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849 results for “linear”
Walking the line: Investigating biophysical characteristics related to wildlife use of linear features
<p>Habitat restoration is a necessary component of wildlife conservation in anthropogenic landscapes. To ensure restoration initiatives achieve the desired effects on wildlife communities, it is useful to investigate how animals use landscape features. Understanding the relationships between wildlife use and ecological cues provides specific and measurable targets that can be used to measure restoration success. In western Canada, linear feature networks formed by seismic lines, pipelines, and roads have altered the boreal forest landscape and resulted in population declines for woodland caribou. Restoration is aimed at supporting caribou recovery by deterring linear feature use by caribou predators and ungulate competitors. Information on how linear feature characteristics facilitate or deter wildlife use supports restoration initiatives by providing specific targets for restoration. Here, we used wildlife track and sign data to investigate biophysical characteristics related to the use of linear features by canines, bears, deer, elk, and moose in caribou ranges of west-central and north-western Alberta and British Columbia. We built generalized linear mixed models consistent with three hypotheses that could explain likely mechanisms for use: 1) ease of movement, 2) risk avoidance, and 3) resource availability (prey and forage).</p> <p>Moose, deer, elk, and bears were more likely to use linear features with either human or game trails. Bears and canines were less likely to use seismic lines with greater lateral vegetation cover and taller vegetation, respectively. Moose, deer, and elk were more likely to use linear features with greater cover of ungulate forage taxa such as willow, birch, sedges, and forbs. These results suggest that restoration focusing on trails, online vegetation structure, and online vegetation type should deter predators and ungulate prey species to the overall benefit of caribou. Our study corroborates the findings of other research recommending structural and functional restoration utilizing high-intensity line blocking and vegetative regeneration. We provide specific targets for linear feature restoration to assist in prioritization according to restoration objectives, which translates to a broader goal of linking local-level restoration actions to landscape-level conservation goals. This approach to restoration has implications for any major system experiencing anthropogenic landscape change.</p>
Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters
<p>Linear instability and quasilinear fluxes calculated with the <a href="https://genecode.org">GENE</a> plasma microturbulence code. The input parameters correspond to variations of ITER baseline scenario parameters calculated by integrated modelling using the <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/wikis/home">QuaLiKiz</a> transport model, as described in <a href="https://iopscience.iop.org/article/10.1088/1361-6587/ab5ae1">P. Mantica et al (2019) Plasma Physics and Controlled Fusion 62 014021</a>. <br> <br> The quasilinear fluxes were calculated with a bespoke saturation rule calibrated to dedicated GENE nonlinear simulations carried out in the same ITER regime. The quasilinear flux dataset was fit with a neural network (NN) regression model, which was then used for ITER baseline integrated modelling and performance projections. Alongside the linear stability dataset, the two separate quasilinear datasets correspond to an unfiltered dataset, and a filtered and data-augmented dataset used for the NN regression. For full details, please see reference [J. Citrin et al (2023) <em>submitted to Physics of Plasmas</em>]. <br> <br> The dimensionless input and output variables correspond to the IMAS gyrokinetic IDS standards. The major radius was taken as the reference length. A key and further details are found below.</p> <table> <caption><strong>Description of CSV file columns</strong></caption> <tbody> <tr> <td>rhoN</td> <td>Normalized toroidal flux coordinate</td> </tr> <tr> <td>ky</td> <td>Binormal wavenumber, normalized to the reference (ion scale) gyroradius</td> </tr> <tr> <td>omt_DT</td> <td>Normalized logarithmic main ion temperature gradient (<span class="math-tex">\(R/L_{Ti}\)</span>)</td> </tr> <tr> <td>omt_el</td> <td>Normalized logarithmic electron temperature gradient (<span class="math-tex">\(R/L_{Te}\)</span>)</td> </tr> <tr> <td>omn_el</td> <td>Normalized logarithmic electron density gradient (<span class="math-tex">\(R/L_{ne}\)</span>)</td> </tr> <tr> <td>s</td> <td>Magnetic shear</td> </tr> <tr> <td>q</td> <td>Safety factor (q-profile)</td> </tr> <tr> <td>gamma</td> <td>Instability growth rate (gyroBohm normalisation with IMAS standard)</td> </tr> <tr> <td>omega</td> <td>Instability frequency (gyroBohm normalisation). Positive frequencies correspond to the ion diamagnetic direction</td> </tr> <tr> <td>kperp2</td> <td>Square of perpendicular wavenumber weighted over poloidal mode structure <span class="math-tex">\(\langle{k_\perp^2}\rangle\)</span></td> </tr> <tr> <td>Q_DT</td> <td>ky-dependent ion heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>Q_el</td> <td>ky-dependent electron heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>G_el</td> <td>ky-dependent electron particle flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>QDT</td> <td>Quasilinear ion heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Qe</td> <td>Quasilinear electron heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Ge</td> <td>Quasilinear electron particle flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>QDT_ITG</td> <td>Quasilinear ion heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_ITG</td> <td>Quasilinear electron heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_ITG</td> <td>Quasilinear electron particle flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>QDT_TEM</td> <td>Quasilinear ion heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_TEM</td> <td>Quasilinear electron heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_TEM</td> <td>Quasilinear electron particle flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> </tbody> </table> <p> </p>
SOLPS-ITER simulations of a vapour box design for the linear device Magnum-PSI
<p>Replication package for manuscript "SOLPS-ITER simulations of a vapour box design for the linear device Magnum-PSI" submitted to PPCF.</p>
Modular nanomagnet design for spin qubits confined in a linear chain
<p>supporting data</p>
Data for: Early radial positional information in the cochlea is optimized by a precise linear BMP gradient and enhanced by SOX2
<p>Positional information encoded in signaling molecules is essential for early patterning in the prosensory domain of the developing cochlea. The sensory epithelium, the organ of Corti, contains an exquisite repeating pattern of hair cells and supporting cells. This requires precision in the morphogen signals that set the initial radial compartment boundaries, but this has not been investigated. To measure gradient formation and morphogenetic precision in developing cochlea, we developed a quantitative image analysis procedure measuring SOX2 and pSMAD1/5/9 profiles in mouse embryos at embryonic day (E)12.5, E13.5, and E14.5. Intriguingly, we found that the pSMAD1/5/9 profile forms a linear gradient up to the medial ~75% of the PSD from the pSMAD1/5/9 peak in the lateral edge during E12.5 and E13.5. This is a surprising activity readout for a diffusive BMP4 ligand secreted from a tightly constrained lateral region since morphogens typically form exponential or power-law gradient shapes. This is meaningful for gradient interpretation because while linear profiles offer the theoretically highest information content and distributed precision for patterning, a linear morphogen gradient has not yet been observed. Furthermore, this is unique to the cochlear epithelium as the pSMAD1/5/9 gradient is exponential in the surrounding mesenchyme. In addition to the information-optimized linear profile, we found that while pSMAD1/5/9 is stable during this timeframe, an accompanying gradient of SOX2 shifts dynamically. Last, through joint decoding maps of pSMAD1/5/9 and SOX2, we see that there is a high-fidelity mapping between signaling activity and position in the regions that will become Kölliker's organ and the organ of Corti. Mapping is ambiguous in the prosensory domain precursory to the outer sulcus. Altogether, this research provides new insights into the precision of early morphogenetic patterning cues in the radial cochlea prosensory domain.</p>
Rove beetle (Staphylinidae) assemblages following the cumulative effect of wildfire and linear footprint in Boreal treed peatlands of northeastern Alberta (Canada)
<p>Cumulative effects of anthropogenic and natural disturbances have become increasingly relevant in the context of biodiversity conservation. Oil and gas (OG) exploration and extraction activities have created thousands of kilometers of linear footprints in boreal ecosystems of Alberta, Canada. Among these disturbances, seismic lines (narrow corridors cut through the forest) are one of the most common footprints and have become a significant landscape feature influencing the maintenance of forest interior habitats and biodiversity. <a name="_Hlk118288505"></a>Wildfire is a common stand-replacing natural disturbance in the boreal forest, and as such, it is hypothesized that its effects can mitigate the linear footprint associated with OG exploration, but