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11,506 results for “Dynamics”
VSA, Analogy, and Dynamic Similarity
<p>This file is the video of a presentation originally scheduled to be given at the <a href="https://sites.google.com/view/vsaworkshop2020/home">Workshop on Developments in Hyperdimensional Computing and Vector Symbolic Architectures</a>, 16 March 2020, Kirchoff-Institute for Physics at Heidelberg University, Heidelberg, Germany. The workshop meeting was cancelled due to the COVID-19 pandemic and this presentation was given as a webinar on 2020-05-18.</p> <p><strong>Extended Abstract</strong></p> <p>It has been argued that analogy is at the core of cognition [7, 1]. My work in VSA is driven by the goal of building a practical, effective analogical memory/reasoning system. Analogy is commonly construed as structure mapping between a source and target [5], which in turn can be construed as representing the source and target as graphs and finding maximal graph isomorphisms between them. This can also be viewed as a kind of dynamic similarity in that the<br> initially dissimilar source and target are effectively very similar after mapping.</p> <p>Similarity (the angle between vectors) is central to the mechanics of VSA/HDC. Introductory papers (e.g. [8]) necessarily devote space to vector similarityand the effect of the primitive operators (sum, product, permutation) on similarity. Most VSA examples rely on static similarity, where the vector representations are fixed over the time scale of the core computation (which is usually a single-pass, feed-forward computation). This emphasises encoding methods (e.g.<br> [12, 13]) that create vector representations with the similarity structure required by the core computation. Random Indexing [13] is an instance of the vector embedding approach to representation [11] that is widely used in NLP and ML. The important point is that the vector embeddings are developed in advance and then used as static representations (with fixed similarity structure) in the<br> subsequent computation of interest.</p> <p>Human similarity judgments are known to be context-dependent (see [3] for a brief review). It has also been argued that similarity and analogy are based on the same processes [6] and that cognition is so thoroughly context-dependent that representations are created on-the-fly in response to task demands [2]. This seems extreme, but doesn’t necessarily imply that the base representations are context-dependent as long as the cognitive process that compares them is<br> context-dependent, which can be achieved by having dynamic representations that are derived from the static base representations by context-dependent transforms (or any functionally equivalent process).</p> <p>An obvious candidate for a dynamic transformation function in VSA is substitution by binding, because the substitution can be specified as a vector and dynamically generated (see Representing substitution with a computed mapping in [8]). This implies an internal degree of freedom (a register to hold the substitution vector while it evolves) and a recurrent VSA circuit to provide the dynamics to evolve the substitution vector.</p> <p>These essential aspects are present in [4], which finds the maximal subgraph isomorphism between two graphs represented as vectors. This is implemented as a recurrent VSA circuit with a register containing a substitution vector that evolves and settles over the course of the computation. The final state of the substitution vector represents the set of substitutions that transforms the static base representation of each graph into the best subgraph isomorphism to the static base representation of the other graph. This is a useful step along the path to an analogical memory system.</p> <p>Interestingly, the subgraph isomorphism circuit can be interpreted as related to the recently developed Resonator Circuits for factorisation of VSA representations [9], which have internal degrees of freedom for each of the factors to be calculated and a recurrent VSA dynamics that settles on the factorisation. The graph isomorphism circuit can be interpreted as finding a factor (the substitution vector) such that the product of that factor with each of the graphs is the<br> best possible approximation to the other graph. This links the whole enterprise back to statistical modelling, where there is a long history of approximating matrices/tensors as the product of simpler factors [10].