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1,067 results for “perturbation”
Data and code for paper "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning"
<p>Simulation results from the NOAA/SWPC Geospace model used in the paper Camporeale et al. (2020) "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning" published in J. Geophys. Res. (2020)</p> <p>MATLAB code is provided to train process the data and train the machine learning model and plot results.</p> <p>Manuscript available on <a href="https://arxiv.org/abs/1912.01038">https://arxiv.org/abs/1912.01038</a></p>
Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities: Further Appendices
<p>Further appendices for <a href="https://arxiv.org/abs/2001.11055">Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities</a>.</p> <p>Includes:</p> <ol> <li>imagenet_examples_appendix.pdf, which contains many examples of context-sensitive ImageNet feature perturbations of different granularities.</li> <li>animations_appendix.zip, which contains animations showing the effect as the feature perturbations are gradually introduced to different images.</li> </ol> <p>Each appendix includes a much more detailed description of its contents.</p>
Experimental parasite community perturbation reveals associations between Sin Nombre virus and gastrointestinal nematodes in a rodent reservoir host
<p>Individuals are often co-infected with several parasite species, yet measuring within-host interactions remains difficult in the wild. Consequently, the impact of such interactions on host fitness and epidemiology are often unknown. We used anthelmintic drugs to experimentally reduce nematode infection and measured the effects on both nematodes and the important zoonosis Sin Nombre virus (SNV) in its primary reservoir (<i>Peromyscus spp.</i>). Treatment significantly reduced nematode infection, but increased SNV seroprevalence. Furthermore, mice that were co-infected with both nematodes and SNV were in better condition and survived up to four times longer than uninfected or singly-infected mice. These results highlight the importance of investigating multiple parasites for understanding interindividual variation and epidemiological dynamics in reservoir populations with zoonotic transmission potential.</p>
Heterothermy as a mechanism to offset energetic costs of environmental and homeostatic perturbations
<p>Environmental and biotic pressures impose homeostatic costs on all organisms. The energetic costs of maintaining high body temperatures (<i>T</i><sub>b</sub>) render endotherms sensitive to pressures that increase foraging costs. In response, some mammals become more heterothermic to conserve energy. We measured <i>T</i><sub>b</sub> in banner-tailed kangaroo rats (<i>Dipodomys spectabilis</i>) to test and disentangle the effects of air temperature and moonlight (a proxy for predation risk) on thermoregulatory homeostasis. We further perturbed homeostasis in some animals with chronic corticosterone (CORT) via silastic implants. Heterothermy increased across summer, consistent with the predicted effect of lunar illumination (and predation), and in the direction opposite to the predicted effect of environmental temperatures. The effect of lunar illumination was also evident within nights as animals maintained low <i>T</i><sub>b</sub> when the moon was above the horizon. The pattern was accentuated in CORT-treated animals, suggesting they adopted an even further heightened risk-avoidance strategy that might impose reduced foraging and energy intake. Still, CORT-treatment did not affect body condition over the entire study, indicating kangaroo rats offset decreases in energy intake through energy savings associated with heterothermy. Environmental conditions receive the most attention in studies of thermoregulatory homeostasis, but we demonstrated here that biotic factors can be more important and should be considered in future studies.</p>
Prioritization of cell types responsive to biological perturbations in single-cell data with Augur
<p>Processed and analysis-ready input data discussed in our Augur procedures.</p>
Data from: History-dependent perturbation response in limb muscle
<p>Muscle mediates movement but movement is typically unsteady and perturbed. Muscle is known to behave non-linearly and with history dependent properties during steady locomotion, but the importance of history dependence in mediating muscles function during perturbations remains less clear. To explore muscle's capacity to mitigate perturbations during locomotion, we constructed a series of perturbations that varied only in kinematic history, keeping instantaneous position, velocity and time from stimulation constant. We find that muscle's perturbation response is profoundly history dependent, varying by four fold as baseline frequency changes, and dissipating energy equivalent to ~6 times the kinetic energy of all the limbs in 5 ms (nearly 2400 W Kg<sup>-1</sup>). Muscle's energy dissipation during a perturbation is predicted primarily by the force at the onset of the perturbation. This relationship holds across different frequencies and timings of stimulation. This history dependence behaves like a viscoelastic memory producing perturbation responses that vary with the frequency of the underlying movement. </p>
Commanded Treadmill Motions for Perturbation Experiments
<p>Sample commanded belt speed and lateral motion time histories generated from Simulink.</p>
A sharper view of Pal 5's tails: Discovery of stream perturbations with a novel non-parametric technique
