Various studies show that people of color are disproportionately exposed to air pollution in the United States. However, it was unclear whether this unequal exposure is due mainly to a few types of emission sources or whether the causes are more systemic. A new study that models peoples’ exposure to air pollution — resolved by race-ethnicity and income level — shows that exposure disparities among people of color and white people are driven by nearly all, rather than only a few, emission source types.
The study led by University of Illinois Urbana Champaign civil and environmental engineering professor Christopher Tessum is published in the journal Science Advances.
“Community organizations have been experiencing and advocating against environmental injustice for decades,” Tessum said. “Our study contributes to an already extensive body of evidence with the new finding that there is no single air pollution source, or a small number of sources, that account for this disparity. Instead, the disparity is caused by almost all of the sources.”
The team used an air quality model to analyze Environmental Protection Agency data for more than 5,000 emission source types, including industry, agriculture, coal electric utilities, light- and heavy-duty gasoline vehicles, diesel vehicles, off-road vehicles and equipment, construction, residential sources, road dust and other miscellaneous small emissions sources. Each source type studied contributes to fine particle air pollution, defined as particles being 2.5 micrometers or less in diameter, the study reports.
To identify patterns of air pollution exposure associated with race-ethnicity and income, the researchers combined the spatial air pollution patterns predicted in their air quality model with residential population counts from the U.S. Census Bureau to identify differences in exposure by race-ethnicity and income.
The researchers found that for the 2014 U.S. total population average, fine particle air pollution exposures from the majority of source types are higher than average for people of color and lower than average for white people. The data indicate that white people are exposed to lower-than-average concentrations from emissions source types that, when combined, cause 60% of their total exposure, the study reports. Conversely, people of color experience greater-than-average exposures from source types that, when combined, cause 75% of their total exposure. This disparity exists at the country, state and city level and for people within all income levels.

Harmful exposures during pregnancy, including some that occur even before pregnancy is recognized, appear to significantly increase a child’s risk for psychiatric or behavioral problems early in life, researchers from Massachusetts General Hospital report.
In their study of 9,290 children from the ages of 9 to 10 living in 21 communities in the United States, the researchers found that children subjected during pregnancy to two or more of six adverse exposures were significantly more likely to have clinically significant scores on the Child Behavior Checklist (CBCL), indicating a higher level of problems such as depression, attention difficulties or anxiety.
The adverse prenatal exposures are unplanned pregnancy; maternal use of alcohol, tobacco or marijuana before pregnancy was recognized; complications during pregnancy (such as high blood pressure or gestational diabetes); and complications during labor and delivery. Pre-term birth or Caesarean delivery were not associated with increased risk.
“While individually these factors had previously been associated with similar risks in prior, often smaller studies, this is the first time that we were able to gauge the effect of cumulative exposures, which were fairly dramatic,” says lead author Joshua L. Roffman, MD, MMSc, director of the Mass General Early Brain Development Initiative.
For example, while children with none of the exposures during their mother’s pregnancy had only a 7% chance of developing clinically significant psychiatric symptoms, this risk increased steeply and in a linear fashion, such that those with four or more of the exposures had a 29% chance of clinically significant symptoms.
As the researchers report in their study, published in the open-access journal PLOS ONE, they saw similar patterns across a range of specific symptoms, from mood and anxiety to attention and thought disturbances.
The associations between prenatal exposures and psychiatric symptoms in childhood held up even when the researchers accounted for other factors that might skew the results, such as the mother’s socioeconomic status, or exposures after birth that are known to increase a child’s risk for psychiatric disorders, such as a traumatic life event.
To validate their findings, Roffman and colleagues also tested them in a separate group of non-twin siblings who differed in their exposures during pregnancy, and here too the data showed that the sibling with the higher number of exposures was at greater risk for more severe symptoms.
A limitation of the results is that they are based on the mother’s recall of events during pregnancy, although the frequency of adverse events closely followed national trends. The study did not measure the effects of maternal infections or stress during pregnancy, although each of these has also been associated with increased risk in previous studies.
Given the additive effects of the common exposures they studied, Roffman and colleagues speculate that the “floor” of risk for psychiatric symptoms may have been raised for children born during the COVID-19 pandemic.
“Our findings reinforce the importance of the prenatal environment for brain health and for reducing risk of psychiatric symptoms in childhood. This brings increased urgency to the need to discover, develop and implement early life interventions that mitigate some of these risks,” says Roffman, who is also an associate professor of Psychiatry at Harvard Medical School (HMS).
Improving child brain development and psychiatric health is the main goal of the Mass General Early Brain Development Initiative, a multidisciplinary collaboration among staff in the Departments of Psychiatry, Obstetrics, Pediatrics and Medicine.