</p> <p>References<br> 1. Blokpoel, M., Wareham, T., Haselager, P., van Rooij, I.: Deep Analogical Inference as the Origin of Hypotheses. The Journal of Problem Solving 11(1), 1–24 (2018)<br> 2. Chalmers, D.J., French, R.M., Hofstadter, D.R.: High-level perception, representation, and analogy: A critique of artificial intelligence methodology. Journal of Experimental & Theoretical Artificial Intelligence 4(3), 185–211 (1992)<br> 3. Cheng, Y.: Context-dependent similarity. In: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI’90), pp. 27–30. Cambridge, MA, USA (1990)<br> 4. Gayler, R.W., Levy, S.D.: A distributed basis for analogical mapping. In: Proceedings of the Second International Conference on Analogy (ANALOGY-2009), pp. 165–174. New Bulgarian University, Sofia, Bulgaria (2009)<br> 5. Gentner, D.: Structure-mapping: A theoretical framework for analogy. Cognitive Science 7(2), 155–170 (1983)<br> 6. Gentner, D., Markman, A.B.: Structure mapping in analogy and similarity. American Psychologist 52(1), 45–56 (1997)<br> 7. Gust, H., Krumnack, U., Kühnberger, K.-U., Schwering, A.: Analogical Reasoning: A core of cognition. KI - Künstliche Intelligenz 1(8), 8–12 (2008)<br> 8. Kanerva, P.: Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation 1, 139–159 (2009)<br> 9. Kent, S.J., Frady, E.P., Sommer, F.T., Olshausen, B.A.: Resonator Circuits for factoring high-dimensional vectors. http://arxiv.org/abs/1906.11684 (2019)<br> 10. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)<br> 11. Pennington, J., Socher, R., Manning, C.D.: GloVe: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014)<br> 12. Purdy, S.: Encoding data for HTM systems. http://arxiv.org/abs/1602.05925 (2016)<br> 13. Sahlgren, M.: An introduction to random indexing. In: Proceedings of the Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering (TKE 2005), Copenhagen, Denmark (2005)</p>
Understanding ENSO dynamics through the exploration of past climates
<p>The palaeoclimate record shows that significant changes in ENSO characteristics took place during the Holocene. Exploring these changes, using both data and models, provides a means of understanding ENSO dynamics. Previous modelling studies have suggested a mechanism whereby changes in the Earth’s orbital geometry explain the strengthening of ENSO over the Holocene. Decreasing summer insolation over the Asian landmass resulted in a weakening of the Asian monsoon system. This led to a weakening of the easterly trade winds in the western Pacific, creating conditions more favourable for El Niño development. To explore this hypothesised forcing mechanism, we use a climate system model to conduct a suite of simulations of the climate of the past 8,000 years. In the early Holocene, we find that the Asian summer monsoon system is intensified, resulting in an amplification of the easterly trade winds in the western Pacific. The stronger trade winds represent a barrier to the eastward propagation of westerly wind bursts, therefore inhibiting the onset of El Niño events. The fundamental behaviour of ENSO remains unchanged, with the major change over the Holocene being the influence of the background state of the Pacific on the susceptibility of the ocean to the initiation of El Niño events.</p>
CHARMM27 dynamics simulation trajectories of α-conotoxin LsIA and its C-terminal carboxylated analogue bound at α3β2 nAChR
<p>The whole simulation trajectories (28 individual trajectories with 27ns for each) contain the coordinates and parameters of atoms with time for α-conotoxin LsIA and its C-terminal carboxylated analogue anchored to rat α3β2 nAChR, respectively. The GROMACS 4.6.5 with the CHARMM27 force field is used for the simulation. The trajectory (.xtc) files are saved every 100ps time for each protein complex only. The portable binary run input (.tpr) files are also uploaded with the data. </p>
Demonstration of 100 Gbit/s active measurements in dynamically provisioned optical paths
<p>New techniques, based on Software-Defined Networks, are used to deploy optical paths dynamically. This demonstration shows how active measurements at 100 Gbit/s are performed to check before operation that the performance requirements are met in terms of capacity, delay or packet loss.</p>
Flow Magnetic Tweezers: Gyrase dynamics in absence of drug Ciprofloxacin
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils in absence of Ciprofloxacin, reaction of Gyrase to external torque and eventual wash with SDS.</p>
The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"
<p>The hindcast and real-time prediction output of FGOALS-f2 V1.0 used in the study named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"</p>
Dynamic Contrast Enhanced MRI Raw Data Acquired with 3D Cones Trajectory
<p>This repository contains the raw data for the second dynamic contrast enhanced (DCE) MRI in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection
<p>Processed TCRbeta and TCRalpha repertoires after mild COVID-19 (Version 2.0: day 85 timepoints added) infection, see preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3">https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3</a></p> <p>and GitHub repository: <a href="https://github.com/pogorely/Minervina_COVID">https://github.com/pogorely/Minervina_COVID</a></p> <p>Two donors (M and W), two biological replicates of PBMC (F1 and F2), CD4+, CD8+, and Memory subpopulations for each post-infection time points (day 15, 30, 37, 45, 85 post-infection), and pre-infection PBMC repertoires sampled in 2019 and 2018. </p>
Representative Structures from Molecular Dynamics Simulations of the Inward Facing and Outward Facing States of LaINDY
<p>This upload is a supplementary data set for the following publication: <a href="https://doi.org/10.7554/eLife.61350">D. B. Sauer, N. Trebesch, J. J. Marden, N. Cocco, J. Song, A. Koide, S. Koide, E. Tajkhorshid, and D.-N. Wang. "Structural basis for the reaction cycle of DASS dicarboxylate transporters." <em>eLife</em>. <strong>9</strong>, e61350. DOI: 10.7554/eLife.61350</a>. Please see the main publication for the methods, analysis, and discussion associated with this data set.</p>
Analysing the data-driven approach of dynamically estimating positioning accuracy (data)
<p>The train/validation/test sets used in the study <strong>"<em>Analysing the data-driven approach of dynamically estimating positioning accuracy</em>"</strong>.</p> <p>Preprint: <a href="https://arxiv.org/abs/2011.10478">https://arxiv.org/abs/2011.10478</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/9500369">https://ieeexplore.ieee.org/document/9500369</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The full dataset used in this study, is the public dataset <em>lorawan_dataset_antwerp.csv </em> (and its related file <em>lorawan_antwerp_gateway_locations.json)</em> which can be access here:</p> <pre><a href="https://zenodo.org/record/3904158#.X4_h7y8RpQI">https://zenodo.org/record/3904158#.X4_h7y8RpQI</a></pre> <p>The above link is related to the following publication, in which the original dataset was published:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p>
Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).
<p>Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).</p> <p>This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2.</p> <p>The models identifiers in the paper correspond to the following terms in the code:</p> <p>Substrate: "13_5_coa"</p> <p>Product: "13_5_coa_desat_fe3"</p> <p>Apoprotein: "13_5_no_ligand"</p> <p>Saturated Lipid: "13_5_nocoa"</p> <p>Desaturated Lipid: "13_5_nocoa_desat_fe3"</p> <p>CoA model: "13_5_coa_nolipid"</p> <p>Substrate-waterbox model: "13_5_coa_waterbox"</p> <p>Saturated Lipid-waterbox: "13_5_nocoa_waterbox"</p>
Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s' Molecular Dynamics
<p><strong>Related publication:</strong><br> Soccio, M.; Martínez-Tong, D.E.; Guidotti, G.; Robles-Hernández, B.; Munari, A.; Lotti, N.; Alegria, A. Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s’ Molecular Dynamics. <em>Polymers</em> 2020, <em>12</em>, 1355.<br> <a href="https://doi.org/10.3390/polym12061355">10.3390/polym12061355</a></p> <p><strong>EUSMI proposal codes:</strong><br> E171100040, E171100043</p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
Sudden_Oak_Death_in_Oregon_Forests: Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests
<p>Release of code associated with the submitted manuscript</p> <p><strong>Authors</strong></p> <p>ZN Kamvar, MM Larsen, AM Kanaskie, EM Hansen, and NJ Grünwald.</p> <p><strong>Title</strong></p> <p>Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests.</p>
Spinning test-body orbiting around Schwarzschild black hole: circular dynamics and gravitational-wave fluxes
