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447 results for “Potassium”
Figure 1 in Quantum yield, chlorophyll, and cell damage in yellow passion fruit under irrigation strategies with brackish water and potassium
Figure 1. Data of precipitation, maximum and minimum air temperatures, and relative humidity of air observed during the experimental period.
Figure 3 in Quantum yield, chlorophyll, and cell damage in yellow passion fruit under irrigation strategies with brackish water and potassium
Figure 3. Electrolyte leakage - % IEL (A) and relative water content - RWC (B) of yellow passion fruit plants 'BRS GA1' as a function of the interaction between the use of brackish water irrigation strategies and potassium doses at 445 and 360 days after transplanting, respectively. Vertical bars represent the standard error of mean (n = 4). Means followed by the same lowercase letters indicate no significant difference between management strategies by the Scott-Knott test (p≤0.05) for the same potassium dose, and the same uppercase letters indicate no significant difference between potassium doses by the Tukey test (p ≤ 0.05) for the same strategy. For details of BWIS see Table 4.
3-Sulfopropyl acrylate potassium-based polyelectrolyte hydrogels: Sterilizable synthetic material for biomedical application
<p>Hydrogels are extensively used in the biomedical field due to their highly valued properties, biocompatibility and antimicrobial activity and resistance to rheological stress. However, determining an efficient sterilization protocol that does not compromise the functional properties of hydrogels is one of the challenges researchers face when developing a material for a medical application. In this work, conventional sterilization methods (steam-, radiation- and gas sterilization) were investigated regarding the influence on the degree of swelling, mechanical performance and chemical effects on the poly 3-sulfopropyl acrylate potassium (pAESO<sub>3</sub>) hydrogel, which is a promising representative for biomedical engineering applications. In summary, no significant changes in the gel properties were observed after sterilization, showing the potential of the selected hydrogel for biomedical applications.</p>
Dinitrogen Reduction and Functionalization by a Siloxide Supported Thulium-Potassium Complex for the Formation of Ammonia or Hydrazine Derivatives
<p>The dinitrogen (N2) chemistry of lanthanides remainsless developed compared to the d-block metals and lanthanide-promoted N2 functionalization chemistry in well-defined lanthanidecomplexes remains elusive. Here we report the synthesis andcharacterization (SQUID, EPR, DFT, X-Ray) of the siloxidesupported heterobimetallic (Tm/K) complexes[{KTm(OSi(OtBu)3)3}2(μ-η2:η2-N2)] (1) and [K3{Tm(OSi(OtBu)3)3}2(μ-η2:η2-N2)] (2). Complex 2 provides a rare example of a metalcomplex of the triply reduced N23- radical. The structure of 2 differsfrom the few previously reported N23- complexes as it presents twoTm and three K cations binding the N23- radical, facilitating N2functionalization. Notably, the K3Tm2-bound N23- moiety reacts withexcess H+ to form NH4Cl in 18% yield, and with MeOTf at roomtemperature to yield the dimethyl hydrazido complex[K2{Tm(OSi(OtBu)3)3}2(μ-(CH3)NN(CH3))] (3). Protonolysis of 3yields MeHN-NMeH·2HCl in 18 % yield.</p>
potassium (2-pyrimidin-2-yl-1H-benzimidazole)dimethylnickel, K(bimpm)Ni(CH3)2
<p>Data Set for compound 1</p>
Data supporting "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study"
<p>Data and code supporting the article "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study".</p> <p> </p>
dataset relate to article: "Functional Characterization of Two Variants at the Intron 6-Exon 7 Boundary of the KCNQ2 Potassium Channel Gene Causing Distinct Epileptic Phenotypes"
<p><strong>Sequencing Analysis performed at Fondazione Besta and carried out as part of the study mentioned at title</strong></p>
Data from: A global reference database in FAOSTAT of cropland nutrient budgets and nutrient use efficiency: nitrogen, phosphorus and potassium, 1961–2020
<p class="MsoNormal">Agricultural nutrient budgets help to identify an excess or an insufficiency in use of fertilizers and other nutrient sources. Nutrient budgets allow for indicators such as the nutrient balance (surplus or deficit) and nutrient use efficiency to be estimated. This can help in the monitoring of agricultural productivity and sustainability globally. The present dataset is a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) in cropland. The database is disseminated in FAOSTAT and provides a global reference, synthesizing and continuously updating the state-of-the-art on this topic. The database covers the period 961 to 2020 for 205 countries and territories, as well as regional and global aggregates. Results indicate the wide range in nutrient use and use efficiencies across regions, nutrients, and time. This dataset introduces improvements over previous work in relation to key nutrient coefficients affecting nutrient budgets and use efficiency estimates. This is especially for nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. </p>
Potassium fertilization effects on cereal yield and soil organic carbon in agricultural ecosystems at the global scale
<p>This dataset includes the raw data of a global meta-analysis study on the responses of cereal yield and soil organic carbon to potassium fertilization in agricultural ecosystems.</p>
A selective small-molecule agonist of G protein-gated inwardly-rectifying potassium channels reduces epileptiform activity in a mouse model of tumor associated epilepsy - Thy1-GCaMP Tumor Electrophysiology
Open the record for dataset details and reuse information.
Discovery of a potent, Kv7.3-selective potassium channel opener from a Polynesian traditional botanical anticonvulsant
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Data from: A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Open the record for dataset details and reuse information.
iSDAsoil: soil extractable Potassium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil extractable Potassium (K) log-transformed predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_log.k_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium mean value,</li> <li>sol_log.k_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: log.k_mehlich3 R-square: 0.773 Fitted values sd: 0.938 RMSE: 0.509 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.3088 -0.2648 -0.0037 0.2639 6.8136 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.907726 6.134422 1.778 0.0754 . regr.ranger 1.004487 0.003878 259.026 <2e-16 *** regr.xgboost -0.004081 0.004739 -0.861 0.3892 regr.cubist 0.084556 0.004346 19.454 <2e-16 *** regr.nnet -2.205286 1.228586 -1.795 0.0727 . regr.cvglmnet -0.064510 0.003933 -16.401 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5092 on 139122 degrees of freedom Multiple R-squared: 0.7725, Adjusted R-squared: 0.7725 F-statistic: 9.451e+04 on 5 and 139122 DF, p-value: < 2.2e-16 </code></pre> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( y / 10 )</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
The potassium channel subunit Kv1.8 (Kcna10) is essential for the distinctive outwardly rectifying conductances of type I and II vestibular hair cells
<p>In amniotes, head motions and tilt are detected by two types of vestibular hair cells (HCs) with strikingly different morphology and physiology. Mature type I HCs express a large and very unusual potassium conductance, g<sub>K,L</sub>, which activates negative to resting potential, confers very negative resting potentials and low input resistances, and enhances an unusual non-quantal transmission from type I cells onto their calyceal afferent terminals. Following clues pointing to K<sub>V</sub>1.8 (KCNA10) in the Shaker K channel family as a candidate g<sub>K,L</sub> subunit, we compared whole-cell voltage-dependent currents from utricular hair cells of K<sub>V</sub>1.8-null mice and littermate controls. We found that K<sub>V</sub>1.8 is necessary not just for g<sub>K,L</sub> but also for fast-inactivating and delayed rectifier currents in type II HCs, which activate positive to resting potential. The distinct properties of the three K<sub>V</sub>1.8-dependent conductances may reflect different mixing with other K<sub>V</sub>1 subunits, such as K<sub>V</sub>1.4 (KCNA4). In K<sub>V</sub>1.8-null HCs of both types, residual outwardly rectifying conductances include K<sub>V</sub>7 (KCNQ) channels. </p> <p>Current clamp records show that in both HC types, K<sub>V</sub>1.8-dependent conductances increase the speed and damping of voltage responses. Features that speed up vestibular receptor potentials and non-quantal afferent transmission may have helped stabilize locomotion as tetrapods moved from water to land.</p>
LCA inventories for metal iodides: Cuprous iodide (CuI), Potassium iodide (KI), Bismuth iodide (BiI3), Silver iodide (AgI), Antimony Triiodide (SbI3).
<p>This dataset contains Life Cycle Assessment (LCA) inventories for a selection of metal iodides, including Cuprous Iodide (CuI), Potassium Iodide (KI), Bismuth Iodide (BiI3), Silver Iodide (AgI), and Antimony Triiodide (SbI3). These are industrial scale inventories, providing comprehensive data on the production of each of these compounds. </p>
Square Antiprismatic Ion Chelation Is a Key Determinant for Potassium Channel Selectivity
<p>Files presented here are archives K_DB.tar.gz, MEMB_DB.tar.gz and PDB70.tar.gz.</p> <p>Archives KDB.tar.gz, MEMB_DB.tar.gz and PDB70.tar.gz contain models of indentified sites for potassium channels (dataset #1), other membrane proteins, excluding potassium channels (dataset #2) and non-membrane proteins form PDB70 (dataset #3). The name of a folder in the dataset corresponds to PDB ID of a protein for which calculation were made. Each folder contain the following files:</p> <ul> <li><PDB_ID>.pdb — the original pdb file.</li> <li><PDB_ID>.ref — file that contains oxygens and nitrogens from original pdb that were used for scanning.</li> <li><PDB_ID>_COMBS.txt — combinations of atoms that were used for calculations.</li> <li><PDB_ID>_alignment_X.pdb — original template that was aligned to the protein atoms. X denotes a number of the alignment.</li> <li><PDB_ID>_site_X.pdb — this pdb file contains eight atoms that form the site for K+ and which were used for the corresponding alignment X.</li> <li><PDB_ID>_RES.txt — the combinations of protein atoms that form the site are written in square brackets. The RMSD value for the alignment to this site is written to the right of them.</li> <li><PDB_ID>_RMSD.log — this file contains RMSD values of the template alignment to the corresponding site.</li> </ul>
Square Antiprismatic Chelation Is a Key Determinant for Potassium Ion Selectivity
<p><strong>DATASETS</strong></p> <p>Archives <em>K_DB.tar.gz</em>, <em>MEMB_DB.tar.gz</em> and <em>PDB70.tar.gz</em> contain models of indentified sites for potassium channels (dataset #1), other membrane proteins, excluding potassium channels (dataset #2) and non-membrane proteins form PDB70 (dataset #3). The name of a folder in the dataset corresponds to PDB ID of a protein for which calculation were made. Each folder contain the following files:</p> <ul> <li><PDB_ID>.pdb — the original pdb file.</li> <li><PDB_ID>.ref — file that contains oxygens and nitrogens from original pdb that were used for scanning.</li> <li><PDB_ID>_COMBS.txt — combinations of atoms that were used for calculations.</li> <li><PDB_ID>_alignment_X.pdb — original template that was aligned to the protein atoms. X denotes a number of the alignment.</li> <li><PDB_ID>_site_X.pdb — this pdb file contains eight atoms that form the site for K+ and which were used for the corresponding alignment X.</li> <li><PDB_ID>_RES.txt — the combinations of protein atoms that form the site are written in square brackets. The RMSD value for the alignment to this site is written to the right of them.</li> <li><PDB_ID>_RMSD.log — this file contains RMSD values of the template alignment to the corresponding site.</li> </ul> <p><strong>SUPPLEMENTARY TABLES S1-S5</strong></p> <p>The file <em>Supporting Information_Tables S1-S5.xlsx </em>contain tables S1-S5 for the supporting information from the original paper. These tables describe potassium binding sites indentifieid in potassium channels, other membrane proteins and randomly chosen non-membrane proteins. (See the article text for details)</p> <p> </p> <p> </p>
Yield and Quality Response of Cassava (Manihot esculenta Crantz) to Nitrogen and Potassium Fertilizer Rates at Arba Minch, Southern Ethiopia
<p><em>Cassava (Manihot esculenta Crentz), is a major staple food crop of the people in most parts of Africa, and plays an important role in terms of food security, employment and income generation for farm families. It is one of important root crops in Ethiopian diet; however there is no research work has been done on rate of nitrogen and potassium fertilizer. Rate of N and K fertilizer are the major factor in determining the quality and quantities of cassava. Thus, this experiment was carried on the influence </em><em>of nitrogen and potassium</em><em> fertilizer rate on yield and qualities of </em><em>cassava </em><em>at Arba Minch. Four levels of N (0, 46, 92 and 138 kg N ha<sup>−1</sup>) and three levels of K (0, 60, and 120 kg K ha<sup>−1</sup>) were arranged in factorial combination in a randomized complete block design (RCBD) with three replications. The results revealed that the main effects of nitrogen fertilizer rates influences all the studied parameters, while the main effect of potassium affects number of tubers per plant and tuber diameter. Interaction effect of N and K influences fresh tuber yield of cassava. The highest fresh tuber yield of cassava was achieved from the combination of 92 kg Nha<sup>-1</sup> with application of 0 kg K ha<sup>−1</sup>, whereas the lowest fresh tuber yield of cassava was obtained from the combination of 0 kg N ha<sup>−1</sup> with 0 kg K ha<sup>−1</sup>. The economic analysis showed that higher net benefit and marginal rate of return were obtained from the application of 92 kg N ha<sup>−1</sup> with 0 kg K ha<sup>−1</sup>. In order to prevent excessive production costs, the use of 92 kg Nha<sup>-1</sup> combined with the application of 0 kg K ha<sup>−1</sup> is recommended. </em></p>
Ab initio umbrella sampling of a potassium ion at the aqueous graphene interface
<p>Ab initio molecular dynamics trajectories obtained with umbrella sampling of a potassium ion at the aqueous graphene interface, where in each trajectory the ion is at a different height from the graphene sheet.</p> <p>This repository contains supplementary data supporting the findings of the paper:</p> <p>L. Joly, R. H. Meissner, M. Iannuzzi, G. Tocci, "Osmotic Transport at the Aqueous Graphene and hBN Interfaces: Scaling Laws from a Unified, First-Principles Description", ACS Nano, 15, 9, 15249–15258 (2021), DOI: 10.1021/acsnano.1c05931.</p>
Ab initio umbrella sampling of a potassium ion at the aqueous hBN interface
<p>Ab initio molecular dynamics trajectories obtained with umbrella sampling of a potassium ion at the aqueous hBN interface, where in each trajectory the ion is at a different height from the hBN sheet.</p> <p>This repository contains supplementary data supporting the findings of the paper:</p> <p>L. Joly, R. H. Meissner, M. Iannuzzi, G. Tocci, "Osmotic Transport at the Aqueous Graphene and hBN Interfaces: Scaling Laws from a Unified, First-Principles Description", ACS Nano, 15, 9, 15249–15258 (2021). DOI: 10.1021/acsnano.1c05931</p>
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