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111 results for “flow dynamics”
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
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Flow Magnetic Tweezers: Gyrase dynamics under 3 different external torque conditions. Part 1/3
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils as well as response of gyrase to external torque of 2, 4 and 8 positive magnet turns. Part 1/3</p>
Flow Magnetic Tweezers: Gyrase dynamics under 3 different external torque conditions. Part 3/3
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils as well as response of gyrase to external torque of 2, 4 and 8 positive magnet turns. Part 3/3</p>
Flow Magnetic Tweezers: Gyrase dynamics in presence of 20 uM drug Ciprofloxacin
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils in presence of 20 uM of Ciprofloxacin, reaction of Gyrase to external torque and eventual wash with SDS.</p>
Flow Magnetic Tweezers: Gyrase dynamics under 3 different external torque conditions. Part 2/3
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils as well as response of gyrase to external torque of 2, 4 and 8 positive magnet turns. Part 2/3</p>
Data from: Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons
White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response.
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Permeability and groundwater flow dynamics in deep-reaching orogenic faults estimated from regional-scale hydraulic simulations
<p>Input file and porosity and permeability datasets to run the case in Figure 3a.</p>
Dynamic light scattering differentiate parameters of blood flow
<p>This dataset demonstrates blood perfusion recordings measurements on the 3rd fingers and wrists simultaneously (sitting position) in volunteers of three groups: healthy volunteers younger group (20 years old), healthy volunteers younger group (~55 years old), patients with Diabetes Type 2 (~55 years old).</p>
Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
The dynamics of bi-directional exchange flows: implication for morphodynamic change within estuaries and sea straits
<p>Environmental and geophysical flows, including dense bottom gravity currents in the ocean and buoyancy-driven exchange flows in marginal seas,<br> are strongly controlled by topographic features.<br> These are known to exert significant influence on both internal mixing and secondary circulations generated by these flows.<br> In such cases, uni-directional or bi-directional exchange flows develop when horizontal density differences<br> and/or pressure gradients are present between adjacent water bodies connected by a submerged channel.<br> The flow dynamics of the dense lower layer depend primarily on the volumetric flux and channel cross-sectional shape,<br> while the stratified interfacial flow mixing characteristics, leading to fluid entrainment/detrainment,<br> are also dependent on the buoyancy flux and motion within the upper (lower density) water mass.<br> For submerged channels that are relatively wide compared to the internal Rossby radius of deformation,<br> Earth rotation effects introduce geostrophic adjustment of these internal fluid motions,<br> which can suppress turbulent mixing generated at the interface and result in the development of Ekman layers that induce secondary,<br> cross-channel circulations, even within straight channels.<br> Moreover, recent studies of dense, gravity currents generated in rotating and non-rotating systems,<br> respectively, indicated that the V-shaped channel topography had a strong influence on both flow distribution<br> and associated interfacial mixing characteristics along the channel.<br> However, such topographic controls on the interfacial mixing and secondary circulations generated by bi-directional exchange flows<br> are not yet fully understood and remain to be investigated thoroughly in the laboratory.<br> Also the effect of mobile bed for bi-directional exchange flows generated in deformable channels along with the physical interactions<br> between the lower dense water flow and the erodible bed sediments<br> will have a strong influence in (re-)shaping the overall channel bed topography (i.e. bed morphodynamics).<br> Consequently, the resulting temporal changes in cross-sectional channel bathymetry (i.e. through erosion and deposition processes)<br> would also be expected to have associated feedbacks on transverse asymmetries in the bi-directional exchange flow structure,<br> as well as on the internal flow stability.</p>
Data from: Linking beaver dam affected flow dynamics to upstream passage of Arctic grayling
Beaver reintroductions and beaver dam structures are an increasingly utilized ecological tool for rehabilitating degraded streams, yet beaver dams can potentially impact upstream fish migrations. We collected two years of data on Arctic grayling movement through a series of beaver dams in a low gradient mountain stream, utilizing radio-telemetry techniques, to determine how hydrology, dam characteristics, and fish attributes impeded passage and movement rates of spawning grayling. We compared fish movement between a "normal" flow year and a "low" flow year, determined grayling passage probabilities over dams in relation to a suite of factors, and predicted daily movement rates in relation to the number of dams each fish passed and distance between dams during upstream migration to spawning areas. We found that the average passage probability over unbreached beaver dams was 88%, though we found that it fell below 50% at specific dams. Upstream passage of grayling was affected by three main characteristics: 1) temperature 2) breach status and 3) hydrologic linkages that connect sections of stream above and below the dam. Other variables influence passage, but to a lesser degree. Cumulative passage varied with distance upstream and total number of dams passed in low versus normal flow years, while movement rates upstream slowed as fish swam closer to dams. Our findings demonstrate that upstream passage of fish over beaver dams is strongly correlated with hydrologic conditions with moderate controls by dam- and fish-level characteristics. Our results provide a framework that can be applied to reduce barrier effects when and where beaver dams pose a significant threat to the upstream migration of fish populations while maintaining the diverse ecological benefits of beaver activity when dams are not a threat to fish passage.
Elastic energy storage in seahorses leads to a unique suction flow dynamics compared to other actinopterygian
<p></p><p>Suction feeding is a dominant prey-capture strategy across actinopterygians, consisting of a rapid expansion of the mouth cavity that drives a flow of water containing the prey into the mouth. Suction feeding is a power-hungry behavior, involving the actuation of cranial muscles as well as the anterior third of the fish's swimming muscles. Seahorses, which have reduced swimming muscles, evolved a unique mechanism for elastic energy storage that powers their suction flows. This mechanism allows seahorses to achieve head rotation speeds that are 50 times faster than fish lacking such a mechanism. However, it is unclear how the dynamics of suction flows in seahorses differ from the conserved pattern observed across other actinopterygians, nor how differenced in snout length across seahorses affect these flows. Using flow visualization experiments, we show that seahorses generate suction flows that are 8 times faster than similar-sized fish, and that the temporal patterns of cranial kinematics and suction flows in seahorses differs from the conserved pattern observed across other actinopterygians. However, the spatial patterns retain the conserved actinopterygian characteristics, where suction flows impact a radially symmetric region of ∼1 gape diameter outside the mouth. Within seahorses, increases in snout length were associated with slower suction flows and faster head rotation speeds, resulting in a trade-off between pivot feeding and suction feeding. Overall, this study shows how the unique cranial kinematics in seahorses are manifested in their suction feeding performance, and highlights the trade-offs associated with their unique morphology and mechanics.</p><p></p>
Relative cerebral flow from dynamic PIB scans as an alternative for FDG scans in Alzheimer's disease PET studies
<p>In Alzheimer’s Disease (AD) dual-tracer positron emission tomography (PET) studies with 2-[<sup>18</sup>F]-fluoro-2-deoxy-D-glucose (FDG) and <sup>11</sup>C-labelled Pittsburgh Compound B (PIB) are used to assess metabolism and cerebral amyloid-β deposition, respectively. Regional cerebral metabolism and blood flow (rCBF) are closely coupled, both providing an index for neuronal function. The present study compared PIB-derived rCBF, estimated by the ratio of tracer influx in target regions relative to reference region (<em>R</em><sub>1</sub>) and early-stage PIB uptake (ePIB), to FDG scans. Fifteen PIB positive (+) patients and fifteen PIB negative (-) subjects underwent both FDG and PIB PET scans to assess the use of <em>R</em><sub>1 </sub>and ePIB as a surrogate for FDG. First, subjects were classified based on visual inspection of the PIB PET images. Then, discriminative performance (PIB+ versus PIB-) of rCBF methods were compared to normalized regional FDG uptake. Strong positive correlations were found between analyses, suggesting that PIB-derived rCBF provides information that is closely related to what can be seen on FDG scans. Yet group related differences between method’s distributions were seen as well. Also, a better correlation with FDG was found for <em>R</em><sub>1</sub> than for ePIB. Further studies are needed to validate the use of <em>R</em><sub>1</sub> as an alternative for FDG studies in clinical applications.</p> <p>The enclosed dataset refers to the work developed at the University Medical Center Groningen and consists of all the data retrieved from the PET images and from clinical assessment used in this work.</p>
Surge-induced Crevasse dynamics of Monacobreen Glacier, Svalbard, through SAR observations and subglacial flow estimations
<p>This repository contains the datasets of glacial surface velocity, subsurface velocities, and the basal shear stress published in the paper 'Surge-induced Crevasse dynamics of Monacobreen Glacier, Svalbard, through SAR observations and subglacial flow estimations' in AGU Earth and Space Sciences. The detailed methodological description along with the computational methods have been discussed in detail in the article.</p>
Dataset on flow dynamics in rivers with riffle-pool morphology: results from case studies and field experiments on the Tagliamento River, Italy
<p>Riffle-pool sequences in rivers, formed due to interactions between river flow, alluvium and vegetation, provide vital ecological services to aquatic organisms and therefore are considered as fundamental habitats in fluvial ecosystems. Nevertheless, the knowledge of associated riffle-pool hydrodynamics is limited because of a lack of high-resolution data collected in rivers and scaling effects present in laboratory studies. Here we present a dataset on turbulent flow structure in riffle-pool sequences of a natural river. Two case studies and two field-based experiments were carried out in a side branch of the braided gravel-bed Tagliamento River in Italy. Our case studies deliver detailed information about the there-dimensional structure of mean and turbulent flows in natural riffle-pool/run and pool-riffle/glide transitions. Field-based experiments completed with the in-stream flume models of a riffle-pool transition and a shallow jet model provide a methodological bridge for linking simplified hydrodynamic theories of shallow jets to complex flow structure documented by our case studies. Therefore, this dataset enables examination of scaling effects and can be widely used for validation of numerical models.</p> <p> </p>
Wave, Flow, and Sediment Dynamics under Strong Winds on a tidal beach
<p>The data are saved as matlab data file.</p> <p>1) ssc_fit.mat is used for producing figure 2.</p> <p>2) Reynolds_shear_stress.mat is used for producing figures 3 and 10.</p> <p>3) hydrodynamics.mat is used for producing figure 4.</p> <p>4) shear_stress_&_ssc.mat is uesd for producing figures 5 and 8.</p> <p>5) SSF.mat is used for producing figures 6 and 11.</p> <p>6) breaking_wave_criteria.mat is used for producing figure 7.</p> <p>7) mob_number.mat is used for producing figure 9.</p>
Data underpinning "Unraveling long-time quantum dynamics using flow equations"
<p>The study of many-body quantum dynamics in strongly-correlated systems is extremely challenging. To date few numerical methods exist which are capable of simulating the non-equilibrium dynamics of two-dimensional quantum systems, in part reflecting complexity theoretic obstructions. In this work, we present a new technique able to overcome this obstacle, by combining continuous unitary flow techniques with the newly developed method of scrambling transforms. We overcome the prejudice that approximately diagonalizing the Hamiltonian cannot lead to reliable predictions for relatively long times. To the contrary, we show that the method works well in both localized and delocalized phases, and makes reliable predictions for a number of quantities including infinite-temperature autocorrelation functions. We complement our findings with rigorous incremental bounds on the truncation error. This approach shows that in practice, the exploration of intermediate-scale time evolution may be more feasible than is commonly assumed, challenging near-term quantum simulators.</p>
A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil Dataset
<p>This dataset contains the reference solutions used for training the two neural networks in 1-, 2- and 3-D cases for the article:"A Data-facilitated Numerical Method for Richards Equation to Model Water Flow Dynamics in Soil" by Zeyuan Song and Zheyu Jiang, submitted to the journal Water Resources Research. </p> <p>This dataset which describes the relationship between the pressure head and number of particles used to train two MLPs in D-GRW based solvers consists of three files, i.e., 1-, 2- and 3-D case study. There are two parts, original reference solutions and reference solutions, corresponding to the original solutions generated by coarse mesh solvers and solutions after data augmentation process, respectively.The dataset is generated by GRW based solvers and simulation results (e.g., Celia's finite difference method). Original reference solutions admit GRW proportionality assumption. We initialize the number of particles by multiplying the initial condition and 1E10. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.