This new research raises new questions about the Sport Concussion Assessment Tool (SCAT), a questionnaire widely used along with other methods to diagnose concussions sustained during sports. Findings were presented at the American Physiological Society annual meeting April 5.

“Our findings highlight the importance of considering the effects of exercise and fatigue in assessing concussions in athletes on the field,” said the study’s first author, Stephanie Iring, a doctoral candidate in the laboratory of Jorge Serrador, an associate professor at the Rutgers School of Health Professions. “While players with a head impact may report more symptoms generally, we have to be cautious in using all symptoms on the assessment since some are common after intense exercise even when there was no head impact.”

A concussion is a traumatic brain injury usually caused by a blow to the head. Although not typically life-threatening, the effects can be serious and long-lasting. About 3.8 million sports-related concussions are reported each year in the United States.

SCAT is a tool designed for use by medical professionals to determine whether a player has suffered a concussion. The assessment includes questions about “red flag” symptoms such as neck pain, headache, muscle weakness and vision problems in addition to tests to assess memory loss and other symptoms.

In previous studies of the tool, researchers compared symptoms in athletes who experienced a blow to the head with people who have been at rest. For the new study, researchers compared SCAT scores in rugby players who had sustained a blow to the head with teammates who had just played an intense rugby match but did not have a head impact. They assessed 209 players, 80 of whom had experienced a head impact and 129 who had not.

Compared with those who did suffer a head injury, those who did have a head injury had significantly more symptoms on the SCAT assessment, reporting 26 symptoms on average. Non-injured players reported about nine symptoms. However, many players without a head injury had symptoms similar to those reported by head-injured players, including fatigue and neck pain.

“Our data shows that exertion during a match increased the number and severity of self-reported symptoms in control players even though they had not experienced a head impact,” Iring said. “This could lead to difficulty differentiating these players from those that had experienced a head impact when using on-field assessments.”

Some symptoms, including headache and “not feeling right,” were more closely associated with having a head injury. This suggests these symptoms might be a stronger indicator of concussion in players who have just finished an intense game, according to researchers. In addition to headache, other symptoms more common in those with a head injury included cognitive-sensory effects, emotional-affective symptoms and hypersensitivity. The researchers suggested further studies are needed to examine how these components can be used along with current physiological measures to better assess a concussion in athletes.

Story Source:

Materials provided by Rutgers University. Original written by Patti Verbanas. Note: Content may be edited for style and length.

The research appears online today in Nature Neuroscience.

“The big game-changer here is discovering locations on the chromosome that speed up or slow down brain aging in worldwide populations. These can quickly become new drug targets,” said Paul Thompson of USC, a lead author on the study and the co-founder and director of the ENIGMA Consortium. “Through our AI4AD (Artificial Intelligence for Alzheimer’s Disease) initiative we even have a genome-guided drug repurposing program to target these and find new and existing drugs that help us age better.”

ENIGMA is working group based at USC that is exploring a vast trove of brain data and has published some of the largest-ever neuroimaging studies of schizophrenia, major depression, bipolar disorder, epilepsy, Parkinson’s disease, and even HIV infection.

To discover the hotspots, or genomic loci, more than 200 ENIGMA-member scientists from all over the world looked for people whose brains were scanned twice with MRI. The scans provided a measure of how fast their brains were gaining or losing tissue in regions that control memory, emotion and analytical thinking.

A million markers screened

After computing brain tissue change rates in 15,000 people of all ages, researchers screened a million markers in their genomes to detect 15 genomic loci — specific, physical locations of genes or other DNA sequences on a chromosome — that were speeding up brain tissue changes.

These loci included some well-known Alzheimer’s risk genes, such as APOE, and some novel ones, Thompson said. The researchers also found overlap with genes involved with depression, schizophrenia and cognitive functioning.

“Some of these genetic variants affect the growth rates of brain substructures in childhood, while others affect the speed of brain tissue loss in older adulthood,” said co-author Neda Jahanshad, an associate professor of neurology at the Keck School of Medicine of USC. “The different parts of the brain have specific genes associated with their rates of change.”

Thompson added, “You can see that APOE — the famous Alzheimer’s gene — hits a couple of brain structures adversely — the hippocampus and amygdala — which also makes sense as they are the brain regions most vulnerable to Alzheimer’s and it seems to speed tissue loss there specifically.”

ENIGMA also has international projects studying childhood brain disorders — from Tourette syndrome and autism to epilepsy. The new list of genes that slow down or speed up brain growth in children provides new leads to pursue in these disorders as well, the researchers said.

About this study

In addition to Thompson and Jahanshad, other USC scientists involved in the study included Sophia Thomopoulos, Joanna Bright, Leila Nabulsi, Linda Ding and Alyssa Zhu, all from the USC Mark and Mary Stevens Neuroimaging and Informatics Institute. For a full list of authors, see the published study.

The study was supported with funding from the National Institutes of Health, including the National Institute on Aging (U01AG068057, R01AG058854, R01AG059874), the National Institute of Mental Health (R01MH117601), the National Institute of Biomedical Imaging and Bioengineering (P41 EB015922), and a Zenith Grant (ZEN-20-644609) from the Alzheimer’s Association.

Story Source:

Materials provided by University of Southern California. Original written by Leigh Hopper. Note: Content may be edited for style and length.

“Our work provides a sort of cooking recipe for patent creation, with a list of ingredients that vary by industrial sector,” says Sandy Dall’erba, professor in the Department of Agricultural and Consumer Economics (ACE) and director of the Center for Climate, Regional, Environmental and Trade Economics (CREATE) at U of I. Dall’erba is a co-author on the study.

“Some sectors depend very much on local input factors, such as the presence of a university, versus elements located further away, such as spending in R&D by another private company. In some cases, this type of collaboration takes place across companies located thousands of miles apart as virtual meetings increasingly have replaced face-to-face meetings,” Dall’erba states.

“In addition, our research measures the extent to which innovation in one sector depends on R&D in the same sector or other sectors. For instance, new patents in the drug and medical industry depend on local and distant R&D in the chemical industry.”

Traditionally, geographical proximity was considered essential for knowledge flows. Clusters such as Silicon Valley’s high-tech industry or Detroit’s automobile industry facilitate face-to-face interactions and informal networking. Economists now acknowledge innovations may be shared across greater distances, but most studies have looked at aggregate results rather than location- and industry-specific patterns.

“We wanted to see the importance of geography for the patterns of knowledge creation in specific industries. We also wanted to examine how information flows across similar or different industries. Finally, we look at spillovers from private and university research and development activities,” says lead author Orsa Kekezi, a scholar at the Swedish Institute for Social Research at Stockholm University. Kekezi began working on this research while she was a visiting scholar at Illinois.

The researchers analyzed knowledge transfers across 853 metropolitan U.S. counties in five manufacturing industries: chemical, drugs and medical, mechanical, computer and communication, and electrical and electronic.

The study uses patent applications as a proxy for knowledge creation, tracking the flow from where patents are created to where they are cited (based on data from the U.S. Patent and Trade Office). This measures the directionality of innovation and identifies the role of external factors in knowledge output.

The core elements in the analysis are intra- and inter-sectoral knowledge spillovers, as well as intra- and interregional flows, defined as local (within county), short distance (neighboring counties in 50-mile radius), and the rest of U.S. (beyond 50 miles). The researchers also looked at the presence of university and private research and development, as well as other factors, such as the number of graduate degree holders and the industrial diversity in a county.

“Overall, the local environment matters greatly for all the sectors. The industry structure in the region, the size of firms, whether there is a university — these elements all matter for innovation. All sectors benefit from the local environment,” Kekezi states.

But the specifics vary by sector and provide a complex picture of interactions. There is no one-size-fits-all approach for the development of patents, and if you look at average results, you’ll miss the intricate details and patterns across sectors and regions.

“While university research matters for all the sectors, there is large heterogeneity here as well,” says study co-author Dongwoo Kang, a research fellow at the Korea Labor Institute, South Korea. “For example, it matters more for the chemical industry and the drugs and medical industry; those sectors really benefit from basic research.

“Universities provide the core research that is needed for the chemical industry to work. Unlike perhaps the mechanical or electric industries, the chemical industry relies more on faculty scholars who are looking at basic processes,” he adds.

