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162 results for “tem”
Teniposide Electron Diffraction (MicroED) Datasets - Glacios TEM with a CETA-D
<p>Electron diffraction datasets collected from nanometer sized crystals of Teniposide (TCI) applied to an electron microscopy grid covered in a pure carbon film. Data were collected using a Thermo Fisher Scientific Glacios Transmission Electron Microscope operated at 200 kV and -193 C using the software package Leginon. The beam diameter is ~600 nm and the total dose is ~2.8e-/A^2. The crystals were continuously rotated and diffraction data was collected with a CETA-D camera in rolling shutter mode. 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. An image of the crystal before centering on the small green dot is also included. Data from six crystals are provided. If processing in DIALS, you will first want to install the proper format class: </p> <p>dxtbx.install_format -u <a href="https://raw.githubusercontent.com/dials/dxtbx_ED_formats/master/FormatSMVCetaD_TUI.py">https://raw.githubusercontent.com/dials/dxtbx_ED_formats/master/FormatSMVCetaD_TUI.py</a></p> <p>A final structure is deposited in the CCDC with code 2015361. </p>
Data from: Unraveling the causes of adaptive benefits of synonymous mutations in TEM-1 β-lactamase
While synonymous mutations were long thought to be without phenotypic consequences, there is growing evidence they can affect gene expression, protein folding and ultimately the fitness of an organism. In only a few cases have the mechanisms by which synonymous mutations affect the phenotype been elucidated. We previously identified 48 mutations in TEM-1 β-lactamase that increased resistance of Escherichia coli to cefotaxime, 10 of which were synonymous. To better understand the molecular mechanisms underlying the beneficial effect of synonymous mutations, we made a series of measurements for a panel containing the 10 synonymous together with 10 non-synonymous mutations as a reference. Whereas messenger levels were unaffected, we found that total and functional TEM protein levels were higher for 5 out of 10 synonymous mutations. These observations suggest that some of these mutations act on translation or a downstream process. Similar effects were observed for some small-benefit non-synonymous mutations, suggesting a similar causal mechanism. For the synonymous mutations, we found that the cost of resistance scales with TEM protein levels. A resistance landscape for four synonymous mutations revealed strong epistasis: None of the combinations of mutations exceeded the resistance of the largest-effect mutation and there were synthetically neutral combinations. By considering combined effects of these mutations, we could infer that functional TEM protein level is a multi-dimensional phenotype. These results suggest that synonymous mutations may have beneficial effects by increasing the expression of an enzyme with low substrate activity, which may be realized via multiple, yet unknown, post-transcriptional mechanisms.
Supplementary material 1 from: Le MD, Rödder D, Nguyen TT, The Pham C, Nguyen TQ, Ong AV, McCormack TEM, Nguyen TT, Le MH, Ngo HT, Ziegler T (2024) Climatic niche modelling and genetic analyses highlight conservation priorities for the Spotted Softshell Turtle (Pelodiscus variegatus). Nature Conservation 55: 67-82. https://doi.org/10.3897/natureconservation.55.114746
Supplementary data
dm4 data for TEM
<p>The TEM data in dm4 format of 1d nanostrucutres</p>
dataset where a Convolutional Neural Network controls a part of the TEM optics and OAM sorter
<p>Here are uploaded the data collected when we succesfully connected a Convolutional Neural Network to the Holo-TEM in Julich. In particular the CNN controls a part of the TEM optics (beam shift x and y, C3 intensity) and the OAM sorter (the Sorter1 and Sorter 2 bias, in particular it is able to change the S1 bias to perfectly match the phase profile generated from the second sorting element).</p>
Electron Diffraction (MicroED) Datasets for C16H21FN5OS - D-malate (Glacios TEM with a CETA-D)
<p>Electron diffraction datasets collected from a chiral pharmaceutical compound cocrystallized with D-malic acid.</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: C<sub>16</sub>H<sub>21</sub>FN<sub>5</sub>OS • D-malate, (N‐(5‐{[(3R)‐3‐[(5‐fluoropyrimidin‐2‐yl)methyl]piperidin‐1‐yl]methyl}‐1,3‐thiazol‐2‐yl)acetamide • D-malate, 483.51 g/mol</p> <p>Structure: CCDC 2132512</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>
