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42 results for “Frequency prediction”
Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021
High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.
Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression
This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Achoma, Peru, described in Lacroix et al. (submitted). Specifically, the repository contains a time series of horizontal ground displacements, obtained from high frequency PlanetScope satellite between 2017 and 2020. It also contains two Digital Elevation Models, one from before the Achoma failure obtained with Pléaides stero images, and the other from just after the Achoma failure obtained with drone imagery.</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 79 PlanetScope scenes were used to produce the time series of horizontal horizontal ground displacements maps. Table 1 provides an overview of these data.</p> <p>Table1: Data used for the creation of this repository</p> <table> <tbody> <tr> <td> <p>Application</p> </td> <td> <p>Platforms</p> </td> <td> <p>Acquisition dates</p> </td> </tr> <tr> <td> <p>Pre-failure DEM</p> </td> <td> <p>Pléiades</p> <p> </p> </td> <td> <p>2017/05/13</p> </td> </tr> <tr> <td> <p>Post-failure DEM</p> </td> <td> <p>Drone</p> </td> <td> <p>2020/06/19</p> </td> </tr> <tr> <td> <p>Horizontal ground displacement</p> </td> <td> <p>PlanetScope</p> </td> <td> <p>79 scenes from 2017/11/27 to 2020/06/17</p> </td> </tr> </tbody> </table> <p><br> </p> <p><strong>Methods</strong></p> <p>The horizontal ground displacement maps, both along the NS and the EW directions (file names NSxxxxxxxx.tif and Ewxxxxxxxx.tif, where xxxxxxxx is the date in the format yyyymmdd) were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) correlation of all the pairs of images using Mic-Mac (Rupnik et al., 2017), (2) masking the low correlation coefficient values (CC<0.7), (3) mosaicking correction, similar to stripe corrections (Bontemps et al., 2018), that we obtained by subtracting the median value of the stacked profile in the along-stripe direction, taking into account only stable areas, (4) least square inversion of the redundant system per pixel, weighted by the time separation between pairs (Bontemps et al., 2018), (5) correction of illumination effects (Lacroix et al., 2019), based on the 2 years of data between November 2017 and December 2019.</p> <p>The pre-failure DEM was computed from Ames Stereo Pipeline (Shean et al. 2016) and the methodology developed in (Lacroix, 2016) applied to the Pléiades stereo images (file name DEM_20170513_shifted_vertical2.tif ).</p> <p>The post-failure DEM was processed using the Structure from Motion-Multi View Stereo (SfM-MVS) methodology with the Agisoft Metashape Professional 1.5.5 software applied on 1824 pictures taken from the drone (file name Achoma_DEM_2020.06.20_UTM19S_50cm.tif ).<br> </p> <p><strong>Acknowledgements</strong></p> <p>P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Huanca, J., Angel, L., Taipe, E.: Data Repository: Time-of-failure prediction of the Achoma landslide, Peru, from high frequency Planetscope satellites. Dataset distributed on Zenodo: 10.5281/zenodo.7866962</p>
Data and Supplement from: Phylogenetic tree instability after taxon addition: Empirical frequency, predictability, and consequences for online inference
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Data from: Complexity of frequency receptive fields predicts tonotopic variability across species
<p><span>Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques. </span></p>
Data from: Sperm metabolic rate predicts female mating frequency across Drosophila species
<p>Female mating rates vary widely, even among closely related species, but the reasons for this variation are not fully understood. Across <i>Drosophila </i>species, female mating frequencies are positively associated with sperm length. This association may be due in part to sperm limitation, with longer-spermed species transferring fewer sperm, or to cryptic female choice. However, a previously overlooked factor is sperm metabolic rate, which may correlate with sperm length. If faster-metabolizing sperm accumulate age-related cellular damage more quickly, then females should remate sooner to obtain fresh sperm. Alternatively, frequent female mating may select for increased sperm competitiveness via increased metabolism. Here, we measure sperm metabolism across 13 <i>Drosophila </i>species and compare these measures to published data on female