Although the mouse is a ubiquitous laboratory model used in research for neurological diseases, results from studies in mice are not always applicable to humans. In fact, more than 90% of drug candidates that show preclinical promise for neurological disorders ultimately fail when tested in humans, in part due to a dearth of knowledge about the differences in astrocytes and other brain cells between the two species.

Astrocytes are crucial to the development and function of the brain, and they play a substantial role in neurological disorders that, nonetheless, is not fully understood. Injury or infection causes astrocytes to go from a resting to reactive state in which they can aid in repairing the brain but can also increase detrimental inflammation.

The scientists studied developing cells purified from mouse and human brain tissue, as well as cells grown in serum-free cultures from astrocytes selected using an antibody-based method developed by the study’s corresponding author.

This technique was necessary because the conventional method of selecting astrocytes by growing them in serum — a mixture of proteins, hormones, fats and minerals — throws them into a reactive state similar to that caused by infection or injury. With the researchers’ strategy, they were able to examine the astrocytes in a healthy state and in controlled conditions of oxidative stress, lack of oxygen and excessive inflammation.

The findings have implications for basic and translational research into neurological disorders such as Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis — conditions whose underlying mechanisms include oxidative stress, lack of oxygen and excessive inflammation.

Because mouse astrocytes stand up to oxidative stress better, the authors suggest that laboratory models for neurodegeneration could be engineered to lessen that resistance, rendering them more human-like. In addition, the mouse astrocyte’s facility for repair in response to lack of oxygen may suggest a new avenue of stroke research. And neuroscientists can take a more informed approach to preclinical studies by accounting for differences in response to inflammation between mouse and human astrocytes, as well as metabolic differences identified in the study.

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“The cell cycle is such a well-studied thing and yet here we are looking at it again for the umpteenth time and a new phase pops out at us,” Plaisier says. “Biology always has new insights to show us, you just have to look.”

The spark for this discovery came through a collaboration with Patrick Paddison, an associate professor at the Fred Hutchinson Cancer Research Center in Seattle, and Dr. Anoop Patel, an assistant professor of neurological surgery at the University of Washington who is also involved in the Fred Hutchinson Cancer Research Center.

Paddison’s team called upon Plaisier to help analyze their brain stem cell data characterized through a process called single-cell RNA sequencing.

“That data turned out to be pretty amazing,” Plaisier says. “It mapped out into this beautiful circular pattern that we identified as all of the different phases of the cell cycle.”

O’Connor developed a new cell cycle classifier tool — called ccAF, or cell cycle ASU/Fred Hutchinson to represent the collaboration between the two institutions — that takes a closer, “high-resolution” look at what’s happening within the growth cycles of stem cells and identifies genes that can be used to track progress through the cell cycle.

“Our classifier gets deeper into the cell cycle because there could be pieces we’re capturing that have important implications for disease,” O’Connor says.

When Plaisier and O’Connor used the ccAF tool to analyze cell data for glioma tumors, they found the tumor cells were often either in the Neural G0 or G1 growth state. And as tumors become more aggressive, fewer and fewer cells remain in the resting Neural G0 state. This means more and more cells are proliferating and growing the tumor.

They correlated this data with the prognosis for patients with glioblastoma, a particularly aggressive type of brain tumor. Those with higher Neural G0 levels in tumor cells had less aggressive tumors.

They also found that the quiescent Neural G0 state is independent of a tumor’s proliferation rate, or how fast its cells divide and create new cells.

“That was an interesting finding from our results, that quiescence itself could be a different biological process,” Plaisier says. “It’s also a potential point where we could look for new drug treatments. If we could push more cells into that quiescent state, the tumors would become less aggressive.”

Current cancer drug treatments focus on killing cancer cells. However, when the cancer cells are killed, they release cell debris into the surrounding area of the tumor, which can cause the remaining cells to become more resistant to the drugs.

“So, instead of killing the cells, if we put them to sleep it could potentially be a much better situation,” Plaisier says.

