As California’s oldest private research university, USC has historically educated a large number of the region’s business leaders and professionals. In recent decades, the university has also leveraged its location in Los Angeles to establish relationships with research and cultural institutions throughout Asia and the Pacific Rim. Reflecting the status of Los Angeles as a global city, USC has the largest number of international students of any university in the United States. In 2011, USC was named among the Top 10 Dream Colleges in the nation.
As of 2011, USC enrolls 17,414 students in its four-year undergraduate program. USC is also home to 20,596 graduate and professional students in a number of different programs, including business, law, social work, and medicine. The university has a “very high” level of research activity and received $560.9 million in sponsored research from 2009 to 2010.
University of Southern California (USC) research articles from Innovation Toronto
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A computer algorithm for analyzing time-lapse biological images could make it easier for scientists and clinicians to find and track multiple molecules in living organisms. The technique is faster, less expensive and more accurate than current methods — and it even works with cell phone images.
A new image analysis technique makes finding important biological molecules — including tell-tale signs of disease — and learning how they interact in living organisms much faster and far less expensive. Called Hyper-Spectral Phasor analysis, or HySP, it could even be useful for diagnosing and monitoring diseases using cell phone images.
Researchers use fluorescent imaging to locate proteins and other molecules in cells and tissues. It works by tagging the molecules with dyes that glow under certain kinds of light — the same principle behind so-called “black light” images.
Fluorescent imaging can help scientists understand which molecules are produced in large amounts in cancer or other diseases, information that may be useful in diagnosis or in identifying possible targets for therapeutic drugs.
Looking at just one or two molecules in cell or tissue samples is fairly straightforward. Unfortunately, it doesn’t provide a clear picture of how those molecules are behaving in the real world. For that, scientists need to expand their view.
“Biological research is moving toward complex systems that extend across multiple dimensions, the interaction of multiple elements over time,” said postdoctoral fellow Francesco Cutrale. He developed HySP with Scott Fraser
, Elizabeth Garrett Chair in Convergent Bioscience and Provost Professor of Biological Science. The work was done at USC’s Translational Imaging Center, a joint venture of USC Dornsife and USC Viterbi School of Engineering.
“By looking at multiple targets, or watching targets move over time, we can get a much better view of what’s actually happening within complex living systems,” Cutrale said.
Currently, researchers must look at different labels separately, then apply complicated techniques to layer them together and figure out how they relate to one another, a time-consuming and expensive process, Cutrale said. HySP can look at many different molecules in one pass.
“Imagine looking at 18 targets,” Cutrale said. “We can do that all at once, rather than having to perform 18 separate experiments and try to combine them later.”
In addition, the algorithm effectively filters through interference to discern the true signal, even if that signal is extremely weak — very much like finding the proverbial needle in a haystack. Recent technology from NASA’s Jet Propulsion Laboratory can also do this, but the equipment and process are both extremely expensive and time-consuming.
In research published Jan. 9 online by the scientific journal Nature Methods, Cutrale and Fraser, along with researchers from Keck School of Medicine, Caltech and the University of Cambridge in the United Kingdom, have used zebra fish to test and develop HySP. In this common laboratory model, the system works extremely well. But what about in people?
“In experimental models, we can use genetic manipulation to label molecules, but we can’t do that with people,” said Fraser. “In people, we have to use the intrinsic signals of those molecules.”
Those inherent signals, the natural fluorescence from biomolecules, normally gets in the way of imaging, Fraser said. However, using this new computer algorithm that can effectively find weak signals in a cluttered background, the team can pinpoint their targets in the body.
Different fluorescent light wavelengths reveal features of a zebra fish embryo. Photo courtesy of Francesco Cutrale.
The scientists hope to test the process in the next couple of years with the help of soldiers whose lungs have been damaged by chemicals and irritants they may have encountered in combat. The researchers will extend a light-emitting probe down into the soldiers’ lungs while the probe records images of the fluorescence in the surrounding tissues. They will then use HySP to create what amounts to a fluorescent map and compare it with that of healthy lung tissue to see if they can discern the damage. If so, they hope to further develop the technology so it may one day help these soldiers and other lung patients receive more targeted treatment.
It might also be possible one day for clinicians to use HySP to analyze cell phone pictures of skin lesions to determine if they are at risk of being cancerous, according to Fraser and Cutrale.
“We could determine if the lesions have changed color or shape over time,” Cutrale said. Clinicians could then examine the patient further to be certain of a diagnosis and respond appropriately.
Cutrale and Fraser see the technology as a giant leap forward for both research and medicine.
“Both scientists at the bench and scientists at the clinic will be able to perform their work faster and with greater confidence in the results,” Cutrale said. “Better, faster, cheaper. That’s the payoff here.”
When political beliefs are challenged, a person’s brain becomes active in areas that govern personal identity and emotional responses to threats, USC researchers find
A USC-led study confirms what seems increasingly true in American politics: People are hardheaded about their political beliefs, even when provided with contradictory evidence.
Neuroscientists at the Brain and Creativity Institute at USC said the findings from the functional MRI study seem especially relevant to how people responded to political news stories, fake or credible, throughout the election.
“Political beliefs are like religious beliefs in the respect that both are part of who you are and important for the social circle to which you belong,” said lead author Jonas Kaplan, an assistant research professor of psychology at the Brain and Creativity Institute at the USC Dornsife College of Letters, Arts and Sciences. “To consider an alternative view, you would have to consider an alternative version of yourself.”
To determine which brain networks respond when someone holds firmly to a belief, USC neuroscientists compared whether and how much people change their minds on non-political and political issues when provided counterevidence.
They discovered that people were more flexible when asked to consider the strength of their belief in non-political statements — for example, “Albert Einstein was the greatest physicist of the 20th century.”
But when it came to reconsidering their political beliefs, such as whether the United States should reduce funding for the military, they would not budge.
“I was surprised that people would doubt that Einstein was a great physicist, but this study showed that there are certain realms where we retain flexibility in our beliefs,” Kaplan said.
The study was published on Dec. 23 in the Nature journal Scientific Reports. Study co-authors were Sarah Gimbel of the Brain and Creativity Institute and Sam Harris, a neuroscientist for the Los Angeles-based nonprofit Project Reason.
For the study, the neuroscientists recruited 40 people who were self-declared liberals. The scientists then examined through functional MRI how their brains responded when their beliefs were challenged.
During their brain imaging sessions, participants were presented with eight political statements that they had said they believe just as strongly as a set of eight non-political statements. They were then shown five counter claims that challenged each statement.
Participants rated the strength of their belief in the original statement on a scale of 1-7 after reading each counter claim. The scientists then studied their brain scans to determine which areas became most engaged during these challenges.
Participants did not change their beliefs much, if at all, when provided with evidence that countered political statements such as, “The laws regulating gun ownership in the United States should be made more restrictive.”
But the scientists noticed the strength of their beliefs weakened by one or two points when challenged on non-political topics, such as whether “Thomas Edison had invented the light bulb.” The participants were shown counter statements that prompted some feelings of doubt, such as “Nearly 70 years before Edison, Humphrey Davy demonstrated an electric lamp to the Royal Society.”
Thoughts that count
The study found that people who were most resistant to changing their beliefs had more activity in the amygdala (a pair of almond-shaped areas near the center of the brain) and the insular cortex, compared with people who were more willing to change their minds.
“The activity in these areas, which are important for emotion and decision-making, may relate to how we feel when we encounter evidence against our beliefs,” said Kaplan, a co-director of the Dornsife Cognitive Neuroimaging Center at USC.
“The amygdala in particular is known to be especially involved in perceiving threat and anxiety,” Kaplan added. “The insular cortex processes feelings from the body, and it is important for detecting the emotional salience of stimuli. That is consistent with the idea that when we feel threatened, anxious or emotional, then we are less likely to change our minds.”
He also noted that a system in the brain, the Default Mode Network, surged in activity when participants’ political beliefs were challenged.
“These areas of the brain have been linked to thinking about who we are, and with the kind of rumination or deep thinking that takes us away from the here and now,” Kaplan said.
The researchers said that this latest study, along with one conducted earlier this year, indicate the Default Mode Network is important for high-level thinking about important personal beliefs or values.
“Understanding when and why people are likely to change their minds is an urgent objective,” said Gimbel, a research scientist at the Brain and Creativity Institute. “Knowing how and which statements may persuade people to change their political beliefs could be key for society’s progress,” she said.
The findings can apply to circumstances outside of politics, including how people respond to fake news stories.
“We should acknowledge that emotion plays a role in cognition and in how we decide what is true and what is not true,” Kaplan said. “We should not expect to be dispassionate computers. We are biological organisms.”
From gene mapping to space exploration, humanity continues to generate ever-larger sets of data — far more information than people can actually process, manage or understand.
Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.
Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at MIT, the University of Waterloo, and the University of Southern California.