<p>We release gravitational wave fluxes at null-infinity from a spinning test-body in circular equatorial orbits around a Schwarzschild black hole. Four different prescriptions are used for the dynamics: the Mathisson-Papapetrou formalism under the Tulczyjew (TUL) spin-supplementary-condition (SSC), the Pirani (PIR) SSC and the Ohashi-Kyrian-Semerak (OKS) SSC, and the spinning particle limit of the effective-one-body Hamiltonian (HAM) of [Phys.~Rev.~D.90,~044018(2014)]. For more details see xxxx .</p> <p>The multipolar fluxes are given for l=2,3 m=1,2,3 at the Boyer-Lindquist radii</p> <p> r = 4 5 6 7 8 10 12 15 20 30 ,</p> <p>in cases they were not computed the data contains a "42". Note that the fluxes in these data files are assumed to contain both the +m and -m contributions, since they are identical for equatorial orbits and aligned spins. <br /> Additionally, the data files contain the key numbers describing the circular dynamics (see paper).</p> <p>Units <span class="math-tex"><em>c</em>=<em>G</em>=1.</span></p>
1st_dataset-Minimal radius of magnetic skyrmions: statics and dynamics
<p>In a broad range of applied magnetic fields and material parameters isolated magnetic skyrmions<br> condense into skyrmion lattices. While the geometry of isolated skyrmions and their lattice<br> counterparts strongly depend on field and Dzyaloshinski–Moriya interaction, this issue has not been<br> adequately addressed in previous studies. Meanwhile, this information is extremely important for<br> applications, because the skyrmion size and the interskyrmion distance have to be tuned for skyrmion<br> based memory and logic devices. In this investigation we elucidate the size and density-dependent<br> phase diagram showing traditional phases in field versus material parameters space by means of<br> Monte-Carlo simulations on a discrete lattice. The obtained diagram permits us to establish that, in<br> contrast to the continuum limit, skyrmions on a discrete lattice cannot be smaller than some critical<br> size and have a very specific shape. These minimal skyrmions correspond to the micromagnetic<br> configuration at the energy barrier between the ferromagnetic and the skyrmionic states.<br> Furthermore, we use atomistic Landau–Lifshitz–Gilbert simulations to study dynamics of the<br> skyrmion annihilation. It is shown that this procees consists of two stages: the continuous skyrmion<br> contraction and its discontinuous annihilation. The detailed analysis of this dynamical process is<br> given.</p>
Dataset for: "Incremental Semiparametric Inverse Dynamics Learning"
<p>Dataset used in the experimental section of the paper:</p> <blockquote> <p>R. Camoriano, S. Traversaro, L. Rosasco, G. Metta and F. Nori, "<strong>Incremental semiparametric inverse dynamics learning,</strong>" <em>2016 IEEE International Conference on Robotics and Automation (ICRA)</em>, Stockholm, 2016, pp. 544-550.<br> <br> doi: 10.1109/ICRA.2016.7487177<br> <br> Abstract: This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.<br> <br> URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487177&isnumber=7487087<br> </p> </blockquote> <p> </p> <p><strong>Description</strong></p> <p>The file "iCubDyn_2.0.mat" contains data collected from the right arm of the iCub humanoid robot, considering as input the positions, velocities and accelerations of the 3 shoulder joints and of the elbow joint, and as outputs the 3 force and 3 torque components measured by the six-axis F/T sensor in-built in the upper arm.</p> <p>The dataset is collected at 10Hz at as the end-effector tracks circumferences with 10cm radius on the transverse (XY) and sagittal (XZ) planes (For more information on the iCub reference frames, see [4]) at approximately 0.6 m/s. The total number of points for each dataset is 10000, corresponding to approximately 17 minutes of continuous operation. Trajectories are generated by means of the Cartesian Controller presented in [5].