Transmission electron microscopy (TEM) image datasets of peptide / protein nanowire morphologies
<p>TEM image dataset containing four nanowire morphologies of bio-derived protein nanowires and synthetic peptide nanowires.</p> <p>The peptide / protein nanowires used in this study were synthesized and imaged by Brian Montz in Prof. Todd Emrick's research group at the Department of Polymer Science and Engineering Department, University of Massachusetts Amherst. </p> <p>We acknowledge financial support from the U.S. National Science Foundation, Grant NSF DMREF #1921839 and DMREF #1921871.</p> <p>Nanowires were classified into either of the four morphologies: bundle, singular, dispersed or network. Each morphology contains 100 images (jpg files).</p> <p>For the dispersed and network morphologies, because these two morphologies are harder to visually distinguish, we have created manual segmentation labels of the nanowires (included in these two morphology folders as png files). Percolation analysis was done on these manually segmented nanowires to provide quantitative metric on whether the nanowires form a network in the image. </p> <p>seg_mask_5_resolutions.zip contains ground truth 2D binary encoding of segmented nanowires at 5 resolutions.</p> <p>encoders_trained_with_optimized_hyperparameter.zip contains 4 sets of encoders trained with either SimCLR or Barlow-Twins self-supervised methods on either generic TEM images, or generic everyday photographic images (each with 5 replicates with different random seed) with optimized hyperparameters.</p> <p>Open-access datasets that have been used during self-supervised training.</p> <ul> <li>2021-CEM500K.zip contains 10,000 images that was used as "generic TEM images" to train the encoders with self-supervised methods, these are a random selection from the CEM500k open-access dataset. DOI: <a href="https://doi.org/10.7554/eLife.65894">10.7554/eLife.65894</a></li> <li>2022-1000-ImageNet.zip contains 1,000 images from the ImageNet1k dataset, each come from a different category. DOI: <a href="http://doi.org/10.1007/s11263-015-0816-y">10.1007/s11263-015-0816-y</a></li> </ul> <p>Open-access datasets that our machine learning workflow have been applied to:</p> <ul> <li>2022-AutoDetect-mNP-morphology.zip contains a selected TEM images of nanoparticles categorized in 3 morphologies from the AutoDetect-mNP datasets: DOI: <a href="http://doi.org/10.6078/D1WT44">10.6078/D1WT44</a> and DOI: <a href="http://doi.org/10.6078/D1S12H">10.6078/D1S12H</a></li> <li>2021-TEM virus.zip contains TEM images of 9 types of viruses from the TEM virus dataset. Matuszewski, Damian; Sintorn, Ida-Maria (2021), “TEM virus dataset”, Mendeley Data, V3, DOI: <a href="http://doi.org/10.17632/x4dwwfwtw3.3">10.17632/x4dwwfwtw3.3</a></li> </ul> <p>The official github page of the implementation of the machine learning models is <a href="https://github.com/arthijayaraman-lab/semi-supervised_learning_microscopy_images">semi-supervised_learning_microscopy_images</a>.</p> <p>If you use the dataset or the codes in the repository linked above, please cite the following <a href="https://doi.org/10.1039/D2DD00066K">manuscript</a>:</p> <p>S. Lu, B. Montz, T. Emrick and A. Jayaraman, <em>Digital Discovery</em>, 2022, <strong>1</strong>, 816-833 , <strong>DOI: </strong>10.1039/D2DD00066K</p>
iGEM-Leiden-2022-Implementation Experiments 2 TEM pictures
<p>TEM pictures Implementation Experiment 2 iGEM Leiden 2022</p>
iGEM-Leiden-2022-Implementation Experiments 3 TEM pictures
<p>TEM pictures Implementation Experiment 3 iGEM Leiden 2022</p>
iGEM-Leiden-2022-Core Experiments 1 TEM pictures
<p>TEM pictures Core Experiment 1 iGEM Leiden 2022</p>
iGEM-Leiden-2022-Preliminary Experiments 6 TEM pictures
<p>TEM pictures Preliminary Experiment 6 iGEM Leiden 2022</p>
iGEM-Leiden-2022-Core Experiments 2 TEM pictures
<p>TEM pictures Core Experiment 2 iGEM Leiden 2022</p>
iGEM-Leiden-2022-Implementation Experiments 1 TEM pictures
<p>TEM pictures Implementation Experiment 1 iGEM Leiden 2022</p>
TEM_Amsterdam
Open the record for dataset details and reuse information.
