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data for Shaping magnetization dynamics in a planar square dot by adjusting its surface anisotropy

Upload contains data needed to create Figures 2 and 4 in the publication Shaping magnetization dynamics in a planar square dot by adjusting its surface anisotropy (<a href="https://arxiv.org/abs/2309.02984">https://arxiv.org/abs/2309.02984</a>)

Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set

Abstract: from [1] <blockquote> Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites. </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4] Post-processing Matlab R2019b License: Creative Commons Attribution 4.0 International Context: Data set supplementing  journal paper: [1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. Content: structure of data set: <ul> <li>04_Equilibration folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> <li>05_UT folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul> Output quantities (columns of *.Dat files): 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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul> References: [1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [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. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

Geochronology and Geochemistry of Late Cenozoic Magmatism on Naozhou Island, Leiqiong Area: Implications for Deep Dynamic Processes

The files of "Metadata 1" and "Metadata 2" are new measured data. The file of "References dataset" is published data from previous studies.

Stage-mediated priority effects and season lengths shape long-term competition dynamics

The relative arrival time of species can affect their interactions and thus determine which species persist in a community. Although this phenomenon, called priority effect, is widespread in natural communities, it is unclear how it depends on the length of growing season. Using a seasonal stage-structured model, we show that differences in stages of interacting species could generate priority effects by altering the strength of stabilizing and equalizing coexistence mechanisms, changing outcomes between exclusion, coexistence, and positive frequency dependence. However, these priority effects are strongest in systems with just one or a few generations per season and diminish in systems where many overlapping generations per season dilute the importance of stage-specific interactions. Our model reveals a novel link between the number of generations in a season and the consequences of priority effects, suggesting that consequences of phenological shifts driven by climate change should depend on specific life histories of organisms.

opencc-by-4.0Apr 2021View details →

Supplementary material for 3D Acoustic-Elastic Coupling with Gravity: The Dynamics of the 2018 Palu, Sulawesi Earthquake and Tsunami

This repository contains the supplementary files for our SC21 submission: "3D Acoustic-Elastic Coupling with Gravity: The Dynamics of the 2018 Palu, Sulawesi Earthquake and Tsunami". It contains the input data for all simulations. For more details, please refer to the included README.md files.   The directory "seissol-sc21-revision-source-code" contains the version of SeisSol that we used.

Original data for "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation"

This upload includes both original and supplementary data for the publication titled "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation" by A. Makogon, J.-M. Noël, F. Kanoufi, and V. Shkirskiy. The preprint version of the paper can be found on ChemRxiv at <a href="https://doi.org/10.26434/chemrxiv-2023-stbm1">https://doi.org/10.26434/chemrxiv-2023-stbm1</a>. The files "DATA_PROCESSING.pdf" and "DATA_PROCESSING.ipynb" contain the developed Python script routine used for data mining and extraction. All figures in the article were generated using this script. For the best experience, we recommend opening the Jupyter Lab files in a Python 3 environment. The "Videos.zip" file houses all original data from the optics experiment, showcasing the evolution of current, averaged normalized reflectivities, and optical movies. The "OriginalData.zip" file contains all raw data from the opto-electrochemical experiments. Refer to the Python script as a guide if you wish to open and process this data. Lastly, "Drafts_of_scripts.zip" includes all the unedited drafts of Python scripts that were developed during the data processing phase.

opencc-by-4.0May 2023View details →

Eliminating Finite-size Effects on the Calculation of X-ray Scattering from Molecular Dynamics Simulations

Data repository for the work with the same name. <a href="https://gitlab.com/asod/grsq_examples/-/tree/final_resubmission?ref_type=tags">Gitlab version</a> All plots for the figures in the work can be generated via <code>plots_resubmission.ipynb</code> <code>This version is accompanying the final resubmission to JCP.</code>   <code>Uses the pypi package <a href="https://pypi.org/project/grsq/">grsq</a></code>

Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management

<ol> <li>Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries.</li> <li>We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. </li> <li>Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. </li> <li>A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings.</li> <li> Synthesis and applications – Our framework provides a tool for 1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and 2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation. </li> </ol>

Fig. 8. A-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 8. A-D − Nomarski (leftmost lane), FITC-immunofluorescence images labeled with anti-α-tubulin monoclonal antibody and their magnified images (middle two lanes), and red fluorescence images (rightmost lane) stained with Acti-stain 555 phalloidin (detection for F-actin) of encysting cells of C. cucullus Nag-1. Each set of photomicrographs arranged in a horizontal row shows an identical cell except for Fig. 8C (FITC image, inset). A − Vegetative cell. B-D − Encysting cells of C. cucullus Nag-1 at 1.5 h (B), 3 h (C) and 3 days (D) after encystment induction. E − Nomarski image (left), red fluorescence images (middle) stained with Acti-stain 555 phalloidin, and a Nomarski image superimposed with a red fluorescence image obtained by Acti-stain 555 phalloidin staining (right) in encysting cells of C. cucullus Nag-1 at 3 h after encystment induction. F − Silver impregnation of a 3-day-aged cyst showing the basal structure of cilia. This photograph was reproduced from our previous work (Watoh et al. 2005, Fig. 9b). ant: anterior end, le: lepidosome, mu: mucus layer, ec/en: ectocyst layer lined with endocyst layer, m: plasma membrane. B − arrowheads: swollen tip of cilia. C − arrowhead: oral apparatus.

