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189 results for “feature model”
dataset related to article "Dysregulation of Muscle-Specific MicroRNAs as Common Pathogenic Feature Associated with Muscle Atrophy in ALS, SMA and SBMA: Evidence from Animal Models and Human Patients"
<p> CINZIA CAGNOLI 0000-0001-6863-6687, MICHELA TAIANA 0000-0001-8257-8831, MONICA NIZZARDO 0000-0001-5447-0882, STEFANIA CORTI 0000-0001-5425-969X, VIVIANA PENSATO 0000-0001-9798-2669, ANNA VENERANDO <a href="https://orcid.org/0000-0002-7489-1833">0000-0002-7489-1833</a>, CINZIA GELLERA <a href="https://orcid.org/0000-0002-3582-665X">0000-0002-3582-665X</a>, SILVIA FENU 0000-0002-5233-6580, DAVIDE PAREYSON 0000-0001-6854-765X, RICCARDO MASSON 0000-0002-9311-452X, LORENZO MAGGI 0000-0002-0932-5173, ELEONORA DALLA BELLA 0000-0001-6267-9651, GIUSEPPE LAURIA 0000-0001-9773-020X, RENATO MANTEGAZZA 0000-0002-9810-5737, PIA BERNASCONI 0000-0003-0869-2104, ANGELO POLETTI 0000-0002-8883-0468, SILVIA BONANNO 0000-0002-8823-6821, STEFANIA MARCUZZO 0000-0001-6893-6372.</p>
Estradiol Enhances the Development of Addition-Like Features in a Female Rat Model of Opioid Use Disorder
<p>This dataset indicates that, as with findings with psychostimulants and alcohol, estradiol enhances vulnerability in females to developing opioid addiction-like features and serious opioid-related health complications in a rat model of opioid use disorder.</p>
Mechanism of building a machine learning model and its main features
<p>This figure is a flowchart summarizing the steps required to build a machine learning model in general way.</p>
Model dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation"
<p>This is the global 1-deg CLM5-SNICAR model simulation dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation".</p>
Jet feature data from PAMIP model simulations
<p>Author: Yvonne Anderson</p> <p>Contact: ee22ya@leeds.ac.uk</p> <p>Dataset created: 21/08/2023</p> <p>Paper title: Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology</p> <p> </p> <p><strong>Dataset information</strong></p> <p>CSV files contain arrays of daily jet feature data for all ensemble member winters for a given model.</p> <p>Dimensions of the arrays are (number of ensemble members, 90 winter days).</p> <p><strong>Filename structure</strong></p> <p>Filenames of CSV files can be interpreted as: timeperiod_jetfeature_model.csv</p> <p><strong>Example filename structure</strong></p> <table> <thead> <tr> <th scope="col">Time period</th> <th scope="col">Jet feature</th> <th scope="col">Model</th> <th scope="col">Example filename</th> </tr> </thead> <tbody> <tr> <td>Present-day</td> <td>Latitude</td> <td>AWI-CM-1-1-MR</td> <td>present-day_jet_latitude_AWI-CM-1-1-MR.csv</td> </tr> <tr> <td>Future</td> <td>Speed</td> <td>HadGEM3-GC31-MM</td> <td>future_jet_speed_HadGEM3-GC31-MM.csv</td> </tr> </tbody> </table> <p><strong>Jet feature description</strong></p> <p>Jet feature data are for the largest mass jet region found on each day of winter, where jet mass is the area weighted jet speed.</p> <p>The jet features and corresponding units contained in the csv files are as follows:</p> <table> <thead> <tr> <th scope="col">Jet feature</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <td>Latitude</td> <td>°</td> </tr> <tr> <td>Speed</td> <td>ms<sup>-1</sup></td> </tr> <tr> <td>Mass</td> <td>ms<sup>-1</sup></td> </tr> <tr> <td>Tilt</td> <td>°</td> </tr> <tr> <td>Area</td> <td>m<sup>2</sup></td> </tr> </tbody> </table> <p><strong>Time periods</strong></p> <p>Time periods are present-day and future, which refer to simulations forced by present-day and future sea ice concentrations, from which the jet features have been extracted.</p> <p><strong>Models</strong></p> <p>Models are AWI-CM-1-1-MR, CanESM5, FGOALS-f3-L, HadGEM3-GC31-MM, IPSL-CM6A-LR and MIROC6 from the Polar Amplification Model Intercomparison Project (PAMIP; https://doi.org/10.5194/gmd-12-1139-2019)</p> <p><strong>Spatial and temporal information</strong></p> <p>Arrays contain daily jet feature data that has been constrained to the North Atlantic region (0-60 &deg; W, 15-75 &deg; N) and to the winter period (December, January and February)</p> <p><strong>Prior processing</strong></p> <ul> <li>Original dataset: netcdf files of daily zonal wind data from Polar Amplification Model Intercomparison Project simulations forced by present-day and future sea ice concentrations</li> <li>850 hPa wind speed data was extracted and regridded to 2.81 ° x 2.81 ° resolution</li> <li>Constrained to North Atlantic region and winter period</li> <li>Wind speed data was filtered using a 10-day Lanczos filter with a 61 day window</li> <li>Jet feature data was extracted for each day in ensemble member winters and saved to numpy arrays</li> </ul> <p><strong>Example code for loading jet variables from csv file</strong></p> <p>To generate a numpy array of jet variable arrays contained in the csv file:</p> <pre><code class="language-python">loaded_jet_variable_arrays = np.genfromtxt((path_to_file/filename.csv'), delimiter=',')</code></pre> <p>To combine arrays for all ensemble member winters, which allows plotting of daily jet feature distributions:</p> <pre><code class="language-python">jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)</code></pre> <p> </p> <p> </p>
Model files for"Impact of Horizontal Resolution (Submesoscale Permitting vs Mesoscale Resolving) on Ocean Dynamic Features in the South China Sea "
<p>These four files are the model configurations as well as model grids for the SP and MR model mentiond in the manuscript titled with "Impact of Horizontal Resolution (Submesoscale Permitting vs Mesoscale Resolving) on Ocean Dynamic Features in the South China Sea", which would be potentially published in the Journal of Earth and Space Science. With the model grids and publicly available forcing field data introduced in the MS, it is easy to generate model input files. Finally, with the configuration files (e.g., .h, .in ), one can easily reproduce all the model results.</p>
Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Using Multimodal Feature Structure Technology
ClinicalTrials.gov study NCT06062316. IPD Sharing: UNDECIDED. Countries: 1. Publications: 25.
Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non-model tree species
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Predictive modeling for clinical features associated with Neurofibromatosis Type 1
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Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo sobre adaptações dos papéis do Scrum</p>
Adaptations of Scrum roles in software projects: Survey and representation tentative with feature models
<p>Vídeo em português</p> <p> </p> <p>This work is supported by CAPES/Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) code 001.</p>
Data from:Identification of landscape features influencing gene flow: how useful are habitat selection models?
Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modelling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider their explicit assumptions. We calculated pairwise genetic distances among 301 Alaskan Dall's sheep (Ovis dalli dalli) using an intensive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important seasonally (rugged terrain, mid-range elevation) were not influential to breeding dispersal. This work underscores that consideration of both habitat selection and landscape genetics models in developing conservation strategies will ensure resources are managed to meet both the immediate survival needs of a species and allow for long-term genetic connectivity.
Data from: A national-scale model of linear features improves predictions of farmland biodiversity
1. Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically-relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies. 2. We assessed whether a novel spatial dataset mapping linear and woody linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites respectively. 3. Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. 4. Synthesis and applications. This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems, and also can potentially be used to assess habitat connectivity. 10-Mar-2017
Data from: Spermatozoa scattering by a microchannel feature: an elastohydrodynamic model
Sperm traverse their microenvironment through viscous fluid by propagating flagellar waves; the waveform emerges as a consequence of elastic structure, internal active moments and low Reynolds number fluid dynamics. Engineered microchannels have recently been proposed as a method of sorting and manipulating motile cells; the interaction of cells with these artificial environments therefore warrants investigation. A numerical method is presented for large-amplitude elastohydrodynamic interaction of active swimmers with domain features. This method is employed to examine hydrodynamic scattering by a model microchannel backstep feature. Scattering is shown to depend on backstep height and the relative strength of viscous and elastic forces in the flagellum. In a 'high viscosity' parameter regime corresponding to human sperm in cervical mucus analogue, this hydrodynamic contribution to scattering is comparable in magnitude to recent data on contact effects, being of the order of 5°–10°. Scattering can be positive or negative depending on the relative strength of viscous and elastic effects, emphasizing the importance of viscosity on the interaction of sperm with their microenvironment. The modulation of scattering angle by viscosity is associated with variations in flagellar asymmetry induced by the elastohydrodynamic interaction with the boundary feature.
