Tech Briefs Magazine - August 2024 - 28

The team elected not to file a patent
application, and instead is offering the
AI software as open source, so it can be
directly used - for free - by any lab.
They believe that this is important for
disseminating the results of their research,
as well as for getting feedback
from users in academic, clinical, and
commercial labs that can help the team
to further refine the software.
As the lab was making more and more
cardiac tissues through innovations
such as milliPillar and multi-organ tissue
models, the increased capabilities
of the tissues required the researchers
to develop a method to more rapidly
quantify the function of cardiomyocytes
(heart muscle cells) and tissues to enable
studies exploring genetic cardiomyopathies,
cosmic radiation,
immune-mediated
inflammation, and
drug discovery.
In the last year and a half, Lead Author
Youngbin Kim and his co-authors
developed a graphical user interface
on top of the code so that biomedical
researchers with no coding expertise
could easily analyze the data with just a
few clicks. This brought together experts
in software development, machine
learning, signal processing, engineering,
and user experience by
lab members.
The study showed that BeatProfiler
could accurately analyze cardiomyocyte
function, outperforming existing tools
by being faster - up to 50 times in
some cases - and more reliable. It detected
subtle changes in engineered
heat tissue force response that other
tools might miss.
" This level of analysis speed and versatility
is unprecedented in cardiac research, "
said Kim. " Using machine learning,
the functional measurements
analyzed by BeatProfiler helped us to
distinguish between diseased and healthy
heart cells with high accuracy and even
to classify different cardiac drugs based
on how they affect the heart. "
The team is working to expand BeatProfiler's
capabilities for new applications
in heart research, including a full
spectrum of diseases that affect the
pumping of the heart, and drug development.
To ensure that BeatProfiler can be
applied to a wide variety of research
questions, they are testing and validating
its performance across additional in vitro
cardiac models, including different
engineered heart tissue models. They
are also refining their machine-learning
algorithm to extend and generalize its
use to a variety of heart diseases and
drug effect classification.
The long-term goal is to adapt BeatProfiler
to pharmaceutical settings to
speed up the testing of hundreds of
thousands of candidate drugs at once.
For more information, contact Holly
Evarts at holly.evarts@columbia.edu;
212-854-3206.
Harnessing Wind and Solar Power Via Neural Networks
Researchers have developed a neural network that can predict wind speed and solar power
density on a daily and hourly basis.
Texas A&M, College Station, TX
T
he ongoing transition from fossil
fuels to renewable energy sources
has never been more important
as climate change and sustainability
awareness continue to rise.
This transition requires a robust
information structure to ensure a
smooth shift. Wind and solar power
are among the most abundant
sources of renewable energy; however,
engineers and researchers in
this field need an established data
pipeline to effectively integrate solar
and wind power into their process
design.
Abdullah Al-Aboosi is an interdisciplinary
doctoral student in the Department
of Multidisciplinary Engineering.
He is working with Aldo
Jonathan Muñoz Vazquez, a multidisciplinary
engineering professor at the
Higher Education Center in McAllen,
on a neural network that they
hope can provide such a pipeline.
The catalyst for this research resulted
from a discussion between Munoz
Vazquez and Al-Aboosi. The idea
evolved into a comprehensive project,
drawing on the expertise of various
28
source for accurate data that the researchers
used to validate their
model's predictions.
In terms of the neural network itDoctoral
student Abdullah Al-Aboosi is researching how
to better position renewable energy as the primary
source of electricity. (Image: Texas A&M Engineering)
collaborators, including Wei Zhan, Dr.
Mahmoud El-Halwagi and Dr. Fadhil
Al-Aboosi with RAPID Manufacturing
Institute for
Process
www.techbriefs.com
self, it can be used to provide a precise
overview of any renewable system's
operation and life cycle
assessment by predicting daily and
hourly wind speed and solar irradiance.
By accurately predicting solar
and wind energy availability, technological
resources and supplies
can be managed more efficiently.
Al-Aboosi hopes this project can
better position renewable energy as
the primary source of electricity in
the industry sector. The project
aims to enable researchers and renewable
energy installation companies
to determine the optimal number
of
solar
panels
and
wind
Intensification.
The
RAPID Manufacturing Institute
provided an invaluable open-access
turbines needed to prevent over or
under production. Such a reduction
could incentivize potential investors
to adopt this technology and
push for a future with cleaner air and
greener electricity.
For more information, contact Lesley
Henton at lshenton@tamu.edu.
Tech Briefs, August 2024
https://www.techbriefs.com
Tech Briefs Magazine - August 2024

Table of Contents for the Digital Edition of Tech Briefs Magazine - August 2024