Fig. 6 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 6. Ca2+/overpopulation-stimulated in vivo phosphorylation of p43 (actin, identified by MS) during resting cyst formation of C. cucullus Nag-1, detected by biotinylated Phos-tag/ECL assays (A), and blots stained with CBB after the biotinylated Phos-tag/ECL detection (B). Figures above the photographs indicate time lapse after onset of encystment induction.

Fig. 5 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 5. Photomicrographs (Nomarski images) (A) and transmission electron micrographs (B) of C. cucullus Nag-1 after onset of encystment induction, showing resorption of cilia. (A) Vegetative cell at 0 h (A-1) and 2.5 h (A-2) after onset of encystment induction. (B) Encysting 3-h-aged cell (B-1) and 4-h-aged cell (B-2). ci: cilia, m: plasma membrane, ec: ectocyst layer, le: lepidosome. (B-2) a different electron micrograph of the same ultrathin section used in a previous paper (Funatani et al. 2010; Fig. 3).

Fig. 3 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 3. Immunoblotting assay using anti α-tubulin antibody showing total α-tubulin content during resting cyst formation of C. cucullus Nag-1. Figures above the photographs indicate time lapse after on- set of encystment induction.

Fig. 2 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 2. Changes of the amount of β-tubulin (p56) and its fragments (p37 and p19) contained in water-soluble fraction during resting cyst formation of C. cucullus Nag-1. Figures above the photographs indicate time lapse after onset of encystment induction.

Fig. 1. 2-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 1. 2-D PAGE showing an alteration of the water-soluble protein composition at 0 h–4 weeks after the onset of encystment induction of C. cucullus Nag-1. Arrowheads indicate the proteins (p56, p37, p19) whose amount uniquely and markedly changed during resting cyst formation. These proteins were identified as β-tubulin and its fragments by MS analysis (see Table 1).

Data from: Urbanization alters the spatiotemporal dynamics of plant-pollinator networks in a tropical megacity

Urbanization is a major driver of biodiversity change but how it interacts with spatial and temporal gradients to influence the dynamics of plant-pollinator networks is poorly understood, especially in tropical urbanization hotspots. Here, we analyzed the drivers of environmental, spatial, and temporal turnover of plant-pollinator interactions (interaction β-diversity) along an urbanization gradient in Bengaluru, a South Indian megacity. The compositional turnover of plant-pollinator interactions differed more between seasons and with local urbanization intensity than with spatial distance, suggesting that seasonality and environmental filtering were more important than dispersal limitation for explaining plant-pollinator interaction β-diversity. Furthermore, urbanization amplified the seasonal dynamics of plant-pollinator interactions, with stronger temporal turnover in urban compared to rural sites, driven by greater turnover of native non-crop plant species (not managed by people). Our study demonstrates that environmental, spatial, and temporal gradients interact to shape the dynamics of plant-pollinator networks and urbanization can strongly amplify these dynamics.

Fig. 7 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics

Fig. 7. Effects of 10 µM taxol (A) and 10 µM cytochalasin B (B) on Ca2+/overpopulation-mediated globulation of C. cucullus Nag-1 (A-1, B-1) and ciliary resorption (A-2, B-2). A-1, B-1 − The rate of encysting (rounded) cells was expressed as a percentage of the total number of tested cells (100 randomly selected cells). Open squares (negative control). The cells were suspended in 1 mM Tris-HCl (pH 7.2) solu- tion without inhibitors at low cell density (<2,000 cells/ml). Under this condition, encystment was hardly induced. Closed circles (positive control). The cells were suspended in an encystment-inducing medium [1 mM Tris-HCl (pH 7.2) and 0.1 mM CaCl2] without inhibitors at high cell density (> 30,000 cells/ml) (Ca2+/overpopulation stimulation). In this condition, the encystment was markedly induced. Open circles (experiment). The cells were suspended in an encystment-inducing medium containing taxol (Ta) or cytochalasin B (CB) at high cell density (> 30,000 cells/ml). Points and attached bars correspond to the means of 5 measurements (100 cells per measurement) obtained from different batches and standard errors, respectively. A-2, B-2 − Length of cilia at 2 h after onset of encystment induction in the presence or absence of taxol (Ta) or cytochalasin B (CB). In the negative control [Induced without 'Ta' (0 h) or Induced without 'CB' (0 h)], the cultured cells were collected, then suspended in encystment-inducing medium, and quickly fixed with 3.7% paraformaldehyde. In the positive control [Induced without 'Ta' (2 h) or Induced without 'CB' (2 h)], the cells were suspended for 2 h in an encystment-inducing medium without inhibitors at high cell density (> 30,000 cells/ml), and then fixed with 3.7% paraformaldehyde. In the experimental groups [Induced with 'Ta' (2 h) or Induced with 'CB' (2 h)], the cells were suspended for 2 h in an encystment-inducing medium containing inhibitors at high cell density (> 30,000 cells/ml), and then fixed with 3.7% paraformaldehyde. Columns and attached bars correspond to the means in 26 cells and standard errors, respectively.

