Friday, October 09, 2026

Scientists identify gene signatures in children with severe COVID-19 infection

University of Southampton

Researchers have identified patterns of gene activity associated with severe COVID-19 in children that are shared with other serious respiratory infections.

The findings, published in BMC Infectious Diseases, offer early clues to how the immune system responds to severe infections, which children are at higher risk and potential targets for treatment.

Lower respiratory tract infections, including COVID-19, respiratory syncytial virus (RSV) and pulmonary tuberculosis (PTB) are major causes of childhood illness and death, particularly in low- and middle-income countries.

Researchers from the University of Southampton and the University of Cape Town, together with international partners, have analysed gene expression in blood samples from 333 children from South Africa.

The team compared samples from children hospitalised with severe COVID-19, RSV and PTB infections with those of children with only mild or asymptomatic COVID-19, and a control group of healthy children.

They identified more than 5,000 genes being expressed differently in children with severe infections. They also discovered ten groups of genes which were expressed in a similar pattern across the different lower respiratory tract infections. These were linked to immune responses, cell regulation and other biological processes involved in infection.

The researchers also identified 82 genes that helped distinguish mild or asymptomatic COVID-19 infection from severe cases, suggesting that gene-expression patterns could potentially be used as an indicator of disease severity.

Dr Negusse Kitaba, Senior Research Fellow at the University of Southampton and lead researcher, said: “These findings provide new insights into the biological pathways involved in disease severity and highlight genes for further investigation as potential biomarkers and therapeutic targets.”

Professor Heather Zar, Director of the South African Medical Research Council Unit on Child & Adolescent Health, University of Cape Town, and Principal Investigator of the three studies in South Africa that informed this work, added: “There’s been limited research examining biological responses to these infections in African children. There’s also been few studies on host gene expression in infants and children with COVID-19 globally, so this study helps to close an important evidence gap.”

Professor John Holloway, Professor of Allergy and Respiratory Genetics from the University of Southampton, said: “This study highlights the value of international collaboration in understanding childhood respiratory disease and provides a foundation for future research into why some children develop severe infection.”

The team say that further research with larger groups of children will be needed to determine whether these promising gene signature discoveries can be developed for clinical use.

Ends

Contact

Steve Williams, Media Manager, University of Southampton, press@soton.ac.uk or 023 8059 3212.

Notes for editors

The study, “Immune transcriptomic differences in paediatric patients with SARS-CoV-2 compared to other lower respiratory tract infections,” is published in BMC Infectious Diseases here: https://link.springer.com/article/10.1186/s12879-026-14343-x
For interviews, please contact Steve Williams, Media Manager, University of Southampton, press@soton.ac.uk or 023 8059 3212.

Additional information

The University of Southampton drives original thinking, turns knowledge into action and impact, and creates solutions to the world’s challenges. Our academics are leaders in their fields, forging links with high-profile international businesses and organisations, and inspiring a 24,000-strong community of exceptional students, from over 135 countries worldwide. Through our high-quality education, the University helps students on a journey of discovery to realise their potential and join our global network of over 300,000 alumni. www.southampton.ac.uk

www.southampton.ac.uk/news/contact-press-team.page

Journal

BMC Infectious Diseases

DOI

10.1186/s12879-026-14343-x

Subject of Research

People

Article Title

Immune transcriptomic differences in paediatric patients with SARS-CoV-2 compared to other lower respiratory tract infections

Article Publication Date

1-Oct-2026

Predicting urban building damage even from cloud-obscured satellite images: SNU professor In Ho Cho’s team develops scientific AI framework

Statistically corrects incomplete satellite imagery obscured by clouds, smoke, and other interference / Enables city-scale building damage prediction immediately after disasters without costly retraining

Seoul National University College of Engineering

image:
City-scale damage prediction results using satellite imagery and image disorder (entropy) before and after a major typhoon
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Credit: © Scientific Reports, originally published in Scientific Reports

A new technology has been developed that can rapidly predict city-scale structural damage even when portions of satellite imagery are obscured by clouds or smoke immediately after a disaster.