With their ccAF tool, they were also able to find new states at the beginning and end of the cell cycle that exist between the commonly known states. These are among the topics for their next phase of research.

“We’re starting to think about ways to dig into those and learn more about the biology of the entry and exit from the cell cycle because those are potentially really important points where the cells will either go into the G1 state or G0,” Plaisier says.

Figuring out what triggers a cell to enter the division cycle or remain in a G0 resting state could help understand the processes behind tumor growth.

“The primary feature of any cancer is that the cells are proliferating,” Plaisier says. “If we could get in there and figure out what the mechanisms are, that might be a place to slow them down.”

Plaisier and O’Connor are making the ccAF classifier tool open source and available in a variety of formats for anyone studying single-cell RNA sequencing data to ease into the process of studying cell cycles.

A researcher in Syracuse University’s College of Arts and Sciences, and a team of scientists from Penn State, have developed a new machine learning technique to holistically assess water quality data in order to detect groundwater samples likely impacted by recent methane leakage during oil and gas production. Using that model, the team concluded that unconventional drilling methods like hydraulic fracturing — or hydrofracking — do not necessarily incur more environmental problems than conventional oil and gas drilling.

The two common ways to extract oil and gas in the U.S. are through conventional and unconventional methods. Conventional oil and gas are pumped from easily accessed sources using natural pressure. Conversely, unconventional oil and gas are acquired from hard-to-reach sources through a combination of horizontal drilling and hydraulic fracturing. Hydrofracking extracts natural gas, petroleum and brine from bedrock formations by injecting a mixture of sand, chemicals and water. By drilling into the earth and directing the high-pressure mixture into rock, the gas inside releases and flows out to the head of a well.

Tao Wen, assistant professor of earth and environmental sciences (EES) at Syracuse, recently led a study comparing data from different states to see which method might result in greater contamination of groundwater. They specifically tested levels of methane, which is the primary component of natural gas.

The team selected four U.S. states located in important shale zones to target for their study: Pennsylvania, Colorado, Texas and New York. One of those states — New York — banned the practice of hydrofracking in 2015 following a review by the NYS Department of Health which found significant uncertainties about health, including increased water and air pollution.

Wen and his colleagues compiled a large groundwater chemistry dataset from multiple sources including federal agency reports, journal articles, and oil and gas companies. The majority of tested water samples in their study were collected from domestic water wells. Although methane itself is not toxic, Wen says that methane contamination detected in shallow groundwater could be a risk to the relevant homeowner as it could be an explosion hazard, could increase the level of other toxic chemical species like manganese and arsenic, and would contribute to global warming as methane is a greenhouse gas.

Their model used sophisticated algorithms to analyze almost all of the retained geochemistry data in order to predict if a given groundwater sample was negatively impacted by recent oil and gas drilling.

The data comparison showed that methane contamination cases in New York — a state without unconventional drilling but with a high volume of conventional drilling — were similar to that of Pennsylvania — a state with a high volume of unconventional drilling. Wen says this suggests that unconventional drilling methods like fracking do not necessarily lead to more environmental problems than conventional drilling, although this result might be alternatively explained by the different sizes of groundwater chemistry datasets compiled for these two states.

The model also detected a higher rate of methane contamination cases in Pennsylvania than in Colorado and Texas. Wen says this difference could be attributed to different practices when drillers build/drill the oil and gas wells in different states. According to previous research, most of the methane released into the environment from gas wells in the U.S. occurs because the cement that seals the well is not completed along the full lengths of the production casing. However, no data exists to conclude if drillers in those three states use different technology. Wen says this requires further study and review of the drilling data if they become available.

According to Wen, their machine learning model proved to be effective in detecting groundwater contamination, and by applying it to other states/counties with ongoing or planned oil and gas production it will be an important resource for determining the safest methods of gas and oil drilling.

Wen and his colleagues from Penn State, including Mengqi Liu, a graduate student from the College of Information Sciences and Technology, Josh Woda, a graduate student from Department of Geosciences, Guanjie Zheng, former Ph.D. student from the College of Information Sciences and Technology, and Susan L. Brantley, distinguished professor in the Department of Geosciences and director of Earth and Environmental Systems Institute, recently had their findings published in the journal Water Research.

The team’s work was funded by National Science Foundation IIS-16-39150, US Geological Survey (104b award G16AP00079), and College of Earth and Mineral Sciences Dean’s Fund for Postdoc-Facilitated Innovation at Penn State.

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Materials provided by Syracuse University. Original written by Dan Bernardi. Note: Content may be edited for style and length.

However, optical tweezers do have flaws. The prolonged interaction with the laser beam can alter molecules and particles or damage them with excessive heat.

Researchers at The University of Texas at Austin have created a new version of optical tweezer technology that fixes this problem, a development that could open the already highly regarded tools to new types of research and simplify processes for using them today.

The breakthrough that avoids this problem of overheating comes out of a combination of two concepts: the use of a substrate composed of materials that are cooled when a light is shined on them (in this case, a laser); and a concept called thermophoresis, a phenomenon in which mobile particles will commonly gravitate toward a cooler environment.

The cooler materials attract particles, making them easier to isolate, while also protecting them from overheating. By solving the heat problem, optical tweezers could become more widely used to study biomolecules, DNA, diseases and more.

“Optical tweezers have many advantages, but they are limited because whenever the light captures objects, they heat up,” said Yuebing Zheng, the corresponding author of a new paper published in Science Advances and an associate professor in the Walker Department of Mechanical Engineering. “Our tool addresses this critical challenge; instead of heating the trapped objects, we have them controlled at a lower temperature.”

Optical tweezers do the same thing as regular tweezers — pick up small objects and manipulate them. However, optical tweezers work at a much smaller scale and use light to capture and move objects.

Analyzing DNA is a common use of optical tweezers. But doing so requires attaching nano-sized glass beads to the particles. Then to move the particles, the laser is shined on the beads, not the particles themselves, because the DNA would be damaged by the heating effect of the light.

“When you are forced to add more steps to the process, you increase uncertainty because now you have introduced something else into the biological system that may impact it,” Zheng said.

This new and improved version of optical tweezers eliminates these extra steps.

The team’s next steps include developing autonomous control systems, making them easier for people without specialized training to use and extending the tweezers’ capabilities to handle biological fluids such as blood and urine. And they are working to commercialize the discovery.

Zheng and his team have much variety in their research, but it all centers on light and how it interacts with materials. Because of this focus on light, he has closely followed, and used, optical tweezers in his research. The researchers were familiar with thermophoresis and hoped they could trigger it with cooler materials, which would actually draw particles to the laser to simplify analysis.

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A team of Virginia Tech researchers from the Department of Mechanical Engineering and the Macromolecules Innovation Institute has created a new type of soft electronics, paving the way for devices that are self-healing, reconfigurable, and recyclable. These skin-like circuits are soft and stretchy, sustain numerous damage events under load without losing electrical conductivity, and can be recycled to generate new circuits at the end of a product’s life.

Led by Assistant Professor Michael Bartlett, the team recently published its findings in Communications Materials, an open access journal from Nature Research.

Current consumer devices, such as phones and laptops, contain rigid materials that use soldered wires running throughout. The soft circuit developed by Bartlett’s team replaces these inflexible materials with soft electronic composites and tiny, electricity-conducting liquid metal droplets. These soft electronics are part of a rapidly emerging field of technology that gives gadgets a level of durability that would have been impossible just a few years ago.

The liquid metal droplets are initially dispersed in an elastomer, a type of rubbery polymer, as electrically insulated, discrete drops.

“To make circuits, we introduced a scalable approach through embossing, which allows us to rapidly create tunable circuits by selectively connecting droplets,” postdoctoral researcher and first author Ravi Tutika said. “We can then locally break the droplets apart to remake circuits and can even completely dissolve the circuits to break all the connections to recycle the materials, and then start back at the beginning.”

The circuits are soft and flexible, like skin, continuing to work even under extreme damage. If a hole is punched in these circuits, the metal droplets can still transfer power. Instead of cutting the connection completely as in the case of a traditional wire, the droplets make new connections around the hole to continue passing electricity.

The circuits will also stretch without losing their electrical connection, as the team pulled the device to over 10 times its original length without failure during the research.

At the end of a product’s life, the metal droplets and the rubbery materials can be reprocessed and returned to a liquid solution, effectively making them recyclable. From that point, they can be remade to start a new life, an approach that offers a pathway to sustainable electronics.

While a stretchy smartphone has not yet been made, rapid development in the field also holds promise for wearable electronics and soft robotics. These emerging technologies require soft, robust circuitry to make the leap into consumer applications.

“We’re excited about our progress and envision these materials as key components for emerging soft technologies,” Bartlett said. “This work gets closer to creating soft circuitry that could survive in a variety of real-world applications.”

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Materials provided by Virginia Tech. Original written by Alex Parrish. Note: Content may be edited for style and length.

Backscatter radios — passive sensors that reflect rather than radiate energy — are known for their low-cost, low-complexity, and battery-free operation, making them a potential key enabler of this future although they typically feature low data rates and their performance strongly depends on the surrounding environment.

Researchers at the Georgia Institute of Technology, Nokia Bell Labs, and Heriot-Watt University have found a low-cost way for backscatter radios to support high-throughput communication and 5G-speed Gb/sec data transfer using only a single transistor when previously it required expensive and multiple stacked transistors.

Employing a unique modulation approach in the 5G 24/28 Gigahertz (GHz) bandwidth, the researchers have shown that these passive devices can transfer data safely and robustly from virtually any environment. The findings were reported earlier this month in the journal Nature Electronics.

Traditionally, mmWave communications, called the extremely high frequency band, is considered “the last mile” for broadband, with directive point-to-point and point-to-multipoint wireless links. This spectrum band offers many advantages, including wide available GHz bandwidth, which enables very large communication rates, and the ability to implement electrically large antenna arrays, enabling on-demand beamforming capabilities. However, such mmWave systems depend on high-cost components and systems.

The Struggle for Simplicity Versus Cost

“Typically, it was simplicity against cost. You could either do very simple things with one transistor or you need multiple transistors for more complex features, which made these systems very expensive,” said Emmanouil (Manos) Tentzeris, Ken Byers Professor in Flexible Electronics in Georgia Tech’s School of Electrical and Computer Engineering (ECE). “Now we’ve enhanced the complexity, making it very powerful but very low cost, so we’re getting the best of both worlds.”

“Our breakthrough is being able to communicate over 5G/millimeter-wave (mmWave) frequencies without actually having a full mmWave radio transmitter — only a single mmWave transistor is needed along much lower frequency electronics, such as the ones found in cell phones or WiFi devices. Lower operating frequency keeps the electronics’ power consumption and silicon cost low,” added first author Ioannis (John) Kimionis, a Georgia Tech Ph.D. graduate now a member of technical staff at Nokia Bell Labs. “Our work is scalable for any type of digital modulation and can be applied to any fixed or mobile device.”

The researchers are the first to use a backscatter radio for gigabit-data rate mmWave communications, while minimizing the front-end complexity to a single high-frequency transistor. Their breakthrough included the modulation as well as adding more intelligence to the signal that is driving the device.

“We kept the same RF front-end for scaling up the data rate without adding more transistors to our modulator, which makes it a scalable communicator,” Kimionis said, adding that their demonstration showed how a single mmWave transistor can support a wide range of modulation formats.

Powering a Host of ‘Smart’ IoT Sensors

The technology opens up a host of IoT 5G applications, including energy harvesting, which Georgia Tech researchers recently demonstrated using a specialized Rotman lens that collects 5G electromagnetic energy from all directions.

Tentzeris said additional applications for the backscatter technology could include “rugged” high-speed personal area networks with zero-power wearable/implantable sensors for monitoring oxygen or glucose levels in the blood or cardiac/EEG functions; smart home sensors that monitor temperature, chemicals, gases, and humidity; and smart agricultural applications for detecting frost on crops, analyzing soil nutrients, or even livestock tracking.

The researchers developed an early proof of concept of this backscatter modulation, which won third prize at the 2016 Nokia Bell Labs Prize. At the time, Kimionis was a Georgia Tech ECE doctoral researcher working with Tentzeris in the ATHENA lab, which advances novel technologies for electromagnetic, wireless, RF, millimeter-wave, and sub-terahertz applications.

Key Enabler of Low Cost: Additive Manufacturing

For Kimionis, the backscatter technology breakthrough reflects his goal to “democratize communications.”

“Throughout my career I’ve looked for ways to make all types of communication more cost-efficient and more energy-efficient. Now, because the whole front end of our solution was created at such low complexity, it is compatible with printed electronics. We can literally print a mmWave antenna array that can support a low-power, low-complexity, and low-cost transmitter.”

Tentzeris considers affordable printing crucial to making their backscattering technology market viable. Georgia Tech is a pioneer in inkjet printing on virtually every material (paper, plastics, glass, flexible/organic substrates) and was one of the first research institutes to use 3D printing up to millimeter-frequency ranges back in 2002.

Other researchers who collaborated on this work included Apostolos Georgiadis and Spyridon Nektarios Daskalakis, both former visiting professors at Georgia Tech now on the faculty of Herriot-Watt University’s School of Engineering and Physical Sciences in Edinburgh.

This work was supported by the National Science Foundation-EFRI, the Defense Threat Reduction Agency (DTRA) and by the European Union Horizon 2020 Research and Innovation Programme under the Marie Sk?odowska-Curie grant agreement.

“Our team was the first to demonstrate that SARS-CoV-2 virus sequences could be identified from environmental swabs collected from hospital surfaces,” said Angela Haczku, a respiratory immunologist and senior author on the study.

Changing cleaning and ICU protocols linked to lower SARS-CoV-2 contamination

In April 2020, a COVID-19 outbreak among hospital staff led an interdisciplinary team of UC Davis researchers to investigate if there was virus contamination of frequently used surfaces in patient serving ICU and staff meeting areas at the UC Davis Medical Center. At that time the role of fomites (surfaces) in spreading the disease was highly debated. They collected multiple samples during the first (April 2020) and the second (August 2020) waves of COVID-19 from surfaces and HVAC filters in the hospital.

The researchers analyzed the surface swabs for SARS-CoV-2 RNA and infectivity and assessed the suitability of the RNA for sequencing.

Despite a significant increase in the number of hospital patients with COVID-19 during the second surge, the team found that only 2% of swabs tested positive in August, compared to 11% of samples collected in April.

“The reduction in the virus contamination was likely due to improved ICU patient management and cleaning protocols,” Haczku said. Haczku is a professor of medicine, director at the UC Davis Lung Center and associate dean for translational research at the UC Davis School of Medicine.

Genome sequence of coronavirus found on surfaces

The study demonstrated that by genome sequencing, SARS-CoV-2 could be detected even from samples that otherwise tested negative (undetectable) by commonly used PCR tests. The results also confirmed that the SARS-CoV-2 RNA picked up from a surface, although containing near- intact genomic sequence, was not infectious. This finding supports the hypothesis that contaminated surfaces may not be a major way for spreading COVID-19 disease.

“For the first time, to our knowledge, we were able to determine the viral genome sequence from surface swab samples obtained in a hospital environment,” said David Coil, project scientist at the UC Davis Genome Center and the first author on the study. “We found SARS-CoV-2 in samples that were tested negative by RT-PCR, suggesting that the sequencing technology is superior for virus detection in environmental samples.”

According to Coil, the genome sequencing performed on the hospital surface swab samples is very important. By getting accurate viral genomic sequences, the researchers could track the source and figure out how an infection moves.

“Our data indicated that the sequences determined for the viral RNA from surfaces were identical to the ones derived from the patients hospitalized in the ICU at the time of sample collection. The ability to identify viral genome sequences from environmental samples may have high public health significance in outbreak surveillance and monitoring the spread of new viral variants,” Haczku said.

This study was funded by a UC Davis CRAFT Award and The Chester Robbins Endowment.

UC Davis co-authors are Timothy Albertson, Christian Sandrock, Daniel G Tompkins, Maya Juarez, Brandt Robinson, Shefali Banerjee, Greg Brennan, Satya Dandekar, Stefan Rothenburg , Ana Stoian, A.J. Campbell, Ivy Jose, Samuel L. Díaz-Muñoz, Stuart H. Cohen, Jonathan A. Eisen, Tracey Goldstein and Alexandre Tremeau-Bravard.

“The Harbin fossil is one of the most complete human cranial fossils in the world,” says author Qiang Ji, a professor of paleontology of Hebei GEO University. “This fossil preserved many morphological details that are critical for understanding the evolution of the Homo genus and the origin of Homo sapiens.”

The cranium was reportedly discovered in the 1930s in Harbin City of the Heilongjiang province of China. The massive skull could hold a brain comparable in size to modern humans’ but had larger, almost square eye sockets, thick brow ridges, a wide mouth, and oversized teeth. “While it shows typical archaic human features, the Harbin cranium presents a mosaic combination of primitive and derived characters setting itself apart from all the other previously-named Homo species,” says Ji, leading to its new species designation of Homo longi.

Scientists believe the cranium came from a male individual, approximately 50 years old, living in a forested, floodplain environment as part of a small community. “Like Homo sapiens, they hunted mammals and birds, and gathered fruits and vegetables, and perhaps even caught fish,” remarks author Xijun Ni, a professor of primatology and paleoanthropology at the Chinese Academy of Sciences and Hebei GEO University. Given that the Harbin individual was likely very large in size as well as the location where the skull was found, researchers suggest H. longi may have been adapted for harsh environments, allowing them to disperse throughout Asia.

Using a series of geochemical analyses, Ji, Ni, and their team dated the Harbin fossil to at least 146,000 years, placing it in the Middle Pleistocene, a dynamic era of human species migration. They hypothesize that H. longi and H. sapiens could have encountered each other during this era.

“We see multiple evolutionary lineages of Homo species and populations co-existing in Asia, Africa, and Europe during that time. So, if Homo sapiens indeed got to East Asia that early, they could have a chance to interact with H. longi, and since we don’t know when the Harbin group disappeared, there could have been later encounters as well,” says author Chris Stringer, a paleoanthropologist at the Nature History Museum in London.

Looking farther back in time, the researchers also find that Homo longi is one of our closest hominin relatives, even more closely related to us than Neanderthals. “It is widely believed that the Neanderthal belongs to an extinct lineage that is the closest relative of our own species. However, our discovery suggests that the new lineage we identified that includes Homo longi is the actual sister group of H. sapiens,” says Ni.

Their reconstruction of the human tree of life also suggests that the common ancestor we share with Neanderthals existed even further back in time. “The divergence time between H. sapiens and the Neanderthals may be even deeper in evolutionary history than generally believed, over one million years,” says Ni. If true, we likely diverged from Neanderthals roughly 400,000 years earlier than scientists had thought.

The researchers say that findings gathered from the Harbin cranium have the potential to rewrite major elements of human evolution. Their analysis into the life history of Homo longi suggest they were strong, robust humans whose potential interactions with Homo sapiens may have shaped our history in turn. “Altogether, the Harbin cranium provides more evidence for us to understand Homo diversity and evolutionary relationships among these diverse Homo species and populations,” says Ni. “We found our long-lost sister lineage.”

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