Interregional spillovers matter less in the chemical industry, so face-to-face contact is important. On the other hand, for the drugs and medical industry, both inter-sectoral and long-distance regional spillovers matter, so geographic proximity is not as necessary. And for the electrical and electronic industry, inter-sectoral short-distance spillovers play a significant role.

The study findings can help companies decide where to locate their establishments.

“The idea that a company values proximity to a university, or where research and development are already happening, still holds true. But we also show the network of innovation is not completely local,” Dall’erba states.

“The main takeaway from the paper is that we should not only look at local spillovers. We should look at knowledge that comes from further away, and from other industries,” Kekezi notes. “New ideas don’t come only by looking at what has been done in one’s field, but also from looking at the broader picture and how we can combine different types of knowledge to create something new.”

The development of COVID-19 vaccines serves as an example, Kang adds.

“The U.S. invested a lot of money in research and development to stimulate innovation in creating the first COVID-19 vaccines. This is typically done through a central cluster that serves as a basis for innovation. But there are also other networks making new COVID-19 vaccines. Our findings imply that not only local activities, but also research and development in other places are important for making new COVID-19 vaccines,” he explains.

The findings can also help improve the design of future local and national innovation policies.

“We need to move away from an approach whereby everything is driven by similar mechanisms and instead understand much better what really works for one industry might be quite different from what works for another industry. It’s not just about promoting industry clusters; it’s more complicated than that. When governments try to promote innovation, they need to define a strategy that works for a specific industry and a specific location,” Dall’erba says.

Known collectively as the human microbiome, these tiny entities influence an astonishing range of activities, from metabolism to behavior and play a central role in health and disease. Some 39 trillion non-human microbes flourish on and within us, in a ceaseless, interdependent bustle. Together, they make up over half of the human body’s cells, though they may possess 500 times as many genes as are found in human cells. Identifying and making sense of this microbial mélange has been a central challenge for researchers.

In a new study, Qiyun Zhu and his colleagues describe a new method for probing the microbiome in unprecedented detail. The technique provides greater simplicity and ease of use compared with existing approaches. Using the new technique, the researchers demonstrate an improved ability to pinpoint biologically relevant characteristics, including a subject’s age and sex based on microbiome samples.

The innovative research holds the promise of rapidly advancing investigations into the mysteries of the microbiome. With such knowledge, researchers hope to better understand how these microbes collectively act to safeguard human health and how their dysfunction can lead to a broad range of diseases. In time, drugs and other therapies may even be tailor-made based on a patient’s microbiomic profile.

Professor Zhu is a researcher in the Biodesign Center for Fundamental and Applied Microbiology and ASU’s School of Life Sciences. The research team includes collaborators from the University of California, San Diego, including co-corresponding author Rob Knight, Zhu’s former mentor.

The group’s research results appear in the current issue of the journal mSystems.

Tools of the trade

Two powerful technologies have been used to help researchers unlock the diversity and complexity of the microbiome, by sequencing the microbial DNA present in a sample. These are known as 16S and metagenomic sequencing. The technique described in the current study draws on the strengths of both methods to create a new way of processing data from the microbiome.

“We borrow some of the wisdom that developed from 16S RNA sequencing and apply it to metagenomics,” Zhu says. Unlike other sequencing methods, including 16S, metagenomics allows researchers to sequence all the DNA information present in a microbiome sample. But the new study shows that the metagenomic approach has room for improvement. “The way people currently analyze metagenomic data is limited, because whole genome data has to first be translated into taxonomy.”

The new technique, known as Operational Genomic Units (OGU) does away with the laborious and sometimes misleading practice of assigning taxonomic categories like genus and species to the multitude of microbes present in a sample. Instead, the method uses individual genomes as the basic units for statistical analysis and simply attempts to align sequences present in a sample to sequences found in existing genomic databases.

By doing this, researchers can get much more fine-grained resolution, which is particularly useful when microbes are present that are closely related in DNA sequence. This is true because most taxonomic classifications are based on sequence similarity. If two sequences differ by less than a certain threshold, they fall into the same taxonomic category, however the OGU approach can help researchers tell them apart.

Further, the method overcomes errors in taxonomy that persist as relics from the pre-sequencing epoch, when different species were defined by their morphology rather than from DNA sequence data.

In addition to improvements in resolution and simplicity, OGU can help researchers analyze data using what are known as phylogenetic trees. As the name implies, these are branching structures that can describe the degree of relatedness between organisms, based on their sequence similarity. Just as two distantly related species like worms and antelope will appear on more distant branches of a phylogenetic tree, so will more distantly related bacteria and other constituents of the microbiome.

Innovations in sequencing

The most widely used technique for probing the microbiome, known as 16S ribosomal RNA sequencing or just 16S, relies on a simple idea. All bacteria have a 16S gene, which is essential to the machinery bacteria need to initiate protein synthesis. The bacterial 16S gene, measuring 1500 base pairs in length, consists of distinct regions. Some of these regions change very little between different bacteria and over evolutionary timeframes, while others are highly variable.

Researchers realized that the conserved and variable regions of the 16S gene allow it to act as a molecular clock, keeping track of bacteria that are more closely or more distantly related, based on their sequence similarity. Thus, the 8 conserved and 9 variable regions of 16S can be used to fingerprint bacteria.

To do this, a microbiome sample is first collected. This could be a fecal sample, to evaluate the gut microbiome, or a sample from the skin or from the mouth. Each body site is home to a different bacterial menagerie.

Next, PCR technology is used to amplify portions of the 16S gene. By sequencing highly conserved regions, a broad swath of bacteria can be identified, while sequencing of variable regions helps narrow the identity of particular bacteria.

Although 16S is an inexpensive and well-developed method, it has limitations. The technique can only give a general idea of the kinds of bacteria present, with limited resolution. In general, 16S is only accurate to the genus level of identification.

Enter metagenomic sequencing. This technique sequences the full genomes of all microbes present in a microbiome sample, (not just bacteria, as with 16S). Metagenomics allows researchers to sequence thousands of organisms in parallel, providing accurate, species-level resolution. The greater resolution however does come with costs. Metagenomic data is far richer and more computationally challenging to analyze than 16S data and more expensive in time and money to process.

A new path for metagenomics

The OGU technique streamlines metagenomic sequencing, while providing even greater resolution. The approach classifies microbes in a sample strictly according to their alignment with a reference database — no taxonomic assignment required. The approach enables researchers to evaluate the degree of species diversity present in a sample.

Compared with 16S and standard metagenomic sequencing, the new approach is superior in ferreting out biologically relevant information. Using the classic Human Microbiome Project dataset of 210 metagenomes sampled from seven body sites of male and female human subjects, the study demonstrates better correlation between body site and host sex.

Next, 6,430 stool samples collected through a random sampling of the Finnish population were analyzed, using both 16S and metagenomic sequencing. The samples belong to a large, randomly sampled cohort of the Finnish population, known as FINRISK. The aim was to predict the age of sampled individuals, based on gut microbial composition. Again, the OGU method outperformed 16S and conventional metagenomic analysis, providing more accurate predictions.

New research drawing on still larger datasets will further enhance the resolution of the new technique and expand the descriptive power of taxonomy-independent analysis.

Similarly, across the globe — in 19 Eastern European countries, 25 Muslim countries and in Israel — low belief in evolution was linked to higher biases within a person’s group, prejudicial attitudes toward people in different groups and less support for conflict resolution.

The findings supported the hypothesis of lead author Stylianos Syropoulos, a Ph.D. candidate in the War and Peace Labof senior author Bernhard Leidner, associate professor of social psychology. They collaborated with co-first author Uri Lifshin at Reichman University in Israel and co-authors Jeff Greenberg and Dylan Horner at the University of Arizona in Tucson. The researchers theorized that belief in evolution would tend to increase people’s identification with all humanity, due to the common ancestry, and would lead to less prejudicial attitudes.

“People who perceive themselves as more similar to animals are also people who tend to have more pro-social or positive attitudes toward outgroup members or people from stigmatized and marginalized backgrounds,” Syropoulos explains. “In this investigation, we were interested in examining whether belief in evolution would also act in a similar way, because it would reinforce this belief that we are more similar to animals.”

In eight studies involving different areas of the world, the researchers analyzed data from the American General Social Survey (GSS), the Pew Research Center and three online crowdsourced samples. In testing their hypothesis about the associations of different levels of belief in evolution, they accounted for education, political ideology, religiosity, cultural identity and scientific knowledge.

“We found the same results each time, which is basically that believing in evolution relates to less prejudice, regardless of the group you’re in, and controlling for all of these alternative explanations,” Syropoulos says.

For example, religious beliefs, like political ideology, were measured separately from a belief or disbelief in evolution, the researchers note. “Regardless of whether one considers religion an important part of their life, belief in evolution relates to less prejudice independently from belief, or lack thereof, in God or any particular religion,” Syropoulos says.

Leidner adds, “This whole effect and pattern seems to be present in all major political systems. It’s very much a human phenomenon, no matter where you are in the world.”

The researchers note that Darwin’s 19^th century theory of evolution has been cited to perpetrate racism, prejudice and homophobia, in part through the phrase, “survival of the fittest,” used to describe the process of natural selection.

“There have been theoretical accounts that predict the opposite of what we found, so it was exciting for us to show that this actually is not the case, that the opposite is true and that belief in evolution seems to have pretty positive effects,” Leidner says.

The U.S.-based study involved data from 1993, 1994, 2000, 2006, 2008, 2010, 2012, 2014, 2016 and 2018 — the years the GSS surveyed Americans about their beliefs in evolution, as well as measures of attitudes toward immigrants, Blacks, affirmative action, LGBTQ people and other social matters.

The data analysis showed unfailingly “that the disbelief in human evolution is the driving factor and most consistent predictor of prejudice in comparison to other relevant constructs,” the paper states.

In the Israel-based study, people with a higher belief in evolution were more likely to support peace among Palestinians, Arabs and Jews. In the study involving countries in the Islamic world, belief in evolution was associated with less prejudice toward Christians and Jews. And in the study based in Eastern Europe, where Orthodox Christians are the majority, a belief in evolution was linked with less prejudice toward gypsies, Jews and Muslims.

Syropoulos posits that a belief in evolution may expand people’s “moral circle,” leading to a sense that “we have more in common than things that are different.”

The findings also suggest that “teaching evolution seems to have side effects that might make for a better or more harmonious society,” Leidner adds.

The next step, the researchers say, is to investigate how evolution is taught in the classroom and work toward developing models to study and strengthen the positive effects.

“These findings reveal a core principle of the nervous system, flexibility in the kinds of calculations the brain makes to represent aspects of the sensory world,” said Krishnan Padmanabhan, Ph.D., an associate professor of Neuroscience and senior author of the study recently published in Cell Reports. “Our work provides scientists with new tools to quantify and interpret the patterns of activity of the brain.”

Researchers developed a model to simulate the workings of the early olfactory system — the network the brain relies on for smelling. Employing computer simulations, they found a specific set of connections, called centrifugal fibers, which carry impulses from other parts of the central nervous system to the early sensory regions of the brain, played a critical role. These centrifugal fibers act as a switch, toggling between different strategies to efficiently represent smells. When the centrifugal fibers were in one state, the cells in the piriform cortex — where the perception of an odor forms — relied on the pattern of activity within a given instant in time. When the centrifugal fibers were in the other state, the cells in the piriform cortex improved both the accuracy and the speed with which cells detected and classified the smell by relying on the patterns of brain activity across time.

These processes suggest the brain has multiple responses to representing a smell. In one strategy, the brain uses a snapshot, like a painting or a photograph, at a given moment to capture the essential features of the odor. In the other strategy, the brain keeps track of the evolving patterns. It is attuned to which cells turn on and off and when — like a symphony.

The mathematical models the researchers developed highlight the critical feature of the nervous system — not only diversity in terms of the components that make up the brain but also how these components work together to help the brain experience the world of smell. “These mathematical models reveal critical aspects of how the olfactory system in the brain might work and could help build brain-inspired artificial computing systems,” Padmanabhan said. “Computational approaches inspired by the circuits of the brain such as this have the potential to improve the safety of self-driving cars, or help computer vision algorithms more accurately identify and classify objects in an image.”

Additional authors include Zhen Chen of the University of Rochester. The research was funded by the National Institutes of Health, the National Science Foundation, the Cystinosis Research Foundation, and the Del Monte Institute for Neuroscience at the University of Rochester Pilot Program.

Story Source:

Materials provided by University of Rochester Medical Center. Original written by Kelsie Smith Hayduk. Note: Content may be edited for style and length.