Urban exodus during the COVID-19 pandemic

Life-course perspective of households with children

Osaka Metropolitan University

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Findings suggest households with children prioritized migration destinations that foster social interaction.

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Credit: Haruka Kato, Osaka Metropolitan University

During the COVID-19 pandemic, widespread migration from urban centers, known as “urban exodus,” occurred. In the context of pandemic-driven urban exodus, households with children emerged as notable migrants due to the spread of COVID-19. However, a research gap exists as to whether the spread of the infection affected changes in the migration destination determinants of households.

Dr. Haruka Kato, a junior associate professor at Osaka Metropolitan University, examined the shifts in migration destination determinants of households with children who mentioned the spread of COVID-19 as a migration motive during the pandemic. This study adopted a life-course perspective of the shift from the pre-pandemic to the pandemic periods. A web-based questionnaire survey was conducted to recruit participants.

Results revealed significant shifts that emphasized the importance of social interaction-related factors. The social interaction determinants included the favorability of communities, community ties, participants’ desire to return to their hometown, and proximity to acquaintances. Conversely, determinants linked to work, the living environment, and housing did not shift significantly to affected groups.

“It is time to use our shared knowledge to examine the social impact of the COVID-19 pandemic.” said Dr. Kato. “This study’s findings suggest that households with children prioritized migration destinations that foster social interaction and enhance their overall appeal.”

The findings were published in Population, Space and Place.

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About OMU 

Established in Osaka as one of the largest public universities in Japan, Osaka Metropolitan University is committed to shaping the future of society through the “Convergence of Knowledge” and the promotion of world-class research. For more research news, visit https://www.omu.ac.jp/en/ and follow us on social media: X, Facebook, Instagram, LinkedIn.

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Journal

Population Space and Place

DOI

10.1002/psp.2869 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

Shifting Migration Determinants for Family Households With Children From a Life-Course Perspective

 

Breast cancer death rates have stopped going down

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Oxford University Press USA

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A new paper in the Journal of Breast Imaging, published by Oxford University Press, indicates that breast cancer mortality rates have stopped declining in women older than age 74, and reconfirms that breast cancer mortality rates have stopped falling in women younger than age 40. This finding for older women is new.

Breast cancer is the second leading cause of cancer deaths in American women, with over 42,000 women dying of the disease in 2024. Before 1990, female breast cancer rates had been rising, and breast cancer mortality rates had been flat or increasing. Since 1990 there has been a steady decline in breast cancer mortality rates, which public health observers attribute both to the widespread use of mammograms and improvements in treatment.

The researchers, Debra Monticciolo and R. Edward Hendrick, assessed cancer mortality rates collected and maintained by the National Center for Health Statistics since 1990. For U.S. women overall breast cancer mortality rates have decreased steadily from 1990 to 2022, falling by 43.5% over that period. The most recent trend has been a decrease of 1.23% per year from 2010 to 2022, the lowest rate of decrease recorded since 1990. For U.S. women ages 20 to 39 (combining all races/ethnicities), breast cancer mortality rates decreased by 2.79% per year from 1990 until 2010, but have remained flat since 2010.

The investigation found that for women 75 years and older, the breast cancer mortality rate decreased by 1.26% per year from 1993 to 2013, when the rate stopped declining. For Asian, Hispanic, and Native American women (of all ages), breast cancer mortality rates have stopped declining over the most recent period: since 2009 for Asian women, since 2008 for Hispanic women, and since 2005 for Native American women.

Previous research indicated that breast cancer mortality rates stopped declining for women under 40 in 2010. The researchers here found that in both younger and older groups, the end of mortality rate decline was primarily due to mortality rates no longer declining for White women under 40 and over 74, as well as unfavorable trends for Hispanic women ages 20-39 years and for Asian, Hispanic, and Native American women 75 and older. Breast cancer mortality rates in Black women continued to decline in all age groups.

The investigators conducting this study contend that mortality rates have stopped declining for women under 40 and over 74 due to significant increases in stage IV breast cancers at diagnosis in these two age groups. Stage IV (metastatic) breast cancer at diagnosis has an extremely poor prognosis: a 31% 5-year survival rate.

This study indicates that increasing rates of advanced stage breast cancer at diagnosis is an important reason breast cancer mortality rates are no longer declining at the rate they once did. The researchers believe that this may be due to healthcare protocols. While the medical community currently recommends a breast cancer assessment for all women by age 25, breast cancer screening is only recommended for women under age 40 who are at higher-than-average risk. Some guidelines discourage women over 74 from screening.

