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162 results for “tem”
A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout
<p>This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.</p>
Electron Diffraction (MicroED) Datasets for Finafloxacin (+)-Menthyl Carbamate (Glacios TEM with a CETA-D)
<p>Electron diffraction datasets collected from finafloxacin (+)-menthyl carbamate </p> <p> </p> <p>Microscope: Thermo Fisher Scientific Glacios Transmission Electron Microscope (SDC1G at NanoImaging Services)</p> <p>Camera: Ceta-D camera (bin 2x2, rolling shutter, noise reduction mode)</p> <p>Collection Software: Leginon (Cheng, et. al. 2021)*</p> <p>Collection Parameters: 200keV, -193C, 20um C2, gun lens 7.1, spot size 10, parallel beam, calibrated camera length 1065.7mm (1100 in UI), oscillation per frame 0.89deg, 222ms exposure time, tilt speed 4 deg/s, rotation -60 to +60 (first ~8 degrees not recorded)</p> <p>Grid: Ted Pella 01840</p> <p>Sample: finafloxacin (+)-menthyl carbamate (C<sub>31</sub>H<sub>37</sub>FN<sub>4</sub>O<sub>6</sub>, 7-[(4aS,7aS)-4-({[(1S,2R,5S)-5-methyl-2-(propan-2-yl)cyclohexyl]oxy}carbonyl)-octahydropyrrolo[3,4-b][1,4]oxazin-6-yl]-8-cyano-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid), 580.66 g/mol</p> <p>Structure: CCDC 2168647</p> <p> </p> <p>* Data have been converted to SMV format with the addition of an offset value to remove negative pixel values. This offset value can be found in the image headers, along with a suggested pedestal value.</p> <p> </p> <p>A data processing tutorial is available for processing data collected with this setup using DIALS: </p> <p><a href="https://dials.github.io/documentation/tutorials/3DED/Biotin.html">https://dials.github.io/documentation/tutorials/3DED/Biotin.html</a></p> <p> </p> <p>Funding: NIH/NIGMS grant number 1R44GM140666</p>
Electron Diffraction (MicroED) Datasets for Finafloxacin (1R)-(+)- α-Methylbenzyl Isocyanate Type A, Unsolved (Glacios TEM with a CETA-D)
<p>Electron diffraction datasets collected from crystals likely composed of finafloxacin and (1R)-(+)- α-methylbenzyl isocyanate</p> <p> </p> <p>Microscope: Thermo Fisher Scientific Glacios Transmission Electron Microscope (SDC1G at NanoImaging Services)</p> <p>Camera: Ceta-D camera (bin 2x2, rolling shutter, noise reduction mode)</p> <p>Collection Software: Leginon (Cheng, et. al. 2021)*</p> <p>Collection Parameters: 200keV, -193C, 20um C2, gun lens 7.1, spot size 10, nano probe mode, parallel beam, calibrated camera length 1065.7mm (1100 in UI), oscillation per frame 0.89deg, 222ms exposure time, tilt speed 4 deg/s, rotation -60 to +60 (first ~8 degrees not recorded)</p> <p>Grid: Ted Pella 01840</p> <p>Sample: finafloxacin (C<sub>20</sub>H<sub>19</sub>FN<sub>4</sub>O<sub>4</sub>,<sub> </sub>7-[(4aS,7aS)-octahydropyrrolo[3,4-b][1,4]oxazin-6-yl]-8-cyano-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid) and (1R)-(+)- α-methylbenzyl isocyanate</p> <p>Structure: Unsolved as of 11/9/2022</p> <p> </p> <p>* Data have been converted to SMV format with the addition of an offset value to remove negative pixel values. This offset value can be found in the image headers, along with a suggested pedestal value.</p> <p> </p> <p>A data processing tutorial is available for processing data collected with this setup using DIALS: </p> <p><a href="https://dials.github.io/documentation/tutorials/3DED/Biotin.html">https://dials.github.io/documentation/tutorials/3DED/Biotin.html</a></p> <p> </p> <p>Funding: NIH/NIGMS grant number 1R44GM140666</p>
Electron microscopy files (SBFSEM & TEM) for "Syncytial nerve net in a ctenophore sheds new light on the early evolution of nervous systems"
<p>4 electron microscopy datasets:</p> <p>1) SBFSEM data of 1-day old ctenophore <em>Mnemiopsis leidyi</em> (animal 1)</p> <p>2) SBFSEM data of 1-day old ctenophore <em>Mnemiopsis leidyi</em> (animal 2)</p> <p>3) SBFSEM data of 1-day old ctenophore <em>Mnemiopsis leidyi</em> (animal 3)</p> <p>4) TEM data of nerve net of 1-day old ctenophore <em>Mnemiopsis leidyi </em></p>
Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) simulations focused on Eight Mile Lake, Alaska and Imnavait Creek, Alaska [2000-2015]
<p>This set of files store model simulations using the biosphere model Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM), developed to simulate biophysical and biogeochemical interactions between the soil, vegetation and atmosphere. To improve predictions of net carbon releases from thawing permafrost, we tested the sensitivity of a suite of model parameters. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek watershed (IMN). These sites are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate, IMN consists of well-drained soils, and EML has historically well-drained soils, however permafrost thaw has altered drainage conditions to wetter soils. Simulations were conducted at a 1km resolution, over a 1,000 km2 area (10x10 km square) centered on two long term ecological research sites in Alaska: Eight Mile Lake located in Interior Alaska (63.8900° N, 149.2535° W), and Imnavait creek watershed located on the northern foothills of the Brooks range (68°37′ N, 149°18′ W).</p> <p>Historical simulations are spanning the 2000 to 2015, and forced using climate simulations from the Climate Research Unit, time series 4.0. We ran 1,000 site level simulations for each model variable. The variables that are produced are gross primary productivity (GPP, in gC.m-2.m-1), net ecosystem exchange (NEE, gC.m-2.m-1), ecosystem respiration (RECO, gC/m2/m-1), active layer thickness (ALT, m), soil temperature (TLAYER,°C) at 5, 10, 40 cm depths, soil moisture (LWCLAYER, m-3/m-3) at 5, 10 cm depths, and snow depth (SNOWDEPTH, m), evapotransipiration(EET, mm/m2/time), potential evapotransipiration (PET, mm/m2/time), leaf area index (LAI, m2/m2), organic layer thickness (OLT, m). The data are stored as compiled csv files, with time as the index, and each model sample output stored in the columns. In addition, there is a postprocessing python script to demonstrate the step and workflow used to generate the individual csv files post processed from the raw model outputs stored as netcdfs.</p>
Histological and TEM data: A major ecological niche of eosinophils in evolving Schistosoma granulomas challenges the eosinophil view as “helminth killer” cells
Open the record for dataset details and reuse information.
Obtenção do óxido de grafeno pelo método de Hummers: Caracterização por DRX, FTIR, Raman e TEM
<p>Vídeo apresentado na XIV Jornada de Ciência e Tecnologia – UEZO no período de 6 a 7 de outubro de 2021</p><p>Projeto de Pesquisa Faperj E-26 -010.002.372/2019</p><p>Projeto de Extensão PROEXT/UEZO/2019 : Estudo e desenvolvimento de oficinas interdisciplinares</p><p>Projeto de Extensão PROEXT /UEZO/2019 Projetos e Debates: Idealização, preparação e apresentação</p>
TEM analysis of WS2 flakes functionalized with Au NP
<p>This dataset contains TEM investigation of WS2 flakes decorated with Au NP.</p>
Cast Al-Si-Mg alloy TEM data
<p>Data from publication.</p> <p>Hardness, conductivity, EBSD and TEM.</p>
FIGURES 15–20. Mallomonas voloshkoae and M. pechlaneri. TEM images. FIGURES 15–18 in Mallomonas voloshkoae sp. nov. (Synurales, Chrysophyceae) and distribution of M. pechlaneri in mountain lakes of Siberia
FIGURES 15–20. Mallomonas voloshkoae and M. pechlaneri. TEM images. FIGURES 15–18. Mallomonas voloshkoae sp. nov. Body scales from different habitats. Figure 15. Unnamed lake 1. Figure 16. Unnamed lake 2. Figure 17. Unnamed lake 3. Figure 18. Lake Frolikha. FIGURES 19–20. Mallomonas pechlaneri. Figure 19. Scales with bristles from Teletskoe Lake. Figure 20. Scales with bristles from Ilchir Lake. Scale bars: Figs 15–20: 2 μm.
