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1,548 results for “Trajectories”
Trajectory Data for publication
<p>Supplementary Data</p>
Supplemental Table and Figures for TSH Trajectories in the ELSA-Brasil Cohort
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Supplemental material for the paper, "Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment"
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Coricello et al. MD Trajectory Data for WT Human ASNS (Dimer)
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Coricello et al. MD Trajectory Data for WT Human ASNS (Enzyme Monomer/Aspartyl-AMP Complex)
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Coricello et al. MD Trajectory Data for WT Human ASNS (Apo Enzyme Monomer)
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Coricello et al. MD Trajectory Data for R142I ASNS Variant
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Alpha-Beta parameter swap trajectories 10.1039/D4CC04415K
<p>alpha monosaccharides simulated with the beta parameters in water boxes for 10.1039/D4CC04415K</p>
Assessing the trajectories of local plant community change: a literature review
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Wind Tunnel Mosquito Trajectory Data
<p>Mosquitoes track odors, locate hosts, and find mates visually. The color of a food resource, such as a flower or warm-blooded host, can be dominated by long wavelengths of the visible light spectrum (green to red for humans) and is likely important for object recognition and localization. However, little is known about the hues that attract mosquitoes or how odor affects mosquito visual search behaviors. We use a real-time 3D tracking system and wind tunnel that allows careful control of the olfactory and visual environment to quantify the behavior of more than 1.3 million mosquito trajectories. We find that CO<sub>2</sub> induces a strong attraction to specific spectral bands, including those that humans perceive as cyan, orange, and red. Sensitivity to orange and red correlates with mosquitoes' strong attraction to the color spectrum of human skin, which is dominated by these wavelengths. The attraction is eliminated by filtering the orange and red bands from the skin color spectrum and by introducing mutations targeting specific long-wavelength opsins or CO<sub>2 </sub>detection. Collectively, our results show that odor is critical for mosquitoes' wavelength preferences and that the mosquito visual system is a promising target for inhibiting their attraction to human hosts.</p>
Population genomic, olfactory, dietary, and gut microbiota analyses demonstrate the unique evolutionary trajectory of feral pigs
<p><span><span><span><span><span><span><span><span><span><span><span>Domestication is an intriguing evolutionary process. Many domestic populations are subjected to strong human-mediated selection, and when some individuals return to the wild, they are again subjected to selective forces associated with new environments. Generally, these feral populations evolve into something different from their wild predecessors and their members typically possess a combination of both wild and human selected traits. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Feralisation can manifest in different forms on a spectrum from a wild to a domestic phenotype. This depends on how the re-wilded domesticated populations can re-adapt to natural environments based on how much potential and flexibility the ancestral genome retains after its domestication signature. Whether feralisation leads to the evolution of new traits that do not exist in the wild or to convergence with wild forms, however, remains unclear. To address this question, we performed population genomic, olfactory, dietary, and gut microbiota analyses on different populations of <i>Sus scrofa </i>(wild boar, hybrid, feral and several domestic pig breeds). Porcine Single Nucleotide Polymorphisms (SNPs) analysis shows that the feral population represents a cluster distinctly separate from all others. Its members display signatures of past artificial selection, as demonstrated by values of <i>F</i><sub>ST</sub> in specific regions of the genome and bottleneck signature, such as the number and length of runs of homozygosity. Generalised <i>F</i><sub>ST</sub> values, re-acquired olfactory abilities, diet, and gut microbiota variation show current responses to natural selection. Our results suggest that feral pigs are an independent evolutionary unit which can persist so long as levels of human intervention remain unchanged.</span></span></span></span></span></span></span></span></span></span></span></p>
msd-errors - LLZO simulation trajectories
<p>Simulation trajectories at a range of temperatures of LLZO, produced using MetalWalls.</p>
MD simulation trajectory and related files for POPC bilayer (GAFFlipid, Gromacs 4.5)
<p>Equilibrated POPC lipid bilayer simulation ran with Gromacs 4.5, GAFFlipid force field (http://dx.doi.org/10.1039/C2SM26007G), 30ns, T=303K, 126 POPC molecules, 3948 water molecules. This data is ran for the nmrlipids.blospot.fi project. More details from nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi. If data is used, please cite nmrlipids.blogspot.fi project and the original publication of the parameters: Dickson et al. Soft Matter, 2012,8, 9617-9627 http://dx.doi.org/10.1039/C2SM26007G.</p>
MD simulation trajectory and related files for POPC bilayer (Chiu et al. Gromos version, Gromacs 4.5)
<p>Equilibrated POPC lipid bilayer simulation ran with Gromacs 4.5, Chiu et al. force field (http://dx.doi.org/10.1021/jp807056c), 50ns, T=298K, 128 POPC molecules, 3552 water molecules. This data is ran for the nmrlipids.blospot.fi project. More details from nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi. If data is used, please cite nmrlipids.blogspot.fi project and the original publication of the parameters (http://dx.doi.org/10.1021/jp807056c).</p>
Data from: Terminal attack trajectories of peregrine falcons are described by the proportional navigation guidance law of missiles
The ability to intercept uncooperative targets is key to many diverse flight behaviors, from courtship to predation. Previous research has looked for simple geometric rules describing the attack trajectories of animals, but the underlying feedback laws have remained obscure. Here, we use GPS loggers and onboard video cameras to study peregrine falcons, Falco peregrinus, attacking stationary targets, maneuvering targets, and live prey. We show that the terminal attack trajectories of peregrines are not described by any simple geometric rule as previously claimed, and instead use system identification techniques to fit a phenomenological model of the dynamical system generating the observed trajectories. We find that these trajectories are best—and exceedingly well—modeled by the proportional navigation (PN) guidance law used by most guided missiles. Under this guidance law, turning is commanded at a rate proportional to the angular rate of the line-of-sight between the attacker and its target, with a constant of proportionality (i.e., feedback gain) called the navigation constant (N). Whereas most guided missiles use navigation constants falling on the interval 3 ≤ N ≤ 5, peregrine attack trajectories are best fitted by lower navigation constants (median N < 3). This lower feedback gain is appropriate at the lower flight speed of a biological system, given its presumably higher error and longer delay. This same guidance law could find use in small visually guided drones designed to remove other drones from protected airspace.
Lagrangian trajectories representing surface drift from the Cape Verde islands
<p><strong>Example trajectories from a biophysical Lagrangian simulation</strong></p> <p>This is 25x 10.000 example trajectories from biophysical experiments performed with Parcels.</p> <p>The trajectories are a tiny subset of a much bigger collection of trajectories that have been simulated with the aim of learning about the fate of particles drifting away from the Cape Verde islands.</p> <p><em>Note, that these trajectories should not be used for biological or physical science but merely serve as study objects for developing, testing, or benchmarking (stasticical) methods and algorithms.</em></p> <p><strong>Details of the experiments</strong></p> <p>The trajectories are taken from 25 sets of biophysical simulations which differ in the year they represent. Particles are seeded between mid of August and start of December of the years 1993 to 2017. They are subject to Ocean surface currents simulated by a high-resolution ocean model and subject to Stokes Drift estimated by a wave simulation data provided by the Copernicus Marine Service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>).</p> <p><strong>Data</strong></p> <p>The full data come as a Zarr store inside of a ZIP file <code>cape_verde_drift_trajectories_1993-2017.zarr.zip</code> which you need to download and unzip to be able to read it, e.g., with Xarray's <code>open_zarr</code> method. There are subsets of 10000 trajectories provided as compressed csv files split into years called <code>cape_verde_drift_trajectories_1-10000_1993.csv.gz</code> ... <code>cape_verde_drift_trajectories_1-10000_2017.csv.gz</code>. And there are subsets of 100 trajectories provided as un-compressed csv files split into years <code>called cape_verde_drift_trajectories_1-100_1993.csv</code> ... <code>cape_verde_drift_trajectories_1-100_2017.csv</code>.</p> <p><strong>Variables and their meaning</strong></p> <ul> <li><code>"obs"</code> contains the time step since the larva started to exist. Each trajectory covers up to 881 daily positions.</li> <li><code>"traj"</code> indicates the trajectory ID.</li> <li><code>"lat"</code> and <code>"lon"</code> contain the horizontal positions in degrees Latitude and Longitude.</li> <li><code>"temp"</code> contains the ambient temperature in degrees Celsius the simulated larva would have felt.</li> <li><code>"time"</code> contains time stamps for each position.</li> <li><code>"z"</code> contains the vertical positions of the simulated larva in meters counted downwards. As <code>"z</code>" does not vary (all particles are at the surface), it is omitted from the CSV files.</li> </ul> <p><strong>Details on the subsetting</strong></p> <p>See <a href="https://nbviewer.org/urls/zenodo.org/record/6826071/files/cape_verde_turtle_subsetting.ipynb">cape_verde_turtle_subsetting.ipynb</a>.</p> <p><strong>Using the data</strong></p> <p>This data set is licensed under a <em>Creative Commons Attribution 4.0 International License</em>.</p> <p>If you use the data, we'd love to get a notice to <a href="mailto:wrath@geomar.de">wrath@geomar.de</a>. This is, however, not required.</p>
Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds
<p>1. Seed dispersal by wind is one of the most important dispersal mechanisms in plants. The key seed trait affecting seed dispersal by wind is the effective terminal velocity (hereafter "terminal velocity", Vt), the maximum falling speed of a seed in still air. Accurate estimates of Vt are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of Vt for slow- or fast-falling seeds, fragile seeds, and seeds with complex falling trajectories.</p> <p>2. We present a new video-based method that estimates the falling trajectory and Vt of wind-dispersed seeds. The design involves a mirror that enables a camera to simultaneously record a falling seed from two perspectives. Automated image analysis then determines three-dimensional seed trajectories at high temporal resolution. To these trajectories, we fit a physical model of free fall with air resistance to estimate Vt. We validated this method by comparing the estimated Vt of spheres of different diameters and materials to theoretical expectations, and by comparing the estimated Vt of seeds to measurements in a vertical wind tunnel.</p> <p>3. Vt estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our Vt estimates for fast-falling seeds are markedly higher than those in an existing trait database. This discrepancy seems to arise because previous estimates inadequately accounted for seed acceleration. </p> <p>4. The presented method yields accurate, efficient and affordable estimates of the three-dimensional falling trajectory and terminal velocity for a wide range of seed types. The method should thus advance the understanding and prediction of wind-driven seed dispersal.</p>
Datasets for "Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population"
<p>Datasets used as examples in "<strong>Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population</strong>", DA Messenger, GE Wheeler, X Liu DM Bortz, arXiv:2204.14141, 2022. </p> <p>Code: <a href="https://github.com/MathBioCU/WSINDy_CellCluster">https://github.com/MathBioCU/WSINDy_CellCluster</a></p> <p>Download code and run <strong>visualize_trajectories.m , </strong>changing file in line 5 accordingly.</p> <p> </p> <p> </p>
KATP MD Simulation Trajectories
<p>MD simulation trajectories of pancreatic KATP channel</p>
Modeled trajectories of the Mediterranean squamates
<p>Mediterranean islands have a high diversity of squamates, although they are unevenly distributed. This variability in the composition of the reptile assemblages across islands may have been influenced by differences in the colonization abilities of these species. To evaluate the dispersal capacities of squamate species, we modeled their sea routes using cost surface models. We estimated the effects of some life-history traits and the phylogenetic signal in the characteristics of the modeled dispersal paths. We hypothesized that a significant phylogenetic signal should be present if the dispersal ability is enhanced by traits shared among evolutionarily related species. The results showed that no phylogenetic signal was present in the characteristics of the dispersal paths (i.e., in the distance traveled/bypassed sea depth). Thus, no superior island-colonizer lineages were detected in Mediterranean Squamata. However, our analyses also revealed that small-sized lizards were superior to other groups of squamates at dispersing over long distances on the sea.Mediterranean islands have a high diversity of squamates, although they are unevenly distributed. This variability in the composition of the reptile assemblages across islands may have been influenced by differences in the colonization abilities of these species. To evaluate the dispersal capacities of squamate species, we modeled their sea routes using cost surface models. We estimated the effects of some life-history traits and the phylogenetic signal in the characteristics of the modeled dispersal paths. We hypothesized that a significant phylogenetic signal should be present if the dispersal ability is enhanced by traits shared among evolutionarily related species. The results showed that no phylogenetic signal was present in the characteristics of the dispersal paths (i.e., in the distance traveled/bypassed sea depth). Thus, no superior island-colonizer lineages were detected in Mediterranean Squamata. However, our analyses also revealed that small-sized lizards were superior to other groups of squamates at dispersing over long distances on the sea.</p>
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.