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Abstract Hypoxia can cause a variety of diseases, including ischemic stroke and neurodegenerative diseases. Within a certain range of partial pressure of oxygen, cells can respond to changes in oxygen. Changes in oxygen concentration beyond a threshold will cause damage or even necrosis of tissues and organs, especially for the central nervous system. Therefore, it is very important to find appropriate measures to alleviate damage. MiRNAs can participate in the regulation of hypoxic responses in various types of cells. MiRNAs are involved in regulating hypoxic responses in many types of tissues by activating the hypoxia-inducible factor (HIF) to affect angiogenesis, glycolysis and other biological processes. By analyzing differentially expressed miRNAs in hypoxia and hypoxia-related studies, as well as the HT22 neuronal cell line under hypoxic stress, we found that the expression of miR-18a was changed in these models. MiR-18a could regulate glucose metabolism in HT22 cells under hypoxic stress by directly regulating the 3’UTR of the Hif1a gene. As a small molecule, miRNAs are easy to be designed into small nucleic acid drugs, so this study can provide a theoretical basis for the research and treatment of nervous system diseases caused by hypoxia. Graphical Abstract

Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene.