Saturday, March 16, 2024

BRAIN STUDIES

Poor neighborhoods linked to elevated dementia risk and faster brain aging


Irrespective of income or education, people living in disadvantaged neighborhoods show early signs of cognitive decline



DUKE UNIVERSITY




DURHAM, NC – Living in a poorer neighborhood is linked to accelerated brain aging and increased dementia risk early in life, regardless of income level or education, a Duke University-led study finds.

The study, which appears March 14 in Alzheimer's & Dementia: The Journal of the Alzheimer's Association, suggests that targeting disadvantaged neighborhoods for dementia prevention programs and encouraging clinicians to consider a patient’s address could help lower dementia risk.

“If you want to prevent dementia, and you’re not asking someone about their neighborhood, you're missing information that's important to know,” said clinical neuropsychologist Aaron Reuben, Ph.D., who led the study as a postdoctoral scholar in the joint lab of Duke University psychology and neuroscience professors Avshalom Caspi, Ph.D., and Terrie Moffitt, Ph.D.

Dementia “blue zones”

Alzheimer's disease is the most common form of dementia, a neurological disorder that robs people of their memories and cognitive skills. An estimated 58 million people around the world today have dementia, which is on course to triple to 150 million by 2050.

Despite the expected rise of cases and the immense emotional and financial toll dementia takes on individuals and families, there are no cures or effective medicines.

Researchers are now looking instead to prevent rather than treat dementia through lifestyle changes, like diet and exercise.

Though opting for more vegetables or bike rides may help strengthen brain health and resilience, Reuben was curious if where people live predicts their future dementia risk better than any combination of individual choices.

“I wanted to understand if there was a geographic patterning to dementia the way there is to longevity, like blue zones,” Reuben said, referring to regions where residents appear to live longer than average. “A lot of individual choices, like what you eat, what you do for fun, or who you spend time with, are constrained by where you live.”

Poor neighborhoods beget dementia risk

Reuben and his colleagues at Duke, as well as collaborators at the University of Michigan, Michigan State University, the University of Otago (NZ), and the University of Auckland, looked at the medical records and addresses of 1.41 million New Zealanders to search for patterns.

The team looked at how well-off or disadvantaged each New Zealander’s address was on a scale from one to ten, using information from the national census on average income, employment, and education levels, as well as transportation accessibility and other related factors.

Similar to smaller-scale studies of people in the United States and England, Reuben and his team found that those residing in the most disadvantaged areas had a 43% increased risk of developing dementia over 20 years of observation.

Reuben said the finding still begged the question whether biological signs for neighborhood-associated neurodegeneration could be seen earlier in adulthood, long before people would show up in clinics with memory complaints.

Accelerated brain aging

Reuben and his team then analyzed data from the Dunedin Study, which has tracked nearly 1,000 New Zealanders since birth, documenting their psychological, social, and physiological health, including brain scans, memory tests, and cognitive self-assessments in adulthood.

Reuben found that study members living in disadvantaged neighborhoods across adulthood had measurably poorer brain health as early as age 45, regardless of their own personal income or education.

“It’s not just what personal resources you have, it’s also where you live that matters,” said Caspi. 

Poorer brain health was seen across a number of measurements, such as fewer or smaller nerve cells in the brain’s information processing areas and less efficient communication between cells across the brain, as well as more atrophy and, potentially, microbleeds.

Study members living in poorer neighborhoods also had visibly older brains at 45 when the researchers looked at MRI scans, with individuals from the most disadvantaged neighborhoods having brains that appeared three years older than expected given their chronological age. They also scored worse on memory tests and reported more problems with everyday cognitive demands, like following conversations or remembering how to navigate to familiar places.

Addressing location for dementia prevention

These results indicate that living in a disadvantaged neighborhood is a risk factor for dementia, Reuben says. How poorer neighborhoods might increase someone’s risk is still unclear, but it could be the result of a number of things associated with deprived areas, such as worse air quality, lower levels of daily social interactions, higher levels of stress, and less walkability.

Combating increased dementia risk stemming from disadvantaged neighborhoods, however, may be simple and low-cost. Community-focused interventions, such as targeting dementia prevention programs to underserved neighborhoods, or developing vacant lots into pocket parks, might help direct resources where they are most needed.

For now, though, Reuben argues that just factoring in someone’s neighborhood early-on is critical to catch and curb accelerated brain aging and dementia risk.

“If you want to truly prevent dementia, you've got to start early, because 20 years before anyone will get a diagnosis, we're seeing dementia’s emergence,” Reuben said. “And it could be even earlier.”

Funding for the study was provided by the National Institutes for Health (R01AG032282, R01AG069939, R01AG049789, P30 AG028716, P30 AG034424, F32ES34238, P30AG066582), UK Medical Research Council (MR/X021149/1), New Zealand Health Research Council (15-265; 16-604), Brain Research New Zealand, New Zealand Ministry of Business, Innovation, and Employment, and the Duke/University of North Carolina Alzheimer’s Disease Research Center Research.

CITATION: “Dementia, Dementia’s Risk Factors and Premorbid Brain Structure are Concentrated in Disadvantaged Areas: National Register and Birth-Cohort Geographic Analyses,” Aaron Reuben, Leah Richmond-Rakerd, Barry Milne, Devesh Shah, Amber Pearson, Sean Hogan, David Ireland, Ross Keenan, Annchen R. Knodt, Tracy Melzer, Richie Poulton, Sandhya Ramrakha, Ethan Whitman, Ahmad R. Hariri, Terrie E. Moffitt, Avshalom Caspi. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, March 14, 2024. DOI: 10.1002/alz.13727

 

What a view: Rice scientists develop a new system to record 2D crystal synthesis in real time



RICE UNIVERSITY
researchers 

IMAGE: 

JUN LOU AND MING TANG

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CREDIT: RICE UNIVERSITY




HOUSTON – (March 15, 2024) – Materials scientists at Rice University are shedding light on the intricate growth processes of 2D crystals, paving the way for controlled synthesis of these materials with unprecedented precision.

Two-dimensional materials such as graphene and molybdenum disulfide (MoS2) exhibit unique properties that hold immense promise for applications in electronics, sensors, energy storage, biomedicine and more. However, their complex growth mechanisms — inconsistent correlations exist between how the conditions for growth affect the shapes of crystals — have posed a significant challenge for researchers.

A research team at Rice’s George R. Brown School of Engineering tackled this challenge by developing a custom-built miniaturized chemical vapor deposition (CVD) system capable of observing and recording the growth of 2D MoS2 crystals in real time. The work is published online in the journal Nano Letters.

Through the use of advanced image processing and machine learning algorithms, the researchers were able to extract valuable insights from the real-time footage, including the ability to predict the conditions needed to grow very large, single-layer MoS2 crystals.

Study co-author Jun Lou, professor and associate chair of the Department of Materials Science and Nanoengineering at Rice, said this interdisciplinary approach represents a significant step forward in the field of scalable synthesis of 2D materials.

“By combining real-time experimental observations with cutting-edge machine learning techniques, we have demonstrated the potential to predict and control the growth of 2D crystals with excellent accuracy,” Lou said.

The research team’s findings have far-reaching implications for the future of 2D materials. Driven by their success with MoS2, the researchers believe that their approach can be extended to other 2D materials and heterostructures, offering a powerful platform for designing and engineering next-generation 2D materials with tailored properties.

“For example, in electronics, being able to robustly synthesize 2D crystals like MoS2 at scale could lead to faster and more efficient devices,” Lou said. “In sensors, it could lead to more sensitive and selective devices.”

“This research is an important step toward realizing the full potential of 2D materials and paves the way for the development of innovative technologies that could revolutionize a wide range of industries,” said Ming Tang, associate professor of materials science and nanoengineering and study co-author.

Joining Lou and Tang on the study from the Rice Department of Materials Science and Nanoengineering are Jing Zhang, Tianshu Zhai, Faizal Arifurrahman, Yuguo Wang, Andrew Hitt, Zelai He, Qing Ai, Yifeng Liu, Chen-Yang Lin and Yifan Zhu.

