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.
Elsevier,
Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021
This book chapter advances SDG #3 and #10 by presenting that (1) some of these depression scales do not correlate, suggesting that they assess different aspects of depression; (2) reports of depression in dementia vary based on depression in dementia scale used; and (3) severe memory impairment may impact the ability to assess depression in the patients using self-reports.
This book chapter advances SDG #3 and #10 by reviewing the extant literature on autophagy in AD and covers recent progress on the molecular mechanisms of NAD+-dependent mitophagy/autophagy regulation and mechanisms underlying the anti-AD potential of NAD+. Further studies to define the NAD+-mitophagy/autophagy axis may shed light on novel therapeutics to treat AD and potentially provide insights into other neurodegenerative diseases.
Elsevier,
Assessments, Treatments and Modeling in Aging and Neurological Disease: The Neuroscience of Aging, Volume , 1 January 2021
This book chapter advances SDG #3 and #10 by reviewing the use of nonhuman primates as a viable model of aging and neurodegeneration research.
This book chapter advances SDG #3 and #10 by reviewing deep brain stimulation as a treatment for AD patients, reviewing the recent studies and issues associated with the treatment.
This book chapter advances SDG #3 and #10 by reviewing the observed epidemiological links between normal and abnormal diurnal and seasonal rhythmicity, cognitive impairment, and ADRD. Then reviewing normal diurnal and seasonal rhythms of brain epigenetic modification and gene expression in model organisms. Finally, reviewing evidence for diurnal and seasonal rhythms of epigenetic modification and gene expression the human brain in aging, Alzheimer's disease, and other brain disorders.
Water harvesting techniques have shown promising outcomes in mitigating risks, increasing yields and delivering positive influences on other ecosystems. A field study was conducted in Northern Jordan to assess the influence of combined in-situ water harvesting techniques, micro-catchment and mulching on soil moisture content, plant morphology, gas exchange [photosynthesis (Pn), transpiration (E), and stomatal conductance (gs)] and midday stem water potential (Ψsmd) of young pistachio (Pistacia vera cv. Ashori) trees.
This study analyzes the effects of a local water market formation on the efficiency of groundwater use productivity. These results demonstrate the role of a market-based groundwater allocation approach under water scarcity conditions.
Elsevier, Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021
In the next 30 years, Alzheimer’s disease cases are predicted to drastically increase. Consequently, there is a critical need for research that can counteract the increasing number of Alzheimer’s disease patients. However, current methods of Alzheimer’s disease research have significant limitations. For example, Alzheimer’s disease research is often restricted by resource, temporal, and recruitment barriers (e.g., participant dropout). Unlike standard research, big data analysis is excellent at investigating complex long-term phenomena such as Alzheimer’s disease.
Improving farming techniques and agricultural methods with advanced technology is becoming more crucial on our fast-evolving and ecologically challenged planet, where challenges such as energy deficiency, drought, global warming, etc., have adverse effects on common agricultural practices. This paper discusses how the hybridization of energy generation from renewable resources and the field of artificial intelligence can aid in optimizing and improving the current best practices followed by farmers. We propose the concept of harnessing natural and artificial wind energy, the turbulence produced by the displacement of air due to the motion of objects such as trains. This energy harnessed can then be used in powering Internet of Things devices that enable AI-based smart farmland monitoring systems. The aim is to solve the challenges facing common agricultural practices in suburban and rural areas where vertical axis wind turbines can be set up, ultimately easing the lives of agriculturalists.