<p>Here we release some data on the Pal 5 stellar stream from the arXiv:1609.01282 paper.</p> <p> The file pal5_measurements.fits stores the Pal 5 stream measurements , as shown on Figure 5 of the paper, with the following columns</p> <ul> <li>phi1 Angle along the stream in degrees ( the coordinate system is defined in the paper)</li> <li>phi2 Angle across the stream in degrees</li> <li>sbstream Central surface brightness of the stream in stars per square arcmin</li> <li>ldens Linear density of the stream in stars/arcmin</li> <li>cumdens Cumulative density of the (leading/trailing) tail from the progenitor</li> <li>width Gaussian sigma of the stream in degrees</li> <li>bgdens density of background stars</li> </ul> <p>The columns with _16, _50, _84 appended to their name refer to the 16%, 50% 84% percentiles inferred from the posterior samples.</p> <p>We also provide the MCMC chains that could be used to measure the covariance matrices of different parameters:</p> <p>The chains are stored in the fits file with the columns</p> <p>phi2_i, logI_j, logSig_j, logB0_j, logB1_j referring to the stream track values at the nodes, log of stream surface brightness, log of stream width, log of the background density and log of background density slope (see paper for more details).</p> <p> </p> <p> </p>
Perturbed Synthetic SWOT Datasets for Testing and Development of a Kalman Filter Approach to Estimate Daily Discharge
<p><strong>1. Introduction</strong></p> <p>Datasets are used to evaluate the performance of a Kalman filter approach to estimate daily discharge. This is a perturbed version of synthetic SWOT datasets consisting of 15 river sections, which are commonly agreed datasets for evaluating the performance of SWOT discharge algorithms (Frasson et al., 2020, 2021). The benchmarking manuscript entitled “A Kalman Filter Approach for Estimating Daily Discharge Using Space-based Discharge Estimates” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p> </p> <p><strong>2. </strong><strong>File description</strong></p> <p>The datasets are generally divided into two categories: river information (River_Info) and time series data (Timeseries_Data). River information provides fundamental and general river characteristics, whereas time series data offers daily reach-averaged data for each reach. In time series data, the data mainly contains three components: true data, perturbed measurements, and true and perturbed flow law parameters (A0, an, and b). For each reach, there are 10000 realizations of perturbed measurements per time step and there are 100 realizations of time-invariant perturbed flow law parameters through a Monte Carlo simulation (Frasson et al., 2023). Moreover, to support our proposed Kalman filter approach to estimate daily discharge, the datasets provide the median of the perturbed discharge, river width, water surface slope, and change in the cross-sectional area, as well as the uncertainty of the perturbed discharge and change in the cross-sectional area based on the interquartile range (Fox, 2015).</p> <p>To support reproducibility and facilitate example usage, we now include a MATLAB code package (<code>KalmanFilter_Code.zip</code>) that demonstrates how to run the Kalman filter approach using the Missouri Downstream case as an example. </p> <p>Datasets are contained in a .mat file per river. The detailed groups and variables are in the following:</p> <p><strong>River_Info</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Good reaches in the study. They were used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>Timeseries_Data</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1, time step.</p> <p>A: Reach-averaged cross-sectional area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>Q_true: True reach-averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>Q_ptb: Perturbed discharge (m<sup>3</sup>/s), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_Q_ptb: Median perturbed discharge (m<sup>3</sup>/s) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_Q_ptb: Uncertainty of the perturbed discharge (m<sup>3</sup>/s), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>W_true: True reach-averaged river width (m). Dimension: Reach, time step.</p> <p>W_ptb: Perturbed river width (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_W_ptb: Median perturbed river width (m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>H_true: True reach-averaged water surface elevation (m). Dimension: Reach, time step.</p> <p>H_ptb: Perturbed water surface elevation (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>S_true: True reach-averaged water surface slope (m/m). Dimension: Reach, time step.</p> <p>S_ptb: Perturbed water surface slope (m/m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_S_ptb: Median perturbed water surface slope (m/m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>dA_true: True reach-averaged change in the cross-sectional area (m<sup>2</sup>). Dimension: Good reach, time step.</p> <p>dA_ptb: Perturbed change in the cross-sectional area (m<sup>2</sup>), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_dA_ptb: Median perturbed change in the cross-sectional area (m<sup>2</sup>) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_dA_ptb: Uncertainty of the perturbed change in the cross-sectional area (m<sup>2</sup>), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>A0_true: True baseline cross-sectional area (m<sup>2</sup>). Dimension: Good reach, 1.</p> <p>A0: Perturbed baseline cross-sectional area (m<sup>2</sup>), including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>na_true: True friction coefficient. Dimension: Good reach, 1.</p> <p>na: Perturbed friction coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>b_true: True exponent coefficient. Dimension: Good reach, 1.</p> <p>b: Perturbed exponent coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p>
Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels
<pre>This repository contains data related to: Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels Amanda J. Lea, Julie Peng, Julien F. Ayroles A preprint of this work can be found here: https://www.biorxiv.org/content/10.1101/2021.11.04.467311v2 Specifically, the filtered, normalized, and batch corrected gene expression data file (31Mar21_all_runs_voom_resid.txt) is provided along with the metadata. We also provide the output from matrix eQTL that was used as input for mashR. Scripts used to generate and analyze these data are provided here: https://github.com/AmandaJLea/LCLs_gene_exp</pre>
Multiplexed single-cell characterization of alternative polyadenylation regulators (HEK293FT & K562 Perturb-seq data)
<p>This site provides access to datasets from the CPA-Perturb-seq <a href="https://www.biorxiv.org/content/10.1101/2023.02.09.527751v1">manuscript</a> Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the <a href="https://github.com/satijalab/PASTA">PASTA</a> (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells. </p> <p>The files include:</p> <p>1. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset </p> <p>2. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset </p> <p>3. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset </p> <p> </p> <p>R code below:</p> <pre><code>library(PASTA) k562 <- readRDS("CPA_K562.Rds") # Add fragments for plotting Fragments(k562) <- CreateFragmentObject(path = "download/CPA_K562_blocks.tsv.gz", cells = Cells(k562)) # visualize polyA site usage PolyACoveragePlot(k562, region ="chr7-26212195-26213351")</code></pre>
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
Open the record for dataset details and reuse information.
Multi-process driven unusually large equatorial perturbation electric fields during the April 2023 geomagnetic storm
<p>Dataset of the article entitled "Multi-process driven unusually large equatorial perturbation electric fields during the April 2023 geomagnetic storm" submitted to <a href="https://www.frontiersin.org/journals/astronomy-and-space-sciences">Frontiers in Astronomy and Space Sciences</a>. The data include the outputs of simulations from the four empirical vertical drift models used in the article: Fejer and Scherliess (1997), Scherliess and Fejer (1999), Kelley and Retterer (2008), and Manoj and Maus (2012).</p>
An LES perturbed parameter ensemble of free-tropospheric cloud-controlling factors on stratocumulus
<p>This dataset contains a perturbed parameter ensemble of large-eddy simulations to assess the effect of two free-tropospheric cloud-controlling factors on stratocumulus clouds properties. The simulations were run on the UK Met Office/NERC cloud model (MONC) for the DYCOMS-II RF01 nocturnal stratocumulus case (Stevens et. al., 2005). Each simulation had the same initial conditions except for the two perturbed parameters, which were the jumps in moisture and temperature at the temperature inversion at cloud top. The corresponding analysis code can be found at this <a href="https://github.com/eers1/dycoms_analysis">dycoms_analysis Github page</a>. </p><p> </p><p>Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S., . . . Zhu, P. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review , 133 (6), 1443–1462. doi: 10.1175/MWR2930.1</p>
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
<p>Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of diseasecompound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.</p>
Data used in the publication: Sensitivity of modeled microphysics to stochastically perturbed parameters
<p>These data support the results presented in the manuscript titled "Sensitivity of modeled microphysics to stochastically perturbed parameters". They consist of results from an idealized single vertical column atmospheric model run for a number of experiments that explore methods of representing model uncertainty. </p>
Temperature perturbation of cellular host-microbe interactions explains continent-wide endosymbiont prevalence
<p>Endosymbioses influence host physiology, reproduction, and fitness, but these relationships require efficient microbe transmission between host generations to persist. Maternally transmitted <i>Wolbachia</i> are the most common known endosymbionts, but their frequencies vary widely within and among host populations for unknown reasons. Here we integrate genomic, cellular, and phenotypic analyses with mathematical models to provide an unexpectedly simple explanation for global <i>w</i>Mel <i>Wolbachia</i> prevalence in <i>Drosophila melanogaster</i>. Cooling temperatures decrease <i>w</i>Mel cellular abundance at a key stage of host oogenesis, producing temperature-dependent variation in maternal transmission that plausibly explains latitudinal clines of <i>w</i>Mel frequencies on multiple continents. <i>w</i>Mel sampled from a temperate climate targets the germline more efficiently in the cold than a recently differentiated tropical variant (~2,200 years ago), indicative of rapid <i>w</i>Mel adaptation to climate. Genomic analyses identify a very narrow list of <i>w</i>Mel alleles—most notably, a derived stop codon in the major <i>Wolbachia</i> surface protein WspB—that underlie thermal sensitivity of cellular<i>Wolbachia</i> abundance and covary with temperature globally. Decoupling temperate <i>w</i>Mel and host genomes further reduces transmission in the cold, a pattern that is characteristic of host-microbe co-adaptation to a temperate climate. Complex interactions among <i>Wolbachia</i>, hosts, and the environment (GxGxE) mediate <i>w</i>Mel cellular abundance and maternal transmission, implicating temperature as a key determinant of <i>Wolbachia</i> spread and equilibrium frequencies, in conjunction with <i>Wolbachia</i> effects on host fitness and reproduction. Our results motivate strategic use of locally selected <i>w</i>Mel variants for <i>Wolbachia</i>-based biocontrol efforts, which currently protect millions of individuals from arboviruses that cause human disease.</p>
Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
<p>This contains data described in detail in our paper, "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery", where we develop a novel algorithm called RPath that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures.</p>
Data from: Kinematic trajectories in response to speed perturbations in walking suggest modular task-level control of leg angle and length
Abstract Navigating complex terrains requires dynamic interactions between the substrate, musculoskeletal and sensorimotor systems. Current perturbation studies have mostly used visible terrain height perturbations, which do not allow us to distinguish among the neuromechanical contributions of feedforward control, feedback-mediated and mechanical perturbation responses. Here, we use treadmill belt speed perturbations to induce a targeted perturbation to foot speed only, and without terrain-induced changes in joint posture and leg loading at stance onset. Based on previous studies suggesting a proximo-distal gradient in neuromechanical control, we hypothesized that distal joints would exhibit larger changes in joint kinematics, compared to proximal joints. Additionally, we expected birds to use feedforward strategies to increase the intrinsic stability of gait. To test these hypotheses, seven adult guinea fowl were video recorded while walking on a motorized treadmill, during both steady and perturbed trials. Perturbations consisted of repeated exposures to a deceleration and acceleration of the treadmill belt speed. Surprisingly, we found that joint angular trajectories and center of mass fluctuations remain very similar, despite substantial perturbation of foot velocity by the treadmill belt. Hip joint angular trajectories exhibit the largest changes, with the birds adopting a slightly more flexed position across all perturbed strides. Additionally, we observed increased stride duration across all strides, consistent with feedforward changes in the control strategy. The speed perturbations mainly influenced the timing of stance and swing, with the largest kinematic changes in the strides directly following a deceleration. Our findings do not support the general hypothesis of a proximo-distal gradient in joint control, as distal joint kinematics remain largely unchanged. Instead, we find that leg angular trajectory and the timing of stance and swing are most sensitive to this specific perturbation, and leg length actuation remains largely unchanged. Our results are consistent with modular task-level control of leg length and leg angle actuation, with different neuromechanical control and perturbation sensitivity in each actuation mode. Distal joints appear to be sensitive to changes in vertical loading but not foot fore-aft velocity. Future directions should include in vivo studies of muscle activation and force-length dynamics to provide more direct evidence of the sensorimotor control strategies for stability in response to belt speed perturbations.
The force response of muscles to activation and length perturbations depends on length history
<p>Recent studies have demonstrated that muscle force is not determined solely by activation under dynamic conditions, and that length history has an important role in determining dynamic muscle force. Yet, the mechanisms for how muscle force is produced under dynamic conditions remain unclear. To explore how muscle force production is determined under dynamic conditions, we investigated the effects of muscle stiffness, activation, and length perturbations on muscle force. First, submaximal isometric contraction was established for whole soleus muscles. Next, the muscles were actively shortened at three velocities. During active shortening, we measured muscle stiffness at L<sub>0 </sub>and<sub> </sub>the force response to time-varying activation and length perturbations. We found that muscle stiffness increased with activation but decreased as shortening velocity increased. The slope of the relationship between maximum force and activation amplitude differed significantly among shortening velocities. Also, the intercept and slope of the relationship between length perturbation amplitude and maximum force decreased with shortening velocities. As shortening velocities were related with muscle stiffness, the results suggest that length history determines muscle stiffness and the history-dependent muscle stiffness influences the contribution of activation to muscle force and the contribution of length perturbations to muscle force. A three-parameter viscoelastic model that included a linear spring and linear damper in parallel with tunable history-dependent spring stiffness proportional to measured muscle stiffness predicted history-dependent muscle force with high accuracy. The results and simulations support the hypothesis that muscle force under dynamic conditions can be accurately predicted as the force response of a history-dependent viscoelastic material to length perturbations.</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.