TEM Characterisation
<p>SmartFan Project public data</p>
Data for "Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase"
<p>Molecular dynamics data associated with the publication: "Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase"</p> <p>Trajectores were generated in GROMACS, and the carbon alpha coordinates were extracted and aligned with the JEDi analysis software. Details of the simulations and analysis are given in the publication.</p> <p>Data in apo.zip contains trajectories for 32 total trajectories of TEM-1, TEM-2, TEM-10, and TEM-52 beta-lactamase, each starting form different 8 crystal structures</p> <p>Data in holo.zip contains 16 trajectories of TEM-1, TEM-2, TEM-10, and TEM-52 beta-lactamase in complex with ampicillin, amoxicillin, cefotaxime, and ceftazidime each.</p> <p>Trajectories files are in comma delimited format, with rows representing degrees for freedom (789 total), and columns representing samples (10000 per trajectory file).</p> <p>Supervised Projective Learning for Orthogonal Completeness (SPLOC) software for analysis as performed in the publication can be found at: https://github.com/BioMolecularPhysicsGroup-UNCC/MachineLearning/tree/master/SPLOC </p>
Dataset for "Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture"
<p>Dataset accompanying the publication "Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture"</p>
Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning
<p>This is a dataset of High-Dose-Electron (HDE) images and Low-Dose-Electron (LDE) images taken with a transmission electron microscopy used in <a href="https://doi.org/10.1017/S1431927621013799">H. Katsuno, Y. Kimura, T. Yamazaki and I Takigawa, Microsc. Microanal. <strong>28</strong> (2022), pp 138--144</a> (<a href="https://arxiv.org/abs/2106.01718">arXiv:2106.01718</a>).</p> <p>There are two LDE images for each HDE image.</p> <p>cf) HDE image is 0001.tif and corresponding LDE images are 0002.tif and 0003.tif.</p> <p> </p> <p>Equipment of TEM:</p> <p>field-emission gun (JEM-2100F, JEOL, Tokyo)</p> <p>OneView IS (Gatan, Inc., Pleasanton, CA, USA)</p> <p> </p> <p>Typical magnification was 25,000x and 30,000x.</p> <p> </p> <p>HDE image</p> <p>The resolution was 4096 x 4096 pixels and its exposure time was 5 s.</p> <p>The typical total doses was 10<sup>10</sup> e<sup>-.</sup></p> <p> </p> <p>LDE image</p> <p>The resolution was 512 x 512 pixels and its exposure time was 3.3 ms.</p> <p>The typical total doses was 10<sup>6</sup> e<sup>-</sup>.</p> <p> </p> <table> <tbody> <tr> <td>Filename</td> <td>Material</td> <td>Total number of images</td> <td>Total number of a pair of HDE and LDE</td> </tr> <tr> <td>train1_Ni.zip</td> <td>Ni</td> <td>336</td> <td>224</td> </tr> <tr> <td>train2_FeNi.zip</td> <td>FeNi</td> <td>390</td> <td>260</td> </tr> <tr> <td>train3_SiC.zip</td> <td>SiC</td> <td>210</td> <td>140</td> </tr> <tr> <td>train4_Silicate</td> <td>Silicate</td> <td>264</td> <td>176</td> </tr> <tr> <td>train5_Alumina</td> <td>Alumina</td> <td>300</td> <td>200</td> </tr> <tr> <td>val1_Ni.zip</td> <td>Ni</td> <td>54</td> <td>36</td> </tr> <tr> <td>val2_FeNi.zip</td> <td>FeNi</td> <td>60</td> <td>40</td> </tr> <tr> <td>val3_SiC.zip</td> <td>SiC</td> <td>30</td> <td>20</td> </tr> <tr> <td>val4_Silicate</td> <td>Silicate</td> <td>36</td> <td>24</td> </tr> <tr> <td>val5_Alumina</td> <td>Alumina</td> <td>60</td> <td>40</td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The ipynb file and model parameters for machine learning are located in <a href="https://github.com/hiroyasukatsuno/Fast-Improvement-Low-Dose-TEMimages">the GitHub page</a>.</p> <p> </p>
Data and code for "Symmetries in TEM imaging of crystals with strain"
<p>Code and data related to manuscript "On Symmetries in TEM imaging of crystals with strain",<br> arXiv preprint arXiv:2206.01689. To be published in Proc. Royal. Soc. A</p> <p> </p>
Fig. 15 in Anatomy of the Tantulocarida: first results obtained using TEM and CLSM. Part I: tantulus larva
Fig. 15 Ovary (TEM, tantulus larva). Microdajus tchesunovi, frontal cross-section through cephalon. a Ovary located in posterior part of cephalon (nuage in cytoplasm indicated by arrowheads). b Developing
Fig. 13 in Anatomy of the Tantulocarida: first results obtained using TEM and CLSM. Part I: tantulus larva
Fig. 13 Brain anatomy, tantulus larva (TEM). Arcticotantulus pertzovi (a, b, d–g), Microdajus tchesunovi (c). a Longitudinal cross-section through middle line of cephalon, brain indicated by dotted outline. b Enlarged part of brain with electron-dense vesicles containing mediator (indicated with arrowhead). c Frontal cross- section through cephalon, brain indicated by dotted outline. d–g Transverse cross-section through cephalon. d Posterior part of brain with neuropil and bi-lobed cortex layer. e Neuropil, enlarged part of (d). f Neurite cell bodies, enlarged part of (d). g Anterior part of brain located dorsally in the cephalon, transverse cross-section. g., gut; np., neuropil; ov., ovary; st., stylet. Scale bars in micrometers
Fig. 11 in Anatomy of the Tantulocarida: first results obtained using TEM and CLSM. Part I: tantulus larva
Fig. 11 Rootlet system of the Tantulocarida (TEM). Microdajus tchesunovi (a, e), Arcticotantulus pertzovi (b–d). a Metamorphosing tantulus, transverse section through anterior part of cephalon showing connection of rootlet system with anterior gut. b Metamorphosing tantulus, rootlet system inside host tissues, transverse section. c Enlarged cuticular wall of the rootlet system. d Newly attached tantulus (rootlet system is not developed yet), longitudinal section through anteriormost part of cephalon medially, stylet puncture in host cuticle indicated with arrowhead. e Metamorphosing tantulus, rootlet system (filled with host content— hemolymph) in host tissues, transverse section. cem., cement; ep., epicuticle; g., gut; h.c., host cuticle; o.d., oral disc; p.c., projections of cephalon; r.s., rootlet system. Scale bars in micrometers
Fig. 10 in Anatomy of the Tantulocarida: first results obtained using TEM and CLSM. Part I: tantulus larva
Fig. 10 Stylet of the Tantulocarida (TEM, a–d; SEM, e). Microdajus tchesunovi (a, d), Arcticotantulus pertzovi (b, c), Serratotantulus chertoprudae (e). a Metamorphosing tantulus, longitudinal section through the midline of cephalon, showing position of stylet. b Tantulus,
Fig. 14 in Anatomy of the Tantulocarida: first results obtained using TEM and CLSM. Part I: tantulus larva
Fig. 14 Cephalic sensory pores, tantulus larva (a–c, g–i—SEM; d–f, j–m— TEM). Microdajus tchesunovi (a–f), Arcticotantulus pertzovi (g–m). a Anterior part of cephalon, dorsolateral view. b, c Posterior part of cephalon, dorsal view. d Anterior part of cephalon, longitudinal section through AI pore. e Posterior part of cephalon, longitudinal section through DI pore. f Posterior part of cephalon, transverse section through pore chamber and beginning of
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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