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11,506 results for “Dynamics”
Haplotype analysis of the mitochondrial DNA d-loop region reveals the maternal origin and historical dynamics among the indigenous goat populations in east and west of the Democratic Republic of Congo (DRC)
<p><span>This study aimed at assessing haplotype diversity and population dynamics of three Congolese indigenous goat populations that included Kasai goat (KG), small goat (SG), and dwarf goat (DG) of the Democratic Republic of Congo (DRC). The 1,169 bp <em>d-loop</em> region of mitochondrial DNA (mtDNA) was sequenced for 339 Congolese indigenous goats. The total length of sequences was used to generate the haplotypes and evaluate their diversities, whereas the hypervariable region (HVI, 453 bp) was analyzed to define the maternal variation and the demographic dynamic. A total of 568 segregating sites that generated 192 haplotypes were observed from the entire <em>d-loop</em> region (1,169 bp <em>d-loop</em>). Phylogenetic analyses using reference haplotypes from the six globally defined goat mtDNA haplogroups showed that all the three Congolese indigenous goat populations studied clustered into the dominant haplogroup A, as revealed by the Neighbor-joining (NJ) tree and median-joining (MJ) network. Nine haplotypes were shared between the studied goats and goat populations from Pakistan (1 haplotype), Kenya, Ethiopia and Algeria (1 haplotype), Zimbabwe (1 haplotype), Cameroon (3 haplotypes), and Mozambique (3 haplotypes). The population pairwise analysis (<em>F<sub>ST</sub></em>) indicated a weak differentiation between the Congolese indigenous goat populations. Negative and significant (<em>p</em>-value < 0.05) values for <em>F</em>u's <em>F</em>s (-20.418) and Tajima's (-2.189) tests showed the expansion in the history of the three Congolese indigenous goat populations. These results suggest a weak differentiation and a single maternal origin for the studied goats. This information will contribute to the improvement of the management strategies and long-term conservation of indigenous goats in DRC</span><span>.</span></p>
Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction
<p>This Zenodo repository provides comprehensive resources for the paper titled "Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction" published on <a href="https://academic.oup.com/bioinformatics/article/41/8/btaf429/8238154">Bioinformatics</a>. We created a dataset of 63,000 molecular dynamics simulations by performing 10 simulations of 10 ns on 6,300 complexes. Neural networks were developed to learn from this data in order to predict the binding affinities of protein-ligand complexes. The implementation of these neural networks are available on <a href="https://github.com/ICOA-SBC/MD_DL_BA" target="_blank" rel="noopener">github</a>. Our collection includes training/benchmark datasets, trained statistical models, and results on test sets (CSV & PDF files).</p> <p> </p> <p><strong>Training/benchmark datasets:</strong></p> <p>Training, validation and test sets are provided to train and evaluate the following neural networks:</p> <ul> <li>Pafnucy, Proli and Densenucy without MD data augmentation (dataset file names contain "initial")</li> <li>Pafnucy, Proli and Densenucy with MD data augmentation (dataset file names contain "MDDA")</li> <li>Pafnucy with/without MD data augmentation and Proli and Densenucy with MD data augmentation were also evaluated on the fep test set (test set file name contain "fep")</li> <li>Timenucy and Videonucy using spatiotemporal learning methods (dataset file names contain "4D")</li> <li>Pafnucy without MD data augmentation and on a reduced training set (dataset file names contain "reduced")</li> </ul> <p>For each training methodology (MD data augmentation and spatiotemporal learning), we provide the data for the whole complex, only the ligand or only the protein. Additionally for spatiotemporal learning, we provide the data with only the ligand using the tracking mode.