Breast cancer mortality rate ratios for Black vs White women show the widest gap for women under age 40 years, suggesting that younger Black women are especially in need of alternatives to our current breast cancer risk assessment, screening, and treatment strategies, according to the authors.

“The fact that breast cancer mortality rates have stopped declining for women over age 74 is an alarming new trend,” said Monticciolo. “This is in addition to women under age 40 no longer seeing mortality rates decline from breast cancer. These groups are exactly those discouraged from breast cancer screening by some U.S. guidelines.”

The paper, “Recent Trends in Breast Cancer Mortality Rates for U.S. Women by Age and Race/Ethnicity,” is available (at midnight on March 6th) at https://doi.org/10.1093/jbi/wbaf007.

To request a copy of the study, please contact:
Daniel Luzer 
daniel.luzer@oup.com

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Journal

Journal of Breast Imaging

DOI

10.1093/jbi/wbaf007 

Method of Research

Data/statistical analysis

Subject of Research

People

Article Title

Recent Trends in Breast Cancer Mortality Rates for U.S. Women by Age and Race/Ethnicity

Article Publication Date

6-Mar-2025

 

AI has ‘great potential’ for detecting wildfires, new study of the Amazon rainforest suggests

Integrating this new technology with current AI systems could help detect wildfires, such as the devastating LA blazes, to enhance earlier warning strategies – experts believe

Taylor & Francis Group

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A type of Artificial Intelligence that mimics the functioning of the human brain could represent a powerful solution in automatically detecting wildfires, plummeting the time needed to mitigate their devastating effects, a new study finds.

The new technology uses an ‘Artificial Neural Networks’ model that combines satellite imaging technology with deep learning (a subset of Artificial Intelligence (AI) and machine learning).

Findings, published in the peer-reviewed International Journal of Remote Sensing, report a 93% success rate when training the model via a dataset of images of Amazon rainforest with, and without, wildfires.

The technology, it is stated, could be used in a complementary nature with existing AI systems to enhance early warning systems and improve wildfire response strategies.

“The ability to detect and respond to wildfires is crucial for preserving the delicate ecological balance of these vital ecosystems, and the future of this Amazon region depends on decisive rapid action,” explains lead author Professor Cíntia Eleutério of the Universidade Federal do Amazonas, in Manaus.

“Our study’s findings could improve wildfire detection in the Amazonian ecosystem and elsewhere in the world, significantly assisting authorities in combating and managing such incidents.”

In 2023 there were 98,639 wildfires in the Amazon alone. The Amazon rainforest, too, accounts for a significant portion (51.94%) of wildfires in the Brazilian biomes. In recent years this area has experienced a notable increase in such incidents.

Currently, monitoring in the Amazon is provided with near, real-time data – however, it has moderate resolutions and the ability to detect details in remote areas or smaller fire outbreaks is limited.

This new technology uses a type of artificial neural network (a machine learning algorithm that uses a network of interconnected nodes to process data in a way that mimics the human brain) called a Convolutional Neural Network (CNN) to classify areas of the rainforest affected by wildfires and improve the issue. The algorithms developed enhance their performance over time through exposure to increasing volumes of data.

The research team, who are all based at the Universidade Federal do Amazonas, used images sourced from the Landsat 8 and 9 satellites to train the CNN. These satellites are fitted with near-infrared and shortwave infrared, which together are critical for detecting vegetation changes, as well as surface temperature alterations.

First, the CNN was trained on a dataset of 200 images of wildfires and an equal number of images without wildfires to ensure a balanced learning approach. Although small, this number of images proved sufficient for the CNN to achieve 93% accuracy during the training phase.

The CNN’s ability to distinguish between images with and without wildfires was then tested using 40 images not included in the training dataset. The model correctly classified 23 of the 24 images with wildfires and all 16 of the images without wildfires, thus underscoring its robustness and capability for generalisation, and showcasing its potential as a tool for effective wildfire detection.

“The CNN model could serve as a valuable addition, enabling more detailed analyses in specific regions. By combining the wide temporal coverage of the current sensors with the spatial precision of our model, we can significantly enhance wildfire monitoring in critical environmental preservation zones,” states co-author Professor Carlos Mendes, who has a PhD in physics.

“The model has the potential to significantly assist competent authorities in combating and managing such incidents, providing an advanced and more localized approach to wildfire detection.

“It serves as a complement to well-established large-scale monitoring systems, such as the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) which are widely used for continuous wildfire detection.”

Going forward, the authors recommend increasing the number of training images for CNN to work on, which “will undoubtedly lead to a more robust model”.

