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42 results for “random walk”
Data in: Aging power spectrum of membrane protein transport and other subordinated random walks
<p>Datasets generated in the report "Aging power spectrum of membrane protein transport and other subordinated random walks". Included data are:</p> <p><strong>Numerical simulations </strong><br> RWdata1.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata3.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata8.mat: 5,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8.<br> RWdataCTRW.mat: 10,000 realizations, continuous time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7.</p> <p><strong>Spectra of simulations</strong><br> PSDdata1.mat: Power spectral density (PSD) of a subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata3.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata8.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8. Four different realization times are used to compute the PDS: 2^15, 2^16, 2^17, and 2^18.<br> PSDs_CTRW.mat: PSD of a continuous-time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.</p> <p><strong>Experimental data of Nav1.6 channels in the soma of hippocampal neurons</strong><br> NavMSDtimes.csv: ensemble-averaged (EA) MSD and time-averaged (TA) MSD. The TA-MSD is measured for three observation times, 64, 128, and 256 frames (3.2, 6.4, and 12.8 s).<br> NavPSD.csv: Power spectral density (PSD) measured for three observation times, 64, 128, and 256 frames.</p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Accurately modeling biased random walks on weighted networks using node2vec+ - Additional data
<p>Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks</p>
Memory Effects in a Random Walk Description of Protein Structure Ensembles
<p>This <a href="https://www.activepapers.org/">ActivePaper </a>file contains all the code and data that was used in generating the figures for the article "Memory Effects in a Random Walk Description of Protein Structure Ensembles" by Gerald R Kneller and Konrad Hinsen, J. Chem. Phys. <strong>150</strong>, 064911 (2019); <a href="https://doi.org/10.1063/1.5054887">https://doi.org/10.1063/1.5054887</a></p>
Quantifying ethnic segregation in cities through random walks
<p><strong>Overview</strong></p> <p>This repository contains the coverage time distributions used to produce the figures and statistics for the paper:</p> <p>S. Sousa, V. Nicosia "Quantifying ethnic segregation in cities through random walks". arXiv: <a href="https://arxiv.org/abs/2010.10462">https://arxiv.org/abs/2010.10462</a></p> <p><strong>Data</strong></p> <p>The <strong>ccp.zip</strong> file contains two subfolders with the coverage time distributions for the US and UK systems. Each file contains a line per node of the network with the format:</p> <pre><code>"Node ID" "[list with the CCT for each fraction c]"</code></pre> <p>Note that each line will always contain 101 columns where the first column identifies the node and the remaining ones represent the average coverage time to reach a fraction c of classes.</p> <p>The <strong>dfa.zip file</strong> contains the following folders:</p> <ul> <li><strong>distances:</strong> each line corresponds to one repetition of the walk, it shows the area travelled by the walker, the length of the trajectory and perimeter.</li> <li><strong>exponents: </strong> The output file contains two columns, respectively for \epsilon and F(\epsilon).</li> <li><strong>results_ids</strong>: Time series of the visited nodes</li> </ul> <p>The <strong>synthetic.zip</strong> file contains the coverage time distributions for the experiment with different lattice sizes (scale-test) and the experiment with distinct spatial patterns for the population distribution (topology-test). The format follows the same as in ccp.zip folder.</p> <p> </p> <p><strong>Code</strong></p> <p>The reader interested in replicating the methods used to create the data can obtain the python scrips in the following repository:</p> <p><a href="https://github.com/segregation-rw/ethnic-segregation-rw">https://github.com/segregation-rw/ethnic-segregation-rw</a></p> <p>Note that the repository also includes the code to simulate the CCT random walks on the adjacency graphs so that the whole simulation can be replicated.</p>
Data from: Effect of ecological momentary assessment, goal-setting and personalized phone-calls on adherence to interval walking training using the InterWalk application among patients with type 2 diabetes – a pilot randomized controlled trial
Objectives: The objective was to investigate the feasibility and usability of structured text-messages, goal-setting and phone-calls on adherence to a 12-week self-conducted interval walking training (IWT) program, delivered by the InterWalk smartphone among patients with type 2 diabetes (T2D). Methods: In a two-arm pilot randomized controlled trial (Denmark, March 2014 to February 2015), patients with T2D (18-80 years with a Body Mass Index of 18 and 40 kg/m2) were randomly allocated to 12 weeks of IWT with (intervention) or without additional support (control). The primary outcome was the difference between groups in accumulated time of interval walking training across 12 weeks. All patients were encouraged to use the InterWalk application to perform IWT for ≥90 minute/week. Patients in the intervention group made individual goals regarding lifestyle change, received automated text-messages once a week, inquiring about exercise adherence. In case of consistent non-adherence, the patients would receive a phone-call inquiring about the reason for non-adherence. The control group did not receive additional support. Information about training adherence was assessed objectively. Usability of structured text-messages was assessed based on response rates and self-reported satisfaction after 12-weeks. Results: Thirty-seven patients with T2D (66 years, 65% female, hemoglobin 1Ac 50.3 mmol/mol) where included (n=18 and n=19 in intervention and control group, respectively). The retention rate was 83%. The intervention group accumulated [95%CI] 345 -7, 698 minutes of IWT more than the control group. The response rate for the text-messages was 83% (68% for males and 90% for females). Forty-one percent of the intervention and 25% of the control group were very satisfied with their participation. Conclusion: The combination of structured text-messages, goal-setting with the possibility of follow-up phone calls are considered feasible interventions to attain training adherence when using the InterWalk app during a 12-week period in patients with T2D. Some uncertainty about the effect size of adherence remains.
Data from: The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States
One of the pervasive challenges in landscape genetics is detecting gene flow patterns within continuous populations of highly mobile wildlife. Understanding population genetic structure within a continuous population can give insights into social structure, movement across the landscape and contact between populations, which influence ecological interactions, reproductive dynamics, or pathogen transmission. We investigated the genetic structure of a large population of deer spanning the area of Wisconsin and Illinois, USA, affected by chronic wasting disease. We combined multi-scale investigation, landscape genetic techniques and spatial statistical modeling to address the complex questions of landscape factors influencing population structure. We sampled over 2,000 deer and used spatial autocorrelation and a spatial principal components analysis to describe the population genetic structure. We evaluated landscape effects on this pattern using a spatial auto-regressive model within a model selection framework to test alternative hypotheses about gene flow. We found high levels of genetic connectivity, with gradients of variation across the large continuous population of white-tailed deer. At the fine scale, spatial clustering of related animals was correlated with the amount and arrangement of forested habitat. At the broader scale, impediments to dispersal were important to shaping genetic connectivity within the population. We found significant barrier effects of individual state and interstate highways and rivers. Our results offer an important understanding of deer biology and movement that will help inform the management of this species in an area where over-abundance and disease spread are primary concerns.
Nonhuman Primate Center-Out and Random-Walk Reaching with Multichannel Motor Cortex Electrophysiology
<p><strong>General Description</strong></p> <p>A rhesus macaque was implanted with a 96-channel microelectrode array (Blackrock Neurotech, Inc.) in the arm area of primary motor cortex (M1). The monkey performed each reaching task with the arm contralateral to the array. We collected broadband data from each electrode at 30 kHz using a 128-channel acquisition system (Cerebus, Blackrock Neurotech, Inc.). To extract spikes, we high pass filtered the broadband data (1st order causal filter, 300 Hz cutoff) and thresholded it using a threshold manually set for each channel (average threshold = 5.2 standard deviations above mean waveform potential). To extract LFPs, we first bandpass filtered the broadband data from 0.3-500 Hz (1st order causal filter), then resampled the signal at 2 kHz, and finally notch filtered it at harmonics of 60 Hz for powerline noise removal. </p> <p>The first task we analyzed was an eight-target center-out reaching task. On each trial, the monkey began by holding at the center of a 10 cm-radius circle of targets for 0.5-0.6 s. Then, one of eight 2 cm square targets spaced at 45° intervals around the circle was illuminated. The monkey had to reach the outer target within 1.5 s and hold for a random time between 0.2-0.4s to obtain a liquid reward. The second task was a random target reaching task. On each trial, the monkey had to acquire a series of 6 randomly positioned targets appearing one-at-a-time, holding each for 0.1 s, to obtain the reward. The targets spanned the majority of the 20-by-20 pixel workspace. </p> <p><strong>Possible Uses</strong></p> <p>These data may be used for testing new BCI decoders or otherwise investigating the motor cortical electrophysiological signals (spikes, local field potentials) during reaching.</p> <p><strong>Variable Names</strong></p> <p>Inside each .mat file is a structure, traditionally called ‘bdf’, or in some cases, ‘out_struct’. We delineate important fields below: </p> <ul> <li> <p><em>bdf.units</em> contains spike timestamps </p> </li> <ul> <li> <p><em>bdf.units.id </em>is a two-element vector [channel, unit]</p> </li> <li> <p><em>bdf.units.ts </em>is an array of spike timestamps for each unit </p> </li> <li> <p>A note on resorting: The<em> bdf.units</em> structure has elements for every sorted unit, plus one element per channel for the unsorted waveforms. In the above example, that amounts to 198 elements. Generally, all these elements will have a unit ID array and some timestamps. However, the waveform data for all recorded spike waveforms in a channel will be found in only one element for that channel (the 255 unit). To illustrate, here is the bdf.units information about channel 16 in the file used to make the above structure map.</p> </li> </ul> </ul> <div> <table> <tbody> <tr> <td> <p>id </p> </td> <td> <p>ts </p> </td> <td> <p>tsAll </p> </td> <td> <p>wv </p> </td> </tr> <tr> <td> <p>[16,0] </p> </td> <td> <p>38x1 double </p> </td> <td> <p>[] </p> </td> <td> <p>[] </p> </td> </tr> <tr> <td> <p>[16,1] </p> </td> <td> <p>3438x1 double </p> </td> <td> <p>[] </p> </td> <td> <p>[] </p> </td> </tr> <tr> <td> <p>[16,255] </p> </td> <td> <p>4199x1 double </p> </td> <td> <p>7675x1 double </p> </td> <td> <p>48x7675 double </p> </td> </tr> </tbody> </table> </div> <ul> <li> <p>There are two sorted units 0 and 1, and a 255 element for unsorted waveforms. Notes: </p> </li> <ul> <li> <p><em>tsAll</em> and <em>wv</em> are only filled out for unit 255 of channel 16. </p> </li> <li> <p>The <em>tsAll</em> field, as its name implies, contains all timestamps recorded on that channel, including those for units 0 and 1, and the unsorted waveforms. A sanity check is that 38+3438+4199 = 7675.</p> </li> <li> <p>The <em>wv</em> field contains the waveforms corresponding to tsAll. Here, there are 48 points per waveform.</p> </li> <li> <p>In general, <em>tsAll</em> will accompany the highest numbered unit.</p> </li> </ul> </ul> <ul> <li> <p><em>bdf.analog.data</em> contains the local field potential (LFP) data</p> </li> <li> <p><em>bdf.analog.ts</em> contains the timestamps for the LFP data as well as the kinematic data </p> </li> <li> <p><em>bdf.pos</em> contains manipulandum position </p> </li> <li> <p><em>bdf.vel </em>contains manipulandum velocity </p> </li> <li> <p><em>bdf.acc</em> contains manipulandum acceleration</p> </li> <li> <p><em>bdf.words</em> contains information about how the run occurred, such as times of trial onset, whether the trial was a success or failure, etc </p> </li> <ul> <li> <p>For <span>center-out</span> data </p> </li> <ul> <li> <p>The <em>bdf.words</em> field can be processed by running the function: tt = co_trial_table(bdf); </p> </li> <li> <p>The numeric array tt will then have Nx10 columns, where each row is a trial and each column gives the following information (this is replicated in the documentation of the function file): </p> </li> <ul> <li> <p>1: Start time </p> </li> <li> <p>2: Bump direction -- -1 for none </p> </li> <li> <p>3: Bump phase -- H (hold), D (delay), or M (movement) </p> </li> <li> <p>4: Bump time </p> </li> <li> <p>5: Target -- -1 for none (e.g., a neutral bump) </p> </li> <li> <p>6: OT on time </p> </li> <li> <p>7: Go cue </p> </li> <li> <p>8: Movement start time </p> </li> <li> <p>9: Trial End time </p> </li> <li> <p>10: Trial result -- R (reward), A (aborted), I (incomplete), or N (no-result) </p> </li> </ul> </ul> <li> <p>For <span>random-walk</span> data </p> </li> <ul> <li> <p>In RW hand-control data, 1 trial consisted of N distinct movements, where N was usually 6 or 8. In other words, N targets were presented successively, and all required to be hit, prior to delivery of a reward. For each target presentation, there is a time-stamp in the field bdf.words. There is also a time-stamp for the event of the cursor first entering the target. This information is presented in two columns, where the first column is the timestamp and the second column is an event code, which indicates:</p> </li> <ul> <li> <p>18: trial-start event flag </p> </li> <li> <p>49: target presentation </p> </li> <li> <p>160: cursor enters target </p> </li> <li> <p>32: success </p> </li> <li> <p>33/35: failure</p> </li> </ul> </ul> </ul> </ul>
Data for "A Random Walk" manuscript
<p>"GO D-G/" includes gene-annotation bipartite graph</p> <p>"LINCS D-G/" includes gene-drug bipartite graph</p> <p>"LINCS G-G/" includes gene-gene co-expression graph for LINCS cell line data</p> <p>"scRNA G-G/" includes gene-gene co-expression graph for single-cell data</p>
Data from: Modeling of the larval response of green sea urchins to thermal stratification using a random walk approach
Larval transport in the ocean can be affected by their vertical position in the water column. In biophysical models that are often used to predict larval horizontal dispersal, generally larval vertical positions are either ignored or incorporated as static parameters. Here, we evaluate the ability of one dimensional random walk based model to predict larval vertical distribution of Strongylocentrotus droebachiensis in response to thermal stratification. Vertical swimming velocities were recorded at various temperatures and used to parameterize the model. Data from a previous laboratory study on the effects of thermal stratification on larval vertical distribution of S. droebachiensis were compared to the model results to evaluate the predictive ability of the model. The model predicts general trends in vertical distribution fairly well, but has a systematic bias which can be explained by un-quantified larval behaviors at the boundaries of the experimental water column. Overall, our behavioral model successfully reproduces the mechanism which regulates larval vertical distribution in response to thermal structure. Collectively, the findings suggest that simple behavioral models parameterized using simple lab experiments can prove useful in estimating the vertical distributions of invertebrate larvae in the laboratory and likely in the ocean. Such models can then be linked to bio-physical models to more accurately predict larval dispersal.
Plastid genome random walks
<p>A common genome composition pattern in eubacteria is an asymmetry between the leading and lagging strands resulting in opposite skew patterns in the two replichores that lie between the origin and terminus of replication. Although this pattern has been reported for a couple of isolated plastid genomes, it is not clear how widespread it is overall in this chromosome. Using a random walk approach, we examine plastid genomes outside of the land plants, which are excluded since they are known not to initiate replication at a single site, for such a pattern of asymmetry. Although it is not a common feature, we find that it is detectable in the plastid genome of species from several diverse lineages. The euglenozoa in particular show a strong skew pattern as do several rhodophytes. There is a weaker pattern in some chlorophytes but it is not apparent in other lineages. The ramifications of this for analyses of plastid evolution are discussed.</p> <p>This dataset contains the random walk plots for the genomes analyzed.</p>
Hard-soled Shoe Versus Short Leg Walking Cast for a Fifth Metatarsal Avulsion Fracture: A Randomized Multicenter Noninferiority Trial
ClinicalTrials.gov study NCT02050698. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Effectiveness of the Walking Intervention Walkadoo: A Randomized Controlled Pilot Trial in an Employee Population
ClinicalTrials.gov study NCT02229409. IPD Sharing: NO. Countries: 1. Publications: 1.
A Randomized Trial of Economic Incentives to Promote Walking Among Full Time Employees
ClinicalTrials.gov study NCT01855776. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Structured Home-based Exercise Versus Walking Advice in Claudication Patients: a Randomized-controlled Trial
ClinicalTrials.gov study NCT04751890. IPD Sharing: YES. Countries: 1. Publications: 3.
A Randomized Control Trial Treating Depression With Yoga and Coherent Breathing Versus Walking in Veterans
ClinicalTrials.gov study NCT03489122. IPD Sharing: YES. Countries: 1. Publications: 0.
A comparison of new cardiovascular endurance test using the 2-minute marching test vs. 6-minute walk test in healthy volunteers: A crossover randomized controlled trial
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Data from: Modeling of the larval response of green sea urchins to thermal stratification using a random walk approach
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Plastid genome random walks
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Data from: Effect of ecological momentary assessment, goal-setting and personalized phone-calls on adherence to interval walking training using the InterWalk application among patients with type 2 diabetes – a pilot randomized controlled trial
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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.