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Aging

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Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

NeuroImage
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adult...

Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances.

Sensors (Basel, Switzerland)
Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum. Recently, Non-intrusive Load Monitoring (NILM) approaches have become the focus of these ...

Pragmatic inferences in aging and human-robot communication.

Cognition
Despite the increase in research on older adults' communicative behavior, little work has explored patterns of age-related change in pragmatic inferencing and how these patterns are adapted depending on the situation-specific context. In two eye-trac...

Effects of age, gender, and hemisphere on cerebrovascular hemodynamics in children and young adults: Developmental scores and machine learning classifiers.

PloS one
A constant blood supply to the brain is required for mental function. Research with Doppler ultrasonography has important clinical value and burgeoning potential with machine learning applications in studies predicting gestational age and vascular ag...

Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms.

Scientific reports
The resident macrophages of the central nervous system, microglia, are becoming increasingly implicated as active participants in neuropathology and ageing. Their diverse and changeable morphology is tightly linked with functions they perform, enabli...

DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.

Computational intelligence and neuroscience
PURPOSE: Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine lea...

Early identification of older individuals at risk of mobility decline with machine learning.

Archives of gerontology and geriatrics
BACKGROUND: The early identification of individuals at risk of mobility decline can improve targeted strategies of prevention.

Interpretable machine learning for high-dimensional trajectories of aging health.

PLoS computational biology
We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine ...

Profiling epigenetic age in single cells.

Nature aging
DNA methylation dynamics emerged as a promising biomarker of mammalian aging, with multivariate machine learning models ('epigenetic clocks') enabling measurement of biological age in bulk tissue samples. However, intrinsically sparse and binarized m...

A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes.

IEEE/ACM transactions on computational biology and bioinformatics
Understanding the ageing process is a very challenging problem for biologists. To help in this task, there has been a growing use of classification methods (from machine learning) to learn models that predict whether a gene influences the process of ...