mating rate and on sperm length. Using fluorescent lifetime imaging microscopy, we quantify NAD(P)H metabolism ex vivo, in intact organs. Phylogenetically controlled regression reveals that sperm metabolic rate is positively associated with sperm length and with female mating frequency. Path analysis shows sperm length driving sperm metabolism and sperm metabolism either driving or being driven by female mating rate. While the causal directionality of these relationships remains to be fully resolved, and the effect of sperm metabolism on sperm aging and/or sperm competitiveness remains to be established, our results demonstrate the importance of sperm metabolism in sexual selection.</p>
Protein vibrational frequencies dataset: Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks
<p>Dataset for machine learning model, based on graph neural network, to predict protein natural frequencies using Graph Neural Networks. </p> <p><strong>Code</strong>: https://github.com/lamm-mit/ProteinMechanicsGNN</p> <p><strong>Paper</strong>: </p> <p>Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks</p> <p>Kai Guo and Markus J. Buehler</p> <p><em>Digital Discovery</em>, 2022, DOI: 10.1039/D1DD00007A</p>
Data from: Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds
<p>This data package includes data and R script belonging to the publication "Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds" by van der Kolk et al. (2024) in Ecology Letters.</p> <p>The script analysis_figures_vanderKolketal2024.R can be used to reproduce all analysis and figures in the manuscript. The Figures are stored in the Output folder.</p> <p>The data folder includes an excel file with metadata explaining all columns in the csv files.</p>
Sway frequencies may predict postural instability in Parkinson's disease: Data
<p>Dataset with raw Center of Pressure (COP) and Center of Mass (COM) time series along with respective wavelet spectrograms.</p> <p>Recorded during 30 seconds of quiet stance. 10 trials per participant. Sampled at 50 Hz.</p> <p>18 individuals with Parkinson's disease, 15 healthy controls.</p> <p>Detailed data description can be found in the word file.</p> <p> </p>
Data from: Frequencies of house fly proto-Y chromosomes across populations are predicted by temperature heterogeneity within populations
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Data from: Complexity of frequency receptive fields predicts tonotopic variability across species
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Data from: Sperm metabolic rate predicts female mating frequency across Drosophila species
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Data from: Plasticity-led evolution: evaluating the key prediction of frequency-dependent adaptation
Plasticity-led evolution occurs when a change in the environment triggers a change in phenotype via phenotypic plasticity, and this pre-existing plasticity is subsequently refined by selection into an adaptive phenotype. A critical, but largely untested prediction of plasticity-led evolution (and evolution by natural selection generally) is that the rate and magnitude of evolutionary change should be positively associated with a phenotype's frequency of expression in a population. Essentially, the more often a phenotype is expressed and exposed to selection, the greater its opportunity for adaptive refinement. We tested this prediction by competing against each other spadefoot toad tadpoles from different natural populations that vary in how frequently they express a novel, environmentally induced carnivore ecomorph. As expected, lab-reared tadpoles whose parents were derived from populations that express the carnivore ecomorph more frequently were superior competitors for the resource for which this ecomorph is specialized—fairy shrimp. These tadpoles were better at utilizing this resource both because they were more efficient at capturing and consuming shrimp and because they produced more exaggerated carnivore traits. Moreover, they exhibited these more carnivore-like features even without experiencing the inducing cue, suggesting that this ecomorph has undergone an extreme form of plasticity-led evolution––genetic assimilation. Thus, our findings provide evidence that the frequency of trait expression drives the magnitude of adaptive refinement, thereby validating a key prediction of plasticity-led evolution specifically and adaptive evolution generally.
Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada
Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.