Figs 45–47. Pseudowigwamma scenozonion TEM whole mounts. 45 in Coccolithophorids in Polar Waters: Wigwamma spp. Revisited
Figs 45–47. Pseudowigwamma scenozonion TEM whole mounts. 45 – complete cell (Arctic / NEW); 46 – detail showing base plate calcification (single layer) and the odd enlarged rim crystallite; Notice also the radial patterning of the organic base plate surface (Arctic Station); 47 – complete cell (Arctic Station).
Fig. 28 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study
Fig. 28. Scheme of the crystal structure in a lorica surface in Eutintinnus angustatus. The primitive unit cell of the crystal is rhombohedral, while three of them form a hexagonal pattern (see white lines). In the simplified model of the crystal structure, the basic units consist of triangles with a diameter of ~ 18 nm, which are interconnected on each side by channels with a diameter of ~ 7 nm. The empty space between the triangles and channels appear as dark areas, having the shape of a cloverleaf, and are ~ 7 nm long. The rim of the triangle and the interconnecting channels visible as bright lines in the image consists of a protein wall, which is ~ 2.5 nm thick. Within the triangles no clear or regular structures could be observed.
Figs 1–9 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study
Figs 1–9. Loricae after mercuric bromophenol blue (1–6) and alcian blue stain (7–9). 1 – Codonella aspera, the staining is restricted to the lorica matrix; 2 – Eutintinnus brandti, the lorica is uniformly stained; 3, 4 – Climacocylis spec., the alveolar texture of the wall is well recognizable; 5, 6 – Rhabdonella spiralis, the alveolar texture, the minute openings, and the spiralled surface ridges are recognizable; 7–9 – Stenosemella ventricosa, lateral (7, 8) and oblique top (9) views. The staining is restricted to the bowl matrix. Scale bars: 50 µm (1, 7–9), 200 µm (2, 3), 40 µm (4), 100 µm (5), and 20 µm (6).
TEM data for Adachi et al
<p>TEM and OPC data for Adachi et al.</p>
Data from: Role of pleiotropy during adaptation of TEM-1 β-lactamase to two novel antibiotics
Pleiotropy is a key feature of the genotype–phenotype map, and its form and extent have many evolutionary implications, including for the dynamics of adaptation and the evolution of specialization. Similarly, pleiotropic effects of antibiotic resistance mutations may affect the evolution of antibiotic resistance in the simultaneous or fluctuating presence of different antibiotics. Here, we study the role of pleiotropy during the in vitro adaptation of the enzyme TEM-1 β-lactamase to two novel antibiotics, cefotaxime (CTX) and ceftazidime (CAZ). We subject replicate lines for four rounds of evolution to selection with CTX and CAZ alone, and in their combined and fluctuating presence. Evolved alleles show positive correlated responses when selecting with single antibiotics. Nevertheless, pleiotropic constraints are apparent from the effects of single mutations and from selected alleles showing smaller correlated than direct responses and smaller responses after simultaneous and fluctuating selection with both than with single antibiotics. We speculate that these constraints result from structural changes in the oxyanion pocket surrounding the active site, where accommodation of CTX and the larger CAZ is balanced against their positioning with respect to the active site. Our findings suggest limited benefits from the combined or fluctuating application of these related cephalosporins for containing antibiotic resistance.
Electron Diffraction (MicroED) Datasets for Ipragliflozin L-Proline (Glacios TEM with a CETA-D)
<p>Electron diffraction datasets collected from ipragliflozin L-proline, a chiral pharmaceutical cocrystal.</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: Ipragliflozin (C<sub>21</sub>H<sub>21</sub>FO<sub>5</sub>S, (2S,3R,4R,5S,6R)-2-{3-[(1-benzothiophen-2-yl)methyl]-4-fluorophenyl}-6-(hydroxymethyl)oxane-3,4,5-triol) and L-proline (C<sub>5</sub>H<sub>9</sub>NO<sub>2, </sub>(2S)-pyrrolidine-2-carboxylic acid)</p> <p>Structure: CCDC 2174023</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>
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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)
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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.