A research team led by Professor In Ho Cho of the Department of Architecture at Seoul National University College of Engineering has presented a scientific artificial intelligence (Scientific AI) and data science framework that uses satellite imagery and various forms of open data to rapidly and accurately predict disaster-related structural damage at the city scale. By incorporating a statistical method for correcting incomplete satellite data, the researchers developed an approach that can estimate structural damage immediately after a disaster without requiring a separate, computationally expensive training stage.

The findings were published in Scientific Reports, an international journal in the Nature Portfolio.

As climate change intensifies and infrastructure systems become increasingly complex, structural damage caused by natural and human-induced disasters—including typhoons, floods, wildfires, earthquakes, and explosions—is occurring on an increasingly broad, city-wide scale. Rapidly determining where structures have been damaged and the extent of that damage immediately after a disaster is a critical first step in rescue operations, setting recovery priorities, and developing disaster-response measures.

In actual disaster settings, however, complete satellite imagery is not always available. Portions of satellite images may be obscured by clouds, smoke, precipitation, shadows, and other factors, which can reduce the performance of subsequent AI or statistical analyses. Existing AI-based damage assessment methods also often require large volumes of training data and substantial computational resources, making them difficult to deploy for immediate damage prediction in the aftermath of a disaster.

To address these challenges, the research team combined a Scientific AI approach, which integrates structural engineering knowledge into artificial intelligence, with a general-purpose statistical imputation method that rapidly corrects incomplete data. Rather than simply learning from data, Scientific AI incorporates engineering knowledge of the physical phenomenon of structural damage into AI-based analysis.

The framework developed in this study uses both pre- and post-disaster satellite imagery together with a range of publicly available data. In particular, it uses image entropy, a measure of disorder in satellite imagery, to analyze the extent of changes in structures and their surrounding environments before and after a disaster and to predict structural damage at the city scale. Entropy indicates how complex and irregular the information within an image has become and can be used to quantify changes in structures and surrounding environments following a disaster.

The researchers also applied a statistical data imputation method to enable damage prediction even when parts of satellite imagery are obscured or missing. Rather than discarding incomplete data or simply filling in missing values with averages, the method uses patterns and similarities within the data to statistically reconstruct missing information. This makes it possible to predict disaster damage even from satellite imagery in which some information has been lost because of clouds, smoke, or other obstructions.

A key feature of the framework is that, unlike state-of-the-art AI approaches, it does not require a costly training stage in which large-scale datasets must be newly assembled and trained. Instead of building extensive training datasets and retraining a model whenever a new disaster occurs, the framework can immediately estimate damage using pre- and post-disaster satellite imagery and publicly available data.

The research team validated the performance of the framework using a case involving damage caused by a major typhoon. The results confirmed that city-scale structural damage and destruction could be rapidly predicted even when using incomplete satellite data together with open data. Notably, even when more than 50% of the imagery was obscured by clouds, the framework successfully predicted the extent of damage to urban buildings beneath the obscured areas with an error of less than 5%.

The study also demonstrated that the approach could potentially be applied to disasters beyond major typhoons, including wildfires. The researchers applied the framework to a major wildfire damage case, demonstrating its potential to be extended to large-scale structural damage prediction across a range of natural and human-induced disasters.

The significance of this work lies in expanding disaster damage prediction beyond a problem of simply interpreting satellite imagery or training artificial intelligence models, instead treating it as a Scientific AI problem that integrates structural engineering knowledge with statistical data correction techniques. In particular, the study demonstrates the potential to rapidly estimate city-scale damage even under the incomplete-data and time-constrained conditions typical of the immediate aftermath of a disaster.

Rapid decision-making is critical in disaster response. If city-scale structural damage and destruction can be quickly identified immediately after a disaster, the information could help determine which areas should receive rescue personnel and equipment first, where recovery efforts should begin, and which areas require additional safety inspections. In the longer term, the approach is expected to have applications in urban disaster-prevention planning, post-disaster recovery strategies, and infrastructure management.

Professor Cho’s primary research focuses on Scientific AI, which combines scientific knowledge with artificial intelligence algorithms to address challenging problems in engineering and science. This study represents an advance that combines Scientific AI and data science to open new possibilities for city-scale disaster damage prediction.

Professor In Ho Cho said, “This study demonstrates that combining structural engineering knowledge with Scientific AI and data science can enable complex engineering information to be predicted rapidly and at low cost.” He added, “It is particularly meaningful that reliable engineering judgments can be derived even from damaged or incomplete image data.”

He continued, “We expect that this approach could eventually be extended to a wide range of fields, including the management of aging structures, image-based performance assessment of machinery, ships, and aircraft, and image-based analysis for defense applications.”

The study involved Professor In Ho Cho of the Department of Architecture at Seoul National University, researchers from Iowa State University, and a researcher from the U.S. Air Force Research Laboratory. The research was supported by the Seoul National University New Faculty Research Settlement Fund.

□ Introduction to the SNU College of Engineering

Seoul National University (SNU) founded in 1946 is the first national university in South Korea. The College of Engineering at SNU has worked tirelessly to achieve its goal of ‘fostering leaders for global industry and society.’ In 12 departments, 323 internationally recognized full-time professors lead the development of cutting-edge technology in South Korea and serving as a driving force for international development.

Journal

Scientific Reports

DOI

10.1038/s41598-026-72069-x

Method of Research

Data/statistical analysis

Example results from applying the developed framework to a major wildfire damage case

Credit

Do ridesharing services affect crime rates?

Wiley

New research published in Economic Inquiry reveals that ridesharing services like Uber and Lyft may reduce crime in some areas.

Based on data collected during the staggered rollout in US cities since 2010, the study showed that ridesharing services were linked to a 4.6% reduced rate of violent crimes, a 5.6% reduced rate of property crimes, and a 10.5% reduced rate of burglaries. No significant effects were observed on larceny, motor vehicle theft, or arson.

Improved employment opportunities generated by ridesharing platforms may have played an important role in these crime reductions.

“My findings suggest that digital platforms may generate public-safety benefits beyond their primary commercial purpose,” said study author Emtiaz Hossain Hritan, PhD, of the University of California, Irvin. “Ridesharing services may complement traditional crime-reduction strategies by improving mobility and expanding employment opportunities.”

URL upon publication: https://onlinelibrary.wiley.com/doi/10.1111/ecin.70087

Additional Information
NOTE: The information contained in this release is protected by copyright. Please include journal attribution in all coverage. For more information or to obtain a PDF of any study, please contact: Sara Henning-Stout, newsroom@wiley.com.

About the Journal
Published since 1962, Economic Inquiry is a highly regarded scholarly journal in economics publishing articles of general interest across the profession. Quality research that is accessible to a broad range of economists is the primary focus of the journal. Join our long list of prestigious authors, including more than 20 Nobel laureates.

About Wiley     
Wiley is a global leader in authoritative content and research intelligence for the advancement of scientific discovery, innovation, and learning. With more than 200 years at the center of the scholarly ecosystem, Wiley combines trusted publishing heritage with AI-powered platforms to transform how knowledge is discovered, accessed, and applied. From individual researchers and students to Fortune 500 R&D teams, Wiley enables the transformation of scientific breakthroughs into real-world impact. From knowledge to impact—Wiley is redefining what's possible in science and learning. Visit us at Wiley.com and Investors.Wiley.com. Follow us on Facebook, X, LinkedIn and Instagram.

Journal

Economic Inquiry

DOI

10.1111/ecin.70087

Article Title

Crime on the Move? The Effect of Ridesharing Services on Crime

Article Publication Date

7-Oct-2026

Biodegradable nanoparticle–technology may improve agricultural production

Wiley

Farmers use a manufactured version of a natural plant hormone called gibberellic acid to improve the health and growth of crops, but it is highly sensitive to heat and light. A study in the Journal of the Science of Food and Agriculture shows that loading gibberellic acid into biodegradable nanoparticles can improve grape yield and quality, making it a promising and environmentally friendly approach for sustainable agricultural production.

Field applications demonstrated that treatment with gibberellic acid–loaded nanoparticles increased grape cluster weight by 104% and length by 67% compared with free gibberellic acid treatment, while berry weight, length, and width increased by 15%, 4%, and 13%, respectively.

“Compared to traditional products, nanocarrier systems ensure more efficient delivery of active substances such as plant growth regulators in agricultural applications,” the authors wrote.

URL upon publication: https://onlinelibrary.wiley.com/doi/10.1002/jsfa.71082

About the Journal
The Journal of the Science of Food and Agriculture publishes high-impact, peer-reviewed research connecting academic and industry leaders worldwide with the latest breakthroughs in food, agriculture, and sustainability. By championing interdisciplinary studies and global collaboration, the journal equips its diverse readership with actionable knowledge to address critical challenges and drive progress toward a sustainable food future.

Journal

Journal of the Science of Food and Agriculture

DOI

10.1002/jsfa.71082

Article Title

Polymeric Nano-Delivery of Gibberellic Acid Enhances Fruit Quality, Yield, and Pedicel Strength in Grapevine (Vitis vinifera L.)

Article Publication Date

7-Oct-2026

Petal “windows” affect the abundance of microbes within flowers

Wiley

Some flowers have petals with small semi-translucent sections. Research in New Phytologist reveals that these “windows” alter the amount of light and heat entering the flower and create conditions that influence microbial abundance.

Field experiments showed that petal windows transmitted more ultraviolet and infrared radiation than pigmented tissue. Also, internal floral temperature predicted microbial numbers, with warmer flowers harboring fewer microbes.

Because microbial communities that live inside flowers can affect nectar quality, this discovery indicates that petal windows may influence how pollinators interact with flowers.

“One of the exciting things about this work is that a seemingly small floral trait can change the physical environment inside a flower in ways that affect its microbial community,” said corresponding author Jessica Williams, a PhD student at Florida Atlantic University. “Because floral microbes can influence nectar traits and plant-pollinator interactions, these results suggest that flower structure may have ecological consequences that extend beyond the flower itself.”

URL upon publication: https://onlinelibrary.wiley.com/doi/10.1111/nph.71608

About the Journal
New Phytologist is an international journal publishing outstanding original research in plant science and its applications. Research falls into five sections: Physiology & Development, Environment, Interaction, Evolution, and Transformative Plant Biotechnology. Topics covered range from intracellular processes through to global environmental change. New Phytologist is owned by the New Phytologist Foundation, a non-profit organization dedicated to the promotion of plant science.

Journal

New Phytologist

DOI

10.1111/nph.71608

Article Title

Petal infrared transmission warms flowers and reduces microbial abundance

Article Publication Date

7-Oct-2026

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Friday, October 09, 2026

Scientists identify gene signatures in children with severe COVID-19 infection

Journal

DOI

Subject of Research

Article Title

Article Publication Date

Predicting urban building damage even from cloud-obscured satellite images: SNU professor In Ho Cho’s team develops scientific AI framework

Journal

DOI

Method of Research

Example results from applying the developed framework to a major wildfire damage case

Credit

Do ridesharing services affect crime rates?

Journal

DOI

Article Title

Article Publication Date

Biodegradable nanoparticle–technology may improve agricultural production

Journal

DOI

Article Title

Article Publication Date

Petal “windows” affect the abundance of microbes within flowers

Journal

DOI

Article Title

Article Publication Date

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