The research was supported by the Welch Foundation (C-1716), the National Science Foundation (2113882, 1929949), the Fulbright Scholar Program, the Air Force Office of Scientific Research (FA9550-21-1-0460) and the Department of Energy (SC0019111).

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This release can be found online at news.rice.edu.

Follow Rice News and Media Relations via Twitter @RiceUNews.

Peer-reviewed paper:

Towards Controlled Synthesis of 2D Crystals by CVD: Learning from the Real-time Crystal Morphology Evolutions | Nano Letters | DOI: 10.1021/acs.nanolett.3c04016

Authors: Jing Zhang, Tianshu Zhai, Faizal Arifurrahman, Yuguo Wang, Andrew Hitt, Zelai He, Qing Ai, Yifeng Liu, Chen-Yang Lin, Yifan Zhu, Ming Tang and Jun Lou

https://pubs.acs.org/doi/10.1021/acs.nanolett.3c04016

Image downloads:

https://news-network.rice.edu/news/files/2024/03/Lou_Tang-b747d187fd7504d7.jpg
CAPTION: Jun Lou and Ming Tang (Rice University)

About Rice:

Located on a 300-acre forested campus in Houston, Rice University is consistently ranked among the nation’s top 20 universities by U.S. News & World Report. Rice has highly respected schools of architecture, business, continuing studies, engineering, humanities, music, natural sciences and social sciences and is home to the Baker Institute for Public Policy. With 4,574 undergraduates and 3,982 graduate students, Rice’s undergraduate student-to-faculty ratio is just under 6-to-1. Its residential college system builds close-knit communities and lifelong friendships, just one reason why Rice is ranked No. 1 for lots of race/class interaction, No. 2 for best-run colleges and No. 12 for quality of life by the Princeton Review. Rice is also rated as a best value among private universities by Kiplinger’s Personal Finance.

 

A green revolution: how our forests are changing and what it means for the planet




AEROSPACE INFORMATION RESEARCH INSTITUTE, CHINESE ACADEMY OF SCIENCES
Spatial distribution, variations, and transitions of different forest management types from 2001 to 2020. 

IMAGE: 

SPATIAL DISTRIBUTION, VARIATIONS, AND TRANSITIONS OF DIFFERENT FOREST MANAGEMENT TYPES FROM 2001 TO 2020.

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CREDIT: JOURNAL OF REMOTE SENSING




A recent study reveals significant shifts in the composition of global forests and their carbon stocks from 2001 to 2020. By leveraging advanced machine learning and change detection techniques, researchers have provided the first and most detailed account to date of how forest management practices are evolving worldwide.

Forests are key to mitigating climate change through carbon absorption. Research supports forest management, like reforestation, for carbon sequestration. Yet, the impact of managed forests on soil diversity and carbon storage is debated. Understanding forest patterns globally is vital but complex due to spectral similarities in imagery, highlighting the need for detailed forest management mapping.

A recent study (doi: 10.34133/remotesensing.0119) published in the Journal of Remote Sensing on February 12, 2024, utilizing cutting-edge machine learning and change detection methodologies, offers an unprecedentedly detailed view of the evolution of forest management practices across the globe, highlighting significant changes in forest types and the management strategies applied to them.

This study meticulously categorized forests into six distinct management types, a novel approach that sheds light on the nuanced interplay between human intervention and forest ecosystems. Utilizing the latest in satellite imagery and machine learning technology, researchers have meticulously traced the evolution of global forest management and its impact on carbon stocks from 2001 to 2020. The analysis revealed a nuanced landscape of change, where losses in natural forest carbon stocks were partially offset by gains in managed forests, including planted forests, oil palm plantations, and agroforestry systems. This compensatory growth suggests a complex balance between economic development and environmental stewardship. The study's nuanced examination extends beyond mere deforestation rates, offering insights into the strategic contributions of different forest management practices to global carbon sequestration efforts.

Lead author Hongtao Xu, from Beijing Normal University, states, "Our findings underscore the dynamic nature of global forests and highlight the significant role of forest management practices in addressing climate change. This study marks a pivotal step toward understanding and optimizing the contribution of forests to carbon sequestration and biodiversity conservation."

This study's insights are vital for policymakers, conservationists, and researchers, providing a data-driven basis for enhancing forest management strategies. By understanding the spatial and temporal changes in forest composition, stakeholders can better align reforestation and conservation efforts with global climate goals.

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References

DOI

10.34133/remotesensing.0119

Original Source URL

https://doi.org/10.34133/remotesensing.0119

Funding information

This research was supported by the BNU-FGS Global Environmental Change Program (grant 2023-GC-ZYTS-01), the High-Resolution Earth Observation Major Special Aerial Observation System (grant 30-H30C01-9004-19/21), and the State Key Laboratory of Earth Surface Processes and Resource Ecology (grant 2023-KF-02).

About Journal of Remote Sensing

The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science. 

Mapping water wonders: a groundbreaking leap in hydrology with NDWFI



AEROSPACE INFORMATION RESEARCH INSTITUTE, CHINESE ACADEMY OF SCIENCES
Flowchart for SW mapping using the time-series NDWFI. 

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FLOWCHART FOR SW MAPPING USING THE TIME-SERIES NDWFI.

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CREDIT: JOURNAL OF REMOTE SENSING





In a significant advancement for hydrological monitoring and water resource management, researchers have developed the Normalized Difference Water Fraction Index (NDWFI), leveraging Landsat imagery and Spectral Mixture Analysis (SMA) within the Google Earth Engine platform. This innovation is pivotal for accurately tracking dynamic and subtle water bodies, crucial for enhancing water security and resilience against extreme hydrological events.

Surface water (SW) is crucial for life, ecosystems, and human activities, serving many functions from climate regulation to supporting biodiversity and agriculture. It's highly dynamic, influenced by climate change, land use alterations, and human interventions like dam construction, making its monitoring essential for effective management and conservation. Traditional methods for water detection face limitations, often missing small or seasonal bodies. Advances in remote sensing offer new techniques for detailed, large-scale water mapping, emphasizing the need for high spatial and temporal resolution to capture SW's complex dynamics and support sustainable management efforts.

Sun Yat-Sen University researchers developed the Normalized Difference Water Fraction Index (NDWFI) using Landsat and Spectral Mixture Analysis on Google Earth Engine, a leap in hydrology. This method enhances tracking of water bodies, improving water security against extreme events. The article (doi: 10.34133/remotesensing.0117) published in the Journal of Remote Sensing on February 21, 2024, it signifies progress in water management by integrating remote sensing and environmental science.

In this study, researchers developed the NDWFI by utilizing Landsat imagery and Spectral Mixture Analysis (SMA) within the Google Earth Engine framework. The technique was meticulously tested across varied terrains, exhibiting a remarkable 98.2% accuracy rate in identifying water bodies, a significant improvement over traditional water detection methods. The use of over 11,000 Landsat images facilitated the creation of detailed surface water maps for Jiangsu Province, China, showcasing NDWFI's ability to discern even the smallest and most transient water features. This method's enhanced precision in capturing the intricacies of water body dynamics marks a crucial advancement in the field of hydrological monitoring, setting a new standard for water resource management and conservation efforts worldwide.

Professor Qian Shi, a lead author of the study, stated, "Our approach using NDWFI significantly improves the accuracy of water detection, especially for small and transient water bodies, which are often overlooked by traditional methods. This advancement opens new avenues for comprehensive hydrological studies and water management strategies."

The NDWFI method presents a significant leap forward in environmental monitoring, offering a more accurate and detailed understanding of SW dynamics. This methodology enhances water security, supports sustainable development, and aids in the adaptation to climate change by providing reliable data for water resource management and policy-making.

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References

DOI

10.34133/remotesensing.0117

Original Source URL

https://doi.org/10.34133/remotesensing.0117

Funding information

This work is granted by the National Science Foundation for Distinguished Young Scholars of China under grant 42225107; in part by the National Key Research and Development Program under grant 2022YFB3903402; and in part by the National Natural Science Foundation of China under grants 61976234, 42171409, and 42171410.

About Journal of Remote Sensing

The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.