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RE: Day at work... When you do everything right and the result is completely wrong :(

in #steemstem • 6 years ago (edited)

Nice post. Some questions/comments:

  1. Spatial content of an object is not a uniquely defined. It depends on how you measure spatial content. For example, a one dimensional fractal still has dimension one in terms of topological dimension, but its box counting dimension might be greater than one.

  2. What is Db? (Box counting dimension maybe?)

  3. On a quick glance it seems that the article gives bounds Db and Mean lac to derive properties. So it it is not really a constant but more a very rough guess. If I understand it correctly they say that they use the whole mitochondria for the computation.

6 years ago in #steemstem by
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     6 years ago 

    1. It was box-counting... I wanted to try Higuchi, but I didn't have Matlab on that computer: https://revistas.eia.edu.co/index.php/Reveiaenglish/article/download/1206/1128

    2. Db,

    FracLac delivers a measure of complexity- a fractal dimension- called the box counting fractal dimension or DB. It is measured from the ratio of increasing detail with increasing scale (ε). The ratio quantifies the increase in detail with increasing magnification or resolution seen in fractals but also in microscopy. The basic technique for calculating the DB used in FracLac is called box counting.

    Page 12, https://imagej.nih.gov/ij/plugins/fraclac/fraclac-manual.pdf

    This approach is very new, and I'm not 100% sure what should I look at...

    The first idea was to do some texture analysis, because under the stress / normal conditions, MTH should change their shape from "rod-like" to "cirlce-like".

    But it was very bad and not reliable...

    Then I've found this MiNa Plug-in, but in reality (poor contrast, overlapped MTH, noise) and it was subjective.

    After I've found this tool for Z-stacks but it's also a complete mess with real-life images.

    My next attempt was, ok, back to "textures", now with some fractals. In the paper, they obtained an incredibly linear dependence between the "network of MTH" and level of stress. But in reality - it was again subjective.

    At the end, I've found that some people used machine learning to distinguish the proportion of 4 different shapes of MTH.

    1. I agree, but it should be really catchy to get images of individual MTH (using visible light, including fluorescence that is needed to properly mark them) because their size is about 1000nm, and that number is at the optical limits of "classical optics", 250-500nm (this is why this approach is very, very new). Now there are super-resolution techniques that allow the resolution to be about 10-20 nm. So still, we will get only very poor images. Does it make sense to observe them as particles - maybe...

    Probably that would be the only solution, to make particles out of them, manually select them into the categories (I have no clear idea how many of them) and after a harsh training - to try...

    I'm constantly seeing such hardly reproducible, subjective analyses that are beautifully packed into some statistics so every biologist can say - woooow!!! It's so perect!

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     6 years ago 

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