</p> <p> </p> <p><strong>Statistical models:</strong></p> <p>We provide the models trained with Pafnucy, Proli, Densenucy, Timenucy and Videonucy. Each models were trained in 10 replicates. </p> <p>For Pafnucy, Proli, Densenucy, we provide the models trained with random and systematic rotations, as well as with or without MD data augmentation.</p> <p>For Proli, Densenucy, Timenucy and Videonucy, we provide the models trained on the whole complex, only the ligand or only the protein.</p> <p>For Pafnucy we also provide the models trained on the reduced set (5932 complexes).</p> <p> </p> <p><strong>Results on test sets (CSV & PDF files):</strong></p> <p>We provide the predictions on the PDBbind v.2016 core set.</p> <ul> <li>For spatiotemporal learning methods (Timenucy and Videonucy), there are predictions for only 83 complexes, as we did not perform simulations on the whole test set.</li> <li>For models trained with MD DA, predictions were carried on the crystallographic structures as well as on the frames extracted from the simulations performed on the test set (augmented test).</li> </ul> <p>Results on the FEP dataset are also provided for Pafnucy, Proli and Densenucy.</p> <p> </p> <p>The Raw MD data (~4.5 To) are stored, and can be visualized/downloaded, on the <a href="https://mdposit.mddbr.eu/#/browse?search=MDBind">MDDB</a>.</p> <p>This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-A0100712496 & 2022-AD011013521) and CRIANN (Grant 2021002).</p>
Supporting data of "Temperature-dependent dynamics of energy stores in Drosophila" article
<p>The data generated in the study: Knoblochová, D., Dharanikota, M., Gáliková, M., and Klepsatel, P.: Temperature-dependent dynamics of energy stores in Drosophila.</p>
Data for: Slip-pulses drive frictional motion of dissimilar materials: universality, dynamics, and evolution
<p>Here, we present data for: Slip-pulses drive frictional motion of dissimilar materials: universality, dynamics, and evolution.</p> <p>Spreadsheets correspond to figures in the paper (SI). Data is organized within a spreadsheet by subfigures, and the '.I' index stands for insets data.</p> <p> </p> <p>For further questions, you may contact me - yonatan.poles@mail.huji.ac.il.</p> <p> </p> <p> </p> <p> </p>
Dynamics Playing a Key Role in the Covalent Binding of Kinase Inhibitors to the Focal Adhesion Kinase
Open the record for dataset details and reuse information.
Impact of Rhizosphere Quantitative Microbiome and Soil Properties on Alkaloid Dynamics in Lycoris aurea
Open the record for dataset details and reuse information.
Nanoscale engineering and dynamical stabilization of mesoscopic spin textures - Experimental Data
<p>Experimental data for "Nanoscale engineering and dynamical stabilization of mesoscopic spin textures".</p> <p>Data is organized by figure in which it is discussed. Filenames are organized by date/timestamp collected. Figures composed of multiple, separately taken datasets have been uploaded into separate folders, organized by subfigure or other identifier.</p> <p>All datasets are matlab (.m) filetype. Each contains three column vectors; the signal and phase of the FFT of readout, and the time at which the readout was performed.</p>
On the layered structure and Seasonal Variation of the Water Exchanges in the Sulawesi Sea Areas as well as the associated dynamics
<p><span>The layered structure and seasonal variation of the water exchanges in the Sulawesi Sea areas, as well as the corresponding dynamics, are investigated using HYCOM data. A net inward water mass transport is identified in the upper and intermediate layers in the Sulawesi Sea and it is compensated by the net outward transport in the deep layer. As an important source of this net inward transport, </span><span>the</span><span> southward flow through </span><span>the </span><span>Sibutu Passage (denoted hereafter as Sibutu Flow) reaches 5.06Sv in winter, accounting for ~43% of the net inflow. However, it </span><span>decreases substantially</span><span> to 1.79Sv in summer</span><span>,</span><span> accounting for only 12% of the