“Previous research has shown face mask wearing can reduce the risk of spreading and contracting the disease. So, we wanted to create a mask that can detect the presence of virus in the air and alert the wearer,” says Yin Fang, the study’s corresponding author and a material scientist at Shanghai Tongji University.

Respiratory pathogens that cause COVID-19 and H1N1 influenza spread through small droplets and aerosols released by infected people when they talk, cough, and sneeze. These virus-containing molecules, especially tiny aerosols, can remain suspended in the air for a long time.

Fang and his colleagues tested the mask in an enclosed chamber by spraying the viral surface protein containing trace-level liquid and aerosols on the mask. The sensor responded to as little as 0.3 microliters of liquid containing viral proteins, about 70 to 560 times less than the volume of liquid produced in one sneeze and much less than the volume produced by coughing or talking, Fang says.

The team designed a small sensor with aptamers, which are a type of synthetic molecule that can identify unique proteins of pathogens like antibodies. In their proof-of-concept design, the team modified the multi-channel sensor with three types of aptamers, which can simultaneously recognize surface proteins on SARS-CoV-2, H5N1, and H1N1.

Once the aptamers bind to the target proteins in the air, the ion-gated transistor connected will amplify the signal and alert the wearers via their phones. An ion-gated transistor is a novel type of device that is highly sensitive, and thus the mask can detect even trace levels of pathogens in the air within 10 minutes.

“Our mask would work really well in spaces with poor ventilation, such as elevators or enclosed rooms, where the risk of getting infected is high,” Fang says. In the future, if a new respiratory virus emerges, they can easily update the sensor’s design for detecting the novel pathogens, he adds.

Next, the team hopes to shorten the detection time and further increase the sensitivity of the sensor by optimizing the design of the polymers and transistors. They are also working on wearable devices for a variety of health conditions including cancers and cardiovascular diseases.

“Currently, doctors have been relying heavily on their experiences in diagnosing and treating diseases. But with richer data collected by wearable devices, disease diagnosis and treatment can become more precise,” Fang says.

The work is supported by National Key Research and Development Program, National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Shanghai Municipal Science and Technology Major Project and the Fundamental Research Funds for the Central Universities.

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A new study by Colorado State University civil engineers examines the hidden costs to building foundations due to sea level rise. They propose a method for inspection and repair to lower the cost associated with deterioration from saltwater corrosion.

The researchers, who are part of the NIST Center for Risk-Based Community Resilience Planning at CSU, say it is important to plan ahead — especially considering there are more than 16 million buildings along the U.S. Atlantic and Gulf coasts.

“This is a problem, and it’s going to cost a lot of money,” said Hussam Mahmoud, a professor in the Department of Civil and Environmental Engineering and co-author on the study.

One at-risk location

Engineers looked at nearly 137,000 residential buildings in low-lying regions in Mobile County, Alabama, and estimated probable foundation damage from saltwater corrosion.

As sea level rises, so does the groundwater table, and saltwater is more corrosive than freshwater. Using existing predictions for sea level rise and water table levels based on wells in Mobile, they predicted how long it would take for saltwater to reach the buildings’ foundations.

Under the most extreme sea level rise projections, they estimated the annual repair cost for foundations in Mobile could total $90 million by 2100.

“The significance here is that this value that we found for one of the saltwater intrusion scenarios is just for 137,000 buildings in Mobile,” Mahmoud said. “If you calculated the cost for the entire East Coast — or from the East to the West Coast — it would be staggering.”

Providing a solution

The engineers developed a formula to help building owners determine when to inspect and repair their buildings based on building location and the probability of saltwater corrosion. They say waiting for the right time to repair the foundation — but not so long that damage has been done — will save building owners money.

They also proposed the use of low-cost sensors to monitor the water table level near a building’s foundation and protect against uncertainties.

The researchers chose to study Mobile because it is an industrial port city with a large economically disadvantaged population. They have started investigating the impact of natural disasters on Mobile’s vulnerable communities to figure out the best way to allocate resources to help them recover in an emergency.

“In order for us to assess social and economic impact, we have to have a good understanding of the impact of the hazard on the built environment,” Mahmoud said.

Next, the team plans to examine structural performance beyond the foundation and how that affects people in the area. For example, how much wind and water surge during a hurricane can a structure withstand before alternative housing or shelters are needed?

