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23 results for “threshold model”
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression
<p>Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point.</p> <p>To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.</p>
Data and code from: Thresholding species distribution models: Simple approaches for land-use planning in multifunctional landscapes
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Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression
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Data from: A framework for developing ecosystem-specific nutrient criteria: integrating biological thresholds with predictive modeling
We present a novel ecosystem-specific framework for developing nutrient criteria from biological thresholds and predictive modeling (BTPM) and an application of this framework to lakes in Michigan, U.S. The four main components for the BTPM framework are: (1) to predict each ecosystem s expected nutrient concentration in the absence of human effects using a predictive model, (2) to identify important biological thresholds along a nutrient gradient (i.e., biological [BIO] benchmarks), (3) to determine each ecosystem s current nutrient concentration, and (4) to use the above information to derive a nutrient criterion for each ecosystem using the BTPM algorithm. The BTPM framework is extremely flexible in that it can be applied to any aquatic ecosystem type or nutrient and the four components can be implemented in a variety of ways. Our BTPM framework has two additional features: it recognizes that prior to human disturbance, ecosystems varied in their natural nutrient concentrations, and it incorporates risk into the decision-making process. In the simplest scheme, a nutrient criterion is set at a BIO benchmark greater than the expected nutrient concentration. However, to protect ecosystems more conservatively, a criterion is set at current lake nutrient concentrations if current is less than the BIO benchmark. In our application of the BTPM framework, we developed total phosphorus (TP) criteria for a diverse set of 374 lakes in MI. The expected lake TP concentrations in the absence of human effects ranged from 3 µg L-1 to 24 µg L-1, suggesting that a single criterion approach would not be appropriate.We found two predominant benchmarks in the biological data along the TP gradient, one for zooplankton metrics at 8 µg L-1, and one for phytoplankton metrics at 18 µg L-1. We present the sequence of analyses and decisions that could be used to apply this approach in a management context using Michigan lakes as an example.
Data from: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex
Birdsong is a complex vocal communication signal, and like humans, birds need to discriminate between similar sequences of sound with different meanings. The caudal mesopallium (CM) is a cortical-level auditory area implicated in song discrimination. CM neurons respond sparsely to conspecific song and are tolerant of production variability. Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics, which could contribute to the emergence of these higher-order functional properties. We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong. Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current. These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies.
Model output for: Rate of mass loss across the instability threshold for Thwaites Glacier determines rate of mass loss for entire basin
<p><strong>Results from “Rate of mass loss across the instability threshold for Thwaites Glacier determines rate of mass loss for entire basin.”</strong></p> <p>The tar files herein contain multi-resolution grounding line position data and 4 km resolution output of modeled fields for all model runs. The region of interest represented is the Thwaites catchment in West Antarctica. The files for modeled fields have been coarsened or “flattened” to 4 km from their original adaptive mesh refinement (AMR) structure.</p> <p>Metadata contents</p> <p><em>1. Grounding line position data – text files</em></p> <p><em>2. Modeled Fields – HDF5 files</em></p> <p><em>3. BISICLES grid and coordinate system</em></p> <p> </p> <p><em>1. Grounding line position data</em></p> <p>Tar Files with "GLposition" in the title contain the annual grounding line positions, at cell faces, over the discretized Thwaites catchment for the specified model run. Each tar file contains a series of text files for a particular model run that used a specific background melt rate. The background marine melt rate is specified in the third field of the tar file name as delimited by the underscore character (“_”). Additionally, the last year of anomalous forcing before it was turned off leaving only the background marine melting is listed in the third field.</p> <p>nonuniformMM indicates the non-uniform background marine melting.</p> <p>uniformMM indicates the uniform background marine melting.</p> <p>260 and 270 are the last model years where anomalous marine melting were applied.</p> <p>After untarring a file, the naming convention for the individual text files is seen to be similar to the name of the respective tar file. The first field as delimeted by the underscore character contains either “glnonMM” or “gluniMM” followed by the last year of anomalous forcing used; e.g “gluniMM260”. The second field indicates the model year. Note that the last year forced is included for all runs.</p> <p>The text files contain three columns of data: an indicator of model resolution followed by <em>x- </em>and<em> y-</em>coordinates, respectively. Location coordinate units are meters and are BISICLES physical coordinates (see 3. BISICLES grid and coordinate system).