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525 results for “amplification”
Atomic spin-controlled non-reciprocal Raman amplification of fibre-guided light
<p>This repository contains the data used in an experiment that demonstrates atomic spin-controlled non-reciprocal Raman amplification of fibre-guided light. For more information, see the following publication:</p> <ul> <li><a href="https://doi.org/10.1038/s41566-022-00987-z">10.1038/s41566-022-00987-z</a></li> <li><a href="https://doi.org/10.48550/arXiv.2107.07272">10.48550/arXiv.2107.07272</a></li> </ul> <p>We provide the data in text files encoded in the Unicode standard UTF-8. In the following, we describe the files in more detail.</p> <p>The measured evolution of the signal transmission presented in Fig. 2<strong>b</strong> is provided in the file “source_data_fig2b.txt”. The file has five columns that are separated by the delimiter “, ”:</p> <ul> <li>the time in microseconds,</li> <li>the signal transmission in the 1→2 direction,</li> <li>the error of the signal transmission in the 1→2 direction,</li> <li>the signal transmission in the 1→2 direction,</li> <li>and the error of the signal transmission in the 1→2 direction.</li> </ul> <p>We provide the theory data in the additional file “theory_fig2b.txt”. It contains three columns that are separated by the delimiter “, ” :</p> <ul> <li>the time in microseconds,</li> <li>the calculated signal transmission in the 1→2 direction,</li> <li>the calculated signal transmission in the 2→1 direction.</li> </ul> <p>In the files “source_data_fig2c.txt”, “source_data_fig2d.txt”, and “source_data_fig3b.txt”, we provide the data of the bar plots in Fig. 2<strong>c</strong>, 2<strong>d</strong>, and 3<strong>b</strong>, respectively. In every file, the first column indicates the measurement direction. The following columns contain the detected mean signal transmission with the corresponding errors for various initial atomic spin states defined by the magnetic quantum number <em>m<sub>F</sub></em>.</p>
Experimental data for Quantum noise limited microwave amplification using a graphene Josephson junction
<p>This dataset was used in our study of "Quantum noise limited microwave amplification using a graphene Josephson junction".</p>
Data and code for figures: Intrinsic Kerr amplification for microwave electromechanics
<p>This directory contains the datasets and code for generating the figures in the research article "Intrinsic Kerr amplification for microwave electromechanics", <em>Appl. Phys. Lett.</em> 124, 243503 (2024).</p>
Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification
<p>Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.</p>
Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"
<p>This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "<strong>Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone</strong>", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a></p> <p>Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. <em>Geophysical Research Letters</em>, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a></p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a></p>
Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplification and flow cytometry
<p>This dataset contains the raw data that were used for the publication entitled, "Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplificaiton and flow cytometry" published in Biosensors and Bioelectronics on 5 October 2024.</p>
Pre-amplification of nucleic acids comparison in the context of sewage metaviromics
<p>Raw data from three different pre-amplification nucleic acids pipelines prior to library preparation for metaviromics of sewage samples.</p>
WUSA surface wave amplification measurements (Schardong et al. 2019)
<p>#Western U.S. surface wave amplification measurements</p> <p>If using this data please cite: <br> Schardong, L., Ferreira, A.M., Berbellini, A. and Sturgeon, W., 2019. The anatomy of uppermost mantle shear-wave speed anomalies in the western US from surface-wave amplification. Earth and Planetary Science Letters, 528, p.115822.</p> <p>Directory format:<br> USA.RAV... = vertical-component Rayleigh wave amplification measurements<br> USA.RAH... = horizontal-component Rayleigh wave amplification measurements<br> USA.TOR... = Love wave amplification measurements</p> <p>File format:<br> motion type index -- mode order (n) -- angular order (l) -- period -- relative local amplification w.r.t. PREM -- estimated error on local amplification</p> <p>The number of data points varies depending on the available data in Hendrik van Heijst's database, and the various selection steps. </p>
