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15 results for “green wave”
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Data from: Migrating bison engineer the green wave
<p>Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world's most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone's bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.</p>
Data and code from: Fruit bat migration matches green wave in seasonal landscapes
<p><span>Migrating </span><span>grazers and carnivores</span><span> respond to seasonal changes in the environment and often match peaks in resource abundance. However, it is unclear if and how frugivorous animals use phenological events to time migration, especially in the tropics.<strong> </strong></span>The straw-colored fruit bat (<em>Eidolon helvum</em>), Africa's most gregarious fruit bat, forms large seasonal colonies throughout much of sub-Saharan Africa. We hypothesized that aggregations of <em>E. helvum</em> match the timing of their migration with phenologies of plant growth or precipitation.<strong> </strong>Using monthly colony counts from across much of the species' range, we matched peak colony size to landscape phenologies and explored the variation among colonies matching the overall closest phenological event.<strong> </strong>Peak colony size was closest to <span>the </span><span>peak</span><span> instantaneous</span><span> rate of green-up</span><span>,</span><span> and sites with closer temporal matching were associated with higher maximum greenness, short growing season, and larger peak colony size. <em>E</em></span><span><em>idolon</em></span><em><span> helvum</span></em><span> seem to time their migrations to move into highly seasonal landscapes to exploit short-lived explosions of food and may benefit from collective sensing to time migrations.<strong> </strong></span>The link between rapid changes in colony size and phenological match may also imply potential collective sensing of the environment. O<span>verall decreasing bat numbers along with various threats might cause this property of large colonies to be lost</span><span>.<strong> </strong></span>R<span>emote sensing data</span><span>, although, indirectly linked to fruiting events,</span><span> can potentially be used to globally describe and predict the migration of frugivorous species in a changing world</span><span>.</span></p>
Data for: Industrial energy development decouples ungulate migration from the green wave
<p>The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed "green-wave surfing"), is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer (<em>Odocoileus hemionus</em>) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behavior before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behavior of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behavior present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.</p>
Data from: A test of the green wave hypothesis in omnivorous brown bears across North America
<p>Herbivorous animals tend to seek out plants at intermediate phenological states to improve energy intake while minimizing consumption of fibrous material. In some ecosystems, the timing of green-up is heterogeneous and propagates across space in a wave-like pattern, known as the green wave. Tracking the green wave allows individuals to prolong access to higher-quality forage. While there is a plethora of empirical support for such behavior in herbivorous taxa, the green wave hypothesis (GWH) is nuanced based on factors such as body morphometrics and digestive capacity. Furthermore, little is known about whether other taxa, such as omnivores, track the green wave. Our objective was to assess whether the GWH can be extended to explain the movements of omnivores. Using GPS collar data from seven populations (n = 127 individuals) of brown bears (<em>Ursus</em> <em>arctos</em>) across their entire North American range, we first tested whether bears tracked the green wave. Using conditional resource selection functions, we found that variation in proxies of vegetative forage quality better-explained movement and habitat selection than proxies of forage biomass in over half of the bears in our study, providing evidence of green wave tracking. Second, we assess factors that explained variation in green wave tracking using linear mixed-effects models. Green wave tracking in brown bears was explained by the variation in availability of green-up within spring home ranges, and how green-up transitioned across those home ranges. Our results demonstrate that the GWH can partially explain movement of a non-migratory omnivorous species, extending the generality of the GWH as a broad predictor of animal space use. The green wave is another resource wave brown bears track, and our findings help predict brown bear space use, which can be used to guide conservation and habitat restoration efforts.</p>
Data from: A test of the green wave hypothesis in omnivorous brown bears across North America
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Data for: Industrial energy development decouples ungulate migration from the green wave
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Data from: Migrating bison engineer the green wave
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Matching the green wave: Growing season length determines embryonic diapause in roe deer
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Data and code from: Fruit bat migration matches green wave in seasonal landscapes
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Data from: A migratory northern ungulate in the pursuit of spring: jumping or surfing the green wave?
The Forage Maturation Hypothesis (FMH) states that herbivores migrate along a phenological gradient of plant development in order to maximize energy intake. Despite strong support for the FMH, the actual relationship between plant phenology and ungulate movement has remained enigmatic. We linked plant phenology (MODIS-NDVI) and space use of 167 migratory and 78 resident red deer (Cervus elaphus) using a space-time-time matrix of "springness", defined as the instantaneous rate of green-up (IRG). Consistent with the FMH, migrants experienced substantially greater access to early plant phenology than residents. Deer were also more likely to migrate in areas where migration led to greater gains in springness. Rather than "surfing the green wave" during migration, migratory red deer moved rapidly from the winter to the summer range, thereby "jumping the green wave". However, migrants, and to a lesser degree residents, did track phenological green-up through parts of the growing season by making smaller-scale adjustments in habitat use. We conclude that, whereas in some systems migration itself is a way to surf the green wave, in others it may simply be a means to re-connect with phenological spring at the summer range.
Data from: A migratory northern ungulate in the pursuit of spring: jumping or surfing the green wave?
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Blue Green Wave hydrological data
<p>The Blue Green Wave of Champs-sur-Marne (France) represents the largest green roof (1 ha) of the Greater Paris Area. The Hydrology, Meteorology and Complexity lab of Ecole des Ponts ParisTech has chosen to convert this architectural building as a full-scale monitoring site devoted to study the performances of green infrastructures in stormwater management. For this purpose, the relevant components of the water balance during a rainfall event have been monitored: rainfall, water content in the substrate and the discharge flowing out of the infrastructure. Data provided by adapted measurement sensors were collected during 78 days between February and May 2018. The related raw data and a python program transforming them into hydrological quantities and providing some first elements of analysis have been made available. These measurements are useful to better understand the processes (infiltration and retention) conducted their hydrological performances, and their spatial variability due to substrate heterogeneity.</p>
Surface wave Green's functions for WASP slip inversion code
<p>Surface wave GFs for use with WASP. File needs to be placed in ./fotran_code/gfs_nm/</p>
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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.