</p> <p>For the first column:</p> <p>1 is 2 km resolution</p> <p>2 is 1 km resolution</p> <p>3 is 500 m resolution</p> <p>4 is 250 m resolution</p> <p>Zero (0) would be the base resolution of 4 km, however, all grounded ice was tagged to refine to level 1 so it does not appear in these files. Additionally, if a region was refined at a high resolution, then the grounding line positions for this region are not reported at any lower resolutions below this.</p> <p> </p> <p><em>2. Modeled fields</em></p> <p>Modeled fields are 4 km resolution in Chombo HDF5 file format. Each tar file contains the annual data as individual HDF5 files for the specified model run. The second field of the tar file as delimited by the underscore character specifies the background melt rate used and the last year of anomalous marine forcing (ramp).</p> <p>NonUniformMM260 indicates the non-uniform background marine melt rate with ramp shutoff after year 260.</p> <p>NonUniformMM270 same as above but ramp shutoff at year 270</p> <p>UniformMM260 indicates the spatially uniform background marine melt rate with ramp shutoff after year 270</p> <p>UniformMM270 same as above but ramp shutoff at year 270</p> <p>HDF5 files: The third field as delimited by the period (“.”) character shows the background melt rate used in individual HDF5 files and the fifth field indicates the model year.</p> <p>Contents of HDF5 files (field name: variable)</p> <p>xVel: velocity in the direction of the x-axis (m/a)</p> <p>yVel: velocity in the direction of the y-axis (m/a)</p> <p>Z_surface: upper ice surface elevation (masl)</p> <p>Z_bottom: underside surface ice elevation (masl)</p> <p>Z_base: bed elevation (masl)</p> <p>basal_friction: Basal friction coefficients</p> <p>div_uh: mass divergence</p> <p>mask: differentiates physical setting of cells (Note that coarsening introduces averages of numbers below at interfaces)</p> <p> grounded ice = 1</p> <p> floating ice = 2</p> <p> ocean = 4</p> <p> rock = 8</p> <p>basalThicknessSource: melt rate (m/a)</p> <p>surfaceThicknessSource: accumulation rate (m/a)</p> <p>surfaceThicknessBalance: sum of melt rate and accumulation rate (m/a)</p> <p> </p> <p><em>3. BISICLES grid and coordinate system</em></p> <p>The BISICLES model uses cell-centered grids with each cell represented by (i, j) pairs that begin numbering at (0,0) typically in the lower left hand corner of a domain. This project maintained the number ordering for the continental dataset such that (i = 366, j = 561) is the lower left cell for the included 4 km resolution HDF5 files and (i = 504, j=732) is the upper right cell. As the resolution is 4 km, this is noted as dx = 4000 in the HDF5 files.</p> <p>Since the data is located at cell centers, the physical coordinates relative to the BISICLES grid for a variable at (i, j) in meters is:</p> <p>(dx*(i + 0.5), dx*(j + 0.5)) = (BISICLES_X, BISICLES_Y)</p> <p>where dx is the cell resolution</p> <p>The translation from BISICLES physical coordinates (m) to polar stereographic projection in meters (standard parallel at -71 degrees) is as follows:</p> <p>(BISICLES_X – 3071500, BISICLES_Y – 3072500)</p> <p><br> </p> <p><br> </p> <p><br> </p> <p><br> </p> <p> </p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs.</p> <p>The dataset includes:</p> <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> <p>1) base-case model and base-case+Noise in one project,</p> <p>2) 30Hz model,</p> <p>3) no_thin_units model,</p> <p>4) no_karst model,</p> <p>5) perfect_illumination model.</p> <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> <p>1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project,</p> <p>2) model with 10x thicker Kapp Starostin layer in the overburden model.</p> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Synthetic seismic models - supplementary data)
<p>This dataset includes pre-loaded seismic models based on digital outcrop models (DOMs) representing 11 different case scenarios with varying signal processing setting (frequency, illumination angle, noise) and different geological models for the DOMs. The case scenarios are set up to investigate detection tresholds in seismic models of thin beds with a high degree of lithological variation. </p> <p>The dataset includes:</p> <ul> <li>A Petrel 2022 project with 11 pre-loaded seismic models based on two different geomodels presented in the paper.</li> <li>The input SGY files for each seismic section and PSF used.</li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Well data for elastic rock parameters - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets of the well data from boreholes on Spitsbergen. it also includes various maps of Svalbard to see where the boreholes are located. Rock parameters were plotted and determined using the blueback toolbox add-on to Petrel. </p> <p>The dataset used for rock parameter statisitcs includes:</p> <ul> <li>A Petrel 2022 project with well data (gamma ray, density, sonic, P-velocity, lithology) from the two boreholes Reindalspasset I (7816/2-1) and Tromsøbreen II (7617/1-2).</li> <li>Excel sheets with the wireline-log data used from the two boreholes. </li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Data from: Investigating the genetic architecture of conditional strategies using the environmental threshold model
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Data from: A framework for developing ecosystem-specific nutrient criteria: integrating biological thresholds with predictive modeling