Other applications, they suggest, for the CNN could also be explored – such as monitoring and controlling deforestation.

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Journal

International Journal of Remote Sensing

DOI

10.1080/01431161.2024.2425119 

Article Title

Identifying wildfires with convolutional neural networks and remote sensing: application to Amazon rainforest

Article Publication Date

6-Mar-2025

 

The future of batteries is in your closet

Scientists show that nylon increases the performance of lithium batteries

King Abdullah University of Science & Technology (KAUST)

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Commercial polymers used in clothes are helping advance battery technology.

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Credit: Xavier Pita

In two new studies published in ACS Energy Letters and Energy Environmental Science, scientists in Saudi Arabia have made a breakthrough that could increase the power and lower the cost of lithium-metal batteries by incorporating nylon into the design. 

Along with their lower carbon dioxide emissions, lithium batteries have a high energy density and are lighter than other batteries. This is why they are used in smartphones small enough to fit into your pocket and in the light, tiny electronics that have allowed us to travel to space. 

Lithium batteries come in two types. Lithium-ion batteries are more common commercially and used in laptops, smartphones and other household products. Lithium-metal batteries, on the other hand, are more energy dense and have wider applications in robotics, transport and other industries. Preventing lithium-metal batteries from realizing their full potential is their safety and longevity. Their production and operation currently involve corrosive, hazardous materials and result in too many parasitic reactions, which are side reactions that result in poorer performance and safety.  

Additives help stabilize battery interfaces, thereby enhancing performance. The studies by the KAUST researchers found that nylon, the same polymer used in clothes, can be dissolved in mild lithium solution to act as an additive for lithium-metal batteries. The result was lithium-metal batteries that were more efficient, had longer lifespans, and showed few parasitic reactions.  

Thus, by examining the chemistry of nylon and lithium interactions, including key molecular bonds, the study shows that the commercial fabric can be dissolved in far milder solvents than previously thought for superior battery performance. 

"Polymers have always been difficult to dissolve in common battery electrolytes. We did an intensive study of the chemical properties and modified the solvation structure and interactions," explained Zhiming Zhao, a postdoctoral scientist at King Abduallah University of Science (KAUST) who authored the study. 

"My research team is dedicated to building renewable energy and storage solutions such as higher energy density and safer batteries to accelerate decarbonization adoption in the Kingdom. This was a discovery that promises cheaper and safer additives and demonstrates the benefits of basic scientific research," said KAUST Professor and Chair of the KAUST Center of Excellence for Renewable Energy and Storage Technologies (CREST) Husam Alshareef, who led the two studies. 

KAUST opened CREST in September 2023 in alignment with its new strategic direction. The Kingdom is investing heavily in sustainable energy technologies, as Saudi Arabia has declared that it will be a net-zero carbon economy by 2060.  

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Journal

ACS Energy Letters

DOI

10.1021/acsenergylett.4c03221 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

New Dissolution Chemistry of Nylon Promises Reversible Li-Metal Batteries

 

Scaling laws in urban ecosystems: A new perspective on city growth

Higher Education Press

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Credit: Gengyuan Liu, Mingwan Wu

As urbanization accelerates, ecosystems are often pushed to the brink, leading to the degradation of ecological services and heightened environmental stress. Traditional urban planning has typically focused on economic and social factors, often sidelining the complex interactions within urban ecosystems. This oversight can result in unsustainable practices, threatening the long-term health of cities and their surrounding environments. In response to this challenge, there is an urgent need for a more integrated approach—one that incorporates ecological principles into the fabric of urban development. Such an approach is essential to safeguard urban resilience and sustainability, ensuring that cities thrive amid rapid growth and changing environmental conditions.

A team of researchers from Beijing Normal University has published a pioneering review (DOI: 10.1007/s11783-025-1924-8) in the October 2024 issue of Frontiers of Environmental Science & Engineering. This research explores the application of scaling laws in urban ecosystems, focusing on the thermodynamic processes that govern urban metabolism. Their goal is to uncover patterns that can help boost ecological resilience within cities, providing a scientific foundation for more sustainable urban planning.

