Several models were tested to assess the uncertainty both in type of diffusion and velocity fields and also the importance of their magnitude. Our results were compared with experimental MRI results of tracer enhancement.ResultsIn models of pure diffusion, the expected amount of tracer in the gray matter reached peak value after 15 h, while the white matter did not reach peak within 24 h with high likelihood. Models of the glymphatic system were similar qualitatively to the models of pure diffusion with respect to expected time to peak but displayed less variability. However, the expected time to peak was reduced to 11 h when an additional directionality was prescribed for the glymphatic circulation. In a model including drainage directly from the brain parenchyma, time to peak occured after 6-8 h for the gray matter.ConclusionEven when uncertainties are taken into account, we find that diffusion alone is not sufficient to explain transport of tracer deep into the white matter as seen in experimental data. A glymphatic velocity field may increase transport if a large-scale directional structure is included in the glymphatic circulation.

| S-EPMC6767654 | biostudies-literature

Smooth velocity fields for tracking climate change.

Project description:Describing the spatial velocity of climate change is essential to assessing the challenge of natural and human systems to follow its pace by adapting or migrating sufficiently fast. We propose a fully-determined approach, "MATCH", to calculate a realistic and continuous velocity field of any climate parameter, without the need for ad hoc assumptions. We apply this approach to the displacement of isotherms predicted by global and regional climate models between 1950 and 2100 under the IPCC-AR5 RCP 8.5 emission scenario, and show that it provides detailed velocity patterns especially at the regional scale. This method thus favors comparisons between models as well as the analysis of regional or local features. Furthermore, the trajectories obtained using the MATCH approach are less sensitive to inter-annual fluctuations and therefore allow us to introduce a trajectory regularity index, offering a quantitative perspective on the discussion of climate sinks and sources.

| S-EPMC8863831 | biostudies-literature

Global Neuromagnetic Cortical Fields Have Non-Zero Velocity.

Project description:Globally coherent patterns of phase can be obscured by analysis techniques that aggregate brain activity measures across-trials, whether prior to source localization or for estimating inter-areal coherence. We analyzed, at single-trial level, whole head MEG recorded during an observer-triggered apparent motion task. Episodes of globally coherent activity occurred in the delta, theta, alpha and beta bands of the signal in the form of large-scale waves, which propagated with a variety of velocities. Their mean speed at each frequency band was proportional to temporal frequency, giving a range of 0.06 to 4.0 m/s, from delta to beta. The wave peaks moved over the entire measurement array, during both ongoing activity and task-relevant intervals; direction of motion was more predictable during the latter. A large proportion of the cortical signal, measurable at the scalp, exists as large-scale coherent motion. We argue that the distribution of observable phase velocities in MEG is dominated by spatial filtering considerations in combination with group velocity of cortical activity. Traveling waves may index processes involved in global coordination of cortical activity.

| S-EPMC4783027 | biostudies-literature

Velocity fields in a collectively migrating epithelium.

Project description:We report quantitative measurements of the velocity field of collectively migrating cells in a motile epithelium. The migration is triggered by presenting free surface to an initially confluent monolayer by using a microstencil technique that does not damage the cells. To avoid the technical difficulties inherent in the tracking of single cells, the field is mapped using the technique of particle image velocimetry. The main relevant parameters, such as the velocity module, the order parameter, and the velocity correlation function, are then extracted from this cartography. These quantities are dynamically measured on two types of cells (collectively migrating Madin-Darby canine kidney (MDCK) cells and fibroblastlike normal rat kidney (NRK) cells), first as they approach confluence, and then when the geometrical constraints are released. In particular, for MDCK cells filling up the patterns, we observe a sharp decrease in the average velocity after the point of confluence, whereas the densification of the monolayer is much more regular. After the peeling off of the stencil, a velocity correlation length of approximately 200 microm is measured for MDCK cells versus only approximately 40 microm for the more independent NRK cells. Our conclusions are supported by parallel single-cell tracking experiments. By using the biorthogonal decomposition of the velocity field, we conclude that the velocity field of MDCK cells is very coherent in contrast with the NRK cells. The displacements in the fingers arising from the border of MDCK epithelia are very oriented along their main direction. They influence the velocity field in the epithelium over a distance of approximately 200 microm.

| S-EPMC2862185 | biostudies-other

Eye Velocity Gain Fields in MSTd During Optokinetic Stimulation.

