custom-made code matlab 2021a Search Results


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MathWorks Inc custom matlab scripts version 2021a
Custom Matlab Scripts Version 2021a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc custom-made matlab scripts version 2023b
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MathWorks Inc custom-made matlab code
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
Custom Made Matlab Code, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc statistical parametric mapping 12
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
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MathWorks Inc 2018a
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
2018a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc matlab 2021a
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
Matlab 2021a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Tucker-Davis Tech pz5-128 neurodigitizer amplifier
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
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Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
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Nikon algorithms imagej
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
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RStudio rstudio (version 2023.06.1+524)
Schematic representation of <t>MATLAB</t> based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).
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Image Search Results


Schematic representation of MATLAB based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).

Journal: Scientific Reports

Article Title: Rational design and synthesis of a novel BODIPY-based probe for selective imaging of tau tangles in human iPSC-derived cortical neurons

doi: 10.1038/s41598-022-09016-z

Figure Lengend Snippet: Schematic representation of MATLAB based algorithm for image analysis. ( A ) The maximum z-projection is calculated for each z-stack, then the background level is automatically retrieved and subtracted. The neurite structure mask is de-fined and applied as a filter to exclude unwanted signals located not in neurite structures. ( B ) An iterative thresholding procedure is used to binarized the image. Starting from the threshold value Thr = 0.2 (20% of the maximum signal) at each iteration, the threshold limit is increased by 0.15 units. The entire threshold image collection is combined to get a well-resolved binarized image. ( C ) The colocalization of the probe (BT1) with the antibodies (T22 and AT8) is calculated. Two well-known colocalization methods are exploited: the scatter plots (orange and green), which allow visualizing the correlation measured by PC coefficient, and the merged binary images (red/yellow and green/yellow), which allow to visualize the co-occurrence measured by M1 coefficient. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).

Article Snippet: On the right, Pearson’s correlation index of AT8 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition ( p = 0.056, MW test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (**** p < 0.0001, MW test; n = 25/3, fileds of view/batches), as determined using the custom-made MATLAB code. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).

Techniques: Software

BT1 binds to hyperphosphorylated and oligomeric tau in OA treated neurons. ( A ) Bar charts showing the fluorescence intensity quantification of (left) T22 signal (** p = 0.002, MW test; n = 53/3, fields of view/batches) and (right) AT8 signals (**** p < 0.0001, MW test; n = 53/6/3, fields of view/batches) in control condition and after the treatment with okadaic acid (50 nM) for 2 h. ( B ) Left , Manders’s colocalization in-dex of T22 staining with TAU1 (green) and BT1 (orange) fluo-rescence signal in control condition (p = 0.48, t-test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (* p < 0.017, MW test; n = 25/3, fileds of view/batches). Right , Manders’s colocalization index of AT8 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition ( p = 0.408, MW test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (**** p < 0.0001, MW test; n = 25/3, fileds of view/batches), as determined using the custom-made MATLAB code. ( C ) On the left, Pearson’s correlation index of T22 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition (*** p = 0.0008, t-test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h ( p = 0.423, t-test; n = 25/3, fileds of view/batches). On the right, Pearson’s correlation index of AT8 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition ( p = 0.056, MW test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (**** p < 0.0001, MW test; n = 25/3, fileds of view/batches), as determined using the custom-made MATLAB code. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).

Journal: Scientific Reports

Article Title: Rational design and synthesis of a novel BODIPY-based probe for selective imaging of tau tangles in human iPSC-derived cortical neurons

doi: 10.1038/s41598-022-09016-z

Figure Lengend Snippet: BT1 binds to hyperphosphorylated and oligomeric tau in OA treated neurons. ( A ) Bar charts showing the fluorescence intensity quantification of (left) T22 signal (** p = 0.002, MW test; n = 53/3, fields of view/batches) and (right) AT8 signals (**** p < 0.0001, MW test; n = 53/6/3, fields of view/batches) in control condition and after the treatment with okadaic acid (50 nM) for 2 h. ( B ) Left , Manders’s colocalization in-dex of T22 staining with TAU1 (green) and BT1 (orange) fluo-rescence signal in control condition (p = 0.48, t-test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (* p < 0.017, MW test; n = 25/3, fileds of view/batches). Right , Manders’s colocalization index of AT8 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition ( p = 0.408, MW test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (**** p < 0.0001, MW test; n = 25/3, fileds of view/batches), as determined using the custom-made MATLAB code. ( C ) On the left, Pearson’s correlation index of T22 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition (*** p = 0.0008, t-test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h ( p = 0.423, t-test; n = 25/3, fileds of view/batches). On the right, Pearson’s correlation index of AT8 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition ( p = 0.056, MW test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (**** p < 0.0001, MW test; n = 25/3, fileds of view/batches), as determined using the custom-made MATLAB code. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).

Article Snippet: On the right, Pearson’s correlation index of AT8 staining with TAU1 (green) and BT1 (orange) fluorescence signal in control condition ( p = 0.056, MW test; n = 25/3, fields of view/batches) and after the treatment with okadaic acid (50 nM) for 2 h (**** p < 0.0001, MW test; n = 25/3, fileds of view/batches), as determined using the custom-made MATLAB code. (Matlab software, version 2021a; URL: https://it.mathworks.com/products/matlab.html?s_tid=hp_products_matlab ).

Techniques: Fluorescence, Control, Staining, Software