Alkaloids as New Leads for Neurodegenerative Diseases

Conventionally, diseases involving the selective loss of neurons are referred to as neurodegenerative diseases. Traditional and more recent compounds have been explored, but they only provide symptomatic benefits and have a large number of negative effects. It will be regarded as a modern vision if stronger molecules are found that can stop the pathophysiology of these diseases. In order to replace existing medications, natural compounds are being developed from plants and other sources. Natural products, including alkaloids that originate from plants, have emerged as potential protective agents against neurodegenerative disorders (e.g., Alzheimer’s and Parkinson’s), psychiatric conditions, and many more. They provided unique lead compounds for medicine. Alkaloids could be exploited as starting materials for novel drug synthesis or, to a lesser extent, used to manage neurodegenerative-related complications due to their diverse mechanistic effects. This chapter aims to highlight the importance of alkaloids as new leads for the development of potential clinical drug candidates for the management and treatment of neurodegenerative diseases

Dynamically‐driven emergence in a nanomagnetic system

Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring‐shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field‐dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non‐linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers