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1,111 results for “Nanoparticles”
Figure 1 in Silver nanoparticles as a potential nematicide against Meloidogyne graminicola
Figure 1: Silver nanoparticles (AgNP) characterization (A) ultra violet-visible (UV–Vis) absorption spectra exhibiting 417 nm absorbance related to surface plasmon resonance of AgNP, (B) X-ray diffractogram showing the different reflections from crystalline planes of AgNP, indicating the formation of face-centered cubic (FCC) structure of AgNP, (C) transmission electron microscopy (TEM) image showing the formation of poly-dispersed spherical AgNP with average size of 20 nm, and (D) high resolution TEM image of single AgNP of 25 nm showing characteristic inter-planar spacing of silver (Ag).
Figure 3 in Silver nanoparticles as a potential nematicide against Meloidogyne graminicola
Figure 3: Effect of silver nanoparticles (AgNP) on root galling by MelOidOgyne graminiCOla on rice seedlings in soilless system. The experiment was repeated, the Trial×Treatment interaction was not significant (P>0.05). Data are means of two trials. All treatments had 10 replications randomized completely.
Accompanying data for the paper "Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles"
<h2>Contributions</h2> <ul> <li><strong>Iaquinta Sarah</strong> did contribute to the first draft edition, the development of the theoretical background of the algorithms and their implementation</li> <li><strong>Khazaie Sharam</strong> did contribute to the revision and edition of the draft, and to the development of the theoretical background of the algorithms</li> <li><strong>Jacquemin Frédéric</strong> did contribute to the project management and to the revision of the article.</li> <li><strong>Fréour Sylvain</strong> did contribute to the project management and to the revision of the article.</li> </ul> <h2>Funding sources</h2> <p>i-Site NExT : Grant/Award Number: ANR-16-IDEX-0007, Région Pays de la Loire and CNRS (French National Centre for Scientific Research).</p> <h2>Data structure and information</h2> <ul> <li>code - <code>np_uptake source and data directory</code> <ul> <li>workflow - <code>scripts to reproduce figures</code></li> <li>np_uptake - <code>source code producing results and figures</code> <ul> <li>figures - <code>utility module to produce figures</code></li> <li>model - <code>see detailed description below</code></li> <li>metamodel_implementation - <code>see detailed description below</code></li> <li>sensitivity_analysis - <code>see detailed description below</code></li> </ul> </li> </ul> </li> </ul> <h3>Detailed description</h3> <h4>Abstract</h4> <p>In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion.</p> <h4>Code</h4> <p>This repository is divided into 4 folders:</p> <ul> <li> <p><em>model</em>: contains the code used to compute the total variation of energy of the interface between a circular NP and a membrane by accounting for the mechanical accommodation of the latter. This folder also contains the routine to determine the final wrapping phase of the system.</p> </li> <li> <p><em>metamodel_implementation</em>: contains a script to check for the representativeness of the dataset used to create a metamodel, a script to create Kriging and PCE metamodels using the Openturns opensource library, and a routine to validate the metamodel that has just been created;</p> </li> <li> <p><em>sensitivity_analysis</em>: contains a script that allows to create samples based on the Kriging metamodels that have been created and exported as .pkl files in the metamodel folder. These samples are then used to the apply sensitivity algorithms. The user can choose among the various sensitivity algorithms provided by Openturns. For PCE metamodels, a routine is implemented to directly get the Sobol indices from the coefficients of the PCE metamodel. The indices can be plotted through plot routines;</p> </li> <li> <p><em>figures</em>: contains a utils script to display the graphs and save them as PNG files with consistency.</p> </li> </ul>
Figure 2 in Silver nanoparticles as a potential nematicide against Meloidogyne graminicola
Figure 2: Ultra violet-visible (UV–Vis) spectra of silver nanoparticles (AgNP), AgNO3, H2O2, and commercial Silvox 500® showing the distinctive features of AgNP which is absent in Silvox 500®.
Data on Effects of the Incorporation of Luminescent Vanadate Nanoparticles in Lithium-borate Glass Matrices by Various Methods
<p><span>The glass-ceramic materials studied in this work are designed using combinations of lithium-vanadate-borate glass matrices and lanthanum/rare earth (RE) vanadate nanoparticles. Three different techniques of sintering of the glass matrix and vanadate nanoparticles are investigated. Morphological characteristics and spectral properties of the glass-ceramic samples obtained by different techniques are investigated and analyzed in comparison with the properties of the original glass matrices. The luminescence spectra of all glass-ceramic samples consist of a wideband glass matrix emission and the characteristic line emission of the RE ions incorporated into the glass matrices as nanoparticles. The RE luminescence of these glass-ceramics is promising for various optoelectronic applications. Recommendations for the next development of new glass-ceramic techniques and materials are discussed.</span><span> </span></p>
Nanoparticle Size Estimation by Scanning Transmission Electron Microscopy and Generative AI
<p>The "raw" directories contain unaltered simulated and experimental data. The train and val directories contain normalized data used to train the models of the manuscript. The dataframes directory contains all information about the atomic models. Exp info contains info about the raw experimental data (excluding the gas-cell data). </p>
Figure 2 in Nitrogen-fixing Cyanothece sp. as a mixotroph and silver nanoparticle synthesizer: a multitasking exceptional cyanobacterium
Figure 2. Disc inhibition zone against MRSA Staphylococcus aureus using (a) gold nanoparticles, (b) silver nanoparticles, and (c) both silver nanoparticles and gold nanoparticles.
Figure 5 in TiO nanoparticles and salinity stress in relation to artemisinin production and ADS and DBR2 expression in Artemisia absinthium L.
Figure 5. The effect of salinity stress and titanium dioxide nanoparticles on the amount of artemisinin in wormwood (the non–identical letters indicate significant difference based on Duncan test P≤ 0.05).
Figure 3 in TiO nanoparticles and salinity stress in relation to artemisinin production and ADS and DBR2 expression in Artemisia absinthium L.
Figure 3. The effect of salinity and titanium dioxide nanoparticles on ADS gene expression in wormwood (non–identical letters indicate significant difference based on Duncan test P≤ 0.05).
Figure 2 in TiO nanoparticles and salinity stress in relation to artemisinin production and ADS and DBR2 expression in Artemisia absinthium L.
Figure 2. (A) Gel electrophoresis of RNA extracted from the leaf; (B) Gel electrophoresis of Standard RT–PCR product of 16s rRNA. M: 100bp DNA size marker. Numbered wells are samples. Gel agarose 1%.
Figure 9 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 9. Means number of deposited eggs by females of tested mites after 4 days post-exposure to α- and γ-Al2O3 NPs at tested concentrations. Different letters denote to significant differences in means at tested concentrations (Duncan test, P ≤ 0.05).
Figure 12 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 12. SEM visualization of α- and γ-Al2O3 NPs aggregation on ventral side of C. mycophagus mite. A = treated female by α-Al2O3 NPs, B = treated female by γ-Al2O3 NPs, C = untreated female.
Figure 7 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 7. Females, nymphal and larval mortality (means ± SE) of C. mycophagus mites, subjected to synthesized α- and γ- Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 5 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 5. Females, nymphal and larval mortality (means ± SE) of M. fungivorus mites, subjected to synthesized α- and γ-Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 14 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 14. Mean growth Inhibition (A) and corresponding percentage (B), of F. oxysporum in response to different concentrations of α and γ-Al2O3 NPs after 5 days of growth at 30 °C and 180 rpm in PDB growth medium (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Figure 6 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 6. Mortality (means ± SE) of M. fungivorus females, nymphs and larvae, concerning α- and γ-Al2O3 NPs at tested concentrations and exposure time.
Figure 10 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 10. Females' mortality (means ± SE) of (A) M. fungivorus and (B) C. mycophagus mites subjected to synthesized α and γ-Al2O3 NPs at different concentrations and exposure time. different letters within the same concentrations are significantly different, Duncan test (P ≤ 0.05).
Figure 13 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 13. Mean growth Inhibition (A) and corresponding percentage (B) of Aspergillus flavus in response to different concentrations of α- and γ-AL2O3 NPs after five days of growth at 30 ℃ and 180 rpm in PDB growth medium. (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Supporting Data for "Synthesis and electrokinetics of cationic spherical nanoparticles in salt-free non-polar media" (Chemical Science, doi:10.1039/c7sc03334f)
<p>TEM micrographs of diblock copolymer micelles (magnification given in file name).</p> <p>Small-angle X-ray (SAXS) and small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]).</p>
Data: In vivo fate of free and encapsulated iron oxide nanoparticles after injection of labelled stem cells
<p>This data set is composed of magnetic resonance images (MRI) that are supporting the article entitled <em>In vivo fate of free and encapsulated iron oxide nanoparticles after injection of labelled stem cells </em>by the same authors. Nanoparticle contrast agents are used to label stem cells and monitor their bio-distribution in pre-clinical models of disease. Due to the impact on the interpretation of imaging results, understanding the <em>in vivo</em> fate of the particles is important. The bio-distribution after intra-cardiac injection of labelled cells with superparamagnetic iron oxide nanoparticles was monitored longitudinally by MRI. </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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
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