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Data from: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex
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Nonlinear dynamic model and rollover threshold of a liquid tank semi-trailer
<p>A refined dynamic model of a tractor-semitrailer, including the nonlinear tire force and tank swing force, is established, and the fluid force and torque in the liquid tank are obtained through simulation by using the fluid mechanics software Fluent. Two kinds of tests, the tire corner stiffness test and tire roll stiffness test, are designed and performed using the KHAT flat bed test rig for tires, and a nonlinear model of the tire is obtained. The driving vehicle body state and handling stability of the liquid tank semi-trailer are studied through the model. The roll angle and lateral accelerationare used as the rollover characterization parameters. The dynamic lateral load transfer rate (LTR) is used as the rollover index to characterize the rollover threshold. The conclusions are of great significance for running status monitoring, rollover early warning and rollover avoidance controlfor liquid tank semi-trailers.</p>
Experimental and model-based investigation of the effect of the free- surface flow regime on the detection threshold of warm water inflow
<p>Experimental and numerical injections of warm water into a free surface flow. Different flow regimes were tested and detection thresholds were determined.</p> <p>Experiments were done in an outdoor flume, in June 2016. They were monitored by Fiber-Optic Distributed Temperature Sensing with a double-ended configuration and 20s time resolution. Two flume flow rates Q were tested. For each, a few injection flow rates were done, leading to a total of 10 experiments characterized by an injection ratio R. Only calibrated data are uploaded.</p> <p>Simulations were carried out on HEC-RAS 5.0, a 1D software developed by the US Army Corps of Engineers, and based on the experimental flume geometry. A total of 90 simulations were tested with different flow rates, injection percentage and thermal contrast ∆T between flowing water and injected water.</p> <p>Supplementary data can be found to understand the flume geometry, hydraulic characteristics (Reynolds, Peclet and Froude numbers) and exchanges with the atmosphere for both simulations and experiments.</p>
Threshold models improve estimates of molt parameters in datasets with small sample sizes
<p>The timing of events in birds' annual cycles is important to understanding life history evolution and response to global climate change. Molt timing is often measured as an index of the sum of grown feather proportion or mass within the primary flight feathers. The distribution of these molt data over time has proven difficult to model with standard linear models. The parameters of interest are at change points in model fit over time, and so least squares regression models that assume molt is linear violate the assumption of even variance. This has led to the introduction of other nonparametric models to estimate molt parameters. Hinge models directly estimate changes in model fit, and have been used in many systems to find change points in data distributions. Here, we apply a hinge model to molt timing, through the introduction of a double-hinge threshold model. We then examine its performance in comparison to current models using simulated and empirical data. We find that the Underhill-Zuchinni (UZ) and Pimm models perform well under many circumstances, and appears to outperform the threshold model in datasets with very high variance. The double-hinge threshold model outperforms the UZ model at low sample sizes of birds in active molt, and shorter molt durations, and provides more realistic confidence intervals at small sample sizes. </p>
Threshold models improve estimates of molt parameters in datasets with small sample sizes
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Nonlinear dynamic model and rollover threshold of a liquid tank semi-trailer
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Data from: Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the Latent Environmental Threshold Model
Conditional strategies are the most common form of discrete phenotypic plasticity. In a conditional strategy, the phenotype expressed by an organism is determined by the difference between an environmental cue and a threshold, both of which may vary among individuals. The Environmental Threshold model (ETM) has been proposed as a mean to understand the evolution of conditional strategies, but has been surprisingly seldom applied to empirical studies. A hindrance for the application of the ETM is that often, the proximate cue triggering the phenotypic expression and the individual threshold are not measurable, and can only be assessed using a related observable cue. We describe a new statistical model that can be applied in this common situation. The Latent Environmental Threshold Model (LETM) allows for a measurement error in the phenotypic expression of the individual environmental cue and a purely genetically determined threshold. We show that coupling our model with quantitative genetic methods allows an evolutionary approach including an estimation of the heritability of conditional strategies. We evaluate the performance of the LETM with a simulation study and illustrate its utility by applying it to empirical data on the size-dependent smolting process for stream-dwelling Atlantic salmon juveniles.
The Progressively Lowered Stress Threshold Model
ClinicalTrials.gov study NCT04305652. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.