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3,264 results for “FAST”
Slow and fast cortical cholinergic arousal is reduced in a mouse model of focal seizures with impaired consciousness
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Comparative transcriptomics of tropical woody plants supports fast and furious strategy along the leaf economics spectrum in lianas
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Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
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Fast-slow traits predict competition network structure and its response to resources and enemies
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Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
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Dsuite - fast D-statistics and related admixture evidence from VCF files
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Marine fish traits follow fast-slow continuum across oceans
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Grow fast but don’t die young: maternal effects mediate life-history tradeoffs of lizards under climate warming
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Data and code from: Viscoelasticity explains fast adaptation in the auditory amplifiers of mammals
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Fast and accurate distance‐based phylogenetic placement using divide and conquer
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Rapid Outer Radiation Belt Flux Dropouts and Fast Acceleration during the March 2015 and 2013 Storms: The Role of ULF Wave Ttansport From a Dynamic Outer Boundary
<p>Duplicate copy of the electron phase space density provided for the Geospace Environment Modeling (GEM) challenge event in March 2013 selected by the <em>Quantitative Assessment of Radiation Belt Modeling</em> focus group. The original copy of the data is available from <a href="https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg">https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg</a></p> <p> </p> <p>Data Providers:<br> Michael G. Henderson (LANL; mghenderson@lanl.gov)<br> Steven K. Morley (LANL; smorley@lanl.gov)</p> <p>This data product provides electron phase space density from the Van Allen Probes<br> ECT suite of instruments. The data are calculated similarly to the method described<br> in Morley et al. (2013), with some differences that are noted below.</p> <p>The files are provided in HDF5 format, so the files are self-describing and contain<br> ISTP-style metadata. The files should be directly readable with:<br> - SpacePy (http://sourceforge.net/p/spacepy)<br> - import the spacepy.datamodel module, use the function fromHDF5 to read the data<br> - Autoplot (http://autoplot.org)<br> - MatLab and IDL provide convience routines for reading HDF5</p> <p>Method<br> ------<br> Starting with directional differential flux data from HOPE, MagEIS and REPT, we<br> calculate the PSD as a function of energy, pitch angle, position and time.<br> Following the same basic method given by Morley et al., we transform this to phase <br> space density as a function of the three adiabatic invariants (M, K, L*); note that<br> where Morley et al. used a relativistic Maxwellian fit to the flux spectrum, these<br> data use a smoothing spline fit so that more complex spectral shapes can be<br> represented. Note also that Morley et al. only used REPT, where these files represent<br> the energy ranges of MagEIS and REPT, but also use HOPE to constrain the fit at low<br> energies.</p> <p>While the pitch angles are determined using the EMFISIS data, all three adiabatic <br> invariants are derived from a magnetic field model. These PSD data files use the<br> Tsyganenko and Sitnov (2005) model (aka TS04, T05 or TS05). The models were run using<br> the "definitive" Qin-Denton data files provided by the RBSP ECT-SOC. These files<br> should be made available through the QARBM google drive. </p> <p><br> Caveats<br> -------<br> These data should be considered preliminary. They have undergone a limited amount of<br> verification and prior to publication the data providers should be contacted. New<br> versions of these data may be generated at some point - we do not expect noticeable <br> changes to the data present.<br> Some gaps may be present in the files that are due to calculation of the adiabatic <br> invariants failing. The issues causing these gaps have been resolved in the underlying <br> software, but the data have not yet been regenerated.</p> <p><br> References<br> ----------<br> Morley, S. K., M. G. Henderson, G. D. Reeves, R. H. W. Friedel, and D. N. Baker (2013), <br> Phase Space Density matching of relativistic electrons using the Van Allen Probes: REPT results,<br> Geophys. Res. Lett., 40, 4798-4802, doi:10.1002/grl.50909.</p> <p>Tsyganenko, N. A., and M. I. Sitnov (2005), <br> Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, <br> J. Geophys. Res., 110, A03208, doi:10.1029/2004JA010798.</p> <p> </p> <p>Also included is the copy of the LANLgeoMag software used in the paper provided on <a href="https://github.com/drsteve/LANLGeoMag">https://github.com/drsteve/LANLGeoMag</a> </p> <p>Copyright (c) 2014, Los Alamos National Security, LLC All rights reserved. Copyright 2014. Los Alamos National Security, LLC. This software was produced under U.S. Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is modified to produce derivative works, such modified software should be clearly marked, so as not to confuse it with the version available from LANL.</p> <p>Additionally, redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of Los Alamos National Security, LLC, Los Alamos National Laboratory, LANL, the U.S. Government, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY LOS ALAMOS NATIONAL SECURITY, LLC AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL LOS ALAMOS NATIONAL SECURITY, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p><br> </p>
Fig. 127 in The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions
Fig. 127. Elaphropeza limosa Shamshev & Grootaert, 2007, ♀ abdomen, lateral view.
