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deconvolution software nikon software (nis-elements ar, nikon)  (Nikon)


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    Nikon deconvolution software nikon software (nis-elements ar, nikon)
    Deconvolution Software Nikon Software (Nis Elements Ar, Nikon), supplied by Nikon, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/nis+elements+deconvolution+software/pm37488906-131-2-7
    Average 90 stars, based on 1 article reviews
    deconvolution software nikon software (nis-elements ar, nikon) - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    Imaging:

    Article Title: Adult hippocampal MeCP2 preserves the genomic responsiveness to learning required for long-term memory formation.
    Article Snippet: Accepted Manuscript Adult hippocampal MeCP2 preserves the genomic responsiveness to learning required for long-term memory formation Kubra Gulmez Karaca, David V.C.. Brito, Benjamin Zeuch, Ana M.M.. Oliveira PII: S1074-7427(18)30029-7 DOI: https://doi.org/10.1016/j.nlm.2018.02.010 Reference: YNLME 6802 To appear in: Neurobiology of Learning and Memory Received Date: 24 November 2017 Revised Date: 4 February 2018 Accepted Date: 9 February 2018 Please cite this article as: Gulmez Karaca, K., Brito, D.V.C., Zeuch, B., Oliveira, A.M.M., Adult hippocampal MeCP2 preserves the genomic responsiveness to learning required for long-term memory formation, Neurobiology of Learning and Memory (2018), doi: https://doi.org/10.1016/j.nlm.2018.02.010 This is a PDF file of an unedited manuscript that has been accepted for publication.

    other:

    Article Title: The effect of single-cell knockout of Fragile X Messenger Ribonucleoprotein on synaptic structural plasticity
    Article Snippet: In vivo imaging of dendritic spines through the cranial window was performed on a two-photon microscope (Ultima Investigator, Bruker Co., Middleton, WI, USA) using a 16x/0.8 NA water-immersion objective (Nikon Instruments, Inc., Melville, NY, USA) and an ultrafast two-photon laser (Mai Tai, Spectra-Physics, Santa Clara, CA, USA) operating at 940 nm wavelength.

    Software:

    Article Title: Ethanol changes Nestin-promoter induced neural stem cells to disturb newborn dendritic spine remodeling in the hippocampus of mice.
    Article Snippet: The projection images were semiautomatically traced by ImageJ software with the Neuron J plugin. .. Images of dendritic spines were deconvoluted with Nikon Software (NIS-Elements AR, Nikon). ..



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    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy <t>deconvolution</t> method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in
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    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy <t>deconvolution</t> method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in
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    Average 90 stars, based on 1 article reviews
    deconvolution software nikon software (nis-elements ar, nikon) - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

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    Nikon nis element deconvolution software
    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy <t>deconvolution</t> method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in
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    Image Search Results


    Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy deconvolution method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in

    Journal: Nature methods

    Article Title: Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.

    doi: 10.1038/s41592-024-02294-7

    Figure Lengend Snippet: Fig. 1 | Implementation and benchmarking of Deconwolf. a, Schematic Deconwolf workflow. WSI, whole-slide image. b,c, In silico generated microtubule images before (ground truth) (b) and after adding artificial noise to simulate a real image (c). Maximum z-projection is shown. d, MSE after deconvolving the image in c using the default Deconwolf mode with scaled heavy ball15 acceleration (DW_SHB), or Deconwolf based on the classic Richardson–Lucy deconvolution method (DW_RL)2,3. The dashed vertical lines indicate the number of iterations needed to reach the minimum MSE. e, As in c after deconvolution with Deconwolf (DW) using default settings. it, number of iterations. t, deconvolution time measured on an 8-Core AMD Ryzen 7 3700X machine. f, As in c using DeconvolutionLab2 (DL2) with default settings at 115 iterations. g, As in

    Article Snippet: Visual inspection of the images in the original dataset showed densely packed clouds of fluorescent dots in different colors inside each nucleus, which could be only partially resolved by applying the commercial deconvolution software (Nikon NIS Elements AR, v5.02.0) incorporated in the OligoFISSEQ image processing pipeline (Fig. 6c).

    Techniques: In Silico, Generated

    Fig. 5 | Deconwolf enables robust detection of individual transcripts in low-magnification smFISH images. a, Breast adenocarcinoma tissue section stained with an smFISH probe targeting MKI67 transcripts (white) and imaged on a widefield microscope using a ×20 air objective, after deconvolution with Deconwolf (DW). Maximum z-projection is shown. Blue, DNA. Scale bars, 200 μm in the large left panel; 10 μm in the two small panels on the right. b,c, Zoom-in of the regions (R1 and R2) marked by the white dashed squares in a. Scale bars, 10 μm. d, Left plot: probability density function (PDF) of smFISH dot fluorescence

    Journal: Nature methods

    Article Title: Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.

    doi: 10.1038/s41592-024-02294-7

    Figure Lengend Snippet: Fig. 5 | Deconwolf enables robust detection of individual transcripts in low-magnification smFISH images. a, Breast adenocarcinoma tissue section stained with an smFISH probe targeting MKI67 transcripts (white) and imaged on a widefield microscope using a ×20 air objective, after deconvolution with Deconwolf (DW). Maximum z-projection is shown. Blue, DNA. Scale bars, 200 μm in the large left panel; 10 μm in the two small panels on the right. b,c, Zoom-in of the regions (R1 and R2) marked by the white dashed squares in a. Scale bars, 10 μm. d, Left plot: probability density function (PDF) of smFISH dot fluorescence

    Article Snippet: Visual inspection of the images in the original dataset showed densely packed clouds of fluorescent dots in different colors inside each nucleus, which could be only partially resolved by applying the commercial deconvolution software (Nikon NIS Elements AR, v5.02.0) incorporated in the OligoFISSEQ image processing pipeline (Fig. 6c).

    Techniques: Staining, Microscopy, Fluorescence