The study takes an innovative approach by applying scaling laws—typically used to describe biological systems—to urban environments. By analyzing energy flows and metabolic processes within cities, the researchers uncovered that urban ecosystems exhibit multistable states. These states reflect different equilibrium points where the demands of urban growth and the provision of ecological services can coexist, fostering a dynamic balance between the two. One of the study's most significant findings is the identification of threshold effects, where small changes in urban planning or environmental conditions can cause dramatic shifts in ecosystem stability. For example, expanding green spaces or implementing sustainable infrastructure can enhance the resilience of urban ecosystems, enabling them to better withstand challenges like climate change and pollution. The study also emphasizes the importance of maintaining ecological infrastructure—such as parks and green corridors—which are critical for supporting the multifunctional needs of urban environments. By optimizing resource use and adopting sustainable practices, cities can build more resilient ecosystems, ultimately improving the quality of life for urban residents.

Dr. Gengyuan Liu, a leading expert in urban metabolism and one of the study’s principal researchers, commented, "Understanding the scaling laws of urban ecosystems is crucial for creating sustainable cities. This research lays the groundwork for predicting ecological tipping points, providing essential insights to guide urban planning toward more resilient and efficient systems."

The implications of these findings are profound, offering a new framework for urban planners and environmental policymakers. By integrating scaling laws into urban design, cities can better anticipate the ecological impacts of their growth and implement strategies to enhance ecosystem services. This approach has the potential to transform urban planning, fostering cities that are more sustainable, resilient, and adaptable to environmental challenges. In the long run, such strategies could improve not only the ecological health of cities but also the economic and social well-being of their residents, creating thriving urban environments for generations to come.

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Journal

Frontiers of Environmental Science & Engineering

DOI

10.1007/s11783-025-1924-8 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Thermodynamic-based ecological scaling theory in urban metabolic framework: a review

 

Possible foundations of human intelligence observed for the first time

For the first time, it has been confirmed that, contrary to previous beliefs, individual neurons represent the concepts we learn, regardless of the context in which we encounter them

IMIM (Hospital del Mar Medical Research Institute)

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A study led by Dr. Rodrigo Quian Quiroga, group leader of the Neural Mechanisms of Perception and Memory Research Group at the Hospital del Mar Research Institute, has allowed scientists to observe for the first time how neurons in the human brain store memories independent of context in which they are acquired. Published in Cell Reports, the study confirms that neurons can distinguish objects or people regardless of their context, enabling the formation of higher and more abstract relationships, which constitutes the basis of human intelligence.

This is the first study to observe this neuronal behavior in humans. Until now, research conducted on animals had shown significant differences in the coding of concepts (such as a specific place, object, etc.) when the context changed. For example, neurons responded very differently if a rat found an object in one location versus another. As a result, it was believed that such memories were stored in different groups of neurons. The study led by Dr. Quian Quiroga has yielded "surprising responses" that contradict previous findings, as neuronal responses to a specific concept remain the same when the context changes, such as remembering having seen a person in different locations. "The basic principle of neuronal coding in humans is the opposite of what has been observed in other species, which has significant implications," notes Quian Quiroga.

Single Neuron Data

The study involved data from nine patients in Argentina and the United Kingdom with treatment for refractory epilepsy, who had electrodes implanted to monitor the activity of specific groups of neurons individually. This allowed researchers to obtain precise recordings of their responses, unlike previous human studies based on fMRI recording, which cannot differentiate individual neurons.

Patients were presented with two stories featuring the same person in different contexts, supported by images. Thanks to the monitoring of individual neurons while performing this task, researchers could observe which groups of neurons were activated and how they responded in the two stories. Specifically, they confirmed that if a neuron responded to a person’s image, the response remained the same in both stories. Furthermore, when patients recounted the story themselves, the same neurons were activated seconds before they referred to the protagonist, and also in the same way for both stories.

"Memories are stored in a much more abstract manner in humans compared to other animals. You can think of concepts or anything else in more abstract terms, independent of the context in which you learned them," explains Dr. Quian Quiroga, suggesting that this could be one of the "foundations of human intelligence." "This ability allows us to make much more abstract and complex associations and inferences than if we were forced to think of each concept within a specific, concrete context," he asserts. In other words, humans can decontextualize their memories to create more abstract thought.

 

Reference Article

Rey HG, Panagiotaropoulos TI, Gutierrez L, Chaure FJ, Nasimbera A, Cordisco S, Nishida F, Valentin A, Alarcon G, Richardson MP, Kochen S, Quian Quiroga R. Lack of context modulation in human single neuron responses in the medial temporal lobe. Cell Rep. 2025 Jan 28;44(1):115218. doi: 10.1016/j.celrep.2024.115218. Epub 2025 Jan 15. PMID: 39823228; PMCID: PMC11781864.

 

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Journal

Cell Reports

DOI

10.1016/j.celrep.2024.115218 

Article Title

Lack of context modulation in human single neuron responses in the medial temporal lobe