Transmission Electron Microscope by LD SEF (2019)
Source. Images some gold nanopraticles 5-10 nm. You can also get crystallographic information directly on the same machine.50,000,000x Magnification by AlphaPhoenix (2022)
Source. This technique has managed to determine protein 3D structures for proteins that people were not able to crystallize for X-ray crystallography.
It is said however that cryoEM is even fiddlier than X-ray crystallography, so it is mostly attempted if crystallization attempts fail.
We just put a gazillion copies of our molecule of interest in a solution, and then image all of them in the frozen water.
Each one of them appears in the image in a random rotated view, so given enough of those point of view images, we can deduce the entire 3D structure of the molecule.
Ciro Santilli once watched a talk by Richard Henderson about cryoEM circa 2020, where he mentioned that he witnessed some students in the 1980's going to Germany, and coming into contact with early cryoEM. And when they came back, they just told their principal investigator: "I'm going to drop my PhD theme and focus exclusively on cryoEM". That's how hot the cryo thing was! So cool.
Super-resolution means resolution beyond the diffraction limit.
They you can observe fluorophores firing one by one. Their exact position is a bit stochastic and beyond the diffraction limit, but so long as there aren't to many in close proximity, you can wait for it to fire a bunch of times, and the center of the Gaussian is the actual location.
From this we see that super-resolution microscopy is basically a space-time tradeoff: the more time we wait, the better spacial resolution we get. But we can't do it if things are moving too fast in the sample.
Tradeoff with cryoEM: you get to see things moving in live cell. Electron microscopy fully kills cells, so you have no chance of seeing anything that moves ever.
Caveats:
- initial illumination to saturate most fluorophores I think can still kill cells, things get harder the less light you put in. So it's not like you don't kill things at all necessarily, you just get a chance not to
- the presence fluorophore disturbs the system slightly, and is not at the same Exact location of the protein of interest
Instead of shining a light over the entire sample to saturate it, you illuminate just a small bit instead.
He was basically saying that this truly brings the resolution to the actual physical limits, going much much beyond 2014 Nobel prize levels.
Illumination patterns for STED microscopy
. Source. It trains the LeNet-5 neural network on the MNIST dataset from scratch, and afterwards you can give it newly hand-written digits 0 to 9 and it will hopefully recognize the digit for you.
Ciro Santilli created a small fork of this repo at lenet adding better automation for:
- extracting MNIST images as PNG
- ONNX CLI inference taking any image files as input
- a Python
tkinterGUI that lets you draw and see inference live - running on GPU
Install on Ubuntu 24.10 with:We use our own
sudo apt install protobuf-compiler
git clone https://github.com/activatedgeek/LeNet-5
cd LeNet-5
git checkout 95b55a838f9d90536fd3b303cede12cf8b5da47f
virtualenv -p python3 .venv
. .venv/bin/activate
pip install \
Pillow==6.2.0 \
numpy==1.24.2 \
onnx==1.13.1 \
torch==2.0.0 \
torchvision==0.15.1 \
visdom==0.2.4 \
;pip install because their requirements.txt uses >= instead of == making it random if things will work or not.On Ubuntu 22.10 it was instead:
pip install
Pillow==6.2.0 \
numpy==1.26.4 \
onnx==1.17.0 torch==2.6.0 \
torchvision==0.21.0 \
visdom==0.2.4 \
;Then run with:This script:
python run.pyIt throws a billion exceptions because we didn't start the Visdom server, but everything works nevertheless, we just don't get a visualization of the training.
The terminal outputs lines such as:
Train - Epoch 1, Batch: 0, Loss: 2.311587
Train - Epoch 1, Batch: 10, Loss: 2.067062
Train - Epoch 1, Batch: 20, Loss: 0.959845
...
Train - Epoch 1, Batch: 230, Loss: 0.071796
Test Avg. Loss: 0.000112, Accuracy: 0.967500
...
Train - Epoch 15, Batch: 230, Loss: 0.010040
Test Avg. Loss: 0.000038, Accuracy: 0.989300One of the benefits of the ONNX output is that we can nicely visualize the neural network on Netron:
Two Photon Microscopy by Nemonic NeuroNex (2019)
Source. Shows a prototype of a two-photon electron microscope on an optical table, and describes it in good detail, well done.One of its main applications is to determine the 3D structure of proteins.
Sometimes you are not able to crystallize the proteins however, and the method cannot be used.
Crystallizing is not simple because:
Cryogenic electron microscopy can sometimes determine the structures of proteins that failed crystallization.
Often used as a synonym for X-ray crystallography, or to refer more specifically to the diffraction part of the experiment (exluding therefore sample preparation and data processing).
- 1958: myoglobin structure resolution (1958). The first protein to be resolved.
- 1965: lysozyme structure resolution (1965). The second protein to be resolved.
- micro.magnet.fsu.edu/index.html OLD website with great design and much love. Some notable things:
As of 2022, this channel is still finding its feet. But it has promise.
Unfortunately it does not show sample preparation, and it does not use controlled cultures, so we are never sure which species are represented.
- phys.org/news/2023-02-muon-detectors-remotely-3d-image.html Using muon detectors to remotely create a 3D image of the inside of a nuclear reactor (2023)
Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Intro to OurBigBook
. Source. We have two killer features:
- topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculusArticles of different users are sorted by upvote within each article page. This feature is a bit like:
- a Wikipedia where each user can have their own version of each article
- a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 3. Visual Studio Code extension installation.
Figure 4. Visual Studio Code extension tree navigation.
Figure 5. Web editor. You can also edit articles on the Web editor without installing anything locally.Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. 
- Infinitely deep tables of contents:
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact