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  • 1
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 120, No. 2 ( 2023-01-10)
    Abstract: The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    RVK:
    RVK:
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2023
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2008
    In:  Annals of the New York Academy of Sciences Vol. 1134, No. 1 ( 2008-06), p. 61-96
    In: Annals of the New York Academy of Sciences, Wiley, Vol. 1134, No. 1 ( 2008-06), p. 61-96
    Abstract: The concentration of materials and energy within cities is an inevitable consequence of dense populations and their per capita requirements for food, fiber, and fuel. As the world population becomes increasingly urban over the coming decades, urban areas will dramatically affect the distribution of nutrients across the face of the planet. In many cities, technological developments and urban planning have been effective at reducing the amount of waste nitrogen that is ultimately exported to downstream surface waters, largely through investments in sanitary sewer infrastructure and wastewater treatment. There are, however, still large cities throughout the developed world that have failed to take advantage of these obvious innovations to reduce their impact on downstream ecosystems. In addition, very few cities have adequately addressed the problems of diffuse nitrogen pollution, instead city infrastructure is often designed to route this N directly into downstream ecosystems. In the developing world, many of these problems are more acute, as rapidly growing urban populations exceed the capacity of limited municipal infrastructure. Reducing urban N pollution of groundwaters and surface waters both locally and globally can only be achieved through cultural and political adaptation in addition to technological innovations. In this review, we will focus on the implications of an increasingly urban world population on local, regional, and global nitrogen cycles and propose a variety of approaches for minimizing and mitigating the impacts of urban N concentration.
    Type of Medium: Online Resource
    ISSN: 0077-8923 , 1749-6632
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2008
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    detail.hit.zdb_id: 2071584-5
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  • 3
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 2022
    In:  Science Vol. 378, No. 6625 ( 2022-12-16), p. 1180-1181
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 378, No. 6625 ( 2022-12-16), p. 1180-1181
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    RVK:
    RVK:
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2022
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    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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