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490 results for “Propagation”
Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans
<p>To survive elevated temperatures, ectotherms adjust the fluidity of membranes by fine-tuning lipid desaturation levels in a process previously described to be cell-autonomous. We have discovered that, in <em>Caenorhabditis elegans,</em> neuronal Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock response (HSR)- causes extensive fat remodelling in peripheral tissues. These changes include a decrease in fat desaturase and acid lipase expression in the intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids. The observed remodelling of plasma membrane is in line with ectothermic adaptive responses and gives worms a cumulative advantage to warm temperatures. We have determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory neurons and TGF-β/BMP are required for signalling across tissues to modulate fat desaturation. We also find neuronal <em>hsf-1</em> is not only sufficient but also partially necessary to control the fat remodelling response and for survival at warm temperatures. This is the first study to show that a thermostat-based mechanism can cell non-autonomously coordinate membrane saturation and composition across tissues in a multicellular animal.</p>
Dataset of "Triggering and propagation of exogeneous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring"
<p>Dataset of "Triggering and propagation of exogeneous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring".</p>
Code to find the cost-effective treatments to stop a propagating front
<p>Invasive species propagation is a common phenomenon, and a major question is how humans would stop its propagation in the most cost-effective manner. This package includes software and simulation results to find the optimal shape of the suppression function under certain conditions.</p> <p>This package accompanies the paper "Optimizing the use of suppression zones for containment of invasive species" by Adam Lampert and Andrew Liebhold. In particular, it includes the complete code for generating Figs. 2-4 in the paper. The file front_complete_code.zip includes the entire code. The file simulation_results.zip includes the results.</p>
A New Open-source Geomagnetosphere Propagation Tool (OTSO) and its Applications - Data
<p>Data files for the computations done with OTSO for the asymptotic cones and effective cut-off rigidities for several neutron monitor stations during three ground-level enhancement events (GLE 66, 70, 71). The computations for GLE 66 and 71 were done using three external geomagnetic field models (TSY89, TSY96, TSY01). Data for the global map of effective cut-off rigidities during GLE70 is also included. Data is in CSV format.</p>
Propagating spatio-temporal activity patterns across macaque motor cortex carry kinematic information
<p>Propagating spatio-temporal neural patterns are widely evident across sensory, motor and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multi-electrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2-D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200–400Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatio-temporal characteristics in refining brain-machine interfaces.</p>
Influence of viscous lubricant on nucleation and propagation of frictional ruptures.
<p>Raw data used for the study of "Influence of viscous lubricant on nucleation and propagation of frictional ruptures."</p> <p>Data includes: high frequency strain gauges measurements, Acceleration measurements, Macroscopic load acquisition (load cells), and photoelasticity measurements.</p>
New1_Korespondensi Propagation of Economic Shocks from the United States, China, The European Union, and Japan to Selected Asian Economies: Does the Global Value Chain Matters?
<p><strong>ABSTRACT</strong></p> <p>A panel vector autoregression (VAR) model is employed to estimate whether growth shocks from the United States (US), China, Japan, and the European Union (EU) can be transferred to selected Asian countries. We examine 1) the effect of shocks through five channels: international trade, monetary policy, finance, global uncertainty, and oil prices; 2) whether a country’s deeper integration with the global value chain (GVC) enhances or decreases the effect of growth shocks from major economies more intensively than trade openness. We found evidence of the shock transfer from major economies to Asia through the five channels. The impact differs across countries depending on their participation in GVC; for example, the impact is high in Indonesia and low in South Korea. Moreover, Asian countries are more exposed to trade shocks through China’s trade channel than other major economies. Zooming in on the channels’ impacts, global uncertainty affects countries’ growth (e.g., Indonesia) more significantly than other channels (i.e., GVC); and Asian countries respond positively to oil prices in the short run but negatively in the long run.</p> <p><strong>Keywords</strong>: Foreign spillovers, global uncertainty, oil prices, global value chain, monetary policy, trade openness, globalization</p> <p><strong>JEL Classification: </strong>F44, F62, O53, E43</p>
Fracture propagation captured by high-speed camera 2: Video (configuration 4)
<p>The video presents the target plate captured by high-speed camera 2 during testing, and shows the damage for configuration 4. A line can be observed between the first and the third impact and a lighter one between the second and third impact. A deeper analysis of the three enlarged holes highlights the location of crack tips and the crack between the first and the third hole. The line between impact 1 and 3 corresponds, to a full crack formation on the plate. On the contrary, there is only an initiation of crack formation between impacts 2 and 3.</p>
Digitizations of seesaw propagating ridges
<p>Digitizations of mid-ocean ridge features in support of "Ridge propagation and the stability of small mid-ocean ridge offsets" by Harper et al., (in prep).</p> <p>Vertical-Gravity-Gradient.kmz is a Google Earth network link to a global vertical gravity gradient image overlay. This was used to identify and digitize features.</p> <p>Digitizations.kmz contains the polyline digitizations of: off-axis traces of seesaw propagators (SSPs); ridge segments and offsets associated with active SSPs; and ridge segments and offsets associated with transform fault offsets (in the same regions as SSPs).</p>
Cross-Shelf Exchange in Antarctic Troughs Driven by Offshore Propagating Dense Water Eddies
<p>This dataset includes the ROMS input files necessary to reproduce a control simulation from our paper, titled "Cross-Shelf Exchange in Antarctic Troughs Driven by Offshore Propagating Dense Water Eddies," as well as the matlab scripts used to generate these input files.</p>
Dataset and supporting materials of "Numerical simulation of orographic gravity waves observed over Syowa Station. Part 1: Wave propagation and breaking in the troposphere and lower stratosphere" by Kohma et al.