only a few studies have examined its effectiveness. We studied the short-term (1 year post-fire) response of rove beetle assemblages to the combined effects of wildfire and linear footprint in forest, edge and seismic line habitats at burned and unburned peatlands along the southwest perimeter of the 2016 Horse River wildfire (Fort McMurray). While rove beetle species richness was higher in seismic lines in both burned and unburned habitats compared to the adjacent peatland, diversity was greater only in seismic lines of burned areas. Abundance was lower in the burned adjacent peatland but similarly higher in the remaining habitats. Assemblage composition on seismic lines was significantly different from that in the adjacent forest and edge habitats within both burned and unburned sites. Moreover, species composition in burned seismic lines was different to either unburned lines or burned forest and edge. <a name="_Hlk89787659"></a><em>Euaesthethus laeviusculus</em> and <em>Gabrius picipennis</em> were indicator species of burned line habitats, are sensitive to post-fire landscape and can occupy wet habitats with moss cover more efficiently than when these habitats are surrounded by unburned forest. Although these results are based on short-term responses, they suggest that wildfire did not reduce the linear footprint, and instead, the cumulative effect of these two disturbances had a more complex influence on rove beetle recovery at the landscape level than for other invertebrates. Therefore, continued monitoring of these sites can become useful to evaluate changes over time and to better understand longer-term biodiversity responses to the cumulative effects of wildfire and linear disturbances in boreal treed peatlands, given the long-lasting effect of such disturbances.</p>
Results of linear ocean model experiment for: Dual wave energy sources for the Atlantic Niño events identified by wave energy flux in case studies
<p>These are sensitivity experiments designed by manipulating the wind forcing that drives the linear ocean model to investigate the difference in equatorial waves in 1999, 2019, and 2021. </p>
Predicting Mathematics Anxiety and Achievement: Unveiling the Significance of Student and Teacher Attributes through Hierarchical Linear Modeling
<p>This study aimed to determine the predictive power of student and teacher characteristics on students' math anxiety and achievement.</p>
Experimental determination of differential scattering coefficients for nickel by means of linearly polarized x-ray radiation
<p>This data set contains the experimental results of differential scattering coefficients for nickel as determined in the publication "Experimental determination of differential scattering coefficients for nickel by means of linearly polarized X-ray radiation".</p>
Frequency Tunable Electromagnetic Vibration Energy Harvester Utilizing Piecewise Linear Nonlinearity Dataset
<p>This is the data gathered during the experiments referenced in the manuscript. All files are .mat and native to Matlab. The variable "time' is the time array gathered during operation corresponding to the 'data' array of the same length. 'data' contains 2-3 columns depending on the file. The first column is always the base displacement voltage output, the second is the mass displacement voltage output, and if a third column is included then this is the measured voltage at the load applied to the harvester.</p>
Allometric parameters and linear breaking strength of intestinal segments
<p>The dataset reports bodyweight, crown-rump-length, and the linear breaking strengths of small and large intestinal segments in German Landrace pigs.</p>
Data: Multilayer integration in silicon nitride: decoupling linear and nonlinear functionalities for ultralow loss photonic integrated systems
<p>This folder contains the data relative to the paper with title:<br> Multilayer integration in silicon nitride: decoupling linear and nonlinear functionalities for ultralow loss photonic integrated systems<br> by<br> Marcello Girardi, Óskar Helgason, Alexander Caut, Magnus Karlsson, Anders Larsson, Victor Torres Company</p> <p>Chalmers University of Technology</p>
Attack time analysis in dynamic attack trees via integer linear programming
<p>Matlab code for the experiments of the paper "Attack time analysis in dynamic attack trees via integer linear programming" by Milan Lopuhaä-Zwakenberg & Mariëlle Stoelinga.</p> <p>The code can be accessed either via the virtual machine in Artifact_MILPforDATs_revised.zip, or through the code directly in Matlab_code.zip. The results are in Paper_results.zip.</p> <p>To run the code, installations of Matlab and Gurobi are required.</p>
Linear machine learning based force matching for amorphous silica: How close are the classical two-body potentials to ab initio calculations?