inflow. Further analysis reveals that </span><span>this</span><span> Sibutu Flow modulates the seasonal variation of the corresponding outward flow at the Makassar Strait, the major exit of the water mass in the Sulawesi Sea. In winter, the enhancement of the Sibutu Flow associated with the greater intrusion of Kuroshio water into the Luzon Strait</span> <span>produces a stronger eastward pressure gradient force, suppressing the surface intrusion of the Mindanao Current. Conversely, this pressure gradient force weakens and shifts westward in summer, permitting a larger water intrusion from the eastern section. Given that the outward Makassar Flow</span><span> is the main passage of </span><span>the Indonesian Throughflow (ITF), our study highlights the essential role of the Sibutu Flow in modulating seasonal variabilities of the ITF via the Makassar Strait and, thus, the transfer of water masses and heat content between the Pacific and Indian Oceans.</span></p>
Results from simulation of Dynamic Precision Medicine 2-Window Trial
<p>Results from the simulation adapted from Beckman et al. PNAS 2012. For details see the preprint "A Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine" https://www.medrxiv.org/content/10.1101/2020.09.24.20201111v2</p> <p> </p> <p>The simualtion code that generated these results can be found at: https://github.com/GU-DPM/EvolutionaryClassifier</p> <p>------------------------------------------------------------------</p> <p>Annotations of simulation outcome files.</p> <p>Simulation outcomes are stored in multiple files.</p> <p>param_ALLDRUG_xxx.txt: parameter configuration values.<br>stopt_ALLDRUG_xxx.txt: survival times for each parameter configuration and each strategy.<br>dosage_ALLDRUG_xxx.txt: dosage combination sequences for each parameter configuration and each strategy.<br>pop_ALLDRUG_xxx.txt: population composition dynamics for each parameter configuration and each strategy.</p> <p>xxx denotes the index of the job when I ran simulations in my cluster. You can simply concatenate the files over all indices.</p> <p><br>param_ALLDRUG_xxx.txt format:</p> <p>Each line denotes a parameter configuration with the following entries:</p> <p>(1)parameter configuration index (one entry).<br>(2)initial population compositions x0 (four entries, x0(S), x0(R1), x0(R2), x0(R12)).<br>(3)growth rate g0 (one entry).<br>(4)drug sensitivity matrix Sa (eight entries, Sa(S,drug1), Sa(S,drug2), Sa(R1,drug1), Sa(R1,drug2), ...).<br>(5)transition rate matrix T (sixteen entries, T(S->S), T(S->R1), T(S->R2), T(S->R12), T(R1->S), T(R1->R1), T(R1->R2), T(R1->R12), ....</p> <p><br>stopt_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the survival times (stopping times) of 10 strategies for each parameter configuration.</p> <p>strategy 0: strategy 0 in the PNAS paper.<br>strategy 1: strategy 2.2 in the PNAS paper.<br>strategy 2: strategy 2.2 in the PNAS paper for first two treatment decisions, followed by strategy 0 in the PNAS paper</p> <p>Each line has 11 entries: parameter configuration index and the survival times of the 10 strategies.</p> <p>The time horizon of the therapy is 1800 days. Each period is 45 days. If the patient is cured (the total tumor population = 0), then the survival time is reported as 1845 days. If the survival time is 1800 days, then the tumor population size is greater than 0 and smaller than the mortal level (1e13).</p> <p><br>dosage_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the dosage combination sequence of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-2).<br>(3)(drug1 dosage,drug2 dosage) at t=0.<br>(4)(drug1 dosage,drug2 dosage) at t=45.<br>...<br>(42)(drug1 dosage,drug2 dosage) at t=1755.</p> <p>If t exceeds the survival time of the strategy, then the drug dosage is set to -1.</p> <p><br>pop_ALLDRUG_xxx.txt format:</p> <p>Each line denotes the population composition dynamics of each strategy for each parameter configuration.</p> <p>(1)parameter configuration index.<br>(2)strategy index (0-9).<br>(3)(S,R1,R1,R12) population size at t=45.<br>(4)(S,R1,R1,R12) at t=90.<br>...<br>(42)(S,R1,R1,R12) at t=1800.</p> <p>If t exceeds the survival time of the strategy, then the population size is set to -1.</p> <p>Notice the dosage combination is reported at the beginning of each period, and the population composition is reported at the end of each period.</p> <p>When the best strategy over strategies 0-8 cures the patient and yields survival time 1800 days, I did not bother running strategy 9 (since it's time consuming). Thus the dosage combination and population composition should be ignored.</p> <p> </p>
Datasets and simulation programs for the thesis 'Terahertz-Induced Dynamics in Strongly Correlated and Magnetic Materials'
<p>This dataset contains all the data and programs used to create the results in my thesis 'Terahertz-Induced Dynamics in Strongly Correlated and Magnetic Materials'.</p> <p>It is separated into individual chapters. Each chapter contains the data inside the figures, the raw data, if applicable, a program file, and a readme.txt offering additional information.</p>
Growth Dynamics and System Models for the Restaurant Industry: Data and Analysis from Taiwanese Chains
<p><span>This dataset includes raw and curated data, system dynamics models, and feedback loop diagrams used in the study of growth dynamics in the restaurant industry, focusing on Taiwanese chains. The data supports the findings presented in the paper "The Growth Dynamics of the Restaurant Industry from Single Store to Chain Store in Taiwan: A Systems Thinking Perspective.”</span></p>
Floquet dynamics of ultracold atoms in optical lattices with a parametrically modulated trapping potential
<p>This dataset includes the data on which the figures of the preprint "Floquet dynamics of ultracold atoms in optical lattices with a parametrically modulated trapping potential" by Usman Ali, Martin Holthaus, Torsten Meier ( https://arxiv.org/abs/2405.02125v1 ) are based and the relevant codes that were used to generate the data.</p> <p>U.A. gratefully acknowledges support from Deutscher Akademischer Austauschdienst (DAAD, German Academic Exchange Service) through a doctoral research grant and we thank the PC2 (Paderborn Center for Parallel Computing) for providing computing time. M.H. has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through Project No. 397122187.</p>
Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"
<p>Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"</p>
Data from: The effect of conspecific density, herbivory, and bamboo on seedling dynamics of a dominant oak in a neotropical highland forest
Conspecific negative density-dependence (CNDD) is one of the main mechanisms influencing diversity maintenance in tropical forests. Tropical highland forests, in contrast to most lowland forests, are commonly dominated by a few tree species, and testing the importance of density dependence effects on seedling establishment of dominant trees may provide insights on the mechanisms regulating population dynamics and forest composition of tropical highlands. We tested the effect of CNDD regulation on seedling survival and recruitment of Quercus costaricensis, a monodominant oak in the Talamanca highland forests of Costa Rica. We used Ripley's K and generalized linear mixed models to test the effects of conspecific density, distance to the nearest adult, density of Chusquea bamboo shoots, and herbivory on the annual survival probability of 3579 seedlings between 2014 and 2017. We did not find a significant effect of CNDD on seedling survival. However, bamboo density and herbivory both significantly decreased oak seedling survival. All seedlings had signs of herbivory and predator satiation may explain the lack of density dependent regulation in seedlings of this species. We argue that the lack of intraspecific density regulation at the seedling stage may contribute to explain the dominance of Q. costaricensis in the highland forests of Costa Rica. Local seedling dynamics of this endemic oak are instead regulated by herbivory and the density of Chusquea.