<p>Dataset and supporting materials of "Numerical simulation of orographic gravity waves observed over Syowa Station. Part 1: Wave propagation and breaking in the troposphere and lower stratosphere" by Kohma, M., K. Sato, D. C. Fritts, & T. S. Lund.</p>
Imaging dataset from: Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron
<div> <span><span>Acute kidney injury (AKI) is a risk factor for chronic kidney disease (CKD), but the cellular mechanisms leading to i</span><span>m</span><span>paired tubular recovery and </span><span>subsequent</span> <span>AKI-CKD-transition are not yet fully understood. In this study, we combined </span><span>transgenic mice to </span><span>monitor</span><span> proliferation in vivo, </span><span>a novel </span><span>injury </span><span>model of AKI, in which </span><span>ischemia</span></span><span><span>-</span></span><span><span>reperfusion injury </span><span>(IRI) was induced in half of the kidney (partial</span></span><span><span>-</span></span><span><span>IRI), and serial intravital 2-photon imaging via an </span><span>Abdominal I</span><span>maging </span><span>W</span><span>indow (AIW) to track tissue remodeling in post- and non-ischemic kidney regions longitudinally over 3 weeks. Our results and novel findings are presented in the associated pre-print "Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron".</span></span><span> </span> </div> <div> <span><span>In this dataset, we </span><span>provide</span><span> the imaging data which we </span><span>acquired</span><span> during the study.</span><span> Overall, the dataset consists of: #1: serial intravital imaging via 2-photon microscopy and genetic identification of proliferating cells in kidneys undergoing partial IRI; #2: serial intravital imaging via 2-photon microscopy and genetic identification of proliferating cells of control, uninjured kidneys; #3: intravital imaging of kidney epithelial cells during intravenous injection of a fluorescent bolus to determine the identity of tubular segments across the nephron; #4: intravital imaging of </span><span>and genetic identification of proliferating cells </span><span>after selective laser injury of </span><span>S1 </span><span>proximal tubule cells; #5 in vivo and ex-vivo imaging of kidney and kidney slices after VCAM1 staining. The data descriptors </span><span>provide</span><span> detailed guidelines on how to place individual imaging files in the context of the pathophysiological states used in the study.</span></span><span> </span> </div> <div> <span><span>Our goal is to </span><span>provide</span><span> an organize</span><span>d</span> <span>and accessible </span><span>unique and unprocessed </span><span>in vivo imaging dataset </span><span>that </span><span>documents </span></span><span><span>in vivo</span></span><span><span> tubule cell remodeling in </span><span>the mouse kidney </span></span><span><span>longitudinally and</span></span> <span><span>during </span><span>physiological and pathological conditions. Sharing </span><span>of </span><span>th</span><span>is</span><span> dataset </span><span>ensures</span><span> </span><span>reproducing</span><span> and expanding </span><span>of </span><span>our preclinical findings of </span><span>tubule injury and (failed)</span><span> recovery during AKI</span><span>. Furthermore, this dataset may also be </span><span>utilized</span> <span>for teaching purposes, as the combination of fluorescent imaging techniques allow</span><span>s</span><span> the </span><span>detail</span><span>ed</span><span> visualization of</span><span> kidney anatomy and physiology over multiple conditions.</span></span> </div>
Data for "Propagation Eects of Slanted Narrow Bipolar Events: A Rebounding-Wave Model Study"
<p>The data for generating the figures in "Propagation Eects of Slanted Narrow Bipolar Events: A<br> Rebounding-Wave Model Study". Each file consists of time (microsecond) and the electric field (V/m).</p>
Ultrafast dark-field X-ray microscopy to image strain wave propagation