Dataset related to the publication "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176
<p>This folder contains the raw data from which the graphs in paper "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176, have been obtained.</p>
Storyline Simulations Data for the paper Athanase et al.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean
<p>Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment).</p> <p>The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model.</p> <p>Parameters naming convention:</p> <p>- Sea Surface Temperature ("tos").</p> <p>- Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs").</p> <p>- Low Clouds Cover ("lcc").</p> <p>- Mixed Layer Depth ("mlotst").</p> <p>- Surface Air Temperature ("tas").</p> <p>- 10 m winds ("u10","v10").</p> <p>All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). </p> <p> </p>
Model output for "Geometric amplification and suppression of ice-shelf basal melt in West Antarctica"
<h2><strong>Content description<br></strong></h2> <p>This data repository contains Úa-MITgcm model output in support of the manuscript 'Geometric amplification and suppression of ice-shelf basal melt in West Antarctica' (De Rydt and Naughten, 2024).</p> <p>In the paper, results from 4 experiments with the coupled ice-ocean model Úa-MITgcm are presented. The experiments are <em>ref_melt</em>, <em>hi_melt</em>, <em>av_melt</em> and <em>var_melt</em> (see Table 1 in De Rydt and Naughten, 2024). Original MITgcm and Úa output files at the start, middle and end of each experiment are available in the .zip-files with corresponding experiment name. Data for other timestamps are available upon request from the authors.</p> <p>The structure of each file is as follows:</p> <table> <tbody> <tr> <td>exp_name.zip/</td> <td>yyyymm/</td> <td> MITgcm/</td> <td> bathymetry.shice</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>data.diagnostics</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>output.nc</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>pload.mdjwf</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>shelfice_topo.bin</td> </tr> <tr> <td> </td> <td> </td> <td>Ua/</td> <td>DataForMIT.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>NewMeltrate.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>UaDefaultRun_yyyymm_dd.mat</td> </tr> </tbody> </table> <p><br><code>yyyymm</code> refers to the start month of the coupling timestep. Note that while the initial state of the ice sheet has been optimized to represent its present-day configuration, the future evolution of the ocean and ice sheet is driven by idealized forcings. The results should not be treated as projections, and the timestamps correspond to model time (in years), not real time.</p> <p><strong>MITgcm diagnostics</strong> (temperature, salinity, velocities and ice-shelf freshwater flux) are stored in <code>output.nc</code> and provided as an average value over the 1-month coupling time interval. The <code>bathymetry.shice</code>, <code>data</code>, <code>data.diagnostics</code>, <code>pload.mdjwf </code>and <code>shelfice_topo.bin</code> files are MITgcm input files for ocean bathymetry, general model parameters, diagnostic model parameters, intial pressure loading under the ice shelf and ice-shelf draft for the corresponding coupling timestep, respectively.</p> <p><strong>Úa data</strong> is provided in <code>UaDefaultRun_yyyymm_dd.mat</code> as a snapshot at the end of the coupling interval (<code>yyyymm_dd</code>). The other files, <code>DataForMIT.mat </code>and <code>NewMeltrate.mat</code> are ice-shelf geometry and MITgcm melt rates at the start of the coupling interval, respectively.</p> <h2><strong>Other resources</strong></h2> <p>The Úa-MITgcm source code and custom Úa-MITgcm configuration files for the experiments are available <a href="https://github.com/knaughten/UaMITgcm/tree/archer2">here</a> and <a href="https://github.com/knaughten/UaMITgcm/tree/archer2/example/ASE_999">here.</a> Scripts to analyse and plot the data are available <a href="https://github.com/janderydt/uamitgcm_ASE">here</a>.</p> <h2><strong>References</strong></h2> <p>De Rydt, J. and Naughten, K.: Geometric amplification and suppression of ice-shelf basal melt in West Antarctica, The Cryosphere, 18, 1863–1888, https://doi.org/10.5194/tc-18-1863-2024, 2024.</p>
Data for the publication "Radiative effects of precipitation on the global energy budget and Arctic amplification"
<p>This dataset includes a set of 15yr simulations using the MIROC6 global aerosol-climate model with 1) diagnostic precipitation, 2) prognostic precipitation without radiative effect of precipitation, and 3) prognostic precipitation with radiative effect of precipitation.</p> <p>The data are used in the manuscript entitled "Radiative effects of precipitation on the global energy budget and Arctic amplification".</p>
