Chemists teach old drug new tricks to target deadly staph bacteria

Emory chemist Bill Wuest, far right, with some of his graduate students, from left: Erika Csatary, Madeleine Dekarske and Ingrid Wilt. Photo by Ann Watson.

"Saying superbugs, one antibiotic at a time,” is the motto of Bill Wuest’s chemistry lab at Emory University. Wuest (it rhymes with “beast”) leads a team of students fighting drug-resistant bacteria — some of the scariest, most dangerous bugs on the planet.

Most recently, they created new molecules for a study published in PNAS. Their work helped verify how bithionol — a drug used to treat parasitic infections — can weaken the cell membranes of “persister” cells of methicillin-resistant Staphylococcus aureus (MRSA), a deadly staph bacterium. They also synthesized new compounds, to learn more about how bithionol works and enhance its potential for clinical use.

“Just before I entered graduate school, my mother was diagnosed with a severe staph infection,” says Ingrid Wilt, a PhD candidate, explaining what drives her passion to tackle MRSA.

“She was in a hospital in the ICU for about two weeks,” Wilt adds. “Luckily, a last-resort antibiotic worked for her and she’s okay now.”

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Cheerleader study highlights need for real-time energy balance

"It's not just how much you eat and what you eat but when you eat it that matters," says Dan Benardot, senior author of the study and a professor of practice at Emory's Center for the Study of Human Health.

It’s well-known that many athletes, especially women athletes and those participating in sports with an aesthetic component, can be chronically energy deficient. A new study suggests that professional cheerleaders also struggle to maintain an optimal balance between energy consumed and energy burned during exercise.

The Journal of Science in Sport and Exercise published the finding, led by researchers at Emory University’s Center for the Study of Human Health and Rollins School of Public Health. The results showed that some study participants had hourly energy balance deficits that were significantly below their estimated energy needs during a typical training day.

“An offensive lineman doesn’t have to worry about what he looks like but appearance matters for professional cheerleaders, and that may affect their food choices,” says Moriah Bellissimo, first author of the study and a graduate student at Rollins. “Some of our study participants reported really low caloric intakes for the amount of physical training they do. Those with the lowest caloric intakes were not eating enough to maintain an optimal body composition of lean mass compared to fat for high-performance athletics.”

“It is not just how much you eat and what you eat but when you eat it that matters,” adds senior author Dan Benardot, professor of practice at Emory’s Center for the Study of Human Health.

Benardot, who is also an emeritus professor of nutrition at Georgia State University, is an expert in the interrelationship between energy intake, body composition and within-day energy balance, and has worked as a team nutritionist for Olympians and professional athletes.

“The body works in real time,” Benardot says. “If you’re not eating enough and not often enough to avoid low blood sugar and high cortisol, your body adapts to this negative energy balance. Your brain will direct the body to find more energy by breaking down muscle mass to satisfy the need for energy. It sets you up for a downward spiral where you continually have to eat less and less to keep from gaining weight.”

The problem is particularly acute for athletes, especially female athletes and those in aesthetic sports, who deplete lean muscle mass at a faster rate than less active people because of the exercise-associated severe energy balance deficit they achieve. The researchers wanted to investigate whether professional cheerleaders, who may train four hours a day practicing dance routines, faced a similar challenge for real-time energy balance as some other female athletes in aesthetic sports.

“I have a vested interest in human performance and nutrition from a personal standpoint,” says Bellissimo, who was a collegiate athlete for five years before entering the Rollins PhD program for Nutrition and Health Science. “I know that how you are eating makes a difference in how you perform.”

Bellissimo says it was challenging to maintain a proper nutritional balance when she was an undergraduate and master’s student, while also competing in Division I volleyball tournaments. She notes that professional cheerleaders often work full-time jobs on top of training and performing and may find it especially challenging to carefully strategize all of their nutritional needs.

For the current study, the researchers conducted 24-hour dietary and activity surveys with professional cheerleaders during an active training period — including an hour-by-hour assessment of what and how much they ate, and hourly energy expenditures throughout the day. They inputted the data into a software tool called NutriTiming®, developed by Benardot, to calculate each participants’ hourly energy balance — and whether they were exercising at a calorie surplus or deficit.

For female athletes, previous research has shown that sustaining an energy balance of plus or minus 300 calories throughout the day is beneficial to avoid the lean tissue breakdown associated with larger energy deficits.

The body mass and body composition of the study participants was also measured, using a bioelectrical impedance analyzer — which painlessly assesses the density of biological tissue.

The results showed that those participants who spent fewer hours in a negative energy balance had a lower, more optimal, percentage of body fat and those who spent more time within the plus-or-minus zone of 300 calories also had a lower percentage of body fat.

The cheerleader study was small and of short duration, but the finding is consistent with other research on female athletes and other populations, Benardot says.

“Athletes expend energy rapidly,” he adds. “They need to eat frequently, just not too much at a time, so their bodies have enough fuel to burn as they need it.”

It is important to study the nutritional needs of people involved in competitive sports and other intensive exercise, both to help them perform at their maximum level and to maintain their health, Bellissimo says. “Research has shown that chronic energy balance deficits in athletes can lead to hormonal imbalances, and that can have long-term health implications,” she says.

Additional authors of the study include Ashley Licata, from the University of Alabama at Birmingham, and Anita Nucci and Walt Thompson, from Georgia State University.

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Emory mathematician to present a proof of the Sensitivity Conjecture

Emory mathematician Hao Huang says that the algebraic tool that he developed to tackle the problem "might also have some potential to be applied to other combinatorial and complexity problems important to computer science.”

The Sensitivity Conjecture has stood as one of the most important, and baffling, open problems in theoretical computer science for nearly three decades. It appears to have finally met its match through work by Hao Huang, an assistant professor of mathematics at Emory University.

Huang will present a proof of the Sensitivity Conjecture during the International Conference on Random Structures and Algorithms, set for Zurich, Switzerland, July 15 to 19.

“I’ve been attacking this problem off and on since 2012,” Huang says, “but the key idea emerged for me just about a week ago. I finally identified the right tool to solve it.”

Huang posted the proof on his home page and it soon generated buzz among mathematicians and computer scientists on social media, who have praised its remarkable conciseness and simplicity.

The Sensitivity Conjecture relates to boolean data, which maps information into a true-false, or 1-0 binary. Boolean functions play an important role in complexity theory, as well in the design of circuits and chips for digital computers.

“In mathematics, a boolean function is one of the most basic discrete subjects — just like numbers, graphs or geometric shapes,” Huang explains.

There are many complexity measures of a boolean function, and almost all of them — including the decision-tree complexity, the certificate complexity, the randomized query complexity and many others — are known to be polynomially related. However, there is one unknown case, the so-called sensitivity of a boolean function, which measures how sensitive the function is when changing one input at a time.

In 1994, mathematicians Noam Nisan and Mario Szegedy proposed the Sensitivity Conjecture concerning this unknown case.

“Their conjecture says the sensitivity of a boolean function is also polynomially related to the other measures,” Huang says. “If true, then it would cease to be an outlier and it would join the rest of them.”

Huang developed an algebraic method for proving the conjecture. “I hope this method might also have some potential to be applied to other combinatorial and complexity problems important to computer science,” he says.

The research was supported in part by the Simons Foundation.

Screams contain a 'calling card' for the vocalizer's identity

"Our findings add to our understanding of how screams are evolutionarily important," says Emory psychologist Harold Gouzoules, senior author of the paper.

By Carol Clark

Human screams convey a level of individual identity that may help explain their evolutionary origins, finds a study by scientists at Emory University.

PeerJ published the research, showing that listeners can correctly identify whether pairs of screams were produced by the same person or two different people — a critical prerequisite to individual recognition.

“Our findings add to our understanding of how screams are evolutionarily important,” says Harold Gouzoules, senior author of the paper and an Emory professor of psychology. “The ability to identify who is screaming is likely an adaptive mechanism. The idea is that you wouldn’t respond equally to just anyone’s scream. You would likely respond more urgently to a scream from your child, or from someone else important to you.”

Jonathan Engelberg is first author of the paper and Jay Schwartz is a co-author. They are both Emory PhD candidates in Gouzoules’ Bioacoustics Lab.

The ability to recognize individuals by distinctive cues or signals is essential to the organization of social behavior, the authors note, and humans are adept at making identity-related judgements based on speech — even when the speech is heavily altered. Less is known, however, about identity cues in nonlinguistic vocalizations, such as screams.

Gouzoules first began researching monkey screams in 1980, before becoming one of the few scientists studying human screams about 10 years ago.

“The origin of screams was likely to startle a predator and make it jump, perhaps allowing the prey a small chance to escape,” Gouzoules says. “That’s very different from calling out for help.”

He theorizes that as some species became more social, including monkeys and other primates, screams became a way to recruit help from relatives and friends when someone got into trouble.

Previous research by Gouzoules and others suggests that non-human primates are able to identify whether a scream is coming from an individual that is important to them. Some researchers, however, have disputed the evidence, arguing that the chaotic and inconsistent nature of screams does not make them likely conduits for individual recognition.

Gouzoules wanted to test whether humans could determine if two fairly similar screams were made by the same person or a different person. His Bioacoustics Lab has amassed an impressive library of high-intensity, visceral sounds — from TV and movie performances to the screams of non-actors reacting to actual events on YouTube videos.

For the PeerJ paper, the lab ran experiments that included 104 participants. The participants listened to audio files of pairs of screams on a computer, without any visual cues for context. Each pair was presented two seconds apart and participants were asked to determine if the screams came from the same person or a different person.

In some trials, the two screams came from two different callers, but were matched by age, gender and the context of the scream. In other trials, the screams came from the same caller but were two different screams matched for context. And in a third trial, the stimulus pairs consisted of a scream and a slightly modified version of itself, to make it longer or shorter than the original.

For all three of the experiments, most of the participants were able to correctly judge most of the time whether the screams were from the same person or not.

“Our results provide empirical evidence that screams carry enough information for listeners to discriminate between different callers,” Gouzoules says. “Although screams may not be acoustically ideal for signaling a caller’s identity, natural selection appears to have adequately shaped them so they are good enough to do the job.”

