Machine learning peeks into nano-aquariums

Date:
August 24, 2020
Source:
University of Illinois at Urbana-Champaign, News Bureau
Summary:
In the nanoworld, tiny particles such as proteins appear to dance
as they transform and assemble to perform various tasks while
suspended in a liquid. Recently developed methods have made it
possible to watch and record these otherwise-elusive tiny motions,
and researchers now take a step forward by developing a machine
learning workflow to streamline the process.



FULL STORY ==========================================================================
In the nanoworld, tiny particles such as proteins appear to dance as
they transform and assemble to perform various tasks while suspended in
a liquid.

Recently developed methods have made it possible to watch and record these otherwise-elusive tiny motions, and researchers now take a step forward
by developing a machine learning workflow to streamline the process.


==========================================================================
The new study, led by Qian Chen, a professor of materials science and engineering at the University of Illinois, Urbana-Champaign, builds upon
her past work with liquid-phase electron microscopy and is published in
the journal ACS Central Science.

Being able to see -- and record -- the motions of nanoparticles
is essential for understanding a variety of engineering
challenges. Liquid-phase electron microscopy, which allows
researchers to watch nanoparticles interact inside tiny aquariumlike
sample containers, is useful for research in medicine, energy and
environmental sustainability and in fabrication of metamaterials,
to name a few. However, it is difficult to interpret the dataset, the researchers said.

The video files produced are large, filled with temporal and spatial information, and are noisy due to background signals -- in other words,
they require a lot of tedious image processing and analysis.

"Developing a method even to see these particles was a huge challenge,"
Chen said. "Figuring out how to efficiently get the useful data
pieces from a sea of outliers and noise has become the new challenge."
To confront this problem, the team developed a machine learning workflow
that is based upon an artificial neural network that mimics, in part,
the learning potency of the human brain. The program builds off of
an existing neural network, known as U-Net, that does not require
handcrafted features or predetermined input and has yielded significant breakthroughs in identifying irregular cellular features using other
types of microscopy, the study reports.

"Our new program processed information for three types of nanoscale
dynamics including motion, chemical reaction and self-assembly of nanoparticles," said lead author and graduate student Lehan Yao. "These represent the scenarios and challenges we have encountered in the
analysis of liquid-phase electron microscopy videos." The researchers collected measurements from approximately 300,000 pairs of interacting nanoparticles, the study reports.

As found in past studies by Chen's group, contrast continues to be
a problem while imaging certain types of nanoparticles. In their
experimental work, the team used particles made out of gold, which is
easy to see with an electron microscope. However, particles with lower elemental or molecular weights like proteins, plastic polymers and other organic nanoparticles show very low contrast when viewed under an electron beam, Chen said.

"Biological applications, like the search for vaccines and drugs,
underscore the urgency in our push to have our technique available
for imaging biomolecules," she said. "There are critical nanoscale
interactions between viruses and our immune systems, between the drugs
and the immune system, and between the drug and the virus itself that
must be understood. The fact that our new processing method allows us
to extract information from samples as demonstrated here gets us ready
for the next step of application and model systems." The team has made
the source code for the machine learning program used in this study
publicly available through the supplemental information section of the
new paper. "We feel that making the code available to other researchers
can benefit the whole nanomaterials research community," Chen said.

See the liquid-phase electron microscopy with combined machine learning
in action: https://www.youtube.com/watch?v=0NESPF8Rwsc

========================================================================== Story Source: Materials provided by University_of_Illinois_at_Urbana-Champaign,_News_Bureau.

Original written by Lois Yoksoulian. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Lehan Yao, Zihao Ou, Binbin Luo, Cong Xu, Qian Chen. Machine
Learning to
Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos. ACS
Central Science, 2020; DOI: 10.1021/acscentsci.0c00430 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200824105607.htm

--- up 6 hours, 50 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)