The nervous system is known as a crucial part of the body and derangement in this system can cause potentially lethal consequences or serious side effects. Unfortunately, the nervous system is unable to rehabilitate damaged regions following seriously debilitating disorders such as stroke, spinal cord injury and brain trauma which, in turn, lead to the reduction of quality of life for the patient. Major challenges in restoring the damaged nervous system are low regenerative capacity and the complexity of physiology system. Synthetic polymeric biomaterials with outstanding properties such as excellent biocompatibility and non-immunogenicity find a wide range of applications in biomedical fields especially neural implants and nerve tissue engineering scaffolds. Despite these advancements, tailoring polymeric biomaterials for design of a desired scaffold is fundamental issue that needs tremendous attention to promote the therapeutic benefits and minimize adverse effects. This review aims to (i) describe the nervous system and related injuries. Then, (ii) nerve tissue engineering strategies are discussed and (iii) physiochemical properties of synthetic polymeric biomaterials systematically highlighted. Moreover, tailoring synthetic polymeric biomaterials for nerve tissue engineering is reviewed.

Our knowledge about encoding and maintenance of spatial memory emphasizes the integrated functional role of the grid cells and the place cells of the hippocampus in the generation of theta rhythm in spatial memory formation. However, the role of astrocytes in these processes is often underestimated in their contribution to the required structural and functional characteristics of hippocampal neural network operative in spatial memory. We show that hippocampal astrocytes, by the secretion of gliotransmitters, such as glutamate, d-serine, and ATP and growth factors such as BDNF and by the expression of receptors and channels such as those of TNFα and aquaporin, have several diverse fuctions in spatial memory. We specifically focus on the role of astrocytes on five phases of spatial memory: (1) theta rhythm generation, (2) theta phase precession, (3) formation of spatial memory by mapping data of entorhinal grid cells into the place cells, (4) storage of spatial information, and (5) maintenance of spatial memory. Finally, by reviewing the literature, we propose specific mechanisms mentioned in the form of a hypothesis suggesting that astrocytes are important in spatial memory formation.

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Pedestrian attributes recognition is an important issue in computer vision and has a special role in the field of video surveillance. The previous methods presented to solve this issue are mainly based on multi-label end-to-end deep neural networks. These methods neglect to apply attributes for defining local feature areas and they suffer from the problems of the bounding box presence. A new framework for jointly human semantic parsing and pedestrian attribute recognition to achieve effective attribute recognition is proposed. By extracting human parts via semantic parsing, both semantic and spatial information can be explored with eliminating of background. The framework also uses multi-scale features to employ rich details and contextual information through proposed attribute recognition-bidirectional feature pyramid network. For baseline network that has a significant impact on the performance, EfficientNet-B3 is selected as a baseline network from The EfficientNet family which provides an appropriate trade-off between the three factors of CNNs scaling (depth/width/resolution). Finally, the proposed framework is tested on datasets PETA, RAP and PA-100k. Experimental results show that our method has superior performance in both mean accuracy and instance-based metrics compared to state-of-the-art results.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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In this work, we introduce a new non-Shilnikov chaotic system with an infinite number of nonhyperbolic equilibrium points. The proposed system does not have any linear term, and it is worth noting that the new system has one equilibrium point with triple zero eigenvalues at the origin. Also, the novel system has an infinite number of equilibrium points with double zero eigenvalues that are located on the z -axis. Numerical analysis of the system reveals many strong dynamics. The new system exhibits multistability and antimonotonicity. Multistability implies the coexistence of many periodic, limit cycle, and chaotic attractors under different initial values. Also, bifurcation analysis of the system shows interesting phenomena such as periodic window, period-doubling route to chaos, and inverse period-doubling bifurcations. Moreover, the complexity of the system is analyzed by computing spectral entropy. The spectral entropy distribution under different initial values is very scattered and shows that the new system has numerous multiple attractors. Finally, chaos-based encoding/decoding algorithms for secure data transmission are developed by designing a state chain diagram, which indicates the applicability of the new chaotic system.

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