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Data from: Dunnock social status correlates with sperm speed, but fast sperm does not always equal high fitness
<p>Sperm competition theory predicts that males should modulate sperm investment according to their social status. Sperm speed, one proxy of sperm quality, also influences the outcome of sperm competition because fast sperm cells may fertilize eggs before slow sperm cells. We evaluated whether the social status of males predicted their sperm speed in a wild population of dunnocks (<em>Prunella modularis</em>). In addition to the traditional analysis of the average speed of sperm cells per sample, we also analyzed subsamples of the fastest sperm cells per sample. In other words, we systematically evaluated the effects of including different numbers of the fastest sperm in our analyses, ranging from the 5-fastest sperm cells to the 100-fastest sperm cells in a sample. We further evaluated whether fitness, defined here as the number of chicks sired per male per breeding season, relates to the sperm speed in the same population. We found that males in monogamous pairings (i.e. low levels of sperm competition), produced the slowest sperm cells whereas subordinate males in polyandrous male-male coalitions, (i.e. high levels of sperm competition), produced the fastest sperm cells. This result was consistent regardless of the number of fastest sperm included in our analyses, but statistical support was conditional on the number of sperm cells included in the analysis. Interestingly, we found no significant relationship between fitness and sperm speed, which suggests that it is possible that the differential mating opportunities across social status leveled out any possible difference. Our study also suggests that it is important to identify biologically meaningful subsets of fastest sperm and cutoffs for inclusions for assessing sperm competition via sperm speed</p>
Data from: Live fast, die old: oxidative stress as a potential mediator of an unexpected life-history evolution
Intraspecific latitudinal patterns in life history are well documented, yet underlying mechanisms of such patterns are poorly understood. To advance our insights in the evolution of latitudinal differences in two key traits, growth rate and lifespan, we evaluated the potential costs of rapid growth in terms of reduced adult lifespan, and the mediatory role of oxidative stress. We studied latitudinal differentiation in routine and experimentally increased (compensatory) larval growth rates, and in adult lifespan under common garden conditions in low- and high-latitude populations of the damselfly <i>Ischnura elegans</i>. The low-latitude populations showed not only higher routine growth rates but also a stronger compensatory growth response after a transient food shortage compared to the high-latitude populations. In contrast with a trade-off scenario, adults of the faster growing low-latitude populations lived longer, had higher levels of antioxidant enzymes, and tended to experience lower oxidative damage. Importantly, these latitudinal patterns were largely mirrored at the treatment level, where experimentally induced compensatory growth rates were associated with neither oxidative damage nor shorter adult lifespans. Moreover, individuals with a higher growth rate after the transient food shortage did not have shorter adult lifespans or higher oxidative damage, but instead showed a stronger antioxidant defense. Our data indicate that an overcompensatory, hormetic response in antioxidant defense, potentially induced by the higher routine growth rates, resulting in less oxidative damage may underlie these unexpected growth-lifespan patterns. Our results highlight the added value of incorporating oxidative stress physiology, and the need to consider multivariate trade-offs in which animals optimize multiple traits, when studying life-history evolution.
Mere presence of co-eater automatically shifts foraging tactics toward "fast and easy" food in humans
Competition for food resources is widespread in nature. The foraging behavior of social animals should thus be adapted to potential food competition. We hypothesized that in the presence of co-foragers, animals would shift their tactics to forage more frequently for smaller food. Because smaller foods are more abundant in nature and allow faster consumption, such tactics should allow animals to consume food more securely against scrounging. We tested whether humans would exhibit such a shift. To prevent subjects from having rivalry, they were instructed to engage in a "taste test" in a laboratory, alone or in pairs. Even though the other subject was merely present and there was no real competition for food, subjects in pairs immediately exhibited a systematic behavioral shift to reaching for smaller food amounts more frequently, which was clearly distinct from their reaching patterns both when eating alone and when simply weighing the same food without eating any. These patterns suggest that behavioral shifts in the presence of others may be built-in tactics in gregarious animals to adapt to potential food competition in social foraging.
Fast and furious: Early differences in growth rate drive short-term plant dominance and exclusion under eutrophication
<p>1. The reduction of plant diversity following eutrophication threatens many ecosystems worldwide. Yet, the mechanisms by which species are lost following nutrient enrichment are still not completely understood, nor are the details of when such mechanisms act during the growing season, which hampers understanding and the development of mitigation strategies.</p> <p>2. Using a common garden competition experiment, we found that early-season differences in growth rates among five perennial grass species measured in monoculture predicted short-term competitive dominance in pairwise combinations and that the proportion of variance explained was particularly greater under a fertilisation treatment.</p> <p>3. We also examined the role of early-season growth rate in determining the outcome of competition along an experimental nutrient gradient in an alpine meadow. Early differences in growth rate between species predicted short-term competitive dominance under both ambient and fertilized conditions and competitive exclusion under fertilized conditions.</p> <p>4. The results of these two studies suggests that plant species growing faster during the early stage of the growing season gain a competitive advantage over species that initially grow more slowly, and that this advantage is magnified under fertilisation. This finding is consistent with the theory of asymmetric competition for light in which fast-growing species can intercept incident light and hence outcompete and exclude slower-growing (and hence shorter) species. We predict that the current chronic nutrient inputs into many terrestrial ecosystems worldwide will reduce plant diversity and maintain a low biodiversity state by continuously favouring fast-growing species. Biodiversity management strategies should focus on controlling nutrient inputs and reducing the growth of fast-growing species early in the season.</p>
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet large test dataset
<p>W+jet events at generator and reconstruction level, used to train analysis-specific generative models.</p> <p>Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features.</p> <p>About 2M events, used for large-scale testing</p> <p>Details in https://arxiv.org/abs/2010.01835</p>
Fast and exact single and double mutation-response scanning of proteins: data and results
<p>Data, data analysis code, and results accompanying the manuscript: <strong>Fast and exact single and double mutation-response scanning of proteins: </strong>https://doi.org/10.1101/2020.10.23.352955</p> <p>In addition, the code needed to generate the data must be downloaded from <a href="https://doi.org/10.5281/zenodo.4123149">https://doi.org/10.5281/zenodo.4123149</a> and put in a subfolder named mutenm/</p>
DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks
<p>Dataset and code used in V Wåhlstrand-Skärström, et al, "DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks", published in Journal of Microscopy. In this work, we develop a new approach for FRAP analysis based on deep neural networks. From a numerical FRAP model developed in previous work, we generate a very large set of realistic, simulated recovery curve data. The data is used for training deep neural network regression models for prediction of e.g. the diffusion coefficient. We compare the performance of the neural network estimation framework to conventional least squares estimation on simulated and <br> experimental data. Herein, the simulated FRAP data used for the training, validation, and test data sets, the experimental data, and the Matlab and Python/Tensorflow code are supplied.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.