Numerical response of predator to prey: Dynamic interactions and population cycles in Eurasian lynx and roe deer

The dynamic interactions between predators and their prey have two fundamental processes; numerical and functional responses. Numerical response is defined as predator growth rate as a function of prey density or both prey and predator densities [dP/dt = f(N, P)]. Functional response is defined as the kill rate by an individual predator being a function of prey density or prey and predator densities combined. Although there are relatively many studies on the functional response in mammalian predators, numerical response remains poorly documented. We studied numerical response of Eurasian lynx (Lynx lynx) to various densities of its primary prey species, roe deer (Capreolus capreolus), and to itself (lynx). We exploited an unusual natural situation, spanning three decades where lynx, after a period of absence in central and southern Sweden, during which roe deer populations had grown to high densities, subsequently recolonized region after region, from north to south. We divided the study area into seven regions, with increasing productivity from north to south. We found strong effects of both roe deer density and lynx density on lynx numerical response. Thus, both resources and intraspecific competition for these resources are important to understand the lynx population dynamic. We built a series of deterministic lynx–roe deer models and applied them to the seven regions. We found a very good fit between these Lotka-Volterra-type models and the data. The deterministic models produced almost cyclic dynamics or dampened cycles in five of the seven regions. Thus, we documented population cycles in this large-predator-large-herbivore system, which is rarely done. The amplitudes in the dampened cycles decreased towards the south. Thus, the dynamics between lynx and roe deer became more stable with increasing carrying capacity for roe deer, which is related to higher productivity in the environment. This increased stability could be explained by variation in predation risk, where human presence can act as prey refugia, and by a more diverse prey guild that will weaken the direct interaction between lynx and roe deer.

Host-pathogen interactions under pressure: a review and meta-analysis of stress-mediated effects on disease dynamics

Human activities have increased the intensity and frequency of natural stressors and created novel stressors, altering host-pathogen interactions, and changing the risk of emerging infectious diseases. Despite the ubiquity of such anthropogenic impacts, predicting the directionality of outcomes has proven challenging. Here, we conduct a review and meta-analysis to determine the primary mechanisms through which stressors affect host-pathogen interactions and to evaluate the impacts stress has on host fitness (survival and fecundity) and pathogen infectivity (prevalence and intensity). We assessed 891 effect sizes from 71 host species (representing seven taxonomic groups) and 78 parasite taxa from 98 studies. We found that infected and uninfected hosts had similar sensitivity to stressors and that responses varied according to stressor type. Specifically, limited resources compromised host fecundity and decreased pathogen intensity, while abiotic environmental stressors (e.g., temperature and salinity) decreased host survivorship and increased pathogen intensity, and pollution increased mortality but decreased pathogen prevalence. We then used our meta-analysis results to develop Susceptible-Infected theoretical models to illustrate scenarios where infection rates are expected to increase or decrease in response to resource limitation or environmental stress gradients. Our results carry implications for conservation and disease emergence and reveal areas for future work.

Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing

This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize, 1. Polishing_stagewise_data.zip - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17." 2. Initial_surface_generation.m - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3). 3. Stage0_fitted_data.mat - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data. 4. Parameter_fitting_Polishing.m - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares with experimental data (Figures 3, 5, 6, 7 and 8 in paper). (The script makes use of other functions defined in #5). 5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m and Gen_hurst.m - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m) 6. Simulated_Annealing.zip - Zip file containing files related to Simulated Annealing Algorithm. Please read the README_Simulated_Annealing.txt for instructions to reproduce the optimized parameter solutions. 7. pub_fig.m - Script containing the formatting options for plots and figures.

Supporting Data for "Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics"

Molecular dynamics topologies (.prmtop), initial coordinates (.inpcrd), and trajectories (.nc) associated with the preprint: https://doi.org/10.1101/2023.07.23.550226. Tarballs are included for each of the six systems, named by the PDB ID for the starting structure and state (ligand/apo). The four replicates of 155ns are included in each tarball in AMBER's netcdf format (.nc). All trajectories have been stripped of solvent and ions and are autoimaged. Input files and an example script for running simulations in AMBER20 are provided (infiles.tar.gz) as well as the topologies and coordinates for the solvated systems (solvated_topologies.tar.gz).