Project description:Lesion studies argue for an involvement of cortical area dorsal medial superior temporal area (MSTd) in the control of optokinetic response (OKR) eye movements to planar visual stimulation. Neural recordings during OKR suggested that MSTd neurons directly encode stimulus velocity. On the other hand, studies using radial visual flow together with voluntary smooth pursuit eye movements showed that visual motion responses were modulated by eye movement-related signals. Here, we investigated neural responses in MSTd during continuous optokinetic stimulation using an information-theoretic approach for characterizing neural tuning with high resolution. We show that the majority of MSTd neurons exhibit gain-field-like tuning functions rather than directly encoding one variable. Neural responses showed a large diversity of tuning to combinations of retinal and extraretinal input. Eye velocity-related activity was observed prior to the actual eye movements, reflecting an efference copy. The observed tuning functions resembled those emerging in a network model trained to perform summation of 2 population-coded signals. Together, our findings support the hypothesis that MSTd implements the visuomotor transformation from retinal to head-centered stimulus velocity signals for the control of OKR.

| S-EPMC4494029 | biostudies-literature

Transformations Based on Continuous Piecewise-Affine Velocity Fields.

Project description:We propose novel finite-dimensional spaces of well-behaved transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization over monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available.

| S-EPMC5889303 | biostudies-other

Simulation-based inference of differentiation trajectories from RNA velocity fields.

Project description:We report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the RNA velocity of single cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories, and constructing a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study. In our analysis, we include differentiation trajectories with varying bifurcated, circular, convergent, and mixed topologies studied in single-snapshot as well as time-series single-cell RNA sequencing experiments. We demonstrate the capability to reconstruct differentiation trajectories, assess the association of RNA velocity-based pseudotime with actually elapsed process time, and identify drawbacks in current state-of-the art trajectory inference approaches.

| S-EPMC9795361 | biostudies-literature

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

Project description:For centuries, flow visualization has been the art of making fluid motion visible in physical and biological systems. Although such flow patterns can be, in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields directly from the images is challenging. We addressed this problem by developing hidden fluid mechanics (HFM), a physics-informed deep-learning framework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic to the geometry or the initial and boundary conditions. We demonstrate HFM for several physical and biomedical problems by extracting quantitative information for which direct measurements may not be possible. HFM is robust to low resolution and substantial noise in the observation data, which is important for potential applications.

| S-EPMC7219083 | biostudies-literature

Young's Experiment with Entangled Bipartite Systems: The Role of Underlying Quantum Velocity Fields.

Project description:We consider the concept of velocity fields, taken from Bohmian mechanics, to investigate the dynamical effects of entanglement in bipartite realizations of Young's two-slit experiment. In particular, by comparing the behavior exhibited by factorizable two-slit states (cat-type state analogs in the position representation) with the dynamics exhibited by a continuous-variable Bell-type maximally entangled state, we find that, while the velocity fields associated with each particle in the separable scenario are well-defined and act separately on each subspace, in the entangled case there is a strong deformation in the total space that prevents this behavior. Consequently, the trajectories for each subsystem are not constrained any longer to remain confined within the corresponding subspace; rather, they exhibit seemingly wandering behavior across the total space. In this way, within the subspace associated with each particle (that is, when we trace over the other subsystem), not only interference features are washed out, but also the so-called Bohmian non-crossing rule (i.e., particle trajectories are allowed to get across the same point at the same time).

| S-EPMC10378373 | biostudies-literature

Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields.

Project description:We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations.

| S-EPMC4891339 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data