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1,026 results for “kinetics”
Data, plotting scripts, and figures for "A physics-based ignition model with detailed chemical kinetics for live fuel burning studies"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "A physics-based ignition model with detailed chemical<br>kinetics for live fuel burning studies" by Diba Behnoudfar and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
Kinetic Study on the Reactivity of Azanone (HNO) toward Cyclic C-Nucleophiles
<p>1. Equipment</p> <p>UV-Vis absorption spectra were collected using an Agilent 8453 spectrophotometer equipped with a photodiode array detector and thermostated cell holder.</p> <p>2. Chemicals</p> <p>The Angeli’s salt stock solution was prepared in 1 mM NaOH. Its concentration was determined by measuring the absorbance at 248 nm (ε = 8.3 · 10<sup>3</sup> M<sup>−1</sup>cm<sup>−1</sup>). The solution was kept on ice. 1-(4-Methoxybenzyl)-2,4-piperidinedione, 2-acetyl-1,3-cyclohexanedione were purchased from Angene Chemical. 1,3-Cyclopentanedione, 2-methyl-1,3-cyclopentanedione, 1,3-cyclohexanedione, 2-methyl-1,3-cyclohexanedione, 1,3-cycloheptanedione, and 2,4-piperidinedione were purchased from Fluorochem, United Kingdom. All other chemicals (of the highest purity available) were sourced from Sigma-Aldrich Corp. All solutions were prepared using deionized water (Millipore Milli-Q system).</p> <p>3. Kinetic Experiments</p> <p>The HNO flux was determined from the rate of FlBA oxidation in aerated aqueous solution of Angeli’s salt, monitored at 490 nm. The initial concentration of Angeli’s salt was equal to 20 µM. Due to the scavenging of HNO by O<sub>2</sub> and other scavengers, the steady-state concentration of azanone is very low. The HNO dimerization was therefore negligible and was not taken into consideration. HNO released from Angeli’s salt reacts either with the HNO scavenger or with the molecular oxygen to form peroxynitrite, which was detected with the use of the FlBA probe (25 µM). Its reaction with ONOO<sup>–</sup> results in the formation of fluorescein. The formation of fluorescein was monitored spectrophotometrically by following the increase in its characteristic absorbance at 490 nm. The reaction mixtures contained Angeli’s salt (20 µM), the fluorescein-based monoborate probe FlBA (25 µM), phosphate buffer (50 mM, pH 7.4), dtpa (100 µM), and the HNO scavenger (at an appropriate concentration). In addition, each solution contained 5% (vol.) CH<sub>3</sub>CN. The rate constants were determined with the assumption that the concentration of molecular oxygen was equal to 225 µM. Each rate constant was determined in at least three independent experiments.</p> <p>The dataset contains ASCII files with UV-Vis spectra of the reaction mixtures recorded for an incubation time of 10 min. (see Artelska, A.; Rola, M.; Rostkowski, M.; Pięta, M.; Pięta, J.; Michalski, R.; Sikora, A.B. Kinetic Study on the Reactivity of Azanone (HNO) toward Cyclic C-Nucleophiles. Int. J. Mol. Sci. (2021), in press.).</p> <p>4. Computational Details</p> <p>Quantum mechanical calculations were performed in the Gaussian G09 suite of programs, Revision E01. Stationary points were found by geometry optimization algorithms with tight convergence criteria except for transition state structure in the reaction of HNO with 1-(4-methoxybenzyl)-2,4-piperidinedione, where default criteria had to be used due to lack of computation convergence. To verify the nature of stationary points, as well as to compute Gibbs free energies, respective frequencies were computed. In calculations, the presence of the water environment was described by the Gaussian default continuum solvation model (IEFPCM). Density functional theory (DFT) functional B2PLYP with Grimme’s D3 dispersion correction (B2PLYP-D3) combined with 6-311+(2df,2p) split valence basis set, was used. The theory level was selected based on the fact that double-hybrid DFT functionals perform well in describing chemical system properties as well as reaction energy barriers, especially when London dispersion corrections are employed.</p> <p>The file AtomicCoordinatesOfStationaryPoints.txt contains geometries of stationary points obtained at the B2PLYP-D3/6-311+(2df,2p) theory level used for Gibbs free energey calculations in computational studies (see Artelska, A.; Rola, M.; Rostkowski, M.; Pięta, M.; Pięta, J.; Michalski, R.; Sikora, A.B. Kinetic Study on the Reactivity of Azanone (HNO) toward Cyclic C-Nucleophiles. Int. J. Mol. Sci. (2021), in press.).</p>
Ensemble statistics for modelled Eddy Kinetic Energy in the Southern Ocean
<p>This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans.</p> <p>This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (<a href="https://www.nemo-ocean.eu/doc">Madec, 2008</a>). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in <a href="https://doi.org/10.1016/j.ocemod.2013.02.004">Brankart, 2013</a>) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015.</p> <p>The sea level anomaly is found according to <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al (2020)</a> and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper.</p> <p>The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.</p>
Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides
<p>We provide here the underlying data of the publication "Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides". Please find the abstract below.</p> <p><strong>Background: </strong>Cellodextrin phosphorylase (CdP; EC 2.4.1.49) catalyzes the iterative β-1,4-glycosylation of cellobiose using α-D-glucose 1-phosphate as the donor substrate. Cello-oligosaccharides (COS) with a degree of polymerization (DP) of up to 6 are soluble while those of larger DP self-assemble into solid cellulose material. The soluble COS have attracted considerable attention for their use as dietary fibers that offer a selective prebiotic function. An efficient synthesis of soluble COS requires good control over the DP of the products formed. A mathematical model of the iterative enzymatic glycosylation would be important to facilitate target-oriented process development.<br> <strong>Results: </strong>A detailed time-course analysis of the formation of COS products from cellobiose (25 mM, 50 mM) and α-D-glucose 1-phosphate (10–100 mM) was performed using the CdP from <em>Clostridium cellulosi</em>. A mechanism-based, Michaelis–Menten type mathematical model was developed to describe the kinetics of the iterative enzymatic glycosylation of cellobiose. The mechanistic model was combined with an empirical description of the DP-dependent self-assembly of the COS into insoluble cellulose. The hybrid model thus obtained was used for kinetic parameter determination from time-course fits performed with constraints derived from initial rate data. The fitted hybrid model provided excellent description of the experimental dynamics of the COS in the DP range 3–6 and also accounted for the insoluble product formation. The hybrid model was suitable to disentangle the complex relationship between the process conditions used (i.e., substrate concentration, donor/acceptor ratio, reaction time) and the reaction output obtained (i.e., yield and composition of soluble COS). Model application to a window-of-operation analysis for the synthesis of soluble COS was demonstrated on the example of a COS mixture enriched in DP 4.<br> <strong>Conclusions:</strong> The hybrid model of CdP-catalyzed iterative glycosylation is an important engineering tool to study and optimize the biocatalytic synthesis of soluble COS. The kinetic modeling approach used here can be of a general interest to be applied to other iteratively catalyzed enzymatic reactions of synthetic importance.</p>
Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes
<p>This dataset continues the dataset accessible by doi 10.5281/zenodo.4311847. The latter also contains all the relevant metadata description.</p>
Coherent and non-coherent Eddy Kinetic Energy and gridded coherent eddy statistics
<p>This dataset includes the processed data used for the paper titled "Climatology, seasonality and trends of oceanic coherent eddies". The original data was obtained from AVISO+ SSH altimetry, Martínez-Moreno, J. <em>et.al (</em>2019)<em> </em>and Chelton, D. B., & Schlax, M. G. (2013).</p> <p> </p> <p>Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/CEKE_climatology</p>
Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, <em>Supercond. Sci. Technol.</em> <strong>37</strong> 075013</p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Data files for the manuscript "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes"
<p>This depository contains the data of all DEM simulations used in the manuscript titled "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes" submitted to Soft Matter in July 2024.</p> <p>The data in the excel file are the measurements obtained after the coarse graining procedure.</p>
Raw data from Qin et al. (2018) "Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles"
<p>Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol. 52(23): 13887-13896. [10.1021/acs.est.8b04436]</p> <p>This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript. </p> <p>The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).</p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling
<p>Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023). </p> <p>Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days.</p> <p>Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename).</p> <p>`average' is the averaged output of the last 100 days.</p> <p>`daily' is the snapshot at the end of the simulation.</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2
<p>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.</p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).</p> <p>2. scripts.zip - scripts to generate figure 2-5 on toy data</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of <em>E. coli </em>metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).</p> <p>2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)</p> <p>3. ODE_solutions.zip - self-explanatory (Figure 2c)</p> <p>4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)</p> <p>5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)</p> <p>6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g</p> <p><strong>The remaining datasets are published in the following links</strong></p> <p><em> - https://doi.org/10.5281/zenodo.7930084</em></p> <p><em> - https://doi.org/10.5281/zenodo.10391802</em></p>
Dataset for the publication: Elucidating the influence of intercalated anions in NiFe LDH on the electrocatalytic behavior of OER: a kinetic study
<p>This contribution comprehends the dataset derived as basis of the publication on alkaline electrochemical oxygen evolution reaction using nickel iron layered double hydroxides (NiFe LDH) with different intercalated anions. In this related study, the dataset was used to analyse the impact of different intercalated anions on the governing reaction mechanism and rate determining step in the electrocatalytic surface mechanism. The dataset comprehends analysis of the three materals: (1) charging current density versus scan rate, (2) X-ray diffraction patterns, (3) polarization curves, (4) steady-state derived Tafel plots, (5) reaction order analysis, (6) impedance spectroscopy, (7) influence of stirring on polarization curves, (8) not iR corrected values for the activity curves, (9) EIS data and (10) Tafel plots obtained through three different methods. </p>
Molecular dynamics trajectories for "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations
<p>The molecular dynamics-generated ensemble dataset for cyclo-(cGHHQKLV), used in the manuscript "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations". The dataset consists of 14 + 6 =20 .dcd files, and one .pdb file for rendering.</p>
Kinetics assessment of the homogeneously catalyzed hydroformylation of ethylene on a Rh-catalyst
<p>Supplementary Information. Section S1, description of dependence of rate and equilibrium coefficients on selected standard state, reaction entropies of reaction steps at different standard states, and derivation of rate equations; Section S2, experimental conditions of data sets selected for model regression and model validation; Section S3, calculation of enthalpies and entropies of solvation explained in more detail and obtained values; and Section S4, calculation of entropies of coordination explained in detail and resulting values</p>
Datasets supporting the publication "Technical Note: in-situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Milsom et al.
<p>Supporting experimental and modelling data for the manuscript entitled "Technical Note: Modelling and in-situ measurements of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Adam Milsom et al. 2023. </p> <p>Includes raw QCM-D data with the numbers at the beginning of the files corresponding to the experiment numbers in the manuscript. </p> <p>Normalised Raman peak area data for modelling and model ensemble outputs, including uptake coefficients. </p>
Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression
<p>Supplemental Data 1 is single-cell response to rapamycin count data first sequenced in this work and deposited in GEO with accession GSE242556. It is a 173348 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Gene', 'Replicate', 'Pool', and 'Experiment') are cell-specific metadata.</p> <p>Supplemental Data 2 is bulk response to rapamycin count data first sequenced in this work. It is a 33 rows × 5847 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 4 columns ('Oligo', 'Time', 'Replicate', and 'Sample_barcode') are sample-specific metadata.</p> <p>Supplemental Data 3 is single-cell count data published as GSE125162 and re-analyzed with the pipeline used for single-cell quantification in this work. It is a 65068 rows × 5850 columns TSV.GZ file where the first row is a header, the first 5843 columns are integer gene counts, and the final 7 columns ('Condition', 'Sample', 'Genotype_Group', 'Genotype_Individual', 'Genotype', 'Replicate', 'Cell_Barcode') are cell-specific metadata.</p> <p>Supplemental Data 4 is the four deep learning models trained in this work. It is a TAR.GZ file containing the final biophysical transcription/decay model, the pre-trained decay model, the velocity prediction model, and the count prediction model. Each model file is an h5 file containing a pytorch model that can be loaded with supirfactor\_dynamical.read().</p> <p>Supplemental Data 5 is the prior knowledge network used to constrain the models for TF interpretability. It is a 1574 rows × 204 columns [Genes x TFs] TSV.GZ file where the first row is a header with TF names, the first column is an index of gene names, and TF-gene interactions are indicated by non-zero values in the matrix. There are 2799 TF-gene interactions.</p> <p><br> Supplemental Table 6 is the oligonucleotide sequences used in this work. It is a TSV file with a header row.</p> <p>Supplemental Table 7 is the yeast strains used in this work. It is a TSV file with a header row.</p> <p>Supplemental Table 8 is gene metadata used in this work (e.g. Ribosomal Protein gene labels, etc). It is a TSV file with a header row.</p> <p>Supplemental Table 9 is FY4/5 growth curve data generated in this work. It is a 20 rows × 7 columns TSV file where the first row is a header with replicate IDs, the first column is an index of times in minutes, and values are cell densities in YPD culture, in units of 10$^6$ cells / mL.</p> <p>Supplemental Data 10 is a TAR.GZ file containing the yeast SacCer3 genome, modified to add UTR sequences, that was used to generate transcripts for kallisto pseudoalignment in this work.</p>
Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"
<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition”<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: “database_ISEA2020.mat”</p>
Arsenic(III) photocatalytic oxidation kinetics (using TiO2 and composite TiO2/Fe2O3 photocatalysts)
<p>Data sets on the photocatalytic oxidation of arsenic(III) using TiO<sub>2</sub> and composite TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> photocatalysts.</p>
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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.