</p> <p> Input (X)</p> <p> columns 1-4: Joint (3 shoulder joints + 1 elbow joint) positions<br> columns 5-8: Joint (3 shoulder joints + 1 elbow joint) velocities<br> columns 9-12: Joint (3 shoulder joints + 1 elbow joint) accelerations</p> <p> Output (Y)</p> <p> Columns 1-3: Measured forces (N) along the X, Y, Z axes by the force-torque (F/T) sensor placed in the upper arm<br> Columns 4-6: Measured torques (N*m) along the X, Y, Z axes by the force-torque (F/T) sensor placed in the upper arm</p> <p> </p> <p><strong>Preprocessing</strong><br> <br> - Velocities and accelerations are computed by an Adaptive Window Polynomial Fitting Estimator, implemented through a least-squares based algorithm on a adpative window (see [2], [3]). Velocity estimation max window size: 16. Acceleration estimation max window size: 25.<br> - Positions, velocities and accelerations are recorded at 9Hz and oversampled to 20 Hz via cubic spline interpolation.<br> - Forces and torques are directly recorded at 20Hz.</p> <p>This dataset was used in [1] for experimental purposes. See section IV therein for further details.</p> <p>For more information, please contact:<br> Raffaello Camoriano - raffaello.camoriano@iit.it<br> Silvio Traversaro - silvio.traversaro@iit.it</p> <p> </p> <p><strong>References</strong><br> <br> [1] Camoriano, Raffaello; Traversaro, Silvio; Rosasco, Lorenzo; Metta, Giorgio; Nori, Francesco, "Incremental Semiparametric Inverse Dynamics Learning", eprint arXiv:1601.04549, 01/2016<br> [2] F. Janabi-Sharifi ; Dept. of Mech. Eng., Ryerson Polytech. Univ., Toronto, Ont., Canada ; V. Hayward ; C. -S. J. Chen, "Discrete-time adaptive windowing for velocity estimation", IEEE Transactions on Control Systems Technology, 1003 - 1009, Vol. 8, Issue 6, Nov 2000<br> [3] https://github.com/robotology/icub-main/blob/master/src/libraries/ctrlLib/include/iCub/ctrl/adaptWinPolyEstimator.h<br> [4] http://wiki.icub.org/wiki/ICubForwardKinematics<br> [5] U. Pattacini; F. Nori; L. Natale; G. Metta; and G. Sandini; “An experimental evaluation of a novel minimum-jerk cartesian controller for humanoid robots,” in Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, Oct 2010, pp. 1668–1674.</p>
The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy.
<p>Data used for the publication of a paper entitled: <strong>The within-host population dynamics of <em>Mycobacterium tuberculosis</em> vary with treatment efficacy.</strong></p> <p>The data were derived from:</p> <p>1. the deep sequencing of serial sputum samples from 12 TB patients,</p> <p>2. the deep sequencing of liquid cultures derived from the expansion of individual colonies <em>in vitro</em>,</p> <p>3. <em>In </em><em>silico</em> simulations of DNA sequencing, populations and mutagenesis.</p> <p>The analytical scripts associated with the generation of the data can be found at:</p> <p>https://github.com/swisstph/TBRU_serialTB/</p> <p><strong>Paper Abstract:</strong></p> <p><strong>Background:</strong></p> <p>Combination therapy is one of the most effective tools for limiting the emergence of drug resistance. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We addressed this question by studying the population dynamics of <em>Mycobacterium tuberculosis</em> within tuberculosis patients undergoing treatment with different combinations of antibiotics.</p> <p><strong>Results:</strong></p> <p>By combining very deep whole genome sequencing (~1,000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy had a clear impact on the population dynamics: sufficient drug pressure bore a clear signature of purifying selection leading to apparent genetic stability. In contrast, <em>M. tuberculosis</em> populations subject to less drug pressure showed markedly different dynamics, including cases of acquisition of additional drug resistance.</p> <p><strong>Conclusions:</strong></p> <p>Our findings show that for a pathogen like <em>M. tuberculosis</em>, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.</p>
Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome
<p>The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 inter-protein and 26 intra-protein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6 and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino-acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/26S-PIPs or the README file.</p>
Setup of the TPV16 benchmark for dynamic rupture verification
<p>Verification benchmark for simulations with dynamic rupture and local time stepping with SeisSol, version Shaking Corals. More details can be found in the publication Uphoff et al. "Extreme scale multi-physics simulations of the tsunamigenic 2004 Sumatra megathrust earthquake", 2017 and on the project homepage (http://www.seissol.org/).</p>
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.