The PeerJ paper is part of an extensive program of research into screams by Gouzoules. In previous work, his lab has found that listeners cannot distinguish acted screams from naturally occurring screams.

In upcoming papers, he is zeroing in on how people determine whether they are hearing a scream or some other vocalization and how they perceive the emotional context of a scream — judging whether it’s due to happiness, anger, fear or pain.

Photo: Getty Images

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The whisper of schizophrenia: Machine learning finds 'sound' words predict psychosis

"Machine learning technology is advancing so rapidly that it's giving us tools to data mine the human mind," says Emory psychologist Phillip Wolff, senior author of the study.

By Carol Clark

A machine-learning method discovered a hidden clue in people’s language predictive of the later emergence of psychosis — the frequent use of words associated with sound. The journal npj Schizophrenia published the findings by scientists at Emory University and Harvard University.

The researchers also developed a new machine-learning method to more precisely quantify the semantic richness of people’s conversational language, a known indicator for psychosis.

Their results show that automated analysis of the two language variables — more frequent use of words associated with sound and speaking with low semantic density, or vagueness — can predict whether an at-risk person will later develop psychosis with 93 percent accuracy.

Even trained clinicians had not noticed how people at risk for psychosis use more words associated with sound than the average, although abnormal auditory perception is a pre-clinical symptom.

“Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes,” says Neguine Rezaii, first author of the paper. “The automated technique we’ve developed is a really sensitive tool to detect these hidden patterns. It’s like a microscope for warning signs of psychosis.”

Rezaii began work on the paper while she was a resident at Emory School of Medicine’s Department of Psychiatry and Behavioral Sciences. She is now a fellow in Harvard Medical School’s Department of Neurology.

“It was previously known that subtle features of future psychosis are present in people’s language, but we’ve used machine learning to actually uncover hidden details about those features,” says senior author Phillip Wolff, a professor of psychology at Emory. Wolff’s lab focuses on language semantics and machine learning to predict decision-making and mental health.

“Our finding is novel and adds to the evidence showing the potential for using machine learning to identify linguistic abnormalities associated with mental illness,” says co-author Elaine Walker, an Emory professor of psychology and neuroscience who researches how schizophrenia and other psychotic disorders develop.

The onset of schizophrenia and other psychotic disorders typically occurs in the early 20s, with warning signs — known as prodromal syndrome — beginning around age 17. About 25 to 30 percent of youth who meet criteria for a prodromal syndrome will develop schizophrenia or another psychotic disorder.

Using structured interviews and cognitive tests, trained clinicians can predict psychosis with about 80 percent accuracy in those with a prodromal syndrome. Machine-learning research is among the many ongoing efforts to streamline diagnostic methods, identify new variables, and improve the accuracy of predictions.

Currently, there is no cure for psychosis.

“If we can identify individuals who are at risk earlier and use preventive interventions, we might be able to reverse the deficits,” Walker says. “There are good data showing that treatments like cognitive-behavioral therapy can delay onset, and perhaps even reduce the occurrence of psychosis.”

For the current paper, the researchers first used machine learning to establish “norms” for conversational language. They fed a computer software program the online conversations of 30,000 users of Reddit, a social media platform where people have informal discussions about a range of topics. The software program, known as Word2Vec, uses an algorithm to change individual words to vectors, assigning each one a location in a semantic space based on its meaning. Those with similar meanings are positioned closer together than those with far different meanings.

The Wolff lab also developed a computer program to perform what the researchers dubbed “vector unpacking,” or analysis of the semantic density of word usage. Previous work has measured semantic coherence between sentences. Vector unpacking allowed the researchers to quantify how much information was packed into each sentence.

After generating a baseline of “normal” data, the researchers applied the same techniques to diagnostic interviews of 40 participants that had been conducted by trained clinicians, as part of the multi-site North American Prodrome Longitudinal Study (NAPLS), funded by the National Institutes of Health. NAPLS is focused on young people at clinical high risk for psychosis. Walker is the principal investigator for NAPLS at Emory, one of nine universities involved in the 14-year project.

The automated analyses of the participant samples were then compared to the normal baseline sample and the longitudinal data on whether the participants converted to psychosis.

The results showed that higher than normal usage of words related to sound, combined with a higher rate of using words with similar meaning, meant that psychosis was likely on the horizon.

Strengths of the study include the simplicity of using just two variables — both of which have a strong theoretical foundation — the replication of the results in a holdout dataset, and the high accuracy of its predictions, at above 90 percent.

“In the clinical realm, we often lack precision,” Rezaii says. “We need more quantified, objective ways to measure subtle variables, such as those hidden within language usage.”

Rezaii and Wolff are now gathering larger data sets and testing the application of their methods on a variety of neuropsychiatric diseases, including dementia.

“This research is interesting not just for its potential to reveal more about mental illness, but for understanding how the mind works — how it puts ideas together,” Wolff says. “Machine learning technology is advancing so rapidly that it’s giving us tools to data mine the human mind.”

The work was supported by grants from the National Institutes of Health and a Google Research Award.

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