“This work will allow us to be proactive about finding solutions quickly to help those in need, instead of waiting until it’s bad,” Mahmoud said.

Helping communities recover from natural disasters is the focus of the CSU-led Center for Risk-Based Community Resilience Planning, a $20 million partnership with the National Institute of Standards and Technology that involves collaborators from more than a dozen other universities.

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Now, researchers at Stevens Institute of Technology have developed an algorithm that can accurately track patients’ level of consciousness based on simple physiological markers that are already routinely monitored in hospital settings. Though still in its early stages, the team’s work — published in the Sept. 15 issue of Neurocritical Care — promises to significantly ease the strain on medical staff, and could also provide vital new data to guide clinical decisions and enable the development of new treatments.

“Consciousness isn’t a light switch that’s either on or off — it’s more like a dimmer switch, with degrees of consciousness that change over the course of the day,” said Samantha Kleinberg, an associate professor in Stevens’ department of Computer Science. “If you only check patients once per day, you just get one data point. With our algorithm, you could track consciousness continuously, giving you a far clearer picture.”

To develop their algorithm, Kleinberg and her Ph.D. student Louis A. Gomez partnered with Jan Claassen, director of Critical Care Neurology at Columbia University, to collect data from a range of ICU sensors — from simple heart rate monitors up to sophisticated devices that measure brain temperature — and used it to forecast the results of a clinician’s assessment of a patient’s level of consciousness. The results were startling: using only the simplest physiological data, the algorithm proved as accurate as a trained clinical examiner, and only slightly less accurate than tests conducted with expensive imaging equipment such as fMRI machines.

“That’s hugely important, because it means this tool could potentially be deployed in virtually any hospital setting — not just neurological ICUs where they have more sophisticated technology,” Kleinberg explained. The algorithm could be installed as a simple software module on existing bedside patient-monitoring systems, she noted, making it relatively cheap and easy to roll out at scale.

Besides giving doctors better clinical information, and patients’ families a clearer idea of their loved ones’ prognosis, continuous monitoring could help to drive new research and ultimately improve patient outcomes.

“Consciousness is incredibly hard to study, and part of the reason is that there simply isn’t much data to work with,” said Kleinberg. “Having round-the-clock data showing how patients’ consciousness changes could one day make it possible to treat these patients far more effectively.”

More work will be needed before the team’s algorithm can be rolled out in clinical settings. The team’s algorithm was trained based on data collected immediately prior to a clinician’s assessment, and further development will be needed to show that it can accurately track consciousness around the clock. Additional data will also be required to train the algorithm for use in other clinical settings such as pediatric ICUs.

Kleinberg also hopes to improve the algorithm’s accuracy by cross-referencing different kinds of physiological data, and studying the way they coincide or lag one another over time. Some such relationships are known to correlate with consciousness, potentially making it possible to validate the algorithm’s consciousness ratings during periods when assessments by human clinicians aren’t available.

For now, though, the Stevens’ team is thrilled to have found a simple, broadly applicable model for automatically assessing patient consciousness in clinical settings. “It was a high-risk, high-reward project,” Kleinberg said. “It was extremely exciting to find we could use these signals to classify patients’ levels of consciousness.”

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Biologists at the University of Cincinnati found that it’s not just the size of its head and body that puts almost everything on a python’s menu. They evolved super-stretchy skin between their lower jaws that allows them to consume prey up to six times larger than similar-sized snakes.

The study, funded in part by a grant from the National Science Foundation, was published in the journal Integrative Organismal Biology.

Since most snakes swallow prey whole, they must have wide mouths to accommodate a meal. Unlike our lower jawbone, the lower jawbones of snakes are not connected, allowing them to open wide.

“The stretchy skin between left and right lower jaws is radically different in pythons. Just over 40% of their total gape area on average is from stretchy skin,” lead author and UC biology professor Bruce Jayne said. “Even after you correct for their large heads, their gape is enormous.”

Pythons are constrictors. They bite their prey and wrap their powerful coils around it, fatally cutting off the animal’s vital blood flow, before consuming it whole at their leisure.

The bigger the prey, the more energy a snake derives from a meal. For pythons, that means not having to hunt as often, which can carry extensive risk in a world full of busy roads and dangerous predators.

Along with pythons, Jayne studied the gape size of brown tree snakes, a mildly venomous arboreal specialist that hunts birds and other animals in the forest canopy. Brown tree snakes were introduced in the 1950s to Guam, wiping out many bird species.

Besides measuring the snakes, Jayne also measured the dimensions and weight of potential prey animals. This allowed Jayne to use snake size to predict the maximal size of its prey and the relative benefits of consuming different types such as alligators, chickens, rats or deer.

Small snakes derive greater benefits in relative prey mass from a modest increase in gape size, the study found. This gives python babies an early advantage in taking on a broader range of prey compared to other snakes their size, Jayne said.

Being big also helps snakes avoid becoming meals themselves. Snakes fall prey to everything from wading birds to minks and raccoons to alligators and other snakes.

“Once those pythons get to a reasonable size, it’s pretty much just alligators that can eat them,” Jayne said. “And pythons eat alligators.”

Like invasive brown tree snakes in Guam, Burmese pythons are wreaking havoc on the ecology of Everglades National Park where they were introduced due to the release of captive animals from the exotic pet trade in the 1980s.

Study co-author Ian Bartoszek works as an environmental science project manager for the Conservancy of Southwest Florida, where he has led a project to track pythons. They implant radio transmitters in male snakes during breeding season to find females before they can lay more clutches of eggs. A big female python can lay more than 100 eggs.

Researchers routinely find deer hooves and the remains of other big animals in their stomachs. Bartoszek photographed one python regurgitating a full-grown white-tailed deer.

“The Everglades ecosystem is changing in real time based on one species, the Burmese python,” Bartoszek said.

The good news is that pythons rarely attack people. Bartoszek said the only defensive encounters he’s had with wild pythons are with females guarding their nests.

“It’s way more dangerous to drive there than to work with the snakes,” he said.

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“We picked the Jezero Crater for Perseverance to explore because we thought it had the best chance of providing scientifically excellent samples — and now we know we sent the rover to the right location,” said Thomas Zurbuchen, NASA’s associate administrator for science in Washington. “These first two science campaigns have yielded an amazing diversity of samples to bring back to Earth by the Mars Sample Return campaign.”

Twenty-eight miles (45 kilometers) wide, Jezero Crater hosts a delta — an ancient fan-shaped feature that formed about 3.5 billion years ago at the convergence of a Martian river and a lake. Perseverance is currently investigating the delta’s sedimentary rocks, formed when particles of various sizes settled in the once-watery environment. During its first science campaign, the rover explored the crater’s floor, finding igneous rock, which forms deep underground from magma or during volcanic activity at the surface.

“The delta, with its diverse sedimentary rocks, contrasts beautifully with the igneous rocks — formed from crystallization of magma — discovered on the crater floor,” said Perseverance project scientist Ken Farley of Caltech in Pasadena, California. “This juxtaposition provides us with a rich understanding of the geologic history after the crater formed and a diverse sample suite. For example, we found a sandstone that carries grains and rock fragments created far from Jezero Crater — and a mudstone that includes intriguing organic compounds.”

“Wildcat Ridge” is the name given to a rock about 3 feet (1 meter) wide that likely formed billions of years ago as mud and fine sand settled in an evaporating saltwater lake. On July 20, the rover abraded some of the surface of Wildcat Ridge so it could analyze the area with the instrument called Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals, or SHERLOC.

SHERLOC’s analysis indicates the samples feature a class of organic molecules that are spatially correlated with those of sulfate minerals. Sulfate minerals found in layers of sedimentary rock can yield significant information about the aqueous environments in which they formed.

What Is Organic Matter?

Organic molecules consist of a wide variety of compounds made primarily of carbon and usually include hydrogen and oxygen atoms. They can also contain other elements, such as nitrogen, phosphorus, and sulfur. While there are chemical processes that produce these molecules that don’t require life, some of these compounds are the chemical building blocks of life. The presence of these specific molecules is considered to be a potential biosignature — a substance or structure that could be evidence of past life but may also have been produced without the presence of life.

In 2013, NASA’s Curiosity Mars rover found evidence of organic matter in rock-powder samples, and Perseverance has detected organics in Jezero Crater before. But unlike that previous discovery, this latest detection was made in an area where, in the distant past, sediment and salts were deposited into a lake under conditions in which life could potentially have existed. In its analysis of Wildcat Ridge, the SHERLOC instrument registered the most abundant organic detections on the mission to date.

“In the distant past, the sand, mud, and salts that now make up the Wildcat Ridge sample were deposited under conditions where life could potentially have thrived,” said Farley. “The fact the organic matter was found in such a sedimentary rock — known for preserving fossils of ancient life here on Earth — is important. However, as capable as our instruments aboard Perseverance are, further conclusions regarding what is contained in the Wildcat Ridge sample will have to wait until it’s returned to Earth for in-depth study as part of the agency’s Mars Sample Return campaign.”

The first step in the NASA-ESA (European Space Agency) Mars Sample Return campaign began when Perseverance cored its first rock sample in September 2021. Along with its rock-core samples, the rover has collected one atmospheric sample and two witness tubes, all of which are stored in the rover’s belly.

The geologic diversity of the samples already carried in the rover is so good that the rover team is looking into depositing select tubes near the base of the delta in about two months. After depositing the cache, the rover will continue its delta explorations.

“I’ve studied Martian habitability and geology for much of my career and know first-hand the incredible scientific value of returning a carefully collected set of Mars rocks to Earth,” said Laurie Leshin, director of NASA’s Jet Propulsion Laboratory in Southern California. “That we are weeks from deploying Perseverance’s fascinating samples and mere years from bringing them to Earth so scientists can study them in exquisite detail is truly phenomenal. We will learn so much.”

More About the Mission

A key objective for Perseverance’s mission on Mars is astrobiology, including caching samples that may contain signs of ancient microbial life. The rover will characterize the planet’s geology and past climate, pave the way for human exploration of the Red Planet, and be the first mission to collect and cache Martian rock and regolith.

Subsequent NASA missions, in cooperation with ESA, would send spacecraft to Mars to collect these sealed samples from the surface and return them to Earth for in-depth analysis.

The Mars 2020 Perseverance mission is part of NASA’s Moon to Mars exploration approach, which includes Artemis missions to the Moon that will help prepare for human exploration of the Red Planet.

JPL, which is managed for NASA by Caltech, built and manages operations of the Perseverance rover.

For more about Perseverance: https://mars.nasa.gov/mars2020/

The default mode network (DMN), which comprises the posterior medial cortex, medial prefrontal cortex, and lateral temporal-parietal regions, has been shown to be engaged in several task-related behaviors. Studies show that DMN activity increases during inward-directed thought and decreases during externally directed tasks requiring focused attention. Despite evidence for a role for the DMN in shaping behavior, little is known about how task-related changes in the DMN influence connectivity with other brain regions. For example, while some observations indicate an indirect relationship between the DMN and the striatum, how the DMN and striatum interact during tasks remains unclear.

To further explore the functions of the DMN, Drs. Dobryakova and Smith applied a novel analysis to the reward task, using behavioral and neuroimaging data from 495 randomly selected individuals in the Human Connectome Project, an open access database of healthy participants. The goals of this network-based psycho-physiological interaction analysis were twofold, according to Dr. Dobryakova, a senior research scientist in the Foundation’s Center for Traumatic Brain Injury Research. “First, to test the effects of reward on connectivity between the DMN and the striatum; and second, whether such connectivity is associated with behavioral and personality characteristics relevant to reward processing,” she explained.

In line with other studies, during the task, they observed decreased activation of the DMN and relative increased activation of other networks. “Most notably, we found that the experience of reward enhanced connectivity between the DMN and the ventral striatum,” Dr. Dobryakova reported, “an effect specific to the DMN. We were also surprised that the strength of this connectivity correlated with personality characteristics relating to openness,” she added.

Greater understanding of the workings of the healthy brain will influence future research and care for individuals with neuropsychiatric syndromes. “Improving our understanding of the interaction of the DMN with other brain networks has the potential to aid clinical research into better treatments for common syndromes such as depression, substance abuse, and schizophrenia,” Dr. Dobryakova concluded.

This research was supported by grants from the National Institutes of Health grants R21-MH113917 (DVS), R03-DA046733 (DVS), RF1-AG067011 (DVS), R01-NS121107 (ED).

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Published in the Cell Press journal iScience, Deep learning models fail to capture the configural nature of human shape perception is a collaborative study by Elder, who holds the York Research Chair in Human and Computer Vision and is Co-Director of York’s Centre for AI & Society, and Assistant Psychology Professor Nicholas Baker at Loyola College in Chicago, a former VISTA postdoctoral fellow at York.

The study employed novel visual stimuli called “Frankensteins” to explore how the human brain and DCNNs process holistic, configural object properties.

“Frankensteins are simply objects that have been taken apart and put back together the wrong way around,” says Elder. “As a result, they have all the right local features, but in the wrong places.”

The investigators found that while the human visual system is confused by Frankensteins, DCNNs are not — revealing an insensitivity to configural object properties.

“Our results explain why deep AI models fail under certain conditions and point to the need to consider tasks beyond object recognition in order to understand visual processing in the brain,” Elder says. “These deep models tend to take ‘shortcuts’ when solving complex recognition tasks. While these shortcuts may work in many cases, they can be dangerous in some of the real-world AI applications we are currently working on with our industry and government partners,” Elder points out.

One such application is traffic video safety systems: “The objects in a busy traffic scene — the vehicles, bicycles and pedestrians — obstruct each other and arrive at the eye of a driver as a jumble of disconnected fragments,” explains Elder. “The brain needs to correctly group those fragments to identify the correct categories and locations of the objects. An AI system for traffic safety monitoring that is only able to perceive the fragments individually will fail at this task, potentially misunderstanding risks to vulnerable road users.”

According to the researchers, modifications to training and architecture aimed at making networks more brain-like did not lead to configural processing, and none of the networks were able to accurately predict trial-by-trial human object judgements. “We speculate that to match human configural sensitivity, networks must be trained to solve broader range of object tasks beyond category recognition,” notes Elder.

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The approach proved more than 90% accurate at predicting outcomes among more than two dozen patients in the intensive care unit at UVA and 100 patients from publicly available data generated at Duke and Harvard. The test, called CovGENE, analyzes genes expressed in a person’s blood to determine whether they may experience a severe disease course with increased risk of death.

“We have come far in the prevention and treatment of COVID-19 in the past two years. Regardless, we still struggle to identify patients at highest risk for severe disease. Our study uses a gene-analysis approach to identify an immune cell signature, distinct from other respiratory illnesses, that correlates with worse outcomes,” said researcher Alexandra Kadl, MD, of UVA Health’s Division of Pulmonary and Critical Care Medicine. “This knowledge has the potential to help evaluate patients’ immune profile with commonly, readily available assays to identify patients at risk for bad outcomes who would benefit from closer monitoring and advanced therapies to aid their recovery.”

Predicting COVID-19 Severity

Based on the promising results of the UVA research, CovGENE’s developer, AMPEL Biosolutions, is seeking to partner with a diagnostic testing company or pharmaceutical company to bring the approach to market as a simple PCR-based blood test.

“This unique collaboration with our colleagues from the University of Virginia has provided an easy and novel means to assess an individual patient’s response to the SARS-CoV-2 virus and predict the clinical outcome,” said Peter Lipsky, MD, AMPEL’s CEO, chief medical officer and co-founder. “Now that this unique approach has been validated, we look forward to its rapid development as a precision-medicine tool that can improve the outcome of patients with COVID-19 and reduce the number of hospitalizations, especially the most vulnerable.”

AMPEL Biosolutions aims to use genomic data to improve healthcare for patients. The company develops gene expression tests for blood or tissue samples to assist doctors in clinical decision-making.

Company co-founder Amrie Grammer, PhD, is an alumna of UVA, having received both her bachelor’s and master’s degree there.

Findings Published

The researchers have published their findings in the journal Frontiers in Immunology. The team consisted of Andrea R. Daamen, Prathyusha Bachali, Catherine A. Bonham, Lindsay Somerville, Jeffrey M. Sturek, Amrie Grammer, Alexandra Kadl and Peter Lipsky. Daamen, Bachali, Grammer and Lipsky are employed by AMPEL BioSolutions.

The work was supported by the National Institutes of Health’s National Heart Lung Blood Institute, grant K23 HL143135; the National Institute of Allergy and Infectious Diseases, grant 1R21AI160334; the COVID-19 Rapid Response Initiative by UVA’s Global Infectious Disease Institute; and The RILITE Foundation.

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