Data from: Contemporary evolution of sea urchin gamete-recognition proteins: experimental evidence of density-dependent gamete performance predicts shifts in allele frequencies over time
Species whose reproductive strategies evolved at one density regime might be poorly adapted to other regimes. Field and laboratory experiments on the sea urchin Strongylocentrotus franciscanus examined the influences of the two most common sperm bindin alleles, which differ at two amino acid sites, on fertilization success. In the field experiment, the Arginine/Glycine (RG) genotype performed best at low densities and the Glycine/Arginine (GR) genotype at high densities. In the lab experiment, the RG genotype had a higher affinity with available eggs, whereas the GR genotype was less likely to induce polyspermy. These sea urchins can reach 200 years of age. The RG allele dominates in old sea urchins, whereas younger sea urchins have near equal RG and GR allele frequencies. A latitudinal cline in RG and GR genotypes is consistent with longer survival of sea urchins in the north and with predominance of RG genotypes in older individuals. The oldest sea urchins were likely conceived at low densities, before sea-urchin predators, like sea otters, were overharvested and sea urchin densities exploded off the west coast. Contemporary evolution of gamete-recognition proteins might allow species to adapt to shifts in abundances and reduces the risk of reproductive failure in altered populations.
Data from: The rate of transient beta frequency events predicts behavior across tasks and species
Beta oscillations (15-29Hz) are among the most prominent signatures of brain activity. Beta power is predictive of healthy and abnormal behaviors, including perception, attention and motor action. In non-averaged signals, beta can emerge as transient high-power 'events'. As such, functionally relevant differences in averaged power across time and trials can reflect changes in event number, power, duration, and / or frequency span. We show that functionally relevant differences in averaged beta power in primary somatosensory neocortex reflect a difference in the number of high-power beta events per trial, i.e. event rate. Further, beta events occurring close to the stimulus were more likely to impair perception. These results are consistent across detection and attention tasks in human magnetoencephalography, and in local field potentials from mice performing a detection task. These results imply that an increased propensity of beta events predicts the failure to effectively transmit information through specific neocortical representations.
Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling
<p>This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling". The code to generate the models and reproduce the results of the comparative study in the above paper is available on this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a></p> <p><strong>Supplementary data</strong>:</p> <p>- The <strong>trained_models</strong> folder contains the results of the trained models.</p> <p>- The folder <strong>data</strong> contains data needed for for the comparative study for the year 2019 in the paper above. This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.</p>
Relative species abundance successfully predicts nestedness and interaction frequency of monthly pollination networks in an alpine meadow
<p>Plant-pollinator networks have been repeatedly reported as cumulative ones that are described with >1 years observations. However, such cumulative networks are composed of pairwise interactions recorded at different periods, and thus may not be able to reflect the reality of species interactions in nature (e.g., early-flowering plants typically do not compete for shared pollinators with late-flowering plants, but they are assumed to do so in accumulated networks). Here, we examine the monthly sampling structure of an alpine plant-pollinator bipartite network over a two-year period to determine whether relative species abundance and species traits better explain the network structure of monthly networks than yearly ones. Although community composition and species abundance varied from one month to another, the monthly networks (as well as the yearly networks described with annual pooled data) had a highly nested structure, in which specialists directly interact with generalist partners. Moreover, relative species abundance predicted the nestedness in both the monthly and yearly networks and accounted for a statistically significant percentage of the variation (i.e., 20%-44%) in the pairwise interactions of monthly networks, but not yearly networks. The combination of relative species abundance and species traits (but not species traits only) showed a similar prediction power in terms of both network nestedness and pairwise interaction frequencies. Considering the previously recognized structural pattern and associated mechanisms of plant-pollinator networks, we propose that relative species abundance may be an important factor influencing both nestedness and interaction frequency of pollination networks.</p>
Fever and Shivering: Frequency and Role in Predicting Serious Bacterial Infection
ClinicalTrials.gov study NCT02760745. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Predictive Efficacy of Ratio of Median Frequency to Spectral Edge Frequency Used in the Depth of Anaesthesia Monitoring on Post-operative Cognitive Functions
ClinicalTrials.gov study NCT06644209. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.
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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.
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