microRNA-9a-5p disrupts the ELAVL1/VEGF axis to alleviate traumatic brain injury.

Plasma microRNA (miR)-9 has been identified as a promising diagnostic biomarker for traumatic brain injury (TBI). This study aims to investigate the possible role and mechanisms of miR-9a-5p affecting TBI. Microarray-based gene expression profiling of TBI was used for screening differentially expressed miRNAs and genes. TBI rat models were established. miR-9a-5p, ELAVL1 and VEGF expression in the brain tissue of TBI rats was detected. The relationship among miR-9a-5p, ELAVL1 and VEGF was tested. TBI modeled rats were injected with miR-9a-5p-, ELAVL1 or VEGF-related sequences to identify their effects on TBI. miR-9a-5p was poorly expressed in the brain tissue of rats with TBI. ELAVL1 was a downstream target gene of miR-9a-5p, which could negatively regulate its expression. Enforced miR-9a-5p expression prevented brain tissue damage in TBI rats by targeting ELAVL1. Meanwhile, ELAVL1 could increase the expression of VEGF, which was highly expressed in the brain tissue of rats with TBI. In addition, ectopically expressed miR-9a-5p alleviated brain tissue damage in TBI rats by downregulating the ELAVL1/VEGF axis. Overall, miR-9a-5p can potentially reduce brain tissue damage in TBI rats by targeting ELAVL1 and down-regulating VEGF expression.

An improved YOLOv5s model for assessing apple graspability in automated harvesting scene.

Introduction: With continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved. Methods: This study introduces a method for detecting multi-occluded apples based on an enhanced YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: APGA, APTUGA, and APUGA, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively. Results: Experimental results indicate that the improved YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for APGA, APTUGA, and APUGA, respectively. Discussion: Compared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment.