Tuesday, September 06, 2022

Shining light on why plastics turn yellow

Peer-Reviewed Publication

AMERICAN CHEMICAL SOCIETY

If you own a retro gaming console or have an old roll of packing tape, you’ve seen how plastics turn yellow as they age. Though the cause of this color change has long been attributed to the formation of molecules that act as dyes — the actual chemical changes that take place remained unexplained. Now, researchers reporting in ACS Applied Polymer Materials have identified surface-based chiral nanostructures as the potential culprit.

Understanding how and why polymers degrade with age is key to designing alternatives that can avoid these pathways, allowing plastic products to have a longer lifespan. For one of the most commonly used plastics, polyethylene, it’s long been suggested that ultraviolet (UV) light — the same light that gives us sunburns — initiates reactions in the backbone of the polymer’s structure that cause the yellow color change. However, though chemical changes to polyethylene’s polymeric backbone have been observed after exposure to UV light, those new structures cannot account for polyethylene’s yellowing. One emerging way to intentionally modify the color and the ways that plastics interact with light is to create nano-sized “supramolecular” structures on their surfaces that impact plastics’ properties in a controllable way. Inspired by these surface-based technologies, Margaret M. Elmer-Dixon, Melissa A. Maurer-Jones and colleagues wanted to see if such nanostructures formed unintentionally by UV light could be the cause of polyethylene yellowing.

The researchers first investigated if potential structures formed on yellowed polyethylene films’ surfaces interacted with circularly polarized light, a type of light whose waves travel with a right- or left-handed rotation. The amount of circularly polarized light absorbed by the film in these experiments changed depending on the film’s orientation, suggesting that the yellowed plastic contains new chemical structures that are chiral, that is, they are directional and aren’t identical to their mirror images. Additional experiments showed that most of the degradation during film yellowing occurred on the surface of the films. The team concluded that chiral chemical structures on the surfaces of the polyethylene films are formed during exposure to UV light and are a potential cause for the yellow color of old plastics. They say that these insights could help researchers design plastic products that last longer before becoming unsightly or unusable.  

The authors acknowledge funding from the University of Minnesota, Duluth, the University of Minnesota McKnight Foundation, and the U.S. Department of Energy.

The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, eBooks and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

To automatically receive news releases from the American Chemical Society, contact newsroom@acs.org.

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Turning over a new leaf: Local mountain climate is affected by leaf area ratio

A research group led by the University of Tsukuba finds that the leaf area index of mixed-forests influences seasonal changes in the formation of a nocturnal cold-air pool at a small mountain basin in central Japan

Peer-Reviewed Publication

UNIVERSITY OF TSUKUBA

forest tower 

IMAGE: IMAGE view more 

CREDIT: UNIVERSITY OF TSUKUBA

Tsukuba, Japan—The changing seasons tell us much about the workings of nature. Now, a research group from Japan has discovered that the seasonal changes of tree leaf growth and shedding can have a big influence on climate even on small, local scales.

Forests act as intermediaries between the atmosphere and land, reducing surface wind speeds and controlling surface heat budgets, as well as indirectly affecting cloud formation and the energy-water cycle. The forest canopy protects the forest floor from sunlight and reduces diurnal variations in surface air temperature. These effects may alter not only forest ecology, but also the surrounding microclimate. For mountain forests, the effects of global climate change on phenology (periodic biological events, e.g., flowering, in relation to climatic conditions) have been shown, such as an extended growing season for deciduous forests. Changes in forest phenology could also alter local circulation and heat budgets of the low-level atmosphere in surrounding environments.

"However, previous studies have not fully considered the contribution of mountain forests to the nocturnal local climate in downstream areas," says senior author of the study, Professor Kenichi Ueno. "This is what we set out to investigate."

Specifically, the researchers sought to clarify the effects of leaf expansion (the stage in deciduous plant phenology where leaves expand from buds to mature leaves) on nocturnal temperature inversion (NTI) in mountain basins. NTI is a key factor that characterizes the local climate in mountainous areas, and much of the mountain slopes in central Japan are covered by deciduous forests.

The research team conducted a three-year study of leaf area index (LAI) at a mixed-forest mountain slope site in a small basin. They observed sudden shifts in the development of the nighttime cold-air pool over the basin that were related to leaf expansion and leaf fall. Specifically, they found weakening of the NTI related to leaf expansion, and strengthening after leaf fall. On the basis of these relationships, the researchers concluded that changes in LAI influenced seasonal changes in the development of the nighttime cold-air pool.

"Our results indicated that changeability in daytime forest heat storage can offset nighttime radiative cooling from the forest canopy," says Professor Ueno. "In short, our research has revealed that the cycle of tree leaf growth and leaf shedding in mountain forests has an observable effect on the local climate."

The results of this study will be applicable to research on the effects of mountain forest processes on nearby areas, such as downwind locations in which human activities are focused, which has important implications for how agricultural areas are designed, and on long-term mountain meteorological records. Future studies are expected to assess the effects of forest phenology of mountain areas on inland nocturnal climates.

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Funding: Field research project (2018-2019), Mountain Science Center, University of Tsukuba

Original Paper

The article, "Development of a nocturnal temperature inversion in a small basin associated with leaf area ratio changes on the mountain slopes in central Japan," was published in the Journal of the Meteorological Society of Japan at DOI: 10.2151/jmsj.2022-047

Correspondence

Associate Professor UENO Kenichi
Faculty of Life and Environmental Sciences, University of Tsukuba

Related Link

Faculty of Life and Environmental Sciences

Large, tasty popcorn kernels with infrared cooking


Peer-Reviewed Publication

AMERICAN CHEMICAL SOCIETY

No movie experience is complete without popcorn, whether plain, buttered, or coated with sweet or savory toppings. Microwaves and counter-top air poppers are common appliances for making this tasty snack at home, but now, a study in ACS Food Science & Technology reports that infrared cooking is yet another way people can make the treat. Using a pilot infrared popping system, the researchers were able to produce a version of the snack that taste-testers liked.

Infrared cooking has been gaining steam in home-sized pizza ovens and outdoor grills because it heats food quickly and evenly, without needing as much energy as other techniques. Previously, Majid Javanmard and colleagues showed that this cooking method is successful at making popcorn. So, to optimize the method for this snack, the team developed a pilot infrared popping system with a rotating chamber that held corn kernels close to two infrared lamps. They tested the new system with three different levels of heat power (600, 700 and 800 W) and found that 700 W produced the highest amount of fully or semi-popped kernels, as well as the largest kernels. Then a sensory panel also determined that the popcorn from 700 W lamps had the best color, taste and firmness, scoring over four on a five-point scale for overall acceptability. Based on their results, the researchers say that their infrared system can make tasty popcorn in a more energy-efficient way than traditional methods. 

The authors acknowledge funding from the Iranian Research Organization for Science and Technology (IROST).

The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, eBooks and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

To automatically receive news releases from the American Chemical Society, contact newsroom@acs.org.

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Machine learning shows links between bacterial population growth and environment

Researchers from the University of Tsukuba find via machine learning that the differentiation in decision-making components for the lag, growth, and saturation phases of bacterial population growth protects the population against extinction

Peer-Reviewed Publication

UNIVERSITY OF TSUKUBA

Tsukuba, Japan—Microbial populations may be small but they are surprisingly complex, making interactions with their surrounding environment difficult to study. But now, researchers from Japan have discovered that machine learning can provide the tools to do just that. In a study published this month in eLife, researchers from the University of Tsukuba have revealed that machine learning can be applied to bacterial population growth to discover how it relates to variations in their environment.

The dynamics of microbe populations are usually represented by growth curves. Typically, three parameters taken from these curves are used to evaluate how microbial populations fit with their environment: lag time, growth rate, and saturated population size (or carrying capacity). These three parameters are probably linked; trade-offs have been observed between the growth rate and either the lag time or population size within species, and with related changes in the saturated population size and growth rate among genetically diverse strains.

"Two questions remained: are these three parameters affected by environmental diversity, and if so, how?" says senior author of the study, Professor Bei-Wen Ying. "To answer these, we used data-driven approaches to investigate the growth strategy of bacteria."

The researchers built a large dataset that reflected the dynamics of Escherichia coli populations under a wide variety of environmental conditions, using almost a thousand combinations of growth media composed from 44 chemical compounds under controlled lab conditions. They then analyzed the big data for the relationships between the growth parameters and the combinations of media using machine learning (ML). ML algorithms built a model based on sample data to make predictions or decisions without being specifically programmed to do so.

The analysis revealed that for bacterial growth, the decision-making components were distinct among different growth phases, e.g., serine, sulfate, and glucose for growth delay (lag), growth rate, and maximum growth (saturation), respectively. The results of additional simulations and analyses showed that branched-chain amino acids likely act as ubiquitous coordinators for bacterial population growth conditions.

"Our results also revealed a common and simple strategy of risk diversification in conditions where the bacteria experienced excess resources or starvation, which makes sense in both an evolutionary and ecological context," says Professor Ying.

The results of this study have revealed that exploring the world of microorganisms with data-driven approaches can provide new insights that were previously unattainable via traditional biological experiments. This research shows that the ML-assisted approach, although still an emerging technology that will need to be developed in terms of its biological reliability and accessibility, could open new avenues for applications in the life sciences, especially microbiology and ecology.

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The study was funded by Japan Society for the Promotion of Science 21K19815 and 19H03215.

Original Paper

The article, "Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity," was published in eLife at DOI: 10.7554/eLife.76846

Correspondence

Associate Professor YING BEIWEN
Faculty of Life and Environmental Sciences, University of Tsukuba

Related Link

Faculty of Life and Environmental Sciences

 

Children with autism benefit when parents are trained to provide at-home interventions

Study shows that after parents receive specialized training, children with autism spectrum disorder markedly improve their social and communication skills.

Peer-Reviewed Publication

BRIGHAM YOUNG UNIVERSITY

Home Interventions 1 

IMAGE: A SUMMARY OF 50 STUDIES FOUND THAT WHEN PARENTS WERE EDUCATED IN WAYS TO OFFER INTERVENTIONS AT HOME, CHILDREN SAW MARKED IMPROVEMENT IN SOCIAL SKILLS AS WELL AS LANGUAGE AND COMMUNICATION SKILLS. view more 

CREDIT: NATE EDWARDS

Training and empowering parents to offer at-home interventions to children with autism spectrum disorder helps children improve in positive behaviors and language communication skills says a new study from BYU.

“We’ve known for a long time that early interventions for children with autism improve learning and social skills at a greater rate than if interventions are offered later,” said Timothy B. Smith, BYU counseling psychology professor. “The problem lies in the bottleneck between the small number of practitioners available and the large numbers of children with symptoms who aren’t receiving treatment. Many can spend months on a waitlist before meeting a clinician.”

Utilizing at-home interventions in conjunction with professional clinical treatment is one way to expand treatment services. The study, recently published in the Journal of Autism and Developmental Disorders, found that when parents were educated in ways to offer interventions at home, children saw marked improvement in social skills as well as language and communication skills when compared to children receiving no specialized home interventions.

Parents could be taught strategies that they can use to help their child develop social, communication, and play skills. For instance, they would be trained on how to help a child focus on desired tasks, or how to take turns when interacting with others. Parents would be empowered to implement these tactics throughout the course of the day.

“There is no scientific justification to not train parents,” said Smith. “A parent can constantly reinforce social behaviors if they know what to look for and how to do it. It’s about meeting the kids where they’re at. It has a potentially remarkable impact on child outcomes.”

The researchers conducted a meta-analysis of over 50 different studies to understand the impact of parent-led interventions. In total, the studies included 2,895 child participants with an average age of five and a half. On average, parents received about 90 minutes of intervention training each week. Impact on child development was measured using direct observation by a professional as well as parent and observer ratings. No differences were observed when the mother, father, or both implemented interventions.

Moderate gains in development while they’re young crescendo over time, said Smith. Children with autism spectrum disorder who benefit from home interventions will enter preschool more prepared and they’ll leave preschool feeling more equipped for kindergarten.

“They’ll get more out of first grade and then second grade, and the effect continues to multiply,” he said. “That trajectory then continues to widen the path where the child will end up across their lifetime.”

When considered with lifelong costs associated with education, social programs and eventually welfare programs that take care of adults with disabilities, Smith estimates that parent-led interventions are a procedure that could save billions of dollars.

Researchers say they’re hopeful that such findings can be used by lawmakers to introduce legislation to add parent training as a covered benefit of insurance policies, like recent changes in federal legislation that offered insurance coverage of professional treatment for children with developmental delays.

“Kids diagnosed with ASD are higher functioning today than even twenty years ago because they’re getting interventions when they’re one or two years old,” said Dr. Tina Taylor, associate dean in the David O. McKay School of Education, and co-author of the study. “We need continued resources to help equip parents help their children. Parents are able and willing to help their children build the skills they need to be successful and make huge contributions to the world.”

Additionally, when pediatricians find symptoms of developmental delays in well child visits, they could immediately make the recommendation for parent training programs, while simultaneously making referrals for professional services.

“Intensive interventions can require 25 hours or more per week, and it’s unrealistic to expect that solely from a professional provider. Parents can have the knowledge and skills to help their children develop,” said Linda Cheng, lead author of the paper and current doctoral student studying educational inquiry, measurement and evaluation in the McKay School. “If we only stay with the traditional methods of treatments, we’re missing an opportunity to help those in need.”

For parents interested in learning more about strategies of parent led interventions, Smith suggests exploring the online resources offered by Project-Impact.

CAPTION

Parents could be trained on how to help a child focus on desired tasks, or how to take turns when interacting with others.

CREDIT

Nate Edwards

Cooling away the pain: Pusan National University researchers develop bioresorbable, implantable device to block pain signals from peripheral nerves

Researchers test the efficacy of a soft, bioresorbable, implantable device to block pain signals from sciatic nerves of rat models

Peer-Reviewed Publication

PUSAN NATIONAL UNIVERSITY

Bioresorbable, implantable devices for cooling of peripheral nerves 

IMAGE: A SOFT, BIORESORBABLE, IMPLANTABLE DEVICE DEVELOPED BY RESEARCHERS FROM PUSAN NATIONAL UNIVERSITY PROVIDES A FOCUSED, REVERSIBLE, AND PRECISE COOLING EFFECT TO BLOCK PAIN SIGNALS FROM PERIPHERAL NERVES view more 

CREDIT: PUSAN NATIONAL UNIVERSITY

Owing to their high efficacy, opioids are used widely for the management of neuropathic pain, despite the increasing rates of opioid addiction and deaths due to overdose. To avoid these side effects, there is an urgent need for pain management approaches that can substitute opioid use.

It is well known that cold temperatures numb the sensation in our nerves. Evidence suggests that cooling peripheral nerves can in fact reduce the velocity and amplitude of neural signals that cause pain, leading to pain relief. What’s great about this approach is that if made possible, it will be completely reversible and non-addictive.

To this end, a team of researchers led by Professor Min-Ho Seo from Pusan National University developed a soft, bioresorbable, implantable device with the potential to cool peripheral nerves in a minimally invasive, focused manner. “Scientists already knew that low temperatures could numb the nerves in the body. But demonstrating this phenomenon with a small device at a clinical level was not an easy task,” said Prof. Seo while discussing the study, which was published in Volume 377 Issue 6601 of Science on June 30, 2022.

To develop the device, the team designed a microfluidics system formed with a bioresorbable material—poly(octanediol citrate)—with interconnects carrying a liquid coolant to a serpentine chamber. To top it off, a Magnesium temperature sensor for real-time temperature monitoring was incorporated at its distal end. The intensity and localization of the cooling effect was regulated by perfluoro pentane (PFP) and dry nitrogen gas (N2)—the two components of the liquid coolant, as well as the geometry of the serpentine chamber.

Next, the team tested the device by implanting it into the sciatic nerves of living rat models with neuropathic pain associated with spared nerve injury. After a three-week evaluation, the team found that the device successfully delivered cooling power to the peripheral nerves of the rats, which led to a reduction in their pain. Fortunately, the delivery of the cooling power occurred in a minimally invasive, stable, and precise manner. What’s more, this application was localized and reversible, and remained effective for almost 15 minutes during one session.

On being submerged in phosphate-buffered saline solution at 75°C, the device, which was made of bioresorbable materials, dissolved within 20 days and got eliminated in approximately 50 days. These findings imply that it has the potential to naturally degrade and get resorbed in the human body.

So, what are the future applications of this device? “The developed device can be used to treat pain after surgery. Since it is connected to an external source of fluid and power like a commercial intravenous (IV) device, it can easily be controlled by the patient. This way, our implantable device will be able to provide targeted and individualized relief without the drawbacks of the addictive pain medications,” said Prof. Seo in response.

With such progress underway, patients with neuropathic pain will finally be able to receive safe and sustainable treatment, without the risk of adverse effects associated with opioid use!

 

***

 

Reference

DOI: https://doi.org/10.1126/science.abl8532

Authors: Jonathan T. Reeder1,2,3, Zhaoqian Xie4,5, Quansan Yang3,6†, Min-Ho Seo2,3,7†, Ying Yan8, Yujun Deng9, Katherine R. Jinkins3, Siddharth R. Krishnan2,3, Claire Liu3,10, Shannon McKay10, Emily Patnaude10, Alexandra Johnson10, Zichen Zhao4,5, Moon Joo Kim11§, Yameng Xu12, Ivy Huang2,3, Raudel Avila6, Christopher Felicelli13, Emily Ray14, Xu Guo4,5, Wilson Z. Ray8,14, Yonggang Huang2,3,6,15, Matthew R. MacEwan8,14, John A. Rogers2,3,6,10,16,17,18

Affiliations:

1Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR, USA

2Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA

3Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA

4State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Dalian University of Technology, Dalian, China

5Ningbo Institute of Dalian University of Technology, Ningbo, China

6Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA

7School of Biomedical Convergence Engineering, College of Information and Biomedical Engineering, Pusan National University, Busan, Republic of Korea.

8Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, USA

9State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China

10Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA

11Department of Chemical Engineering, Northwestern University, Evanston, IL, USA

12The Institute of Materials Science and Engineering, Washington University, St. Louis, MO, USA

13Department of Pathology, Northwestern University, Evanston, IL, USA 14Department of Biomedical Engineering, Washington University, St. Louis, MO, USA

15Departments of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA

16Department of Chemistry, Northwestern University, Evanston, IL, USA

17Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA

18Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA

 

About Pusan National University
Pusan National University, located in Busan, South Korea, was founded in 1946, and is now the no. 1 national university of South Korea in research and educational competency. The multi-campus university also has other smaller campuses in Yangsan, Miryang, and Ami. The university prides itself on the principles of truth, freedom, and service, and has approximately 30,000 students, 1200 professors, and 750 faculty members. The university is composed of 14 colleges (schools) and one independent division, with 103 departments in all.    

Website: https://www.pusan.ac.kr/eng/Main.do

 

About Assistant Professor Min-Ho Seo
Dr. Min-Ho Seo received his B.S. degree (Magna cum laude, 2011) in nanomechatronics engineering from Pusan National University and his M.S. and Ph.D. degrees (2013 and 2018, respectively) in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST). Between 2018 and 2019, he was a postdoctoral research fellow at the Information and Electronics Research Institute at KAIST, following which from 2019 until 2020, he joined the Center for Bio-integrated Electronics at Northwestern University in USA as a postdoctoral researcher.

Since 2020, he has been affiliated with the School of Biomedical Convergence Engineering, Pusan National University, where he works as an assistant professor. His research interests include nano/microelectromechanical systems, biomedical devices and electronics, flexible and wearable electronics, and physical, chemical, hydrogen, and optical sensor devices and electronics.

