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University of Texas at Austin, Texas Advanced Computing Center


The complex journey of red bloods cells through microvascular networks

A team of researchers used a state-of-the-art simulation code to study the behavior of red blood cells flowing through physiologically realistic microvascular networks. They observed that red blood cells frequently jam for brief periods before proceeding downstream, causing temporary increases in vascular resistance. The findings have ...

Uncovering decades of questionable investments

Researchers at University of Texas at Austin analyzed 40 years of stock prices, finding that high-beta stocks do not outperform those with low betas. Controlling for lottery-like characteristics, the seminal theory is empirically supported, revealing price pressure from investors as the main cause of the anomaly.

SourceUniversity of Texas at Austin, Texas Advanced Computing Center·JournalJournal of Financial and Quantitative Analysis·DateJan 17, 2018

Tailoring cancer treatments to individual patients

A team of researchers is developing complex computer models to predict how cancer will progress in individual patients. They use advanced computing resources, including those at the Texas Advanced Computing Center, to analyze patient-specific data from imaging tests and biopsies, as well as other factors.

SourceUniversity of Texas at Austin, Texas Advanced Computing Center·JournalComputer Methods in Applied Mechanics and Engineering·DateJan 3, 2018

Scientists enlist supercomputers, machine learning to automatically identify brain tumors

A team of researchers from the University of Texas at Austin has created an accurate and efficient method to characterize gliomas, the most common and aggressive type of primary brain tumor. Their system combined biophysical models with machine learning algorithms to analyze Magnetic Resonance imaging data, achieving top results in a c...

How pythons regenerate their organs and other secrets of the snake genome

Scientists study Burmese pythons' ability to regenerate organs after feeding, identifying key genes that drive regenerative growth. The team also explores the genetic basis of evolution in snakes and lizards, shedding light on the mechanisms behind unique traits such as venom composition and reproductive differences.

Machine learning lets scientists reverse-engineer cellular control networks

Researchers have developed a machine learning model that can predict the outcome of cellular interactions and design new cancer treatments. The Stampede supercomputer enabled the team to run billions of simulations, allowing them to identify patterns in the data and create a system capable of predicting laboratory results.

Preventing blood clots with a new metric for heart function

Researchers developed a new metric to predict blood clots in the left ventricle, which is more accurate than current methods. The E-wave propagation index can be calculated using standard diagnostic tools and clinical procedures, and has been validated with data from patients who experienced post-heart attack blood clots.

SourceUniversity of Texas at Austin, Texas Advanced Computing Center·JournalInternational Journal of Cardiology·DateJan 30, 2017