Kerr reversal in Josephson meta-material and traveling wave parametric amplification datasets
<p>This repository contains raw data for results presented in article "Kerr reversal in Josephson meta-material and traveling wave parametric amplification" (Preprint : arXiv:2101.05815). All data is stored in numpy(numpy.org) array format.</p> <p><strong>Please site any usage to original publication.</strong></p> <p><br> # Gain data</p> <p> The data used for generating Fig. 3(a) of main text:<br> <br> - Gain_6_freq contains frequency axis, gain_6 contains corresponding gain data when the device is pumped at 6 GHz<br> <br> - Gain_8_freq contains frequency axis, gain_8 contains corresponding gain data when the device is pumped at 8 GHz<br> <br> - Gain_10_freq contains frequency axis, gain_10 contains corresponding gain data when the device is pumped at 10 GHz</p> <p><br> # Saturation data</p> <p> The data used for generating Fig. 3(d) of main text:</p> <p> - saturation_6_pow contains input signal power at 6.05 GHz and saturation_6_gain contains gain as a function of the same when device is pumped at 8 GHz.</p> <p> - saturation_9.5_pow contains input signal power at 9.5 GHz and saturation_9.5_gain contains gain as a function of the same when device is pumped at 8 GHz.</p> <p><br> # Noise data</p> <p> The raw data used for fitting noise performance of the TWPA as depicted in Fig. 4 of main text:</p> <p> - noise_without_TWPA_temperature : contains noise temperature of the amplification chain without TWPA, in<br> Kelvin.<br> - noise_without_TWPA_sys_gain_dB : contains gain of the amplification chain without TWPA, in dB.</p> <p> - noise_freq : contains frequency axis for the measured PSD.</p> <p> - noise_thermal_source_temperature : contains temperature data of the thermal noise source.</p> <p> - noise_with_TWPA_PSD_vs_thermal_source_temperature : contains PSD measured with 200 MHz RBW as a function<br> of frequency and temperature of thermal noise source.</p> <p><br> # Transmission data</p> <p> Normalized transmission through the device.</p> <p> - transmission_flux : contains quantized flux axis for the measured transmission.</p> <p> - transmission_freq : contains frequency axis for the measured transmission.</p> <p> - transmission : contains transmission as a function of frequency and quantized flux, in dB.</p> <p><br> # Dispersion data</p> <p> Dispersion through the device.</p> <p> - dispersion_freq : contains frequency axis for the measured dispersion.</p> <p> - dispersion_phase_PCB : contains phase accumulation when RF switch is in PCB position as a function of<br> frequency, in radians.<br> - dispersion_flux_mA : contains flux axis for the measured dispersion, in mA.</p> <p> - dispersion_phase_device : contains phase accumulation when RF switch is in device position as a function<br> of frequency and flux, in radians.</p>
eDNAssay: a machine learning tool that accurately predicts qPCR cross-amplification
<p>Environmental DNA (eDNA) sampling is a highly sensitive and cost-effective technique for wildlife monitoring, notably through the use of qPCR assays. However, it can be difficult to ensure assay specificity when many closely related species cooccur. In theory, specificity may be assessed in silico by determining whether assay oligonucleotides have enough base-pair mismatches with nontarget sequences to preclude amplification. However, the mismatch qualities required are poorly understood, making in silico assessments difficult and often necessitating extensive in vitro testing—typically the greatest bottleneck in assay development. Increasing the accuracy of in silico assessments would therefore streamline the assay development process. In this study, we paired 10 qPCR assays with 82 synthetic gene fragments for 530 specificity tests using SYBR Green intercalating dye (n = 262) and TaqMan hydrolysis probes (n = 268). Test results were used to train random forest classifiers to predict amplification. The primer-only model (SYBR Green-based) and full-assay model (TaqMan probe-based) were 99.6% and 100% accurate, respectively, in cross-validation. We further assessed model performance using six independent assays not used in model training. In these tests the primer-only model was 92.4% accurate (n = 119) and the full-assay model was 96.5% accurate (n = 144). The high performance achieved by these models makes it possible for eDNA practitioners to more quickly and confidently develop assays specific to the intended target. Practitioners can access the full-assay model via eDNAssay (https://NationalGenomicsCenter.shinyapps.io/eDNAssay), a user-friendly online tool for predicting qPCR cross-amplification.</p>
Dataset for: Ultrarapid detection of SARS-CoV-2 RNA using a reverse transcription-free exponential amplification reaction, RTF-EXPAR
<p>A dataset is reported for a rapid isothermal method for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19. The procedure uses an unprecedented reverse transcription–free (RTF) approach for converting genomic RNA into DNA. This involves the formation of an RNA/DNA heteroduplex whose selective cleavage generates a short DNA trigger strand, which is then rapidly amplified using the exponential amplification reaction (EXPAR). Deploying the RNA-to-DNA conversion and amplification stages of the RTF-EXPAR assay in a single step results in the detection, via a fluorescence read-out, of single figure copy numbers per microliter of SARS-CoV-2 RNA in under 10 min. In direct three-way comparison studies the assay has been found to be faster than both PCR and loop-mediated isothermal amplification (LAMP), while being just as sensitive. The assay protocol involves the use of standard laboratory equipment and is readily adaptable for the detection of other RNA-based agents.</p>
Global amplification factors for tsunami inundation
<p><strong>Global tsunami amplification factors used for estimation of tsunami run-up, based on wave input from offshore mariograms.</strong></p> <p>The off-shore maximum surface elevation is multiplied by the factor to achieve the MIH estimation. Two sets of factors are included, one for the Mediterranean Sea (Glimsdal et al., 2019) and one set of global factors (Løvholt et al., 2012). The factors depends on the wave period, polarity and coastal location.</p> <p>The primary objective of this service will be to give scientists a tool for regional tsunami hazard evaluations, based on time series extracted by tsunami propagation models, without running computational costly run-up models along the coastlines. Example on global evaluation of MIH is found in Figure 2.</p> <p>The model will provide the following features:</p> <ul> <li>Interface for extraction of the wave characteristics from off-shore mariograms (time history of the surface elevation at a gauge)</li> <li>Extract the amplification factors based on the wave characteristics</li> <li>Calculation of the maximum inundation height</li> </ul> <p>Examples on Python-codes that can be used to calculate factors for different types of input will be added soon.</p> <p>The method is described in the following open access publications:</p> <p><a href="https://link.springer.com/article/10.1007%2Fs00024-019-02091-w">https://link.springer.com/article/10.1007%2Fs00024-019-02091-w</a></p> <p>This service is part of the <strong><a href="https://tsunamidata.org/">candidate Thematic Core Service for tsunami</a></strong> and will be integrated into the <a href="https://www.ics-c.epos-eu.org/"><strong>Integrated Core Service Data </strong></a>Portal of the <a href="https://www.epos-eu.org/"><strong>European Plate Observing System (EPOS)</strong>.</a></p>
In-silico PCR results for PSSC primer sets exhibiting >50% amplification rate
<p>using in-silico PCR, 2,161 genomes from<em> </em>the <em>Pseudomonas syringae</em> species complex were amplified using 16 established PCR primer sets, allowing one mismatch per primer. Each file here contains amplicons generated from a single primer set, with each amplicon sequence named with the GenBank accession number for the associated genome.</p>
Research data supporting "Platinum Nanocatalyst Amplification: Redefining the Gold Standard for Lateral Flow Immunoassays with Ultra-Broad Dynamic Range"
<p>Research data supporting the publication: Loynachan C. N., et al., 2017, ACS Nano, DOI: http://dx.doi.org/10.1021/acsnano.7b06229.</p>
A Seasonally Delayed Sea Ice Response and Arctic Amplification during the Last Glacial Inception
<p>Presented here is the datasets and corresponding codes used in creating Figures of the article "<strong><span>A Seasonally Delayed Sea Ice Response and Arctic Amplification during the Last Glacial Inception</span></strong>".</p>
Fig. 2 in Optimisation Of Dna Extraction And Rapd-Pcr Amplification For Population Genetic Analysis Of Daphnia Cucullata Sars, 1862 (Crustacea: Cladocera)
Fig. 2. RAPD fingerprints results from different samples of Daphnia cucullata with primers OPA-03 and OPA-05 (M- marker, 1-11 runners- different samples of Daphnia cucullata; 12- control) using RAPD-PCR 10 × Taq buffer with (NH4)2SO4.
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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.