Stimuli used in the experiments reported by Wallis et al., "A parametric texture model based on deep convolutional features closely matches texture appearance for humans"
<p># Stimuli for "A parametric texture model based on deep convolutional features closely matches texture appearance for humans" by Wallis et al.</p> <p>This repository contains the stimuli used in</p> <p>Wallis, Funke, Ecker, Gatys, Wichmann and Bethge (submitted). A parametric texture model based on deep convolutional features closely matches texture appearance for humans. </p> <p>Code can be found in a complementary archive (http://doi.org/10.5281/zenodo.438029; stored separately due to license restrictions on images).</p> <p>The first experiment reported in the paper uses the stimuli in the subdirectory `stimulus_set_3`; the second experiment uses `stimulus_set_4`.</p> <p>## License</p> <p><strong>The original texture images (stored in `stimulus_set_3/raw_textures`) and their derivatives (`stimulus_set_3/preprocessed_ims/` and `stimulus_set_4/preprocessed_ims/`) remain copyright of www.textures.com (shared here with permission for scientific, non-commercial purposes).</strong></p> <p>Other images are shared under a CC-BY-NC license.<br> </p>
Fractal geometry features of aerosol particle and its contribution to atmospheric optical property: development of Fractal Aerosol Cluster Model and its validation of atmospheric visibility during a heavy haze event
<p>-------------------------<br>Content of the dataset<br>-------------------------<br>****** the experiment case (EXP) ; the control case (CTR) ******</p> <p>1. Meteorological elements.tar contains observational and simulated data for T2, WS, RH, and PM2.5 time series, which can be used to plot Figure 4 and build Table 2</p> <p>2. Planar distribution.tar contains the horizontal spatial distribution data of aerosol extinction coefficients simulated by CTR and EXP for the four typical moments selected in this paper, which can be used to plot Figures 5, 6, and 7</p> <p>3. PM.rar contains the vertical profile data of simulated Particulate Matter concentrations by CTR and EXP during the study period in the paper, which can be utilized for drawing Fig. 11.</p> <p>4. Timeseries.tar contains observational and simulated data for time series of atmospheric visibility and surface shortwave radiation, which can be used to plot Figures 5, 6, 7, 8, S1, and build Table 3</p> <p>5. wrfbiochemi.rar contains the biogenic emissions data for simulation both for CTR and EXP.</p> <p>6. wrffirechemi.rar contains the biomass burning emissions data for simulation both for CTR and EXP.</p> <p>7. wrfchemi.rar contains the Anthropogenic emissions data for simulation both for CTR and EXP.</p> <p>8. The file module_optical_averaging.F contains the main code of the improved visibility model, the Fractal Aerosol Cluster Model</p> <p>(FACM), which is coupled to WRF-Chem and used by EXP. It is located in the chem/ directory and called by optical_driver.F.</p> <pre> </pre> <p> </p> <p>-------------------------</p> <p>Contact information</p> <p>-------------------------</p> <p> </p> <p>Zhenxin Liu</p> <p>liuzhenxin@nuist.edu.cn</p> <p> </p> <p> </p>
ProtFound-V Pretrained Models and Features
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3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data
<p>Supplementary data of 3D-MSNet</p>
Advanced Iterative Model for Lumpy Skin Disease Prediction Using Fine-grained Feature Fusion and Adaptive Transfer Learning
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Modeling the signatures of interaction in Type II supernovae: UV emission, high-velocity features, broad-boxy profiles
<p>Model spectra</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.