Molecular dynamics simulations of 20 complexes from the Protein-Protein Docking Benchmark
<p>We selected 20 complexes from the Protein-Protein Docking Benchmark 5.0 dataset based on structure resolution and parameterization difficulty. For each complex, we conducted a standard 1 µs-long molecular dynamics (MD) simulation in the NPT ensemble (at 1 atm and 300 K, following a 2 ns NVT equilibration) for the bound receptor, unbound receptor, bound ligand and unbound ligand. We set up all systems using Amber ff14SB<sup> </sup>and its recommended TIP3P water model, running MD simulations with Amber 16. For the 80 (single chain structure) MD, we sampled 500 frames for each simulation and computed the average prediction confidence.</p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
Dynamic web page change content detection
<p>This dataset contains 4 parts. "SimilarWeb dataset with screenshots" is created by scraping web elements, their CSS, and corresponding screenshots in three different time intervals for around 100 web pages. Based on this data, the "SimilarWeb dataset with SSIM column" is created with the target column containing the structural similarity index measure (SSIM) of the captured screenshots. This part of the dataset is used to train machine learning regression models. To evaluate approach, "Accessible web pages dataset" and "General use web pages dataset" parts of the dataset are used.</p>
Unveiling the Role of Acrocomia aculeata Palms in Savanna Hydrology: Insights from Bark Morphology and Stemflow Dynamics
<p>In this study, we investigated stemflow in <em>Acrocomia aculeata</em> trees in an Amapá savanna, in an Amapá savanna, focusing on the influence of structural traits on stemflow volumes. Diameter at breast height, crown area and height of trees were determined. Bark hydrophobicity and texture were also investigated. During the sampling period, total precipitation was 1,744.3 mm, with stemflow accounting for 0.71% of this total. This value is lower than previously reported, highlighting the variability of stemflow across different environments and study conditions. We found significant diversity in stemflow volumes among the studied individuals, which were categorized into three classes: high (more than 0.18mm), medium (between 0.10-0.18mm) and low (less than 0.10mm). . Our analysis revealed a strong correlation between bark contact angle and stemflow volumes, indicating that surfaces with higher hydrophobicity favor greater water flow to the soil. This emphasizes the relevance of bark physical properties in regulating stemflow. Principal component analysis indicated that the combination of stemflow and bark contact angle, along with characteristics such as crown geometry and height, explains most of the variability in stemflow volumes. However, other morphological characteristics such as diameter at breast height and projected crown area showed no significant correlation with stemflow, suggesting bark morphology might play a more determining role in stemflow than initially thought. Significant structural differences in bark surfaces were observed through scanning electron microscopy and optical microscopy, indicating that bark texture, including the presence or absence of grooves and furrows, influences stemflow capacity. This study enhances our understanding of the complex interactions between palm tree morphological and dendrometric characteristics and stemflow, highlighting the importance of considering bark hydrophobicity and structural properties in forest hydrology studies. The results suggest the need to explore bark characteristics beyond its morphology to understand stemflow dynamics in savanna ecosystems.</p>
Probing the viscosity of Venus mantle from dynamic topography at Baltis Vallis
<p>The Baltis Vallis channel on Venus preserves a record of long-wavelength deformation generated by a convecting mantle, providing a unique window into the planet's geodynamics. Notably, the observed topography along the channel is not downhill, suggesting complex interactions between surface processes and mantle dynamics. We statistically compare the observed dynamic topography of Baltis Vallis with dynamic topographies generated by a suite of stagnant-lid mantle convection models to constrain Venus' interior dynamics. Baltis Vallis's relatively young age (likely less than 250 Myr) and low root-mean-square relief of 217 m indicate vigorous convection in Venus's mantle, with a Rayleigh number greater than 4x10<sup>8</sup>, implying a mantle viscosity 1-2 orders of magnitude lower than Earth's. This difference may result from either a water-rich, less-degassed interior or a higher-temperature mantle beneath an insulating lid. Additionally, our simulations suggest that melt advection may dominate heat transport on Venus, potentially leading to non-linear temperature profiles in the crust. Upcoming missions such as <em>VERITAS </em>and <em>EnVision </em>will deliver higher-resolution gravity and topographic data, providing further constraints on Venus's present-day internal dynamics and the origin of Baltis Vallis.</p>
FIGURE 3 in A host, a parasite, and a predator: the dynamics of successive invasions in the eastern Mediterranean
FIGURE 3. Charybdis longicollis collected off Ashdod, Israel, November 2022, at 40 m depth, male, CW 28.9 mm, bearing 6 mature externae (photo: Oz Rittner).
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.