Lab websitehttp://sites.google.com/view/mhseogroup

ORCID ID: Prof. Min-Ho Seo:0000-0002-9990-9227

Preparing for future coronavirus variants using artificial intelligence

Peer-Reviewed Publication

ETH ZURICH

SARS-​CoV-2 is constantly mutating and each new variant often catches the world by surprise. Take for example the highly mutated Omicron variant that emerged last November and required health authorities to develop a rapid response strategy even though, initially there were no answers to important questions: How protected are vaccinated and previously infected people against the new variant? And are antibody therapies still effective against this new version of the virus?

Researchers led by Professor Sai Reddy from the Department of Biosystems Science and Engineering at ETH Zurich in Basel have now developed a way of using artificial intelligence to answer such questions, potentially even in real-​time immediately after a new variant emerges.

Exploring the multitude of potential variants

Since viruses mutate randomly, no one can know exactly how SARS-​CoV-2 will evolve in the coming months and years and which variants will dominate in the future. In theory, there is virtually no limit to the ways in which a virus could mutate. And this is the case even when considering a small region of the virus: the SARS-​CoV-2 spike protein, which is important for infection and detection by the immune system. In this region alone there are tens of billions of theoretical possible mutations.

That’s why the new method takes a comprehensive approach: for each variant in this multitude of potential viral variants, it predicts whether or not it is capable of infecting human cells and if it will be neutralized by antibodies produced by the immune system found in vaccinated and recovered persons. It is highly likely that hidden among all these potential variants is the one that will dominate the next stage of the COVID-​19 pandemic.

Synthetic evolution and machine learning

To establish their method, Reddy and his team used laboratory experiments to generate a large collection of mutated variants of the SARS-​CoV-2 spike protein. The scientists did not produce or work with live virus, rather they produced only a part of the spike protein, and therefore there was no danger of a laboratory leak.

The spike protein interacts with the ACE2 protein on human cells for infection, and antibodies from vaccination, infection or antibody therapy work by blocking this mechanism. Many of the mutations in SARS-​CoV-2 variants occur in this region, which allows the virus to evade the immune system and continue to spread.

Although the collection of mutated variants the researchers have analysed comprises only a small fraction of the several billion theoretically possible variants – which would be impossible to test in a laboratory setting – it does contain a million such variants. These carry different mutations or combinations of mutations.

By performing high-​throughput experiments and sequencing the DNA from these million variants, the researchers determined how successfully these variants interact with the ACE2 protein and with existing antibody therapies. This indicates how well the individual potential variants could infect human cells and how well they could escape from antibodies.

The researchers used the collected data to train machine learning models, which are able to identify complex patterns and when given only the DNA sequence of a new variant could accurately predict whether it can bind to ACE2 for infection and escape from neutralizing antibodies. The final machine learning models can now be used to make these predictions for tens of billions of theoretically possible variants with single and combinatorial mutations and going far beyond the million that were tested in the laboratory.

Next-​generation antibody therapy

The new method will help develop the next generation of antibody therapies. Several of such antibody drugs were developed to treat the original SARS-​CoV-2 virus and approved for use in the United States and Europe. Among these, five antibody drugs were removed from clinical use and many others under clinical development were discontinued because they could no longer neutralise the Omicron variant. To address this challenge, the new method may be applied to identify which antibodies have the broadest activity.

“Machine learning could support antibody drug development by enabling researchers to identify which antibodies have the potential to be most effective against current and future variants,” says Reddy. The researchers are already working with biotechnology companies that are developing next generation COVID-​19 antibody therapies.

Identifying variants able to escape immunity

Additionally, the method developed at ETH Zurich could be applied to support the development of next generation COVID-​19 vaccines. The focus here is on identifying virus variants that still bind to the ACE2 protein – and can therefore infect human cells – but cannot be neutralised by the antibodies present in vaccinated and recovered people. In other words, variants that can escape the human immune response. This was indeed the case with the Omicron variant that escaped from most antibodies and this winter resulted in many breakthrough infections in vaccinated and previously infected people. Therefore, similar to antibody therapies, it is a major advantage if vaccines could induce antibodies that provide protection against potential future viral variants.

“Of course, no one knows which variant of SARS-​CoV-2 will emerge next,” Reddy says. “But what we can do is identify key mutations that may be present in future variants, and then work to develop vaccines in advance that provide a broader range of protection against these potential future variants.”

Faster decision making for public health

Finally, this machine learning method can also support public health, as when a new variant emerges, it can rapidly make predictions on whether antibodies produced by existing vaccines will be effective. In this way, it can accelerate the decision-​making process related to vaccinations. For example it may be that people who received a particular vaccine produce antibodies that are not effective against a new variant and should thus receive booster vaccinations as soon as possible.

Reddy points out that the technology could also be adapted for other circulating viruses, such as influenza, as predicting future influenza variants may support the development of seasonal flu vaccines.

This research was funded by the Botnar Research Centre for Child Health as part of the Fast Track Call for emergency response to the COVID-​19 pandemic.

Reference

Taft JM, Weber CR, Gao B, Ehling RA, Han J, Frei L, Metcalfe SW, Overath M, Yermanos A, Kelton W, Reddy ST: Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-​CoV-2 receptor binding domain, Cell, 31 August 2022 (Journal Pre-​Proof), doi: 10.1016/j.cell.2022.08.024

Technology transfer deficits jeopardize climate targets

Peer-Reviewed Publication

INSTITUTE FOR ADVANCED SUSTAINABILITY STUDIES E.V. (IASS)

Many developing countries have made their nationally determined climate contributions submitted under the Paris Agreement conditional on receiving climate finance, technology transfer, and capacity-building support. However, developed countries have so far failed to deliver tech transfer to the extent promised. According to a new study, public-private partnerships and other energy initiatives can only partially make up for this shortfall. While their role in supporting the growth of low-carbon energy systems in the Global South is proving crucial, their contribution in terms of technology transfer is insufficient.

Developed countries have pledged to provide US$100 billion annually from public and private sources for climate finance starting in 2020. Technology transfer is an important part of this: While developing and emerging countries need climate finance to build out clean energy solutions, knowledge is pivotal to harnessing their benefits.

This has not been achieved to date - and not only because climate financing is lacking. "Most patents for low-carbon technologies are held by companies in the Global North. This gives them a significant competitive advantage. They only share their knowledge when it is beneficial for them," explains co-author Andreas Goldthau (Institute for Advanced Sustainability Studies, Potsdam/University of Erfurt). China is the only emerging market that has successfully attracted technology transfer through foreign direct investment. In order to tap into the Chinese market, companies were willing to "transfer" their technologies, i.e. share their knowledge.

China's recipe for success is not easily transferable

China's success in building a low-carbon technology sector can be broadly attributed to the high innovation capacity of Chinese industry as well as various policy measures. "These include the promotion of joint ventures and knowledge transfer, but also local content requirements that compel foreign investors to use products or services made in China. China was able to push through these measures by leveraging its large and profitable market," says lead author Silvia Weko (IASS/University of Erfurt). In other developing and emerging economies, similar efforts have proven ineffective or even counterproductive.

There, foreign investment in low-carbon energy systems and associated knowledge transfer remains critically insufficient. As a consequence, many developing countries continue to invest in predominantly fossil fuel technologies. There are concerns that countries may become locked in to high-carbon energy systems as a result.

A stronger focus on promoting low-carbon technology transfer is needed

What options are available to countries that want to increase the transfer of technology but are unable to achieve this through market mechanisms or policy? Technology transfer initiatives, such as public-private partnerships or platforms like the United Nations Climate Technology Center and Network (CTCN), work to advance energy transitions in the Global South. Such initiatives were intended to fill the gap in the market, but their track record is mixed, according to the IASS researchers' analysis.

Weko and Goldthau identified 71 initiatives that include technology transfer among their goals. Many of these are active in countries where only a small proportion of the population has access to electricity. Their efforts to support the development of sustainable energy systems in these countries are largely successful. However, just 26 of the 71 initiatives studied actually pursue technology transfer activities.

In order to increase knowledge transfer to developing countries and emerging economies, industrialized countries must keep their funding promises and provide greater support to the United Nations Climate Technology Center and Network, the researchers argue. The transfer gap cannot be closed with the current patchwork approach. Trade and regional cooperation also offer opportunities for countries to pool resources and demand in order to negotiate better terms.