<p>Please later see our manuscript (in submission) for details.</p>
Translocation of linearized full-length proteins through an engineered nanopore under opposing electrophoretic force
<p>This database contains raw electrophysiology data and MD data, organised in two parts: part 1 corresponds to electrophysiology traces and part 2 corresponds to MD files. For detailed information see below.</p> <p><strong>Part 1: electrophysiology data </strong></p> <p>Data separated in two main categories: main text data and SI (only) data. The electrophysiology data is named in the following format: </p> <p>main_FnX_CytK mutant_buffer_substrate_cis_applied potential, where n = figure number and X = panel</p> <p>Data from the main text:</p> <p><strong>Figure 2</strong>:</p> <p>--> C) CytK WT + S1 substrate (file names start with main_F2C_CytK WT)</p> <p>--> D) CytK 2E-4D (K128D K155D Q145D S151D) + S1 substrate (main_F2D...)</p> <p>--> E) CytK 2E-4D (K128D K155D Q145D S151D) + tzatziki substrate (main_F2E...)</p> <p>--> F) CytK 2E-4D (K128D K155D Q145D S151D) + mujdei substrate (main_F2F...)</p> <p><strong>Figure 4</strong>:</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + malE219a substrate (main_F4A...)</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + H152A-GBP substrate (main_F4B...)</p> <p>--> CytK 2E-4D (K128D K155D Q145D S151D/ 4D) + W30G-W133L-DHFR substrate (main_F4C...)</p> <p> </p> <p><strong>Supporting information figures </strong></p> <p>general name: SI_Sx_CytK mutant_buffer_substrate_cis_applied potential, where x = number figure from supporting information</p> <p> </p> <p>Figure S4. S1 translocation through the K128D K155D CytK mutant nanopore --> SI_S4...</p> <p>Figure S5. S1 translocation through the K128D K155D Q145D CytK mutant nanopore --> SI_S5...</p> <p>Figure S6. S1 translocation through the K128D K155D T147D CytK mutant nanopore --> SI_S6...</p> <p>Figure S7. Translocation of S1 through the 2E-1D-1Q-Q122D-CytK nanopore --> SI_S7...</p> <p>Figure S8. Translocation of S1 through 2E-4D-CytK nanopores --> see F2D main text (main_F2D...)</p> <p>Figure S9. Tzatziki and the CytK 2E-2D nanopore --> SI_S9...</p> <p>Figure S10. Tzatziki translocation through the K128D Q145D S151D K155D CytK nanopore --> see F2D main text (main_F2E...)</p> <p>Figure S11. Tzatziki translocation through the K128D K155D Q145D CytK nanopore --> SI_S11...</p> <p>Figure S12. Tzatziki translocation through the K128D K155D T147D CytK mutant nanopore --> SI_S12...</p> <p>Figure S14. Translocation of mujdei through 2E-4D-CytK nanopores --> see F2D main text (main_F2F...)</p> <p>Figure S22. Translocation of malE219a through 2E-4D-CytK nanopores in 2 M urea --> see F4A main text (main_F4A...)</p> <p>Figure S23: Translocation GBP H152A through the 2E-4D CytK nanopore in 2.4 M urea --> see F4B main text (main_F4B...)</p> <p>Figure S24. Translocation of W30G-W133L-DHFR through 2E-4D-CytK nanopore in 2.6 M urea --> see F4B main text (main_F4B...)</p> <p>Figure S26. MalE219a translocation through the 2E-4D CytK mutant in 1 M and 1.8 M Gu.HCl --> SI_S26... (1 M GuHCl and 1.8 M GuHCl are included in the file name)</p> <p>Figure S27: WT-CytK tested with the malE219a and malE219aD10ssrA proteins in 1.5 M Gu.HCl --> SI_S27...</p> <p>Original SDS-PAGE gels of the substrates in a powerpoint file</p> <p> </p> <p><strong>Part 2: MD data</strong></p> <p>The data corresponding to the MD simulations is bundled in a zip file named MD_files.zip</p> <p>This file contains the following subfiles linked to <strong>main text Figure 3</strong>:</p> <p><em>panel A</em>:</p> <p>- main_F3A_CytK_E2-D2 </p> <p>- main_F3A_CytK_E2-D3</p> <p>- main_F3A_CytK_E2-D4</p> <p>- main_F3A_CytK_WT</p> <p><em>panels C, D and E</em>:</p> <p>- main_F3CDE_CytK_E2-D4_rep1</p> <p>- main_F3CDE_CytK_E2-D4_rep2</p> <p>- main_F3CDE_CytK_E2-D4_rep3</p> <p>and files linked to the supporting information <strong>Figure S20</strong>:</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep1</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep2</p> <p>- SI_S20_ABC_CytK_E2-D4_TZA11_rep3</p>
Data for the paper: Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows
<p>These are the data files for the paper:</p> <p>Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows</p> <p>authored by: Victor Chéron, Fabien Evrard, and Berend van Wachem</p> <p>#Files<br> Temporally averaged drag, lift and torque coefficients are written in .txt files stored in the folder ResultsCoefficients.<br> Python scripts used to derive the correlations are stored in the folder PythonScripts.<br> Results of an example simulation are provided in the folder SimulationResults.<br> A Python script with the final correlations of the manuscript is also included.</p> <p>#ResultsCoefficients<br> The .txt files are split per coefficient, aspect ratio and shear rate, which can be identified by the name of the .txt file<br> The results obtained for the torque coefficient of the particle of aspect ratio 2.5 for a uniform flow configuration are given in the file:<br> ### Uniform-Torque-Angles-Size2-5.txt<br> The results obtained for the lift coefficient of the particle of aspect ratio 10 for a shear rate 0.2 configuration are given in the file:<br> ### Shear02-Lift-Angles-Size10.txt<br> In these files, the results are ordered per orientation angle and particle Reynolds number.</p> <p>#PythonScripts<br> The python scripts for processing the data are split among three main functions in two files:<br> - Getter.py (reads the .txt files storing the coefficients - separate functions for the drag, lift and torque coefficients - as well as fill up the missing points for spherical particles using the Kurose and Komori correlations)<br> - generalmain (calls the Getter.py function). The Getter.py is called from the generalmain.py file. (run python3<br> generalmain.py). This will return a 1D column vector ordering the variables used to derive the correlations:<br> - Coefficients<br> - Reynolds number<br> - Orientation Angle<br> - Shear rate<br> - Aspect ratio<br> - Additional coefficients.</p> <p>The Python script ManuscriptCorrelations.py has the correlations as derived in the manuscript. This routine can be used to query the correlations, for plotting them or using them in a simulation.</p> <p><br> #SimulationResults<br> Simulation results of one case are provided:<br> - Aspect ratio 2.5, particle Reynolds number 200, orientation angle 90, Shear rate 0.2<br> The fields and particles information are stored in hdf5 file format.<br> A .xmf wrapper file is provided to read the simulation results in paraview.<br> Data up to 40 seconds of real time are provided due to storage limits.</p> <p><br> This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 448292913.</p>
Data from: Ocean deoxygenation caused non-linear responses in the structure and functioning of benthic ecosystems
<p><span>The O<sub>2 </sub>content of the global ocean has been declining progressively over the past decades, mainly because of human activities and global warming. Nevertheless, how long-term deoxygenation affects macrobenthic communities, sediment biogeochemistry and their mutual feedback remains poorly understood. Here, we evaluate the response of the benthic assemblages and biogeochemical functioning to decreasing O<sub>2 </sub>concentrations along the persistent bottom-water dissolved O<sub>2</sub> gradient of the Estuary and Gulf of St. Lawrence (QC, Canada). We report several of non-linear biodiversity and functional responses to decreasing O<sub>2</sub> concentrations, and identify an O<sub>2</sub> threshold that occurs at approximately at 63 µM. Below this threshold, macrobenthic community assemblages change, and bioturbation rates drastically decrease to near zero. Consequently, the sequence of electron acceptors used to metabolize the sedimentary organic matter is squeezed towards the sediment surface while reduced compounds accumulate closer (as much as 0.5 to 2.5 cm depending on the compound) to the sediment-water interface. Our results illustrate the capacity of bioturbating species to compensate for the biogeochemical consequences of hypoxia and can help to predict future changes in benthic ecosystems.</span></p>
Lattice-tip Versus Irrigated-tip Catheter for Linear Ablation of the Cavotricuspid Isthmus. A Multicenter, Randomized Study.
ClinicalTrials.gov study NCT07078760. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Trial Comparing the Effects of Linear Versus Nonlinear Aerobic Training in Women With Operable Breast Cancer
ClinicalTrials.gov study NCT01186367. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Non-Linear Imaging of Skin In Vivo
ClinicalTrials.gov study NCT05410964. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
ScienceDex guides
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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.