Scientists are developing a completely new ‘brain stress test’ for evaluating the mental status of patients with Parkinson’s disease, the second most prevalent neurodegenerative disease worldwide. It involves awakening the ‘ghosts’ hidden in specific networks of the brain to predict the onset of hallucinations.
“We’re developing something similar to a cardiac stress test, but instead of testing the heart, we’re testing the brain,” says EPFL neuroscientist Olaf Blanke.
EPFL scientists are providing a new way to evaluate the onset of hallucinations in patients with Parkinson’s disease with the help of a newly developed brain stress test, focusing their research on presence hallucinations. They also provide evidence of a promising biomarker for predicting the severity of the disease’s progression. The results of their three-tier study, involving 56 patients affected by the disease and recruited at several centers in Switzerland and Spain, are published in today’s edition of Science Translational Medicine (STM).
“An important challenge with hallucinations is that they occur spontaneously, that their occurrence cannot be predicted, that many patients may not openly report them, possibly out of fear, and that it is currently very challenging for physicians to quantify their occurrence, phenomenology and intensity,” continues Fosco Bernasconi, co-first author of the paper. “We have established a robotic-medical device and procedure that can provoke a specific hallucination, the presence hallucination, safely and under controlled conditions in a hospital setting.”
A spectrum of hallucinations in Parkinson’s
Parkinson’s disease is well-known for leading to slowness of movement, muscle stiffness, as well as uncontrollable shaking of the limbs. But alterations of movement are far from being the only symptom of the disease.

The Pfizer-BioNTech and Moderna coronavirus vaccines are 94 percent effective at preventing hospitalization in fully vaccinated adults 65 or older, according to a small study released by the Centers for Disease Control and Prevention on Wednesday.The findings, which are consistent with the clinical trial results, are the first real-world evidence from the United States that the vaccines protect against severe Covid-19. Older adults are at the highest risk of hospitalization and death from the disease. More than 573,000 people have died across the country related to the virus, according to a New York Times database, and as of Tuesday, 141.8 million people have received at least one dose of one of three federally authorized vaccines, including about 96.7 million people who have been fully vaccinated.“These findings are encouraging and welcome news for the two-thirds of people aged 65 and up who are already fully vaccinated,” Dr. Rochelle Walensky, the C.D.C. director, said in a statement. “Covid-19 vaccines are highly effective and these real-world findings confirm the benefits seen in clinical trials, preventing hospitalizations among those most vulnerable.”The study is based on data from 417 patients who were admitted to 24 hospitals in 14 states between January 1 and March 26. Approximately half were 75 or older.Both the Pfizer and Moderna vaccines require two shots, spaced three to four weeks apart. Older adults who were partially vaccinated — that is, they had received one dose of the vaccine more than two weeks prior — were 64 percent less likely to be hospitalized with the coronavirus than unvaccinated seniors, the researchers reported.The vaccines did not reduce hospitalization rates in people who had received their first dose less than two weeks prior. The body requires time to mount an effective immune response, and people are considered fully vaccinated two weeks after the last dose in the series.“This also highlights the continued risk for severe illness shortly after vaccination, before a protective immune response has been achieved and reinforces the need for vaccinated adults to continue physical distancing and prevention behaviors,” the scientists wrote.

A higher ratio of green spaces at the county level is associated with a lower racial disparity in coronavirus infection rates, according to a new study. It is the first study to report the significant relationship between the supply of green spaces and reduced disparity in infectious disease rates.
The research team included William Sullivan, a landscape architecture professor at the University of Illinois Urbana-Champaign, and was led by Bin Jiang, a landscape architecture professor at The University of Hong Kong who received his Ph.D. at Illinois, and Yi Lu, an architecture professor at City University of Hong Kong. They reported their findings in the journal Environment International.
Previous studies by Sullivan, Jiang and Lu have shown that green spaces have positive effects on health. Access to green spaces is associated with improved cognitive performance, reduced mental fatigue and stress, reduced impulsiveness and aggressiveness, increased sense of safety, reduced crime rate, increased physical activity and increased social cohesion.
Prior studies also provide strong evidence that green spaces may mitigate racial disparities in health outcomes. However, none have looked at the effect on disparities in infectious diseases. Most studies examining the racial disparity in coronavirus infections have focused on its association with socio-economic status or pre-existing chronic disease factors.
For this study, the researchers identified 135 of the most urbanized counties in the U.S., with a total population of 132,350,027, representing 40.3% of the U.S. population. They collected infection data from county health departments from late January to July 10, 2020, and used it to calculate the infection rates for Black and white residents of the counties, while controlling for differences in income, pre-existing chronic diseases and urban density.
The data showed that the average infection rate for Black residents was more than twice that of white residents — 497 per 100,000 people for white individuals versus 988 per 100,000 people for Black individuals.

As of April 2021, about 2.8 million people worldwide have died of COVID-19. Early in the pandemic, researchers developed accurate diagnostic tests and identified health conditions that correlated with worse outcomes. However, a clinical predictor of who faces the highest risk of being hospitalized, put on a ventilator or dying from the disease has remained largely out of reach.
This week in mSphere, an open-access journal of the American Society for Microbiology, researchers describe a two-step prognostic test that can help predict a patient’s response to infection with SARS-CoV-2. The test combines a disease risk factor score with a test for antibodies produced early in the infection. It could be administered at the time of diagnosis to help guide therapeutic choices before the most severe symptoms appear, the researchers said.
“You can predict with really high sensitivity that someone is going to have a severe case of COVID-19,” said Emily Sanders, a graduate student in the lab of chemical biologist Gregory Weiss, Ph.D, at the University of California, Irvine. Sanders led the study together with Sanjana Sen and Kristin Gabriel, also graduate students in Weiss’s lab.
Most diagnostic tests search for antibodies associated with interrupting the virus. Weiss said the group initially set out to develop their own diagnostic, following this same strategy, but quickly realized that plenty of competing tests would soon be available. Instead, they pivoted to focus on other, unstudied antibodies — ones that wouldn’t necessarily disrupt the virus or help the immune system fight the infection.
“Everyone else was looking for the good antibodies that neutralize the virus,” said Weiss, senior author on the study. “We weren’t seeing enough about antibodies that are unhelpful.”
“Being able to identify a ‘bad’ antibody response helps to fill in a research gap,” said Gabriel.
Previous studies have predicted that the SARS-CoV-2 particle has more than 55 epitopes, or sites on the virus where antibodies can attach. The best studied epitopes to date are those found on the S, or spike, protein, but the virus has three other structural proteins, each with epitopes worth investigating, said Sen. Using ELISAs, or enzyme-linked immunosorbent assays, the researchers compiled a list of antibodies that might correlate with worse prognosis and ultimately focused on one that attaches to epitope 9 on the N, or nucleocapsid, protein. The group also developed a tool that used data on factors including age, sex, and pre-existing health conditions to produce a disease risk factor score (DRFS).
They tested their tool on a group of 86 people who had tested positive for the coronavirus. Patients whose tests revealed the presence of epitope 9 antibodies were more likely to have prolonged illness and worse outcomes than people without the antibodies. Of the 23 people in the study who did have the antibodies associated with epitope 9, a high DRFS predicted disease severity with more than 92% sensitivity.
The test uses technology and tools readily available in testing labs, Weiss said. “Detecting antibodies is super easy to do,” he said, and an inexpensive prognostic test could inform treatment decisions early in the disease progression. The researchers found that the epitope-9 antibodies become detectable between 1 and 6 days after the onset of symptoms.
“We’ve all had friends and family impacted by this disease,” said Weiss, “and we wanted to do something that might be useful, that might help the physicians who are being overwhelmed.”
Story Source:
Materials provided by American Society for Microbiology. Note: Content may be edited for style and length.

