The current trends in environment protection and sustainable development demand the investigations in the field of searching new perspective sources for any production. Oat husks seems to be a perspective resource. Cellulose isolated from oat wastes can be used for the production of different materials such as hydro and aerogels, bacterial, nanocrystal and nanofibrillated cellulose, cellulose based composites and ethers. All these materials can be spent in different industries: production of pharmaceuticals, medicine (including targeted delivery of drugs, sponges etc.), agriculture (controlled delivery of pesticides or fertilizers), electronics, production of films, membranes, plastics, coatings, paints, biodegradable packages. In this review, we will demonstrate prospects for using oat husks to obtain cellulose and for obtaining materials based on the main oat husk polysaccharide. This review will evaluate the main achievements in this area. The analysis will allow a reader to form an analytical opinion about the prospects for using oat husks as a source for obtaining valuable products.

The increasing growth in knowledge about functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technologies development in recent decades, have led to the emergence of a large number of brain-computer interfaces and neuroprosthetics for the regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. Hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for prediction of hippocampal signals in CA1 region based on received biological input in CA3 region. We compare results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. Proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.