This four month cycle of peaks and valleys for covid infections ought to get someone’s attention. (https://www.worldometers.info/coronavirus/) I sure have not heard anything about “four months”.
I think this image shows pretty nicely how little, except for cosmetics, the image processing of a cropped portion of a multimer involved in immune defense and differentiation in epithelia shows here affects the raw data (LUT grayscale plots). There are small variations in the length of the “arm” which are more likely due to that actual tracing in ImageJ than any difference due to processing. That said, gaussian blur tends to increase the dimensions slightly, highpass filtering increases the grayscale, and unsharp mask creates greater peak heights. So the peak number and the smoothness of the plot can be improved with processing, and the peak height, but no real gain in information is obtained.
The greater task is to look carefully at the molecule, deciding whether tracings of the beginning and ending of close, twisted and overlapping arms (as plotted) (made by hand) make sense.
Images below show plots (color coded as to image processing filters used) and the actual images and trace-lines (screen print from ImageJ tracing). In this case: filters, each named, were performed in photoshop; plots were made in excel as traced in ImageJ; calculator.net was used for determining means and SD of arm length and peak number; corelDRAW was used to normalize the x axis (y axis remained unchanged from original plots) and to color code the plot lines.
Next task is to measure peak widths and heights. Unsharp mask (amount 300, 40px radius, 20 threshold) followed by a 10px gaussian blur provided the smoothest plot. The High peak (left) and low peak height for the next three peaks, and a subsequent high peak seems to be a pretty prevalent pattern for arms in this molecule.
This is the plot for unsharp mask and gaussian blur. It is so remarkably like the very first plots I made on this protein. Since 99.34 nm was close to 100 i used a 100 set of 1 nm dividers to mark out the valleys of these peaks. Interesting features are the absolute regularity of the first five peaks, not in height, but in width, and the first and 4th peaks being higher, and the series of similar size peaks in the middle of the plot.
High pass filtering can help sort out background from data, especially microscopic data. Here is a summary about the high pass filter when used alone, and with gaussian blur. The subject matter is a segment of a multimer (either round or linear) and the brightest peak is where the tracing for gray scale LUT tables begins. Each trace is shown, and along with that a plot of the peak height (and soon to be measured peak width (at mid height and at the base). The plots of the arms shown at the bottom of the figure are color coded with the plots shown in the graph (graph made in image J, highpass filter and gaussian blur made with photoshop 6. Figure assembeled in CorelDRAW x5. THis is NOT surfactant protein A or D, but another multimer involved in innate immunity.
Has anyone else noticed a speech pattern from the previous president. It is like a set of words become a mantra, perhaps a meditative experience, or a soothing mantra, maybe even a prayer list, a mandala, a rosary or prayer beads. The following quote made me think of this.
This is the quote “”The county has, for whatever reason, also refused to produce the network routers. We want the routers, Sonny, Wendy, we got to get those routers, please. The routers. Come on, Kelly, we can get those routers. Those routers. You know what? We’re so beyond the routers, there’s so many fraudulent votes without the routers. But if you got those routers, what that will show, and they don’t want to give up the routers. They don’t want to give them. They are fighting like hell. Why are these commissioners fighting not to give the routers?”
I think all will
again be well when
the garden begun
change have you
Back again to then
The dreams of your
Father and talents
of the family.
I think that the
direction can be changed
that not needed,
to become more
like Saturn, in
a ring about the planet.
I think you will,
once the swirling air
why you were called
then, for no more
than one clear minute.
And that moment
Is maybe more but
no less than
nights, than mornings
and in between,
spent in the
eye of the hurricane,
grasping for an
hold on to.
There is a dilemma for researchers in terms of their choice of image processing programs. The industry giants charge considerable amounts (matlab is exorbitant), what used to be high prices for CDs but now for online subscriptions. Does the US taxpayer need to pay for these services – it doesn’t seem quite right that we pay industry graphics prices to do science which benefits all of humanity (hopefully).
I have spent the better part of the year (not full time but avidly) trying to figure out who has the image processing program that is the easiest to use (i am talking here about real ease of use…. as I come to this field as a visual learner, without apologies, but find much lacking in the so called “intuitiveness” of many programs). I am also interested in the least expensive program as i dont want to feed the giants in the commercial world. Another goal is to see whether all the bells and whistles that are touted in the new image processing programs really make a difference for scientists who are wanting to do some relatively simple things…. get rid of dirt, get rid of pixels, identify and enhance hard to see details, to put statistics ahead of guessing about shapes, peaks and the like.
I began 50 years ago with wet-darkroom prints, and the simplest tablet and stylus, haha i don’t even remember the name of the setup but could look that up in a materials and methods published somewhere. I used camera lucidas and pencil, then digital images. The earliest processing was just a hand waving (dodging and burning) then using those same terminologies, photoshop appears.
The earliest plates for publication were made on matboard with a drymount press and tissue. The earliest program that i used for making images into figures was the first version of CorelDRAW (it was version 3) cracked on 11 pink 3.25 inch floppy discs. These were mostly plots and line drawings.
50 years later, I still use CorelDRAW (x5 and 19), CorelDRAW Photopaint (x5 and 19) and still use Photoshop (6 and 2021), and have in the past year examined ImageJ, Gwyddion, and GIMP. It is difficult not to be biased because CorelDRAW has had both raster and vector elements that in my way of thinking are more than adequate for both processing images, measuring features, and creating diagrams. CorelDRAW has been nicely compatible with Photoshop and Illustrator (though the reverse has never been as smooth). I have used images with 100 layers or more imported into CorelDRAW from Photoshop with ease.
So the purpose of this is to determine whether it is possible to do image processing with the same filters and measuring options in the open source software as is done with the high priced industry standards.
The test today is the unsharp mask filter in GIMP. I do like the ease with which the sliders can be manipulated with instant preview updates…. i don’t much like the actual “typing” from the number pad into the slider. But using the surfactant protein D images (that I have used from published papers (in this case those shown here are some good and not so good images from Arroyo et al (AFM of surfactant protein D). Regardless of the original image quality, GIMP (open source image processing software) does a great job of masking.
Pay special attention to the small masked areas in the collagen like domain of each of the SP-D trimers…. yep…. 3 to 4 small peaks beside the glycosylation peak, especially nice in lower right image, and in that one and the lower left image… a tiny peak on the side of the N termini peaks is a new little peak (of which i am trying to determine the relative incidence). Error bars are what i have used for measurments and are visible, most are 100nm but the tiny one is 20nm, and lower center has no error bar.
Determining a beginning and an end creates a dilemma for measuring LUT plots. There are many variables playing on where to start the plot-line and where to end it. The total distance from one end of the CRD of a hexamer to the other CRD can be measured in at least these ways. 1. brightest spot to brightest spot, 2. straightest line (whether that means beginning or ending the line in a place where one of the three elements of the CRD are spread apart creating a darker and shorter line path.
Yep…. coronavirus globe glass and polymer clay ornaments that are made to be accurate representations of the surface look of the corona virus. Just for fun and remembering the headaches of this particular pandemic. Order from the website here.