Tiny molecules called nanobodies, which can be designed to mimic antibody structures and functions, may be the key to blocking a tick-borne bacterial infection that remains out of reach of almost all antibiotics, new research suggests.
The infection is called human monocytic ehrlichiosis, and is one of the most prevalent and potentially life-threatening tick-borne diseases in the United States. The disease initially causes flu-like symptoms common to many illnesses, and in rare cases can be fatal if left untreated.
Most antibiotics can’t build up in high enough concentrations to kill the infection-causing bacteria, Ehrlichia chaffeensis, because the microbes live in and multiply inside human immune cells. Commonly known bacterial pathogens like Streptococcus and E. coli do their infectious damage outside of hosts’ cells.
Ohio State University researchers created nanobodies intended to target a protein that makes E. chaffeensis bacteria particularly infectious. A series of experiments in cell cultures and mice showed that one specific nanobody they created in the lab could inhibit infection by blocking three ways the protein enables the bacteria to hijack immune cells.
“If multiple mechanisms are blocked, that’s better than just stopping one function, and it gives us more confidence that these nanobodies will really work,” said study lead author Yasuko Rikihisa, professor of veterinary biosciences at Ohio State.
The study provided support for the feasibility of nanobody-based ehrlichiosis treatment, but much more research is needed before a treatment would be available for humans. There is a certain urgency to coming up with an alternative to the antibiotic doxycycline, the only treatment available. The broad-spectrum antibiotic is unsafe for pregnant women and children, and it can cause severe side effects.

Analysis of data from a lightning mapper and a small, hand-held radiation detector has unexpectedly shed light on what a gamma-ray burst from lightning might look like — by observing neutrons generated from soil by very large cosmic-ray showers. The work took place at the High Altitude Water Cherenkov (HAWC) Cosmic Ray Observatory in Mexico.
“This was an accidental discovery,” said Greg Bowers, a scientist at Los Alamos National Laboratory and lead author of the study published in Geophysical Research Letters. “We set up this system to study terrestrial gamma-ray flashes — or gamma-ray bursts from lightning — that are typically so bright you can see them from space. The idea was that HAWC would be sensitive to the gamma-ray bursts, so we installed a lightning mapper to capture the anatomy of the lightning development and pinpoint the lightning processes producing them.”
The team, including Xuan-Min Shao and Brenda Dingus also from Los Alamos, used a small, handheld particle detector, expecting that a terrestrial gamma-ray flash would generate a clear gamma-ray signal in the small particle detector.
“Our system ran for almost two years, and we saw a lot of lightning,” said Bowers. But during those storms, they did not observe anything that looked like terrestrial gamma-ray flashes. “We did, however, see large count-rate bursts during clear, fair-weather days, which made us scratch our heads.”
HAWC data gathered during these times showed that, in every case, the large array that comprises HAWC had been overwhelmed by extremely large cosmic-ray showers — so large that the Los Alamos researchers couldn’t estimate their size.
UC Santa Cruz collaborator David Smith found that these fair-weather bursts had previously been observed by scientists in Russia, who called them “neutron bursts,” and determined that they were the result of neutron production in the soil around the impact point of cosmic ray shower cores.
Previous work that simulated these events had only considered hadrons — a type of subatomic particle — in the core of the showers. In addition to hadrons and other particles, cosmic-ray shower cores also contain a lot of gamma rays.
For this work, William Blaine, also of Los Alamos, simulated large cosmic ray-showers, and included both hadrons and gamma rays. “We were able to match our observations with the simulations,” said Bowers. “We found that the gamma rays produce the same type of neutron burst as the hadrons.”
This study suggests that any natural phenomena that produces a beam of gamma-rays pointed towards the ground (such as downward terrestrial gamma-ray flashes), could produce a similar “neutron burst” signature. This is significant for future terrestrial gamma-ray flash observation modeling efforts.
“It tell us that you can’t just model the gamma rays hitting your detector, you’ll also have to consider the neutron burst that’s happening nearby,” said Bowers.
The HAWC Observatory comprises an array of water-filled tanks high on the flanks of the Sierra Negra volcano in Puebla, Mexico, where the thin atmosphere offers better conditions for observing gamma rays. When gamma rays strike molecules in the atmosphere they produce showers of energetic particles. When some of those particles strike the water inside the HAWC detector tanks, they produce flashes of light called Cherenkov radiation. By studying these Cherenkov flashes, researchers reconstruct the sources of the showers to learn about the particles that caused them.
Story Source:
Materials provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length.

