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10 results for “Brownian Motion”
Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state (Data Underlying Figures)
<p>Data underlying figures in A. Bain 'Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state' RSC Environmental Science: Nano, 2022. </p> <p>CA = citric acid<br> NaCl = sodium chloride<br> AS = ammonium sulfate</p> <p>rho = difference in density (g/cm^3)<br> Rh = % relative humidity<br> radius is in micrometers<br> Pe0 are the calculated dimensionless Peclet numbers<br> </p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - photon timetag simulation files
<p>Attached are the photon timestamp and channels simulated for different 3D diffusing molecules simulations at different conditions (simulation was performed by PyBroMo).</p> <p>Each of the files has, in its name, a code. The meaning of the codes are as following:</p> <pre>f32445 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model </pre> <pre>a01f8f - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>9ff667 - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>71154a - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>ad926d - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>1ab235 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a numerical PSF model</pre> <pre>d00978 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a numerical PSF model</pre> <p> </p> <pre>2469bb - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model </pre> <pre>4be121 - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>a7088f - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>023983 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>653f61 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>4f06ee - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <pre>dec32c - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <p> </p> <pre>85b0a1 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Numerical PSF model</pre> <pre>964ef3 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Numerical PSF model</pre> <p> </p> <pre>f28f6e - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Gaussian PSF model</pre> <pre>c311dd - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Gaussian PSF model</pre>
Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
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Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - raw figures of all quantities from all simulations
<p>We performed many simulations in many conditions, calculated many quantities and reported them in the main paper. However, the journal manuscript did not place for so many figures. Nevertheless, the reported values of all simulations rely on the results that were also summarized in the figures. These figures were produced by the Jupyter notebooks analyzing the simulation results.</p> <p>Each figure file has the following nomenclature:</p> <p>TypeOfFigure_concentration_diffusionCoefficient_1or2pop_simulationDuration(long)_Type of PSF.</p> <p>For example:</p> <p><em>molecule_positions_pure_and_impure_bursts_varying_F_C62_D22_5_psfNumeric</em> - report on the molecular position histograms of pure and impure bursts for varying F values, in the simulation with C=62pM, D=22.5 um^2/s, a NUmerical PSF model and 60s duration of simulation (if nothing appears, it was 60s; if the word <em>long</em> appears, it was 180s).</p>
Data from: Revisiting Brownian motion as a description of animal movement: a comparison to experimental movement data
Characterization of patterns of animal movement is a major challenge in ecology with applications to conservation, biological invasions and pest monitoring. Brownian random walks, and diffusive flux as their mean field counterpart, provide one framework in which to consider this problem. However, it remains subject to debate and controversy. This study presents a test of the diffusion framework using movement data obtained from controlled experiments. Walking beetles (Tenebrio molitor) were released in an open circular arena with a central hole and the number of individuals falling from the arena edges was monitored over time. These boundary counts were compared, using curve fitting, to the predictions of a diffusion model. The diffusion model is solved precisely, without using numerical simulations. We find that the shape of the curves derived from the diffusion model is a close match to those found experimentally. Furthermore, in general, estimates of the total population obtained from the relevant solution of the diffusion equation are in excellent agreement with the experimental population. Estimates of the dispersal rate of individuals depend on how accurately the initial release distribution is incorporated into the model. We therefore show that diffusive flux is a very good approximation to the movement of a population of Tenebrio molitor beetles. As such, we suggest that it is an adequate theoretical/modelling framework for ecological studies that account for insect movement, although it can be context specific. An immediate practical application of this can be found in the interpretation of trap counts, in particular for the purpose of pest monitoring.
Raw data files for the analysis of Brownian motion of single DNA molecules
<p>Raw data files for the analysis of Brownian motion of single DNA molecules.</p>
Brownian Motion or Mean Reversion? A Parameter Based Approach to Stochastic Process Selection
<p>Data set and supporting files for article "Brownian Motion or Mean Reversion? A Parameter Based Approach to Stochastic Process Selection"</p> <ul> <li>ADF Tests log commodities SA (Excel and PDF files) – EViews outputs (18) of Augmented Dickey-Fuller tests run on all 9 Seasonally Adjusted time series (S A) with trend and intercept, and with only intercept.</li> <li>Commodities data historical x Inflation (Excel file) – Time series used in the paper in 3 versions: <ul> <li>In historical monthly averages prices US$,</li> <li>In December 2018 US$ prices (with US inflation adjustment in appropriate tab),</li> <li>In December 2018 US$ prices Seasonally Adjusted (S A) from EViews output.</li> </ul> </li> <li>commodities log SA data Ev1 (Excel file) – for the log of 9 commodities time series in Dec 2018 values, after STL decomposition in EViews: original series, seasonally adjusted, seasonal factor and trend</li> <li>commodities log SA Regressions (Excel and PDF files) - EViews outputs of OLS regression on log of seasonally adjusted times series: Dx = α + β x.</li> <li>GBMVaratAVG, GBMVaratPaths, MRMVaratAVG, MRMVaratPaths (Matlab codes) – Matlab codes for Variance ratio tests for GBM and MRM, sample paths (200) and averages of 2,500 paths.</li> <li>Series Seasonal data decomposition (PDF file) – graphical display of seasonal decomposition of log of 9 price series into: original series, trend, seasonal factor, remainder, and seasonally adjusted.</li> <li>Variance Ratio Comm SA (Excel file) - EViews outputs of variance ratio tests run of seasonal adjusted log of 9 price series in Dec 2018 values. </li> <li>ADF Tests log commodities SA (Excel and PDF files) – EViews outputs (18) of Augmented Dickey-Fuller tests run on all 9 Seasonally Adjusted time series (S A) with trend and intercept, and with only intercept.</li> <li>Commodities data historical x Inflation (Excel file) – Time series used in the paper in 3 versions: <ul> <li>In historical monthly averages prices US$,</li> <li>In December 2018 US$ prices (with US inflation adjustment in appropriate tab),</li> <li>In December 2018 US$ prices Seasonally Adjusted (S A) from EViews output.</li> </ul> </li> <li>commodities log SA data Ev1 (Excel file) – for the log of 9 commodities time series in Dec 2018 values, after STL decomposition in EViews: original series, seasonally adjusted, seasonal factor and trend</li> <li>commodities log SA Regressions (Excel and PDF files) - EViews outputs of OLS regression on log of seasonally adjusted times series: Dx = α + β x.</li> <li>GBMVaratAVG, GBMVaratPaths, MRMVaratAVG, MRMVaratPaths (Matlab codes) – Matlab codes for Variance ratio tests for GBM and MRM, sample paths (200) and averages of 2,500 paths.</li> <li>Series Seasonal data decomposition (PDF file) – graphical display of seasonal decomposition of log of 9 price series into: original series, trend, seasonal factor, remainder, and seasonally adjusted.</li> <li>Variance Ratio Comm SA (Excel file) - EViews outputs of variance ratio tests run of seasonal adjusted log of 9 price series in Dec 2018 values. </li> </ul>
Data from: Revisiting Brownian motion as a description of animal movement: a comparison to experimental movement data
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Chromatin States and their diverse protein occupancy drive genome organization in S. cerevisiae through heterogeneous Brownian motion
GEO Series GSE105075. Saccharomyces cerevisiae. 4 samples. Type: Other.
Geometric Brownian Motion of stock indexes and financial market uncertainty in the context of non-crisis and financial crisis scenarios
<p>Supplementary file for the manuscript submitted in the <em>Special Issue of <strong>Entropy</strong> “Statistical Physics and Social Sciences”.</em></p>
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