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447 results for “Potassium”
Input files for simulation of potassium channels using the AMOEBA polarizable force field
<p>This dataset contains input Tinker xyz and key files for the simulation of KcsA potassium channels in DOPC bilayer, a simple script for converting CHARMM pdb file to Tinker xyz file, and modified Tinker source code to support one-dimensional position restraints.<br> "params.tar.gz" contains a description of the force field modifications.<br> <br> To use "mod2", add the following lines to the key file.</p> <pre><code>#compatible with amoebabio18.prm polarize 5 1.4500 0.3900 3 polarize 11 1.4500 0.3900 9 polarize 3 1.7500 0.3900 1 5 7 50 225 227 polarize 9 1.7500 0.3900 1 7 11 50 225 227</code></pre> <p> </p>
Effects of factorial nitrogen, phosphorus, and potassium with micronutrient addition and Host Community on Fungal Endophyte Diversity at Cedar Creek Ecosystem Reserve, Minnesota, USA, 2014
The microbes contained within free-living organisms can alter host growth, reproduction, and interactions with the environment. In turn, processes occurring at larger scales determine the local biotic and abiotic environment of each host that may affect the diversity and composition of the microbiome community. Here, we examine variation in the diversity and composition of the foliar fungal microbiome in the grass host, Andropogon gerardii, across a factorial nitrogen, phosphorus, and potassium addition experiment in Minnesota, USA. We found limited evidence of direct effects of nutrients on endophyte diversity. Instead, the effects of nutrients on endophyte diversity appeared to be mediated by accumulation of plant litter and plant diversity loss. Specifically, nitrogen addition is associated with a 40% decrease in plant diversity and an 11% decrease in endophyte richness. Although nitrogen, phosphorus, and potassium addition increased aboveground live biomass and decreased relative Andropogon cover, endophyte diversity did not covary with live plant biomass or Andropogon cover. Our results suggest that fungal endophyte diversity within this focal host is determined in part by the diversity of the surrounding plant community and its potential impact on immigrant propagules and dispersal dynamics. Our results suggest that elemental nutrients reduce endophyte diversity indirectly via impacts on the local plant community, not direct response to nutrient addition.
Research data supporting "Tin phosphide anodes for potassium-ion batteries: insights from crystal structure prediction"
<p>This dataset contains the output files of crystal structure prediction calculations (density-functional theory relaxations, bandstructures, phonon calculations, GIPAW-NMR calculations) on the ternary K-Sn-P phase diagram. All calculations were performed with the CASTEP DFT package (https://www.castep.org/) and the "matador" Python library (https://github.com/ml-evs/matador).</p> <p><strong>Contents:</strong></p> <ul> <li>"convergence_tests.zip": contains the results of convergence tests on the K-P system at two levels of accuracy "polish" and "searches" on the corresponding edge of the K-Sn-P ternary system</li> <li>"phonons.zip": contains CASTEP output files for phonon calculations on the predicted low-lying phases on the corresponding edge of the K-Sn-P phase diagram</li> <li>"polish.zip": contains CASTEP output files of relaxations on the corresponding edge of the K-Sn-P system at the "polish" level of accuracy using various different xc-functionals or external pressures.</li> <li>"searches.zip": contains ".res" files that provide the relaxed structure from each different crystal structure prediction method on the corresponding edge of the K-Sn-P system.</li> <li>"bulk_modulus.zip" contains CASTEP output files for calculation of E(V) curves for low-lying KP phases with different xc-functionals.</li> <li>"nmr.zip" contains CASTEP output files for GIPAW-NMR calculations of chemical shifts for low-lying K-Sn-P phases.</li> <li>"spectral.zip" contains CASTEP and OptaDOS output files for projected bandstructure and DOS calculations of low-lying K-Sn-P phases.</li> <li>"digests.zip" contains JSON representations of all the structures from polish and searches, broken down into K-P and K-Sn-P specific digests.</li> </ul>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</p>
Data for Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates
<p>This repository contains data and scripts to reproduce results that are presented in the manuscript: Van Laere, J., Merckx, R., Hood-Nowotny, R., Dercon, G. (2023) Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. <em>Front. Plant Sci</em>. 14:1222558 doi: 10.3389/fpls.2023.1222558</p>
Datasets to Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Poly(ethylene oxide)-based Electrolytes for Solid-State Potassium Metal Batteries with Prussian Blue Positive Electrode"</strong></p> <p>published in ACS Appl. Polym. Mater. (DOI: <a href="https://doi.org/10.1021/acsapm.2c00014">10.1021/acsapm.2c00014</a> ) / <a href="https://doi.org/10.1021/acsapm.2c00014">https://doi.org/10.1021/acsapm.2c00014</a></p> <p>Specifically, the following measurements are provided:</p> <p>Electrochemical cell tests of liquid and solid electrolytes ("CYCLING_" & Ratecapability test)</p> <p>Solid electrolyte characterization:</p> <p>Differential Scanning Calorimetry ("DSC_")</p> <p>Electrochemical Impedance Spectroscopy ("EIS_")</p> <p>Rheological measurements ("RHEO_")</p> <p>X-ray diffraction data ("XRD_")</p>
Figure 3 in Evaluation of potassium borate as a volatility-reducing agent for dicamba
Figure 3. Temperature and relative humidity following herbicide application in 2020 at the locations in (A) Fayetteville and (B) Newport, AR, from July 7, 2020, through July 8, 2020 (30 h after application).
Figure 2. Exponential 2p in Evaluation of potassium borate as a volatility-reducing agent for dicamba
Figure 2. Exponential 2p curve ((O*Exp(*rate), O = scale, b = growth rate) fit to potassium tetraborate tetrahydrate (KBo) concentration and total dicamba recovered from polyurethane foam and filter paper from the two KBo rate titration experiments conducted in 2020; R2 value displays the percentage of variability explained by the fit of the line. Black dots in the middle represent mean recovered dicamba of the respective KBo concentration, and gray dots above and below the mean represent the SE.
Figure 1 in Evaluation of potassium borate as a volatility-reducing agent for dicamba
Figure 1. (A) Images of low tunnels and implementation of trial in the field, and (B) placement of air samplers and treated soil flats between two rows of bioindicator soybean located underneath a 1.5 m by 6 m by 1.2 m plastic-covered tunnel in Fayetteville, AR, in 2020.
Molecular Dynamics Simulations of Hydrophilic (QTY) Potassium Ion Channels in Water
<p>You can find here the molecular dynamics (MD) trajectories of QTY proteins in water performed for the "Computational engineering of water-soluble potassium ion channels through QTY transformation" manuscript. Please cite our paper and the previous Zenodo dataset when referring to or using this data. If you have any questions, please contact me (Eva Smorodina) at ribes.ev@gmail.com. Thank you!<br><br>Smorodina, E. (2024). Molecular Dynamics Simulations of Hydrophobic (cryo-EM and Native) and Hydrophilic (QTY) Potassium Ion Channels [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10592842</p>
Figure 3 in Effects of temperature on mortality of quagga mussels (Dreissena bugensis) exposed to potassium chloride and copper-based molluscicides in high conductivity waters
Figure 3. Comparison of measured mortality for adult mussels exposed to copper at 10 °C and copper concentrations over time for Experiment 1b (A), which had 50% less biomass and lower mean specific conductivity than Experiment 4 (C) with log-logistic dose-response model fits. Colored bands are 95% confidence intervals and points are mortality values from replicate bioboxes. Measured copper concentrations in bioboxes for B) Experiment 1b and D) Experiment 4. Solid horizontal lines are target concentrations, dashed horizontal lines are mean concentration over the entire experiment duration.
Figure 2 in Effects of temperature on mortality of quagga mussels (Dreissena bugensis) exposed to potassium chloride and copper-based molluscicides in high conductivity waters
Figure 2. Measured mortality for adult mussels exposed to KCl at A) 10 °C, B) 18 °C, and C) 22 °C with log-logistic doseresponse model fits. Colored bands are 95% confidence intervals and points are mortality values from replicate bioboxes.
Figure 1 in Effects of temperature on mortality of quagga mussels (Dreissena bugensis) exposed to potassium chloride and copper-based molluscicides in high conductivity waters
Figure 1. Variation in specific conductivity in A) Lake Piru and B) control bioboxes within experimental periods. Specific conductivity from moderate conductivity Lake Ontario and Minnesota lakes (≈ 300 µS/cm; Moffitt et al. 2016; Luoma et al. 2018) is provided for reference.
Figure 5 in Effects of temperature on mortality of quagga mussels (Dreissena bugensis) exposed to potassium chloride and copper-based molluscicides in high conductivity waters
Figure 5. Comparison of measured mortality for adult mussels exposed to copper at 10 °C and copper concentrations over time for Experiment 1a (A), which received only a single dose of copper and Experiment 1b, which included refreshed copper treatments (C) with log-logistic dose-response model fits. Colored bands are 95% confidence intervals and points are mortality values from replicate bioboxes. Measured copper concentrations in bioboxes for B) Experiment 1a (without refresh) and D) Experiment 1b (with refresh). Solid horizontal lines are target concentrations, dashed horizontal lines are mean concentration over the entire experiment duration.
Figure 4 in Effects of temperature on mortality of quagga mussels (Dreissena bugensis) exposed to potassium chloride and copper-based molluscicides in high conductivity waters
Figure 4. Measured mortality for adult mussels exposed to copper (Earthtec QZ®) at A) 10 °C, C) 18 °C, and E) 22 °C with loglogistic dose-response model fits. Colored bands are 95% confidence intervals and points are mortality values from replicate bioboxes. Measured copper concentrations in bioboxes at B) 10 °C, D) 18 °C, and F) 22 °C. Solid horizontal lines are target concentrations, dashed horizontal lines are mean concentration over the entire experiment duration.
Figure 4 in Nitrogen and potassium synergism influences the yield and quality of Dioscorea cayennensis
Figure 4. Total yield (A) and marketable yield (B) of tubers as a function of nitrogen and potassium fertilization (** significant at 1%). MY = Marketable yield. CV = Coefficient of variation.
Figure 6 in Nitrogen and potassium synergism influences the yield and quality of Dioscorea cayennensis
Figure 6. SPAD index (A) and leaf nitrogen (B), phosphorus (C) and potassium (D) contents as a function of nitrogen and potassium fertilization (**, * significant at 1%, and 5%, respectively). PC = Phosphorus content. KC = Potassium content.
Figure 2 in Nitrogen and potassium synergism influences the yield and quality of Dioscorea cayennensis
Figure 2. Average tuber mass as a function of nitrogen and potassium fertilization (**, * significant at 1%, and 5%, respectively). TM = Tuber mass.
Figure 1 in Nitrogen and potassium synergism influences the yield and quality of Dioscorea cayennensis
Figure 1. Maximum and minimum air temperature, relative air humidity, and precipitation during the period of cultivation for the yam.
Figure 5 in Nitrogen and potassium synergism influences the yield and quality of Dioscorea cayennensis
Figure 5. Root-knot (A) and dry rot (B) of yam tubers as a function of nitrogen and potassium fertilization (**, * significant at 1%, and 5%, respectively).
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International Brain Laboratory public data
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OpenNeuro
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