Mangrove-dominated estuaries host a diverse microbial assemblage that facilitates nutrient and carbon conversions and could play a vital role in maintaining ecosystem health. In this study, we used 16S rRNA gene analysis, metabolic inference, nutrient concentrations, and δ13C and δ15N isotopes to evaluate the impact of land use change on near-shore biogeochemical cycles and microbial community structures within mangrove-dominated estuaries.
The crisis provoked by COVID-19 has rapidly and profoundly affected Latin America. The impacts are seen not only in infection and mortality rates, but also in the economic decline and increased inequality that plague the region, problems which have been exacerbated as a result of the pandemic.
Background: Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segmentation is an active research field, with many recent models using deep learning. Most current state-of-the art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. This raises the question whether these methods are capable of recognizing the hippocampus on a different domain, that of epilepsy patients with hippocampus resection.
This book chapter advances SDG #3 and #10 by providing therapeutic strategies that can be employed in clinical trials for AD in DS will be discussed as well as their underlying scientific rationale.
This book chapter advances SDG #3 and #10 by providing a brief history of PET imaging and the radiotracers that have had a significant impact for measuring the three signature AD-related neuropathologies related to AD and provides an overview of the research utilizing PET imaging in the DS population
This book chapter advances SDG #3 and #10 by discussing the advantages of performing genetic studies in people with DS, and then discussing the role of reported genes that are known to be associated with AD risk in adults with DS or in the general population. It also discusses how future longitudinal multiomic and imaging study can enhance our understanding of the biology of AD.
Elsevier,
Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021
This book chapter advances SDG #3 and #10 by discussing the operational aspects of deep learning solutions for Alzheimer’s disease, including the review of the advantages and limitations of using deep learning, and future directions on the applications of deep learning to Alzheimer’s disease.
This book chapter advances SDG #3 and #10 by systematically appraises the concepts and promising benefits of AI technology within healthcare for AD risk prediction across communities, and its possible concerns to be tackled prior to large-scale implementation.
Elsevier,
Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021
This book chapter advances SDG #3 and #10 by providing evidence that behavioral treatments are more effective than most pharmacological therapies at managing depression in Alzheimer’s disease.
This book chapter advances SDG #3 and #10 by stressing that a population health approach and a focus on promoting equity in health and access to care are critical to reducing the risk of AD and other dementias.