<p>This dataset includes DF-XRM data obtained at an X-FEL source.<br> The sample is a diamond single crystal with a strain wave propagating through the center, which is imaged in a stroboscopic fashion.<br> This data originated the LCLS, see <a href="https://arxiv.org/abs/2211.01042">arXiv:2211.01042</a> for a description of the experiment.<br> Run 538 contains a time series with 1 ns between each step, run 540 contains a time series with 72 ns steps (one full period) while run 536 contains a rocking curve.</p> <p>Each run is divided into hdf5 files with 2000 shots each. Each hdf5 file has then been zipped using the BZIP2 algorithm to reduce the overall size.</p> <p>Each run contains header information with the status of the X-FEL beam and the laser (the laser excites the strain wave). The shots where the X-FEL beam is off should be used to subtract the detector background. Run 536 additionally contains motor positions for the goniometer.</p> <p>Please note: for run 538 and 540 there are ~120 shots at each position. However, the timing tool is unreliable, giving a ±1 ns error, and to analyze this data the correct time of each shot must be first identified. This can be done by looking at the position of the strain wave.<br> <br> Using python, the data may be unziped, opened, and visualized in the following way:</p> <pre><code class="language-python">import zipfile import numpy as np import h5py #unzip with zipfile.ZipFile(f'538_0.zip', 'r') as zip_ref: zip_ref.extractall('') with h5py.File(f'538_0.hdf5', 'r') as f: # print contents of file for key in f.keys(): print(key, f[key].shape, f[key].dtype) # approximate the detector background mask = np.array(f[b'lightStatus_xray']) == 0 Images_DF_Xray_OFF = np.array(f['images_dark_field_arm'][mask,:,:]) df_noise = np.median(Images_DF_Xray_OFF, axis = 0) # extract desired frames ims = np.array(f['images_dark_field_arm'][120*1:120*2]) im = np.average(ims, axis = 0) # plot the detector background and the average of the selected frames import matplotlib.pyplot as plt fig, axes = plt.subplots(1,2, figsize = (4, 12), dpi = 300) axes[0].imshow(df_noise, vmin = np.percentile(df_noise,1), vmax = np.percentile(df_noise,99)) axes[1].imshow(im-df_noise, vmin = np.percentile(im-df_noise,1), vmax = np.percentile(im-df_noise,99))</code></pre> <p> </p>
Ray-tracing data for "Propagation of very oblique chorus waves near a plasmaspheric plume boundary"
<p>The dataset consists of ray-tracing results, background density models, and the figure codes for the manuscript "Propagation of very oblique chorus waves near a plasmaspheric plume boundary". The dataset and codes are in Matlab.</p>
Estimated site amplifications at DONET stations, 1D velocity models, and movies for seismic wave propagation in a local 1D models
<ul> <li>Site amplification factors of vertical (V) and horizontal (H) components at DONET stations at each frequency band (1-2, 2-4, 4-8, and 2-8 Hz)</li> <li>Anelastic attenuations are estimated during least square inversions for maximum S-wave amplitudes at DONET and F-net stations.</li> <li>Local 1D velocity structure models used in this study</li> <li>Animation movie of seismic wave propagation in local 1D models <ul> <li>Movies can be available using <a href="https://www.videolan.org/vlc/index.en_GB.html">the VLC media player</a></li> </ul> </li> </ul> <p>Details of the method and results were described in the paper:<br><span>Takemura, S.</span>, <span>Emoto, K.</span>, & <span>Yabe, S.</span> (<span>2024</span>). <span>Revisiting seismic energy of shallow tremors: Amplifications due to site and propagation path effects near the Nankai Trough</span>. <em>Journal of Geophysical Research: Solid Earth</em>, <span>129</span>, e2024JB029168. <a href="https://doi.org/10.1029/2024JB029168">https://doi.org/10.1029/2024JB029168</a></p>
Radial Hydraulic Fracturing Experiment: 3 Cycles of Fracture Propagation, Arrest, and Closure in Molasse de Villarlod Sandstone - Sample M04
<h4><strong>Overview</strong></h4> <p>This dataset encompasses detailed measurements from a lab-scale radial hydraulic fracturing experiment conducted on a cubic sample of Molasse de Villarlod sandstone, designated as Sample M04. The sandstone, sourced from a quarry in Fribourg, Switzerland, is known for its porosity (18.1%) and permeability, making it an ideal material for studying hydraulic fracture processes. The primary focus of the experiment was to observe and analyze the propagation, arrest, and closure of hydraulic fractures under controlled triaxial stress conditions.</p> <h4><strong>Experimental Setup</strong></h4> <p>The experiment was conducted on a cubic sandstone sample with dimensions of 25 × 25 × 25 cm. The sample was placed in a truetriaxial frame that applied confining stresses in all three principal directions:</p> <ul> <li><strong>Vertical Confining Stress:</strong> 5 MPa</li> <li><strong>Horizontal Confining Stress:</strong> 15 MPa</li> </ul> <p>A <strong>viscous glycerol fluid (0.57 Pa.s) containing a UV additive</strong> was used as the fracturing fluid. This fluid was injected through a 1/8'' high-pressure tube cemented with epoxy into a centrally drilled hole within the sample. An axisymmetric notch was created at the injection point to facilitate fracture initiation and promote the planarity of the fracture.</p> <p>The experiment was designed to simulate three cycles of fracture initiation, propagation, arrest, and closure. The closure of the fracture was occured by the leakoff of the fracturing fluid into the surrounding porous medium.</p> <h4><strong>Acoustic Monitoring</strong></h4> <p>To capture the dynamics of fracture propagation and closure, the experiment employed both passive and active acoustic monitoring systems:</p> <ol> <li> <p><strong>Passive Acoustic Monitoring:</strong></p> <ul> <li><strong>Sensors:</strong> 16 Vallen VS150-M passive piezoelectric sensors were used to capture Acoustic Emissions (AEs) within the frequency range of 50 kHz to 600 kHz.</li> <li><strong>Signal Processing:</strong> Continuous signal analysis and denoising were performed on the captured AE data. The STA/LTA algorithm was applied to the denoised signal to identify potential p-wave arrivals, providing insights into the fracture mechanics.</li> </ul> </li> <li> <p><strong>Active Acoustic Monitoring:</strong></p> <ul> <li><strong>Transducers:</strong> A total of 64 piezoelectric transducers were integrated into the loading platens, with 32 acting as sources and 32 as receivers. The array included 10 shear-wave and 54 longitudinal-wave transducers.</li> <li><strong>Signal Generation and Acquisition:</strong> A Ricker excitation signal with a central frequency adjustable between 300 and 750 kHz was generated and amplified using a high-power amplifier. The signal was routed to one of the 32 source transducers via a multiplexer, and the resulting signals were recorded simultaneously by the 32 receiver transducers at a sampling frequency of 50 MHz. Each source was excited 50 times to improve the signal-to-noise ratio, with the complete acquisition sequence taking approximately 2.5 seconds.</li> </ul> </li> </ol> <h4><strong>Additional Measurements</strong></h4> <p>In addition to acoustic monitoring, several other key measurements were recorded during the experiment:</p> <ul> <li><strong>Fluid Injection Parameters:</strong> The pressure and rate of fluid injection were continuously monitored.</li> <li><strong>Flat-Jack and Piston Parameters:</strong> The pressures and volumes exerted by each pair of flat-jacks were recorded at a frequency of 1 Hz.</li> <li><strong>Fracture Opening Measurement:</strong> An eddy current sensor, an electromagnetic inductive device, was placed inside the wellbore at the notch/inlet to directly measure the fracture opening.</li> </ul> <p>All measurements were synchronized using a dedicated LabView application to ensure consistency across the dataset.</p> <h4><strong>Conclusion</strong></h4> <p>This dataset provides a comprehensive view of the hydraulic fracturing behavior of Molasse de Villarlod sandstone under controlled laboratory conditions, with a focus on the processes of fracture propagation, arrest, and closure. The dataset includes raw and processed acoustic data, fluid injection metrics, and direct observations of fracture opening. It is an invaluable resource for researchers and engineers studying hydraulic fracturing, rock mechanics, and related fields. The data is suitable for detailed analysis and modeling of fracture mechanics in porous, permeable sandstones.</p> <h4><strong>Note</strong></h4> <p>Since the fracture did not extend to the boundaries of the sample, the sample was subsequently cut, and a core was extracted. This core was then sent for CT-scan analysis, which was used to reconstruct the residual fracture surfaces and assess their roughness. The dataset from this analysis is available in the Related Work section via the provided URL (Talebkeikhah, M. (2024). CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M04 Sample [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13357511" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13357511</a>).</p> <h4><strong>Processing code</strong></h4> <p>Follow the <strong>URL repositories</strong> below to access to the codes for processing these dataset.</p> <p><a href="https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX">https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX</a></p> <p><a href="https://github.com/GeoEnergyLab-EPFL/FracLowRate">https://github.com/GeoEnergyLab-EPFL/FracLowRate</a></p> <p><strong>Contact and Support</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Propagating spatio-temporal activity patterns across macaque motor cortex carry kinematic information
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Code to find the cost-effective treatments to stop a propagating front
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Propagating Gottesman-Kitaev-Preskill states encoded in an optical oscillator
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