• miura starred neurobiology-ut/PHILOW (2024/10/08 18:20)

http://github.com/cmci

• Measurements of Intensity Dynamics at the Periphery of the Nucleus | SpringerLink (2019/10/24 16:37)

  Annotations: run(”command”[, ”options”]) Executes an ImageJ menu command. The optional second argument contains values that are automatically entered into dialog boxes (must be GenericDialog or OpenDialog). Use the Command Recorder (Plugins>Macros>Record) to generate run() function calls. Use string concatenation to pass a variable as an argument. With ImageJ 1.43 and later, variables can be passed without using string concatenation by adding “&” to the variable name. selectWindow(”name”) Activates the window with the title ”name”. getTitle() Returns the title of the current image. code/code_block1_ChannelSplitting.ijm1 orgName = getTitle();2 run("Split Channels");3 c1name = "C1-" + orgName;4 c2name = "C2-" + orgName;5 selectWindow(c1name);6 c1id = getImageID();7 selectWindow(c2name);8 c2id = getImageID(); selectImage(id) isOpen(id) isActive(id) selectImage(id) Activates the image with the specified ID (a negative number). If id is greater than zero, activates the ID-th image listed in the Window menu. The ID can also be an image title (a string). code/code_block2_recordNucSeg.ijm1 selectWindow("C1-NPCsingleNucleus.tif");2 run("Gaussian Blur...", "sigma=1.50 stack");3 4 //run("Threshold...");5 setAutoThreshold("Otsu dark");6 setOption("BlackBackground", true);7 run("Convert to Mask", "method=Otsu background=Dark calculate black");8 //run("Threshold...");9 run("Analyze Particles...", "size=800-Infinity pixel circularity=0.00-1.00 show=Masks display exclude clear include stack");10 run("Invert LUT");11 run("Duplicate...", "title=[Mask of C1-NPCsingleNucleus-1.tif] duplicate range=1-15");12 selectWindow("Mask of C1-NPCsingleNucleus.tif");13 run("Options...", "iterations=2 count=1 black edm=Overwrite do=Nothing");14 run("Dilate", "stack");15 selectWindow("Mask of C1-NPCsingleNucleus-1.tif");16 run("Erode", "stack");17 imageCalculator("Difference create stack", "Mask of C1-NPCsingleNucleus.tif", "Mask of C1-NPCsingleNucleus-1.tif");18 selectWindow("Result of Mask of C1-NPCsingleNucleus.tif"); code/code_block2_recordNucSegV2.ijm1 orgID = getImageID();2 run("Gaussian Blur...", "sigma=1.50 stack");3 4 //run("Threshold...");5 setAutoThreshold("Otsu dark");6 setOption("BlackBackground", true);7 run(Convert to Mask", "method=Otsu background=Dark calculate black");8 //run("Threshold...");9 run("Analyze Particles...", "size=800-Infinity pixel circularity=0.00-1.00 show=Masks display exclude clear include stack");10 dilateID = getImageID();11 run("Invert LUT");12 run("Duplicate...", "title=[Mask of C1-NPCsingleNucleus-1.tif] duplicate range=1-15");13 erodeID = getImageID();14 //selectWindow("Mask of C1-NPCsingleNucleus.tif");15 selectImage(duplicateID);16 run("Options...", "iterations=2 count=1 black edm=Overwrite do=Nothing");17 run("Dilate", "stack");18 //selectWindow("Mask of C1-NPCsingleNucleus-1.tif");19 selectImage(erodeID);20 run("Erode", "stack");21 //imageCalculator("Difference create stack", "Mask of C1-NPCsingleNucleus.tif","Mask of C1-NPCsingleNucleus-1.tif");22 imageCalculator("Difference create stack", dilateID, erodeID); run("Duplicate...", "title=[Mask of C1-NPCsingleNucleus-1.tif] duplicate range=1-15"); options = "title = dup.tif duplicate range=1-" + nSlices run("Duplicate...", options); code/code_block2_recordNucSegV3.ijm1 orgID = getImageID();2 run("Gaussian Blur...", "sigma=1.50 stack");3 4 setAutoThreshold("Otsu dark");5 setOption("BlackBackground", true);6 run("Convert to Mask", "method=Otsu background=Dark calculate black");7 run("Analyze Particles...", "size=800-Infinity pixel circularity=0.00-1.00 show=Masks display exclude clear include stack");8 dilateID = getImageID();9 run("Invert LUT");10 options = "title = dup.tif duplicate range=1-" + nSlices;11 run("Duplicate...", options);12 erodeID = getImageID();13 selectImage(dilateID);14 run("Options...", "iterations=2 count=1 black edm=Overwrite do=Nothing");15 run("Dilate", "stack");16 selectImage(erodeID);17 run("Erode", "stack");18 imageCalculator("Difference create stack", dilateID, erodeID);19 selectImage(dilateID);20 close();21 selectImage(erodeID);22 close();23 selectImage(orgID);24 close(); code/code_block3_measurements.ijm1 orgName = getTitle();2 run("Split Channels");3 c1name = "C1-" + orgName;4 c2name = "C2-" + orgName;5 selectWindow(c1name);6 c1id = getImageID();7 selectWindow(c2name);8 c2id = getImageID();9 opt = "area mean centroid perimeter shape integrated limit display redirect=None decimal=3";10 run("Set Measurements...", opt);11 selectImage(c1id);12 run("Create Selection");13 run("Make Inverse");14 selectImage(c2id);15 run("Restore Selection");16 run("Measure"); code/code_block3_MeasurementOverTime.ijm1 orgName = getTitle();2 run("Split Channels");3 c1name = "C1-" + orgName;4 c2name = "C2-" + orgName;5 selectWindow(c1name);6 c1id = getImageID();7 selectWindow(c2name);8 c2id = getImageID();9 opt = "area mean centroid perimeter shape integrated limit display redirect=None decimal=3";10 run("Set Measurements...", opt);11 for (i =0; i < nSlices; i++){12 selectImage(c1id);13 setSlice(i + 1);14 run("Create Selection");15 run("Make Inverse");16 selectImage(c2id);17 setSlice(i + 1);18 run("Restore Selection");19 run("Measure");20 } code/code_block2_recordNucSegV3_function.ijm1 function nucseg(orgID){2 //orgID = getImageID();3 selectImage(orgId);4 run("Gaussian Blur...", "sigma=1.50 stack");5 6 setAutoThreshold("Otsu dark");7 setOption("BlackBackground", true);8 run("Convert to Mask", "method=Otsu background=Dark calculate black");9 run("Analyze Particles...", "size=800-Infinity pixel circularity=0.00-1.00 show=Masks display exclude clear include stack");10 dilateID = getImageID();11 run("Invert LUT");12 options = "title = dup.tif duplicate range=1-" + nSlices;13 run("Duplicate...", options);14 erodeID = getImageID();15 selectImage(dilateID);16 run("Options...", "iterations=2 count=1 black edm=Overwrite do=Nothing");17 run("Dilate", "stack");18 selectImage(erodeID);19 run("Erode", "stack");20 imageCalculator("Difference create stack", dilateID, erodeID);21 resultID = getImageID();22 selectImage(dilateID);23 close();24 selectImage(erodeID);25 close();26 selectImage(orgID);27 close();28 run("Clear Results");29 return resultID;30 } code/code_final.ijm1 orgName = getTitle();2 run("Split Channels");3 c1name = "C1-" + orgName;4 c2name = "C2-" + orgName;5 6 selectWindow(c1name);7 nucorgID = getImageID();8 nucrimID = nucseg(nucorgID);9 10 selectWindow(c2name);11 c2id = getImageID();12 opt = "area mean centroid perimeter shape integrated display redirect=None decimal=3";13 run("Set Measurements...", opt);14 for (i =0; i < nSlices; i++){15 selectImage(nucrimID);16 setSlice(i + 1);17 run("Create Selection");18 run("Make Inverse");19 selectImage(c2id);20 setSlice(i + 1);21 run("Restore Selection");22 run("Measure");23 }24 25 function nucseg(orgID){26 selectImage(orgId);27 run("Gaussian Blur...", "sigma=1.50 stack");28 29 setAutoThreshold("Otsu dark");30 setOption("BlackBackground", true);31 run("Convert to Mask", "method=Otsu background=Dark calculate black");32 run("Analyze Particles...", "size=800-Infinity pixel circularity=0.00-1.00 show=Masks display exclude clear include stack");33 dilateID = getImageID();34 run("Invert LUT");35 options = "title = dup.tif duplicate range=1-" + nSlices;36 run("Duplicate...", options);37 erodeID = getImageID();38 selectImage(dilateID);39 run("Options...", "iterations=2 count=1 black edm=Overwrite do=Nothing");40 run("Dilate", "stack");41 selectImage(erodeID);42 run("Erode", "stack");43 imageCalculator("Difference create stack", dilateID, erodeID);44 resultID = getImageID();45 selectImage(dilateID);46 close();47 selectImage(erodeID);48 close();49 selectImage(orgID);50 close();51 run("Clear Results");52 return resultID;53 } code/code_plotResults.ijm1 // store normalized total intensity values in an array2 intA = newArray(nResults);3 for (i = 0; i < nResults; i++)4 intA[i] = getResult("RawIntDen", i) / getResult("IntDen", 0);5 6 //prepare x-axis values7 t = Array.getSequence(intA.length);8 9 // get the statistics of the total intensity array.10 Array.getStatistics(intA, amin, amax, amean, astdDev);11 12 // Create the plot13 Plot.create("Total Intensity at Nuclear Membrane", "Time", "Intensity");14 Plot.setLimits(0, intA.length, amin * 0.9, amax * 1.1);15 Plot.setColor("red", "red");16 Plot.setLineWidth(3);17 Plot.add("circle", t, intA);18 Plot.setFontSize(14);19 Plot.addLegend("Normalized Total Intensity");
• Filopodyan: An open-source pipeline for the analysis of filopodia | JCB (2017/09/25 14:07)

  ImageJ plugin + R
• Mutation of the α-tubulin Tuba1a leads to straighter microtubules and perturbs neuronal migration | JCB (2017/09/25 13:58)

  interesting analysis of MT dynamics using Matlab
• Prepatterning by RhoGEFs governs Rho GTPase spatiotemporal dynamics during wound repair | JCB (2017/09/25 13:44)

  Tags: ImageJ R Intensity profiling and fitting
• Control of actin polymerization via the coincidence of phosphoinositides and high membrane curvature | JCB (2017/09/25 13:40)

  analysis using ImageJ peak-fit, Imaris & metamorph
• R for Data Science (2017/07/30 01:56)

  Annotations: filter() only includes rows where the condition is TRUE; it excludes both FALSE and NA values Missing values are always sorted at the end: cumsum(), cumprod(), cummin(), cummax() Offsets: lead() and lag() allow you to refer to leading or lagging values. Instead of relying on ==, use near(): (flights, time_hour, air_time, everything()) 5.4 Select columns with select() rename(flights, tail_num = tailnum) matches x1, x2 and x3
• Content Service  |  Apps Script  |  Google Developers (2017/07/28 00:49)

  "function doGet(request) {"
• google apps script image to text ocr (2017/07/28 00:42)
• ImageJ Fiji のインストールと Fiji - バイオ系だけどプログラミング始めました (2017/07/25 14:39)

  Annotations: STOMのデータ解析
• R for Data Science (2017/06/12 23:08)

  Annotations: Alt + - (the minus sign). Type “this” then press Cmd/Ctrl + ↑.
• Deploying Node.js Microservices to AWS using Docker | @RisingStack (2017/06/09 00:25)

  Annotations: Search for ECS Optimized to locate the Amazon ECS-Optimized AMI. Amazon created this image for use with its EC2 Container Service. We won't be using ECS and will opt instead to use Docker and later, Docker Swarm. This choice will allow you to use the skills you acquire here on other cloud providers such as Google Cloud and Microsoft's Azure. The reason we're using an ECS Optimized AMI is because it has Docker pre-installed!
• R for Data Science (2017/05/06 21:55)

  Annotations: You can avoid this type of repetition by passing a set of mappings to ggplot(). ggplot2 will treat these mappings as global mappings that apply to each geom in the graph. In other words, this code will produce the same plot as the previous code: ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +   geom_point(mapping = aes(color = class)) +   geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE) facet_grid(drv ~ cyl) coord_quickmap() sets the aspect ratio correctly for maps. This is very important if you’re plotting spatial data with ggplot2 (which unfortunately we don’t have the space to cover in this book). 3.5 Facets group = drv If you place mappings in a geom function, ggplot2 will treat them as local mappings for the layer. It will use these mappings to extend or overwrite the global mappings for that layer only. This makes it possible to display different aesthetics in different layers. ggplot2 provides over 20 stats for you to use. Each stat is a function, so you can get help in usual way, e.g. ?stat_bin. To see a complete list of stats, try the ggplot2 cheatsheet. .prop.., group = 1
• Challenges in long-term imaging and quantification of single-cell dynamics : Nature Biotechnology : Nature Research (2017/04/22 13:02)

  Annotations: Cell segmentation, cell tracking and phenotype quantification. Continuous long-term imaging of single cells at high spatiotemporal resolution provides researchers with voluminous multidimensional, information-rich data sets. These can be mined to analyze a large number of cell phenotypical attributes, including molecular dynamics, morphology and behavior, and relationships and evolving dynamics across generations (Fig. 3). The wealth of these data can be fully explored only with the use of efficient automatic cell segmentation, tracking and phenotype quantification algorithms. For segmentation, a large number of approaches have been developed to date70, 71, but most methods are not broadly generalizable and are currently limited to the specific types of experiments, cells or images for which they were designed. Unfortunately, as others have said, “rather than converging to a robust, unified solution, it thus seems that the field is diverging, and by now almost as many cell segmentation methods have been developed as there exist cell analysis problems”72.
• Manning | Real-World Machine Learning (2017/01/14 15:31)
• Manning | Java 8 in Action (2017/01/12 20:16)

  Tags: java
• Yes you should understand backprop – Medium (2016/12/20 17:29)
• Reading data from google sheets into R | (2016/11/23 18:50)
• SimpleITK/GettingStarted - KitwarePublic (2016/11/21 23:16)

  "a visual guide to SimpleITK in Java"
• SimpleITK Notebooks (2016/11/21 23:12)
• Maven Quick Guide (2016/11/19 14:14)

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