College students across the country struggle with a vicious cycle: Test anxiety triggers poor sleep, which in turn reduces performance on the tests that caused the anxiety in the first place.
New research from the University of Kansas just published in the International Journal of Behavioral Medicine is shedding light on this biopsychosocial process that can lead to poor grades, withdrawal from classes and even students who drop out. Indeed, about 40% of freshman don’t return to their universities for a second year in the United States.
“We were interested in finding out what predicted students’ performance in statistics classes — stats classes are usually the most dreaded undergrad class,” said lead author Nancy Hamilton, professor of psychology at KU. “It can be a particular problem that can be a sticking point for a lot of students. I’m interested in sleep, and sleep and anxiety are related. So, we wanted to find out what the relationship was between sleep, anxiety and test performance to find the correlation and how it unfolds over time.”
Hamilton and graduate student co-authors Ronald Freche and Ian Carroll and undergraduates Yichi Zhang and Gabriella Zeller surveyed the sleep quality, anxiety levels and test scores for 167 students enrolled in a statistics class at KU. Participants completed an electronic battery of measures and filled out Sleep Mood Study Diaries during the mornings in the days before a statistics exam. Instructors confirmed exam scores. The study showed “sleep and anxiety feed one another” and can hurt academic performance predictably.
“We looked at test anxiety to determine whether that did predict who passed, and it was a predictor,” Hamilton said. “It was a predictor even after controlling for students’ past performance and increased the likelihood of students failing in class. When you look at students who are especially anxious, it was almost a five-point difference in their score over students who had average levels of anxiety. This is not small potatoes. It’s the difference between a C-minus abd a D. It’s the difference between a B-plus and an A-minus. It’s real.”
Beyond falling grades, a student’s overall health could suffer when test anxiety and poor sleep reinforce each other.

The highly infectious SARS-CoV-2 variant that recently emerged in South Africa, known as B.1.351, has scientists wondering how existing COVID-19 vaccines and therapies can be improved to ensure strong protection. Now, researchers reporting in ACS’ Journal of Medicinal Chemistry have used computer modeling to reveal that one of the three mutations that make variant B.1.351 different from the original SARS-CoV-2 reduces the virus’ binding to human cells — but potentially allows it to escape some antibodies.
Since the original SARS-CoV-2 was first detected in late 2019, several new variants have emerged, including ones from the U.K., South Africa and Brazil. Because the new variants appear to be more highly transmissible, and thus spread rapidly, many people are worried that they could undermine current vaccines, antibody therapies or natural immunity. Variant B.1.351 bears two mutations (N501Y and E484K) that can enhance binding between the receptor binding domain (RBD) of the coronavirus spike protein and the human ACE2 receptor. However, the third mutation (K417N; a lysine to asparagine mutation at position 417) is puzzling because it eradicates a favorable interaction between the RBD and ACE2. Therefore, Binquan Luan and Tien Huynh from IBM Research wanted to investigate potential benefits of the K417N mutation that could have caused the coronavirus to evolve along this path.
The researchers used molecular dynamics simulations to analyze the consequences of the K417N mutation in variant B.1.351. First, they modeled binding between the original SARS-CoV-2 RBD and ACE2, and between the RBD and CB6, which is a SARS-CoV-2-neutralizing antibody isolated from a recovered COVID-19 patient. They found that the original amino acid, a lysine, at position 417 in the RBD interacted more strongly with CB6 than with ACE2, consistent with the antibody’s therapeutic efficacy in animal models. Then, the team modeled binding with the K417N variant, which changes that lysine to an asparagine. Although this mutation reduced the strength of binding between the RBD and ACE2, it decreased the RBD’s binding to CB6 and several other human antibodies to a much greater extent. Thus, variant B.1.351 appears to have sacrificed tight binding to ACE2 at this site for the ability to evade the immune system. This information could prove useful to scientists as they work to enhance the protection of current vaccines and therapies, the researchers say.
Story Source:
Materials provided by American Chemical Society. Note: Content may be edited for style and length.